مقایسه اجتماعی و سلامت:آیا داشتن دوستان و همسایگان ثروتمند تر شما را بیمار می کند؟

Abstract Do richer friends and neighbors improve your health through positive material effects, or do they make you feel worse through the negative effect of social comparison and relative deprivation? Using the newly available National Social Life, Health, and Aging Project (NSHAP) data set that reports individuals' income positions within their self-defined social networks, this paper examines whether there is an association between relative position and health in the US. Because this study uses measures of individuals' positions within their self-defined social groups rather than researcher-imputed measures of relative position, I am able to more precisely examine linkages between individual relative position and health. I find a relationship between relative position and health status, and find indirect support for the biological mechanism underlying the relative deprivation model: lower relative position tends to be associated with those health conditions thought to be linked to physiological stress. I also find, however, that only extremes of relative position matter: very low relative position is associated with worse self-rated physical health and mobility, increased overall disease burden, and increased reporting of cardiovascular morbidity; very high relative position is associated with lower probabilities of reporting diabetes, ulcers, and hypertension. I observe few associations between health and either moderately high or moderately low positions. This analysis suggests that the mechanism underlying the relative deprivation model may only have significant effects for those at the very bottom or the very top.

Introduction and background Empirically, income has consistently been shown to be highly correlated with health; in both aggregate and micro studies, richer people have better health and longer life expectancies (for reviews, see Adler et al., 1993 and Adler and Ostrove, 1999). There also appears to be a strong correlation between income inequality and health. Whether comparing across countries, U.S. states, or smaller sampling regions, those geographic areas with higher levels of inequality also appear to have higher rates of mortality (see for example, Daly et al., 1998, Kaplan et al., 1996, Kennedy et al., 1996 and Waldmann, 1992; or the review by Wilkinson and Pickett, 2006).1 One hypothesis put forth to explain these associations between health and both income and income inequality is that health is determined, not only by one's absolute material resources, but also by one's relative position (Marmot and Wilkinson, 2001, Wilkinson, 1996 and Wilkinson, 1997). According to this hypothesis, poor people have worse health not only because they are less able to afford health-promoting goods, but also because they experience health deficits related to the gap between their own circumstances and those of others. These health deficits are thought to stem from the psychosocial effect of finding oneself less worthy in social comparisons – in other words, from the stress of being of lower social rank. According to the relative deprivation hypothesis, the greater this difference between one's circumstances and those of others, the worse one's health. In the aggregate, then, the more unequal a society – that is, the greater the difference between a society's haves and have-nots – the worse the health of society's poorest members because of relative deprivation. This leads to worse overall (average) health, and so we observe the negative association between income inequality and health. That low social rank has negative health effects is somewhat supported by animal studies. Among non-human primates, lower social rank is associated with higher levels of stress hormones, which if chronically elevated, lead to worse immune function, increased inflammation, and increased susceptibility to disease (Cohen et al., 1997 and Sapolsky et al., 1997). Most primate studies, however, do not attempt to exogenously change social rank; they therefore cannot rule out the possibility that an unobserved factor affects both an animal's social dominance and its immune status, or that susceptibility to infection leads to low social rank. The one study that does manipulate the social status of monkeys finds a clear effect of rank on stress hormone response but does not directly evaluate health outcomes (Shively, Laber-Laird, & Anton, 1997). Among humans, the hypothesis that low relative position has an independent effect on health has been more difficult to test. The biggest problem, aside from humans' natural aversion to the randomization of their economic status, has been that, within a society, lower income implies both lower absolute resources and lower relative position; stated differently, lower income is perfectly correlated with lower relative (economic) position within a given society. Because of this difficulty, individual-level studies that have attempted to test the relative deprivation hypothesis have evaluated, not the effect of an individual's relative position, but rather the effect of income inequality of someone's area of residence – state, MSA, or census sampling unit – on his or her health (for example, Fiscella and Franks, 1997, Mellor and Milyo, 2002 and Sturm and Gresenz, 2002). Notably, unlike the studies that use aggregate data – which consistently report a negative association between inequality and health – these individual-level studies find no relationship between the inequality of an individual's geographic area and his or her health. These tests, however, do not quite estimate the effect of relative position on health. As Eibner and Evans (2005) point out, two individuals may experience the same level of inequality because they live in the same community, but their relative position within the community may be very different – and it is their relative position that affects health. These individual-level tests of inequality therefore do not truly test the relative deprivation hypothesis. Attempts to test this hypothesis by constructing individual-level measures of relative deprivation have met with mixed results. In these papers, relative deprivation is typically determined by an individual's income relative to the incomes of those who have the same demographic