Session 9Assessing Welfare Impact of PBL
Operations: Applications
Introductory Course on Economic Analysis of Policy-Based Lending Operations
7 June 2007
Guntur SugiyartoEconomics and Research Department
• Program design should begin with proposed reforms,sector analysis, and expected benefits
• Problem: proposing a policy reform measure as part ofprogram design; since it implicitly assumes reformmeasures and their benefits are already been linked.
OED’s Special Evaluation Study:Program Lending
Example:
A project proposal implies the underlying assessment ofthe relative priority and the incremental benefits of theproject could generate, including justification forallocating scarce resources.
Implication:This calls for refining and further developing the conceptual and analytical framework for understanding ex-ante economic analysis of policy and institutional reforms.
Different Paths to Achieve the Goal
Path 3
Path 1
Path 2
Path influences:- Policy- Institution- Investment patterns
t0 tn
Sectorindicator
time
Objective: Assess and present viable options to facilitate theselection of the least-cost and best sequence mix withverifiable and valid assumptions.
This means:
• Rely on quantitative and qualitative assessments to identifyand measure the impact.
• Descriptive analysis is required to assess the economic andsocial situation as starting point to assessing possibleimpacts. It also provide guidance for further analyses, suchas partial or general equilibrium analysis.
• Applications of methods require a clear statement ofassumptions.
• Increasingly rigorous analytical techniques require greateruse of data, resources, and capacity.
Different Paths to Achieve the Goal
Coverage of PBL and Possible Impact Measures
Coverage:
• Agriculture Sector: Price, income/consumption/poverty, effectiveness
• Financial Sector: Rate, income, effectiveness of programs.
• Economic Reform: Price, income, effectiveness of programs.
• Public Sector: Revenue, income, effectiveness of programs.
• Education: Access, Attendance, Grade, effectiveness of delivery.
• Health: Access, Health Condition, effectiveness of delivery.
• Governance: Revenue, income, effectiveness of programs.
• Capital Market: Price, rate, effectiveness of programs.
Examples: Descriptive Statistics
0
50
100
150
200
Days for Starting a Business
Duration (days) 8 30 31 42 46 59 63 88 94 168
Sing
Ban
Mal
Thai
Chi
Phili
Viet
Indi
Ca
Indo
0
500
1,000
1,500
2,000
Costs of Starting a Business
US$ Cost 103 129 135 144 239 244 249 272 961 1,551
Indo
Viet
Chin
Thail
India
Phili
Sing
Ban
Mala
Cam
• Average
• Standard Deviation or Variation
• Other descriptive measures
Note: Notice the vulnerability of poverty across countries
Source: Poverty in Asia: Measurement, Estimates and Prospects. KI 2004
Estimating population parameters
Examples: Inference Statistics
Examples: Non-parametric Statistics
-0.20.40.60.81.01.21.41.61.82.0
Business Obstacles 1.81 1.42 1.40 1.34 1.30 1.03
Stability Business Operat'n
Labor Financing Taxation Infra
Business Constraints
Source: ICS INO 2004
Effects of Decentralization Program
Source: ICS INO 2004
Hasty and big bang approach of decentralization in Indonesia has contributed to the worsening of main aspects of IC, especially in
creating more uncertainty and corruption.
Examples: Non-parametric Statistics
Examples: Index and ClassificationMicrosoft Excel
Worksheet
1
East AsiaChina, People's Rep. of 56.79 63.04 3.85 88.67 83.76 59.22 20Hong Kong, China … … 11.54 … … 11.54 35Korea, Rep. of … … 44.87 … 100.00 72.44 10Mongolia 26.47 17.39 15.38 78.00 56.41 38.73 28Taipei,China … … … … … … …
Southeast Asia
Brunei Darussalam … … … … 99.15 99.15 1Cambodia 5.59 0.00 32.05 7.19 47.86 18.54 34Indonesia 83.82 95.65 51.28 44.23 94.02 73.80 9Lao PDR 26.47 39.13 47.44 18.52 68.38 39.99 27Malaysia 100.00 100.00 0.00 82.57 99.15 76.34 8Myanmar … … … 36.38 95.73 66.05 16Philippines 60.29 72.83 12.82 45.53 71.79 52.65 24Singapore … … 7.69 … … 7.69 36Thailand 100.00 100.00 24.36 65.14 68.38 71.58 12Viet Nam 99.41 100.00 39.74 43.79 75.21 71.63 11
South Asia
Bangladesh 0.00 17.39 58.97 2.18 52.99 26.31 31Bhutan … … … 64.92 … 64.92 17India 0.00 11.96 57.69 0.00 70.09 27.95 30Maldives … … … 39.43 85.47 62.45 19Nepal 35.00 46.74 20.51 0.44 75.21 35.58 29Sri Lanka 89.41 96.74 50.00 41.61 66.67 68.89 14
Traffic Lights Indicating Relative Attainment Index
Proportion of Population Below $1 Purchasing Power
Parity (PPP) Per Day
Poverty Gap Ratio (Incidence Multiplied by Depth of Poverty)
Share of Poorest Quintile in National
Consumption
Region/Country Rank
Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5
Prevalence of Underweight
Children Under 5 Years of Age
Proportion of the Population Below Minimum Level of
Dietary Energy Consumption
Goal 1
Eradicate extreme
poverty and hunger
Lower - % in the Red light
Upper - % in the Green light25
25
Change all at the same time
25
Lower - % in the Red lightUpper - % in the Green lightChange Individually
25
25 25 25 25
25 2525 25
25
25
Back to Summary
Lower 25% Between 25% and 75% Upper 25%
Figure 2. Goal 1 Index: Eradicate extreme poverty and hunger
0 5 10 15 20 25 30 35 40 45
Singapore
Hong Kong,China
Cambodia
Afghanistan
Bangladesh
India
Nepal
Mongolia
Lao PDR
%40 45 50 55 60 65 70 75 80 85 90
Papua New Guinea
Timor-Leste
Philippines
Tajikistan
Pakistan
China
Maldives
Uzbekistan
Bhutan
Myanmar
Turkmenistan
Sri Lanka
Solomon Is.
