Modelling Heaped Duration Data: An Application to Neonatal Mortality

Wiji Arulampalam, Valentina Corradi and Daniel Gutknecht
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Valentina Corradi: Surrey University
Daniel Gutknecht: Oxford University

CAGE Online Working Paper Series from Competitive Advantage in the Global Economy (CAGE)

Abstract: In 2005, the Indian Government launched a conditional cash-incentive program to encourage institutional delivery. This paper studies the effects of the program on neonatal mortality using district-level household survey data. We model mortality using survival analysis, paying special attention to the substantial heaping present in the data. The main objective of this paper is to provide a set of sufficient conditions for identi cation and consistent estimation of the baseline hazard accounting for heaping and unobserved heterogeneity. Our identi cation strategy requires neither administrative data nor multiple measurements, but a correctly reported duration and the presence of some at segments in the baseline hazard which includes this correctly reported duration point. We establish the asymptotic properties of the maximum likelihood estimator and pro- vide a simple procedure to test whether the policy had (uniformly) reduced mortality. While our empirical ndings do not con rm the latter, they do indicate that accounting for heaping matters for the estimation of the baseline hazard.

Keywords: Discrete Time Duration Model; Heaping; Measurement Error; Parameters on the Boundary; Neonatal Mortality. (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-hea
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http://www2.warwick.ac.uk/fac/soc/economics/resear ... 2014_arulampalam.pdf

Related works:
Journal Article: Modeling heaped duration data: An application to neonatal mortality (2017)
Working Paper: Modelling Heaped Duration Data: An Application to Neonatal Mortality (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:cge:wacage:207

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