Electricity Sector Reform; Quality of service; Data Envelopment Analysis; Performance gap; East Africa
Abstract :
[en] The electricity sector has globally been subject to reforms since the 1990s. The reforms consisted of unbundling vertically integrated monopolies and attracting the private sector with a view of improving quality of service (QoS) and technical efficiency. In some East African countries, however, the electricity sector remains vertically integrated. Controlling electricity losses has been difficult, resulting in poor QoS. This paper analyzes and compares the performance of the East African power sector with regard to QoS. A non-parametric approach, Data Envelopment Analysis (DEA) was used to estimate the technical efficiency scores and the Total Factor Productivity Change (TFPC) for productivity improvement under two models, generation and transmission-distribution (TD). Data comprising two outputs and three inputs was collected in Burundi, Ethiopia, Kenya, Rwanda, Tanzania, and Uganda for the period 2008-2017. On average, the East African power sector exhibits performance gaps of 20% for the generation model, and 22% for the TD model. In the generation model, it exhibits Decreasing Returns to Scale (DRS) at a frequency of 34 out of 60, compared to 16 for Increasing Returns to Scale (IRS) and 10 for Constant Returns to Scale (CRS). However, in the TD model, IRS are the most dominant, with a frequency of 31 out of 60 compared to 19 and 10 for DRS and CRS respectively. Inefficiency is largely attributed to excess inputs, including high-voltage transmission line lengths and electricity losses, as well as a shortage of outputs, such as the number of customers. The study also shows a global productivity improvement, which is linked to efficiency change for the generation model and technological change for the TD model. Specifically, countries that have attracted the private sector into the generation and/or distribution sectors have improved their productivity compared to others with state-owned utilities.
Disciplines :
Microeconomics Economic systems & public economics
ARES - Académie de Recherche et d'Enseignement Supérieur [BE] Ecole Doctorale de l'Université du Burundi
Commentary :
The Paper has been presented in the 2nd EAC Science, Technology and Innovation conference held in Bujumbura, Burundi from 27th-29th october 2021 on the theme: The Role of Science, Technology, and Innovation in EAC Regional Integration and Socio-Economic Development in the phase of Covid-19 Pandemic. The conference aimed to provide a platform for the players in Science, Technology, and Innovation in the East African Community and beyond to share their experiences and results within the knowledge and technology generation, translation, and transfer chain.
Alizadeh, R., Gharizadeh, R., Soltanisehat, L., Soltanzadeh, E., & Lund, P. D. (2020). Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach. Energy Economics, 91, 104894. https://doi.org/10.1016/j.eneco.2020.1048 94
Bacon, R. (2018). Taking Stock of the Impact of Power Utility Reform in Developing Countries A Literature Review. World Bank Policy Research Working Paper, 8460, 64. Retrieved from https://openknowledge.worldbank.org/h
Badunenko, O., & Kumbhakar, S. C. (2017). Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter? European Journal of Operational Research, 260(2), 789–803. https://doi.org/10.1016/j.ejor.2017.01.025
Bagdadioglu, N., Waddams Price, C. M., & Weyman-Jones, T. G. (1996). Efficiency and ownership in electricity distribution: A non-parametric model of the Turkish experience. Energy Economics, 18, 1–23. https://doi.org/10.1016/0140-9883(95)00042-9
Balza, L. H., Jimenez Mori, R., Macedo, D., & Mercado, J. (2020). Revisiting private participation, governance and electricity sector performance in Latin America ☆. The Electricity Journal, 33, 106798. https://doi.org/10.1016/j.tej.2020.106798
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science. https://doi.org/10.1287/mnsc.30.9.1078
Barabutu, J., & Lee, H. (2018). An empirical analysis of the efficiency determinants in the Southern African electricity sector: evidence and policy implications. Geosystem Engineering, 21(1), 31–42. https://doi.org/10.1080/12269328.2017.13 53445
Bimenyimana, S., Asemota, G. N. O., & Li, L. (2018). The state of the power sector in Rwanda: A progressive sector with ambitious targets. Front. Energy Res., 6(July). https://doi.org/10.3389/fenrg.2018.00068
Bongo, M. F., Ocampo, L. A., Magallano, Y. A. D., Manaban, G. A., & Ramos, E. K. F. (2018). Input–output performance efficiency measurement of an electricity distribution utility using super-efficiency data envelopment analysis. Soft Computing, 22, 7339–7356. https://doi.org/10.1007/s00500-018-3007-2
Çelen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA and Tobit) analysis. Energy Policy, 63, 300–310. https://doi.org/10.1016/j.enpol.2013.09.03 4
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Coelli, T. J. (1996). A Guide to FRONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation (No. 07). New England.
