Employment Protection, Technology Choice, and Worker ... · Introduction Model and Calibration Results Employment Protection, Technology Choice, and Worker Allocation Eric J. Bartelsman1,

IntroductionModel and Calibration

Results

Employment Protection, Technology Choice, andWorker Allocation

Eric J. Bartelsman1, Pieter Gautier1, and Joris de Wind2

1Vrije Universiteit Amsterdam; Tinbergen Institute; IZA Bonn

2CPB Netherlands

November 30, 2012 – NBB, Brussels

Bartelsman, Gautier, de Wind EPL and Technology Choice


Results

Outline

1 IntroductionMotivationStylized Facts

2 Model and CalibrationTheoryCalibration and Simulation

3 ResultsSimulationEmpirical Results



Results

MotivationStylized Facts

Productivity Divergence Puzzle

EU productivity almost caught up to US by 1995. Since then, thegap has widened again

1985 1990 1995 2000 20050.7

0.8

0.9

1

1.1

year

US

= 1

Labor productivity EU15 vs US

source: EUKLEMS, Market sector,ppp-adjusted real output per hourBartelsman, Gautier, de Wind EPL and Technology Choice


Results


Divergence continues in recent data

1996-2005 2006-2010

US 2.3 1.3EU-15 1.5 .7UK 2.3 .05

Source: The Conference Board Total Economy Database



Results


Main explanations productivity divergence

Macro/industry: EU productivity almost caught up to US by1995. Since then, the gap has widened again

Main explanation: In the US, ICT capital deepening and TFPincrease likely associated with ICT and other intangiblesinvestments boosted aggregate output.But: why does investment in ICT and associated intangibleslag in the EU?Traditional explanations, such as unfavorable factor prices, donot explain it.



Results


Potential Storyline on ICT investment

Investments in ICT and related intangibles increases thevariance of firm outcomes

ICT-related innovations require firm to experiment in themarketMarkets becomes more contestable and the innovation mayleads to winner-take-all situations

High variance technology is good for productivity,

good news can be leveragedbad news is bounded by option to fire/close but ...

Firing Costs and inflexibility in EU makes this type ofintangible investment less profitable

Payoff depends on scaling up successful innovations, closingunsuccessful ones



Results


ICT and Increase in Performance Spread

Source: Brynjolfsson et al.Bartelsman, Gautier, de Wind EPL and Technology Choice


Results


ICT and Increased Job Flows

Gross Job Flows Entry-Exit Job Flows1986–94 1995–04 1986–94 1995–04

Hi ICT Inds 17.5 23.1 6.8 10.4Lo ICT Inds 17.5 18.6 8.1 8.1

Source: Authors’ calculation from micro-aggregated file from LBD



Results


An Aside: Micro-aggregated EU indicators

Provision of metadata. Approval of access. Disclosure analysis

of cross-country tables. Disclosure analysis of Publication

Res

earc

her

Policy Question Research Design Program Code

Publication

Net

wor

k Metadata

Network members

Cross-country Tables

NS

Is

Distributed micro data research



Results


Productivity Heterogeneity and Allocation

Manufacturing Excluding ICT, 2003-2009, Finland and Sweden.Labor productivity, Employment Share and Employment Growth byProductivity Quartile



Results



Manufacturing Excluding ICT, 2003-2009, Italy and the UnitedKingdom. Labor productivity, Employment Share and EmploymentGrowth by Productivity Quartile



Results



Market Services Excluding ICT, 2003-2009, Finland and Sweden.Labor productivity, Employment Share and Employment Growth byProductivity Quartile



Results


ICT and Dispersion

σc,i ,t = α + γBBIc,i ,t + FE + εc,i ,t (1)

FE : country, industry, time fixed effects

Std. Deviation of firm-level productivity distribution regressed onBroadband intensity

Levels First-differences

γ 0.47(5.02)

.28(2.59)

R2 0.52 0.03D.F. 1180 1021Fixed effects ctry, ind, time ctry, ind, timeSource: Eurostat: Firm-Level ICT Impacts Project



