U.S. Department of Labor (DOL) 

Employment and Training Administration (ETA) 

Privatization and Employment 

in the Implementation of 

Temporary Assistance for Needy Families (TANF): 

A Multi-Level Analysis 

Prepared by: 

Adam M. Butz, Ph.D. 

Political Science 

University of Kentucky in Lexington 

This paper was funded, either wholly or in part, with Federal funds from the U.S. 

Department of Labor, Employment and Training Administration (ETA) under Contract 

Number DOLJ061A20380. The contents of this publication do not necessarily reflect 

the views or policies of the Department, nor does mention of trade names, commercial 

products, or organizations imply endorsement of same by the U.S. Government. This 

paper was one of seven prepared under the 2009 ETA Research Papers Program, 

which competitively awarded doctoral and post-doctoral students funding to conduct 

original research and prepare scholarly papers on topics relating to the workforce 

investment system. 
Introduction

A growing literature in public administration and governance examines the 

effect of organizational form and administrative ownership on policy outcomes and 

bureaucratic performance, namely the impact of “privatization” in public service 

provision. At its core, privatization involves the utilization of market forces to operate 

in formerly public-based industries and public services (GAO, 1997; Winston, 2002; 

and Kemp, 2007), and has in recent decades garnered attention among governments 

both internationally and in the United States as a tool of administrative governance 

(Hodge, 2000 and Salamon and Lund, 1989). 

In the context of American policy implementation, privatization refers most 

often to the “contracting-out” of publicly funded administrative activities to private, forprofit and non-profit, “third-party” vendors. These private sector entities then perform 

the functions of traditional government agencies at the street-level, delivering public 

goods directly to citizens. In other words, private organizations perform the day-to-day 

administration of government-sponsored activities from refuse collection to prison 

maintenance and human support services (Hodge, 2000). 

While concepts and application of privatization may vary, the basic thrust of 

privatization research remains very similar, linking market forces to performance, or 

system outputs (Winston, 2002). The lure of free-market gains has led countries with 

transitional economies to privatize state-owned enterprises, such as the agriculture and 

textile industries in China, with the hopes of improving economic productivity and 

performance at the system-level, increasing production yields or gross domestic product 

(Li and Rozelle, 2000). In America, which already has an established liberal economic 

system, privatization is manifested in private entities undertaking the administrative 

1 
responsibilities of public policy implementation, with the expectation of improving 

bureaucratic service quality and reducing operating costs at the street-level (Kemp, 

2007; Brudney, 2005; Winston, 2002; Boyne, 1998; and Savas 1988)1. 

Although privatization is achieving heightened popularity as a means to deliver 

public services, the implications of privatization for bureaucratic functioning, especially 

in the social policy arena, remain unknown and strikingly understudied. One school of 

thought argues that privatizing human support services, such as employment training or 

public assistance programs, will open up competition and choice among alternative 

providers, producing superior outcomes for program clients (Savas, 1998). Another line 

of reasoning asserts that social service provision is rife with ambiguity, complexity, and 

market failure, and hence privatized entities will be unable to deliver human support 

services more effectively and equitably than government agencies (Heinrich, 2001 and 

Hodge, 2000). 

This research endeavors an original examination of privatization and 

redistributive social welfare policy in America. An era of welfare reform culminated in 

1996 with passage of the Personal Responsibility Work Opportunity Reconciliation Act 

(PRWORA), resulting in public assistance programs that center on employing welfare 

recipients rather than simply distributing cash assistance (Soss, 2001). This shift in 

bureaucratic functioning associated with welfare-to-work means that welfare offices are 

now required to serve prospective employees, meaning that administrative success is 

measured in terms of their ability to move welfare clients into the labor force. It is of 

 

1

 The term “street-level” was coined most famously by Lipsky (1980) and refers to operations taking 

place on the front-lines of public service provision (i.e., garbage collection, mail delivery, medical care, 

and employment assistance), the actors or stakeholders undertaking/receiving service provision 

(organizational management, employees, and clients being served), and the local environment in which 

they function. 

2 
interest to Federal employment agencies alongside state and local policymakers to 

better understand the efficacy of these administrative arrangements in producing 

employment and earnings for low-income individuals and families. 

Traditional models of social policy implementation are being replaced with 

decentralized and contractually privatized methods of service delivery. Yet, the 

determinants of welfare contracting across jurisdictions and the implications for the 

employment outcomes of low-income welfare recipients remain an empirical mystery in 

need of exploration. 

Three empirical questions set the stage for this research: 

1. What are the antecedents of welfare contracting across the American states? 

Why are states more or less inclined to privatize welfare services? 

2. How does variation in administrative ownership influence the program 

outcomes of welfare clients? Are welfare clients served under private providers 

working more often and earning more income? 

3. Is privatized welfare provision being administered equitably across different 

clientele groupings? 

Opportunities, Motivations, and Welfare Contracting 

Both the flexibility and restrictiveness of PRWORA legislation has increased the 

administrative role of the private sector in delivering welfare services. In short, states 

have the freedom to uniquely administer PRWORA’s lead public assistance program, 

Temporary Assistance to Needy Families (TANF), but also face restrictive Federal 

performance directives that make privatization an ever-appealing option (Sanger, 2003). 

The impetus for welfare privatization on one hand stems from the inherent 

flexibility of PRWORA legislation, which gives states the ability to pursue unique 

bureaucratic approaches outside of the public arena. TANF eases important restrictions 

in terms of case management, granting private entities the authority to determine 

3 
program eligibility and compliance; functions that were once strictly under public 

control (Sanger, 2003; Winston, 2002). For the first time, states have the ability to 

contract-out entire welfare offices to private entities that administer the program from 

start (eligibility) to finish (case closure). While TANF provides an opportunity for 

states and localities to increase the role of private organizations in welfare 

administration, it by no means guarantees this outcome. TANF also generates 

motivations for states to pursue alternative methods of delivery. 

Welfare devolution and sub-national autonomy was accompanied by a set of rigid 

Federal performance standards mandating work requirements for TANF clients and 

general caseload reduction. States and localities have broad latitude in adopting and 

administering TANF policies; however, federal block-grant financing is contingent 

upon performance in moving welfare recipients into the workforce2

. TANF mandates 

that after 24 months in the program, welfare clients must participate in employment 

activities for a minimum of 30 hours per week in exchange for monetary support. 

Furthermore, recipients can only receive cash assistance for a total of 60 months under 

federal time-limit restrictions (Soss, 2001). TANF is meant to provide temporary cash 

assistance in route to full employment and self-sufficiency. 

This type of intense client-case manager interaction (i.e. servicing potential 

employment candidates) was foreign to many public welfare agencies, which had 

minimal experience turning welfare recipients into successful employees. In turn, states 

have responded to the national dictum of employment assistance and caseload reduction 

 

2

 TANF abolished the federal “entitlement” to welfare financing, characterized by an open-ended 

matching-fund system in which the government would subsidize state welfare operations by matching 

state contributions nearly dollar for dollar. The new financing regime is based on “block grants” that 

afford the states a fixed amount of TANF dollars to spend on welfare administration, meaning that states 

now bear the full economic brunt of any welfare servicing costs exceeding the block-grant allotment. 

4 
by experimenting with fresh bureaucratic approaches, creating an increased role for 

private entities in the delivery of TANF services (Sanger, 2003; Winston, 2002). 

Given the opportunities and motivations to privatize welfare services, states have 

indeed heeded the call. As of 2001, every state but South Dakota has instituted some 

form of TANF privatization, often contracting substantial portions of their TANF 

dollars with private, for-profit and non-profit, service providers (GAO, 2003). 

Unfortunately, the popularity of privatized social services has not attracted 

commensurate scholarly attention, and the discipline still lacks definitive answers about 

both the origins and effectiveness of welfare contracting. The first section of this report 

briefly explores patterns in welfare contracting across states. The second section 

examines the quality of TANF program outcomes across various welfare service 

providers within the state of Florida. 

Exploring the Determinants of Welfare Contracting 

PRWORA affords private entities unprecedented authority to determine program 

eligibility and manage the daily operations of welfare offices; however, it is state and 

local officials that ultimately choose to direct administrative authority to private 

vendors or not. The budding embrace of privatization in welfare implementation across 

states is evident, but the factors that explain the presence or absence of privatization 

across states and localities remain largely unknown. Through shifting to a system of 

potentially privatized service delivery, federal legislation grants policy researchers an 

exclusive opportunity to explore the patterns in privatized contracting occurring across 

sub-national jurisdictions. We know that state and local governments are establishing 

privatized administrative arrangements, but we know little about how these 

5 
arrangements are formed and the ultimate ramifications for bureaucratic functioning and 

performance. This paper expands the contracting literature through undertaking an 

initial exploration into the potential determinants of welfare privatization across the 

American states. 

