EFFICIENCY AND INCREASING PRODUCTIVITY OF THE MANUFACTURING INDUSTRY OF EAST JAVA PROVINCE

  • Lusi Sulistyaningsih Fakultas Ekonomi dan Bisnis, Universitas Airlangga
  • Nita Ma’rufah Fakultas Ekonomi dan Bisnis, Universitas Airlangga
  • Samsu Puji Estika Fakultas Ekonomi dan Bisnis, Universitas Airlangga
Keywords: Technical efficiency, productivity, convergence, divergence, manufacturing industry

Abstract

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 (β), sigma convergence (σ), gamma convergence (γ), 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

References

Abramovitz, M. (1986). Catching Up, Forgoing Ahead, and Falling Behind. Journal of Economic History, 46(2), 385-406.

Badan Pusat Statistik. (2009). Klasifikasi Baku Lapangan Usaha indonesia. Cetakan III. Jakarta: Badan Pusat Statistik.

Badan Pusat Statistik. (2012). Indikator Ekonomi Desember 2012. Jakarta: Badan Pusat Statistik.

Badan Pusat Statistik. (2012). OECD Energy Intensity: Measures, Trends, and convergence. Energy Efficiency, 5(4), 583-597.

Badan Pusat Statistik. (2015). Indikator Ekonomi Desember 2015. Jakarta: Badan Pusat Statistik.

Badan Pusat Statistik. (2015). Klasifikasi Baku Lapangan Usaha Indonesia 2015. Jakarta: Badan Pusat Statistik.

Badan Pusat Statistik. (2016). Indikator Ekonomi Mei 2016. Jakarta: Badan Pusat Statistik.

Badan Pusat Statistik. (2017). Conditional convergence in Australia's energy consumption at the sector level. Energy Economics, 62, 396-403.

Badan Pusat Statistik. (2017). Convergence in energy consumption per capita across the US states, 1970–2013: An exploration through selected parametric and non-parametric methods. Energy Economics, 62, 404-410.

Badan Pusat Statistik Provinsi Jawa Timur. (2018). Provinsi Jawa Timur dalam Angka 2018. Surabaya: Badan Pusat Stastistik Provinsi Jawa Timur.

Balk, B. M. (2001). Scale Efficiency and Productivity Change. Journal of Productivity Analysis, 15(1), 159-183.

Barro, R. J., & Martin, X. S. (1996). The Classical Approach to Convergence Analysis. The Economic Journal, 106(437), 1019-1036.

Coelli, T. J., Rao, D. S., Donnell, C. J., & Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis. Queensland: Springer Science & Business Media.

Diskaya, F., Emir, S., & Orhan, N. (2011). Measuring the Technical Efficiency of Telecommunication Sector within Global Crisis: Comparison of G8 Countries and Turkey. Procedia Social and Behavioral Sciences, 24(1), 206-218.

Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120(3), 253-290.

Flokou, Angeliki, Aletras, V., & Niakas, D. (2017). A Window-DEA Based Efficiency Evaluation of the Public Hospital Sector in Greece during the 5 Year Economic Crisis. PLOS ONE, 1-26.

Hao, Y., Shuo, W., & Zhang, Z.-Y. (2015). Examine the convergence in Per Capita Energy Consumption in China with Breakpoints. Energy Procedia Vol. 75 , 2617-2625.

Khayyat, N. T. (2015). Energy Demand in Industry: What Factors Are Important? New York: Springer Science & Business Media Dordrecht.

Liddle, B. (2012). Revisiting World Energy Intensity Convergence for Regional Differences. Applied Energy, 87, 3218-3225.

Lubis, R. R. (2014). Analisis Efisiensi Teknis, Alokatif dan Ekonomi Produksi Nanas di Kabupaten Subang, Provinsi Jawa Barat. Skripsi S1.

Mohammadi, H., & Ram, R. (2012). Cross-country convergence in energy and electricity consumption, 1971–2007. Energy Economics, 34, 1882–1887.

Nicholson, W., & Snyder, C. (2010). Microeconomic Theory Basic Principle and Extensions. USA: Thomson, South-Western.

Paredes, M. (2015, December 29). Inputs and Production Function. Retrieved from cupdf.com.

Porcelli, F. (2009). Measurement of Technical Efficiency: A-brief Survey on Parametric and Non-Parametric Techniques.

Rusydiana, A. S. (2013). Data Envelopment Analysis, CRS dan VRS. Retrieved from DEA Center.

Tangen, S. (2002). Understanding the Concept of Productivity. Proceeding of the 7th Asia Pacific Industrial Engineering and Management Systems Conference (AIPEMS2000), Taipei.

Valdés, B. (2003). An Application of Convergence Theory to Japan’s Post-WWII “Economic Miracle”. Journal of Economic Education, 34(1), 61-81.

Vincova, I. K. (2005). Using DEA Models to Measure Efficiency. BIATEC, 8(1), 24-28.

Wajdi, M. F. (2010). Pengukuran Kinerja dalam Industri Kecil. Universitas Muhammadiyah Surakarta Online Journal, 101-111.

Published
2019-03-30
How to Cite
Lusi Sulistyaningsih, Nita Ma’rufah, & Samsu Puji Estika. (2019). EFFICIENCY AND INCREASING PRODUCTIVITY OF THE MANUFACTURING INDUSTRY OF EAST JAVA PROVINCE. East Java Economic Journal, 3(1), 43-66. https://doi.org/10.53572/ejavec.v3i1.24
Section
Articles