characteristics (e.g. gender, race, age, education, region of residence, occupation). Kondo, Kawachi, Subramanian, Takeda, and Yamagata (2008) unambiguously find a relationship between relative deprivation and worse self-reported health, while Jaffe, Eisenbach, Neumark, and Manor (2005), and Yngwe, Fritzell, Lundberg, Diderichsen, and Burstrom (2003) report a similar relationship in men but no such relationship among women. Others, like Eibner and Evans, 2005 and Gravelle and Sutton, 2006, and Jones and Wildman (2008), find that empirical associations between relative position and health tend to be very sensitive to the measures of deprivation used as well as to the form of the models being estimated. In this paper, I empirically examine the relationship between relative position and health but address an important issue that has not been attended to in previous studies. A difficulty that arises in the empirical study of relative deprivation is that individuals' reference groups are unobserved. Consequently, an individual's relative position must be imputed by the researcher, most often by defining someone's relative income as this person's income relative to the incomes of those with similar demographic characteristics. Thus, the imputed relative income of, say, Chief Justice John G. Roberts, Jr., would be his income relative to the income of – as of the time of this writing – white males, approximately 50 years old, living in Washington, DC. While this method of constructing reference groups is reasonable given data constraints, it is not unproblematic. First, there is a fair amount of sociological evidence suggesting that someone's reference group is not typically as broad as his geographic region or demographic classification. Reference groups tend to be more localized and are mostly limited to family, friends, neighbors, work colleagues, and others people know personally (for overviews, see Frank, 1985 and Merton, 1957). Moreover, while it is true that these localized reference groups tend to be comprised of people who have the same demographic characteristics as the primary individual (McPherson, Smith-Lovin, & Cook, 2001), it is unclear whether demographic and geographic groupings are accurately capturing that individual's reference group. For example, that the social network of a Supreme Court Justice tends to consist of lawyers does not mean that we would be accurately representing his or her socioeconomic reference group by pooling all U.S. lawyers or indeed even all Washington, DC lawyers. Justices' professional reference groups are more likely to be comprised of federal judges or law professors or professionally elite non-lawyers. Further, anthropological work provides evidence for the importance of locally defined norms and cultural models in specifying the criteria for social status; consonance with these local cultural models has been shown to be associated with health status (Dressler et al., 2005, Dressler and Bindon, 2000 and Dressler et al., 1998), and standard demographic variables do not adequately capture these localized norms and models. We suspect, then, that individuals are likely to be drawing, for their reference group, from a pool of demographically similar people in a way that is idiosyncratic to the individual and his/her local environment, and that is biased in important unobserved ways. Consequently, geographic or demographic classifications may not simply be innocuous approximations of actual reference groups but may be misleading indicators of them. For this reason, this paper focuses on local reference groups as identified by the individual. Note that reference groups are ‘local’ in the sense of being part of one's social, work, or kin network, but need not be geographically concentrated; for example, two brothers may reflexively compare themselves to each other even though one lives in New York and the other in San Francisco. To account for the social locality of reference groups, this paper uses newly available data on individuals' positions within their local network to construct measures of relative position. These local measures will allow us to test the relative deprivation hypothesis by examining whether the mechanism behind the theory of relative deprivation could be operating: if the relative deprivation hypothesis holds and relative income has an effect on health independent of absolute income, we should observe associations between local position (relative to a reference group) and health status. In addition to these relative position measures, this data set contains a particularly rich set of health indicators. I can thus examine associations between relative position and a broad range of health status measures. Most studies examining health use either a self-reported summary measure of health or the rather extreme outcome of death. This paper uses indicators that capture a range of well-being and morbidity. Like other papers, this paper looks at self-reported health, but I also consider reports of diagnoses of important health conditions such as diabetes, cancer, arthritis, and reports of previous strokes and heart attacks. I also use a body mass index measure calculated from height and weight measurements, and interviewer-recorded blood pressure readings. Since the health effect of social comparison is posited to work through a specific physiological stress pathway, the various measures of health will help me detect different ways in which the stress mechanism underlying the relative deprivation hypothesis may influence health. In brief, this paper finds a relationship between relative position and health status, and finds indirect support for the biological mechanism underlying the relative deprivation model. I also find, however, that only extremes of relative position matter: very low relative position is associated with worse self-rated physical health and mobility, increased overall disease burden, and increased reporting of cardiovascular morbidity; very high relative position is associated with lower probabilities of reporting diabetes, hypertension, and ulcers. I observe few associations between health and either moderately high or moderately low positions. This analysis suggests that the mechanism underlying the relative deprivation model may only have significant effects for those at the very bottom or the very top.

