Social Policies

The nonprofit sector has become the major provider of mandated human services (Salamon Reference Salamon and Salamon2003), and the social policies from which human service organizations obtain resources may discriminate against racial and ethnic out-groups by undersubscribing benefits to them. Racial and ethnic isolation concentrates the effects of these deprivations in spatially defined neighborhoods (Massey and Denton, Reference Massey and Denton1993). Thus when nonprofit human service organizations attempt to locate in and serve these neighborhoods, their ability to mobilize institutional, political, or economic resources may be challenged, because in serving disadvantaged groups who are the target of discriminatory social policies, they too face a scarce resource environment.

Sometimes these policies are explicitly racialized. Scholars have suggested that widely shared moral valuations of target client groups can influence the policymaking process (Schneider and Ingram, Reference Schneider and Ingram1993; Steensland Reference Steensland, Hitlin and Vaisey2010), resulting in policies that undersubscribe public benefits to groups that are constructed as morally devalued and oversubscribe benefits to highly valued groups. For example, research shows that policy features of redistributive social programs for poor people are less generous and more restrictive when the public believes that the target population is a racial minority (Fording Reference Fording, Schram, Soss and Fording2003; Howard Reference Howard1999; Radcliff and Saiz, Reference Radcliff and Saiz1995; Soss et al., Reference Soss, Schram, Vartanian and O’Brien2001).

Yet even social policies that do not explicitly target racial and ethnic out-groups for disinvestment may deprive minority neighborhoods of resources. For example, William J. Wilson (Reference Wilson2009) notes that the conservative fiscal policies of Ronald Reagan and George W. Bush, while they were not overtly racial, resulted in drastic cuts to federal aid for social programs on which poor and minority groups depend. These cuts had a greater impact in metropolitan areas whose populations had become predominately minority. Since nonprofit human services are highly dependent on public resources (Boris et al., Reference Boris, Leon, Roeger and Nikolova2010; Salamon Reference Salamon and Clotfelter1992), cuts in public aid may increase their risk of disbanding.

Spatial Isolation

As a form of structural racism, spatial isolation is thought to enable dominant groups to deprive out-groups of resources by strategically disinvesting in the neighborhoods in which they live (Alba and Logan, Reference Alba and Logan1993; Alba et al., Reference Alba, Logan and Stults2000; Kasarda Reference Kasarda1989; Logan et al., Reference Logan, Alba, McNulty and Fisher1996; Logan and Molotch, Reference Logan and Molotch1987; Massey and Denton, Reference Massey and Denton1993; Peterson and Krivo, Reference Peterson and Krivo2010; Sharkey Reference Sharkey2013; Squires and Kubrin, Reference Squires and Kubrin2005; Tilly Reference Tilly1998; Vélez Reference Vélez, Peterson, Krivo and Hagan2006). Racial and ethnic isolation influences discriminatory allocation patterns by: (1) increasing the salience of racial and ethnic categories, and (2) facilitating the disinvestment of out-groups.

First, the confluence of spatial borders and racial categories raises the salience of the boundaries between races by increasing the social distance between racial categories, or the awareness of difference or nearness experienced between categories on each side of the racial divide (Fox and Guglielmo, Reference Fox and Guglielmo2012; Lamont and Molnar Reference Lamont and Molnar2002; Tilly Reference Tilly1998). Research has demonstrated that greater racial isolation at the block or census tract level increases White hostility toward racial out-groups, presumably because it reduces direct inter-group contact which would challenge Whites’ negative stereotypes about out-groups (Baybeck Reference Baybeck2006; Oliver and Wong, Reference Oliver and Wong2003; Rocha and Espino, Reference Rocha and Espino2009). It may also lead to more prejudicial policy preferences (Kinder and Mendelberg, Reference Kinder and Mendelberg1995). Second, racial and ethnic isolation enables the disinvestment of out-groups by allowing dominant groups to channel resources away from the neighborhoods in which the out-groups reside (Massey Reference Massey2007; Wimmer Reference Wimmer2008).

Discriminatory Resource Allocation

The institutional perspective on organizations complements the model of structural racism and racial segregation by proposing that legitimacy is the key to organizational survival for highly institutionalized organizations such as nonprofit human services (DiMaggio and Powell, Reference DiMaggio and Powell1983; Meyer and Rowan, Reference Meyer and Rowan1977). It argues that the ability of the organization to mobilize resources and survive depends on the legitimacy it can garner from key institutional audiences, such as institutional funders and the general public (DiMaggio and Powell, Reference DiMaggio and Powell1983; Meyer and Rowan, Reference Meyer and Rowan1977). As defined by Suchman (Reference Suchman1995), legitimacy is “a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions…” (p. 574). Location in neighborhoods heavily populated by out-groups may undermine organizational legitimacy with key institutional audiences, and as a result may compromise access to needed resources.

