text: 109,81,1048200598330511360,436029997,Noah Haber,"New on arXiv: This paper identifies a policy-relevant causal effect, BUT 1) Controls for almost nothing in the main spec 2) Uses only basic regression methods 3) Has no meaningful effect size estimate 4) Is not a natural experiment Curiosity stoked yet? <LINK> No? How about these: 5) Uses a threshold for effect estimation, BUT 6) Does not measure the effect across the threshold (i.e. is not regression discontinuity) 7) Uses p-values because in this setting they are more sensible than CIs",https://arxiv.org/abs/1810.01971,"South Africa's disability grants program is tied to its HIV/AIDS recovery program, such that individuals who are ill enough may qualify. Qualification is historically tied to a CD4 count of 200 cells/mm3, which improve when a person adheres to antiretroviral therapy. This creates a potential unintended consequence where poor individuals, faced with potential loss of their income, may choose to limit their recovery through non-adherence. To test for manipulation caused by grant rules, we identify differences in disability grant recipients and non-recipients' rate of CD4 recovery around the qualification threshold, implemented as a fixed-effects difference-in-difference around the threshold. We use data from the Africa Health Research Institute Demographic and Health Surveillance System (AHRI DSS) in rural KwaZulu-Natal, South Africa, utilizing DG status and laboratory CD4 count records for 8,497 individuals to test whether there are any systematic differences in CD4 recover rates among eligible patients. We find that disability grant threshold rules caused recipients to have a relatively slower CD4 recovery rate of about 20-30 cells/mm3/year, or a 20% reduction in the speed of recovery around the threshold. ","Disability for HIV and Disincentives for Health: The Impact of South