تئوری نئو ماتریالیستی و رابطه زمانی بین نابرابری درآمد و تغییر طول عمر

Abstract This study uses a neo-materialist perspective to develop theoretical predictions regarding temporal ties between income inequality and change in population health. The argument focuses on the relationship between income inequality and adoption of longevity-enhancing innovations. It asserts that longevity change should be influenced by preexisting levels of income inequality and that, consequently, income inequality can cause differential longevity improvement across jurisdictions even if inequality levels remain unchanged. State-level U.S. data from 1970 to 2000 are used to jointly model the effects of initial levels and change in income inequality on 10-year life expectancy change. Results confirm that states with higher levels of inequality experienced less subsequent improvement in life expectancy. Contrary to findings from prior research, analyses also reveal a strong negative association between change in inequality and change in longevity once initial levels of inequality and other state characteristics are controlled. Finally, direct tests of the relationship between income inequality and the adoption of innovations in quality of medical care indicate that the two are highly related and that differences in the average quality of care can account for the negative cross-sectional association between income inequality and life expectancy.

Introduction One great demographic shift in economically advanced nations over the past two centuries has been the dramatic growth in life expectancy. Empirical work has attributed longevity gains to improvements in material standard of living and the implementation of health-enhancing innovations in public health and medicine, though scholars have debated the relative importance of each (Catalano and Frank, 2001, Deaton, 2006, Fogel, 2004, McKeown, 1976, Preston and Haines, 1991 and Szreter, 1988). A relatively recent literature has asserted that income inequality also has an important influence on health and mortality. Wilkinson (1992) went so far as to assert that it is the principal cause of health differences among wealthy nations. After an initial flurry of supportive cross-sectional findings, the empirical tide turned against the inequality hypothesis in more recent years (see Lynch, Harper, & Davey Smith, 2003 for a discussion), though supportive results do continue to emerge (e.g., Backlund et al., 2007, Ram, 2005, Subramanian and Kawachi, 2006 and Zimmerman and Bell, 2006). One indirect critique of the inequality hypothesis is that focusing on inequality is unwarranted because it is not a major cause of change in mortality/longevity. Lynch, Davey Smith, Harper, and Hillemeier (2004) find that changes in inequality over time fail to match up in any consistent way with changes in mortality. Although it is readily apparent that inequality, in isolation, is not a principal driver behind life expectancy improvements over time, it does not necessarily follow that income inequality has no important role in influencing changes in longevity or, in particular, differential change across populations. Ultimately, improvements in health outcomes result from adoption of beneficial innovations – whether they be innovations that improve medical care, provide for public goods such as drinking water or waste disposal, increase food production, etc. And one claim of “neo-materialist” theory (Davey Smith, 1996 and Lynch et al., 2000) is that income inequality harms population health because it weakens societies' willingness to make investments that promote the common good. If true, inequality could play an important role in affecting the rate of health change by influencing adoption of the innovations that are proximal determinants of heath improvements. An important issue, particularly when discussing the relationship between inequality and change in health outcomes, is what the temporal ties between inequality and health look like. A number of authors have discussed the question of latency periods between exposure and emergence of observable health outcomes ( Blakely et al., 2000, Lynch et al., 2004, Mellor and Milyo, 2003 and Subramanian and Kawachi, 2003). In this paper I use a neo-materialist perspective to discuss an issue that has received less attention: the temporal relationship between inequality as a distal cause and the adoption of innovations that are proximate determinants of health change. The argument I present asserts a tie between longevity change and preexisting levels of inequality. It does not necessarily include (nor exclude) links between longevity growth and change in inequality. Rather, I argue that within a dynamic technological environment we should observe differential improvement in life expectancy between low and high-inequality populations, even in the absence of any differential change in inequality. One implication of the argument is that results from previous empirical studies of change in inequality and change in population health are likely to be biased by failure to account for the effect of initial levels of inequality on subsequent health change. I test the proposed temporal ties using data on life expectancy and income inequality in the United States for a three-decade period, from 1970 to 2000. The tests include examinations of both the relationship between levels of inequality and subsequent longevity change, and of how observed change-on-change associations are affected by the inclusion of controls for initial levels of inequality and other state characteristics. I also use data on the quality of medical care to directly examine the relationship between income inequality and adoption of health-enhancing innovations.

