Operationalizing the Salience of Race to State Social Policy: A Comparison of Approaches with Application to TANF

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https://doi.org/10.1007/s42972-021-00028-z ORIGINAL PAPER

Operationalizing the Salience of Race to State Social Policy:

A Comparison of Approaches with Application to TANF

Vincent A. Fusaro¹

Accepted: 9 May 2021

Abstract

The USA’s system of dual federalism affords states substantial discretion over the design and implementation of important social welfare programs. Social theory pos- its causal mechanisms through which the politics of race and racism might influence state policy, and a substantial body of empirical scholarship links the salience of race in state politics to policy design. There is no single accepted method for operationalizing the salience of race to policy and policymaking in quantitative studies, however. How do different measures relate, and what are the implications for analysis? I compare multiple possible variables for measuring racial salience in state policy, including measures of population and social program demographics and measures of White racial attitudes. Attitudinal measures are constructed using both disaggregation and multi-level regression and post-stratification. I consider their convergent and discriminant validity through correlations and use them as predictors in models of state Temporary Assistance for Needy Families policy. The predictors generally, though not exclusively, demonstrate high convergent validity and lead to similar inferences in empirical modeling. The high convergence of most measures means studies of the relationship between race and policy at the state level will often lead to similar conclusions regardless of method used to operationalize racial salience. By extension, however, it is difficult to evaluate theories regarding underlying causal mechanisms.

Keywords Temporary assistance for needy families · Welfare policy · Race and policy · State politics

* Vincent A. Fusaro fusarov@bc.edu

1 Boston College School of Social Work, Chestnut Hill, Boston, MA, USA / Published online: 12 June 2021

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Introduction

States play key roles in many aspects of social policy in the United States. Decentraliza- tion of policymaking authority has a number of benefits, including facilitation of policy learning and more efficient solutions to complex problems (Kollman et al., 2000; Shipan & Volden, 2012). There are concerning aspects to policy devolution, however, such as the threat of a “race to the bottom” in welfare benefits generosity and accessibility (Brueckner, 2000). State control over policy can also reflect racial disparities. Even absent a causal link, the uneven geographic distribution of racial and ethnic groups across the USA means that any variation in state policy can lead to differential treatment and policy effects that follow a racialized pattern. In at least some policy areas, such as cash welfare, states in which race is a salient aspect of policymaking tend to adopt less generous and more punitive approaches compared to other states (Fellowes & Rowe, 2004; Fusaro, 2020; Soss et al., 2001, 2011).

It remains important for researchers to identify these patterns and explain their underlying mechanisms. To the latter end, scholars have advanced theories link- ing the salience of race and racism to policymaking (Alesina et al., 2001; Gilens, 1999; Lee & Roemer, 2006; Soss et al., 2011). Empirically testing claims about the relationship between racial and ethnic politics and policy at the state level, however, rests on measurement of the salience of race to state policymaking and policy implementation. Researchers have used a number of methods to operationalize racial salience in quantitative studies of state policy. A common approach to measurement is simple use of racial and ethnic demographics, such as the proportion of the state population (Rodgers & Tedin, 2006; Rodgers et al., 2008) or the relevant program caseload (Bentele & Nicoli, 2012; Fellowes & Rowe, 2004;

Soss et al., 2001) composed of different identity groups. Others have used state demographics as the foundation for indices of diversity (Hero & Tolbert, 1996).

Still others, following public opinion scholarship indicating a link between racial attitudes and policy attitudes among Whites (Gilens, 1999), have used measures of prevailing White racial attitudes (Brace et al., 2002; Fusaro, 2020; Highton, 2011; Johnson, 2001; Percival, 2009). How, though, do these measures relate to one another?

Different social theories point to different avenues by which the politics of race and racism might influence decision-making in seemingly non-race-related policy domains. By extension, analysts attempting to test a particular theory of race and racism and policy design can choose the measure that best operationalizes that theory.

Assessing these theories to determine which provides a stronger explanation for observed phenomena, however, requires differentiation in the measures–discriminant validity. If different measures are operationalizing distinct concepts, they should not be strongly correlated. On the other hand, if they are closely correlated, it is evidence of high convergent validity, or measurement of the same or very similar constructs (Carlson & Herdman, 2012). In the case of high convergent validity, testing theories against one another to refine identification of causal mechanisms is difficult, but different measures might be useful as robustness checks.

