Iran's Food Industry Value-Added Growth Accounting: The Endogenous Growth Theory Approach

Document Type : Scientific paper

Authors

1 Ph.D. Student, Department of Economics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

2 Department of Economic, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

3 Department of Economics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

Abstract

The purpose of this study is to account for the value-added and total factor productivity (TFP) growth of 17 food and beverage industries with the classification of two-digit ISIC by the endogenous growth theory approach in the period of 2003 to 2019. The results of the research showed that in the production process, the labor force and capital stock are complementary and the yield trend is upward relative to the scale, which is compatible with the endogenous growth theory. The analysis of value-added growth indicated that classical production inputs (capital stock and labor force) play a dominant role in the growth of food and beverage industries. However, a considerable portion of their contribution to the value-added growth is due to the mix with technology. The findings showed that technology has affected the productivity of the labor force more than the productivity of capital stock and on this basis, the share of labor force-embedded technology in value-added growth is high and the share of capital-embedded technology is low. In addition, the traditional growth accounting method in estimating the share of total productivity is associated with an underestimation error, and in the new growth accounting theory, the contribution of free technology to growth is significant.
.

Keywords

Main Subjects


Abtahi, S. H., & Kazemi, B. (2015). Productivity (Principles, Basics, Methods of Increase and Measurement), Fojan Publishing (in Persian).
Aghion, P. & Peter H. (1992). A Model of Growth Through Creative Destruction. Econometrica, 60, (2), 323-351.
Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37.
Bader, F., & Rahimifard, S. (2020). A methodology for the selection of industrial robots in food handling. Innovative Food Science & Emerging Technologies, 64, 102379.
Baldini, C., Bava, L., Zucali, M., & Guarino, M. (2018). Milk production Life Cycle Assessment: A comparison between estimated and measured emission inventory for manure handling. Science of The Total Environment, 625, 209-219.
Bai, Y., & Zhang, J. (2010). Solving the Feldstein–Horioka puzzle with financial frictions. Econometrica, 78(2), 603-632.
Bassem, B. S. (2014). Total factor productivity change of MENA microfinance institutions: A Malmquist productivity index approach. Economic Modelling, 39, 182-189. 
Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 3(1), 153-169.
Clairand, J. M., Briceno-Leon, M., Escriva-Escriva, G., & Pantaleo, A. M. (2020). Review of energy efficiency technologies in the food industry: trends, barriers, and opportunities. IEEE Access, 8, 48015-48029.
Dashti, Gh., Sani, F., Ghahramanzadeh, M., Sani, R. (2019). Analysis and Measurement of Productivity Growth of Total Factors of Production in the Dairy Industry of Iran. Animal Science Research Journal (Agricultural Knowledge), 29 (1), pp. 61-76 (in Persian).
Deininger, K., & Jin, S. (2005). The potential of land rental markets in the process of economic development: Evidence from China. Journal of Development Economics, 78(1), 241-270.
Fathabadi, M., Soufi Majidpour, M. (2018). Higher Education, Technical Efficiency and Total Productivity Changes; Evidences of Iran's Manufacturing Industries. Research and Planning in Higher Education Journal, 24 (2), pp. 27-51 (in Persian).
Feng, CH; Wang, M; Liu, G; Huang, J. (2017). Sources of economic growth in China from 2000–2013 and its further sustainable growth path: A three-hierarchy meta-frontier data envelopment analysis, Economic Modelling Journal, 64, 334-348.
Fissel, B; Felthoven, R; Kasperiski, S; O’Donnell, CH. (2015).
 Decomposing productivity and efficiency changes in the Alaskahead and gut factory trawl fleet, Marine Policy Journal, 62, 337–346.
Gallup, J. L., & Sachs, J. D. (2000). Agriculture, climate, and technology: why are the tropics falling behind? American Journal of Agricultural Economics, 82(3), 731-737.
Hammond, G. W., & Thompson, E. C. (2008). Determinants of income growth in metropolitan and nonmetropolitan labor markets. American Journal of Agricultural Economics, 90(3), 783-793.
Hastie, T., & Tibshirani, R. (1993). Varying‐coefficient models. Journal of the Royal Statistical Society: Series B (Methodological), 55(4), 757-779.
Herrendorf, B., Rogerson, R., & Valentinyi, A. (2014). Growth and structural transformation. Handbook of economic growth, 2, 855-941.