Conclusion Given the fact that temporal ties in any relationship between income inequality and health are likely to be complex, empirical research requires greater theoretical guidance in developing specifications to gauge effects of inequality. In this paper I have used a neo-materialist perspective to argue that, within an environment of emerging health-enhancing innovations, preexisting levels of inequality should affect changes in population health. Consistent with that claim, results presented here show that state-level longevity improvement over the course of a decade is lower in states where income inequality was higher at the beginning of the decade. The results presented in this paper also show that findings from previous analyses of the relationship between change in inequality and change in population health suffer serious bias as a result of failure to control for the effect of initial levels of inequality and other state characteristics. Using a first-differences specification I find – consistent with previous studies using first-differences or state fixed-effects – no relationship between inequality change and longevity change. But once the effect of the initial conditions is taken into account, we observe a strong negative association between inequality change and longevity change. One prediction from the theoretical discussion is that effects of inequality will be contingent on the pace of innovation-driven health change, with inequality having a larger impact on longevity change when overall life expectancy improvement is greater. The results of decade-by-decade analyses support that prediction, as the estimated effects decline over time in tandem with overall slowing in the rate of improvement in life expectancy. Due to a lack of reliable time series data on adoption of health-enhancing innovations over time, it is not possible to directly test for mediating effects of innovation adoption in the multi-period change analyses. But cross-sectional results using recent data on variation in adoption of innovations in medical care across states are consistent with the theory. Higher income inequality is a strong predictor of lower average quality of care, and the negative cross-sectional relationship between income inequality and life expectancy can largely be accounted for by the associated lower quality of medical care. A few considerations should be taken into account in interpreting the results of the multiperiod change models. One would expect the results to understate the effects of inequality since they capture only effects occurring within the decade, whereas some mortality may occur with a greater lag. Given relatively high intertemporal correlations between levels of inequality, attenuation bias may be less serious for the estimated effects of initial levels as some lagged effects from past periods are picked up, making up for effects of current levels that may be pushed into later periods. Change-on-change effects will tend to be more attenuated than those of level-on-change due to low correlations between inequality changes across periods. By including initial levels of inequality and other state characteristics, as well as controls for unobserved region-specific causes of change, the analyses presented in this paper go substantially further than past work in addressing potential heterogeneity bias. It is nonetheless possible that important confounders remain. One fundamental question is whether income inequality itself affects longevity, or if it is simply one manifestation societies that are less concerned with egalitarianism and tend to “underinvest in human capital (e.g., education), health care, and other factors that promote health” (Kawachi et al., 1997, p. 1491). The challenge involved in untangling the effects of income inequality from those of the institutional characteristics that spawn it is heightened by the fact the relationship between two may reciprocal and reinforcing. While societies' sociopolitical dispositions towards egalitarianism (rather than income inequality itself) could plausibly account for the level-on-change effects, they seem less likely to explain the change-on-change relationship. That relationship could be explained by direct effects of inequality through innovation adoption, but it is also entirely possible that the change-on-change effects work through individual income or psychosocial mechanisms. Could individual income or psychosocial factors also explain the observed level-on-change effects? The preceding aggregate-level analyses do not, of course explicitly rule out those mechanisms (Wagstaff & van Doorslaer, 2000), but it is not obvious why either of those theories would predict – as I argue neo-materialist theory does – that levels of inequality would influence future longevity change. The preceding analyses demonstrate both theoretically and empirically the potential influence of levels of inequality on the pace of subsequent improvement in longevity. The results also show that attention to potential temporal ties between inequality and population health can have major impacts on empirical results. It is hoped that this work will help motivate advances in both theoretical consideration and empirical modeling of those ties.