Search for: All records

Abstract BackgroundFactors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes. MethodsWe rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank’s High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences. ResultsIn all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd). ConclusionWith SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust. 

Abstract Theoretical units of interest often do not align with the spatial units at which data are available. This problem is pervasive in political science, particularly in subnational empirical research that requires integrating data across incompatible geographic units (e.g., administrative areas, electoral constituencies, and grid cells). Overcoming this challenge requires researchers not only to align the scale of empirical and theoretical units, but also to understand the consequences of this change of support for measurement error and statistical inference. We show how the accuracy of transformed values and the estimation of regression coefficients depend on the degree of nesting (i.e., whether units fall completely and neatly inside each other) and on the relative scale of source and destination units (i.e., aggregation, disaggregation, and hybrid). We introduce simple, nonparametric measures of relative nesting and scale, asex anteindicators of spatial transformation complexity and error susceptibility. Using election data and Monte Carlo simulations, we show that these measures are strongly predictive of transformation quality across multiple change-of-support methods. We propose several validation procedures and provide open-source software to make transformation options more accessible, customizable, and intuitive. 

Abstract The past few years have seen an emergence of populist leaders around the world, who have not only accrued but also maintained support despite rampant criticism, governance failures, and the ongoing COVID pandemic. The Philippines’ Rodrigo Duterte is the best illustration of this trend, with approval ratings rarely dipping below 80 percent. What explains his high levels of robust public support? We argue that Duterte is an ethnopopulist who uses ethnic appeals in combination with insider vs. outsider rhetoric to garner and maintain public support. Moreover, we argue that ethnic affiliation is a main driver of support for Duterte, and more important than alternative factors such as age, education, gender, or urban vs. rural divides. We provide evidence of Duterte's marriage of ethnic and populist appeals, then evaluate whether ethnicity predicts support for Duterte, using 15 rounds of nationally representative public opinion data. Identifying with a non-Tagalog ethnicity (like Duterte) leads to an 8 percent increase in approval for Duterte, significantly larger than any other explanatory factor. Among Duterte supporters, a non-Tagalog ethnicity is associated with 19 percent increase in strong versus mild support. Ethnicity is the only positive and significant result, suggesting that it strongly explains why Duterte's support remains robust. Alternative explanations, such as social desirability bias and alternative policy considerations, do not explain our results. 

In principle, aid from donor organizations to developing countries should be based on need and the likelihood of positive impact, but political biases may intrude into decisions about aid allocations. This article elaborates a theory about why a particular form of bias, one based on partisan affiliations, can affect where aid goes and whether the goals of aid are met. Party networks can facilitate coordination of decisions and leverage bureaucratic capacity, but they can also ensure that resources, such as aid, stay in the control of copartisans to boost reelection goals. The empirical analysis evaluates whether partisan bias is evident in the locations and impact of World Bank agricultural aid to India. The authors analyze georeferenced data on aid projects, election results, and cropland coverage at the levels of state parliamentary electoral constituencies and administrative districts from 1995 to 2008. They find that alignment between local legislators and the political parties that govern state and national governments is associated with a greater number of new aid projects and with anomalous changes in cropland coverage. The evidence is consistent with arguments that partisan bias works primarily through patronage to achieve strategic party goals.