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In this paper, I compare several measures of the salience of race with respect to Blacks to politics and policy in the states. First, I examine their correlations to consider how closely the measures are related—an assessment of convergent and discriminant validity. I then apply each variable to an analysis of three aspects of state Temporary Assistance for Needy Families cash assistance policy (TANF;

“welfare”)—coverage, generosity, and presence of a family cap—to consider the research implications of the interrelationships of the measures. TANF makes a useful illustrative case because it varies considerably across states and has often been found to follow a racialized pattern of design and implementation (Bentele

& Nicoli, 2012; Fellowes & Rowe, 2004; Soss et al., 2001, 2011). The tested measures include demographic and attitudinal items. The latter measures come from three sources: variables developed by other researchers, originally constructed from survey data using disaggregation (a large cross-tabulation by state), and originally constructed using multi-level regression with post-stratification (MRP;

a model-based procedure) (Lax & Phillips, 2009b). In general, I find that the measures are highly correlated and lead to similar, although not identical, inferences as predictor variables.

Background

Race and American Social Policy

The politics of race and racism profoundly influenced the development of America’s welfare state. The nation’s earliest forays into social welfare—state “mother’s pen- sion” programs for low-income, primarily widowed mothers—included both explicit and implicit barriers to participation by Black families (Skocpol, 1992; Ward, 2005).

Scholars have often charged that the Social Security Act of 1935 (SSA), the foundation of the modern American welfare state, was shaped by racism, as support- ers were forced to make concessions to racially conservative but highly influential Southern legislators that resulted in exclusion of Blacks from many of the Act’s pro- tections (Lieberman, 1995; Quadagno, 1994). While this view has been contested, particularly with regard to the social insurance components of the SSA (Beland, 2005; Davies & Derthick, 1997; Rodems & Shaefer, 2016), excluding agricultural and domestic workers nonetheless had racialized effects due to the disproportion- ate presence of Blacks in these jobs. Schickler (2013), using data from some of the earliest known public opinion surveys in the USA, finds that a link between support for racially liberal policies and economically interventionist policies emerged in the general public during the New Deal era. The politics of race and the politics of social welfare, then, were intertwined from the formative years of the American welfare state.

Social theory offers insights into the ways the politics of race might continue to influence social welfare in the USA. Some analysts have concluded the overrepresentation of people of color among the socioeconomically disadvantaged makes

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racism a key explanation for the USA’s unusual welfare state relative to other advanced democracies.¹ In this view, members of the racial majority both have greater policy influence than minorities and disfavor redistribution because racial and ethnic minorities are the perceived beneficiaries (Alesina et al., 2001; Lee &

Roemer, 2006). This process could operate both by affecting policy preferences generally and by leading White voters who might associate with a left-leaning party to instead support a right-leaning party (Lee & Roemer, 2006).

More nuanced theories of policy racialization have also been proposed. Soss et al.’s (2011) racial classification model (RCM), for example, draws upon research in psychology, political science, and other disciplines to understand when policy may come to follow a race-oriented pattern of design and implementation. The RCM holds that policymakers at all levels, whether street-level bureaucrats or national elected officials, require decision-making shortcuts. Racial stereotypes, when race is a relevant consideration in the target population, can serve as such a shortcut. The distance between the content of the stereotype and the desired outcome of the policy then influences whether the policymaker supports punitive approaches. Brown (2013), examining state TANF policy, proposes a spillover effect between more overtly race-related conflicts and implicitly racialized policies such as welfare. An event such as a controversy over Confederate symbols creates incentives for officials to support strict policy approaches.

Racialized patterns of policy adoption and implementation have been seen in a number of policy areas. One of the most-studied has been traditional “welfare,”

direct cash aid to low-income families with children. Federal assumption of economic relief under Aid to Dependent Children (ADC, later renamed Aid to Fami- lies with Dependent Children, AFDC) during the Great Depression still left the states, and even localities, with a great deal of authority. Both implicit and explicit exclusions on participation by Blacks frequently resulted (Lieberman, 1998; Ward, 2005). The late 1960s and early 1970s saw many of these exclusionary practices overturned, most notably through a series of Supreme Court decisions, creating a de facto entitlement to cash aid for qualifying families. Even in this era, however, states with larger populations of Blacks tended to offer less generous benefits (Orr, 1976).

In 1996, the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA; “welfare reform”) ended entitlement, replacing AFDC with Temporary Assistance for Needy Families (TANF). Structured as a block grant with additional state effort requirements, TANF affords states considerably more control over program rules, behavioral requirements, and even how fiscal resources are allocated—

they need not be directed to cash aid or services for cash aid recipients but can be used for many purposes broadly consistent with the stated goals of PRWORA (Center on Budget & Policy Priorities, 2018; Falk, 2017; Fusaro, 2020). Follow- ing reform, states in which race is a salient aspect of politics—most especially with respect to Blacks—tended to adopt stricter rules than peer states (Fellowes & Rowe,

1 Some scholars argue the US welfare state is not underdeveloped relative to other democracies. Rather, it more often operates through different means, such as tax credits and services (Garfinkel et al., 2010;

Howard, 2007; Mettler, 2011).

