We examine the causal effect of health insurance on mortality using the universe of low-income adults, a dataset of 37 million individuals identified by linking the 2010 Census to administrative tax data. Our methodology leverages state-level variation in the timing and adoption of Medicaid expansions under the Affordable Care Act (ACA) and earlier waivers and adheres to a preregistered analysis plan, a rarely used approach in observational studies in economics. We find that expansions increased Medicaid enrollment by 12 percentage points and reduced the mortality of the low-income adult population by 2.5 percent, suggesting a 21 percent reduction in the mortality hazard of new enrollees. Mortality reductions accrued not only to older age cohorts, but also to younger adults, who accounted for nearly half of life-years saved due to their longer remaining lifespans and large share of the low-income adult population. These expansions appear to be cost-effective, with direct budgetary costs of $5.4 million per life saved and $179,000 per life-year saved falling well below valuations commonly found in the literature. Our findings suggest that lack of health insurance explains about five to twenty percent of the mortality disparity between high- and low-income Americans. We contribute to a growing body of evidence that health insurance improves health and demonstrate that Medicaid’s life-saving effects extend across a broader swath of the low-income population than previously understood.
Saved by Medicaid: New Evidence on Health Insurance and Mortality from the Universe of Low-Income Adults
When ELIZA meets therapists: A Turing test for the heart and mind
“Can machines be therapists?” is a question receiving increased attention given the relative ease of working with generative artificial intelligence. Although recent (and decades-old) research has found that humans struggle to tell the difference between responses from machines and humans, recent findings suggest that artificial intelligence can write empathically and the generated content is rated highly by therapists and outperforms professionals. It is uncertain whether, in a preregistered competition where therapists and ChatGPT respond to therapeutic vignettes about couple therapy, a) a panel of participants can tell which responses are ChatGPT-generated and which are written by therapists (N = 13), b) the generated responses or the therapist-written responses fall more in line with key therapy principles, and c) linguistic differences between conditions are present. In a large sample (N = 830), we showed that a) participants could rarely tell the difference between responses written by ChatGPT and responses written by a therapist, b) the responses written by ChatGPT were generally rated higher in key psychotherapy principles, and c) the language patterns between ChatGPT and therapists were different. Using different measures, we then confirmed that responses written by ChatGPT were rated higher than the therapist’s responses suggesting these differences may be explained by part-of-speech and response sentiment. This may be an early indication that ChatGPT has the potential to improve psychotherapeutic processes. We anticipate that this work may lead to the development of different methods of testing and creating psychotherapeutic interventions. Further, we discuss limitations (including the lack of the therapeutic context), and how continued research in this area may lead to improved efficacy of psychotherapeutic interventions allowing such interventions to be placed in the hands of individuals who need them the most.