Solidarity Responsibility Job Incentive Scheme Master Thesis Daan Struyvenv3

FACULTÉ DES SCIENCES SOCIALES ET POLITIQUES/ SOLVAY BRUSSELS SCHOOL OF ECONOMICS AND MANAGEMENT

MEMOIRE P r é s e n t é e n v u e d e l ' o b t e n t i o n d u M a s t e r e n

I n g é n i e u r d e g e s t i o n , à f i n a l i t é s p é c i a l i s é e

Combining solidarity and responsibility by improving employment

incentives for Belgian decentralized entities

Daan Struyven

Directeur: Professeur Mathias Dewatripont

Commissaires: Professeurs Estelle Cantillon, François Rycx & Ariane Szafarz

Experts : Professeurs Rudi Vander Vennet & Magali Verdonck

Président du jury : Professeur Bruno Van Pottelsberghe

Année académique 2008-2009

ii

ACKNOWLEDGEMENTS

It is a great pleasure to thank the people who made this thesis possible.

First, I am very grateful to my supervisor, Mathias Dewatripont, for proposing the research topic, for

steering me in the right direction and for providing his thoughtful and frequent feedback with

energy, ambition, humor and perfectionism. He allowed me to discover, taste and enjoy research.

I would like to thank the committee members Estelle Cantillon and Ariane Szafarz, for staying in the

jury for this thesis, although I switched to a topic which might be less at the centre of their research

interest. I would like to thank Estelle Cantillon for her feedback of my initial thesis work, which was

highly appreciated by the professors of the Indian Institute of Management Bangalore. I am very

grateful to the other committee members François Rycx, Rudi Vander Vennet and Magali Verdonck

for their helpful comments and suggestions.

I am grateful to Didier Baudewijns, Koen Hendrickx, Dirk Horelbeeke and Luc Masure. They provided

me with and explained me the rich HERMREG model and database developed by the Planning Office.

I would like to express my full gratitude to Jean-Paul Abraham, Koen Algoed, Laurent Bouton,

Michael Castanheira, Antonio Estache, Paul De Grauwe, Catherine Dehon, Renaud Foucart, Wim

Moesen, Geert Noels, Jan Smets, Guy Tegenbos, Bruno Vanderlinden, Paul Van Rompuy and David

Veredas for their insights, feedback and suggestions. Those stimulating interactions made the time

fly.

I also thank my friends Alexandra, An, Fiona, François, Henri, Koen, Maarten, Pieter, Thomas K. and

Yana for their friendship. My friends Jérôme, Nicolas and Thomas D. provided me with very smart

feedback, which I highly appreciated. Aakash and Nicolas deserve a special mention for the incredible

time we spent in India. I will not forget their support when I took the difficult decision, on the exotic

beach of Pondichéry, to switch my thesis topic in December 2008.

I would like to thank my parents, Ine and Hans, for their unconditional love and support. They are my

sounding board and rock. Finally, I would like to thank my sister Heleen and my brother Robbert.

Heleen drilled my layout skills. Robbert allowed me to put this work in perspective, despite all the

passion which drove me along this journey.

iii

ABSTRACT

The job bonus malus is a hot topic on the Belgian political agenda. But could it be useful? And could it work

in practice?

First, we show that a job bonus malus could be useful for the Belgian federation because it could lead to

better activation incentives. Better activation incentives could lead to higher employment rates since

governments tend to react to financial incentives. Today Regions bear the financial pain of activation

without any significant financial gain. Activating an unemployed costs the Regions around €21,800 and

brings in around €3,200 in Flanders, €1,200 in Wallonia and €1,100 in Brussels. Significant activation gains

could lead to decentralized policy reactions in fields such as education and active labor market policies.

These policies have a high job creating potential according to our international and Belgian literature review.

Second, we show that the job bonus malus could be effective. To be effective, coupling benchmarking of

regional employment rate variations to mobility premiums is an attractive option. Such a scheme could be

fair and hard to manipulate. Such a scheme could significantly raise activation incentives while controlling

budget risk and fostering interregional cooperation. We work out and simulate the budget impact of several

schemes to generate a maximum of lessons for policymakers. Covered schemes include Belgian and

international benchmarking, various targets and various incentivization sizes.

Key words: Belgium, incentives, federalism, solidarity, responsibility, employment rate, job bonus malus, risk.

iv

TABLE OF CONTENT ACKNOWLEDGEMENTS ............................................................................................................................ii ABSTRACT ................................................................................................................................................ iii TABLE OF CONTENT ................................................................................................................................. iv LIST OF TABLES ....................................................................................................................................... vii LIST OF CHARTS ....................................................................................................................................... ix SUMMARY ............................................................................................................................................... xi 1. INTRODUCTION ........................................................................................................................... - 1 - 2. RATIONALE FOR JOB BONUS MALUS .......................................................................................... - 6 -

2.1. Today activation incentives are limited at the decentralized level ................................. - 6 - 2.1.1. Federal return .............................................................................................................. - 6 - 2.1.2. Regional return ............................................................................................................ - 9 - 2.1.3. Community Return .................................................................................................... - 13 -

2.2. Employment rates have to converge upward for sustainability Belgian social model . - 16 - 2.2.1. Status-quo baseline course of employment rates is not an option .......................... - 16 - 2.2.2. Current economic crisis and ageing cost urge for upward employment rate convergence .............................................................................................................................. - 17 - 2.2.3. Credible perspective of upward employment rate convergence might contribute to alleviate Belgian political deadlock ........................................................................................... - 18 -

3. LESSONS FROM CONTRACT THEORY ......................................................................................... - 19 - 3.1. Job bonus malus raises four issues contract theory can shed light on ......................... - 19 - 3.2. Five incentive models illustrating issues in bonus malus design................................... - 20 -

3.2.1. Principal-agent baseline model with observable actions .......................................... - 20 - 3.2.2. Principal-agent model with uncertain results from observable actions .................. - 20 - 3.2.3. Principal-agent model with uncertain results from hidden actions ......................... - 21 - 3.2.4. Principal-agent multitasking model .......................................................................... - 22 - 3.2.5. Principal-multiple agents model and relative performance evaluation ................... - 23 -

3.3. Key Lessons .................................................................................................................... - 24 - 3.3.1. Job externalities create an opportunity for a win-win incentive scheme ................. - 24 - 3.3.2. Uncertain results raise a risk aversion issue .............................................................. - 24 - 3.3.3. Imperfectly observable actions raise a moral hazard issue ...................................... - 24 - 3.3.4. Multiple tasks raise an effort-substitution issue ....................................................... - 25 - 3.3.5. Multiple agents create an opportunity for relative performance evaluation ........... - 25 -

4. WHICH PERFORMANCE INDICATORS COULD BE DESIGNED? ................................................... - 26 - 4.1. Scheme design questions, scheme features and scheme trade-offs ........................... - 26 -

4.1.1. Five answers to five questions .................................................................................. - 26 - 4.1.2. Three trade-offs ......................................................................................................... - 28 -

4.2. Options to overcome trade-offs .................................................................................... - 30 - 4.3. Analysis features of schemes proposed by academics and politics .............................. - 31 -

4.3.1. The two Van Rompuy proposals ................................................................................ - 32 - 4.3.2. The Van der Linden proposal ..................................................................................... - 32 - 4.3.3. The Vandenbroucke-Marcourt proposal ................................................................... - 33 -

4.4. Shortlist of three considered performance indicators .................................................. - 33 - 4.5. Translation of three performance indicators into financial formula’s .......................... - 34 -

4.5.1. Absolute total employment rate variations .............................................................. - 34 - 4.5.2. Relative total employment rate variations................................................................ - 35 - 4.5.3. Absolute total employment rate variation controlled for external factors with regression .................................................................................................................................. - 35 -

5. EMPIRICAL ANALYSIS OF HOW WELL SHORTLISTED INDICATORS WORK ................................. - 36 - 5.1. Objectives of empirical analysis .................................................................................... - 36 -

v

5.2. Exploring risk reduction potential with descriptive statistics ....................................... - 37 - 5.2.1. Cumulative variations less risky than yearly variations ............................................. - 37 - 5.2.2. Strong Flanders-Wallonia correlation provides support for yearly and cumulative Belgian benchmarking ............................................................................................................... - 38 - 5.2.3. Strong mutual Flanders-Wallonia interaction employment rate variations provides support for yearly Belgian benchmarking ................................................................................. - 39 - 5.2.4. Strong correlation between variations of three neighbors, Flanders and Wallonia provides support for cumulative neighbor benchmarking........................................................ - 40 - 5.2.5. Brussels has relatively uncorrelated and volatile employment rate variations ........ - 41 -

5.3. Reducing risk of yearly employment rate variation indicator ....................................... - 42 - 5.3.1. Benchmarking ............................................................................................................ - 42 - 5.3.2. Estimating impact external factors with regressions ................................................ - 43 -

5.4. Reducing risk of cumulative employment rate variations............................................. - 46 - 5.5. Reducing shortlist to empirically effective indicators ................................................... - 47 - 5.6. Fine-tuning shortlisted and empirically effective indicators ......................................... - 48 -

5.6.1. Business cycle and Brussels ....................................................................................... - 48 - 5.6.2. Demographics ............................................................................................................ - 48 - 5.6.3. Cooperation issues and mobility premiums .............................................................. - 49 - 5.6.4. A more precise employment rate? ............................................................................ - 50 -

6. BRUSSELS AND THE JOB INCENTIVE SCHEME............................................................................ - 51 - 6.1. Brussels has a special labor market ............................................................................... - 51 -

6.1.1. Intense economic interactions in the Brussels Metropolitan Area ........................... - 51 - 6.1.2. International city with atypical job demand sector structure .................................. - 52 - 6.1.3. Skill mismatch and language challenge ..................................................................... - 52 -

6.2. Relatively uncorrelated and highly volatile employment rate ...................................... - 54 - 6.3. Risk reduction techniques fail ....................................................................................... - 54 - 6.4. Policy options for indicators for Brussels ...................................................................... - 55 -

6.4.1. Interactions with hinterland require cooperative scheme ....................................... - 55 - 6.4.2. Atypical job structure suggests focus on private jobs ............................................... - 56 - 6.4.3. Skill mismatch and language challenge suggest partial community returns ............ - 56 - 6.4.4. Failure classical risk reductions suggests international city benchmarking or smaller incentivization size .................................................................................................................... - 56 -

7. DECENTRALIZED POLICIES CAN INCREASE EMPLOYMENT RATES SIGNIFICANTLY .................... - 57 - 7.1. Policies impact employment ......................................................................................... - 57 - 7.2. Which policies are decentralized? ................................................................................. - 59 -

7.2.1. Job-related competencies ......................................................................................... - 59 - 7.2.2. Education ................................................................................................................... - 60 -

7.3. Impact and potential of active labor market policies.................................................... - 61 - 7.3.1. Impact ........................................................................................................................ - 61 - 7.3.2. Potential Belgian decentralized entities .................................................................... - 64 -

7.4. Impact and potential of education policies ................................................................... - 66 - 7.4.1. Impact ........................................................................................................................ - 66 - 7.4.2. Potential .................................................................................................................... - 67 -

7.5. Conclusion ..................................................................................................................... - 69 - 8. TARGETS ON FINANCIALLY NEUTRAL PATH .............................................................................. - 70 -

8.1. Planning Office HERMREG baseline scenario ................................................................ - 71 - 8.1.1. How does the HERMES-HERMREG system work? ..................................................... - 71 - 8.1.2. Which demographical projections underpin the HERMREG forecasts? ................... - 73 - 8.1.3. What would HERMREG baseline scenario forecast if financial crisis is integrated? . - 74 - 8.1.4. Lessons from HERMREG forecasts ............................................................................ - 76 -

vi

8.2. Which realistic scenario’s can we imagine for Flanders? ............................................. - 77 - 8.3. Which speed of interregional convergence do we target for Brussels and Wallonia? . - 77 - 8.4. Different problem definitions lead to different families of targets .............................. - 77 - 8.5. National relative performance targets .......................................................................... - 78 - 8.6. Lisbon absolute performance targets ........................................................................... - 79 - 8.7. Semi Danish-style absolute targets ............................................................................... - 80 - 8.8. European relative performance targets ........................................................................ - 80 - 8.9. Reality checks of targets and scenario selection ........................................................... - 81 -

8.9.1. Employment rate variations European Regions in 1999-2007 .................................. - 81 - 8.9.2. Employment rate variations European countries in 1995-2007 ............................... - 82 - 8.9.3. Scenario selection with reality check ........................................................................ - 83 -

8.10. Conclusion with visualization of four selected scenario’s ............................................. - 86 - 9. SIZE OF INCENTIVIZATION ......................................................................................................... - 89 -

9.1. Definition of incentivization size coefficients ................................................................ - 89 - 9.2. Factors one might look at to fix incentivization size coefficients .................................. - 90 - 9.3. High-powered Regional incentivization size .................................................................. - 92 - 9.4. Medium-powered Regional incentivization size ............................................................ - 93 - 9.5. Low-powered Regional incentivization size ................................................................... - 94 - 9.6. Advantages and disadvantages of the three incentivization size options .................... - 94 -

10. SIMULATING THE IMPACT OF THE SCHEME .............................................................................. - 95 - 10.1. Budget Impact without policy link ................................................................................. - 96 - 10.2. Impact scheme on net cost of increasing employment rates with activation policies - 103 -

11. CONCLUSION ........................................................................................................................... - 107 - 12. BIBLIOGRAPHY ......................................................................................................................... - 109 -

12.1. Books ........................................................................................................................... - 109 - 12.2. Articles in journals ....................................................................................................... - 110 - 12.3. Articles in newspapers................................................................................................. - 112 - 12.4. Articles in books .......................................................................................................... - 112 - 12.5. Unpublished documents ............................................................................................. - 114 - 12.6. Databases .................................................................................................................... - 117 -

13. APPENDICES ............................................................................................................................. - 118 - 13.1. Newspaper articles related to this thesis .................................................................... - 118 -

13.1.1. Article scan from De Standaard ............................................................................... - 118 - 13.1.2. Article website screen shot from Le Soir ................................................................. - 119 -

13.2. Community budget response to GDP increases .......................................................... - 122 - 13.3. Descriptive statistics of Regional employment rate variations ................................... - 125 - 13.4. Risk reduction results of benchmarking of yearly employment rate variations ......... - 128 - 13.5. Risk reduction results of benchmarking of cumulative employment rate variations . - 132 - 13.6. Benchmarking of weighted average indicator for Brussels ......................................... - 136 - 13.7. Methodological challenges in assessing the impact of active labor market policies .. - 138 - 13.8. Modeling the impact of education on growth ............................................................ - 139 - 13.9. Benchmarking education potential of Belgian communities ...................................... - 143 - 13.10. Functioning of the HERMES forecasting model ........................................................... - 146 - 13.11. HERMREG baseline forecasts in May 2007 before the crisis ...................................... - 149 - 13.12. European relative performance targets ...................................................................... - 150 -

13.12.1. Catching-up with employment levels of best similar Regions ............................ - 150 - 13.12.2. Increasing employment levels at same speed as best similar Regions ............... - 151 -

13.13. Has Regional growth become more labor intensive? ................................................. - 152 - 13.14. Governments react to financial incentives: brief literature review ............................ - 155 -

vii

LIST OF TABLES Table 2.1:Average annual cost per unemployed person and its components 1987-2002(in €1000) . - 7 - Table 2.2:Growth rate and break-down of annual cost of an unemployed person 1987-2002(in %) - 7 - Table 2.3:Unemployed budget cost estimated with a linear regression on the basis of the effective value in 1987 and the price evolution in 1987–2007 ......................................................................... - 8 - Table 2.4:Unemployed budget cost in 2002-2007 estimated with a linear regression(in €1000) ...... - 8 - Table 2.5:Shares of revenue categories in Belgian GDP in 2007(in %)................................................ - 9 - Table 2.6:Regional budget impacts when Regional GDP increases by €100 in 2007(in €)............... - 10 - Table 2.7:Regional budget impact associated with one activation in 2007(in €1000 ) .................... - 11 - Table 2.8:Net Regional returns on activation through ALMP expenses in 2007(in € 1000) ............ - 12 - Table 2.9:Community budget impacts if Regional GDP increases with €100 in 2007(in €) ............. - 13 - Table 2.10:Community budget impacts associated per activated unemployed in 2007(in €1000) . - 13 - Table 2.11:Community-, Regional- and federal budget impacts per activation in 2007(in €1000) .. - 14 - Table 2.12:Shares of Community-, Regional- and federal budget impacts in total return associated with activation of an unemployed in 2007(in %) .............................................................................. - 15 - Table 2.13:Expected evolution Regional employment rates until 2013 including crisis impact(in percentage points) ............................................................................................................................. - 16 - Table 4.1:Main features of most concrete academic and political scheme proposals ..................... - 31 - Table 5.1:Standard deviation of 5-year and yearly employment rate variations ............................. - 38 - Table 5.2:Correlations between Regional employment rate variations in 1981-2007 ..................... - 38 - Table 5.3:Results regressions of Regional yearly employment rate variations ................................ - 39 - Table 5.4:Correlations between 5-year employment rate variations of Belgian Regions and their neighbor countries ............................................................................................................................ - 40 - Table 5.5:Results external factor regressions ................................................................................... - 45 - Table 6.1:Sector weights in employment in Brussels and its benchmarking Regions in 2007(in %) - 52 - Table 6.2: Employment qualification structure in Brussels and its peers in 2007(in %) ................... - 52 - Table 7.1:Correlations between unemployment rate and policy variables for 1982-2003 .............. - 58 - Table 7.2:Actors currently involved in main job-related competencies in Belgium ......................... - 60 - Table 7.3:National active labor market expenses compared to passive labor market expenses and compared to GDP in 2007(in %) ........................................................................................................ - 64 - Table 7.4:Effect of an increment of one year of schooling on the probability of being unemployed for an average citizen at working age in 2004(in %) ............................................................................... - 66 - Table 8.1:Regional demographic projections: percentage change on 2000 figures in the number of residents between 15 and 64 years old over the period 2000-2060(in %) ....................................... - 73 - Table 8.2:Expected impact of financial crisis on national number of working residents(in 1000) ... - 75 - Table 8.3:Expected evolution of the Regional number of working residents at horizon 2013 ........ - 75 - Table 8.4:Expected evolution Regional employment rates at horizon 2013(in percentage points) - 76 - Table 8.5:Regional employment targets under a slow national convergence scenario ................... - 78 - Table 8.6:Regional employment targets under a fast national convergence scenario ..................... - 78 - Table 8.7:Regional employment targets under a slow Lisbon scenario ............................................ - 79 - Table 8.8:Regional employment targets under a fast Lisbon scenario ............................................. - 79 - Table 8.9:Regional employment targets under a slow semi Danish scenario................................... - 80 - Table 8.10:Regional employment targets under a fast semi Danish scenario .................................. - 80 - Table 8.11:Statistics of average yearly employment rate variations of 332 EU Regions 1999-2007(in percentage points) ............................................................................................................................. - 82 - Table 8.12:Statistics of average yearly employment rate variations of 20 European countries for 1995-2007(in percentage points) ...................................................................................................... - 83 - Table 8.13:Reality check metrics for rejected scenario’s .................................................................. - 84 - Table 8.14:Reality check metrics for accepted scenario’s................................................................. - 85 - Table 9.1:Total returns per activated resident with high-powered incentives(in €1000) ................ - 92 -

viii

Table 9.2:Total returns per activated resident with medium-powered incentives(in €1000) .......... - 93 - Table 9.3:Total returns per activated resident with low-powered incentives(in €1000) ................. - 94 - Table 13.1:Community budget impacts if Regional GDP increases with €100 in 2007(in €) ......... - 124 - Table 13.2:Regional yearly employment rate variations descriptive statistics in 1981-2007......... - 125 - Table 13.3:Regional five-year employment rate variations descriptive statistics in 1981-2007 .... - 126 - Table 13.4:Correlations between employment rate variations of Belgian Regions and neighbor countries in 1984-2007.................................................................................................................... - 126 - Table 13.5:Standard deviations of Regional yearly employment rate variations with Belgian benchmarking .................................................................................................................................. - 128 - Table 13.6:Standard deviations of Regional employment rate variations with international benchmarking .................................................................................................................................. - 128 - Table 13.7:Standard deviations of Regional five year employment rate variations 1985-2007 with Belgian Benchmarking ..................................................................................................................... - 132 - Table 13.8:Standard deviations of Regional five year employment rate variations 1988-2007 with international benchmarking ............................................................................................................ - 132 - Table 13.9:Correlations between variation of weighted average of employment- and job-rate of Brussels and variation of employment rates in Flanders and Wallonia for 1981-2007 .................. - 136 - Table 13.10: Risk level of weighted average indicator with a 25% weight for 1981-2007 ............. - 137 - Table 13.11: Risk level of weighted average indicator with a 50% weight for 1981-2007 ............. - 137 - Table 13.12: Risk level of weighted average indicator with a 75% weight for 1981-2007 ............. - 137 - Table 13.13:Education public spending per student in 2006(in €).................................................. - 143 - Table 13.14:Distribution of students in last two degrees of secondary education per education type in 2001(in %) ........................................................................................................................................ - 143 - Table 13.15:PISA results in 2006 ..................................................................................................... - 143 - Table 13.16:Percentage of students with at least one year delay in sixth year in 2003-2004 ....... - 143 - Table 13.17:Share of residents knowing language(s) in 2006 ......................................................... - 144 - Table 13.18:Distribution of students per group of university studies for 2000-2001 .................... - 144 - Table 13.19:Shanghai Academic ranking of World Universities in 2008 ......................................... - 145 - Table 13.20:Number of patents per million inhabitants in 2002 .................................................... - 145 - Table 13.21:Expected evolution Regional growth rates at horizon 2013(%) .................................. - 149 - Table 13.22:Expected evolution Regional number of working residents at horizon 2013 ............ - 149 - Table 13.23:Expected evolution Regional employment rates at horizon 2013 .............................. - 149 - Table 13.24:Regional employment targets under a European peer level caching up scenario ...... - 150 - Table 13.25:Regional employment targets under a European peer speed caching up scenario.... - 151 - Table 13.26:Flanders’ elasticities of employment rate with respect to GRP for 1980-2007 .......... - 153 - Table 13.27:Brussels’ elasticities of employment rate with respect to GRP for 1980-2007 ........... - 153 - Table 13.28:Wallonia’s elasticities of employment rate with respect to GRP for 1980-2007 ........ - 154 -

ix

LIST OF CHARTS Chart 5.1:Regional yearly employment rate variations 1981-200(in percentage points) ............... - 41 - Chart 6.1:Number of jobs and number of working residents as percentages of the working age population in Belgian Regions in 2007 .............................................................................................. - 51 - Chart 6.2:Required training for job offers in Brussels, Wallonia and Flanders in 2007(in %) ........... - 53 - Chart 6.3:Regional employment rate per training level in 2007(in %) .............................................. - 53 - Chart 7.1:Impact of active labor market programs (right scale) on effect (left scale) of benefits on unemployment rate in 20 OECD countries........................................................................................ - 62 - Chart 7.2:Breakdown of national active labor market expenses in 2006(in %) ................................ - 64 - Chart 7.3:Breakdown of Regional active labor market expenses in 2007(in %) ............................... - 65 - Chart 8.1:Distribution of average yearly employment rate variations of 332 EU Regions 1999-2007(in percentage points) ............................................................................................................................. - 81 - Chart 8.2:Distribution of average yearly employment rate variations of 20 European countries 1995-2007(in percentage points) ............................................................................................................... - 82 - Chart 8.3:Targeted business-cycle adjusted average employment rate levels under Lisbon scenario’s 2008-2035 ......................................................................................................................................... - 87 - Chart 8.4:Targeted Business- cycle adjusted average employment rate levels under Danish scenario’s 2008-2035 ......................................................................................................................................... - 88 - Chart 10.1:Bonus malus incentive flows for Flanders with slow Semi-Danish targets and medium-powered premiums ........................................................................................................................... - 97 - Chart 10.2:Bonus incentive flows for Flanders with slow Semi-Danish targets and medium-powered premiums .......................................................................................................................................... - 98 - Chart 10.3:Bonus malus incentive flows for Wallonia with slow Semi-Danish targets and medium-powered premiums ........................................................................................................................... - 99 - Chart 10.4:Bonus incentive flows for Wallonia with slow Semi-Danish targets and medium-powered premiums ........................................................................................................................................ - 100 - Chart 10.5:Marginal impact of schemes with different incentivization sizes on net ALMP cost to achieve employment rate variations for Flanders .......................................................................... - 105 - Chart 10.6:Marginal impact of schemes with different incentivization sizes on net ALMP cost to achieve employment rate variations for Wallonia .......................................................................... - 106 - Chart 13.1:Regional yearly employment rates 1981-2007(in %) ................................................... - 125 - Chart 13.2:Regional five-year employment rate variations 1985-2007(in percentage points)..... - 126 - Chart 13.3:Sector shares in Regional GRPs in 2007(in %) ............................................................... - 127 - Chart 13.4:Effective and relative yearly employment rate variations of Brussels with Belgian interregional benchmarking 1981-2007(in percentage points) ...................................................... - 129 - Chart 13.5:Effective and relative employment rate variations of Brussels with neighbor benchmarking 1984-2007(in percentage points) .................................................................................................... - 129 - Chart 13.6:Effective and relative yearly employment rate variations of Flanders with Belgian interregional benchmarking 1981-2007(in percentage points) ...................................................... - 130 - Source: HERMREG Planning Office .................................................................................................. - 130 - Chart 13.7:Effective and yearly relative employment rate variations of Flanders with neighbor country benchmarking 1984-2007(in percentage points) ............................................................... - 130 - Chart 13.8:Effective and relative employment rate variations of Wallonia with Belgian interregional benchmarking 1981-2007(in percentage points) ............................................................................ - 131 - Chart 13.9:Effective and relative employment rate variations of Wallonia with neighbor country benchmarking 1984-2007(in percentage points) ............................................................................ - 131 - Chart 13.10:Effective and relative five year employment rate variations of Brussels with Belgian interregional benchmarking 1985-2007(in percentage points) ...................................................... - 133 - Chart 13.11:Effective and relative five year employment rate variations of Brussels with neighbor benchmarking 1984-2007(in percentage points) ............................................................................ - 133 -

x

Chart 13.12:Effective and relative five year employment rate variations of Flanders with Belgian interregional benchmarking 1985-2007(in percentage points) ...................................................... - 134 - Chart 13.13:Five year effective and relative employment rate variations of Flanders with neighbor country benchmarking 1988-2007(in percentage points) ............................................................... - 134 - Chart 13.14:Effective and relative five year employment rate variations of Wallonia with Belgian interregional benchmarking 1985-2007(in percentage points) ...................................................... - 135 - Chart 13.15:Five year effective and relative employment rate variations of Wallonia with neighbor country benchmarking 1988-2007(in percentage points) ............................................................... - 135 - Chart 13.16: A flowchart of the HERMES model ............................................................................. - 147 -

xi

SUMMARY

The job bonus malus has been set on the Belgian political agenda by 120 Belgian economists via a

text discussed in De Standaard and Le Soir on 26th of January 2008. In December 2008 Walloon and

Flemish Ministers of Labour and Economy J.C. Marcourt and F. Vandenbroucke formulated a job

bonus malus proposal. The idea behind the job bonus malus is simple. Belgian Regions and/or

Communities, the so-called decentralized entities, receive boni when jobs are created. With these

boni, public policy investments in jobs are(partially) paid back. This pay-back perspective could lead

to more and better policy investments and to more jobs. Two questions arise. First, could the job

bonus malus be useful? Second, how could the job bonus malus work in practice?

The job bonus malus could be useful to improve activation incentives. Better activation incentives

could lead to higher employment rates because governments react to financial incentives such as tax

base elasticities, financing of own expenditures and bail-outs by other governments.

Higher employment rates are necessary to fund the social security and the ageing cost and to get the

Belgian political federation out of its current political, budgetary and economic deadlock.

Today Regions bear the financial pain of activation without any significant financial gain. Activating

an unemployed costs the Regions around €21,800 and brings in around €3,200 in Flanders, €1,200 in

Wallonia and €1,100 in Brussels.

Significant activation gains could lead to decentralized policy reactions. Decentralized education-,

active labor market- and economic policies have a high job creation potential. This potential is shown

by our international and Belgian literature review. With a year of extra schooling, the average

Walloon citizen at working age decreases his probability of being unemployed by around 17.2%.

The job bonus malus could be effective. To be effective, coupling benchmarking of regional

employment rate variations to mobility premiums might be an attractive scheme option. Such a

scheme could be fair and hard to manipulate. Such a scheme could also significantly raise activation

incentives while controlling budget risk and fostering interregional cooperation.

Employment rate variations measure an objective and desirable end: increasing the activity of the

working age population. Employment rate variations are hard to manipulate.

Employment rate variations are fair both for Regions with weaker and stronger starting positions.

Initially weaker Regions are not penalized for bad past performances because only progress matters.

xii

Initially stronger Regions are not penalized for good past performances since targets could be set at

relatively less ambitious levels. We define targets such that Regions receive no bonus or malus if they

achieve certain employment rate variation targets. Targets can be set on the basis of Lisbon

objectives, Belgian interregional convergence paths or European benchmarking. European

benchmarking could be based on leading countries such as Denmark. Suppose Flanders bridges 50%

of the employment rate gap with Denmark at horizon 2020. Suppose that Brussels and Wallonia

achieve this level in 2030. To follow this Semi-Danish path, Brussels, Flanders and Wallonia should

join the 17%, 42% and 20% fastest European employment rate climbers. Budget impact simulations

show that schemes based on Semi-Danish or Lisbon targets are ambitious for Wallonia and Brussels.

To protect Wallonia and Brussels against the vicious circle, one could limit incentive flows to boni.

Coupling employment rate variations of a certain Region to financial incentive flows, could

significantly improve activation incentives. The size of the incentive flows could be set such that

Regions can finance investments with scheme boni or such that total budget returns on activation are

equally shared between the federal and Regional level. Equal return sharing is possible if Flanders,

Wallonia and Brussels receive around €10,800, €13,700 and €12,700 per activated resident.

Budget risk could be controlled by Belgian or international benchmarking. By comparing the

employment rate variations of two similar Regions, part of the business cycle risk is filtered out.

Indeed, similar Regions are exposed to the same international business cycle. Evaluating employment

rate progress since the previous year and evaluating cumulative employment rate progress are two

attractive options. The first option reduces the risk of a vicious circle and provides long-run activation

dividends to the federal budget. The second option encourages long-run Regional investments. If

policymakers opt for assessing yearly employment rate variations, then Belgian benchmarking of

Flanders and Wallonia relative to one another reduces the budget risk. Risk is then reduced by

around 55% and 47%. If policymakers opt for cumulative employment rate variations, then Belgian

and neighbor benchmarking appear as two effective techniques to reduce the risk supported by

Flanders and Wallonia. For Brussels, benchmarking is not the magical solution to reduce risk.

Cooperation could be fostered by mobility premiums. Mobility premiums reward the activation of

neighbor residents. Employment rate incentives reward Regions for the activation of their own

residents. Mobility premiums are useful in Brussels where around 304,000 commuters go working.

To conclude, the job bonus malus could lead to higher activation incentives and higher employment

rates. Employment rate variation benchmarking and mobility premiums combine solidarity and

responsibility by stimulating activation and by fostering cooperation while controlling budget risk.

- 1 -

1. INTRODUCTION

Today, Belgium is at a triple cross-road. Employment, the sustainability of the Belgian social security

system and the accountability of Regions and Communities are at stake. Here are a couple of facts to

draw the picture of this triple cross-road.

According to employment forecasts, no Belgian Region will reach the Lisbon 70% employment rate

target in 2015. Today, a job gap separates Flanders, Wallonia and Brussels. The employment rate in

Flanders is 11.1 and 8.8% above the values in Brussels and Wallonia. This gap is expected to widen by

1.2 and 1.5% at horizon end 2013, except if new measures are taken. And in the current turbulent

economic context, expected job losses total 37,000 in 2009 and 53,000 in 2010.

The sustainability of the Belgian social security is at stake. The High Council of Finance forecasts a

deterioration of the structural deficit up to 5% in 2015. Between 2018 and 2050, ageing-related

extra costs will rise to 4.2% of GDP. It took 192 days to form an interim government after the federal

elections of 10th of June 2007. Until now, very few measures have been taken to overcome the

opposition between, on the one hand, Flemish calls for more decentralized policy autonomy and

financial accountability and, on the other hand, a Frenchspeaking “non” coupled to the wish to

maintain interpersonal and hence interregional solidarity.

In terms of accountability of the decentralized entities, a job incentive paradox is ingrained within the

Belgian federation. On the one hand, decentralized entities possess more and more policy levers to

create jobs and to activate residents. Think about active labor market and regional economic policies

for the Regions and education for the Communities. On the other hand, when a resident is activated,

more than 82% of the financial budget returns on activation are reaped by the federal level. Indeed,

it is the federal level which collects taxes and social contributions and who pays unemployment

benefits.

The Belgian federation faces a triple challenge. First, it has to lift up regional employment rates to get

its budget back on track and to finance ageing costs. Second, the federation has to get out of the

political stalemate, which seems quite irresponsible in the light of the dramatic current economic and

financial crisis. Third, it has to improve financial accountability and incentives for decentralized

entities to create jobs and foster growth. Within the context of this triple challenge, 120 Belgian

economists put the principle of the job incentive scheme on the agenda via a text discussed in De

Standaard and Le Soir of 26th January 2008. Walloon and Flemish Ministers of Labor and Economy

- 2 -

J.C. Marcourt and F. Vandenbroucke formulated a proposal for a job incentive scheme in December

2008. The core idea behind the job incentive scheme is relatively simple. If decentralized entities can

share in financial returns on activation, then they can win back part of their public policy investments

in jobs. This pay-back perspective could foster the quality and quantity of these investments, which

might ultimately result into higher employment rates and safeguard interpersonal and interregional

solidarity.

The main questions of this thesis are the following: “Is it possible to implement such a job incentive

scheme? Could it work in practice? What are the main questions, trade-offs and difficulties which

arise when one wants to design such a scheme? Could such a scheme enhance the credibility of an

upward employment rate convergence scenario as one of the key steps to lift up employment rates,

to get out of the current political deadlock, to finance the ageing cost and to solve the job incentive

paradox?”

This research question might seem relatively ambitious. Therefore, we think it is crucial to limit the

scope of this thesis. In this thesis we do not cover three main questions related to our topic. First, we

do not discuss the institutional structure of Belgium. Therefore, we do not assess whether the

current distribution of competences, incomes, expenditures, assets and liabilities between the

federal, regional and community level is optimal. Second, we only study employment incentives for

Belgian political entities. We do not cover labor law-related employment incentives for employers

and employees. Third, we do not compare the advantages and disadvantages of the job incentive

scheme with the advantages and disadvantages of tax decentralization, another big issue on the

political agenda.

Our target audience is double. We target on the one hand academics and on the other hand citizens,

media and policy makers1. But how to deal with this duality? Because of the scientific intentions of

this master thesis, we aim to help and inform decision-makers by proposing facts, trade-offs and

elements of solutions. But since we cannot discuss, test and simulate an infinity of schemes, we have

to make choices at various points. But clearly, this does not mean that we advocate these options.

The main idea is to draw a maximum of lessons from the simulations. We want to be policy relevant.

Therefore, the proposed schemes and their statistical verifications cannot be too complex. All

empirical work and all computations have to be considered as first cuts.

1 In this perspective we summarized some of our idea’s in the newspaper articles Struyven(2009a) and Struyven(2009b).

The articles in De Standaard and Le Soir can be found in Appendix 13.1. of this thesis.

- 3 -

We now introduce the 10 chapters of this work in a nutshell.

In chapter 2, we explain the rationale for a job incentive scheme. First, we quantify the job incentive

paradox. Today, if Regions invest in resident activation, the federal level reaps the financial benefits

with a budget return of €29,000 per activated resident. Regions, which have to invest approximately

€21,800 in active labor market policies to activate one resident, only get back a very small amount.

Regional total incomes only increase by around €3,200 in Flanders, €1,200 in Wallonia and €1,100 in

Brussels when respectively Flemish, Walloon or Brussels’ residents get a job. Second, we argue that

the job incentive scheme might contribute to the upward convergence of employment rates. This

upward convergence seems absolutely necessary in the light of the budgetary, the ageing and the

political challenges faced by the Belgian federation.

In chapter 3, we briefly discuss the main models of the theory of incentives to formulate five key

lessons for the incentive scheme. First, job externalities create a win-win opportunity. Indeed, more

decentralized incentives and better efforts can increase employment rates. Higher employment rates

reduce the federal budget costs of unemployment while increasing the decentralized incomes trough

incentive boni. Second, we underscore the necessity to study schemes which maximize incentives

while controlling budget risk for decentralized entities. Third, we argue that the incentive scheme

should not insure decentralized entities against bad results which are the consequence of bad efforts.

Entities should only be protected against bad luck. Fourth, we argue that performance should be

coupled to global indicators rather than to specific indicators. Excessively specific indicators might

induce decentralized entities to neglect important non-rewarded tasks. Fifth, we argue that the

exposure of the three Regions and their peers to the international business cycle creates an

opportunity for benchmarking. Benchmarking could filter out risk for a given level of incentives.

In chapter 4, we discuss the economic advantages of assessing global results rather than efforts or

specific results. We present the trade-offs of penalizing versus rewarding only, absolute versus

relative performance evaluation and of assessing yearly rather than long-run results. We analyze the

salient features of schemes proposed by Van Rompuy, Van der Linden and Vandenbroucke and

Marcourt. We work out formula’s for schemes based on absolute and relative performance

evaluation of employment rate variations.

Chapter 5 leverages the HERMREG regional employment database. The database goes back to 1980.

We test how well certain performance indicators might improve incentives, how they might limit risk

exposure and how they might avoid any unintended consequences. If one opts for yearly variations,

then evaluating Flanders’ and Wallonia’s performance relative to one another brings down risk by

- 4 -

respectively 55% and 47%. This is the consequence of the 89% correlation between the yearly

employment rate variations of Wallonia and Flanders. If one opts for assessing cumulative variations,

then Belgian benchmarking and France-Netherlands-Germany neighbor benchmarking are two

effective risk reduction techniques for Flanders and Wallonia. For Brussels, benchmarking with other

Belgian Regions or with our neighbor countries, does not seem to reduce risk significantly.

Chapter 6 shows that Brussels has a special labor market. The special labor market is characterized

by the daily inflow of around 304,000 commuters, an atypical sector structure, a skill mismatch and a

language challenge. For Brussels, benchmarking is not the magical solution to reduce risk. The

scheme could take into account the specificities of Brussels. Mobility premiums could foster

commuting. Reinvestments of boni into education could tackle the skill and language challenges.

Chapter 7 summarizes the important potential of decentralized entities to increase employment

rates. Policy changes explain around 74% of the cross-country differences in unemployment changes.

