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Measuring technology inequality across African countries using the concept of efficiency Gini coefficient

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Abstract

In the context of global climate change, much hope is placed in technological progress's ability to address environmental problems. However, the persistence of technology inequality across countries undermines environmental technology innovation’s contribution to environmental issues. In such a context, this paper introduces a new approach that combines Data Envelopment Analysis (DEA) with the Gini coefficient to gauge technology inequality. Additionally, decomposition analysis is adopted to identify the driving factors affecting technology diffusion. Empirically, the proposed approach is applied to 49 African countries from 2000 to 2017. The results of our study show that, although the unified efficiency of African countries improved slightly during the study period, there is still much room for improvement in terms of economic prosperity and environmental performance. Secondly, there is group heterogeneity between two groups of African countries (low-efficiency group and high-efficiency group) under both managerial and natural disposability. Thirdly, the inequality decomposition revealed that cross-group inequality is the source of group heterogeneity and the main barrier to technology diffusion, followed by within-group inequality. Finally, environmental technology progress exhibits a low contribution to enhancing sustainability in Africa due to technological inequality persistence. In future, it would be quite meaningful to perform sector-level analysis, which could provide more detailed information on sustainability.

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Notes

  1. It should be noted that the regional grouping is done following Ohene-Asare et al. (2020). Countries with no or multiple regional affiliations are grouped according to their position on the map.

References

  • Adams, E. A. (2018). Intra-urban inequalities in water access among households in Malawi’s informal settlements: Toward pro-poor urban water policies in Africa. Environmental Development, 26, 34–42.

    Google Scholar 

  • Adom, P. K. (2019). An evaluation of energy efficiency performances in Africa under heterogeneous technologies. Journal of Cleaner Production, 209, 1170–1181.

    Google Scholar 

  • AfDB. (2020). African economic outlook: Developing Africa’s workforce for the future. Annual Yearly Review Study. https://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/African_Economic_Outlook_2018_-_EN.pdf.

  • Alhassan, A. L., & Tetteh, M. L. (2017). Non-interest income and bank efficiency in Ghana: A two-stage DEA bootstrapping approach. Journal of African Business, 18(1), 124–142.

    Google Scholar 

  • Amowine, N., Ma, Z., Li, M., Zhou, Z., Azembila Asunka, B., & Amowine, J. (2019). Energy efficiency improvement assessment in Africa: An integrated dynamic DEA approach. Energies, 12(20), 3915.

    Google Scholar 

  • Amowine, N., Ma, Z., Li, M., Zhou, Z., Yaw Naminse, E., & Amowine, J. (2020). Measuring dynamic energy efficiency in Africa: A slack-based DEA approach. Energy Science & Engineering, 8(11), 3854–3865.

    Google Scholar 

  • Asongu, S. A., Nnanna, J., & Acha-Anyi, P. N. (2020). Inequality and gender economic inclusion: The moderating role of financial access in Sub-Saharan Africa. Economic Analysis and Policy, 65, 173–185.

    Google Scholar 

  • AUC/OECD. (2018). Africa’s development dynamics 2018: Growth. Addis Ababa/OECD Publishing. https://doi.org/10.1787/9789264302501-en

  • Awodumi, O. B., & Adeleke, A. M. (2016). Non-renewable energy and macroeconomic efficiency of seven major oil producing economies in Africa. Zagreb International Review of Economics and Business, 19(1), 59–74.

    Google Scholar 

  • Aye, G. C., Gupta, R., & Wanke, P. (2018). Efficiency in South African agriculture: A two-stage fuzzy approach. Benchmarking, 25(8), 2723–2759.

    Google Scholar 

  • Ayompe, L. M., Davis, S. J., & Egoh, B. N. (2020). Trends and drivers of African fossil fuel CO2 emissions 1990–2017. Environmental Research Letters, 15(12).

