Correlates of Volunteerism and Charitable Giving in the 50 United States

Cities have been described as environments where there is sensory overload (Milgram, 1970). One way urbanites adapt is to spend less time on each input. Consequently, people who live in cities have reduced moral and social involvement with others. One implication of this proposition is that people will be less helpful in urban than in rural areas. Steblay's (1987) meta-analytic study of the research literature on this proposition supports it. Specifically, Steblay's analysis of the 52 tests of the urban-rural differences in terms of where the helping occurred found that people were less helpful in an urban than a rural environment. One reason why urbanites have high sensory overload is high population density. In an examination of the relationship between population density and helping, Levine, Martinez, Brase, and Sorenson (1994) examined six types of helping across thirty-six cities in the United States. In general, they found that people living in denser cities were less helpful. However, this effect occurred on only three of their six measures of helping. Specifically density was negatively correlated with picking up a dropped pen, helping someone with a hurt leg, and making change for a quarter. Density was not correlated with helping a blind person cross the street, mailing a lost letter, and giving to United Way. Levine et al. concluded that urban-rural differences are more likely to occur for spontaneous than planned helping, though this delineation is not clear-cut, as they acknowledged. The present study also examines the correlation between population density and helping but examines these variables in the 50 United States. To the author's knowledge no previous research has examined the relationships at the state level. Furthermore, helping behavior is operationalized in terms of volunteerism and charitable giving. As previously stated, Levine et al. found a negative but not statistically significant correlation between population density in a city and charitable giving. They did not include volunteerism. Although both exemplify helping behavior, volunteerism is giving of one's time, whereas charitable giving is giving of one's monetary resources. Both volunteerism and charitable giving exemplify planned, long-term giving. Research on this topic often focuses on whom and why people volunteer and/or give to charity (Schroeder, Penner, Dovidio, & Piliavin, 1995). For example, Schroeder et al. indicated that people who have more financial resources are more likely to donate their time and money. Levine et al. also demonstrated that economic indicators were correlated with charitable giving. The foregoing might suggest that people who volunteer also give. This may not always be the case. For example, a national survey indicated that although 42% of the adults surveyed gave and volunteered, 46% only gave, 2% only volunteered, and 10% did neither (Independent Sector, 2001). As DeVoe and Pfeffer (2010) demonstrated, people sometimes prefer to give their money rather than their time (i.e., volunteer). Indeed volunteerism and charitable giving may not be significantly positively correlated at the level of the 50 United States (Whitehead & Kitzrow, 2012). If volunteerism is more time dependent than charitable giving, then population density may impact volunteerism more than charitable giving. As Milgram proposed, people in denser, overloaded situations spend less time on each input. On this basis the study is designed to test the following hypotheses. First, population density and volunteerism will be negatively correlated. Second, because charitable giving is the giving of one's monetary resources, it is more likely to correlate with variables related to income. Therefore, charitable giving will be correlated with individual income tax rates and median household income. METHOD To examine the relationship between population density, volunteerism, and charitable giving, individual income tax rates, and median household income of residents in the 50 United States, archival data were used. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.