Decomposition of socio-economic inequality in health among an elderly cohort in India

Dinkar Kuchibhotla* and Robert Rosenman** Ill health is commonly thought to be concentrated in disadvantaged groups, especially in developing countries, but causal factors are not always well-known. Using a comprehensive data set of an elderly cohort in India, we decompose socio-economic inequality in a categorical measure of health namely self-assessed health (SAH). To adjust for potential cohort and categorical bias when individuals assess their health, we first construct an unbiased continuous prediction of health using the reported categorical response and objective information on individual health conditions, variables on socioeconomic status and health resource use. We use this predicted measure to assess health inequality, and then decompose it to find factors that contribute to the observed inequality in health. We find that health is concentrated among the wealthier cohort. Correlates of income, out of work, and elderly female help explain the inequality we find. KEY WORDS: Self -Assessed Health; Concentration Index; inequalities; decomposition; India

* Doctoral Student, School of Economic Sciences, Washington State University, Pullman, WA. dinkar.kuchibhotla@email.wsu.edu ** Professor of Economics, School of Economic Sciences, Washington State University, Pullman, WA

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Introduction Over the past few decades’ researchers have increasingly focused attention on measuring inequalities in economic and social indicators around the world. As a result, evidence has started to emerge in developing countries, including India, about adverse health outcomes being concentrated among the poor and marginalized communities (Planning Commission, 2011; Dreze and Sen, 2002; Desai et al., 2010)1. When poor and marginalized social groups experience a surfeit of adverse health outcomes, it limits their ability to fully participate in the process of economic development. Effective policy to counter such concentration requires more than simply quantifying the inequalities that exist, what is also needed is evidence that helps identify covariates of health inequalities. Identification and quantification of these covariates will help policy maker’s direct attention to key factors that help reduce these inequalities. Since health is a broadly defined2, difficult to measure multi-dimensional concept, it is not surprising that studies on health inequality in the developed world have mostly focused on health care access and health care delivery rather than on distribution of health itself across subgroups of the population (Yienprugsawan et al., 2007; World Health Organization, 2005-08). But while health care access and delivery are important covariates of health, they are not the only causal factors. Perhaps more importantly, the quality of people’s lives and their ability to contribute and enjoy in the fruits of economic development depends greatly on their health, not the healthcare they receive. Thus, Nobel laureate Amartya Sen has been quoted as arguing that inequalities in health are more worrisome than inequalities in most other spheres (O’Donnell et al., 2007). The World Health Organization (WHO) has defined inequity in health as “systematic differences in health” that are “avoidable by reasonable action” and are “quite simply unfair” (Planning Commission, 2011). To fill the void in understanding the multi-dimensional concept that is “health” we use self-assessed health (SAH) as our measure. When compared with objective measures like the incidence of various diseases, there is evidence showing that SAH is a “very valuable” source of data on health status (Idler and Benyamini, 1997). Even in a developing economy like India, which faces problems of poverty, income inequality and uneven access to healthcare3 SAH cannot be dismissed as unreliable indicator of health on the ground that socially disadvantaged individual’s fail to perceive and report illness as their assessment is based on their social

1 Morbidity provides one such example. Females have a greater occurrence of short and long-term morbidity (Desai et al., 2010). Morbidity also shows a downward trend in urban areas compared with rural areas. Across income categories, the top quintile shows a far lower short-term morbidity level than the bottom quintile (Desai et al., 2010). 2 According to WHO, health is a state of complete physical, mental and social well-being and not merely absence of disease or infirmity. 3 Recent estimates of poverty using the head count ratio (Planning Commission, 2009-10) show almost 30 per cent of the Indian population is poor with wide variation reflected along social strata, occupation categories and regions. Low levels of education, a fact evident in our data, reinforce these disparities.

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experience4. A more recent paper by Munoz et al. (2013) points to a wealth of studies from all over the world that attest to the robust nature of the relationship between self-assesments of health with mortality and morbidity. In order to address imbalances in health outcomes, India has tried several measures over the past decades including doubling the share of public financing on health by 2017 from a meager 1.2 per cent of Gross Domestic Product (GDP) in 2009, one of the lowest in the world. Simultaneously efforts are underway to introduce Universal Health Coverage (UHC) so that economic development leads to an increase in health and well being of all citizens (Planning Commission, 2011). But for these policies to be successful, concerted action is required to target social covariates of health which are conditions under which people are born, grow, live, work and age including the health system (Planning Commission, 2011). We believe that social covariates also impinge on SAH. Therefore our research aims to make a two-fold contribution to the literature by first quantifying inequality in adult health and secondly identifying covariates that cause health inequalities to help policy planners to formulate better health care policies5 targeting marginalized communities. Our analytical approach follows two steps. First we obtain an unbiased measure of SAH and proceed to measure inequality in health with a concentration curve (CC) and associated concentration index (CI)6. Statistician P.C. Mahalnobis proposed the CC as a tool to generalize the Lorenz Curve (Kakwani, 1980). In a second step we decompose the CI to help explain what contributes to any found inequality. Decomposition analysis is a popular tool used in labor economics and has recently found favor in health economics. 7 Our research focuses attention on the problem of health inequality among an elderly cohort in India. We focus on this group for several reasons. First, well-being of the elderly has received little attention in the Indian context (Desai, et al. 2010). This lack of attention is worrisome as the proportion of the elderly in the overall population is expected to grow in the coming decades partly due to rising life expectancy (Desai, et al. 2010). Second, care for the elderly has traditionally come from within the joint family system8, an institution whose existence is under threat due to rapid urbanization (Desai, et al. 2010). At the same time new structures have not sprung up at the same rate to take the place of the joint family system. As a result, health inequality among the elderly

4 Using recent data from India, Subramanian et al. (2009) find that illiterate individuals do not underreport poor health when compared to those who are literate lending some support for the use of SAH in developing countries. 5 Munoz et al. (2013) point to argument that public health policies have been criticized for responding to medical components of health and illness whereas individuals have adopted a more complex and holistic definition of health. 6 In the income inequality literature, the Lorenz curve and associated Gini coefficient are used to quantify inequalities. 7 Unlike the Oaxaca decomposition for differences in wages used in labor economics, our decomposition method which follows Wagstaff et al.(2003) identifies factors contributing to inequality in health across the entire distribution of a measure of socioeconomic status (SES). The decomposition method is adapted from Rao (1969). 8 Joint family is a multigenerational family system based on intergenerational reciprocity (Desai et al., 2010).

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sub-population may present a special and persistent problem even more resistant to policy intervention than health inequality among other sub-populations in India. Although many studies have used decomposition analysis to understand covariates of health and health outcomes, few pertain to developing countries (Yiengprugsawan et al., 2007; Wagstaff, 2003; Morasae, 2012; Zere, 2011). Methods Measuring Inequality using a Concentration Index Our primary measure to assess inequalities in health is the concentration index (CI), a tool developed in the literature on income inequalities. Although a number of tools have been developed to measure socioeconomic inequalities in health, only two measures including the concentration curve (CC) and the associated CI meet three important criteria required to serve as a comprehensive measure of inequality (Wagstaff et al., 1991)9. 1. The measure reflects the experiences of the entire population. 2. The measure is sensitive to changes in the distribution of the entire population across socioeconomic groups. 3. The measure ensures that socioeconomic dimensions to inequalities in health are accounted for. A measure that does encompass the entire population, the Lorenz curve for health, focuses solely on health and thus fails to capture socioeconomic dimensions that contribute to health inequality. Another measure---the range of health, captures the health of richest class and poorest class while ignoring the intermediate class. Since our aim is to understand to what extent inequalities in health are systematically related to SES, one would not be able to judge if inequalities in health are sensitive to inequalities in SES if these criteria are not met. The CC for health plots the cumulative proportions of health in the population against the cumulative proportion of population starting with the most disadvantaged and ending with the least disadvantaged based on a measure of SES or living standards10. A representative CC for health is shown in Figure 1. If “good” health is equally distributed across socioeconomic distribution then the health CC will coincide with diagonal. If good health is concentrated among the higher socioeconomic group, the CC lies below the diagonal. The greater the area between the diagonal and the CC, the greater is the inequality.