  • Bahrini, R. (2017). Efficiency analysis of islamic banks in the middle east and north Africa region: A bootstrap DEA approach. International Journal of Financial Studies, 5(1), 7.

    Google Scholar 

  • Baloch, M. A., Danish, K., & S. U. D., Ulucak, Z. Ş., & Ahmad, A. (2020). Analyzing the relationship between poverty, income inequality, and CO2 emission in Sub-Saharan African countries. Science of the Total Environment, 740, 139867.

    CAS  Google Scholar 

  • Ben Abdelkader, I., & Mansouri, F. (2019). Performance of microfinance institutions in the MENA region: A comparative analysis. International Journal of Social Economics, 46(1), 47–65.

    Google Scholar 

  • Biney, E., Amoateng, A. Y., & Ewemooje, O. S. (2020). Inequalities in morbidity in South Africa: A family perspective. SSM - Population Health, 12, 100653.

    Google Scholar 

  • BP. (2020). BP statistical review of world energy. https://www.bp.com/en/global/ corporate/energy-economics/statistical-review-of-world-energy.html.

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Google Scholar 

  • Cook, W. D., & Zhu, J. (Eds.). (2014). Data envelopment analysis (Vol. 208). Springer.

  • Cooper, W. W., Seiford, L. M., & Zhu, J. (Eds.). (2011). Handbook on data envelopment analysis (Vol. 164). Springer.

  • Copenhagen Centre on Energy Efficiency. (2015). Accelerating Energy Efficiency: Initiatives and Opportunities, Africa. Copenhagen Denmark. https://c2e2.unepdtu.org/kms_object/accelerating-energy-efficiency-initiatives-and-opportunities-africa/.

  • Costa, M. (2016). Overlapping component and inequality decomposition: A simulation study for the Gini index. METRON, 74(2), 193–205.

    Google Scholar 

  • Dai, C., Qin, X. S., Chen, Y., & Guo, H. C. (2018). Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach. Journal of Hydrology, 561, 322–334.

    Google Scholar 

  • De Coninck, H., & Sagar, A. (2015). Technology in the 2015 Paris Climate Agreement and beyond. ICTSD Programme on Innovation, Technology and Intellectual Property. Issue Paper, 42. International Centre for Trade and Sustainable Development, Geneva, Switzerland, www.ictsd.org.

  • De Magalhães, L., & Santaeulàlia-Llopis, R. (2018). The consumption, income, and wealth of the poorest: An empirical analysis of economic inequality in rural and urban Sub-Saharan Africa for macroeconomists. Journal of Development Economics, 134, 350–371.

    Google Scholar 

  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society—Series B, 39(1), 1–38.

    Google Scholar 

  • Desta, T. S. (2016). Are the best African banks really the best? A Malmquist data envelopment analysis. Meditari Accountancy Research, 24(4), 588–610.

    Google Scholar 

  • Docquier, F., & Iftikhar, Z. (2019). Brain drain, informality and inequality: A search-and-matching model for sub-Saharan Africa. Journal of International Economics, 120, 109–125.

    Google Scholar 

  • Falchetta, G., Pachauri, S., Byers, E., Danylo, O., & Parkinson, S. C. (2020). Satellite observations reveal inequalities in the progress and effectiveness of recent electrification in sub-Saharan Africa. One Earth, 2(4), 364–379.

    Google Scholar 

  • Getachew, Y. Y., & Turnovsky, S. J. (2020). Redistribution, inequality, and efficiency with credit constraints: Implications for South Africa. Economic Modelling, 93, 259–277.

    Google Scholar 

  • Gini, C. (1914). Sulla misura della concentrazione e della variabilita de caratteri. Atti Del Reale Istituto Veneto Di Scienze. Lettere Ed Arti., 73, 1203–1248.

    Google Scholar 

  • Girsberger, E. M., Méango, R., & Rapoport, H. (2020). Regional migration and wage inequality in the West African economic and monetary union. Journal of Comparative Economics, 48(2), 385–404.

