Measuring and Explaining Political Sophistication through Textual Complexity

Political scientists lack domain‐specific measures for the purpose of measuring the sophistication of political communication. We systematically review the shortcomings of existing approaches, before developing a new and better method along with software tools to apply it. We use crowdsourcing to perform thousands of pairwise comparisons of text snippets and incorporate these results into a statistical model of sophistication. This includes previously excluded features such as parts of speech and a measure of word rarity derived from dynamic term frequencies in the Google Books data set. Our technique not only shows which features are appropriate to the political domain and how, but also provides a measure easily applied and rescaled to political texts in a way that facilitates probabilistic comparisons. We reanalyze the State of the Union corpus to demonstrate how conclusions differ when using our improved approach, including the ability to compare complexity as a function of covariates.     

Structural Topic Models for Open‐Ended Survey Responses

Collection and especially analysis of open‐ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open‐ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.     

Confidence‐building measures at sea: The challenges ahead in Southeast Asia

Southeast Asia faces a range of challenges in adopting maritime confidence‐ and trust‐building measures in the very promising atmosphere of political endorsement that exists after the ASEAN Regional Forum, with possible changes to the strategic balance caused by maritime rearmament within the region. This paper briefly analyses the roles or activities of maritime forces in the region on the basis of their sensitivity to scrutiny: the hard or contentious kinds of activities, usually warlike, that navies will only practise together in an environment of considerable trust, and the soft, usually non‐warlike activities, that they could undertake cooperatively with fewer security concerns. From this list of shared roles two lists of possible CBMs are derived, the easy (or likely‐to‐succeed) and the hard (or less‐likely), based on the level of sensitivity of the activities to be undertaken cooperatively.     

A Method of Automated Nonparametric Content Analysis for Social Science

The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunately, even a method with a high percent of individual documents correctly classified can be hugely biased when estimating category proportions. By directly optimizing for this social science goal, we develop a method that gives approximately unbiased estimates of category proportions even when the optimal classifier performs poorly. We illustrate with diverse data sets, including the daily expressed opinions of thousands of people about the U.S. presidency. We also make available software that implements our methods and large corpora of text for further analysis.     

Interactive Learning Online at Public Universities: Evidence from a Six‐Campus Randomized Trial

Online instruction is quickly gaining in importance in U.S. higher education, but little rigorous evidence exists as to its effect on student learning. We measure the effect on learning outcomes of a prototypical interactive learning online statistics course by randomly assigning students on six public university campuses to take the course in a hybrid format (with machine‐guided instruction accompanied by one hour of face‐to‐face instruction each week) or a traditional format (as it is usually offered by their campus, typically with about three hours of face‐to‐face instruction each week). We find that learning outcomes are essentially the same—that students in the hybrid format are not harmed by this mode of instruction in terms of pass rates, final exam scores, and performance on a standardized assessment of statistical literacy. We also conduct speculative cost simulations and find that adopting hybrid models of instruction in large introductory courses has the potential to significantly reduce instructor compensation costs in the long run.     

Varieties of consumer credit data regimes: A regulatory governance approach

Credit markets are expanding, and with them also the automated, large‐scale commercialization of personal credit data. The increasing use of data and scores for commodified decision making lends greater urgency to the study of credit data regulatory regimes. This article promotes a comparative regulatory governance perspective as the basis for theory‐driven, multidimensional measurement. In order to measure consumer protection, we distinguish three different subregimes (collection, profiling, and use) and construct a two‐dimensional index of consumer protection (market restriction and user empowerment). We then assess the index and demonstrate its applicability and validity, building on empirical analysis of the regulatory regimes in the United States, France, Sweden, and Israel for the year 2019. Our approach points to a new direction in researching and measuring regulatory regimes in a comparative manner, which looks beyond national analysis toward an in‐depth understanding of other, equally important, levels of variation.     

