Dynamic versus static inefficiency assessment of the Polish meat‐processing industry in the aftermath of the European Union integration and financial crisis

Abstract

This paper assesses the dynamic inefficiency of the Polish meatprocessing industry during the period between 2004 and 2012. This study employs also a comparison of dynamic with static inefficiency measures to address the importance of accounting for adjustment costs when measuring a firm's inefficiency. Dynamic and static cost inefficiencies and their decomposition into technical, allocative, and scale inefficiency are derived using Data Envelopment Analysis (DEA). Results show that firms' low levels of dynamic cost inefficiency are mainly due to dynamic allocative inefficiency rather than technical and scale inefficiency. The 2008 financial crisis appears to hamper firms' dynamic technical performance, but has also a positive influence on the dynamic allocative and scale inefficiencies. We further show that the average static measures tend to underestimate all inefficiency components compared to dynamic counterparts. [EconLit citations: C61, D61, L66].

Managerial and program inefficiency for European meat manufacturing firms: A dynamic multidirectional inefficiency analysis approach

“…Bontemps et al (2012) found technical inefficiency levels for the poultry industry in France that are similar to our findings. Regarding dynamic inefficiency studies of meat processing industry, our results are similar to those of Kapelko (2017b) findings for the meat processing industry in Poland.…”

Dynamic Cost Inefficiency of the European Union Meat Processing Firms

“…The dynamic inefficiency indicators (cost, technical and allocative inefficiencies) were estimated for each year separately, relative to the metafrontier based on observations of firms in all regions, and to a region‐specific frontier. Following previous literature (e.g., Kapelko et al ., 2014; Dakpo, 2015; Kapelko, 2017), the directional vector used in the computations is g x = x 0 n , n = 1, …, N , for variable inputs and g I = 0.2 K 0 f , f = 1, …, F , for investment. As noted in section 3, we assume CRS technology in our calculations.…”

“…The firms’ output are measured as revenues as reported in the firm’s profit and loss accounts. Previous studies on efficiency in the meat processing industry also applied one single output (e.g., Ali, 2007; Keramido et al, 2011; Kapelko, 2017). Fixed assets represent quasi‐fixed inputs; the value of fixed assets in a given year t was measured as the opening value of fixed assets from the firm’s balance sheet.…”

“…Measuring the level of firms’ inefficiency using a dynamic approach explicitly accounts for the intertemporal linkages of production decisions. In the literature, two main categories of dynamic efficiency models are distinguished (Kapelko, 2017): (i) dynamic network models, in which outputs of and investment in some production stages are used as inputs to other stages across multiple periods (see Färe & Grosskopf 1996; Nemoto & Goto 2003; Tone & Tsutsui 2010, 2014); (ii) adjustment cost theory grounded models in which the adjustment costs related to investments in quasi‐fixed assets are incorporated to account for the intertemporal linkages of the firms’ production decisions (see Silva & Stefanou 2003, 2007; Kapelko et al ., 2014; Silva et al ., 2015; Ang et al ., 2018). We follow the latter approach.…”

“…The literature assessing dynamic inefficiency is rather sparse in the food manufacturing sector, including the meat processing industry. Previous studies of dynamic inefficiency include Kapelko (2017), who focuses exclusively on the meat processing industry in Poland without comparing different EU regions. Kapelko & Oude Lansink (2017) analyse and compare firms at the regional EU level, but focus on dairy manufacturing firms and only on dynamic technical inefficiency.…”