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2004; Gais & Weaver, 2002; Soss et al., 2001, 2011). They also experienced steeper caseload declines in the initial years following reform (Bentele & Nicoli, 2012), offered lower benefits (Fellowes & Rowe, 2004; Gais & Weaver, 2002), and devoted fewer resources to traditional cash benefits out of their overall pool of TANF funds (Fusaro, 2020). Continuing a trend that began in the earliest years of cash redistribution to low-income families in the USA, the politics of race and the politics of “welfare” under TANF were intermingled.

It is important to note that, while often found in studies of TANF, the link between state policy and the politics of race is not evident in all aspects of the program. Soss et al. (2001), for example, did not find evidence of a relationship between composition of the cash assistance caseload and adoption of strict work requirements in the initial phases of reform. Bentele and Nicoli (2012) state that racial composition of the caseload is predictive of caseload declines in the earliest years of TANF, but not once the program had matured. Finally, Volden (2016) did not find an association between population demographics and patterns of interstate policy learning—the process of states changing their own policies in response to the experiences of other states—in the initial years of TANF. It is overly simple to attribute all aspects of state TANF policy to the influence of race and racism on politics, yet the connection is found routinely enough to devote effort to understanding its boundaries and underlying causes.

While TANF and its predecessor programs ADC/AFDC are perhaps the most- studied cases of racially patterned policy implementation, they are not the only examples of this trend. Soss et al. (2011) observe a relationship between Black population percentage and the elimination of state General Assistance programs (cash benefits programs targeting populations not eligible for other aid). The salience of race appears to be related to reduced Medicaid spending (Hero & Tolbert, 1996;

Howard, 2007) and lower unemployment insurance benefits (Howard, 2007). The role of race in criminal justice policy has long been of interest to analysts, and at the state level, a relationship has been found between White racial attitudes, state population demographics, and the services such as education and mental health treatment available to aid prisoners in reentry into society (Percival, 2009).

Measuring Racial Salience

Despite the frequency with which researchers have examined the relationship between race and state social policy, there is no single agreed-upon method for operationalizing the salience of race to state politics. The majority of studies use demographic measures, whether the racial and ethnic demographics of the state (Rodgers

& Tedin, 2006) or of a more specific relevant population, such as a program caseload (Bentele & Nicoli, 2012; Fellowes & Rowe, 2004; Soss et al., 2001). More complex variations on demographics have also been used, such as Hero and Tolbert’s (1996) diversity indices that summarize state racial and ethnic variation in a single number.

Given that theories of the influence of race and policy often reference racial attitudes, an alternative approach is to operationalize racial salience using a measure

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of White racial affect. In the context of welfare, Johnson (2001) modeled state welfare spending as a function of both the diversity of the state and White attitudes toward Blacks as measured by an index computed from General Social Survey (GSS) questions on racial integration. Other researchers have used the same question set to gauge the salience of race to state policy and politics in other areas (Brace et al., 2002; Howard, 2007; Mas & Moretti, 2009; Percival, 2009). Considering the implications of changes to Voting Rights Act enforcement in the wake of Shelby County v. Holder, a 2013 case in which the Supreme Court decided the formula used to determine which states and localities required federal approval of changes to election practices, Elmendorf and Spencer (2014) produced estimates of state and district-level White racial attitudes (explicit stereotyping) using the 2008 National Annenberg Election Survey and a Cooperative Campaign Analysis Project survey dataset. Highton (2011) evaluated the role of racism in the 2008 presidential election using a measure of social distance between Blacks and Whites from the Pew Research Center Values Study.

Even when considering attitudinal measures, there are competing alternatives not just between data sources, but methods of variable construction. Most of these attitudinal measures are produced using disaggregation, or cross-tabulation by state. This method has limitations, including the need for extremely large sample sizes often met by pooling multiple surveys or multiple years of survey data. An alternative procedure, multi-level regression and post-stratification (MRP), has seen increasing use in recent years (Lax & Phillips, 2009b). With MRP, the analyst estimates a multi-level model of opinion using individual-level demographic characteristics and group-level geographic characteristics. Predictions for each “type” of individual—combination of individual characteristics and geographic location—are generated from the model, then weighted by the count of the “type” in the population (Gelman & Hill, 2007; Lax & Phillips, 2009b). MRP has been applied to areas such as LGBT rights (Lax & Phillips, 2009a), attitudes toward health care reform (Gelman et al., 2010), general policy preferences on a left-right spectrum (Tausanovitch & Warshaw, 2013), and social trust (Fairbrother

& Martin, 2013). Most relevant for this discussion, Elmendorf and Spencer (2014) use MRP to estimate the prevalence of negative stereotyping of Blacks by Whites at the district level (they use disaggregation on the same variable to produce state-level estimates).

