- Political salience and regime resilienceSebastian Schweighofer-Kodritsch, Steffen Huck, and Macartan Humphreys2022
We study a version of a canonical model of attacks against political regimes where agents have an expressive utility for taking political stances that is scaled by the salience of political decision-making. Increases in political salience can have divergent effects on regime stability depending on costs of being on the losing side. When regimes have weak sanctioning mechanisms, middling levels of salience can pose the greatest threat, as regime supporters are insufficiently motivated to act on their preferences and regime opponents are sufficiently motivated to stop conforming. Our results speak to the phenomenon of charged debates about democracy by identifying conditions under which heightened interest in political decision-making can pose a threat to democracy in and of itself.
- Political and social correlates of covid-19 mortalityConstantin Manuel Bosancianu, Kim Yi Dionne, Hanno Hilbig, Macartan Humphreys, and 3 more authors2020
What political and social features of states help explain the distribution of reported Covid-19 deaths? We survey existing works on (1) state capacity, (2) political institutions, (3) political priorities, and (4) social structures to identify national-level political and social characteristics that may help explain variation in the ability of societies to limit Covid-19 mortality. Accounting for a simple set of Lasso-chosen controls, we find that measures of interpersonal and institutional trust are persistently associated with reported Covid-19 deaths in theory-consistent directions. Beyond this, however, patterns are poorly predicted by existing theories, and by arguments in the popular press focused on populist governments, women-led governments, and pandemic preparedness. Expert predictions of mortality patterns associated with state capacity, democracy, and inequality, do no better than chance. Overall, our analysis highlights the challenges our discipline’s theories face in accounting for political responses to unanticipated, society-wide crises.
- Does registration reduce publication bias? Evidence from medical sciencesAlbert Fang, Grant Gordon, and Macartan Humphreys2015
There is increasing support for the use of research registries in social sciences. One possible advantage of the use of a registry is that it would limit the scope for publication or analysis biases that result from selecting statistically significant results. However, to date, there is surprisingly little evidence for the claim that registration will reduce these biases. We look to historical data from medical publishing for evidence, comparing the distribution of p-values before and after the introduction of registration in prominent journals. We couple this analysis with a pre-analysis survey of medical experts and social scientists to assess their prior expectations of the impact of registration on medical publishing and to assess their perceptions on the specificity and sensitivity of our test of effects. Although there is evidence of publication bias in medical studies, our registered analyses uncovered no evidence that registration affected that bias, leading us to moderately downgrade our confidence in the curative effects of registration.
- Policing politicians: citizen empowerment and political accountability in Uganda preliminary analysisMacartan Humphreys, and Jeremy Weinstein2012
- Bounds on least squares estimates of causal effects in the presence of heterogeneous assignment probabilitiesMacartan Humphreys2009
In many contexts, treatment assignment probabilities differ across strata or are correlated with some observable third variables. Regression with covariate adjustment is often used to account for these features. It is known however that in the presence of heterogeneous treatment effects this approach does not yield unbiased estimates of average treatment effects. But it is not well known how estimates generated in this way diverge from unbiased estimates of average treatment effects. Here I show that biases can be large, even in large samples. However I also find conditions under which the usual approach provides interpretable estimates and I identify a monotonicity condition that ensures that least squares estimates lie between estimates of the average treatment effects for the treated and the average treatment effects for the controls. The monotonicity condition can be satisfied for example with Roy-type selection and is guaranteed in the two stratum case.