Soren Jordan’s research while affiliated with Auburn University and other places

Media stories on the economy tout automation as one of the biggest contemporary technological changes in America and argue that many Americans may lose their jobs because of it. Politicians and financial elites often promote a policy of Universal Basic Income (UBI) as a solution to the potential unemployment caused by automation, suggesting Americans should support UBI to protect them from this technological disruption. This linkage and basic descriptive findings are largely untested: we don’t know much about whether Americans support UBI, see automation as a threat to their job, or connect the two in any meaningful way. Using a Mechanical Turk survey of 3600 respondents, we examine the relationship between Americans’ perception of how much automation threatens their jobs, how much automation actually threatens their jobs, and their support for UBI. Our results indicate that while the public does not view automation as the same threat that elites do, Americans who believe their jobs will be automated are more likely to support UBI. These relationships, however, vary considerably by political party.

Investing in cryptocurrency has become more popular among Americans. Despite this, politicians and social scientists know almost nothing about the politics of cryptocurrency in the American public. By analyzing an original, nationally representative survey of 2500 American respondents, we create the first robust profile of the personalities, demographics, and political attitudes of cryptocurrency owners. We show that Americans who report hardship from inflation are more likely to own cryptocurrency, suggesting that when inflation is high, Americans may be more likely to use cryptocurrency as a medium of exchange and store of value. Americans who favor lower government spending and are more inclined toward conspiratorial thinking are also more likely to own cryptocurrency. Finally, there is a personality to cryptocurrency owners, with those open to new experiences more likely to own it and the conscientious less likely to own it. Our results have implications for how the American public may use cryptocurrency going forward.

Machine learning models, especially ensemble and tree‐based approaches, offer great promise to legislative scholars. However, they are heavily underutilized outside of narrow applications to text and networks. We believe this is because they are difficult to interpret: while the models are extremely flexible, they have been criticized as “black box” techniques due to their difficulty in visualizing the effect of predictors on the outcome of interest. In order to make these models more useful for legislative scholars, we introduce a framework integrating machine learning models with traditional parametric approaches. We then review three interpretative plotting strategies that scholars can use to bring a substantive interpretation to their machine learning models. For each, we explain the plotting strategy, when to use it, and how to interpret it. We then put these plots in action by revisiting two recent articles from Legislative Studies Quarterly.

Mummolo and Peterson improve the use and interpretation of fixed-effects models by pointing out that unit intercepts fundamentally reduce the amount of variation of variables in fixed-effects models. Along a similar vein, we make two claims in the context of random effects models. First, we show that potentially large reductions in variation, in this case caused by quasi-demeaning, also occur in models using random effects. Second, in many instances, what authors claim to be a random effects model is actually a pooled model after the quasi-demeaning process, affecting how we should interpret the model. A literature review of random effects models in top journals suggests that both points are currently not well understood. To better help users interested in improving their interpretation of random effects models, we provide Stata and R programs to easily obtain post-estimation quasi-demeaned variables.

We execute an original survey experiment to examine the extent and determinants of support for a nascent policy issue, universal basic income (UBI), in the American public. We explore the effects of how UBI is framed (either in the context of values or the context of policy), finding three key results. First, UBI is primarily a Democratic and liberal policy. Second, negative arguments against UBI move support for UBI more than positive arguments. Third, and surprisingly, respondents are equally affected by both policy‐driven and value‐driven arguments about UBI. In conclusion, an increase in messaging about UBI is likely to widen existing partisan differences in UBI support. These differences are unlikely to be won over by policy or values arguments.

We develop and assess an account of ideological asymmetries in public support for the Supreme Court. We find that specific support for the Supreme Court is more strongly negatively related to perceptions that the Court is overly liberal than perceptions that the Court is overly conservative. Our findings provide a more complete theoretical account of dynamics in specific support for the Supreme Court and indicate a mechanism behind the recent decline in the Supreme Court’s public standing.

An electoral model predicts that polarized and alienated voters lead to unstable elections, like phase transitions in an Ising model. Such physics-inspired models may help political scientists devise electoral reforms to quench instability.

In this paper we introduce dynamac, a suite of Stata programs designed to assist users in modeling and visualizing the effects of autoregressive distributed lag models, as well as testing for cointegration. We discuss the bounds cointegration test proposed by Pesaran, Shin and Smith (2001), which we have adapted into a Stata program. Since the resulting models can be dynamically complex, we follow the advice of Philips (2018) by introducing a flexible program designed to dynamically simulate and plot a variety of types of autoregressive distributed lag models, including error-correction models.