Jason Kerwin

@jkerwin.bsky.social

2720 Followers 1178 Following

Development Economist @ UW. Hawaiʻi born, Stanford alum, Michigan PhD. Views my own. My website: http://jasonkerwin.com

Graph of treatment effects (text on graph shows the numbers)
Ursa Major illustration
Kenyan GDP per capita trend vs Tanzania and Uganda
Regression table showing that treatment X imprecision is a significant predictor of endline risk beliefs.
Regression table showing that treatment X imprecision is a significant predictor of endline risk beliefs.
Strip plots of imprecision against various covariates, showing no correlations.
Graphs of imprecision in annual (left) and per-act (right) imprecision against the level of risk beliefs. Both graphs have inverse U shapes.
Picture of a survey question. H1a. If 100 women, who do not have HIV, each have an HIV-positive sex partner for one year, and do not use condoms when having sex, how many of the women do you think will have HIV at the end of the year? Number: H1b. Do you really think that 50 of the women would get HIV, or are you just not sure? 1. I really think it's 50 0. I'm just not sure What is your best guess? H1c. Could it be more? If so, what is the most women out of 100 that you think could possibly get the virus? Number: 997. No, it could not be more. H1d. Could it be less? If so, what is the fewest women out of 100 that you think could possibly get the virus? Number: 997. No, it could not be less.
Updating of Risk Beliefs in Response to New Information by Baseline Imprecision Notes: Graphs show the prior and posterior distributions of risk beliefs using a Beta distribution with expected value mu = 0:5 and two different levels of the dispersion parameter nu. The new information has n = 100 observations and an average risk of x = 0:10, i.e. 90 failures and 10 successes.
Graph of the standard deviation of a Bernoulli random variable against its expectation (i.e. the probability), which is a semicircle.
Navigating Ambiguity: Imprecise Probabilities and the Updating of Disease Risk Beliefs Jason T. Kerwin and Divya Pandey September 26, 2023 Abstract Probabilistic risk beliefs are key drivers of economic and health decisions, but people are not always certain about their beliefs. We study these "imprecise probabilities", also known as ambiguous beliefs. We show that imprecision is measurable separately from the levels of risk beliefs. People with higher levels of imprecision update their beliefs more in response to a randomized information treatment, and new information also causes changes in imprecision levels. We show that we can map our data onto both a standard Bayesian model and a version that is designed to handle imprecise probabilities; these models can match some features of our data but not all of them. Imprecise probabilities have important implications for our understanding of decisionmaking and for the design of programs intended to change people's minds.
Max Dorélien Kerwin
Population projections showing a peak in 2085 and then a collapse