The Employment and Output Effects of Short-Time Work in Germany · PDF file ·...

Introduction The Model Steady State Aggregate Shocks Employment Conclusion Appendix

The Employment and Output Effects ofShort-Time Work in Germany

Russell Cooper1 Moritz Meyer 2 Immo Schott 3

1Penn State 2The World Bank 3Universite de Montreal

Social Statistics and Population Dynamics SeminarMcGill

March 8th, 2017


Motivation

In Germany the 2008 recession led to:

Large negative effect on GDP & total hours workedSmall effect on unemploymentStark contrast with other OECD economies‘German Labor Market Miracle’

Possible explanation: Short-Time Work (STW)

Our question:

Can STW save jobs?And if yes, at what cost?


GDP Growth (year-to-year)

−10

−5

05

GD

P g

row

th

1995q1 2000q1 2005q1 2010q1 2015q1Time

DEU USA

OECD AUT

ESP FRA

Micro Data Hours Change


Unemployment Rate

50

100

150

200

250

2005q1 =

100

1995q1 2000q1 2005q1 2010q1 2015q1Time

DEU USA

OECD AUT

ESP FRA

Micro Data


What is Short-Time Work (STW)?

Labor market policy instrument

Goal: Mitigating cyclical shocksChange labor demand via intensive margin (hours vs. workers)UI compensates workers for lost income (60-67%)Absent STW, unilateral reductions in hours worked are illegalUse of STW is subject to strict set of legal requirements Details

The ‘STW policy’: 2009 - 2010

Gov’t dramatically reduced eligibility criteria & burden of proofMaximum duration increased from six to 18, and then 24monthsJune 2009: Around 60’000 establishments and 1’500’000workers Graph


Summary of Results

Can STW save jobs?

Economic press, Government, Unions→ We find a positive effect on employment

What are the costs?

Reduced form vs. structural model‘Reallocation channel’→ STW prevents reallocation of labor→ negative effect on GDP


Literature

Work Sharing: Burdett & Wright (1989), Hunt (1998,1999), Marimon & Zilibotti (2000), Kudoh & Sasaki (2011)

German Labor Market: Krause & Uhlig (2011), Burda &Hunt (2011), Cahuc & Carcillo (2011), Balleer et al. (2016)

Factor allocation: Hsieh & Klenow (2007), Bartelsman et. al(2013)

Multi-worker firms: Cooper, Haltiwanger, & Willis (2007),Elsby & Michaels (2013), Stole & Zwiebel (1996)


Data

Afid-Panel Indusriebetriebe from German Statistical Office

Universe of manufacturing plants, annual panel 1995-2010

Up to 68’000 observations, of which we use ≈ 39’000

Variables: Revenue, Employment, Hours Worked, . . . Sumstats

Advantages

June 2009: 80.4% (41%) of workers (firms) using STW werelocated in manufacturingHeavy concentrating of employment in MittelstandNo sampling bias

Disadvantages

No direct information on STW


Changes in Total Hours: Extensive and Intensive Margins

−.1

−.0

50

.05

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

Employment Hours per Employee


Distribution of changes in annual hours per worker:1995-2008

05

10

15

Fre

quency

<−

.3

−.3

<x<

.2

−.2

<x<

−.1

%

−.1

<x<

−.0

5

−.0

5<

x<

−.0

25

−.0

25<

x<

−.0

1

inactiv

e

.01<

x<

.025

.025<

x<

.05

.05<

x<

.1

.1>

x>

.2

.2>

x>

.3

x>

.3



05

10

15

20

Fre

quency

<−

.3

−.3

<x<

.2

−.2

<x<

−.1

%

−.1

<x<

−.0

5

−.0

5<

x<

−.0

25

−.0

25<

x<

−.0

1

inactiv

e

.01<

x<

.025

.025<

x<

.05

.05<

x<

.1

.1>

x>

.2

.2>

x>

.3

x>

.3

1995−2008 2009



05

10

15

20

Fre

quency

<−

.3

−.3

<x<

.2

−.2

<x<

−.1

%

−.1

<x<

−.0

5

−.0

5<

x<

−.0

25

−.0

25<

x<

−.0

1

inactiv

e

.01<

x<

.025

.025<

x<

.05

.05<

x<

.1

.1>

x>

.2

.2>

x>

.3

x>

.3

1995−2008 2009

2010


Model - Overview

Basic Model

Hours Contraints & STW

Aggregate Shocks

Quantitative Results: Counterfactuals


Model - Ingredients

Workers and multi-worker Firms

Firms face idiosyncratic productivity shocks ε

Decreasing returns to scale in production

Total labor input L = h · nFrictional labor market produces rents

Nash-BargainingMatching Function M = m(U,V ),Labor Market Tightness θ = V

U

Vacancy-filling probability q = MV

Distribution of firms over (ε, n)


Model - Timing

Firm enters period with n−1 workers and productivity ε

Choose n workers and average hours h

Negotiate wage with n workers

Produce output


Model - Firm’s Problem

V (ε, n−1) = maxh,n

{εF (h · n)− ω(h, n, ε) · h · n −

cvq

(n − n−1)1+ + β

∫V (ε′, n)dG (ε′|ε)

},

ω(·) is a wage schedule

cv is a linear vacancy creation cost

1+ is an indicator for when a firm is hiring



FOC Hours

εFL(h · n)− ω(h, n, ε) − ωh(h, n, ε) · h = 0

FOC Employment (if ∆n 6= 0)

εhFL(h·n)−ω(h, n, ε)·h−ωn(h, n, ε)·nh−cvq1

++βD(ε, n) = 0,

where D(ε, n) ≡∫Vn(ε′, n)dG (ε′|ε)



FOC Hours

εFL(h · n)− ω(h, n, ε) − ωh(h, n, ε) · h = 0

FOC Employment (if ∆n 6= 0)

εhFL(h·n)−ω(h, n, ε)·h−ωn(h, n, ε)·nh−cvq1

++βD(ε, n) = 0,

where D(ε, n) ≡∫Vn(ε′, n)dG (ε′|ε)


Model - Worker’s Problem

W e(ε, n) = ω(h, ε, n)·h−ξ(h)+βEε′|ε[sW ′u + (1− s)W e(ε′, n′)

].

W u = b + βE(ε′,n′)

[(1− φ)W ′u + φW e(ε′, n′)

].


Model - Wages

Workers and Firm share surplus of match

Decreasing return to scale → surplus changes for each workerNash bargaining over marginal surplus (Stole & Zwiebel(1996))

Firm’s marginal surplus for matching with a worker:

S(ε, n) = εhFL(h · n)− ω(h, n, ε)h − ωn(h, n, ε)hn + βD(ε, n)

Surplus is shared according to

W e(ε, n)−W u =η

1− ηS(ε, n).


Model - Wages

Wage solves differential equation

ω(h, ε, n) · h = (1− η) [b + ξ(h)] +

η

[εhFL(h · n) + φ

cvq− ωn(h, n, ε) · h · n

]Assume F (L) = Lα = nαhα

ω(h, ε, n) · h = (1− η) [b + ξ(h)] + η

[εαhαnα−1

1− η(1− α)+ φ

cvq

]Negotiated at t = 0


Model - Optimal Labor Demand

Combine wage with FOCs to get H(ε, n) and N (ε, n−1).

The optimal hours choice:

H(ε, n) =

[εαnα−1

ξ′(h) (1− η(1− α))

] 11−α

The optimal employment choice:

N (ε, n−1) =

ψ−1v (ε) if ε > ψv (n−1),

n−1 if ε ∈ [ψ(n−1), ψv (n−1)] ,

ψ−1(ε) if ε < ψ(n−1),

Graph


Hours Constraint and STW

Standard hours = h. Firm cannot set h < h

Policy parameter for STW: Ξ

Ξ ∈ [0, h]Constraint changes to h− Ξ

The optimal hours policy function becomes

H(ε, n) = max

{h− Ξ,

[εαnα−1

ξ′(h) (1− η(1− α))

] 11−α}.

STW use has to be approved by gov’t


Model - Calibration (Ξ = 0)

Parameter Meaning Value Reason

Calibrated

β Discount factor .9967 Annual r = 4%γ Matching elasticity .6 Petrongolo & Pissarides (2001)µ Matching efficiency .1622 θ = 0.091α F (L) = Lα .65 Cooper et al. (2007)ε Mean of ε 1 Normalizationb Unemployment benefit .024 Average employment = 98.5ξ0 Disutility of work (scale) .124 Average hours = 1η Worker bargaining power .413 Labor share 0.76

Table: Model Parameters.


