Foreign Ownership and the Distribution of Wages in Hungary, 1992-2000: An Unconditional Quantile Decomposition Approach

GÁBOR ANTALInstitute of Economics - HASCentral European University

SEBA – IE/CASS – IE/HAS ConferenceJune 30, 2011

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)

Introduction ••••

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 (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

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 •••••

• 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)

Sample

Data •••••

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)

Methodology •••••

Estimated Effects of FDI on Unconditional Quantiles of Wage Distribution

Results ••••••

0.1

5.3

.45

.6

0 .2 .4 .6 .8 1Quantile

1992

2000

Foreign

0.1

.2.3

.4

0 .2 .4 .6 .8 1Quantile

1992

2000

Foreign

Men Women

Results of Aggregate Decomposition - Men

-.2

0

.2

.4

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile

Total change Composition effect

Wage structure effect

Approximation error

Reweighting error

Results ••••••

Results of Detailed Decomposition - Men

-.3

-.2

-.1

0

.1

.2

.3

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile

Foreign Education

Experience

Occupation

Region

Industry

Other

Results ••••••

Composition Effects - Men

-.05

0

.05

.1

.15

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile

Foreign Education

Experience

Occupation

Region

Industry

Results ••••••

Wage Structure Effects - Men

-.15

0

.15

.3

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile

Foreign Education

Experience

Occupation

Region

Industry

Constant

Results ••••••

Contribution of FDI to Changes in Log Wage Differentials

90-10 90-50 50-10

Men Total Change 0.376 0.187 0.189 FDI Composition Effect 0.021 0.034 -0.013 FDI Wage Structure Effect -0.001 0.003 -0.004

Women Total Change 0.350 0.170 0.180 FDI Composition Effect 0.010 0.018 -0.008 FDI Wage Structure Effect 0.013 0.001 0.003

Results ••••••

Distribution of Foreign Ownership Share in 2000

0

10

20

30

40

50

60

Per

cent

of F

irms

0 20 40 60 80 100Foreign Ownership Share

•Only firms with positive foreign share:

Within-Firm Representation of Workers

0

2

4

6

8

10

Den

sity

0 .2 .4 .6 .8 1Share of workers observed

Mean = 0.255, Median = 0.085

All firms

0

5

10

15

Den

sity

0 .2 .4 .6 .8 1Share of workers observed

Mean = 0.132, Median = 0.074

Emp>20

0

5

10

15

Den

sity

0 .2 .4 .6 .8 1Share of workers observed

Mean = 0.088, Median = 0.070

Emp>100

0

10

20

30

40

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008Year

Foreign share in business sector employmentPercent of foreign firms in business sectorPercent of workers employed by foreign firms in LEEDPercent of foreign firms in LEED

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

Descriptives

1992 2000

Foreign Employment Share (%) 4.6 30.5

Domestic Foreign Domestic Foreign

Monthly Earnings 116.1 152.4 131.5 202.4 (71.1) (104.7) (134.7) (225.2) Female (%) 37.4 46.7 37.0 46.0 Education (%)

Elementary 32.9 33.8 23.6 19.6 Vocational 32.7 33.5 38.4 33.7 High school 27.1 23.5 29.9 32.7 University 7.3 9.2 8.2 14.0

Experience 22.1 20.4 23.1 19.3 (10.6) (10.5) (10.9) (10.9)

Descriptives – cont.Occupation (%)

Elementary Occupations 11.1 10.8 9.6 5.4 Skilled Manual Workers 48.3 58.2 50.5 53.3 Service Workers 9.2 5.0 10.9 7.2 Clerks 6.8 5.4 5.9 6.0 Associate Professionals 12.7 9.6 12.1 14.5 Professionals 6.2 6.9 2.9 6.2 Managers 5.7 4.0 8.2 7.3

Industry (%) Agriculture 18.3 0.2 12.3 0.6 Mining 0.2 0.0 0.3 0.0 Food&Beverages 6.2 11.1 6.5 7.6 Textile 5.4 12.3 6.8 9.8 Wood&Paper 2.6 2.6 3.1 2.5 Chemicals 4.8 3.7 2.7 9.5 Minerals&Water 5.3 4.9 6.7 7.5 Machines&Equipment 8.8 43.1 9.8 26.2 Utilities 3.0 0.0 2.8 5.1 Construction 6.1 8.8 6.3 1.8 Retail Trade 9.5 7.2 7.3 7.4 Wholesale Trade 4.0 4.0 4.0 5.2 F.I.R.E. 1.5 0.1 4.5 5.9 Business Services 2.6 1.1 4.9 3.6 Other Services 21.7 0.8 22.0 7.3

N 74,724 3,869 59,987 29,932

(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 •••••

• 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

RIF Regression I

Methodology •••••

• 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

RIF Regression II

Methodology •••••

•Decompose mean overall change in unconditional quantiles between end and base period:

• Aggregate decomposition with DFL (1996) reweighting

•Detailed decomposition with DFL (1996) reweighting

Unconditional Quantile Decomposition

Methodology •••••

Foreign Effects by Quantile in RIF Regressions Men

Women

1992 2000 1992 2000

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

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

0

10

20

30

40

50

Num

ber o

f for

eign

firm

s

0 20 40 60 80 100Average wage percentile

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

0

10

20

30

40

Num

ber o

f for

eign

firm

s

0 20 40 60 80 100VLOG percentile