Bayesian treatment effects due to a subsidized health program: The case of preventive health care utilization in Medellin (Colombia)

Andrés Ramírez-Hassan, Rosember Guerra Urzola

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We analyze the treatment effects due to patients' status, covered or uncovered by the subsidized health program in Medellin (Colombia), on the number of preventive health care visits to physicians. We use a Bayesian endogenous switching model that allows interaction effects as well as endogeneity due to patients' status. This framework allows the calculation of the posterior distributions of heterogeneous treatment effects and presents Bayesian learning of the covariance between the two potential outcomes, a relevant policy-maker parameter, even though we do not observe individuals in both states at the same time. We found that there are self-selection effects as well as moral hazard, which may imply adverse consequences for the health care system in Colombia.

Original languageEnglish
Number of pages30
JournalEmpirical Economics: A quarterly journal of the Institute for Advanced Studies
DOIs
Publication statusAccepted/In press - 2020

Fingerprint

Treatment Effects
Colombia
Healthcare
Health
utilization
Moral Hazard
Potential Outcomes
health care
Endogeneity
Bayesian Learning
Interaction Effects
health
Posterior distribution
Imply
physician
Treatment effects
Health care utilization
interaction
Model
learning

Keywords

  • DEMAND
  • Endogenous switching
  • INSURANCE
  • OUTCOMES
  • Ordered response
  • Potential outcome
  • SELECTION MODEL
  • Subsidized health program
  • Treatment effects

Cite this

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abstract = "We analyze the treatment effects due to patients' status, covered or uncovered by the subsidized health program in Medellin (Colombia), on the number of preventive health care visits to physicians. We use a Bayesian endogenous switching model that allows interaction effects as well as endogeneity due to patients' status. This framework allows the calculation of the posterior distributions of heterogeneous treatment effects and presents Bayesian learning of the covariance between the two potential outcomes, a relevant policy-maker parameter, even though we do not observe individuals in both states at the same time. We found that there are self-selection effects as well as moral hazard, which may imply adverse consequences for the health care system in Colombia.",
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AB - We analyze the treatment effects due to patients' status, covered or uncovered by the subsidized health program in Medellin (Colombia), on the number of preventive health care visits to physicians. We use a Bayesian endogenous switching model that allows interaction effects as well as endogeneity due to patients' status. This framework allows the calculation of the posterior distributions of heterogeneous treatment effects and presents Bayesian learning of the covariance between the two potential outcomes, a relevant policy-maker parameter, even though we do not observe individuals in both states at the same time. We found that there are self-selection effects as well as moral hazard, which may imply adverse consequences for the health care system in Colombia.

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KW - Ordered response

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