What's representation got to do with it? Comparing public reactions to diversity among government employees and government contractors

Abstract

The literature on representative bureaucracy is largely focused on government agencies and little attention has been paid to representation within private sector contractors providing services on behalf of government. A survey experiment, administered on a nationally representative panel collected by YouGov, is used to assess whether the public evaluates the distributive justice of government programs differently if the programs are implemented by either contractors or government officials, and whether this changes when the public is provided information on the diversity of those actors. We find that perceptions of distributive justice are no different with government or contractor delivery, nor do they change in response to diversity information. The findings imply that perceptions of distributive justice may only vary between contractors and government, and in response to diversity information, when the public are presented with information about program failure or obvious inequities.

Points for practitioners

Nationally representative survey data indicates that the general public may be more concerned with program failure rather than the demographic composition of the organization that delivers the service.

When performance is the same between government and private contractors, the public views the program outcomes as equally fair.

Diversity, on its own, is not enough to enhance the public's assessment of government decisions.

When engaging with different communities, managers should remember that perceptions of government may be informed by assumptions about who may benefit from government programs and racial stereotypes.

Keywords

Representative bureaucracy distributive justice government contractors

Introduction

Representative bureaucracy scholarship considers the relationship between the composition of government agencies, how the exercise of bureaucratic discretion can advance democratic outcomes, and how the public ultimately responds to such decisions (Krislov, 1974; Meier, 1993; Mosher, 1968). However, in recent years, this conversation has been complicated by the fact that modern governance often involves third parties. The public may very well view the activities of private contractors differently when compared to public employees. As such, there is a need to expand representative bureaucracy scholarship to consider private contractors. This research asks the question: do distributive justice perceptions of government programs change when (a) the public knows the diversity of the agency and/or (b) when service is delivered by government or a private sector contractor? Evaluating the public's reaction to government diversity is a different question from evaluating the effects of actions of diverse bureaucrats under active representation. We argue that the public's reaction to representation is just as important a puzzle to consider within representative bureaucracy scholarship.

An on-line survey experiment is used to evaluate the research questions, using a 2 × 2 design, where respondents were told: (a) the program was implemented by either government employees or contractors; and (b) either the diversity of the employees implementing the program, or no information about staffing diversity. We find that perceptions of distributive justice did not change between government and contract program delivery, and distributive justice perceptions also did not change when respondents were provided with information on the diversity of program employees. The null result in the experiment is noteworthy as it was conducted on a nationally representative sample.

Literature review

The concept of representative bureaucracy arose out of the dilemma posed by bureaucrats using discretion, but removed from direct public or political control (Mosher, 1968). Scholars distinguish between passive, active, and symbolic forms of representation. Passive representation considers the demographic compositions of government agencies and the degree to which they reflect their community; Bradbury and Kellough (2011) provide an overview of studies that examine the determinants of passive representation in government agencies and its perceived benefits. Active representation considers how bureaucrats will exercise discretion in order to make decisions favoring the community that they represent (Meier, 1993, 2019). Finally, symbolic representation focuses on how members of the public respond after directly interacting with diverse bureaucrats, finding that members of the public will exhibit different behaviors or attitudes solely because they interacted with a bureaucrat who “looks like them” (Riccucci et al., 2016).

Although representative bureaucracy scholarship has increasingly focused on active and symbolic representation, there is value in studying the effects of passive representation. For example, Riccucci et al. (2014) note that “a passively representative bureaucracy may influence how the public perceives the legitimacy and performance of an agency” (537). However, the representative bureaucracy literature offers limited theoretical explanation of why such perceptions should change. To explain this, we turn to the concept of distributive justice.

Distributive justice focuses on the extent to which allocation decisions are perceived as fair (Colquitt et al., 2005). Distributive justice has been used to study the public's reactions to interactions with various government actors (Tyler, 1984; Tyler and Caine, 1981). Importantly, an increase in distributive justice has been linked with increases in perceptions of legitimacy of authority (Lind and Tyler, 1988; Tyler and Lind, 1992). Specifically, if the public assesses a particular outcome to be fair, the public is more likely to view the decision-maker as legitimate and is more likely to voluntarily comply with rules issued by that authority.