Results


ICT and Dispersion

Employment Adjustment by ICT Intensity

‘Hockey Stick’ Chart, Data appear similar to real stuff



Results


ICT and Dispersion

Table: Measures of Dispersion by ICT intensity

Prod. (TS) Output (TS) Prod. (XS) Output (XS)Country ICT=0 ICT=1 ICT=0 ICT=1 ALL ICT=1 ALL ICT=1DK .037 .044 .057 .068 .23 .24 .29 .32FI .036 .079 .043 .097 .25 .27 .30 .33FR .040 .034 .047 .031 .21 .18 .21 .19NL .016 .019 .012 .017 .22 .24 .20 .21NO .031 .070 .043 .082 .32 .35 .33 .35SE .039 .067 .101 .141 .33 .37 .49 .52

Source: ESSLIMIT: Dispersion of Productivity and Output Growth, from

firm-level timeseries and firm-level cross sections



Results


Risky versus Safe Sectors, US vs EU

1990 1995 2000 20050.45

0.5

0.55

0.6

year

Employment share risky sector

US

EU

1990 1995 2000 20051.2

1.5

1.8

2.1

year

Relative productivity risky sector

US

EU

Source: EUKLEMS: ppp-adjusted output and employee hours; Eurostat

Firm-level ICT Impacts Project: Broadband penetration (industry

riskiness indicator)



Results


Risky versus Safe Sectors, EU by EPL

1990 1995 2000 20050.45

0.5

0.55

0.6

year

Employment share risky sector

EU low EPL

EU high EPL

1990 1995 2000 20051.2

1.5

1.8

2.1

year

Relative productivity risky sector

EU low EPL

EU high EPL

Source: EUKLEMS: ppp-adjusted output and employee hours; Eurostat

Firm-level ICT Impacts Project: Broadband penetration (industry

riskiness indicator)



Results

TheoryCalibration and Simulation

Selection of Related literature

ICT and Productivity

van Ark, O’Mahony, and Timmer (2008); Bloom, Sadun, VanReenen (2012);Brynjolfsson, McAfee (2011)

Employment Protection

Autor, Kerr, and Kugler (2008); Samaniego (2006); Bassanini,Nunziata, and Venn (2009); Cunat and Melitz (2010)

Labor Search

Mortensen and Lentz (2008); Brugemann (2012); Koenigerand Prat (2007)



Results


A model with firing cost and choice of technology: Setup

2 technologies/ sectors compete for workers

0: safe sector, known technology and productivity (i.e.Pissarides, 2000)1: risky sector, gets draws from prod. distribution (i.e.Mortensen Pissarides, 1994)

Labor market states: (non-participation), unemployment,employed in safe sector, employed in risky sector

Why productivity dispersion? –¿ Search frictions

EPL makes using the risky technology more expensive.



Results


A model with firing cost and choice of technology: Setup

Firms pay a fixed fee (c0, c1) to post a vacancy in either thesafe or risky sector, until profit opportunities are exhausted

In the safe sector, productivity, y , is known ex-ante.In the risky sector,y + x , job starts at the safe level ofproductivity and shocks arrive at rate λ : draw x ∼ N(µ, σ2)Conditional on a draw below an endogenous productivitythreshold, xd , the job ends and the firm pays an exit cost, k

There is an exogenous job destruction rate, δ, for both sectors(no exit fee applies).

Surplus is shared between firm and worker through Nashbargaining

Workers decide between home production or engage in costlysearch for a job in either sector

No on-the-job search



Results


Vacancies and Jobs

Value of vacancy

rV0 = −c0 +m0

θ0[J0 − V0] = 0

rV1 = −c1 +m1

θ1[J1(0)− V1] = 0.

Value of a job for the firm

rJ0 = y − w0 − δJ0.

rJ1(x) = y + x−w1(x)− δJ1+λ

( ∫ xuxd

[J1(z)− J1(x)] dF (z)

−F (xd ) (J1 + k)

).



Results


Employment and unemployment

Value of unemployment

rU = b+m0 [max [0,E0 − U ]] +m1 [max [0,E1(0)− U ]] .

Value of employment

rE0 = w0 − δ [E0 − U ]

rE1(x) = w1(x)− (δ + λF (xd )) [E1(x)− U ]

+λ∫ xu

xd[E1(z)− E1(x)] dF (z)



Results


Equilibrium

Nash bargaining

Free entry

Beveridge curve, Steady state flow conditions

Endogenous job destruction threshold



Results


Equilibrium

Free entry conditions (no arbitrage)

Job creation condition sector 0, (m0θ0

)

Job creation condition sector 1, (m1θ1

)

Participation constraints:

Job destruction sector 1: J1(xd ) = −kValue of marginal job for worker: E1(xd ) = U



