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Daily RC Article 115

Unraveling Healthcare Demand: Bridging Gaps Between Economic Theory and Real-World Factors

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Economic theory provides a powerful but incomplete guide to the empirical determinants of health care demand. Health economists generally assume that the demand for health care derives from a demand for health. We consume health services as either an investment in our future health, to cope with chronic illness, or recover from acute illnesses or accidents. Rarely medical care is availed simply because it is enjoyed, but we seek it for our health. Health then, along with price (or its proxy health insurance), income, and consumer preferences, play the main role in formal economic models of consumer decision-making about health care.

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Yet, this theory takes us only so far. Although central to demand, economic theory is agnostic as to how individuals form preferences. It is suspected that individuals vary in attitudes and preferences toward risk, willingness to trade-off better health tomorrow for increased consumption today, and likes and dislikes. As a result, people with the exact same economic resources may respond differently to the same medical circumstances. However, these individual preferences are almost never directly observed – they are difficult to measure outside of controlled experiments. Health care prices and income too are almost never observed in the way we would like.

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However, empirical studies consistently demonstrate that a wide range of sociodemographic characteristics including age, ethnicity, sex, and education are strongly correlated with health care use. These rarely appear directly in the theoretical models. Sociodemographic characteristics are intended to be included in empirical models because it is believed that they are correlated with otherwise unmeasured preferences toward health or capture dimensions of a person's health (e.g., age), or both. However, interpretation is difficult because these proxy measures are often confounded with so many different unobserved aspects of individuals and their environment.

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Economic theory also posits that health care demand is jointly determined with supply. In practice, empirical models of consumer demand are almost never jointly estimated with models of supply because of data limitations. Instead, these empirical models assume that observed health care use is equal to consumer demand (technically, short-run supply is assumed to be perfectly elastic at the margin). Researchers sometimes add measures of provider supply and other market characteristics on an ad hoc basis to individual characteristics in empirical models of demand. However, interpretation here is problematic. For example, physicians tend to locate in areas with high demand. Thus, a measure of supply, like physicians per capita, will tend to reflect back demand rather than being a causal determinant of demand.

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How then do we decide which determinants to include in our empirical models of health care demand? And how do we interpret them? Although, theory is by no means definitive, economic principles can still be appealed to understanding the relationships among the theoretical and proxy determinants. Statistical principles also play a role. Overall, competing concerns about usefulness of particular variables as predictors of health care use and the potential biases they introduce must be confronted.

The article delves into the complexities of understanding healthcare demand through economic theory. While economic models primarily focus on health, price, income, and preferences, they often overlook individual variations in attitudes, risk, and unmeasured preferences. Empirical studies reveal strong correlations between healthcare use and sociodemographic factors, like age and education, not directly factored into theoretical models. Incorporating supply factors into demand models poses challenges due to data limitations and intertwined relationships. Determining which determinants to include in empirical models involves a nuanced consideration of economic and statistical principles, addressing biases and predictors' utility in understanding healthcare utilization.
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