Results Probit and OLS estimates According to the probit and OLS estimates (Table 2), relative position is associated with certain self-reported morbid conditions; this association, however, appears limited to extreme relative positions. In particular, very low rank is associated with a higher Charlson index – that is, of reporting more health conditions predictive of mortality. At the other extreme, very high rank is associated with lower probabilities of reporting diagnoses of hypertension and diabetes. Neither moderately low nor moderately high relative positions are associated with any health measures. Table 2. Probit and OLS estimates of health in relation to local position and income. Health measure Marginal effect or coefficient (standard error) Very low rank Low rank High rank Very high rank Income Self-rated physical health measures Poor or fair physical health (0 = excellent/very good/good, 1 = fair/poor) 0.168* (0.082) 0.091 (0.061) −0.041 (0.047) −0.028 (0.091) −0.047** (0.018) Difficulty in walking a block (0 = no difficulty, 1 = some diff/much diff/unable) 0.126* (0.067) 0.044 (0.039) −0.052 (0.047) −0.093 (0.065) −0.043*** (0.015) Self-reported morbidity Cardiovascular morbidity (0 = none, 1 = diagnosis) 0.126 (0.078) 0.040 (0.050) −0.041 (0.048) −0.066 (0.075) −0.016 (0.019) Hypertension (0 = none, 1 = diagnosis) −0.037 (0.074) −0.052 (0.053) −0.056 (0.048) −0.213** (0.088) −0.005 (0.021) Diabetes (0 = none, 1 = diagnosis) 0.025 (0.056) 0.007 (0.036) 0.015 (0.028) −0.122** (0.057) −0.024 (0.016) Arthritis (0 = none, 1 = diagnosis) 0.134* (0.077) 0.006 (0.052) 0.003 (0.053) −0.056 (0.126) −0.011 (0.026) Cancer (0 = none, 1 = diagnosis) −0.015 (0.041) 0.003 (0.030) 0.019 (0.030) 0.103 (0.065) −0.003 (0.015) Ulcer (0 = none, 1 = diagnosis) 0.064 (0.051) 0.041 (0.032) 0.064 (0.043) −0.067* (0.037) −0.007 (0.014) Charlson-based morbidity index (0, 1, 2, …, 9 conditions) 0.657*** (0.151) 0.261* (0.149) 0.087 (0.099) −0.082 (0.346) −0.074 (0.057) Interviewer-based measures Obese (0 = non-obese, 1 = obese) −0.004 (0.079) 0.035 (0.042) 0.028 (0.062) −0.014 (0.099) −0.024 (0.023) Systolic blood pressure (mm Hg) 1.831 (3.228) −1.091 (2.029) 0.183 (1.435) −3.506 (4.939) 0.506 (0.867) Diastolic blood pressure (mm Hg) 0.949 (1.795) −1.113 (1.010) −0.186 (0.885) −2.588 (2.110) 0.005 (0.465) Hypertensive based on BP, Pr{hypertensive} −0.015 (0.090) −0.049 (0.044) −0.043 (0.045) −0.195 (0.117) 0.034 (0.025) Pre-hypertensive or hypertensive based on BP, Pr{pre-hypertensive or hypertensive} 0.045 (0.056) −0.041 (0.058) −0.023 (0.038) −0.047 (0.070) −0.004 (0.018) ***Significant at α = 0.01; **significant at α = 0.05; *significant at α = 0.10. All regressions include age, age2, race, gender, education, marital/cohabitation status, partner's education, log of per capita assets, and the self-esteem measure. Interviewer-based blood pressure regressions also include diagnosis of hypertension. All reported results are from probit regressions with the exception of results for the Charlson-based comorbidity index, systolic blood pressure, and diastolic blood pressure, which are from OLS regressions. Table options One interesting result is that, once we disaggregate health into individual conditions, the association between health and income is less evident. We observe an association with income for the two self-rated aggregate health measures, but not for individual health diagnoses, and not for the objective measures of obesity and blood pressure. One reason for this result may be that disaggregation of health conditions leads to too few cases for each condition to be able to discriminate any effect, especially if the effect size is small. A second reason for the lack of association with income may be that, for this age group of 57–85, income may not be a good measure of material resources. Several studies (see for example, House et al., 1990 and Sorlie et al., 1995) have shown that the health-income gradient diminishes after middle-age; furthermore, for those who are at or near the end of their working lives, the stock of accumulated assets rather than the current flow of income may better reflect an individual's material resources. Income is often used as a measure of resources because it is typically the only measure available in survey data; Robert and House (1996) have shown, however, that when both asset and income data are available, assets are more predictive of health status than income