Like marketers, fundraisers are constantly on the hunt for new sources of income. But selling a charity is unlike selling other products because donors are motivated by generosity, while consumers are moved by self-interest. When they buy a product from the supermarket, consumers receive a tangible good. With charity, donors receive a sense of good will.
Like other product marketers, fundraisers try to find the most promising targets. Most fundraisers rely on lists of people who have given to a cause in the past, and as anyone who has given knows, most charities swap mailing lists. A donator to the relief agency CARE may soon find request letters from other international food assistance programs. A second targeting method is to locate large donor areas using a national database of actual donations. Few of these exist because few charities collect nationally. A third way to target likely donors is through nationally compiled marketing models. These models are traditionally based on income and purchasing preferences. The last two methods offer the widest range of fundraising opportunities because they include many millions more potential donors than a mailing list.
The United Way collects data from all its local member organizations on annual donations. While many local chapters do not report data, 161 counties were represented in the Southwest (including California) and Northeast in 1996. Not every United Way in these counties reported contributions, but rough estimates can be made on average household donations to the United Way. Wyoming County, Pennsylvania, is the United Way's hot spot: household donations averaged $380.02, according to these data. In the Southwest, San Francisco topped the list with average donations of $169.68. Washington, DC also ranked high, with donations of $328.65, although donations in this area may be skewed by visitors from other states currently residing in the city. The average household donation for all 161 counties was just $35.56.
CACI, a geodemographic firm, produces a statistic on expected total giving to charity at the county level. For the 161 United Way counties, they note average household donations of $64.73 in 1997, some $29.17 higher than the United Way recorded figure. Even accounting for inflation there is still a healthy difference. The CACI statistic includes all charities, so their higher figure is to be expected. However, some surprises occur when counties are compared amongst themselves. The hot spot for giving according to CACI is Morris, New Jersey, a high-income area, with average household contributions of $117.53. Second and third runners-up are Nassau County, New York, and Somerset, New Jersey, which are both wealthy suburbs of New York City. The top figure in CACI's model of the Southwest is Orange County, California, with donations of $94.42 per household. For the top counties noted by the United Way, CACI expected Wyoming County to donate only $58.47 per household and San Francisco to contribute only $61.51.
The reason for these differences is that CACI's expectations are based on income while the United Way's actual donations are based on income plus what researchers call "social capital." Social capital is the cohesion of a community. It defines generosity, improves economic growth, lowers the crime rate, and keeps families together. Social capital is difficult to measure, although studies by researchers like Robert Putnam and Sidney Verba, both of Harvard University, have explored the subject extensively.
Social capital has also been studied by the Independent Sector, a nonprofit research institute in Washington, D.C., that has sponsored biennial surveys on attitudes toward charities since 1988. Surveys of 1,500 adults a year were conducted by the Gallup Organization, a Princeton, New Jersey polling firm. Gallup has produced a series of reliable predictor variables for giving to charity, including income, education, affiliation with religious organizations, and involvement in the community.
Information from the Gallup polls can be used to select variables for a nationwide analysis at the county level. Data on membership organizations, for example, are available in the 1995 County Business Patterns produced by the Census Bureau. These data show that the annual payroll of county-based membership organizations has a high correlation with United Way data in the 161 counties, but payroll and CACI data have low correlations.
Counties with a high median household income also have high annual giving in the CACI model. Yet there is almost no correlation, between United Way giving and income. This difference betrays the bias of CACI's statistical model, which is partially based on income. Giving to charity is in many ways a social phenomenon. Many people with low household incomes give money to charity because they believe it is the right thing to do.
The gap between demographics and giving shows up clearly when the CACI and United Way data are placed on maps. According to CACI, charitable giving should be high in the affluent counties of (1) Litchfield, Connecticut, (2) Chester, Pennsylvania, and (3) Delaware, Pennsylvania. But these three counties rank near the bottom in giving to the United Way. Yet high giving and high incomes do seem to go together in (4) Fairfield, Connecticut, and the nearby counties of Hartford, Connecticut; and Mercer, Morris, and (5) Somerset, New Jersey. The rule also holds true in Baltimore, Maryland.
Most interesting are the counties where few are wealthy, but giving is high. (6) Apache County, Arizona, is one of America's poorest places, and it is almost completely covered by the Navajo Indian reservation. Yet United Way giving is in the "average-to-high" category. The same pattern holds in (7) Yuba County, California; in (8) Steuben County, New York, the home of Corning, Inc.; and in central Boston (Suffolk County, Massachusetts) and Philadelphia, Pennsylvania.
Do these conclusions mean that fundraisers should use United Way data to decide where to launch a campaign? Not necessarily. These data are incomplete, and they do not measure local preferences for specific charities. When people give to the United Way, they are supporting a large national social service agency. Other counties might rank higher in support for the arts or for education. CACI data cannot measure the social factors we have seen that influence the decision to give to charity, but they do reflect income-and in some situations, income may be the key factor. Until a truly reliable model is developed that allows for good identification of likely fundraising hot spots by type of organization, it may be best to stick to the mailing lists.