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People with Disabilities in India: From Commitment to Outcomes

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of age and disability status. Multivariate analysis of the NSS data provides more precise <strong>in</strong>sights<br />

<strong>in</strong><strong>to</strong> the determ<strong>in</strong>ants of employment among PWD. Potential determ<strong>in</strong>ants <strong>in</strong>clude demographic<br />

characteristics (gender, age, marital status), rural/urban, disability characteristics (type of<br />

disability, severity of disability, disability at birth), human capital characteristics (education,<br />

vocational tra<strong>in</strong><strong>in</strong>g, work experience).<br />

5.18. Table 5.6 presents results of a probit model of employment among PWD us<strong>in</strong>g the 58 th<br />

round of the NSS. 128 The results confirm some of the earlier f<strong>in</strong>d<strong>in</strong>gs. The follow<strong>in</strong>g<br />

characteristics are associated <strong>with</strong> higher probabilities of employment among the PWD sample:<br />

• liv<strong>in</strong>g <strong>in</strong> rural areas, where the likelihood of PWD be<strong>in</strong>g employed is over 20 percent higher<br />

than <strong>in</strong> urban areas and highly significant statistically. This is consistent <strong>with</strong> anecdotal<br />

evidence of greater accommodation of PWD <strong>in</strong> rural work sett<strong>in</strong>gs and possible <strong>in</strong>come<br />

effects.<br />

• be<strong>in</strong>g a male<br />

• hav<strong>in</strong>g a disability s<strong>in</strong>ce birth, <strong>with</strong> the effect more pronounced <strong>in</strong> rural areas<br />

• hav<strong>in</strong>g a hear<strong>in</strong>g, speech, or locomo<strong>to</strong>r disability. The positive sign of the coefficient of the<br />

multiple disability dummy <strong>in</strong> the rural subsamples is surpris<strong>in</strong>g.<br />

• be<strong>in</strong>g married has a relatively strong positive effect on the probability of be<strong>in</strong>g employed for<br />

males, but a negative effect for women.<br />

• <strong>in</strong>creased age is positively associated <strong>with</strong> the probability of employment. There is a<br />

quadratic effect <strong>in</strong> age <strong>in</strong> both male and female subsamples, <strong>with</strong> the probability of<br />

employment grow<strong>in</strong>g at a decreas<strong>in</strong>g rate.<br />

• hav<strong>in</strong>g a postgraduate education (<strong>with</strong> the positive effect much stronger for women) and<br />

hav<strong>in</strong>g vocational tra<strong>in</strong><strong>in</strong>g.<br />

5.19. In contrast <strong>to</strong> the above positive impacts among PWD, there are several variables<br />

which are associated <strong>with</strong> a lower probability of be<strong>in</strong>g employed. These <strong>in</strong>clude:<br />

• hav<strong>in</strong>g a mental illness has a strong (and statistically highly significant) negative impact on<br />

the probability of employment. For those <strong>with</strong> mental illness, the effect is much stronger <strong>in</strong><br />

urban than rural areas, which is consistent <strong>with</strong> f<strong>in</strong>d<strong>in</strong>gs from NIMHANS that mental illness<br />

may be more stigmatiz<strong>in</strong>g <strong>in</strong> urban than rural sett<strong>in</strong>gs (perhaps <strong>in</strong> part due <strong>to</strong> the nature of<br />

work, but also perhaps related <strong>to</strong> the additional stigma attach<strong>in</strong>g <strong>to</strong> diagnosis). 129<br />

• hav<strong>in</strong>g mental retardation has an even stronger negative impact on the probability of be<strong>in</strong>g<br />

employed, and is also highly significant statistically.<br />

5.20. Surpris<strong>in</strong>gly, across all sub-samples, the educational level dummies have coefficients that<br />

are close <strong>to</strong> zero, <strong>with</strong> the exception of hav<strong>in</strong>g a postgraduate education. Thus, overall it appears<br />

that work experience rather than education <strong>in</strong>creases the probability of be<strong>in</strong>g employed for PWD.<br />

5.21. For a number of characteristics, there is clear heterogeneity <strong>in</strong> the impact of certa<strong>in</strong><br />

characteristics between men and women and between rural and urban areas. Some<br />

characteristics (e.g. the strength of the impact of a hear<strong>in</strong>g disability on employment) show<br />

weaker impacts for both men and women <strong>in</strong> urban than rural areas. Others (e.g. like the impact of<br />

be<strong>in</strong>g married and the impact of vocational tra<strong>in</strong><strong>in</strong>g) show clearer gender rather than locational<br />

differences. One example is the impact of postgraduate education among different PWD groups.<br />

Another is the strength of the negative impact of mental illness on employment, <strong>with</strong> the effect<br />

far more pronounced <strong>in</strong> urban areas as noted.<br />

128 It also accounts for the stratified sample design <strong>with</strong> weights from schedule 26 of the 58 th round.<br />

129 Insert NIMHANS reference.<br />

-92-

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