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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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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DOI: https://doi.org/10.1007/s10888-017-9374-6
Keywords
- Vulnerability to poverty
- Distributional regression
- Generalized additive model for location
- Scale and shape
- Receiver operating characteristic curve