Thailand
Viet Nam
Korea, Rep. of
Indonesia
%0 20 40 60 80 100 120
Malaysia
Armenia
Kazakhstan
Kyrgyz Rep.
Fiji Islands
Kiribati a
Azerbaijan
Brunei
%
Examples: Index and Classification
Examples: RegressionRegression Results of Individual Investment Climate Indicators
Explanatory Variables Labor Productivity Sales Growth Export
Share TFP
Labor
Share of workers with more than 12 years of schooling -0.002 0.000 0.070 -0.002
(1.35) (0.58) (2.04)** (1.17)
Share of workers who use computer in their work 0.013 0.001 0.15 0.008
(4.18)*** (0.85) (2.32)** (2.55)**
Share of temporary workers 0.004 0.009 0.291 0.009
(0.75) (3.06)*** (2.37)** (1.59)
Share of workers with training 0.004 0.000 0.155 0.000
(1.72)* (0.27) (2.83)*** (0.19)
Firm provides either in-house or outside training to its workers 0.509 0.009 12.206 0.210
(3.76)*** (0.12) (3.94)*** (1.55)
Labor quality index 0.171 0.001 5.568 0.053
(3.07)*** (0.04) (4.34)*** (0.94)
Poverty Predictor Modeling
A. General Variables: Scoring Types oh house (options with different score) Living area Type of roof Type of wall Type of floor Type of WC Source of drinking water Cooking facility Frequency of eating Ownership of sofa Ownership of refrigerator assets Ownership of refrigerator assets Saving etc
B. Policy/Project Concerned Variables Schooling of school age children Proportion of Illiterate Head unemployed Head/wife Illiterate Diploma/university graduate
Total Score 100 %
Examples: IO Modeling
1998 UK Germany
France Italy Netherlands
Other Total
GNP Direct 0.437 0.443 0.448 0.447 0.446 0.423 0.437 D+I 0.559 0.561 0.572 0.568 0.564 0.503 0.556 D+I+H 0.706 0.708 0.720 0.716 0.711 0.681 0.702 D+I+H+G 1.022 1.020 1.041 1.036 1.030 0.985 1.015 EMPLOYMENT (per million expenditure) Direct 61.093 60.761 58.936 61.486 65.413 59.042 60.603 D+I 80.640 79.996 79.321 81.298 84.440 78.129 80.034 D+I+H 95.477 94.619 94.239 96.157 99.263 92.300 94.679 D+I+H+G 130.775 129.790 130.231 131.981 135.029 126.361 129.741 GOVERNMENT INCOME Direct 0.137 0.138 0.141 0.140 0.141 0.133 0.137 D+I 0.165 0.166 0.169 0.169 0.168 0.160 0.165 D+I+H 0.233 0.232 0.238 0.237 0.236 0.225 0.232 D+I+H+G 0.344 0.343 0.351 0.349 0.349 0.332 0.342
0
50
100
150
200
Total Effects on GNP
1998 131.91 54.43 21.15 24.26 13.97
2001 194.33 64.80 28.65 39.33 18.98
UK Germany
France
Italy Netherlands
0
20
40
60
80
Total Effects on Government Income
1998 44.47 18.29 7.12 8.18 4.73
2001 66.01 21.85 9.64 13.16 6.48
UK Germa France Italy Nether
0
5,000
10,000
15,000
20,000
Total Effects on Employment (FTE)
1998 16,887 6,923 2,645 3,091 1,831
2001 16,349 5,478 2,431 3,334 1,598
UK Germa France Italy Nether
Microsoft Excel Worksheet
Effects of Exports
Examples: SAM Multiplier AnalysisSouth-KoreaDefourny and Thorbecke (1984) : relative importance of paths of the multipliereffects on households headed by unskilled workers that arise from an injection inthe processed foods sector.IndonesiaKeuning and Thorbecke (1992): higher income rural and urban households weremore influenced by government current expenditure injections than by exports.Thorbecke and Jung (1996): growth in agriculture and agriculture-related activitiestend to do more to alleviate poverty than growth in industrial, or even serviceactivities, even after accommodating the various multiplier effects.IndiaSAM Multiplier Analysis of Informal Households: Application to an Indian ArchetypeEconomy (Sinha, 2000).ChinaSAM-based Multiplier Analysis for China’s Economy (Li Shantong et al. 2004).OthersAgriculture-based Development: A Sam Perspective On Central Viet Nam (Bautista2000), Distributional Impacts of Agricultural Growth in Pakistan: A MultiplierAnalysis, Dorosh et al. mimeo; Structural Characteristics of the Economy ofMozambique: A SAM-based Analysis (Arndt, et al. 2000). Distributive impacts ofalternative agricultural policies: a SAM-based analysis (ROCCHI et al. 2005); etchttp://www.iioa.org
Economy-wide multipurpose, open economy, based on SAM for 1993,comparative static and real sector.
