GENDER AND RACE IN THE LABOR MARKET

Transcription

1 GENDER AND RACE IN THE ABOR MARKET

2 GENDER AND RACE IN THE ABOR MARKET Sponsored by a Grant TÁMOP /2/A/KMR Course Material Developed by Department of Economics, Faculty of Social Sciences, Eötvös oránd University Budapest (ETE) Department of Economics, Eötvös oránd University Budapest Institute of Economics, Hungarian Academy of Sciences Balassi Kiadó, Budapest

3

4 ETE Faculty of Social Sciences, Department of Economics GENDER AND RACE IN THE ABOR MARKET Author: Anna ovász Supervised by Anna ovász June 2011

5 GENDER AND RACE IN THE ABOR MARKET Week 5 Measuring discrimination II: other methods using databases Anna ovász

6 iterature for next week Bertrand Mullainathan 2004 Goldin Rouse 2000 Further recommended reading: Heckman 1998

7 Estimation of group-level relative productivity from production functions Unexplained wage gap from wage equations discrimination, since there may be unobserved group-level differences in productivity. How can we take these into account? Firms differ in output (revenue) and in the demographic composition of their workforces. Over time, a given firm s workforce composition and output varies Can use to estimate the effect of the ratio of different worker groups on output/productivity

8 Goal Estimate the relative productivities and wages of various worker groups (gender, age, education) MP n / MP 0 w n / w 0 wage discrimination (or efficiency wages, compensating wage differentials) Research questions: Do differences in the relative productivity of various worker groups explain their wage differentials? (For example, the gender wage gap) Kertesi Köllő (2002): the wage and productivity of young skilled workers increased relative to unskilled worker in Hungary. Do firms set relative wages closer to relative productivities since the transition (more efficient wage setting)? Increased competition decreased the gender wage gap (Becker)

9 Benefits of the methodology Mincerian wage equations (residual wage gap): estimate of discrimination is consistent if: We can measure all differences in group-level productivity. Production function augmented with workforce composition relative productivity of worker groups This relative productivity estimate includes the effect of unobservable and observable differences in productive characteristics Makes it possible to take systematic differences in grouplevel productivity into account iterature: Hellerstein Neumark (1999), Dostie (2006), Van Biesebroek (2007), Hellerstein Neumark (2005), Zhang és Dong (2009), ovász Rigó (2009)

10 Main steps Step 1: Estimation of production function augmented with workforce composition relative productivities Step 2: Estimation of firm-level wage equation relative wages Step 3: Test: Relative productivity = relative wage?

11 ln Y Methodology: production function (Hellerstein Neumark 1999) lny = α lnk + β lnm + γ lnq Workers are perfect substitutes: Q N n0 φ 0 : productivity of reference group Estimated equation: n n 0 0 N n1 01 n Can easily calculate relative productivities based on NS estimates: n n N n1 0 N n K M 0 ln ln ln0 ln ln 1 1 n1 0 n 1 n Z u φ n / φ 0 = MP n /MP 0 Z: industry, year, ownership, (firm fixed effects)

12 Estimation strategy Worker groups: Gender: male (G), female (F) Age: <40, 40< Education: elementary or secondary school (E), higher education (U) 8 worker groups (interactions) 7 relative productivity parameters ln Y N n K M 0 ln ln ln0 ln ln 1 1 n1 0 n Z u Reference group: male, below 40, no diploma

13 Estimation strategy Q simplification: 1. Constant relative productivity: For example, gender difference is the same within each age group Traditional wage equation estimation also assumes this if there are no interaction terms The number of relative productivity parameters decreases to 3 2. Equiproportional assumption: For example, the ratio of women is the same within each age group Number of parameters: 3 Ratios of worker groups are estimated for larger groups

14 Estimation strategy Equation with constraints : ln Y 0 ln1 ln1 ln F 1 ln 1 1 F K U U 1 Z u ln Most studies use both constraints (e.g. Hellerstein Neumark 1999 and 2004; Hellerstein Neumark Troske 1999, 1999; Van Biesebroeck, 2007; Dostie, 2006) M ln O O

15 Production function problems Differences over time or between industries (structural): Divide sample: into time periods by industries Measurement of labor inputs (Q) Determining worker groups (which characteristics, how many categories) Measurement error: we estimate the firm-level ratio of worker groups from the sample of workers in the dataset Unobserved productivity shocks Firm fixed effects evinsohn and Petrin (2003) method

