Scholars have studied the carceral state extensively. However, little is known about the ‘shadow’ carceral state, coercive institutions lacking even the limited safeguards of the carceral state. Pretrial incarceration is one such institution. It often lasts months and causes large resource losses. Yet it is imposed in rushed hearings, with wide discretion for bail judges. These circumstances facilitate quick, heuristic judgments relying on racial stereotypes of marginalized populations. We merge court records from Miami-Dade with voter records to estimate the effect of this ‘shadow’ institution on turnout. We find that quasi-randomly assigned harsher bail judges depress voting by Black and Hispanic defendants. Consistent with heuristic processing, these racial disparities result only from inexperienced judges. Unlike judge experience, judge race does not matter; minority judges are as likely to impose detention and reduce turnout. The ‘shadow’ carceral state undermines democratic participation, exacerbating racial inequality.

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method.

This study examines how the Russian invasion of Ukraine and the subsequent Western responses influence Chinese public opinion on the use of force. Using two original, preregistered online survey experiments, first in June 2022 and then in June 2023, we show that the Russian invasion is associated with a modest but statistically significant increase in Chinese support for using military force in international affairs in general and against Taiwan in particular. However, information on Western military measures aiding Ukraine curbs the modest impact of the invasion. Such information is especially effective in reducing support for an outright military invasion of Taiwan. Causal mediation analyses reveal that the Russian invasion influences public opinion by inducing optimism regarding military success and pessimism regarding peaceful resolution of the conflict. These findings suggest that foreign military aggression and subsequent international countermeasures can sway domestic public opinion on using military force.

Ethnic voting is an important phenomenon in the political lives of numerous countries. In the present paper, we propose a theory explaining why ethnic voting is more prevalent in certain localities than in others and provide evidence for it. We argue that local ethnic geography affects ethnic voting by making voters of ethnicity that finds itself in the minority fear intimidation by their ethnic majority neighbors. We provide empirical evidence for our claim using the data from round 4 of the Afrobarometer survey in Ghana to measure the voters’ beliefs that they are likely to face intimidation during electoral campaigns. Using geocoded data from rounds three and four of the Afrobarometer, as well as data from the Ghana Demographic and Health Survey, we find no evidence for local public goods provision as an alternative mechanism.

We introduce a method for scaling two datasets from different sources. The proposed method estimates a latent factor common to both datasets as well as an idiosyncratic factor unique to each. In addition, it offers a flexible modeling strategy that permits the scaled locations to be a function of covariates, and efficient implementation allows for inference through resampling. A simulation study shows that our proposed method improves over existing alternatives in capturing the variation common to both datasets, as well as the latent factors specific to each. We apply our proposed method to vote and speech data from the 112th U.S. Senate. We recover a shared subspace that aligns with a standard ideological dimension running from liberals to conservatives, while recovering the words most associated with each senator’s location. In addition, we estimate a word-specific subspace that ranges from national security to budget concerns, and a vote-specific subspace with Tea Party senators on one extreme and senior committee leaders on the other.

Since most social science research relies on multiple data sources, merging data sets is an essential part of researchers’ workflow. Unfortunately, a unique identifier that unambiguously links records is often unavailable, and data may contain missing and inaccurate information. These problems are severe especially when merging large-scale administrative records. We develop a fast and scalable algorithm to implement a canonical model of probabilistic record linkage that has many advantages over deterministic methods frequently used by social scientists. The proposed methodology efficiently handles millions of observations while accounting for missing data and measurement error, incorporating auxiliary information, and adjusting for uncertainty about merging in post-merge analyses. We conduct comprehensive simulation studies to evaluate the performance of our algorithm in realistic scenarios. We also apply our methodology to merging campaign contribution records, survey data, and nationwide voter files. An open-source software package is available for implementing the proposed methodology.