Barriers to Entry, Barriers to Entry, Deregulation and Deregulation and

Workplace TrainingWorkplace Training

Andrea Bassanini (OECD, ERMES and Andrea Bassanini (OECD, ERMES and IZA)IZA)

Giorgio Brunello (Padova, Cesifo and Giorgio Brunello (Padova, Cesifo and IZA)IZA)

MotivationMotivation• Large literature on product market institutions Large literature on product market institutions

– regulation, barriers to entry – and growth.– regulation, barriers to entry – and growth.

• Large empirical literature on competition and Large empirical literature on competition and performance (productivity, innovation, etc…)performance (productivity, innovation, etc…)

• Empirical work using regulation data: Empirical work using regulation data: deregulation can foster growth by affecting:deregulation can foster growth by affecting:– employment (Kugler and Pica, Fiori et al)employment (Kugler and Pica, Fiori et al)– productivity growth (Nicoletti and Scarpetta)productivity growth (Nicoletti and Scarpetta)– investment in capital stock (Alesina et al)investment in capital stock (Alesina et al)– innovation (Aghion et al)innovation (Aghion et al)– business startups (Djankov et al)business startups (Djankov et al)

Does it affect training?Does it affect training?

• Human capital accumulation Human capital accumulation important engine of productivity important engine of productivity growthgrowth

• One channel in the relationship One channel in the relationship between deregulation and between deregulation and productivity growth?productivity growth?

Previous literature: TheoryPrevious literature: Theory

• Common idea in labour economics: Common idea in labour economics: competitioncompetition trainingtraining because because rentsrents and firms pay for most of workplace and firms pay for most of workplace training (e.g. Acemoglu and Pischke, training (e.g. Acemoglu and Pischke, 1999)1999)

– Only exception Autor (2001), but limited to Only exception Autor (2001), but limited to induction training (training as a selection induction training (training as a selection device: THS firms)device: THS firms)

-.2

-.1

0.1

.2tr

ain

ing

-3 -2 -1 0 1 2regulation

Training and deregulation (deviations from sectoral means) in energy, transport and communication. 15 EU countries. 1995-2002

Our contribution: Theory Our contribution: Theory

• We develop a model of price competition We develop a model of price competition with identical firms and find that lower with identical firms and find that lower barriers to entry increase training barriers to entry increase training

• Intuition: two effectsIntuition: two effects– Competition reduces rents per unit of output Competition reduces rents per unit of output

and thus training (and thus training (rent effectrent effect))– Competition increases the sensitivity of Competition increases the sensitivity of

output to relative prices (and training reduces output to relative prices (and training reduces relative prices) (relative prices) (business-stealing effectbusiness-stealing effect))

• Autor shows that training and firm Autor shows that training and firm concentration (Herfindhal index) are concentration (Herfindhal index) are negatively correlated in the US THS industrynegatively correlated in the US THS industry

• Goerlitz and Stiebale (2008) find no effect of Goerlitz and Stiebale (2008) find no effect of mark-ups or firm concentration on training mark-ups or firm concentration on training using German data.using German data.

• Our previous exploratory work (BBBDL) on a Our previous exploratory work (BBBDL) on a multi-country aggregate panel finds negative multi-country aggregate panel finds negative correlation between the OECD index of correlation between the OECD index of regulation and trainingregulation and training

Previous Literature: Previous Literature: EmpiricsEmpirics

Our contribution: EmpiricsOur contribution: Empirics

• We match large industry-level datasets We match large industry-level datasets on regulation and training – allowing us on regulation and training – allowing us to control for all aggregate effects – and to control for all aggregate effects – and we find that deregulation increases we find that deregulation increases trainingtraining

• We also look at the relationship between We also look at the relationship between markups and training, and instrument markups and training, and instrument the former with regulationthe former with regulation

RoadmapRoadmap

• MotivationMotivation

• LiteratureLiterature

• The Theoretical ModelThe Theoretical Model

• The Empirical SetupThe Empirical Setup

• The DataThe Data

• ResultsResults

• ConclusionsConclusions

Key assumptionsKey assumptions

• Firms and workers are identical: share the same Firms and workers are identical: share the same technology, demand elasticity, outside options and technology, demand elasticity, outside options and training (and hiring) costs. training (and hiring) costs.