in older populations. NSHAP is one of the few surveys that includes self-reported assets, so I repeat the above analysis conditioning on assets (Table 3). A potentially serious disadvantage of using assets rather than income, however, is a reduction in sample size. With income alone, the sample size is 1580 observations; with assets alone, the sample is reduced to 1408 observations. (A sample of both income and assets reduces the sample size even further to 1297 observations. The resultant standard errors are large, and I do not report the results for that analysis here.) I compute the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) for each of the models, first using income as the resource variable and then separately using assets. I find that all of the asset models strictly dominate the corresponding income models by both the AIC and BIC criteria (results available upon request), and so focus on the results from the asset models here. Table 3. Probit and OLS estimates of health in relation to local position and assets. Health measure Marginal effect or coefficient (standard error) Very low rank Low rank High rank Very high rank Assets Self-rated physical health measures Poor or fair physical health (0 = excellent/very good/good, 1 = fair/poor) 0.223*** (0.084) 0.119* (0.070) 0.000 (0.035) −0.012 (0.114) −0.012** (0.005) Difficulty in walking a block (0 = no difficulty, 1 = some diff/much diff/unable) 0.155*** (0.052) 0.064** (0.031) 0.019 (0.038) −0.016 (0.083) −0.025*** (0.007) Self-reported morbidity Cardiovascular morbidity (0 = none, 1 = diagnosis) 0.161** (0.063) 0.028 (0.039) −0.007 (0.037) −0.023 (0.098) 0.005 (0.008) Hypertension (0 = none, 1 = diagnosis) −0.030 (0.075) −0.058 (0.052) −0.032 (0.056) −0.280*** (0.084) −0.027** (0.011) Diabetes (0 = none, 1 = diagnosis) −0.008 (0.041) −0.037 (0.037) −0.010 (0.036) −0.127** (0.053) −0.015* (0.009) Arthritis (0 = none, 1 = diagnosis) 0.149* (0.079) 0.008 (0.047) 0.007 (0.047) 0.042 (0.125) −0.010 (0.010) Cancer (0 = none, 1 = diagnosis) 0.004 (0.045) 0.013 (0.035) 0.020 (0.026) 0.093 (0.092) −0.007 (0.005) Ulcer (0 = none, 1 = diagnosis) 0.054 (0.041) 0.019 (0.030) 0.048 (0.036) −0.093*** (0.034) −0.003 (0.005) Charlson-based morbidity index (0, 1, 2, …, 9 conditions) 0.632*** (0.156) 0.178 (0.106) 0.072 (0.082) 0.079 (0.304) −0.051* (0.029) Interviewer-based measures Obese (0 = non-obese, 1 = obese) −0.002 (0.068) 0.055 (0.062) 0.046 (0.046) 0.014 (0.116) −0.018 (0.016) Systolic blood pressure (mm Hg) 4.455 (3.547) −1.669 (2.025) 1.071 (1.298) −1.015 (5.305) 0.424* (0.242) Diastolic blood pressure (mm Hg) 1.125 (1.848) −0.662 (1.864) −0.260 (1.099) −0.568 (0.837) 0.093 (2.039) Hypertensive based on BP, Pr{hypertensive} 0.050 (0.067) −0.047 (0.058) −0.007 (0.048) −0.089 (0.141) 0.023** (0.010) Pre-hypertensive or hypertensive based on BP, Pr{pre-hypertensive or hypertensive} 0.104* (0.056) −0.019 (0.031) −0.013 (0.036) −0.071 (0.100) 0.005 (0.008) ***Significant at α = 0.01; **significant at α = 0.05; *significant at α = 0.10. All regressions include age, age2, race, gender, education, marital/cohabitation status, partner's education, log of per capita assets, and the self-esteem measure. Interviewer-based blood pressure regressions also include diagnosis of hypertension. All reported results are from probit regressions with the exception of results for the Charlson-based comorbidity index, systolic blood pressure, and diastolic blood pressure, which are from OLS regressions. Table options From Table 3, we see that we obtain similar results using assets as we do using income. All health measures that were related to relative position when we conditioned on income are also related when we condition on assets. Some results, such as those for cardiovascular morbidity and self-rated physical health and self-rated difficulty in walking a block, are more statistically significant. Notably, even though statistical power is in principle reduced with a smaller sample size (when we move from income to assets), the standard errors of the two sets of estimates are of similar sizes. With the asset models, we see that only the extreme relative positions are strongly associated with health status. Very low relative position is associated with worse self-rated health and worse self-rated mobility; it is also associated with an increased probability of reporting cardiovascular morbidity and a higher Charlson morbidity index. At the