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This paper is the result of a nationwide study of polling place dynamics in the 2016 presidential election. Research teams, recruited from local colleges and universities and located in twenty-eight election jurisdictions across the United States, observed and timed voters as they entered the queue at their respective polling places and then voted. We report results about four specific polling place operations and practices: the length of the check-in line, the number of voters leaving the check-in line once they have joined it, the time for a voter to check in to vote (i.e., verify voter’s identification and obtain a ballot), and the time to complete a ballot. Long lines, waiting times, and times to vote are closely related to time of day (mornings are busiest for polling places). We found the recent adoption of photographic voter identification (ID) requirements to have a disparate effect on the time to check in among white and nonwhite polling places. In majority-white polling places, scanning a voter’s driver’s license speeds up the check-in process. In majority nonwhite polling locations, the effect of strict voter ID requirements increases time to check in, albeit modestly.

In this paper, we introduce an innovative method to diagnose electoral fraud using vote counts. Specifically, we use synthetic data to develop and train a fraud detection prototype. We employ a naive Bayes classifier as our learning algorithm and rely on digital analysis to identify the features that are most informative about class distinctions. To evaluate the detection capability of the classifier, we use authentic data drawn from a novel data set of district-level vote counts in the province of Buenos Aires (Argentina) between 1931 and 1941, a period with a checkered history of fraud. Our results corroborate the validity of our approach: The elections considered to be irregular (legitimate) by most historical accounts are unambiguously classified as fraudulent (clean) by the learner. More generally, our findings demonstrate the feasibility of generating and using synthetic data for training and testing an electoral fraud detection system. 2 See Lehoucq (2003) for an extensive review of the literature on electoral fraud. 3 Digit analysis using Benford's Law is an example of such a method. Benford's Law specifies that in a collection of numbers, the first possible digits should not occur with equal frequency. Widely applied to financial auditing (Drake and Nigrini 2000), conformity with Benford's Law has also been used to detect manipulation of economic indicators (Nye and Moul 2007), campaign

This paper investigates the opportunities for non-democratic regimes to rely on fraud by documenting the alteration of vote tallies during the 1988 presidential election in Mexico. In particular, I study how the alteration of vote returns came after an electoral reform that centralized the vote-counting process. Using an original image database of the vote-tally sheets for that election and applying Convolutional Neural Networks (CNN) to analyze the sheets, I find evidence of blatant alterations in about a third of the tallies in the country. This empirical analysis shows that altered tallies were more prevalent in polling stations where the opposition was not present and in states controlled by governors with grassroots experience of managing the electoral operation. This research has implications for understanding the ways in which autocrats control elections as well as for introducing a new methodology to audit the integrity of vote tallies.

Federalism is widely lauded for its ability to manage deep social divisions and promote efficient policy in democratic systems, but it has been criticized for its impact on party system nationalization. In this paper, we explore the role of formal and informal institutions on party system nationalization in the Mexican political system, focusing on legislative politics. In Mexico, an end of one-party rule transformed the nature of center-periphery relations, empowering subnational actors and giving them incentives to act on the national stage. Using an original dataset, we show that these changes resulted in national parties dividing along state lines on policy decisions, and that the magnitude of these divisions depends primarily on 1) the informal centralization of career resources, 2) the extent to which parties are ideological and programmatic, and 3) the personal vote incentives of electoral rules.

We provide an introduction of the functioning, implementation, and challenges of convolutional neural networks (CNNs) to classify visual information in social sciences. This tool can help scholars to make more efficient the tedious task of classifying images and extracting information from them. We illustrate the implementation and impact of this methodology by coding handwritten information from vote tallies. Our paper not only demonstrates the contributions of CNNs to both scholars and policy practitioners, but also presents the practical challenges and limitations of the method, providing advice on how to deal with these issues.

A generalized distrust in Mexican local elections raises the question of whether electoral corruption has vanished or remains a prevalent practice in the country. To answer this question, I analyze the 2010 gubernatorial elections, exploiting a feature of the country's electoral system: within each electoral precinct, voters are assigned to polling stations according to their childhood surname. Consequently, the only difference between voters in contiguous polling stations should be their last names. Given that political preferences are seldom correlated with voters' names, I use suspicious differences in turnout levels across contiguous polling stations to identify fraudulent practices. The findings of this article indicate that nondemocratic enclaves that actively obstruct the completion of Mexico's democratic transition still remain today.

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