The rise of automation has transformed economies around the world. We examine how its effects spill over and affect people's views about environmental issues and policies. We argue that the long-term economic threat posed by automation is expected to reduce environmental concern amongst those affected due to a deprioritization of problems with high levels of uncertainty and that require deep reforms to be addressed. Therefore, we expect automation risk to subsequently reduce support of environmental policy that imposes immediate direct costs, such as carbon taxation. Meanwhile, support for policies with diffuse costs, such as environmental subsidies, will only be affected by automation indirectly, to the extent that it reduces individuals' general environmental concern. Using European Social Survey data from 2002 to 2018 for 23 European countries, our analysis reveals that individuals exposed to automation are less likely to hold environmental concerns and less supportive of carbon taxes that impose immediate visible costs. Mediation analysis suggests that automation reduces support for environmental policies through its negative effect on environmental concern, with this effect being larger for subsidies. Our findings have important implications for understanding how structural transformations in the economy shape individuals' preferences for tackling long-term societal problems like climate change.

This study investigates the impact of exposure to foreign goods in the marketplace on the policy preferences and political behavior of US consumers. Using a survey experiment, we simulate a realistic consumption experience with well-known brands of sports utility vehicles. Our findings reveal that exposure to foreign brands intensifies hostility towards immigrants and trade among respondents holding pre-existing nationalist attitudes while also increasing their support for Trump as a presidential candidate. Exposure to foreign brands has the opposite effect on the preferences and behavior of cosmopolitans. Our results demonstrate that consumption in an increasingly diversified marketplace can drive a bottom-up process of trade policy polarization. Our study has significant implications for understanding the contemporary political backlash against economic globalization and standard models of international trade based on the ``love of variety."

How does an immigration shock affect politicians’ discourses? This study examines the sudden influx of Venezuelan migrants into Latin American countries. We argue that such events alter politicians’ agendas, creating opportunities to frame new issues from their perspectives. Analyzing over 3 million tweets by parliament members from 2013 to 2021 in Chile and Peru, we employ computational textanalysis methods, from simple dictionaries to complex techniques like unsupervised topic analysis and OpenAI, along with an instrumental variable strategy. Our results suggest that after the immigration shock, politicians emphasized the immigration issue without any party family monopolizing it. We find little evidence that regional exposure explains the issue’s salience, suggesting a disconnection from local experiences. Our findings reveal a novel channel for increased salience: right-wing politicians criticized the Venezuelan regime and socialism instead of increasing anti-immigration sentiment, while left-wing politicians promoted pro-immigration attitudes consistent with contact theory. This work enhances our understanding of the politicization of immigration in South-South contexts in the digital age.

The analysis of multilevel data is common in political science and the social sciences more generally. We examine the case where random community-level effects cluster geographically in space. Ignoring this spatial dependence leads to inefficient coefficient estimates and overconfident standard errors. We propose a two-step spatial feasible generalized least squares estimator that, under empirically identifiable conditions, provides relatively efficient coefficient estimates and accurate standard errors compared to the maximum likelihood estimation of non-spatial models.

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Are protests contained to their specific space and time or do they have the ability to spread across borders and in the future? This question has interested scholars of social movements and political behavior for decades but the literature provides a mixed picture on whether protests diffuse throughout time and space. Using protest event analysis and novel spatiotemporal autoregressive distributed lag (STADL) models designed to capture both temporal and spatial dependence in the same model, we find significant dependencies across both time and space. Protests in one time period shape the onset of protests in the future and protests in one country increase protests in a neighboring country. These results help us understand the dynamics of protest diffusion and have important implications for the study of political behavior and social movements.

The political science discipline faces significant disparities in the representation and participation of underrepresented minorities in graduate education. This lack of diversity among political scientists results in a narrower range of questions being explored within the field. Furthermore, the underrepresentation is particularly pronounced in the political methodology subfield, limiting the scope of tools and perspectives to those predominantly shaped by white scholars. This article proposes a template for teaching and mentoring undergraduate students from underrepresented backgrounds to enhance their opportunities in graduate programs. We examine the Mobilization and Political Economy program, a summer research initiative aimed at minority-serving institutions and underrepresented minorities, designed to equip participants with the tools to study social movements, political mobilization, and structural inequality.