    Google Scholar 

  • Grunewald, N., Jakob, M., & Mouratiadou, I. (2014). Decomposing inequality in CO2 emissions: The role of primary energy carriers and economic sectors. Ecological Economics, 100, 183–194.

    Google Scholar 

  • Haal, K., Smith, A., & van Doorslaer, E. (2018). The rise and fall of mortality inequality in South Africa in the HIV era. SSM - Population Health, 5, 239–248.

    Google Scholar 

  • Hollander, M., Wolfe, D. A., & Chicken, E. (2013). Nonparametric statistical methods (3rd ed.). Wiley.

  • Huang, R., & Tian, L. (2021). CO2 emissions inequality through the lens of developing countries. Applied Energy, 281, 116043.

    CAS  Google Scholar 

  • Ibidunni, A. S., Ufua, D. E., Okorie, U. E., & Kehinde, B. E. (2019). Labour productivity in agricultural sector of Sub-Sahara Africa (2010–2017): A data envelopment and panel regression approach. African Journal of Economic and Management Studies, 11(2), 207–232.

    Google Scholar 

  • IEA. (2019). Africa energy outlook, Paris: International Energy Agency. https://www.iea.org/africa2019. Accessed January 28, 2021.

  • International Energy Agency. (2020). Data and statistics. https://www.iea.org/data-and-statistics?country=SOUTHAFRIC&fuel=CO2emissions&indicator=CO2ByGDP. Accessed February 08, 2021.

  • International Monetary Fund. (2021). World Economic Outlook (October 2021) - GDP, current prices. https://www.imf.org/external/datamapper/NGDPD@WEO/AFQ/DZA/ZAF/MAR/NGA/EGY?year=2019. Accessed November 1, 2021.

  • Jiang, S., Zhang, Z., Ren, H., Wei, G., Xu, M., & Liu, B. (2021). Spatiotemporal characteristics of urban land expansion and population growth in Africa from 2001 to 2019: Evidence from population density data. ISPRS International Journal of Geo-Information, 10(9), 584.

    Google Scholar 

  • Krafft, C., & Alawode, H. (2018). Inequality of opportunity in higher education in the Middle East and North Africa. International Journal of Educational Development, 62, 234–244.

    Google Scholar 

  • Lambert, P. J., & Aronson, J. R. (1993). Inequality decomposition analysis and the Gini coefficient revisited. The Economic Journal, 103, 7.

    Google Scholar 

  • Li, A., Zhang, A., Zhou, Y., & Yao, X. (2017). Decomposition analysis of factors affecting carbon dioxide emissions across provinces in China. Journal of Cleaner Production., 141, 1428–1444.

    CAS  Google Scholar 

  • Liao, H., Du, J., & Wei, Y. M. (2013). Energy conservation in China: Key provincial sectors at two-digit level. Applied Energy, 104, 457–465.

    Google Scholar 

  • Mahalik, M. K., Mallick, H., Padhan, H., & Sahoo, B. (2018). Is skewed income distribution good for environmental quality? A comparative analysis among selected BRICS countries. Environmental Science and Pollution Research, 25(23), 23170–23194.

    CAS  Google Scholar 

  • Maji, I. K. (2019). Impact of clean energy and inclusive development on CO2 emissions in sub-Saharan Africa. Journal of Cleaner Production, 240, 118186.

    CAS  Google Scholar 

  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60.

    Google Scholar 

  • Marti, L., & Puertas, R. (2020). Analysis of the efficiency of African countries through their Ecological Footprint and Biocapacity. Science of the Total Environment, 722, 137504.

    CAS  Google Scholar 

  • Nandelenga, M. W., & Oduor, J. (2020). Asymmetric analysis of finance—Inequality nexus: Evidence from sub-Saharan Africa. The Journal of Economic Asymmetries, 22, e00184.