Active labor market, regional economic and education policies are largely decentralized. Various

empirical studies show that active labor market policies can have a positive net impact on

employment rates. The impact increases if the right interventions, such as counseling schemes, are

well-designed, accompanied with other positive measures and regularly evaluated. Belgian Regions

could increase the job impact of their active labor market policies. Therefore, they could invest more

in training rather than in direct job creation. Education stimulates employment both directly and

indirectly by fostering growth and innovation. One year of extra schooling decreases directly the

probability of being unemployed by around 17.2% for the average Walloon citizen at working age.

Chapter 8 lists several options for employment rate variation targets. We define targets such that

Regions receive no bonus or malus if they achieve certain employment rate variation targets. Targets

could be set on the basis of Lisbon objectives, Belgian interregional convergence paths or European

benchmarking. European benchmarking could be based on leading countries such as Denmark.

Suppose Flanders bridges 50% of the employment rate gap with Denmark at horizon 2020. Suppose

that Brussels and Wallonia achieve this level in 2030. To follow this Semi-Danish path, Brussels,

Flanders and Wallonia should join the 17%, 42% and 20% fastest European employment rate

climbers.

Chapter 9 discusses the potential size of incentivization. To fix the size of incentivization,

policymakers might look at several factors. These factors are related to risk and to budgets. Major

risk-related factors are the current exposure of decentralized budgets to (business cycle) risk and the

level of risk aversion. Major budget-related factors are the budget sizes and the potential for

- 5 -

decentralized to generate cash in bad days. In bad days cash could be generated through borrowing,

taxation or cost-cutting. The size of incentivization could be set such that Regions can finance

investments with scheme boni or such that total budget returns on activation are equally shared

between the federal and Regional level. Equal sharing of the return on activation between the

federal and Regional level is possible if Flanders, Wallonia and Brussels receive around €10,800,

€13,700 and €12,700 per activated resident.

The budget- and job impacts of some schemes are simulated in chapter 10. Simulations aim to draw

a maximum of lessons rather than to confirm or reject any options. Budget impact simulations show

that schemes based on Semi-Danish or Lisbon targets are ambitious for Wallonia and Brussels. If one

wants to protect Wallonia and Brussels against the vicious circle risk, limiting incentive flows to boni

is an option. For the job impact simulations, we do not assume a precise policy reaction because this

would be to speculative. We nevertheless argue that decentralized Belgian entities would react to

financial activation incentives created by job bonus schemes. This arguing is based on significant

international empirical evidence of government reactions to various financial incentives such as tax

base elasticities, financing of own expenditures by other governments and bail-outs by other

governments. Therefore, we present a range of job impact figures triggered by a range of active labor

market policy reactions for several incentivization sizes rather than estimating one precise job impact

figure for a given incentivization size.

In chapter 11, we repeat the two take-away points of this work. First, the job bonus malus could lead

to higher activation incentives and higher employment rates. Second, coupling benchmarking of

employment rate variations to mobility premiums could be an attractive option. This option could

combine solidarity and responsibility by stimulating activation and fostering interregional

cooperation while controlling budget risk.

- 6 -

2. RATIONALE FOR JOB BONUS MALUS In this chapter we argue that a job bonus malus might be useful for the Belgian federation for at least

two reasons. First, it might improve financial returns, gained by decentralized governments when

they activate their residents. We show in 2.1. that these activation returns are low today, compared

to the federal returns and compared to the decentralized cost of activation. Second, these better

incentives might lead to more and more effective public decentralized investments in active labor

market policies and education and might, ultimately, result in an upward convergence of the

Regional Belgian employment rates. Indeed, we argue in 2.2. that employment rates have to

converge upward to finance the social security and to ensure the sustainability of the Belgian

federation.

2.1. TODAY ACTIVATION INCENTIVES ARE LIMITED AT THE DECENTRALIZED LEVEL We compute in 2.1. financial returns on job creation for the federal, Regional and Community

budgets in respectively 2.1.1., 2.1.2. and 2.1.3. The Regional and Community activation returns result

from the positive budget response to an increase in Regional GDP, which is associated to job

creation. We discuss the aggregate incentive policy implications of those returns in 2.1.4.

2.1.1. FEDERAL RETURN

2.1.1.1. Federal budget cost per unemployed person in 2002

The Planning Office defines the federal cost per unemployed as an average cost based on average

values of unemployment benefits, average wages and average tax rates. The evaluation is quite

simple: the budget cost is the sum of the unemployment benefit cost2 for an average employee in

the private sector with an average wage and the opportunity cost in terms of taxes3 (both direct and

indirect taxes) and in terms of social contributions (both employee and employer contributions). The

Planning Office estimates the total cost in 2002 at €25,700 per unemployed person.

2 This unemployment benefit covers complete unemployment assessed on the basis of the average yearly amount and the

average unemployment duration as communicated by O.N.E.M.-R.V.A. 3 The loss in terms of taxes is the difference between on the one hand direct and indirect taxes paid if the persons would

have been working and on the other hand taxes paid during unemployment.

- 7 -

Table 2.1:Average annual cost per unemployed person and its components 1987-2002(in €1000)

‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02

Total cost 16.4 16.2 16.6 17.6 18.5 19.5 20.3 20.8 21.1 21.6 22.2 22.7 23.5 24.1 24.9 25.7

Unemployment benefit 5.1 5.2 5.4 5.5 5.9 6.0 6.3 6.4 6.6 7.0 7.1 7.3 7.5 7.7 8.0 8.5

Loss social contributions 7.2 7.0 7.4 7.9 8.3 9.1 9.3 9.4 9.4 9.5 9.8 9.8 10.2 10.3 10.6 10.9

Loss taxes 4.1 4.0 3.9 4.2 4.3 4.5 4.7 5.1 5.1 5.2 5.3 5.5 5.8 6.1 6.3 6.3

Source: De Bresseleers et al(2004)

Table 2.2:Growth rate and break-down of annual cost of an unemployed person 1987-2002(in %)

Share in total cost ‘87 Share in total cost ‘02 Growth rate ‘87-‘02

Total cost 100.0 100 3.0

Unemployment benefit 30.9 33.0 3.5

Loss social contributions 44.1 42.6 2.8

Loss taxes 25.0 24.5 2.9

Source: De Bresseleers et al(2004)

2.1.1.2. Federal budget cost of an unemployed in 2007 with inflation

regression

The most recent estimate of the federal budget cost of an unemployed person goes back to 2002.

Therefore we have to estimate the value for 2007 on the basis of the price evolution4. We obtain a

very strong empirical linear relationship between the Consumer Price Index (base 1996) and the

budgetary cost of unemployment with a R squared of more than 99%:

𝐶𝑜𝑠𝑡 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑡 = −€9,575 + €315 ∗ 𝐶𝑃𝐼𝑏𝑎𝑠𝑒 1996,𝑡 + 휀𝑡 (2.1)

4 We opted for this regression after obtaining less good estimates of the budget cost between 1987 and 2002 by

multiplying the budget cost of 1987 with the price index: the estimation errors are quite large (up to 12.0% in 2002) despite a very strong correlation between the CPI and the budget cost of 99.6%. The reason is that budgetary cost increased faster ,at a growth rate of 3.0%, than the CPI which increased at 2.1% on average.

- 8 -

With this regression the estimation errors are limited and vary in a range from -1.56 % in 1999 to

+3.88 % in 1987 between 1987 and 20025. With relationship (2.1) we estimate the total budget cost

in 2007 at €29,000 per year per unemployed person.

Table 2.3:Unemployed budget cost estimated with a linear regression on the basis of the effective

value in 1987 and the price evolution in 1987–2007

‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02

Total cost(K€) 16.4 16.2 16.6 17.6 18.5 19.5 20.3 20.8 21.1 21.6 22.2 22.7 23.5 24.1 24.9 25.7

Estimated cost(K€) 15.8 16.1 16.9 17.8 18.7 19.3 20.1 20.8 21.3 21.9 22.4 22.7 23.1 23.9 24.8 25.3

Estimation error(%) -3.88 -0.54 1.31 1.16 0.61 -0.82 -1.00 0.05 0.85 1.33 1.06 0.33 -1.56 -0.57 -0.48 -1.37

Source: De Bresseleers et al (2004)and own computations

Table 2.4:Unemployed budget cost in 2002-2007 estimated with a linear regression(in €1000)

2002

6 2003 2004 2005 2006 2007

25,33 25,89 26,63 27,64 28,30 28,99

Source: De Bresseleers et al (2004) and own computations

5 The value of the t-statistic of the price index coefficient equals 3.91.

6 The value for 2002 is the value computed by the Planning Office, whereas values beyond 2002 are own estimations on the

basis of linear regression (2.1).

- 9 -

2.1.2. REGIONAL RETURN Since Regional incomes are not directly coupled to employment but to GDP, we first compute the

impact of employment on GDP. Then we estimate the Regional budget return on activation.

2.1.2.1. Regional GDP variation associated with employment rate variation

We compute the impact of the Regional employment rate on the Regional GDP7 without taking into

account the multiplier-effect8. We argue that activation leads to the payment of an average wage.

The wage payment results into an increase of GDP according to the extra revenues approach9 to

compute GDP. The idea behind this extra revenues methodology to compute GDP is the following.

Any extra sales, resulting from extra production, allow to pay capital- (Yk) and labor (Yl)revenues, to

save for capital expenses (through accounting Depreciations- Dep) and to pay indirect taxes net of

subsidies (Ti-Subs):

𝐺𝐷𝑃 = 𝑌𝑘 + 𝑌𝑙 + 𝐷𝑒𝑝𝑟 + 𝑇𝑖 − 𝑆𝑢𝑏𝑠 (2.2)

Thus, coming back to wages, the increase of GDP (Δ GDP) can be estimated on the basis of the

increase of wages (𝛥 𝑌𝑙 ) if we suppose a locally constant unitary elasticity between GDP and wages:

𝛥 𝐺𝐷𝑃 = 𝛥 𝑌𝑙 ∗ 𝐺𝐷𝑃

𝑌𝑙 (2.3)

Wages represent 50.2 % of GDP in 2007 whereas they represented 52.4% in 2002.

Table 2.5:Shares of revenue categories in Belgian GDP in 2007(in %)

Labour Revenues 50.2

Gross Profit margin 38.5

Indirect Taxes (net of Subsidies) 11.3

Gross Domestic Product 100

Source : Institute for National Accounts

Bresseleers et al(2004) compute a gross average wage for private workers (i.e. manual and

intellectual workers). For 2002, the average gross wage is estimated at €25,040 per worker. We

estimate GDP growth associated with activation at €47,814 per worker in 2002 with equation (2.3).

7 Noticing that employment and GDP are jointly determined.

8 Estimating correctly the multiplier-effect of job creation on GDP seems very difficult from a statistical point of view

because of the bicausality of growth and employment. 9 There are three ways to assess GDP growth. One can measure extra production (value added), extra expenditures

(consumption, investments, public expenses and the net expense of foreign goods) and extra revenues generated by extra production. Here, we focus on the third extra revenues approach.

- 10 -

On the basis of GDP- and price evolutions in 2002-2007, we estimate GDP growth associated with

activation at €58,417 per worker in 2007.

2.1.2.2. Regional budget impacts associated with Regional employment rate

variation

Table 2.6 illustrates how Regional budgets react to changes in Regional GDP as computed by Algoed

(2009)10. The total impact gives the combined response of the three types of Regional revenues: i)

personal revenue tax revenues transferred ii) additional funds of new expenditure competences and

iii) Regional tax revenues.

Table 2.6:Regional budget impacts when Regional GDP increases by €100 in 2007(in €)

Impact budget Fla Impact budget Wal Impact budget Bru

GDP increase of 100 € in Bru 1.34 0.84 1.88

GDP increase of 100 € in Fla 5.55 1.39 0.38

GDP increase of 100 € in Wal -0.90 2.01 0.39

Source: Algoed(2009)

We distinguish volume-, substitution- and solidarity grant effects. There are volume effects since the

personal revenue tax grant and the grant for new expenditures competences are indexed to Belgian

GDP. Own tax revenues are also linked to Regional GDP. Substitution effects arise because changes in

the personal revenue tax revenue in Region i increase the personal revenue tax transfer for Region I,

which affects negatively the funds at the disposal of Regions j and k. Finally, a change in a Region’s

GDP, affects solidarity transfers, which are based on the relative divergence of the Region’s tax

revenues. Table 2.6 teaches us that Belgian Regions only win €1.8811 up to €5.55 when their Regional

GDP increases with €100. Knowing that GDP growth associated with activation equals approximately

€58,417 per worker in 2007 and with the results of Table 2.6, we compute the Regional budget

returns on activation in Table 2.7. Regional returns per activated resident are limited and vary

between €1,100 in Brussels and €3,200 in Flanders.

10

Algoed assumes that the GRP-elasticity of personal tax revenues and the GDP-elasticity of own tax revenues both equal one. 11

The limited return for Brussels and Wallonia arises because the solidarity grant, received by the weaker Region, reduces since the personal tax revenue gap between the weaker Region and the national average, which is the driver of the solidarity grant, reduces. The negative impact of an increase of the Walloon GDP on Flanders’ budget arises because the negative substitution effect dominates the positive volume effect.

- 11 -

Table 2.7:Regional budget impact associated with one activation in 2007(in €1000 )

Budget Fla Budget Wal Budget Bru

Bru 0.8 0.5 1.1

Fla 3.2 0.8 0.2

Wal -0.5 1.2 0.2

Source: Own computations

2.1.2.3. Regional active labor market expenses

Since we now know how much activation brings in for a Region, let us now turn to how much

activation costs for a Region. We compute this cost with Estevão(2003). We assume the three

Regions to increase their active labor market policy (ALMP) expenses with a certain amount resulting

into a total12 Regional employment rate variation with one percentage point. We consider this

investment as a recurring investment with an immediate and certain return and compare this

investment cost with the yearly recurring Regional returns from a one percentage point employment

rate increase. Hence, we obtain a yearly net return. We assume that the ALMP-employment rate

relationship in Estevão is verified in the three Regions in 200713. Estevão defines as dependent

variable the share of the working age population employed in the business sector14. The main

explanatory variable concerns the ALMP expenditures. He computes total ALMP expenditures as a

share of GDP. ALMP expenditures cover expenditures on public employment services and

administration, labor market training, youth measures, subsidized employment and measures for the

disabled. ALMPs are normalized by GDP and not by unemployment15. He tests the following

equation, where X is a vector of control variables16 capturing changes in institutions and the business

cycles, Y a vector of year dummies to control for common shocks, C a vector of country dummies,

and ε the error term, for fifteen17 industrial countries between 1993 and 2000:

𝐵𝐸𝑖 ,𝑡 = 𝛽1 × 𝐴𝐿𝑀𝑃𝑖,𝑡 + 𝛽2 × 𝑋𝑖 ,𝑡 + 𝛽3 × 𝑌𝑡 + 𝛽4 × 𝐶𝑖 + 휀𝑖 ,𝑡 (2.4)

12

Estevão has studied empirically the link between ALMP-expenses and business employment whereas we are interested (because of data constraints) in total employment. Since total employment is the sum of public and private employment, we make the cautious assumption that public employment grows at the same speed as business employment because of the ALMP expenses. 13

Estevão did not publish the coefficient values for Belgium with respect to the country dummies or interaction terms. It would be interesting to know if the Belgian employment rate responds differently to ALMP-expenses than the rates in other countries. But the dataset (8 datapoints per country) seems relatively small to estimate these country dummies. 14

The restriction to the business sector avoids overestimating importance of ALMPs. 15

Countries with lower employment rates spend relatively more in ALMPs but these effects are attenuated by controlling for institutions, other country-specific factors and economic shocks. He applies this normalization to avoid positive bias toward estimating a positive effect of ALMPs unemployment. 16

As main control variables, he proposes per capita GDP, technological growth, extent of economic openness and the share of GDP spent on passive labor market policy (PLMPs). 17

The fifteen countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Netherlands, Norway, New Zealand, Spain, Sweden, the United Kingdom and the United States.

- 12 -

We notice in (2.4) that the employment rate during the year t of Region i (BEit) only depends on the

ALMP-expenditures during the year t of Region i (ALMP it ). In a more sophisticated lead and lag

model, we could also imagine adding as explanatory variables the ALMP-expenditures of previous

years (ALMP it-1 ,ALMP it-2,…). In (2.4), current ALMP expenditures (ALMP it ) also capture the effects of

ALMP-expenditures in the past (ALMP it-1, ALMP it-2, …), given the strong correlation between current

and past expenditures. One can expect a positive effect of past ALMP-expenditures on the

employment rate today since activated residents probably continue working for more than one year

since the day of training, activation, coaching, etc given their higher human capital. By making the

assumption of a permanent18 ALMP-increase and by computing yearly immediate and certain returns,

we do not have to make any assumption on the depreciation rhythm or the risk profile of past stocks

of ALMP-investments. We have a model determining employment rates and wages19. Here is

Estevão’s key result20: “If the share of ALMP expenditures in GDP increases with one percentage

point, then the business employment rate increases with 1.88 percentage points.” Therefore,

applying this result at a macro-level, if Belgium wants to lift up its employment rate by 1 percentage

point, then it should increase its ALMP expenditures from €4.5 Billion to €6 Billion. At a micro-level,

assuming equal costs across Regions, €21,800 should be invested in ALMP expenses to activate one

unemployed, which would otherwise not have found a job, in every Region anno 2007.

2.1.2.4. Regional net returns

Table 2.8 is clear. Regions are not encouraged to invest in ALMP expenses. Activating one

unemployed gives a net cost of €18,600 in Flanders, €20,700 in Wallonia and € 20,700 in Brussels.

Table 2.8:Net Regional returns on activation through ALMP expenses in 2007(in € 1000)

Entity Activation of one unemployed in Fla

Activation of one unemployed in Wal

Activation of one unemployed in Bru

Bru Reg 0.2 0.2 1.1

Bru Reg (incl. ALMP cost) 0.2 0.2 -20.7

Fla Reg 3.2 -0.5 0.8

Fla Reg (incl. ALMP cost ) -18.6 -0.5 0.8

Wal Reg 0.8 1.2 0.5

Wal Reg (incl. ALMP cost) 0.8 -20.7 0.5

Source: Own computations based on Estevão(2003)

18

The assumption regarding the permanent character of this increase seems realistic since the bonus malus scheme aims achieving permanent and structural changes in the policies of decentralized entities. 19

Many of the expected effects of ALMPs on employment occur through variations in wages, which are also a function of ALMPs. So, wages are excluded from the employment rate specification and the estimated effect of ALMPs on employment rates should already incorporate shifts in wage setting. 20

This result is statistically significant at a 5% level and has been obtained with an Ordinary Least Squares method. The t-stat is also significantly different from zero and equals 4.08.

- 13 -

2.1.3. COMMUNITY RETURN Again, like for the Regions, we first compute the impact of activation on GDP, which influences on its

turn the budget of the Community.

2.1.3.1. Impact of Regional GDP on Community budgets

We compute the impact of Regional GDP-increases on Community budgets in a methodology similar

to Algoed(2009). The methodology is based on partial derivatives of Community budget components

with respect to GDP. We base our computations in appendix 13.2. on the clear overview of the four

types of revenues of the French and Flemish Communities21 in Van der Stichele and Verdonck(2001):

the value added tax (VAT)-grant, the personal revenue tax (PIT)-grant, the Radio-TV fee and

Government funding in respect of foreign students. Table 2.9 teaches us that Communities do not

gain much money from GDP increases.

Table 2.9:Community budget impacts if Regional GDP increases with €100 in 2007(in €)

Impact Budget Fla Comm Impact Budget Fre Comm

Increase GDP Fla with 100 € 1.82 0.18

Increase GDP Wall with 100 € -0.25 2.05

Increase GDP Bru with 100 € 1.16 0.92

Source: Own computations based on Conjunctuurnota 2008, Projet de loi de Finances pour l’année 2008 : “Exposé des

Motifs” and NBB, Belogstat figures.

2.1.3.2. Community budget impacts associated with Regional employment

rate variation without multiplier effect

Table 2.10 teaches us that financial incentives for Communities to activate residents in the Region of

jurisdiction, are very low and vary between €1,200 and €500 per activated resident.

Table 2.10:Community budget impacts associated per activated unemployed in 2007(in €1000)

Entity Activation of one unemployed in Fla

Activation of one unemployed in Wal

Activation of one unemployed in Bru

Fla Comm 1.1 -0.1 0.7

Fre Comm 0.1 1.2 0.5


Motifs, HERMREG and NBB, Belogstat figures.

21

To keep it simple, we neglect the German Community and also the transfers between French Community and Brussels-Capital Region (Cocof) and Walloon Region linked to transfers of competences.

- 14 -

2.1.4. Total returns

Table 2.11:Community-, Regional- and federal budget impacts per activation in 2007(in €1000)

Number Entity Activation one

unemployed in Fla

Activation one unemployed in

Wal


Bru

(1) Fla Reg 3.2 -0.5 0.8

(2) Fla Reg (incl. ALMP cost ) -18.6 -0.5 0.8

(3) Fla Comm 1.1 -0.1 0.7

(4) Wal Reg 0.8 1.2 0.5

(5) Wal Reg (incl. ALMP cost) 0.8 -20.7 0.5

(6) Bru Reg 0.2 0.2 1.1

(7) Bru Reg (incl. ALMP cost) 0.2 0.2 -20.7

(8) Fre Comm 0.1 1.2 0.5

(9)=-(1)-(4)-(6) Fed (Financing Law Reg) -3.0 0.7 -1.2

(10)=-(3)-(8) Fed (Financing Law Com) -1.2 -1.1 -1.2

(11) Fed unemployment cost 29.0 29.0 29.0

(12)=(9)+(10)+(11) Fed net 24.8 28.7 26.5

(13)=(2)+(3)+(5)+(7)+(8)+(12) Total Return (incl. ALMP) 8.5 8.8 8.3

(14)=(1)+(3)+(4)+(6)+(8)+(12) Total Return (excl. ALMP) 30.3 30.6 30.1

Source: 0wn computations Lines 1,4 and 6 reflect the impact of Regional GDP increases, associated with activation, on Regional budgets

22.

Lines 2,5 and 7 are the same Regional figures but we take into the account the activation cost23

of one unemployed based on Estevão(2003). Lines 3 and 5 are the Community returns based on own computations regarding the impact on Community incomes of Regional GDP increases associated with activation. Line 11 is gives the total federal budgetary cost of an unemployed person which is an up-dated figure of De Bresseleers et al (2004).

22

The Regional GDP increase, associated with the activation of an unemployed, is based on an up-date of De Bresseleers et al(2004). Regional budget impacts of Regional GDP variations are based on Algoed(2009). 23

ALMPs consist mainly in training, targeted subsidies to job creation, public employment services and other expenditures aimed at promoting employment. Non targeted policies to lower labor costs are not included in this definition, as they are considered general macroeconomic policies.

- 15 -

Table 2.12:Shares of Community-, Regional- and federal budget impacts in total return associated

with activation of an unemployed in 2007(in %)

Channel Entity Activation of one unemployed in

Fla

Activation of one unemployed in

Wal

Activation of one unemployed in

Bru

Indirect: GDP,PIT* -> Financing Law

Fla Reg 10.7 -1.7 2.6


Fla Comm 3.5 -0.5 2.3


Wal Reg 2.7 3.8 1.6


Bru Reg 0.7 0.7 3.6


Fre Comm 0.4 3.9 1.8

Direct: Unemployment benefits, taxes, social

contributions

Federal 82.0 93.7 88.1

Regions, Communities, Federal

Total Return 100.0 100.0 100.0

Source: Own computations *The abbreviation PIT stands for personal income taxes which increase in GDP. In the column “Channel”, we distinguish direct returns of activation through higher taxes, social contributions and lower unemployment benefits and indirect returns through the positive impact of activation on GDP which on its turn drives Regional incomes via the Financing Law.

What are the incentive implications of the summary results in tables 2.11 and 2.12? Taking into

account the ALMP-cost, the three Regions have a limited financial interest in investing to increase the

employment rate. Excluding the ALMP-cost, the financial incentives for Wallonia and Brussels are

not significant (respectively 3.8 and 3.6% of the total return). The incentives for the Flemish Region

represent 10.7% of the total return. Moreover, the returns are not direct neither transparent

because the Financing Law is (i) complex and (ii) linked to Regional GDP and not to Regional

employment. The same story holds for the Communities: incentives for the Flemish and French

Communities represent 3.5 and 3.9% of the total return. The federal level is clearly the biggest

winner when Regional employment rates increase, especially when residents in the lagging Regions

are activated. The federal returns are important, direct and transparent.

We clearly showed that today, activation incentives are limited at the decentralized level. Let us now

turn to the second main argument for the bonus malus: employment rates have to convergence

upward in Belgium both for socio-economic and political stability reasons.

- 16 -

2.2. EMPLOYMENT RATES HAVE TO CONVERGE UPWARD FOR SUSTAINABILITY BELGIAN

SOCIAL MODEL

2.2.1. STATUS-QUO BASELINE COURSE OF EMPLOYMENT RATES IS NOT AN OPTION Table 2.13 provides us with two key messages. First, no Belgian Region has reached nor will reach

the Lisbon 70% employment rate targets in 2013, considered as hurdle rate to finance the ageing

cost. Second, the current employment rate gap (11.1 percentage points between Flanders and

Brussels and 8.8 percentage points between Flanders and Wallonia) is expected to widen with 1.2

and 1.5 percentage points at horizon end 2013 at unchanged policies.24

If we want to close this interregional gap, while at the same time bringing all Regions to sustainable

Lisbon target conform employment rate levels, then we have to achieve this upward employment

rate convergence. Let us now turn to the socio-economic and political arguments for this upward

convergence.

Table 2.13:Expected evolution Regional employment rates until 2013 including crisis impact25(in

percentage points)

Employment rate end ‘07

Employment rate end'13

Average yearly variation employment rate '08-'13


Bru 55.7 56.0 0.06 0.33

Fla 66.8 68.3 0.25 0.40

Wal 58.0 58.0 0.00 0.20

Source: Own computations based on Planning Office(2008)

24

Although the Planning Office does not explain the widening employment rate gap between Flanders and the two other Regions, we can outline the main reasons. For Brussels, demographics play an important role. In Brussels, the active population grows with 3.6 percentage points between 2007 and 2010 whereas the active population in Flanders only grows with 1.5 percentage point. On the other hand, the number of working residents is expected to grow faster is Brussels with a compounded average growth rate of 1.35% versus 0.89% in Flanders between the end of 2007 and the end of 2013 in the baseline scenario which includes the impact of the financial crisis. For Wallonia, the main reason is the lower GRP growth driven by an unfavorable sector structure. Indeed, the fastest growing sectors over the period 2007-2013, in the baseline scenario drafted before the financial crisis, namely market services and construction with growth rates of 2.3% and 3.3% have relatively low shares in Wallonia’s GRP (respectively 38.4% and 5.7%) compared to the relatively high shares of those sectors in Flanders’ GRP (respectively 43.3% and 6%). The second reason for the lower employment rate growth in Wallonia is demographic: the active population is expected to grow with 2.3% over 2007-2010 compared to 1.5% in Flanders. 25

We estimate the number of lost jobs at 66,000 in Belgium in 8.1.3. on the basis of Planning Office data and we suppose they are (definitively) lost in 2009.

- 17 -

2.2.2. CURRENT ECONOMIC CRISIS AND AGEING COST URGE FOR UPWARD EMPLOYMENT

RATE CONVERGENCE

In May 2009, The European Commission estimated the Belgian public deficit for 2009, at unchanged

policies, at 6.1% of GDP. For the first time since 2002, public debt would exceed Belgian GDP in 2010.

In March 2009, the High Council of Finance estimated the structural budget deficit26 at 2.7% of GDP.

At unchanged policies, the structural deficit might deteriorate up to 5% in 2015 due to the rising

ageing costs, increasing at 0.5 to 0.6% of GDP every two years, and due to the interest rate snowball

effect.

Between 2018 and 2050, ageing-related extra costs will rise to 4.2% of GDP. This estimate depends

on critical assumptions concerning demographics, employment rates and productivity gains. The High

Council of Finance and the Ageing Commission stress the need to increase the employment rate to

finance our social security by increasing the financing base while reducing the number of recipients

of unemployment benefits.

In the current turbulent economic content, many workers fall into unemployment. The job bonus

malus can incentivize decentralized entities to coach, train and educate them to avoid them falling

into structural unemployment and to become victims of hysteresis effects. To conclude, the

deteriorating situation of Belgian public finances, both in the short as in the medium run, urges for

significant employment rate increases, as necessary condition to finance the ageing cost.

26

The structural deficit assesses the underlying public financial result, independent of temporary or reversible factors such as the business cycle, international relative price levels or one-shot budget measures. The structural deficit is assessed on the basis of the estimation of the cyclical deviation of effective GDP from the GDP growth trend line, which is the so-called output gap.

- 18 -

2.2.3. CREDIBLE PERSPECTIVE OF UPWARD EMPLOYMENT RATE CONVERGENCE MIGHT

CONTRIBUTE TO ALLEVIATE BELGIAN POLITICAL DEADLOCK Since the 2007 elections, the political debates about institutional reforms have been characterized by

immobility. Once can represent this deadlock as a stylized opposition between, on the one hand,

Flemish calls for more decentralized policy autonomy and financial accountability and, on the other

hand, a Frenchspeaking “non”, coupled to the wish to maintain interpersonal and hence interregional

solidarity.

But is solidarity necessarily at odds with responsibility? No, as outlined by Dewatripont(2009), a

credible job bonus malus might “ensure that federated entities manage to gradually converge so that

solidarity is not forever unidirectional as far as cross-regional flows are concerned”.

Dury et al(2008) quantify how interregional employment rate upward convergence could reduce or

even eliminate North-South transfers. If employment rates converge to 68.1% in the three Regions at

horizon 2030, then financial interregional transfers will change dramatically. Flanders would turn

from a net-payer (1.9% of Belgian GDP in 2007) into a net-receiver (0.2% of Belgian GDP in 2030).

Brussels would pay a transfer equal to 1.1% of Belgian GDP in 2030, compared to 0.1% today. The

interregional transfers, received by Wallonia, would drop from 2.1% of GDP in 2007 to 0.9% of GDP in

2030.

To conclude, the job bonus malus could enhance the credibility of this convergence scenario as one

of the key steps to get out the current political deadlock, which seems quite irresponsible in the light

of the dramatic current economic and financial crisis.

- 19 -

3. LESSONS FROM CONTRACT THEORY

3.1. JOB BONUS MALUS RAISES FOUR ISSUES CONTRACT THEORY CAN SHED LIGHT ON Contract theory often analyses problems where one person (the agent) acts on behalf of the other

(the principal). In general, those contracts work well when the agent is an expert and when the agent

and principal share common interests. The job relationship between the decentralized and the

federal government shares many features of an agent-principal problem: the decentralized level

takes actions for jobs (ALMP’s, education, industrial policy, etc) while budgetary returns from job

investments (i.e. saved unemployment benefits, extra taxes and extra social security contributions)

are federal. A job-bonus malus schemes introduces decentralized financial returns. In this chapter

we base ourselves on the text book Contract theory of Dewatripont and Bolton(2005) and on the

articles of Holmström(1979), Holmström(1982) and Holmstrom and Miligrom(1991). This chapter

focuses on four main issues.

First, employment results of local job policies are uncertain and partially random. It is not because

the local level invests a certain amount of money a in best practice job policies, that a certain number

of jobs q will be created. Companies react to the business cycle and to institutional changes when

they hire and fire. How can we make sure that the business cycle risk is shared optimally? How

should we take into account incentives and the natural aversion of governments to risk? Today,

federates entities are insured against bad employment results. This reduces the decentralized

incentives to avoid those bad results. This is the famous moral hazard problem.

Second, the job actions, the decentralized level undertakes, are difficult to observe or hidden for the

federal government. Despite some international monitoring mechanisms, the federal government

does not have a clear answer to the question: “How optimal are decentralized actions for job

creation”? Indeed, scientific consensus on how optimal job policies should look like is not complete.

Third, creating jobs is about multitasking. Multiple categories of unemployed, from young to old and

from short-term to structural, should go to work. Both the short-term quantity of new jobs and the

long-term quality of jobs matter.

Fourth, multiple agents (governments) strive for more jobs both in and out of Belgium. All have to

cope with the international business cycle. This makes relative performance evaluation possible.

For every issue, we summarize the main contract theoretic models and what the application of the

models can teach us for the job bonus malus.

- 20 -

3.2. FIVE INCENTIVE MODELS ILLUSTRATING ISSUES IN BONUS MALUS DESIGN

3.2.1. PRINCIPAL-AGENT BASELINE MODEL WITH OBSERVABLE ACTIONS

Consider an employer and an employee. The employee (hereafter individual 1) has an initial

endowment of time, which she can keep for herself or sell to the employer (hereafter individual 2) as

labor services. We suppose that utility for the employee u(l,t) and for the employer U(l,t) increase

with purchasing power t (from wages and from selling output) and time l (kept free for leisure and

invested in production process). How many hours of work will the employee be willing to offer and

what wage will she be paid? We assume that both parties are rational and maximize their payoff. If

we denote li the working time, ti the purchasing power and μ the relative bargaining power, then

parties solve the following joint surplus maximization problem:

𝑀𝑎𝑥 𝑈 𝑙11, 𝑡1 + 𝜇 𝑢 𝑙2 , 𝑡2 3.1 𝑙𝑖 , 𝑡𝑖 If utility functions are strictly increasing and concave, then the two parties can increase their payoff

by exchanging efforts for money. The following maximum first order condition implies that the

employer increases working time, as long as the hourly wage (the employer wants to pay) is higher

than the value of one hour of leisure for the employee :

𝑈𝑙

𝑈𝑡=

𝑢 𝑙

𝑢𝑡 (3.2)

3.2.2. PRINCIPAL-AGENT MODEL WITH UNCERTAIN RESULTS FROM OBSERVABLE ACTIONS Suppose now that the output/performance q produced by the agent can be either zero (failure) or

one (success) q ɛ{0,1}. The probability of success Pr(q=1/a)=p(a) is strictly increasing and concave in

the agent’s efforts a. Assume p(0)=0, p(∞)=1. Principal and agent utilities are respectively given by

V(q-w) and u(w)-Ψ(a), where Ψ(a)=a is the effort cost in utility units. The solution is based on two

idea’s: (i) The principal sets a wage wi , which maximizes his expected value anticipating the agent’s

effort ai associated to this wage and (ii) the agent should be better off working than exerting the

outside option (with utility uext).

𝑀𝑎𝑥 𝑝 𝑎 𝑉 1 − 𝑤1 + 1 − 𝑝 𝑎 𝑉 −𝑤𝑂 3.3 𝑎𝑖 , 𝑤𝑖

- 21 -

𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜

𝑝(𝑎)𝑢(𝑤1) + (1 − 𝑝(𝑎)) 𝑢(𝑤𝑂) − 𝑎 > 𝑢𝑒𝑥𝑡 (3.4 𝐼𝑅)

Since the agent’s choice of action is observable and verifiable, the agent’s compensation w can be

made contingent on the action choice. The wage and effort depend on relative risk aversions: when

the principal is risk-neutral, the agent is fully insured through a constant wage w*. Contrarily, when

the agent is risk-neutral, then the principal is fully insured by passing through all output fluctuations

to the agent, earning a highly variable compensation scheme w1*-w0

*=1. In all cases, the marginal

productivity of effort (in terms of produced output) is equated with its marginal cost (in terms of

wage) for the principal.

3.2.3. PRINCIPAL-AGENT MODEL WITH UNCERTAIN RESULTS FROM HIDDEN ACTIONS

Suppose now that the agent’s actions are hidden, that is we cannot make compensation contingent

on actions. The principal’s utility maximization- and the agent participation equations (3.3) and (3.4)

are solved and moreover the agent will choose the effort a to maximize her surplus:

𝑀𝑎𝑥 𝑝(𝑎)𝑢(𝑤1) + 1 − 𝑝 𝑎 𝑢(𝑤𝑂) − 𝑎 (3.5) 𝑎

If the agent is risk neutral, then the agent will absorb all the risk and the surplus of the principal will

not depend on the cycle. If the principal is risk neutral or if both are risk averse, then the optimal

insurance is distorted: the agent gets a larger (smaller) share of the surplus in case of high (low)

performance. In order to induce efforts, the agent is rewarded for success and penalized for failure.

Paradoxically, the principal predicts efforts and realizes that the wage only corresponds to good or

bad luck but needs to commit to such as scheme to induce effort.

- 22 -

3.2.4. PRINCIPAL-AGENT MULTITASKING MODEL Suppose now that the agent undertakes two tasks a1 and a2, each providing an output q1 and q2

27. We

also have three simplifying assumptions: (i) constant absolute risk averse (CARA) risk preferences28;

(ii) a quadratic effort cost function Ψ(a)=1/2ca2+ δa1a2,where δ is strictly positive because raising

effort on the first task raises the marginal cost of effort of the second task; the so-called effort

substitution problem29 and (iii) a linear compensation scheme w=t+s1q1+s2q2 with fixed and

performance related components t and si. The principal chooses t,s1 and s2 to maximize his payoff30:

max 𝑎1 1 − 𝑠1 + 𝑎2 1 − 𝑠2 − 𝑡. 3.6 t,s1 and s2

The agent chooses ai to maximize her certainty equivalent compensation31:

And the agent should be better off working than doing nothing:

𝑡 + 𝑠1𝑎1 + 𝑠2𝑎2 −1

2 𝑐1𝑎1

2 + 𝑐2𝑎22 − (3.7 𝐼𝑅)

𝛿𝑎1𝑎2 − 𝜂/2 𝑠1 𝜎12 + 𝑠2 𝜎2 2 > 𝑤𝑒𝑥𝑡

32

The optimal effort ai33

increases with the performance-related return si, the marginal cost of the

second task cj and decreases with the return of the second task sj. The fact that increasing rewards

for the second task s2 decreases the effort for the first task a1, is the so-called effort substitution

problem.

27

Assume outputs to be a linear function of efforts, that is qi=ai+εi , with σi the variance of εi and σij the variance between εi and εj. 28

The risk preferences are represented by the following negative exponential utility function: u(w,a)=-e -η(w- Ψ(a))

where η>0 is the agent’s coefficient of absolute risk aversion (η=-u’’/u’). 29

In that case, the two tasks are called technologically dependent/substitutes. 30

The principal anticipates ai, to be set rationally by the agent, and pays such a package that the agent is expected to be better off when she works rather than going for the outside option. 31

The compensation is then equal to her expected net compensation net of her effort cost and net of a risk premium. The risk premium, for a given s, is increasing in the coefficient of risk version and in the variance of outputs. 32

wext equals the certain monetary equivalent of the default wage. 33

Mathematically, the effort for task i will be equal to 𝑎𝑖 = (𝑠𝑖𝑐𝑗 – 𝛿𝑠𝑗 )/(𝑐𝑖𝑐𝑗 – 𝛿2).

