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Economic Development, Industrialization, and Poverty Eradication: A Benchmarking Analysis of Developing, Emerging, and Developed Countries
1
Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
2
Business and Economic School, Instituto Superior de Gestão, Av. Mal. Craveiro Lopes 2A, 1700-284 Lisbon, Portugal
3
CEG-IST, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1040-001 Lisbon, Portugal
4
University of New England, Armidale NSW 2350, Australia
5
CEFAGE, Faculdade de Economia, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
*
For correspondence.
Academic Editor:
Received: 7 November 2023 Accepted: 31 January 2024 Published: 27 February 2024
Abstract
This study utilizes benchmarking techniques to monitor productivity change in relation to Sustainable Development Goals (SDGs) 1, 8, and 9, addressing the challenges faced by countries in interpreting measures. The first SDG 1, “No Poverty”, aims to completely eliminate poverty. The objective of SDG 8, “Decent Work and Economic Growth”, is to foster comprehensive economic advancement. Finally, SDG 9, “Industry, Innovation, and Infrastructure”, focuses on the creation of durable and sustainable infrastructure, as well as promoting innovation to drive economic progress. Economic development, job creation, wealth creation, and poverty eradication are crucial for sustainable development. However, there is no other study estimating the evolution of countries’ performance in terms of these SDGs, whether countries have converged or not, and how each of these SDGs contributes to this performance development. This is the main goal of the present study, which compares 85 countries (2010–2020) from different profiles (developing, emerging, and developed) in terms of several SDG indicators. We applied data envelopment analysis (DEA) and Malmquist productivity indices that quantify changes in efficiency and technology over time to assess productivity dynamics and improvements. Results showed that emerging countries showed the highest productivity development, followed by developing countries and finally developed countries. The slower productivity development in developed countries indicates stagnation, allowing emerging countries to converge in terms of wealth creation, distribution, and poverty reduction.
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Keywords
sustainable development goals; economic development; poverty eradication; United Nations; data envelopment analysis; Malmquist index
Copyright © 2024
Delgado et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use and distribution provided that the original work is properly cited.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Cite this Article
Delgado, A., Caldas, P., & Varela, M. (2024). Economic Development, Industrialization, and Poverty Eradication: A Benchmarking Analysis of Developing, Emerging, and Developed Countries. Highlights of Sustainability, 3(1), 84–103. https://doi.org/10.54175/hsustain3010007
References
1.
Karahasan, B. C. (2023). To make growth reduce poverty, industrialize: Using manufacturing to mediate the effect of growth on poverty. Development Policy Review, 41(4), e12689. https://doi.org/10.1111/dpr.12689
2.
Halkos, G., & Gkampoura, E.-C. (2021). Where do we stand on the 17 Sustainable Development Goals? An overview on progress. Economic Analysis and Policy, 70, 94–122. https://doi.org/10.1016/j.eap.2021.02.001
3.
Yang, B., Usman, M., & Jahanger, A. (2021). Do industrialization, economic growth and globalization processes influence the ecological footprint and healthcare expenditures? Fresh insights based on the STIRPAT model for countries with the highest healthcare expenditures. Sustainable Production and Consumption, 28, 893–910. https://doi.org/10.1016/j.spc.2021.07.020
4.
McMillan, M., & Zeufack, A. (2022). Labor productivity growth and industrialization in Africa. Journal of Economic Perspectives, 36(1), 3–32. https://doi.org/10.1257/jep.36.1.3
5.
Fonseca, L. M., Domingues, J. P., & Dima, A. M. (2020). Mapping the sustainable development goals relationships. Sustainability, 12(8), 3359. https://doi.org/10.3390/su12083359
6.
Brown, P., & James, D. (2020). Educational expansion, poverty reduction and social mobility: Reframing the debate. International Journal of Educational Research, 100, 101537. https://doi.org/10.1016/j.ijer.2020.101537
7.
