The Demand for Aid and the Supply of Development

Though citizens in developing countries are the ostensible beneficiaries of international development, projects and policies are designed well above those on the ground. This dissertation collects three papers on the consequences of international development from the perspective of these intended beneficiaries. In the first paper, I argue that citizens in societies inundated with foreign aid have preferences for different types aid projects, favoring certain donors, certain sectors, and certain implementation styles over others. I develop a model in which the political returns to satisfying voter preferences motivate the distribution of aid by a recipient government. The results of this model correspond to the optimal distribution of aid projects given citizen demand. I estimate the demand for many types of aid projects using a conjoint experiment fielded in Uganda and compare this demand to the observed allocation of aid. In the second paper, I focus on the unintended political consequences of internal displacement during civil war, a decision prioritized by domestic governments but made possible with the help of international donors. Using a randomized response experiment, I show that returned internally displaced peoples in Northern Uganda are often the targets of vote buying in post-conflict elections and suggest that the removal of citizens from their land causes a severe economic shock, making the displaced particularly susceptible to vote buying. In the final paper, I explore the unintended economic consequences of government fragmentation. While the creation of new subnational administrative units intends to bring the government "closer to the people", I argue that many fragmented units lack the requisite administrative capacity to fulfill the provision of public goods. Combining remote-sensed development data in Burkina Faso with a difference-in-differences design, I show that communities within newly created units are often left behind

Considering climate and conflict conditions together to improve interventions that prevent child acute malnutrition

Despite early warning signs about threats to food security, humanitarian interventions often lag behind these warning signs. Climate and conflict conditions are among the most important factors preceding food system failures and malnutrition crises around the world. Research shows how conflict and climate conditions can upend functional food and economic systems, but this research does not address the severe health impacts of these conditions on infants and young children. Translating quantitative research findings into humanitarian interventions requires geographical detail, resulting in location-specific alerts of risks of food insecurity. We describe how the use of readily available, spatially referenced quantitative data can support targeted interventions for nutrition resiliency. Effective humanitarian programmes for targeted nutrition interventions require real-time datasets on food security drivers and models that can provide actionable guidance to mitigate negative impacts of conflict and climate conditions on the people most susceptible to food insecurity. Although treatment of acute malnutrition is important, treating existing malnutrition is not enough. Instead, action to prevent acute malnutrition should be taken to minimise suffering and to maximise wellbeing, particularly in contexts prone to worsening climate and conflict conditions

Conflict and climate factors and the risk of child acute malnutrition among children aged 24–59 months: a comparative analysis of Kenya, Nigeria, and Uganda

Acute malnutrition affects a sizeable number of young children around the world, with serious repercussions for mortality and morbidity. Among the top priorities in addressing this problem are to anticipate which children tend to be susceptible and where and when crises of high prevalence rates would be likely to arise. In this article, we highlight the potential role of conflict and climate conditions as risk factors for acute malnutrition, while also assessing other vulnerabilities at the individual- and household-levels. Existing research reflects these features selectively, whereas we incorporate all the features into the same study. The empirical analysis relies on integration of health, conflict, and environmental data at multiple scales of observation to focuses on how local conflict and climate factors relate to an individual child’s health. The centerpiece of the analysis is data from the Demographic and Health Surveys conducted in several different cross-sectional waves covering 2003–2016 in Kenya, Nigeria, and Uganda. The results obtained from multi-level statistical models indicate that in Kenya and Nigeria, conflict is associated with lower weight-for-height scores among children, even after accounting for individual-level and climate factors. In Nigeria and Kenya, conflict lagged 1–3 months and occurring within the growing season tends to reduce WHZ scores. In Uganda, however, weight-for-height scores are primarily associated with individual-level and household-level conditions and demonstrate little association with conflict or climate factors. The findings are valuable to guide humanitarian policymakers and practitioners in effective and efficient targeting of attention, interventions, and resources that lessen burdens of acute malnutrition in countries prone to conflict and climate shocks

Empirical studies of factors associated with child malnutrition: highlighting the evidence about climate and conflict shocks

