Academic journal article The European Journal of Comparative Economics

A Comparison of Pre- and Post-Crisis Efficiency of OECD Countries: Evidence from a Model with Temporal Heterogeneity in Time and Unobservable Individual Effects

Academic journal article The European Journal of Comparative Economics

A Comparison of Pre- and Post-Crisis Efficiency of OECD Countries: Evidence from a Model with Temporal Heterogeneity in Time and Unobservable Individual Effects

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1.Introduction

The importance of taking measures of efficiency and productivity, as well as their further benchmarking to improve the performance of any economic system, is recognised. These measures are success indicators and performance metrics (Fred et al, 2008, p.7-15). In general, to estimate efficiency one can compare observed performance (values) to some optimal values, or to some maximum potential output obtained from the available input. Optimum values can be defined in terms of the production possibilities of countries. Although "true" potential is unknown, it is possible to observe best practice, its evolution over time and its variation among countries. Thus, it refers to an operation on a best-practice "frontier" that leads to the identification of countries with the best performance, and further benchmarking performance of the rest against those of the best. Efficiency in this case is derived as the evaluation of observed outputs as compared to maximum potential outputs obtainable from the given inputs. This defines efficiency as technical efficiency.

Technical efficiency, or its opposite term - inefficiency, is a heterogeneous phenomenon and varies both over time and across countries. According to Kose et al. (2008), heterogeneity across countries matters, despite the common evolution of business cycles. Macro factors largely drive heterogeneities since they define initial conditions for business and ways in which economies absorb shocks. Nowadays, economies are increasingly interconnected and integrated in all areas of economic activity. The literature has already highlighted the role of heterogeneity and interdependency in economic development (e.g., Chaserant and Harnay, 2013; Tamborini, 2014). The significant role of interdependency was also demonstrated during the recent global financial crisis (e.g. Dallago, 2013; Vollmer and Bebenroth, 2012). It is possible to assume that an estimation of efficiency on a macro level is sensitive to heterogeneity. Ignoring heterogeneity on a macro level may cause estimates to become highly biased which may lead to misinterpretations. This, therefore, is the motivation behind a study of technical efficiency on a macro level with respect to heterogeneity in various dimensions.

Classical approaches to heterogeneity are based on panel models, which try to account for heterogeneity, including unobserved heterogeneity, by using dummy variables or structural assumptions on an error term (Baltagi, 2005; among others). Nevertheless, this approach has limitations, because unobserved heterogeneity is assumed to be constant over specified time. Extending classical models with a factor structure is one of the effective ways to deal with unobserved time-varying heterogeneity. This approach can provide a parsimonious specification which identifies the effects of unobserved heterogeneity on the outcomes of interest, allowing for access to time-varying technical efficiency.

This paper focuses mainly on shifts in technical efficiency of OECD countries that are caused by the global financial crises, heterogeneity and interdependencies. The motivation for this is instigated by the great variety in the initial economic conditions and development of OECD countries on the one hand, and their high integratability, on the other hand. In this study OECD countries are analyzed as production units. Their outputs are real GDP and export of goods and services. Whilst inputs are limited to labor (the number of employed), capital (gross fixed capital formation) and import of goods and services. Thus, a dataset is formed for 34 OECD countries1, including the abovementioned 2 outputs, 3 inputs, and covering the 2000Q1-2014Q4 period.

This paper contributes to previous literature by computing and comparing technical efficiency in terms of productivity growth for each OECD country taking into consideration an arbitrary temporal heterogeneity through time to minimize bias and improve inference. …

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