    Google Scholar 

  • Ngobeni, V., Breitenbach, M. C., & Aye, G. C. (2020). Technical efficiency of provincial public healthcare in South Africa. Cost Effectiveness and Resource Allocation, 18(3).

  • Nock, D., Levin, T., & Baker, E. (2020). Changing the policy paradigm: A benefit maximization approach to electricity planning in developing countries. Applied Energy, 264, 114583.

    Google Scholar 

  • Nyiwul, L. M. (2019). Climate change mitigation and adaptation in Africa: Strategies, synergies, and constraints. In T. Sequeira & L. Reis (Eds.), Climate change and global development. Contributions to Economics. Springer.

  • Nyiwul, L. (2021). Climate change adaptation and inequality in Africa: Case of water, energy and food insecurity. Journal of Cleaner Production, 278, 123393.

    Google Scholar 

  • Odhiambo, N. M. (2020). Financial development, income inequality and carbon emissions in sub-Saharan African countries: A panel data analysis. Energy Exploration and Exploitation, 38(5), 1914–1931.

    CAS  Google Scholar 

  • Ogundele, O. J., Pavlova, M., & Groot, W. (2020). Socioeconomic inequalities in reproductive health care services across Sub-Saharan Africa. A systematic review and meta-analysis. Sexual and Reproductive Healthcare, 25, 100536.

    Google Scholar 

  • Ohene-Asare, K., & Turkson, C. (2019). Total-factor energy efficiency and productivity of ECOWAS states: A slacks-based measure with undesirable outputs. Journal of African Business, 20(1), 91–111.

    Google Scholar 

  • Ohene-Asare, K., Tetteh, E. N., & Asuah, E. L. (2020). Total factor energy efficiency and economic development in Africa. Energy Efficiency, 13(6), 1177–1194.

    Google Scholar 

  • Ouedraogo, N. S. (2017). Africa energy future: Alternative scenarios and their implications for sustainable development strategies. Energy Policy, 106, 457–471.

    Google Scholar 

  • Paramati, S. R., Shahzad, U., & Doğan, B. (2022). The role of environmental technology for energy demand and energy efficiency: Evidence from OECD countries. Renewable and Sustainable Energy Reviews, 153.

  • Penn World Table. (2020). https://www.rug.nl/ggdc/productivity/pwt/.

  • Pham, N. M., Huynh, T. L. D., & Nasir, M. A. (2020). Environmental consequences of population, affluence and technological progress for European countries: A Malthusian view. Journal of Environmental Management, 260, 110143.

    Google Scholar 

  • Sarkodie, S. A., & Adams, S. (2020). Electricity access, human development index, governance and income inequality in Sub-Saharan Africa. Energy Reports, 6, 455–466.

    Google Scholar 

  • Sia, D., Tchouaket, N., Hajizadeh, M., Karemere, H., Onadja, Y., & Nandi, A. (2020). The effect of gender inequality on HIV incidence in Sub-Saharan Africa. Public Health, 182, 56–63.

    CAS  Google Scholar 

  • Stuch, B., Alcamo, J., & Schaldach, R. (2021). Projected climate change impacts on mean and year-to-year variability of yield of key smallholder crops in Sub-Saharan Africa. Climate and Development, 3(3), 268–282.

    Google Scholar 

  • Sueyoshi, T., & Goto, M. (2016). Undesirable congestion under natural disposability and desirable congestion under managerial disposability in U.S. electric power industry measured by DEA environmental assessment. Energy Economics, 55, 173–188.

    Google Scholar 

  • Sueyoshi, T., & Goto, M. (2018). Environmental assessment on energy and sustainability by data envelopment analysis. Wiley (2018 John Wiley & Sons (ed.)). https://www.wiley.com/en-us/Environmental+Assessment+on+Energy+and+Sustainability+by+Data+Envelopment+Analysis-p-9781118979341.

