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

Introduction • •••

Motivation I

• • • 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 ≈40,000 business units ▫ Spanning 1986-2008  Long spells for diff-in-diff analysis and matching

Introduction •• ••

Motivation II

• 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?

Introduction ••• •

Research Question

• 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)

Introduction ••••

Contribution

• • • 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)

Data • ••••

Employee Information

•

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 (5 th workers, also 25 th and 15 th for nonproduction) for production ▫ All workers in small firms (<20 employees in 1996-2001, <50 since 2002) ▫ Earnings, gender, age, education, occupation, date of hiring, location of plant

Data •• •••

Employer Information

•

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

Data ••• ••

Key Variables: Wages and Ownership

• • 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

Data •••• •

Weighting and Longitudinal Links

• • • 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

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)

Methodology • ••••

Estimation Method

• 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)

Results • ••••• Estimated Effects of FDI on Unconditional Quantiles of Wage Distribution

Men

Foreign

Women

Foreign 0 .2

.4

Quantile .6

.8

1992 2000 1 0 .2

.4

Quantile .6

.8

1992 2000 1

Results •• •••• Results of Aggregate Decomposition - Men .4

.2

0 -.2

0 .2

Total change .4

Quantile .6

Composition effect Wage structure effect .8

Approximation error Reweighting error 1

Results ••• ••• Results of Detailed Decomposition - Men .3

.2

.1

0 -.1

-.2

-.3

0 .2

Foreign .4

Quantile .6

Education Experience Occupation Region .8

Industry Other 1

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

.1

.05

0 -.05

0 .2

Foreign .4

Quantile .6

Education Experience Occupation Region .8

Industry 1

Results ••••• • Wage Structure Effects - Men .3

.15

0 -.15

0 .2

Foreign .4

Quantile .6

Education Experience Occupation Region .8

Industry Constant 1

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

Men

Total Change FDI Composition Effect FDI Wage Structure Effect

Women

Total Change FDI Composition Effect FDI Wage Structure Effect

90-10

0.376 0.021 -0.001 0.350 0.010 0.013

90-50

0.187 0.034 0.003 0.170 0.018 0.001

50-10

0.189 -0.013 -0.004 0.180 -0.008 0.003

Distribution of Foreign Ownership Share in 2000 • Only firms with positive foreign share: 60 50 40 30 20 10 0 0 20 40 60 Foreign Ownership Share 80 100

2 6 4 8 10 Within-Firm Representation of Workers All firms 15 Emp>20 10 Mean = 0.255, Median = 0.085

Mean = 0.132, Median = 0.074

5 0 0 .2

.4

.6

Share of workers observed 15 .8

1 Emp>100 0 0 .2

.4

.6

Share of workers observed .8

1 10 5 0 0 Mean = 0.088, Median = 0.070

1 .2

.4

.6

Share of workers observed .8

Foreign Penetration in Sample and in Business Sector • Only firms with more than 20 employees 40 30 20 10 0 1986 1988 1990 1992 1994 1996 Year 1998 2000 2002 Foreign share in business sector employment Percent of foreign firms in business sector Percent of workers employed by foreign firms in LEED Percent of foreign firms in LEED 2004 2006 2008

Descriptives Foreign Employment Share (%) Monthly Earnings Female (%) Education (%)

Elementary Vocational High school University

Experience Domestic 116.1 (71.1) 37.4 32.9 32.7 27.1 7.3 22.1 (10.6)

1992

4.6 Foreign 152.4 (104.7) 46.7 33.8 33.5 23.5 9.2 20.4 (10.5)

2000

Domestic 131.5 (134.7) 37.0 23.6 38.4 29.9 8.2 23.1 (10.9) 30.5 Foreign 202.4 (225.2) 46.0 19.6 33.7 32.7 14.0 19.3 (10.9)

Descriptives – cont.

Occupation (%)

Elementary Occupations Skilled Manual Workers Service Workers Clerks Associate Professionals Professionals Managers

Industry (%)

Agriculture Mining Food&Beverages Textile Wood&Paper Chemicals Minerals&Water Machines&Equipment Utilities Construction Retail Trade Wholesale Trade F.I.R.E. Business Services Other Services

N 11.1 48.3 9.2 6.8 12.7 6.2 5.7 18.3 0.2 6.2 5.4 2.6 4.8 5.3 8.8 3.0 6.1 9.5 4.0 1.5 2.6 21.7 74,724 10.8 58.2 5.0 5.4 9.6 6.9 4.0 0.2 0.0 11.1 12.3 2.6 3.7 4.9 43.1 0.0 8.8 7.2 4.0 0.1 1.1 0.8 3,869 9.6 50.5 10.9 5.9 12.1 2.9 8.2 12.3 0.3 6.5 6.8 3.1 2.7 6.7 9.8 2.8 6.3 7.3 4.0 4.5 4.9 22.0 59,987 5.4 53.3 7.2 6.0 14.5 6.2 7.3 9.5 7.5 26.2 5.1 1.8 7.4 5.2 0.6 0.0 7.6 9.8 2.5 5.9 3.6 7.3 29,932

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

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

Methodology •••• • RIF Regression II • Functional form assumption: • For the τ th 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

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

Foreign Effects by Quantile in RIF Regressions

Men Women

1 st Decile 2 nd Decile 3 rd Decile 4 th Decile Median 6 th Decile 7 th Decile 8 th Decile 9 th Decile N 1992 0.152** (0.021) 0.190** (0.023) 0.204** (0.028) 0.229** (0.035) 0.262** (0.041) 0.292** (0.044) 0.347** (0.051) 0.386** (0.058) 0.424** (0.057) 44,072 2000 0.366** (0.033) 0.326** (0.022) 0.313** (0.018) 0.307** (0.018) 0.310** (0.020) 0.331** (0.025) 0.349** (0.029) 0.364** (0.032) 0.451** (0.045) 50,495 1992 0.188** (0.021) 0.250** (0.029) 0.287** (0.036) 0.312** (0.046) 0.304** (0.045) 0.277** (0.047) 0.255** (0.036) 0.246** (0.032) 0.242** (0.035) 31,887 2000 0.297** (0.030) 0.338** (0.028) 0.336** (0.026) 0.303** (0.022) 0.271** (0.020) 0.264** (0.023) 0.244** (0.027) 0.261** (0.033) 0.331** (0.039) 37,235

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

50 40 30 20 10 0 0 20 40 60 Average wage percentile 80 100

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

40 30 20 10 0 0 20 40 60 VLOG percentile 80 100