Model - Estimation (Ξ = 0)

Moment Data ModelL−NL

= δφ+δ

.09 .09

∆h < |5%| (annual) .538 .542∆n < |5%| (annual) .476 .440

cv(n)/cv(h) 5.63 5.66

Distance L(Θ) - 0.001382

Table: Estimated Parameters


Steady state results - no policy

Match inactivity regions of Hours and Employment changes

Match the relative variability of hours and employment

Value of leisure = 13.24% of average wages

Firms spend on average 1.07% of monthly wage bill onrecruiting costs

Labor costs of posting vacancies are 32.66% of the averagemonthly worker wage

Labor market tightness θ = VU = 0.091

Monthly job-finding rate of 6.22%

US ≈ 30% (Hall (2006))


Steady state results - Hourly wage

11.1

1.21.3

1.41.5

80

100

120

0.145

0.15

0.155

0.16

0.165

0.17

HoursEmployment

Wa

ge

Wage is decreasing in n and h

Effect via marginal product of labor & disutility

More productive firms are large

Positive relationship between size and wages


Steady state results - The Hours Constraint h = 1

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5Average Hours

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

F(x

)

Empirical CDF of Hours

no STWSTW

Constraint can be binding in steady state

h prevents hours reductions, firms use extensive margin


Aggregate Shocks

Π =

Ahigh Alow AΞ

Ahigh ρ 1− ρ 0Alow 1− ρ ρ 0AΞ 1− ρ ρ− π π

Average duration of STW is six months: π

Solve similarly to Krusell & Smith (1998)

Firms need to forecast q′ which depends on the cross-sectionaldistribution


Effect of STW

Simulation of economy

Let STW policy become active in period t = 200

no negative productivity shocks


IRF - Effect of STW

50 100 150 200 250 300 350

0

0.5

1

1.5

2Aggregate Productivity

50 100 150 200 250 300 350

0.98

0.99

1

1.01Output

50 100 150 200 250 300 350

0.96

0.98

1

1.02

1.04

1.06

Employment

50 100 150 200 250 300 350

0.97

0.98

0.99

1

1.01Total Hours Worked

50 100 150 200 250 300 350

0.96

0.98

1

Average Hours

50 100 150 200 250 300 350

0.62

0.64

0.66

0.68

Vacancy-filling probability

50 100 150 200 250 300 350

0.99

0.995

1

1.005

1.01

1.015

Hourly Wages

50 100 150 200 250 300 350

0

0.1

0.2

0.3

0.4

0.5Fraction of Firms using STW


Effect of STW

Simulation of economy

Let STW policy become active in period t = 200

no negative productivity shocks

Partial Equilibrium: Keep q fixed


IRF - Effect of STW - PE

50 100 150 200 250 300 3500

0.5

1

1.5

2Aggregate Productivity

GE

PE

50 100 150 200 250 300 350

0.98

0.985

0.99

0.995

1

1.005

1.01Output

50 100 150 200 250 300 3500.95

1

1.05

1.1

Employment

50 100 150 200 250 300 350

0.97

0.98

0.99

1

1.01Total Hours Worked

50 100 150 200 250 300 350

0.94

0.96

0.98

1

Average Hours

50 100 150 200 250 300 350

0.63

0.64

0.65

0.66

0.67

0.68

0.69Vacancy-filling probability

50 100 150 200 250 300 3500.99

0.995

1

1.005

1.01

Hourly Wages

50 100 150 200 250 300 3500

0.1

0.2

0.3

0.4

0.5



Effect of STW

STW increases employment but has a negative effect onoutput.