9 Another tool is the slope index of inequality and the associated relative index of inequality, useful for studying group level inequalities. Since our data are measured at the individual level we prefer the CC and CI. 10 Since both SES and living standards measures are used in this literature we will use them somewhat interchangeably. The issue of appropriateness of the SES/living standards measure in establishing inequality is beyond the scope of this paper.

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The CI is defined as twice the area between the CC and the diagonal. It provides a measure of the extent of inequalities in health associated with SES. Since the CI provides a measure of magnitude of inequality it gives us the advantage of making comparisons across time and space that the CC does not permit. Conventionally, the CI takes a negative value when the CC lies above the diagonal and is positive when the CC lies below the diagonal. For unbounded outcomes, it has been shown that the CI lies in the range [−1, 1] (O’Donnell et al., 2007 and Appendix A) where the sign of the CI indicates the direction of the relationship between the variables and the magnitude reflects the strength of the relationship.11 In our application we measure propensity to be in good health. Let L(h) be the mathematical formula for the CC. Then the concentration index (CI) in its most general form is given by 𝐶𝐼 = 1 − 2∫ 𝐿(ℎ)𝑑ℎ.1

0 Alternatively, the CI for a measure of health ℎ can be represented as

𝐶𝐼 =2𝑛𝜇

�ℎ𝑖𝑟𝑖

𝑛

𝑖=1

− 1 −1𝑛

where 𝑟𝑖 = 𝑖/𝑛 is the fractional rank in the SES variable and µ is the mean of ℎ. We are interested in the distribution of ℎ by SES. We measure SES12 by monthly household per capita expenditure (MPCE).13 Decomposing the CI To identify the contributions of different factors to inequality in health we decompose the CI using a four-step process: Step 1: Calculate the CI for a measure of Health Using the formula14 introduced above we calculate the CI for health as 𝐶𝐼 = 2

𝑛𝜇 ∑ ℎ𝑖𝑟𝑖𝑛

𝑖= − 1 (1)

11 If we measure “good health” and it is concentrated among the wealthy, the CC would lie below the diagonal. Alternatively, the CC can be defined for poor health with the interpretation that if it lies above the diagonal then poor health is concentrated among the poor and vice versa. Hence, when measuring poor health, the CI takes a negative value if poor health is concentrated among the poor and is positive when poor health is concentrated among the rich. 12 Some studies use an asset-based index for the ranking variable but the two competing ranking measures are equivalent as long as they produce similar rankings and as long as rank differences are uncorrelated with health (Wagstaff & Watanabe, 2003). We use MPCE, as we do not have detailed information on household assets. 13 Though our measure of MPCE underreports the true MPCE it is a reasonable proxy for relative ranking of households (NSSO, 2006). 14 The last term 1/n drops out as n becomes very large.

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Step 2: Estimate coefficients for the health model Represent the relationship between health and its covariates as ℎ𝑖 = 𝛼 + ∑ 𝛽𝑘𝑘 𝑥𝑘𝑖 + 𝜀𝑖 (2) where ℎ𝑖 is a continuous measure of health, 𝑥𝑘 is a set of covariates of health status and 𝜀𝑖~𝑁(0,𝜎2). Estimates for 𝛽𝑘 are obtained through normal regression analysis. Step 3: Compute the CI for covariate terms In a manner analogous to that for health, the CI’s for the 𝑘 covariates of health (denoted 𝐶𝐼𝑘) over which we intend to decompose the CI are computed. Each 𝐶𝑘 is obtained by replacing the measure of health in equation (1) with the respective covariate. Step 4: Decompose the CI for health We use the results from Step 1, 2 and 3 to decompose the CI for the health outcome by (Wagstaff et al., 2003). 𝐶𝐼 = ∑ (�̂�𝑘�̅�𝑘/𝜇)𝐶𝐼𝑘 + 𝐺𝐶𝜀/𝜇𝑘 (3) where �̅�𝑘 is the mean of kth covariate, 𝐶𝐼𝑘 is the CI for kth determinant calculated as 𝐶𝐼𝑘 = 2

𝑛�̅�𝑘∑ 𝑥𝑘𝑖𝑟𝑖 − 1𝑛𝑖=1 , is the mean of the health variable, is the estimate of

obtained from the regression (2) and 𝐺𝐶𝜖/𝜇 is computed as a residual between the summation term on the right-hand-side, referred to as the sum of contributions of all 𝑘 factors, and the CI computed for health in step 115. It is a measure of the unexplained inequality in health. A few comments are in order about the CI and its properties. These comments also illustrate the challenges in applying the CI to the case at hand. First, strictly speaking a CI is only an appropriate measure of socioeconomic inequalities of health when it is measured on a ratio scale16 with non-negative values (O’Donnell et al., 2007). More often than not, we are faced with health outcomes that are not measurable on a ratio scale such as SAH. The problem is that CI cannot be directly computed for categorical data but one can be constructed for cardinal variables. Second, the CI is invariant to scalar multiplication but not to a linear transformation of the health variable. Several methods have been proposed to calculate CI for non-ratio scale variables. Researchers have relied on a technique that collapses a j>2 categorical ordered outcome variable into a binary outcome measure (for example, good and poor health), but Wagstaff and Van Doorslaer (1994) show this dichotomization is sensitive to the cut off

15 For interested readers we offer a short tutorial for obtaining equation (3) in Appendix B. 16 Ratio scale variable allow us to make statements such as person A has twice as much of X as person B in a meaningful way.

µ ˆkβ kβ

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value. An alternative approach proposed by Wagstaff and Van Doorslaer (1994) preserves the J categories by transforming the variable under the assumption that it reflects a continuous underlying latent variable. Both these methods do not account for cohort bias associated with self assessed health measures. But subjective health measures are prone to bias (Kerkhofs and Lindeboom, 1995) due to willingness to conform to social norms. We, suspect that our outcome measure may be biased for similar reasons. To illustrate how bias might influence reported SAH we refer to Table 1, which presents the frequency of selected health conditions by caste, gender and age group. Although mean values of current SAH are almost identical for the low caste group in comparison to the higher caste group, the latter group is two to three times more likely to report chronic conditions like hypertension and diabetes than members of the former group. This empirical finding lends support to the fact that individuals might be referencing their immediate cohort while reporting SAH despite the proportions of objective health conditions being different. We also find that while mean SAH declines as age increases (lower numbers of SAH indicate worse health), the proportions for diabetes and hypertension do not show a clear trend in either direction. Females seem to be fairly consistent in their behavior, reporting worse SAH outcomes compared with their male counterparts while at the same time reporting higher proportions of most other health conditions versus their male peers. To overcome this potential bias and the categorical nature of our SAH variable, we innovate on an approach used by Vaillant and Wolff (2012) and Vallejo-Torres (2011) with an ordered probit model of SAH based on reported objective health measures along with age, gender and caste. We assume our (discrete) health measure, SAH, denoted as ℎ𝑖 is tied to an underlying (latent) continuous measure of self-assessed health, ℎ𝑖∗, in the following manner. ℎ𝑖 = 1 𝑖𝑓 − ∞ ≤ ℎ𝑖∗ < 𝛼1 ℎ𝑖 = 2 𝑖𝑓 𝛼1 ≤ ℎ𝑖∗ < 𝛼2 : : ℎ𝑖 = 𝐽 𝑖𝑓 𝛼𝐽−1 ≤ ℎ𝑖∗ < +∞ Where the 𝛼𝑗’s represent the cut-offs or thresholds for each health category i.e. 𝑗 = 1,2,3 … 𝐽. In line with convention we set 𝛼0 = −∞ and 𝛼𝑗 = +∞. where the underlying variable ℎ𝑖 is explained by ℎ𝑖∗ = 𝒙𝒊𝜷 + 𝒛𝒊𝜸 + 𝜀𝑖 (4) where 𝒙𝒊 and 𝒛𝒊 are vectors of explanatory variables, 𝜷 and 𝜸 are conformable vectors of parameters and 𝜀𝑖~𝑁(0,𝜎2). In (4) we are distinguishing between two types of

