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 • •••
• 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 •• ••
• 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 ••• •
• 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 ••••
• 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 • ••••
• 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 and 15 th for production workers, also 25 th 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

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

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

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

Data •••••
• 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 • ••••
• 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
.1
0
-.1
-.2
.3
.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
90-10
Men
Total Change
FDI Composition Effect
0.376
0.021
FDI Wage Structure Effect -0.001
Women
Total Change 0.350
FDI Composition Effect 0.010
FDI Wage Structure Effect 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 1998 2000 2002 2004 2006 2008
Year
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

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
30.5
Foreign
131.5
(134.7)
37.0
23.6
38.4
29.9
8.2
23.1
(10.9)
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
4.8
5.3
8.8
3.0
6.1
9.5
4.0
18.3
0.2
6.2
5.4
2.6
1.5
2.6
21.7
74,724
11.1
48.3
9.2
6.8
12.7
6.2
5.7
3.7
4.9
43.1
0.0
8.8
7.2
4.0
0.2
0.0
11.1
12.3
2.6
0.1
1.1
0.8
3,869
10.8
58.2
5.0
5.4
9.6
6.9
4.0
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
9.6
50.5
10.9
5.9
12.1
2.9
8.2
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
5.4
53.3
7.2
6.0
14.5
6.2
7.3

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

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

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