• No exogenous turnoverNo exogenous turnover

• Demand elasticity Demand elasticity if number of firms if number of firms

• No physical capital. There is only one type of labourNo physical capital. There is only one type of labour

Specific training: Time lineSpecific training: Time line

• Stage 0: firms decide entry (Stage 0: firms decide entry (mm enter) enter)

• Stage 1: Firms hire Stage 1: Firms hire TT workers and provide workers and provide them with a (costly) amount of training them with a (costly) amount of training

• Stage 2: Final wages Stage 2: Final wages WW and employment and employment LL are set; production occurs. are set; production occurs. – Wages and employment are bargained over Wages and employment are bargained over

(Efficient bargaining between union and (Efficient bargaining between union and employer).employer).

Specific trainingSpecific training

• Two alternative settings:Two alternative settings:

• CommitmentCommitment: The employer commits to : The employer commits to employ all trainees (e.g. long notice employ all trainees (e.g. long notice periods).periods).– LL = = TT

• No CommitmentNo Commitment: The employer cannot : The employer cannot commit to employ all trainees.commit to employ all trainees.– LL < < T T

• L>T not feasible: training ONLY in the first L>T not feasible: training ONLY in the first periodperiod

Commitment case: setup (see Blanchard Giavazzi 2003)

PP

mY

Y ii

0' ),( gmg

ie

iALY

iie LL )1(

Training costs per trainee: 2)(

2i

ic

Nash Bargaining

Nash maximand:

iiiiiii WLYPLVW ln)1()(ln

Bargaining over wages only:

))1(()1( VAPVVL

YPW ii

i

iii

Net profits:

P

P

m

Y

APA

V

P

P i

i

i

i

ii )1(2)1(

)1()1(2

Training Decisions

with L=T

ii

iiii TTP

VTA

Y

m

2)1()1()1(

211

1

First order conditions:

T :

)1()1(2)1(1

2

i

i

i

i

APA

V

P

P

:

iiii TAT

P

P

)1(

11

Optimal Training Incidence

0)1(22

P

V

i

There is a unique positive solution All firms invest in the same amount of training per trainee Prices are the same for all firms (Pi / P = 1)

)1(

)1(

A

;

1

; )1(

A

Long Run Equilibrium with Entry

Entry condition:

1

[MM]

First order condition for training in the long run

)1(

A [TT]

which imply the linear relationship

)1(

A

TT

MMτ

θ

Reducing entry barriersReducing entry barriers

• Lower barriers rotate the TT curve Lower barriers rotate the TT curve downwards because they reduce net downwards because they reduce net profits per unit of outputprofits per unit of output

• Lower barriers increases the number Lower barriers increases the number of firms of firms MM shifts out and MM shifts out and training increases because output training increases because output gains from reducing unit costs go up gains from reducing unit costs go up

No commitment case:No commitment case:• Starting from the commitment equilibriumStarting from the commitment equilibrium

– Bargaining over L leads to L>T (not feasible)Bargaining over L leads to L>T (not feasible)– Reason: the parties consider training costs as bygonesReason: the parties consider training costs as bygones

• Accomodating strategy in the first stage: set Accomodating strategy in the first stage: set TT large large enough to accommodate L=T in second stage enough to accommodate L=T in second stage – Any largeAny large-T-T strategy is always dominated by a small strategy is always dominated by a small-T-T

strategy strategy Intuition: training costs are not internalised in the bargaining Intuition: training costs are not internalised in the bargaining stage, therefore training costs can be cut only by tying own stage, therefore training costs can be cut only by tying own hands and setting a non accommodating strategy with lower hands and setting a non accommodating strategy with lower TT

• Commitment equilibrium holds even in absence of Commitment equilibrium holds even in absence of commitmentcommitment

• Key reason: firms cannot train in the second periodKey reason: firms cannot train in the second period

General trainingGeneral training

• If hiring costs > training costs: as aboveIf hiring costs > training costs: as above

• Otherwise: no firm trains, training paid Otherwise: no firm trains, training paid by workers, wages set by the market (as by workers, wages set by the market (as in Becker)in Becker)

• Workers invest till MB=MCWorkers invest till MB=MC

• Firms demand training to max profitsFirms demand training to max profits

General training: equilibrium

Hiring costs per worker: 2)(

2i

ih

Training costs if paid by the worker: 2)(

2i

ic

Equilibrium conditions:

)(' W

iiiii LWAL

P

P))((

11 '

1

A

Empirical strategy: Empirical strategy:

• Compare a group of industries affected by Compare a group of industries affected by deregulation (certain services plus utilities) deregulation (certain services plus utilities) to a group not (or less) affected to a group not (or less) affected (manufacturing)(manufacturing)