Results


Parameters from the literature

Parm. Value Description Motivation

y 1 productivity safe sector normalizationr 0.004 monthly interest rate Pissarides (2009)β 1− η Nash bargaining share worker Hosios conditionb 0.4 unemployment benefits Shimer (2005)η 0.5 matching elasticity Pissarides (2009)ξ 0.3 matching efficiency normalization



Results


Parameters matching US labor market stocks and flows

Parm. Value Description Motivation

l 0.77 size labor force size labor force (OECD LFS)δ 0.026 Poisson rate ex. job destr. ex. job destr. (JOLTS, EUKLEMS)c0 0.2092 vacancy costs safe sector stocks, flows (OECD LFS, JOLTS, EUKLEMS)c1 0.4184 vacancy costs risky sector stocks, flows (OECD LFS, JOLTS, EUKLEMS)



Results


Target output

y is average output in the risky sector

(1− s) workers have not received a shock and produce y

s workers produce on average y + 11−F (xd )

∫ xuxd

zdF (z)

solve for s using the steady state flow equation

λ (1− F (xd )) (1− s) e1 = (δ + λF (xd )) se1

Average output per worker in the risky sector is

y = y + s1

1− F (xd )

∫ xu

xdzdF (z) = y +

λ

δ + λ

∫ xu

xdzdF (z) .

Use the formula’s for the truncated normal distribution.



Results


Target variance and endogenous job destruction

The variance of output per worker in the risky sector is

σ2 = s1

1− F (xd )

∫ xu

xd(y + z − y)2 dF (z) + (1− s) (y − y)2

=λ

δ + λ

(∫ xu

xdz2dF (z)− λ

δ + λ

(∫ xu

xdzdF (z)

)2)

.



Results


Target risky sector job creation and destruction

λF (xd ) = srisky − δ.

m1

θ1=

(r + λ + δ) c1−(1− β) (σxd + (r + λ + δ) k)



Results


Matching US cross-sectional productivity distribution

Solve 4 equations (σ2, y , m1θ1

, λF (xd )) for 4 unknowns λ, µ, σ, kfor median workerParameter Value Description

λ 0.1410 Poisson rate productivity shockµ 0.0653 mean productivity shockσ 0.4989 standard deviation productivity shockk 1.2227 firing costs

endog. job destruction (JOLTS, EUKLEMS)cross-sectional mean (EUKLEMS)cross-sectional variance (BHS)stocks and flows (OECD LFS, JOLTS, EUKLEMS)



Results

SimulationEmpirical Results

Increase in Riskiness, under different Firing Costs

0.2 0.3 0.4 0.5 0.60

0.2

0.4

0.6

0.8

1

σ

Share risky

benchmark

high cf = 6

0.2 0.3 0.4 0.5 0.60.95

1

1.05

1.1

1.15

1.2

σ

Productivity

benchmark

high cf = 6



Results


Model Simulation: Comparative Statics

Benchmark σ = 0.50 High σ = 0.75k = 1.25 k = 3 k = 7 k = 1.25 k = 3 k = 7

xd -0.729 -1.010 -1.688 -0.781 -1.048 -1.693λF (xd ) 0.008 0.002 0.000 0.018 0.010 0.001

y 1.100 1.071 1.055 1.182 1.135 1.071S1 (0) 3.012 2.905 2.871 3.315 3.125 2.900

θ1 1.167 1.085 1.059 1.413 1.2560 1.081e1 0.410 0.413 0.419 0.586 0.446 0.418θ0 0.408 0.550 0.596 0.017 0.256 0.557e0 0.316 0.319 0.315 0.110 0.276 0.315u 0.043 0.037 0.035 0.073 0.047 0.037

e1/e 0.566 0.565 0.571 0.842 0.617 0.570π 1.056 1.040 1.032 1.153 1.084 1.040Ω 0.965 0.962 0.958 0.988 0.981 0.964



Results


Cross country / industry Regressions

Employment share, employment growth, or productivity isregressed on industry riskiness indicator interacted with exitcosts

depc,i ,t = α + βkc,t + γkc,tR(σ)i + FE + εc,i ,t X

Where kc,t is country (and time) specific exit cost indicator

OECD Employment protection index (EPL)WB Cost of Doing Business Exit Cost, Cost Recovery, FiringCosts

and R(σ)i is industry riskiness indicator

Variance of firm-level productivity distributionRatio of top quartile productivity to mean productivityPercentage of workers with broadband access



