Energy and Non-Energy Inputs Substitution Possibilities in Nigeria's Manufacturing Sector: A Translog Cost Function Approach

This study investigates the substitution possibilities between energy and non-energy inputs in Nigeria's manufacturing sector from 1981 to 2023. Utilizing a transcendental logarithmic (translog) cost function estimated via iterated Seemingly Unrelated Regression (iSUR), we compute both Allen and Morishima elasticities of substitution to analyze factor relationships. Results reveal significant substitution possibilities: capital and energy are substitutes with a Morishima elasticity (MES) averaging 3.66, while energy and labor show substitutability with an MES of 2.32. Conversely, capital and labor emerge as complements (MES = -1.94), suggesting that technological upgrading in this context requires simultaneous investments in human capital. These findings have crucial implications for energy and industrial policy, particularly in the context of energy price reforms and carbon taxation. We demonstrate that the Morishima elasticity provides more policy-relevant information than conventional Allen elasticities by capturing changes in input ratios rather than partial adjustments.

Input Substitution, Translog Cost Function

1. Berndt, E. R., & Wood, D. O. (1975). Technology, prices, and the derived demand for energy. The Review of Economics and Statistics, 57(3), 259–268. [Google Scholar] [Crossref]

2. Blackorby, C., & Russell, R. R. (1989). Will the real elasticity of substitution please stand up? (A comparison of the Allen/Uzawa and Morishima elasticities). The American Economic Review, 79(4), 882–888. [Google Scholar] [Crossref]

3. Christensen, L. R., Jorgenson, D. W., & Lau, L. J. (1973). Transcendental logarithmic production frontiers. The Review of Economics and Statistics, 55(1), 28–45. [Google Scholar] [Crossref]

4. Cirera, X., & Maloney, W. F. (2017). The innovation paradox: Developing-country capabilities and the unrealized promise of technological catch-up. World Bank Publications. [Google Scholar] [Crossref]

5. Haller, S. A., & Hyland, M. (2014). Energy-related technological change and energy efficiency in Ireland. *The Energy Journal, 35(4), 1–26. [Google Scholar] [Crossref]

6. Koetse, M. J., de Groot, H. L. F., & Florax, R. J. G. M. (2008). Capital-energy substitution and shifts in factor demand: A meta-analysis. Energy Economics, 30(5), 2236–2251. [Google Scholar] [Crossref]

7. National Bureau of Statistics (NBS). (2018). Annual abstract of statistics. Federal Republic of Nigeria. [Google Scholar] [Crossref]

8. Ogunleye, E. K., & Akinbami, J. F. K. (2016). Diesel-electricity substitution in Nigeria's industrial sector: A translog cost function approach. Energy Policy, 88, 635–647. [Google Scholar] [Crossref]

9. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368. [Google Scholar] [Crossref]

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