The African Comprehensive HIV/AIDS Partnerships.

Background: Botswana is regarded as a leader of progressive HIV/AIDS policy, as the first country in sub-Saharan Africa to establish a free, national antiretroviral therapy program. In light of such programmatic successes, it is important to evaluate the potentially changing relationship of HIV/AIDS to the wellbeing of individuals, households, and institutions in the country. Methods: We evaluate the effects of HIV-related illness on absenteeism and earnings several years after the start of the national treatment program among a random sample of adults in Botswana using survey data from 3999 individuals aged 15 to 49, using quasi-experimental methods. We compare absenteeism between individuals with and without HIV-related illness, using a propensity score matching approach. We then estimate the effect of HIV-related illness on earnings using a Heckman selection model to account for selection into the workforce. We stratify our analyses by sex. Results: Men and women with HIV-related illness were absent by about 5.2 and 3.3 additional days, respectively, in the month prior to the survey compared to matched controls, and earned approximately 38% and 43% less, respectively, in the month prior to the survey compared to those without HIV-related illness. Conclusions: HIV-related illness appears to increase absenteeism in this sample and dramatically reduce earnings. The findings suggest a need for policies that confer greater financial security to individuals with HIV/AIDS in Botswana.

We use data from a serological study that experimentally varied financial incentives for participation to detect and characterize selection bias. Participants are from neighborhoods with substantially lower COVID-19 risks. Existing methods to account for the resulting selection bias produce wide bounds or estimates that are inconsistent with the population. One explanation for these inconsistent estimates is that the underlying methods presume a single dimension of unobserved heterogeneity. The data suggest that there are two types of nonparticipants with opposing selection patterns. Allowing for these different types may lead to better accounting for selection bias.

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