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

Public understanding of violence against women, and appropriate solutions to tackling gender-based violence, have changed enormously over the past 50 years. In this paper, we study how violence against women is practically understood through organizational efforts to frame and combat it in the United States. We use topic modeling and dictionary-based content analysis to explore the missions and programming of 918 service and advocacy nonprofits directly involved in anti-violence work between 1998 and 2016. We find that, in contrast to earlier foci on direct crisis intervention, anti-violence organizations increasingly understand violence against women as a multifaceted problem that must be addressed by comprehensive programming. We also find that nonprofits increasingly use medicalized, criminal-legal, and bureaucratic language to describe their work, underscoring the tensions of institutionalization.

Nonprofit organizations are influenced by multiple institutional logics. However, data and methodological limitations have restricted scholars to classifying organizations solely according to activity-based logics and hindered investigation of alternative logics. This article presents a method for measuring organizational identity-a critical component of the multiple logics framework. To illustrate our method, we focus on religious identity. Using dictionary-based text analysis, we analyze Form 990 data to identify nonprofit organizations that share the same activity-based logic (e.g., education, arts, and health care) but operate with different identity-based logics (religious vs. secular). To demonstrate the value of measuring organizational identity at scale, we compare religiously identified organizations with their secular counterparts. Results suggest there are approximately 670,000 religious and religiously identified organizations in the nonprofit sector and illustrate the importance of operationalizing multiple institutional logics for nonprofit research. Extensions include creating additional dictionaries and large-scale measures of other organizational identities, including race/ethnicity, gender, LGBTQIA+, and politics.

This article theorizes and uses global and longitudinal data on gender quota laws to investigate how levels and dimensions of democracy affect the adoption of different quotatypes. Our results demonstrate that countries at middle levels of the democracy scale are more likely to adopt quotas. Within this diverse group of countries, those that have relatively low levels of electoral contestation (i.e., limited political rights) are most likely to adopt reserved seats. On the other hand, the likelihood of adopting candidate quotas is highest in countries where the protection of civil liberties (i.e., individual freedoms of association, etc.) is moderately high. Our findings suggest that different levels and dimensions of democracy provide political actors with incentives and constraints that create distinct trajectories for quota adoption.

The United States is currently in the midst of a long, historic cultural transformation-redefining our collective representation to be inclusive of diverse sexual and gender identities. A core logic advancing this inclusion is to discursively recognize an expanded set of discrete, deconstructed identities-gay and lesbian expands to LGBT, LGBTQ, LGBTQIA+, and so on. But a newer logic stipulates that inclusion arises through using constructive identities that encompass many fluid experiences under a single term (e.g., "queer"). To understand inclusive change, the authors leverage a unique mesolevel site of cultural (re)production: service and advocacy nonprofit organizations. Using event history models, the authors investigate inclusive language change by 735 organizations from 1998 to 2016. They supplement analyses of administrative data with semistructured interviews with 13 nonprofit leaders, providing converging evidence. Findings showcase how bottom-up, horizontal, and top-down pressures explain both the inclusion of discrete identity labels and the shift to constructive logics.

Questions on voluntary association memberships have been used extensively in social scientific research for decades. Researchers generally assume that these respondent self-reports are accurate, but their measurement has never been assessed. Respondent characteristics are known to influence the accuracy of other self-report variables such as self-reported health, voting, or test scores. In this article, we investigate whether measurement error occurs in self-reports of voluntary association memberships. We use the 2004 General Social Survey (GSS) questions on voluntary associations, which include a novel resource: the actual organization names listed by respondents. We find that this widely used voluntary association classification scheme contains significant amounts of measurement error overall, especially within certain categories. Using a multilevel logistic regression, we predict accuracy of response nested within respondents and interviewers. We find that certain respondent characteristics, including some used in research on voluntary associations, influence respondent accuracy. Inaccurate and/or incorrect measurement will affect the statistics and conclusions drawn from the data on voluntary associations.

Looking to supplement common economic indicators, politicians and policymakers are increasingly interested in how to measure and improve the subjective well-being of communities. Theories about nonprofit organizations suggest that they represent a potential policy-amenable lever to increase community subjective well-being. Using longitudinal cross-lagged panel models with IRS and Twitter data, this study explores whether communities with higher numbers of nonprofits per capita exhibit greater subjective well-being in the form of more expressions of positive emotion, engagement, and relationships. We find associations, robust to sample bias concerns, between most types of nonprofit organizations and decreases in negative emotions, negative sentiments about relationships, and disengagement. We also find an association between nonprofit presence and the proportion of words tweeted in a county that indicate engagement. These findings contribute to our theoretical understanding of why nonprofit organizations matter for community-level outcomes and how they should be considered an important public policy lever.