other extreme, a very high position is associated with a decreased probability of reporting hypertension, a decreased probability of reporting diabetes, and a decreased probability of reporting an ulcer. In general, a very low relative position appears to increase the probability of reporting poor health or a morbid condition by between 16 and 22 percentage points; a very high relative position decreases the probability of reporting a morbid condition by between 9 and 28 percentage points. Moderately low and moderately high positions are not linked to any health conditions except the self-rated mobility measure. In addition, there is no association between relative position and reports of arthritis or cancer, nor does there appear to be a relationship between relative position and obesity, nor between relative position and either systolic or diastolic blood pressure. These results appear to be robust. I estimate each of the models with and without the perception bias and self-esteem measures, with and without state fixed effects, and with and without reweighting for non-response. The resultant estimates across the different specifications are similar to those reported in this paper and are available upon request. Gender differences The previous models control for gender differences through a gender dummy variable, but there may well be more complex differences in the relationship between relative position and health for men versus women (for a review of sex differences in mortality, morbidity, and physiological responses to stress, see Kudielka & Kirschbaum, 2004 and Waldron, 1983). To investigate this possibility, I re-estimate the models for males and females separately. Table 4 and Table 5 report results for the male subsample and the female subsample, respectively. Table 4. Probit and OLS estimates of health in relation to local position and assets (male subsample). Health measure Marginal effect or coefficient (standard error) Very low rank Low rank High rank Very high rank Assets Self-rated physical health measures Poor or fair physical health (0 = excellent/very good/good, 1 = fair/poor) 0.387*** (0.145) 0.136* (0.081) 0.019 (0.045) 0.040 (0.163) −0.017*** (0.006) Difficulty in walking a block (0 = no difficulty, 1 = some diff/much diff/unable) 0.184 (0.116) 0.018 (0.038) −0.007 (0.038) −0.051 (0.072) −0.025*** (0.008) Self-reported morbidity Cardiovascular morbidity (0 = none, 1 = diagnosis) 0.129 (0.090) 0.039 (0.054) −0.016 (0.056) 0.054 (0.163) 0.006 (0.012) Hypertension (0 = none, 1 = diagnosis) −0.100 (0.127) −0.111 (0.084) −0.051 (0.064) −0.285*** (0.109) −0.030* (0.016) Diabetes (0 = none, 1 = diagnosis) −0.046 (0.069) −0.020 (0.058) 0.022 (0.045) −0.085 (0.093) −0.022* (0.013) Arthritis (0 = none, 1 = diagnosis) 0.203 (0.131) 0.005 (0.084) 0.032 (0.055) −0.203 (0.170) 0.002 (0.016) Cancer (0 = none, 1 = diagnosis) 0.016 (0.050) 0.016 (0.033) 0.088** (0.039) 0.267* (0.146) −0.004 (0.007) Ulcer (0 = none, 1 = diagnosis) −0.013 (0.059) 0.003 (0.050) 0.040 (0.045) 0.003 (0.008) Charlson-based morbidity index (0, 1, 2, …, 9 conditions) 0.704** (0.321) 0.287 (0.192) 0.241* (0.125) 0.177 (0.485) −0.045 (0.039) Interviewer-based measures Obese (0 = non-obese, 1 = obese) 0.106 (0.095) 0.044 (0.098) 0.067 (0.070) 0.101 (0.159) 0.002 (0.016) Systolic blood pressure (mm Hg) 8.620** (4.136) −5.275*** (1.868) 0.477 (2.271) −0.383 (6.834) 0.881** (0.422) Diastolic blood pressure (mm Hg) 4.029* (2.230) −0.652 (1.123) 0.911 (1.155) 3.476 (2.551) 0.342 (0.228) Hypertensive based on BP, Pr{hypertensive} 0.076 (0.082) −0.102* (0.056) 0.014 (0.078) 0.038 (0.185) 0.039** (0.018) Pre-hypertensive or hypertensive based on BP, Pr{pre-hypertensive or hypertensive} 0.139** (0.064) −0.083* (0.050) −0.024 (0.058) −0.006 (0.103) 0.018 (0.012) ***Significant at α = 0.01; **significant at α = 0.05; *significant at α = 0.10. All regressions include age, age2, race, gender, education, marital/cohabitation status, partner's education, log of per capita assets, and the self-esteem measure. Interviewer-based blood pressure regressions also include diagnosis of hypertension. All reported results are from probit regressions with the exception of results for the Charlson-based comorbidity index, systolic blood pressure, and diastolic blood pressure, which are from OLS regressions. Conditions for which there are no reported results mean that there is insufficient variation in the subsample to identify the