Coelli, T. J., Gautier, A., Perelman, S., & Saplacan-Pop, R. (2013). Estimating the cost of improving quality in electricity distribution: A parametric distance function approach. Energy Policy, 53, 287–297. https://doi.org/10.1016/j.enpol.2012.10.06 0
Coelli, T., & Perelman, S. (1999). A Comparison of parametric and non-parametric distance functions: With application to European railways. European Journal of Operational Research, 117, 326–339. https://doi.org/10.1016/S0377-2217(98)00271-9
da Silva, A. V., Azevedo Costa, M., Ahn, H., Lúcia, A., & Lopes, A. L. M. (2019). Performance benchmarking models for electricity transmission regulation: Caveats concerning the Brazilian case. Utilities Policy, 60, 1–10. https://doi.org/10.1016/j.jup.2019.100960
Dertinger, A., and Hirth, L. (2020). Reforming the electric power industry in developing economies evidence on efficiency and electricity access outcomes. Energy Policy, 139(February), 111348. https://doi.org/10.1016/j.enpol.2020.1113 48
Eberhard, A., Gratwick, K., & Kariuki, L. (2018). A review of private investment in Tanzania’s power generation sector. Journal of Energy in Southern Africa, 29(2), 1–11. https://doi.org/http://dx.doi.org/10.171 59/2413-3051/2018/v29i2a4389 Published
Ervural, B. C., Zaim, S., & Delen, D. (2018). A two-stage analytical approach to assess sustainable energy efficiency. Energy, 164, 822–836. https://doi.org/10.1016/j.energy.2018.08.2 13
Estache, A., Tovar, B., & Trujillo, L. (2008). How efficient are African electricity companies? Evidence from the Southern African countries. Energy Policy, 36, 1969–1979. https://doi.org/10.1016/j.enpol.2008.02.01 1
Fare, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. The American Economic Review, 84(1), 66–83.
Färe, R., & Primont, D. (1995). Multi-output production and duality: theory and Applications (Springer S). New York: Kluwer Academic Publishers. https://doi.org/10.1017/CBO97811074153 24.004
Gore, C. D., Brass, J. N., Baldwin, E., & Maclean, L. M. (2019). Political autonomy and resistance in electricity sector liberalization in Africa. World Development, 120, 193–209. https://doi.org/10.1016/j.worlddev.2018.0 3.003
Imam, M. I., Jamasb, T., & Llorca, M. (2019). Sector reforms and institutional corruption: Evidence from electricity industry in Sub-Saharan Africa. Energy Policy, 129, 532–545. https://doi.org/10.1016/j.enpol.2019.02.04 3
Jaraite, J., & Di Maria, C. (2012). Efficiency, productivity and environmental policy: A case study of power generation in the EU. Energy Economics, 34(5), 1557–1568. https://doi.org/10.1016/j.eneco.2011.11.01 7
Kumbhakar, S. C., & Tsionas, M. G. (2021). Dissections of input and output efficiency: A generalized stochastic frontier model. International Journal of Production Economics, 232, 107940. https://doi.org/10.1016/j.ijpe.2020.107940
Leibenstein, H. (1979). X-Efficiency: From Concept toTheory. Challenge, 22(4), 13–22.