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explanatory variables; 𝒙𝒊 represents a vector of factors that explain the true health status and 𝒛𝒊 represents a vector of variables suspected of causing bias in self-assessed health. The probability of an observed outcome (ℎ) for given values of 𝒙 is Pr(ℎ = 𝑗|𝒙) = Pr (𝛼𝑗−1 ≤ ℎ∗ < 𝛼𝑗|𝒙) Our measure of bias adjusted health denoted, ℎ�𝑖, is calculated as ℎ�𝑖 = 𝒙𝒊𝜷� + 𝒛�𝜸� (5) where 𝜷� and 𝜸� are estimated with an ordered probit model based on (4) and 𝒛� are the sample means of 𝒛. Application: Our data, which comes from a nationally representative sample of the Indian population conducted in 2004, captures a host of information on health conditions and subjective indicators of health along with socio-demographic variables. We focus on the elderly cohort for our analysis. Using two different measures as the dependent variable – current SAH and relative SAH -- we construct continuous measures of underlying health by using predicted values from the parameters obtained from an ordered probit of SAH. Current SAH measures subjective health on a scale of 1-3 with lower values representing poor health and higher values representing very good/excellent health. On average the elderly cohort reports that it is in good health. Relative SAH, the second subjective health variable, is measured on a scale of 1-5 with lower values representing worse health compared to a year ago and higher values representing much better health. On this count our elderly cohort on average reported that their health was nearly the same compared to a year ago. We explain SAH with two classes of variables, those that are indicators of true health status, and those that we believe may cause cohort bias. Cohort group bias arises, as people tend to reference their immediate cohort while reporting SAH. We include three variables in our probit model---gender, age and caste--- that could potentially bias SAH and illustrate with examples. Table 2 provides basic statistics for all the variables used in the ordered probit estimation. We include Male as an explanatory variable because in a developing country like India, bias with respect to gender can arise because females tend to be discriminated on account of cultural factors such as preference for a male child. Discrimination against females can take the form of inadequate provision of nutrition in childhood to exclusion of females from inheriting their parents’ property, so much so that the social schism between females and males becomes

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very wide in certain cases. Therefore, a female with the same health conditions as her male counterpart may rate herself as far better in health than her male counterpart would rate himself. Table 2 shows a little over half of our elderly sample is male. Gender is not the only form of social stratification in India17; caste is another influential variable with potential to cause bias. A unique feature of the Indian sub-continent, caste system divides society into four exclusive groups that are mutually exhaustive, hereditary, endogamous, and occupation specific (Deshpande, 2000). Traditionally, occupations have been determined based on membership of caste with better paying and more respectable jobs concentrated in the hands of the upper (higher) castes. People from lowest tier of Indian society have been relegated to menial jobs associated with low self-esteem and considered impure (Gupta, 2000). Furthermore, there is evidence that shows villages and urban areas are clustered around caste groups. Because of low social status and harsh living conditions health problems are likely more commonplace among low caste individuals who, when comparing themselves to others in the low caste, may have a higher SAH compared with a high caste person suffering from same ailments. We include Lower Caste, a binary variable to capture the influence of caste. It takes a value of one if a person belongs to lowest echelons of Indian society and zero otherwise. Our measure of caste is fairly broad based that incorporates not only membership of the traditional caste system but also includes the other major marginalized segment of society--tribal population. By this definition, members of the Lower Caste constitute about 23.8 per cent of our sample. Our final candidate as a bias-causing variable is age. Age has the potential to cause bias, as people tend to compare their health status with others in their close cohort. Given the same profile of diseases, an elderly person is likely to report higher SAH compared to his younger counterpart. The data reveal that our elderly cohort has an average age just less than 68 years with a wide range from 60 years to 110 years. We caution that these same variables can have real health consequences as well; males and higher castes people have more and better access to healthcare, hence could improve true health. Increasing age brings with it more ill health. This confounds our ability to reveal bias. We return to this point later. We now turn our attention to true indicators of health, conditions and measures that capture the seriousness of these conditions. We have indicators of thirty seven different health conditions that capture a wide range of ailments from gastro-intestinal problems to functional disabilities like speech and hearing, each of these conditions is binary coded with a value of one if the condition is present and zero

17 Although MPCE could also bias SAH, we save it for calculating our CI to avoid the possibility of rigging our health variable. We thank Ben Cowan for pointing this out.

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otherwise. By far joint disease is the most commonly occurring ailment in our data with over seven per cent of the elderly cohort reporting its occurrence. This is not surprising given that old age is often associated with such problems. Cataract, an ailment also associated with ageing is reported by four per cent of the cohort. Overall the data reveal that diabetes and hypertension occur more commonly in our elderly sample when compared to diseases like malaria or tuberculosis that are common in a tropical setting in India. Since specific health conditions in our model do not necessarily capture the seriousness of health conditions, we include six measures of resource use; Treatment, Hospitalized, Days Hospitalized, Days Treated Before Hospitalization and Days Treated After Hospitalization, and Surgery, that are aggregated over individual health conditions. Treatment is a binary coded variable taking a value of one if a person received some sort of treatment for any health condition they had at the time the survey was conducted and zero otherwise. Approximately 31 per cent of our sample received treatment of some sort for existing health conditions. The variable Hospitalized is binary coded with a value of one if a person had been hospitalized for any reported health condition in the 365 day period prior to the canvassing of the survey and zero otherwise. About seven per cent of our sample reported they had been hospitalized. Days Hospitalized measures the number of days a person in our sample spent in hospital receiving treatment for existing health conditions. The elderly in our sample spent on average just under a day in hospital for treatment in the 365-day reference period for any ailment. Furthermore, they received on average about 10 days of treatment prior to an episode of hospitalization and a little over 5 days of treatment following an episode of hospitalization. Surgery is an indicator variable with value of one if a person underwent surgery for any ailment in the 365-day reference period prior to the canvassing of the survey and zero otherwise. Slightly less than 2 per cent of the sample underwent surgery for an existing health condition. The ordered probit estimates, which are given in Table 3, help us to better understand the influence of the direct health variables and potential bias-causing variables on SAH. The confounding of potentially real health effects and potential bias from gender, caste and age makes it impossible to separate any clear bias. If there is any bias it is dominated by the real impact of the variable on health. All the parameter estimates for these variables have signs that would be expected from a real impact of these variables on health. Age and caste have negative coefficients18 while Male has a positive coefficient. All estimates have p-values less than 0.01. We suspect, but cannot show, that bias could be lowering the magnitude of estimates but there is no way to test for this effect.19

18 A negative coefficient on caste means that health deteriorates for an individual placed in the lower caste category versus his higher caste counterpart. 19 We emphasize that these are not biased estimates, but that these variables may bias SAH.

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As expected, all the indicators of health conditions have a negative and significant influence (except for worm infestation) on SAH. Almost all our resource use variables that indicate seriousness of health conditions also have signs as anticipated, except for surgery. Predicted SAH are shown in Table 4. As expected, the unadjusted predicted SAH reported in row 2 takes on a range of negative values (SAH as reported in the Likert scale responses are constrained to be positive by the survey instrument). For comparison purposes, we also report the predicted SAH adjusted for bias (by using mean values for age, gender and caste) in row 3. Further analyses of the predicted SAH are shown in Table 5. Column 2 details average values for predicted SAH with no adjustments. Comparing entries for Lower Caste Males versus Higher Caste Males we find that both groups report similar health. The same is true when we compare females across caste groups. In column 3 we adjust predicted SAH to account for bias on account of all three potential bias causing variables. Adjusting for bias implies that higher caste males are in worse health than lower caste males. The same is true for females. Columns 3 and 4, which represent partial adjustments for bias, also reveal the same trend as the case of full adjustment. We stress that the way we correct (adjust) for bias removes any real impact of these variable on health; hence they will understate the actual contribution of Male towards better SAH, and understate the real contribution of Lower Caste and Age towards poorer SAH. We provide concentration indices for predicted SAH in Table 6. Our calculations show that health is concentrated among the rich (CI=0.00638471)20. For comparison purposes we also report concentration indices based on various types of adjustments. We find that our measure of health is fairly robust at least in direction to different types of adjustments except when we include MPCE in our predicted health model (see column 6). This last result is somewhat odd and improbable as it suggests that good health is concentrated among the poor (O’Donnell et al., 2007). Thus, we argue that including MPCE in our predicted health model leads to a biased measure of health as individuals tend to reference their income cohort group while reporting health.21 We do a linear decomposition of the concentration index based on the unadjusted predicted SAH. The covariates used in this model, partly based on covariates used in previous studies (Yiengprugsawan et al., 2007) are reported in Table 7. These

20 Following Munoz et al. (2013) we tried an OLS specification that yielded a narrower range for our predicted health variable but gives us a similar CI. We prefer the ordered probit specification as it takes into account extreme values. 21 The rich with better access to health care and living conditions would expect to be in better overall health compared with their poorer counterparts. But if a rich and a poor person face the same objective health conditions the latter might think he/she is in better overall health compared to the former thus overrating his/her SAH, hence the negative CI when we include MPCE.

