1,721,020 research outputs found
Replication Data for: Just in time: Political policy cycles of land reform
No full textAbstract: While the political budget cycle literature focuses on the manipulation of existing policies, an analysis of the impact of the passage of redistributive policies themselves remains absent. I contend that policy passage is a strategically timed signal to voters used before elections to benefit the incumbent. Using aggregate data on land reforms in India from 1957 to 1992, I find that reforms are indeed timed before elections. Second, using historical survey data, I show that land issues remain a strong signal to Indian voters over time, even in states that have already enacted reforms. These findings provide evidence for political policy cycles
Replication Data for: Just in time: Political policy cycles of land reform
No full textAbstract: While the political budget cycle literature focuses on the manipulation of existing policies, an analysis of the impact of the passage of redistributive policies themselves remains absent. I contend that policy passage is a strategically timed signal to voters used before elections to benefit the incumbent. Using aggregate data on land reforms in India from 1957 to 1992, I find that reforms are indeed timed before elections. Second, using historical survey data, I show that land issues remain a strong signal to Indian voters over time, even in states that have already enacted reforms. These findings provide evidence for political policy cycles
Replication Data for: Have Your Cake and Eat it Too? Cointegration and Dynamic Inference from Autoregressive Distributed Lag Models
No full textAlthough recent articles have stressed the importance of testing for unit-roots and cointegration in time series analysis, practitioners have been left without a straightforward procedure to implement this advice. I propose using the autoregressive distributed lag model and bounds cointegration test developed by Pesaran, Shin and Smith (2001) as an approach to dealing with some the most commonly encountered issues in time series analysis. Through Monte Carlo experiments I show that this procedure performs better than existing cointegration tests under a variety of situations. I illustrate how to implement this strategy with two step-by-step replication examples. To further aid users, I have designed software programs in order to test and dynamically model the results from this approach
Replication Data for: Have Your Cake and Eat it Too? Cointegration and Dynamic Inference from Autoregressive Distributed Lag Models
No full textAlthough recent articles have stressed the importance of testing for unit-roots and cointegration in time series analysis, practitioners have been left without a straightforward procedure to implement this advice. I propose using the autoregressive distributed lag model and bounds cointegration test developed by Pesaran, Shin and Smith (2001) as an approach to dealing with some the most commonly encountered issues in time series analysis. Through Monte Carlo experiments I show that this procedure performs better than existing cointegration tests under a variety of situations. I illustrate how to implement this strategy with two step-by-step replication examples. To further aid users, I have designed software programs in order to test and dynamically model the results from this approach
Replication Data for: How to Avoid Incorrect Inferences (While Gaining Correct Ones) in Dynamic Models
No full textA flurry of current interest in time series has focused on clarifying equation balance, fractional integration, and cointegration testing. Despite this, a number of recent suggestions may continue to lead scholars towards incorrect inferences. In this comment, I investigate the likelihood of drawing both correct and incorrect inferences under a variety of stationary and non-stationary data-generating processes. I extend previous work in this area by focusing on both short- and long-run effects using several popular model specifications. Given these findings, I conclude by offering a variety of recommendations to practitioners about how they can best specify their model
Replication Data for: Does the @realDonaldTrump Really Matter to Financial Markets?
No full textDoes the @realDonaldTrump really matter to financial markets? Research shows that new information about the likely future policy direction of government affects financial markets. In contrast, we argue that new information can also arise about the likely future policy commitment of government, affecting financial markets as well. We test our argument using data on US president Donald J. Trump’s Mexico-related policy tweets and the US dollar/Mexican peso exchange rate. We find not only that Trump’s Mexico-related tweets raised Mexican peso volatility while his policy views were unknown, but also thereafter, as they clarified his commitment to his Mexico-related policy goals. We argue that, by helping politicians disseminate policy information to voters and helping voters hold governments accountable for their policy performance, social media allows investors to gather information about the future policy direction and policy commitment of government, especially those run by newcomers whose policy direction and commitment are unknown
Replication Data for: Does job insecurity shape policy preferences? An experimental manipulation of labor market risk.
No full textResearch on political behavior and policy preferences has long argued that economic or labor-market risk should motivate support for social policy, especially social insurance. We test this expectation about political behavior using a survey experiment in the nationally-representative 2020 US Cooperative Congressional Election Study, through which we manipulate perceptions of labor market risk. Though our results suggest that our treatment successfully induced greater perceived labor market insecurity among respondents, we find no support for the expectation that risk of job loss translates into preferences for unemployment insurance policy design. We further find that Republicans react to the suggestion of macroeconomic change (either positive or negative) with a preference for rolling back unemployment insurance benefits, while Democrats’ policy preferences are not significantly changed by the treatment. This result raises interesting questions for future analysis and research
Replication Data for: Does the @realDonaldTrump Really Matter to Financial Markets?
No full textDoes the @realDonaldTrump really matter to financial markets? Research shows that new information about the likely future policy direction of government affects financial markets. In contrast, we argue that new information can also arise about the likely future policy commitment of government, affecting financial markets as well. We test our argument using data on US president Donald J. Trump’s Mexico-related policy tweets and the US dollar/Mexican peso exchange rate. We find not only that Trump’s Mexico-related tweets raised Mexican peso volatility while his policy views were unknown, but also thereafter, as they clarified his commitment to his Mexico-related policy goals. We argue that, by helping politicians disseminate policy information to voters and helping voters hold governments accountable for their policy performance, social media allows investors to gather information about the future policy direction and policy commitment of government, especially those run by newcomers whose policy direction and commitment are unknown
Replication Data for: Integrating the Use of Statistical Software into Undergraduate Political Methodology Courses
No full textTeaching undergraduate political methodology courses is a challenging task, yet has garnered little pedagogical discussion within the discipline. With the growing use of technology in the classroom, as well as the growing demand for data science and data literacy in our society, better understanding how we use statistical software in these courses is warranted. In this short paper, we shed light on current practices in teaching political methodology courses, with a particular emphasis on the use of statistical software. Combining an analysis of 93 course syllabi with a quantitative survey of research method instructors, we provide key information on the structure of these courses and how they incorporate statistical software. Our results reflect the growing importance of data literacy within the discipline, and suggest that more intentional discussions of research method pedagogy are needed in the future
Replication Data for: "Point Break: Using Machine Learning to Uncover a Critical Mass in Women's Representation"
No full textDecades of research has debated whether women first need to reach a “critical mass” in the legislature before they can effectively influence legislative outcomes. This study contributes to the debate using supervised tree-based machine learning to study the relationship between increasing variation in women’s legislative representation and the allocation of government expenditures in three policy areas: education, healthcare, and defense. We find that women’s representation predicts spending in all three areas. We also find evidence of critical mass effects as the relationships between women’s representation and government spending are nonlinear. However, beyond critical mass, our research points to a potential critical mass interval or critical limit point in women’s representation. We offer guidance on how these results can inform future research using standard parametric models
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