Pluralism and Political Conflict in Indonesia

Page range: 81-10

Piety and Redistributive Preferences in the Muslim World

This article tests two competing theories of the relationship between piety and redistributive preferences in the Muslim world. The first, drawn from the new political economy of religion, holds that more pious individuals of any faith should oppose redistributive economic policies. The second, drawn from Islamic scripture, holds that pious Muslims should favor more redistributive economic policies. Employing survey data from twenty-five countries, the authors find that there is no clear relationship between piety and redistributive preferences among Muslims. The authors find that more pious Muslims are less likely to favor government efforts to eliminate income inequality, but they find only inconsistent evidence that more pious Muslims support governments taking responsibility for the well-being of the poor. The findings offer little evidence to suggest that either scriptural or organizational factors unique to Islam create distinct economic policy preferences

The Exclusionary Foundations of Embedded Liberalism

Abstract: Analyses of embedded liberalism have focused overwhelmingly on trade in goods and capital, to the exclusion of migration. We argue that much as capital controls were essential components of the embedded liberal compromise, so too were restrictions on the democratic and social rights of labor migrants. Generous welfare programs in labor-receiving countries thrived alongside inclusionary immigration policies, but this balanced arrangement was only tenable if migrants were politically excluded in their destination countries. That is, embedded liberalism abroad rested on exclusionary political foundations at home. In bringing together the IPE literature on the “globalization trilemma” with the comparative politics of citizenship, we provide a novel account of how embedded liberalism worked politically, with implications for current debates about the fate of the liberal order in a time of populist resurgence

Gender Representation and Strategies for Panel Diversity: Lessons from the APSA Annual Meeting

ABSTRACT: Gender representation is a pervasive problem in political science. We draw on evidence from the 2017 and 2018 American Political Science Association (APSA) Annual Meeting programs to discuss diversity and representation in large political science disciplinary conferences. APSA program divisions differ substantially in their gender representation: although some are representative of their organized-section membership, others are not, and some sections are particularly likely to feature “manels.” We present representation data by organized section, with discussions of what representation looks like and identifying different types of representation goals. We conclude by offering guidelines for increasing gender representation, for both future submitters and program chairs

Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic

ObjectiveTo study the U.S. public's health behaviors, attitudes, and policy opinions about COVID-19 in the earliest weeks of the national health crisis (March 20-23, 2020).MethodWe designed and fielded an original representative survey of 3,000 American adults between March 20-23, 2020 to collect data on a battery of 38 health-related behaviors, government policy preferences on COVID-19 response and worries about the pandemic. We test for partisan differences COVID-19 related policy attitudes and behaviors, measured in three different ways: party affiliation, intended 2020 Presidential vote, and self-placed ideological positioning. Our multivariate approach adjusts for a wide range of individual demographic and geographic characteristics that might confound the relationship between partisanship and health behaviors, attitudes, and preferences.ResultsWe find that partisanship-measured as party identification, support for President Trump, or left-right ideological positioning-explains differences in Americans across a wide range of health behaviors and policy preferences. We find no consistent evidence that controlling for individual news consumption, the local policy environment, and local pandemic-related deaths erases the observed partisan differences in health behaviors, beliefs, and attitudes. In further analyses, we use a LASSO regression approach to select predictors, and find that a partisanship indicator is the most commonly selected predictor across the 38 dependent variables that we study.ConclusionOur analysis of individual self-reported behavior, attitudes, and policy preferences in response to COVID-19 reveals that partisanship played a central role in shaping individual responses in the earliest months of the COVID-19 pandemic. These results indicate that partisan differences in responding to a national public health emergency were entrenched from the earliest days of the pandemic

Lagged Explanatory Variables and the Estimation of Causal Effects

Across the social sciences, lagged explanatory variables are a common strategy to confront challenges to causal identification using observational data. We show that "lag identification"--the use of lagged explanatory variables to solve endogeneity problems--is an illusion: lagging independent variables merely moves the channel through which endogeneity biases causal estimates, replacing a "selection on observables" assumption with an equally untestable "no dynamics among unobservables" assumption. We build our argument intuitively using directed acyclic graphs, then provide analytical results on the bias resulting from lag identification in a simple linear regression framework. We then present simulation results that characterize how, even under favorable conditions, lag identification leads to incorrect inferences. These findings have important implications for current practice among applied researchers in political science, economics, and related disciplines. We conclude by specifying the conditions under which lagged explanatory variables are appropriate for identifying causal effects

Lagged Explanatory Variables and the Estimation of Causal Effects

Across the social sciences, lagged explanatory variables are a common strategy to confront challenges to causal identification using observational data. We show that "lag identification"--the use of lagged explanatory variables to solve endogeneity problems--is an illusion: lagging independent variables merely moves the channel through which endogeneity biases causal estimates, replacing a "selection on observables" assumption with an equally untestable "no dynamics among unobservables" assumption. We build our argument intuitively using directed acyclic graphs, then provide analytical results on the bias resulting from lag identification in a simple linear regression framework. We then present simulation results that characterize how, even under favorable conditions, lag identification leads to incorrect inferences. These findings have important implications for current practice among applied researchers in political science, economics, and related disciplines. We conclude by specifying the conditions under which lagged explanatory variables are appropriate for identifying causal effects