Guo, H., Liang, D., Sun, Z., Chen, F., Wang, X., Li, J., et al. (2022). Measuring and evaluating SDG indicators with Big Earth Data. Science Bulletin, 67(17), 1792–1801. https://doi.org/10.1016/j.scib.2022.07.015
8.
Winsemius, H. C., Jongman, B., Veldkamp, T. I. E., Hallegatte, S., Bangalore, M., & Ward, P. J. (2018). Disaster risk, climate change, and poverty: assessing the global exposure of poor people to floods and droughts. Environment and Development Economics, 23(3), 328–348. https://doi.org/10.1017/S1355770X17000444
9.
Arriani, R. R., & Chotib. (2021). The Correlation of SDG 1 and 8 and Spatial Effect of Human Development Index in Central Java. IOP Conference Series: Earth and Environmental Science, 940, 012063. https://doi.org/10.1088/1755-1315/940/1/012063
10.
Giles-Corti, B., Lowe, M., & Arundel, J. (2020). Achieving the SDGs: Evaluating indicators to be used to benchmark and monitor progress towards creating healthy and sustainable cities. Health Policy, 124(6), 581–590. https://doi.org/10.1016/j.healthpol.2019.03.001
11.
Jacob, A. (2017). Mind the Gap: Analyzing the Impact of Data Gap in Millennium Development Goals’ (MDGs) Indicators on the Progress toward MDGs. World Development, 93, 260–278. https://doi.org/10.1016/j.worlddev.2016.12.016
12.
Cherchye, L., Moesen, W., Rogge, N., & Van Puyenbroeck, T. (2007). An introduction to ‘benefit of the doubt’composite indicators. Social Indicators Research, 82, 111–145. https://doi.org/10.1007/s11205-006-9029-7
13.
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141, 61–94. https://doi.org/10.1007/s11205-017-1832-9
14.
Camanho, A. S., & Dyson, R. G. (2006). Data envelopment analysis and Malmquist indices for measuring group performance. Journal of Productivity Analysis, 26, 35–49. https://doi.org/10.1007/s11123-006-0004-8
15.
Tone, K. (2004). Malmquist productivity index. In Handbook on data envelopment analysis (pp. 203–227). Springer, Boston.
16.
Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50(6), 1393–1414. https://doi.org/10.2307/1913388
17.
Bali Swain, R., & Ranganathan, S. (2021). Modeling interlinkages between sustainable development goals using network analysis. World Development, 138, 105136. https://doi.org/10.1016/j.worlddev.2020.105136
18.
Dang, H.-A. H., & Serajuddin, U. (2020). Tracking the sustainable development goals: Emerging measurement challenges and further reflections. World Development, 127, 104570. https://doi.org/10.1016/j.worlddev.2019.05.024
19.
Leal Filho, W., Azeiteiro, U., Alves, F., Pace, P., Mifsud, M., Brandli, L., et al. (2018). Reinvigorating the sustainable development research agenda: the role of the sustainable development goals (SDG). International Journal of Sustainable Development and World Ecology, 25(2), 131–142. https://doi.org/10.1080/13504509.2017.1342103
20.
Eisenmenger, N., Pichler, M., Krenmayr, N., Noll, D., Plank, B., Schalmann, E., et al. (2020). The Sustainable Development Goals prioritize economic growth over sustainable resource use: a critical reflection on the SDGs from a socio-ecological perspective. Sustainability Science, 15, 1101–1110. https://doi.org/10.1007/s11625-020-00813-x
21.
Barbier, E. B., & Burgess, J. C. (2019). Sustainable development goal indicators: Analyzing trade-offs and complementarities. World Development, 122, 295–305. https://doi.org/10.1016/j.worlddev.2019.05.026
22.
Leal Filho, W., Lovren, V. O., Will, M., Salvia, A. L., & Frankenberger, F. (2021). Poverty: A central barrier to the implementation of the UN Sustainable Development Goals. Environmental Science and Policy, 125, 96–104. https://doi.org/10.1016/j.envsci.2021.08.020
23.
