Does morbidity matter? Perceived health status in explaining the share of healthcare expenditures

Abstract

We argue that the demand for healthcare services can be better explained by individual need based variables rather than by macro variables such as the Gross Domestic Product (GDP) per capita and the share of public healthcare expenditures. This study introduces a self-rated health variable called morbidity that describes individual needs for health care – healthy individuals need less health care than sick ones – and that is measured through personal interviews conducted by the Organization for Economic Co-operation and Development (OECD). In addition, stationary properties of the series are considered in order to understand the effect of shocks to expenditure behaviour on health care. Stationary test results show that we should not only use differenced values for the model variables but also incorporate time-specific effects into the model. Using the appropriate specification and accounting for the time effect, we find evidence supporting the hypothesis that the share of healthcare expenditure in GDP rises with the increased need for health care. The need for health care is also found to be more important than per capita GDP when explaining the change in the share of healthcare expenditures for the examined countries.

Keywords:

• healthcare expenditures
• morbidity
• two-way model

JEL Classification:

• I10
• H51
• C23

Notes

¹ OECD Health Data (2006). GDP per-capita values for 2006 for Spain, Finland and USA were about 27 K, 40 K and 44 K USD, respectively, in 2006.

² As health care is a normal good, for example, a higher per-capita GDP results in an increase in per-capita healthcare expenditures. In addition, higher healthcare expenditures can be regarded as a positive investment in human capital, and, in turn, as an input to production, which causes an increase in GDP. Healthier individuals are more energetic and physically and mentally robust, and thus more productive. They are also less likely to be absent from work due to illness.

³ See McGee et al. (1999), Burstrom and Fredlund (2001), Heistaro et al. (2001) and Hillen et al. (2000) for the positive relationship between self-rated and actual health outcomes.

⁴ See for example Gerdtham et al. (1992).

⁵ Recent analyses by the WHO show that countries vary considerably on disease incidences. If individuals in a country get sick frequently, the need for health care and health spending is usually high (WHO, 2009).

⁶ See Kanavos and Mossialos (1999) and Gerdtham and Jönsson (2000) for more discussion about the issue.

⁷ Structural breaks specific to individual countries can be captured by other indicators such as public healthcare expenditure, because such expenditure correlates with fluctuations in the business cycle in that country. We note that IPS tests can be sensitive to the inclusion of structural breaks and should be approached with a substantial degree of skepticism.

⁸ See Matteo (2005) for a discussion of the significance of technological innovations.

⁹ The share of elderly people in the total population and the morbidity variables are stationary; therefore, the differenced values are also stationary, regardless of the specific effects.

¹⁰ In a simulation study, Karlsson and Löthgren (2000) found that for small-t panels, LL-tests exhibit distortions. However, based on our results, t-values are large enough to model the series as stationary when time-specific effects are accounted for. See Maddala and Wu (1999) for arguments about the shortcomings of Levin and Lin tests and of IPS tests and for a comparison of these two tests with the Fisher test. Also see Banerjee (1999) for an overview and discussion of these tests. Also see Im et al. (1997) for a power comparison of Levin and Lin tests with t- and LM-tests for different N and T and when errors are serially correlated.

¹¹ These results are consistent with the studies of Hansen and King (1996) and Jewell et al. (2003).

¹² Although our tests suggested that the omission of time-specific effects would lead to a nonstationary process, we also applied a one-way model. Thus, we included only the country effect in our panel estimation. According to the estimation results, the morbidity variable provided no statistically significant explanation for the share of healthcare expenditures in explaining the share of healthcare expenditures. The results suggested that GDP per capita and public share were the only factors that could explain healthcare expenditures. However, note that the inclusion of time-specific effects into the model in addition to the country-specific effects greatly improved the test statistics.

¹³ Another way of interpreting a negative coefficient would be to look at short-term effects. When the economy grows quickly, people may feel healthier and need less medical attention. In addition, the higher opportunity cost of missed working days or the decrease in productivity due to treatment might encourage individuals to postpone treatment for their health problems. On the other hand, stagnant economies cause personal stress levels to rise. Indeed, physiological stress is believed to be a strong trigger for significant health problems, such as diabetes and cancer. Thus, people may need more medical attention but may spend less on health care when the economy grows quickly.

¹⁴ His argument is based on political decision-making process.

¹⁵ For a study which found negative effect of public financing on medical spending see Gerdtham et al. (1992). For a study which found positive effect of public financing on medical spending see Leu (1986).