Estimating the Health Time Preference Rate and Analyzing Its Trend in Certain Countries (Case Study: Countries with Higher than Average Income)

Document Type : Research Paper

Authors

1 Ph.D. student, Tarbiat Modares University, Tehran, Iran.

2 Associate Professor of Economics, Tarbiat Modares University, Tehran, Iran

3 Associate Professor of Economics, Institute of Economic Research, Tarbiat Modares University, Tehran, Iran.

4 Associate Professor of Agricultural Economics, Institute of Economic Research, Tarbiat Modares University, Tehran, Iran

10.22034/epj.2025.19743.2404

Abstract

Purpose: Individual attitudes towards risk shape a broad set of decisions related to important outcomes such as savings and investments, occupational choice and labor supply, retirement decisions, insurance and health services purchase, health behaviors and lifestyles (Banks et al., 2019). Despite the limited resources available and with the aim of obtaining the maximum benefit, these decisions sometimes have only short-term effects and, in some cases, their effects are shifted to the future. The question of whether a person’s choice is to achieve more benefit in the present or in the future relates to the concept of time preference. Knowledge of the time preference rate of individuals in the field of health is a key variable affecting intertemporal utility and is considered as one of the fundamental concerns in health economics macro policies.
Methodology: This study employs the MIMIC model to evaluate the shifts in the health-related time preference rate in several countries with upper middle incomes during a 20-year period from 2000 to 2019. The MIMIC model explains the relationship between observable variables and an unobservable one by minimizing the distance between the sample covariance matrix and the covariance matrix predicted by the model. In the research model, the hidden variable is the health time preference rate (HTP). The variables used in our study are divided into two major categories including indicator variables and causal variables. The causal variables are LLE (the logarithm of life expectancy), LGDP (logarithm of per capita income), UNILO (unemployment rate), PR (political risk rating), SE (enrollment, tertiary–% gross), and INFG (inflation, GDP deflator). The indicator variables are OOP (out-of-pocket expenditure–% of current health expenditure), SUI (suicide mortality rate–per 100.000 population), LEGA (prosperity index), MIG (migration rate), and LINSU (life insurance premium rate). The studied countries are a number of upper-middle income countries according to the World Bank classification, namely Brazil, Iran, Malaysia, Mexico, Russia, Jordan, Azerbaijan, Argentina, Kazakhstan, and South Africa. After modeling, first the stationarity and autocorrelation tests were performed to ensure the existence of a long-term relationship between the variables. Before estimating the model, the appropriate variable was selected to normalize the model. In the following, the hidden variable (i.e. the health time preference rate) and its trend were predicted. It should be noted that all the steps were taken in the STATA software.
Findings and Discussion: In order to investigate the stationary of the variables, different tests were performed. According to the results, all the variables are stationary, i.e. I(0) or I(1). Also, a co-integration test was performed to ensure a long-term relationship between the variables. To determine the best conditions, the model was first normalized for all the causal variables. According to the results, the best model is the normalization based on the LEGA variable. Also, the GOF test was used to confirm the fit of the model. Based on the results of the MIMIC model predicting the hidden variable, the trend of health time preference rate for all the studied countries is presented in separate graphs. During the period under study, the trend of the time preference rate for health in Brazil, Iran, Malaysia, Mexico, and Russia was downward with a steep slope, indicating an improvement in people’s attitudes toward health in these countries and a reduction in risky behaviors in order to achieve greater future benefits. This trend was also downward in Jordan and Azerbaijan, albeit with a very low slope. In Argentina, Kazakhstan, and South Africa, the trend of the time preference rate for health was rising, with Argentina’s trend being steeper than that of the other two countries. This indicates a decline in people’s attention to the health in the present and, consequently, a decline in their desire to invest in it for the future.
Conclusions and Policy Implications: People’s behavior is clearly related to their time horizon. As people’s time preference rate increases, planning becomes increasingly myopic. This study employs the MIMIC model to evaluate the shifts in the health-related time preference rate in several upper-middle-income countries. The obtained trends for the health time preference rate show the impacts of the policies adopted in the field of health on people's attitudes towards a healthier life in the future. Prohibition of smoking in public places, restricting laws or controls on the consumption of alcohol and drugs, policies to promote professional and public sports, healthy diet recommendations, expansion of health education and similar cases are among these policies. Since policy makers try to achieve the goals of promoting the health status of citizen, it seems warning, and awareness programs such as health education and counseling programs can be effective in this regard.

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Main Subjects

References

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The Journal of Economic Policy
Volume 17, Issue 34
August 2025
Pages 372-408

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