- 23 -

3.2.5. PRINCIPAL-MULTIPLE AGENTS MODEL AND RELATIVE PERFORMANCE EVALUATION Suppose finally a principal engaged in a contractual relationship with n agents. When individual

outputs are observable, then relative performance evaluation can be used to improve the trade-off

between risk and effort. Suppose that we have a first agent W and a second agent F. They produce

outputs qw and qf, which are individually observable and imperfectly correlated34. We have qw=aw+εw

+αεf and qf=af+εf +αεw. The contracts for the two agents are linear, contain a fixed component zi, a

component related to the own performance qi and a component related to the performance of the

the other agent qj35

. Also assume that we have a CARA utility function36 and a quadratic effort cost37.

The principal writes symmetric contracts38 with the two agents maximizing his payoff:

max 𝐸 𝑞1 − 𝑤1 3.8 𝑎1 , 𝑧1 , 𝑣1 𝑎𝑛𝑑 𝑢1

while each agent chooses effort to maximize her payoff:

𝑎1 ɛ 𝑎𝑟𝑔 𝑚𝑎𝑥 𝐸 −𝑒 −𝜂 (𝑤1− 𝛹(𝑎)) (3.9 IC)

It can be shown that the effort39 of the first agent, we will call her agent W, increases with the

variable component vw and decreases with the marginal cost of effort c. The principal’s problem is

solved sequentially. For any given vw ,uw40 is determined to minimize risk. Optimal uw is negative when

q1 and q2 are positively correlated (α>0). The first agent W is penalized for a better performance of

the second agent F. Better performance of the second agent F is likely to be due to a high realization

of εf which affects positively the first agents’ output. This scheme reduces/filters out exposure of the

first agent W to a common shock affecting both outputs, and thus reduces the variance of the first

agent’s W compensation. The variable vw is then set to optimally trade off risk sharing and incentives.

To conclude, those schemes reduce risk, for a given level of incentives provision, but they might

undermine cooperation and coordination because negative results of peers have a positive financial

impact.

34

εw and εf are independently normally distributed with mean zero and variance σ2.

35 The linear contract compensation structures are as follows :ww=zw+vwqw+uwqf and wf=zf+vfqf+ufqw

36 The CARA utility function is as follows: u(w,a)=-e

-η(w- Ψ(a)).

37 We have the following effort cost function: Ψ(a)=1/2ca

2.

38 The terms of the contract are subject to the individual rationality constraint of the agent.

39 The precise value of aw equals vw/c.

40 Precise values of uw and vw equal -2𝑣𝑤

𝛼

1+𝛼2 and 1+𝛼2

1+ 𝛼2+ 𝜂𝑐𝜎2(1− 𝛼2).

- 24 -

3.3. KEY LESSONS

3.3.1. JOB EXTERNALITIES CREATE AN OPPORTUNITY FOR A WIN-WIN INCENTIVE SCHEME As outlined in the baseline model 3.2.1., sub-optimal efforts can be increased to an optimal level

through a win-win contract. Low decentralized job investment incentives and poor Belgian

employment results suggest sub-optimal decentralized efforts. A job bonus malus could repair

distorted incentives. Therefore the decentralized level increases job efforts and achieves better job

results in a win-win process: the federal level bears lower budget costs from unemployment (cfr.

increased output and sales for employer) and the decentralized level has higher incomes through the

job bonus (cfr. hourly wage for employee).

3.3.2. UNCERTAIN RESULTS RAISE A RISK AVERSION ISSUE As outlined in the second model 3.2.2., employment results of decentralized policies are uncertain

and partially random. Federal and decentralized job results and hence budgets are exposed to

business cycle-and job policy risks41. Optimal risk sharing involves a trade-off. How to provide

maximum incentives to the decentralized level by coupling decentralized bonus-malus incomes to

efforts or results if we want at the same time limit decentralized budget instability? An optimal

bonus malus scheme features two risk properties. First, such a scheme takes into account degrees of

relative risk aversions. Assuming that neither the decentralized nor the federal governments are fully

risk-neutral, an optimal scheme exposes both levels to some extent of financial job risk. Second, such

a scheme is coupled to indicators which minimize randomness: exogenous shocks should be filtered

out to limit risk exposure. All techniques limiting risk for a given level of incentives42, can be win-win.

3.3.3. IMPERFECTLY OBSERVABLE ACTIONS RAISE A MORAL HAZARD ISSUE As outlined in the third model 3.2.3., unobservable actions cause a deviation from the first best social

optimum achieved under observable actions as outlined in 3.2.2. Indeed, under observable actions,

local compensation can be coupled to efforts rather than to results. Contrarily to results, efforts are

not biased by exogenous shocks, exposing the decentralized level to risk. To limit this risk exposure,

today, the decentralized level is insured against bad employment results. This reduces the

decentralized incentives to avoid those bad results. This is the famous moral hazard problem arising

from hidden efforts to create jobs.

41

Policies are local, federal, European and international. 42

Or we can conversely also think about polices which increase incentives for a given level of risk.

- 25 -

The job bonus malus envisions to alleviate this moral hazard problem. By limiting insurance against

bad results to results, which are the consequence of bad luck, the decentralized level has to bear the

consequences of bad efforts. Ultimately, the decentralized level will increase efforts to avoid those

bad results of bad work.

3.3.4. MULTIPLE TASKS RAISE AN EFFORT-SUBSTITUTION ISSUE As in the fourth model 3.2.4., creating jobs is about multitasking. Holmstrom et al(1991) explain that

incentives for a certain task can be provided in two ways: “Either the task itself is rewarded, or the

marginal opportunity cost for a task can be lowered by removing or reducing the incentives on

competing tasks.” Decentralized financial and time resources for activation are limited. Therefore,

the effort-substitution problem arises when a job bonus malus is coupled to a specific indicator, let us

say structural unemployment. Under such as scheme, raising effort on one task, let us say activating

structurally unemployed, increases marginal cost for other tasks, let us say activating freshly

unemployed. Similarly, job indicators focusing on short-term quantitative job gains (e.g. indicators

positively coupled to the amount of new temporary agency workers), could be in conflict with the

long-run goal to create high-quality jobs ensuring maximum macro-economic labor productivity

through an optimal match between the employee’s skills set and the employer’s skills requirements.

The multitasking literature tells us that a job bonus malus should be either coupled to a global

indicator or to a specific indicator, where we control for the effort-substitution issue. Such a specific

indicator should control for the risk that non-rewarded tasks (e.g. activating non-rewarded

categories) are neglected. Finally, if certain parameters are difficult to measure and crucial for the

federal level (e.g. job quality43), then low-powered incentives can be appropriate.

3.3.5. MULTIPLE AGENTS CREATE AN OPPORTUNITY FOR RELATIVE PERFORMANCE

EVALUATION As outlined in the fifth model 3.2.5. , bonus-malus schemes can improve the trade-off between effort

and insurance by using decentralized result-contingent indicators. The exposure of several

Regions/countries to similar exogenous business cycle- and policy shocks, allows to improve

incentives, for a given level of insurance, by filtering out common shocks via benchmarking

techniques.

43

For an interesting overview of several indicators and definitions of job quality we refer to Davoine et al(2007). Davoine et al(2007) link job quality to aspects such as skills, lifelong learning and career development, gender equality, health and safety at work, flexibility and security, inclusion and access to the labor market, work organization and work life balance, social dialogue and workers involvement, diversity, non discrimination and overall economic performance and productivity.

- 26 -

4. WHICH PERFORMANCE INDICATORS COULD BE

DESIGNED? To design the incentive performance scheme, we already outlined the goals and contract theoretical

principles in respectively chapter 2 and chapter 3. But how could one measure performance? This

chapter aims to answer this question by exploring the following sub-questions. In 4.1. we explore the

questions and the trade-offs one has to ask when designing such a scheme. In part 4.2., we discuss

the most concrete indicators that have been proposed by politicians and academics. In 4.3, 4.4. and

4.5. we respectively analyze the following questions: Are there any smart options to overcome the

trade-offs? Which shortlist of indicators will we consider in the more operational chapters of this

work? And how do we translate all those ideas in concrete financial formula’s?

4.1. SCHEME DESIGN QUESTIONS, SCHEME FEATURES AND SCHEME TRADE-OFFS We formulate eight questions one has to answer to select a performance indicator. With economic

reasoning and judgment, we suggest our preferred answers to the first five questions we raise . For

the last three questions, we remain uncommitted and we present the advantages of the different

potential answers to three questions in the form of three scheme trade-offs.

4.1.1. FIVE ANSWERS TO FIVE QUESTIONS Here are the five questions to which we suggest specific answers:

(i) Measuring results rather than measuring efforts

As outlined in chapter 3, one can measure efforts or results. We argued in 2.2. that improving

employment results is the ultimate goal of this scheme. We will illustrate in chapter 7 that that

defining good activation- or education efforts is not only difficult, but sometimes also subjective: the

optimality of, for instance, various labor market policies is a controversial issue in the academic

community. Defining good results seems less difficult, subjective and controversial. Finally, assessing

efforts could reduce decentralized autonomy in the way policies are designed and implemented. To

conclude, we consider measuring results rather than efforts because this seems to be the most goal-

effective, feasible and autonomy respectful option.

- 27 -

(ii) Measuring global results rather than specific results

Global indicators are for instance the employment rate or the unemployment rate. Examples of

specific indicators are the structural unemployment rate or the number of activations by the

Regional Public Employment Services; Actiris, VDAB and FOREM.

Specific indicators reflect relatively more the targeted specific efforts44 and relatively less noise than

global results. For instance, specific metrics of structural unemployment rate reduction probably

reflect the efforts to activate the structurally unemployed rather than the business cycle compared

to global metrics such as the total unemployment rate.

On the other hand, specific indicators exhibit unintended consequences: as outlined in 3.3.4., effort-

substitution can push rewarded entities to neglect non-rewarded tasks (e.g. activating school leavers

or freshly unemployed). Finally, the problems this scheme aims to tackle (job incentive paradox,

interregional employment gap, difficult financing of ageing cost, unsustainability Belgian social

security,…) are so global, that it seems more opportune to measure global rather than specific

results.

To conclude, we consider global rather than specific indicators although they might signal more noise

because we want to avoid effort-substitution and because the problem is global and

multidimensional.

(iii) Considering the employment rate rather than the unemployment rate or employment

Should one evaluate employment, employment rate or unemployment rate? To avoid decentralized

entities to improve their results cosmetically by manipulating the data rather than taking real actions,

the performance indicator should be non-manipulable and transparent. Therefore we prefer the

employment rate or the number of jobs to the unemployment rate. Indeed, unemployed workers

should not be induced to exit the labor force which might be a theoretical option to reduce the

unemployment rate. We prefer the employment rate to employment because the employment rate

is a politically visible indicator that can be easily benchmarked. Morover the employment rate is a

proxy for the capability to finance social security costs since it indicates the relative importance of

the active and non-active persons within the population at working age.

44

As rightly pointed out by Dewatripont(2009), “the relevant concept in this debate is that of ‘structural underemployment and/or unemployment’, not the cyclical component of unemployment that we are starting to witness right now because of the financial crisis; this latter one has to be dealt with by macro-economic policy coordinated at EU and even world level”.

- 28 -

(iv) Measuring progress in results rather than final results

Measuring final results means penalizing Regions for their weaker initial position, inherited from the

period before the introduction of the scheme. This seems to be at odds with the goal to ensure static

justice. Therefore, we consider measuring progress in results rather than final results.

(v) Being more ambitious for Regions with more potential

We do not want to penalize Regions for their weaker initial position. Similarly, we do not want to

penalize Regions with stronger initial positions for their positive performances in the past. Indeed,

requiring the same speed of progress from those Regions closer to the frontier, seems not only

unrealistic but also at odds with the goal to ensure dynamic justice. Moreover, as outlined in 2.2., we

want to foster interregional convergence in order to bring all Regions to very high employment rates.

To conclude, to ensure dynamic justice, to foster convergence and to aim high for all Regions, we

consider faster speeds of progress for Regions with a lower initial position.

4.1.2. THREE TRADE-OFFS (i) Penalizing on top of rewarding?

In a classical insurance context, the word bonus malus refers to penalizing as to rewarding. But what

are the advantages and disadvantages of such a symmetrical incentivization compared to an

asymmetrical incentivization where boni only flow from the central to the decentralized level? From

a pure incentives point of view, this question is not essential. Indeed, incentives arise from marginal

returns and the expectations of missing a bonus or having to pay a malus imply same marginal

returns and investment decisions. At the end of the day, there is no free lunch and someone will

receive the return if jobs are created since they reduce budgetary costs of unemployment.

Algoed(2009) argues a symmetrical bonus malus to be better for the federal level. It would lead to a

more equal sharing of the business cycle risk between the federal and decentralized levels. Second, it

would be better for the federal budget, which has the bear the burden of the ageing cost and which

struggles with important structural deficits.

On the other hand, such a symmetrical bonus malus seems not only politically difficult but also

economically dangerous. Imagine a Region has bad luck leading to bad results. Under a bonus-malus,

these bad results can create a dangerous vicious circle where bad luck leads to penalties etc.

To conclude, we face a trade-off of between, on the one hand, sensitivity to the vulnerable federal

budget, which is the main advantage of a bonus-malus, and, on the other hand, political feasibility

and elimination of the vicious circle risk, which is the main advantage of a bonus scheme.

- 29 -

(ii) Measuring absolute or relative performance?

As will be discussed in chapter 7, employment rate variations result from many factors. Decentralized

entities can influence some of them (e.g. active labor market policies, education, Regional

economy,…) but others are out of their control, such as the international business cycle or federal

institutional changes in labor law, wages or taxation. Can we eliminate the impact of these external

factors? As explained in chapter 3, filtering out external factors allows to incentivize more for a given

level of risk.

In chapter 5, we show that relative performance evaluation of strongly correlated entities is a

powerful technique to reduce risk. On the other hand, relative performance means than a certain

entity can increase its rewards by either increasing its own output, which is a positive incentive, or by

decreasing the output of its peer, which provides a negative cooperation incentive. Insofar the entity

has an impact on the output of its peer (e.g. in a context of Belgian benchmarking where commuters

living in one Region represent an important part of the working force of the other Region), this

second effect can lead to sabotage, lower coordination or political frictions.

To conclude, we face a a trade-off between on the one hand risk reduction, which is the main

advantage of relative performance evaluation, and on the other hand positive cooperation incentive

provision, which is the main advantage of absolute performance evaluation.

(iii) Measuring yearly progress or cumulative progress since introduction scheme?

One can measure the employment rate progress since the previous year. Alternatively, one might

take a more long-run view and assess the cumulative progress since the introduction of the scheme

(until a fixed scheme evaluation date).

As we will explain in chapter 7, we cannot forecast from which point in time on and for how long a

public decentralized investment in, for instance, active labor market policies or in schooling, will

boost the employment rate. If the activation effects, triggered by decentralized investments, are

spread over time, then it seems reasonable to couple decentralized rewards to the cumulative

progress over time. Moreover, yearly progress variations might exhibit unintended consequences for

Regions who progress very rapidly during the first years of the scheme’s lifetime. Indeed, on the one

hand, federal financial gains from this activation might recur for many years after the investments.

On the other hand, it might be unrealistic for the decentralized entity to book significant progress,

exceeding ambitious progress targets if the entity took a kick-start. Therefore, the uncertain duration

of federal gains from decentralized investments and the goal to reward Regions, having booked

important progress from the beginning, plead in favor of cumulative progress metrics.

- 30 -

Nevertheless, a vicious circle scenario might occur for a Region, investing heavily in the beginning

without any important results during the first years. In that case, bad early results continue impacting

cumulative results for the many years after the early years of the scheme launch. Moreover,

measuring cumulative progress allows the federal budget to benefit from cumulative level returns

(so-called federal dividends) from employment rate increases in the past.

To conclude, we seem to be facing a trade-off between on the one hand encouraging long-run

investments, which is the advantage of measuring cumulative progress, and on the other hand

reducing the vicious circle risk while letting the federal level benefit from long-run activation

dividends, which is the double key advantage of measuring yearly progress.

4.2. OPTIONS TO OVERCOME TRADE-OFFS

(i) Combining the advantages of a bonus and a bonus-malus?

The main disadvantage of the asymmetrical bonus consisted in the extra pressure it might put on the

federal budget. But, why not combining an asymmetrical bonus with a New Belgian Deal on a

different sharing of total public financial resources and expenditure competences between the

federal and the decentralized entities? This New Belgian Deal could take more into account the

difficult federal financial situation and its important liabilities for the future. Such an asymmetrical

bonus, coupled to a New Belgian Deal, seems politically feasible, financially positive for the federal

level and well-equipped to avoid a vicious circle doom scenario. Chapter 10 quantifies this double

approach. Another option might be to subtract current mali from future boni.

(ii) Combining the advantages of absolute and relative performance evaluation?

Is it possible to combine risk reduction and positive cooperation incentive provision? A first option

might be to benchmark results with results booked by peers outside the Belgian federation.

International benchmarking can be effective in terms of risk reduction insofar that employment

evolutions are sufficiently correlated and exposed to common external factors. A second option is to

couple Belgian benchmarking to positive cooperation- or mobility incentives. These mobility

incentives can off-set the unintended negative cooperation incentive effect of Belgian relative

performance evaluation. Chapter 5.6. will work out a formula for mobility incentives.

- 31 -

(iii) Measuring yearly progress or cumulative progress since introduction scheme?

One option to overcome this trade-off would be to design a combined indicator which would be a

weighted average of recent and cumulative progress. We nevertheless have a slight preference for

the pure cumulative option. We could also measure cumulative progress during a relatively short test

period, let us say five years, before evaluating the scheme after this test period. By doing so, the

potential vicious circle risk, linked to the cumulative option, might be minimized.

4.3. ANALYSIS FEATURES OF SCHEMES PROPOSED BY ACADEMICS AND POLITICS Several academics and Ministers Marcourt and Vandenbroucke proposed principles for some

potential schemes. Since the devil is in the details, we focus on the four most concrete proposals Van

Rompuy(2007), Van der Linden(2007) and Vandenbroucke(2009). Table 4.1 provides an overview of

the key features of those four schemes.

Table 4.1:Main features of most concrete academic and political scheme proposals

Name proposal Van Rompuy (1)

Van Rompuy (2)

Van der Linden Marcourt-Vandenbroucke

Results/Efforts Results Results Results Results

Global/Specific results Specific Specific Semi-global Global

Indicator Marginal unemployment benefits

Unemployment benefits – contribution in financing benefits

Number of placements

Number of employed residents

Progress/Level Progress Level Progress45

Progress

Yearly/Cumulative progress

Cumulative - Yearly Yearly

More ambition high-potential Region?

No No No No

Penalizing? Yes Yes No Not directly

Relative/absolute evaluation

Absolute Relative Absolute Absolute

45

Van der Linden proposes to assess the level of placements, which roughly corresponds to the progress of the number of employed persons besides the number of persons who lose their job. So, although he proposes a level metric, the idea is clearly to reward the employment performance progress.

- 32 -

4.3.1. THE TWO VAN ROMPUY PROPOSALS The first proposal is based on the idea of financial accountability for long-run unemployment benefits

at the margin. The scheme compares the number of structural unemployed during a certain year

with the number in the reference year. If long-run unemployment has dropped, then federal

unemployment benefit savings are refunded to the decentralized level. Conversely, when structural

unemployment increases with respect to the reference year, then Regions should finance this extra

benefits with their own resources.

As explained in 4.1.1., this proposal has the interesting feature to focus on structural unemployment

which is less sensitive to the business cycle than total unemployment. But, the specific scope of this

proposal might create some effort-substitution problems, as explained in 3.3.4., and might be too

narrow to provide incentives for achieving the ambitious motives behind the scheme we consider in

chapter 2.

The second proposal is based on the idea of Regional financing of the level of long-term

unemployment benefits. The scheme compares the Regional unemployment benefits with the

national unemployment benefits, weighted by the share of the Region in the national wage bill tax

base. If a Region has relatively more unemployed persons and relatively lower taxed wage masses

than the other Regions, then it will have to finance this difference with its own resources.

This proposal assesses the level of results rather than the progress in results. We think this might be

unfair with respect to Regions with weaker initial positions, as we explained in 4.1.1.

4.3.2. THE VAN DER LINDEN PROPOSAL Van der Linden proposes to pay yearly bonus placement premiums linked to the number of activated

unemployed persons. He proposes to couple the size of the premium to the job duration, possibly to

non manipulable job characteristics, anticipated Regional shocks, etc.

Two interesting features concern (i) the business cycle risk elimination technique and (ii) mobility and

cooperation incentives. The business cycle risk could be eliminated by coupling the premiums to the

GDP growth of our neighbor countries. We test this technique empirically in 5.3.2. Mobility-,

commuting- and cooperation incentives could be provided by splitting the total premium for a cross-

border activation into a component for the Region of residence and a second component funded to

the Region of workplace. Nevertheless, we consider the number of placements as a too specific

metric to tackle the global problems defined in chapter 2.

- 33 -

4.3.3. THE VANDENBROUCKE-MARCOURT PROPOSAL

The Regional Ministers of Employment propose to check the effectiveness of every Region, i.e. the

number of employed persons living in the Region every year and compare it to the previous year and

to the previous maximal level. If the effectiveness has increased, then the Regions receive a one-shot

bonus. If there is a drop, this is noted down as a kind of debit balance, which will be credited in

possible future bonuses. Since the boni only reward improvements with respect to the previous year

(“the progress”) and not the improved cumulative effectiveness (“the cumulative level”), the

recurrent profits are for the federal level.

We argue that this proposal is very welcome and would like to underscore the win-win rationale

behind the scheme where both the decentralized level (only boni) and the federal level (federal

dividend from recurrent progress) could be better off. Nevertheless, we wonder if it would not be

better to reward cumulative progress to foster decentralized investments with long-run payoffs.

Second, in the case of business cycle or demographic shocks, the indicator could signal noise rather

than effective decentralized policies. We now turn to the selection of empirically studied indicators.

4.4. SHORTLIST OF THREE CONSIDERED PERFORMANCE INDICATORS On the basis of the five desired scheme features and the three scheme trade-offs, we propose the

following shortlist of indicators which we will study empirically in detail in the next chapters:

Absolute total46 employment rate47 variations;

Relative total employment rate variations48;

Absolute total employment rate variation controlled for external factors (e.g. demographics

and international business cycle) with statistical techniques.

For every of these three considered performance indicators, we think it might be interesting to

consider the bonus malus, bonus, yearly and cumulative versions.

46

There are good reasons to prefer to look at the private employment rate, which reflects economic progress, rather than the total employment rate which also reflects the creation of public jobs. But we are restricted by data availability in our empirical tests in Chapter 5. We only have total employment Regional series even if we would have preferred private employment Regional series. 47 Technically spoken, we propose to use the administrative employment rate, based on employment in the National Account and on the population at labor age (Institute for National Statistics), rather than the European harmonized rate which is not based on the population but on a sample survey. We also think a validation by a scientific committee of the figures could control the risk of data manipulation. 48

In Chapter 5.6. we illustrate how this relative performance evaluation could be coupled to commuting premiums to off-set its cooperation disadvantage.

- 34 -

4.5. TRANSLATION OF THREE PERFORMANCE INDICATORS INTO FINANCIAL FORMULA’S

Hereunder, we formulate and explain financial formula’s for the three indicators shortlisted in 4.4. In

general, the incentive flow (IFi,t) for a Region i during year a year t is proportional to the performance

indicator (PIi,t) and to the incentivization size coefficient (ISCi,t), expressing which amount will be

funded per unit of performance:

𝐼𝐹𝑖,𝑡 = 𝐼𝑆𝐶𝑖,𝑡 × 𝑃𝐼𝑖,𝑡 (4.1)

We do not discuss separately bonus malus and bonus versions since formula’s are very similar. For

the latter, negative values of the performance indicator result into zero financial incentive flows

whereas, for the former, incentive flows will always have the same sign as the performance indicator.

4.5.1. ABSOLUTE TOTAL EMPLOYMENT RATE VARIATIONS The key idea is to fix targets for employment rate variations for a Region i during the year t

(ΔERi,t,)and to check whether this variation was above or under the target (ΔERi,t,targ,). In the former

case, the absolute performance indicator (APIi,t) is positive, in the latter case, the indicator is negative

under a bonus-malus scheme and zero under a bonus scheme:

𝐴𝑃𝐼𝑖,𝑡 = ∆𝐸𝑅𝑖,𝑡 − ∆𝐸𝑅𝑖 ,𝑡 ,𝑡𝑎𝑟𝑔 (4.2)

If one aims to reward short-run progress since the previous year, then the yearly employment rate

variation in the year t (ΔERi,t,year,) is the difference between the employment rate this year (ERi,t) and

the employment rate last year (ERi,t-1):

𝛥𝐸𝑅𝑖 ,𝑡 ,𝑦𝑒𝑎𝑟 = 𝐸𝑅𝑖 ,𝑡 − 𝐸𝑅𝑖,𝑡−1 (4.3)

If one aims to reward cumulative progress since the introduction of the scheme, then the cumulative

employment rate variation in the year t (ΔERi,t,cum,) is the difference between the employment rate

this year (ERi,t) and the employment rate in the reference year(ERi,,t_ref ):

𝛥𝐸𝑅𝑖 ,𝑡 ,𝐶𝑢𝑚 = 𝐸𝑅𝑖,𝑡 − 𝐸𝑅𝑖 ,𝑡_𝑟𝑒𝑓 (4.4)

- 35 -

In this cumulative case, the average yearly cumulative employment rate variation(𝛥𝐸𝑅𝑖 ,𝑡,𝐶𝑢𝑚 _𝐴𝑣𝑔 )

since the introduction of the scheme, can be easily computed:

𝛥𝐸𝑅𝑖 ,𝑡 ,𝐶𝑢𝑚 _𝐴𝑣𝑔 =𝐸𝑅𝑖,𝑡−𝐸𝑅𝑖,𝑡_ 𝑟𝑒𝑓

𝑡−𝑡𝑟𝑒𝑓 (4.5)

4.5.2. RELATIVE TOTAL EMPLOYMENT RATE VARIATIONS We compare the absolute performance of Region i (𝐴𝑃𝐼𝑖,𝑡)with the absolute performance of a

comparable peer (𝐴𝑃𝐼𝑝𝑒𝑒𝑟 ,𝑡) to filter out external factors. We explain in 5..3.1. the economic

assumptions, concerning the exposure to common external factors, behind equation (4.6):

𝑅𝑃𝐼𝑖,𝑡 = 𝐴𝑃𝐼𝑖,𝑡 − 𝐴𝑃𝐼𝑝𝑒𝑒𝑟 ,𝑡 = ∆𝐸𝑅𝑖,𝑡 − ∆𝐸𝑅𝑖,𝑡 ,𝑡𝑎𝑟𝑔 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 ,𝑡𝑎𝑟𝑔 (4.6)

4.5.3. ABSOLUTE TOTAL EMPLOYMENT RATE VARIATION CONTROLLED FOR EXTERNAL

FACTORS WITH REGRESSION We filter out external factors from the absolute employment rate variation of Region i(∆𝐸𝑅𝑖 ,𝑡 ,)by

approximating the impact of external factors on the employment rate variation (∆𝐸𝑅𝑖 ,𝑡 ,𝑝𝑟𝑜𝑥𝑦 _𝑒𝑥𝑡 ) to

obtain a filtered absolute employment rate variation (𝐹𝐴𝐸𝑅𝑉𝑖,𝑡 ):

𝐹𝐴𝐸𝑅𝑉𝑖,𝑡 = ∆𝐸𝑅𝑖,𝑡 , − ∆𝐸𝑅𝑖,𝑡 ,𝑝𝑟𝑜𝑥𝑦 _𝑒𝑥𝑡 (4.7)

We will explain in 5.5 the empirics of the risk filtering techniques. Let us now turn to the empirical

analysis of the shortlisted indicators in the next chapter, namely chapter 5.

- 36 -

5. EMPIRICAL ANALYSIS OF HOW WELL SHORTLISTED

INDICATORS WORK

5.1. OBJECTIVES OF EMPIRICAL ANALYSIS We formulated a shortlist of indicators in 4.4. on the basis of scheme objectives in chapter 2, lessons

from contract theory in chapter 3 and a discussion of scheme features, proposals and trade-offs in

4.1., 4.2. and 4.3. But how risky are these indicators? Are there any unintended consequences, such

as cooperation issues, one has to deal with? What happens if we test the shortlisted indicators with

historical real data?

As explained in 4.2., measuring yearly and cumulative progress might both be interesting options. In

4.3., we proposed to evaluate an eventual cumulative progress scheme after 5 years. Therefore, we

will not only study the yearly employment rate variations, but also the 2-year, 3-year, 4-year and 5-

year variations. We propose three ways to reduce risk. First, Belgian benchmarking refers to the

situation where a Belgian Region is taken as peer (see equation ((4.6)). Second, one might also go for

international benchmarking49.Third, regressions might estimate the impact of external factors (see

equation (4.7.)).

In part 2, we explore the potential of those three risk reduction techniques with basic descriptive

statistics. We propose five statements. First, cumulative employment rate variations are less risky

than yearly employment rate variations. Second, the strong correlation between Flanders’ and

Wallonia’s yearly and cumulative employment variations provides support for yearly and cumulative

Belgian benchmarking. Third, the strong mutual interaction between Flanders’ and Wallonia’s yearly

employment rate variations provides support for yearly Belgian benchmarking. Fourth, the relatively

important correlation between the cumulative employment rate variations of our three neighbors

and the cumulative variations of Flanders and Wallonia’s provides perspectives for cumulative

neighbor benchmarking. Fifth, Brussels is special with relatively uncorrelated and volatile yearly and

cumulative employment rate variations.

49

As international peers, we opt for the three main neighbor countries (Netherlands, France and Germany) because they buy more than 54% of Belgian exports. Their economic progress, which is reflected in their GDP- and employment performances, drives Belgian export-related jobs and is hence a good proxy for the effect of the international business cycle on the Belgian economy. We notice that in 2008, Germany bought 21.7% of Belgian exports, France 19%, the Netherlands 13.4% and the group of European Union Member States 84.5%.

- 37 -

In part 3, we analyze the effectiveness of benchmarking and of regression techniques to reduce the

risk of yearly employment rate variations. We find that (i) comparing yearly progress of Flanders and

Wallonia reduces risk significantly and (ii) estimations of external factors50 with regressions do not

yield unambiguous results.

In part 4, we analyze the effectiveness of benchmarking to reduce the risk of cumulative employment

rate variations. For Flanders and Wallonia, benchmarking progress relative one to each other and

relative to the average cumulative progress of our main three neighbors, reduces an important part

of total risk.

In part 5, we reduce our shortlist to empirically effective indicators. In part 6, we discuss how we

could fine-tune the proposed indicators by taking into account demographics, the business cycle in

Brussels and cooperation issues.

5.2. EXPLORING RISK REDUCTION POTENTIAL WITH DESCRIPTIVE STATISTICS

We illustrate the five statements cited in 5.1 on the basis of descriptive tables and charts with

respect to employment rate (variations). Major charts and tables are found in this chapter 5.2 and

complementary charts and tables can be found in appendix 13.3.

5.2.1. CUMULATIVE VARIATIONS LESS RISKY51

THAN YEARLY VARIATIONS Table 5.1 is clear: employment rate variations measured cumulatively over 5 years are less volatile

than yearly employment rate variations. This finding is also reflected in the 5-year curves on Chart

13.2, which are flatter than the yearly curves on Chart 13.1. The relatively low cumulative standard

deviations are expected since cumulative metrics generally smooth out periodical effects.

Nevertheless, as explained in chapter 4, a vicious circle scenario might occur for a Region, investing

heavily in the beginning, without any important results during the first years. In that case, bad early

results continue impacting cumulative results for the many years after the early years of the scheme

launch. It takes time before recent positive results are reflected in cumulative averages.

50

We distinguish two important external factors, which drive employment rate variations: the international business cycle and demographic shocks. 51

We define the risk of a performance indicator as the standard deviation of the indicator.

- 38 -

Table 5.1:Standard deviation of 5-year and yearly employment rate variations

Bru Fla Wal

Cumulative variations 1985-2007 over 5 years 0.53 0.39 0.44

Variations 1985-2007 over 1 year 0.91 0.56 0.50

Variations 1981-2007 over 1 year 0.86 0.74 0.63

Source: Own computations based on HERMREG

5.2.2. STRONG FLANDERS-WALLONIA CORRELATION PROVIDES SUPPORT FOR YEARLY AND

CUMULATIVE BELGIAN BENCHMARKING

Table 5.2. teaches us that Flemish and Walloon employment rate variations are strongly correlated.

These correlation figures are reflected visually in Charts 13.1, 5.1. and 13.2 where the Flemish curve

(in red) and the Walloon curve (in green) often move together. This strong correlation suggests an

exposure of both employment rates to common external factors52. As explained in the multiple agent

model in 3.2.5, this strong correlation between Flanders and Wallonia suggests an important risk

reduction potential for Belgian Flanders-Wallonia benchmarking.

Table 5.2:Correlations between Regional employment rate variations in 1981-200753

1 year 3 year 5 year

Bru-Fla 0.25 0.48 0.41

Bru-Wal 0.28 0.59 0.56

Fla-Wal 0.89 0.89 0.95


52

Two main analyzed common external factors, impacting the employment rate variations, are the international business cycle and Belgian federal institutional changes. 53

The correlation coefficients over the sub-periods 1981-1995 and 1995-2007 are slightly from those over the whole period 1981-2007: 0.04 and 0.34 between Flanders and Brussels, 0.10 and 0.23 between Brussels and Wallonia and finally 0.89 and 0.86 between Wallonia and Flanders.

- 39 -

5.2.3. STRONG MUTUAL FLANDERS-WALLONIA INTERACTION EMPLOYMENT RATE

VARIATIONS PROVIDES SUPPORT FOR YEARLY BELGIAN BENCHMARKING Can we explain and predict yearly Regional employment rate variations with employment rate

variations of its peers? For every Region, we test three regressions: a Belgian, a neighbor and a

mixed regression with as respective explanatory variables the employment rate variations of the

other two Belgian Regions, our three main neighbor countries and finally the combination of those

five factors. Here is an example of a mixed regression equation estimated for Wallonia:

ΔERWal ,t = Const + β1 ∗ ΔERBru ,t + β2 ∗ ΔERFla ,t + β3 ∗ ΔERNed ,t + β4 ∗ ΔERGer ,t + β5 ∗

ΔERFra ,t + εWal ,t (5.1)

Table 5.3:Results regressions of Regional yearly employment rate variations

Source: Own computations based on HERMREG and on Eurostat Legend: i)3 stars (***) if the variable is significant at 1 %, ii) 2 stars (**)if the variable is significant at 5 % and (iii) 1 star (*) if the variable is significant at 10 %.

Explanatory variables (horizontal)

Dependent variables (vertical)

Belgian (1981-2007) Neigbor (1984-2007) Mixed (1984-2007)

Bru Flanders Wallonia Bru Flanders Wallonia Bru Flanders Wallonia

Constant coef. -0.11 0.24 -0.18 -0.18 0.19 -0.02 -0.24 0.22 -0,18

t-stat -0.55 3.71*** -2.9*** -0.88 1.98* -0.25 -0.96 3.08*** -2,29**

Bru coef. 0.00 0.04 0.04 -0.04

t-stat 0.02 0.68 0.46 -0.43

Flanders coef. 0.01 0.75 0.31 0.77

t-stat 0.02 9.42*** 0.46 4.82***

Wallonia coef. 0.36 1.05 -0.29 0.73

t-stat 0.61 9.42*** -0.43 4.82***

Netherlands coef. -0.07 0.17 0.13 -0.08 0.07 0.00

t-stat -0.51 2.39** 1.83* -0.51 1.39 0.01

Germany coef. 0.26 0.16 0.08 0.23 0.10 -0.04

t-stat 1.80* 2.19** 0.99 1.34 1.71* -0.64

France coef. 0.78 0.20 0.29 0.80 -0.04 0.16

t-stat 3.74*** 1.87* 2.62** 3.16*** -0.36 1.55

Adjusted R squared 0.07 0.78 0.80 0.41 0.49 0.42 0.35 0.78 0.68

Standard Error Regression

0.86 0.34 0.29 0.69 0.36 0.36 0.72 0.25 0.25

- 40 -

Table 5.3 teaches us one main message per Region. For Brussels, it is very difficult to explain its

employment rate variation with the employment rate variation of an intuitive54 peer. For Flanders,

what happens in Wallonia, explains a very important part of the employment dynamics55

. At first

sight, Netherlands and Germany also seem to explain a part of the job dynamics in Flanders56. But, in

a mixed regression, Germany and the Netherlands are not significant any more. Germany, the

Netherlands and Wallonia are probably exposed in a similar way to the international business cycle

as Flanders. But this similarity is much stronger for Wallonia than for our neighbor countries. For

Wallonia, the results are symmetrical to those for Flanders. Both France and Flanders explain

Wallonia’s employment rate variations but the explanatory power of Flanders is much stronger.

5.2.4. STRONG CORRELATION BETWEEN VARIATIONS OF THREE NEIGHBORS, FLANDERS

AND WALLONIA PROVIDES SUPPORT FOR CUMULATIVE NEIGHBOR BENCHMARKING

The last column of Table 5.4. is clear: average variations in our three main neighbor countries and

Flanders, Wallonia (and Brussels to a lesser extent) are strongly correlated if we take the cumulative

view57. At the country level, the Netherlands and France seem to be closely correlated with

respectively Flanders and Brussels.

Table 5.4:Correlations between 5-year employment rate variations of Belgian Regions and their

neighbor countries

Netherlands France Germany Unweighted ave-rage 3 neighbors

Bru 0.12 0.71 0.30 0.71

Fla 0.91 0.15 0.57 0.93

Wal 0.74 0.49 0.54 0.93

Source: Own computations based on Eurostat

54

In the Belgian regression, nor the Flemish neither the Walloon variations have a significant impact. France’s variation has a positive and significant impact. But, this result is not completely satisfying neither from a econometric point of view (the adjusted R squared equals only 41%) nor from a economic point of view. Indeed, is it intuitive to explain the employment dynamics of a Belgian city zone with the dynamics of a country, which is not exposed to typically Belgian dynamics? 55

The adjusted R squared of the Belgian regression for Flanders is 78% and the p-value associated to Wallonia’s impact on Flanders is smaller than 1/1000. 56

P-values, associated to the impact of employment rate variations in the Netherlands and Germany, equal 2 and 4%. 57

Table 13.4 shows that 1 year correlations between Flanders and Wallonia on the one hand and our neighbors on the other hand are also relatively important but not as important as for the cumulative metrics.

- 41 -

5.2.5. BRUSSELS HAS RELATIVELY UNCORRELATED AND VOLATILE EMPLOYMENT RATE

VARIATIONS Brussels is a special: (i) in Chart 5.1., the blue Brussels’ employment rate variation curve does not

move together with the other two curves (ii) the dispersion of variations is high in Brussels58

(iii) it is

the only Region with a negative average employment rate variation of -0.11 percentage points (iv) in

Table 5.2, we notice that the Brussels’ employment rate is relatively uncorrelated with Belgian

Regions in comparison with the strong Flanders-Wallonia link. Chapter 6 is dedicated to the

specificity of Brussels and tries to outline some explanatory reasons for this Brussels exception59 and

to propose some tailor-made scheme principles.