Consist of 18 sectors/commodities, 8 types of labour, 5 kinds of capital,10 categories of household, a firm, the government, and the rest of theworld (ROW).
Wage stickiness (for agriculture & production workers), flexible capitalprices, Armington specifications, and imperfect substitutability betweenfactors and capitals.
A downward sloping demand curve for exports and price taker forimports.
Fixed aggregate investment and a ‘planned government consumption’.
Current account deficits residual, clearing S-I balance.
A fixed exchange rate and price of the ROW as the numéraire.
Examples: CGE ModelCGE Model for Indonesia : Main Features
Welfare Costs of Indirect Taxation and Tariffs
Value (%)Sectoral Output
Total Output
Sectoral Tax Total Tax
Food Crops 485.8 2.4 1.4 0.1 193.7 3.0Other Agriculture 499.6 2.5 1.2 0.1 139.0 3.1Mining 145.9 0.7 0.4 0.0 45.6 0.9Food Processing 10427.7 51.8 16.4 1.8 168.0 65.3Textile 741.3 3.7 0.9 0.1 54.4 4.6Construction 282.2 1.4 1.4 0.1 101.6 1.8Papers and Metals 1018.8 5.1 3.1 0.2 87.5 6.4Chemical -620.2 -3.1 -1.0 -0.1 80.4 -3.9Utilities 45.7 0.2 0.6 0.0 106.5 0.3Trades 2959.4 14.7 5.4 0.5 78.5 18.5Restaurant 1025.1 5.1 5.6 0.2 128.2 6.4Hotel 138.5 0.7 4.0 0.0 94.2 0.9Land Transport 279.8 1.4 1.5 0.1 89.3 1.8Other Trans & Com. 114.4 0.6 0.7 0.0 90.9 0.7Bank and Insurance 168.6 0.8 0.9 0.0 104.7 1.1Real estate 839.2 4.2 4.9 0.2 104.5 5.3Public services 322.6 1.6 1.2 0.1 106.0 2.0Personal services 401.5 2.0 2.4 0.1 123.5 2.5Total 20151.1 100.0 3.5 3.5 126.2 126.2
Welfare Costs Welfare Costs as % ofSectoral Indirect Taxation
Value (%) Sectoral Output
Total Output
Sectoral Tax Total Tax
Food Crops 13.93 0.28 0.98 0.02 25.11 0.22Other Agriculture 40.12 0.80 8.92 0.06 84.44 0.63Mining 30.97 0.61 1.28 0.04 80.50 0.48Food Processing 365.03 7.24 13.96 0.52 117.57 5.71Textile 2.70 0.05 3.09 0.00 13.45 0.04Construction 280.93 5.58 5.73 0.40 100.99 4.39Papers and Metals 2408.84 47.81 6.89 3.42 71.70 37.68Chemical 1870.88 37.13 9.91 2.66 83.43 29.27Total 5038.63 100.00 7.16 7.16 78.83 78.83
Welfare Costs as % ofWelfare CostsSectoral Tariffs
Effects of 2% Tariff Reduction
E conom ic Ind icators A T B (% ) M acroeconom ic A ggregates G ross D om estic Product 0.017 E m ploym ent 0.035 E xternal C ondition R eal Export -0 .050 R eal im port 0 .197 Trade B alance -2 .874 W elfare of H ouseholds D om estic A bsorption 0 .078 H ousehold Incom e 0 .063 H ousehold R eal C onsum ption 0 .051 E quivalent V ariation (E V ) EV -A ll H ousehold 123.677 EV -Farm er 37.435 EV -R ural H ousehold 31.883 EV -U rban H ousehold 54.856
CGE Application in Nepal
CGE Micro simulation in the Philippines
CGE Micro simulation in the Philippines
Thank You !