• Cross-country data: add country variation in Cross-country data: add country variation in deregulation measures: the intensity of deregulation measures: the intensity of deregulation varies across countries for deregulation varies across countries for each sector (more variation and possibility each sector (more variation and possibility of controlling for macroeconomic effects)of controlling for macroeconomic effects)

• Confounders are controlled by a set of Confounders are controlled by a set of country by time, country by sector and country by time, country by sector and sector by time dummiessector by time dummies

Empirical Setup

ictictictict YXTP 210

ictctiticict

Y = measures of deregulation

ictictictict LIXTP 210

LI = Lerner Index, instrumented by Y. We decompose the effect of regulation on training into the effect on the Lerner index (positive) and the effect of the Lerner index on training (negative if the business stealing effect prevails)

DataData

• OECD industry-specific regulatory OECD industry-specific regulatory indicatorsindicators

• Eurostat Labour Force SurveyEurostat Labour Force Survey

• OECD STAN database on output, OECD STAN database on output, imports and labourimports and labour

Definition of training in the Definition of training in the datadata• Training flow in the 4 weeks preceding the Training flow in the 4 weeks preceding the

interview interview

• Sensitivity: we also estimate training stocks Sensitivity: we also estimate training stocks using the inventory method (Dearden, Reed using the inventory method (Dearden, Reed and van Reenen 2006, Conti 2005)and van Reenen 2006, Conti 2005)

• We focus on fully employed individuals We focus on fully employed individuals aged 25 to 54aged 25 to 54

• We collapse data by country year and We collapse data by country year and industryindustry

.04

.06

.08

.1.1

2(m

ean)

edu

c4w

n

1992 1994 1996 1998 2000 2002YEAR

training participation rate

training incidence – affected industries - average across EU

OECD regulation indicatorsOECD regulation indicators

• Cover 3 industries: utilities (electricity Cover 3 industries: utilities (electricity gas), transport (air road rail) and gas), transport (air road rail) and communicationcommunication

• Detailed indicators of sector specific entry Detailed indicators of sector specific entry barriers, public ownership, market share of barriers, public ownership, market share of dominant player, vertical integration and dominant player, vertical integration and price controlsprice controls

• Range from 0 – no regulation – to 6, Range from 0 – no regulation – to 6, maximum regulationmaximum regulation

• We focus on barriers to entry indicators We focus on barriers to entry indicators (but also check with broader ones)(but also check with broader ones)

IndicatorsIndicators

• BEVI: legal limitations on number of BEVI: legal limitations on number of companies and rules on vertical companies and rules on vertical integration of industriesintegration of industries

• REGNO: BEVI plus price controls REGNO: BEVI plus price controls

1994 1996 1998 2000 2002YEAR

BEVI REGNO

regulation indicators – affected industries - average across EU

Services and manufacturingServices and manufacturing• Following the SMP in 1992, the time profile of Following the SMP in 1992, the time profile of

regulation in manufacturing can be taken as regulation in manufacturing can be taken as flat for most EU countries, while regulation flat for most EU countries, while regulation has changed in serviceshas changed in services

• Diff in diff approach: regulation constant in Diff in diff approach: regulation constant in manufacturing and time varying in servicesmanufacturing and time varying in services

• Final post-SMP sample: 15 countries, 13 Final post-SMP sample: 15 countries, 13 sectors at most 7 years (more than 1200 obs.)sectors at most 7 years (more than 1200 obs.)

Additional confounders at Additional confounders at country x sector x time levelcountry x sector x time level• Industry-specific import-weighted real Industry-specific import-weighted real

exchange ratesexchange rates• Employment growth (growing industries may Employment growth (growing industries may

train more)train more)• Cyclical effects – employment and worked Cyclical effects – employment and worked

hours gaps using the HP filterhours gaps using the HP filter• GenderGender• EducationEducation• Union densityUnion density• EPL interacted with US job turnover by industryEPL interacted with US job turnover by industry• R&D IntensityR&D Intensity

Table 1. Estimates of training as function of the index of product market regulation REGNO, which excludes public ownership. Panel A: Linear specification estimated by OLS (1) (2) (3) (4) (5)

-0.014 -0.015 -0.014 -0.017 -0.015 Regulation, excluding public own. (REGNO) [3.24]*** [3.29]*** [3.00]*** [3.47]*** [3.29]***