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covariates account for a host of variables that influence health, ranging from socio-economic factors to geography. This excludes both health conditions as well as health resource variables but includes MPCE. Age is our first covariate. Since health is expected to deteriorate with age we expect this variable to play an important role. We divide our sample into five different age groups.22 Each age cohort group is an indicator variable taking a value of one if a person belongs to the cohort and zero otherwise. The young-elderly (Age60-64) form the largest segment of our sample at nearly 36%. The bulk of our sample (65%) is concentrated in the Age60-64 and Age65-69 categories. We interact each age cohort variable with indicator variables for gender---male and female---treating Male60_64 as our reference category. Lower Caste is our second variable as it can play a crucial role in determining access to resources through discriminatory treatment handed out to certain castes. We use MPCE as our third covariate to represent control over resources leading to greater access to health care and improved living conditions. Our data reveal that MPCE varies vastly around an average of 743 rupees (local currency). Lower Caste is measured in the same way as in our predicted health model. Currently Married is an indicator variable taking a value of one if currently married and zero if a person is never married, widowed, divorced or separated. Nearly 60% of our elderly cohort is currently married with wide differences between males and females. Of all elderly males in our sample, 80% are currently married. This number drops to 40% for elderly females driven by the large number of widows in the female cohort. Number of Children Living (sons and daughters) is a continuous variable with an average of nearly four living children per elderly person. This variable along with the whether a person is currently married provides proxy measures for family life as opposed to the availability of family to provide health care. Economic Independence is a binary coded variable taking a value of one if a person is entirely independent in financial terms and zero otherwise. Only 34 per cent of the elderly cohort is financially independent which brings us to the role of family as an institution of support in a country where public means of supporting the elderly are often either absent or lacking. Education impacts lives in many ways. At one level human capital theory argues that education is known to improve health status, Groot (2000). This improvement in health status can come about in many ways such as awareness about personal hygiene, information on treatments. But education is also important for the economic well-being of households and investments in the next generation, etc. (Desai et al., 2010). To capture the influence of education we include four binary

22 Using age and male separately in our decomposition model did not change the sign of other contributing factors. MPCE, Female80 and No Work are the most important factors contributing to inequality. Moreover, using age categories allows us to narrow down differences given the wide age range of our elderly sample.

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coded variables in our model--No Education, Primary Education, Secondary Education and Higher Education. No Education serves as our omitted category. A majority of our sample (61%) received no education. Primary Education is an indicator variable taking a value of one if a person achieved literacy without schooling or went on to primary school. A little over a fifth of the sample attained literacy in this fashion. Secondary Education is again an indicator variable that takes a value of one if a person went to middle, secondary or higher secondary school and zero otherwise. The data show that only 12% of the sample, which, recall, are only elderly, received a secondary education. Our final education category is Higher Education that takes a value of one if a person received higher secondary education or above and zero otherwise. Only a small fraction (5.6%) of our sample received higher education. Since access to a good clean source of drinking water is crucial for health we include Direct Water Source as a binary coded variable taking a value of one if the household to which the individual belongs receives a major part of its drinking water supply either through tap water or bottled water and zero otherwise. We include this as a control variable in our model because water borne diseases such as cholera are caused by lack of access to clean sources of drinking water (Qadeer, 2011). Less than half (44%) of our sample has access to such a source of drinking water. It is likely that persons with access to such a source of drinking water are more likely to be in good health compared with their peers who lack such access.

The next set of indicator variables deal with labor force participation. Researchers have used occupational status variables to isolate low status manual labor jobs from other jobs (Yiengprugsawan et al., 2007). As our data do not allow us to undertake a detailed classification by occupational category, we restrict ourselves to a slightly different type of classification. We divide the elderly cohort into two broad categories; those in the labor force and those outside it, using a standard definition that classifies a person as a labor force participant if he/she is working or is not working but is actively looking for work. All those who do not fall into this category for whatever reason are treated as being outside the labor force. The labor force participants are further sub-divided into salaried workers, self-employed, and casually employed workers. The non-labor force participants were sub-divided into two categories with a view to separate those who voluntarily chose not to work (including pensioners, beggars, prostitutes, etc.) from those could not work due to disability.

Salaried is an indicator variable that takes a value of one if a person received a regular salary or wages and zero otherwise. Only a small percentage (1.6%) of our sample was engaged in such work. Jobs, which pay a regular salary, are typically associated with the public sector and organized private sector. These numbers are along expected lines given evidence that Indian labor force is largely employed in the rural sector and the age for retirement in the formal sector is 60-62 years. Since these jobs are much sought after they are placed on a higher pedestal compared to

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casual work. We treat salaried as our reference category.

A second labor force participation variable is Self-Employed which takes a value of one if a person was engaged in a household enterprise as a worker, helper, employer or was unemployed and zero otherwise. By this definition nearly a quarter of our sample was self-employed. Casual Work (daily wager) is an indicator variable with value one if a person was engaged as a casual wage laborer and zero otherwise. Typically in rural areas of India such jobs involve public works related to construction of roads, digging of ponds, along with participation in employment generation schemes sponsored by the government. The nature of the tasks involving casual labor requires a fair amount of physical labor. Thus, one would expect such people to be in good health but concentrated among the poorer sections. Around six per cent of the elderly sample was engaged in such jobs.

As far as non-labor force participants are concerned, No Work is a binary coded variable with value one if a person was voluntarily engaged in tasks that are not typically associated with the labor force such as attending to domestic duties, along with those engaged in begging and prostitution and zero otherwise. By far this group forms the largest component among the occupational categories with 63% of the elderly staying out of labor force. Disabled is our last category taking a value of one if a person is unable to work due to a disability and zero otherwise. A little less than five per cent of the sample falls in this category.

To capture regional effects, we divide the country into five broad geographic regions (North, South, East, West and Central) using indicator variables to capture residence in each region. Since different regions exhibit varying levels of development we expect to capture the influence of uneven regional development on health. To further illustrate this point we cite evidence that shows India’s northern region performs poorly when it comes to gender equality (Desai et al., 2010) whereas the eastern region suffers from high levels of poverty and lacks infrastructure. Southern region though not economically wealthy tends to outperform other regions of the country as far as socio-demographic factors are concerned. We find that the largest component of the elderly (30%) resides in the northern region. As modern health infrastructure shows a pro urban bias, we introduce indicator variables for rural and urban areas to capture the influence of this factor. A majority (63%) of our sample resides in rural areas. We interact each of the geographic region variables with the location variables. East-Rural is the reference category.

In Table 8 we present the results of the linear health model used to decompose inequality in predicted SAH. The results show that health declines as one ages irrespective of gender. The decline in SAH of females is sharper when compared to their male counterparts among the very elderly. An increase in MPCE leads to a decrease in health, which is surprising.

Being currently married is beneficial to health whereas an increase in the number of

14

children living is detrimental to health. The latter result though seemingly counter-intuitive can be explained by the fact that having lots of children can increase longevity even for those in poor health23. Economic independence is beneficial to health of the elderly, as one would expect.

Our primary and secondary education variables have signs that are unexpected as one would think that education helps improve health. The coefficient on higher education though in the right direction is insignificant. Access to a direct source of drinking water improves health, as expected. With respect to our labor force participation variables, participation improves health while non-participation is detrimental to health. Our region-location variables are mostly insignificant.