An abundant literature investigates the predictors of state and local contracting 

decisions and with good reason. Prior to any bureaucratic arrangement impacting 

administrative functioning and program outputs, the decision to “make or buy” service 

provision must be undertaken by government actors. Because of the recent dramatic 

changes in welfare policy rules and administrative structuring taking place across the 

American landscape, it is imperative that the discipline begin to unlock the puzzles as to 

why governments are instituting specific bureaucratic arrangements under TANF. 

While scholars have conducted studies of contracting across multiple policy areas (Bel 

and Fageda, 2007; Brudney, 2005; Price and Riccucci, 2005; Boyne, 1998; and 

Ferris, 1986), this research represents the first systematic exploration of the potential 

factors that influence patterns in welfare contracting across the American states. 

Dependent Variables

Three separate dependent variables capturing variation in TANF privatization 

are utilized in cross-sectional analyses at the state-level. All measures originate from 

the U.S. General Accounting Office (GAO) and are regrettably available for only one 

point in time, the year 2001 (GAO, 2002), thus this research is limited to “static” 

Ordinary Least Squares (OLS) regression analysis3. 

 

3

 OLS regression is a popular statistical technique used to uncover linear quantitative relationships 

through estimating unknown parameters that express how much a dependent variable changes (and in 

what direction) with a one-unit movement in an independent variable. These estimated parameters 

6 
The first dependent variable represents a state’s general proclivity toward 

privatizing TANF administration, and is measured as the total-value of privatized TANF 

contracts as a percentage of total TANF spending (TVPrivate)4. This measure serves as 

the primary dependent variable of interest, representing a state’s fiscal effort directed 

toward privatized TANF contracting. Univariate statistics illustrate that Washington 

DC and Mississippi contracted the greatest proportion of TANF funds to private entities 

(74 and 71 percent, respectively), while South Dakota chose to retain pure public 

administration. The average state in 2001 had 15.16 percent of their total TANF dollars 

contracted-out to private providers with a standard deviation of 16.31 percent, 

indicating the large amount of variability present in the TVPrivate measure5. 

The same study by the GAO additionally published the percentage of privatized 

TANF funds devoted to both non-profit and for-profit organizations, also for the year 

2001. These measures represent the next stage of the contracting decision when 

jurisdictions choose the particular types of private organizations that will deliver 

services. The data shows that states overwhelmingly preferred to contract services with 

non-profit providers. On average, states devoted approximately three-fourths (74.45 

percent) of privatized funds toward non-profit organizations, leaving the average state 

with just 25.55 percent of privatized funds afforded to for-profit firms. These measures 

 known as “beta coefficients” tell researchers whether or not relationships are statistically significant (i.e., 

effects distinguishable from zero), and tell researchers the unit change in the dependent variable for a 

one-unit shift in an explanatory variable. In the multivariate regression analysis found here, the beta 

coefficients represent the unit change in the dependent variable for a one-unit change in an independent 

variable, “controlling for” or holding all other independent variables constant. 

4

 The “total TANF spending” denominator in this measure includes both Federal block grant dollars and 

state “maintenance of effort” (MOE) funds. 5

 Washington DC is excluded from the statistical analysis because it lacks values for several of the 

independent variables that are only measured for the 50 states. 

7 
(NonProfit and ForProfit) will allow us to understand not only why states contract-out 

generally, but also why they tend to favor certain organizational ownership. 

Independent Variables 

What factors might potentially explain variation in TANF contracting patterns 

across states? Why are states more or less likely to direct TANF funds to private firms? 

The antecedents of TANF contracting across states are borrowed largely from research 

by Boyne (1998) and Brudney (2005) and include four sets of explanatory variables. 

Also included are two supplementary hypotheses that are germane to welfare politics 

specifically and may further explain why states are more or less likely to contract-out 

TANF dollars. All premises are discussed further below. 

Service Supply and Marketplace Competition 

 According to public choice theory, it is not organizational form necessarily, but 

rather marketplace competition in service supply that yields performance gains. In turn, 

when state agencies are deciding how much of their TANF dollars to contract-out, they 

should have the competitive environment in mind and will likely only privatize 

substantial amounts of resources when sufficient competition among rival providers 

exists (Brudney, 2005). When private entities compete amongst each other for 

government contracts, program costs are more likely to be reduced and quality more 

likely to improve, and thus privatization should be a more appealing option in “make or 

buy” decisions. Utilizing proxies from past research (see Brudney, 2005), the log of 

8 
total number of service business establishments and the log of total population in a state 

are hypothesized to be positively related to TANF contracting6

. 

H1: States with larger populations and greater numbers of service establishments will 

contract-out a greater proportion of TANF funds to private entities. 

Public Employee Strength 

A second set of variables that may explain variation in TANF contracting is 

endorsed by both Boyne (1998) and Brudney (2005), and relates to the strength of 

public employees in terms of labor rights and union representation. The Federal 

Association of State and Local Municipal Employees has deplored privatization efforts 

perceiving the movement to privatize public services as a threat to their occupational 

well-being. Public unions argue that private entities in welfare implementation will 

simply “profit off the poor” rather than bring improved quality and bureaucratic 

efficiency (Berkowitz, 2001 and Brophy-Baermann, 2006). States with a stronger base 

of public employees should encounter heightened resistance to privatization, and thus 

the strength of public employees should be negatively associated with TANF 

contracting. 

For the sake of parsimony in a 50-state analysis, this paper employs one 

consistent measure of public employee strength found in past research, the ratio of 

government employees to total state population. Although this measure does not tap 

into the power of public unions directly, researchers argue that it is a valid indicator of 

the relative strength of public employees to oppose privatization efforts. Furthermore, a 

 

6

 Log of state population is admittedly not an ideal proxy for marketplace competition. This variable 

more than likely is capturing several different indicators beyond the presence of multiple providers, such 

as levels of urbanism, social service demand, and propensity for policy innovation. It is oftentimes 

included in studies of administrative contracting and for exploratory purposes is included in the analysis 

presented here. 

9 
consistent negative association between public-employee ratio measures and 

administrative contracting is found in the literature, while union-based measures have 

decidedly mixed effects (Boyne, 1998 and Brudney, 2005). Thus, this research 

hypothesizes that a state government’s full-time equivalent (FTE) employment per 

10,000 population will be negatively related to TANF contracting. 

H2: Public employee strength will be negatively related to TANF contracting. States 

with a higher percentage of full-time government employees will contract-out fewer 

TANF funds to private entities. 

Political and Ideological Factors 

A consistent theme in the research on contracting decisions involve political and 

ideological factors that can enhance or impede privatization efforts. The trend toward 

privatization in public service provision in recent decades has been ignited by 

conservative elites with stalwarts championing the involvement of the private sector in 

public affairs and policy implementation. Although many liberal Democratic elites 

eagerly signed onto PROWRA in 1996, others on the left remained openly skeptical of 

the reform measures, arguing that effective service provision for the impoverished can 

only be achieved through direct and sustained government involvement. For instance, 

one New York Senator claimed that imposing time limits on welfare benefits and 

removing public responsibility for the poor represented “the most brutal act of social 

policy since Reconstruction”, and that these approaches would “invite an urban crisis 

unlike anything we have known since the 1960s” (Rector, 2006). 

When states are deciding to contract-out TANF funds, the policy preferences of 

state government actors should shape the acceptance and prevalence of privatized 

administration. Ideological conservatism embraces the free market ideals that undergird 

10 
administrative privatization, while liberal ideology tends to focus on equity and 

accountability in provision that is more readily achieved with government involvement. 

This research hypothesizes that the ideological liberalism of state governments 

(measures provided from Berry, et. al., 1998) will be negatively related to the 

percentage of TANF funds directed at private service providers7

. 

H3: The liberalism of state governments will be negatively related to TANF 

contracting. More liberal states will contract-out fewer TANF funds to private entities 

than relatively conservative states. 

Fiscal Conditions 

 The final set of antecedents informed by past research center on the fiscal 

conditions of states that can provide motivation to privatize social services and free-up 

state resources (Kodrzycki, 1998 and Brudney, 2005). These scholars succinctly point 

out that fiscal pressure on governments (and the motivation to assuage such pressures) 

comes from two-sides of the ledger. On the one hand, states require revenues to run 

government-sponsored operations, while on the other hand, they must deal with citizen 

demand for services. 