Tax Toleration and Tax Compliance: How Government Affects the Propensity of Firms to Enter the Unofficial Economy

How do government‐supplied institutional benefits and the taxation and regulation of producers affect the propensity of private firms to enter the unofficial economy and evade taxation? We propose a model in which the incentive of firms to operate underground depends on tax rates relative to firm‐specific thresholds of tax toleration that are decisively affected by quality of governance—in particular by the presence of high‐grade institutions delivering services enhancing official production that anchor profit‐maximizing firms to the official economy. Some key predictions of the model concerning the determinants of firms' tax toleration and tax compliance receive broad support from empirical analyses of enterprise‐level data from the World Bank's World Business Environment Surveys.     

Tree‐Based Models for Political Science Data

Political scientists often find themselves analyzing data sets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in “big data,” as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high‐dimensional data sets. In this article, we introduce a family of tree‐based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in data sets with many (potentially irrelevant) covariates. We introduce the basic logic of tree‐based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.     

Emerging,and old,dilemmas for food security in Latin America

The present text offers a summary of some of the crucial food security problems in Latin American (LA) countries. This region, like many others in the globe, confronts such as food safety difficulties. The summary offers also an analysis of some technologically based solutions for LA food security issues. As a general call, the present review article calls for a collaborative and transdisciplinary approach to think deeper about how to solve food security problems, putting the focus on enabling technologies within a context of social, market, and global trends to achieve food and nutritional security. The review is positioned far from techno‐optimisms and, at the same time, away from purely economic‐based solutions. Conversely, this review embraces the whole complexity that brings economy‐nature‐technology triad. Embracing such complex relationships between technology, nature and the socio‐economy aspects of food security LA issues. Such complexity also triggers a need for a solution that considers a renewed approach to sustainability in the whole food value chain from production to consumption. Sustainability in the value chain opens up a much‐needed approach to LA food security that broadens, and refresh, our understanding of this fundamental socio‐economic and technical phenomena.     

“What have you done for me lately?” Re-thinking local representation

Scholars have studied the influence that constituents exert on elected representatives’ action in national parliaments at length. Still, academic pundits have usually confined local representation to distributive policies and casework, and limited local legislators’ focus to a territorial perspective. In this study, I try and propose a more nuanced theory of local representation, and I use automated text analysis to capture elected representatives’ propensity to serve functional as well as territorial interests. In an effort to provide empirical backing to my theoretical argumentation, I present an analysis of Italian legislators’ behavior which shows that deputies are willing to divert public spending to their district but also to favor the interests of specific economic sectors. Scholars have already acknowledged the multidimensional character of political representation at the national level, my analysis offers theoretical justification and empirical evidence to support doing so at the local level as well.     

Between continuity and change: The EU's mechanism of differentiated value integration

How does the European Union integrate new values into the text of its treaties? A growing body of literature indicates that, in the past three decades, new norms and values have entered the EU's discourse, resulting in what is usually termed ‘normative power Europe’. Yet the research and knowledge to‐date about the EU's discursive assimilation of new values and norms is surprisingly poor. As any institutional change, such integration has the potential to undermine the coherence of the EU's identity and thus also its objective to ‘speak with one voice’. This article explores the EU's discursive management of the continuity‐versus‐change imperative by analysing the integration of new values into the text of its treaties. This issue is addressed based on a quantitative content analysis on the full texts of European founding treaties between the 1950s and 2009. Findings show that the distribution of the EU's values in the text is not uniform: while the language of market economy and democracy is pervasive, the values of peace, European identity, rights and social justice are mentioned less frequently and in restricted linguistic environments. To account for the differences in the integration of values into the EU's treaty discourse, the article develops the notion of a discursive mechanism of differentiated value integration (MDVI). This rationale echoes the logic of differentiation in policy implementation employed by the EU. It is claimed here that, applied in the European discursive arena, MDVI allows radically different readings of the same text. This helps the EU to maintain a coherent value identity while at the same time enabling change.     

