The last decades have seen important progress in the economic analysis of institutions, with increasing concern about the need to ‘unbundle’ this concept and the diversity of situations it covers. It is so because of the complexity of the systems the concept intends to capture and the ambiguity of definitions often perceived as catch-all ideas without a clear connection to a research strategy. This essay contributes to the literature emphasising that overcoming these difficulties requires a theoretical framework identifying and characterising distinct institutional layers. The content of this framework is substantiated through the analysis of the nature and role of the long-ignored intermediate layer of ‘meso-institutions’. Meso-institutions designate devices and transmission mechanisms linking general rules, norms and beliefs established at the macro-institutional level with their perception, adaptation, and implementation (or challenge) by the actors populating the micro-level. Operationalising this framework relies on a research strategy that proceeds from a ‘substantive theory’ of institutions to the collection and processing of ‘empirical evidences’ through the development of ‘auxiliary theories’ designed to capture specific institutional objects. References to several empirical studies support the relevance of this approach.

This article develops the first dynamic method for systematically estimating the ideologies and other traits of nearly the entire federal judiciary. The Jurist-Derived Judicial Ideology Scores (JuDJIS) method derives from computational text analysis of over 20,000 written evaluations by a representative sample of tens of thousands of jurists as part of an ongoing, systematic survey initiative begun in 1985. The resulting data constitute not only the first such comprehensive federal-court measure that is dynamic, but also the only such measure that is based on judging, and the only such measure that is potentially multi-dimensional. The results of empirical validity tests reflect these advantages. Validation on a set of several-thousand appellate decisions indicates that the ideology estimates predict outcomes significantly more accurately than the existing appellate measures, such as the Judicial Common Space. In addition to informing theoretical debates about the nature of judicial ideology and decision-making, the JuDJIS initiative might lead courts scholars to revisit some of the lower-court research findings of the last two decades, which are generally based on static, non-judicial models. Perhaps most importantly, this method could foster breakthroughs in courts research that, until now, were impossible due to data limitations.

Strengthening the research workforce is essential for meeting the evolving needs and challenges in the health and biomedical fields. To do so effectively, it requires an understanding of how the experiences of a researcher shift over time and how one’s research career evolves, particularly as supports are put in place to foster research. This narrative review provides a summary of published individual-level assessment measures and survey tools from 2000–2024. All measures were abstracted, classified, and coded during analyses to describe the areas of focus, and they were organized into one of six research categories. The review identified a range of measures and methods across all categories. However, the measures were often narrow, focused on outputs, and not ideal for assessing the full range of experiences a researcher may have throughout their career. The most common metrics were related to research productivity and bibliometric measures. Our review of survey tools revealed a gap in comprehensive approaches available to assess an individual’s research experience, efforts, supports, and impact. As efforts expand to evaluate and study the research workforce, tools that focus on a broad range of individual-level measures, tied to specific underlying constructs and drawn from the literature, may prove useful.

We apply a synthesis review to revisit the concept, measurement, and operationalisation of social inclusion and exclusion in the context of comparative social policy, integrating the vast literature on the concepts, with the aim of elucidating a clearer understanding of the concepts for use by scholars and policymakers around the planet. In turn, we outline the conceptual development of the concepts, how they have been operationalised through social policy, and how they have been measured at the national and individual levels. Through our review, we identify limitations in extant conceptualisation and measurement approaches and suggest directions for refining conceptual and measurement frameworks to enhance their utility in social inclusion policy, emphasising the concepts’ multidimensional, multilevel, dynamic, and relational essence and highlighting their connection to related concepts such as social capital, social integration, and social citizenship.