parameter for this health condition. Table options Table 5. Probit and OLS estimates of health in relation to local position and assets (female subsample). Health measure Marginal effect or coefficient (standard error) Very low rank Low rank High rank Very high rank Assets Self-rated physical health measures Poor or fair physical health (0 = excellent/very good/good, 1 = fair/poor) 0.114 (0.080) 0.092 (0.063) −0.031 (0.061) −0.008 (0.007) Difficulty in walking a block (0 = no difficulty, 1 = some diff/much diff/unable) 0.148** (0.075) 0.114 (0.069) 0.053 (0.057) 0.022 (0.154) −0.028** (0.012) Self-reported morbidity Cardiovascular morbidity (0 = none, 1 = diagnosis) 0.136** (0.066) 0.004 (0.054) 0.020 (0.046) 0.002 (0.008) Hypertension (0 = none, 1 = diagnosis) 0.037 (0.099) 0.001 (0.103) −0.011 (0.084) −0.275** (0.136) −0.020 (0.013) Diabetes (0 = none, 1 = diagnosis) 0.008 (0.046) −0.062 (0.054) −0.052 (0.062) −0.008 (0.012) Arthritis (0 = none, 1 = diagnosis) 0.085 (0.089) 0.018 (0.079) −0.044 (0.086) 0.360*** (0.080) −0.033* (0.020) Cancer (0 = none, 1 = diagnosis) −0.032 (0.044) 0.006 (0.064) −0.062 (0.038) −0.013 (0.009) Ulcer (0 = none, 1 = diagnosis) 0.071 (0.064) 0.031 (0.041) 0.050 (0.056) −0.016 (0.085) −0.011* (0.006) Charlson-based morbidity index (0, 1, 2, …, 9 conditions) 0.433* (0.217) 0.010 (0.120) −0.155 (0.197) 0.053 (0.217) −0.080* (0.043) Interviewer-based measures Obese (0 = non-obese, 1 = obese) −0.116 (0.103) 0.081 (0.067) 0.020 (0.077) −0.072 (0.222) −0.043 (0.026) Systolic blood pressure (mm Hg) 1.162 (4.327) 2.787 (2.537) 1.939 (1.881) −1.200 (5.017) −0.187 (0.383) Diastolic blood pressure (mm Hg) −1.636 (2.719) −0.477 (1.465) −1.438 (1.142) −5.106** (2.262) −0.172 (0.272) Hypertensive based on BP, Pr{hypertensive} 0.017 (0.111) 0.027 (0.092) −0.024 (0.080) −0.242** (0.096) 0.004 (0.013) Pre-hypertensive or hypertensive based on BP, Pr{pre-hypertensive or hypertensive} 0.038 (0.093) 0.061 (0.056) 0.008 (0.051) −0.115 (0.198) −0.022 (0.015) ***Significant at α = 0.01; **significant at α = 0.05; *significant at α = 0.10. All regressions include age, age2, race, gender, education, marital/cohabitation status, partner's education, log of per capita assets, and the self-esteem measure. Interviewer-based blood pressure regressions also include diagnosis of hypertension. All reported results are from probit regressions with the exception of results for the Charlson-based comorbidity index, systolic blood pressure, and diastolic blood pressure, which are from OLS regressions. Conditions for which there are no reported results mean that there is insufficient variation in the subsample to identify the parameter for this health condition. Table options Analyzing subsamples generates new methodological issues. Although the subsampling decreases the accuracy of some estimates because of smaller sample size (so that a loss of statistical significance with the subsamples may not be meaningful), it can be useful to look at the associations that persist or emerge as a result of the subsampling. For both men and women separately, there remains a strong protective effect against hypertension when the respondents are of very high rank. In addition, for men, there remains a strong association between very low rank and poor self-rated health. For women, there remains a strong association between very low rank and self-rated mobility, as well as between very low rank and increased probability of cardiovascular morbidity. In addition, we see for the first time associations between relative position and the objective measures. Women who are in very high positions have lower diastolic blood pressure readings and lower probabilities of having hypertensive blood pressure readings. Among men, we do not see a similar protective association with high rank, but we do observe a symmetric association with low rank. That is, men who report very low rank have higher systolic blood pressure readings and higher probabilities of having hypertensive blood pressure readings. For the blood pressure readings, then, we see a protective association with high rank in women and a deleterious association with low rank in men. These results suggest that, even for apparently the same health condition, the mechanism through which relative position operates could be very different for men and women. Relative position and causality In general, it is not possible to interpret the above estimates as unambiguously