Making work pay: evidence from Serbia S. Randjelović, M. Vladisavljević, S. Vujić, & J. Žarković-Rakić EUROMOD 2nd Research Workshop Bucharest, October 2012

Outline Motivation Impact of tax & benefit systems on work decisions In-work benefits (IWB): intro Aim of the paper Policy design: WTC in Serbia Methodology Results Conclusions

Motivation High inactivity rate of 40.3% High informal employment rate of 17.8% (in 2007) Inactivity rates particularly high among: – low-educated individuals – those with low skills – women Inactivity rates by educational level and gender, 2011 Level of education MenWomen Primary Secondary Tertiary

Motivation These groups have low earnings capacity → financial payoffs from staying in or seeking employment are often limited Incentive problems are aggravated by high tax burdens on labour income and by social benefits design Those taking up low-paid employment see that large part of their gross earnings is consumed by income taxes, social contributions or reduced social benefits => Need incentives to make (formal) work pay

Tax Wedge & Progressivity in Serbia

In-Work Benefits - Purpose In-work benefits (or making work pay policies) = Means-tested transfers given to individuals conditional on their employment status, designed to: 1)Create a significant gap between the incomes of people in and out of work 2)Encourage entry into the labour market 3)Ensure higher living standard and help reduce poverty of low-income people 4)Encourage formality Literature: – Figari (2010) – Figari (2011) – Bargain & Orsini (2006) – Orsini (2005)

WFTC in Serbia – Policy Design (1) British Working Family Tax Credit (WFTC) as a role model – Why? Effectiveness and comparability with results of studies for other Med. countries – Fiscal effect=0.34% of GDP – ignoring special elements (disability, 50+ years, child care, etc.) Three family based and one individual WTC scheme: – Family WFTC 1 – single person working full time – Family WFTC 2 – lone parents and couples working part time (16/30 hrs) – Family WFTC 3 – lone parents and couples working full time – Individual WTC – individual working at least 16 hrs

WFTC in Serbia – Policy Design (2)

Aim of the Paper The most of empirical studies on IWB evaluation focus on developed countries –...lack of such studies for transition economies (effects might not be the same since it depends on the structural features of economy) Aims of the paper: – Contribute to empirical literature on labor supply and redistribution effects of IWB in transition economy Simulating the effects of introduction of British WTC scheme in Serbia The first analysis of that kind in Serbia – Analyze the performances of IWB in transition compared to developed economies with the similar labor market structure

Methodology Steps of the estimation of labor supply and redistribution effects of WTC in Serbia: 1.Estimating wage equation and imputing wages for those who are not working (Heckman 2-step estimator) 2.Discrete labor choice: 0, 20 or 40 hrs per week, typical household 3.SRMOD: computing household disposable income (9 combinations) 4.Estimating preferred labor/leisure – consumption combination by means of utility function 5.Introduction of WFTC (in SRMOD) – back to step 3, 4 and 5

Methodology SRMOD – Tax and benefit micro-simulation model for Serbia – Static model: individual behaviour (employment, childcare, saving, etc. are all assumed to be exogenous to the tax-benefit system) – Baseline fiscal system: 2007 – Data: Living standards Measurement Survey from 2007 (5,535 hh/17,335 individuals) Labour Supply Model (LSM) – Is fully integrated with the static model – Used to derive the budget sets under the baseline and reformed scenarios – Impose revenue neutrality conditions taking into account the behavioural reactions SRMOD + LSM => Behavioural tax and benefit model

Descriptives (Working Age Pop 15-64) FemalesMalest-testSig. MeanStd. Err.MeanStd. Err. Demographic variables Age ** Married *** Children < Children Children * Education No school *** Primary ed *** Secondary ed *** Tertiary ed BA, MA or PhD Sample Unweighted 5,9455,826 Weighted 2,592,7632,495,567

1 Conditional on being salaried employee 2 Heckman sample (age 18-64) 3 Heckman sample (age 18-64) + Conditional on being unemployed or “other inactive“ Descriptives (Working Age Pop 15-64) FemalesMalest-testSig. MeanStd. Err.MeanStd. Err. Labour market characteristics Employee *** Employer or self-employed *** Family worker *** Farmer *** Unemployed ** Pensioner *** Student *** Inactive *** Sick or Disabled Other *** Hours worked and hourly wage Hours worked *** Hours worked *** Gross hourly wage *** Imputed hourly wage *** Imputed hourly wage *** Heckman sample Unweighted 3,4603,044 Weighted 1,546,7711,322,314