Maksimov, V., Wang, S. L., & Luo, Y. (2017). Reducing poverty in the least developed countries: The role of small and medium enterprises. Journal of World Business, 52(2), 244–257. https://doi.org/10.1016/j.jwb.2016.12.007
24.
Blicharska, M., Teutschbein, C., & Smithers, R. J. (2021). SDG partnerships may perpetuate the global North–South divide. Scientific Reports, 11, 22092. https://doi.org/10.1038/s41598-021-01534-6
25.
Chen, M., Sinha, A., Hu, K., & Shah, M. I. (2021). Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development. Technological Forecasting and Social Change, 164, 120521. https://doi.org/10.1016/j.techfore.2020.120521
26.
Wang, C., Quan, Y., Li, X., Yan, Y., Zhang, J., Song, W., et al. (2022). Characterizing and analyzing the sustainability and potential of China’s cities over the past three decades. Ecological Indicators, 136, 108635. https://doi.org/10.1016/j.ecolind.2022.108635
27.
Wen, B., Musa, S. N., Onn, C. C., Ramesh, S., Liang, L., Wang, W., et al. (2020). The role and contribution of green buildings on sustainable development goals. Building and Environment, 185, 107091. https://doi.org/10.1016/j.buildenv.2020.107091
28.
Nhemachena, C., Matchaya, G., Nhemachena, C. R., Karuaihe, S., Muchara, B., & Nhlengethwa, S. (2018). Measuring baseline agriculture-related sustainable development goals index for Southern Africa. Sustainability, 10(3), 849. https://doi.org/10.3390/su10030849
29.
Odey, G. O., Alawad, A. G. A., Atieno, O. S., Carew-Bayoh, E. O., Fatuma, E., Ogunkola, I. O., et al. (2021). COVID-19 pandemic: Impacts on the achievements of sustainable development goals in Africa. Pan African Medical Journal, 38, 251. https://doi.org/10.11604/pamj.2021.38.251.27065
30.
Romano, G., Ferreira, D. C., Marques, R. C., & Carosi, L. (2020). Waste services’ performance assessment: The case of Tuscany, Italy. Waste Management, 118, 573–584. https://doi.org/10.1016/j.wasman.2020.08.057
31.
Li, Z., Crook, J., & Andreeva, G. (2017). Dynamic prediction of financial distress using Malmquist DEA. Expert Systems with Applications, 80, 94–106. https://doi.org/10.1016/j.eswa.2017.03.017
32.
Wang, D. D. (2019). Performance assessment of major global cities by DEA and Malmquist index analysis. Computers, Environment and Urban Systems, 77, 101365. https://doi.org/10.1016/j.compenvurbsys.2019.101365
33.
Huang, B., Zhang, L., Ma, L., Bai, W., & Ren, J. (2021). Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index. Energy, 228, 120481. https://doi.org/10.1016/j.energy.2021.120481
34.
Tachega, M. A., Yao, X., Liu, Y., Ahmed, D., Li, H., & Mintah, C. (2021). Energy efficiency evaluation of oil producing economies in Africa: DEA, malmquist and multiple regression approaches. Cleaner Environmental Systems, 2, 100025. https://doi.org/10.1016/j.cesys.2021.100025
35.
Amaral, C., Pedro, M. I., Ferreira, D. C., & Marques, R. C. (2022). Performance and its determinants in the Portuguese municipal solid waste utilities. Waste Management, 139, 70–84. https://doi.org/10.1016/j.wasman.2021.12.020
36.
Ferreira, D. C., Marques, R. C., & Pedro, M. I. (2018). Explanatory variables driving the technical efficiency of European seaports: An order-α approach dealing with imperfect knowledge. Transportation Research Part E: Logistics and Transportation Review, 119, 41–62. https://doi.org/10.1016/j.tre.2018.09.007
37.