States with a greater capacity to raise tax revenues will feel less fiscal strain than 

those states with a reduced capacity and will not have the same pressure to pursue 

innovative cost-cutting measures such as privatization in TANF implementation. When 

 

7

 The ideological orientation of state governments is constructed from five indicators that combine 

 ideology scores of governors and state legislators of both parties, weighted by the proportional 

composition of the respective parties in the state legislature. The ideology scores of state legislators are 

derived from interest group ratings (Americans for Democratic Action and the American Federation of 

Labor and Congress of Industrial Organization Committee on Political Education) of U.S. Congressmen 

(based upon some assumptions about ideological congruence between state and national policymakers). 

These ratings range from 0-100 with a score of 0 indicating pure conservative voting positions in 

Congress and 100 representing a strictly liberal voting record. The ideology scores of governors are 

derived from the average ideology score of state legislators of the same party (based upon some 

assumptions about ideological congruence between state legislators and governors that share party 

identification). Higher values on the index indicate more liberal orientations. 

11 
state governments have a reduced capacity to generate tax revenue, they are motivated 

to hunt for bureaucratic efficiency and privatization is one probable solution to doing 

“more with less” (Brudney, 2005 and Greene, 1996). This research employs a measure 

of fiscal capacity developed by Yilmaz et al. (2007) that combines a state’s revenue 

capacity with its need for outlaying public expenditures. States with stronger fiscal 

capacity have an expansive tax revenue base and score higher on the index, whereas 

states with weaker fiscal capacity generally have a small revenue base and more often 

need to expend resources, and score lower on the capacity index8. 

H4: Fiscal capacity will be negatively related to TANF contracting. 

 On the opposite side of the fiscal coin, states handle state citizenries that demand

public services and have the potential to drain state fiscal resources. When states must 

craft and implement social policy in the face of heightened needs or demand for public 

services, levels of fiscal stress rise as states exhaust resources and expenditure. In the 

TANF context more specifically, the financing structure shifted to block grants 

awarding states a “fixed-amount” of welfare funds and mandated that states put up 

“maintenance of effort” (MOE) funds that reflected previous welfare effort. In short, 

the generous matching entitlement found under Aid to Families with Dependent 

Children (AFDC) has been replaced by a system that potentially adds fiscal stress on 

state budgets, as states must now bear the financial brunt of any services that extend 

beyond the fixed block grant. 

 

8

 The Yilmaz et al. fiscal capacity measure includes several indicators of tax revenue capacity or the 

ability to raise revenues (tax capacity, tax effort, etc.) and relates those indicators to a state’s need for 

expenditures. Higher values indicate stronger fiscal capacity. See Yilmaz et al. (2007) for more details. 

12 
Thus, states with a greater level of need or demand for cash assistance should be 

more likely to contract-out services, exploring innovative and efficient ways of serving 

program clients. States with lower levels of social service demand should feel less 

fiscal pressure and less desire to seek third-party administration. Although the Yilmaz 

et al. fiscal capacity measure does encompass program demand to a certain degree, this 

study also includes straightforward measures of social service demand. In the welfare 

policy literature, two measures routinely capture service demand. These include the 

state poverty rate and ratio of welfare recipients to total population, both of which 

should be positively associated with TANF privatization. 

H5: Program demand will be positively related to TANF contracting. States with 

higher poverty rates and higher ratios of TANF recipients will contract-out a greater 

proportion of TANF funds to private entities. 

Racial Politics 

A long history in political science research documents the negative connection of 

African Americans to the welfare state. Negative stereotypes of African Americans as 

“lazy” and “undeserving” have been found to incite restrictive welfare spending 

preferences among White Americans (Gilens, 1999). Research in state policy adoptions 

has consistently shown that a greater presence of African Americans leads to diminutive 

monetary benefits and punitive program rules (Key, 1949; Hero, 1999; Soss et al., 2001; 

and Fellows and Rowe, 2004). These racial considerations can be extended to TANF 

administration in that states with more minorities should be interested in shedding direct 

public responsibility for the “marginalized” or “hard-to-serve” poor away from 

government and embrace third-party solutions. 

13 
H6: The percentage of a state’s TANF rolls comprised of African Americans will be 

positively related to TANF contracting. 

Lower-Class Mobilization 

Research in state welfare policy has consistently found that when lower-class 

citizens are mobilized and participate actively at the ballot box, they are rewarded with 

generous cash benefit levels and eased program requirements (Avery and Peffley, 2005 

and Hill, Leighley, and Hinton-Andersson, 1995). Interest groups representing poor 

individuals such as the Welfare Information Network have published several works 

documenting the potential pitfalls of “profiteering off of the poor”, arguing that firms 

will decrease client attention and service quality to maximize profits (Yates, 1998). 

When individuals with depressed socioeconomic status are mobilized, participating in 

legislative advocacy and in the voting booth, it is in the electoral interest of 

policymakers to react with policy “responsiveness”, favoring a greater role for 

governmental implementation over privatized approaches (Avery and Peffley, 2005 and 

Hill, Leighley, and Hinton-Andersson, 1995). This research hypothesizes that lowerclass mobilization at the ballot box in a state is negatively related to TANF 

privatization. 

H7: Voter turnout among lower-class citizens vis-à-vis upper-class voters will be 

negatively related to the proportion of TANF funds allocated to private service 

providers 

State-Level Privatization Model: 

У(Privatization)s = β0 + β1(Competition/Size)s + β2(PubEmployee)s + 

β3(Ideology)s + β4(Capacity)s + β5(Demand)s + β6(Race)s + 

β7(Mobilization)s

s = state 

14 
Analysis and Findings 

The estimations of TANF contracting across the American states are found in 

Table 1. From the results in the baseline TVPrivate model, it appears that patterns in 

TANF contracting are determined by at least four primary sets of factors: state size, 

fiscal capacity, minority caseloads, and low-income mobilization. The TVPrivate

model accounts for a modest 31 percent of the variation in privatized TANF spending 

(R2 = .306), but several variables are statistically significant in expected ways as 

predicted by theory9. 

An interesting initial observation of the results in Table 1 is the lack of 

significance among several familiar variables thought to be associated with contracting 

decisions. The strength of public employee measure is insignificant in every model. 

Every measure of social service demand, including the state TANF caseload-topopulation ratio and poverty rate, is insignificant, along with the state ideology variable. 

Liberalism among state governments does not yield fewer TANF contracts just as 

conservatism does not increase privatization, most likely highlighting the pragmatic 

nature of privatization as a universally accepted, all-purpose administrative strategy, 

embraced by both liberal and conservative governments alike. Previous research has 

failed to uncover any consistent ideological link to privatization, and scholars such as 

Price and Riccucci (2005) have documented a positive association between liberalism 

and administrative contracting. The politics of TANF privatization seemingly extend 

beyond conventional left-right ideological divisions. 

 

9

 None of the independent variables were statistically significant in either the ForProfit or NonProfit 

models. This is most likely because the state-level data is highly skewed toward non-profit contracting 

(essentially 75% of TANF dollars went to non-profits so there is little variation to explain in the models). 

15 
Table 1. Determinants of TANF 

Contracting Across the American 

States, 2001 

IVs TVPrivate S.E. 

Competition 

Log-Pop 

Log-Bus 

.447* (.262) 

1.25 (2.21)

PubEmploy 

FTE PerCap -.023 (.049)

Ideology 

GovtID .067 (.109)

Capacity 

FiscalCap -.523** (.228)

Demand 

CasePop 

Poverty

.858 (.768)

-2.27 (1.77)

Mobilization 

UC-Turnout .192* (.103)

Minority 

AA-Caseload .331* (.168)

N 

R2

 50 

 .306 

Note: OLS coefficients in bold, with robust 

standard errors clustered by states in parentheses. 

**p<.05; *p<.10. 

 As predicted by theory, TANF contracting is more prevalent in more populous, 

presumably more competitive states, where numerous rival contractors set the necessary 

conditions for successful ventures into the private sector. As seen in Table 1, increasing 

by one unit on the log-population scale increases the proportion of TANF funds devoted 

to private entities by approximately 0.45 percent (.447, p < .10). Admittedly, the 

population variable may be capturing much more than the presence of multiple potential 

service providers. More populous states also tend to be more urbanized and innovative, 

and possess the resources to draft professional contracts and monitor the activities of 

private entities, thus public accountability can be maintained without the government 

administering services in-house (Brudney, 2005). Because the arguably more valid 

16 
measure of marketplace competition (the log of service business establishments) was 

insignificant, it is unclear that policymakers are responding to the presence of multiple 

providers specifically, or rather responding to other underlying factors present in more 

populous states. 

Interestingly, states with higher percentages of African Americans on the welfare 

rolls are also more likely to contract out TANF services to private organizations (.331, 

p < .05). Considering the well-documented racial connection to the American welfare 

state, this could very well be evidence that state governments are attempting to shed 

direct public responsibility and accountability for this historically disadvantaged group. 