Some research suggests that public officials can be discriminatory in the spatial distribution of government benefits to nonprofit human service organizations that locate in neighborhoods of color. Eve Garrow (Reference Garrow2014) shows that greater neighborhood poverty increases the likelihood that nonprofit human service organizations will receive government funding. However, in neighborhoods with a high percentage of Black residents, greater poverty reduces the likelihood that the organization will receive government funding. Jennifer Wolch and colleagues (2005) find that human service organizations in minority neighborhoods have less access to government funding than organizations in White neighborhoods. In an analysis of Community Development Block Grant data in the San Francisco Bay area, Els De Graauw and colleagues (2013) show how government funding to nonprofit organizations that serve and represent immigrant groups depended on regional differences in how local government officials socially constructed those groups. They found that elected officials in suburban immigrant destinations were less likely than officials in the urban core to fund the organizations that served foreign-born residents, concluding that “a key obstacle for immigrants residing in municipalities outside traditional gateway cities is that they are not viewed as legitimate interlocutors and civic partners by city decision makers” (p. 117).

Widely shared attitudes about target groups also shape the giving patterns of institutional audiences such as the general public and corporations. Julian Wolpert (Reference Wolpert1993) demonstrated that higher rates of charitable donations were positively correlated with per capita income and with giving that was directed toward services used by the donors rather than toward more charitable social services that targeted out-groups and the poor. He concluded that residential segregation separated donor capacity from geographic centers of need, leading to highly regressive effects at the neighborhood level. And although no research has examined the relationship between residential segregation and corporate giving, evidence suggests that corporations may avoid controversial issues that might cause negative publicity and the loss of customers (Froelich Reference Froelich1999; Powell and Friedkin, Reference Powell, Friedkin and DiMaggio1986; Useem Reference Useem and Powell1987). It follows that they may be less likely to fund nonprofit human services that locate in devalued neighborhoods. The cumulative effects of these forms of disinvestment may deprive nonprofit human service organizations of key resources needed for survival.

The history of the “Weed and Seed” program in the Los Angeles area provides an example of how widely shared negative attitudes held by stakeholder groups about minority neighborhoods can divert critical organizational resources such as legitimacy and funding away from the nonprofit human services in the neighborhoods, even when the neighborhoods are targeted for support. The program, which was initiated by the Bush administration in response to the Los Angeles civil unrest of 1992, aimed to root out the criminal element in poor, minority neighborhoods affected by the riots (“weed”) and fund social services, which had been severely neglected (“seed”). When it became apparent that the criminal justice element would be prioritized over social service funding, local community groups, leaders, and the city council members representing the targeted areas argued that the implementation would be “both racist and repressive because the program would impose a warfare approach to the resolution of the pressing problems confronting the residents of the target communities” (Johnson et al., Reference Johnson, Farrell and Jackson1994, p. 22). The project was renamed, and control was handed over to local government with community input. Yet community leaders worried that funding patterns would continue to exacerbate existing disparities. According to James Johnson and colleagues (1994), local community leaders were still suspicious of the local city council that had not, in recent decades, “generated much assistance for the residents of the target communities. Instead, the power of the pocketbook...seemingly has dictated the allocation of resources in the city during this period” (p. 22).

Combining a structural view on race and the institutional perspective on organizations, I propose that the survival prospects of nonprofit human service organizations are influenced by a racial hierarchy that privileges organizations in White neighborhoods at the same time that it reinforces the disadvantaged status of organizations in neighborhoods occupied by racial and ethnic minorities (Bobo Reference Bobo, Krysan and Lewis2004; Bonilla-Silva Reference Bonilla-Silva2001; Feagin Reference Feagin2010; Marable Reference Marable, Krysan and Lewis2004). I examine the relationship between organizational disbanding and the percentage of Blacks, Latinos, and Whites—three of the most populous racial and ethnic groups in Los Angeles County—in the neighborhood.^{Footnote 2}

Together, these groups comprised about 85% of the total population in the county in 2000.^{Footnote 3} In general, Whites are positioned at the top of the ethnoracial hierarchy. Blacks, who have experienced a unique history of oppression and discrimination in the U.S. context, are at the bottom, and Latinos fall somewhere in between (Bobo and Hutchings, Reference Bobo and Hutchings1996; Link and Oldendick, Reference Link and Oldendick1996). However, ethnoracial hierarchies vary across time and space (Gullickson Reference Gullickson2010; Wimmer Reference Wimmer2008), and may be different in Los Angeles County than in other areas.