Chart 5.1:Regional yearly employment rate variations 1981-200(in percentage points)

Source: Based on HERMREG

58

The standard deviation of the yearly employment rate variation is the highest for Brussels with 0.86 percentage points versus 0.63 percentage points for Wallonia and 0.74 percentage points for Flanders (see Table 13.6). 59

One major factor might be the very services-oriented sector structure of the Brussels economy with a high number of public jobs. For the sector structure, see chart 13.3.

-2

-1,5

-1

-0,5

0

0,5

1

1,5

2

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Bru Fla Wal

- 42 -

5.3. REDUCING RISK OF YEARLY EMPLOYMENT RATE VARIATION INDICATOR

5.3.1. BENCHMARKING What are the economic assumptions, concerning the exposure of employment rate variations of a

certain Region and its peers to common external factors, which underpin equation (4.6)= (5.2)?:


The three Belgian Regions and their peers are exposed to common external factors impacting their

employment rate (e.g. the international business cycle). Assume that the employment rate variation

∆𝐸𝑅𝑖,𝑡can be split into a intrinsic net component, independent of external factors, (∆𝐸𝑅𝑖 ,𝑡 ,𝑛𝑒𝑡 ) and a

component due to the impact of external factors (∆𝐸𝑅𝑖,𝑡 ,𝑒𝑥𝑡 ):

∆𝐸𝑅𝑖,𝑡 = ∆𝐸𝑅𝑖 ,𝑡 ,𝑛𝑒𝑡 + ∆𝐸𝑅𝑖,𝑡 ,𝑒𝑥𝑡 (5.3)

Assume that the impact of the external factors is identical for the two peers:

∆𝐸𝑅𝑖,𝑡 ,𝑒𝑥𝑡 = ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 ,𝑒𝑥𝑡 (5.4)

Equation (5.4) defines a relative employment rate variation (𝑅𝐸𝑅𝑉𝑖,𝑡),which is independent of the

external factors:

𝑅𝐸𝑅𝑉𝑖,𝑡 = ∆𝐸𝑅𝑖,𝑡 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 = ∆𝐸𝑅𝑖 ,𝑡,𝑛𝑒𝑡 + ∆𝐸𝑅𝑖,𝑡 ,𝑒𝑥𝑡 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 ,𝑛𝑒𝑡 + ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡,𝑒𝑥𝑡 5.5

= (∆𝐸𝑅𝑖,𝑡 ,𝑛𝑒𝑡 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 ,𝑛𝑒𝑡 )

We can write the relative performance indicator (𝑅𝑃𝐼𝑖,𝑡 ) as a metric based on this relative

employment rate variation (𝑅𝐸𝑅𝑉𝑖,𝑡) independent of the external factors by substituting equation

(5.5) in equation (5.2):

𝑅𝑃𝐼𝑖,𝑡 = ∆𝐸𝑅𝑖,𝑡 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 − ∆𝐸𝑅𝑖,𝑡 ,𝑡𝑎𝑟𝑔 − ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 ,𝑡𝑎𝑟𝑔 (5.6)

= 𝑅𝐸𝑅𝑉𝑖,𝑝𝑒𝑒𝑟 ,𝑡 − ∆𝐸𝑅𝑖 ,𝑡,𝑡𝑎𝑟𝑔 + ∆𝐸𝑅𝑝𝑒𝑒𝑟 ,𝑡 ,𝑡𝑎𝑟𝑔

In appendix 13.4, we search the best risk-reducing peer for all the three Belgian Regions. Therefore,

we compute in Tables 13.5 and 13.6 the percentage of the employment rate variation risk, that can

be filtered out by benchmarking, to obtain the relative employment rate variation 𝑅𝐸𝑅𝑉𝑖,𝑝𝑒𝑒𝑟 ,𝑡 .

- 43 -

We also check graphically if benchmarking flattens out employment rate variation curves in Charts

13.4 until Chart 13.9.

We conclude that (i) relative yearly performance evaluation of Flanders and Wallonia reduces the risk

with 55% for the former and 47% for the latter (ii) benchmarking does not reduce risk for Brussels in

a spectacular manner (iii) neighbor benchmarking is not very effective to reduce risk of yearly

employment rate variations60.

5.3.2. ESTIMATING IMPACT EXTERNAL FACTORS WITH REGRESSIONS Rather than trying to identify the impact of external factors, by looking at what happens at the level

of the peers’ employment rate variations, we are now searching for direct proxies for two main

exogeneous factors. The two analyzed factors are the international business cycle and demographics.

We assume61 that the employment rate variation (𝛥𝐸𝑅𝑖 ,𝑡) can be reasonably well estimated as a

linear function of (i) decentralized policy results (DECPOLi,t) ; (ii) the international business cycle (BC,t)

and (iii) demographics (DEM0i,t):

ΔERi,t = Cst + β1

∗ BCi,t + β2 ∗ DEMOi,t + εi,t (5.7)

Under that assumption, the residual term εi,t should be a good proxy for the decentralized policy

results DECPOLi,t :

εi,t = ΔERi,t − Cst + β1

∗ BCi,t + β2 ∗ DEMOi,t = f 𝐷𝐸𝐶𝑃𝑂𝐿𝑖,𝑡

; noise (5.8)

The estimators β1 and β2 allow us to compute the expected employment rate variation, having

controlled for the business cycle and for demographics (ΔERi,t ). The estimated residual εi,t should

then be a good proxy for the decentralized policy impact on employment rate variations:

εi,t = ΔERi,t − Cst − β1

∗ BCi,t − β2 ∗ DEMOi,t = 𝐷𝐸𝐶𝑃𝑂𝐿𝑖,𝑡

(5.9)

60

Benchmarking Flanders with the Netherlands increases the risk, Flanders has to support, by 25%. Benchmarking Flanders with our neighbors decreases risk by 32%. Nevertheless, the best way to reduce risk for Flanders remains benchmarking with Wallonia. We assume this better Belgian risk reduction results are the consequence of the country effect: every year different national governments in Europe take decisions which impact the employment rate in their own country. In that respect, Flanders, Brussels and Wallonia are exposed to the same federal country policy impact- risk whereas the neighbor countries take their own decisions. 61

A literature review in Chapter 7 suggests that this assumption is reasonable.

- 44 -

To compute this controlled employment rate variation, we define proxies for the international

business cycle and for demographics. As first cut62 proxy for the business cycle (BCi,t), we propose

the average growth rate of the combined GDP of Belgium’s three key neighbors (i.e. Netherlands,

Germany, and France). We propose as proxy for the demographic effect the growth rate of the

population between 15 and 64 years (pop15-64,i,t) since the employment rate, by definition, can

change due to both demographic and employment reasons63:

DEMOi,t = pop 15−64,i,t−pop 15−64,i ,t−1

pop 15−64,i ,t−1 (5.10)

We draw two main lessons from the regression results in table 5.5.

First, demographics matters for employment in the three Regions but not in a homogeneous way.

Results in Flanders and Wallonia are intuitive: the more people at working age, the more difficult it

becomes for Flanders and Wallonia to increase their employment rate. The order of magnitude of

the results is also expected64. In Brussels, the sign of the significant65 demographic impact on the

employment rate is positive and hence opposite to the signs for Flanders and Wallonia and relatively

counter-intuitive.

62

One could try to improve this proxy by testing as second cut proxies the Eurozone GDP growth rate, the growth rates of GDP of this year, the previous year, etc to take lead and lag effects into account. One could also test a synthetic Eurozone sector-GDP taking into account the sector weights of every Region. 63

The employment rate changes either because the number of working residents (=first semi-endogenous job effect) changes and/or because the size of the population between 15 and 64 years old (=second exogeneous demographic effect) changes. 64

On average, for a 1% increase of the working age population, employment rates in Flanders and Wallonia decrease, all other things equal, by respectively 1.54 and 0.67 percentage points. Those values are in the range we can expect mathematically with a simple back-of-the-envelope-calculation. Suppose that the number of working residents (work res) is constant during one year. Suppose the active population increases by 1% up to 1.01* 𝑝𝑜𝑝15−64,𝑖 ,𝑡−1. The growth rate of the

population at working age is hence 1%.. The employment rate variation then equals: 𝑤𝑜𝑟𝑘 𝑟𝑒𝑠

1.01∗ 𝑝𝑜𝑝 15−64,𝑖 ,𝑡−1−

𝑤𝑜𝑟𝑘 𝑟𝑒𝑠

𝑝𝑜𝑝 15−64,𝑖 ,𝑡−1=

𝑤𝑜𝑟𝑘 𝑟𝑒𝑠

𝑝𝑜𝑝 15−64,𝑖 ,𝑡−1×

100−101

101 = 𝐸𝑅𝑖,𝑡−1 ×

−1

101

If we take for Wallonia ER initial=60%, then the mathematically expected employment rate variation, for a growth rate of 1 % of the working age population, equals a 0.606 percentage points decrease. 65

For a 1% increase of the working age population, the employment rate grows with 0.48 percentage points. An assumption to explain this sign relates to the relatively high responsiveness of the population in Brussels to improving job opportunities. Indeed, Brussels is a Region with high economic migration in- and out- streams. If migration flows to Brussels tend to increase when job market conditions are favorable, then one can expect a positive relationship between working age population and employment rate. We probably face a bicausality or chicken and egg problem. Population does not only drive employment performances but employment performances do also drive population.

- 45 -

The second lesson is that GDP neighbor growth matters in Flanders and in Brussels. Neighbor GDP

growth has a positive and significant impact on employment rate variations in Flanders and also in

Brussels, but to a lesser extent66

. In Wallonia, neighbor GDP growth has, relatively surprisingly, no

significant impact on the evolution of the employment rate.

To conclude, ambiguous regression results suggest us to stay cautious with this kind of difficult to

communicate, relatively non-transparent risk reduction techniques. Last but not least, we show in

13.13 that macro-economic relationships, such as the relationship between GDP and growth, have

not been stable in the past. Therefore, they will probably not remain stable in the future.

Table 5.5:Results external factor regressions

Dependent variables (vertical)

ER-variation Bru ER-variation Fla ER-variation Wall

Explanatory variables (horizontal)

Constant coefficient -0.74 0.21 -0,07

t-stat -1.99** 0.97 -0,28

Growth rate 15-64 population in own Region

coefficient 0.48 -1.54 -0,67

t-stat 2.27** -5.34*** -1,99**

Average GDP growth Netherlands, Germany, France

coefficient 0.22 0.23 0,11

t-stat 1.67* 3.07*** 1,30

Goodness of fit

Adjusted R squared 0.13 0.56 0.14

Standard Error of Regression 0.80 0.49 0.58

Source: Own computations based on HERMREG and on Eurostat

66

Since the p-value is 10 %, the relationship is not significant at a 5% threshold but significant at a 10% threshold.

- 46 -

5.4. REDUCING RISK OF CUMULATIVE EMPLOYMENT RATE VARIATIONS We stressed in 5.2.1. that cumulative employment rate variations are less volatile than yearly

employment rate variations. In a certain sense, measuring cumulative rather than yearly variations

could also be considered as an efficient risk reduction technique, which might reduce the added

value of other risk reduction techniques67. Nevertheless, the standard deviation is probably a too

restrictive metric of risk. Indeed, a decentralized entity can also get into trouble if it has to pay

constant but highly negative mali. Then the risk of a vicious circle is important even if the standard

deviation of the performance indicator is zero.

In appendix 13.5, we search for the best risk-reducing peer for all the three Belgian Regions.

Therefore, we compute in Tables 13.7 and 13.8 the percentage of the employment rate variation risk,

that can be filtered out. We also check graphically if this benchmarking flattens out the employment

rate variation curves in Charts 13.10 until Chart 13.15.

For Flanders respectively Wallonia, Belgian benchmarking is very effective and reduces risk by 68%

and respectively 67%. Again for Flanders and Wallonia, international benchmarking with the average

of our three key neighbors brings down the standard deviation of the relative 5-yearly employment

rate variations to respectively 13% and 14% by 53% and 38%.

Flanders and Wallonia do not seem to be optimal peers with high risk-reducing potential for Brussels.

The only peer, allowing significant risk reduction of the cumulative employment rate variation, is

France, with a risk reduction of 53%. Nevertheless, this result is not very intuitive since both the

economic sector structure and national institutional factors differ in Brussels from the economic and

institutional setting in Brussels.

67

Therefore, we only focus in 5.4. on simple benchmarking techniques, which gave some coherent results, and we will not develop the regressions techniques, which led to inconsistent and difficult to interpret results.

- 47 -

5.5. REDUCING SHORTLIST TO EMPIRICALLY EFFECTIVE INDICATORS In 4.4., we shortlisted three performance indicators that we wanted to test empirically: (i) Absolute

total employment rate variations (ii) Relative total employment rate variations and (iii) Absolute total

employment rate variations controlled for external factors with regressions. We proposed to

distinguish for every of these three indicators the bonus, bonus malus, cumulative and yearly

variants. Part 5.3 taught us that this third type of regressions-based indicator is too ambiguous and

not transparent enough to be effective.

If one opts for yearly variations, then evaluating Flanders’ and Wallonia’s performance relative one

to each other brings down risk by respectively 55% and 47%. Therefore, Belgian benchmarking seems

more effective for Flanders and Wallonia than international benchmarking since peer assessment,

relative to the three main neighbors, only reduces risk with respectively 32% and 21%. For Brussels,

we did not find any intuitively comparable peer with important risk reduction capabilities.

If one opts for assessing cumulative variations, then one has to notice first that 5-yearly cumulative

variations are less volatile than 1-year variations. For Flanders and Wallonia, mutual Belgian

benchmarking and neighbor benchmarking are two effective risk reduction techniques. Nevertheless,

the risk reduction results of the first Belgian method (respectively 68% and 67% ) are clearly better

than the result of the second neighbor method (respectively 53% and 38%). For Brussels, we did not

find any peer with a comparable economic structure coupled to important risk reduction results.

To sum up, both in the yearly and cumulative cases, performance evaluation of Flanders and

Wallonia, relative one to each other, seems to be an intuitive solution with empirical foundations.

Nevertheless, the relative assessment, relative to the Netherlands, Germany and France, could be an

attractive second-best solution in terms of risk reduction if Belgian peer-assessment is rejected for

political, symbolical or cooperation reasons. For Brussels, relative performance evaluation, relative

to other Belgian Regions or our neighbor countries, does not seem to be the magical solution in

terms of risk reduction.

- 48 -

5.6. FINE-TUNING SHORTLISTED AND EMPIRICALLY EFFECTIVE INDICATORS For Brussels, we could not eliminate the business cycle in a consistent and intuitive manner. For none

of the three Regions, we were able to filter out demographic shocks. For Flanders and Wallonia, we

proposed relative performance metrics. Could this be detrimental for cooperation? And finally, is it

possible to fine-tune and improve the relative employment rate variation metric?

5.6.1. BUSINESS CYCLE AND BRUSSELS The international business cycle could maybe be filtered out in the future by finding similar

international cities (although their own non-Belgian institutional dynamics), by adding lead and lag

effects, or by developing a synthetic sector-weighted business cycle proxy specific for Brussels. We

will dedicate chapter 6 to the specificity of Brussels.

5.6.2. DEMOGRAPHICS We could not filter out the impact of the working age population growth68, on the variation of the

employment rate, in an unambiguous and intuitive manner in 5.3.2 with regressions. Those kinds of

relationships are probably to complex and too unstable to serve as basis for a bonus malus scheme.

But, as will be outlined in Section 8.1.2., demographic projections heavily vary across Regions and

should be taken into account. But are there any alternatives to eliminate demographic shocks?

One option might be to assess Regions on the basis of an indicator, which depends less on

demographics than the employment rate, for instance, the absolute number of working residents or

jobs. But, we think total employment is a less suitable indicator than the employment rate for three

reasons. First, total employment is probably also sensitive to demographics. Second, employment

rate variations are simple, politically visible indicators. Third, benchmarking employment rates is

much easier than benchmarking total employment levels.

A second option concerns the employment rate variation targets (∆𝐸𝑅𝑖,𝑡 ,𝑡𝑎𝑟𝑔 ). As shown in equations

(4.4) and (4.5), these targets directly impact the performance indicator. One could take into account

the demographics dimension. Indeed, for a given employment rate target and a projection of the

active population, it is easy to compute the number of extra working residents to be activated. We

will do these computations in chapter 8, which is dedicated to target fixing. This reality check

approach seems by far the most simple, transparent and clear way to take into account

demographics.

68

This growth of the working age population is not expected to be spectacular in the short, medium and long run, except for the coming years in Brussels, as we outline in Chapter 8.1.2.

- 49 -

5.6.3. COOPERATION ISSUES AND MOBILITY PREMIUMS Relative performance evaluation (RPE) allows to reduce risk, for a given level of incentives, if the

relatively evaluated peers are exposed to a common shock. But the main drawback of RPE concerns

the negative incentives provided for cooperation. Increasing interregional mobility of workers is one

of the key levers to reduce unemployment in Belgium. The proposed performance indicators

incentivize Regions on the basis of their own employment rate. This own employment rate can be

increased by the activation of residents of the own Region. Does this imply incentives to favor own

residents and sabotage the activation of residents of the other Region? Would such a sabotage effect

differ under an APE and a RPE scheme? We formulate the problem with the APE- and RPE equations

(4.2) and (4.6) and verify possibilities to off-set the problem. Let us look at the problem from, for

instance, the perspective of Wallonia. We distinguish APE and RPE with Flanders. Wallonia can

increase its absolute performance (𝐴𝑃𝐼𝑊,𝑡 ) by activating Walloon residents (i.e. increasing 𝐸𝑅𝑊,𝑡):

𝐴𝑃𝐼𝑊,𝑡 = ∆𝐸𝑅𝑊,𝑡 − ∆𝐸𝑅𝑊,𝑡 ,𝑡𝑎𝑟𝑔 (5.11)

Wallonia’s relative performance (𝑅𝑃𝐼𝑊,𝑡 ) can be fostered by activating own residents (i.e. increasing

𝐸𝑅𝑊,𝑡) and by avoiding the activation of residents of Flanders(i.e. avoiding increase of 𝐸𝑅𝐹,𝑡 ):

𝑅𝑃𝐼𝑊,𝑡 = 𝐴𝑃𝐼𝑊,𝑡 − 𝐴𝑃𝐼𝐹,𝑡 = ∆𝐸𝑅𝑊,𝑡 − ∆𝐸𝑅𝑊,𝑡 ,𝑡𝑎𝑟𝑔 − ∆𝐸𝑅𝐹,𝑡 − ∆𝐸𝑅𝐹,𝑡 ,𝑡𝑎𝑟𝑔 (5.12)

But can Wallonia avoid the activation of Flemish workers? Which forms could mobility, cooperation

or sabotage take? Basically, mobility can take two forms. First, a Walloon resident might start

working in Flanders ( i.e. Wres-> Fjob), which only increases the employment rate in Wallonia. Second,

a Flemish resident might start working in Wallonia (i.e. Fres-> Wjob), which only increases the

employment rate in Flanders.

The French Community and Wallonia Region are responsible for schooling and active labor market

policies for Walloon residents. That is why Walloon decentralized entities have a high influence on

the probability that the first type of interregional exchange (Wres-> Fjob) might take place. The

influence of Walloon decentralized entities on the probability that the second type of interregional

exchange (Fres-> Wjob) takes place seems more limited. The most important lever for this second type

of exchange (Fres-> Wjob) is the quality of communication of Walloon job offers to Flemish public

employment agencies and to Flemish workers by Walloon employment agencies:

In the case of RPE, Walloon decentralized entities have an incentive to sabotage the second type of

exchange (Fres-> Wjob). Indeed, Walloon incentive flows are negatively coupled to the employment

rate variation in Flanders. If Walloon decentralized entities might be capable to foster the activation

- 50 -

of own residents in Wallonia (Wres-> Wjob)rather than hosting Flemish residents to fill the job offer

(Fres-> Wjob), then marginal incentives are completely opposite. Even with APE, Walloon decentralized

entities have an interest in activating a Walloon resident rather than a Flemish resident. We face a

neighbor-resident-substitution problem. How could mobility incentives69 repair these distorted

cooperation incentives under employment rate-based schemes?

The goal is to fix premiums, received by Wallonia per Flemish commuter 𝑀𝑜𝑏𝑊,𝐹 𝑟𝑒𝑠→𝑊 job , such

that Wallonia is financially indifferent in terms of incentive flows (IFw),between offering jobs to own

residents(Wres-> Wjob) and hosting Flemish residents(Fres-> Wjob):

𝐼𝐹𝑊,𝑊 𝑟𝑒𝑠→𝑊 job = 𝐼𝐹𝑊,𝐹 𝑟𝑒𝑠→𝑊 job + 𝑀𝑜𝑏𝑊,𝐹 𝑟𝑒𝑠→𝑊 job (5.13)

Suppose the incentive per activated Walloon resident for Wallonia equals €10K:

𝐼𝐹𝑊,𝑊 𝑟𝑒𝑠→𝑊 job =€10K

W res →W job (5.14)

Then, under APE and RPE, incentives per activated Flemish resident equal €0 and €-5.6K70. If we want

Wallonia to be indifferent between activating Walloon and Flemish residents, then it should receive

mobility premiums per activated Flemish commuter working in Wallonia, equal to €10K per activated

Flemish resident under APE and equal to €15.6K per activated Flemish resident under RPE.

5.6.4. A MORE PRECISE EMPLOYMENT RATE? Is the total employment rate a too global metric? One could think about various fine-tuning options:

(i) Considering private rather than total employment since public employment can be easily

created by decentralized entities without a necessarily high added value for these jobs;

(ii) Excluding students from the employment rate computations to avoid giving governments

incentives to reduce the length of schooling;

(iii) Considering a full-time equivalent employment rate to avoid that decentralized entities

increase the employment rate by splitting up full- time jobs in part- time jobs.

Unfortunately, because of data constraints, we could not test these fine-tuning strategies empirically.

69

Two options for mobility incentives are possible. First, we can link the performance to a weighted average of, on the one hand, the employment rate and, on the other hand, the ratio between employment and the population at working age (i.e. weighted average option). Second we can add to the employment rate of the incentivized Region a premium proportionate to the number of commuters living in the other two Regions working in the incentivized Region (i.e. Adding commuting term/mobility premium option).We develop the second mobility premium option. 70

Wallonia’s relative performance is negatively proportionate to the Flemish employment rate. That is why Wallonia has a malus per activated Flemish resident equal in absolute value to the bonus per activated Walloon resident, corrected for the ratio between the active populations of both Regions. This ratio equals 100 vs. 56 in 2007.

- 51 -

6. BRUSSELS AND THE JOB INCENTIVE SCHEME Chapter 5 illustrated the failure of risk reduction techniques for Brussels. This chapter tries to identify

the reasons why the Brussels labor market is so special and suggests some incentivizing principles

one should keep in mind.

6.1. BRUSSELS HAS A SPECIAL LABOR MARKET

6.1.1. INTENSE ECONOMIC INTERACTIONS IN THE BRUSSELS METROPOLITAN AREA The Brussels Metropolitan Region Benchmarking Analysis states that Brussels as an economic area

exceeds the political borders of the Brussels Region71. “Today, Brussels city provides its surrounding

economic area with high quality jobs, higher education- and public transport infrastructure and

cultural facilities. On the other hand, the surrounding areas provide places to live for the people

working in the city, and areas for production or for down streamed functions.” The net commuting

inflow to Brussels consisted of 304,000 workers every working day in 2007. This flow results in the

double face of Brussels (Chart 6.1): on the one hand Brussels has been the engine of job creation,

resulting into a high ratio of employment with respect to the working age population, on the other

hand a high part of this working population is not active.

Chart 6.1:Number of jobs and number of working residents as percentages of the working age population in Belgian Regions in 2007

Source: HERMREG

71

This results in a definition of the Brussels Metropolitan Region (denoted as BMR) consisting of three administrative units: the Brussels Capital Region (Nuts 1), Walloon Brabant (Nuts 2) and Halle-Vilvoorde (Nuts 3).

97,4

55,762,0

66,8

51,458

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

Employment/ working age population working residents/working age population

Bru Fla Wal

- 52 -

6.1.2. INTERNATIONAL CITY WITH ATYPICAL JOB DEMAND SECTOR STRUCTURE Within Belgium, Brussels has relatively few industry jobs and a relatively high number of service

sector jobs (Chart 13.3), especially in the political, financial and multinational company area. Even

within a peer group of other important European cities72

, the weights of the political sector, lobbies

and finance stand out (Table 6.1). On the other hand, “Brussels underperforms compared to its peers

in areas such as tourism, trade and transport.73

”

Table 6.1:Sector weights in employment in Brussels and its benchmarking Regions in 2007(in %)

Brussels Average European Benchmarking cities

Political Sector 29.9 25.5

Lobbies 1.1 0.6

Banking and Insurance 7.4 4.8

Source: Brussels Metropolitan Region Benchmarking Analysis

6.1.3. SKILL MISMATCH AND LANGUAGE CHALLENGE The labor market in Brussels suffers from a skill mismatch: abundant low-skilled residents do not

have access to abundant high-skilled jobs. Highly qualified employment is important in Brussels, both

in a Belgian (Chart 6.2) and an international perspective (Table 6.2). Similarly, speaking at least two

languages, is key in Brussels to find a job (for more than 44% of the jobs) compared to only 15.1%

and 24.5% of the jobs in Wallonia and in Flanders.

This skill mismatch and language challenge result in employment rates for lower educated persons in

Brussels more than 20% under the level of this rate in Flanders. The Business Route 2018 proposes “a

sustainable growth strategy based on talent development through appropriate training and private

initiatives and a more flexible labor market. ”74

Table 6.2: Employment qualification structure in Brussels and its peers in 2007(in %)

Highly qualified employment Non qualified employment

Brussels 45 28

Average european Benchmarking city 22 28

Source: Brussels Metropolitan Region Benchmarking Analysis

72

The Benchmarking Regions selected in the Brussels Metropolitan Region Benchmarking Analysis are Dublin, Edinburgh, Randstad, London, Paris, Lyon, Madrid, Milan, Zurich, Vienna, Frankfurt, Berlin and Stockholm. 73

“ Een routeplan voor metropool Brussel”: press message on 19-11-2008 of Beci, UWA, VOKA and VBO-FEB. 74

“ Een routeplan voor metropool Brussel”: press message on 19-11-2008 of Beci, UWA, VOKA and VBO-FEB.

- 53 -

Chart 6.2:Required training for job offers in Brussels, Wallonia and Flanders in 2007(in %)

Source: High Council of Employment

Chart 6.3:Regional employment rate per training level in 2007(in %)

Source:Steunpunt Werkgelegenheid en Economie website

0

5

10

15

20

25

30

35

40

45

50

No education Bachelor Master Total higher education

Bru

Fla

Wal

0

10

20

30

40

50

60

70

Low educated Medium educated Highly educated

Bru

Fla

Wal

- 54 -

6.2. RELATIVELY UNCORRELATED AND HIGHLY VOLATILE EMPLOYMENT RATE

The three specificities of Brussels outlined in 6.1., are reflected in the correlations and volatilities of

chapter 5. First, the Brussels yearly employment rate variation is quite uncorrelated with the

evolutions in Wallonia (28%) and Flanders (25%), whereas the variations between Flanders and

Wallonia show a 89% correlation75. Second, the volatility of the Brussels yearly employment rate

variation is respectively 14% and 27% higher in Brussels than in Flanders and Wallonia76.

6.3. RISK REDUCTION TECHNIQUES FAIL

As explained above, risk reduction through Belgian benchmarking works well for Flanders and

Wallonia (reduction of 55% and 47% of the yearly variation risk supported by Flanders and

Wallonia77) but fails for Brussels. Indeed in Brussels, 1-yearly evaluations relative to Flanders and

Wallonia increase the risk by 13% and 6 %.

75

The correlations of the 5-year employment rate variations of Brussels-Flanders, Brussels-Wallonia and Flanders-Wallonia were 41%, 56% and 95%. 76

The volatility of Brussels’ 5-yearly employment rate variation is respectively 36% and 21% higher in Brussels than in Flanders and Wallonia. 77

Risk reduction results for 5-yearly variations are very similar to the yearly results, with a good risk reduction potential for Flanders and Wallonia and less good results for Brussels. Indeed, for Flanders/Wallonia, Belgian benchmarking reduces risk by 68%/ 67%

- 55 -

6.4. POLICY OPTIONS FOR INDICATORS FOR BRUSSELS

6.4.1. INTERACTIONS WITH HINTERLAND REQUIRE COOPERATIVE SCHEME

As explained in 5.6.3. ,employment rate indicators provide incentives for the activation of residents

of the own Region but provide zero or even negative incentives for the creation of jobs, filled in by

commuters. Nevertheless, commuters are of vital importance to the economy of Brussels. Brussels

thus requires a more cooperative scheme where both resident activation and non-resident job

creation are fostered.

Technically speaking, two options can be imagined to achieve this double goal. First, we might apply

what we explained 5.6.3. We might add mobility premiums per commuter to the basic incentive

flows, based on the employment rate. Second, we can compute a weighted average of the relative

number of jobs and the relative number of working residents (in that case, one double counts a

resident having a job in his own Region). This weighted average indicator (WAIi) increases both in the

number of jobs in Region i (JOBSi) and in its employment rate (ERi):

WAii = a × ERi + 1 − a × JOBS i

pop 15−64,i ,t (6.1)

We test in appendix 13.6. whether using this 1-year weighted average indicator variation yields

better results in terms of risk reduction than using the 1-year employment rate variation for Brussels

for several values of a. We observe three facts. First, the correlation increases in the weight of the

job rate78. Second, the correlation always remains under 50 %. Third, for Brussels, national relative

performance evaluation does not allow to reduce risk significantly, regardless of which indicator we

take. The main advantage of the first option is that we avoid double counting jobs filled in by own

residents.

78

This result is quite intuitive since the job rate in Brussels and the employment rate variation in the other two Regions can both reflect the evolution of the number of commuters working in Brussels but living in the other two Regions.

- 56 -

6.4.2. ATYPICAL JOB STRUCTURE SUGGESTS FOCUS ON PRIVATE JOBS

As explained in 5.6.4, it might be more opportune to reward private employment performance rather

than public employment performance. Since Brussels hosts a high number of Belgian and European

direct and indirect public jobs, this reasoning is especially relevant for Brussels.

6.4.3. SKILL MISMATCH AND LANGUAGE CHALLENGE SUGGEST PARTIAL COMMUNITY

RETURNS79

As stated in Dewatripont et al(2007), education and training are key for Brussels. “The inefficient and

unequal education policies are an important factor driving high unemployment rates in Brussels and

Wallonia.” Since the French and Flemish communities are responsible for education in Brussels, boni

flowing to the Brussels Region could be shared among the Brussels Region, the Flemish and French

communities in a mutual cooperation agreement.

6.4.4. FAILURE CLASSICAL RISK REDUCTIONS SUGGESTS INTERNATIONAL CITY

BENCHMARKING OR SMALLER INCENTIVIZATION SIZE

Belgian benchmarking did not reduce risks for Brussels. In 5.6.1, we proposed to find similar

international cities (although their own non-Belgian institutional dynamics), to add lead and lag

effects or to develop a synthetic sector-weighted business cycle proxy specific for Brussels. If these

alternative risk reduction techniques do not work, then we propose to go for absolute performance

evaluation in Brussels, while at the same time reducing financial risk via lower incentive size

coefficients. The idea is simple: since the indicator is more noisy in Brussels, one should reduce the

importance of the indicator in terms of financial impact for Brussels80.

79

This reasoning can also be held for the other two Regions. 80

For theoretical foundations of asymmetric federal systems, we refer to Congleton(2006).

- 57 -

7. DECENTRALIZED POLICIES CAN INCREASE

EMPLOYMENT RATES SIGNIFICANTLY Making decentralized entities financially accountable for their employment performances is based on

the following important assumption81:“Decentralized entities have the competencies and the

potential to increase their employment rates significantly”. We discuss this assumption in three

statements. First, policies and institutions matter for employment. Second, some important job

stimulating policies82 are largely decentralized. Third, Belgian decentralized entities have the

potential to do much better in those policy fields. In 7.1. we illustrate that policies and institutions

matter in general for employment. In 7.2. we explain which of these policies are decentralized. In

7.3., we study the impact and potential of active labor market policies. In 7.4. we study the impact of

education on employment and growth. In 7.5. we summarize our main findings.

7.1. POLICIES IMPACT EMPLOYMENT Can policies impact employment or is employment mainly driven by non-policy variables such as the

international business cycle? Most studies zoom in one/(a group of) determinant(s)of employment.

To have an international overview of the policy and non-policy determinants, we base ourselves on

the OECD Employment Outlook 2007. For Belgian determinants, we cite Van Der Linden(2001) and

Bodart et al(2008).

The Employment Outlook estimates unemployment rates on the basis of policy variables, control

variables (for instance the business cycle),time- and country effects and sometimes interaction

terms. Estimating the employment rate is more complex: you have to look both at determinants of

unemployment and determinants of labor force participation. “On average, changes in policies and

institutions explain two-thirds of non-cyclical unemployment changes over the past two decades.”

When changes in the output gap are taken into account, changes in polices explain 74% of the cross-

country variance of observed unemployment changes between 1982 and 2003. Policy strategies for

reducing the unemployment rate are numerous (Table 7.1): from reducing tax wedges,

unemployment benefits or red tape in the product market over centralizing and/or coordinating

wage bargaining to investing in active labor market programs. To compare the relative importance of

those policies, the impact of a historically typical reform – corresponding to one standard deviation83-

81

As rightly pointed out by Foucart(2009) in a reaction on Struyven(2009b), “The bonus malus scheme relies on the assumption that Regions are responsible for employment policy.” 82

We think about education, active labor market polices and industrial policy which are respectively Community- and Region competences.

- 58 -

has been computed for every variable. Historically typical reforms of the average benefits

replacement rate (i.e. 4,7 percentage points), the tax wedge (2.8 percentage points) and product

market regulation stringency index (1 unit) result84 in a fall of the unemployment rate of respectively

0.5, 0.7 and 0.5 percentage points. Macro-economic conditions do also matter: historically

typical85adverse shocks like a drop in productivity growth, a deterioration in the terms of trade and

an increase in long-run real interest rates increase the unemployment rate by about 0.3, 1.1 and 0.5

percentage points. These findings confirm that macro shocks can explain unemployment fluctuations

over the business cycle, as well as the evolution of structural unemployment if those shocks persist

over time.

Table 7.1:Correlations between unemployment rate and policy variables for 1982-2003

Policy lever Proxy for policy lever Correlation

Average replacement rate ** Metric of generosity (in time and level) of unemployment benefits

0,12

Tax wedge*** Labor cost- take home pay 0,48

Employment protection legislation Index of stringency of employment protection legislation

-0,02

Union density Proxy of bargaining power 0,04

Product market regulation *** Index of stringency of anti-competitive

product market regulation 0,19

Active labor market policies *** Expenditures per worker as share of GDP per capita

-0,46

Legend: ”***, **, * means statistically significant at , respectively levels of 1%, 5% and 10%. Source: OECD (2007)

The most complete Belgian study assessing both policy and non-policy variables, is Van der Linden

and Dor (2001). With econometric methods, theoretical foundations86 and a large database, they

cover the evolution of Belgian’s private employment rate from 1961 to 1990. Their conclusion is that

both structural and labor market policy variables can contribute to a rise in the employment rate, but

the latter only to “ a limited extent.”

Three structural parameters yield expected results. If productivity grows by 1%, then private

employment rate increases by 0.76%. Business cycle indicators also have a positive and significant

impact. Finally, when the three-months rate of job termination increases by 1%, then the private

84

By contrast, the impact of employment protection legislation and union density is statistically insignificant. 85

Historically typical deviations of variables corresponds to a one standard deviation of these variables. 86

They model a labor market with four states: insured unemployment, uninsured unemployment, training and regular employment. Since labor market policies impact wages and the number of vacant jobs, their model allows do determine tightness endogenously. Matching efficiency, trainings, rate of job termination, job duration and sanctions impact directly the employment rate for a given level of tightness.

- 59 -

employment rate decreases by 0.11%. The frequency of strikes, as a proxy of bargaining power of the

employees, has a positive impact on employment rates.

Two labor market polices variables yield expected results: the elasticities of the private employment

rate, with respect to the yearly number of trainings per unemployed person and the replacement

rate, are respectively 0.10 % and -0.29%. The authors explain the unintended negative elasticity of

the private employment rate with respect to the percentage of sanctioned unemployed of -0.13%

persons in the following way:“If the efficiency of the sanctioned in the matching process is

sufficiently lower than those of the insured unemployed87, than sanctions can be adverse for the

employment rate.”

Bodart et al(2008) quantify how current and past economic growth drive the evolution of

employment. The correlation coefficient between the average economic growth for the last four

quarters and the current quarterly employment growth rate equals 65%. To stabilize employment, a

yearly economic growth rate of 1% is necessary. By including the impact of past employment rate

growth on current employment rate growth, the model with a memory explains 65% of the variation

of the growth rate of employment. Finally, their final VAR model88 teaches us that “a permanent

increase of the GDP growth rate with 1 percentage point, leads, in the long run, to an increase of the

employment growth rate with 0.86 percentage points.”

7.2. WHICH POLICIES ARE DECENTRALIZED?

7.2.1. JOB-RELATED COMPETENCIES In Belgium, many actors are involved in job-related policies. The federal and Regional level share

labor market competencies. The federal level is in charge of fields linked to taxation and social

security. The federal level manages unemployment benefits, wage setting, hiring subsidies,

reductions in social security contributions and labor law. Regions train, activate and place workers

through the public employment services89. The Regions also play a role in the allocation of

unemployment benefits: they report the behavior of job seekers to the unemployment insurer (i.e.

RVA-ONEM). In some sectors, wages are bargained within joined bargaining committees that are

regionalized. In other sectors, wages are bargained within the firms. The Communities are in charge

of education and professional training. 87

Moreover, sanctions often hit the persons for who activation requires the highest total effort. Therefore, remaining activation efforts, after the sanctions, will often focus on persons for who activation requires the lowest total effort. Ultimately, those persons often would have anyway found a job without the activation efforts. 88

VAR models explain current values of variables both with the evolution of those variables in the past and the evolution today and in the past of the explanatory variables. In their VAR-model, they also controlled for the impact of the wage level evolution on employment. 89

Public employment services are VDAB in Flanders, ACTIRIS in Brussels and FOREM in Wallonia.

- 60 -

To conclude, competencies are highly spread. There are more and more calls for increasing

homogeneity and accountability through further decentralization of job-related competencies. The

Marcourt-Vandenbroucke proposal aims to keep only labor law, wage setting and welfare state

provisions federal. For a scientific overview of the pros and cons90 of decentralization, we refer to

Van der Linden(2009).

Table 7.2:Actors currently involved in main job-related competencies in Belgium

Issue Instrument Federal government+ federal social

security

Regions Communities Social partners

Private firms

Insurance Unemployment benefits

V v v

Insurance Employment protection legislation

V V

Labour allocation

Wage setting V V V

Labour allocation

Placement agencies V V

Labour allocation

Training and ALMPs V V V

Labour allocation

Education V

Labour allocation

Professional training, life-long learning

V

Labour allocation

Hiring subsidides V v

Financing Reductions in social security contributions

V

Source: Van Der Linden(2008) Legend: (V)=” major actor” and (v)= “minor actor”

7.2.2. EDUCATION Communities are completely responsible for education except for some minor residual federal

responsibilities91.

90

As pros for decentralization Van der Linden gives the possibilities to increase preference matching, reduce coordination- and information costs whereas the cons refer to minimizing positive externalities, scale economies, risk diversification and information gains, … 91

The residual federal responsibilities are the setting of the minimum age with the responsibility to learn, language rules in schools and financing of foreign university students.

- 61 -

7.3. IMPACT AND POTENTIAL OF ACTIVE LABOR MARKET POLICIES

We now zoom in on a major employment driver in the hands of the Regions; active labor market

policies.

7.3.1. IMPACT

Active labor market polices include employment services, training, public works, wage and

employment subsidies and self-employment assistance. They aim at achieving economic, social and

political objectives. We focus on two main economic objectives: reducing the risk of unemployment

and increasing the earnings capacity of workers. ALMPs may impact employment through two

major92 channels (Estevão, 2003). First, more efficient matching between vacancies and job seekers,

because of trainings and/or better searching and lower employers’ recruiting costs, may increase

labor demand. Second, productivity increases (through trainings or on the-job-learning) would shift

labor demand up and lift employment(and wages). In Appendix 13.7, we explain how difficult it is to

assess the net impact of active labor market policies and their cost-effectiveness.

Very few surveys assess the overall impact of aggregated ALMP expenditures on employment

outcomes because most of them focus on one type of/(group of) intervention(s). In chapter 2, we

used the key result of the most complete counter-example; Estevão(2003). “ If the share of ALMP

expenditures in GDP increases with one percentage point, then the business employment rate

increases with 1.88 percentage points.”The OECD Employment Outlook stresses another, indirect but

positive channel of the ALMP impact on employment. ALMPs reduce the negative disincentives

effects of generous unemployment benefits on employment rates by about 20%. “In countries with a

strong emphasis on activation polices, like Denmark and the Netherlands, unemployment benefits

have a statistically insignificant effect on unemployment.”

92

Another channel might be the impact of ALMPs on the period a worker stays in the labor force, resulting in stronger competition, lower wages and more jobs.

- 62 -

Chart 7.1:Impact of active labor market programs (right scale) on effect (left scale) of benefits on

unemployment rate in 20 OECD countries

Legend: Left scale variable: Percentage-point impact of one standard deviation (4.7 percentage points) increase of the average replacement rate Right scale variable: Average ALMP expenditures per unemployed persons as a percentage of GDP per capita in 2000/2001, Source: OECD (2007)

Since the number of studies zooming in on the specific impact of on type of intervention is

numerous, we based ourselves on the most exhaustive and recent international survey we could find

by Betcherman et al(2004). The bottom-line of this-meta study, regrouping the results of 159

studies93, is that some type of well-designed interventions can be cost-effective. But those programs

are “not a panacea for unemployment.” Therefore, “governments should be realistic about what

ALMPs can achieve and should focus on design optimization, continuous performance evaluation and

cost-effectiveness assessment.”

Here are the conclusions for every type of intervention. First, employment services94 generally have

“positive impacts on post-program employment and earnings of participants.” The cost-benefit ratio

93

The difficulty in these meta-studies is to guarantee the quality of the combined studies. Betcherman et al(2004) base their selection on the methodology, which should be based on control-group techniques. 94

They include counseling, placement assistance, job matching, labor exchanges, job-search courses, etc.

****** *********************

******

************

**

***

-90

-60

-30

0

30

60

90

120

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

Unit

ed S

tate

s

Japan

Can

ada

Aust

rali

a

Unit

ed K

ingdom

Ital

y

Spai

n

New

Zea

lan

d

Fin

lan

d

Aust

ria

Port

ugal

Bel

giu

m

Fra

nce

Sw

itze

rlan

d

Ger

man

y

No

rway

Sw

eden

Den

mar

k

Irel

and

Net

her

lan

ds

Unw

eighte

d a

ver

age

- 63 -

is very often positive. Nevertheless, employment services should be coupled to labor demand-

fostering policies to be fully effective. Second, training for the unemployed has often a positive

impact on employment rates but no significant impact in terms of higher earnings. Third, retrainings

for workers in mass layoffs are only successful (but generally costly) if they include a comprehensive

package of employment services to accompany the retraining. Fourth, training for youth is much less

effective in terms of labor market outcomes than investing earlier in the education system to reduce

drop-outs. Fifth, wage employment subsidies “often do not have a positive impact and have

substantial deadweight and substitution costs” even if targeting and monitoring can help. Sixth,

public works “can be an effective short-term safety net” but are less successful in improving future

labor market participation.

To sum up, ALMPs can have a positive net impact on employment rate outcomes and can be cost-

effective, especially if the right intervention is well-designed, accompanied with other positive

measures and regularly evaluated and improved95.

The positive impact of employment services (e.g. counseling schemes) seems the less controversial.

To illustrate this, we refer to two employment services studies in the Continental European context:

Crépon et al(2006) and Cockx et al(2007). Crépon et al(2006) assess the positive effect of a job search

assistance scheme, introduced in France in 2001, on both shorter duration of unemployment and

lower recurrence of unemployment. The program is shown to reduce incidence of recurrence (=exit

to unemployment),one year after employment, from 33% to 26% and to increase the exit to

employment after 900 days up by 3 to 10%..

Cockx et al(2007)evaluate the impact of the “Plan for Coaching of unemployed”, introduced in 2004

in Belgium, on transition to work. This reform intensified the training and coaching given by the

Regional public employment services. It also formalized and provided credibility to the follow-up

procedure in the form of a letter, recalling the insured unemployed individual his duty to seek a job,

as necessary condition to safeguard his unemployment benefit entitlement. Since the measure was

first introduced for the 25-29 years old, the control group could focus on 30 years old who did not

take part in the program yet. By measuring the transition to work, the exemptions of the duty to seek

a job, trainings, education and sanctions, the authors came to the following conclusion: “the recall

letter, sent by RVA-ONEM in the context of the follow-up procedure, increased the probability of

transition to employment significantly in Flanders and Wallonia.”

95

Introducing the job incentive scheme would probably intensify policy evaluations. This could be considered as an extra channel of positive impact.

- 64 -

7.3.2. POTENTIAL BELGIAN DECENTRALIZED ENTITIES Until now we suggested that investing in better active labor market policies can determine positive

employment evolutions. Now we verify to which extent the Belgian Regions can improve the

effectiveness of their active labor market policies, both from a quantitative as a qualitative point of

view.

A quantitative assessment of total Belgian ALMP expenditures96 suggests that they are relatively high

compared to the European average (Table 7.3). However, Sweden spends significantly more on active

labor market expenses, especially compared to its passive labor market expenses. Moreover,

Belgium spends a very high share of those ALMPs on direct job creation at the expense of training

(Chart 7.2), although our literature review suggests that training investments are much more cost-

effective than direct job creation measures. The low share of trainings is found across the three

Regions (Chart 7.3). The share of direct job creation is especially high in Brussels and in Wallonia.

Table 7.3:National active labor market expenses compared to passive labor market expenses and

compared to GDP in 2007(in %)

ALMP/PLMP ALMP/GDP

Belgium 49 0.86 EU-27 43 0.51 Sweden 118 1.13 Source: Eurostat

Chart 7.2:Breakdown of national active labor market expenses in 2006(in %)

Source: Eurostat

96

Active labor market expenses consist according to the OECD classification of training (category 2), job rotation and sharing (category 3), employment incentives (category 4), supported employment and rehabilitation (category 5), direct job creation (category 6) and start-up incentives (category 7).

0

5

10

15

20

25

30

35

40

45

50

Belgium EU-27 Sweden

Training (cat 2)

Job rotation/sharing (cat 3)

employment incentives (cat 4)

supported employment (cat 5)

direct job creation (cat 6)

start-up incentives (cat 7)

- 65 -

Chart 7.3:Breakdown of Regional active labor market expenses in 2007(in %)

Source: Eurostat

From a more qualitative point of view, we pinpoint the ALMP policies, where Belgium would not be

up to research-based standards or advices, according to the OECD Employment Outlook 2007 report:

Placement- and training registration should take place before unemployment benefit

registration can start;

The reporting on search efforts and the unemployment status is done by post rather than

through in person-visits at local offices;

The number of direct referrals97 per unemployed person is either below OECD standards (i.e.

in Flanders) or not registered (i.e. in Brussels and Wallonia);

The take-off of intense interview and individual action plans comes relatively late in Belgium

(i.e. after six to nine months in Flanders and after seven months in Wallonia except for

youngsters in Wallonia who are followed-up after two months of unemployment spell);

The participation in ALMP programs (e.g. training, information sessions, …) is only mandatory

in the case where the Public Employment Service counselor explicitly requests it.

97

A direct referral refers to a vacancy which is communicated by the public employment service office to the job seeker.

0

10

20

30

40

50

60

70

80

Fla Wal Bru

Training (cat 2)

employment incentives (cat 4)

supported employment (cat 5)

direct job creation (cat 6)

- 66 -

7.4. IMPACT AND POTENTIAL OF EDUCATION POLICIES

7.4.1. IMPACT Education is today considered as a main driver of innovation and economic growth, which on their

turn, determine employment evolutions. Other studies quantify the direct impact of education on

employment.

In Appendix 13.8, we summarize the main trends in the literature concerning the impact of education

on growth. According to the micro-economic models of returns on education, individuals can

increase their wage with an extra year of studying by approximately 8%.

To get a broader picture of the society effects, more macro-economic approaches have been

adopted. The neo-classical approach stresses that a country can grow by increasing the education

level of its population. The technology-approach stresses the level of education as a key success

factor to absorb new technologies. Krueger et al(2001) combine these approaches and estimate both

the positive effects on growth of both the initial level and of the increase of the education level with

one year at respectively 0.5% and 8% per year.

Recently, endogenous growth theories have explained technological progress itself and consider

innovation as key for growth. The closer a country gets to the technological frontier, the more it has

to innovate rather than imitate and the higher are its returns from investing in highly-qualified

superior education according to Aghion(2003).

De La Croix et al(2004) estimate the effect of the duration of schooling on the probability of being

employed for Belgians who are already participating in the labor market with a probit-model. They

assume that the likelihood of getting a job, as function of education and other explanatory variables

(age, gender, age squared and student), follows a cumulative normal distribution. One year of extra

schooling decreases the probability of being unemployed by around 17.2% in Wallonia, 7.3% in

Brussels and 13.2% in Flanders (Table 7.4)

Table 7.4:Effect of an increment of one year of schooling on the probability of being unemployed

for an average citizen at working age in 2004(in %)

Fla -13.2

Bru -7.3

Wal -17. 2

Source: Own computations based on De La Croix et al(2004)

- 67 -

7.4.2. POTENTIAL

We assess the potential for improvement of the contribution of both primary, secondary and

superior education systems to economic growth and employment in the two communities by

looking at key figures for education inputs and -outputs.

7.4.2.1. Primary and secondary education

With respect to inputs, Belgium spent 4.1% of its GDP in 2007 on primary- and secondary education

in 2006. This spending is 0.4% above the EU-19 level according to OECD data. Within Belgium,

Flanders spends respectively 14% and 10% more per pupil than the French Community in primary

and secondary schools (Table 13.13). The share of students, benefiting from general education, is

25% higher in the French Community than in the Flemish Community (Table 13.14). The High Council

of Employment underscored the problem of the lack of technical qualifications within the active

population in Belgium in general but specifically in the French Community.

With respect to outputs, the most recent PISA98 results, concerning the skills of students of 15 years

old in mathematics, reading and sciences, are positive for Flanders and negative for the French

Community (Table 13.15). The French Community is not only under average in the three fields but

also has the tendency to reproduce social inequities with standard deviations above the average

OECD standard deviation of 100. On top of the poor results of 15-year old students, students in the

French Community tend to struggle more with at least one year of delay in their school career(Table

13.16). Weber et al (2006) stress that the education system in Wallonia could significantly improve

the knowledge of other languages than French: 57% of the population confirms in a survey to “speak

only French” (Table 13.17).

98

PISA studies use standardized surveys to compare performance of students within the participating 48 countries. Indicators are constructed such that the average OECD country gets a score of 500, with 68% of the students getting a score between 400 and 600 and 95% getting a score between 300 and 700.

- 68 -

7.4.2.2. Superior education

With respect to inputs, Belgian universities have lower total year budgets per student (€11,

300/student) than the European average 99(€16,100/student) according to Aghion et al(2008). The

Flemish Community spent 8% more per university student than the French Community in 2006.100

Deschamps et al(2006) signal an important decrease of the real amount, spent per university

student, in French Community of 17% between 1991 and 2004.

Demeulemeester(2006) notices that students’ choices for university curricula reflect “cultural

differences”. Indeed, 27.8% of French university students opt for curricula with a classical high social

status (law, medicine and civil engineering) in contrast to the mere 17.7% in Flanders,

Demeulemeester assumes that “French students privilege social status as key decision factor for

studies whereas Flemish students privilege economic returns” (Table 13.18).

With respect to outputs, both Communities perform relatively well in the Shanghai Academic ranking

2008 given their financial resources. Every Community hosts two universities in the second tier

(positions 100 to 151). The Shanghai index is a weighted average of six different indicators of

research performance101.Flanders is the only Belgian Region with a patent density above the EU-15

average (Table 13.20). One could wonder: “Why do relatively good research performances in the

French Community do not lead to higher levels of knowledge and technology transfers,

approximated by the patent density?”

99

This European average is estimated on the basis of the average within the following countries with a sufficiently high response rate: Denmark, Germany, Ireland, Italy, Netherlands, Spain, Sweden, Switzerland and UK. 100

The budgets are around €8,675 € per student per year in Flemish Community versus around €8,000 per student per year in French Community. 101

One can criticize several features of the ranking (arbitrary weights, focus on research rather than teaching, too little importance of social sciences, bias in favor of “large institutions”, …). Therefore those Shanghai rankings (Table 13.19) should be assessed carefully.

- 69 -

7.5. CONCLUSION

We learnt in 7.1. that non business-cycle related changes in (un)employment and in cross-country

unemployment differences can be largely explained by polices and institutions. In 7.2, we show that

decentralized entities have major competences with job impact such as active labor market policies

and education. Nevertheless other major policy variables are still federal (e.g. unemployment

benefits, employment protection legislation, wage setting, targeted hiring subsidies and social

contribution reductions).

In 7.3, we illustrated that well-designed active labor market policies can increase the employment

outcomes while being cost-effective. Belgium should probably invest more in public employment

services and less in direct job creation with respect to the criterion of cost-effectiveness.

In 7.4, we explained how both education levels and changes in education levels can foster growth

and employment. The 17.2% decrease in the probability of being unemployed per extra year of

education for Wallonia, illustrates the high activation potential of investing in education. Belgian

spending on primary and secondary education levels is slightly above the EU average. Nevertheless

the French Community exhibits sub-optimal results in mathematics, sciences, reading and languages.

The Shanghai ranking suggests relatively good research performances in the two communities, which

are not reflected in the low patent density in the French Community. To conclude, the French

Community has significant potential to increase the economic return of education by (i) increasing

the efficiency of resources invested in primary and secondary education (ii) providing incentives to

students steering them to curricula with higher expected labor market returns and (iii) increasing

budgets for universities, which have been reduced during the last years, remembering that

universities train highly-skilled workers, who are crucial in knowledge economies close to the

economic frontier102.

102

For the positive correlation between the budgets of universities and their performance, see Aghion et al (2008).

- 70 -

8. TARGETS ON FINANCIALLY NEUTRAL PATH In chapter 4, we proposed absolute (APIi,t )and relative performance indicators (RPIi,t) for a certain

Region i in the year t in formula’s (4.2) and (4.6) on the basis of employment rate variation targets

(∆𝐸𝑅𝑖,𝑡 ,𝑡𝑎𝑟𝑔 ). But how can we fix realistic though ambitious targets?

Chapter 7 showed that employment rate evolutions are partially driven by external factors such as

the business cycle. Therefore, it is almost impossible to fix yearly employment rate targets such that

they will be exactly achieved under optimal decentralized policies. That is why we study in this

chapter 8 average employment rate variation targets which are business-cycle adjusted. Assessing

cumulative103 progress and/or evaluating relative performances allows to filter (part of) the business

cycle out of the performance indicators in order to obtain realistic targets.

As most robust104 starting point for fixing these business-cycle adjusted average yearly employment

rate variation targets, which we will from now on call targets, we study the baseline future scenario

forecasted by the Planning Office HERMREG model in 8.1.

As outlined in 2.2.3, we want to provide a credible perspective of interregional upward employment

rate convergence to alleviate the Belgian political deadlock. Upward convergence targets will be fixed

by, on the one hand, studying realistic paths for Flanders in 8.2., and, on the other hand, targeting

several potential speeds of convergence for Brussels and Wallonia with respect to Flanders’ path in

8.3. In 8.4., we show how different problem definitions can lead to different families of targets. For

every family of targets, concrete targets are computed in 8.5, 8.6, 8.7 and 8.8. In 8.9, we verify how

realistic the target families are. We explain our preference for a restricted group of targets in 8.10.

103

In chapter 5, we proposed absolute performance evaluation, Belgian and neighbor relative performance evaluation if one opts to assess cumulative progress We also recommended Belgian relative performance evaluation if one opts to assess yearly progress. 104

The HERMREG data of the Planning Office are the most robust ones although two caveats. First, we have to integrate the impact of the financial crisis on employment since the latest Belgian Regional HERMREG-model forecasts have been computed in May 2007 before the start of the financial crisis. Second, despite the papers by Bassilière et al(2008) and Bossier et al(2000) and some telephone interviews with employees of the Planning Office, having worked on the model, it has not been easy to distinguish key drivers of Regional differences in the model. Nevertheless, we have to make predictions and no better alternative seems at hand than the HERMREG model. And even if the forecasted levels of employment might be uncertain, Regional differences might be quite precisely forecasted because of the strong correlation between Flanders and Wallonia, which we outlined in chapter 5.

- 71 -

8.1. PLANNING OFFICE HERMREG BASELINE SCENARIO We set the context of targets by presenting the baseline scenario forecasted by Planning Office.

Therefore we explain the HERMREG model in 8.1.1. Since demographic projections are an important

element in forecasting employment rates, they are presented in 8.1.2 for the three Belgian Regions.

We finally update105 HERMREG forecasts by integrating the impact of the financial crisis in 8.1.3.

8.1.1. HOW DOES THE HERMES-HERMREG SYSTEM WORK?

Regional strategic plans106

created the need for projection tools including the Regional dimension.

Therefore the Planning Office and the three Regional statistical offices developed the HERMES-

HERMREG system. It is a macro-econometric model simulating the economic evolution in thirteen

sectors and three Regions. This system works in two phases. At a first top-stage, HERMES projects

national aggregates such as incomes, production, expenditures, consumption and employment on

the basis of international demand, factor input prices, public policy and demographics. General

characteristics and the functioning of the HERMRES model are detailed in Appendix 13.10.

At a second down- stage, endogenous107 Regional keys are estimated and used to break down

national forecasts into Regional forecasts for value added, employment, investments, wages and

productivity for every sector. The shift-share method is used to break down growth rates of the five

key variables, for instance employment in branch i in Region j at time t Yij,t, into three terms: a

shared national term nt, a shifted sector term si,t and a Regional component rij,t:

𝑦𝑖𝑗 ,𝑡 = 𝑛𝑡 + 𝑠𝑖,𝑡 + 𝑟𝑖𝑗 ,𝑡 (8.1)

The growth differential rij,t is estimated108, with a vector of variables Xij characterizing Regional

dynamics for 13 sector branches and 3 Regions from 1980 to 2004, and where eij,t is the residual

term:

𝑟𝑖𝑗 ,𝑡 = ß𝑖𝑗 × 𝑋𝑖𝑗 ,𝑡 + 𝑒𝑖𝑗 ,𝑡 (8.2)

105

We integrate the impact of the financial crisis into the most recent HERMREG May 2007 data on the basis of an interview with Professor Vander Vennet and on the basis of national Planning Office February 2009 national employment up-dated forecasts. 106

Examples of Regional strategic plans are Vlaanderen in actie for Flanders, Le Plan Marshall for Wallonia and het Contract voor de Economie en de Tewerkstelling for Brussels. 107

Endogenous means determined within the model. 108

The estimation is done with the Ordinary Least Squares (OLS) regression method.

- 72 -

Here are two examples109

of variables Xij,t explaining growth differentials rij,t. For the health care sub-

branch within the market services branch, the size of the cohort of persons older than 65 years has a

positive impact on the growth rate of the health-care branch. For the construction branch, the

interest rate has a negative impact on the growth rate of the construction branch.

Commuting could contribute to the absorption of asymmetric shocks and to the reduction of

interregional differences in unemployment and in incomes. The net commuting flow Mj,t in a certain

year t towards a Region j is driven by employment opportunities in Regions of residence rj,t and by

employment opportunities in the Region of work wj,t and where ej,t is the residual term:

𝑀𝑗 ,𝑡 = 𝛼 × 𝑟𝑗 ,𝑡 + 𝛽 × 𝑤𝑗 ,𝑡 + 𝑒𝑗 ,𝑡 (8.3)

Finally notice that the HERMES-HERMREG system has to be top-down since demand data, which are

the driving force behind production in the model, do not exist in Belgium at a Regional level110. To

conclude, final Regional projections are computed on the basis of growth equations (8.1) and (8.2),

commuting equation (8.3), assumptions on the international and national context and finally on the

basis of socio-demographic labor supply projections.

109

The two examples have been given by Didier Baudewijns, economist at Planning Office, during a telephone interview on 20

th of February 2009.

110 We do not know the precise values of consumption in Wallonia, Brussels and Flanders. Regional investments are the

only demand component known at the Regional level.

- 73 -

8.1.2. WHICH DEMOGRAPHICAL PROJECTIONS UNDERPIN THE HERMREG FORECASTS? Table 8.1 illustrates that Flanders will be much more exposed to ageing than Brussels. In fact, in

Flanders persons at working age will only represent 57% of the population in 2060 versus 66% today.

The size of the working age population will decrease slightly between 2007 and 2060 (Table 8.1).In

Brussels, the share of persons at working age in the population will equal 62% in 2060. Between 2020

and 2040, the Flemish population at working age will shrink by 3.5%. This poses a double challenge.

On the one hand, new workers, with sufficient skills to replace the retirees, will have to be found to

grasp growth opportunities. On the other hand, ageing costs will have to be funded.

For Brussels, the challenge will be the activation of the extra entrants in the working age cohort. The

size of this cohort will increase by 12.1% between 2007 and 2020 (Table 8.1). The demographic

pattern of Wallonia lies somewhere between the Flemish and Brussels’ pattern. The Walloon

population clearly gets older since the share of persons at working age in the population will drop by

7% at horizon 2030. But the total number of persons at working age increases by around 9.6%

between 2000 and 2060 (Table 8.1).

Table 8.1:Regional demographic projections: percentage change on 2000 figures in the number of

residents between 15 and 64 years old over the period 2000-2060(in %)

2000 2007 2010 2020 2030 2040 2050 2060

Fla 0.0 2.5 4.0 5.4 3.4 1.9 1.7 2.0

Bru 0.0 8.6 12.2 20.7 22.7 21.8 22.2 23.4

Wal 0.0 4.3 6.6 9.6 10.2 10.8 12.5 13.9

Source: Planning Office(2008)

- 74 -

8.1.3. WHAT WOULD HERMREG BASELINE SCENARIO FORECAST IF FINANCIAL CRISIS IS

INTEGRATED? The assumptions behind the HERMREG forecasts in May 2007 are out-dated because of the financial

crisis. For instance, HERMREG assumes GDP growth rates for the Euro area and for the United Sates

of 2.0 and 2.7% over the period 2009-2012 whereas the latest Economist Consensus May 2009

forecasts predict 2009 GDP growth rates for the Euro area and for the United Sates of -3.7 and-2.9%.

We do not have any other alternative than integrating the financial crisis impact into this May 2007

forecasts, which are outlined in Appendix 13.11. In order to integrate the crisis’ impact, we base

ourselves on the downward revisions of national employment aggregates, forecasted by the Planning

Office in February 2009. We compute the net long-run impact of the crisis in Table 8.2 as the

difference between the bullish forecasted employment variation in 2009, computed before the crisis,

and the bearish forecasted employment variation in 2009 computed during the crisis111. The crisis

might result in a reduction of the number of working residents in Belgium by 62,000112 units and into

an increase of the number of unemployed by 60,000 units113. This forecast falls within the range of

estimates by KBC, IRES and the National Bank Director Jan Smets114.

111

Implicitly, we make two assumptions. First, we suppose that the crisis has no impact on employment levels in 2008. Second, we suppose that all the jobs lost, because of the crisis, are lost in 2009 but are also definitively lost. The first assumption seems reasonable since companies always react with a delay to a changing business cycle in their hiring- and firing behavior. Professor R. Vander Vennet quantifies this delay at a lag of 18 months between GDP- and employment shocks. Professor R. Vander Vennet qualified this second assumption as reasonable by referring to the keynesian hysteris effects and to the types of jobs which are lost such as jobs in the banking- or car manufacturing sector because of structural lower demand or because of definitive outsourcing. 112

The most recent forecasts of the Planning Office on 20th

May of 2009 estimate the impact of the crisis on employment at 37,000 lost jobs in 2009 and 53,000 jobs in 2010. 113

This figure of 60,000 is computed according to the same methodology as the job loss computed in Table 8.2 and is the difference between 64,400 units and 4,500 units. 114

Our impact forecast based on Planning Office figures is 40% lower than the pessimistic estimate of KBC Chief Economist Bart Van Craeynest who estimated the number of extra unemployed losing their job because of the crisis at 100,000 in De Morgen of 12 January 2009. Our impact estimate is 30% lower than the estimate of Institut de Recherches Economiques et Sociales de l’Université Catholique de Louvain (IRES), which estimated the reduction in working residents at 90 000 in January 2009. Finally, our impact estimate is 16% higher than the estimated number of lost jobs by the National Bank Director Jan Smets of 50,000 during a speech at the 5

th Economic Congres of Tienen around the theme of “Mobilizing all

talents” in March 2009.

- 75 -

Table 8.2:Expected impact of financial crisis on national number of working residents(in 1000)

Variation of the number of working residents during ‘09-forecasts in May 2007 before crisis 37.8

Variation of the number of working residents during ‘09-forecasts in February 2009 during crisis -23.8

Net forecasted impact of crisis on the number of working residents -61.6

Source: Own computations based on Planning Office(2008) and Planning Office website on February 2009

We assume an equal relative impact of the crisis on employment aggregates across the three

Regions. Mathematically, we suppose that every Region looses 1.4 % of its working residents, which

is equal to the Belgian loss in percentage.115

On the basis of the HERMREG May 2007 forecasts and

on the basis of the crisis impact estimate, we can compute Regional projections for the number of

working residents and Regional employment rates in respectively tables 8.3 and 8.4.

The three Regions are all expected to progress both in terms of the number of working residents and

in terms of employment rates. Brussels will increase its number of working residents at the fastest

pace with a 1.79% growth rate between 1st of January 2010 and the 31st of December 2013.

Nevertheless, employment rate gaps are expected to deepen between, on the one hand Flanders

and Brussels by 0.07 percentage points per year, and on the other hand Flanders and Wallonia with

0.20 percentage points per year between the start of 2010 and the end of 2013116.

Table 8.3:Expected evolution of the Regional number of working residents at horizon 2013

Increase of the number of working residents ’08-'13(1000)

Growth rate of the number of working residents ‘08-‘13(%)

Growth rate of the number of working residents ‘10-‘13(%)

Bru 32.3 1.35 1.79

Fla 119.0 0.72 0.89

Wal 45.5 0.56 0.79


115

62,000 fewer working residents represents 1.4 % of 4.5 Million Belgian working residents. 116

Although the Planning Office does not describe the reasons of the widening employment rate gap between Flanders and the two other Regions, we can outline some main reasons. For Brussels, the main reason seems to be demographic: the active population grows by 3.6 percentage points between 2007 and 2010 whereas the active population in Flanders only grows by 1.5 percentage point. On the other hand, the number of working residents is expected to grow faster is Brussels with a compounded average growth rate of 1.35% versus 0.89% in Flanders, between the end of 2007 and the end of 2013, in the baseline scenario which includes the impact of the financial crisis. For Wallonia, the main reason is the lower GRP growth driven by an unfavorable sector structure. Indeed, the fastest growing sectors over the period 2007-2013 in the baseline scenario drafted before the financial crisis, namely market services and construction with growth rates of 2.3% and 3.3% have relatively low shares in Wallonia’s GRP (respectively 38.4% and 5.7%) compared to the relatively high shares of those sectors in Flanders’ GRP (respectively 43.3% and 6%). The second reason for the lower employment rate growth in Wallonia is demographic: the active population is expected to grow with 2.3 % over 2007-2010 compared to 1.5% in Flanders.

- 76 -

Table 8.4:Expected evolution Regional employment rates at horizon 2013(in percentage points)

Employment rate '08

Employment rate '13



Bru 55.7 56.0 0.06 0.33

Fla 66.8 68.3 0.25 0.40

Wal 58.0 58.0 0.00 0.20


8.1.4. LESSONS FROM HERMREG FORECASTS

First, the three Regions are all expected to increase their number of working residents and their

employment rates at horizon 2013 although a very difficult year 2009.

Second, there is no convergence between Flanders and Wallonia although the fact that the GDP

growth rate gap is expected to decrease from 0.9% in 1993-1999 to 0.2% year at horizon 2013.

Nevertheless, Flanders is expected to increase both its number of working residents and its

employment rate at a faster pace than Wallonia between 2007 and 2013.

Third, Brussels’ job performance heavily depends on the selected parameter. It has the best

prospects in terms of increasing its number of working residents (with a growth rate of 1.79% over

the period 2009-2013) but only second-best employment rate variations forecasts, with a 0.33%

increase per year. This clearly reflects Brussels’ demographics: the number of residents between 15

and 64 years old in Brussels, is expected to grow by almost 15% between 2007 and 2060.

To conclude, the amplitude of the upward interregional convergence challenge is important.

Convergence requires faster speeds of progress in Wallonia and Brussels than in Flanders.

Nevertheless, the amplitude of the challenge depends on the chosen parameter.

- 77 -

8.2. WHICH REALISTIC SCENARIO’S CAN WE IMAGINE FOR FLANDERS? We distinguish three realistic employment rate evolution scenario’s for Flanders until 2020 and

classify them from conservative to ambitious. First, Flanders could stabilize its employment rate

around the 67.3% level achieved at the end of 2008. Second, Flanders could envision achieving the

Lisbon 70% cap as outlined in the Vlaanderen in Actie117 strategy. Third, Flanders could envision

going beyond the Lisbon target and strive to reduce, for instance, 50% of the gap with Denmark. In

2007, Denmark had an employment rate of 77.1%. To bridge half of the gap, Flanders should increase

its employment rate to 71.9%.

8.3. WHICH SPEED OF INTERREGIONAL CONVERGENCE DO WE TARGET FOR BRUSSELS

AND WALLONIA? If one aims at interregional convergence, one could fix two speeds of convergence for Brussels and

Wallonia with respect to realistic scenario’s for Flanders. First, one could think about a slow

convergence speed resulting in full convergence in 2030 and second, a fast convergence speed

resulting in full convergence in 2025.

8.4. DIFFERENT PROBLEM DEFINITIONS LEAD TO DIFFERENT FAMILIES OF TARGETS In chapter 2, we outlined several arguments for the job incentive scheme. Main arguments are

providing a credible perspective to achieve the Lisbon 70% target, funding ageing and alleviating the

Belgian political deadlock trough interregional convergence. In function of the main problem one

defines, different families of targets can be proposed. We propose four families of targets.

First, if we only want to solve Belgian interregional disparities, without requiring that Flanders does

achieve Lisbon targets, then one needs national relative performance targets. Second, if we want all

Regions to achieve Lisbon employment rates of 70%, then one needs Lisbon absolute performance

targets. Third, if we want to push Flanders closer to the European top in the medium run and

Brussels and Wallonia in the long run, then one needs semi Danish-style absolute targets. Fourth, If

we want all Belgian Regions to catch-up with similar European Regions, then one needs European

relative performance targets. Notice that the first three families of targets indirectly also imply

national convergence. Now, we work out some concrete options for the four cited target families.

117

Vlaanderen in Actie ’s website (http://www.vlaandereninactie.be/nlapps/docs/default.asp?id=416) mentions the targets to increase the global employment rate to at least 70% in 2020 through a yearly average increase of at least 0.5 percentage points. For immigrants, disabled and elderly above 50 years old, this growth rate should exceed the 1 percentage point to achieve, for instance, the European employment rate for elderly of 50%.

http://www.vlaandereninactie.be/nlapps/docs/default.asp?id=416

- 78 -

8.5. NATIONAL RELATIVE PERFORMANCE TARGETS The idea is to limit the scope of the problem. One could focus on reducing Belgian interregional

disparities as a way to alleviate the Belgian political deadlock, as outlined in 2.2.3. Flanders is

rewarded if it increases its employment rate above the level of today. If Flanders stabilizes its

employment rate level around the 67.3% level, achieved at the end of 2008, then Flanders will

receive no bonus or malus118. As explained in 8.3, we distinguish two speeds of convergence in Tables

8.5 and 8.6.

How should the three Regions perform to move on the slow convergence path? Flanders should keep

its employment rate constant. To have completely caught up in 2030, Brussels and Wallonia should

increase their employment rate with 0.6 and 0.5 percentage points per year on average. To move on

the fast convergence path, and to have completely caught up in 2025, Brussels and Wallonia should

increase their employment rate with 0.8 and 0.6 percentage points per year on average.

Table 8.5:Regional employment targets under a slow national convergence scenario

Employment rates (%)

Average yearly employment rate increase (% points)

Number of working residents

'09 '20 ‘10-'20 Increase '10-'20 (1000)

Growth rate'10-'20 (%)

Bru 55.6 61.3 0.6 91 2.0

Fla 67.3 67.3 0.1 35 0.2

Wal 58.0 62.5 0.5 155 1.1


Table 8.6:Regional employment targets under a fast national convergence scenario


Average yearly employment rate increase (% points)


'09 '20 ‘10-'20 Increase '10-'20 (1000)


Bru 55.6 63.4 0.8 107 2.3

Fla 67.3 67.3 0.1 35 0.2

Wal 58.0 64.1 0.6 194 1.4


118

We suppose the scheme is introduced and starting to impact employment performances from the 1st

of January of 2010.

- 79 -

8.6. LISBON ABSOLUTE PERFORMANCE TARGETS Here Flanders has to lift its employment rate up to the Lisbon 70% threshold at horizon 2020. Here

the other Regions have to achieve the Lisbon cap in 2030 under the slow convergence path and in

2025 under the fast convergence path. Under the slow path, Brussels and Wallonia should increase

their employment rate with 0.7 and 0.6 percentage points per year on average. Under the fast

convergence path, Brussels and Wallonia should increase their employment rate with 1.0 and 0.8

percentage points per year on average.

Table 8.7:Regional employment targets under a slow Lisbon scenario


Average yearly employment rate increase(% points)


'09 '20 ‘10-'20 Increase '10-'20 (1000)

Growth rate' 10-'20 (%)

Bru 55.6 62.7 0.7 108 2.2

Fla 67.3 70.0 0.3 186 0.6

Wal 58.0 63.9 0.6 207 1.3


Table 8.8:Regional employment targets under a fast Lisbon scenario




'09 '20 ‘10-'20 Increase '10-'20 (1000)


Bru 55.6 65.2 1.0 128 2.6

Fla 67.3 70.0 0.3 186 0.6

Wal 58.0 66.0 0.8 257 1.6


- 80 -

8.7. SEMI DANISH-STYLE ABSOLUTE TARGETS

Here Flanders has to lift its employment rate up to 71.9 % to bridge 50% of the expected gap at the

end of 2009, between the Flemish employment rate of 66.7% and the Danish value of 77.1%, at

horizon 2020. Under the slow convergence path, Brussels and Wallonia should increase their

employment rate with 0.8 and 0.7 percentage points per year on average. To stay on the fast

convergence path, Brussels and Wallonia should increase their employment rate with 1.1 and 0.9

percentage points per year on average.

Table 8.9:Regional employment targets under a slow semi Danish scenario




'09 '20 ‘10-'20 Increase '10-'20 (1000)

Growth rate '10-'20 (%)

Bru 55.6 62.0 0.8 116 2.4

Fla 67.3 71.9 0.5 265 0.8

Wal 58.0 63.4 0.7 231 1.5


Table 8.10:Regional employment targets under a fast semi Danish scenario




'08 '20 ‘10-'20 Increase '10-'20 (1000)


Bru 55.6 66.5 1.1 138 2.8

Fla 67.3 71.9 0.5 265 0.8

Wal 58.0 67.3 0.9 288 1.8


8.8. EUROPEAN RELATIVE PERFORMANCE TARGETS In Appendix 13.12, we fix targets by requiring that Belgian Regions catch-up with employment rates

or employment rate variations of the best performing Regions within clusters of comparable Regions.

We do not discuss this family of targets in the centre of this work because those targets might be too

complex and because they might widen the gap between Belgian Regions.

- 81 -

8.9. REALITY CHECKS OF TARGETS AND SCENARIO SELECTION We verify how realistic those targets are. Therefore, we compare targeted average employment rate

variations with variations of EU-Regions and -countries in the past and also with forecasted Belgian

Regional variations under the HERMREG baseline scenario. In Appendix 13.13, we show that Belgian

growth has become more labor intensive since 1995. This increasing labor-intensity of growth has

been suggested in European literature by Dew-Becker et al(2008). Therefore, it should become

easier, for a given level of GDP growth, to achieve a fixed set of employment rate variation targets.

8.9.1. EMPLOYMENT RATE VARIATIONS EUROPEAN REGIONS IN 1999-2007

Chart 8.1 and table 8.11 illustrate that some European Regions achieved very high employment rate

variations whereas other others struggled with decreasing employment rates. Cantabria increased its

employment rate by 2.1 percentage points per year whereas the most important yearly employment

rate decrease equaled -1.1 percentage points per year, achieved by Sud-Vest Oltenia. The median

Region increased its employment rate with 0.50 percentage points on average per year which was

slightly better than Flanders’ increase with 0.40 percentage points per year. Brussels and Wallonia

achieved mediocre variations of 0.08 and 0.28 percentage points on average per year. Only

respectively 24 and 36% of the European Regions did worse.

Chart 8.1:Distribution of average yearly employment rate variations of 332 EU Regions 1999-

2007(in percentage points)

Source: Eurostat

-1,2

-0,7

-0,2

0,3

0,8

1,3

1,8

0%

4%

7%

10

%

14

%

17

%

20

%

23

%

27

%

30

%

33

%

37

%

40

%

43

%

47

%

50

%

53

%

57

%

60

%

63

%

67

%

70

%

73

%

77

%

80

%

83

%

86

%

90

%

93

%

96

%

10

0%

- 82 -

Table 8.11:Statistics of average yearly employment rate variations of 332 EU Regions 1999-2007(in

percentage points)

Average 0.4

Median 0.5

Maximum 2.1

Minimum -1.1

Standard deviation 0.5

90th

percentile 73.5

80th

percentile 71.0

20th

percentile 58.2

Percentage Regions slower than Fla 47%

Percentage Regions slower than Bru 24%

Percentage Regions slower than Wal 36%

Source: Eurostat

8.9.2. EMPLOYMENT RATE VARIATIONS EUROPEAN COUNTRIES IN 1995-2007

When we benchmark Belgium’s performances since 1995, then we notice that cross-country

differences are smaller than cross-region differences. Important differences nevertheless persist. The

Belgian performance of 0.48 percentage points employment rate increase on average per year, is

slightly under the European average of 0.56 percentage points variation per year.

Chart 8.2:Distribution of average yearly employment rate variations of 20 European countries

1995-2007(in percentage points)

Source: Eurostat

0,000,200,400,600,801,001,201,401,60

- 83 -

Table 8.12:Statistics of average yearly employment rate variations of 20 European countries for

1995-2007(in percentage points)

Average 0.56

Median 0.45

Standard deviation 0.36

90th

percentile 1.01

80th

percentile 0.70

20th

percentile 0.28

Source: Eurostat

8.9.3. SCENARIO SELECTION WITH REALITY CHECK

In tables 8.13 and 18.14, we verify how realistic the various scenarios are for every Region.

Therefore, one can compare the targeted employment rate variation with the baseline scenario

variation119

. Second, we look at the values of the employment rate increases for 20 European

countries in the period 1995-2007. We compute the percentage of countries, who performed less

well than the targeted variation for each scenario.

By analyzing table 8.13, we decide to eliminate three families of target scenario’s on the basis of two

reasons. We eliminate the HERMREG Baseline, the European peer level and the European speed

catching-up scenario because those scenario’s foster interregional divergence120. We also think that

the national convergence scenario’s lack ambition for Flanders, with respect to the European

historical averages, since no European country exhibited a worse employment rate increase in 1995-

2007 than the rate targeted under this scenario for Flanders.

119

We refer to the baseline scenario which includes the impact of the financial crisis. 120

The divergence fostering-character of European benchmarking is relatively intuitive. If you ask a good-performer today to become a top-performer tomorrow, and if you ask a bad-performer today to become a medium-performer tomorrow, then differences will only increase if the top-performer progresses faster than the medium-performer. Nevertheless, the idea of comparing with peers is not necessarily bad if you are more ambitious for the bad-performer than the good-performer in terms of speed of convergence or relative performance within their classes of peers. But complexity in putting realistic tough ambitious peer targets is so high, that many unintended consequences may appear.

- 84 -

Table 8.13:Reality check metrics for rejected scenario’s

Scenario Region ER-variation ‘10-‘20 (% points)

% of 20 EU-countries with lower ER-increase’95-‘07

HERMREG baseline (incl. financial crisis)

Bru 0.3 28%

Fla 0.4 34%

Wal 0.2 0%

Slow national convergence Bru 0.6 75%

Fla 0.1 0%

Wal 0.5 63%

Fast national convergence Bru 0.8 82%

Fla 0.1 0%

Wal 0.6 77%

European peer level caching up

Bru 1.0 90%

Fla 0.7 79%

Wal 0.6 74%

European peer speed catching- up

Bru 0.4 34% Fla 0.8 83%

Wal 0.4 34%

Source: Own computations based on Planning Office(2008), Eurostat and Stevens et al(2007)

By analyzing Table 8.14, we finally select two families of scenario’s which result into interregional

convergence. First, we have the Lisbon scenario’s, where Flanders achieves Lisbon targets in 2020,

and where the other two Regions catch-up with the Lisbon level in 2025, under the fast scenario, and

in 2030 under the slow scenario. Second, we have the Semi- Danish employment rate scenario’s. In

those scenario’s Flanders bridges 50% of the gap with Denmark’s employment rate level anno 2007

in 2020 and the other two Regions catch-up with this level in 2025, under the fast scenario, and in

2030 under the slow scenario.

As explained in 8.4, the final choice of a scenario, among those four scenario’s within those two

families of scenario’s, depends on two questions. The first question is: “Do we want to restrict the

ambition of the bonus-malus scheme to reducing the activity gap between the three Regions or is it a

opportunity to provide Flanders with extra incentives to climb to the European top at horizon

2020121?”In the first case, Lisbon targets are appropriate. In the second case, semi- Danish scenario’s

seem optimal. The second question is: “Should the speed of interregional convergence be fast or

slow?”.

121

The case for providing extra incentives to climb to the European top seems to make sense since simply achieving the Lisbon targets would correspond to an average employment rate increase lower than the one achieved by 73% of the European countries in 1995-2007.

- 85 -

Finally, Table 8.14 shows that it is not obvious to judge if those four ambitious scenario’s are realistic

for Wallonia and Brussels. If you compare the targeted speeds of progress, with their own speed in

the past or with baseline speeds estimated by the Planning Office, then the targets seem very

ambitious. For instance, Wallonia should increase its employment rate under the Fast Danish

scenario at a pace which is 4.6 times faster than the pace under the baseline scenario. On the other

hand, benchmarking targets with the performances of the 20 European countries in 1995-2007,

shows that no target requires Brussels or Wallonia to be with the 8% fastest climbers.

Table 8.14:Reality check metrics for accepted scenario’s

Scenario Region ER-variation ‘10-‘20 (% points)

% of 20 EU-countries with lower ER-increase’95-‘07

Slow Lisbon Bru 0.7 81%

Fla 0.3 27%

Wal 0.6 76%

Fast Lisbon Bru 1.0 88%

Fla 0.3 27%

Wal 0.8 82%

Slow semi Danish

Bru 0.8 83%

Fla 0.5 58%

Wal 0.7 80%

Fast semi Danish

Bru 1.1 91%

Fla 0.5 58%

Wal 0.9 86%


- 86 -

8.10. CONCLUSION WITH VISUALIZATION OF FOUR SELECTED SCENARIO’S The Planning Office expects the employment rate gap to widen between, on the one hand, Flanders

and, on the other hand, Wallonia and Brussels. Brussels’ employment rates will be heavily impacted

by the expected increase of its population at working age.122

The choice of final targets depends on the definition of the problem(s) the scheme aims to tackle. If

you want the scheme to be sufficiently ambitious for the three Regions, while at the same time

providing incentives to bridge the gap between the three Regions, then two families of targets seem

appropriate. In Chart 8.3, we visualize two Lisbon scenario’s, where Flanders achieves the Lisbon cap

in 2020, and where the other two Regions catch-up with the Lisbon level in 2025 or in 2030. Chart

8.4. visualizes Semi- Danish employment rate scenario’s, where Flanders reduces the gap with

Denmark’s employment rate level anno 2007 by 50% in 2020 by achieving an employment rate of

71.9% , and where the other two Regions catch-up with this level in 2025 or in 2030.

To conclude, the amplitude of the challenge to lift up Wallonia’s and Brussels’ employment rates to

and/or above 70% seems important, especially if we benchmark these targets with their own past

performances or with the forecasted performances by the Planning Office. Achieving those long-run

targets of convergence would require Brussels and Wallonia to progress at a speed realized by the

20% fastest European countries over the last 10 years.

122

The population at working age is expected to increase by 12.1% increase between 2007 and 2020.

- 87 -

Chart 8.3:Targeted business-cycle adjusted123 average employment rate levels under Lisbon

scenario’s 2008-2035


123

This chart results from a simulation and should not be misunderstood. We simply apply the targeted average employment rate increases, on which the scheme should depend, to the forecasted levels for December 2009. We cannot predict the employment rate levels accurately because of for instance the business cycle impact.

54

56

58

60

62

64

66

68

70

08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Slow Brussels

Flanders

Slow Wallonia

Fast Brussels

Fast Wallonia

- 88 -

Chart 8.4:Targeted Business- cycle adjusted average employment rate levels under Danish

scenario’s 2008-2035


54

56

58

60

62

64

66

68

70

72

08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Slow Brussels

Flanders

Slow Wallonia

Fast Brussels

Fast Wallonia

- 89 -

9. SIZE OF INCENTIVIZATION In 2.1. we showed that financial activation incentives at the decentralized level are too limited today.

In 4.5. we defined the concept of incentivization size coefficients. But how important should those

incentivization size coefficients be? What are optimal levels of decentralized financial activation

incentives? In 9.1. we recapitulate the definition of incentivization size coefficients. In 9.2. we list

factors, one might look at to fix the values of the incentivization size coefficients, and we propose

three approaches to fix those values. In 9.3., 9.4. and 9.5. we fix three sets of values of the

incentivization size coefficients for every Region. In 9.6. we discuss the main advantages and

disadvantages of those three sets of coefficient values.

9.1. DEFINITION OF INCENTIVIZATION SIZE COEFFICIENTS

We recapitulate here the macro- definition of incentivization size coefficients we proposed in Section

4.5. The incentivization size coefficient (ISCi,t) expresses which amount will be funded from the

federal to the decentralized level per unit of performance. Therefore the incentive flow (IFi,t) for a

Region i during year a year t is proportional to the performance indicator (PIi,t) and to the

incentivization size coefficient:

𝐼𝐹𝑖,𝑡 = 𝐼𝑆𝐶𝑖,𝑡 × 𝑃𝐼𝑖,𝑡 (9.1)

At a macro level, incentivization size coefficients correspond to the amount, funded by the federal to

the decentralized level, per employment rate percentage point variation on top of the targeted

variation under target scenario’s we selected in 8.10. At a micro-level, incentivization size coefficients

can also be expressed as the amounts, funded by the federal to the decentralized level, per activated

resident on top of the targeted number of activated residents. In this chapter, we set coefficients at a

micro-level. But it is relatively easy to compute the macro-equivalents of micro-coefficients by taking

into account the size of the Regional working age population.

- 90 -

9.2. FACTORS ONE MIGHT LOOK AT TO FIX INCENTIVIZATION SIZE COEFFICIENTS In general, fixing values of incentivization size coefficients involves two, in a certain sense orthogonal,

questions. First, how should we provide incentives to decentralized entities to act optimally with

respect to the factors, which they control, and which impact the performance indicators? Second, to

which extent, should decentralized entities bear risk, generated by the exposure of the performance

indicators to external factors, which they cannot control? For instance, should decentralized entities

also bear (a part of) the business cycle risk? In the specific context of this work, as outlined in chapter

1, we will only answer the first question. We consider the second question as out of the scope of this

work and, therefore, we propose schemes, which minimize the exposure of decentralized entities to

the business cycle risk.

In general, the following factors would strengthen the case for relatively high incentivization size

coefficient values:

A low current exposure of decentralized budgets to business cycle variations;

Low degrees of risk aversion of Regions124;

An important size of decentralized budgets125;

The potential for decentralized budgets to avoid the vicious circle by generating cash in bad;

days through borrowing potential126, fiscal autonomy127or cost-cutting potential;

An important extent of decentralization of policies with an impact on employment128.

We think the following three approaches to fix the values of the incentivization size coefficients

might be reasonable. First, one might quantify and compare the employment policy impact, that

several entities have, and incentivize every level with incentivization size coefficients which are

proportional to the policy impact of every political level. This first approach applies some kind of

impact-return proportionality principle. Second, one might focus on one of the most important

decentralized policy levers, such as active labor market policies. One might fix coefficients such that

decentralized investments in this area become self-sustainable. We equal investment amounts and

expected scheme boni according to a self-financing principle. Third, one might argue that the 124

The concept of risk aversion is very difficult to define and quantify for political entities like, for instance, Regions. 125

In 2006, expenditures in Billion Euros equaled 6.1 in the Walloon Region, 7.6 in the French Community, 2.7 in the Brussels Region and 20.0 in the Flemish merged Region+ Community entity. 126

Current debt levels as share of GDP of the decentralized entities equal 2.5% in the French Community, 2.5% in the Brussels Region, 8.4% in Walloon Region and 0.27% in Flemish Region. Those debt levels suggest that there is some room for increasing debt levels if this would be necessary. Nevertheless, this reasoning should be tempered since, at the end of the day, those same citizens, living in the three Regions, also bear the burden of the important federal public debt. 127

The potential for decentralized entities to raise tax rates in bad days seems limited since OECD-data dating from 2007 cited in Algoed et al(2007) suggest that only 39% of decentralized expenditures are covered by own taxes of the decentralized entities. 128

For a more detailed discussion on the decentralized entities’ impact on employment variables, we refer to Chapter 7.

- 91 -

uncertainty on the policy impact is so high, that the first and second approaches might be difficult to

implement with accuracy. Therefore, one might prefer to share the total job return, that we

computed in Table 2.11., according to reasonable sharing-principles.

Let us now turn to Section 3, where three examples of values of coefficients will be computed. The

first example is based on the self-financing principle and the second and third examples are based on

the reasonable sharing-principle. We will rank the three examples from the most incentivizing to the

least incentivizing one and we will respectively call them the high-powered set, the medium-powered

set and the low-powered set of Regional incentivization size coefficients.

- 92 -

9.3. HIGH-POWERED REGIONAL INCENTIVIZATION SIZE We apply the self-financing principle on the ALMP- investments of the Regions which should pay for

themselves. If we take the results in Table 2.11. as given, then we can fix incentivization size

coefficients at €18,600 for the Flemish Region and at €20,700 for the Brussels’ and Walloon Region

per activated resident. The resulting financial returns are exhibited in Table 9.1.

Table 9.1:Total returns per activated resident with high-powered incentives(in €1000)

Entity Activation one unemployed in Fla

Activation one unemployed in Wal

Activation one unemployed in Bru

Fla Reg 21.8 -0.5 0.8

Fla Reg (incl. ALMP cost ) 0.0 -0.5 0.8

Fla Comm 1.1 -0.1 0.7

Wall Reg 0.8 21.8 0.5

Wall Reg (incl. ALMP cost) 0.8 0.0 0.5

Bru Reg 0.2 0.2 21.8

Bru Reg (incl. ALMP cost) 0.2 0.2 0.0

Fre Comm 0.1 1.2 0.5

Fed (Financing Law Reg) -3.0 0.7 -1.2

Fed (Financing Law Com) -1.2 -1.1 -1.2

Fed unemployment cost 29.0 29.0 29.0

Fed bonus malus -18.6 -20.7 -20.7

Fed net 6.2 8.0 5.8

Total Return (incl. ALMP) 8.5 8.8 8.3

Total Return (excl. ALMP) 30.3 30.6 30.1


- 93 -

9.4. MEDIUM-POWERED REGIONAL INCENTIVIZATION SIZE

We apply the reasonable-sharing principle between the Regions and the federal level. We require

that Regional returns, excluding ALMP-investments, equal the federal return. If we take the results in

Table 2.11. as given, then we can fix incentivization size coefficients per activated resident at

€10,800 for the Flemish Region and at €13,700 for Walloon Region and at €12,700 for the Brussels’

Region. The resulting financial returns are exhibited in Table 9.2

Table 9.2:Total returns per activated resident with medium-powered incentives(in €1000)

Entity Activation one

unemployed in Fla


Wal


Bru

Fla Reg 14.0 -0.5 0.8

Fla Reg (incl. ALMP cost ) -7.8 -0.5 0.8

Fla Comm 1.1 -0.1 0.7

Wall Reg 0.8 14.9 0.5

Wall Reg (incl. ALMP cost) 0.8 -6.9 0.5

Bru Reg 0.2 0.2 13.8

Bru Reg (incl. ALMP cost) 0.2 0.2 -8.0

Fre Comm 0.1 1.2 0.5

Fed (Financing Law Reg) -3.0 0.7 -1.2

Fed (Financing Law Com) -1.2 -1.1 -1.2

Fed unemployment cost 29.0 29.0 29.0

Fed bonus malus -10.8 -13.7 -12.7

Fed net 14.0 14.9 13.8

Total Return (incl. ALMP) 8.5 8.8 8.3

Total Return (excl. ALMP) 30.3 30.6 30.1


- 94 -

9.5. LOW-POWERED REGIONAL INCENTIVIZATION SIZE We apply the reasonable-sharing principle between the Regions and the federal level. We require

that Regional returns, excluding ALMP-investments, equal 50% of the federal return. If we take the

results in Table 2.11. as given, then we can fix incentivization size coefficients per activated resident

at €6,100 for the Flemish Region, at €8,800 for Walloon Region and at €8,100 for the Brussels’

Region. The resulting financial returns are exhibited in Table 9.3.

Table 9.3:Total returns per activated resident with low-powered incentives(in €1000)

Entity Activation one unemployed

in Fla


Wal


Bru

Fla Reg 9.4 -0.5 0.8 Fla Reg (incl ALMP cost ) -12.5 -0.5 0.8 Fla Comm 1.1 -0.1 0.7 Wall Reg 0.8 9.9 0.5 Wall Reg (incl ALMP cost) 0.8 -11.9 0.5 Bru Reg 0.2 0.2 9.2 Bru Reg (incl ALMP cost) 0.2 0.2 -12.6 Fre Comm 0.1 1.2 0.5 Fed (Financing Law Reg) -3.0 0.7 -1.2 Fed (Financing Law Com) -1.2 -1.1 -1.2 Fed unemployment cost 29.0 29.0 29.0 Fed bonus malus -6.1 -8.8 -8.1 Fed net 18.7 19.9 18.4 Total Return (incl ALMP) 8.5 8.8 8.3 Total Return (excl ALMP) 30.3 30.6 30.1 Source: Own computations

9.6. ADVANTAGES AND DISADVANTAGES OF THE THREE INCENTIVIZATION SIZE OPTIONS The high-powered scheme involves higher Regional activation incentives. The expected positive

impact on policies and employment outcomes is therefore higher. On the other hand, the Regional

level has to share more budgetary risk. Therefore, if targets are not achieved by, for instance the

weaker Region, despite good Regional efforts129, then the vicious circle risk increases in the size of

the incentivization. Ultimately, we face a trade-off between a high expected Regional policy impact

and a low Regional budget vicious circle risk. 129

Potential reasons for this bad performance despite good efforts are numerous. First, despite the possibility to pursue reality-checks, as we did in Chapter 8, targets might be unrealistic. Second, one might over-estimate the potential of decentralized entities to impact the employment rate despite the literature review we presented in Chapter 7. Third, one might face important time lags between decentralized investments on the one hand and performance improvements and bonus returns on the other hand. In the case of relative performance evaluation, another reason might be the asymmetrical impact of external shocks on two Regions, which is at odds with the equal relative impact assumption we made in 5.3.1, despite the high Flanders-Wallonia correlations we computed in Chapter 5. Moreover, further studies are needed to assess the risk of disincentivizing the federal level in the case of too high powered Regional incentive schemes.

- 95 -

10. SIMULATING THE IMPACT OF THE SCHEME

In chapter 2, we argued that incentive schemes might contribute to the upward convergence of

employment rates because of a significant and stimulating impact of positive employment

performances on the budgetary situation. In chapter 4, we explained the risk of creating a vicious

circle under a malus system with too ambitious targets. Therefore, to strike the balance, the budget

impact of employment rate variations has to be, on the one hand, sufficient to change policy

behavior but, on the other hand, not too excessive to avoid a vicious circle. But how important is the

budget impact of the schemes we proposed as options in previous Chapters? And do these schemes

significantly change the total cost of increasing the employment rate through better policies? How

many extra jobs could be created with those schemes for a given net investment in active policies?

The goal of this chapter 10 is to draw lessons from the simulations rather than to confirm or

advocate certain options. The fact that we simulated the impact of certain schemes does not mean

we think those schemes are perfect or that they do not feature any significant disadvantages.

In 10.1, we neglect decentralized policy costs. On the basis of given employment rate variations, we

assess the budget impact of schemes for the Flemish and Walloon Region. We consider both Belgian

relative and absolute performance evaluation and we limit our simulations to yearly assessment

schemes and discuss how cumulative assessment might change the curves.

In 10.2, we introduce decentralized active labor market policy costs and we link them to employment

rate variations. We show how different incentivization sizes have different impacts on the net

decentralized budget cost to increase employment rates, where the net cost includes marginal

incentive boni. Our simulations cover yearly assessment schemes and we discuss how cumulative

assessment might shift the curves.

- 96 -

10.1. BUDGET IMPACT WITHOUT POLICY LINK We simulate the budget impact of the introduction of bonus malus and bonus schemes for Flanders

and Wallonia in function of their Regional employment rate. We consider yearly performance

evaluation with slow Semi-Danish targets130 and medium-powered incentivization size coefficients131.

Charts 10.1, 10.2, 10.3. and 10.4 exhibit incentive flows for Flanders and respectively Wallonia which

are always a function of their own employment rate performances. In the case of relative

performance evaluation, incentive flows are also a negative function132 of employment performances

of their peers, respectively Wallonia and Flanders. As explained conceptually in 3.3.5. and as

quantified in chapter 5, Belgian yearly relative performance evaluation, allows to reduce risk

significantly by filtering out the exposure to the business cycle. But Charts 10.1, 10.2 , 10.3 and 10.4

also exhibit incentive flows in the case of absolute performance evaluation. The absolute incentive

flow curves are colored in green. Formula (10.1.), which we already presented as equation (5.2.) in

chapter 5, shows that, for a given employment rate performance of Flanders/Wallonia, absolute

incentive flows are exactly equal to relative incentive flows when the respective peers

Wallonia/Flanders achieve their targets.


What happens if we evaluate cumulative rather than yearly performances? As explained in 4.1.2, if

employment rate variations are permanent and if we assess average cumulative employment rate

variations, then the Regions will not only reap the benefits during the first year of the improvement,

but also during the following years. Nevertheless, incentive flows, due to the performance leap in, let

us say year t1, will decrease over time, during the years t2, t3, etc133.

130

Those slow Semi-Danish average employment rate variation targets of 0.47 % for Flanders, 0.70% for Wallonia and 0.82 % for Brussels are computed such that Flanders reduces the gap with Denmark’s employment rate level with 50% in 2020 by achieving a rate of 71.9% and where the other two Regions catch-up with this level in 2030. 131

According to the medium-powered incentivization approach, incentivization size coefficients per activated resident are fixed at €10,800 for the Flemish Region, at €13,700 for Walloon Region and at €12,700 for the Brussels’ Region. 132

We explained in 5.6.3. that this relative performance evaluation does not necessarily have to be at odds with promoting cooperation and mobility if this scheme is coupled to mobility premiums. These premiums could slightly modify curves 10.1 and 10.2 since incentive flows for Flanders/Wallonia would increase in the number of activated commuters from Wallonia/Flanders working in Flanders/Wallonia. 133

If we assume a financial incentive flow f in year t1, as direct return for a given employment rate improvement e in year t1, and if we assess cumulative average employment rate variations during the years t1, t2, etc, then the returns will decrease according to a harmonious series. The respective returns during the years t1, t2, t3, t4, and t5 will be equal to f, f/2, f/3, f/4 and f/5. The cumulative total return over the 5 years, without taking into account the time value of money, will equal 2.28f.

- 97 -

Chart 10.1:Bonus malus incentive flows for Flanders with slow Semi-Danish targets and medium-

powered premiums

Legend: The baseline employment rate variation forecasted by the Planning Office (including the impact of the financial crisis) for Wallonia is 0.20% per year. The slow- Semi Danish target for Wallonia is 0.70% per year. The green curve exhibits incentive flows both in the absolute performance case as in the relative performance case when Wallonia achieves its targets. Source: own computations

-800

-600

-400

-200

0

200

400

600

800

-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

Ince

nti

ve F

low

(M

n €

/yea

r)

Employment rate variation (percentage point/year)

bonus-malus if Wal achieves baseline

bonus-malus if Wal status-quo

bonus-malus if Wal achieves target/ if absolute performance evaluation Fla

1% expenditures budget Fla(Region+Comm)

- 98 -

Chart 10.2:Bonus incentive flows for Flanders with slow Semi-Danish targets and medium-powered

premiums

Legend: The baseline employment rate variation forecasted by the Planning Office (including the impact of the financial crisis) for Wallonia is 0.20% per year. The slow- Semi Danish target for Wallonia is 0.70% per year. The green curve exhibits incentive flows both in the absolute performance case as in the relative performance case when Wallonia achieves its targets. Source: own computations

0

100

200

300

400

500

600

700

800

-1 -0,7 -0,4 -0,1 0,2 0,5 0,8 1,1 1,4

Ince

nti

ve F

low

(M

n €

/yea

r)


bonus if Wal achieves baseline

bonus if Walstatus-quo

bonus if Wal achieves target/if absolute performance evaluation Fla

absolute value 1% expenditures budget Fla(Region+Comm)

- 99 -

Chart 10.3:Bonus malus incentive flows for Wallonia with slow Semi-Danish targets and medium-

powered premiums

Legend: The baseline employment rate variation forecasted by the Planning Office (including the impact of the financial crisis) for Flanders is 0.40% per year. The slow- Semi Danish target for Flanders is 0.47% per year. The green curve exhibits incentive flows both in the absolute performance case as in the relative performance case when Flanders achieves its targets. Source: own computations

-600

-400

-200

0

200

400

-1 -0,8-0,6-0,4-0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

Ince

nti

ve F

low

(M

n €

/yea

r)


bonus-malus if Fla achieves baseline

bonus-malus if Fla status-quo

bonus-malus if Fla achieves target/ if absolute performance evaluation Wal

1% expenditures budget Wall(Region)

5% expenditures budget Wall (Region)

- 100 -

Chart 10.4:Bonus incentive flows for Wallonia with slow Semi-Danish targets and medium-

powered premiums

Legend: The baseline employment rate variation forecasted by the Planning Office (including the impact of the financial crisis) for Flanders is 0.40% per year. The slow- Semi Danish target for Flanders is 0.47% per year. The green curve exhibits incentive flows both in the absolute performance case as in the relative performance case when Flanders achieves its targets. Source: own computations

0

50

100

150

200

250

300

350

400

-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

Ince

nti

ve F

low

(M

n €

/yea

r)


bonus if Fla achieves baseline

bonus if Fla status-quo

bonus if Fla achieves target /if absolute performance evaluation Wal

absolute value 1% expenditures budget Wall(Region)

absolute value 5% expenditures budget Wall (Region)

- 101 -

Which two main lessons can we draw from those four charts 10.1, 10.2, 10.3 and 10.4?

The first lesson arises from comparing bonus charts 10.2 and 10.4 with bonus malus charts 10.1 and

10.3. The key lessons from the bonus malus versus bonus trade-off discussion in chapter 4.1.2., are

now visualized. Bonus malus schemes provide symmetrical incentives for all levels of Regional

performance. Curves 10.1 and 10.3 are linear. Symmetrical schemes also limit the federal cash out-

flows to the case, where good Regional performances result in important federal budget gains, and

where part of those gains flow back to the Regional level. But, a vicious circle might hit the weaker

Region if this Region performs poorly. Chart 10.3, shows that, if Wallonia performs exceptionally

poorly, it might have to pay a malus equivalent to 5% of its budget. Bonus schemes avoid this vicious

circle but they do not provide symmetrical incentives. Curves 10.2 and 10.4 are locally horizontal:

there is no local incentive to improve results from very poor to poor if this ultimately yields the same

zero incentive flow. Last but not least, bonus schemes increase the pressure on the federal budget,

which is already vulnerable, as we outlined in 2.2.2.

Nevertheless, some advantages of the bonus and bonus malus options can be combined by

implementing a bonus malus scheme which looks like a bonus. First, we reduce the general

generosity of the Finance Law by translating curves 10.1 and 10.3 vertically downward along the

vertical axis with a certain distance134. Second, the federal level only pays positive cash-flows to the

Regional level, which translates curves 10.1 and 10.3 upward along the vertical axis with the same

distance. With this technique, we can obtain exactly the same curves as in Chart 10.1 and 10.3. This

solution might combine several advantages such as symmetrical incentives, psychological

advantages, “no mali” and respect of the vulnerable federal budget constraint.

The second lesson arises when we compare Flemish Charts 10.1 and 10.2 with Walloon Charts 10.3

and 10.4. Is the probability for Wallonia to fall into a vicious circle much higher than for Flanders?

How does the financial impact of the baseline HERMREG scenario differ across Regions? Let us

answer those questions for relative performance evaluation before moving to absolute performance

evaluation.

Compare Flanders and Wallonia under relative evaluation by looking at curves in Charts 10.1 and

10.3. Assume that their peers Wallonia and Flanders follow the HERMREG baseline path with yearly

employment rate variations of respectively 0.20% for Wallonia and 0.40% for Flanders. When should

Flanders and Wallonia pay mali exceeding 1% of their budget? Graphically, we can reformulate the

same question by verifying for which employment rate variation values of Flanders and Wallonia the

134

We might nevertheless ask ourselves if this is feasible for the Walloon and Brussels Regions.

- 102 -

yellow peer baseline curves and the purple 1% budget curves cross? We observe that Flanders, on

the one hand, should pay mali equivalent to at least 1% of its budget, if Flanders’ employment rate

drops by more than 0.50 percentage points per year. On the other hand, Wallonia should pay mali

equivalent to at least 1% of its budget, if Wallonia’s employment rate increases by less than 0.40%

percentage points per year. Therefore the probability to fall into a vicious circle seems much higher

for Wallonia than for Flanders.

Compare now Flanders and Wallonia under absolute evaluation by looking at the green curves in

Charts 10.1 and 10.3. We observe that Flanders, on the one hand, should pay mali equivalent to at

least 1% of its budget, if Flanders’ employment rate decreases. On the other hand, Wallonia should

pay mali equivalent to at least 1% of its budget, if Wallonia’s employment rate increases by less

than 0.50% percentage points per year

But, why is the simulated impact of this scheme under the baseline scenario financially more

favorable to Flanders than to Wallonia? First, slow-semi Danish targets are more ambitious for

Wallonia than for Flanders with targeted employment rate variations of respectively 0.70% per year

for Wallonia and 0.47% per year for Flanders. Second, under the baseline scenario, the employment

rate gap between Flanders and Wallonia widens with 0.20% per year. Third, the Flemish budget is

more important than the Walloon budget, both in absolute figures as in relative figures. We define

the relative budget as the budget per member of the active population. The relative Walloon budget

is smaller mainly because of the merger between the budget of the Flemish Community and the

Flemish Region. The Flemish Community and Region can, to a certain extent, share the burden.

- 103 -

10.2. IMPACT SCHEME ON NET COST OF INCREASING EMPLOYMENT RATES WITH

ACTIVATION POLICIES It would be ideal to present the expected number of created jobs for every scheme. Unfortunately,

we cannot compute accurate forecasts unless we want to use very simplistic assumptions. Indeed,

predicting the impact of incentives on jobs requires formulating two complex assumptions.

First, one has to make a Government objective function assumption. What will the policy reaction be

of both decentralized and federal governments to the new financial incentive structure? To which

extent will the level and/or the efficiency of relevant policy investments, such as ALMP-and

education investments, increase? An accurate answer to this question would be based on the

definition of a government objective function. But how to weight several relevant elements in this

function such as financial incentives, electoral incentives, the general interest and special interests?

We consider that we do not have information or empirical research results to make such an

assumption with sufficient accuracy. We nevertheless argue that there would very probably be a

positive policy reaction. The policy reaction is expected be positive since governments tend to react

to financial incentives, according to our literature review of government reactions to financial

incentives in Appendix 13.14. We show that there is extensive empirical evidence of government

reactions to various financial incentives such as tax base elasticities, common pool problems and soft

budget constraints. This empirical evidence strongly suggests that decentralized Belgian entities

would react to financial activation incentives, created by job bonus schemes.

Therefore, we do not predict a precise policy reaction, but we will study the impact of a range of

policy reactions. Indeed, Charts 10.5 and 10.6 assume that Regional ALMP-investments, from

respectively the Flemish and Walloon governments, will increase with a certain amount within a

range from €0 Million up to respectively €400 Million for Flanders and €250 Million for Wallonia.

Second, once we have predicted the (range of potential) policy reaction(s) to the scheme, then we

introduce the policy elasticity of the employment rate. By how many percentage points will the

employment rate increase given the higher level and/or quality of ALMP- and education

investments? We only focus on the impact of ALMP- policies and neglect the education impact even

if the education activation potential, as we outlined in 7.4.2., seems very important135. With respect

135

We consider that the uncertainty concerning the question by how much and for how long education-investments increase expected employment rates is to too high to make accurate forecasts.

- 104 -

to the active labor market elasticity of the employment rate, we leverage the key result136 of

Estevão(2003) that we presented in 2.1.2. On the basis of this key result, we estimated the yearly

ALMP- cost to activate one unemployed at €21,800. On Charts 10.5 and 10.6, we visualize how

different incentivization sizes of yearly137 evaluation schemes have different impacts on the net

Regional budget cost to increase employment rates. This cost includes incentive bonus flows. To

isolate the stylized leanings from Charts 10.5 and 10.6 about how different schemes change the cost

of increasing employment rates, we make some simplifying assumptions138.

What do we learn from Charts 10.5 and 10.6? If we read the charts by following a horizontal line,

then we can notice how a given marginal net ALMP-investment results into a higher number of

created jobs for a more important size of incentivization. The idea is that part of the gross investment

is recovered via positive marginal incentive flows. If we read the charts by following a vertical line,

then we can notice how the cost, to achieve a certain marginal objective of employment rate

variation, decreases in the size of the incentivization. We illustrate the horizontal interpretation both

for Flanders and for Wallonia.

Assume the Flemish government invests €200 Million extra in ALMP’s during a certain year. Then, the

subsequent increase of the employment rate depends on the scheme and equals 0.26 percentage

points without the scheme, 0.39 percentage points with the low-powered scheme and 0.63

percentage points with the medium-powered scheme. Assume Wallonia invests €130 Million more

in ALMPs during a certain year. Then, the subsequent increase of the employment rate depends on

the scheme and equals 0.28 percentage points without the scheme, 0.49 percentage points with the

low-powered scheme and 0.83 percentage points with the medium-powered scheme.

To conclude, we showed in 10.1 that Wallonia has to make important progress if it wants to reap

significant and positive bonus-benefits from schemes based on interregional convergence targets,

such as the semi-Danish targets. We showed in 10.2., that, from a marginal point of view,

136

The key result can be formulated in the following way:“ If the share of ALMP expenditures in GDP increases with one percentage point, then the business employment rate increases with 1.80 up to 1.96 percentage points with a 95% probability”. We compute this confidence interval on the basis of the t-stat and the number of data points which equals 120, which is the product of 15 countries and 8 yearly data points per country. 137

As explained in footnote 129, if one opts for cumulative evaluation rather than yearly evaluation, Regions will benefit during several years from permanent employment increases, but at a decreasing rhythm. The red, blue and green curves in Charts 10.5 and 10.5 would rotate clockwise: the total cost of employment rate variation would decrease compared to the yearly evaluation scheme. 138

First, we focus on extra employment rate variations on top of those necessary to achieve targets. Second, we neglect mobility premiums. Our charts, really show the marginal impact of extra investments on extra employment rate variations. Lifting the first assumption would imply vertical translations of the curves in Charts 10.5 and 10.6. Third, we suppose constant returns of active labor market policies although they will be probably decreasing. It is quite intuitive to think that the efficiency of active labor market policies, which are mainly focused on labor supply, decreases if labor demand is totally insufficient.

- 105 -

incentivization does reduce the net cost of increasing the employment rate. The stimulating effect of

this cost reduction increases in the incentivization size coefficent. And this result is thus independent

of the targets and of the question: “Who pays to whom?”. We definitely think further simulations

might be very insightful. Further simulations might forecast policy, employment, and budget

reactions for a broader set of schemes for different performance indicators, targets and

incentivization size coefficients with more accuracy.

Chart 10.5:Marginal impact of schemes with different incentivization sizes on net ALMP cost to achieve employment rate variations for Flanders


0

50

100

150

200

250

300

350

400

0 0,1 0,2 0,3 0,4 0,5

Net

ALM

P in

vest

me

nt

cost

ne

cess

ary

to a

chie

ve

em

plo

yme

nt

rate

var

iati

on

(M

n €

/yea

r)


Net cost ALMP- no scheme

Net cost ALMP- low powered

Net cost ALMP-medium powered

Net Cost ALMP-high powered

- 106 -

Chart 10.6:Marginal impact of schemes with different incentivization sizes on net ALMP cost to

achieve employment rate variations for Wallonia


0

50

100

150

200

250

0 0,1 0,2 0,3 0,4 0,5

Net

ALM

P in

vest

me

nt

cost

ne

cess

ary

to

ach

ieve

em

plo

yme

nt

rate

var

iati

on

(M

n €

/yea

r)


Net cost ALMP- no scheme

Net cost ALMP- low powered

Net cost ALMP-medium powered

Net Cost ALMP-high powered

- 107 -

11. CONCLUSION

The job bonus malus is a hot topic on the Belgian political agenda. But can it work?

We argued that the job bonus malus could be a good idea. It could be a good idea because it could

lift up employment rates and secure the financing of the Belgian social security by improving

activation incentives. Today Regions bear the financial pain of activation without any significant

financial gain. Activating an unemployed costs the Regions around €21,800 and brings in around

€3,200 in Flanders, €1,200 in Wallonia and €1,100 in Brussels.

We showed that Belgian and/or international benchmarking of employment rate variations could

incentivize Regions and Communities to invest in activation while insuring them against bad luck.

Evaluating employment rate progress since the previous year and evaluating cumulative

employment rate progress could be two attractive options. The first option reduces the risk of a

vicious circle and provides long-run activation dividends to the federal budget. The second option

encourages long-run regional investments.

If policymakers opt for assessing yearly employment rate variations, then Belgian benchmarking of

Flanders and Wallonia relative to one another reduces the budget risk created by the bonus malus.

Risk is reduced by respectively 55% and 47%. If policymakers opt for assessing cumulative

employment rate variations, then Belgian and neighbor benchmarking could be effective techniques

to reduce the budget risk supported by Flanders and Wallonia.

For Brussels, benchmarking is not the magical solution to reduce risk. Every day around 304,000

commuters go working in Brussels. Therefore, employment rate based incentives could be coupled to

mobility premiums. Employment schemes reward Regions for the activation of their own residents

whereas mobility premiums reward the activation of neighbor residents.

Belgian decentralized entities could increase employment rates through renewed education and

active labor market policies. The policy potential is underscored by our Belgian and international

literature review. For instance, with a year of extra schooling, the average Walloon citizen of working

age decreases his probability of being unemployed by around 17.2%.

- 108 -

We have defined targets such that Regions receive no bonus or malus if they achieve certain

employment rate variation targets. Targets could be set on the basis of Lisbon objectives, Belgian

interregional convergence paths or European benchmarking. European benchmarking could be based

on leading countries such as Denmark. Suppose Flanders bridges 50% of the employment rate gap

with Denmark at horizon 2020. Suppose also that Brussels and Wallonia achieve this level in 2030. To

follow this Semi-Danish path, Brussels, Flanders and Wallonia should join the 17%, 42% and 20%

fastest European employment rate climbers.

