: State capacity is a key concept for research in public policy and political science. Despite its importance, there is no broadly accepted measure of state capacity in the existing literature, and frequently used measures of capacity have not been examined for their validity. We begin with an explicit definition of state capacity -the state's ability to implement public policy -and connect this definition to a measurable outcome of state capacity -the state's taxation of income. We show that this measure, income taxes as a percentage of total tax revenue, is a useful indicator of state capacity and meets higher standards of measurement validity than other tax-based indicators. We also compare our measure to the most common existing indicators of state capacity to show that income taxation is a better theoretical and statistical measure of states' effectiveness in policy implementation.
Networks can affect a group's ability to solve a coordination problem. We utilize laboratory experiments to study the conditions under which groups of subjects can solve coordination games. We investigate a variety of different network structures, and we also investigate coordination games with symmetric and asymmetric payoffs. Our results show that network connections facilitate coordination in both symmetric and asymmetric games. Most significantly, we find that increases in the number of network connections encourage coordination even when payoffs are highly asymmetric. These results shed light on the conditions that may facilitate coordination in real-world networks.
a b s t r a c tScholars in the social sciences use network theory to study a range of collective action problems. Often the goal is to identify how the structure of the network affects efforts to coordinate or cooperate, and research suggests that adding connections to a network can improve the performance of groups faced with such tasks. On the other hand, theory and empirics also suggest that additional connections can degrade the performance of a network. If connections can have negative effects then it is important to consider if there are alternatives to adding connections to a network that can also improve network performance. Because a primary function of connections in a network is to disseminate information, providing individuals with more information about the network may act as a substitute for adding connections to a network. We test experimentally whether providing subjects with more information about the structure of networks can improve coordination. We find that a more complete view of the network leads to faster coordination, but the magnitude of this effect depends on network structure. These results suggest that changing what actors know about a network can improve outcomes without having to add connections that may impede overall performance.
The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting cases to study causal mechanisms. We outline a general approach for selecting cases for pathway analysis: a mode of qualitative research that is part of a mixed-method research agenda, which seeks to (1) understand the mechanisms or links underlying an association between some explanatory variable, X1, and an outcome, Y, in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. The gist of our approach is that researchers should choose cases for comparison in light of two criteria. The first criterion is the expected relationship between X1/Y, which is the degree to which cases are expected to feature the relationship of interest between X1 and Y. The second criterion is variation in case characteristics or the extent to which the cases are likely to feature differences in characteristics that can facilitate hypothesis generation. We demonstrate how to apply our approach and compare it to a leading example of pathway analysis in the so-called resource curse literature, a prominent example of a correlation featuring a nonlinear relationship and multiple causal mechanisms.
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