Differences in Measurement Matter

In research testing social theory relating racism and the politics of race to a seemingly race-neutral policy area, the choice of measure should operationalize hypotheses following from that theory. Theories focusing on overrepresentation of people of color among those experiencing economic disadvantage lend themselves to operationalization using population or caseload demographics when applied to the states (Alesina et al., 2001). Conversely, if the image of welfare policy is racialized generally (Gilens, 1999), then local differences in racial attitudes are more consequential, and an attitudinal variable is appropriate. A nuanced consideration of some of these theories might use both constructs simultaneously, with racial demographics influencing the image of the target population (whether among policymakers or the general public)

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and depth of stereotypes moderating the relationship between that image and policy attitudes (and ultimately, policy design) (Soss et al., 2011). Measures of the salience of race to state politics must be distinguishable to demonstrate that one theory is superior to another in explaining racialized patterns of policy adoption and implementation.

Research Questions

The previous discussion leads to two conclusions. First, it is important to study the influence of the politics of race and racism on subnational policy design and implementation. Seemingly non-race-oriented policy areas can reflect race-based patterns.

Identifying and further explaining these patterns, as well as considering their con- sequences, are key tasks for social research. Second, there is no clear “correct” way to measure racial salience at the state level, as existing theories support use of both attitudinal and demographic measures. Analysts have simply chosen between the two or, in a very small number of studies, included both types of measures using structural models (Johnson, 2001; Percival, 2009).

The properties of these various measures, particularly their interrelationships, remain largely unexamined. Almost all these approaches have a high degree of face validity—they would seem to measure appropriate constructs. But, if they are to be used to empirically evaluate theory, it is also important to assess other forms of validity. In particular, do they have discriminant validity, allowing the analyst to clearly test particular relationships? Alternatively, given that racial attitudes and racial context are associated at the individual level (Branton & Jones, 2005), do they have a high degree of convergent validity as measures of closely related constructs?

There is some existing work in this vein. Fording (2003), in considering the relationship between racial context and state section 1115 waivers under AFDC, also examines the relationship between racial context and stereotyping of the work ethic of Blacks. He concludes that demographic measures are at least partly a stand-in for attitudes and that racial context may be used as a substitute for a measure of racial affect.

Howard (2007), meanwhile, examined several welfare state programs (TANF benefit levels, Medicaid spending, and unemployment benefits) using a series of models that included specifications with either demographic measures or racial attitude measures. In the case of TANF benefits, a statistically significant relationship was found regardless of measure, unemployment benefits were only statistically significant with demographic measures, and Medicaid was only significant with a specific demographic variable—a composite Black and Hispanic population measure. Even given a small body of existing studies that have broached the subject, then, the performance of various measures of racial salience remains an open question.

Methods

I examined several measures operationalizing the salience of race with respect to Blacks to state policymaking. The candidate variables include demographic measures and both existing and original attitudinal measures. Having identified measures,

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I next examine their correlations to understand how strongly they are associated with one another, an assessment of convergent and discriminant validity. Finally, I use each measure in example empirical models of various aspects of state TANF policy—coverage, benefits generosity, and presence of a family cap—to practically examine if different measures lead to different conclusions. Note that the focus here is on measures of racial salience with respect to Blacks. Research has found a link between salience of issues with regard to Hispanics or Latinos and state policy, but less consistently and sometimes less strongly than with regard to Blacks. Attitudes toward Hispanics or Latinos among non-Hispanic Whites and their relationship to other opinions are also conditional on other factors (Fox, 2004), and it is even possible that attitudes toward undocumented immigrants may be more policy-relevant than attitudes toward people of Hispanic or Latino ethnicity (Hussey & Pearson- Merkowitz, 2012). These issues warrant consideration, but for the present study, it is clearer to focus primarily on operationalizing the salience of considerations with regard to Blacks to state policy.

Demographic Measures

I examine two demographic measures. The first variable is the percentage of the state population identifying as Black. This data is drawn from the 2000 US Cen- sus public use file via DataFerret (U.S. Census Bureau, 2016). The second variable is the percentage of the Temporary Assistance for Needy Families cash assistance caseload identifying as Black (Administration for Children & Families, 2017). The proportion of the caseload identifying as Hispanic is also included in the example policy models as a control variable.

Brace General Social Survey Integration Measure

One measure of racial affect is drawn from existing research, a variable created by Brace et al. (2002) gauging White attitudes toward racial integration. Brace and col- leagues (2002) generate this measure from five items in the General Social Survey (GSS) asking the respondent questions such as whether White and Black students should go to the same school and whether Whites have a right to keep Blacks out of their neighborhoods. The authors achieve sufficient sample size for disaggregation by pooling GSS samples from 1974 to 1998. The state-level variable of attitudes toward integration is the mean response within the state to the scale generated from the five items. In its original form, larger values indicate greater general acceptance of racial integration. To maintain consistency in interpretation across all attitude variables (larger values indicate more prejudicial attitudes), I transformed this variable by subtracting the original value from 1. All subsequent references to the Brace et al. (2002) measure are with respect to this transformed variable. Note that the Brace et al. (2002) measure is only available for forty-three states, as not all states were included in the 1974 to 1998 GSS data file used in developing their estimates.

