Q: Can you say more about <strong>disability </strong>and the interaction between personal impairment and social barriers?

Historically, disability was thought of as being a personal attribute of a human being. For example, one would be considered disabled if one had their left leg amputated. This is consistent with a medical model for understanding disability, and it was the primary conceptualization in most countries until the 1990s, when a more social model began to emerge. The framing of a problem matters because it suggests or limits the potential solutions and interventions. If the problem is a missing left leg and the situation is framed as a tragedy , the logical intervention is charity . If the problem is a missing leg and the situation is framed as a medical issue , the logical intervention is medical treatment . However, if the problem is that someone with such an impairment cannot access a school, for example, the situation is framed as a social issue and the needed intervention is at the level of institutional policy change towards universal design and accessibility . Because after all, in a world full of ramps, a person who uses a wheelchair is not disabled. More info: In a development context, the United Nations also defines disability socially, as the result of interaction between personal impairments and environmental barriers that hinder one’s participation in society. Reference: The Convention on the Rights of Persons with Disabilities (CRPD), a 2006 human rights treaty signed by 168 nations including India, led by the United Nations Department of Economic and Social Affairs.

Q: How do you interpret the ppo/gologit <strong>regression coefficients</strong> in this case?

I want to share a somewhat oversimplified explanation first . For all variables in the model that are dichotomous indicators, which is all variables except income and age, a positive coefficient means that a score of “yes” on that indicator increases the likelihood of higher categorical levels of the SWB outcome. A negative coefficient means that a score of “yes” increases the likelihood of the present or lower categorical levels of the SWB outcome. Of course, because this is not a regular ordered logit model, these coefficients sometimes vary depending on the outcome level we’re looking at. Coefficients are the same at all levels only for those terms for which the proportional odds, or the parallel lines assumptions, held. (If that assumption was upheld for all terms, I would have used an ordered logit though, so it’s not that simple to interpret, unfortunately.) A PPO/gologit model is interpreted by considering the coefficients at each level of the ordered outcome. So in this case, we have three levels of an ordered outcome, from low to high they are suffering, struggling, and thriving, numerically identified by 1, 2, and 3. The regression coefficients reflect the effects of each of the 11 variables in the model, the key predictors and the controls, and the coefficient represents the odds of a respondent being either above the lowest outcome category (of suffering), or of being in the highest outcome category (which is thriving). Revisit the equation for the model too, noting the role of this probability of being "> j".

Question 1

Can you say more about disability and the interaction between personal impairment and social barriers?

Accepted Answer

Historically, disability was thought of as being a personal attribute of a human being. For example, one would be considered disabled if one had their left leg amputated. This is consistent with a medical model for understanding disability, and it was the primary conceptualization in most countries until the 1990s, when a more social model began to emerge. The framing of a problem matters because it suggests or limits the potential solutions and interventions. If the problem is a missing left leg and the situation is framed as a tragedy, the logical intervention is charity. If the problem is a missing leg and the situation is framed as a medical issue, the logical intervention is medical treatment. However, if the problem is that someone with such an impairment cannot access a school, for example, the situation is framed as a social issue and the needed intervention is at the level of institutional policy change towards universal design and accessibility. Because after all, in a world full of ramps, a person who uses a wheelchair is not disabled.

More info: In a development context, the United Nations also defines disability socially, as the result of interaction between personal impairments and environmental barriers that hinder one’s participation in society. Reference: The Convention on the Rights of Persons with Disabilities (CRPD), a 2006 human rights treaty signed by 168 nations including India, led by the United Nations Department of Economic and Social Affairs.

Question 2

How do you interpret the ppo/gologit regression coefficients in this case?

Accepted Answer

I want to share a somewhat oversimplified explanation first. For all variables in the model that are dichotomous indicators, which is all variables except income and age, a positive coefficient means that a score of “yes” on that indicator increases the likelihood of higher categorical levels of the SWB outcome. A negative coefficient means that a score of “yes” increases the likelihood of the present or lower categorical levels of the SWB outcome.

Of course, because this is not a regular ordered logit model, these coefficients sometimes vary depending on the outcome level we’re looking at. Coefficients are the same at all levels only for those terms for which the proportional odds, or the parallel lines assumptions, held. (If that assumption was upheld for all terms, I would have used an ordered logit though, so it’s not that simple to interpret, unfortunately.)

A PPO/gologit model is interpreted by considering the coefficients at each level of the ordered outcome. So in this case, we have three levels of an ordered outcome, from low to high they are suffering, struggling, and thriving, numerically identified by 1, 2, and 3. The regression coefficients reflect the effects of each of the 11 variables in the model, the key predictors and the controls, and the coefficient represents the odds of a respondent being either above the lowest outcome category (of suffering), or of being in the highest outcome category (which is thriving). Revisit the equation for the model too, noting the role of this probability of being "> j".

Subjective Well-Being in India