Using microsimulation model to get things right: a wage equation for Poland Leszek Morawski, University of Warsaw Michał Myck, DIW - Berlin Anna Nicińska, University of Warsaw Project financially supported from the EFS and the Polish Ministry of Labor and Social Policy

Outline The wage equation and the selection problem. Polish individual wage level data and previous work on the wage equation. Using SIMPL in the analysis of wages. Data and results. Summary and conclusions.

Estimating the earnings/wage equation: Mincerian Approach: Marginal productivity regressed on education and potential experience: E[ln(Wage)]=Const+b 1 Schooling+b 2 Experience +b 3 Experience 2 The important problem of sample selection. Heckman selection corrected model as solution to the problem. (Heckman, 1979; Amemiya 1985, Duan et al., 1983)

Instrumental variables in the Heckman model Conditions for a good instrument: partially correlated with the decision whether to work uncorrelated with earnings from work Common instruments used in the Heckman model: demographic characteristics: number and age of children; income out of work: income from assets, house ownership, non-labor income from data, simulated non-labor income,

Why do we need a wage equation? Analysis of wage differentiation by individual characteristics: rate of return from education; analysis of gender wage discrimination. The expected wage rate predicted upon wage equation estimates is crucial for analysis of effects of financial incentives on labor market behavior: analyses of the replacement rates; analyses of tax wedge among not working persons; modeling labour supply.

Wage equations in Poland Individual-level wage data in Poland: Firm-level (Z-12): gross individual wages, few individual characteristics, no household characteristics; BAEL (Polish LFS): net wages, poor wage quality, little information on other household incomes; BBGD (Polish HBS) : net wages, good wage data quality, detailed information on household characteristics and incomes. So far studies on the wage equation in Poland: based on net not gross earnigs (BBGD, BAEL); exclude earings of those employed in small companies (Z-12); do not account for sample selection; for example: Keane & Prasad, 2002; Newell & Socha, 2005; Grajek, 2001; Hartog et al., 2004; Rutkowski, 1994

Using SIMPL to get things tight: SIMPL – a microsimulation model for Poland: run on the Houshold Budgets Survey (BBGD 2003 and 2005); net-gross converter to be able to run the model on gross earnings; generating non-labor incomes for families and households based upon Polish benefit system and other household members’ income; Instrumental variables used in the estimation: disposable family income if not working; disposable income of other families in a household (equivalised); control dummy variable for a multi-family household.

The sample and assumptions Data from the Polish Household Budgets Survey (BBGD) 2005 Selection criteria: age: 18-59; does not receive a disability pension; not retired; not self-employed; not a day-time student. Dependent variable in wage regressions - full-time earnings, part-time wages assumed to be half-time. outliers over the 99.5 centile threshold excluded.

Estimates: Three broad types of estimations: net monthly wages (two-step Heckman and linear estimates). gross monthly wages (two-step Heckman and linear estimates). gross monthly wages (two-step Heckman) for men and women separetely. Principal regressors: age, education dummies, disability dummies; dummies for number of children, presence of a child aged less than 7; married dummy, public or private sector; region dummies, town size dummies;

Results: gross wage Dependent variable: gross monthly wage Heckman two-stepOLS wage equationparticipationwage equation age **0.1836**0.1274** age squared ** ** ** married **0.7496**0.1109** child aged less than ** ** secondary academic **0.4555**0.3678** secondary technical **0.6743**0.3710** post-secondary professional **0.8928**0.4472** higher education **1.2944**0.7601** female ** ** ** other regressors yes family non-labor income ** household non-labor income ** multifamily household ** Number of observations Rho Lambda **

Results: gross wage Dependent variable: gross monthly wage Heckman two-stepOLS wage equationparticipationwage equation age **0.1836**0.1274** age squared ** ** ** married **0.7496**0.1109** child aged less than ** ** secondary academic **0.4555**0.3678** secondary technical **0.6743**0.3710** post-secondary professional **0.8928**0.4472** higher education **1.2944**0.7601** female ** ** ** other regressors yes family non-labor income ** household non-labor income ** multifamily household ** Number of observations Rho Lambda **

