Social Indicators Research

, Volume 141, Issue 1, pp 111–130 | Cite as

Evaluating the Efficiency of Governmental Excellence for Social Progress: Focusing on Low- and Lower-Middle-Income Countries

  • Hyeri Choi
  • Min Jae ParkEmail author


As the “weak-institutions trap” is increasingly recognized as the major hindrance to growth, the advantages offered by e-government may provide an opportunity for these counties to escape this trap. From a stance emphasizing the importance of a state’s fundamental capacity, this study advances the literature by including a country’s e-government development as another dimension of government capacity. With this perspective, we examine how efficiently each country’s governmental capacity is enhancing social progress performance in low- and lower-middle-income countries by applying data envelopment analysis. The results of the efficiency test were then combined with income level, used widely to categorize countries, for clustering analysis, aiming to discover certain characteristics or typologies across countries. The results offer a guide to how efficiently (in comparison with others) each country’s governmental excellence is yielding the outcome of social progress, the nature of their limitations, and how countries at a similar economic level are performing, as a potential benchmarking target.


Government efficiency Social progress index DEA Clustering Developing countries 


  1. Acemoglu, D., Johnson, S., & Robinson, J. A. (2004). Institutions as the fundamental cause of long-run growth. Cambridge, MA: National Bureau of Economic Research.Google Scholar
  2. Adeya, C. N. (2002). ICTs and poverty: A literature review. Ottawa: IDRC.Google Scholar
  3. Afonso, A., Romero-Barrutieta, A., & Monsalve, E. (2013). Public sector efficiency: evidence for Latin America. Inter-American Development Bank, Fiscal and Municipal Management Division, Discussion Paper IDB-DP-279. IDB, Washington.Google Scholar
  4. Afonso, A., Schuknecht, L., & Tanzi, V. (2005). Public sector efficiency: An international comparison. Public Choice, 123(3), 321–347. Scholar
  5. Al Nagi, E., & Hamdan, M. (2009). Computerization and e-government implementation in Jordan: Challenges, obstacles and successes. Government Information Quarterly, 26(4), 577–583. Scholar
  6. Alkire, S. (2005). Why the capability approach? Journal of Human Development, 6(1), 115–135. Scholar
  7. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.Google Scholar
  8. Banker, R. D., Cooper, W. W., Seiford, L. M., Thrall, R. M., & Zhu, J. (2004). Returns to scale in different DEA models. European Journal of Operational Research, 154(2), 345–362.Google Scholar
  9. Basu, S. (2004). E-government and developing countries: An overview. International Review of Law, Computers and Technology, 18(1), 109–133. Scholar
  10. Berlage, L., & Terweduwe, D. (1988). The classification of countries by cluster and by factor analysis. World Development, 16(12), 1527–1545. Scholar
  11. Bertocchi, G., & Guerzoni, A. (2012). Growth, history, or institutions: What explains state fragility in sub-Saharan Africa? Journal of Peace Research, 49(6), 769–783. Scholar
  12. Birdsall, N. (2007). Do no harm: Aid, weak institutions and the missing middle in Africa. Development Policy Review, 25(5), 575–598. Scholar
  13. Blancard, S., & Hoarau, J. F. (2013). A new sustainable human development indicator for small island developing states: A reappraisal from data envelopment analysis. Economic Modelling, 30, 623–635.Google Scholar
  14. Burnside, C., & Dollar, D. (2000). Aid, policies, and growth. The American Economic Review, 90(4), 847–868.Google Scholar
  15. Case, A., Lubotsky, D., & Paxson, C. (2002). Economic status and health in childhood: The origins of the gradient. The American Economic Review, 92(5), 1308–1334.Google Scholar
  16. Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (Eds.). (2013). Data envelopment analysis: Theory, methodology, and applications. Berlin: Springer.Google Scholar
  17. 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
  18. Choi, H., Park, M. J., & Rho, J. J. (2017). Two-dimensional approach to governmental excellence for human development in developing countries: Combining policies and institutions with e-government. Government Information Quarterly. Scholar
  19. Cordella, A., & Bonina, C. M. (2012). A public value perspective for ICT enabled public sector reforms: A theoretical reflection. Government Information Quarterly, 29(4), 512–520. Scholar
