FDI and Wages: Evidence from LEED in Hungary

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

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 factormarkets, large FDI inflow • Supportive policy, taxabatements/subsidiesforforeignfirms • 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 • LEED for Hungary • Many 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 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 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

7. 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 time • Majority of workers linked within firm

8. 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 Sample Selection

9. Definition of Foreign Ownership andEarnings 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

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 + 1lnKj +2lnMj+3lnempj + δ1lnempjFOjt-1+ ΣλktINDkYEARt+ ujt lnwbillj = β0 + β1lnempj +δ2lnempjFOjt-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 1999-2005 • 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 WageLevels

45. RelativeProductivity and Wages