16 Methodology: firm-level wage equation (Hellerstein Neumark 1999) lnw Aggregation of individual wage equations Dependent variable: weighted sum of worker wages, OR firm-level wage bill a w N n a K b M c w c c 0 ln ln ln 0 ln ln 1 1 n1 w0 n d Z u Benefits of firm-level estimation: Simultaneous estimation of production and wage equations Straightforward hypothesis testing Two firm-level variables All wage-related costs

17 inear estimation NS (Stata:nlsur) is slow and difficult to implement, so usually estimate linear approximation As long as approximation is: ln Y 0 ln K ln F, the Estimated equations (Stata: sureg): lnw 0 ln K ln 1 ln F F F 1 F F 1 F F F O O O O F F F U U Z Z u u

18 Data Hungarian Wage and Employment Survey 1986, 1989, Matched employer-employee dataset: worker variables (wage, education, gender, age, occupation) and firm variables (revenue, size, ownership, industry, capital, material and wage costs) All firms with at least 20 employees, sample of smaller firms 6.5% of blue collar workers, 10% of white collar workers sampled on average Panel in terms of firms, not workers

19 Data sample restrictions Only firms with at least 50 employees Only those with at least 5% of their workers included in the sample 47,928 firm-years 1,245,577 worker-years 15,804 firms 10,155 with at least 10 workers 5,624 with at least 20 workers

20 ln Y 0 Data variables ln K Ratio of worker groups within each firm, each year: from worker-level dataset Y (output): value added (VA) W (wage): firm s wage bill K (capital) Z controls ln F F O O F U Z u

21 Results women Female-male productivity wage 4,0 Gap 3,0 2,0 1,0 0,0-1,0 1986, ,0-3,0

22 Results by skill level Diploma - no diploma productivity 8,0 6,0 wage Gap 4,0 2,0 0,0-2,0 1986, ,0-6,0

23 Results by age Above 40 - below 40 productivity wage 1,4 Gap 1,2 1,0 0,8 0,6 0,4 0,2 0,0-0,2 1986,

24 Summary The female-male wage productivity-wage gap decreased after the transition. Women are paid in line with their productivity no evidence of discrimination. Highly skilled have a negative gap: they are underpaid. Workers above 40 are overpaid Productivity decreased compared to younger workers significantly following the transition: skill obsolence

25 Results: old and new firms Female-male wage-productivity gap, FE 0,5 0,4 0,3 0,2 0,1 0,0-0,1-0, pooled old new Female-male estimates, pooled sample, FE Female-male estimates, old firms, FE 1,2 1,1 1,0 0,9 0,8 0,7 0, rel wage rel prod 1,2 1,1 1,0 0,9 0,8 0,7 0, rel wage rel prod

26 Results: old and new firms Degree - no degree, wage-productivity gap, FE 0,0-0,2-0,4-0,6-0,8-1,0-1, pooled old new 2,0 1,8 1,6 1,4 1,2 1,0 0,8 Degree-no degree estimates, pooled sample, FE rel wage rel prod 2,0 1,8 1,6 1,4 1,2 1,0 0,8 Degree-no degree estimates, old firms, FE rel wage rel prod

27 Results: old and new firms Above 40 - below 40, wage-productivity gap, FE 0,3 0,2 0,1 0,0-0,1-0,2-0, pooled old new 1,2 Above 40-below 40 estimates, pooled sample, FE rel wage 1,2 Above 40-below 40 estimates, old firms, FE rel wage 1,1 rel prod 1,1 rel prod 1,0 1,0 0,9 0,9 0, ,

28 Indirect tests Use the implications of discrimination models to test for the presence of discrimination For example: the relationship between the ratio of minority workers and profit: (taste-based) discriminating employers are not profit-maximizing. Hellerstein Neumark Troske (1995): negative significant relationship between profits and the ratio of female workers

29 The effect of competition on discrimination ovász 2009 The log female-male wage gap decreased from 0.31 to 0.18 following the transition: The change is mostly unexplained (Campos és Joliffe 2004) Were discriminating employers forced out of the market due to increased competition? If yes: empirical evidence of discrimination against women Becker (1957): an increase in product market competition will decrease discrimination in the long-run Empirical testing opportunity: Rapid liberalization of Hungarian markets: sudden, large change in the level of competition arge, representative matched employer-employee database, long time period:

30 Statistics Relative Wage of Women Year Relative Wage Source: CSO

31 Gender wage gap in Hungary, Source: WES database

32 Empirical strategy Step 1: estimation of unexplained wage gaps: For every firm j and year t: lnw i = α t + β t X i + δ FE it + ε i X ij = worker characteristics (education, experience, occupation) FE i = female dummy δ = firm-level residual wage gap = upper-bound estimate of discrimination Step 2: testing the effect of competition: δ = α t + β 1 CM kt + β 2 N t + ε CM kt : competition measures in industry k and year t N t : controls (year, region, industry fixed effects) Becker s implication: β 1 < 0

33 Competition measures Concentration ratio (1-HHI: for ease of evaluation) 3 digit industries, based on Tax Authority revenue data 0=monopoly, 1=perfect competition Export share (export revenue/revenue) 3 digit industries, based on Tax Authority revenue and export data 0=no export, 1=all export Import penetration ratio (import/revenue+importexport) 3 digit industries, based on Tax Authority revenue, Customs import data 0=no import, 1=all import Price Cost Margin (profit/revenue) 3 digit industries, based on Tax Authority revenue data All increase as competition increases

34 Empirical issues Collective agreements decrease wage discrimination Subsamples based on presence of agreement 2 step estimation: the wage gap Weighting the second step based on the standard errors from the first step Unobservable industry characteristics Industry fixed effects: estimate the effect within industries of changes over time Selection bias: exit of low-skilled women Worker controls, subsamples by skill level Identification: is there sufficient variation within industries?

35 0 HHI in Changes in competition over time Changes in Industry Concentration Ratios HHI in 1989

36 Changes in competition over time Changes in Industry Export Shares Export share in 1989

37 Data Hungarian WES: 1986, 1989, Matched employer-employee data Panel in firms, not workers Worker characteristics: gender, age, education, occupation, potential experience, workplace Firm characteristics: size, industry, region, ownership Sample: Firms with at least 20 employees At least 2 men and 2 women in the sample (for FE) Private sector only

38 Results: δ = αt + β1cmkt + β2nt + ε 1-HHI Import penetration Export share All industries Manufacturing ** (0.018) 0.094** (0.036) (0.041) ** (0.025) (0.032) ** (0.043) * (0.054) 0.129** (0.027) ** (0.048) * (0.056) (0.032) ** (0.048) Year dummies Y Y Y Y Industry FE N Y N Y Weighted Y Y Y Y Number of observations R squared

39 Results: δ = αt + β1cmkt + β2nt + ε All industries Manufacturing Price Cost Margin ** (0.051) ** (0.035) ** (0.075) ** (0.031) Import penetration (0.034) (0.036) (0.091) (0.063) Export share (0.032) (0.045) * (0.026) (0.046) Year dummies Y Y Y Y Industry FE N Y N Y Weighted Y Y Y Y Number of obs R squared

40 Results: by presence of collective agreements 1-HHI Import penetration Collective Wage Agreement No Collective Wage Agreement * (0.022) (0.053) (0.063) (0.042) ** (0.024) (0.057) (0.054) (0.053) Export share (0.072) (0.091) ** (0.049) (0.082) Year dummies Y Y Y Y Industry FE N Y N Y Weighted Y Y Y Y Number of obs R squared

41 Results: by skill level High skilled Medium and low skilled 1-HHI Import penetration (0.036) (0.157) (0.037) (0.051) ** (0.033) 0.386** (0.073) * (0.043) (0.035) Export share (0.209) (0.056) ** (0.069) (0.054) Year dummies Y Y Y Y Industry FE N Y N Y Weighted Y Y Y Y Number of obs R squared

42 Summary The results support Becker s implication that an increase in competition decreases the unexplained wage gap. How big is the effect? The observed changes in competition levels can explain roughly 26% of the decrease in the gender wage gap. Import results contradictory?

43 Meta-analysis: the effect of competition International comparison: relationship between the gender wage gap and the legal/economic environment Weichselbaumer & Winter- Ebmer Method: meta-analysis: Dependent variable: wage gap estimates from international studies Explanatory variables: competition (Economic freedom index), legislature (equal treatment laws) Results: Competition decreases the wage gap Equal treatment laws do so as well