Hsiao, C. (2014). Analysis of panel data. Cambridge university press.
Isazadeh, S., Soufi Majidpour, M. (2017). Total Productivity Growth of Production Factors, Technological Progress, Efficiency Changes: Empirical Evidence from Iran's Manufacturing Industries. Economic Modeling Journal, 11 (4), pp. 29-48 (in Persian).
Jahangard, E., Feizabadi, F. (2019). Analyzing Sources of Productivity Growth of Total Factors of Production in Iran's Economy. New Economy and Business Journal, 14 (4), pp. 1-25. (in Persian).
Jin, S., Huang, J., Hu, R., & Rozelle, S. (2002). The creation and spread of technology and total factor productivity in China's agriculture. American Journal of Agricultural Economics, 84(4), 916-930.
Jones, C. I. (2002). Sources of US economic growth in a world of ideas.
 American Economic Review, 92(1), 220-239.
Jorgenson, D. W., & Stiroh, K. J. (2000). US economic growth at the industry level. American Economic Review, 90(2), 161-167.
Kalio, A; Mutenyo, J; Owuor, G. (2012). Analysis of Economic Growth in Kenya: Growth Accounting and Total Factor Productivity, Applied Economics, 1(6), 2-22.
Kodan, R., Parmar, P., & Pathania, S. (2020). Internet of things for food sector: Status quo and projected
potential. Food Reviews International, 36(6), 584-600.
Komeijani, A., Mahmoodzadeh, M. (2008). The role of Information and Communication Technology in Iran's Economic Growth (Growth Accounting Approach). Economic Research Journal, 8 (29), pp. 75-107. (in Persian).
Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2021). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, 1-13.
Li, D., Chen, J., & Gao, J. (2011). Non‐parametric time‐varying coefficient panel data models with fixed effects. The Econometrics Journal, 14(3), 387-408.
Mahmoudi, N., Hosseinpour, A., Rezaie, M. (2019). Analysis of the Total Productivity of Production Factors in Selected Sectors despite the Economic Sanctions Index. Economic Research Journal, 54 (3), pp. 659-693 (in Persian).
Mahmoodzadeh, M., Fathabadi M. (2016). Driving Factors of Total Productivity of Production Factors in Iran's Manufacturing Industries. Journal of Economic Modeling Studies, 26 (4), pp. 141-165 (in Persian).
Mahmoodzadeh, M., Mousavi, M. H., Paknahad, F. (2015). Accounting for the Growth of Added Value in Iran's Manufacturing Industries with an Emphasis on Information Technology. Economic Modeling Journal, 9 (4), pp. 41-64 (in Persian).
Mahmoodzadeh, M., Zeitoon Nejad Moosavian, S. A. (2012). Measuring and Analyzing the Sources of Economic Growth in the Mining Sector in Iran. Macroeconomic Research Journal, 7 (13), pp. 121-142. (in Persian).
Meeusen, W., & van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 435-444.
Mia, A. Bassem, I. (2016). Productivity and its determinants in microfinance institutions (MFIs): Evidence from South Asian countries, Economic Analysis and Policy, 51, 32-45.
Mia, M. A., Chandran, V. G. R. (2015). Measuring Financial and Social Outreach Productivity of Microfinance Institutions in Bangladesh. Social Indicators Research, 1-23.
Miller, D. J. (2002). Entropy-based methods of modeling stochastic production efficiency. American Journal of Agricultural Economics, 84(5), 1264-1270.
Oh, D.H. Lee, Y.G. (2016). Productivity decomposition and economies of scale of Korean fossil-fuel power generation companies: 2001-2012. Energy, 100, 1-9.
Pope, R. D., & LaFrance, J. T. (2013). Robust error specification in a production system. American Journal of Agricultural Economics, 95(3), 669-684.
Sharma, S; Sylwester, K, Margono, H. (2007). Decomposition of total factor productivity growth in U.S. states, The Quarterly Review of Economics and Finance, 47(2), 215-241.
Shee, A., & Stefanou, S. E. (2015). Endogeneity corrected stochastic production frontier and technical efficiency. American Journal of Agricultural Economics, 97(3), 939-952.
Sheng, Y., Ding, J., & Huang, J. (2019). The relationship between farm size and productivity in agriculture: Evidence from maize production in Northern China. American Journal of Agricultural Economics, 101(3), 790-806.
Sickles, R. C. (2005). Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings. Journal of Econometrics, 126(2), 305-334.
Swan, T. W. (1956). Economic growth and capital accumulation. Economic Record, 32(2), 334-361.
van Geest, M., Tekinerdogan, B., & Catal, C. (2021). Design of a reference architecture for developing smart warehouses in industry 4.0. Computers in Industry, 124, 103343.
Wang, X., Yamauchi, F., & Huang, J. (2016). Rising wages, mechanization, and the substitution between capital and labor: evidence from small scale farm system in China. Agricultural Economics, 47(3), 309-317.