The increasing multimodality (e.g., images, videos, links) of social media data presents opportunities and challenges. But text-as-data methods continue to dominate as modes of classification, as multimodal social media data are costly to collect and label. Researchers who face a budget constraint may need to make informed decisions regarding whether to collect and label only the textual content of social media data or their full multimodal content. In this article, we develop five measures and an experimental framework to assist with these decisions. We propose five performance metrics to measure the costs and benefits of multimodal labeling: average time per post, average time per valid response, valid response rate, intercoder agreement, and classifier’s predictive power. To estimate these measures, we introduce an experimental framework to evaluate coders’ performance under text-only and multimodal labeling conditions. We illustrate the method with a tweet labeling experiment.

Emotions and their sociopolitical impact have received increasing scholarly attention. However, it remains largely unclear whether emotional expression within surveys is subject to social desirability bias. By drawing on impression management theory and the disclosure decision model, I argue that emotional expression is likely prone to social desirability bias in interviewer-administered survey modes and test my hypotheses on mixed-mode ANES data. The findings demonstrate that respondents significantly underreport negative emotions—anger and fear—when interviewed face-to-face as compared to online. Furthermore, positive emotions, such as hope and pride, are not exempt from biased reporting related to interview mode. These results highlight the risks of estimating emotions and their salience by either relying on interviewer administration or combining survey modes.

A growing literature explores the effect of economic inequality in citizens’ surrounding environment on their political attitudes and behavior. This literature typically relies on measures of income concentration or gap-size, which reflect under-tested presumptions about how citizens perceive the economic conditions surrounding them. Utilizing survey data to explore perception of economic inequality in Americans’ residential environment, this note finds that measures capturing income concentration or gap-size perform poorly relative to a measure capturing the joint prevalence of “haves” and “have-nots.” These results suggest that commonly used measures of economic inequality may not fully capture the features of people’s daily environment used to perceive the existence or magnitude of inequality. The results guide future research toward using contextual indicators that treat inequality as a compound phenomenon involving manifestations of poverty and affluence.

Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and priming biases in three experimental designs that vary when moderators are measured: pre-treatment, post-treatment, or a randomized choice between the two. We derive nonparametric bounds for interactions between the treatment and the moderator under each design and show how to use substantive assumptions to narrow these bounds. These bounds allow researchers to assess the sensitivity of their empirical findings to priming and post-treatment bias. We then apply the proposed methodology to a survey experiment on electoral messaging.

We use data on Latino children in the United States who have been randomly assigned calculation tests in English or Spanish to check for the so-called bilingual advantage, the notion that knowing more than one language improves individuals’ other cognitive skills. After controlling for different characteristics of children and their parents, as well as children's time in the US, we find a bilingual advantage among children who read or write in English and Spanish but not for those who only speak or understand both languages. In particular, bilingual readers or writers perform one-fourth to one-third of a standard deviation better than monolingual children, equal to learning gains of an additional school year. Applying the Oster test, we find that selection on unobservables would need to be 3–4 times stronger than selection on observables to explain away our results. The bilingual advantage is stronger among children in two-parent households with siblings and for those at the upper end of the ability distribution.

Over the past quarter-century, the literature on gender, peace, and security has evolved into a substantial interdisciplinary field. In this line of work, researchers have investigated the interplay between state security and women’s security, or how gender equality at the state level affects the occurrence of international and intranational conflict. The conclusion is that more gender-equal countries are less prone to engage in warfare, pointing toward a link between women’s security and national security. Various indicators have been used to capture gender equality in this literature, such as the representation of women in parliamentary roles, the proportion of women participating in the labor force, and school enrollment among girls relative to boys.

The standard measure of authoritarianism asks respondents about desirable qualities in children. Although these questions are gender-neutral, respondents may differ in the gender of the child in their heads when answering. The items also may tap into gendered expectations about boys' and girls' behavior. We conducted three experiments that randomly assigned respondents to be asked about a child, a boy, or a girl in the items. We compare the means, measurement properties, and correlation between authoritarianism and other important variables across the conditions. Asking respondents about a girl creates significant differences in the level and measurement of authoritarianism, which is partially driven by the respondents' sexism. There are, fortunately, few other significant differences in the correlates of authoritarianism.