Once again the substantive impact is arguably trivial as a one percentage point increase 

in the African American TANF caseload increases privatized TANF spending by .33 

percent, but the variable achieves a high level of statistical significance and should not 

be overlooked in future research of social service contracting. Consistent with the 

broader welfare policy literature, race additionally appears to be a determining factor in 

TANF contracting decisions10. 

On the other hand, states are seemingly less likely to privatize TANF funds when 

they have strong fiscal capacity. As hypothesized, the OLS beta coefficient is negative 

indicating that states with higher values on the Capacity variable are less likely to 

contract TANF funds with private providers. Another way to interpret the Capacity

variable is that states with weaker fiscal capacity are more apt to privatize TANF 

services. Since privatization is largely perceived to be an effective approach to 

 

10 The contracting models included controls for urbanism (percentage of the population that lives in an 

urban area) and region (South, or Non-South) that could potentially explain away the racial connection to 

TANF contracting. These two variables were insignificant to the estimations and did not meaningfully 

change the racial caseload coefficient. 

17 
achieving bureaucratic efficiency or doing “more with less”, states with a weakened 

capacity to collect tax revenue and fund human support programs are more likely to 

utilize the private sector in their administrative operations (-.523, p < .10). Through 

contracting government services to the private sector, policymakers in low-capacity 

states can continue to maintain an adequate level of public goods, satisfying constituent 

desires for services while simultaneously reducing the daily administrative 

responsibilities of the government. 

An opposing point of view argues that privatization should accompany fiscal 

capacity because policymakers will respond to public employee demands for continued 

employment when public budgets are stressed (see Pallesen, 2005). Although there is 

some empirical support for the notion of increased contracting with fiscal capacity, an 

inverse relationship between capacity and TANF contracting exists for several potential 

reasons. TANF services represent a relatively small portion of state budgets and the 

influence of established public welfare bureaucracies have arguably been weakened by 

social service retrenchment associated with conservative reforms such as the Workforce 

Investment Act (WIA), Job Training and Partnership Act (JTPA), and PRWORA. 

Additionally, most Americans hold unfavorable views toward government-sponsored 

public assistance programs, thus when policymakers confront fiscal constraint they may 

be more likely to remove direct government involvement in these activities, embracing 

private sector solutions to welfare administration. 

Lastly, although there is little evidence in this analysis to suggest that state 

governments respond to tangible program demand in the form of higher welfare 

caseloads or deepening poverty conditions when contracting TANF dollars, there is 

18 
some evidence that policymakers are responsive to electoral pressures from low-income 

voters. When upper-income voters turnout at a greater rate at the polls vis-à-vis lower 

income earners, fewer TANF funds are directed at private firms (.192, p < .10)11. Put 

another way, there is an upper-class participatory bias in favor of privatization in that 

higher turnout rates among wealthier voters is associated with increased contracting and 

privatization of government social services; whereas, when poor populations are 

mobilized electorally, they are awarded with increased public control over welfare 

services. Just as mobilized interest groups can impact TANF policy adoptions (Avery 

and Peffley 2003), their influence also extends to administrative structuring in the 

modern welfare state. 

Exploring patterns in TANF contracting is an appropriate first step in this 

research, but the principal concerns of this paper deal with the efficacy of TANF 

contracting in producing superior program outcomes. If privatization is successful then 

TANF clients should be participating in work activities to a greater degree and exiting 

welfare because of employment and earnings. The next section examines the impact of 

privatization on welfare clients and the outcomes they experience in the TANF 

program. 

 

11 The Avery and Peffley mobilization measure is constructed as the percentage of upper-class voters 

divided by the percentage of lower-class voters multiplied by 100. Higher scores on the mobilization 

measure indicate greater upper-class turnout vis-à-vis lower class voters. Thus, the positive coefficient 

for the Mobilization variable indicates that upper-class turnout yields increased contracting, validating the 

notion that lower-class mobilization yields fewer TANF contracts consistent with existing theoretical 

arguments. 

19 
Linking Privatization and TANF Outcomes 

In the era of entrepreneurial government and welfare reform, policy authority 

and responsibility of social service implementation has shifted downward to state and 

local governments, and these governments have openly embraced privatized contracting 

structures to deliver human support services at the street-level (Winston, 2002). The 

basic rationale for privatization revolves around familiar microeconomic and publicchoice arguments that private markets, opening up competition and choice between 

providers and consumers, increases service quality and reduce costs of service 

provision12. In short, proponents of privatization argue that contracting with third-party 

vendors boosts performance and improves administrative outputs, producing the highest 

quality or greater quantity of services while expending the fewest resources or inputs13. 

Brief Review of the Literature 

Proponents of privatization herald the improved quality and cost efficiency when 

market forces are introduced into government sponsored activities (Savas, 1988). As 

appealing as the basic arguments for privatizing public services may be on the surface 

of pure microeconomic or public-choice theory, the effect of privatization on 

bureaucratic performance and client outcomes remains an empirical question in need of 

objective analysis. There is no assurance that privatized organizational forms produce 

services of higher quality and lower cost, and market theory points to several conditions 

 

12 Social-choice theory is essentially a microeconomic rational-choice framework applied to government 

officials, bureaucratic agents, and the mass populace (Pack, 1987). Private firms subjected to rival 

competitors and client choice will rationally seek improved quality and production at the lowest costs so 

that the government will continue contracting with these entities. The livelihood of the private agency 

and its employees is dependent upon its performance and ability to maximize profit. 

13 Brudney (2005) confirms this rationale in his survey of state administrators. The administrators cited 

reduced costs and improved quality, along with increased flexibility in operations as the primary reasons 

for pursuing privatized contracting. 

20 
that must be present, such as marketplace competition and informed consumer choices, 

before superior performance will be realized. In turn, scholarship operating under the 

rubric of “new public management” has begun the analytical task of linking 

privatization to program outcomes14. 

 

Scholarly pursuits linking privatization to bureaucratic performance outside of the 

social policy and welfare arena are substantial, but the primarily case-study 

methodologies are limited and the empirical evidence is decidedly mixed. For instance, 

Boyne (1998) performs a meta-analysis of several quantitative studies examining 

privatization and administrative outcomes across various activities such as garbage 

collection, asphalt lying, and fire and police protection. The evidence reveals that 

privatized contracting in general reduces the costs of service provision but does not 

necessarily improve service quality. The analysis warns that cost reduction in 

privatization may often come at the expense of quality, as underbidding on contracts is 

eventually felt by a strained labor force on the front lines. Other studies do find that 

privatization improves quality while at the same time reducing costs in routine 

maintenance services such as refuse collection and asphalt laying (Bendick, Jr., 1989 

and Donahue, 1989), but the limited nature of the quantitative evidence leaves any 

empirical generalities on shaky ground. 

Administrative research in the social policy arena is relatively sparse, but a number 

of studies do support the purported benefits of privatization. For instance, Price (1997) 

finds that private, for-profit firms are more effective in treating clients in drug abuse 

14 See Lynn, Heinrich, and Hill (2000) for an introduction to the empirical strategies of “new governance” 

that focuses on the “science” of street-level administration in terms of explaining and predicting 

“outcomes” or bureaucratic “performance”. If research can isolate the factors that improve/hinder 

bureaucratic outcomes then improvements in public service functioning can occur more readily. 

21 
programs. Rosenau (2003) finds that privatized medical care facilities induce quality 

medical services at lower prices and overall program cost. Lastly, Heinrich and 

Fournier (2004) and Heinrich (2004) find that patients in substance abuse programs 

served by profit seekers and non-profits are less likely to relapse into drug abuse and 

more likely to be working full-time. Other studies are not so sanguine toward the 

prospect of privatization in social policy administration, finding that the performance 

improvement in job-training programs is minimal or non-existent as public agencies 

routinely outperform their privatized peers (see Heinrich, 2000 and Hodge, 2000). 

Administrative activities that assist human clients, such as employment assistance 

or public safety, are more ambiguous (and arguably complex) and it is not clear that 

governments benefit through economies-of-scale or savings from avoiding investment 

in heavy equipment. Human support services in particular have a decidedly lowincome, comparatively disadvantaged clientele, whose concentrated demand for 

attention and occupational guidance can cut into privatized profits and eventually 

undermine efforts to assist the poor. With these contradictory findings in mind, it 

becomes apparent that the researchers and policymakers lack the ability to consistently 

predict the effects of privatization on social services. 

