Modeling heaped duration data: An application to neonatal mortality

Wiji Arulampalam, Valentina Corradi, Daniel Gutknecht

Research output: Contribution to journalArticlepeer-review

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 substantial heaping, a form of measurement error, present in the data. The main objective of this paper is to provide a set of sufficient conditions for identification and consistent estimation of the (discretized) baseline hazard accounting for heaping and unobserved heterogeneity. Our identification strategy requires neither administrative data nor multiple measurements, but a correctly reported duration point and the presence of some flat segment(s) in the baseline hazard. We establish the asymptotic properties of the maximum likelihood estimator and derive a set of specification tests that allow, among other things, to test for the presence of heaping and to compare different heaping mechanisms. Our empirical findings do not suggest a significant reduction of mortality in treated districts. However, they do indicate that accounting for heaping matters for the estimation of the hazard parameters.

Original languageEnglish (US)
Pages (from-to)363-377
Number of pages15
JournalJournal of Econometrics
Volume200
Issue number2
DOIs
StatePublished - Oct 2017

Keywords

  • Discrete time duration model
  • Heaping
  • Measurement error
  • Neonatal mortality
  • Parameters on the boundary

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint

Dive into the research topics of 'Modeling heaped duration data: An application to neonatal mortality'. Together they form a unique fingerprint.

Cite this