GÁBOR ANTALCentral European UniversityInstitute of Economics - HAS

JOHN S. EARLECentral European University

W.E. Upjohn Institute

ÁLMOS TELEGDYCentral European UniversityInstitute of Economics - HAS

EACES WorkshopApril 8, 2010

CEU, BudapestSeptember 24, 2009

FDI and Wages:Evidence from LEED in Hungary

Motivation: Employer Wage Effects

Employer effects on wages (Abowd et al., 1999; Haltiwanger et al. 2007)

Questions: What firm characteristics associated with high/low wage? Neutral or biased across types of workers? What explains?

selection measurement unmeasured heterogeneity wage policy productivity/rents

Motivation: FDI

Ownership: distinguished characteristic of employer (residual rights)

Policy ambivalence towards FDI+ Source of finance, technologies, markets and new

jobs- Prohibited in strategic sectors, regulatory burdens

Major issue in shaping policies towards FDI: Worker outcomes in foreign-owned enterprises

Why Is Hungary Different?

During the 90’s liberalization of factor markets, large FDI inflow

Supportive policy, tax abatements/subsidies for foreign firms

Foreign owners likely to be very different from domestic owners

Capacity for improvement (technology, know-how, knowledge of market economy, access to financing)

Gaps in the industrial structure Low wage country

Contribution

LEED for HungaryMany ownership switches: 905

594 acquisitions 311 divestments

Long time series (20 years: 1986 - 2005) Mean of pre-treatment years: 3.2 Mean of post-treatment years: 5.7

Effects on wage structureExamine explanations for foreign wage

premium

Data I

Employee information: Hungarian Wage Survey Includes all firms with >20 employees plus random sample

of small (11-20 employees in 1996-99, 5-20 in 2000-05) Workers sampled randomly based on birth date (5th and

15th for production workers, also 25th for nonproduction) All workers in small firms (<20 employees in 1996-2001,

<50 since 2002)

Employer information: Hungarian Tax Authority Data

All legal entities using double-entry bookkeeping Total employment = all employees in Hungary

Data II

Data weighted to represent corporate sector Worker weights within firm Firm weights

Sample size 2,331,566 worker-years 29,169 enterprises

Firms are linked over timeMajority of workers linked within firm

Sample Selection

Sample of firms Only the corporate sector Only industries where any ownership change involving

foreign investors Only firms with switches ≤ 2 (14 firms dropped)

Worker sample Full time workers Age 15 -74

Definition of Foreign Ownership and Earnings

Foreign ownership > 50 percent of the firm’s shares owned by foreign

owners (same results with >10 percent) Distinguishing acquisitions (594), divestments (311)

and greenfield investments (2,140)Earnings

Monthly base salary Overtime Regular bonuses and premia, commissions, allowances Extraordinary bonuses based on previous year’s

records

Evolution of Ownership and Earnings

0

20

40

60

80

100

120

1986 1989 1992 1994 1996 1998 2000 2002 2004

Year

Percent foreign firms Percent foreign employmentAverage real wage (1986=100)

Composition of Workforce by Ownership

Domestic Foreign

Earnings 115.9 177.5 (101.3) (188.7) Female 38.5 45.0 Education Elementary 29.1 19.4 Vocational 32.8 31.9 High school 29.4 33.0 University 8.7 15.7 Experience 22.6 19.8 (11.0) (10.9)

New Hire 10.8 15.2 Manager 6.4 5.0 N 2,019,957 311,649

Firm Characteristics by Ownership I

Domestic Foreign

Tangible Assets 284 1,152 (7,564.0) (11,262.0)

Employment 54 111 (574) (443)

Labor Productivity 20.2 63.4 (213.8) (274.2)

Exporter 0.12 0.48 Export Share 0.40 0.60 (0.30) (0.34)

Estimation

lnwijt = + Xitβ + δFOREIGNjt-1+ ΣγjREGIONj + ΣλtYEARt + uijt

i = workersj = firmst = time

Specifications I

Controls (Xit):

(1) No additional controls(2) Gender, education category, potential

experience(3) + interactions(4) + manager, new hire dummies

Dynamics: Ownership interacted with event time

Specifications II

Error term (uijt): OLS Firm fixed effects (FE) ~29,000 FE combined with narrowly defined worker groups (GFE)