The few studies that exist on welfare privatization are generally informal, 

qualitative case studies of privatized implementation within specific states and 

localities, or administrative manuals instructing states on how to contract-out welfare 

services in hopes of achieving effective performance (Curtis and Copeland, 2003; 

Liebshutz, 2000; Stevenson, 2003; Breaux, Duncan, and Keller, 2002; Iverson, 2000; 

and Pavetti, Wemmerus, and Johnson, 1999). While these studies often provide in22 
depth descriptions of bureaucratic structures and the potential effects of welfare 

privatization, none focus specifically on linking privatization to employment outcomes 

and none systematically examine variation in privatization and performance across a 

large number of cases. 

One published study systematically examines the relationship between TANF 

contracting and performance outcomes across “workforce regions” in the state of 

Florida (Crew and Lamothe, 2003). The authors find that regions utilizing government 

agencies performed on par and often more proficiently than private sector providers. 

Interestingly, government agencies were found to provide welfare services at lower cost 

than private, for-profit organizations. These public-based organizations also induced 

superior quality across several aggregate performance measures, such as work 

participation rates and average client earnings. 

While this singular piece of evidence lends credence to the notion that privatization 

will do little to improve bureaucratic functioning and the outcomes of welfare clients, it 

only represents one study of privatization in TANF implementation and ultimately fails 

to examine the program outputs of individual program participants. Privatizing TANF 

services may not work to improve client services but more rigorous and comprehensive 

tests are warranted. 

Why Privatize? The Role of Ownership and Market Forces 

The structural and internal organizational characteristics of private firms 

themselves should improve agency performance (Kemp, 2007; McConnell et. al, 2003; 

and Donahue, 1989). Most importantly, ownership in private organizations is held by 

an entrepreneur or few identifiable owners who have a clear, vested self-interest in 

23 
improving their firms operations, procuring and renewing government contracts over 

rival firms while maximizing revenues and financial self-enrichment. Private sector 

firms are inherently more accustomed to competitive forces and less formalized 

internally than government agencies, characterized by a relatively streamlined and 

decentralized organizational structure of adaptive decision-making and operations. 

Private actors are at liberty to design unique and innovative operations that will 

most readily increase revenues in the case of profit-seekers or maximize goodwill in the 

case of non-profits. Profit-seekers and non-profit managers have the flexibility to 

directly control production activities and resource allocation, adapting to changing 

business climates through briskly expanding or reducing the workforce or swiftly 

manipulating core business functions to meet existing and foreseeable challenges 

(Winston, 2002). In short, private firms tend to exude fluidity and flexibility in terms of 

personnel and operations that should lead to improved performance vis-à-vis 

government agencies. 

Private organizations retain the ability to openly hire and fire employees, 

meaning that they can rapidly increase capacity to meet demands or reduce excesses 

when necessary. Conversely, public organizations oftentimes encounter entrenched 

public employee unions and rigid civil-service requirements that impede flexibility in 

the hiring and firing of public employees (Brudney, 2005). Additionally, government 

agencies are oftentimes plagued by rigidity in terms of standard operating procedures 

and regulatory delays that routinely get the moniker of “red-tape” ( Kemp, 2007). 

Profit-seekers and non-profits, less hampered by burdensome regulations, can more 

easily operate void of red-tape obstruction and can respond immediately to the changing 

24 
task environments, thereby improving performance and the subsequent employment 

outcomes for TANF program clients. 

Ownership in government agencies, on the other hand is diffused across the 

broader tax-paying citizenry fostering the conditions for administrative stagnancy and 

ineffectiveness. When private sector firms fail to perform adequately, the adverse 

effects of contract and revenue loss are felt disproportionately by the primary owners 

and shareholders, who in turn have an undeviating economic interest to remain in 

business and maximize personal financial security. American citizens, the “owners” of 

government agencies, lack any such compelling economic interest in improving the 

functions of government agencies. Potentially ineffective government-based social 

service operations will only marginally affect individual taxpayers (because only a 

fraction of the citizenry utilizes means-tested social service programs, and any increased 

costs of government inefficiency will be spread across all taxpayers). In a similar vein, 

individual lobbying efforts for collective bureaucratic improvement are likely unfruitful 

(Donahue, 1998). 

Political rather than economic considerations and diluted organizational 

ownership among government agencies hampers the pursuit of quality improvement and 

optimal program outcomes in policy implementation. As administrative scholar, John 

D. Donahue puts it, “At best, activities [of government agencies] will drift out of 

alignment with the public interest in a more or less random way. A city government is 

probably more likely to repave Park Street, even when Maple Street gets more traffic 

and has more potholes, than is a private company to make blue sweatshirts when 

customers are clamoring for red ones” (p. 51). 

25 
Potential Pitfalls of Privatization

Market Failure 

Although many herald the performance gains that should follow administrative 

privatization, a substantial number of policy scholars espouse the potential pitfalls of 

taking such actions. One set of arguments rests on the notion that “perfect” and hence 

efficient, effective markets will rarely exist in the social policy arena and that market 

failure will provoke inferior and ineffective outcomes for program clients. There is 

some doubt that private firms will face sufficient competition from rivals in the 

contractual bidding process or that competition will remain substantial after awarding 

an initial contract (Gilman, 2001 and Sclar, 2000). If adequate competitive pressures 

are absent, private service providers will operate in an environment much like 

government monopolies, rarely, if ever, fearing contractual replacement by rivals and 

the motivation to improve performance will wane. 

A second possible market failure can occur among consumers of TANF 

services. Market theories assert that consumers must have both perfect information and 

provider choice in order for maximum service efficiency to be realized; however, 

consumer information and choice are often inadequate and unfeasible in the market for 

human support services. Welfare clients or “customers” by their very nature are 

relatively void of the resources needed to make informed consumer choices or seek 

viable alternatives and often are beholden to one particular local service provider 

(Gilman, 2001). In populous metropolitan settings, TANF clients may have the ability 

to seek services among multiple providers choosing the provider that best suits their 

needs, but that is no guarantee. More physically isolated rural clients will more than 

26 
likely be limited to only one option for supportive services. The inability of welfare 

clients to make informed decisions among viable alternatives should undermine the 

motivations of private firms to enhance service quality (Warner, 2004). 

Complexity and Ambiguity in Human Support Services 

Another set of arguments against privatization hinge on the type of services 

being provided. In the provision of “hard” services, such as asphalt laying or refuse 

collection, administrative outputs are physically tangible and easily observed and 

measured (Hodge, 2000). When hard services are performed, the providers’ objectives 

are clearly outlined and there is little room for flexibility or discretion in activities 

taking place at the street-level. Social services, on the other hand, are known as “soft” 

services in that they are directed at a human-based clientele where complexity and 

ambiguity in service provision abounds. 

Social service administrators are expected to handle human subjects, and in the 

case of TANF, aid these program clients toward fruitful employment and ultimate selfsufficiency. The programmatic tasks of hard services are substituted with more 

complex and arguably challenging endeavors that involve deep interpersonal attention 

and more front-line decision-making by case managers. These providers must educate 

the clients on program rules, ascertain extensive details into work and educational 

histories, and properly evaluate barriers related to child-care, transportation, and 

substance or domestic abuse. Case managers then must seek to address the various 

barriers, assist with job searches and, in some cases, supplement educational and other 

skills deficits, and closely monitor attempts to find employment. 

27 
A pressing concern among critics of privatization is that private (especially 

profit-seeking) firms will respond to complex and challenging administrative tasks by 

undertaking “creaming”, directing attention and resources disproportionately at those 

clients most likely to succeed in the program, while clients with debilitating barriers to 

program success receive inferior services and outcomes (Kemp, 2007 and Gilman, 

2001). Government agencies and non-profit organizations, immune to profit motives 

and shareholder pressure, should provide more equitable and service provision across 

all clientele groupings within the TANF client pool (Gilman, 2001 and Bendick, 1989). 

This is the point at which individual-level data becomes critical in the empirical 

analyses. Through disaggregating client-level data this research more accurately 

captures the dynamics taking place between organizational form, client characteristics, 

and individual employment outcomes. Those disadvantaged clients, with the lowest 

levels of education or prior work experience, for instance, may become worse off under 

privatized administration relative to their more advantaged peers. This research begins 

to dissect these unique interactive relationships by moving beyond wholly aggregate 

methodologies. 

The Case for Utilizing Client-Level Data 

Previous research utilizes the county (or county-level equivalents) as the level of 

analysis to measure both privatization (i.e., TANF provider type) and program outputs 

(i.e., work participation). While this unit-of-analysis is perfectly logical for measuring 

TANF providers that operate largely at the county level, this research argues that 

utilizing the individual-level outcomes of welfare clients will provide the clearest 

28 
picture of how administrative actions are impacting actual clients participating in the 

program. 