Research shows that people are able to form multiple types of fairness judgments, including in response to events that they did not personally experience (Skarlicki and Kulik, 2004). Referred to as a third-party justice reaction, this perception is elicited through a multistep process (Brockner et al., 2015; Skarlicki and Kulik, 2004). First, the observer attempts to determine who is responsible for the allocation decision; then the observer considers whether a different decision could have been made. If responsibility can clearly be attributed and the actor could have acted differently, a fairness reaction is triggered. This is likely to be magnified if the observer identifies with the party receiving the allocation (Brockner et al., 2015; Skarlicki and Kulik, 2005). Greater identification with the recipient because of a similar demographic, professional, or economic background, for example, is likely to inspire a stronger reaction to the event. This is especially the case if the individual is observing patently unfair treatment, because the perceived injustice also threatens the identity of the observer.

To link third-party justice reactions and representation, the research presented here tests whether the public evaluates the allocation of government benefits as more or less fair when given information about the race of recipients and the racial make-up of the organization making the allocation decision. This fairness reaction is likely when the public can clearly assign responsibility for the allocation to specific actors, such as government or contractor employees, and is likely to be magnified when the observer identifies with the recipient of the allocation decision.

Contracting out and representation

Governments make extensive use of private and non-profit organizations to deliver services (Petersen et al., 2015). Scholarship has considered whether there is a relationship between the percent age of minority government contracting officials and the awarding of contracts to minority-owned businesses (Brunjes and Kellough, 2018), and how outsourcing impacts levels of staff diversity within government agencies (Brown and Kellough, 2020). However, the representative bureaucracy scholarship has not considered whether the public will consider diversity differently when evaluating the actions of contractors instead of government employees.

Prior research on public attitudes to contracting out presents a complex picture. Thompson and Elling (2000) and Johnson et al. (2019) found that the public believed that state functions that directly impact safety or wellbeing should remain under public control. Hvidman and Andersen (2016) offer some support, finding that publicly run hospitals in Denmark are perceived as more benevolent than their private counterparts even when performance is equal. However, Meier et al. (2019) found no differences in the perception of public and private hospitals regardless of performance.

Dawkins (2021) evaluated responses to both service success and service failure and found that private garbage collection changes public perceptions of local government compared to public garbage collection. Two different vignette studies found that respondents blame contractors for service failures (Piatak et al., 2017), and politicians are blamed less for private garbage collection failures compared to public collection failures (Marvel and Girth, 2016). A different study on poor road quality found no differences in respondent perceptions of blame assigned to local politicians for public or private road maintenance efforts (James et al., 2016). These studies each included arguments on the importance of attribution in rendering a judgment. Combined, the majority of these studies found that the public has different perceptions of public and private delivery in situations of service failure.

Overall, the literatures on outsourcing, representative bureaucracy, and justice perceptions suggest that the public will have more positive perceptions of distributive justice regarding government services when the organization providing the service looks like them (Berg and Dahl, 2020; Dawkins, 2021; Riccucci et al., 2016; Skarlicki and Kulik, 2004). Perceptions of distributive justice will be inspired when an observer can clearly identify who is responsible for a decision and when the observer identifies with the program recipient. Finally, different assumptions about government and contractor performance are likely to lead individuals to evaluate the same performance of the two actors differently. Combined, we propose the following hypotheses (see Figure 1):

H1: Information about employee representation will change citizen perceptions of distributive justice, compared to no information about employee representation.

H2: Information about government program implementation will change citizen perceptions of distributive justice, compared to information about private sector contractor implementation.

H3: Information about both employee representation and private sector contractor implementation will change citizen perceptions of distributive justice, compared to being given no information about representation and program delivery.

H4: Minorities will report different distributive justice perceptions when provided with information about private sector contractor implementation and representation, compared to White respondents.