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Original Measures of White Racial Affect NAES Disaggregation Measure

Prior to the 2008 Presidential election, the online version of the National Annen- berg Election Survey (NAES) included a six-question battery of racial stereotyping measures (Annenberg Public Policy Center, 2010). Each question is a scale scored 0 to 100, with 100 indicating perfect agreement with a statement and 0 perfect disa- greement. White and Latino respondents were each asked, first, about the degree to which they feel their own racial group is hardworking, intelligent, and trustwor- thy; the next three questions asked whether the respondent agreed with these state- ments when applied to Blacks.² Similar to Elmendorf and Spencer (2014), I create an overall racial affect measure for adult Whites by taking the difference between the response to each item for Blacks and the respondent’s own racial group, coded such that higher values indicate more positive views of Whites than Blacks. I then sum each of these differences to create an overall racial affect score. Finally, I create an indicator variable coded “1” if the respondent scored in the top quartile in-sample on this measure.

The NAES has a very large sample, approximately 20,000 respondents to the questions used here,³ and disaggregation is therefore a valid and useful technique.

Additionally, the racial attitude questions were administered by web survey, which elicits more honest responses to sensitive topics than other modes of administration (Kreuter et al., 2008). After restricting to non-Hispanic White respondents only, the final sample size is 15,372 observations. Disaggregation is simply a matter of calcu- lating the fraction of White respondents within each state expressing negative views of Blacks (here using included sampling weights).

NAES MRP Measure

The procedures for implementing MRP are based on those outlined by Kastellec et al. (2014). They are also similar to those Elmendorf and Spencer (2014) used to produce district-level estimates of racial bias in their consideration of the implications of changes to Voting Rights Act enforcement following the Supreme Court decision in Shelby County v. Holder.

Using the 2008 NAES data described above, I estimated a multi-level logit model of the probability of a White respondent expressing negative views of Blacks. The model included gender, age (coded categorically), and level of education (coded categorically). Respondents were nested in states and states in geographic region based on U.S. Department of Commerce Bureau of Economic Analysis (2016) regions:

New England, Mideast, Great Lakes, Plains, Southeast, Southwest, Rocky Mountain,

2 For Black respondents, Whites are the reference group for the outgroup attitudes questions.

3 This sample size refers to the online sample. The NAES also has a telephone survey with an even larger sample, approximately 60,000 respondents, but does not include the racial attitudes questions. The sample is restricted to non-Hispanic White respondents for this analysis.

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and Far West. I also define a ninth region containing Alaska and Hawaii. The model incorporates three state-level variables associated with White racial views: proportion of the state population identifying as Black, socioeconomic environment as measured by proportion of the population (age 25–65) holding a bachelor’s degree or higher drawn from the 2006 to 2008 American Community Survey (United States Census Bureau, 2016), and a dissimilarity index measuring White-Black segregation (Population Studies Center, 2016).

ANES MRP Measure

The NAES is unusual in its very large sample size, which boosts confidence in both the disaggregation and MRP results. Smaller sample sizes are far more common in social surveys, and a key advantage of MRP is the ability to leverage these typical national data sources to produce state-level estimates (Gelman & Hill, 2007; Lax &

Phillips, 2009b). Here, I use the American National Election Studies (ANES) survey to produce a state-level measure of Whites’ feelings of warmth toward Blacks.

The ANES survey has been routinely fielded since 1948 and is one of the premier sources of data on political attitudes, opinion, and behavior in the American general public. The ANES uses a stratified sampling scheme to achieve national—but not state-level—representativeness (American National Election Studies, 2019).

To produce the measure, I pool the 2002, 2004, and 2008 iterations of the ANES survey. As with the NAES data, I restrict the sample to White non-Hispanic respondents only. Even with pooling, the ANES sample is much smaller than the NAES sample, 3166 respondents. The ANES in these years also did not sample respondents from Alaska and Hawaii and had only a small number of respondents in some low-population states. It is, then, a more typical social survey than the NAES and suffers from the problems that MRP addresses—small overall samples can still produce meaningful state-level estimates, and estimates can still be produced even in states with few or no respondents (Gelman & Hill, 2007; Kastellec et al., 2014; Lax

& Phillips, 2009b). The ANES includes a battery of “feeling thermometer” questions asking the respondent to rate, on a scale of 0 to 100, feelings of coldness (0) or warmth (100) toward a number of social and political groups.⁴ I take the difference between the thermometer response for attitudes toward Blacks and attitudes toward Whites to create a continuous measure of discriminatory attitudes. Similar to the NAES procedure, I create a binary variable using the top quartile as a cut point.

