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

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 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

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 (5 th and 15 th for production workers, also 25 th 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

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

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

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

Composition of Workforce by Ownership

Firm Characteristics by Ownership I

Estimation lnw ijt =  + X it β + δFOREIGN jt-1 + Σγ j REGION j + Σλ t YEAR t + u ijt i = workers j = firms t = time

Specifications I Controls (X it ): (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 (u ijt ):  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

Wage Effects by Type of Investment: FE

Wage Effects by Type of Investment: Matching

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 lnoutput j =  0 +  1 lnK j +  2 lnM j +  3 lnemp j + δ 1 lnemp j FO jt-1 + Σ λ k t IND k YEAR t + u jt lnwbill j = β 0 + β 1 lnemp j +δ 2 lnemp j FO jt-1 + Σλ k t IND k YEAR t + v jt Hypothesis: MP FO /MP DO = W FO /W DO that is: (  3 + δ 1 )/  3 = (β 1 + δ 2 )/ β 1

Productivity and Wages: Results and Tests MP FO /MP DO = W FO /W DO  General foreign effect:8.9% > 6.5%  Acquisition effect:12.4% > 7.9%

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 ( 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.

Composition of Workforce I Foreign effect for incumbent workers

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

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

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 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

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

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

Firm Characteristics by Ownership II

Tests of Covariate Balance

Foreign Wage Premium: OLS

Foreign Wage Premium: Alternative Specifications

Dynamics: OLS

Dynamics: FE

Dynamics: Matching and OLS

Dynamics: Matching and FE

Dynamics: GFE

Dynamics: Matching and GFE

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

Relative Productivity and Wages