To assess the contribution of these covariates to overall inequality in predicted SAH we turn to Table 9. In column 2 we list the elasticity of each covariate expressed as the first term in equation 3: (�̂�𝑘�̅�𝑘/𝜇). Column 3 merely details the concentration indices for all the covariates. Each entry in column 4 is obtained as the product of the respective entries in column 2 and 3. Summing across elements of Column 4 and then subtracting the sum from the overall CI in the top row of Column 4 gives us the residual component. In column (5), standard errors are calculated using a bootstrapping technique, as there is no analytical way to obtain them (O’Donnell et al., 2007). We replicate the decomposition exercise 500 times to compute the standard deviation of the contribution of each factor (Vallejo-Torres, 2011).

The results show that MPCE is the biggest contributor to inequality in predicted health in favor of the rich followed by Female80, and No work. The fact that MPCE is the largest factor responsible for inequality in our predicted health variable in favor of the rich is not surprising given evidence from other researchers that attribute income and its correlates as the major factor for inequality in adverse health outcomes in favor of the poor (Wagstaff et al., 2003).

We replicate our analysis with a second measure of health; SAH relative to SAH in the previous year. These results exhibit a similar pattern (see Table 10). The potential bias causing variables have the same sign as in the case of current SAH. Again, bias could be lowering the magnitude of estimates but again there is no way to test for this effect. As regards to health conditions and resource use variables, all have a negative influence on health except Surgery and Malaria . The predicted relative SAH reported in Table 11 take on a range of negative values. Table 12 reports average values of predicted SAH with and without various adjustments. The results reveal that irrespective of adjustments members of the higher caste are in worse health than their lower caste counterparts compared to a year ago.

23 In our sample, elderly with poor predicted health reported greater mean number of children compared to those in good health.

15

Before we turn to the decomposition model, we report in Table 13 the concentration indices. As in the case of current health not adjusting for bias implies that we have a positive CI (column 2). Our measure is robust (in direction) to types of adjustments. However including MPCE in the predicted health model reverses the sign of the CI (column 6). We explain away this odd result in the same fashion as in the case current health. Table 14 lists the results of our linear health model. Our key variables gender, age, and Lower Caste show signs along expected lines and are highly significant. All other variables are broadly similar to the case of current health including MPCE, which surprisingly has a negative sign. The decomposition results (Table 15) show that MPCE continues to be the single biggest contributor to inequality in favor of the poor in relative health followed by Female80 and No work. Conclusion: In this paper, we explore the nature of inequality in SAH among an elderly cohort in India. Our objective has been two-fold; first to obtain an unbiased measure of SAH and second to measure inequality in predicted SAH and then to decompose any found inequality among our elderly cohort. We have registered modest success in our attempt at tackling the issue of bias on account of individuals referencing their close cohort in assessing their SAH due to confounding caused by real effects of our suspected bias causing variables (age, gender and caste) on health. Despite this problem we have managed to overcome judgmental bias arising due to the categorical nature of our outcome variable by making use of a predicted measure of SAH. To this extent, our measure of health should be free from at least one major source of bias. We find that predicted SAH (both measures) is concentrated among the richer sections of society. Since inequalities in predicted SAH may be the result of inequalities in the determinants of health, decomposing these inequalities allows us to assess the relative importance of each factor in generating inequalities in the outcome of interest. The decomposition exercise reveals that MPCE is the largest contributor to inequality in health (both current and relative) in favor of the rich. Other important sources of inequality include No work and Female80. India is a particularly interesting case to study given the developing nature of the economy and a growing elderly population. In the end our study could have benefitted from data on other sections of population to make it broad based.

16

Acknowledgements: The authors would like to thank Bidisha Mandal and Ben Cowan for providing valuable comments. All errors rest with the authors.

17

Figure 1: Concentration Curve

Figure 1

Cumulative % of good health

Cumulative % of population by SES variable

Line of equality

Concentration Curve

0% 100%

100%

18

Appendix A Proof: Bounds of CI for continuous outcomes The reader is advised to look at Figure 1 while referring to the following proof. The CI has been defined in the above discussion as twice the area between diagonal (line of equality) and CC.

𝐶𝐼 = 2 �𝐴𝑟𝑒𝑎 𝑜𝑓 𝑙𝑜𝑤𝑒𝑟 𝑡𝑟𝑖𝑎𝑛𝑔𝑙𝑒 − � 𝐿(ℎ) 𝑑ℎ1

0� = 2[

12−� 𝐿(ℎ) 𝑑ℎ

1

0]

Multiplication by 2 helps to conveniently restrict the limits of the CI to [−1, 1].24 It can easily be verified that the lower bound of CI is -1 when the value of integral goes to 1 i.e. in that case all the health is concentrated in the poor. On the other hand the upper bound of 1 is reached when the value of integral goes to 0. Appendix B Decomposition of CI We can obtain the decomposition equation (equation 3) by substituting equation 2 into 1, which gives us 𝐶𝐼 = 2

𝑛𝜇∑ (𝛼 +𝑛𝑖=1 ∑ 𝛽𝐾𝐾 𝑥𝑘𝑖 + 𝜖𝑖)𝑟𝑖 − 1

Making use of the fact that �̅�𝑖 = 1/2 we have 𝐶𝐼 = 2

𝑛𝜇[𝑛𝛼2

+ ∑ (𝑛𝑖=1 𝛽1𝑥1𝑖 + 𝛽2𝑥2𝑖 + ⋯+ 𝛽𝑘𝑥𝑘𝑖 + 𝜖𝑖)𝑟𝑖]− 1

= 2𝑛𝜇

[𝑛𝛼2

+ 𝛽1 ∑ (𝑛𝑖=1 𝑥1𝑖)𝑟𝑖 + 𝛽2 ∑ (𝑛

𝑖=1 𝑥2𝑖)𝑟𝑖 + ⋯+ 𝛽𝑘 ∑ (𝑛𝑖=1 𝑥𝑘𝑖)𝑟𝑖 + ∑ (𝑛

𝑖=1 𝜖𝑖)𝑟𝑖] − 1

Substituting (𝐶𝑘 + 1) 𝑛�̅�𝑘2

= {∑ 𝑥𝑘𝑖𝑛𝑖=1 𝑟𝑖}, using 𝐺𝐶𝜖

𝑛2

= ∑ 𝜖𝑖𝑟𝑖𝑛𝑖=1 and 𝜇 = 𝛼 +

∑ 𝛽𝐾𝐾 𝑥𝑘 we get 𝐶𝐼 = ∑ (𝛽𝐾𝐾 �̅�𝑘/𝜇)𝐶𝐼𝐾 + 𝐺𝐶𝜖/𝜇 This equation is the same as equation 3 detailed in the measurement section.

24 For binary variables the bounds of the concentration index depend on its mean, Wagstaff (2005).

19

Note on survey design and data source Our data comes from the 60th round on Healthcare and Morbidity (2004), the most recent period for which data is available, conducted by the National Sample Survey Organization (NSSO), an agency of the Government of India. This nation-wide survey adopted a multi-stage stratified sampling design with census villages and urban blocks forming the first stage units, households formed the ultimate stage units. Villages and blocks are often organized on the basis of caste. A total of about 73,000 households were surveyed in the process. Information was collected at two levels through interviews: household level for characteristics of the household and individual level for data on health conditions. As far as possible information relating to health conditions was gathered from individual members themselves except in the case of children. In the survey elderly respondents were asked two questions about SAH. The first question asked respondents to rate their “own perception about state of health” into three categories namely--excellent/very good, good/fair or poor (NSSO, 2006). A second question asked respondents to rate their “own perception about relative state of health compared to previous year” as much better, somewhat better, nearly the same, somewhat worse or worse. This question helps us understand whether an individual believes his or her health is in decline. We believe respondents may choose their answers consistent with how they compare to their cohort with respect to age, gender and caste. If health conditions vary widely between castes or economic groups, self-assessing ones health against a close cohort may mean that a relative ranking of one’s health to the comparison cohort could also vary, making SAH biased upward for individuals in groups with poor health conditions overall.

