The determinants of life expectancy: an analysis of the OECD health data.

• Author: Shaw, James W.
• Position: Organization for Economic Cooperation and Development

1. Introduction

   This study is concerned with understanding the determinants of life expectancy in developed countries. The level (and variability) of life expectancy has important implications for individual and aggregate human behavior; it affects fertility behavior, economic growth, human capital investment, intergenerational transfers, and incentives for pension benefit claims (Zhang, Zhang, and Lee 2001: Coile et al. 2002). From the social planners perspective, it has implications for public finance. For example, Gradstein and Kaganovich (2004) conclude that increasing longevity results in increasing public funding of education and economic growth. Cremer, Lozachmeur, and Pestieau (2004, p. 2260) argue that early retirement "puts pressure on the financing of healthcare and pension schemes [and this pressure] is made worse by growing longevity." While typically assumed strictly exogenous for the purpose of policy analysis, it has been argued that life expectancy (or more broadly "'health") is predetermined by behavioral and policy variables in what can be loosely described as a production function for health. Estimating this function is the goal of this study.

   Auster, Leveson, and Sarachek (1969) were the first economists to study a population production function for health: a regression of state-level mortality rates on medical care and environmental variables. Today their research motivations and questions remain compelling. Indeed, given the size and rapid growth of health care-related industries and recent public interest in containing medical and insurance costs, it could be argued that understanding the socioeconomic determinants of societal health is more important today than ever. Moreover, issues concerning the government's role in sponsoring basic medical and pharmaceutical research: in regulating drug, alcohol, and tobacco consumption; and in promoting healthy lifestyles are all particularly newsworthy. The research questions related to public health are obvious. If societal health can be measured as life expectancy or mortality rates, what are the various socioeconomic factors that increase or decrease it? Can the marginal effects of these factors be disentangled? If so, which of these factors produces the largest health benefits (or costs) to society? These questions are as important now as when first posed by Auster, Leveson, and Sarachek in 1969.

   Since Auster, Leveson, and Sarachek, several economic studies have attempted to answer these questions using data from the United States or multiple countries. (1) Many of these have used aggregate data from the member countries of the OECD to explain cross-country mortality rates or life expectancies. (2) While the empirical results are mixed, the general consensus is that population life expectancy (or mortality) is a function of environmental measures (e.g., wealth, education, safety regulation, infrastructure), lifestyle measures (e.g., tobacco or alcohol consumption), and health care consumption measures (e.g., medical or pharmaceutical expenditures). However, the appropriate econometric methodology lot disentangling these effects and its meaning for the relative importance (statistical or economic) of the estimated effects is more contentious.

   These methodological issues are most vividly illustrated in the few studies that have focused on pharmaceutical expenditures as a separate input to life expectancy. These include Peltzman (1987), Babazono and Hillman (1994), Lichtenberg (1996, 1998), Frech and Miller (1999), and Miller and Frech (2000). For example, Peltzman (1987) examined the effects of wealth and prescription drug laws on infectious disease mortality and on poisoning mortality across middle-income countries in a generalized least squares (GLS) framework. He found that wealth variables significantly decreased both disease and poisoning mortality rates, while prescription drug laws had a significant and positive effect on poisoning mortality only. The implication of the latter result was that mandatory prescription drug enforcement may lead to more frequent accidental poisonings (or deaths due to the overconsumption of pharmaceuticals, as interpreted by Peltzman). Peltzman also considered a GLS regression of life expectancy at birth on wealth and government health expenditures and found only wealth to be a significant determinant. His life expectancy variable was an average for the entire population (including males and females) of each country and was only for a single age stratum (at birth) in each country. He also ignored lifestyle variables in his regressions.

   To fully appreciate the vast differences in methodological approaches in pharmaceutical studies, compare Peltzman's analysis to that of Frech and Miller (1999), a subset of whose findings were also published in Miller and Frech (2000). Frech and Miller partitioned OECD data into age strata (life expectancy at birth, age 40 years, and age 60 years) and estimated separate life expectancy regressions for each stratum (pooling data for males and females). The determinants in each stratum regression were wealth, some lifestyle variables (alcohol, tobacco, and animal fat consumption), and pharmaceutical and nonpharmaceutical medical expenditures. Using country-level OECD data, they found that pharmaceutical expenditures had a significant and positive effect on life expectancy and that this increased with age. They also found that tobacco consumption (as measured using the concatenated percentages of males and females who smoked in each country) was not a significant determinant of life expectancy at any age. Frech and Miller's study is among the better investigations that have sought to estimate a population health production function since properly constructed measures of health care and pharmaceutical expenditures were used, and the effects of environmental and lifestyle factors were also taken into account. (3)

   This study considers a life expectancy production function similar to that of Frech and Miller but with some methodological innovations that have meaningful effects on the magnitude of our results. First, our data distinguish life expectancies by age (40, 60, and 65 years) and by gender. However, specification tests indicate that pooling the life expectancy data across gender and age strata cannot be rejected. Therefore, we pool the data across the distribution of ages to produce a larger effective sample size and include interactions with age and gender variables to disentangle marginal effects for different age--gender strata. Second, this study uses a nonparametric jackknife technique to quantify the sampling variability of coefficient estimates. None of the previously mentioned studies employed resampling methods to derive variance estimates. Third, we include a measure of fruit and vegetable consumption in our regression. Fourth, we also use a different measure of tobacco consumption that yields different inferences compared to those of Frech and Miller. Finally, and most important, we find that failure to adjust for the effect of a country's age distribution may create an omitted-variable bias in the coefficients for other determinants of life expectancy. (4) This bias emphasizes the importance of age distribution variability in macroeconomic and public economic studies that incorporate aggregate life expectancy or mortality as a determinant of economic behavior.

   We discuss these methodological issues and their effects on our results in the remainder of the paper. Accordingly, the next section of this paper discusses our methodology. In a following section, we discuss the results, with an emphasis of the effects of lifestyle factors and pharmaceutical consumption on life expectancy. In particular, we calculate and discuss the changes in lifestyle and pharmaceutical expenditures necessary to promote an additional year of life in different age--gender strata. Couched in these terms, our results suggest policy tactics lot improving societal longevity and behavioral strategies for extending individual life expectancy. We close with a discussion of the robustness of our results, a brief discussion of some of the limitations of our work, and our conclusions.

2. Methods

   Sample

   Data are taken from the OECD Health Data 20(10 database, which contains aggregate data on the health care systems of 29 of the 30 OECD countries. The database includes over 1200 indicators spanning the period 1960 to 1999, with official data up to 1998 and selected estimates for 1999. In particular, the data set includes various measures of health status (morbidity and mortality), health care resources and utilization, health expenditures and financing, as well as information relating to population demographics, nonmedical determinants of health (alcohol and tobacco consumption), and economic references (GDP and monetary conversion rates). (5) Variable definitions and descriptive statistics are presented in Table 1, and a complete explanation of the data is contained in the Data Appendix.

   Since we are using data on OECD countries, inferences drawn from this study are valid only for more developed countries. We recognize the importance of ascertaining the determinants of population life expectancy in developing countries. However, it has been shown in many studies that public health services, such as clean water supply and sanitation services, provide the biggest benefits to societal health in these countries. According to Miller and Frech (2000, p. 34), these "services are a matter of civil engineering rather than healthcare." Since our study focuses on the health care determinants of life expectancy, we select a sample containing developed countries only. In addition, data regarding drug consumption in developing nations are limited, precluding a detailed analysis of the effect of drug consumption in these countries.

   We hypothesize that health care and lifestyle factors will have...