  • Sueyoshi, T., & Yuan, Y. (2017). Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention. Energy Economics, 66, 154–166.

    Google Scholar 

  • Sueyoshi, T., Li, A., & Gao, Y. (2018). Sector sustainability on fossil fuel power plants across Chinese provinces: Methodological comparison among radial, non-radial and intermediate approaches under group heterogeneity. Journal of Cleaner Production, 187, 819–829.

    Google Scholar 

  • Sueyoshi, T., Li, A., & Liu, X. (2019). Exploring sources of China’s CO2 emission: Decomposition analysis under different technology changes. European Journal of Operational Research, 279(3), 984–995.

    Google Scholar 

  • Sueyoshi, T., Liu, X., & Li, A. (2020). Evaluating the performance of Chinese fossil fuel power plants by data environment analysis: An application of three intermediate approaches in a time horizon. Journal of Cleaner Production, 277, 121992.

    Google Scholar 

  • Sueyoshi, T., Qu, J., Li, A., & Liu, X. (2021). A new approach for evaluating technology inequality and diffusion barriers: The concept of efficiency Gini coefficient and its application in Chinese provinces. Energy, 235, 121256.

    CAS  Google Scholar 

  • Sueyoshi, T., Qu, J., Li, A., & Xie, C. (2020). Understanding the efficiency evolution for the Chinese provincial power industry: A new approach for combining data envelopment analysis-discriminant analysis with an efficiency shift across periods. Journal of Cleaner Production., 277, 1223711.

    Google Scholar 

  • Sueyoshi, T., Yuan, Y., & Goto, M. (2017a). A literature study for DEA applied to energy and environment. Energy Economics, 62, 104–124.

    Google Scholar 

  • Sueyoshi, T., Yuan, Y., Li, A., & Wang, D. (2017b). Methodological comparison among radial, non-radial and intermediate approaches for DEA environmental assessment. Energy Economics, 67, 439–453.

    Google Scholar 

  • Sueyoshi, T., Zhang, R., Qu, J., & Li, A. (2021). New concepts for environment-health measurement by data envelopment analysis and an application in China. Journal of Cleaner Production, 312, 127468.

    Google Scholar 

  • Sulemana, I., Nketiah-Amponsah, E., Codjoe, E. A., & Andoh, J. A. N. (2019). Urbanization and income inequality in Sub-Saharan Africa. Sustainable Cities and Society, 48, 101544.

    Google Scholar 

  • Suzuki, M. (2015). Identifying roles of international institutions in clean energy technology innovation and diffusion in the developing countries: Matching barriers with roles of the institutions. Journal of Cleaner Production, 98, 229–240.

    Google Scholar 

  • Tchamyou, V. S., Erreygers, G., & Cassimon, D. (2019). Inequality, ICT and financial access in Africa. Technological Forecasting and Social Change, 139, 169–184.

    Google Scholar 

  • Top, M., Konca, M., & Sapaz, B. (2020). Technical efficiency of healthcare systems in African countries: An application based on data envelopment analysis. Health Policy and Technology, 9(1), 62–68.

    Google Scholar 

  • UNEP. (2017). “Atlas of Africa Energy Resources.” https://www.icafrica.org/en/knowledge-hub/article/atlas-of-africa-energy-resources-329/. Accessed March 05, 2021.

  • U.S. Energy Information Administration. (2020). U.S. Energy Information Administration EIA. United States. Accessed from: https://www.eia.gov/international/data/world/total-energy/total-energy-consumption.

  • Uzar, U. (2020). Is income inequality a driver for renewable energy consumption? Journal of Cleaner Production, 255, 120287.

    Google Scholar 

  • van Heerden, C., & Coetzee, J. (2019). Re-examining the ability to explain future south African banking share returns: A data envelopment analysis approach. South African Journal of Economic and Management Sciences, 22(1).