~400,000NN PS matching (e, lp, w, expshare 1 and 2 years

before acqusition; quadratic polynom.) 325 acqd, 279 control firms; 330,510 obs. PS: normalize around acquisition year, weight controls Exact matching on 2-digit industry and year OLS, FE, GFE Good covariate balance

Wage Effects by Type of Investment: OLS

(1) (2) (3) (4)

Greenfield 0.364** 0.382** 0.385** 0.402** Pre-Acquisition Domestic 0.335** 0.285** 0.284** 0.297** Acquisition 0.438** 0.416** 0.417** 0.428** Divestment 0.201** 0.209** 0.210** 0.216** Post-Divestment Domestic 0.172** 0.173** 0.174** 0.188** Do-Fo-Do 1 0.174* 0.164** 0.166** 0.175** Do-Fo-Do 2 0.349** 0.336** 0.337** 0.344** Do-Fo-Do 3 0.283** 0.281** 0.281** 0.290** Fo-Do-Fo 1 0.279* 0.266* 0.261* 0.286* Fo-Do-Fo 2 0.419** 0.437** 0.435** 0.441** Fo-Do-Fo 3 0.357** 0.482** 0.483** 0.495** Ind. Characteristics No Yes Yes Yes Job Characteristics No No No Yes

Wage Effects by Type of Investment: FE

(1) (2) (3) (4)

Firm FE Acquisition 0.179** 0.152** 0.153** 0.159** Divestment 0.071** 0.062* 0.060* 0.058* Do-Fo-Do 2 0.247** 0.218** 0.218** 0.223** Do-Fo-Do 3 0.154* 0.143* 0.144* 0.156** Fo-Do-Fo 1 -0.068 -0.095* -0.099** -0.081* Fo-Do-Fo 3 0.009 0.013 0.010 0.012

Group Effects Acquisition 0.115** 0.122** Divestment 0.078* 0.071* Do-Fo-Do 2 0.200** 0.207** Do-Fo-Do 3 0.097 0.116* Fo-Do-Fo 1 -0.129** -0.107** Fo-Do-Fo 3 0.009 0.020 Ind.

Characteristics No Yes Yes Yes

Job Characteristics No No No Yes

Wage Effects by Type of Investment: Matching

(1) (2) (3) (4)

Matching OLS Acquisition 0.227** 0.177** 0.178** 0.179** Do-Fo-Do 2 0.104 0.105* 0.105* 0.104* Do-Fo-Do 3 0.034 0.045 0.046 0.045

Matching FE Acquisition 0.132** 0.115** 0.116** 0.118** Do-Fo-Do 2 0.142** 0.124** 0.125** 0.127** Do-Fo-Do 3 0.033 0.045 0.048 0.058

Matching GE Acquisition 0.096** 0.099** Do-Fo-Do 2 0.124** 0.128** Do-Fo-Do 3 0.023 0.038 Ind.

Characteristics No Yes Yes Yes

Job Characteristics No No No Yes

What Might Explain Higher Wages with FDI?

Observed foreign wage difference could be related to:

Selection At firm and worker level before treatment Change in workforce composition after treatment

(observed and unobserved) Attrition correlated with ownership and wages

Measurement error, differences in job attributes Working conditions (hours, job security) Undeclared wages and employment Structure of compensation (fringe benefits, incentive

pay...)

What Might Explain Higher Wages with FDI?

Observed foreign wage difference could be related to

Productivity and rents Restructuring Technological advantage, technology-skill

complementarity On-the-job training Efficiency wages Export status Rent sharing, unions

Productivity and Wages: Estimation

SUR modell: 2 equations, demeaned at the firm level

lnoutputj = 0 + 1 lnKj + 2 lnMj +3 lnempj +δ1 lnempj FOjt-1+ Σ λkt INDkYEARt + ujt

lnwbillj = β0 + β1 lnempj +δ2 lnempj FOjt-1+ ΣλktINDkYEARt + vjt

Hypothesis: MPFO/MPDO = WFO/WDOthat is: (3 + δ1)/ 3 = (β1 + δ2)/ β1

Productivity and Wages: Results and Tests

MPFO/MPDO = WFO/WDO

General foreign effect: 8.9% > 6.5% Acquisition effect: 12.4% > 7.9%

All foreign Chi2 (Breusch-Pagan) test of independence 10711.33 (0.000)