20

Table 1: Frequency of selected health conditions reported by caste, age and gender Hypertension

(%) Diabetes

(%) Cataract

(%) Joint/Bone

Diseases (%)

Current SAH

(Mean) Lower Caste

2.950 1.469 4.204 6.04 1.806

Higher Caste

6.393 4.487 3.703 7.412 1.808

Female 6.247 3.536 4.22 8.372 1.773 Male 4.925 3.99 3.44 5.85 1.841

Age 60-64 4.672 3.369 2.226 5.273 1.931 Age 65-69 5.354 4.143 3.705 6.752 1.824 Age 70-74 6.352 4.293 5.017 8.972 1.723 Age 75-79 7.512 3.813 5.360 9.551 1.666 Age 80+ 6.534 2.970 6.779 9.294 1.554

21

Table 2: SAH and factors influencing SAH

Var. Mean Std. Dev. Min. Max. Obs. Outcome Vars. Cur. SAH 1.807468 0.5154743 1 3 33127 Rel. SAH 2.926767 0.731683 1 5 33127 Explanatory Vars. Bias Causing (z) Male 0.5099768 0.499908 0 1 33127 Lower Caste 0.2384158 0.4261211 0 1 33127 Age 67.60839 6.935438 60 110 33127 Covariates (x) Diarrhea 0.0049506 0.0701875 0 1 33127 Gastritis 0.0234552 0.1513464 0 1 33127 Worm Infestation 0.000966 0.0310657 0 1 33127 Amoebiosis 0.0014792 0.0384319 0 1 33127 Jaundice 0.0006943 0.0263408 0 1 33127 Heart Disease 0.0287681 0.1671566 0 1 33127 Hypertension 0.0557249 0.2293932 0 1 33127 Respiratory 0.0154255 0.1232396 0 1 33127 Tuberculosis 0.0062487 0.0788024 0 1 33127 Asthma 0.0350469 0.1839013 0 1 33127 Joint Disease 0.0708486 0.2565756 0 1 33127 Kidney 0.0075467 0.0865447 0 1 33127 Prostate 0.0018716 0.043222 0 1 33127 Gynecological 0.000966 0.0310657 0 1 33127 Neurological 0.0136143 0.115885 0 1 33127 Psychological 0.0023546 0.0484675 0 1 33127 Conjunctivitis 0.0040149 0.0632365 0 1 33127 Glaucoma 0.0056148 0.0747221 0 1 33127 Cataract 0.0382166 0.1917216 0 1 33127 Skin Disease 0.0063694 0.0795553 0 1 33127 Goiter 0.0007849 0.0280047 0 1 33127 Diabetes 0.0376732 0.1904075 0 1 33127 Under Nutrition 0.0005434 0.0233042 0 1 33127 Anemia 0.0025055 0.0499931 0 1 33127 Malaria 0.0011773 0.0342919 0 1 33127 Whopping Cough 0.0060072 0.077274 0 1 33127 Unknown Fever 0.0095089 0.0970501 0 1 33127 Filariasis/ Elephantiasis

0.0010867 0.0329481 0 1 33127

Locomotor Disability

0.0251155 0.1564782 0 1 33127

22

Visual Disability 0.0267154 0.1612527 0 1 33127 Speech Disability 0.0019018 0.0435685 0 1 33127 Hearing Disability 0.0240589 0.1532345 0 1 33127 Diseases of Mouth/Teeth/Gum

0.0029281 0.0540337 0 1 33127

Accidents/Fractures

0.0084221 0.0913863 0 1 33127

Cancer/Tumors 0.0041356 0.0641765 0 1 33127 Other Diagnosed Ailments

0.0435898 0.2041838 0 1 33127

Other Undiagnosed Ailments

0.0156066 0.1239496 0 1 33127

Treatment 0.3138226 0.4640522 0 1 33127 Hospitalized 0.0716636 0. 257934 0 1 33127 Days Hospitalized 0.953965 6. 498131 0 350 33127 Days Treated Before Hospitalization

9.98059 100.1413 0 3996 33127

Days Treated After Hospitalization

5.33441 34.41821 0 1786 33127

Surgery 0.0187762 0.1357359 0 1 33127 Notes:

1. Certain conditions like STD, Eruptive diseases; Mumps, Diphtheria and Tetanus were dropped due to low frequencies.

2. Dataset was purged of elderly above the age of 110 (N=17) to account for outliers.

3. All observations not reporting the outcome variables (N=1635), independence (N=24), household expenditure (N=2), caste (N=7), education (N=9), drinking water source (N=5) and treatment for ailment (N=5) were dropped

23

Table 3: Ordered Probit Estimates of Current SAH Vars. Coeff. Robust S.E.

Bias Causing (z)

Male 0.203*** 0.014 Lower Caste -0.0924*** 0.0165 Age -0.0335*** 0.0011 Other Covariates (x)

Diarrhea -0.458*** 0.11 Gastritis -0.365*** 0.0501 Worm Infestation -0.174 0.253 Amoebiosis -0.479** 0.24 Jaundice -0.477* 0.28 Heart Disease -0.365*** 0.0455 Hypertension -0.160*** 0.0344 Respiratory -0.470*** 0.0615 Tuberculosis -1.140*** 0.0992 Asthma -0.754*** 0.0437 Joint -0.451*** 0.0296 Kidney -0.599*** 0.0877 Prostate -0.675*** 0.165 Gynecological -0.958*** 0.22 Neurological -0.932*** 0.0695 Psychological -0.741*** 0.15 Conjunctivitis -0.238** 0.113 Glaucoma -0.648*** 0.0956 Cataract -0.488*** 0.0395 Skin -0.336*** 0.0861 Goiter -0.573** 0.29 Diabetes -0.167*** 0.0396 Under Nutrition -0.916*** 0.315 Anemia -0.673*** 0.153 Malaria -0.505** 0.238 Whop Cough -0.365*** 0.0854 Unknown Fever -0.417*** 0.08 Filariasis -0.424* 0.242 Locomotor -1.039*** 0.0522 Visual -0.470*** 0.0496 Speech -0.337* 0.183 Hearing -0.308*** 0.0481 Mouth -0.327** 0.139 Accidents -0.714*** 0.0826 Cancer -1.390*** 0.14 Others-Diagnosed -0.552*** 0.0376

24

Others-Undiagnosed -0.823*** 0.0645 Treated -0.145*** 0.0251 Hospitalized -0.283*** 0.0483 Days Hospitalized -0.00573** 0.00225 Days Treated Before Hospitalization

-0.000249*** 0.0000908

Days Treated After Hospitalization

-0.0000737 0.000285

Surgery 0.306*** 0.0661

Cut1 -3.283*** 0.0756 Cut2 -0.721*** 0.0733 N 33,127 Log-likelihood -21960 Chi Square 4838 Pseudo-Rsq 0.124 *** p<0.01, ** p<0.05, * p<0.1 Note: Certain conditions like STD, Eruptive diseases; Mumps, Diphtheria and Tetanus were dropped due to low frequencies

25

Table 4: SAH as reported with the Likert scale versus Predicted SAH Mean Std. Deviation Min Max SAH as reported with the Likert scale

1.807468 0.5154743 1 3

Predicted SAH (Unadjusted)

-2.514892 0.5731518 -6.365982 -1.806874

Predicted SAH (Adjusted)

-2.514892 0.4796497 -6.18876 -2.183433

Values of predicted health (current) obtained using coefficients of the ordered probit model to make a linear prediction of health as detailed in equation 5. Values of health closer to zero are interpreted as indication of good health. Table 5: Differences in Mean Predicted SAH Across Groups No Adjustment

(2) Adjustment 1

(3) Adjustment 2

(4) Adjustment 3

(5)