-0.141 -0.129 -0.147 -0.141 Percentage with low education

[3.11]*** [2.73]*** [3.06]*** [3.10]*** -0.065 -0.069 -0.073 -0.064

Percentage with intermediate education [1.51] [1.37] [1.64] [1.48] 0.070 0.068 0.076 0.075

Percentage females [1.82]* [1.71]* [1.82]* [1.90]*

Import-weighted real exchange rate

-0.025 [1.02]

-0.021 [0.82]

-0.025 [1.03]

-0.025 [1.03]

Log worked hours gap

-0.207 [1.94]*

-0.268 [2.42]**

-0.213 [1.81]*

-0.208 [1.94]*

Percentage large firms

0.005 [0.29]

Age

-0.000 [0.12]

Employment growth

0.012 [0.41]

Logarithm of R&D intensity

-0.004 [1.33]

Union density

0.000 [0.55]

EPL times US job turnover

-0.007 [0.11]

Estimated elasticity of training wrt regulation -0.472 -0.494 -0.490 -0.500 -0.492 Country by sector dummies yes yes yes yes Yes Country by year dummies yes yes yes yes Yes Sector by year dummies yes yes yes yes Yes Number of observations 1236 1224 1188 1061 1224

-0.3

-0.2

-0.1

0.0

NO

R

AU

T

BE

L

ITA

SP

A

NE

D

GB

R

GR

E

GE

R

FR

A

IRE

FIN

SW

E

PO

R

DN

K

1% confidence interval: upper bound estimated coefficient 1% confidence interval: lower bound

Country excluded

Sensitivity to country coverage

Table 3. Estimates of training stock as a function of REGNO. Alternative measures Coefficient Elasticity

-0.062 -0.204 Training stock – base measure [2.62]***

-0.130 -0.426 Training stock – alternative measure based on observed growth rates

[4.51]*** -0.037 -0.122

Training stock – alternative measure assuming 2% steady state growth [2.15]** -0.037 -0.123

Training stock – alternative measure assuming 2.5% steady state growth [2.16]**

Table 4. Estimates of training as function of the Lerner index, instrumented with REGNO. Dependent variable: training participation rates. Panel A: Linear model, 2SLS (1) (2) (3) (4) (5)

-1.323 -1.419 -1.391 -1.187 -1.456 Lerner Index [1.90]* [2.00]** [1.82] * [2.27]** [1.92]*

-0.167 -0.169 -0.176 -0.171 Percentage with low education [2.49]** [2.48]*** [2.71]** [2.49]** -0.074 -0.071 -0.086 -0.083 Percentage with intermediate education [1.22] [1.18] [1.47] [1.37] 0.142 0.141 0.109 0.144 Percentage females [2.31]** [2.25]** [2.10]** [2.27]**

Import-weighted real exchange rate -0.001 [0.03]

-0.000 [0.01]

-0.014 [0.46]

0.001 [0.05]

Log worked hours gap -0.042 [0.23]

-0.110 [0.59]

-0.042 [0.26]

-0.031 [0.17]

Percentage large firms 0.040 [1.24]

Age 0.000 [0.18]

Employment growth 0.054 [0.84]

Logarithm of R&D intensity -0.017 [2.11]*

Union density -0.002

[0.96]

EPL times US job turnover 0.149

[1.05]

Durbin-Wu-Hausman exogeneity test (2(1)) 23.75*** 24.70*** 27.86*** 25.89*** 27.64***

Coeff. of REGNO in the first-stage regression 0.013 [2.67]***

0.013 [2.71]***

0.013 [2.56]**

0.016 [3.20]***

0.013 [2.58]***

Elasticity of training to the Lerner Index -1.613 -1.749 -1.744 -1.381 -1.789 Derivative of training wrt REGNO -0.017 -0.018 -0.019 -0.019 -0.019 Elasticity of training wrt REGNO -0.445 -0.486 -0.488 -0.497 -0.488 Country by sector dummies yes yes yes yes yes Country by year dummies yes yes yes yes yes Sector by year dummies yes yes yes yes yes Number of observations 1120 1108 1084 985 1108

Empirical Setup: GLM

ictictictict YXGTP )( 210

ictctiticict

Y = measures of deregulation

G = logit function (exp(X)/(1+exp(X)

ictictictict LIXGTP )( 210

LI = Lerner Index, instrumented by Y. More suitable in the case of fractional data obtained by averaging microdata from sample of unknown size. Robust QMLE estimator can be devised independently of distributional assumptions on the error term (Papke and Wooldridge, 1996).