Results


Regression Results

Labor share Labor growth TFP growth

γ −1.08(2.94)

−.86(3.00)

−1.12(2.48)

R2 .84 .20 .07D.F. 5025 4979 4396Fixed effects ind mean+trends ctry,ind,time ctry,ind,time

t-statistic in parenthesis. Period: 1995-2005; Industry rank: productivity

variance; ExitCost: EPLRegular. See Appendix ?? for country and

industry listing. Robust estimation of error variances using 2-way industry

and country clusters for employment, industry clusters for TFP



Results


Robustness γ: by sub-period

sub-periodSample 1995-2000 2000-2005 1995-2005

EU −.71(5.04)

−.84(5.49)

−.77(7.15)

EUN −0.87(6.72)

−.94(6.89)

−0.91(9.27)

EURO −.63(3.32)

−.63(7.15)

−.62(4.62)

OECD −0.85(7.85)

−0.94(8.09)

−0.89(10.81)

ALL −0.98(9.52)

−1.04(9.57)

−1.01(12.98)

t-statistic in parenthesis. Industry rank: productivity variance; ExitCost:

EPLRegular; Fixed Eff: industry means & trends. See appendix for

country listing.



Results


Robustness γ: by riskiness and exit cost

Industry sub-sampleCountry sub-sample Low risk High risk

Low firing cost −2.27(2.71)

−4.39(3.62)

High firing cost −.98(.94)

−2.71(2.81)

t-statistic in parenthesis. Period: 1995-2005; Industry Rank: productivity

variance; ExitCost: EPLRegular; Fixed Eff: industry means & trends.

Robust errors clustered 2-way by industry and country. See Appendix ??

for country and industry listing.



Results


Robustness γ: by k and R(σ)

Riskiness indicatorExit Cost DSLpct P4/P Variance

Exitloss% −4.99(2.63)

−3.55(1.83)

−3.16(1.67)

Exitcost% −22.76(3.00)

−16.20(1.87)

−13.03(1.62)

Firerule −.42(.66)

−.36(.70)

−.32(.69)

Firecost −4.34(2.37)

−3.88(2.84)

−3.09(3.03)

EPLoverall −1.07(2.33)

−.82(2.42)

−.66(2.39)

EPLregular −1.29(2.70)

−1.11(2.55)

−1.08(2.96)

t-statistic in parenthesis. Period: 1995-2005; Fixed Eff: industry means

& trends. See Appendix for indicator definitions and country and industry

listing. Robust errors clustered 2-way by industry and country.



Results


Employment Share: Entry Costs vs Exit Costs

Entry Cost Indicator only γentry γentry γexit

Starting a Business - # of procedures −.20(1.64)

−.09(.97)

−.94(2.85)

Starting a Business - time (days) −.17(2.27)

−.11(1.37)

−.76(1.93)

Starting a Business - cost (pct of capital) −.67(1.04)

−.29(.56)

−1.03(3.34)

Difficulty of hiring (index) −2.12(1.65)

−.39(.30)

−1.04(2.35)

Barriers to entrepreneurship −.71(1.11)

−.27(.66)

−1.05(3.28)

Barriers to entrp. license and permits .20(1.00)

.14(.73)

−1.04(2.77)

none. (only exit cost: EPLRegular) −1.08(2.94)

t-statistic in parenthesis. Period: 1995-2005; Industry rank: productivity variance;

Exit Cost: EPLRegular; Fixed Eff: industry means & trends. Robust errors

clustered 2-way by industry and country.



Results


Robustness γ: by random R(σ)

0 200 400 600 800 1000 1200−1.5

−1

−0.5

0

0.5

1

1.5Random rankings

γ

95% CI

Estimates of γ and confidence interval from 1200 regressions usingrandom draws from all possible permutations of industry riskinessrankings.Exit cost indicator: EPL Regular, ALL countries, 1995-2005, Fixed

Effects: mean & trend



Results


Long term effects of the crisis? Productivity

0.3 0.4 0.5 0.6 0.7 0.80

0.2

0.4

0.6

0.8

1Employment share risky sector

c1

0.3 0.4 0.5 0.6 0.7 0.80.95

1

1.05

1.1

1.15

1.2Relative productivity risky sector

c1


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Employment Protection, Technology Choice, and Worker ... · Introduction Model and Calibration Results Employment Protection, Technology Choice, and Worker Allocation Eric J. Bartelsman1,