Lower Caste Male

-2.427862 -2.484404 -2.457125 -2.384777

Lower Caste Female

-2.617973 -2.477553 -2.443926 -2.581237

Higher Caste Male

-2.42147 -2.52924 -2.543124 -2.429613

Higher Caste Female

-2.608232 -2.521588 -2.526576 -2.625272

Everyone -2.514892 -2.514892 -2.514892 -2.514892 Col. 2: Values for lower/higher caste male/female are calculated at actual values for male/female, caste and age, all other variables also at actual values Col. 3: Values for lower/higher caste male/female are calculated at overall sample means for male/female, caste and age; all others at actual values Col. 4: Values for lower/higher caste male/female are calculated setting age at actual values and male/female, caste at their overall sample means respectively; all other variables at actual values Col. 5: Values for lower/higher caste, male/female are calculated setting male/female at actual values and age, caste at their overall sample means respectively, all other variables at actual values

26

Table 6: Concentration Indices for Predicted SAH (Current) No Adjustment

(2)

Adjustment 1

(3)

Adjustment 2

(4)

Adjustment 3

(5)

Adjustment 5

(6) Concentration Index

(C)

0.00638471 0.00514986 0.00850668 0.00497447 -0.01105271

Col 2: Predicted health calculated at actual values for all covariates Col 3: Adjustments were made at sample mean values for caste, male/female and age, all other variables at actual values. Col 4: Adjustments were made only for caste and gender at sample mean values, all other variables at actual values Col 5: Adjustments were made only for age and caste at sample mean values, all other variables at actual values Col 6: Adjustments were made only for age, caste, male/female and income at sample mean values, all other variables at actual values

27

Table 7: Determinants of Predicted SAH Mean S.D. Min Max Obs. Age60-64 0.3567 0.4790 0 1 33127 Age65-69 0.2893 0.4534 0 1 33127 Age70-74 0.1877 0.3905 0 1 33127 Age75-79 0.0800 0.2712 0 1 33127 Age80 0.0864 0.2810 0 1 33127 Male 0.5100 0.5000 0 1 33127 Female 0.4900 0.5000 0 1 33127 Lower Caste 0.2384 0.4261 0 1 33127 MPCE 743.32 652.60 0 25500 33127 Cur. Married 0.6011 0.4897 0 1 33127 Children Living 3.9580 2.3452 0 88 33127 Econ. Independence 0.3432 0.4748 0 1 33127 No Education 0.6118 0.4874 0 1 33127 Primary Education 0.2119 0.4087 0 1 33127 Secondary Education 0.1207 0.3258 0 1 33127 Higher Education 0.0556 0.2291 0 1 33127 Dir. Water Source 0.4435 0.4968 0 1 33127 Self Employed 0.2443 0.4297 0 1 33127 Salaried 0.0155 0.1235 0 1 33127 Casual Work 0.0585 0.2348 0 1 33127 No Work 0.6343 0.4816 0 1 33127 Disabled 0.0473 0.2123 0 1 33127 South 0.2429 0.4288 0 1 33127 North 0.2961 0.4565 0 1 33127 West 0.1276 0.3337 0 1 33127 East 0.2467 0.4311 0 1 33127 Central 0.0698 0.2548 0 1 33127 Urban 0.3623 0.4807 0 1 33127 Rural 0.6377 0.4807 0 1 33127

1. Number of children living was calculated by replacing missing values with zeros for sons and daughters living as there is no clear way of distinguishing a missing value from a zero based on the wording of the question. Not giving an answer does not necessarily imply a missing value.

2. MPCE: Monthly per capita household expenditure is total household consumption expenditure for last 30 days divided by household size and is measured in local currency units. It is an aggregate of purchases, receipts in exchange of goods and services, free collection and home produced stock in the last 30 days. It includes imputed values of goods and services that were not purchased but procured for consumption.

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Table 8: OLS Estimates for Predicted SAH (Unadjusted) Vars. Coeff. Robust SE Male65_69 -0.191*** 0.00855 Male70_74 -0.410*** 0.0106 Male75_79 -0.605*** 0.0153 Male80 -0.889*** 0.0166 Female60_64 -0.148*** 0.00897 Female65_69 -0.332*** 0.0098 Female70_74 -0.559*** 0.0121 Female75_79 -0.697*** 0.0161 Female80 -1.076*** 0.0187 MPCE -4.87e-05*** 7.52E-06 Lower Caste -0.0758*** 0.00618 Cur. Married 0.0223*** 0.00594 Children Living -0.00588*** 0.00123 Econ. Independence 0.0462*** 0.00687 Primary Education -0.0420*** 0.00718 Secondary Education -0.0308*** 0.00934 High Education 0.00878 0.0135 Water direct 0.0188*** 0.00612 Self Employed 0.0133 0.019 Casual Work 0.0386* 0.0202 No Work -0.0744*** 0.0191 Disabled -0.485*** 0.025 North Rural 0.011 0.00842 North Urban 0.0304*** 0.0113 Central Rural 0.0134 0.013 Central Urban 0.0184 0.0182 South Rural -0.0268*** 0.00964 South Urban 0.0101 0.011 East Urban -0.00741 0.0121 West Rural -0.0334*** 0.0119 West Urban -0.0155 0.0135 Constant -2.070*** 0.0215 N 33,127 R-sq 0.339 Adjusted R-sq 0.338 F 469.5 *** p<0.01, ** p<0.05, * p<0.1

29

Table 9: Decomposition of Predicted SAH (Unadjusted) Covariates Elasticity

(2)

Concentration Index

(3)

Contribution CI=0.00638471

(4)

Std. Errors (5)

Male65_69 .01089286 -.01887113 -.00020556 .3452454 Male70_74 .01579351 .00737504 .00011648 .26526351 Male75_79 .01027377 .04502927 .00046262 .20969488 Male80 .01557386 .04373757 .00068116 .23015199 Female60_64 .01022262 -.02373836 -.00024267 .38623095 Female65_69 .01923573 -.02962912 -.00056994 .35144135 Female70_74 .02016189 -.0058726 -.0001184 .27766562 Female75_79 .01032839 .06840788 .00070654 .22624406 Female80 .01811337 .10069533 .00182393 .17617626 MPCE .01438114 .34462626 .00495612 482.94191 Lower Caste .00718826 -.2222419 -.00159753 .43459607 Cur. Married -.00532884 .0158645 -.00008454 .48048072 Children Living

.00924705 -.01915127 -.00017709 2.1532992

Econ. Independence

-.00630985 .09361916 -.00059072 .47744729

Primary Education

.00353621 .1205674 .00042635 .41600736

Secondary Education

.00147727 .3818055 .00056403 .33218171

Higher Education

-.00019413 .66944335 -.00012996 .20969488

Direct Water Source

-.00331965 .20818868 -.00069111 .49638372

Self Employed -.00129292 -.05071241 . 00006557 .43907354 Casual Work -.00089902 -.33077433 .00029737 .22224939 No work .01876119 .05579706 .00104682 .48328784 Disabled .00912031 -.15169051 -.00138346 .21398031 North Rural -.00087427 -.10813449 .00009454 .4173396 North Urban -.00116573 .40177862 -.00046837 .26203306 Central Rural -.00024686 -.47307135 .00011678 .21398031 Central Urban -.0001708 .09938857 -.00001698 .13308427 South Rural .00146385 -.14223974 -.00020822 .33664024 South Urban -.00042263 .36016096 -.00015221 .28931541 East Urban .00021547 .34637754 .00007463 .28064483 West Rural .00089417 -.13984082 -.00012504 .26203306 West Urban .00037148 .46621324 .00017319 .25874679 Residual 0.00160595 Std. Errors in col.5 are bootstrapped std. errors for the contribution of factors

