Political professionals and scholars maintain that raising money early in the election season is critical to a successful campaign, having downstream consequences on a candidate’s future fundraising potential, the stiffness of competition she will face, and her likelihood of electoral victory. In spite of early money’s perceived importance, there is no common operationalization for money as “early.” Moreover, existing measures often fail to reflect definitional aspects of early money. In this paper, we first lay out a theoretical framework regarding the utility of early campaign fundraising for candidates. We argue that early fundraising can be expressed as two conceptually distinct quantities of interest centered on either a candidate’s own fundraising performance (candidate-centered) or her fundraising performance relative to her electoral competitors (election-centered). We next lay out steps for operationalizing candidate- and election-centered measures of early fundraising. Lastly, we demonstrate that both our proposed measures for early campaign fundraising are predictive of a candidate’s future fundraising and electoral success. By putting forward a set of best practices for early money measurement and, additionally, producing off-the-shelf measures for early fundraising in U.S. House elections, we fill an important gap in scholarly research on the measurement of money in politics.

Despite the ongoing success of populist parties in many parts of the world, we lack comprehensive information about parties’ level of populism over time. A recent contribution to Political Analysis by Di Cocco and Monechi (DCM) suggests that this research gap can be closed by predicting parties’ populism scores from their election manifestos using supervised machine learning. In this paper, we provide a detailed discussion of the suggested approach. Building on recent debates about the validation of machine-learning models, we argue that the validity checks provided in DCM’s paper are insufficient. We conduct a series of additional validity checks and empirically demonstrate that the approach is not suitable for deriving populism scores from texts. We conclude that measuring populism over time and between countries remains an immense challenge for empirical research. More generally, our paper illustrates the importance of more comprehensive validations of supervised machine-learning models.

This article offers an evaluation of cross-national measures of ethnic socio-economic inequality. It demonstrates that the measures differ in important ways regarding empirical scope, conceptualization, measurement and aggregation. Despite significant advances in the measurement of ethnic inequality, all measures have shortcomings, such as limited and biased coverage, as well as measurement error from the underlying data sources. Moreover, the empirical convergence between conceptually similar measures is strikingly low: some of the measures show no or even negative covariation. Four replication studies also indicate that extant measures of ethnic inequality are generally not interchangeable. Scholars should therefore take the various features highlighted in this evaluation into account before employing any of them. Based on this conclusion, the article offers multiple suggestions for improving existing measures and developing new ones.

Recent accounts of American politics focus heavily on urban–rural gaps in political behavior. Rural politics research is growing but may be stymied by difficulties defining and measuring which Americans qualify as “rural.” We discuss theoretical and empirical challenges to studying rurality. Much existing research has been inattentive to conceptualization and measurement of rural geography. We focus on improving estimation of different notions of rurality and provide a new dataset on urban–rural measurement of U.S. state legislative districts. We scrutinize construct validity and measurement in two studies of rural politics. First, we replicate Flavin and Franko (2020, Political Behavior, 845–864) to demonstrate empirical results may be sensitive to measurement of rural residents. Second, we use Mummolo and Nall’s (2017, The Journal of Politics, 45–59) survey data to show rural self-identification is not well-captured with objective, place-based classifications, suggesting a rethinking of theoretical and empirical accounts of rural identity. We conclude with strategies for operationalizing rurality using readily available tools.

State capacity is a key concept for research in public policy and political science. Despite its importance, there is no broadly accepted measure of state capacity in the existing literature, and frequently used measures of capacity have not been examined for their validity. We begin with an explicit definition of state capacity – the state's ability to implement public policy – and connect this definition to a measurable outcome of state capacity – the state's taxation of income. We show that this measure, income taxes as a percentage of total tax revenue, is a useful indicator of state capacity and meets higher standards of measurement validity than other tax-based indicators. We also compare our measure to the most common existing indicators of state capacity to show that income taxation is a better theoretical and statistical measure of states’ effectiveness in policy implementation.