Panel Data Estimation Techniques and Mark Up Ratios

Abstract

1. Introduction Despite the changing face of the business economy, manufacturing still plays a key role in Europe's prosperity. The manufacturing industry in Europe has for decades been through a process of structural changes. The current and sudden economic crisis that has affected the Euro zone area and especially the Mediterranean countries (i.e Greece, Spain and Portugal) during the last years has pointed more than ever before to the importance of adjustment and structural change. Indeed, there is a compelling need for a better understanding and more insight into the competitive pressure that individual economic sectors experience, the adjustment performance of sectors and countries, and the institutional framework that directly impacts the need and the capabilities of change (Fafaliou and Polemis, 2013). The estimation of the market power has been of interest to economists for a long time and there is a substantial body of literature assessing the main elements of competition in various countries and industries. The majority of the empirical studies apply Roeger (1995) methodology in order to estimate industry markups. Most of these studies consent that mark up ratios exceed unity denoting the absence of competitive conditions in certain sectors/industries (see for example Martins et al, 1996; Christopoulou and Vermeulen, 2012; Borg, 2009; Molnar, 2010; Molnar and Bottini, 2010). The approach adopted in this paper is to empirically estimate the level of significant market power (SMP) by adapting the methodology introduced by Roeger (1995). This methodology is based on the hypothesis that in a situation of perfect competition the selling price is equal to marginal cost. The equality of marginal cost and price is essential for the efficiency of the economy since, first, competitive markets can achieve higher productivity levels, and second, competition provides consumers with products of higher quality, increased variety and lower prices (Rezitis and Kalantzi, 2013). However, this condition does not apply in a less competitive environment (i.e oligopoly markets, monopolies), since the price deviates from marginal cost. Therefore, the ratio between the selling price and marginal cost assesses the competitiveness of the market. However, while selling price is directly observable, the marginal production cost is not. This drawback was overcome by Hall (1988) and Roeger (1995) who both showed that under a perfect competition, the nominal growth rate of the Solow residual is independent of the nominal capital productivity growth rate. It then follows that the coefficient linking the nominal growth rate of the Solow residual to the nominal capital productivity growth is the Lerner Index defined as the ratio of the difference between price and marginal cost (Borg, 2009). Despite the great number of empirical studies devoted on this topic, few of them, have investigated the competitive conditions of the services industry. Concretely, none of the studies has examined the level of competition in the Greek services sectors. This paper aims to cover this gap in the empirical literature. This model estimates the mark up ratios for the two industries over the period 1970-2007 by applying Ordinary Least Squares (OLS) and Two Stage Least Squares (TSLS) in a panel data set. The remainder of this paper is organised as follows. Section 2 reviews the literature on the methods of estimation of market power. Section 3 discusses the data and outlines the methodology applied. Section 4 illustrates and evaluates the results of the empirical analysis and, finally, Section 5 concludes the paper. 2. Survey of the Literature The majority of the empirical studies apply Roeger (1995) methodology in order to estimate industry markups (see Table 1). Considering the above, Martins et al, (1996) applies the Roeger (1995) approach extended to include intermediate goods, in order to estimate markups in the manufacturing industries for 14 OECD countries including Greece as well, over the period 1970-1992 by using the OECD STAN database. …