30

Table 10: Ordered Probit Estimates of Relative SAH Var. Coeff. Robust S.E. Bias Causing (z) Male 0.103*** 0.0124 Lower Caste -0.0304** 0.0147 Age -0.0195*** 0.000969 Other Covariates (x) Diarrhea -0.274** 0.108 Gastritis -0.182*** 0.0505 Worm Infestation -0.26 0.235 Amoebiosis -0.000495 0.222 Jaundice -0.162 0.296 Heart Disease -0.260*** 0.046 Hypertension -0.148*** 0.0328 Respiratory -0.302*** 0.0595 Tuberculosis -0.458*** 0.102 Asthma -0.475*** 0.0404 Joint -0.299*** 0.0276 Kidney -0.375*** 0.0929 Prostate -0.660*** 0.151 Gynecological -0.706*** 0.18 Neurological -0.687*** 0.0681 Psychological -0.283* 0.159 Conjunctivitis -0.398*** 0.102 Glaucoma -0.328*** 0.0883 Cataract -0.323*** 0.0357 Skin -0.238*** 0.0874 Goiter -0.29 0.273 Diabetes -0.126*** 0.0391 Under Nutrition -0.444 0.332 Anemia -0.536*** 0.141 Malaria 0.238 0.258 Whopping Cough -0.242*** 0.0854 Unknown Fever -0.288*** 0.0734 Filariasis -0.485** 0.243 Locomotor -0.614*** 0.0461 Visual -0.307*** 0.0435 Speech -0.516*** 0.157 Hearing -0.193*** 0.0404 Mouth -0.0663 0.124 Accidents -0.724*** 0.0867 Cancer -0.956*** 0.134 Other-Diagnosed -0.415*** 0.0362 Other-Undiagnosed -0.483*** 0.0594 Treatment -0.032 0.0234 Hospitalized -0.112** 0.0478

31

Days Hospitalized -0.00332* 0.00188 Days Treated Before Hospitalization

-0.000170** 7.62E-05

Days Treated After Hospitalization

-3.59E-05 0.000279

Surgery 0.245*** 0.0701 Cut1 -3.510*** 0.0683 Cut2 -2.285*** 0.0661 Cut3 -0.344*** 0.065 Cut4 0.457*** 0.066 N 33,127 P-value 0.0000 Psuedo-Rsq 0.0421 Log Likelihood -33480 *** p<0.01, ** p<0.05, * p<0.1 Note: Certain conditions like STD, Eruptive diseases; Mumps, Diphtheria and Tetanus were dropped due to low frequencies

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Table 11: Relative SAH as reported with the Likert scale versus Predicted Relative SAH Variable Mean Std. Dev. Min Max Relative SAH as reported with the Likert scale

2.926767 0 .731683 1 5

Predicted Relative SAH (unadjusted)

-1.466696 0.3399418 -3.742246 -0.7825432

Predicted Relative SAH (adjusted)

-1.466691 0.2865144 -3.841489 -0.9579608

Values of relative health obtained using coefficients of the ordered probit model to make a linear prediction of health as detailed in equation 5. Values of relative health closer to zero are interpreted as indication of good health. Table 12: Differences in Mean Predicted Relative SAH Across Groups No Adjustment

(2) Adjustment 1

(3) Adjustment 2

(4) Adjustment 1

(5)

Lower Caste Male -1.401752 -1.444618 -1.428841 -1.39439

Lower Caste Female

-1.502827 -1.446862 -1.427414 -1.499135

Higher Caste Male

-1.424611 -1.474101 -1.482082 -1.423873

Higher Caste Female

-1.520383 -1.472406 -1.475354 -1.524679

Everyone -1.466696 -1.466691 -1.466699 -1.466691 Col. 2: Values for lower/higher caste male/female are calculated at actual values for male/female, caste and age; all other variables also at actual values Col. 3: Values for lower/higher caste male/female are calculated at overall sample means for male/female, caste and age,;all others at actual values Col. 4: Values for lower/higher caste male/female are calculated setting age at actual values and male/female, caste at their overall sample means respectively; all other variables at actual values Col. 5: Values for lower/higher caste, male/female are calculated setting male/female at actual values and age, caste at their overall sample means respectively; all other variables at actual values

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Table 13: Concentration Indices for Predicted Relative SAH (Unadjusted) No

Adjustment (2)

Adjustment 1

(3)

Adjustment 2

(4)

Adjustment 3

(5)

Adjustment 4

(6) Concentration

Index (C) 0.00708181 0.00498131 0.00833131 0.00482812 -0.00723195

Col 2: Predicted health calculated at actual values for all covariates Col 3: Adjustments were made at sample mean values for caste, male/female and age, all other variables at actual values. Col 4: Adjustments were made only for caste and gender at sample mean values, all other variables at actual values Col 5: Adjustments were made only for age and caste at sample mean values, all other variables at actual values Col 6: Adjustments were made only for age, caste, male/female and income at sample mean values, all other variables at actual values

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Table 14: OLS Estimates for Predicted Relative SAH (Unadjusted) Vars. Coeff. Robust SE Male65_69 -0.111*** 0.00502 Male70_74 -0.239*** 0.00620 Male75_79 -0.357*** 0.00924 Male80 -0.524*** 0.0101 Female60_64 -0.0719*** 0.00534 Female65_69 -0.180*** 0.00580 Female70_74 -0.313*** 0.00725 Female75_79 -0.398*** 0.00990 Female80 -0.618*** 0.0112 MPCE -.0000288*** 4.47e-06 Lower Caste -0.0194*** 0.00368 Cur. Married 0.0168*** 0.00356 Children Living -0.00349*** 0.000753 Econ. Independence 0.0252*** 0.00410 Primary Education -0.0239*** 0.00429 Second Education -0.0176*** 0.00558 High Education 0.00799 0.00807 Water Direct 0.0134*** 0.00363 Self Employed 0.0138 0.0117 Casual Work 0.0264** 0.0124 No work -0.0380*** 0.0117 Disabled -0.280*** 0.0153 North Rural 0.00459 0.00499 North Urban 0.0145** 0.00674 Central Rural 0.00488 0.00769 Central Urban 0.00360 0.0111 South Rural -0.0206*** 0.00574 South Urban 0.00259 0.00656 East Urban -0.00670 0.00726 West Rural -0.0230*** 0.00705 West Urban -0.0160* 0.00819 Constant -1.224*** 0.0132 Observations 33,127 R-square 0.329 Adjusted R-square 0.329 F 441.7 *** p<0.01, ** p<0.05, * p<0.1

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Table 15: Decomposition of Predicted Relative SAH (Unadjusted) Elasticity

(2)

Concentration Index

(3)

Contribution CI=0.00708181

(4)

Std. Errors (5)

Male65_69 .01089408 -.01877748 -.00020456 .34099348 Male70_74 .01577326 .00740066 .00011673 .27766562 Male75_79 .01038115 .04652681 .000483 .20079002 Male80 .01574971 .04333436 .0006825 .20079002 Female60_64 .00853483 -.02331001 -.00019895 .38949636 Female65_69 .01784424 -.02996727 -.00053474 .34939971 Female70_74 .01939515 -.00639059 -.00012395 .28357862 Female75_79 .0101126 .06861037 .00069383 .19615543 Female80 .01786325 .10068055 .00179848 .2053005 MPCE .01460201 .34465907 .00503271 483.15282 Lower Caste .00314638 -.222074 -.00069873 .44336118 Cur. Married -.00688534 .01601712 -.00011028 .48434821 Children Living .00941256 -.01911854 -.00017995 2.1343371 Econ. Independence

-.00588684 .09379107 -.00055213 .47551681

Primary Education

.00345857 .12050191 .00041676 .38120156

Secondary Education

.00144534 .38207053 .00055222 .34939971

Higher Education

-.00030244 .66994506 -.00020262 .24853075

Direct Water Source

-.00406617 .20842553 -.00084749 .49947467

Self Employed -.00230394 -.05035332 .00011601 .42253173 Casual Work -.00105541 -.33100858 .00034935 .2746395 No work .01643876 .05555861 .00091331 .48486512 Disabled .00902202 -.15012597 -.00135444 .22224939 North Rural -.00062441 -.10838066 .00006767 .38787226 North Urban -.00095608 .40180812 -.00038416 .31320312 Central Rural -.00015422 -.47275372 .00007291 .16513795 Central Urban -.00005743 .09902895 -5.687e-06 .11760809 South Rural .00193441 -.14249606 -.00027565 .36881982 South Urban -.00018605 .35980435 -.00006694 .30030045 East Urban .00033489 .34665016 .00011609 .24499806 West Rural .00105772 -.1399277 -.000148 .25199803 West Urban .00065692 .46619245 .00030625 .25874679 Residual 0.001252267 Std. Errors in col.5 are bootstrapped std. errors for the contribution of factors

36

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