The size of incentivization could be set such that Regions can finance investments with scheme boni

or such that total budget returns on activation are equally shared between the federal and regional

level. Equal sharing of the return on activation between the federal and Regional level is possible if

Flanders, Wallonia and Brussels receive around €10,800, €13,700 and €12,700 per activated resident.

Budget impact simulations show that schemes based on Semi-Danish or Lisbon targets are ambitious

for Wallonia and Brussels. If one wants to protect Wallonia and Brussels against the vicious circle risk,

then incentive flows could be limited to boni. On the other hand, Brussels and Wallonia start from a

lower base and have more room for activation. Last but not least, the incentive scheme could trigger,

on top of reforms in decentralized policy areas, job reforms at the federal level. Cockx et al (2009)

propose to couple decentralization and incentivization to “structural reforms of labor market

institutions” at the federal level.

To conclude, we return to our research question: “Can the job incentive scheme work?” Yes, the

scheme could work properly. To work properly and to achieve a real win-win solution, maximizing

incentives while controlling risk could be the double goal. Of course, targets and incentivization sizes

are highly political topics. Within such a political topic, an economist can only list costs and benefits

of several options. Those options deserve further research. Further research could tackle Brussels,

mobility premiums, the number of created jobs and the compared effects of tax decentralization and

of the job scheme. Beyond the job scheme, the application of the solidarity-responsibility paradigm

seems promising inside Belgium and outside Belgium. Within federations like Belgium, Germany, Italy

or Canada this paradigm could be applied to activation, education, tax collection and public good

provision.

We do hope that this work is a useful contribution of economic facts and figures to the challenge of

making this job incentive scheme work as one of the key steps to get the Belgian federation out of

the current political and economic deadlock.

- 109 -

12. BIBLIOGRAPHY

12.1. BOOKS

1. Aernoudt, R.(2006), Wallonie, Flandre, je t’aime moi non plus. Antimanifeste sur les relations

entre les Flamands et les Wallons, Roularta Books, Roeselare.

2. Aghion P. and E. Cohen(2004), Education et Croissance: Rapport pour le Conseil D’Analyse

Economique, La documentation Française, Paris.

3. Ahmad E. and G. Brosio(2006), Handbook of Fiscal Federalism, Edward Elgar Publishing Inc.,

Northampton.

4. Becker, G.(1964), Human capital: a theoretical and empirical analysis with special reference

to education, Columbia University Press, New York.

5. Brennan G. and J. Buchanan(1980), The Power to Tax: Analytical Foundations of a Fiscal

Constitution, Cambridge University Press, Cambridge.

6. Cockx B. and B. Van Der Linden(2009), Does it make sense to regionalize labour market

institutions?, Re-bel e-books, Brussels.

7. Dewatripont M. and P. Bolton(2005), Contract Theory, The MIT Press, Cambridge.

8. Niskanen, W.(1971), Bureaucracy and Representative Government, Aldine-Atherton, Chicago

& New York.

9. Rodden J., E. Gunnar and J. Litvack(2003), Fiscal Decentralization and the Challenge of Hard

Budget Constraints, The MIT Press, Cambridge.

- 110 -

12.2. ARTICLES IN JOURNALS

10. Aghion P., M. Dewatripont, C. Hoxby, A. Mas-Colell and A. Sapir(2008), “Higher aspirations:

An agenda for reforming European universities”, Bruegel Blueprint Series, 5, 1 – 70.

11. Algoed K., D. Heremans and T. Peeters(2007), “Voorrang geven aan meer financieel- fiscale

verantwoordelijkheid in een nieuwe staatshervorming”, Leuvense Economische Standpunten,

115, 1 – 39.

12. Benhabib J. and M. Spiegel(1994), “The role of human capital in economic development:

evidence from aggregate cross-country data”, Journal of Monetary Economics, 34, 143 – 173.

13. Bodart V., P. Ledent and F. Shadman(2008), ”Comment la croissance économique et le coût

salarial déterminent-ils l’emploi en Belgique?”, Regards Economiques, 58, 1 – 13.

14. Bucovetsky, S.(1991), “Asymmetric Tax Competition”, Journal of Urban Economics, 30, 67 –

181.

15. Cockx B., A. Defourny, M. Dejemeppe and B. Van der Linden(2007),”Bevordert het plan voor

de begeleiding en opvolging van werklozen de overgang naar werk?”, Regards Economiques,

49, 1 – 20.

16. Conway K. and A. Houtenville(2001), “Elderly Migration and Fiscal Policy: Evidence from the

1990 Census Migration Flows”, National Tax Journal, 54, 103 – 124.

17. DelRossi A. and R. Iman(1999), “Changing the price of pork: the impact of local cost sharing

on legislators’ demand for distributive public goods”, Journal of Public Economics, 82, 247 –

273.

18. Deschamps R. and V. Schmitz(2006), “Le financement de l’enseignement supérieur et le

contexte budgétaire de la communauté française de Belgique” , Reflets & Perspectives de la

vie économique, XLV, 47 – 57.

19. Dewatripont, M.(2009), ”Gouvernance francophone ‘responsable’, condition d’une politique

socio-économique efficace et solidaire en Belgique ”, Reflets & Perspectives de la vie

économique, XLVIII, 15 – 21.

20. Dewatripont M., I. Jewitt and J. Tirole(2000), “Multitask agency problems: focus and task

clustering”, European Economic Review, 44, 869 – 877.

21. Giligan T. and J. Matsusaka(1995), “Deviations from constituent interests: the role of

legislative structure and political parties in the states”, Economic Inquiry, 33, 383 – 401.

22. Ginsburgh V. and S. Weber(2006), “La dynamique des langues en Belgique”, Regards

Economiques, 42, 1 – 15.

- 111 -

23. Holmström, B.(1979), “Moral hazard and Observability”, Bell Journal of Economics, 10, 74 –

91.

24. Holmström, B.(1982), “Moral hazard in Teams”, Bell Journal of Economics, 13, 324 – 40.

25. Holmstrom B. and P. Miligrom(1991),”Multitask Principal-Agent Analyses: Incentive

Contracts, Asset Ownership, and Job Design”, Journal of Law, Economics and Organization, 7,

24 – 52.

26. Knight, B.(2003b), “Parochial interests and the centralized provision of local public goods:

Evidence from congressional voting on transportation projects”, Journal of Public Economics,

88, 845 – 866.

27. Krueger A.B. and M. Lindahl(2001), “Education for Growth: Why and For Whom?”, Journal of

Economic Litterature, XXXIX, 1101 – 1136.

28. Mankiw G., D. Romer and D. Weil(1992), “A Contribution to the Empirics of Economic

Growth”, Quarterly Journal of Economics, 107, 407 – 437.

29. Maskin E. and J. Tirole(2004), ”The Politician and the Judge: Accountability in government”,

American Economic Review, 94, 1034 – 1054.

30. Nelson R. and E. Phelps(1966), “Investment in humans, technological diffusion and economic

growth”, American Economic Review, 56, 69 – 75.

31. Service d’analyse économique de l’IRES(2009), “Perspectives économiques 2009” , Regards

Economiques, 65, 1 – 10.

32. Solow, R.(1956), “A Contribution to the Theory of Economic Growth”, Quarterly Journal of

Economics, 70, 65 – 94.

33. Spence, M.(1973), “Job Market Signaling”, Quarterly Journal of Economics, 87, 355 – 374.

34. Van Der Linden, B.(2007), “Un regard sur le rapport de l’OCDE: des emplois pour les jeunes”,

Regards Economiques, 56, 1 – 8.

35. Van Der Linden, B.(2009), “Selon quels critères (dé)centraliser les interventions publiques sur

le marché du travail?”, Regards Economiques ,68, 1 – 8.

36. Wilson, J.(1991), “Tax Competition with Interregional Differences in Factor Endowments”,

Regional Science and Urban Economics, 21, 423 – 452.

- 112 -

12.3. ARTICLES IN NEWSPAPERS

37. Algoed, K.(2008), “Gezocht: regering met oog voor het algemene belang”, De Standaard, 22nd

december 2008.

38. Foucart, R.(2009), ”Faut-il un bonus-malus régional pour la politique de l’emploi?”, Le Soir, 3th

April 2009 .

39. Struyven, D.(2009a), “Beloon regio’s voor hun activeringsbeleid”, De Standaard, 31st March

2009.

40. Struyven, D.(2009b), “Le bonus-malus intelligent pour les Régions ”, Le Soir, 31st March 2009.

41. Van Craeynest, B.(2009), ”Crisis kost minstens 100,000 Belgische banen”, De Morgen, 12th

January 2009.

42. Vandenbroucke F. and J.C. Marcourt(2008), ”Samen arbeidsmarkt dynamiseren”, De

Standaard ,8th december 2008.

12.4. ARTICLES IN BOOKS

43. Bayenet B. and G. Pagano(2007),”Vivre ensemble ou séparément… les leçons du fédéralisme

financier”, in L’espace Wallonie-Bruxelles : Voyage au bout de la Belgique, edited by B.

Bayenet, H. Capron and P. Liégeois, De Boeck, 79 – 102.

44. Bayenet B., H. Capron H. and P. Liégeois(2007),”La sécurité sociale, coûts et découp(l)ages :

la fin de la solidarité?”, in L’espace Wallonie-Bruxelles : Voyage au bout de la Belgique, edited

by B. Bayenet, H. Capron and P. Liégeois, De Boeck, 249 – 282.

45. Bouton L. and V. Verardi(2008), “Le fédéralisme budgétaire”, in L’impôt et la politique fiscale

en Belgique, edited by C. Valenduc, Institut Belge des Finances Publiques, Brussels.

46. Cantillon B. and V. De Maesschalck(2007) , “Sociale zekerheid, transferten en federalisme in

België”, in Gedachten over sociaal federalisme ,edited by B. Cantillon, Acco, 131 – 162.

47. Capron, H.(2007) , “Fédéralisme, transferts interrégionaux et croissance régionale”, in

Réformer sans tabous.10 questions pour la Belgique de demain, edited by M. Castanheira and

J. Hindriks, De Boeck & Larcier, 47 – 64.

48. Cattoir P. and M. Verdonck(2002), “Péréquation financière et fédéralisme”, in Autonomie,

solidarité et coopération, edited by P. Cattoir, P . De Bruycker, H. Dumont, H. Tulkens and E.

Witte, Larcier, 307 – 355.

- 113 -

49. Congleton, R.(2006),“Asymmetric federalism and the political economy of decentralization ”,

in Handbook of fiscal federalism, edited by E. Ahmad and G. Brosio, Edward Elgar Publishing,

131 – 153.

50. De Callatay, E.(2007),“Réformer la solidarité dans un Etat réformé”, in Gedachten over

sociaal federalisme, edited by B. Cantillon, Acco, 163 – 183.

51. Demeulemeester, J.L.(2007), ”Capital Humain et éducation en Communauté Française de

Belgique ”, in L’espace Wallonie-Bruxelles : Voyage au bout de la Belgique, edited by B.


52. Deschamps, R.(2007a), ”Fédéralisme ou scission du pays : l’enjeu des finances publiques

régionales”, in L’espace Wallonie-Bruxelles : Voyage au bout de la Belgique, edited by B.


53. Deschamps, R.(2007b) , “La politique de l’emploi et la négociation salariale dans l’état fédéral

belge”, in Gedachten over sociaal federalisme, edited by B. Cantillon, Acco, 93 – 107.

54. Eraly A. and J. Hindriks(2007) , “Le principe de responsabilité dans la gestion publique”, in

Réformer sans tabous.10 questions pour la Belgique de demain, edited by M. Castanheira and

J. Hindriks, De Boeck & Larcier, 195 – 208.

55. Meunier O., M. Mignollet and M. Mulquin(2007), ”Les transferts interrégionaux en

Belgique”, in L’espace Wallonie-Bruxelles : Voyage au bout de la Belgique, edited by B.

Bayenet, H.Capron and P. Liégeois, De Boeck, 283 – 306.

56. Plasman, R .(2007),”Politiques d’emploi et négociations salariales”, in L’espace Wallonie-

Bruxelles : Voyage au bout de la Belgique, edited by B. Bayenet, H. Capron and P. Liégeois,

De Boeck, 227-248.

57. Sapir A. and E. De Callatay(2007) , “La réforme des marches du travail, des biens et services

et du capital en Belgique”, in Réformer sans tabous.10 questions pour la Belgique de demain,

edited by M. Castanheira and J. Hindriks, De Boeck & Larcier, 143 – 157.

58. Van der Linden, B.(2008) , “Quelles réformes pour nos institutions du marché du travail?

Réflexions autour d’un certain nombre de pistes”, in Gedachten over sociaal federalisme ,

edited by B.Cantillon, Acco, 29 – 69.

59. Van Rompuy, P.(2007) , “Werkloosheidsverzekering in een federale staat met een toepassing

op België”, in Gedachten over sociaal federalisme, edited by B. Cantillon, Acco, 11 – 27.

- 114 -

12.5. UNPUBLISHED DOCUMENTS

60. Acemoglu D., P. Aghion and F. Zilibotti(2002), “Distance to Frontier, Selection and Economic

Growth”, NBER Working Paper 9066.

61. Algoed K. and D. Heremans(2008), “The incentive effects of the Belgian Financial

Arrangements for the future”, Working Paper published for steunpunt beleidsrelevant

onderzoek 2007-2011 fiscaliteit en begroting.

62. Algoed K. and D. Heremans(2009),”The empirics of vertical and horizontal imbalances: the

Belgian case”, working paper drafted for the International Atlantic Economic Society.

63. Algoed K., D. Heremans and T. Peeters(2008a), “Een staatshervorming als reddingsboei voor

de overheidsfinanciën”, Vives Beleidspaper nr. 1.

64. Algoed K.,D. Heremans and T. Peeters(2008b), ”Vertical and horizontal fiscal imbalances in

Belgium“, powerpoint presented on the seminar Rethinking Belgium’s Foundations in

Brussels on December 11th 2008.

65. BAK Basel Economics(2007), “Brussels Metropolitan Region Benchmarking Analysis 2007”,

report published by economics consultancy in December 2007.

66. Bassilière D., F. Bossier, F. Caruso, D. Hoorelbeke, O. Lohest(2008), “Vijfentwintig jaar

Regionale ontwikkelingen: Een overzicht op basis van de databank van de het HERMREG-

model”, Planning Paper 104 published by the Planning Office in April 2008.

67. Bassilière D., F. Bossier, F. Caruso, K. Hendrickx, D. Hoorelbeke, O. Lohest(2008), ”Uitwerking

van een Regionaal projectiemodel: Een eerste toepassing van het HERMREG model op de

nationale economische vooruitzichten 2007-2012”, common working paper published by

Planning Office and Statistical Institutes of the Brussels, Flanders and Wallonia Regions in

January 2008.

68. Beci(2008), “Een routeplan voor metropool Brussel”, press message published in November

2008 by the employer organizations Beci, UWA, VOKA and VBO-FEB.

69. Betcherman G., K. Olivas and D. Amit(2004), “Impacts of Active Labor Market Programs: New

evidence from evaluations with particular attention to developing and transition countries”,

Social Protection Discussion Paper of the World Bank Social Protection Unit 0402.

70. Bossier F., I. Bracke, P. Stockman and F. Vanhorebeek(2000), “ A description of the HERMES 2

model for Belgium”, Planning Office Working Paper 5-00 published in July 2000.

71. Cattoir, P.(2002), “Fiscal federalism and Regional integration: Lessons from Belgium”, text

submitted for the Conference Fiscal federalism in the Mercosur: the challenges of Regional

integration in Porto Alegre in June 2002.

- 115 -

72. Crépon B., M. Dejemeppe and M. Gurgand(2006),”Counseling the unemployed: does it lower

unemployment duration and recurrence?”, working paper finished on January 11,2006.

73. de la Croix D. and V. Vandenberghe(2004), “Human capital as a factor of growth and

employment at the Regional level. The case of Belgium” , paper drafted in March 2004 for

the European Commission by the Department of Economics of Université Catholique de

Louvain.

74. Davoine L. and C. Erhel(2007), ”Monitoring employment quality in Europe: European

Employment Strategy indicators and beyond”, working paper from the Centre d’Economie de

la Sorbonne at the Université Paris Panthéon-Sorbonne.

75. De Bresseleers V., N. Fasquelle, K. Hendrickx, L . Masure, M . Saintrain, B. Scholtus and P.

Stockman(2004), “Coût budgétaire d’un chômeur de 1987 à 2002”, up-date of the Planning

Paper 79 of the Planning Office.

76. De Clerck W. and L. Van Wichelen(2008), “Hoe actief is het arbeidsmarktbeleid in

Vlaanderen, België en Europa?”, Steunpunt WSE report 2008.

77. Decoster A., G. Verbist G. and D. Verwerft(2008), ”Solidarity: Regions or persons”,

powerpoint presentation presented on the seminar Rethinking Belgium’s Foundations in

Brussels on December 11th 2008.

78. Dewatripont M. and R. Vander Vennet(2008),“Hervorming van de Belgische instellingen:

Combinatie van flexibiliteit en coördinatie”, text discussed in interviews of Belgian press

channels like Knack, De Standaard, Le Soir on the 26th January 2008.

79. Dewatripont M., F. Docquier, F. Thys-Clément, H. Capron, M. Mignolet, M. Allé, J.-F . Thisse,

A. Sapir, C. Valenduc and J.-F. Husson(2007), “Wallonie et Bruxelles: Défis et opportunités

économiques” , Recommandations formulated at the occassion of the 17th Congrès des

Economistes Belges de Langue Française in November 2007.

80. Dew-Becker I. and R.J. Gordon(2008), “The Role of Labor-Market Changes in the Slowdown of

European Productivity Growth”, Working Paper CEPR Version published on February 19th

2008.

81. Dury D., B. Eugène, G. Langenus, K. Van Cauter and L. Van Meensel(2008), “Intergewestelijke

overdrachten en solidariteitsmechanismen via de overheidsbegroting”, Working Paper of the

National Bank of Belgium.

82. Estevão, M.(2004), “Do Active Labor Market Policies Increase Employment?”, IMF working

paper 03/234.

83. European Commission(2007), ”Employment in Europe 2007”, policy report of the European

Commission.

- 116 -

84. Gérard M.(2002), ”Fiscal federalism in Belgium”, text submitted for the International

Symposium on Fiscal Imbalance in Québec.

85. Hoge Raad Van Financiën(2008), “Jaarlijks verslag Studiecommissie voor de vergrijzing”,

report of the Ageing Commission published in Juin 2008.

86. Hoge Raad voor de Werkgelegenheid(2008),“Verslag 2008”, annual report of the Superior

Council of Employment.

87. Knight B.(2003a), “Common Tax Pool Problems in Federal Systems”, working paper of the

Brown University.

88. Lisbon Strategy National Refom Programme 2005-2008(2007), “Progress Report 2007”.

89. OECD(2007), “OECD Employment outlook”, OECD report.

90. Petterson- Lidbom P. and M. Dahlberg(2003), “An Empirical Approach for Evaluating Soft

Budget Constraints”, Working Paper 28 of the Department of Economics of the Uppsala

University.

91. Shah, A.(2005), “Fiscal Decentralization and Fiscal Performance”, World Bank Policy Research

Working Paper 3786.

92. Stevens E ., S. De Winne and L. Sels(2007), “Europa Regionaal. Arbeidsmarktprestaties in een

comparatief perspectief”, Policy report of Steunpunt WSE .

93. Van Der Linden, B.(2005), “Equilibrium evaluation of active labor market programmers

enhancing matching effectiveness”, working paper supported by IZA.

94. Van Der Linden, B.(2008), ”Wage formation, payroll taxation and labor market policies:

should they be decentralized?”, powerpoint presented on the seminar Rethinking Belgium’s

Foundations in Brussels on December 11th 2008.

95. Van Der Linden B. and E. Dor(2001), “The net effect of unemployment benefits, sanctions

and training on regular employment”, working paper supported by the Belgian Government.

96. Van Der Stichele G. and M. Verdonck(2002), “The Lambermont Agreement: Why and How?”,

text submitted for the International Symposium on Fiscal Imbalance in Québec.

- 117 -

12.6. DATABASES

97. Conjunctuurnota with federal and decentralized government budgets published by the

Federal State Agency of Finance.

98. Eurostat website.

99. HERMREG Regional employment database.

100. Onderwijsstatistieken Vlaanderen website.

101. Planning Office website.

102.Projet de Loi de Finances pour l’année budgétaire 2008 : “Exposé des motifs”.

103.Public Federal Service of Economy website.

104.National Institute of Statistics website.

105.National Bank of Belgium website including Belgostat database.

106.Services des Statistiques de la Communauté Française website.

107.Steunpunt Werk en Sociale Economie website.

108.Shangai Academic Ranking 2008 website.

109.Vlaanderen in Actie website.

- 118 -

13. APPENDICES

13.1. NEWSPAPER ARTICLES RELATED TO THIS THESIS

13.1.1. ARTICLE SCAN FROM DE STANDAARD

- 119 -

13.1.2. ARTICLE WEBSITE SCREEN SHOT FROM LE SOIR

Daan Struyven Etudiant à Solvay Brussels School of Economics and Management (ULB)

Le “ bonus-malus intelligent ” pour les Régions

mardi 31 mars 2009, 08:43

Un fossé d’emplois sépare la Flandre, la Wallonie et Bruxelles. Le taux d’emploi en Flandre y est

respectivement de 9,1 % et de 11,3 % plus élevé qu’en Wallonie et à Bruxelles.

Ce pays pourrait s’appuyer sur un système bonus-malus, qui récompense/pénalise les Régions en

fonction des efforts voire des résultats des politiques actives. Le système est comparable à une

assurance avec bonus-malus pour un conducteur automobile, qui paie plus ou moins en fonction du

nombre d’accidents provoqués.

Pourquoi ? Aujourd’hui, celui qui agit bien est pénalisé. Une Région qui investit en coûteuses

mesures créatrices d’emplois génère beaucoup de retombées pour l’Etat fédéral. Avec un bonus-

malus, les Régions pourraient récolter les fruits de leurs investissements d’activation. Cela pourrait

mener à davantage d’investissements et d’emplois. La solidarité entre les Régions serait ainsi mieux

garantie. Les jobs supplémentaires pour les Wallons et les Bruxellois contribueraient à réduire l’écart

avec la Flandre, les transferts et la pression politique et financière sur la Sécurité sociale.

Le principe du “ bonus-malus d’emploi “ a été porté à l’agenda il y a 14 mois par 120 économistes

universitaires du nord comme du sud du pays, dans un article d’opinion dans le Standaard et Le Soir

le 26 janvier 2007. Les ministres wallon et flamand de l’emploi, J.-C. Marcourt et F. Vandenbroucke,

ont formulé une proposition fin 2008, mais l’accord de principe a été “ mis au frigo “ récemment.

Néanmoins, ce bonus-malus est un impératif pour une fédération belge durable. Opérationnaliser

une telle responsabilisation n’est pas chose triviale. L’emploi est la résultante de plusieurs facteurs ;

certains sont sous le contrôle des autorités publiques fédérées (politique d’emploi, enseignement) ;

d’autres non (cycle économique international, décisions fédérales). Tout comme un conducteur évite

certains accidents en étant prudent mais ne contrôle ni les autres chauffeurs ni l’état de la route.

Comment opérationnaliser ce bonus-malus intelligent ?

- 120 -

Question 1 : évaluer les résultats ou les efforts ? Des experts internationaux peuvent évaluer les

efforts régionaux. Le Pacte de stabilité et de croissance et le contrôle budgétaire par le Conseil

supérieur des Finances sont des exemples opérationnels d’évaluation des efforts. Si l’effort sérieux

ne mène pas au résultat suite à l’adversité de certains facteurs externes, alors il n’y a ni pénalité

budgétaire ni “ cercle vicieux “. Le bonus-malus peut également être directement lié aux résultats

comme le taux d’emploi : un concept objectif et une fin en soi.

Question 2 : mesurer des résultats spécifiques ou des résultats globaux ? Des exemples d’indicateurs

spécifiques sont “ le nombre d’emplois trouvés par le Forem “ ou “ la chute du nombre de chômeurs

de longue durée “. Les taux d’emploi et de chômage sont globaux. Des effets pervers caractérisent

les indicateurs spécifiques : le Forem portera-t-il par exemple encore autant d’attention à

accompagner les employés récemment licenciés si l’incitation est uniquement liée à la chute du

chômage de longe durée ? Pour résoudre un problème global, des indicateurs globaux, non

manipulables, comme le taux d’emploi, s’imposent.

Question 3 : faut-il également pénaliser, en plus de récompenser ? L’accord Vandenbroucke-

Marcourt prévoyait uniquement des flux financiers du niveau fédéral vers le régional. Les bonis

évitent le “ cercle vicieux “, semblent politiquement réalistes et sont positifs pour les budgets

régionaux. Néanmoins, l’impact serait plus favorable pour le budget fédéral (structurellement

déficitaire) dans système de bonus-malus que dans un système de bonus. Pénaliser et récompenser,

cela induit les mêmes incitations économiques.

Question 4 : comment éliminer l’impact des facteurs externes sur les résultats ? En basse

conjoncture, l’incitation peut être liée au ralentissement de l’économie européenne.

Les techniques de “ benchmarking “ (de “ comparaison des régions “) permettent de récompenser les

régions qui performent relativement bien, malgré la basse conjoncture. Le benchmarking peut être

effectué avec des régions belges ou des régions non belges. Le benchmarking belge évite de

pénaliser les Régions quand le niveau fédéral prend des mesures nuisibles à l’emploi dans les trois

Régions (par exemple, l’augmentation de l’imposition du travail). Comparons la situation à celle de

deux collègues ouvriers travaillant à la chaîne de production (dont le salaire est lié au nombre de

pièces traitées) qui n’ont pu travailler à la suite d’une panne de la chaîne. En évaluant la production

de façon comparative, on évite de pénaliser les deux ouvriers pour le problème technique.

L’évaluation comparative de l’évolution des taux d’emploi en Flandre et en Wallonie permet

- 121 -

d’éliminer la grande majorité des facteurs externes et plus de 50 % du risque total. En effet, les taux

d’emploi wallons et flamands sont fortement corrélés (89 %).

Il est donc possible d’opérationnaliser ce concept de la “ solidarité responsable “. Plusieurs options

chiffrées intelligentes peuvent être imaginées.

Une option serait de fixer le taux d’emploi de 70 % – visé par le Pacte de Lisbonne – comme objectif

central. Selon le Plan “ Vlaanderen in actie “, la Flandre devrait passer ce cap en 2020. Comme la

Wallonie et Bruxelles démarrent d’une “ position initiale moins favorable “, on pourrait envisager

qu’ils doivent atteindre les 70 % en 2025 (selon une trajectoire de convergence rapide) ou en 2030

(selon une trajectoire de convergence moins rapide). Dans ce cas-là, si les Régions suivent ce

parcours, elles ne recevraient ou paieraient ni bonus ni malus. La majorité des régions européennes

ont augmenté leurs taux d’emploi depuis 1995. Pour atteindre les objectifs de “ la trajectoire

Lisbonne rapide “, la Flandre, la Wallonie et Bruxelles devraient rejoindre les pelotons des 73 %, 18 %

et 12 % des “ grimpeurs européens “ les plus rapides. Si une région avance plus vite, elle se verrait

alors octroyer un pourcentage du retour budgétaire fédéral de la création d’emploi.

Parmi toutes ces options, il est capital d’optimiser à la fois la logique incitative et la logique

d’assurance contre les facteurs externes. Après les élections, le bonus-malus reviendra certes à

l’agenda politique. Un débat scientifique et politique intense permettra au bonus-malus de devenir

un vrai win-win pour toutes les autorités et les citoyens du pays.

- 122 -

13.2. COMMUNITY BUDGET RESPONSE TO GDP INCREASES

In 2.1.3. we compute the Community return on activation based on the response of Community

budgets to economic growth. In this Appendix 13.2. we compute this response of Community

budgets on Regional GDP-increases in a methodology very similar to Algoed(2009). We base our

computations on the clear overview of the four types of revenues of the French and Flemish

Communities 139 by Van der Stichele et al(2002). The four types of revenues are the value added tax

(VAT)- grant, the personal revenue tax (PIT)-grant, the Radio and TV fee and Government funding in

respect of foreign students. The first two are coupled to Regional GDP through indexation or fair tax

return mechanisms whereas the last two are independent of Regional GDP. We assume that real

growth is independent both of inflation and of the number of students. Thus we focus on the impact

of Regional GDP growth on Regional VAT- and PIT- grants. We first explain how the VAT- and PIT-

grants are computed in equations (13.1) until (13.6). Second, we explain and compute in Table 13.1

the budget response by distinguishing volume- and substitution effects. When computing this

response, we assume that the GRP-elasticity of personal tax revenues equals one.

Let us start with the personal revenue tax transfers. The total amount attributed in respect of the

personal revenue tax transfer depends on the initial amount, fixed in the Special Financing Act of

1989, and is adjusted every year to changes in CPI and national GDP:

𝑑𝑡 = 𝑑𝑡−1 ∗ 1 +𝑃𝑡−𝑃𝑡−1

𝑃𝑡−1 ∗ 1 +

𝐺𝐷𝑃𝑡 −𝐺𝐷𝑃𝑡−1

𝐺𝐷𝑃𝑡−1 (13.1)

= 𝑑𝑡−1 ∗ (1 + 𝜋𝑡) ∗ (1 + 𝛾𝑔𝑑𝑝 ,𝑡)

This amount is distributed between the communities according to the contribution to national

personal revenue tax revenues (with a 80-20 key for personal tax revenue revenues paid in Brussels):

𝑑𝐹,𝑡 = 𝑑𝑡 ×𝑃𝐼𝑇𝐹 ,𝑡

𝑃𝐼𝑇𝑡 + 0.2 × 𝑑𝑡 ×

𝑃𝐼𝑇𝐵 ,𝑡

𝑃𝐼𝑇𝑡 (13.2)

𝑑𝐹𝑟𝑒 ,𝑡 = 𝑑𝑡 ×𝑃𝐼𝑇𝑤 ,𝑡

𝑃𝐼𝑇𝑡 + 0.8 × 𝑑𝑡 ×


𝑃𝐼𝑇𝑡 (13.3)

139

To keep it simple, we neglect the German Community and also the transfers between French Community and Brussels-

Capital Region (Cocof) and Walloon Region linked to transfers of competences.

- 123 -

The total value added tax grant in year t (vt) consists of the indexed Pre-Lambermont Agreement

amount vt,prel and the new Post-Lambermont amount vt,postl which is not only linked to prices but also

to the evolution of Gross National Revenue:

𝑣𝑡 = 𝑣𝑝𝑟𝑒𝑙 ,𝑡 + 𝑣𝑝𝑜𝑠𝑡𝑙 ,𝑡 (13.4)

= 𝑣𝑝𝑟𝑒𝑙 ,𝑡−1 × 1 + 𝑃𝑡−𝑃𝑡−1

𝑃𝑡−1 +

𝑣𝑝𝑜𝑠𝑡𝑙 ,𝑡−1 × 1 + 𝑃𝑡−𝑃𝑡−1

𝑃𝑡−1 × (1 +

0,91∗ (𝐺𝑁𝐼𝑡 −𝐺𝑁𝐼𝑡−1)

𝐺𝑁𝐼 𝑡−1)

= 𝑣𝑝𝑟𝑒𝑙 ,𝑡−1 ∗ (1 + 𝜋𝑡) + 𝑣𝑝𝑜𝑠𝑡𝑙 ,𝑡−1 ∗ (1 + 𝜋𝑡) ∗ (1 + 0,91 ∗ 𝛾𝑔𝑛𝑖 ,𝑡)

The pre-Lambermont agreement amount is distributed according the students ratio whereas the

post- Lambermount amounts are distributed according to a double time-varying key depending both

on the student ratio as on the contributions to federal personal income taxes. Van der Stichele et al

(2002) explain that the fair return principle will become more and more important over time: “The

percentage of the VAT transfer apportioned according to the key applied to the personal revenue tax

transfer will increase as follows:40% in 2003, 45% in 2004, 50% in 2005, 55% in 2006, 60% in 2007,

65% in 2008, 70% in 2009, 80% in 2010, 90% 2011, and 100% in 2012”. Hence, in 2007, Flemish and

French communities receive:

𝑣𝐹,𝑡 = 𝑣𝑝𝑟𝑒𝑙 ,𝑡 × 𝑆𝑇𝑈𝐷𝐹 ,𝑡

𝑆𝑇𝑈𝐷𝑡 + 0.6 × 𝑣𝑡, 𝑝𝑜𝑠𝑡𝑙 ×

𝑃𝐼𝑇𝐹 ,𝑡

𝑃𝐼𝑇𝑡+ 0.2 ×


𝑃𝐼𝑇𝑡 + 0.4 × 𝑣𝑝𝑜𝑠𝑡𝑙 ,𝑡 ×

𝑆𝑇𝑈𝐷𝐹 ,𝑡

𝑆𝑇𝑈𝐷𝑡 13.5

𝑣𝐹𝑟𝑒 ,𝑡 = 𝑣𝑝𝑟𝑒𝑙 ,𝑡 ×𝑆𝑇𝑈𝐷𝐹𝑟𝑒 ,𝑡

𝑆𝑇𝑈𝐷𝑡 + 0,6 × 𝑣𝑡, 𝑝𝑜𝑠𝑡𝑙 ×

𝑃𝐼𝑇𝐹𝑟𝑒 ,𝑡

𝑃𝐼𝑇𝑡+ 0,8 ×


𝑃𝐼𝑇𝑡 + 0.4 × 𝑣𝑝𝑜𝑠𝑡𝑙 ,𝑡 ×

𝑆𝑇𝑈𝐷𝐹𝑟𝑒 ,𝑡

𝑆𝑇𝑈𝐷𝑡 (13.6)

Let us now move to the Community budget response. There are volume effects since the personal

revenue – and the value added tax grants are indexed to GDP.140Next, substitution effects take place

since the horizontal distribution of the grants depends on GDP, via the effect of GDP on the personal

income tax revenues of the Regions where the Community has jurisdiction. This is the case for the

personal revenue -and the value added tax grants. We also take into account the commuting effect,

because a change in the GDP of a certain Region may influence another Region’s Gross Regional

Product, by using the GDP-spillover effects on GRP data of Algoed(2009). Table 13.1 tells us that

Communities do not gain much money from GDP increases.

140

The GDP indexation is complete for the former and only for the Post-Lambermont agreement component for the latter.

- 124 -

Table 13.1:Community budget impacts if Regional GDP increases with €100 in 2007(in €)

Impact Budget Fla Comm Impact Budget Fre Comm

Increase GDP Fla with 100 € 1,82 0,18

Increase GDP Wall with 100 € -0,25 2,05

Increase GDP Bru with 100 € 1,16 0,92


Motifs” and NBB- Belogstat figures.

- 125 -

13.3. DESCRIPTIVE STATISTICS OF REGIONAL EMPLOYMENT RATE VARIATIONS In chapter 5.2., we explored the potential of risk reduction by outlining the main descriptive statistics

of employment rate variations. In this Appendix 13.3., we provide some extra charts and tables to

complete the analysis.

Chart 13.1:Regional yearly employment rates 1981-2007(in %)

Source: HERMREG Planning Office Table 13.2:Regional yearly employment rate variations descriptive statistics in 1981-2007

Mean Median Maximum Minimum Standard Deviation

Bru -0.11 -0.20 1.70 -1.40 0.86

Fla 0.24 0.40 1.20 -1.70 0.74

Wal 0.00 0.20 0.90 -1.70 0.63

Source: HERMREG Planning Office

50

52

54

56

58

60

62

64

66

68

19

80

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Bru Fla Wal

- 126 -

Chart 13.2:Regional five-year employment rate variations 1985-2007(in percentage points)


Table 13.3:Regional five-year employment rate variations descriptive statistics in 1981-2007

Mean Median Maximum Minimum Standard Deviation

Bru -0.13 -0.20 0.92 -0.82 0.53

Fla 0.36 0.38 0.84 -0.74 0.39

Wal 0.09 0.10 0.52 -0.98 0.44


Table 13.4:Correlations between employment rate variations of Belgian Regions and neighbor

countries in 1984-2007

Netherlands France Germany Unweighted ave-rage 3 neighbors

Bru 0.06 0.63 0.36 0.37

Fla 0.50 0.41 0.46 0.60

Wal 0.42 0.53 0.28 0.50

Source: Eurostat and HERMREG

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

11

98

5

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Bru

Fla

Wal

- 127 -

Chart 13.3:Sector shares in Regional GRPs in 2007(in %)

Source: National Bank

0

10

20

30

40

50

60

Industry- non construction

Construction Transport & Communication

Other market services

non-market services

Bru

Fla

Wal

- 128 -

13.4. RISK REDUCTION RESULTS OF BENCHMARKING OF YEARLY EMPLOYMENT RATE

VARIATIONS

In this Appendix 13.4., we quantify how benchmarking can reduce the volatility of employment rate

variations. Therefore we look at tables and charts.