Children who experience poor nutrition during the first 1000 days of life are more vulnerable to illness and death in the near term, as well as to lower work capacity and productivity as adults. These problems motivate research to identify basic and underlying factors that influence risks of child malnutrition. Based on a structured search of existing literature, we identified 90 studies that used statistical analyses to assess relationships between potential factors and major indicators of child malnutrition: stunting, wasting, and underweight. Our review determined that wasting, a measure of acute malnutrition, is substantially understudied compared to the other indicators. We summarize the evidence about relationships between child malnutrition and numerous factors at the individual, household, region/community, and country levels. Our results identify only select relationships that are statistically significant, with consistent signs, across multiple studies. Among the consistent predictors of child malnutrition are shocks due to variations in climate conditions (as measured with indicators of temperature, rainfall, and vegetation) and violent conflict. Limited research has been conducted on the relationship between violent conflict and wasting. Improved understanding of the variables associated with child malnutrition will aid advances in predictive modeling of the risks and severity of malnutrition crises and enhance the effectiveness of responses by the development and humanitarian communities

Replication Data for: Government Fragmentation, Administrative Capacity, and Public Goods: The Negative Consequences of Reform in Burkina Faso

Replication data and code for Government Fragmentation, Administrative Capacity, and Public Goods: The Negative Consequences of Reform in Burkina Fas

Visualizing trends in food security across Africa, 2009–2020: Data and animations at a grid-cell level

The Famine Early Warning Systems Network (FEWS NET) has been appraising food security in numerous countries around the world since 1985. Multiple times per year, FEWS NET reports scores for current situation assessments and future projections of food security. The scores are measured on a five-level index scale and gauged for the geographic units of livelihood zones. These zones vary in size and do not remain static, which complicates comparison of food security within and across countries and over time. To facilitate such analysis and interoperability with other sources, we transformed available raw data to the units of geospatial grid-cells that have a uniform, static resolution of 0.5° × 0.5°, a common format of data used in research across diverse disciplines. FEWS NET provides public online access to shapefiles reflecting reports back to 2009. Separate shapefiles capture assessments and projections, with further delineation by the index score. Each shapefile can comprise a complex (multi)polygon, without clear differentiation among livelihood zones. Overlaying a geospatial grid allows disaggregation of the (multi)polygons to standard units. We performed the transformation to grid-cells on the shapefiles for all 25 countries (including Yemen) that FEWS NET tracked within regional groupings of East, Southern, and West Africa from July 2009–October 2020. For each report cycle, each grid-cell was assigned scores of the assessment and near-term and medium-term projections, based on the raw data for the corresponding livelihood zone. In addition, we calculated a value of bias in medium-term projections relative to subsequent assessments, which can be used as a metric for validation of accuracy. This article provides access to the grid-cell data on assessment and projection scores and bias values. In addition, we present time-lapse animated maps as tools to visualize historical patterns and trends in these indicators across Africa. Our related research article employed the grid-cell data to evaluate the accuracy of FEWS NET projections, including as a function of variation in humanitarian assistance, climate conditions, and violent conflict (Backer and Billing [1]). Researchers can likewise use the grid-cell data to conduct further validation of food security projections and to examine the relationship of assessments and projections to potential drivers and consequences. The data and animations are also valuable to stakeholders throughout the international community seeking to learn and disseminate knowledge about the tendencies of food security projections on a broad scale

Leveraging a Multi-Method Approach to Improve Mass Atrocity Forecasting

Forecasting mass atrocities is a central concern for academics, policymakers, and practitioners, but determining where and when mass atrocities will occur is far from straightforward. Over the last few decades, researchers around the world have developed several forecasting models. Some of these models—like the Political Instability Task Force model or the Australia Forecasting Project model—have emphasized quantitative assessments of the risk of mass atrocity. Others—like the UN Framework of Analysis for Atrocity Crimes—have focused on how case-specific factors coalesce to impact the onset of mass atrocity. In this article, we suggest that a multi-methods framework that capitalizes on the strengths of each of these approaches enhances the ability to correctly forecast mass atrocities. Specifically, we rely upon case-based analysis that identifies combinations of factors that are associated with the absence of atrocities as well as two quantitative approaches geared toward predicting the onset of mass atrocity. After integrating results, we assess how well the forecasts fare and discuss the possible uses of our multi-methods approach