Chi2 (MP ratio=Wage ratio) in foreign relative 10.91 to domestic firms (0.001)

Acquisitions Chi2 (Breusch-Pagan) test of independence 10696.34 (0.000) Chi2 (MP ratio=Wage ratio) in foreign relative 29.82 to domestic firms (0.000)

Further Productivity Evidence: “Catch-Up”

Why is the wage effect of FDI so large in Hungary?Distance from the frontier and the transitionDivide period into early (<1999) and late (>1998)

Larger effects earlierDivide FDI acquisition targets into state and private

Larger effects for state-owned targets

=> Part of large effect in Hungary may be catch-up. FDI to developed countries may have little effect.

Composition of Workforce I

Foreign effect for incumbent workers (1) (4) Firm-Worker Effects Acquisition 0.052** 0.052** (0.016) (0.016) R2 0.072 0.072 Matching with Acquisition 0.044* 0.044* Firm-Worker Effects (0.022) (0.022) R2 0.093 0.093 Individual Characteristics

No Yes

Job Characteristics No Yes

Composition of Workforce II

Stock of university graduates and young workers increases after acquisition

LPMs with individual characteristics on LHS, acquisition dummy on RHS; FE estimation

More hiring after acquisition (mostly one year after), in favor of young high-skilled

LPMs with new hire dummy on LHS, acquisition dummy interacted with individual characteristics on RHS; FE estimation

Separation rates: to be done

Composition of Firms

Acquisitions weakly correlated with wages and firm exit

Probit with firm-level exit on LHS, acquisition dummy interacted with log wagebill on RHS

Foreign Acquisitions and Wage Structure

Fixed Effects Matching (FE)

Group Effects Matching (GFE)

Female 0.009 0.013 0.004 0.004 Vocational 0.013 0.014 -0.018 -0.009 High school 0.037** 0.052** 0.003 0.023 University 0.161** 0.136** 0.082** 0.115** Experience -0.001 -0.005** -0.002 -0.002 Exp2 * 100 -0.000 0.007* -0.003 -0.001 New Hire -0.009 0.014 0.013 0.008 Manager 0.115** 0.032 0.120** 0.059 Foreman 0.073** -0.005 0.060* 0.002 R2 0.405 0.490 0.164 0.199

Measurement I

Hypothesis: Higher working hours at acquired firmsMonthly paid hours for 1999-2005Tests:

Monthly vs hourly earnings Same effect

Hours as a dependent variable No foreign effect

Hours as a covariate Leaves foreign effect unchanged

Caveat: Overtime probably mismeasured for non-production workers, and hard to test for production separately, since no wage effect

Measurement II

Hypothesis: Domestic firms are more likely to underreport wages Aux. hypotheses: Probability of cheating is lower in big

enterprises and in industries with a low cheating index (Elek and Szabó 2008)

Tests: LPM for 1[w < minw + 3%]

Negative foreign effect (not high enough to explain total wage difference)

Foreign interacted with size Zero/positive effect (reject hypothesis)

Foreign interacted with industry cheating index Zero/negative correlation (reject hypothesis)

Conclusions

OLS: foreign wage premium is 36 percentFE, GFE, matching premium is 9–17 percentDivestment effect is 40-50% of acquisition effectAll worker types benefit; high educated the most5% premium for incumbent workers, composition

change in favor of young high-skilledResults not driven by measurement errorProductivity best candidate for explaining the gap

Previous Studies I

Firm-level data: Positive, sometimes large foreign wage premium

Controls for employment composition or LEED:Smaller effects, sometimes insignificant

The premium varies by skill groupTreatment of selection bias is important

Previous Studies II

Many datasets are not real LEED, but firm-level data with information on composition

Short time series (usually ≤ 5 years) Matching only on immediate pre-acquisition

yearFew ownership changes with enough pre- and

post treatment observationsMost studies from developed countries

exposed to FDI for a long timeWage structure: mostly skilled-unskilled

Firm Characteristics by Ownership II

Domestic Foreign

Industry Agriculture 8.4 1.9 Industry 25.7 43.4 Construction 10.6 2.2 Trade 28.7 27.0 FIRE 4.2 8.1 Business Services 9.2 8.8 Other Services 13.0 8.5