Study design and methods

We conducted a vignette experiment that focused on the allocation of grants to minority-owned small businesses. The use of minority-owned businesses was to avoid activating prejudices that might be associated with welfare (see Wetts and Willer, 2018). The experiment was administered online by YouGov to 1500 US residents, aged 18 or above, in May 2019 as part of a larger survey on attitudes about race and politics. The full survey was conducted after the receipt of Institutional Review Board (IRB) approval and informed consent was obtained for all participants. Participants were debriefed after the study was completed.

The full sample was based upon stratified sampling of the American Community Survey and weighted to reflect the US population. We discarded any respondent who failed the attention check or returned incomplete responses. This produced a final sample of 1042, for a useable sample rate of 69%. As YouGov screens for and replaces overly fast and other invalid responses (see YouGov, 2017), there was no need to impose other data-quality control criteria. YouGov provided survey weights, which are applied in the analyses below, to ensure the sample is representative of the US, based on the American Community Survey.

Respondents were randomly assigned to one of four experimental conditions (see Figure 2). Participants were presented with information that informed them that last year, grants in their state were awarded by either a government agency or a “private sector contractor.” In two conditions, respondents were told that 74% of employees with the government agency or a private sector contractor were White and 26% were minorities. In the other two conditions, no diversity information was presented. After seeing the vignette with one of the four treatments, respondents were then asked about their distributive justice perceptions.

Measuring distributive justice

In organizational justice studies, scholars typically ask individuals to reflect on treatment they personally experienced, usually in the workplace. Neither of these situations is present in this study. As a result, distributive justice is measured using three items, two from research earlier in the justice canon, and one reflecting a more recent development. Specifically, the study uses two items previously used in Tyler and Blader (2003) and Lind et al. (1993):

The allocation of small business grants in my state last year was fair.

Overall, how would you rate the acceptability of the small business grant program?

The third survey question, “The allocation of small business grants in my state last year was biased,” was modeled after Colquitt et al.’s (2015) findings indicating scholars should not assume that low fairness perceptions are the same thing as high perceptions of bias (see Appendix Table A1 for a list of all survey items used). Responses for all three questions were based on a 7-point Likert scale; the third question was then reverse coded. Factor analysis of the three items exhibits a Cronbach's alpha of 0.724. The dependent variable used in the analysis is an additive index of the three items, with a score of 3 indicating low perceptions of distributive justice and 21 indicating high perceptions of distributive justice.

Measuring race and ethnicity of respondents

The race and ethnicity of the respondent were collected with one survey item that asked participants to identify all the racial or ethnic descriptions that applied to them from a list of seven options. In the empirical analysis, respondents who identified themselves as White only, and did not check any other category, were coded as White. Respondents selecting any other option or combination of options were coded as minority respondents. This aggregation results in 30.38% of the weighted sample as minorities. The number of respondents does not allow for a more fine-grained analysis of differences in perceptions between minority groups. See Table 1 for the demographic and summary statistics.

Table 1.

Descriptive statistics.

Variable	Mean	SD	Minimum	Maximum	n
Distributive justice	13.04	3.374	3	21	1042
Racial resentment	8.854	3.358	3	15	1042
Social desirability	6.692	2.602	3	15	1042
Model minorities	3.289	1.348	1	5	1042
Age	49.361	16.955	19	87	1042
Variable	Proportion
Race/Ethnicity
White	69.62
Black	10.71
Hispanic	12.59
Asian	2.49
Native American	0.78
Mixed	2.32
Other	1.34
Middle Eastern	0.15
Female	50.52
Public employee	8.34
Small business owner	9.24
Party
Republican	37.64
Independent	15.62
Democrat	46.74
Education: 4-year college and higher	31.55
Income = <$40k per year	38
Employment: Full-time	40.94

