Yongzheng Yang’s research while affiliated with Renmin University of China and other places

In contexts with high-income inequality, are people more or less inclined to support those in need through philanthropy? Using data from the 2016 wave of the China Labor-Force Dynamics Study, this study examines (i) how income inequality is associated with charitable giving to high-redistributive (e.g., poverty alleviation) and low-redistributive (e.g., arts and culture) causes and (ii) the household and contextual level variation for this relationship. We conduct this study in the unique context of China, where the Communist Party and Chinese government actively encourage charitable giving to play a role in diminishing income inequality. Empirical results show that in China, income inequality has no significant relationship with charitable giving to high-redistributive causes. We do find some support for a small moderating effect of education. Those with a higher level of education living in provinces with higher income inequality give more to high-distributive causes. We also find support for a small negative association with charitable giving to low-redistributive causes, which indicates that people in a context with higher income inequality are less inclined, although not substantially, to give to low-redistributive causes.

This practice paper articulates the key learnings for philanthropic organizations based on their experiences of the COVID‐19 pandemic. Which actions can philanthropic organizations take to best support community needs during times of crisis? To answer this question, we synthesize information about how philanthropic organizations responded during the early COVID‐19 crisis (spring―fall 2020) across 11 countries: Australia, Austria, Finland, Germany, Iceland, Israel, Norway, Sweden, the Republic of Korea, the Russian Federation, and the United States of America. Results indicate four key actions that we recommend philanthropic organizations take during times of crisis: (1) Assess community needs; (2) engage with volunteers and donors; (3) communicate effectively and strategically with volunteers, donors, and the public; and (4) focus on equity.

There has been a steady increase in research studying the role of gender in prosocial behavior, such as charitable giving and volunteering. We provide an extensive review of the interdisciplinary literature and derive hypotheses about three different pathways that lead men and women to differ in their display of giving and volunteering: pathways through social capital, motivations, and resources. We test these hypotheses across 19 countries by analyzing 28,410 individuals, using generalized structural equation models. Our results support previous research, conducted in single countries, that there are distinct different pathways that lead men and women to engage in giving and volunteering: Women report stronger motivations to help others, but men report more of the financial resources that make giving and volunteering possible. The gendered pathways to giving and volunteering that lead through social capital, educational achievement, and financial security vary by country.

How can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods from a research design perspective and highlights key applications in our field. We define CSS as a set of computationally intensive empirical methods for data management, concept representation, data analysis, and visualization. What makes the computational methods “social” is that the purpose of using these methods is to serve quantitative, qualitative, and mixed-methods social science research, such that theorization can have a solid ground. We illustrate the promise of CSS in our field by using it to construct the largest and most comprehensive database of scholarly references in our field, the Knowledge Infrastructure of Nonprofit and Philanthropic Studies (KINPS). Furthermore, we show that through the application of CSS in constructing and analyzing KINPS, we can better understand and facilitate the intellectual growth of our field. We conclude the article with cautions for using CSS and suggestions for future studies implementing CSS and KINPS.

Charitable giving is often seen as voluntary prosocial behavior. However, compulsory donations are not uncommon in countries such as China. This paper explores this understudied phenomenon by examining the impact of compulsory donations on voluntary and total charitable giving. Building upon the previous literature from multiple perspectives and using panel data from the China Labor-force Dynamics Survey, this study proposes an integrated framework to explain how compulsory donations affect voluntary and total giving and empirically tests the possible impact. Results from random-effects logistic regressions and tobit models demonstrate the dual role of compulsory donations in China. On the one hand, making compulsory donations crowds out voluntary charitable giving, while on the other hand, the practice increases total charitable giving because of its imperfect crowding-out effect on voluntary giving.

Political ideology not only influences political activities, but also apolitical fields such as charitable giving. However, empirical studies regarding political ideology and charitable giving have yielded mixed results. To find out the effect size and explain the variation in effect sizes, we deploy a meta-analysis to estimate the average effect size and examine the potential moderators from four perspectives. Following scientific data collection and coding procedures, we identify 421 effect sizes from 31 empirical studies. Our meta-analysis results suggest that political conservatives are significantly more charitable than liberals at an overall level, but the relationship between political ideology and charitable giving varies under different scenarios. Furthermore, meta-regression results indicate that the measure of charitable giving, the type of charitable giving, and controlling for religiosity can account for the variation in effect sizes.

In this paper, we examine whether and how the institutional context matters when understanding individuals’ giving to philanthropic organizations. We posit that both the individuals’ propensity to give and the amounts given are higher in countries with a stronger institutional context for philanthropy. We examine key factors of formal and informal institutional contexts for philanthropy at both the organizational and societal levels, including regulatory and legislative frameworks, professional standards, and social practices. Our results show that while aggregate levels of giving are higher in countries with stronger institutionalization, multi-level analyses of 118,788 individuals in 19 countries show limited support for the hypothesized relationships between institutional context and philanthropy. The findings suggest the need for better comparative data to understand the complex and dynamic influences of institutional contexts on charitable giving. This, in turn, would support the development of evidence-based practices and policies in the field of global philanthropy.

Public policy can directly or indirectly affect private philanthropy. Although previous studies have investigated the role of tax incentives and government grants to nonprofits, scholars do not pay much attention to how public welfare receipt affects philanthropic behavior. This study fills the gap by examining the impact of public assistance use on individual charitable giving using data from the USA and China. We employ propensity score matching to reduce the issue of selection bias and adopt logistic regression and the Tobit model to answer our research question. Our analysis demonstrates the different impacts of public assistance use on charitable giving in the two countries. In the USA, public assistance income is negatively associated with secular giving, and prior public assistance use is negatively related to religious and total giving. However, in China, there is no statistically significant relationship between public assistance use and charitable giving.

How can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods, and highlights key applications in our field. Based on a typical design of empirical social science research, we define CSS as a set of computationally intensive empirical methods for data organization, concept representation, data analysis, and visualization. What makes the computational methods “social” is that the purpose of using these methods is to serve empirical social science research, such that theorization can have a solid ground. We illustrate the promise of CSS in our field by using it to construct the largest and most comprehensive database of scholarly references in our field so far, the Knowledge Infrastructure of Nonprofit and Philanthropic Studies (KINPS). Furthermore, we show that through the application of CSS in the analyses of the KINPS, our field’s knowledge and knowledge producing activities can be advanced, which is a core requisite for the development of our field as a discipline. We conclude the article with cautions for using CSS and suggestions for future research directions implementing CSS and the KINPS.