Tests of Covariate Balance

Normalized Difference Treated-Controls

Probability of Rejecting Inequality of Means

One Year Before Acquisition

Average Earnings 0.042 0.473 Sales 0.058 0.317 Employment 0.068 0.247 Capital 0.016 0.787 Export Share 0.003 0.954

Two Years Before Acquisition

Average Earnings 0.036 0.520 Sales 0.053 0.362 Employment 0.058 0.312 Capital -0.060 0.291

Foreign Wage Premium: OLS

(1) (2) (3) (4) (5)

Foreign 0.371** 0.371** 0.373** 0.385** 0.355** Individual Characteristics Female -0.218** -0.218** -0.204** -0.173** Vocational 0.118** 0.103** 0.093** 0.105** High school 0.378** 0.372** 0.314** 0.266** University 0.951** 0.944** 0.777** 0.716** Experience 0.025** 0.025** 0.021** 0.019** Experience2* 100 -0.037** -0.036** -0.031** -0.027** Job Characteristics

New Hire -0.114** -0.093** Manager 0.411** 0.460** Foreman 0.254** 0.293** Interactions between gender, education and experience

No No Yes Yes Yes

Industry effects No No No No Yes R2 0.124 0.369 0.372 0.402 0.461

Foreign Wage Premium: Alternative Specifications

(1) (2) (3) (4)

Firm FE Foreign 0.172** 0.147** 0.147** 0.151** R2 0.066 0.330 0.334 0.406 Group Effects Foreign 0.124** 0.128** R2 0.071 0.163 Matching and OLS Foreign 0.148** 0.126** 0.126** 0.129** R2 0.100 0.432 0.436 0.484 Matching and FE Foreign 0.124** 0.108** 0.108** 0.106** R2 0.090 0.405 0.410 0.490 Matching and GE Foreign 0.094** 0.093** R2 0.076 0.197 Individual Characteristics

No Yes Yes Yes

Job Characteristics No No No Yes

Dynamics: OLS

0

.2

.4

.6

-5- -4 -3 -2 -1 0 1 2 3 4 5+

OLS CI

Dynamics: FE

0

.1

.2

.3

.4

-5- -4 -3 -2 -1 0 1 2 3 4 5+

FE CI

Dynamics: Matching and OLS

-.1

0

.1

.2

.3

-5- -4 -3 -2 -1 0 1 2 3 4 5+

Matching with OLS CI

Dynamics: Matching and FE

-.1

0

.1

.2

.3

-5- -4 -3 -2 -1 0 1 2 3 4 5+

Matching with FE CI

Dynamics: GFE

0

.1

.2

.3

-5- -4 -3 -2 -1 0 1 2 3 4 5+

GFE CI

Dynamics: Matching and GFE

-.1

0

.1

.2

.3

-5- -4 -3 -2 -1 0 1 2 3 4 5+

Matching with GFE CI

Productivity and Wages I

If productivity increases, wages may rise as well, and differentials may come closer to relative MPs

SUR models: productivity and wage equations, error terms allowed to be correlated

SUR model I: labor productivity and average wages RHS: ACQ, ind-year interactions

SUR model II: TFP and wagebill RHS TFP: lnK, lnM, lnL, ACQ*lnL, ind-year interactions H=university-educated; L=less than university

Productivity and Wage Levels

(1) (2) Average Compensation Foreign 0.096** (0.004) Acquisition 0.099** (0.005) Divestment 0.031** (0.006) Labor Productivity Foreign 0.118** (0.007) Acquisition 0.166** (0.009) Divestment -0.015 (0.010) P ( βcomp = βlp) 0.000 0.000 Corr ( ucomp, ulp) 0.472 0.472 Breusch-Pagan test 0.000 0.000

Relative Productivity and Wages

Output Foreign 0.166** (0.038) Low Skill -0.139** (0.028) High Skill 0.022** (0.005) Foreign * Low Skill -0.010 (0.044) Foreign * High Skill 0.040** (0.014)

Wage Foreign 0.127** (0.031) Low Skill 0.024 (0.023) High Skill 0.024** (0.004) Foreign * Low Skill -0.062 (0.036) Foreign * High Skill 0.028** (0.011)