Control variables

As our research focuses on allocation of government benefits and diversity, the models must include consideration of prejudice. We draw upon the concept of racial resentment or the notion of symbolic racism to examine this. Symbolic racism holds that racial prejudice follows from believing stereotypes that portray minorities—and Blacks in particular—as violating White cultural norms regarding hard work, self-help, and self-reliance (Henry and Sears, 2002). These cultural norms form the basis of assessments of deservingness (Jensen and Petersen, 2017). Racial resentment measures the degree to which Whites accept prejudicial stereotypes about minorities, and perceive minorities as deserving. Prejudiced Whites see interventions by government to assist minorities as undeserved. We argue that deservingness calculations will be affected by racial stereotypes and that racial resentment offers a way to measure stereotypes. To measure racial resentment, we use a 3-item scale with slightly revised items from the 2016 American National Elections Studies (ANES) survey (see Appendix Table A1 for a list of all survey items used). To control for ordering effects, the questions on racial resentment were shown in a random order. The answers were combined in an additive index, scaled from 3 (low racial resentment) to 15 (high racial resentment). These items exhibit a Cronbach's alpha of 0.814.

The analysis also includes controls for age, gender, political party identification, highest level of completed education, state where the respondent lives, employment status, income, and whether the respondent owns a small business. Individuals were further asked if they worked in the public sector. We accounted for social desirability effects using three items from Berinsky and Lavine (2012). Finally, we include an item to ensure that respondents are not simply rejecting government interventions on behalf of a minority group, which we refer to as the model minorities variable. This item is adapted from the 2001 and 2010 Cooperative Congressional Election Study.

Methods

Analysis of the hypotheses is implemented in two steps. First, analyses of variance (ANOVAs) are used to examine the effect of the treatments on distributive justice perceptions. ANOVAs do not include the control variables and can be viewed as a more detailed version of a t-test when comparing multiple means (Acock, 2014). For each hypothesis, the second step is then to evaluate the relationships with an ordinary least squares (OLS) regression, with robust standard errors and the weights provided by YouGov, including all of the control variables described previously. To evaluate Hypothesis 1, the regression includes a dummy variable indicating whether the respondent was provided with diversity information about the agency or not, while the regression for Hypothesis 2 includes an additional dummy variable indicating whether the respondent was told the program was administered by government or by private sector contractors. For Hypothesis 3, the diversity information and contractor dummies are interacted to examine their joint effects. Finally, for Hypothesis 4, the regression includes a triple interaction term between diversity information, contractor provision, and minority respondents.

Results

In the sample, the average weighted distributive justice perception was 13.109 (SD 3.353), where the distributive justice index has a range of 3–21, with 21 indicating high perceptions of justice (Table 1). Furthermore, 69.62% of respondents are White, and 9.24% have owned small businesses. The results provide no support for the hypotheses (see Table 2). For the first hypothesis, the ANOVA analysis identifies no significant differences in distributive justice perceptions in response to the diversity information treatment (F = 0.14, df = 1041, p = 0.706) (see panel A in Table 3). Similarly, the coefficient for the diversity information treatment is not significant in the OLS model (see column H1 in Table 4). Next, the ANOVA results do not support the second hypothesis that people will respond differently to contractor service delivery (F = 0.10, df = 1041, p = 0.747) (see panel B in Table 3). Likewise, the regression coefficient for contractor provision is not significant (see column H2 in Table 4). Combined, the results indicate that distributive justice perceptions are not responsive to either information about agency diversity or information about contractor delivery.

Table 2.

Summary of findings.

Hypothesis	ANOVA results	OLS results
H1: different distributive justice perceptions when provided information about employee representation	No	No
H2: different distributive justice perceptions when grants awarded by private sector contractors	No	No
H3: different distributive justice perceptions when provided information about both representation and contractors awarding grants	No	No
H4: minority respondents will report different distributive justice perceptions when provided information about both representation and contractors awarding grants	No	No

The third hypothesis suggests distributive justice perceptions will be different when given information about both workplace diversity and contractor provision. The ANOVA analysis provides no support for this argument (F = 0.26, df = 1041, p = 0.856) (see panel C in Table 3). The average distributive justice score for the no diversity information and government provision treatment is 13.033 (standard error (SE) 0.203; confidence interval (CI) 12.635, 13.43), compared to the average distributive justice score for the diversity information provided and contractor provision treatment of 13.042 (SE 0.206; CI 12.637, 13.44). In Figure 3, the overlapping CIs visually demonstrate the lack of statistical difference in the average distributive justice scores for all treatment permutations in this ANOVA. Similarly, the variable representing the interaction of diversity information and contractor provision is not significant in the regression (see column H3 in Table 4).

