برآورد نرخ ترجیح زمانی سلامت و تحلیل روند آن در کشورهای منتخب (مطالعه موردی کشورهای با درآمد بالاتر از متوسط) | ||
| سیاست گذاری اقتصادی | ||
| دوره 17، شماره 34، مهر 1404، صفحه 372-408 اصل مقاله (893.44 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/epj.2025.19743.2404 | ||
| نویسندگان | ||
| راضیه محمدی صابر* 1؛ عباس عصاری2؛ امیرحسین مزینی3؛ لطفعلی عاقلی3 | ||
| 1دکتری اقتصاد سلامت، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2دانشیار دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس ، تهران، ایران | ||
| 3دانشیار پژوهشکده اقتصاد، دانشگاه تربیت مدرس، تهران، ایران | ||
| چکیده | ||
| چکیده آگاهی از نرخ ترجیح زمانی افراد در حوزه سلامت بعنوان متغیر کلیدی تاثیرگذار بر مطلوبیت بین دورهای، یکی از موضوعات اساسی در سیاستگذاری های کلان حوزه اقتصاد سلامت میباشد. این مطالعه با استفاده از مدل MIMIC به استخراج روند نرخ ترجیح زمانی سلامت در تعدادی از کشورهای با درآمد بالاتر از متوسط طی سال های 2000 تا 2019 پرداخته است. نتایج نشان میدهد روند نرخ ترجیح زمانی سلامت در اردن نزولی با شیب بسیار کم و در برزیل، ایران، مالزی، مکزیک، روسیه و آذربایجان نزولی با شیب زیاد میباشد. شیب نزولی بدست آمده نشان دهنده بهبود قابل توجه نگرش افراد به موضوع سلامت و بالتبع پرهیز از رفتارهای پرخطر در زمان حال به منظور کسب منافع بیشتر در آینده در کشورهای مذکور میباشد که البته این موضوع در اردن کمتر محسوس بوده است. روند بدست آمده برای نرخ ترجیح زمانی سلامت در قزاقستان صعودی با شیب بسیار کم و در آرژانتین و آفریقای جنوبی صعودی با شیب زیاد میباشد که بیانگر کاهش توجه افراد به موضوع سلامت در بازه مورد بررسی و کاهش تمایل افراد به سرمایه گذاری در آن برای آینده میباشد. تفاوت در روندهای بدست آمده برای نرخ ترجیح زمانی سلامت نشان دهنده تفاوت در تغییرات صورت گرفته در نگرش افراد نسبت به وضعیت سلامتیشان طی زمان و نیز اثرگذاری متفاوت سیاست های دولتی در حوزه سلامت میباشد. به نظر میرسد برنامه ها و سیاست های هشدار دهنده و آگاهی بخش از جمله برنامه های آموزش و مشاوره سلامت، موجب تقلیل نرخ ترجیح زمانی برای سلامت شود. | ||
| کلیدواژهها | ||
| ترجیح زمانی؛ سلامت؛ کشورهای با درآمد بالاتر از متوسط؛ مدل میمیک | ||
| عنوان مقاله [English] | ||
| Estimating the Health Time Preference Rate and Analyzing Its Trend in Certain Countries (Case Study: Countries with Higher than Average Income) | ||
| نویسندگان [English] | ||
| Raziyeh Mohammadi Saber1؛ Abbas Assari2؛ Amir Hosseain Mozayani3؛ Lotfali Agheli3 | ||
| 1Ph.D. student, Tarbiat Modares University, Tehran, Iran. | ||
| 2Associate Professor of Economics, Tarbiat Modares University, Tehran, Iran | ||
| 3Associate Professor of Economics, Institute of Economic Research, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژهها [English] | ||
| Time Preference, Health, Countries with Higher than Average Income, MIMIC model | ||
| مراجع | ||
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Aghdam, K., & Milaelmi, Z. (2019). Analysis of the Factors that Create Poverty in Iran through the Pseudo-Panel Data Approach. The Journal of Economic Policy, 11(21), 25-53. (In persian) Ahari, A., Pourreza, A., Sari, A., Sheldon, T. A., & Moeni, M. (2019). Private and social time preference for health outcomes: A general population survey in Iran. PLOS ONE, 14(2), e0211545. https://doi.org/10.1371/journal.pone.0211545. Attema, A. E., & Brouwer, W. (2012). In Search of a Preferred Preference Elicitation Method: A Test of the Internal Consistency of Choice and Matching Tasks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1988984. Attema, A. E., & Versteegh, M. M. (2012). Would You Rather Be Ill Now, Or Later?. Health Economics, 22(12), 1496–1506. https://doi.org/10.1002/hec.2894. Attema, A. E., Bleichrodt, H., Gao, Y., Huang, Z., & Wakker, P. P. (2016). Measuring Discounting without Measuring Utility. American Economic Review, 106(6), 1476–94. https://doi.org/10.1257/aer.20150208. Bartczak, A., Chilton, S., & Meyerhoff, J. (2015). Wildfires in Poland: The Impact of Risk Preferences and Loss Aversion on Environmental Choices. Ecological Economics, 116, 300–09. https://doi.org/10.1016/j.ecolecon.2015.05.006. Bleichrodt, H., Gao, Y., & Rohde, K. I. M. (2016). A Measurement of Decreasing Impatience for Health and Money. Journal of Risk and Uncertainty, 52(3), 213–31. https://doi.org/10.1007/s11166-016-9240-0. Böhm-Bawerk, E. v. (1890). The Positive Theory of Capital. United Kingdom: Macmillan. Brody, A., & Von Mises, L. (1951). Human Action: A Treatise on Economics. Political Science Quarterly, 66(4), 606. https://doi.org/10.2307/2145453. Cairns, J. A., & Van Der Pol, M. M. (1997). Saving Future Lives. A Comparison of Three Discounting Models. Health Economics, 6(4), 341–50. https://doi.org/10.1002/(sici)1099-1050(199707)6:4<341::aid-hec277>3.0.co;2-y. Cairns, J., & Van Der Pol, M. (1999). Do People Value Their Own Future Health Differently from Others' Future Health?. Medical Decision Making, 19(4), 466-72. https://doi.org/10.1177/0272989x9901900414. Cen, X., Johnston, D. W., Kung, C. S. J., Shields, M. A., & Sun, E. C. (2021). The Link between Health and Economic Preferences: Evidence from 22 OECD Countries. Health Economics, 30(4), 915–20. https://doi.org/10.1002/hec.4225. Chao, L. W., Szrek, H., Sousa Pereira, N., & Pauly, M. V. (2009). Time Preference and Its Relationship with Age, Health, and Survival Probability. Judgment and Decision Making, 4(1), 1–19. https://doi.org/10.1017/s1930297500000668. Cropper, M. L., Aydede, S. K., & Portney, P. R. (1991). Discounting Human Lives. American Journal of Agricultural Economics, 73(5), 1410–1415. Portico. https://doi.org/10.2307/1242393. Dallali, R., & Barzani, M. (2016). Impact of Time Preference on Economic Growth. The Economic Research, 16(3), 155-179. (In Persian) Dastjerdi, R., & Dallali, R. (2004). An Introduction to Subjective Origins of Interest from Bohm-Bawerk's Viewpoint (Acritical Study). The Quarterly Journal of the Economic Research, 13(24), 69-89. (In Persian) Eslamloueyan, K., & Ostadzad, A. H. (2014). Estimating the Rate of Time Preference for Iran: A Recursive Algorithm. Journal of Economic Research (Tahghighat-E-Eghtesadi), 49(2), 267-294. (In Persian) Frederick, S., Loewenstein, G., & O’donoghue, T. (2002). Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature, 40(2): 340–51. https://doi.org/10.2307/j.ctvcm4j8j.11. Galizzi, M. M., Harrison, G. W., & Miraldo, M. (2018). Experimental Methods and Behavioral Insights in Health Economics: Estimating Risk and Time Preferences in Health. Contributions to Economic Analysis, (Vol.4). 1–21. https://doi.org/10.1108/s0573-855520180000294001. Goda, G. S., Levy, M., Manchester, C. F., Sojourner, A. J., & Tasoff, J. (2018). Predicting Retirement Savings Using Survey Measures of Exponential-Growth Bias and Present Bias. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3249876. Goda, G., Levy, M., Manchester, C., Sojourner, A. J., & Tasoff, J. (2018). Predicting Retirement Savings Using Survey Measures of Exponential-Growth Bias and Present Bias. IZA Discussion Papers, 1-63. https://www.econstor.eu/handle/10419/185222. Hardisty, D. J., & Weber, E. U. (2009). Discounting Future Green: Money versus the Environment. Journal of Experimental Psychology: General, 138(3), 329–40. https://doi.org/10.1037/a0016433. Harrison, G. W., Hofmeyr, A., Ross, D., & Swarthout, J. T. (2018). Risk Preferences, Time Preferences, and Smoking Behavior. Southern Economic Journal, 85(2), 313–48. Portico. https://doi.org/10.1002/soej.12275. Howden, S.E., McColl, B., Glaser, A., Vadolas, J., Petrou, S., Little, M.H., Elefanty, A.G., & Stanley, E.G. (2016). A Cas9 Variant for Efficient Generation of Indel-Free Knockin or Gene-Corrected Human Pluripotent Stem Cells. Stem Cell Reports, 7(3), 508-517. Hu, Y. et al. (2021). Review for “Global Burden and Attributable Risk Factors of Acute Lymphoblastic Leukemia in 204 Countries and Territories in 1990–2019: Estimation based on Global Burden of Disease Study 2019.”