Industry structural inefficiency and potential gains from mergers and break-ups: an empirical approach

In this paper an encompassing empirical strategy is presented which is able to decompose an indicator of industrial structural inefficiency into its sources components. The main purpose of the analysis is to bring a number of ideas floating in the efficiency literature together in the same empirical industrial organization model and extend them to a more comprehensive definition of industry inefficiency. The tool used to reach this goal is the directional distance function (DDF) representation of the data envelopment analysis (DEA) data generated technology. As it is standard in the linear activity analysis model, all the discussion is based only on the assumption that measures of inputs and outputs quantities are available, without reference to prices. Decomposing the industrial structural inefficiency indicator into different components the following effects have been identified: 1) inefficiencies arising from firms operating on a large size that can be split into smaller more productive units (size inefficiencies); 2) efficiency gains that can be realized thanks to the merger of small firms (merger inefficiencies); 3) re-allocation of inputs and outputs in order to bring firms toward an optimal production plan (re-allocation inefficiencies). After defining the static industry inefficiency indicator, a dynamic decomposition of productivity change will be proposed. Productivity change itself is decomposed into technical change and efficiency components. The methodology is applied to healthcare data on public hospitals in Australia. The empirical results point to the fact that technical inefficiency of individual hospitals accounts only for less than 15% of the total inefficiency of the industry. The most part of industry inefficiency has been found to be organizational. Size inefficiency is the most prominent component accounting for around 40% of the total inefficiency of the industry.