  20. Cruz-Cázares, C., Bayona-Sáez, C., & García-Marco, T. (2013). You can’t manage right what you can’t measure well: Technological innovation efficiency. Research Policy, 42(6–7), 1239–1250. Scholar
  21. Cutler, D., Deaton, A., & Lleras-Muney, A. (2006). The determinants of mortality. The Journal of Economic Perspectives, 20(3), 97–120.Google Scholar
  22. Doryan, E. (2001). Poverty, human development and public expenditure: Developing actions for government and civil society, in equity and health: Views from the Pan American Sanitary Bureau. Pan American Health Organization, Washington: PAHO Press.Google Scholar
  23. Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management is dead—Long live digital-era governance. Journal of Public Administration Research and Theory, 16(3), 467–494. Scholar
  24. Ebrahim, Z., & Irani, Z. (2005). E-government adoption: Architecture and barriers. Business Process Management Journal, 11(5), 589–611. Scholar
  25. Estevez, E., & Janowski, T. (2013). Electronic governance for sustainable development—Conceptual framework and state of research. Government Information Quarterly, 30(Supplement 1), S94–S109. Scholar
  26. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290.Google Scholar
  27. Fountain, J. E. (2004). Building the virtual state: Information technology and institutional change. Washington: Brookings Institution Press.Google Scholar
  28. Gerbec, D., Gašperič, S., Šmon, I., & Gubina, F. (2004). Determining the load profiles of consumers based on fuzzy logic and probability neural networks. IEE Proceedings-Generation, Transmission and Distribution, 151(3), 395–400.Google Scholar
  29. Gupta, S., & Verhoeven, M. (2001). The efficiency of government expenditure: Experiences from Africa. Journal of Policy Modeling, 23(4), 433–467. Scholar
  30. Haddad, L., Alderman, H., Appleton, S., Song, L., & Yohannes, Y. (2003). Reducing child malnutrition: How far does income growth take us? The World Bank Economic Review, 17(1), 107–131.Google Scholar
  31. Hauner, D., & Kyobe, A. (2010). Determinants of government efficiency. World Development, 38(11), 1527–1542. Scholar
  32. Heeks, R. (2002). Reinventing government in the information age. In R. Heeks (Ed.), Reinventing government in the information age: International practice in IT-enabled public sector reform (pp. 9–21). London: Routledge.Google Scholar
  33. Jang, D., Eom, J., Park, M. J., & Rho, J. J. (2016). Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers. Energy Policy, 88, 11–26.Google Scholar
  34. Janowski, T. (2016). Implementing sustainable development goals with digital government—Aspiration-capacity gap. Government Information Quarterly, 33(4), 603–613. Scholar
  35. Layne, K., & Lee, J. (2001). Developing fully functional e-government: A four stage model. Government Information Quarterly, 18(2), 122–136.Google Scholar
  36. Liang, L., Li, Y., & Li, S. (2009). Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA. Expert Systems with Applications, 36(3, Part 2), 5895–5899. Scholar
  37. Luk, S. C. Y. (2009). The impact of leadership and stakeholders on the success/failure of e-government service: Using the case study of e-stamping service in Hong Kong. Government Information Quarterly, 26(4), 594–604. Scholar
  38. Maumbe, B. M., Owei, V., & Alexander, H. (2008). Questioning the pace and pathway of e-government development in Africa: A case study of South Africa’s Cape Gateway project. Government Information Quarterly, 25(4), 757–777. Scholar
  39. Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179.Google Scholar
  40. Moon, M. J. (2002). The evolution of e-government among municipalities: Rhetoric or reality? Public Administration Review, 62(4), 424–433.Google Scholar
  41. Murtagh, F., & Legendre, P. (2014). Ward’s hierarchical agglomerative clustering method: Which algorithms implement ward’s criterion? Journal of Classification, 31(3), 274–295.Google Scholar
  42. Nielsen, M. M. (2016). E-governance and stage models: Analysis of identified models and selected Eurasian experiences in digitising citizen service delivery. Electronic Government, an International Journal, 12(2), 107–141.Google Scholar
  43. Onda, K., Crocker, J., Kayser, G. L., & Bartram, J. (2014). Country clustering applied to the water and sanitation sector: A new tool with potential applications in research and policy. International Journal of Hygiene and Environmental Health, 217(2–3), 379–385. Scholar
  44. Pang, G., & Herrera, S. (2005). Efficiency of public spending in developing countries: An efficiency frontier approach (Vol. 3645). World Bank Policy Research Working Paper.Google Scholar