Step 1. Wage Imputation Heckman 2-Step Estimator Because working may be systematically correlated with unobservables that affect the wage offer, using only working people might produce biased estimators of the parameters in the wage offer equation: – E ndogenous sample selection (OLS is biased and inconsistent) For people who are in the labour force, we observe the wage offer as the current wage For those currently out of the workforce, we do not observe the wage offer We want to know how different factors, such as education, work experience, number of children, etc. affect the wage an individual could earn in the labour force

Step 2. Simulating Discrete Choice Labor Decisions Imputing household gross income corresponding to a discrete set of working time alternatives (inactivity (0h), part-time (20h) and full- time (40h)) Use tax and benefit micro-simulation model for Serbia (SRMOD) in order to compute the corresponding set of disposable incomes With combinations of 0, 20 and 40 hours worked, we generated 9 scenarios for couples and 3 scenarios for singles

Three Scenarios for Singles: Descriptives Simulated individual disposable income by hours worked MeanStd. Dev. H = 07, ,648.1 H = 2010, ,395.7 H = 4018, ,398.6 Number of individuals (unweighted) Number of individuals (weighted) 2,959 1,318,324

Step 3. Preferences Estimations (1) - Intro - Discrete labour supply model (Van Soest, 1995) In order to estimate preference parameters in the utility function, we apply simulated maximum-likelihood estimation on a conditional or logit function (McFadden, 1974) –...we will do the same with mixed logit function Direct estimation of preferences over hours and income Sample: 1,467 females and 2,595 males

Step 3. Preferences Estimations (2) - Utility Function Specification - A person chooses the number of hours of work in order to maximize the utility U ij (derived by individual i from making choice j ) on the basis of ‘preferences’ over hours and income: The likelihood for a sample of observed choices can be derived from that expression and maximized s.t. a budget constraint to estimate the parameters of function U. We assume quadratic specification of the deterministic part of the utility function as in Blundell et al. (2000):

Notes: Tertiary education is omitted category. Children = dummy variable for having children 0-6 years. Income divided by 10,000. Step 3. Preference Estimates (clogit) - Results AllFemalesMales Income *** *** x Aged over ** * x Primary ed ** x Secondary ed *** *** x Children Income *** *** Hours *** *** *** x Aged over ** ** x Primary ed ** x Secondary ed ** *** x Children Hours *** *** *** Income x Hours * Fixed cost of work (omitted) x Children Observations Log-likelihood Wald test: Chi2 (14) ***367.86***690.14***

Results: Discrete Choice of LS Weekly working hours Observed frequencies Predicted frequencies Labor Supply (Own Wage) Elasticities FemalesMales hourspartic.hourspartic Observed and Predicted Freq.

AllFemalesMales Income *** ** x Aged over x Primary ed (omitted)0.594 x Secondary ed * x Children Income *** *** Hours *** *** x Aged over ** ** x Primary ed * (omitted) x Secondary ed * ** x Children Hours *** *** *** Income x Hours *** Fixed cost of work (omitted) x Children Observations Log-likelihood Wald test: Chi2 (14)699.40***213.31***535.83*** Notes: Tertiary education is omitted category. Children = dummy variable for having children 0-6 years. Income divided by 10,000. Pref. Est. (clogit): Without Disp.Inc. = 0

Results: Discrete Choice of LS, DispY=0 Weekly working hours Observed frequencies Predicted frequencies Labor Supply (Own Wage) Elasticities Observed and Predicted Freq. FemalesMales hourspartic.hourspartic

Results: Effects of WITC on Labor Supply and Employment (Singles 15-64) Employment rate femalemaletotal no wtc35.6%49.9%43.7% individual wtc37.4%53.1%46.3% increase in employment1.8pp3.2pp2.6pp * weighted data Hours worked femalemaletotal no wtc individual wtc increase in hours worked0.1pp-0.2pp-0.1pp *weighted data

Forthcoming Steps Estimating preferences for the couples Simulation of effects of WFTC Comparison of the results with other countries – Portugal, Spain, Italy and Greece? Figari (2010) – WITC performs better in terms of labor supply, but worse in terms of inequality than WFTC The same indication in Serbia (Gini, EMTR)? – CEE? Formulating final conclusions

Instead of Conclusion: Questions for Discussion Imputing wages for inactive and unemployed separately? Taking into account informality (tax evasion)? Comparability of WTC scenarios with British WTC dependent on the fiscal size of the programs? Utility function specification improvement? Trade off between including/not including disposable income = 0 (better fit or more reasonable elasticties)? When modelling individuals, using individual or hh income (at the moment – individual)?

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