# **1 Introduction**

The overdraft facility<sup>2</sup> policy from private financial institutions provides financial support to increase firms‘ accessibility to private financing sources. However, according the IMF (2005) the overdraft facility policy has often been criticized for its negative effects by impairing the development of an innovative private financial sector and for making firm highly dependent on government support policy measures. Even though there have been some qualitative remarks on the effectiveness of the policy, an evaluation of the overdraft facility policy has not been conducted systematically in terms of methodology and data, especially in Eastern European Union Industrial firms. This study aims to fill the gap in the existing literatures.

In this work, we evaluate the effect of the overdraft facility policy in terms of human capital (production workers trained), technological capital (expenditures of R&D), competitiveness (sales export level) and the firm‘s technical efficiency in the Eastern European Union Industrial firms, by comparing overdraft facilitated firms and non- overdraft facilitated firms. The amount of funds allocated to the overdraft facility policy is huge and the number of targeted firms is large. Therefore, we need to investigate its effectiveness and to provide background information for further evolution of the policy.

This work utilizes an original dataset that covers manufacturing firms in 11 Eastern European countries (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Slovak Republic, and Slovenia). Moreover, we analyze the effectiveness of the overdraft facility in four industrial sectors (Beverages, Food, Garments, and Metals and machinery).

Methodologically, reliable policy evaluation should solve the ̳selectivity‘ problem. According to Muthen and Jöreskog (1983), selectivity problems can occur whenever one tries to estimate population parameters from a nonrandom sample. The sample may be nonrandom because only firms with certain characteristics are selected into the sample (sample selection), or because firms participate voluntarily in the sample (self-selection). Selective samples can also occur because firms fall out of the sample for various reasons, despite an initial random sample (attrition). According to Jaffe (2002), if we cannot control the selectivity problem, we might over- or underestimate the true effects of the policy.

In order to deal with the selectivity issue, we adopt propensity score matching estimators, which have recently been applied to firm-level studies (Yasar and Rejesus 2005; Loof and Heshmati 2005; Oh et al. 2009). Propensity score matching was chosen because: (i) it reduces (although does not eliminate) selection biases, (ii) it reduces the limitation from matching on many observable variables for finite data, and (iii) it is best suited to the structure of the available data. For example, because baseline data was not collected at the outset of the program and detailed secondary source data were not available to construct an appropriate baseline, evaluation techniques such as difference-in-difference, which would have removed non-observable biases between the treatment and control groups, could not be utilized. The difference-in-difference compares the difference between the treatment and control groups before (first difference) and

> 2 An overdraft facility is defined as a flexible account that allows firms to draw upon in the event their account balance becomes negative.

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