Results: gross wage Dependent variable: gross monthly wage Heckman two-stepOLS wage equationparticipationwage equation age **0.1836**0.1274** age squared ** ** ** married **0.7496**0.1109** child aged less than ** ** secondary academic **0.4555**0.3678** secondary technical **0.6743**0.3710** post-secondary professional **0.8928**0.4472** higher education **1.2944**0.7601** female ** ** ** other regressors yes family non-labor income ** household non-labor income ** multifamily household ** Number of observations Rho Lambda **

Results: gross wage Dependent variable: gross monthly wage Heckman two-stepOLS wage equationparticipationwage equation age **0.1836**0.1274** age squared ** ** ** married **0.7496**0.1109** child aged less than ** ** secondary academic **0.4555**0.3678** secondary technical **0.6743**0.3710** post-secondary professional **0.8928**0.4472** higher education **1.2944**0.7601** female ** ** ** other regressors yes family non-labor income ** household non-labor income ** multifamily household ** Number of observations Rho Lambda **

Results: gross wage, separately by gender Dependent variable: gross monthly wage wage equationparticipationwage equationparticipation female male higher education **1.4853**0.8364**0.8380** post-secondary professional **1.0301**0.4546**0.4817** secondary technical **0.8003**0.4484**0.4775** secondary academic **0.5741**0.3693**0.1687** age **0.1676**0.2236**0.3530** age squared ** ** ** married **0.5066**0.5045**1.2280** child aged less than * ** *0.1825** other regressors yes family disposable income if not working **-0,000047** household income ** ** multifamily household * * Rho Lambda0.3397** ** Number of observations

Results: gross wage, separately by gender Dependent variable: gross monthly wage wage equationparticipationwage equationparticipation female male higher education **1.4853**0.8364**0.8380** post-secondary professional **1.0301**0.4546**0.4817** secondary technical **0.8003**0.4484**0.4775** secondary academic **0.5741**0.3693**0.1687** age **0.1676**0.2236**0.3530** age squared ** ** ** married **0.5066**0.5045**1.2280** child aged less than * ** *0.1825** other regressors yes family disposable income if not working **-0,000047** household income ** ** multifamily household * * Rho0.657**0.850** Lambda0.3397**0.4907** Number of observations

Results: gross wage, separately by gender Dependent variable: gross monthly wage wage equationparticipationwage equationparticipation female male higher education **1.4853**0.8364**0.8380** post-secondary professional **1.0301**0.4546**0.4817** secondary technical **0.8003**0.4484**0.4775** secondary academic **0.5741**0.3693**0.1687** age **0.1676**0.2236**0.3530** age squared ** ** ** married **0.5066**0.5045**1.2280** child aged less than * ** *0.1825** other regressors yes family disposable income if not working **-0,000047** household income ** ** multifamily household * * rho0.657**0.850** Lambda0.3397**0.4907** Number of observations

Results: gross wage, separately by gender Dependent variable: gross monthly wage wage equationparticipationwage equationparticipation female male higher education **1.4853**0.8364**0.8380** post-secondary professional **1.0301**0.4546**0.4817** secondary technical **0.8003**0.4484**0.4775** secondary academic **0.5741**0.3693**0.1687** age **0.1676**0.2236**0.3530** age squared ** ** ** married **0.5066**0.5045**1.2280** child aged less than * ** *0.1825** other regressors yes family disposable income if not working **-0,000047** household income ** ** multifamily household * * rho Lambda0.3397**0.4907** Number of observations

Summary of results: Net-gross wages - relatively small differences between estimates (wages in logs) on female dummy: Heckman model: in net wages vs in gross; linear model:-0.23 in net wages vs in gross; on higher education: Heckman model: 0.91 in net wages vs 1.02 in gross; linear model: 0.69 in net linear vs 0.76 in gross. High underestmated impact of a number of variables in linear regression in comparison to Heckman estimates: For gross wages: on higher education: 0.76 (linear) vs 1.02 (Heckman); on post-sec. professional education: 0.45 vs 0.65 on secondary technical education: 0.37 vs 0.52 on being married: 0.11 vs 0.23; on gender: vs -0.36; on severe disability: vs

Conclusions Small differences between estimates for net and gross wages since models estimated in logs. Significant differences between estimates for the OLS and the selection corrected model. Statistically significant estimates on instrumental variables in participation equation. Selection clearly important. Significant differences in the returns to various characteristics by gender.