Hypothesis 4 is also not supported by either the ANOVA or regression analyses, indicating that minority respondents have similar distributive justice perceptions to White respondents when presented with information about agency diversity and contractor delivery. The ANOVA does not demonstrate changes in distributive justice perceptions in response to the treatments (F = 1.15, df = 1041, p = 0.329) (see panel D of Table 3). When individuals are provided diversity information and told that the grant program was administered by government, White respondents have an average justice score of 13.275 (SE 0.251) compared to an average justice score for minorities of 13.230 (SE 0.390). The regression results do not support Hypothesis 4; the variable interacting diversity information, contractor provision, and minority respondents is not significant (β= −0.741, SD 0.984) (see column H4 in Table 4). In addition to the interaction term being insignificant, the coefficient for minority on its own is not significant in any of the four regression models—minorities did not exhibit different distributive justice perceptions in any of the experimental conditions.

Table 3.

Analysis of variance (ANOVA) results.

Panel A: One-way ANOVA of perceived distributive justice, by information treatment
No racial information	13.07
Racial information	13.15
df: 1041	F: 0.14	p: 0.7059
Panel B: One-way ANOVA of perceived distributive justice, by provider treatment
Government	13.14
Contractor	13.08
df: 1041	F: 0.10	p: 0.7473
Panel C: Multi-way ANOVA, allowing interaction of information and provider treatments
	df	F	p-value
Model	3	0.26	0.8564
Racial information	1	0.15	0.7026
Provider	1	0.12	0.7337
Interaction	1	0.52	0.4726
Total	1041
Panel D: Multi-way ANOVA, allowing interaction of treatments with respondent race
	df	F	p-value
Model	7	1.15	0.3286
Racial information	1	0.00	0.9494
Contractor	1	1.80	0.1806
Minority	1	2.31	0.1291
Interaction of treatments	1	0.56	0.4538
Interaction of information with race	1	0.83	0.3615
Interaction of contractor with race	1	0.02	0.8981
Triple interaction	1	4.45	0.0352
Total sample	1041

Table 4.

Regression results.

	H1	H2	H3	H4
Diversity information treatment	0.243		0.364	0.403
	[0.220]		[0.299]	[0.361]
Contractor treatment		−0.189	−0.083	0.015
		[0.220]	[0.303]	[0.364]
Diversity information*contractor			−0.225	−0.042
			[0.434]	[0.519]
Diversity information*minority				−0.117
				[0.659]
Contractor*minority				−0.254
				[0.668]
Diversity informationcontractorminority				−0.741
				[0.984]
Minority	−0.064	−0.087	−0.078	0.279
	[0.273]	[0.271]	[0.273]	[0.431]
Racial resentment	0.132	0.13	0.131	0.13
	[0.055]*	[0.055]*	[0.055]*	[0.055]*
Party: Republican	0.838	0.828	0.834	0.798
	[0.299]**	[0.299]**	[0.299]**	[0.300]**
Adjusted R²	0.06	0.06	0.06	0.06
n	1042	1042	1042	1042

Note: Robust standard errors provided in brackets. Survey weights developed by YouGov were applied to these regressions. Other control variables were not significant and are excluded from the table to save space. The dependent variable is an index measuring perceived distributive justice.

As noted previously, the regressions include a number of control variables that are worth briefly examining. Republicans consistently exhibit higher distributive justice perceptions than Democrats. Furthermore, an increase in racial resentment is associated with an increase in distributive justice perceptions across all four models. To say this another way—individuals with higher levels of self-reported prejudice perceive the grant allocations to exhibit more distributive justice. Finally, distributive justice perceptions did not vary for women, for individuals working in the public sector, or for individuals who owned a small business.

