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We compare the nature of party systems across bicameral legislatures using newly available data on upper chamber elections. We examine the similarity in the composition of political parties between the lower and upper chambers (partisan congruence) and introduce a novel measure that captures differences in the nationalization of parties between the two chambers (nationalization congruence). We explore variations in these measures across countries and over time and demonstrate that the power of the upper chamber (symmetry) is linked to both forms of congruence. Moreover, we apply these measures to understand how the interaction between congruence and symmetry—two key dimensions of bicameralism—influences policymaking, focusing on government spending patterns. Our findings reveal that partisan and nationalization congruence can have contrasting implications for government spending in symmetric bicameral systems but have negligible implications in asymmetric bicameral systems. 

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 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. 

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.