Therefore, to determine the relative ethnoracial positioning of Blacks, Latinos, and Whites in Los Angeles County, I assess whether the nominal racial distinctions used by the census correspond to meaningful social differences in the lives of residents. In particular, I focus on ethnoracial differences in socioeconomic status, marginality (i.e., the extent to which groups are excluded from socioeconomic institutions and statuses that signify social inclusion), and political participation and representation (Soss and Bruch, Reference Soss and Bruch2008). A comparison along these dimensions suggests that in 2000, Whites in Los Angeles County were positioned at the top of the ethnoracial hierarchy, while the relative disadvantage of Blacks and Latinos varied across measures (results are available on request). For example, of the three groups, Whites had the highest median income, the lowest poverty rate (socioeconomic status), the lowest unemployment rate, and the highest educational attainment (marginality). Latinos fared better than Blacks on some indicators (median income, unemployment) and the same or worse on others (poverty rate, educational attainment) (U.S. Bureau of the Census 2000). Whites also appeared to have an advantage in terms of political participation. For example, Blacks and Whites registered to vote at greater rates than Latinos, while Whites turned out to vote at higher rates (50%) than Blacks and Latinos (40%) (Los Angeles Urban League and United Way of Greater Los Angeles 2005).

I expect that the ethnoracial hierarchy in Los Angeles County will be replicated in patterns of disbanding. Therefore, organizations that locate in predominately Black or Latino neighborhoods should experience an increased risk of disbanding. Conversely, organizations that locate in neighborhoods containing high percentages of Whites should experience a reduced risk of disbanding.

Taking a perspective that those ethnic communities with potentially homogenous interests could provide a cultural, social, and political foundation for the development of neighborhood human services (Iglehart and Becerra, Reference Iglehart and Becerra2011; Jenkins Reference Jenkins1988), an alternative hypothesis is that location in places with high levels of minority-group concentration lowers the risk of organizational disbanding. For example, in response to the systematic exclusion of Blacks from mainstream social services, a system of Progressive Era charity organizations arose to provide for social service needs and to promote racial equality in Black neighborhoods (Gordon Reference Gordon1991; Jackson Reference Jackson1978; O’Donnell Reference O’Donnell2001). Scholars have noted that the concentration of minorities can signal homogenous demand to which the nonprofit sector may respond with more services (Bielefeld et al., Reference Bielefeld, Murdoch and Waddell1997). Minority neighborhoods could also benefit from co-ethnic social networks, voting blocs and political coalitions that can translate service needs into resources (Barreto et al., Reference Barreto, Segura and Woods2004; Barreto et al., Reference Barreto, Villarreal and Woods2005).

Disbanding

In the spring of 2011, the study team followed up the organizations surveyed in 2002 to ascertain whether they were active or had disbanded. An organization was determined to have disbanded when a knowledgeable informant or other source (e.g., information obtained from the Internet) verified that the organization was no longer active. Ninety-three organizations fit this description. The outcome is a dichotomous variable that indicates whether the organization had disbanded as of the spring of 2011, coded 0 for organizations that were active and 1 for organizations that had disbanded.

Independent Variables

The unit of analysis for neighborhood-level variables is the census tract. Los Angeles County is multiethnic, and mixing is greater at the levels of zip codes, communities, and cities than in census tracts and blocks (Clark Reference Clark, Waldinger and Bozorgmehr1996). Therefore, using a unit of analysis larger than the census tract would mask much of the striking racial homogeneity at the neighborhood level. In addition, evidence suggests that service utilization is localized. Rebecca Kissane’s (Reference Kissane2010) ethnographic study of women living in a high-poverty neighborhood in Philadelphia demonstrates that geographic distance (sometimes even one mile was deemed too far to travel to obtain services) reduced service utilization outside of the local neighborhood. Quantitative analyses also demonstrate that service utilization is localized (Allard et al., Reference Allard, Tolman and Rosen2003) and deteriorates beyond a one-mile radius (Bielefeld et al., Reference Bielefeld, Murdoch and Waddell1997). Census tract location was ascertained by asking the respondent for the address of the organization’s service location in 2001.^{Footnote 7} Census variables are based on 2000 census data. Percent Black in the neighborhood is constructed from percent Black. Percent Latino in the neighborhood is constructed from percent Hispanic. Percent White in the neighborhood is constructed from percent White. Non-Hispanic data are used to prevent double-counting.

