We seek to better understand the demand side of vote buying: the conditions under which voters participate in, eschew, tolerate, or punish the exchange of targeted material benefits for votes. We ask whether voters perceive vote buying as substituting for local public goods provision in office, or whether they think that candidates who buy votes will excel at securing local public goods. Voters who place great value on future public goods may opt out of vote buying if they believe they are substitutes and punish vote‐buying candidates at the polls. We explore these issues in a nationwide survey in Nepal. Multiple survey experiments provide evidence that Nepali voters perceive vote buying and local public goods provision as substitutes. Voters who hold this view also express a preference for candidates who do not engage in vote buying, implying they prioritize public goods provision, although this latter result is not causally identified.

Measuring levels of electoral integrity is essential to the study of both democracy and nondemocracy, as well as to interrogations of movements between systems in the form of democratization and autocratization. This chapter discusses how electoral integrity can be measured using expert data to capture both difficult-to-observe aspects of electoral integrity and more standard factual data, such as voter turnout. It first lays out the advantages and issues with these types of data and then operationalizes the concept of electoral integrity as used throughout this volume—“as a set of principles to be achieved in elections which empower the everyday citizen and help to realize the ideals of democracy”—using Varieties of Democracy (V-Dem) data to form a new Electoral Integrity Index (EII). We subject the EII to a series of validation tests, including convergent validity analysis comparing it to the Electoral Integrity Project’s Principles of Electoral Integrity (PEI) index and V-Dem’s Clean Elections Index. We conclude by presenting descriptive patterns in electoral integrity worldwide using the EII and discussing best practices surrounding EII use in future research.

Most crossnational indices of democracy rely centrally on coder judgments, which are susceptible to personal bias and error, and also require expensive and time-consuming coding by experts. The few measures based exclusively on observable indicators are either dichotomous or rely on a few rather crude proxies. This project lays out an approach to measurement based on observables that aims to preserve the nuanced quality of subjectively coded democracy indices. First, we gather data for a wide range of observable indicators, X´, that capture different aspects of the democratic process. Next, we use supervised random forest machine learning to predict Z using factual indicators, X´, creating an observable-to-subjective score mapping (OSM). The mapping that provides the best cross-validated fit to the outcome serves as an alternate index, Z´, for that conceptualization of democracy.Information loss from Z to Z´ is minimal for indices centered on an electoral conception of democracy and this loss may be advantageous for some purposes. It is free of idiosyncratic coder errors arising from misinformation, slack, or biases for or against a regime. It is also less susceptible to systematic bias that may arise from coders’ inferences about a country’s regime status, e.g., from the ideology of the current ruler. The data collection procedure and mode of analysis is fully transparent and replicable, and the procedure is cheap to produce, easy to update, and offers coverage for all polities with sovereign or semisovereign status, surpassing the sample of any existing index. We show that this expansive coverage makes a big difference to our understanding of some causal questions.

A growing literature posits that vote buying dynamics depend on characteristics of the context and its voters. We explore vote buying in Nepal using a multi-methods approach combining survey experiments, semi-structured interviews, and focus group discussions. We find that vote buying in Nepal aligns somewhat with other contexts. A list experiment reveals approximately 25% of Nepali voters receive a voter-buying offer and, in an unmonitored but contingent exchange, the same percentage vote for the offeror candidate or party. Cash and other private goods are the most common offers. In contrast to findings from other contexts, however, voter education level is the strongest predictor of refraining from vote buying in Nepal, and wealth is not a significant predictor. Our list experiment also finds that, in Nepal, clientelism appears to be a socially undesirable activity. Overall, our results support the increasingly dominant viewpoint that vote buying is highly context dependent.

This document outlines the methodological considerations, choices, and procedures guiding the development of the Varieties of Democracy (V-Dem) project.

During the past decade, analyses drawing on several democracy measures have shown a global trend of democratic retrenchment. While these democracy measures use radically different methodologies, most partially or fully rely on subjective judgments to produce estimates of the level of democracy within states. Such projects continuously grapple with balancing conceptual coverage with the potential for bias (Munck and Verkuilen 2002; Przeworski et al. 2000). Little and Meng (L&M) (2023) reintroduce this debate, arguing that "objective" measures of democracy show little evidence of recent global democratic backsliding.1 By extension, they posit that time-varying expert bias drives the appearance of democratic retrenchment in measures that incorporate expert judgments. In this article, we engage with (1) broader debates on democracy measurement and democratic backsliding, and (2) L&M's specific data and conclusions.

This Codebook includes all the variables that V-Dem is compiling in the v14 dataset (2024).

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Multiple well-known democracy rating projects—including Freedom House, Polity,and V-Dem—have identified apparent global regression in recent years. These measures rely on partly subjective indicators, which could, in principle, suffer from rater bias. For instance, Little and Meng (2023) argue that shared beliefs driven by the current zeitgeist could lead to shared biases that produce the appearance of democratic backsliding in subjectively coded measures. To assess this argument, and the strength of the evidence for global democratic backsliding, we propose an observable-to-subjective score mapping (OSM) methodology that uses only easily observable features of democracy to predict existing indices of democracy. Applying this methodology to three prominent democracy indices, we find evidence of backsliding, but beginning later and not as pronounced as suggested by some of the original indices. Our approach suggests that particularly the Freedom House measure is out of track with the recent patterns in observable indicators and that there has been a stasis or, at most, a modest decline in the average level of democracy.

