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Vulnerability to poverty revisited: Flexible modeling and better predictive performance

The Journal of Economic Inequality volume 16, pages 439–454 (2018)Cite this article

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.

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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).

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Affiliations

  1. Chair of Statistics, University of Goettingen, Humboldtallee 3, 37073, Goettingen, Germany

    Maike Hohberg, Katja Landau, Thomas Kneib & Walter Zucchini

  2. Chair of Development Economics, University of Goettingen, Platz der Goettinger Sieben 3, 37073, Goettingen, Germany

    Stephan Klasen

Authors
  1. Maike Hohberg
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  2. Katja Landau
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  3. Thomas Kneib
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  4. Stephan Klasen
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  5. Walter Zucchini
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Correspondence to Maike Hohberg.

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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

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Keywords

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