Control Variables

I control for alternative explanations of disbanding to factor out their effects. Organizational legitimacy enhances access to resources, which in turn promotes organizational survival (Aldrich and Auster, Reference Aldrich and Auster1986). Location in high-poverty neighborhoods can increase the legitimacy of nonprofit human service organizations by signaling that they have a commitment to improving the life conditions of the local residents. Government funders in particular may be more willing to fund nonprofits that locate in high-poverty neighborhoods because allocation rules for human services typically consider the degree of poverty in the organization’s location (Salamon Reference Salamon and Clotfelter1992). Conversely, some scholars have argued that greater poverty may result in insufficient local investment to support a vibrant sector, which could lower organizational life chances (Wilson Reference Wilson1987; Wolpert Reference Wolpert1993). The variable for poverty is measured by the poverty rate in the organization’s location based on 2000 census data on “people in poverty,” normalized by the number of people for whom they could ascertain poverty status. The poverty rate is measured as a continuous variable (0–100%).

Because residential segregation by race may concentrate the greater economic deprivation and other disadvantages found among minorities in spatially defined communities (Massey and Denton, Reference Massey and Denton1993), residents are constrained in their capacity to support local organizations through fee-for-service. Such constraint may increase the risk of disbanding. A variable for percent fee income is constructed from a response to an item that asks how much of the organization’s total revenue came from fees-for-service in the most recent fiscal year. Data collected from the organizations’ IRS 990 tax forms was used to replace missing data.

Endorsements of key institutional actors such as government funders are thought to enhance organizational survival by providing material resources and increasing the legitimacy of the organization (Baum and Oliver, Reference Baum and Oliver1991; Wiewel and Hunter, Reference Wiewel and Hunter1985). A variable for any government funding is constructed from a response to two items that ask how much of the organization’s revenue comes from government grants and contracts and how much comes from government reimbursements in the previous fiscal year. The sum of these two responses is used to create a dichotomous variable indicating receipt of any government funding in the most recent fiscal year, coded 0 for organizations that received no government funding and 1 for organizations that received any government funding. When available, I replace missing data with revenue data collected from the organizations’ IRS 990 tax forms.

Organizational ecologists point to smallness as an explanation for disbanding (Hannan and Freeman, Reference Hannan and Freeman1989). Larger organizations may be less likely to fail because their size signals success and they are therefore viewed as more legitimate than smaller organizations. Larger organizations may also have more slack resources and can more easily raise capital than smaller organizations (Aldrich and Auster, Reference Aldrich and Auster1986). Organizational size is measured as total expenditures during the preceding fiscal year. Respondents were asked to report the total expenditures of the organization. Expenditure data collected from the organizations’ IRS 990 tax forms were used to replace missing data. Because the data are skewed, I created five dummy variables for the following expenditure categories: (1) less than 100,000; (2) 100,000–500,000; (3) 500,000–1 million; (4) 1 million–5 million; and (5) over 5 million.

The “liability of newness” thesis (Hannan and Freeman, Reference Hannan and Freeman1989, p. 81) argues that young organizations lack the exchange relationships and legitimacy required to maximize survival chances and are thus more likely to fail than older, established organizations. The organization’s age is computed by subtracting the year of founding from 2002.

Many ecological studies on organizational populations validate the idea that greater competition for resources leads to an increased likelihood of organizational disbanding (Baum and Oliver, Reference Baum and Oliver1991; Hannan and Carroll, Reference Hannan and Carroll1992). In their study of hospitals, for example, Renee Hsia and colleagues (2011) found that location in a competitive market was positively related to closure. A competition variable is constructed from the following items—How much competition does your organization experience from other nonprofit organizations: a) getting financial resources?; b) recruiting staff?; and c) attracting clients? Responses to all competition questions are coded on a scale with 0 indicating ‘‘none,’’ 1 ‘‘some,’’ 2 ‘‘a fair amount,’’ and 3 ‘‘a great deal.’’ The results of a factor analysis warrant the inclusion of all three items in the scale (alpha 5 .72).^{Footnote 8} Values range from 0, indicating no competition from nonprofit organizations on all three dimensions, to 9, indicating ‘‘a great deal’’ of competition from nonprofit organizations on all three dimensions.