Matos, R., Ferreira, D. C., & Pedro, I. (2021) Economic analysis of Portuguese public hospitals through the construction of quality, efficiency, access, and financial related composite indicators. Social Indicators Research, 157, 361–392. https://doi.org/10.1007/s11205-021-02650-6
38.
Nunes, A. M., & Ferreira, D. C. (2022). Social inequity and health: From the environment to the access to healthcare in composite indicators, the Portuguese case. In W. Leal Filho, D. G. Vidal, M. A. P. Dinis, & R. C. Dias (Eds.), Sustainable Policies and Practices in Energy, Environment and Health Research Addressing Cross-cutting Issues. World Sustainability Series. Springer, Cham. https://doi.org/10.1007/978-3-030-86304-3_21
39.
Cherchye, L., Moesen, W., Rogge, N., & Van Puyenbroeck, T. (2011). Constructing composite indicators with imprecise data: A proposal. Expert Systems with Applications, 38(9), 10940–10949. https://doi.org/10.1016/j.eswa.2011.02.136
40.
Rogge, N. (2018). On aggregating benefit of the doubt composite indicators. European Journal of Operational Research, 264(1), 364–369. https://doi.org/10.1016/j.ejor.2017.06.035
41.
Álvarez, I. C., Barbero, J., & Zofío, J. L. (2020). A Data Envelopment Analysis Toolbox for MATLAB. Journal of Statistical Software, 95(3), 1–49. https://doi.org/10.18637/jss.v095.i03
42.
Ferreira, D. C., Marques, R. C., Pedro, M. I., & Santos, G. (2022). PPP hospitals in Portugal: What does benchmarking tell us about their relative performance? In S. Verweij, I. van Meerkerk, & C. Casady (Eds.), The Performance Advantage of Public-Private Partnerships: An International Assessment of Empirical Evidence. Edward Elgar. https://doi.org/10.4337/9781800889200.00012
43.
Kerstens, K., & Van de Woestyne, I. (2014). Comparing Malmquist and Hicks-Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data. European Journal of Operational Research, 233(3), 749–758. https://doi.org/10.1016/j.ejor.2013.09.009
44.
Ferreira, D. C., & Marques, R. C. (2016). Malmquist and Hicks–Moorsteen productivity indexes for clusters performance evaluation. International Journal of Information Technology & Decision Making, 15(5), 1015–1053. https://doi.org/10.1142/S0219622016500243
45.
Lind, N. (2019). A Development of the Human Development Index. Social Indicators Research, 146, 409–423. https://doi.org/10.1007/s11205-019-02133-9
46.
Resce, G. (2021). Wealth-adjusted Human Development Index. Journal of Cleaner Production, 318, 128587. https://doi.org/10.1016/j.jclepro.2021.128587
47.
O’Sullivan, A., & Sheffrin, S. M. (2003). Economics: Principles in Action. Pearson Prentice Hall.
48.
Ferreira, D. C., Caldas, P., Varela, M., & Marques, R. C. (2023). A geometric aggregation of performance indicators considering regulatory constraints: An application to the urban solid waste management. Expert Systems with Applications, 218, 119540. https://doi.org/10.1016/j.eswa.2023.119540
49.
Ferreira, D. C., Marques, R. C., & Nunes, A. M. (2021). Pay for performance in health care: a new best practice tariff-based tool using a log-linear piecewise frontier function and a dual–primal approach for unique solutions. Operational Research, 21, 2101–2146. https://doi.org/10.1007/s12351-019-00502-3
50.
Ferreira, D. C., Figueira, J. R., Greco, S., & Marques, R. C. (2023). Data envelopment analysis models with imperfect knowledge of input and output values: An application to Portuguese public hospitals. Expert Systems with Applications, 231, 120543. https://doi.org/10.1016/j.eswa.2023.120543
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