  • von Fintel, D., & Orthofer, A. (2020). Wealth inequality and financial inclusion: Evidence from South African tax and survey records. Economic Modelling, 91, 568–578.

    Google Scholar 

  • Wang, X., Shao, Q., Nathwani, J., & Zhou, Q. (2019). Measuring wellbeing performance of carbon emissions using hybrid measure and meta-frontier techniques: Empirical tests for G20 countries and implications for China. Journal of Cleaner Production, 237, 117758.

    Google Scholar 

  • Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249(1), 378–389.

    Google Scholar 

  • Xaba, S. T., Marwa, N., & Mathur-Helm, B. (2019). Efficiency evaluation of agricultural cooperatives in Mpumalanga: An empirical study using the DEA approach. African Journal of Economic and Management Studies, 11(1), 51–62.

    Google Scholar 

  • Xu, B., & Lin, B. (2017). Does the high-tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model. Environmental Impact Assessment Review, 63, 44–58.

    Google Scholar 

  • Xu, B., & Lin, B. (2018). Investigating the role of high-tech industry in reducing China’s CO2 emissions: A regional perspective. Journal of Cleaner Production, 177, 169–177.

    Google Scholar 

  • Xu, B., & Lin, B. (2019). Can expanding natural gas consumption reduce China’s CO2 emissions? Energy Economics, 81, 393–407.

    Google Scholar 

  • Xu, B., & Lin, B. (2021). Investigating spatial variability of CO2 emissions in heavy industry: Evidence from a geographically weighted regression model. Energy Policy, 149, 112011.

    CAS  Google Scholar 

  • Yao, X., Zhou, H., Zhang, A., & Li, A. (2015). Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis. Energy Policy, 84, 142–154.

    CAS  Google Scholar 

  • Zhang, Y., Mao, Y., Jiao, L., Shuai, C., & Zhang, H. (2021). Eco-efficiency, eco-technology innovation and eco-well-being performance to improve global sustainable development. Environmental Impact Assessment Review, 89, 106580.

    Google Scholar 

  • Zhou, Z. (2016). Machine learning. Tsinghua University Press.

  • Zhu, H., Xia, H., Guo, Y., & Peng, C. (2018). The heterogeneous effects of urbanization and income inequality on CO2 emissions in BRICS economies: Evidence from panel quantile regression. Environmental Science and Pollution Research, 25(17), 17176–17193.

    CAS  Google Scholar 

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Acknowledgments

The authors are grateful to the anonymous referees for their valuable suggestions.

Funding

This paper is supported by the National Natural Foundation of China (Grant No. 71873078&71603148&), Taishan Scholars, Shandong Provincial Natural Science Foundation (Grant No. ZR201807060746), Young Scholars of Ideology and Culture Propaganda of Publicity Department, CCCPC, Humanities And Social Sciences Research Major Project of Shandong University (Grant No. 21RWZD16) and SDU Outstanding Scholar.

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  1. The Center for Economic Research, Shandong School of Development, Shandong University, Jinan, 250100, China

    Jingjing Qu, Aijun Li & Morié Guy-Roland N’Drin

  2. Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, 01 BP 1303, Abidjan 01, Ivory Coast

    Morié Guy-Roland N’Drin

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  1. Jingjing Qu
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JQ contributed to methodology and programming. AL reviewed, edited the manuscript, supervised, and revised the expressions. MG-RN’D collected the data, wrote the original draft, reviewed, edited the manuscript, and revised the expressions. All authors read and approved the final manuscript.

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Correspondence to Morié Guy-Roland N’Drin.

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Appendix

Appendix

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Table 9 Regions members
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Table 10 Descriptive statistics of inputs outputs variables by sub-region
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Qu, J., Li, A. & N’Drin, M.GR. Measuring technology inequality across African countries using the concept of efficiency Gini coefficient. Environ Dev Sustain 25, 4107–4138 (2023). https://doi.org/10.1007/s10668-022-02236-3

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