20th International Scientific Conference AMSE Applications of Mathematics and Statistics in Economics 2017 Szklarska Poręba, Poland 30 August 2017 – 3 September 2017 473 APPLICATION OF LOGISTIC REGRESSION IN COUNTERFACTUAL IMPACT EVALUATION OF GRADUATE PRACTICE MEASURE IN SLOVAKIA LUCIA SVABOVA University of Zilina, The Faculty of Operation and Economics of Transport and Communications, Department of Economics, Univerzitna 8215/1, 010 26 Zilina, Slovakia email: lucia.svabova@fpedas.uniza.sk MAREK DURICA University of Zilina, The Faculty of Operation and Economics of Transport and Communications, Department of Quantitative Methods and Economic Informatics, Univerzitna 8215/1, 010 26 Zilina email: marek.durica@fpedas.uniza.sk Abstract In the paper we discuss the results of Counterfactual impact evaluation of Graduate practice that is one of Active labour market policy (ALMP) interventions in Slovakia for unemployed jobseekers. Counterfactual impact evaluation is usually made using various multivariate statistical methods; one of the most used methods is Propensity score matching. Propensity score for every individual jobseeker means the probability of taking a part of an ALMP intervention and can be obtained using logistic regression model. Counterfactual evaluation is based on comparison of placeability and sustainability of treated and non-treated jobseekers on open labour market and is very important for valuation of impact of intervention not only on individual jobseekers and their employability but also for valuation of whole intervention and its economic impact. Key words: Logistic regression, Propensity score matching, Counterfactual impact evaluation, Active labour market policy, Intervention JEL Codes: J08, J68 DOI: 10.15611/amse.2017.20.39 1. Introduction Public programs are designed to reach certain goals and beneficiaries. Programs might appear potentially promising before implementation yet fail to generate expected impacts or benefits. The obvious need for impact evaluation is to help policy makers to decide whether programs are generating intended effects; to promote accountability in the allocation of resources across public programs; and to fill gaps in understanding what works, what does not, and how measured changes in well-being are attributable to a particular project or policy intervention. (Khandker et al., 2010) The main challenge of an impact evaluation is to determine what would have happened to the beneficiaries if the intervention had not existed. (Holmlund, 2001) That is, to determine the income of beneficiaries in case of absence of the intervention. A beneficiary’s outcome in the absence of the intervention would be its counterfactual. A program or policy intervention seeks to alter changes in the well-being of beneficiaries. Ex post, outcomes of this intervention on intended beneficiaries, such as employment or expenditure, are quantified.