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    • The results in Table show that for the total sample

    2018-10-24

    The results in Table 3 show that, for the total sample, the education coefficient is reduced by 10% in the binary component and 23% in the conditional component with the inclusion of the occupational categories. Models 4 and 5 in Table 2 present the analogous models with controls for wealth instead of educational attainment. In Model 4, higher asset values are significantly associated with a lower likelihood of any limitations as well as fewer limitations among those that have them. The net worth coefficients remain significant for the total sample (but not always for the sex-specific samples) in the presence of the occupation variables (Model 5) but are reduced by more than 35% in the binary component and about 25% in the conditional component (Table 3, “gross effect”). Model 6 in Table 2 includes all socioeconomic measures. As shown in Table 3 (“net effect”), the AN-2728 Supplier in the education coefficients is 9% in the binary component and 21% in the conditional component, and, for wealth, the corresponding reductions are at least 13% and 9% for the binary and conditional components respectively. The second question in the analysis is: do job categories as a group remain statistically significant after controlling for education and wealth? As shown in Table 2 model 3, when education level is included in the model, job categories remain a significant predictor of both components of the mobility limitation model for the total sample and for females (but not males). When wealth is held constant, job categories also remain significant, in this case, for all models (Table 2, model 5). Finally, for the total sample and the female (but not the male) sample, job categories are significantly associated with limitations in both components of the model when education and wealth are held constant (Table 2, model 6). Thus, the relationship between physical work conditions, as represented by job categories, and mobility limitations is robust to inclusion of education and wealth, except in the case of males and education. We speculate that the result for males may be due to a stronger tie between education and type of work among men than among women, but we have no means of testing this conjecture. The third question in this analysis is: which occupational categories are associated with the highest rates of mobility limitations at older ages, as a whole and by gender? To answer this question, we use the estimates from the regression models shown in Table 2 and Appendix D to assess the magnitude of the inequalities by job category. In particular, we identify job categories associated with the highest rates of mobility limitations, for the total sample and by sex. Fig. 2 presents the results of Model 6 for each of the job categories shown in Table 1 in terms of the overall mean number of limitations, calculated by combining estimates from the two components of the hurdle model, as described earlier (Appendix D). Predicted values for each component of the hurdle model are shown in Appendix E. These estimates were obtained by retaining all variables except occupation at their observed values and then aggregating the individual estimates to obtain the binary and conditional components. To avoid estimates with very large sampling errors, Fig. 2 presents estimates only for categories (overall or by sex) with at least 50 cases.
    Discussion With population aging and longer life expectancies, the prevalence of older age disability is increasing in low, middle, and high income countries (World Health Organization, 2011). Wong et al. (2011) argue that the burden of disability is heavier in lower and middle income countries like Mexico, because older people faced a high prevalence of infectious disease and poverty in their youth, but also greater longevity and burgeoning chronic disease in more recent years. As older age disability has become more common, socioeconomic inequality in older age disability is more apparent (World Health Organization, 2011). A clear understanding of the root causes of this socioeconomic inequality is essential to develop measures to reduce the burden of older age disability.