Discussion and conclusion

This article asks: “What's representation got to do with it?” The results of the experiment suggest that the answer to this question is: nothing. Scholars have argued that increasing representation in government agencies will lead the public to view the actions of government as more legitimate (Bradbury and Kellough, 2011; Krislov, 1974; Riccucci et al., 2016). In this study, providing information on representation did not impact perceptions of distributive justice, contracting did not initially appear to impact justice perceptions, nor is there a joint impact of representation and contracting. Since the analysis is based on a nationally representative sample and uses previously tested survey items to measure key concepts, these findings should provide some confidence that representation information alone is not likely to change the public's perception of government, and differences in the means of service delivery do not inspire different assessments of fairness.

In this study, minorities and Whites exhibited similar distributive justice perceptions; neither responded in unique ways to the treatments. Justice scholarship suggests that people can have justice reactions to events they do not personally experience, and that this is more likely to occur if the observer identifies with the individual experiencing the event (Brockner et al., 2015; Skarlicki and Kulik 2004). In this case, we hypothesized that minority respondents would identify with the minority small business owners. The findings do not support the identification argument. We included the phrase “in your state” to inspire identification between our respondents and the businesses receiving the grant. However, it is possible that the phrase “in your state” did not elicit a feeling of connection. Future research could evaluate whether different justice reactions are elicited by placing the program “in your local community.” Alternatively, rather than relying on initiating a third-party justice reaction, a vignette could describe a situation where the respondent is personally “experiencing” some sort of outcome from a decision made by a government official or a contractor.

Another possible explanation for the null results is that minority respondents were grouped together into a single category. We did not have enough African American or Asian respondents in each treatment group and the control group, for example, to evaluate their specific perceptions. This potentially masks the variations in attitudes that exist within and between different minority populations in the US. Future research could oversample minority respondents to allow for careful disaggregation. For example, it would be interesting to know if different communities respond differently to diversity information.

Importantly, we caution against the interpretation that our findings suggest that Americans’ distrust in government is such a strong cultural touchstone that anyone living in the US would develop a similar orientation towards government. Although national surveys demonstrate consistently low levels of trust in government (see, e.g., Pew Research Center, 2023), assumptions that perceptions are similar across groups would ignore the unique experience of different groups targeted by government for differential and especially negative treatment—for example, African Americans’ experience with racial inequities in the criminal justice system (Rucker and Richeson, 2021), or Asian American experiences under the Asian Exclusion Acts. Political science has long found that lived political realities amongst different groups shape their attitudes to government (Dupree and Hibbing, 2023; Howell and Fagan, 1988). Importantly, we do not need to assume these perceptions—we can measure them. In future studies, it may be interesting to control for a global perception of government fairness, in addition to asking about the fairness of a specific decision.

The null results may further be attributable to setting performance at believable levels, and not explicitly describing the performance as excellent or failing. We deliberately set the performance and representation levels at a rate that would be believable to avoid extreme responses for performance that would be obviously imbalanced. Most studies evaluating responses to contractor and government service provision measure reactions after descriptions of service failure following the rationale from negativity bias that losses are felt more strongly (e.g., James et al., 2016; Johnson et al., 2019; Marvel and Girth, 2016; Piatak et al., 2017).

One implication of our null results may be that justice perceptions are not likely to vary unless government/contractor performance is obviously failing or unequal. This would be in accordance with the justice scholarship that indicates a justice violation is interpreted as an identity threat which inspires a justice reaction (Skarlicki and Kulik, 2004). Future research may consider whether reactions to government or contractor failure are stronger or weaker when given information about the diversity of the government/contractor decision-makers. For example, the James et al. (2016) experiment could be administered in the US (it was originally administered in the context of English local government) with the addition of information about the racial make-up of the city council members.

