Skip to main content

Vulnerability to poverty revisited: Flexible modeling and better predictive performance

Abstract

This paper analyzes several modifications to improve a simple measure of vulnerability as expected poverty. Firstly, in order to model income, we apply distributional regression relating potentially each parameter of the conditional income distribution to the covariates. Secondly, we determine the vulnerability cutoff endogenously instead of defining a household as vulnerable if its probability of being poor in the next period is larger than 0.5. For this purpose, we employ the receiver operating characteristic curve that is able to consider prerequisites according to a particular targeting mechanism. Using long-term panel data from Germany, we build both mean and distributional regression models with the established 0.5 probability cutoff and our vulnerability cutoff. We find that our new cutoff considerably increases predictive performance. Placing the income regression model into the distributional regression framework does not improve predictions further but has the advantage of a coherent model where parameters are estimated simultaneously replacing the original three step estimation approach.

References

  1. Amemiya, T.: The maximum likelihood and the nonlinear three-stage least squares estimator in the general nonlinear simultaneous equation model. Econometrica 45(4), 955 (1977)

    Article  Google Scholar 

  2. Atkinson, A.B.: Social indicators: The EU and social inclusion. Oxford University Press, Oxford (2002)

    Book  Google Scholar 

  3. Bergolo, M., Cruces, G., Ham, A.: Assessing the predictive power of vulnerability measures: Evidence from panel data for Argentina and Chile. J. Income Distrib. 21(1), 28–64 (2012)

    Google Scholar 

  4. Biewen, M., Jenkins, S.P.: A framework for the decomposition of poverty differences with an application to poverty differences between countries. Empir. Econ0 30(2), 331–358 (2005)

    Article  Google Scholar 

  5. Calvo, C., Dercon, S.: Vulnerability to individual and aggregate poverty. Soc. Choice Welf. 41(4), 721–740 (2013)

    Article  Google Scholar 

  6. Celidoni, M.: Vulnerability to poverty: An empirical comparison of alternative measures. Appl. Econ. 45, 1493–1506 (2013)

    Article  Google Scholar 

  7. Chaudhuri, S.: Assessing vulnerability to poverty: concepts empirical methods and illustrative examples. Columbia University, Mimeo (2003)

    Google Scholar 

  8. Chaudhuri, S., Jalan, J., Suryahadi, A.: Assessing household vulnerability to poverty from cross-sectional data: A methodology and estimates from Indonesia. Discussion Paper Series 0102-52 Department of Economics. Columbia University, New York (2002)

    Google Scholar 

  9. Christiaensen, L.J., Subbarao, K.: Towards an understanding of household vulnerability in rural Kenya. J. Afr. Econ. 14(4), 520–558 (2005)

    Article  Google Scholar 

  10. Dutta, I., Foster, J., Mishra, A.: On measuring vulnerability to poverty. Soc. Choice Welf. 37(4), 743–761 (2011)

    Article  Google Scholar 

  11. Egan, J.P.: Signal Detection Theory and ROC Analysis. Academic Press Series in Cognition and Perception Academic Press. NY, New York (1975)

    Google Scholar 

  12. Eilers, P.H.C., Marx, B.D.: Flexible smoothing with B-splines and penalties. Stat. Sci. 11(2), 89–121 (1996)

    Article  Google Scholar 

  13. Feeny, S., McDonald, L.: Vulnerability to multidimensional poverty: Findings from households in Melanesia. J. Dev. Stud. 52(3), 447–464 (2016)

    Article  Google Scholar 

  14. Frick, J.R., Jenkins, S.P., Lillard, D.R., Lipps, O., Wooden, M.: Die internationale Einbettung des Sozio-oekonomischen Panels (SOEP) im Rahmen des Cross-National Equivalent File (CNEF). Vierteljahrsh. Wirtschaftsforschung 77(3), 110–129 (2008)

    Article  Google Scholar 

  15. Gaiha, R., Imai, K.: Measuring vulnerability and poverty: Estimates for rural India. Research Paper 2008/040 UNU-WIDER. Helsinki, Finland (2008)

    Google Scholar 

  16. Günther, I., Harttgen, K.: Estimating households vulnerability to idiosyncratic and covariate shocks: A novel method applied in Madagascar. World Dev. 37(7), 1222–1234 (2009)

    Article  Google Scholar 

  17. Hoddinott, J., Quisumbing, A.: Methods for microeconometric risk and vulnerability assessments. Social Protection Discussion Paper Series 0324 The World Bank. DC, Washington (2003)

    Google Scholar 

  18. Jha, R., Dang, T.: Vulnerability to poverty in Papua New Guinea in 1996. Asian Econ. J. 24(3), 235–251 (2010)

    Article  Google Scholar 

  19. Klasen, S., Lange, S.: How narrowly should anti-poverty programs be targeted? Simulation evidence from Bolivia and Indonesia. Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 213, Courant Research Centre PEG (2016)

  20. Klasen, S., Waibel, H.: Vulnerability to poverty. Palgrave Macmillan, UK, London (2013)

    Book  Google Scholar 

  21. Klein, N., Kneib, T., Lang, S., Sohn, A.: Bayesian structured additive distributional regression with an application to regional income inequality in Germany. Ann. Appl. Stat. 9(2), 1024–1052 (2015)