Panel B: GLM specification estimated by QMLE (1) (2) (3) (4) (5)

Regulation, excluding public own. (REGNO) -0.141 -0.161 -0.175 -0.151 -0.167

[3.84]*** [4.40]*** [4.22]*** [4.25]*** [4.51]***

Percentage with low education -2.005 -1.938 -2.045 -1.988 [4.38]*** [4.03]*** [4.38]*** [4.36]***

Percentage with intermediate education -0.679 -0.646 -0.737 -0.638 [1.93]* [1.76]* [2.12]* [1.80]*

Percentage females 0.737 0.672 0.643 0.713 [1.91]* [1.71]* [1.69]* [1.84]*

Import-weighted real exchange rate

-0.275 [1.78]*

-0.263 [1.67]*

-0.382 [2.44]**

-0.276 [1.80]*

Log worked hours gap

-2.269 [2.13]***

-3.186 [2.79]***

-1.939 [1.85]*

-2.229 [2.09]**

Percentage large firms

0.203 [0.66]

Age

0.012 [0.64]

Employment growth

0.364 [0.95]

Logarithm of R&D intensity

-0.069 [1.83]*

Union density

0.001 [0.18]

EPL times US job turnover

-1.828 [1.57]

Estimated elasticity of training wrt regulation -0.279 -0.316 -0.345 -0.319 -0.329 Country by sector dummies yes yes yes yes Yes Country by year dummies yes yes yes yes Yes Sector by year dummies yes yes yes yes Yes Number of observations 1236 1224 1188 1061 1224

Panel B: GLM, Two-step IV estimates (1) (2) (3) (4) (5)

Lagged Lerner Index -11.08** -12.44** -13.62* -9.36** -13.59** [-69.09,-0.05] [-71.03,-0.87] [-130.0,0.04] [-44.74,-0.96] [-87.96,-1.41]

Residual first stage 11.41* [-0.48,66.48]

12.49** [0.59,69.65]

13.64* [-0.56,130.0]

9.14** [0.56,38.04]

13.68** [1.45,89.30]

Percentage with low education -2.20* -2.31* -2.26** -2.23* [-4.13,0.02] [-4.93,0.32] [-4.04,-0.13] [-4.43,0.45]

Percentage with intermediate education -0.76 -0.75 -0.85 -0.82 [-2.50,1.56] [-2.60,3.45] [-2.27,0.84] [-3.00,1.57]

Percentage females 1.27* 1.31* 0.86 1.29 [-0.43,4.02] [-0.66,6.34] [-0.48,2.90] [-0.50,4.85]

Import-weighted real exchange rate -0.19

[-1.18,1.07] -0.01

[-1.92,1.23] -0.18

[-0.99,0.78] 0.02

[-1.15,1.37]

Log worked hours gap -2.27

[-5.96,7.05] -1.31

[-6.50,15.19] -0.55

[-4.70,4.35] -0.40

[-6.05,9.22]

Percentage large firms 0.55

[-1.30,2.35]

Age 0.02

[-0.07,0.13]

Employment growth 0.71

[-3.53,3.80]

Logarithm of R&D intensity -0.18*

[0.50,0.01]

Union density -0.03*

[-0.24,0.04]

EPL times US job turnover -0.70

[-5.24,13.63]

Coeff. of REGNO in the first-stage regression 0.013** [0.008, 0.017]

0.013** [0.008, 0.017]

0.013** [0.008, 0.018]

0.016** [0.011, 0.021]

0.013** [0.008, 0.018]

Elasticity of training to the Lerner Index -0.651 -0.795 -0.729 -0.589 -0.795 Derived elasticity of training wrt REGNO -0.289 -0.359 -0.329 -0.317 -0.350 Country by sector dummies yes yes yes yes yes Country by year dummies yes yes yes yes yes Sector by year dummies yes yes yes yes yes Number of observations 1120 1108 1084 985 1108

ConclusionsConclusions

• An increase in product market An increase in product market deregulation generates a sizeable deregulation generates a sizeable increase in training incidence (10 percent increase in training incidence (10 percent reduction in regulation increases training reduction in regulation increases training incidence by 2.8 to 5 percent in the incidence by 2.8 to 5 percent in the exposed industriesexposed industries

• These findings suggest that an important These findings suggest that an important link in the relationship between link in the relationship between deregulation and productivity growth is deregulation and productivity growth is the investment in human capital taking the investment in human capital taking place in firmsplace in firms

Thank you!!Thank you!!––