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Slavko Bezeredi & Ivica Urban Institute of Public Finance, Zagreb

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. Goals.

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Slavko Bezeredi & Ivica Urban Institute of Public Finance, Zagreb

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  1. Distribution of marginal effective tax rate in Croatia: do taxes and benefits prevent people from getting employed? SlavkoBezeredi & IvicaUrban Institute of Public Finance, Zagreb 2013EUROMODresearch workshop UniversityofLisbon, October 2013

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

  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

  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= marginaleffectivetax rate (METR) X,Y, T, B = household’s pre-fiscalincome, post-fiscalincome, taxesandbenefits

  5. Model pre-fiscalincome = Q’s grosswage + + othergrossincomes in Q’s household

  6. Model Notavailableinthedataset... working not-working „selection problem” – becausethe „not-working” are outofsample iworks idoesnot work

  7. Data • EU-SILC Croatia for 2010 • 6,403 householdswith 16,948 members • investigated: long-termunemployedandinactivepeopleaged 16to 65 • pensioners, students and unable to work are excluded from the analysis

  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(0working hours) • Inactive: 684 persons who declared themselves as “housewifes” or “other inactive” during the greater part of the year (0 working hours)

  9. Populationstructure 16 to 65 years

  10. Variables

  11. Probit regression (marginaleffects) Threemodels: (1) Notworking are unemployedandinactivetogether (2) unemployedonly (3)inactiveonly

  12. Wage regression

  13. METR - results Unemployed Inactive

  14. METR by groups

  15. Decompositionof METRfor peoplewith METR>50%

  16. Conclusion • distribution of METR for long-termunemployed 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% ofunemployedand 71% ofinactivehavelow METR (<30%) • veryhigh METR (>70%) for 3.2% • particularlyvunerablepersons: (a) with three and more children, (b) whose spouses are also inactive or unemployed, (c) withprimaryeducation • the results suggest that policies to make work pay should target thesemost vulnerable groups

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