Key: endogeneity of q

Positive employment response more than twice as large in PE

Output falls by almost 1%

Heterogeneous effect on firms


IRF - Recession without STW

5 10 15 20 25

0.99

1

1.01Aggregate Productivity

5 10 15 20 250.97

0.98

0.99

1Output

5 10 15 20 251

1.05

1.1Unemployment Rate

5 10 15 20 250.98

0.99

1Total Labor Input L

5 10 15 20 250.985

0.99

0.995

1Average Hours

5 10 15 20 251

1.01

1.02

1.03q

5 10 15 20 250.997

0.998

0.999

1Hourly Wages

5 10 15 20 250

0.5

1Fraction of Firms using STW

no STW


IRF - Recession with STW

5 10 15 20 25

0.99

1

1.01Aggregate Productivity

5 10 15 20 250.96

0.98

1Output

5 10 15 20 251

1.05

1.1Unemployment Rate

5 10 15 20 250.94

0.96

0.98

1Total Labor Input L

5 10 15 20 250.94

0.96

0.98

1Average Hours

5 10 15 20 251

1.01

1.02

1.03q

5 10 15 20 250.995

1

1.005Hourly Wages

5 10 15 20 250

0.2

0.4


no STW

STW


Productivity Effects

5 10 15 20 25 300.996

0.998

1

1.002

1.004

1.006

1.008

1.01

1.012

1.014Correlation of Employment and Productivity

no STW

STW

Figure: Cross-sectional correlation between productivity and employment


Employment Effects for firms with ∆ε < 0

5 10 15 20 25-9.4

-9.2

-9

-8.8

-8.6

-8.4

-8.2

-8

-7.8Average Employment Change

5 10 15 20 250.88

0.9

0.92

0.94

0.96

0.98

1

1.02Average Hours

no STW

STW


Job Creation and Job Destruction

5 10 15 20 25-0.2

0

0.2

0.4

0.6

0.8Job Destruction

no STW

STW

5 10 15 20 25-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06Job Creation


Robustness

Role of parameters (see paper)

Role of labor market institutions

Flexibility, h < 1

Alternative: Hiring Credits

cheaper, but less effectiveLarge initial effect on U via JD


Model Predictions

Germany 2009:

labor productivity per worker -4.9%labor productivity per hour -2.2%Less job creation in sectors with more STW Graph

in line with model prediction


Conclusion

Can STW save jobs?

Economic press, Government, Unions→ We find a positive effect on employment

What are the costs?

Reduced form vs. structural model‘Reallocation channel’→ STW prevents reallocation of labor→ negative effect on GDP of around 1%


Thank you


Employment Policy

0 1 2 3 4 5 6 7 8

Log Employment Today

0

1

2

3

4

5

6

7

8

Log

Empl

oym

ent T

omor

row

Employment Policy Function for levels of productivity

Figure: Firm’s Employment Policy N (ε, n−1) as a function of productivity.

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Change in Total Hours Worked

−6

−4

−2

02

4T

ota

l H

ours

gro

wth

, %

1995 2000 2005 2010 2015Time

DEU USA

OECD AUT

ESP FRA

back


Summary Statistics

Count Mean SD IQR p10 p50 p90

N 38,839 98.5 142.6 73.8 19.4 48.2 228.0H 33,617 156,300 20,576 11,694 3,578 8,366 35,107

H/N 34,303 135.8 35.7 31.6 104.5 134.0 167.9PY 39,180 1,531,785 3,106,538 1,116,285 101,242 474,343 3,766,944

Table: Summary Statistics

Note: Summary statistics for Employment N, Hours H, Hours per EmployeeH/N, and Revenues PY . The table shows average values over all years.Revenues are deflated to 2005 Euros.

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Rules for STW

1 Hours reduction must not be preventable (overtime, holidays)

2 The firm must be unable to compensate the work stoppagewith permissible variations in intra-firm working hours

3 At least a third of the firm’s workforce must suffer an earningsloss of at least 10%.

4 Reduction in working time must be temporary. The maximumduration of STW is six months. After this time full-timeemployment should be restored.

Hours worked will be paid as usual

Remanence costs for the firm

The gov’t will compensate workers for 60% (67%) of earningsloss

back


STW use by Workers and Firms

0500

1000

1500

Work

ers

020

40

60

Firm

s

2008m1 2010m1 2012m1 2014m1 2016m1

Time

Firms Workers

back


Hours Change Distribution

<-.20 -.20--.10 -.10--.05 Inactive .05-.10 .10-.20 >.200

0.1

0.2

0.3

0.4

0.5

0.6

Data

Model

back


Employment Change Distribution

<-.20 -.20--.10 -.10--.05 Inactive .05-.10 .10-.20 >.200

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Data

Model

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Job Creation

.98

.99

11.0

11.0

21.0

3Log J

ob C

reation (

2004 =

1)

2005 2010 2015year

Manufacturing Services

Figure: Job Creation, in logs, normalized to 2004 values. Source: GermanEmployment Agency.

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