The MRP procedures are similar to the NAES processes, but the predictive model is slightly different. It is still a multi-level logit model which, like the NAES model, includes gender, categorical age, and categorical level of education as individual- level covariates and proportion of the working-age population with a bachelor’s degree or greater, proportion of the state population identifying as Black, and the segregation index as group-level covariates. Unlike the NAES model, region is not included.

4 The thermometer is top-coded in the publicly reported data, with a code of “97” indicating a response of 97 to 100.

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State Policy Models

I estimate a set of fairly simple models of state social policy using each of the previ- ously described variables as predictors. Given how frequently a relationship is found between state TANF policy and racial salience, I select policy variables from that program (Fellowes & Rowe, 2004; Fusaro, 2020; Soss et al., 2001, 2011). These variables include cash benefits coverage as indicated by the number of families receiving TANF aid to the estimated count of families in poverty (Center on Budget

& Policy Priorities, 2013), the maximum cash benefit for a family of three (Uni- versity of Kentucky Center for Poverty Research, 2017), and an indicator for presence of a family cap (a rule under which no additional benefit or a reduced benefit is given to a unit if a child was conceived while the mother was already receiving assistance) (Urban Institute, 2015). The continuous variables are modeled using linear regression while the binary variable is modeled using Firth logistic regression, a logit model accounting for a small sample size (Firth, 1993).

Control variables in the models include the proportion of the cash assistance caseload identifying as Hispanic, government ideology operationalized using Berry et al. (2013) government liberalism scores, state unemployment rate, gross state product per capita, and unmarried birth rate. I iteratively estimate models for the years 2001 to 2010 (n = 50 in each year for each model except for models using the Brace et al. (2002) measure, where n = 43) to examine whether and how results change across time. In the interest of parsimony, full model results are not presented.

Rather, the estimated coefficients and 90% confidence intervals on the racial salience variables are shown graphically to facilitate easy comparison both between measures and across time. Descriptive statistics (means and standard deviations) of the racial salience measures and the control variables are presented in Table 1. Over- all, within-state and between-state standard deviations are shown for variables for which multiple years of data are available.

Results

Correlations Between Measures of Racial Salience

As shown in Table 2, the measures generally correlate highly with one another. The strongest correlations are between the two MRP-based variables, estimated from the NAES and the ANES (r = 0.91), and the two demographic variables, state population percentage Black and percentage of the cash assistance caseload identifying as Black (r = 0.92), respectively. Other correlations are also fairly strong, however. The two demographic variables correlate with the NAES MRP measure at 0.86 each, while they correlate with the ANES MRP measure and the NAES disaggregation measure at between 0.60 and 0.69—weaker, but still notable. The weakest correlations are with the Brace integration attitudes measure, which correlates particularly poorly with the two demographic variables. Its correlation with Black caseload percentage is weak but statistically significant (0.32) while its correlation with state Black population percentage (0.28) fails to achieve significance.

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Measures Used as Predictors

Since multiple measures are tested across 10 years for three different aspects of state TANF policy, there are 180 models. Estimates of the coefficients are therefore presented graphically. Figures 1, 2, and 3 plot the key results for each aspect of TANF policy, with Fig. 1 presenting coefficients on racial salience from models of TANF cash benefits, Fig. 2 models of coverage, and Fig. 3 presence of a state welfare family cap. Within each figure, plots are organized by the independent variable operationalizing racial salience.

Results for models from each year, 2001 to 2010, are shown. The dots indicate the point estimate of the coefficient and the bars the 90% confidence interval. If the bar crosses

Table 1 Descriptive statistics

N = 50 for NAES MRP, NAES disaggregation, ANES MRP, and state population Black %. N = 43 for Brace integration attitude measure. N = 500 for all time-varying measures (n = 50, t = 10)

Mean Standard Deviation

NAES MRP 27.02 6.18

NAES disaggregation 27.37 9.92

ANES MRP 20.04 5.07

Brace 27.19 9.82

State Black population % 10.48 9.55

Caseload % Black 33.46 Overall: 25.92

Between: 26.03 Within: 2.53 Log TANF benefit, family of 3 5.97 Overall: 0.39 Between: 0.38 Within: 0.06

TANF-to-poverty 0.34 Overall: 0.19

Between: 0.17 Within: 0.06

Hispanic caseload % 13.94 Overall: 16.18

Government ideology 0.51 Overall: 0.24

Unemployment rate 5.65 Overall: 1.99

GSP/capita 0.04 Overall: 0.009

Unmarried birthrate 0.37 Overall: 0.07

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the zero line, the result is not statistically significant. If the bar does not cross the zero line, the result is statistically significant (at 0.05 in a one-tailed test or 0.10 in a two-tailed test). Estimates for TANF coverage and TANF benefits are linear regression coefficients while the estimates for presence of a family cap are logistic regression coefficients.