Studies that examine aggregate performance measures can fall prey to inferential 

bias known as the “ecological fallacy” because these researchers are making inferences 

about the outcomes of program clients from aggregate outputs, measured at the state, 

county, or agency level, for instance (Steel and Holt, 1996). The outcomes of chief 

interest to administrative scholars studying policy implementation operate at the clientlevel and occur firsthand among individuals, not states, counties, or welfare agencies. 

Aggregate measures “average-out” the individual variation in client outcomes occurring 

at the street-level and in the process may encumber accurate causal inference. 

Another advantage of employing data on clients rests on the predictive side of 

the empirical analysis. When aggregate outcome and explanatory measures are 

analyzed, the individual-level factors of clients that presumably impact eventual 

administrative outputs are largely ignored. Data on program clients includes a set of 

individual-level predictors, creating an improved ability to statistically control for the 

distinctive traits of participating clients that can impact eventual outcomes. This ability 

will afford key insight into the (client-level) reasons that individuals vary in their 

outcomes, while more completely isolating the independent effect of contextual 

measures (such as TANF provider type) on bureaucratic performance. 

 A final example of the analytical leverage inherent with client-level data is 

found in the unique hypotheses related to the potential effects of privatization that can 

be tested among exclusive subsets of TANF clientele. In short, the individual-level data 

can be disaggregated according to diverse client characteristics, allowing for distinctive 

29 
“interactive” effects with privatization to be explored. Through sub-setting the clients 

along “hard-to-serve” dimensions, such as racial identity, educational attainment, or 

marital status, we can better understand how these particular client types are fairing in 

various administrative environs. 

Quantitative Methodology 

Conceptualizing and Operationalizing Administrative Performance 

Research operating under the title of “new governance” or “new public 

management” suggests that researchers can (and should) attempt to analyze unique 

administrative arrangements and quality of the results that they produce, so that we can 

improve government functioning and public well-being (Heinrich and Lynn, 2000 and 

Ingraham and Lynn, 2004). Just as we can judge the performance of economies in 

terms of the outputs produced (such as GDP, inflation, and stock market returns), we 

can scrutinize social policy implementation in terms of the program outputs produced 

by front-line workers and experienced by the clients they serve. A better understanding 

of why agencies and individuals achieve various outcomes can eventually lead to 

knowledge of “best practices” that will maximize bureaucratic effectiveness at the 

street-level. 

In short, this particular framework borrows from theories of political economy 

and conceptualizes performance as the quality or superiority of outputs and outcomes 

achieved by administrative stakeholders and the clients served through public policy 

implementation. The specific outputs or performance measures under scrutiny will vary 

with the objectives or goals of specific policies being implemented. For instance, 

30 
policy analysis of Head Start educational programs will more than likely examine 

outputs related to educational achievement, operationalized perhaps as standardized test 

or other aptitude test scores (see Currie and Thomas, 1995) whereas studies of 

substance abuse programs will inspect performance outputs related to sobriety and 

employment (see Heinrich and Fournier, 2004). 

In the case of welfare implementation, the performance objectives of 

administrators had traditionally been to provide timely cash payments and undertake 

routine case management procedures (i.e., determine eligibility). Under TANF, the 

objectives shifted from timely benefit allocation to a decidedly work-centered approach 

(Soss, et. al., 2001 and Fellows and Rowe, 2004). Apart from explicitly promoting subnational policy autonomy, the stated goals (and subsequent performance objectives) of 

PROWRA are to promote employment and ultimately self-sufficiency, reducing longterm dependency on public assistance through sustained attachment to the labor force. 

Additional policy goals, including increasing rates of marriage and reducing out-ofwedlock births, are significant but doubtless secondary goals to increasing employment 

among low-income Americans and reducing chronic welfare dependency in America. 

PROWRA and its reauthorized legislative forms mandate that clients receiving 

assistance beyond 24 months must be engaged in full-time work activities that are 

tightly defined by Federal legislation (i.e., vocational training, community service, or 

actual employment). In order to curb long-term dependency, Federal legislation also 

mandated that clients receive cash assistance for no more than 60 months. States did 

have wide latitude to relax the strict national guidelines, exempting clients from work 

requirements or extending time limits, but the fact remains that maximum Federal block 

31 
grant subsidies are based upon moving individuals off the rolls and into a life of selfsufficiency. 

The dependent variables capturing the quality of program outputs are measured 

and analyzed at the individual-level across TANF clients in multi-level models. In the 

quantitative analysis, employment-based program outputs that are of keen interest to 

both policymakers and Federal agencies alike are the primary focus here. Client-level 

outputs are utilized in multi-level models discussed in detail below and will include 

items such as individual work participation, employment status, earnings from 

employment, and sanction for non-compliance with program requirements15. 

Intrastate Analysis: A Hierarchical Approach

 This research employs an intrastate “hierarchical” methodology, known as 

multi-level modeling or hierarchical linear modeling (HLM), in which individual-level 

data are analyzed alongside higher-level contextual measures, such as TANF provider 

type (i.e., for-profit, non-profit, or public administration). In essence, HLM techniques 

allow the program outputs of individuals to be modeled simultaneously as a function of 

both client characteristics and measures at higher levels of analysis. 

HLM techniques have recently garnered popularity in social science research 

because of the distinctive ability to model outcomes with (linear and non-linear) 

 

15 In the original plan for this research, a variable for “unsubsidized employment” was included in the 

pilot of the survey but upon further investigation it was included as an optional survey item (secondary to 

the baseline employment question) and was left blank by nearly every respondent. Instead, this research 

includes a “sanction” variable that captures when clients receive punishment for non-compliance with 

program rules. When clients fail to abide by TANF program rules, perhaps working less than the 

required 30 hours per week or missing a scheduled meeting with a caseworker, clients are temporarily 

“sanctioned” off the welfare rolls. These clients oftentimes lose monetary benefits and employment 

resources, further hampering efforts to find meaningful attachment to the labor force. Through inclusion 

of this variable, researchers can better understand which clients are more likely to be sanctioned and 

which provider types sanction at higher/lower rates. 

32 
equations that estimate individual (level-1) and contextual (level-2) parameters 

simultaneously. In education research, for example, scholars can observe how pupillevel achievement varies across pupil-level traits while also observing the independent 

and interactive effects of higher-level variables related to families, schools, or school 

districts16. Administrative researchers of policy implementation and performance 

operating within the “new governance” or “new public management” frameworks 

recognize the hierarchical nature of relationships that are of interest to this research 

(Lynn, Hienrich, and Hill, 2000 and Ingraham and Lynn, 2004). 

Under the “new governance” framework, administrative outputs (O) are 

conceived as a function of five sets of variables that include client characteristics (C), 

administrative structures (S), environmental factors (E), policy treatments (T), and 

managerial roles and actions (M). This research is centered on examining variation in 

administrative structures (S), and understanding how privatized delivery structures 

interact with client characteristics (C) and the local environment (E) to impact TANF 

client outputs. In the multi-level models constructed here, level-1 variables represent 

the outcomes and characteristics of TANF clients, while level-2 variables are measured 

across counties or county-level equivalents and capture the local administrative, 

economic, and political environments in which implementation takes place. 

 

16 HLM simultaneously estimates two models: first, it estimates parameters among individual-level 

variables, while also estimating how individual-level outcomes vary across higher-level variables. HLM 

is a methodological improvement because instead of aggregating individual-level data to higher levels 

(then performing an OLS regression), individual-level slopes and intercepts are allowed to vary randomly 

across higher levels of analysis. Individuals nested within higher-level contexts are likely to share 

specific unobserved characteristics with level-2 variables. This violates the independence assumptions of 

non-hierarchical OLS techniques and assumptions regarding the (random and normal) distribution of 

error terms, thus HLM should produce more efficient estimations. 

33 
In order to capture more direct measures of the independent variable of utmost 

concern, TANF provider type, one must observe variation in welfare contracting taking 

place within states, where an exclusive blend of private and public agencies administer 

localized welfare services. TANF service providers overwhelmingly operate at the 

county-level, and counties (not states) often have sole responsibility for TANF 

implementation and contracting decisions (Fording et al., 2007 and Winston, 2002). 

This means that any macro, interstate analysis must be abandoned in favor of an 

intrastate approach that examines variation in privatized arrangements and individual 

client outcomes within particular states. Because welfare providers do predominantly 

vary at the county-level within states, undertaking an intrastate approach that 

exclusively examines county-level differences is methodologically appropriate17. 

