research

Dissertation Project: Decision-making by consensus has been the preferred collective choice rule by innumerable human groups throughout history, yet majority rule dominates contemporary political institutions. When societies that traditionally made decisions by consensus are forced to make decisions by majority rule we might expect an incongruence between culture—the default preference for a collective choice rule—and institutions—the actual rule used to make group decisions. I plan to test this incongruence hypothesis among the Tswana of Botswana, known for their consensus traditional institutions. Using a lab-in-the-field experiment, I hypothesize that cultural and institutional congruence leads to more efficient decisions and greater citizen satisfaction with decision-making processes. Leveraging variation in the ’consensus tradition’ across Tswana polities in Botswana and South Africa I expect that consensus institutions are demanded in localities with less exposure to colonialism and a history of ethnic diversity. I will also explore the mechanisms through which consensus persists, including aversion to inequality, conformity and aversion to institutional innovation.

Scarification—the practice of creating permanent facial marks to signal group belonging—has historically served as an identity marker among West African societies. In Ghana, the practice is prevalent among northern ethnic groups but less common in southern populations. Following episodes of ethnic conflict in the 1990s, scarred individuals were displaced to urban centers like Accra, where they faced discrimination and limited job opportunities. Fieldwork in Accra’s Old Fadama slum highlighted the occupational traps created by scarification, as scarred individuals often found themselves confined to low-paying, ingroup-centered jobs. The proposed exploratory study seeks to measure discrimination and implicit biases through lab-in-the-field experiments using AI-generated portraits of individuals with tribal marks and behavioral games. Based on these findings, we will collaborate with local organizations to design an experiment promoting cross-group integration and explore how new social networks can improve the economic and social outcomes of displaced populations.

The aim of this paper is to challenge conventional notions of political development that prioritize the state and Western-style democracy as universal benchmarks. First, I present political development as defined by modernization theory (comparative politics) and neo-evolutionary theory (anthropology). Next, I argue that these approaches inadequately capture the diversity of political systems across societies. I propose to redefine political development based on normative principles of political equality and legitimacy: a more politically developed society is one that promotes political equality and can transition between equality and inequality with the consent of its population. I illustrate the points using old and new archeological and anthropological evidence of alternative forms of political life in small and non-state societies.

On what basis can we claim a scholarly community understands a phenomenon? Social scientists generally propagate many rival explanations for what they study. How best to discriminate between or aggregate them introduces myriad questions because we lack standard tools that synthesize discrete explanations. In this paper, we assemble and test a set of approaches to the selection and aggregation of predictive statistical models representing different social scientific explanations for a single outcome: original crowd-sourced predictive models of COVID-19 mortality. We evaluate social scientists’ ability to select or discriminate between these models using an expert forecast elicitation exercise. We provide a framework for aggregating discrete explanations, including using an ensemble algorithm (model stacking). Although the best models outperform benchmark machine learning models, experts are generally unable to identify models’ predictive accuracy. Findings support the use of algorithmic approaches for the aggregation of social scientific explanations over human judgement or ad-hoc processes.

Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs.