"Income and Public Service Demands: Comparative Voter Efficacy in Brazil"

Abstract
Many models of public service distribution in democracies predict the poor will have high voting leverage over distributive policy due to a numerical advantage in universal suffrage political competition. Empirical studies do not bear this prediction out, especially in highly unequal democracies, where canonical models predict the poor will have the greatest leverage. This paper proposes an argument that explains the apparently low weight placed by politicians on the preferences of the poor with respect to public service policy in unequal democracies. I show that even when accountability mechanisms function properly in democracy, the poor may find themselves at an electoral disadvantage. This occurs when the poor's (likely higher) public service demands are divided more symmetrically across competing services. When the better-off pile the weight of their votes on fewer services, their votes are more responsive to a unit shift in spending, even if their total service demands are lower. This leads the spending priorities of vote-maximizing, tactically-spending politicians to more closely reflect the preferences of the more concentrated demands of the better-off than of those with higher total state dependence for services. I illustrate the argument and its implications using a study of local public health service allocation in Brazil in the context of a shock to the public primary care service dependency-level of a subset of poor voters induced by a federal transfer program. I contrast voter demands for services with vote responsiveness to service spending using original survey data I collected in Brazil in the two weeks prior the 2012 municipal elections. This research updates our understanding of accountability in unequal democracies, suggesting that the poor do not necessarily fail to hold democratic politicians accountable as many theories would suggest; rather, democratic politicians may have the incentive to prioritize the preference-ranking of the less state-dependent over those more dependent on the public services in question if the less-dependent also have less diffuse service preferences.


"The Spillover Effects of Excludable Cash Transfers: What the Miracle Cure for Development Woes Means for Infant and Child Mortality"

Abstract
Brazil's Bolsa Familia (Family Stipend) Program is the world's flagship conditional cash transfer program. The excludable transfer is conditioned upon school attendance and the use of primary health care services---a design often lauded by the development community for its ability to produce long-term improvements in human capital in addition to providing short-term poverty relief. However, researchers have found the anticipated health benefits of the Program to be somewhat elusive. This article relies on evidence from aggregate and individual-level datasets on infant mortality rates to investigate the ambiguous findings regarding the role Bolsa Familia plays in overall health outcomes of the poor. The analyses suggest that a failure to account for possible spillover effects (i.e., violations of the SUTVA assumption) of the Program with respect to the non-recipient poor could undergird the considerable variability in causal claims regarding the relationship between the Bolsa Familia Program and health outcomes.


"Learning from Social Data When Researchers and Social Actors Share Prior Beliefs"

Abstract
Bayesian analyses are often critiqued on the basis of dubious exchangeability claims regarding the data (as are frequentist analyses regarding claims of independence). Not only must observed data be exchangeable, but prior “data” must be as well, and the observed data must also be exchangeable with the prior data—an assumption typically not explicitly justified by the practitioner. I focus on the last of these assumptions, which is likely to be violated in the social sciences where many of our “observations” are informed by human behavior and, therefore, by the prior beliefs of the humans taking the observed actions. Social or psychological biases that characterize the beliefs of a society will thus inform the observed data, violating the exchangeability assumption. One of the common defensive arguments offered by many Bayesian practitioners is that as long as there is some component of new information in the observed data, repeated observation-updating cycles will still eventually produce a posterior distribution that is highly informative (similar to the idea of consistency in frequentist statistics). In frequentist statistics we have power analyses—a way of estimating how much data we need to get desirable properties from our estimator. This paper develops a model that parameterizes the degree of non-exchangeability between the observed data and the prior data and offers a standard way to calculate how many observations are needed to achieve an arbitrary (parameterized) definition of an “informative” estimator in a single iteration of updating, or the number of updating iterations needed given a fixed observation size “n” at each iteration. Though I focus on non-exchangeability between observed data and prior data, the same type of analysis may be applied to any non-exchangeability between any data.