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Vulnerability to poverty revisited: Flexible modeling and better predictive performance

Maike Hohberg (), Katja Landau, Thomas Kneib, Stephan Klasen and Walter Zucchini
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Maike Hohberg: University of Goettingen
Katja Landau: University of Goettingen
Thomas Kneib: University of Goettingen
Walter Zucchini: University of Goettingen

The Journal of Economic Inequality, 2018, vol. 16, issue 3, No 6, 439-454

Abstract: 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.

Keywords: Vulnerability to poverty; Distributional regression; Generalized additive model for location; Scale and shape; Receiver operating characteristic curve (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10888-017-9374-6

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