gÁbor antal central european university institute of economics - has john s. earle central european...
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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 employment
Average 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)
~400,000
NN 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/WDO
that 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 firms
Monthly 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)