This study examines public perceptions towards technological change, focusing on subjective concerns, sociotropic views, actual exposure, and their correlation with policy preferences. The research categorizes individuals into enthusiasts, those personally or sociotropically concerned, and exposed groups, analyzing their demographics and political inclinations. Enthusiasts, primarily older, male, upper-class, non-routine workers, and Democrats, view technological advancements as beneficial for enhancing productivity in American companies. In contrast, those expressing concerns tend to be younger, female, lower-class, routine workers, non-white, and Republicans, displaying apprehension towards these changes. Individuals who have directly experienced technological integration are predominantly male, non-white, upper-class, and engaged in non-routine jobs. Policy preferences vary significantly across groups: those exposed to automation support Trump-like policies and display anti-trade, anti-immigration, and ethnocentric tendencies. In contrast, enthusiasts favor more open immigration, pro-trade, and pro-offshoring policies, and are less ethnocentric. The study also highlights that sociotropic concerns and future anxieties correlate more strongly with political views than current personal fears. Despite this, the correlation with objective measures such as RTI is weak.

The rise of populism across democracies is one of the greatest challenges to the existing world order since World War II. Yet, we only have very limited tools to measure populism within and across countries. Existing approaches rely on human coding, expert surveys, or diction ary-based analysis and suffer from high costs, low comparability, or low validity. To help overcome these limitations, we offer a new middle ground strategy between the expensive but rich qualitative reading of populist language and the cheaper, but coarsest machine coding of sets of texts. We extend the Parsing Unstructured Language into Sentiment-Aspect Representations (PULSAR) project to identify specific sentences and paragraphs as carrying populist narratives. We parse ``us'' versus ``them'' frames, as well as who is determined by the speaker as protecting or threatening ``the people''. We also code common aspects of political speeches such as agreement, opposition, and judgment holder that are not populist-specific. Our system uniquely matches the ``thin-centered'' definition of populism, such that not all utterances from parties ascribed to a populist worldview will carry populist content. To illustrate our contribution, we train our model using recent US Presidential campaig n speeches. Our method has important implications for studying populism's rise worldwide, it’s common patterns and local distinctiveness, especially for countries where researchers cannot afford costly systems.

Research on the political consequences of economic inequality focuses almost exclusively on relative inequality, using measures such as percentile ratios and gini coefficients for empirical analysis. Measures of relative inequality facilitate empirical comparison across space and time, but they do not always match theories that connect economic and political inequality. We demonstrate with a simple theoretical model that proportionate increases in income, gains that preserve levels of relative inequality in the population but increase levels of absolute inequality, generate greater inequality in campaign contributions from the poor and rich. Using data from U.S. Congressional Districts, we show empirically that greater levels of absolute inequality, rather than relative inequality, are in fact associated with larger differences in the rate at which rich and poor constituents make campaign contributions.

Political economists have explored the implications of firm heterogeneity for trade politics, but existing studies do not explain how the effects of labor politics distribute across firms. This paper contributes by analyzing the impact of wage bargaining by firm size. It empirically tests theoretical expectations about the uneven distribution of effects by looking at a drastic change in labor market policies in Uruguay, where the government instituted coordinated wage bargaining and a minimum wage (MW) increase, causing a regulatory shock for all firms. However, small firms were more exposed to the MW increase than their large counterparts. Adopting a Fuzzy-DID approach, I demonstrate that small firms were less able to increase wages, faced higher formalization costs, and lost the most skilled workers. These findings have important implications for understanding wage bargaining politics and firm heterogeneity in developing countries, which I discuss.

Esta investigación analiza las transformaciones en las relaciones laborales y el sindicalismo en Uruguay en el periodo 2005 - 2014, a través del estudio del caso del Sindicato Único de la Construcción y Anexos. En primer lugar, se sistematizaron los logros sindicales vinculados a las condiciones de trabajo : libertad sindical, mejoras en la estabilidad laboral, cambios en la duración de la jornada, regulaciones de la cantidad y calidad del trabajo, mejoras en seguridad e higiene y avances en la no discriminación laboral. En segundo lugar, se obtuvo evidencia de que más del 66% del contenido de los acuerdos colectivos referían a aspectos no salariales. Por último, el factor organizativo fue identificado como clave para estas conquistas y se observó una revitalización sindical, a través de relaciones de colaboración entre actores, reformas internas, formas alternativas de participación solidarias y fortalecimiento institucional.