1. FDI and Wages: Evidence from LEED in Hungary
GÁBOR ANTAL
Central European University
Institute of Economics - HAS
JOHN S. EARLE
W.E. Upjohn Institute
ÁLMOS TELEGDY
EACES Workshop
April 8, 2010
CEU, Budapest
September 24, 2009

2. 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

3. 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

4. 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

5. Contribution Effects on wage structure
LEED for Hungary
Many ownership switches: 905
594 acquisitions
311 divestments
Long time series (20 years: )
Mean of pre-treatment years: 3.2
Mean of post-treatment years: 5.7
Effects on wage structure
Examine explanations for foreign wage premium

6. Data I Employee information: Hungarian Wage Survey
Includes all firms with >20 employees plus random sample of small (11-20 employees in , 5-20 in )
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 , <50 since 2002)
Employer information: Hungarian Tax Authority Data
All legal entities using double-entry bookkeeping
Total employment = all employees in Hungary

7. Data II 2,331,566 worker-years 29,169 enterprises
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 time
Majority of workers linked within firm

8. Sample Selection Sample of firms Worker sample
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

9. Definition of Foreign Ownership and Earnings
> 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

10. Evolution of Ownership and Earnings

11. Composition of Workforce by Ownership

12. Firm Characteristics by Ownership I

13. Estimation
lnwijt =  + Xitβ + δFOREIGNjt-1+ ΣγjREGIONj + ΣλtYEARt + uijt i = workers j = firms t = time

14. Specifications I Controls (Xit): No additional controls
Gender, education category, potential experience
+ interactions
+ manager, new hire dummies
Dynamics: Ownership interacted with event time

15. 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

16. Wage Effects by Type of Investment: OLS

17. Wage Effects by Type of Investment: FE

18. Wage Effects by Type of Investment: Matching

19. 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...)

20. 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

21. 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

22. Productivity and Wages: Results and Tests
MPFO/MPDO = WFO/WDO
General foreign effect: 8.9% > 6.5%
Acquisition effect: 12.4% > 7.9%

23. Further Productivity Evidence: “Catch-Up”
Why is the wage effect of FDI so large in Hungary?
Distance from the frontier and the transition
Divide period into early (<1999) and late (>1998)
Larger effects earlier
Divide 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.

24. Composition of Workforce I
Foreign effect for incumbent workers

25. 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

26. 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

27. Foreign Acquisitions and Wage Structure

28. Measurement I Hypothesis: Higher working hours at acquired firms
Monthly paid hours for
Tests:
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

29. 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)

30. Conclusions OLS: foreign wage premium is 36 percent
FE, GFE, matching premium is 9–17 percent
Divestment effect is 40-50% of acquisition effect
All worker types benefit; high educated the most
5% premium for incumbent workers, composition change in favor of young high-skilled
Results not driven by measurement error
Productivity best candidate for explaining the gap

31. 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 group
Treatment of selection bias is important

32. 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 year
Few ownership changes with enough pre- and post treatment observations
Most studies from developed countries exposed to FDI for a long time
Wage structure: mostly skilled-unskilled

33. Firm Characteristics by Ownership II

34. Tests of Covariate Balance

35. Foreign Wage Premium: OLS

36. Foreign Wage Premium: Alternative Specifications

37. Dynamics: OLS

38. Dynamics: FE

39. Dynamics: Matching and OLS

40. Dynamics: Matching and FE

41. Dynamics: GFE

42. Dynamics: Matching and GFE

43. 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

44. Productivity and Wage Levels

45. Relative Productivity and Wages