Table 13.5:Standard deviations of Regional yearly employment rate variations with Belgian

benchmarking

Standard deviation

percentage change of standard deviation on standard deviation of effective employment rate variation

Fla effective 0.74 0%

Fla-Wal 0.33 -55%

Fla-Bru 0.98 74%

Wal effective 0.63 0%

Wal-Fla 0.33 -47%

Wal-Bru 0.91 86%

Bru effective 0.86 0%

Bru- Fla 0.98 13%

Bru- Wal 0.91 6%


Table 13.6:Standard deviations of Regional employment rate variations with international

benchmarking

Standard deviation


Fla 0.74 0% Fla- Ned 0.93 25% Fla- 3 neighbor 0.44 -32% Wal 0.63 0% Wal-Fra 0.60 -4% Wal-3 neighbor 0.50 -21% Bru 0.86 0% Bru- Fra 0.70 -22% Bru- 3 neighbor 0.81 -6% Source: HERMREG Planning Office

- 129 -

Chart 13.4:Effective and relative yearly employment rate variations of Brussels with Belgian

interregional benchmarking 1981-2007(in percentage points)


Chart 13.5:Effective and relative employment rate variations of Brussels with neighbor

benchmarking 1984-2007(in percentage points)

Sources: HERMREG Planning Office & Eurostat

-2,5

-2

-1,5

-1

-0,5

0

0,5

1

1,5

2

2,5

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Bru Bru-Fla Bru-Wal

-3

-2,5

-2

-1,5

-1

-0,5

0

0,5

1

1,5

2

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Bru Bru- Fra Bru- 3 neighbor

- 130 -

Chart 13.6:Effective and relative yearly employment rate variations of Flanders with Belgian



Chart 13.7:Effective and yearly relative employment rate variations of Flanders with neighbor

country benchmarking 1984-2007(in percentage points)


-2,5

-2

-1,5

-1

-0,5

0

0,5

1

1,5

2

2,5

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Fla Fla-Wal Fla-Bru

-3,5

-3

-2,5

-2

-1,5

-1

-0,5

0

0,5

1

1,5

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Fla Fla-Ned Fla- 3 neighb

- 131 -

Chart 13.8:Effective and relative employment rate variations of Wallonia with Belgian interregional



Chart 13.9:Effective and relative employment rate variations of Wallonia with neighbor country



-2

-1,5

-1

-0,5

0

0,5

1

1,5

2

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Wal Wal-Fla Wal-Bru

-2

-1,5

-1

-0,5

0

0,5

1

1,5

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Wal Wal- Fra Wal- 3 neighbor

- 132 -

13.5. RISK REDUCTION RESULTS OF BENCHMARKING OF CUMULATIVE EMPLOYMENT RATE

VARIATIONS Table 13.7:Standard deviations of Regional five year employment rate variations 1985-2007 with

Belgian Benchmarking

standard deviation percentage change of standard deviation on standard deviation of effective employment

rate variation

Fla effective 0.39 -

Fla-Wal 0.13 -68%

Fla-Bru 0.52 32%

Wal effective 0.38 -

Wal-Fla 0.13 -67%

Wal-Bru 0.45 17%

Bru effective 0.53 -

Bru-Fla 0.52 -3%

Bru-Wal 0.45 -16%

Sources: HERMREG Planning Office

Table 13.8:Standard deviations of Regional five year employment rate variations 1988-2007 with

international benchmarking

standard deviation


Fla effective 0.28 -

Fla-Ned 0.32 16%

Fla-3 neighbor

0.13 -53%

Wal effective 0.22 -

Wal-Fra 0.33 49%

Wal-3 neighbor

0.14 -38%

Bru effective 0.54 -

Bru-Fra 0.25 -53%

Bru-3 neighbor

0.43 -20%


- 133 -

Chart 13.10:Effective and relative five year employment rate variations of Brussels with Belgian



Chart 13.11:Effective and relative five year employment rate variations of Brussels with neighbor


Source: HERMREG and EUROSTAT

-1,5

-1

-0,5

0

0,5

1

1,5

Bru Bru-Fla Bru-Wal

-1,5

-1

-0,5

0

0,5

1

1,5

19881989199019911992199319941995199619971998199920002001200220032004200520062007

Bru Bru- Fra Bru- 3 neighbor

- 134 -

Chart 13.12:Effective and relative five year employment rate variations of Flanders with Belgian



Chart 13.13:Five year effective and relative employment rate variations of Flanders with neighbor



-1

-0,5

0

0,5

1

1,5

Fla Fla-Bru Fla-Wal

-1,50

-1,00

-0,50

0,00

0,50

1,00

19881989199019911992199319941995199619971998199920002001200220032004200520062007

Fla-Ned Fla-Neighbor Fla

- 135 -

Chart 13.14:Effective and relative five year employment rate variations of Wallonia with Belgian



Chart 13.15:Five year effective and relative employment rate variations of Wallonia with neighbor



-1,2

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

Wal Wal-Bru Wal-Fla

-1,2

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

Wal Wal- Fra Wal- 3 neighbor

- 136 -

13.6. BENCHMARKING OF WEIGHTED AVERAGE INDICATOR FOR BRUSSELS In chapter 5 and 6, we showed that Brussels has a special labor market. This specificity is reflected in

relatively low correlations between employment rate variations of Brussels and other peers, inside

and outside Belgium. In chapter 6, we proposed to give some importance to commuters in the

performance indicator for Brussels. In appendix 13.6, we test if this weighted average indicator is

more correlated and can reduce risk. As weights for the employment rate a in equation (6.1) we

respectively use 100, 75, 50 and 25%. To test the exposure to a common factor, we compute in table

13.9 correlations between the evolution of the weighted average for Brussels and employment rates

in the other two Regions. We observe: (i) that the correlation increases with the weight of the job

rate141

(ii) the correlation always remains under 50%.

Table 13.9:Correlations between variation of weighted average of employment- and job-rate of

Brussels and variation of employment rates in Flanders and Wallonia for 1981-2007

ER variation Fla ER Variation Wal

ER Variation Bru 0.25 0.28

3/4 *ER+ 1/4* employment/working age pop Bru 0.35 0.36

1/2*ER+ 1/2* employment/working age pop Bru 0.44 0.42

1/4 *ER+ 3/4* employment/working age pop- Bru 0.50 0.47

Source: HERMREG

The conclusion from Tables 13.10 until 13.12 is identical as in previous chapters: for Brussels,

national relative performance evaluation does reduce risk significantly, whatever the indicator.

141

This increasing correlations is quite intuitive since the job rate in Brussels and the employment rate variation in the other two Regions can both reflect the evolution of the number of commuters working in Brussels but living in the other two Regions.

- 137 -

Table 13.10: Risk level of weighted average indicator with a 25% weight for 1981-2007

Variation for Brussels st dev

change of st dev on st dev of effective employment rate variation(%)

ER 0.86

1/4 *ER+ 3/4* employment/working age pop

0.92 7%

1/4 *ER+ 3/4* employment/working age pop- ER Fla

0.84 -10%

1/4 *ER+ 3/4* employment/working age pop- ER Wal

0.83 -1%

Source: HERMREG



change of st dev on st dev of effective employment

rate variation(%)

ER 0,86

1/2*ER+ 1/2* employment/working age pop 0,85 -1%

1/2 *ER+ 1/2* employment/working age pop- ER Flanders

0,85 0%


0,82 -4%

Source: HERMREG



change of st dev on st dev of effective employment

rate variation(%)

ER 0,86

3/4 *ER+ 1/4* employment/working age pop 0,83 -3%

3/4 *ER+ 1/4* employment/working age pop- ER Flanders

0,90 5%


0,85 -1%

Source: HERMREG

- 138 -

13.7. METHODOLOGICAL CHALLENGES IN ASSESSING THE IMPACT OF ACTIVE LABOR

MARKET POLICIES

In chapter 7.3.1. we summarized the impact of active labor market polices on employment on the

basis of literature. In this appendix 13.7, we focus on the methodological challenges, faced by

economists, who want to assess this impact in a scientific way. Evaluating active labor market policies

is about answering two questions. First, how do programs impact the future labor market situation of

participants? Second, are those programs cost-effective?

To answer the first question, tracking post-program experiences of participants is not enough. We

have to compare those experiences with what would have happened to workers had they not

participated. Since this counterfactual cannot be directly observed, rigorous evaluation studies

construct control groups of people, who did not participate in the program but ideally are identical

to participants in all other ways. This challenge is complicated by the fact that some relevant features

of participants may be difficult to observe (e.g., ambition, drive, etc). Control groups are constructed

with experimental techniques or with quasi-experimental techniques. Experimental techniques

randomly assign individuals to treatment- and control groups. Quasi-experimental techniques draw a

control group from a survey/database that includes individuals, with characteristics that fit the

targets of intervention.

To answer the first question, we also have to include all the general equilibrium effects. For instance,

some employment gains might have occurred without the program. Then we face a deadweight loss.

Gains might also be at the expense of non-participants, in which case we have a substitution effect.

To answer the second question, gains should be compared with all the types of costs: cost of

administering the program, delivering the services and the participants’ opportunity costs.

- 139 -

13.8. MODELING THE IMPACT OF EDUCATION ON GROWTH

In chapter 7.4.1. we summarized the impact of education on employment. In this appendix 13.8, we

provide more details and we base ourselves on Aghion et al(2004).They review the important

literature regarding the positive impact of education on growth, which is of course on its turn a main

driver of employment. The return on education investments is analyzed according to three angles:

micro-economic returns for an individual, macro-economic returns for society in exogenous142

growth models and finally macro-economic returns for society in endogenous143 growth models.

Micro-economic returns of investing in education were first conceptualized in Becker’s theory of

human capital. According to Becker(1964), individuals continue studying as long as the expected

wage gain exceeds the income loss (lost wage and tuition fee). At the equilibrium, the return of

education followed by individual i 𝑅𝑒 should equal the marginal wage increase for individual i in the

following Mincer equation:

ln(𝑤𝑎𝑔𝑒𝑖) = 𝑤0 + 𝑅𝑒 × # 𝑌𝑒𝑎𝑟𝑠 𝑒𝑑𝑢𝑖 (13.7)

For most countries, the productivity gain, of an extra year of studying, 𝑅𝑒 is estimated at 8 %.

Nevertheless, this return measurement on the basis of the wage increase has two main caveats. First,

the wage increase does not only reflect productivity gains from extra training but also signals talents.

Indeed individuals, who pursue their studies, have in general the talent to study at a reasonable

effort cost, according to the filter view of education developed by Spence(1973). Not subtracting the

signal value of education from the wage increase leads to an overestimation of the productivity gain

of education. Second, the wage increase does no capture positive externalities144 for society as a

whole . This leads to an under-estimation of the productivity gain of education.

Because of those two caveats, methods to measure macro-economic rather than micro-economic

returns have been proposed. We distinguish both exogeneous and endogenous growth models.

Within the exogeneous growth models, two main channels have been described to explain the

positive impact of education on growth. The neo-classical approach focuses on the growth rate of

the education level and has been proposed by Solow(1956): “Countries who increase the education

142

Exogenous growth models do not explain the drivers of technological progress. 143

Endogenous growth models explain the drivers of technological progress 144

The main positive externalities of education are better education for children of educated persons, technological innovation and the spill-overs captured by team members of educated persons.

- 140 -

level of their work force make their human capital more productive”. The technology approach

focuses on the education level and has been proposed by Nelson et al(1966):”Countries with higher

education levels absorb new technologies at a higher pace”.

According to the first neo-classical approach, human capital plays the same role in production as

physical capital. Increasing the number of studied years within a country allows to increase the

efficiency of the working force. This approach predicts that the growth rate of GDP per capita should

be proportional to the growth rate of the education level, the proportionality factor being equal to

the macro-economic return of education. This neo-classical prediction has been supported

empirically in the article by Mankiw et al(1992). They illustrate with cross-country data, for the

period 1960-1985, the positive and significant impact of the growth rate of the education rate of the

12- to 17 years old on the GDP per capita growth rate.

According to the second technological approach, low-skilled and high-skilled workers do not only

differ in terms of labor productivity but also in terms of their capacity to absorb rapidly technological

innovations into their way of working. Benhabib et al(1994) quantify the positive impact of the

education level (rather than its growth rate) on the GDP per capita growth rate between 1965 and

1985.

So, what do we know today? Is economic growth about having a high education level and/or about

increasing the education level? Recent econometric work of Krueger et al(2001), stresses that the

three following factors drive economic growth (ΔYi145):

education levels S0,i146

;

changes in education levels ΔSi147

;

initial wealth Zi,0148

.

We have the following equation:

𝛥𝑌𝑖 = 𝛽0 ×∗ 𝑆0,i + 𝛽1 × 𝛥𝑆𝑖 + 𝛽2 × 𝑍i,0 + 휀𝑖 (13.8)

145

The variable is measured as the change in the logarithm of GDP per capita. 146

The variable is measured as the average number of years of education of the working population. 147

The variable is measured as the variation of the average number of years of education of the working population. 148

The variable is measured as the logarithm of GDP per capita at the beginning of the period.

- 141 -

The estimation of this equation for 90 countries149 leads to three key results. First, we have a positive

impact of the initial level of schooling on growth of 0.5% per year reflected by a t-student value

associated with β0 of 5.43. Second, we have a macro-economic return due to an increase of the

education level, with one year, of 8%150 ,reflected by a t-student value of 3.93 associated with β1.

Third, we have a catching-up effect of 0.8% per year, reflected by a t-student value of 3.44 associated

with β2. This empirical confirmation of the second technology approach illustrates the necessity to

study the link between education and growth in endogenous theories of growth, where technological

progress itself is explained.

Let us now turn to macro-economic returns of investing in education in endogenous growth models.

Endogenous growth theories consider innovation as the driver of growth. Innovation, defined as the

emergence of new products, procedures and organization schemes, takes place within companies as

a result of creative destruction under the impetus of high-skilled workers. Education, research and

development drive growth in all countries, whatever their level of technological development. On the

one hand, in countries close to the technological frontier, education increases the supply of potential

researchers and developers. This supply increase reduces the cost of R&D. On the other hand, in

countries further from the technological frontier, education fosters the imitation and absorption of

technologies launched in other countries (e.g. green revolution in agriculture).

This distinction in function of the distance from the technological frontier lies at the basis of the new

institutional approach developed by Aghion et al(2002). The core idea is that the precise impact of

structural policies, such as education and competition policy, on productivity growth depends on the

distance from the technological frontier.

They predict that “the closer a country gets to the frontier, the higher the return on superior

education investments” on the basis of the following two assumptions. First, developed countries

tend to innovate whereas developing countries tend to imitate. Second, innovation relies rather on

highly-qualified persons whereas imitation can be done by lowly-qualified workers.

149

We have four data points per country where each data point covers a decade in the period from 1960 to 2000. 150

Since we can assimilate the change in the logarithm of GDP per capita to the GDP per capita growth rate, we have a lin- lin model where a one year increase in average schooling duration, all other things being equal, increases the GDP per capita growth rate with 0.08 percentage points per year.

- 142 -

This theoretical prediction is empirically verified with panel data from 20 OCDE countries:

the closer to the frontier, the higher the return on superior education151;

if countries reduce the distance to the frontier152 below the 24% threshold, than returns on

superior education exceed returns on lower education.

Aghion(2002) proposes another argument to privilege investments in superior education for

countries close to the frontier: in order to capture the benefits from waves of new information-and

communication technologies, education systems should train innovators rather than imitators.

151

From a statistical point of view, the interaction term between the proximity to the frontier and the number of superior years studied in the country (defined as the product of both) is positive and significant in a panel data regression of the growth rate of per capita GDP. 152

The frontier is defined as the per capita GDP of the USA.

- 143 -

13.9. BENCHMARKING EDUCATION POTENTIAL OF BELGIAN COMMUNITIES

Table 13.13:Education public spending per student in 2006(in €)

Flemish French Flemish-French (as % of Flemish cost)

Primary 3929 3387 14

Secondary 6696 6058 10

Sources: http://statistique.cfwb.be and www.ond.vlaanderen./onderwijstatistieken

Table 13.14:Distribution of students in last two degrees of secondary education per education type

in 2001(in %)

Source: Public Federal Service of Economy

Table 13.15:PISA results in 2006

Maths Reading Science

Mean st dev Mean st dev Mean st dev French 490 109 473 110 486 103 Flemish 543 98 522 105 529 93

Source: PISA results OECD Regional report 2006

Table 13.16:Percentage of students with at least one year delay in sixth year in 2003-2004

Flemish French

General 16 29

Technical 44 68

Professional 57 78 Sources: http://statistique.cfwb.be and www.ond.vlaanderen./onderwijstatistieken

Flemish French

General 49 64 Technical 31 18

Professional 20 18

http://statistique.cfwb.be/

http://www.ond.vlaanderen./onderwijstatistieken

http://statistique.cfwb.be/

http://www.ond.vlaanderen./onderwijstatistieken

- 144 -

Table 13.17:Share of residents knowing language(s) in 2006

Bru Fla Wal

Only French 18 1 57 Only Dutch 4 28 1

French & Dutch 53 57 17 French & English 40 42 16 Dutch & English 34 51 7

French & Dutch & English 31 40 7 Source: Weber and Ginsburgh(2006)

Table 13.18:Distribution of students per group of university studies for 2000-2001

French Flemish

Human Sciences Philosophy & Literature 16.4 15.8 Law 13.0 13.2 Psychology-Pedagagoy 9.4 11.4 Economics 12.9 13.4 Political Sciences 9.4 9.7 Total 61.1 63.3

Sciences Sciences 12.8 10.8 Applied Sciences 8.6 5.4 Bio-engineering 2.9 4.2 Total 24.3 20.4

Health Sciences Medecine 6.2 2.6 Dentist 0.6 0.2 Pharma 2.1 2.1 Other health 5.8 11.3 Total 14.6 16.2 Source: Demeulemeester(2007)

- 145 -

Table 13.19:Shanghai Academic ranking of World Universities in 2008

Ranking Institution Alumni Award Hici N&S PUB PCP

101-151 Univ Ghent 7.9 15.5 16.3 7.7 52.7 29.5

101-151 Univ Leuven 0.0 0.0 21.9 15.6 50.8 24.8

101-151 Univ Libre Bruxelles 19.4 18.9 12.6 14.3 31.6 26.8

101-151 Univ Louvain 12.5 13.6 17.9 12.4 42.8 27.7

201-302 Univ Antwerp 0.0 0.0 12.6 13.3 33.2 25.4

201-302 Univ Liege 9.7 0.0 10.3 12.8 30.3 24.6

303-401 Vrije Univ Brussel 15.8 0.0 0.0 9.4 26 21.7 Legend:

i) Alumni (10% weight): The number of alumni from the university winning Nobel Prizes in physics, chemistry, medicine, and economics and Fields Medals in mathematics

ii) Award (20% weight): The number of university faculty winning Nobel Prizes in physics, chemistry, medicine, and economics and Fields Medals in mathematics

iii) Hici (20% weight): The number of articles (co-)authored by university faculty published in Science Citation Index Expanded and Social Sciences Citation Index categories

iv) N&S (20% weight):The number of articles (co-)authored by university faculty published in Nature and Science

v) PUB (20% weight): The number of highly cited researchers from the university in 21 broad subjects

vi) PCP (10% weight): The academic performance with respect to the size of the university Source: http://www.arwu.org/

Table 13.20:Number of patents per million inhabitants in 2002

Wallonia Bru Flanders EU 15

109.9 120.4 161.5 159.5 Source: Capron(2007)

http://www.arwu.org/

- 146 -

13.10. FUNCTIONING OF THE HERMES FORECASTING MODEL We start chapter 8 by presenting the forecast of the HERMES-HERMREG system with respect to

future Regional employment rates in Belgium. In appendix 13.10, we detail the national HERMRES

and the HERMREG Regional component of the system. Chart 13.16 gives a general simplified

flowchart of the complete HERMES model. HERMES contains 3100 equations, thirteen branches153

and five sectors154

. The main general characteristics of the model are as follows. It consists of eight

main groups of equations, the so-called blocks: production, employment policies, prices and wages,

consumption and investments, external trade, energy emissions, the public sector and interest rates.

HERMES works in five phases by linking eight blocks. It starts from national demand (households

block) and international demand (external trade block). Second, on the basis of demand, the model

computes production (production block): marginal profitability of production capacity is computed

for all sectors for all production factors (capital, labor155

, energy and intermediary inputs) on the

basis of anticipated input prices (prices and wages block and interest rates block). Third, those

production costs drive prices of goods (prices and wages block). Fourth, on the basis of production

and prices, disposable income(demand on households block) is computed for all agents, taking into

account taxes (public sector block). Finally, resulting pollution impact of consumption and production

is computed (energy and greenhouse gases emissions block).

153

The thirteen branches are agriculture, energy, intermediate goods, equipment goods, construction, transport and communication, trade and horeca, credit and insurance, health care, other market services, general government services, and other non-market services. 154

The five sectors are households, non-profits, corporates, public administrations and the rest of the world. 155

A special block (the modeling and simulation of employment policies) is dedicated to the labor production factor to compute working time, the impact of employment policies and the behavior of low- wage workers.

- 147 -

Chart 13.16: A flowchart of the HERMES model

Source: Bossier et al(2004)

The production block explains how production factors (labor, energy, capital and intermediate

inputs) are determined for thirteen branches and includes the labor demand production-

employment link. The point of departure is a production function with four factors. The function

determines new production capacity on the basis of marginal factor inputs. Producers minimize their

costs and take into account two factors to fix their optimal factor inputs: (i) the marginal capacity -

generated by one extra unit of production factor, which is the so-called the neo-classical marginal

technical coefficient, and (ii) anticipated factor prices. On the basis of this optimal factor input and

taking into account current capacity utilization rate156,producers fix their desired factor demand157.

For labor demand, the model computes first the total number of hours effectively worked for each

branch. The number of jobs is then calculated knowing average branch labor time. Gross investment

is based on investments of last period158, marginal capital-output ratio, input-and output prices and

156

This is clearly an example of a supply effect within the in general more demand-oriented HERMES-HERMREG system. 157

Technically spoken, factor demand is fixed according to an error correction mechanism. 158

In this way, the HERMRES-model incorporates an acceleration mechanism.

- 148 -

optimal capital input. Finally notice that factor demand is determined more straightforwardly for

non-industrial sectors where output is difficult to measure. Producers opt for the factor which

minimizes their marginal cost function.

Now we turn to prices and wages. Production prices are determined at each branch as a mark-up to

the average production cost which is, itself, a function of the pricing of the production factors.

Capital cost depends on the marginal capital-output coefficients (for industrial branches), real

interest-, depreciation- and corporate tax rates. Wages are driven by inflation, tensions on the labor

market159

, employers’ social security contributions, branches’ productivity and average national

productivity. For projections, nominal wages are exogenised to take into account the wage norm.160

We now provide a brief overview of the key elements in other blocks. In the demand of households

block, consumption is based on the life cycle hypothesis and driven by future expected income and

current wealth in the long run and by inflation and unemployment rate in the short run. In the

external trade block, exports and imports are based on the demand volume (demand for import and

world demand for Belgian goods and services for exports) and price competitiveness of producers.

In the public sector block, five public administrations161 intervene at all stages, such as price

formation (VAT, subsidies, …), net disposable income (taxes, benefits, ..) and investment policy which

all on their turn influence behavior of economic agents. Public current transactions are divided into

resources and expenditures.

Finally, labor supply is computed outside the model with a separate socio-demographic module. It is

determined bottom-up per age category, sex and Region. The demographic forecasts of the Federal

Planning Office and the National Institute of Statistics are coupled to projections of the activity rate.

Four young age categories, activity rate is determined on the basis of recently observed trends. For

middle-and older age categories, the probability, that a worker gets one year older and remains

member of labor supply, is computed. The goal is to take into account sociological phenomena such

as the increasing labor market participation of women. Policy shifts (e.g. increase retirement age for

women, the Generation Pact, etc) are also explicitly taken into account.

159

Labor market tensions are approximated by the unemployment rate. 160

The wage norm stipulates that the nominal wage cost increase cannot exceed the weighted average wage growth of Belgium’s three main trade partners: Germany, France and the Netherlands. 161

The five public administrations are the federal government, communities, local authorities, social security and the EU.

- 149 -

13.11. HERMREG BASELINE FORECASTS IN MAY 2007 BEFORE THE CRISIS The assumptions behind the HERMREG May 2007 data are out-dated as outlined in 8.1.3. In our

baseline model, we integrate the impact of the changing scenario directly into the number of

working residents rather than indirectly through changing all the growth assumptions in table 8.2.

This Appendix 13.11. nevertheless presents the main May 2007 forecasts on which the up-dated

forecasts in 8.1.3. are based.

Concerning Regional projections for economic growth, the model does not forecast any convergence

between the outputs of Flanders and Wallonia despite the fact that the GDP growth rate gap is

expected to become smaller. The number of working residents is expected to increase faster in

Brussels than in Flanders, especially compared to Wallonia. Employment rate gaps are expected to

deepen between, on the one hand, Flanders and Brussels by 0.22 percentage points per year and, on

the other hand, Flanders and Wallonia by 0.27 percentage points per year between 2007 and 2013.

Table 13.21:Expected evolution Regional growth rates at horizon 2013(%)

Bru Fla Wal

Expected GDP growth rate 2007-2013 1.9 2.2 2.0

GDP growth rate 2000-2006 2.3 2.1 1.9

GDP growth rate 1993-1999 1.9 2.5 1.6

Expected GDP growth rate 2007-2013 - GDP growth rate 2000-2006 -0.4 0.1 0.1

Expected GDP growth rate 2007-2013 - GDP growth rate 1993-1999 0.0 -0.3 0.4


Table 13.22:Expected evolution Regional number of working residents at horizon 2013

Variation of number of working residents ’10-’13 (1000)

Growth rate working residents ‘10-‘13 (%)

Bru 38.0 1.58

Fla 157.1 0.94

Wa 63.8 0.79


Table 13.23:Expected evolution Regional employment rates at horizon 2013

Employment rate ‘08 (% points)

Employment rate ‘13 (% points)

Average yearly variation employment rate ‘10-‘13 (% points)

Bru 55.7 56.8 0.18

Fla 66.8 69.2 0.40

Wal 58.0 58.8 0.13


- 150 -

13.12. EUROPEAN RELATIVE PERFORMANCE TARGETS

Here targets are set to allow to catch-up with comparable peers in terms of employment rate levels

and/or employment rate variations.

13.12.1. CATCHING-UP WITH EMPLOYMENT LEVELS OF BEST SIMILAR REGIONS The target for every Belgian Region is to catch-up with the Region, which has the highest

employment rate anno 2007, within a cluster of comparable international Regions. We use six static

clusters of Stevens et al(2007). They classify 93 European Regions within six clusters with the

hierarchical Ward method followed by a non-hierarchical method applied to five employment-

related stock variables162. Cluster one has the highest average employment rate, cluster two has the

second highest, etc. Wallonia and Brussels should catch-up earlier (in 2015) before Flanders (in

2020). Once they caught-up with their international peers, they should continue increasing their

employment rate at the same speed until national interregional convergence.

Flanders belongs to the third cluster, which, anno 2005, contains thirteen Regions with an average

employment rate of 65.7%. Anno 2007, the highest employment rate within the cluster is achieved

by Westösterreich with a score of 73.9%. Brussels and Wallonia belong to the sixth cluster, which

anno 2005 has an average employment rate of 52.2%. Anno 2007, the highest employment rate

within the cluster, is achieved by Slovakia with 60.7%.

Under this scenario, the employment rate gap between Flanders and Wallonia would be widened by

0.10% per year because Wallonia is benchmarked with a less good performing Region than Flanders.

Table 13.24:Regional employment targets under a European peer level caching up scenario




'09 '20 ‘10-'20 Increase '10-'20 (1000)


Bru 55.6 65.7 1.0 131 2.7

Fla 67.3 73.9 0.7 348 1.1

Wal 58.0 63.6 0.6 200 1.3

Source: Own computations based on Planning Office(2008) and Stevens et al(2007)

162

The five stock variables are the 2005 total employment rate, female employment rate, elderly employment rate for persons between 55 and 64 years old and the lower educated employment rate and finally the unemployment rate.

- 151 -

13.12.2. INCREASING EMPLOYMENT LEVELS AT SAME SPEED AS BEST SIMILAR

REGIONS

The target is to achieve employment rate variation rates, achieved from 1999 until 2007 by the best

dynamic performer within each cluster. Now we use six dynamic clusters based on five stock

variables and five flow variables163.

Flanders belongs to the third cluster. Anno 2005, the average employment rate variation for the

thirteen Regions within this cluster is 0.34% per year. Anno 2007, the highest average employment

rate increase is achieved by Nord Ovest with 0.83% per year.

Brussels and Wallonia belong to the sixth cluster which, anno 2005, exhibits an average employment

rate variation of 0.15% per year. Anno 2007, the highest employment rate variation is achieved by

Slovakia with 0.40% per year. Under this scenario, the employment rate gap between Flanders, on

the one hand, and Wallonia and Brussels, on the other hand, would be widened by 0.40% year

because Wallonia and Brussels are benchmarked with a less well performing Region than Flanders.

Table 13.25:Regional employment targets under a European peer speed caching up scenario




'09 '20 ‘10-'20 Increase '10-'20 (1000)


Bru 55.6 59.1 0.4 79 1.7

Fla 67.3 75.8 0.8 427 1.3

Wal 58.0 61.6 0.4 152 1.0

Source: Own computations based on Planning Office(2008 )and Stevens et al(2007)

163

The five flow variables are 2005 total employment rate, female employment rate, elderly employment rate (55-64) and lower educated employment rate (25-64) and finally the unemployment rate.

- 152 -

13.13. HAS REGIONAL GROWTH BECOME MORE LABOR INTENSIVE?

Lifting up Regional employment rates to Lisbon levels is an important challenge. Regional

policymakers do not completely control their Regional GDP evolution. Therefore, one could ask the

questions:”Which GDP growth rate would it take to get there?” Have Belgian Regional growth paths

become more labor intensive since 1995 as suggested in European literature by Dew-Becker et

al(2008)”?

We leverage our HERMREG database to compute Regional elasticities of the employment rate with

respect to GRP164 휀𝐺𝑅𝑃𝑖

𝐸𝑅𝑖 since 1980 for the whole period (1980-2007) and for two sub-periods (1981-

1995 and 1995-2007). Technically, first-cut elasticity values are computed relying on the definition of

elasticity165:

휀𝐺𝑅𝑃𝑖

𝐸𝑅𝑖 = 𝑔𝑟𝑜𝑤𝑡 𝑟𝑎𝑡𝑒 𝐸𝑅 𝑖

𝑔𝑟𝑜𝑤𝑡 𝑟𝑎𝑡𝑒 𝐺𝑅𝑃 𝑖

= ∆𝐸𝑅 𝑖 ,𝑡 𝐸𝑅 𝑖 ,𝑡−1

∆𝐺𝑅𝑃 𝑖 ,𝑡 𝐺𝑅𝑃 𝑖 ,𝑡−1 (13.9)

Those elasticity results are recomputed with a more robust statistical method. A regression of the

Regional employment rates on Regional GRP’s, both expressed in natural logarithms, involves the

coefficient βi . This coefficient stands for the elasticity of the employment rate for Region i with

respect to its own GRP 휀𝐺𝑅𝑃𝑖

𝐸𝑅𝑖 :

𝐿𝑛 𝐸𝑅𝑖 ,𝑡 = 𝑐𝑖 + 𝛽𝑖 × 𝑙𝑛 𝐺𝑅𝑃𝑖,𝑡 (13.10)

How interpreting the elasticity coefficient βi? Suppose a Region increases its employment rate from

60% to 60.6%, while its GRP grows during this boom year with 4%. Then the elasticity βi equals 25%

which is the ratio between 1 % and 4%.

We now compute elasticities using equation (13.10) for the three Regions.

For Flanders, Table 13.26 provides two key messages: (i) the overall elasticity equals approximately

34% and (ii) the elasticity is higher in the second period than in the first period.

164

We rely on GRP-data since GDP data are not available for the Regions in our statistics system. 165

Regional Sample elasticity is defined as the average/median of Regional yearly elasticities, which are defined as the quotient of, on the one hand, the growth rate of the employment rate in percentage (which may not be confused with the variation of the employment rate in percentage points) and, on the other hand, the growth rate of the GDP.

- 153 -

Table 13.26:Flanders’ elasticities of employment rate with respect to GRP for 1980-2007

Estimated elasticity of employment rate with respect to GRP

p-value166

Adjusted R squared167

Goodness of fit168

Fla (1980-2007) 0.34 0.00 0.90 high

Fla (1980-1994) 0.19 0.00 0.56 medium Fla(1995-2007) 0.31 0.00 0.91 high Source: Own computations based on HERMREG

In Brussels, the overall elasticity is difficult to estimate with counter-intuitive negative signs and a

low overall goodness of fit score. Although estimation problems in the total and in the first period ,

the elasticity is higher, positive and significant in the second period169.

Table 13.27:Brussels’ elasticities of employment rate with respect to GRP for 1980-2007


p-value

Adjusted R squared

Goodness of fit

Bru (1980-2007) -0.10 0.03 0.13 low

Bru (1980-1994) -0.42 0.00 0.65 high

Bru (1995-2007) 0.33 0.00 0.79 high


There are two key observations for Wallonia. First, the overall elasticity is difficult to estimate with

counter-intuitive negative signs and a low overall goodness of fit score. Second, although estimation

problems in the total and in the first period, the elasticity is higher, positive and significant in the

period170.

166

The p-value is defined as the probability that the null hypothesis is true given the sample. Here it refers to the probability that GRP does not impact employment rate given the HERMREG data. The lower is the p-value, the more significant is the impact of growth on employment rates. 167

The adjusted R squared is an indicator that measures the goodness of fit of the model with the data and it increases if the estimated regression line fits real data. It also penalizes models with too many variables by decreasing in the number of variables. The higher the adjusted R squared is, the better is the regression. 168

To summarize the info contained in the p-value and the adjusted R squared, for readers who are less experienced with statistics, we define the goodness of fit. The fit is high if the adjusted R squared is higher than 60% and if the p-value is below 5%. The fit is medium if the adjusted R squared is higher than 20 % and if the p-value is below 5%. The fit is low if the p-value is above 5 % or if the adjusted R squared is below 20%. 169

The second period data exhibit a high goodness of fit, because both the p-value and the adjusted R square confirm solid regression results. 170

The second period data exhibit a high goodness of fit, because both the p-value and the adjusted R square confirm solid regression results.

- 154 -

Table 13.28:Wallonia’s elasticities of employment rate with respect to GRP for 1980-2007


p-value

Adjusted R squared

Goodness of fit

Wal (1980-2007) 0.11 0.00 0.44 medium

Wal (1980-1994) 0.02 0.77 -0.07 low

Wal (1995-2007) 0.24 0.00 0.84 high


To conclude, for Flanders, the elasticities are always straight-forward to measure and they increase

over time around an average of approximately 30%. For Wallonia and Brussels, estimation problems

lead to uncertain and ambiguous results in the period 1980-1994. But the period 1995-2007 exhibits

positive, significant and unambiguous positive elasticities of approximately respectively 24 and 33%.

- 155 -

13.14. GOVERNMENTS REACT TO FINANCIAL INCENTIVES: BRIEF LITERATURE REVIEW

We would like to predict whether decentralized policymakers would react to such a job incentive

scheme. Therefore one has the answer the following questions: “ What do economists know about

government objective functions? Do governments react to financial incentives? ”. We briefly classify

economic theories, concerning financial government incentives, before presenting some recent

empirical studies.

Bouton et al(2008) distinguish two generations of theories in public economics. Until approximately

1990, the first generation consisted of normative theories. Around 1990, the second generation of

more positive theories was born.

The first normative generation opposes the Arrow–Musgrave–Samuelson school and the public

choice school. The Arrow–Musgrave–Samuelson school supposes that governments maximize social

welfare. The public choice school considers that politicians maximize their own utility under the

constraints of the institutional system where they work in (Niskanen, 1971). Governments would like

to gather as much as financial resources as possible (Brennan et al, 1980).

The second positive generation is less based on axioms and incorporates new tools from economic

research. Main new tools cover principal–agent frameworks, information economics and game

theory.

In this brief literature review, we want to be as much fact-based as possible. Therefore, we focus on

empirical studies from the second generation. We outline three main empirical arguments to

illustrate that governments tend to react to financial incentives. Discussed financial incentives are

triggered by the revenue potential of specific tax base elasticities, common pool problems and soft

budget constraints.

First, governments tend to react to financial incentives triggered by the specific elasticity of certain

tax bases. We briefly discuss how governments react to elasticity differences, due to the size of a

jurisdiction and how they react to elasticity differences across population segments. Tax competition

between smaller and larger regions, in terms of the number of residents, each possessing the same

shares of capital and labor, is qualified as asymmetric. For instance, Bucovetsky(1991) and

Wilson(1991) show that governments from relatively smaller regions compete more intensively for

capital through tax rate reductions. Therefore, smaller regions end up with a lower tax rate because

smaller regions anticipate a higher elasticity of capital tax bases. Conway et al(2001) document how

- 156 -

American state governments react to the relatively higher financial potential and mobility of elderly.

The elderly are quite mobile because they do not work at a fixed place. Governments react to this

incentive by trying to attract them with fiscal and spending policies. Empirically observed tax

preferences for elderly are numerous. Examples of tax preferences for elderly are exemptions from

social security benefits, exemptions from pension income taxation, credit deductions for elderly, etc.

Tax revenue loss estimates, due to elderly tax preferences, vary from 2 to 18 percent for the

analyzed states according to Conway et al(2001).

Second, decentralized governments react to financial incentives triggered by the common pool

problem. The common pool problem results from central financing of the provision of local public

goods and services. The common pool reaction to financial incentives is empirically shown by

DelRossi et al(1999), Gilligan et al(1995) and Knight(2003b). Knight(2003a) gives an overview of the

empirical evidence on this common pool problem. DelRossi et al(1999) analyze the sizes of water

projects, chosen by United States members of parliament, before and after the introduction of the

Water Resources Development Act of 1986. This Act increased the percentage of costs, financed by

decentralized governments. The overall price elasticity of demand for spending on water projects

was estimated at -35%. Other studies start from a logical prediction. Gilligan et al(1995) predict and

show that total spending increases when the number of districts increases, such that the fraction of

project costs financed by local governments decreases. They show that decentralized government

expenditures in the United Sates increased between 1960 and 1990 in the number of seats in the

state legislature. Knight(2003b) shows that American legislators support projects with relatively high

own-jurisdiction spending and reject projects where spending is relatively more concentrated in

other jurisdictions, such that associated tax-costs are high. The logarithm of own spending and the

logarithm of tax prices have a significant impact on the probability of supporting the project

according to the Probit model. One standard deviation increases in the logarithm of own spending

and in the logarithm of tax prices are associated with respectively an increase and a decrease in the

probability of supporting by 15.6 and 7.0 percentage points. Nevertheless, excessive decentralized

spending can be limited. Shah(2005) shows that laws, which put constraints on decentralized

government behavior, have limited strategic behavior in countries such as Brazil, India, Russia and

South Africa.

- 157 -

Third, governments, who expect to be bailed out by a superior organization in case of financial

trouble, tend to react to this financial soft budget constraint incentive. They react by increasing

expenditures and/or debt level. Rodden et al(2003) and Petterson – Lidbom et al(2003) document

this reaction to the soft budget constraint empirically. Rodden et al(2003) present several soft budget

constraint case studies from various countries. The various countries are classified into decentralized

OECD countries (e.g. United States, Canada, Norway, Germany), developing countries with histories

of federalism and fiscal decentralization (e.g. Argentina, Brazil and India) and finally newly

decentralizing countries in transition such as China, South Africa, Ukraine and Hungary. Rodden et

al(2003) conclude that “most of the case studies illustrate fiscal, political and financial factors that

make the central government vulnerable to manipulation by the subnational government, which as a

result is unable to just say no and stand by it”. Let us now turn to an empirical case study of Sweden.

Petterson – Libdom et al(2003) consider previous bail-out experiences of Swedish local governments

and bail-out experiences of neighbor governments as a good proxy for the expectation to be bailed

out in the future. On the basis of 1,697 bailouts of local governments by the Swedish central

government between 1974 and 1992, the authors show the existence of the soft budget constraint.

“A local government increases its level of debt by 6–10 percent if it expects to be bailed out with

probability one as compared with the likelihood is zero due to previous experience of being bailed

out, while the effect on debt from bailouts of its geographical neighbors is roughly four times as

large.”

To conclude, there is significant empirical evidence of government reactions to various financial

incentives such as tax base elasticities, common pool problems and soft budget constraints. This

empirical evidence strongly suggests that decentralized Belgian entities would react to financial

activation incentives created by job bonus schemes.

Date post:	27-Jan-2015
Category:	Technology
Upload:	daans
View:	107 times
Download:	1 times

Solidarity Responsibility Job Incentive Scheme Master Thesis Daan Struyvenv3

Technology