Advocacy can enhance the legitimacy of organizations and their worthiness of support in the eyes of policymakers and institutional funders, increasing survival chances. Nicole Marwell (Reference Marwell2004) showed how community-based organizations engage their local politicians to ensure continued fiscal support, and Eve Garrow (Reference Garrow2011, Reference Garrow2014) found that advocacy increases the likelihood that nonprofit human services will acquire government funding. Advocacy is a dichotomous variable, scored 0 for those who answer “no” to the question, “Is your organization actively involved in advocating or promoting certain policy issues, or the interests of a certain group or groups?,” and 1 for those who answer “yes” to this question.

Some research suggests that the distribution of organizational resources is influenced by the political influence of neighborhoods (Marwell Reference Marwell2004, Reference Marwell2007). Minority neighborhoods with strong political participation and influence may be able to obtain more organizational resources than politically disengaged neighborhoods. To test for this possibility, I include a measure of voter turnout at the census tract level. Voter turnout is the percentage of the population in the census tract that voted in the 2002 general election (Statewide Database 2002). Interaction terms are created by dichotomizing the variable at the median and multiplying it by the variables for the percentage of Blacks and the percentage of Latinos in the census tract to test the possibility that increased political participation in the census tract reduces the positive association between minority concentration and the risk of organizational disbanding.

The use of institutionalized management tools can enhance organizational legitimacy and increase survival prospects because they are interpreted as an indication of strong management and leadership (Brody Reference Brody2005). Martin Ruef and W. Scott (Reference Ruef and Scott1998) demonstrated that the use of highly institutionalized management practices improved the survival chances among a sample of hospitals. A scale for the use of management tools is constructed from the following five items—In the last three years, has your organization: a) undertaken a market analysis?; b) developed a strategic plan?; c) implemented a program evaluation system?; d) reorganized your administrative or management structure?; and e) implemented a new fiscal or cost control system? Responses are coded 1 for “yes” and 0 for “no” and are aggregated into a scale that ranges from 0 to 5 (alpha=5.65).

I also control for the effect of leadership stability and experience on the chances of disbanding by including a variable for executive turnover. The variable is constructed from a response to the question, “In the past three years how many different executive directors or CEOs has your organization had?”

The matching of services to residents’ needs could enhance survival prospects by increasing the organization’s legitimacy and relevance within the community context. To control for service area, I include a series of dummy variables based on the National Taxonomy of Exempt Entities (NTEE) codes, as indicated by the National Center for Charitable Statistics on data compiled from the IRS Form 990. I collapsed the NTEE codes into the following service areas: child care clinical services, crime and legal services, individual assistance, youth development, basic needs services, special needs services, and advocacy (See Table 1A in the Appendix for full descriptions of the service areas). Because nonprofit human service organizations often operate in multiple service areas, the categories used in this analysis represent the organization’s service area that receives the greatest share of expense allocations.

Los Angeles County has experienced a considerable influx of immigrants (particularly Latinos) in recent decades (Clark Reference Clark, Waldinger and Bozorgmehr1996). Ecological theory suggests that organizations adapt their services to the attributes of local residents and may experience structural inertia when those attributes change, creating a mismatch between neighborhood needs or preferences and organizational services (Hannan and Freeman, Reference Hannan and Freeman1989). Organizations that respond to the needs of new populations—for example by providing services in multiple languages—should increase their legitimacy in the neighborhood and, in turn, their survival prospects. To control for this possibility, I include a dichotomous measure to the question, “Does your organization provide services in any language other than English?,” scored 0 for organizations that answer “no” and 1 for those who answer “yes” to the question.

Population density is a potential indicator of demand. Greater density in the organization’s location may translate to more demand for services and greater survival prospects. In addition, densely populated urban cores have longer histories of community development and organizational infrastructure from which nonprofits may benefit (De Graauw et al., Reference De Graauw, Gleeson and Bloemraad2013). The measure uses 2000 Census data to calculate the number of persons per square mile.

Descriptive statistics and pairwise correlations for all variables are shown in Tables 1 and 2. Table 1 indicates that about 14% of the organizations sampled in 2002 disbanded by 2011.

Table 2.

Pairwise Correlations for All Continuous Variables

Analytic Technique

I use logistic regression with survey estimation procedures to correct for stratification of the sample. The nested structure of the data (organizations within census tracts) is treated as ignorable.^{Footnote 9} Missing data range from 0% for the dependent variable to 14% for the fees variable, with an average percentage for the independent variables of 3.8% missing. Using the multivariate normal method, I imputed all variables with missing values, and included all of the variables in the dataset in the imputation model to maximize the amount of information available to estimate missing values. To impute the missing data, I created five multiple imputation datasets using STATA 12 for a sample size of 655.^{Footnote 10}