Using microsimulation model to get things right: a wage equation for Poland

1. 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

2. 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.

3. Estimating the earnings/wage equation: • Mincerian Approach: Marginal productivity regressed on education and potential experience:E[ln(Wage)]=Const+b1Schooling+b2Experience +b3Experience2 • The important problem of sample selection. • Heckman selection corrected model as solution to the problem.(Heckman, 1979; Amemiya 1985, Duan et al., 1983)

4. 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,

5. 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 wedgeamong not working persons; • modeling labour supply.

6. 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

7. 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.

8. 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.

9. 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;

10. Results: gross wage

11. Results: gross wage

12. Results: gross wage

13. Results: gross wage

14. Results: gross wage, separately by gender

15. Results: gross wage, separately by gender

16. Results: gross wage, separately by gender

17. Results: gross wage, separately by gender

18. Summary of results: • Net-gross wages - relatively small differences between estimates (wages in logs) • on female dummy: • Heckman model: -0.33 in net wages vs -0.36 in gross; • linear model: -0.23 in net wages vs -0.25 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 0.76in gross. • High underestmated impact of a number of variablesin linear regressionin 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: -0.25 vs -0.36; • on severe disability: -0.44 vs -0.60.

19. 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.