Florida represents one immediate state of interest for the intrastate HLM 

analyses. In the aftermath of PROWRA, Florida pursued extensive second-order 

devolution, granting 24 “workforce regions” (operating in 67 Florida counties) the 

autonomy to contract-out services to either private, public, or a mix of agency types, 

and allowed private entities to operate entire local welfare offices18. Florida’s general 

demographic diversity and pursuit of decentralized bureaucratic control results in 

meaningful variation in provider types and street-level operations across workforce 

regions, making this state a popular choice for welfare reform researchers. To date, all 

systematic quantitative studies published on TANF privatization and performance 

 

17 One could feasibly pool data across multiple states and perform a multi-state analysis, but it would 

require the collection of localized provider type data to correctly match clients to welfare services. 

Unfortunately, county-level TANF contracting data is not readily available across space or time. 18 The Florida Department of Children and Families (DCF) did retain power to determine initial TANF 

eligibility. Private entities in Florida are potentially in charge of all case management and monitoring 

activities but cannot determine who is initially eligible to participate in the program. 

34 
examine Florida (Crew and Lamothe, 2003; Fording et. al., 2007), and the ready 

availability of provider type data in Florida makes this state an ideal place to begin 

studying privatization and outputs across TANF clients. 

Client-Level Data: Outcomes and Predictors 

 Data for individual TANF clients in Florida comes from a generous database 

compiled by the U.S. Department of Health and Human Services (DHHS) and includes 

six years of observations (2000-2005) for both “open” and “closed” TANF cases19. 

Individual program outcomes for “open” TANF cases serving as the dependent 

variables in the multi-level models include participation in work activities, earnings 

from employment, and sanctions for non-compliance. The work participation and 

sanction variables are all operationalized in a dichotomous fashion (meeting 

participation requirements (1) or not (0) and received sanction (1) or not (0)). Earnings 

from employment are measured continuously as the total income from work activities 

reported from the previous month in the TANF program20. 

The dependent variables employed in the “closed” case multi-level models 

include reason for closure and earnings from employment. Monthly earnings from 

employment are measured analogous to the “open” cases. There are two closure

 

19 “Open” cases represent TANF clients that were currently participating in the Florida TANF program at 

the time of DHHS data collection. “Closed” cases represent TANF clients who had previously 

participated in the TANF program, but were not currently receiving public assistance when the DHHS 

survey was administered. Open and closed TANF cases were surveyed separately and represent two 

distinct datasets, but both clientele groupings answered a relatively similar battery of survey questions, 

allowing for continuity and comparisons to be made among these different types of TANF clients. By 

examining open TANF cases, this research can better understand how current clients are fairing in the 

TANF program; whereas, examining closed cases will elucidate the reasons for case closure and earning 

potential after leaving the TANF program. See the Appendix for descriptive statistics that highlight the 

consistency of the individual-level variables among the two datasets. 

20 An “employment status” dependent variable was also included in the analysis for “open” cases, but the 

lack of statistical significance among the explanatory variables warrants its removal from the tables. 

35 
variables. The first represents closure due to employment or earnings and another 

relates to closure due to sanction for non-compliance. Both are measured in a 

dichotomous fashion (1 if case closes for reasons of employment/earnings and 0 if 

otherwise; 1 if case closes due to non-compliance (sanction) and 0 if case closes for 

other reasons). 

Individual predictors of program outputs for both “open” and “closed” cases 

include race, gender, education, and marital status. African Americans, Hispanics, and 

females have suffered through a history of occupational discrimination and continue to 

face unique challenges to employment that might incite substandard program outputs. 

Next, levels of education should predict program success in that clients with higher 

levels of education are theorized to have an easier time procuring employment and 

finding jobs that offer elevated wages. Lastly, clients that are married presumably have 

greater domestic stability and fewer burdens in their search for employment and might 

have more successful program outcomes than single clients. 

Client race, gender, education, and marital status are operationalized as a set of 

dummy variables (African American (1) or not (0); Hispanic (1) or not (0); female (1) 

or male (0); Less than 12 years of education (1) or 12 or more (0); and single (1) or 

married (0)). 

H8: Client characteristics affect the TANF program outputs experienced by clients. 

African American, Hispanic, single, and female identity will be negatively related to 

work participation, earnings from employment, and closure due to employment, and 

positively related to sanction for non-compliance. Education will be positively related 

to employment outputs and negatively related to case sanctioning. 

36 
Operationalizing Administrative Ownership and the Local Environment 

The provider type data used in this preliminary analysis is measured across 

counties in Florida and is measured from available data for the years 2000 - 2005 as a 

series of annual dummy variables. Crew and Lamothe’s data is available for the years 

1997-2001, thus the provider type and client-level data align only for the years 2000 

and 2001. Data from Fording et al. (2007) is available for the year 2004. Data for the 

years 2002, 2003, and 2005 were collected and coded by the author. The analysis 

follows the basic 3-part coding scheme developed by Crew and Lamothe in which 

workforce regions choose to retain governmental administration (Public), or contract 

out to either for-profit (ForProfit) or non-profit (NonProfit) organizations. Public

serves as the reference category throughout the analysis, thus the basic expectation is to 

observe higher quality employment outcomes (more work participation, higher 

earnings, and more closure due to employment) and fewer punitive sanctions among 

clients within for-profits and non-profits vis-à-vis public agencies21. 

Welfare contracting is pervasive and varied within the state of Florida over the 

2000-2005 time period examined in this study. TANF privatization is not exclusive to 

any particular geographic region in Florida, and privatization (especially with forprofits) has been increasing over the six years of observations. In 2000 and 2001 

government agencies operated TANF offices in nearly one in three Florida counties, but 

accounted for less than fifteen percent of welfare offices in 2004 and 2005. Public 

 

21 Provider type data is included for all years and Florida regions except for First Coast and Alachua in 

2002, 2003, and 2005. Mirroring the mutually exclusive coding scheme of Crew and Lamothe each 

region is coded either for-profit, non-profit, or public depending on which provider type was most 

prevalent in each respective fiscal year. The vast majority of locales chose to contract exclusively with 

either for-profits, non-profits, or public agencies, but a minority did utilize a hybrid approach. For 

instance, a blend of providers operates in Miami-Dade but non-profits are most common from year to 

year and thus it is coded as non-profit throughout the entirety of the dataset. 

37 
welfare agencies oftentimes work in tandem with the Department of Children and 

Families and are primarily housed local community colleges (Gulf Coast Community 

College in Bay County, St. Petersburg College in Pinellas County, and Pensacola 

Community College in Escambia County for example), or other institutions of higher 

learning (Florida Atlantic University in Palm Beach County). 

A variety of non-profits operate TANF offices across the state of Florida. 

Approximately one-third of Florida counties contract with non-profit organizations, and 

that figure stays relatively steady across the 2000-2005 time period. Catholic Charities 

and Goodwill Industries were awarded contracts over the six year period examined here 

in counties such as Miami-Dade, Broward, and Leon-Tallahassee. Other locally-based 

non-profits operating TANF offices are the Florida Institute for Workforce Innovation 

(Highlands County), Workforce Connection, Inc. (Okaloosa and Walton Counties), and 

Experience Works (Duval, Baker, Putnam, and Nassau Counties). 

Several for-profit firms operate TANF offices in Florida, and for-profit 

contracting was increasing during the 2000-2005 time period. This is largely due to 

Affiliated Computer Services (ACS), the most prevalent for-profit administrative entity 

in the state of Florida. By 2004, ACS operated welfare offices in approximately onethird of Florida counties. There is not much turnover in welfare providers from year to 

year in Florida, but ACS has managed to successfully increase TANF market share 

across counties. Other profit-seeking firms operating in Florida include the Paxen 

Group (Pasco and Hernando Counties), Kaiser Group (Brevard County), and the 

Training Institute (Martin and St. Lucie Counties). 

38 
Two additional control variables are included in the preliminary analysis 

presented below. The average county unemployment rate captures the local 

occupational environment within which clients and administrators must operate. 

Secondly, the county percentage of the vote for the Democratic candidate in the 2000 

presidential election is utilized as an indicator of the local political environment. 

Intrastate Hierarchical Logit Model: 

Level 1: Ln [Pic/1-P ic] (Program Outputs) = β0 + β1(African 

American)iwr + β2(Latino)iwr + β4(Gender)iwr + β5(Marital Status)iwr + 

β6(Education)iwr 

 Level 2: β0c = γ00 + γ1(Public)wr + γ1(ForProfit)wr + γ2(NonProfit)wr 

+ γ3(Unemployment)wr + γ4(Ideology)wr 

iwr = Individual client in workforce region 

wr = workforce region 

Analyses of Interactive Effects across Client Groupings

Utilizing data on TANF clients and the accompanying individual controls, 

allows for a set of interesting analyses related to racial politics and “hard-to-serve” 

TANF clientele. One line of the anti-privatization argument suggests that private firms, 

in their effort to maximize profit and retain contractual authority, will curtail the quality 

of services to those especially hard-to-serve clients or “marginal” populations such as 

African American or low educated clients (Gilman, 2001). 