Examining the plots, some patterns emerge. First, for all three outcome variables, the fraction of the caseload identifying as Black produces statistically significant results in almost all years (it fails to achieve significance for TANF coverage in 3 years). It is negatively associated with TANF coverage and TANF benefits and positively associated with adoption of a family cap. A very similar pattern emerges for the population percentage Black variable, albeit with different magnitudes for the coefficients. The estimates are also less precise, resulting in some years where the coefficient is not significant (4 years for TANF coverage, 1 year for family cap).

Turning to the attitudinal measures, all results are identically signed—each measure is negatively associated with coverage and benefit levels and positively associated with a family cap. There are inconsistent results both within and between measures with regard to statistical significance. The Brace integration measure is significantly associated with coverage in the two earliest years and the four latest years. It is always significant with regard to benefit levels, and it is never significant for presence of a family cap. The ANES MRP measure is statistically significant for all years with respect to benefit levels and, with the exception of the year 2001, coverage. It is not, however, statistically significant in any year as a predictor of presence of a family cap. The NAES disaggregation measure is statistically significant for 3 years with regard to coverage, 1 year for benefits, and 3 years for presence of a family cap. The NAES MRP measure is generally significant for coverage and benefit levels (with the exception of 2 years for benefits) and for family cap in the early years of the panel but not after 2005. Overall, all of the measures except the NAES disaggregation measure are usually predictive of benefit levels and coverage. The results are much less consistent for presence of a family cap, for which the demographic predictors are fairly consistently associated with a cap and the two NAES measures are occasionally associated with a cap.

Table 2 Bivariate correlations of racial salience measures

Values are Pearson’s correlation coefficients. 2008 value used for caseload demographics. N = 50 except for correlations using Brace measure, where n = 43

***p < 0.001; **p < 0.01; *p < 0.05

NAES MRP NAES disaggregation

ANES MRP Brace State Black

population % Caseload

% Black

NAES MRP 1.00

NAES disaggregation 0.73*** 1.00

ANES MRP 0.91*** 0.62*** 1.00

Brace 0.52*** 0.44** 0.62*** 1.00

State Black population % 0.86*** 0.62*** 0.67*** 0.28 1.00

Caseload % Black 0.86*** 0.60*** 0.69*** 0.32* 0.92*** 1.00

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Discussion

The analyses indicate that many measures of the salience of race at the state level, at least with regard to Temporary Assistance for Needy Families, are closely related. Across two demographic measures, two White racial attitude measures estimated from survey data using disaggregation (one created for this analysis and one from a previous study), and two White racial attitude measures created using multi-level regression with post-stratification, the measures are generally strongly correlated. Used in models of state TANF policy, they also often, albeit not always, lead to parallel inferences, with identical sign and pattern of significance. The magnitudes of the relationships do differ, however, but that is not sur- prising as they are different constructs and have different distributions.

These findings suggest a high degree of convergent validity—the different measures of racial salience will lead the analyst to similar inferences. Convergent validity comes at the expense of discriminant validity, however. It is difficult to clearly test theoretical relationships between race, racism, and policy design due to the close correlations of most measures. Were one to use multiple measures, such as one attitudinal variable and one demographic variable, as predictors in

Fig. 1 Coefficient plots of OLS regression models with log transformed TANF cash benefit for family of three as dependent variable. Models repeated for each year, 2001-2010. Dots indicate point estimate of the coefficient on the predictor listed at top of each plot for a given year, bars are 90% confidence intervals. Models control for government ideology, unemployment rate, gross state product per capita, and unmarried birth rate. N = 50 for all models except those using Brace integration attitudes measure, where n = 43

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an empirical model of some aspect of state policy, a high degree of collinearity would be expected. Point estimates of the relationships would be uncertain, with inflated standard errors, and potentially unreliable in both sign and magnitude.

Estimating models with only one measure is an obvious solution, but that lim- its understanding of underlying pathways between demographics, racial attitudes, and programmatic choices in the policy design process.

The high convergent and low discriminant validity do not mean that there is no utility to the availability of multiple measures. They could be used as robustness checks, for example, to examine the durability of findings in different but closely related models. Doing so leverages the strengths of convergence. More complex models accounting for the interrelationships between racial context and racial attitudes in influencing policy are also possible (Johnson, 2001; Percival, 2009). At the very least, however, analysts should acknowledge the challenges in disentangling these constructs when discussing patterns of state policy adoption.

Two predictors stand out as somewhat unusual compared to the others. First, the Brace et al. (2002) measure of White attitudes toward racial integration correlates poorly with the other measures. The data source used by Brace et al. (2002), the General Social Survey with samples from 1974 to 1998 pooled to create a

Fig. 2 Coefficient plots of OLS regression models with TANF-to-poverty ratio as dependent variable.