Despite the lack of support for our hypotheses, two controls were significant in all of the regression models: levels of racial resentment and identification with the Republican Party. As noted earlier, we use racial resentment as an indirect measure of racial prejudice. As levels of prejudice increase, the perceived distributive justice of the grant allocations also increased, suggesting that individuals with high levels of resentment viewed the allocation mirroring the population as especially fair. In effect, they may have been reassured that the results were not more skewed in favor of minority-owned businesses if they were concerned about so-called “reverse discrimination.” Similarly, Republicans reported higher levels of distributive justice perceptions. Although the experiment noted the grant program was administered by their states, the racially charged nature of national political conversations during the Trump administration (see, e.g., Alphonso, 2020), when this experiment was administered, might have made Republicans especially sensitive to representation cues. If scholars pursue the suggestions to revisit the James et al. (2016) experiment, we recommend controlling for racial resentment to ensure any measured outcome accounts for potentially more harsh judgments of minority government leaders.

Importantly, researchers should exercise caution in using one-shot experiments to examine the public's perception of government programs and representation. People's reactions to diversity are exceedingly complex. Even though experimental studies of symbolic representative bureaucracy have found differences in people’s reported willingness to engage in certain behaviors (e.g., Riccucci et al., 2016), this is not the same thing as measuring actual behavior, nor is it the same thing as measuring perceptions of government fairness. Assumptions about diversity and government may be so sticky that a one- or two-sentence treatment is not enough to overcome a life-time of political rhetoric and experience with government (Berg and Dahl, 2020).

Experimental research has been criticized for decoupling from political values and institutional pressures (Bertelli and Riccucci, 2022). It is possible that the difficulty of influencing perceptions about government and diversity, combined with the shortcoming of experimental work, mean that we would be better off exploring these questions in real-life interactions with government programs. Symbolic representative bureaucracy studies have considered whether direct personal interaction with a street-level bureaucrat of a similar or different background changes perception of the treatment received (see, e.g., Gade and Wilkins, 2012; Theobald and Haider-Markel, 2009). This suggests that future research should assess fairness perceptions after people have directly and personally interacted with a service provided by government or a private sector contractor, where the race or ethnicity of the service provider is somewhat or very clear, while controlling for racial resentment.

This research has a number of practical implications. First, the general public appears to care little about the actual composition of public agencies and government-funded contractors when performance is not obviously failing. Historically, the US government has required government contractors both to pursue non-discrimination and to expand diversity among their employees (Kellough, 2006). The pursuit of social equity is a worthy goal and one that is critical for public administration but the general public may be largely indifferent if performance is otherwise adequate. As such, policies that aim to improve diversity are likely to meet with indifference despite the benefits detailed above. Second, the general public appears to be similarly indifferent to the publicness of service delivery, consistent with Meier et al. (2019). Our findings indicate that people form judgments towards the public sector based on underlying social attitudes (see also Marvel, 2016), rather than who is providing the service. Public managers must be aware that public opinion regarding matters such as service performance or contracting decisions may simply reflect deeply ingrained prejudices or political partisanship rather than the matter at hand.

Although representative bureaucracy scholars have long argued that levels of representation should impact public perceptions of government, the research presented here tested this argument directly and considered whether service delivery by government employees or contractors may make a difference. To date, representation scholarship has not considered contractors as potential actors serving subgroup interests. It is useful for representation scholars to know that public reactions do not change regardless of whether services are being provided by government or contractors. Our findings provide evidence that this is not important, but further testing is required.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Russell Sage Foundation (grant number 1807–07074).

ORCID iDs

Keith Baker

Ellen V. Rubin

Keith Baker is an associate professor at SUNY (State University of New York) Brockport. His work focuses the organization and governance of state–society relationships. He earned his PhD from the University of Birmingham, UK.

Ellen V. Rubin is an associate professor at the Rockefeller College of Public Affairs at the University at Albany, State University of New York. Her work focuses on public personnel management including understanding perceptions of fairness in the public sector workplace and organizational performance. She earned her PhD from the University of Georgia.

Stephen Weinberg is a research associate at the Center for Policy Research at the University at Albany, State University of New York. He earned his PhD from Harvard in Economics and is an expert in behavioral economics and health policy.

Christopher T. Stout is an associate professor at Oregon State University. His research interests include racial and ethnic politics, gender and politics, political behavior, representation, and Congress. He earned his PhD at the University of California, Irvine.

Appendix

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