Llorca, M., Orea, L., & Pollitt, M. G. (2016). Efficiency and environmental factors in the US electricity transmission industry. Energy Economics, 55, 234–246. Retrieved from http://dx.doi.org/10.1016/j.eneco.2016.02. 004
Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable and Sustainable Energy Reviews, 70, 1298–1322. Retrieved from http://dx.doi.org/10.1016/j.rser.2016.12.0 30
Mbangala, M., & Perelman, S. (1997). L’efficacité technique des chemins de fer en Afrique subsaharienne: une comparaison internationale par la méthode de DEA. Revue d’économie Du Développement, 5(3), 91– 115.
Meera, S. N., Rao, R. B., Ganesan, & Prathibha, D. K. (2016). Growth and Performance of Electricity Sector in Rwanda-A Descriptive Analysis. Sona Global Management Reviews, 10(3), 106–126.
Mohsin, M., Hanif, I., Taghizadeh-hesary, F., &Abbas, Q. (2021). Nexus between energy efficiency and electricity reforms: A DEA-Based way forward for clean power development. Energy Policy, 149, 112052. https://doi.org/10.1016/j.enpol.2020.1120 52
Njeru, G., Gathiaka, J., & Kimuyu, P. (2020). Technical Efficiency of Thermal Electricity Generators in Kenya. International Journal of Energy Economics and Policy, 10(3), 340–347.
Nsabimana, R. (2020). Electricity Sector Organization and Performance in Burundi. Multidisciplinary Digital Publishing Institute Proceedings, 58(1), 26. https://doi.org/10.3390/WEF-06938
Pereira de Souza, Marcus V., Souza, Reinaldo C., Pessanha, José Francisco M., da Costa Oliveira, Carlos H., Diallo, M. (2014). An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities. Socio-Economic Planning Sciences, 48, 169–174. https://doi.org/10.1016/j.seps.2014.03.002
Petridis, K., Ünsal, M. G., Dey, P., & Örkcü, H. H. (2019). A novel network data envelopment analysis model for performance measurement of Turkish electric distribution companies. Energy, 174, 985– 998. https://doi.org/10.1016/j.energy.2019.01.0 51
Plane, P. (1999). Privatization, technical efficiency and welfare consequences: The case of the Cote d’Ivoire Electricity Company (CIE). World Development, 27(2), 343–360. https://doi.org/10.1016/S0305-750X(98)00139-9
Real, F. J. R., & Tovar, B. (2020). Revisiting electric utilities ’ efficiency in the Southern African Power Pool, 1998-2009. Journal of Energy in Southern Africa, 31(1), 1998–2009.
Ritten, C. J., Peck, D., Ehmke, M., and Patalee, M. A. B. (2018). Firm Efficiency and Returns-to-Scale in the Honey Bee Pollination Services Industry. Journal of Economic Entomology, 111(3), 1014–1022. https://doi.org/10.1093/jee/toy075
See, K. F., & Coelli, T. (2012). An analysis of factors that influence the technical efficiency of Malaysian thermal power plants. Energy Economics, 34(3), 677–685. https://doi.org/10.1016/j.eneco.2011.09.00 5
Valasai, G. Das, Uqaili, M. A., Memon, H. U. R., Samoo, S. R., Mirjat, N. H., & Harijan, K. (2017). Overcoming electricity crisis in Pakistan: A review of sustainable electricity options. Renewable and Sustainable Energy Reviews, 72, 734–745. https://doi.org/10.1016/j.rser.2017.01.097
Wang, Z., and Feng, C. (2015). Sources of production inefficiency and productivity growth in China: A global data envelopment analysis. Energy Economics, 49, 380–389. https://doi.org/10.1016/j.eneco.2015.03.00 9
Xie, B.-C., Gao, J., Chen, Y., & Deng, N.-Q. (2018). Measuring the efficiency of grid companies in China: A bootstrapping non-parametric meta-frontier approach. Journal of Cleaner Production, 174, 1381–1391. https://doi.org/10.1016/j.jclepro.2017.11.0 16