. https://doi.org/10.1002/hon.2936/v2/review1. Johnson, A. S., Rae, J., & Mixter, C. W. (1907). The Sociological Theory of Capital. Political Science Quarterly, 22(1), 162. https://doi.org/10.2307/2140931. Karl, F. M., Holle, R., Schwettmann, L., Peters, A., & Laxy, M. (2018). Time Preference, Outcome Expectancy, and Self-Management in Patients with Type 2 Diabetes. Patient Preference and Adherence, 26(12), 1937–45. https://doi.org/10.2147/ppa.s175045. Kemptner, D., Jürges, H., & Reinhold, S. (2011). Changes in Compulsory Schooling and the Causal Effect of Education on Health: Evidence from Germany. Journal of Health Economics, 30(2), 340-540. doi: 10.1016/j.jhealeco.2011.01.004. Lawless, L., Drichoutis, A. C., & Nayga, R. M. (2013). Time Preferences and Health Behaviour: A Review. Agricultural and Food Economics, 1(1), 1-19. https://doi.org/10.1186/2193-7532-1-17. Leonard, T., & Shuval, K. (2017). Behavioral Economics: Tools for Promotion of Physical Activity. Behavioral Economics and Healthy Behaviors, 70–89. https://doi.org/10.4324/9781315637938-5. Mischel, W. Ebbesen, E.B., & Zeiss, A.R. (1972). Cognitive and Attentional Mechanisms in Delay of Gratification. J Pers Soc Psychol, 21(2), 204-18. doi: 10.1037/h0032198. Mohammadisaber, R., Assari, A., Mozayani, A., & Agheli, L. (2022). Estimation of the Health Time Preference Rate in Iran and Its Trend in Selected Countries. Ph.D. Thesis in Health Economics. Tarbiat Modares University. (In persian) Norrgren, L. (2021). Time Preferences, Illness, and Death. Working Papers in Economics, 812, University of Gothenburg, Department of Economics. Robberstad, B. (2005). Estimation of Private and Social Time Preferences for Health in Northern Tanzania. Social Science & Medicine, 61(7), 1597–1607. https://doi.org/10.1016/j.socscimed.2005.03.013. Shirdel, R., Sadeghi, H., Assari, A., & Abdoli, G.H. (2017). Estimation of Social Discount Rate in Iran with using Social Time Preference Approach. Quarterly Journal of Fiscal and Economic Policies, 5(18), 7-24. (In persian) Shoda, Y., Mischel, W., Peake, P. K. (1990). Predicting Adolescent Cognitive and Self-regulatory Competencies from Preschool Delay of Gratification: Identifying Diagnostic Conditions. Developmental Psychology, 26(6), 978–986. https://doi.org/10.1037/0012-1649.26.6.978. Soofi, M., Sari, A. A., & Najafi, F. (2020). The Effect of Individual Time Preferences on Smoking Behavior: Insights from Behavioral Economics. Iranian Journal of Public Health, 49(9), 1787-1795. https://doi.org/10.18502/ijph.v49i9.4100. Touray, M. M. L., Cohen, D. R., Williams, S. R. P., Alam, M. F., Groves, S., Longo, M., & Gage, H. (2022). Overweight/Obesity and Time Preference: Evidence from a Survey among Adults in the UK. Obesity Facts, 15(3), 428–41. Portico. https://doi.org/10.1159/000522651. Van der Pol, M., Walsh, D., & McCartney, G. (2015). Comparing Time and Risk Preferences across Three Post-Industrial UK Cities. Social Science & amp; Medicine, 140, 54–61. https://doi.org/10.1016/j.socscimed.2015.07.010. West, R., McNabb, R., Thompson, A., Sheldon, T., & Grimley Evans, J. (2003). Estimating Implied Rates of Discount in Healthcare Decision-Making. Health Technology Assessment, 7(38), 1-60. https://doi.org/10.3310/hta7380. WHO World Health Assembly Adopts Global Strategy on Diet, Physical Activity and Health (2004). Nutrition & Food Science, 34(5). https://doi.org/10.1108/nfs.2004.01734eab.024. WHO. Global Strategy on Diet, Physical Activity and Health. http://wwwwhoint/dietphysicalactivity/factsheet_ inactivity/en/. | ||
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