Overdraft Facility Policy and Firm's Performance: An Empirical Analysis in Eastern European Union Industrial Firms

Abstract

This article evaluates the effect of the overdraft facility (or line of credit) policy by comparing a large sample of overdraft facilitated firms and matched non-overdraft facilitated firms from Eastern Europe at sector level. The sample firms are compared with respect to rates of different performance indicators including: technical efficiency (a Data Envelopment Analysis -DEA -approach is applied to estimate technical efficiency level for individual sectors), production workers trained, expenditures of R&D, and export activity. In order to avoid the selectivity problem, propensity score matching methodologies are adopted. Results suggest that a certain level of overdraft facility given to a firm would be needed to stimulate investment in R&D, which will eventually result in growth in productivity.

Policy evaluation and efficiency: a systematic literature review

“…In particular, we observe that propensity score matching (Castillo and Salem, 2013;Papadimitriou and Johnes, 2019) and difference-in-differences (Hitt and Tambe, 2016;Pan et al, 2019;Baláź et al, 2020;He et al, 2020) are the more common techniques, and sometimes they are used as complementary tools (Lin and Zhu, 2019;Ma et al, 2020). Recent studies by D'Inverno et al ( 2021) (and Mergoni et al 2020) translated the idea of the identification strategy of the regression discontinuity design in the realm of frontier estimation.…”

“…2 In the literature on efficiency for policy evaluation the Banker, Charnes and Cooper (CCR) model encountered great success, in particular in the field of education and firms' productivity. However, only few studies rely entirely on the CCR model (for education see Soares de Mello et al, 2006;Coupet and Barnum, 2010;Montoneri et al, 2012, and for firms' productivity see Kao and Hwang, 2010;Castillo and Salem, 2013). The majority of papers uses the CCR model in combination to the BCC model, proposed by Banker et al (1984) to relax the constant return to scale assumption, and to investigate the possible presence of scale economies (Halkos and Tzeremes, 2013;Du et al, 2014;Schubert and Yang, 2016;Natesan and Marathe, 2017;Baláź et al, 2020).…”

“…These techniques have been used to analyze the sensitivity of efficiency scores relative to sample variations of the estimated frontier and aim to mitigate them. Therefore, they are particularly useful in the presence of noisy input or output measures, which are common in the field of environment (Halkos and Tzeremes, 2010a;Czyźewski et al, 2020), innovation (Castillo and Salem, 2013;Halkos and Tzeremes, 2013), and education (Tochkov et al, 2012;Papadimitriou and Johnes, 2019). A further innovation was provided by Cazals et al (2002) and Daraio and Simar (2007) with the introduction of robust and conditional models as they allow to investigate the effect of possible environmental variables on the efficiency scores, without assuming the separability condition.…”

“…To overcome these issues, few attempts have been made in the literature by implementing classical econometric techniques for causal inference. In particular, we observe that propensity score matching (Castillo and Salem, 2013; Papadimitriou and Johnes, 2019) and difference‐in‐differences (Hitt and Tambe, 2016; Pan et al., 2019; Baláź et al., 2020; He et al., 2020) are the more common techniques, and sometimes they are used as complementary tools (Lin and Zhu, 2019; Ma et al., 2020). Recent studies by D'Inverno et al.…”

“…et al (2006),Chang et al (2009),Coupet and Barnum (2010),Montoneri et al (2012),Castillo and Salem (2013),Whittaker et al (2017),Cordero and Tzeremes (2018),Papadimitriou and Johnes (2019),Ding et al (2020Ding et al ( (2008,Zhang et al (2008),Waldo (2007),Hsu and Hsueh (2009),Tochkov et al (2012),Mallikarjun et al (2014),Morfeldt and Silveira (2014),Seth and Patel (2014),Halkos et al (2015),Ke et al (2015),Hitt and Tambe (2016),Santín & Sicilia (2018),Carrington et al (2018),Vieira et al (2018), Czyźewski et al (2020), Xin-gang et al (2020), Johnes and Virmani (2020), Titl and De Witte (2021) 9 Compare CRS-VRS Glass et al (2006), Halkos and Tzeremes (2011a, 2011b), Sahin et al (2011), Zhang et al (2011), Sav (2012), Halkos and Tzeremes (2013), Baláź et al (2020), Czyźewski et al (2020) 8 Scale efficiency Halkos and Tzeremes (2009), Halkos and Tzeremes (2010b, 2010a), Jiménez-Sáez et al (2011), Jiménez-Sáez et al (2013), Du et al (2014), Schubert and Yang (2016), Natesan and Marathe (2017) # Panel 2: Orientation 14 Output-oriented Glass et al (2006), Cherchye et al (2007a), Chang et al (2009), Kantabutra and Tang (2010), Zhang et al (2011), Montoneri et al (2012), Sav (2012), Tochkov et al (2012), Haelermans and De Witte (2012), Asatryan and De Witte (2015), Natesan and Marathe (2017), Santín & Sicilia (2018), Cordero et al (2018), Johnes and Virmani (2020) 22 Input-oriented McCallion et al (2000), Glass et al (2006), Waldo (2007), Halkos and Tzeremes (2009), Hsu and Hsueh (2009), Halkos and Tzeremes (2010b, 2010a), De Witte and Saal (2010), Puenpatom and Rosenman (2008), Zhang et al (2008), Halkos and Tzeremes (2011a, 2011b), Sahin et al (2011), Halkos and Tzeremes (2013), Hitt and Tambe (2016), Schubert and Yang (2016), Natesan and Marathe (2017), Cordero et al (2017), Cordero and Tzeremes (2018), Vieira et al (2018), D'Inverno et al (2021), Titl and De Witte (. (2006), Mallikarjun et al (2014), Morfeldt and Silveira (2014) 5 Slack-based model Seth and Patel (2014), Ke et al (2015), Wang et al (2019)(super), Baláź et al (2020), Czyźewski et al (2020) 6 Distance function Jiménez-Sáez et al (2011), Jiménez-Sáez et al (2013), Halkos et al (2015), Pan et al (2019), Lin and Zhu (2019), Zhang et al (2009), Tochkov et al (2012), Halkos and Tzeremes (2013), Du et al (2014), Ke et al (2015), Hitt and Tambe (2016), Schubert and Yang (2016), Vieira et al (2018), Santín & Sicilia (2018), Baláź et al (2020), Xin-gang e...…”