Models repeated for each year, 2001-2010. Dots indicate point estimate of the coefficient on the predictor listed at top of each plot for a given year, bars are 90% confidence intervals. Models control for government ideology, unemployment rate, gross state product per capita, and unmarried birth rate. N = 50 for all models except those using Brace integration attitudes measure, where n = 43

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very large analytical sample, is the oldest of the constructs considered here. It is possible that data from those earlier periods are simply not representative of attitudes in the 2000s. Alternatively, attitudes toward racial integration may simply no longer be relevant—racism might now manifest itself in other ways. The measure is also only available for forty-three states, and perhaps the dropped cases explain the discrepancy.

The second measure warranting some additional discussion is the NAES disaggregation measure. While all of the other measures have a clear relationship with TANF cash benefit levels, the NAES disaggregation measure is only statistically significant in a single year (2001). The issue could be technical. The survey has a large representative sample and is seemingly ideal for estimating attitudes, including racial attitudes, using traditional disaggregation. Nonetheless, researchers have found that MRP can produce better estimates than disaggregation even with large samples (Lax & Phillips, 2009b). Perhaps the disaggregation estimates are simply less accurate than the MRP estimates in this case. Alternatively, the MRP measure produced from the ANES incorporates proportion of the state population identifying as Black as a predictor variable and is thus in a way a composite measure of racial demographics and White racial attitudes.

Fig. 3 Coefficient plots of Firth logit models with presence of a welfare family cap as dependent variable. Models repeated for each year, 2001-2010. Dots indicate point estimate of the coefficient on the predictor listed at top of each plot for a given year, bars are 90% confidence intervals. Models control for government ideology, unemployment rate, gross state product per capita, and unmarried birth rate. N = 50 for all models except those using Brace integration attitudes measure, where n = 43

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Overall, this analysis parallels Fording’s (2003) consideration of both state-level racial attitudes and welfare caseload demographics, concluding that the two are so closely related that demographic data might be at least a partial proxy for attitudinal data in this realm. However, while the measures often lead to similar conclusions, they do not always do so. Depending on the modeling strategy, sample size (i.e., whether the analyst uses a single year of data or pools multiple years and uses an analytical technique appropriate for panel data), and other covariates, one type of measure could achieve statistical significance while the other does not. If a researcher is concerned about this possibility, it may be useful to repeat analyses with different measures of racial salience and consider any differences.

Limitations

A few methodological limitations warrant mention. First, the attitudinal measures examined in this study are not a comprehensive catalog of measures of racial attitudes.

There are some alternative constructs such as racial resentment (belief that people of color receive unearned advantages in society) (Kinder & Sanders, 1996) that may be relevant to the politics of social welfare but which the analysis did not consider. The variables that were examined were chosen to provide the reader with a selection of possible approaches. Second, the example empirical models are deliberately simple—linear and small-sample logit models repeated for a series of years for three select outcomes. Suites of control variables are, of necessity, small. More advanced and fully specified models (e.g., estimated by pooling multiple years of observations in a panel) might produce different point estimates or inferences. Finally, TANF was selected as a test case specifi- cally because previous research has found a correlation between important components of the program and the salience of race to state policymaking. There are many aspects of TANF and of state social policy more generally in which null or conditional findings might be expected with regard to a relationship with race and racism, and these are not explored here (e.g., Volden’s (2016) findings regarding policy learning between states).

Regardless, this article addresses an important issue in the investigation of the relationship between racial and ethnic politics and state policy—does the choice of measure matter and, if so, how?

Conclusion

States are central to the design and administration of important programs in the Ameri- can welfare state. Devolving social policy to the state level, though, brings with it the risk of policy reflecting social disparities. Racialized patterns of policy design and implementation are evident in programs such as TANF, unemployment insurance, and Medicaid (Fellowes & Rowe, 2004; Howard, 2007; Soss et al., 2001, 2011). Social theory has forwarded several explanations for these patterns (Alesina et al., 2001; Brown, 2013; Soss et al., 2011). Empirical operationalization of these theories, as well as con- tinued investigation of the correlates of state policymaking, is dependent on measurement of the salience of race and racism at the state level.

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This article considered several methods for operationalizing the salience of race to state policy with respect to Blacks, including commonly used demographic measures and less-used, but theoretically important, measures of state-level variation in White racial attitudes. Among the attitudinal items, both existing and original measures were examined, including variables created from survey data using disaggregation and multi- level regression with post-stratification. After examining the correlation between these measures, I then compared their performance in models of state TANF policy over a 10-year period. In general, the measures are highly correlated and frequently produce similar results in empirical models. These findings indicate a high degree of convergent validity but low discriminant validity. In turn, it is difficult to isolate the roles of either racial context or majority group racial attitudes on policy. The availability of multiple related measures is nonetheless useful, however, providing opportunities for robustness checks or more complex models reflecting their entanglement.

Funding This work was funded by the Fahs-Beck Fund for Research and Experimentation.

Data Availability Data are public use data. Analysis files available upon request.

Code Availability Available upon request.

Declarations

Conflict of Interest The author declares no competing interests.

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