GÁBOR ANTAL Institute of Economics - HAS Central European University

1. Foreign Ownership and the Distribution of Wages in Hungary, 1992-2000: An Unconditional Quantile Decomposition Approach SEBA – IE/CASS – IE/HAS Conference June 30, 2011 GÁBOR ANTAL Institute of Economics - HAS Central European University

2. Introduction •••• Transition provides fruitful setting to investigate changes in wage distribution Wage determination became decentralized within a couple of years Changes affecting both supply and demand side of labor market Hungary displayed largest level of earnings inequality before transition (Rutkowski 1996) AND largest growth in earnings inequality between 1994 and 2005 (OECD 2007) Special data source Firm-level data on ≈400,000 business units Linked employer-employee dataset of 2.9 million worker-year observations on workers employed by≈40,000business units Spanning 1986-2008 Long spells for diff-in-diff analysis and matching Motivation I

3. Introduction •••• Largest volume of FDI in region during nineties (OECD 2000) remaining high later More ownership switches for identification than any other study in literature Foreign owners may differ more from domestic ones than in developed economies Only FDI’s effect on conditional average wages analyzed Unconditional wages? Differences across the distribution? Motivation II

4. Introduction •••• What would have happened to the unconditional wage distribution (wage inequality) in 2000, had the share of foreign employment remained at its 1992 level? Is FDI’s effect the same across the distribution? Is it rather a composition effect or a wage structure effect? Literature context Effect of (de)unionization on wage inequality in the US DiNardo et al. (1996), DiNardo and Lemieux (1997), Firpo et al. (2007, 2008) Wage arrears and wage inequality in Russia Lehmann and Wadsworth (2007) Research Question

5. Introduction •••• No study yet to explicitly analyze FDI’s effects on unconditional wage distribution Application of a newly developed decomposition method in this context Typical paper in literature on FDI and wages: FDI’s effect on conditional mean wages Firm-level: Conyon et al. (2002), Lipsey and Sjöholm (2004), Feliciano and Lipsey (2006), Girma and Görg (2007), Brown et al. (2010) LEED: Martins (2004), Almeida (2007), Heyman et al. (2007), Huttunen (2007), Earle and Telegdy (2008) Some analysis of effect on wage structure in a few studies Huttunen (2007), Almeida (2007), Eriksson and Pytliková (2011), Heyman et al. (2011) Contribution

6. Data ••••• Hungarian Wage Survey Conducted in 1986, 1989, and then yearly 1992-2008 Includes all firms with >20 employees plus random sample of small (11-20 employees in 1996-99, 5-20 in 2000-08) Workers sampled randomly based on birth date in medium and large firms (5th and 15th for production workers, also 25th for nonproduction) All workers in small firms (<20 employees in 1996-2001, <50 since 2002) Earnings, gender, age, education, occupation, date of hiring, location of plant Employee Information

7. Data ••••• Hungarian Tax Authority Data 1992-2008: All legal entities using double-entry bookkeeping Total employment in data ≈ All business sector employees in Hungary 1986-1992: Sample of firms from HWS Balance sheet and income statement items, employment, legal form, industry, county of HQ LEED: HWS and HTA data linked through firm identifier Employer Information

8. Data ••••• Monthly gross earnings As reported by the employer (contrast with HH surveys, e.g. CPS) Monthly base salary + Overtime pay + Regular bonuses and premia, commissions, allowances… + Tenure-proportional extraordinary bonuses based on previous year’s records Foreign ownership status If >50% share of total equity Large number of ownership switches Can distinguish types of ownership histories Key Variables: Wages and Ownership

9. Data ••••• Three set of weights Worker weights within firm to account for different sampling schemes of BC and WC workers Firm weights in LEED to weight up to business sector employment Firm weights in HTA data to account for differences in firms size and for pre-1992 sample size Firms are linked over time ≈50% of workers linked within firm based on birth date and other individual characteristics Weighting and Longitudinal Links

10. Data ••••• Sample Selected from LEED; years 1986, 1989, 1992-2008 (current focus: 1992-2000) For-profit firms in business sector with more than 20 employees with not more than 2 ownership switches in industries with any foreign presence Full-time workers aged 15-74 25,031 companies (16,790 in 1992-2000) 2,498,412 worker-years (797,250 in 1992-2000)

11. Methodology ••••• Detailed decomposition of unconditional wage changes by quantile, based on recentered influence functions (RIF) RIF: Measures the effect of a perturbation in a distribution on some distributional statistic (Hampel 1974) Key idea: Effect of changes in distribution of covariates on wage distribution captured by RIF regression (Firpo et al. 2009) A decomposition analogous to O-B decomposition of changes in mean can be performed with help of RIF regressions (Firpo et al. 2007) Estimation Method

12. Results •••••• Estimated Effects of FDI on Unconditional Quantiles of Wage Distribution Men Women

13. Results •••••• Results of Aggregate Decomposition - Men

14. Results •••••• Results of Detailed Decomposition - Men

15. Results •••••• Composition Effects - Men

16. Results •••••• Wage Structure Effects - Men

17. Results •••••• Contribution of FDI to Changes in Log Wage Differentials

18. Distribution of Foreign Ownership Share in 2000 • Only firms with positive foreign share:

19. Within-Firm Representation of Workers

20. Foreign Penetration in Sample and in Business Sector • Only firms with more than 20 employees

21. Descriptives

22. Descriptives – cont.

23. Methodology ••••• (Recentered) Influence Functions • Consider a perturbation in wage distribution : • Then IF and RIF of the distributional statistic : • If “moves” towards : • Change in given by: • where

24. Methodology ••••• RIF Regression I • Consider the (unconditional) wage distributions as: • where is a vector of covariates distributed as • Then the IIF becomes: • Ceteris paribus effect of location shift in distr. of covariate , so that is given by

25. Methodology ••••• RIF Regression II • Functional form assumption: • For the τth quantile, the estimated RIF is equal to • where is the sample quantile and is a kernel density estimate • and the data generating process in year is given by

26. Methodology ••••• Unconditional Quantile Decomposition • Decompose mean overall change in unconditional quantiles between end and base period: • Aggregate decomposition with DFL (1996) reweighting • Detailed decomposition with DFL (1996) reweighting

27. Foreign Effects by Quantile in RIF Regressions

28. Foreign Presence by Quantiles of Firm-Level Average Wages (2005)

29. Foreign Presence by Quantiles of Within-Firm Variances of Log Wages (2005)