    Article  Google Scholar 

  22. Krause, P., Ritz, D.: EU-Indikatoren zur sozialen Inklusion in Deutschland. Vierteljahrsh. Wirtschaftsforschung 75(1), 152–173 (2006)

    Article  Google Scholar 

  23. Landau, K.: Messung der Vulnerabilität der Armut: Eine statistische Analyse mit deutschen Paneldaten Dissertation. Universität Göttingen, Göttingen (2012)

    Google Scholar 

  24. Ligon, E., Schechter, L.: Measuring vulnerability. Econ. J. 113(486), C95–C102 (2003)

    Article  Google Scholar 

  25. Ligon, E., Schechter, L.: Evaluating different approaches to estimating vulnerability. Social Protection Discussion Paper Series 0410 The World Bank. DC, Washington (2004)

    Google Scholar 

  26. McCarthy, N., Brubaker, J., De La Fuente, A.: Vulnerability to poverty in rural Malawi. Policy Research Working Paper WPS7769 The World Bank. DC, Washington (2016)

    Google Scholar 

  27. McDonald, J.B., Ransom, M.: The Generalized Beta Distribution as a Model for the Distribution of Income: Estimation of Related Measures of Inequality. In: Chotikapanich, D. (ed.) Modeling Income Distributions and Lorenz Curves, Economic Studies in Equality, Social Exclusion and Well-Being, vol. 5, pp 147–166. Springer, New York (2008)

  28. Moser, C.O.: The asset vulnerability framework: Reassessing urban poverty reduction strategies. World Dev. 26(1), 1–19 (1998)

    Article  Google Scholar 

  29. Novignon, J., Nonvignon, J., Mussa, R., Chiwaula, L.S.: Health and vulnerability to poverty in Ghana: evidence from the Ghana living standards survey round 5. Health Econ. Rev. 2, 11 (2012)

    Article  Google Scholar 

  30. Pritchett, L., Suryahadi, A., Sumarto, S.: Quantifying vulnerability to poverty: A proposed measure, applied to Indonesia. Policy Research Working Paper WPS2437 The World Bank. DC, Washington (2000)

    Google Scholar 

  31. Ravallion, M.: How relevant is targeting to the success of an antipoverty program? World Bank Res. Obs. 24(2), 205–231 (2009)

    Article  Google Scholar 

  32. Rigby, R.A., Stasinopoulos, D.M.: Generalized additive models for location, scale and shape. J. Royal Stat Soc.: Ser. C (Appl. Stat.) 54(3), 507–554 (2005)

    Article  Google Scholar 

  33. Selezneva, E., Van Kerm, P.: A distribution-sensitive examination of the gender wage gap in Germany. J. Econ. Inequal. 14(1), 21–40 (2016)

    Article  Google Scholar 

  34. Skoufias, E., Quisumbing, A.R.: Consumption insurance and vulnerability to poverty: A synthesis of the evidence from Bangladesh, Ethiopia, Mali, Mexico and Russia. Eur. J. Dev. Res. 17(1), 24–58 (2005)

    Article  Google Scholar 

  35. Sohn, A., Klein, N., Kneib, T.: A Semiparametric Analysis of Conditional Income Distributions. Schmollers Jahrbuch 135, Proceedings of the 11th International Socio-Economic Panel User Conference (SOEP 2014) (2015)

  36. Stasinopoulos, D.M., Rigby, R.A.: Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23(7) (2007)

  37. Stauder, J., Hüning, W.: Die Messung von Äquivalenzeinkommen und Armutsquoten auf der Basis des Mikrozensus. Statistische Analysen und Studien NRW 13 (2004)

  38. Suryahadi, A., Sumarto, S.: Poverty and vulnerability in Indonesia before and after the economic crisis. Asian Econ. J. 17(1), 45–64 (2003)

    Article  Google Scholar 

  39. Thi Nguyen, K.A., Jolly, C.M., Bui, C.T.P.N., Le, T.H.T.: Climate change, rural household food consumption and vulnerability: The case of Ben Tre province in Vietnam. Agric. Econ. Rev. 16(2), 95–109 (2015)

    Google Scholar 

  40. Thompson, M.L., Zucchini, W.: On the statistical analysis of ROC curves. Stat. Med. 8(10), 1277–1290 (1989)

    Article  Google Scholar 

  41. Zereyesus, Y.A., Embaye, W.T., Tsiboe, F., Amanor-Boadu, V.: Implications of non-farm work to vulnerability to food poverty-recent evidence from northern Ghana. World Dev. 91, 113–124 (2017)

    Article  Google Scholar 

  42. Zhang, Y., Wan, G.: How precisely can we estimate vulnerability to poverty? Oxf. Dev. Stud. 37(3), 277–287 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

We thank two anonymous referees and Stephen Jenkins for helpful comments on earlier versions of this paper. We are grateful for funding from the Ministry of Science and Culture (Lower Saxony).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Maike Hohberg.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hohberg, M., Landau, K., Kneib, T. et al. Vulnerability to poverty revisited: Flexible modeling and better predictive performance. J Econ Inequal 16, 439–454 (2018). https://doi.org/10.1007/s10888-017-9374-6

Download citation

Keywords

  • Vulnerability to poverty
  • Distributional regression
  • Generalized additive model for location
  • Scale and shape
  • Receiver operating characteristic curve