EFFICIENCY AND INCREASING PRODUCTIVITY OF THE MANUFACTURING INDUSTRY OF EAST JAVA PROVINCE - Lusi Sulistyaningsih; Nita Ma’rufah; Samsu Puji Estika (Kantor Perwakilan Bank Indonesia Provinsi Jawa Timur, 2019)
This study aims to analyze technical efficiency and to find out the main contributors to the productivity of the manufacturing industry in East Java, which increased in 2007-2015. On the other hand, this research also aims to determine the direction of the efficiency of the manufacturing industry sub-sector and the factors that influence the level of efficiency of the manufacturing industry. The level of efficiency that approaches the frontier signifies convergence, while divergence indicates that the level of efficiency that has not approached the frontier. There are 24 manufacturing industry sectors that were observed in this study. To calculate the value of technical efficiency, this study uses Data Envelopment Analysis (DEA) with a bootstrapping approach with 2000 times the number of iterations. While the calculation of productivity changes in this study uses the Malmquist Index. Conditional beta convergence analysis (?), sigma convergence (?), gamma convergence (?), and stochastic convergence is used to analyze efficiency convergence. The results of the score of technical efficiency analysis show that the manufacturing industry in East Java is still in an inefficient condition with the electrical equipment industry being the highest efficiency score sector, while the furniture industry is the most inefficient sector. Technological progress at 54 percent each year is a major contributor to the increase in manufacturing industry productivity by 55 percent during 2007-2015. On the other hand, the results of testing four convergence models show different results, sigma, gamma, and stochastic convergence indicating the convergence of manufacturing industry efficiency, while conditional beta convergence testing shows an indication of the divergence in manufacturing industry efficiency. The GMM estimation results show that factors that significantly affect the efficiency of manufacturing industries are energy intensity, capital-labor ratio, capital intensity, and industrial share. JEL: D24, L60