causal. This is so because reference groups (and consequently relative positions within these groups) are fundamentally endogenous. Put differently, individuals use, for their social comparisons, reference groups that are always at least partially chosen (although individuals may not choose their kin, they can and do choose their friends, and to a lesser degree, their co-workers and neighbors). The above estimates would only be causal if we believed that health and all other characteristics correlated with health were irrelevant for the choice of reference group (and position within that reference group). If we believe that health determines the choice of reference group, we have a reverse causality problem; if we believe that some other characteristic that is correlated with health affects the choice of reference group, we have an omitted variables' problem. Although I proxy for an important omitted variable – psychological disposition – the general problem of omitted variables and reverse causality remains. Even so, it is instructive to look at the statistical association between relative position and health. We are helped by the fact that the reverse causal story and many omitted variables' stories suggest a clear direction of bias. Specifically, in thinking about reverse causality, we would expect that poor health leads to low relative position, and so if there were any reverse causality, there would be an upward bias in our estimate. Similarly, when we consider likely omitted variables – aspirational tendencies, preferences towards risk-seeking behaviors, low motivation, high discount rates –we might think that these omitted factors would bias our estimate upward. Thus, one way in which these non-causal estimates might be helpful is that they suggest an upper bound on the causal effect coming through relative position. That is, we could assume hypothetically that there were no reverse causality or omitted variables and interpret the estimate as giving us the largest possible causal effect of relative position on health. In this way, we would be able to assess whether relative income, once we condition on absolute material resources, has the potential to have a large effect on health. If the estimate is large, we have reason to investigate further into the mechanisms underlying this potentially large effect. If, however, the upper bound estimate is small or zero (which I find for some health conditions and for some relative position statuses), this tells us that relative deprivation may not be an important mechanism underlying health disparities in these instances; we would then, for these particular cases, reasonably focus our efforts on investigating other mechanisms and causes of disparities. In a similar way, we can look at patterns in the relationship between relative position and health even if the estimates are not causal. We might, for example, look at asymmetries. In particular, it might be possible that being in a low relative position has a negative effect on health relative to being in an average position, but being in a high relative position confers no protective effect relative to being in an average position. Or, as suggested by the foregoing analysis, relative position is relevant only if individuals are at the extreme ends of the relative position spectrum. Examining these asymmetries, even if the estimates are not causal, will help us to focus our attention on the social locations that have the potential for the greatest impact on health. Finally, we can consider these estimates in the context of the biological theory underlying the relative deprivation hypothesis. The mechanism through which relative deprivation is conjectured to affect health is the chronic physiological stress induced by unfavorable social comparison. The stress hormones that are released trigger biological changes have been strongly linked to certain health conditions, while the evidence is mixed or narrower for the relationship between stress and other conditions. For example, there is strong evidence for cardiovascular disease resulting from the stress pathway (McEwen, 1998), but narrower evidence for cancer, with only some neoplasms appearing to be affected by psychosocial stress (Chida, Hamer, Wardle, & Steptoe, 2008). By examining the non-causal estimates for individual conditions, we can refine our views about the importance of the relative deprivation hypothesis for specific conditions.