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

Service and advocacy organizations have long struggled to find the appropriate language to name traumatic experiences when working with vulnerable populations. Organizations have been pressed to adopt either “victim”-based language or “survivor”-based language, with both terms seen as having mutually exclusive meanings. However, despite academic and popular debates, no recent studies have documented trends in language used to describe traumatic experiences, whether of sexual and relationship violence, or of experiences of war, disaster, or major illness. In this research note, we use administrative data from the Internal Revenue Service to analyze how 3,756 service and advocacy organizations use trauma-related language between 1998 and 2016. Descriptive analysis shows that survivor language has been on the rise as victim language declined. Victim remains a common way to name trauma, however, and survivor tends to join, rather than displace, victim terminology. Further analysis also points to gendered use of both terms. Victim and survivor are used together most often in organizations that work with trauma experienced by women and in the field of sexual and relationship violence. We suggest these findings indicate a more complex story of how communities of language users emerge, which aligns with recent sociological treatments of discourse.

The United States has long relied on private organizations to provide public services to poor communities. However, while the federal government’s support of the civic sector through grants and contracts is well studied, little research investigates how it subsidizes voluntary organizations through national service programs, such as Volunteers in Service to America (VISTA). In this article, we assess whether nonprofits that receive VISTA members show higher levels of donations and volunteers than matched nonprofits that did not receive VISTA members in the years following the Great Recession. We find that nonprofits that participated in the VISTA program had higher numbers of volunteers 2 years after participation, suggesting that national service was effective at supporting local organizations and building local civic infrastructure during an economic recovery. We also follow VISTA receiving organizations from 2010 to 2016 in a longitudinal design, finding a robust relationship of VISTA service and volunteering. These findings suggest VISTA is a resource for organizations and invite further research on the relationship between national service and anti-poverty work.

The V-Dem Dataset v11.1 covers 202 countries, with a year coverage: 1789-2020 483 V-Dem indicators, 82 indices and 5 high-level indices.

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 V-Dem Dataset V10 covers 202 countries, with a year coverage: 1789-2019. 470+ V-Dem indicators, 82 indices and 5 high-level indices.

Nonprofits offer services to disadvantaged populations, mobilize collective action, and advocate for civil rights. Conducting this work requires significant resources, raising the question: how do nonprofits succeed in increasing donations and volunteers amid widespread competition for these resources? Much research treats nonprofits as cold, rational entities, focusing on overhead, the "price" of donations, and efficiency in programming. We argue that nonprofits attract donors and volunteers by connecting to their emotions. We use newly available administrative IRS 990 e-filer data to analyze 90,000 nonprofit missions from 2012 to 2016. Computational text analysis measures the positive or negative affect of each nonprofit's mission statement. We then link the positive and negative sentiment expressed by nonprofits to their donations and volunteers. We differentiate between the institutional fields of nonprofits-for example, arts, education, social welfare-distinguishing nonprofits focused on social bonding from those focused on social problems. We find that expressed positive emotion is often associated with higher donations and volunteers, especially in bonding fields. But for some types of nonprofits, combining positive sentiment with negative sentiment in a mission statement is most effective in producing volunteers. Auxiliary analyses using experimental and longitudinal designs provide converging evidence that emotional language enhances charitable behavior. Understanding the role of emotion can help nonprofit organizations attract and engage volunteers and donors.

Many nonprofit organizations rely on donations to fund their programs, and a robust literature predicts donations in large-scale quantitative studies. The focus, however, is almost exclusively on the financial characteristics of the organizations, leaving the social context underexplored. In this article, we theorize how ecological context, organizational identity, and social network ties can shape donations. We use the new Internal Revenue Service (IRS) release of e-filed nonprofit reporting forms to consider 95,518 501(c)3 nonprofits around 2015. Using lagged regression models, we find that organizations within a more favorable ecological context, those that use appeals to religion, and organizations with more volunteers report more donations. Furthermore, stressing affiliation with a geographic location is associated with more donations only under certain ecological conditions. The article concludes with a discussion of the implications of these results for nonprofit organizations and social theories regarding what influences donations to organizations.

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Over the last fifty years, gender quotas have transformed the composition of national legislatures worldwide. But a lack of systematic cross-national longitudinal data limits the questions research ...

In the late twentieth century, researchers began calling attention to declining social capital in America and the potential consequences of this trend for a healthy society. While researchers empirically assessed the decline in social capital from the mid-1900s onward, this line of research diminished when the major source of data, the General Social Survey, stopped fielding critical questions in 2004. We do not know, therefore, whether social capital, especially associational social capital, has declined, stabilized, or even increased in a twentyfirst century America. In this paper, we develop a new measure of associational social capital using a confirmatory factor analysis of six indicators from the Civic Engagement Supplement to the Current Population Survey for 2008–2011 and 2013. Our findings support previous research suggesting that associational social capital does not seem to be declining over time. However, we do find evidence of a nonlinear decrease in associating during the Great Recession years. Across the entire time period, though, membership in groups has not declined and there has been little practical change in the amount of time that individuals spend with neighbors. Our analysis of the variance of social capital also shows no general change in the national dispersion of social capital from 2008 to 2013. The paper advances the measurement of social capital and updates our understanding of its possible decline.

The V-Dem Dataset V9 covers 202 countries, with a year coverage: 1789-2018. 450+ V-Dem indicators, 81 indices and 5 high-level indices.