1. Distribution of marginal effective tax rate in Croatia: do taxes and benefits prevent people from getting employed? Slavko Bezeredi & Ivica Urban Institute of Public Finance, Zagreb 2013 EUROMOD research workshop University of Lisbon, October 2013

2. Goals Do taxes and benefits prevent people from getting employed in Croatia? How high is the marginal effective tax rate (METR) for long-term unemployed and inactive people?...speculating (in our model) whether to remain out of work or to get employment...people from a micro-data sample 2

3. Problems (A) Calculate net household income, taxes and benefits, paid/received miCROmod – microsimulation model...uses new 2010 Croatian income survey (harmonised with EU-SILC) (B) Obtain gross wages for unemployed and inactive, because they are not available in the sample Wage regression – „selection problem” – tobit II model 3

4. Model A person Q is planning what to do in the next one-year period...calculates what would be her household’s income in two different hypothetical states: “0” remains unemployed or inactive “1” gets employed at full-time job M = marginal effective tax rate (METR) X, Y, T, B = household’s pre-fiscal income, post-fiscal income, taxes and benefits 4

5. Model 5 pre-fiscal income = Q’s gross wage + + other gross incomes in Q’s household

6. Model 6 Not available in the dataset... working not-working „selection problem” – because the „not-working” are out of sample i works i does not work

7. Data EU-SILC Croatia for 2010 6,403 households with 16,948 members investigated: long-term unemployed and inactive people aged 16 to 65 pensioners, students and unable to work are excluded from the analysis 7

8. Data Workers: 4.460 persons who worked more than 1000 hours during the year and reported a positive gross wage Unemployed: 1.616 persons who declared themselves as unemployed during the whole year (0 working hours) Inactive: 684 persons who declared themselves as “housewifes” or “other inactive” during the greater part of the year (0 working hours) 8

9. Population structure 9 GenderAgeEducation levelChildren ShareMF16-2526-5556-650-89-12>1201-2>2 Share in pop. 10049.750.320.258.121.821.964.213.971.024.43.6 Employed 43.946.241.524.059.121.518.847.865.136.364.055.2 Self-employed 4.76.52.90.76.14.64.24.65.83.86.96.4 Unemployed 15.816.015.718.317.110.017.916.49.815.814.521.5 Housewifes 5.00.19.80.55.86.815.22.50.54.55.59.7 Other inactive 0.60.70.50.80.50.81.10.50.20.70.50.2 Students 11.711.911.655.11.10.016.111.27.216.40.30.4 Unable to work 0.91.20.6 1.00.82.50.50.01.10.30.0 Pensioners 17.5 17.40.09.255.624.216.411.421.48.06.6 Total 100 Working 48.552.744.424.765.326.023.052.470.940.170.961.6 16 to 65 years

10. Variables 10 VariableDescription GenderFemale = 1; Male = 0 AgeAge in years Married1= married Divorced1= divorced Children 1Number of children aged 0-6 Children 2 Number of children aged 7-14 Children 3Number of children aged 15-18 Zagreb1 = lives in Zagreb PPDS1 = lives in PPDS Primary educ.1 = primary education Secondary educ.1 = secondary education Tertiary educ.1 = tertiary education Health problems1 = yes; 0 = no Other income Natural logarithm of household market income (excluding a person’s wage) per member divided by 100 Hourly wageNatural logarithm of hourly wage in HRK Activity 11 = Sections A, B and C Activity 21 = Sections D, E and F Activity 31 = Sections G, H and I Activity 41 = Sections J, K, L and M Activity 51 = Sections N through U Manager1 = Managerial position

11. Probit regression (marginal effects) 11 Working vs. unemployed & inactive Working vs. unemployed Working vs. inactive 1 234 Gender-0.149 *** -0.066 *** -0.110 *** Age0.056 *** 0.048 *** 0.010 *** Age^2/100-0.071 *** -0.059 *** -0.014 *** Married0.094 *** 0.106 *** -0.013 * Divorced 0.176 *** 0.144 *** 0.017 * Children 1 -0.047 *** -0.043 *** -0.015 *** Children 2 -0.037 *** -0.031 *** -0.011 *** Children 3 -0.0070.000-0.009 * Zagreb 0.081 *** 0.052 *** 0.028 *** PPDS -0.063 *** -0.049 *** -0.021 *** Secondary education 0.318 *** 0.219 *** 0.131 *** Tertiary education 0.351 *** 0.258 *** 0.070 *** Health problems -0.225 *** -0.222 *** -0.056 *** Other income0.0000.003 * -0.002 ** Number of Obs 674060565124 Wald chi2(13) 1000.91580.05676.3 Prob > chi2 000 Pseudo R-Squared 0.15940.10670.4364 Log Pseudolikelihood -932144.14-820416.68-270966.86 Three models: (1) Not working are unemployed and inactive together (2) unemployed only (3) inactive only

12. Wage regression 12 Wage equations Coefficients ACoefficients B Gender-0.147 *** -0.156 *** Age0.028 *** 0.032 *** Age^2/100-0.020 * -0.025 *** Zagreb 0.184 *** 0.189 *** PPDS -0.037 * -0.041 ** Secondary education 0.218 *** 0.245 *** Tertiary education 0.602 *** 0.641 *** Health problems -0.074-0.091 ** Activity 1-0.088 *** Activity 2-0.063 ** -0.062 ** Activity 3-0.114 *** Activity 40.108 *** Manager0.311 *** Heckman's lambda -0.052 Constant 2.657 *** 2.533 *** Number of Obs 4440 Wald chi2(13) 127.86137.66 Prob > chi2 00 Pseudo R-Squared 0.3471 Log Pseudolikelihood 0.450270.45023

13. METR - results 13 Unemployed Inactive

14. METR by groups 14 UnemployedInactive 000 peopleMean>70(%)000 peopleMean>70(%) All 417 33.23.2 155 29.73.2 Gender Men 207 33.74.0 10 31.11.1 Women 210 32.72.4 145 29.63.3 Age 16-25 90 31.86.2 7 40.015.2 26-55 266 33.62.7 102 30.63.8 56-65 61 33.81.0 46 26.30.0 Education Primary 108 37.910.5 101 29.94.9 Secondary 276 31.40.7 52 29.30.0 Tertiary 34 32.60.0 2 31.80.0 Children 0 217 30.72.1 47 27.00.0 1 or 2 152 34.21.7 82 27.80.7 3 or more 48 41.212.7 26 40.816.8 Marital status No spouse 175 30.83.0 17 29.30.8 Employed or pensioner 168 31.50.1 117 27.20.2 Inactive or unemployed 74 42.110.2 21 42.519.3

15. Decomposition of METR for people with METR>50% 15

16. Conclusion distribution of METR for long-term unemployed and inactive; for various subgroups for majority of unemployed and inactive people METR is relatively low, and should not be the factor detrimental to entering employment 55% of unemployed and 71% of inactive have low METR (<30%) very high METR (>70%) for 3.2% particularly vunerable persons: (a) with three and more children, (b) whose spouses are also inactive or unemployed, (c) with primary education the results suggest that policies to make work pay should target these most vulnerable groups 16