Do text messages increase voter registration? Evidence from RCTs with a local authority and an advocacy organisation in the UK

In the wake of the Covid-19 pandemic, text messages have become an increasingly attractive tool of voter registration. At the same time, in countries without automated registration, advocacy organisations play a more prominent role in supplementing the efforts of official bodies in registering voters. However, most available, robust evidence on whether voter registration campaigns work is based on campaigns conducted by official bodies charged with electoral registration. We present the results of two RCTs that aimed to increase voter registration in the UK using SMS-text messages, relying mainly on behavioural messaging. One was conducted by a local authority, while the other was implemented by an issue advocacy organisation that had no prior involvement in voter registration. In line with previous findings, the local authority’s text messages resulted in an increased registration rate of eight percentage-points, which translates into a three percentage-point increase in voter turnout. However, the advocacy organisation’s text messages neither increased voter registration, nor turnout, no matter whether the text message offered a personal follow-up conversation, or not. Given that many voter registration campaigns are run by advocacy organisations and text messages are an increasingly important mobilisation tool, this raises questions about the scope conditions of existing findings.     

Adjusting for Confounding with Text Matching

We identify situations in which conditioning on text can address confounding in observational studies. We argue that a matching approach is particularly well-suited to this task, but existing matching methods are ill-equipped to handle high-dimensional text data. Our proposed solution is to estimate a low-dimensional summary of the text and condition on this summary via matching. We propose a method of text matching, topical inverse regression matching, that allows the analyst to match both on the topical content of confounding documents and the probability that each of these documents is treated. We validate our approach and illustrate the importance of conditioning on text to address confounding with two applications: the effect of perceptions of author gender on citation counts in the international relations literature and the effects of censorship on Chinese social media users.     

Increasing the transparency of stated choice studies for policy analysis: Designing experiments to produce raw response graphs

We believe a lack of transparency undermines both the credibility of, and interest in, stated choice studies among policy makers. Unlike articles reporting the results of contingent valuation studies, papers in the stated choice literature rarely present simple tabulations of raw response data (that is, a table or graph showing the percentage of respondents agreeing to purchase a good or service, or vote for a proposed management plan as a function of price). We describe an approach for adding “policy” tasks to a standard orthogonal‐in‐attribute‐levels research design that allows the researcher to plot raw responses as a function of changes in only one characteristic of the offered good or service. We demonstrate this approach using data from a stated choice study of private demand for cholera and typhoid vaccines in Kolkata, India, carried out in the summer of 2004. © 2006 by the Association for Public Policy Analysis and Management.     

How to Analyze Political Attention with Minimal Assumptions and Costs

Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large-scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the topic of specific speeches. Our method estimates, rather than assumes, the substance of topics, the keywords that identify topics, and the hierarchical nesting of topics. We use the topic model to examine the agenda in the U.S. Senate from 1997 to 2004. Using a new database of over 118,000 speeches (70,000,000 words) from the Congressional Record , our model reveals speech topic categories that are both distinctive and meaningfully interrelated and a richer view of democratic agenda dynamics than had previously been possible.     

The Effects of California's Paid Family Leave Program on Mothers’ Leave‐Taking and Subsequent Labor Market Outcomes

This analysis uses March Current Population Survey data from 1999 to 2010 and a differences‐in‐differences approach to examine how California's first in the nation paid family leave (PFL) program affected leave‐taking by mothers following childbirth, as well as subsequent labor market outcomes. We obtain robust evidence that the California program doubled the overall use of maternity leave, increasing it from an average of three to six weeks for new mothers—with some evidence of particularly large growth for less advantaged groups. We also provide evidence that PFL increased the usual weekly work hours of employed mothers of 1‐ to 3‐year‐old children by 10 to 17 percent and that their wage incomes may have risen by a similar amount.     

Information,Agreement Design,and the Durability of Civil War Settlements

Civil war is usually examined from the perspective of commitment problems. This approach provides considerable insight regarding which civil war agreement provisions reduce the chance of renewed fighting. Yet, additional insight can be gained by examining information asymmetries as a potential cause of civil war recurrence. We argue that significant uncertainty regarding military capabilities may persist after fighting ends and that this uncertainty may lead to the breakdown of peace. However, carefully designed peace agreements can guard against renewed civil war by calling for international monitoring, making the belligerents submit military information to third parties, and providing for verification of this information. Our empirical analysis of 51 civil war settlements between 1945 and 2005 shows that these provisions significantly reduce the risk of new civil war. Encouraging the adoption of these provisions may be a useful policy in the international community's effort to establish peace in civil‐war‐torn societies.