Measuring Technical Efficiency of the Japanese Professional Football (Soccer) League (J1 and J2)

This is the first paper to measure the efficiency of the Japan Professional Football League clubs both the first and the second divisions. In Chapter 1, a non-parametric method Data Envelopment Development (DEA) is used and the data covers six seasons from 2005 to 2010. The input variables are payroll, cost besides payroll, and total assets. The output variables are attendance, revenue, and points awarded. I use different output combinations in order to check the sensitivity of the efficiency of the clubs after the original composition. This is also the first research to include more than one division of the Professional Football League and hence, the promotion and relegation impact on the efficiency can be analyzed using unique data such as Tokyo Verdy 1969. Tokyo Verdy 1969 operated inefficiently in the second division because it spent so much on inputs hoping for promotion. It was efficient when in the first division. The results indicate that athletic rank in the league is not correlated with the efficiency scores. The efficient clubs in the second division are all ranked at the bottom in the league and this is because they have limited resource inputs, no expectation to promote, and because the expansion policy of the league precludes relegation. Chapter 2 is an extension of Chapter 1. In this chapter I check the exogenous factors impacting the efficiency scores but not involved in the DEA analysis as the input variables. I aim to estimate the relationship between the input-oriented DEA efficiency scores under the constant returns to scale assumption and use an exogenous variable ordinary least square (OLS) model to check the relationship between the efficiency scores and exogenous variables. I regress the DEA efficiency scores on all of the exogenous variables collected from various resources during the sample period. Chapter 3 estimates the productivity and efficiencies of the football clubs in Japan Professional Football League. This chapter is an extension of the first chapter. In this chapter I check the dynamic change of Total Factor Productivity (TFP) based on the calculation of the Malmquist Index, which consists of efficiency change and technical change between two time periods. Additionally, the production frontier used in this chapter was built by the non-parametric input-oriented CRS DEA approach as applied in the first chapter. Based on the results of the Malmquist Index, we find if the change in the TFP growth as increasing, declining or remaining the same