Political engagement is deeply enmeshed with online activity. However, there has been a lack of publicly available cross-country datasets enabling researchers and policymakers to understand how politics and digital space intersect. This paper introduces the Digital Society Project (DSP), which aims to provide systematic, cross-country measurement of political institutions and behavior that manifest online or are affected by online activity. Using the Varieties of Democracy Project infrastructure, DSP provides annual global data from 2000 to 2021. The dataset features 35 indicators measuring online censorship, politicization of social media, coordinated information operations, foreign influence, monitoring of domestic politics, and regime cyber capacity. This article introduces the DSP data collection effort, overviews the resulting dataset, and validates key indicators by conducting a series of diagnostic tests. We demonstrate that the DSP dataset aligns with extant datasets measuring internet freedom and offers expanded coverage across countries and over time. We analyze two case studies, walking through how the DSP data can be used to extend existing work on China to generalize this case to other contexts, and examining in depth the case of Ethiopia, which differs the most between DSP and Freedom House's Freedom on the Net.

Political scientists increasingly use crowdworkers to produce data, predominantly in the context of coding researcher-curated text or to retrieve simple data from the internet. In this article, we provide a theoretical and empirical basis for understanding when crowdworkers can provide data of sufficient quality to substitute for other types of coders. First, we introduce a typology of data-producing actors - experts, trained coders and crowds - and hypothesize factors that affect the substitutability of crowdworkers. We then examine how crowdworkers perform across coding tasks that vary along multiple dimensions of difficulty: information verifiability, availability and complexity. The results provide scope conditions bounding the substitutability of crowdworkers in political science applications. Although crowds can substitute for trained coders in the context of relatively simple information retrieval tasks, there is little evidence that crowdworkers can substitute for experts, whose tasks require both information retrieval and data synthesis.

Models for converting expert-coded data to estimates of latent concepts assume different data-generating processes (DGPs). In this paper, we simulate ecologically valid data according to different assumptions, and examine the degree to which common methods for aggregating expert-coded data (1) recover true values and (2) construct appropriate coverage intervals. We find that the mean and both hierarchical Aldrich–McKelvey (A–M) scaling and hierarchical item-response theory (IRT) models perform similarly when expert error is low; the hierarchical latent variable models (A-M and IRT) outperform the mean when expert error is high. Hierarchical A–M and IRT models generally perform similarly, although IRT models are often more likely to include true values within their coverage intervals. The median and non-hierarchical latent variable models perform poorly under most assumed DGPs.

Addiction medicine is a dynamic field that encompasses clinical practice and research in the context of societal, economic, and cultural factors at the local, national, regional, and global levels. This field has evolved profoundly during the past decades in terms of scopes and activities with the contribution of addiction medicine scientists and professionals globally. The dynamic nature of drug addiction at the global level has resulted in a crucial need for developing an international collaborative network of addiction societies, treatment programs and experts to monitor emerging concerns at national, regional, and global levels. In this protocol, methodological details of running longitudinal surveys at national, regional, and global levels through the Global Expert Network of the International Society of Addiction Medicine (ISAM-GEN) are presented. The surveys will be developed by global experts in addiction medicine on treatment services, service coverage, comorbidities, treatment standards and barriers, emerging drug addictions and/or dynamic changes in treatment needs across the world. Survey participants in categories of (1) addiction societies/associations, (2) addiction treatment programs, (3) addiction experts/clinicians and (4) related stakeholders will respond to these longitudinal global surveys. The results will be analyzed and cross-examined with available data and peer-reviewed for publication.

* Pemstein is first author as the primary developer of the measurement model; Marquardt, Tzelgov and Wang are equal second authors due to their essential contributions to model development. Medzihorsky is third author for his technical contributions to model implementation. Krusell, Miri and von R¨omer are equal fourth authors for their contributions as data managers during initial model implementation. The authors would like to thank the other members of the V–Dem team for their suggestions and assistance. We also thank Michael Coppedge, Christopher Fariss, Jon Polk

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.

Part I sets forth the V-Dem conceptual scheme. Part II discusses the process of data collection. Part III describes the measurement model along with efforts to identify and correct errors.

The international community spends significant sums of money on democracy promotion, focusing especially on producing competitive and transparent electoral environments. In theory, aid empowers a variety of actors, increasing competition and government responsiveness. We argue that to fully understand the effect of aid on democratization one must consider how democracy aid affects specific country institutions. Building on theory from the democratization and democracy promotion literature, we specify more precise causal linkages between democracy assistance and elections. Specifically, we hypothesize about the effects of democracy aid on the implementation and quality of elections. Building on canonical work, we test these hypotheses, using V-Dem's detailed elections measures to examine the impact of democracy aid. Intriguingly, we find that there is no consistent relationship between democracy and governance aid and the improvement of disaggregated indicators of election quality, but aggregate measures still capture a relationship. We suggest that current evidence is more consistent with election-enhancing aid following democratization than with democratization following such aid.

A key obstacle to measurement is the aggregation problem. Where indicators tap into common latent traits in theoretically meaningful ways, the problem may be solved by applying a data-informed (“inductive”) measurement model, for example, factor analysis, structural equation models, or item response theory. Where they do not, researchers solve the aggregation problem by appeal to concept-driven (“deductive”) criteria, that is, aggregation schemes that do not presume patterns of covariance across observable indicators. This article introduces a novel approach to scale construction that builds on the properties of concepts to solve the aggregation problem. This is accomplished by regarding conceptual attributes as necessary-and-sufficient conditions arrayed in an ordinal scale. While different sorts of scales are useful for different purposes, we argue that “lexical” scales are in many cases superior for research questions where it is relevant to combine the differentiation of an ordinal scale with the distinct, meaningful categories of a typology.

ABSTRACTThe traditional process of peer review and publication has come under intense scrutiny in recent years. The time seems propitious for a consideration of alternatives in political science. To that end, we propose a Peer Review and Publication Consortium. The Consortium retains the virtues of the traditional peer-review process (governed by academic journals) while also mitigating some of its vices.

This Codebook includes all the variables that V-Dem is compiling in 2020 dataset.