  45. Park, H.-S., & Jun, C.-H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications, 36(2), 3336–3341.Google Scholar
  46. Po, R.-W., Guh, Y.-Y., & Yang, M.-S. (2009). A new clustering approach using data envelopment analysis. European Journal of Operational Research, 199(1), 276–284. Scholar
  47. Prasetyo, A. D., & Zuhdi, U. (2013). The government expenditure efficiency towards the human development. Procedia Economics and Finance, 5, 615–622. Scholar
  48. Putu, S. H. M. N., Jan van Helden, G., & Tillema, S. (2007). Public sector performance measurement in developing countries: A literature review and research agenda. Journal of Accounting and Organizational Change, 3(3), 192–208. Scholar
  49. Razmi, M., Abbasian, E., & Mohammadi, S. (2012). Investigating the effect of government health expenditure on HDI in Iran. Journal of Knowledge Management, Economics and Information Technology, 5, 1–13.Google Scholar
  50. Rodríguez Domínguez, L., García Sánchez, I. M., & Gallego Álvarez, I. (2011). Determining factors of e-government development: A worldwide national approach. International Public Management Journal, 14(2), 218–248. Scholar
  51. Rojas, M. (2011). The ‘measurement of economic performance and social progress’ report and quality of life: Moving forward. Social Indicators Research, 102(1), 169–180.Google Scholar
  52. Rose, W. R., & Grant, G. G. (2010). Critical issues pertaining to the planning and implementation of e-government initiatives. Government Information Quarterly, 27(1), 26–33. Scholar
  53. Rostow, W. W. (1959). The stages of economic growth. The Economic History Review, 12(1), 1–16.Google Scholar
  54. Sachs, J. (2006). The end of poverty: Economic possibilities for our time. New York: Penguin.Google Scholar
  55. Samoilenko, S., & Osei-Bryson, K.-M. (2008). Increasing the discriminatory power of DEA in the presence of the sample heterogeneity with cluster analysis and decision trees. Expert Systems with Applications, 34(2), 1568–1581. Scholar
  56. Samoilenko, S., & Osei-Bryson, K.-M. (2013). Using data envelopment analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system. Omega, 41(1), 131–142. Scholar
  57. Schuppan, T. (2009). E-government in developing countries: Experiences from sub-Saharan Africa. Government Information Quarterly, 26(1), 118–127. Scholar
  58. Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA. Journal of Econometrics, 46(1), 7–38. Scholar
  59. Sen, A. (1992). Inequality reexamined. Oxford: Clarendon Press.Google Scholar
  60. Siqueira, I. R. D. (2014). Measuring and managing ‘state fragility’: The production of statistics by the World Bank, Timor-Leste and the g7+. Third World Quarterly, 35(2), 268–283. Scholar
  61. Strauss, J., & Thomas, D. (1998). Health, nutrition, and economic development. Journal of Economic Literature, 36(2), 766–817.Google Scholar
  62. Sugden, R. (1993). Welfare, resources, and capabilities: A review of inequality reexamined by Amartya Sen. [Inequality reexamined, Amartya Sen]. Journal of Economic Literature, 31(4), 1947–1962.Google Scholar
  63. Suri, T., Boozer, M. A., Ranis, G., & Stewart, F. (2011). Paths to success: The relationship between human development and economic growth. World Development, 39(4), 506–522. Scholar
  64. Teicher, J., & Dow, N. (2002). E-government in Australia: Promise and progress. Information Polity, 7(4), 231–246.Google Scholar
  65. Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Berlin: Springer.Google Scholar
  66. UNPAN. (2012). UN E-government Survey 2012: E-government for the people. New York: UNPAN.Google Scholar
  67. Vu, K. M. (2013). Information and communication technology (ICT) and Singapore’s economic growth. Information Economics and Policy, 25(4), 284–300. Scholar
  68. Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180. Scholar
  69. Whitmore, A. (2012). A statistical analysis of the construction of the United Nations E-Government Development Index. Government Information Quarterly, 29(1), 68–75. Scholar
  70. Yang, K., & Rho, S.-Y. (2007). E-government for better performance: Promises, realities, and challenges. International Journal of Public Administration, 30(11), 1197–1217. Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Business and Technology ManagementKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea
  2. 2.Department of Business AdministrationSeoul School of Integrated Sciences and Technologies (aSSIST)SeoulRepublic of Korea

Personalised recommendations