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Improving Socioeconomic Opportunities for the Poor: A Study of Poverty Measurement in Romania

Chapter

Abstract

This chapter estimates the extent and trend of poverty in Romania during transition, using grouped distributional data and Lorenz curve interpolation. It asks the question: do estimates derived from grouped distributional data differ from those in the literature derived from survey data? It finds that the poverty estimates introduced in this chapter are only broadly in line with the literature for the period 2002–2005 and document a substantial decline in poverty. The estimates for the late 1990s suggest that poverty in Romania fluctuated considerably during this period, which reflects the trend estimated in part of the literature. However, there are significant differences in the size of the estimated measures. This is also the case between estimates found in the literature derived from survey data. The chapter concludes by discussing the broader implications of using grouped distributional data and Lorenz curve interpolation in poverty research in Romania and elsewhere.

Keywords

Poverty Estimation Grouped data POVCAL Romania 

Notes

Acknowledgments

I would like to acknowledge the invaluable support of Monica Silaghi and Camelia Minoiu during the writing of the thesis on which this contribution is based. Recently, Camelia Minoiu, Christopher Pollitt, and Greg Porumbescu reviewed an earlier draft and provided a number of helpful comments.

References

  1. Alam, A., Murthi, M., Yemtsov, R., Murrugarra, E., Dudwick, N., Hamilton, E., et al. (2005). Growth, poverty and inequality: Eastern Europe and the former soviet union. Washington, DC: Europe and Central Asia Region World Bank.CrossRefGoogle Scholar
  2. Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95, 476–487.CrossRefGoogle Scholar
  3. Bresson, F. (2009). On the estimation of growth and inequality elasticities of poverty with grouped data. Review of Income and Wealth, 55(2), 266–302.CrossRefGoogle Scholar
  4. Chen, S., Datt, G., & Ravallion, M. (2007). POVCAL, a program for calculating poverty measures from grouped data. DEC-RG, World Bank: Washington, DC.Google Scholar
  5. Chen, S., & Ravallion, M. (2001). How did the world’s poorest fare in the 1990s? Review of Income and Wealth, 47(3), 283–300.CrossRefGoogle Scholar
  6. Chen, S., & Ravallion, M. (2006). China’s (uneven) progress against poverty. Journal of Development Economics, 82(1), 1–42.Google Scholar
  7. Chen, S., & Wang, Y. (2001). China’s growth and poverty reduction: recent trends between 1990 and 1999 (World Bank Policy Research Working Paper No. 2651), Washington, DC: World Bank.Google Scholar
  8. Coudouel, A., Hentschel, J. S., & Wodon, Q. T. (2001). Poverty measurement and analysis with Annex A: Technical notes. In J. Klugman (Ed.), Poverty reduction strategy sourcebook. Washington, DC: World Bank.Google Scholar
  9. Figini, P., & Santarelli, E. (2006). Openness, economic reforms, and poverty: Globalization in the developing countries. Journal of Developing Areas, 39(2), 129–151.CrossRefGoogle Scholar
  10. Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–766.CrossRefGoogle Scholar
  11. Fusco, A., & Dickes, P. (2006). Rasch model and multidimensional poverty measurement (IRISS Working Paper 2006-02). Differdange, Luxembourg: CEPS/INSTEAD.Google Scholar
  12. Kakwani, N. (1980). On a class of poverty measures. Econometrica, 48(2), 437–446.CrossRefGoogle Scholar
  13. Karshenas, M. (2004). Global poverty estimates and the millennium goals: Towards a unified framework (ILO Employment Strategy Paper No. 2004/5). Geneva, Switzerland: International Labor Organization.Google Scholar
  14. Klasen, S. (2008). Economic growth and poverty reduction: Measurement issues using income and non-income indicators. World Development, 36(3), 420–445.CrossRefGoogle Scholar
  15. Milanovic, B. (1998). Income, inequality and poverty during the transition from planned to market economy. World Bank: Washington, DC.Google Scholar
  16. Minoiu, C., & Reddy, S. G. (2008a). Estimating poverty and inequality from grouped data: How well do parametric methods perform? Journal of Income Distribution, 18(2), 24.Google Scholar
  17. Minoiu, C., & Reddy, S. G. (2008b). Chinese poverty: Assessing the impact of alternative assumptions. Review of Income and Wealth, 54(4), 572–596.CrossRefGoogle Scholar
  18. Molnar, M. (2000). Poverty measurement and income support in Romania. In S. Hutton & G. Redmond (Eds.), Poverty in transition economies. London: Routledge.Google Scholar
  19. Pritchett, L. (2006). Who is not poor? Dreaming of a world truly free of poverty. The World Bank Research Observer, 21(1), 1–23.CrossRefGoogle Scholar
  20. Son, H. H., & Kakwani, N. (2006). Global estimates of pro-poor growth (UNDP−IPC Working Paper No. 31). Brasilia, Brazil: United Nations Development Programme: International Poverty Center.Google Scholar
  21. Subbarao, K., & Mehra, K. (1995). Social assistance and the poor in Romania (Education and social policy discussion paper series No. 66). Washington, DC: World Bank.Google Scholar
  22. Teșliuc, E., Pop, L., & Panduru, F. (2003). Poverty in Romania: Profile and trends during 1995–2002 (In: World Bank, Romania Poverty Assessment Volume II: Background Papers, Report No. 26169-RO). Washington, DC: Europe and Central Asia Region, World Bank.Google Scholar
  23. Villasenor, J. A., & Arnold, B. C. (1989). Elliptical Lorenz curves. Journal of Econometrics, 40, 327–338.CrossRefGoogle Scholar
  24. World Bank. (1997). Romania: Poverty and social policy (Report No. 16462-RO, in two volumes: Volume 1 Main Report and Volume 2 Statistical Annex). Washington, DC: World Bank.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Griffiths School of ManagementEmanuel University of OradeaOradeaRomania

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