When served by private, especially for-profit providers, these hard-to-serve 

clients may be given less or inferior services and experience sub-optimal program 

outcomes, participating in fewer work activities and for lower wages. Disadvantaged 

clients may also be sanctioned more often than advantaged peers. The hard-to-serve 

39 
may find it relatively difficult to achieve quality program outcomes irrespective of the 

administrative environment but should be served more equitably by public and nonprofit service providers, providing the best chance for program success. Only HLM 

analyses that simultaneously analyze the impact of distinctive provider types and client 

characteristics will help to flesh out these empirical puzzles. 

 Through utilizing cross-level interaction terms, the following analysis 

additionally examines the potentially relationship between privatization and program 

outcomes of African American clients and those program participants that lack a high 

school education22. 

Analysis and Findings

The results of the preliminary pooled multi-level estimations for the years 2000 - 

2005 are arranged into eight (8) tables23 24. Tables 2-5 contain the baseline noninteractive HLM estimations (Tables 2 and 4) and subsequent “odds ratios” (Tables 3 

and 5) for both open and closed TANF cases. In short, privatized arrangements are only 

 

22 Cross-level interactions in HLM test weather higher-level variables moderate or condition the 

relationship between individual-level variables. In the interaction models presented here, welfare 

providers are hypothesized to condition the relationship between race/education and program outputs. 

In other words, the interactive models demonstrate how the outputs of African Americans and loweducated clients vary across administrative ownership and test whether individual effects vary in 

significant ways across differing provider types. 

23 All hierarchical estimations were performed in HLM (version 6.06). 

24 It can be argued that pooling the entire TANF sample is methodologically inappropriate. Because the 

TANF caseload is traditionally and disproportionately comprised of singe females, this group should 

exhibit unique program dynamics and be examined separately from male and married clients. However, 

approximately one quarter of the Florida TANF clients are married in both the open and closed case 

datasets, providing sufficient variation for inclusion of a marital status variable in the final pooled 

models. Secondly, the HLM models were estimated separately for females only and the substantive 

results do not change. The reason removing men from the datasets only has a negligible impact on the 

coefficients is largely because there are very few men in the datasets originally. Women make up 93.3% 

and 92.5% of the open and closed TANF cases respectively, thus removing men does not meaningfully 

change parameter estimates. There should be some caution in interpreting the highly skewed gender 

variable in the pooled models, but the other coefficients remain all but unchanged when men are removed 

from the sample. 

40 
seldom found to significantly impact the quality of program outcomes for both open and 

closed TANF cases in Florida, and they often impact TANF clients in an inconsistent 

and unexpected manner. 

 Table 2 presents the baseline (non-interactive) results from the pooled HLM 

estimations for open TANF cases in Florida. The individual-level (level-1) predictors 

are often significant in expected ways. Female, single, and low-educated status is 

associated with less participation in full-time work activities. Female and low-educated 

TANF clients additionally earn less monthly income from employment in the initial 

model, and low-educated clients are associated with higher rates of sanctioning than 

more educated peers. While the Latino variable proves to be insignificant to the 

estimations, African American identity is negatively related to work participation and 

positively related to the probability of being sanctioned (.634; p <.05). 

Turning to the level-2 measures in Table 2, only one provider type variable 

achieves statistical significance. Clients served by profit-seekers earn nearly $30 (-

28.86; p < .10) less in monthly earnings than those served by public agencies. One 

note of caution concerning the level-2 coefficients. Multi-level models are appropriate 

to analyze hierarchical data and should produce unbiased estimates, but these findings 

cannot be interpreted as definitively causal at this juncture. No effort is directed at 

explicitly modeling contracting decisions below the state-level, thus it is assumed 

throughout the analysis that workforce regions privatize welfare at random. This 

assumption is likely tenuous. Localities may be contracting for reasons that are 

systematically related to unobserved individual characteristics and outcomes. In other 

words, it might be that for-profit TANF clients are earning less monthly income because 

41 
for-profit contracting is more likely to occur among more disadvantaged clientele. 

Hence, this analysis should be perceived as exploratory and associative rather than 

causal and definitive. 

 While the regression coefficients for monthly earnings can be interpreted in a 

straightforward manner, the coefficients for the logistic HLM regressions are not 

directly interpretable, thus “odds ratios” are calculated in Table 3 for both the work 

participation and sanction models because these outcomes are measured in a 

dichotomous fashion25. Odds ratios calculate the probability of one event occurring 

(i.e., participating in full-time work activities) as opposed to another event occurring 

(i.e., not participating in full-time work activities) and will be used to discuss the 

substantive findings throughout the rest of the paper. 

 The statistically significant odds ratios presented in the work participation model 

in Table 3 (column 1) are all less than one, indicating a reduced odds of participating in 

full-time work activities. Females and single clients have a 46.4 percent (1.00 - .536) 

and 55.7 percent (1.00 - .443) reduced odds of participating in full-time work activities 

relative to males and married clients. While the odds of being in full-time work 

activities is a paltry 4.1 percent (1.00 - .959) less for those clients without a high school 

education, the odds that African Americans are participating in full-time work activities 

is a robust 35.3 percent (1.00 - .647) less than for non-African American clients. 

 

25 Odds ratios are utilized to indicate the “odds” of an event taking place relative to the “odds” that an 

event will not take place and is calculated as the probability of an event occurring (P) divided by the 

probability of an event not occurring (1 – P). Because logistical regression coefficients represent the 

change in the “log of odds” and are not directly interpretable, odds ratios are often used to put coefficients 

into a substantive context. An odds ratio of “1” indicates that one event is just as likely to occur as 

another. An odds ratio > 1 indicates an event is more likely to occur than another, while < 1 indicates an 

event is less likely to occur. For instance, an odds ratio of “1.60” means that the odds of one event 

occurring is 1.6 times more likely than another, or that the odds of an event occurring is 60 percent (1.60 

– 1.00) more likely than another. 

42 
Table 2. Pooled HLM Analysis of TANF Privatization 

and Client Outcomes for Open Cases, 2000-05 

IVs WorkPart Earnings Sanction 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

-.623*** -15.27* .069 

(.142) (8.94) (.047) 

-.041*** -52.45*** .094* 

(.005) (18.37) (.052) 

-.815*** -4.72 .064 

(.069) (4.78) (.241) 

 -.435*** 5.27 .634** 

(.092) (5.18) (.032) 

 .098 9.06 -.163 

(.074) (6.56) (.401) 

Contextual Level 

ForProfit 

NonProfit 

 .049 -28.86* .083 

(.086) (16.05) (.055) 

-.081 7.13 -.079 

(.085) (6.01) (.054) 

N 4624; 67 4624 4624 

Note: Multi-level logit and regression (Earnings) coefficients in bold, with 

robust standard errors clustered by county in parentheses. ***p<.001, **p<.05, 

*p<.10. 

The odds ratios in the sanctioning model in Table 3 indicate that those clients 

lacking a high school education are 1.11 times (or 11%) more likely to have had their 

case sanctioned for non-compliance than more educated clients. A similar positive 

relationship is even more dramatic for African American clients, whose odds of being 

sanctioned for non-compliance are 89 percent or nearly two times (odds ratio = 1.89; p 

< .05) greater than clients that are not African American. Put another way, for every 

non-African American TANF client that is sanctioned in Florida, there are nearly two 

African Americans sanctioned. 

43 
Table 3. Pooled HLM Analysis Odds Ratios 

for Open Cases, 2000-2005 

IVs WorkPart Sanction 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

.536*** 1.07 

(.076) (.505) 

.959*** 1.11* 

(.005) (.005)

.443*** .938 

(.007) (.241) 

.647*** 1.89**

(.021) (.519) 

1.10 .851 

(.081) (.340) 

Contextual Level 

ForProfit 

NonProfit 

1.05 1.49 

(.090) (.668) 

.923 -.684 

(.078) (.054) 

N 4624; 67 4624 

Note: Odds ratios in bold, with robust standard errors clustered 

by county in parentheses. ***p<.001, **p<.05, *p<.10. 

Tables 4 and 5 contain the baseline logistical regression results (Table 4) and 

subsequent odds ratios (Table 5) of the pooled HLM estimations for closed TANF cases 

in Florida. Once again, the individual-level variables largely behave in an expected 

fashion. In the pooled closed case results found in Tables 4 and 5, lacking a high school 

education is negatively associated with case closure due to employment or earnings, and 

low educated clients earn slightly less monthly income from employment (-15.94; p < 

.01). Female and unmarried clients also earn less monthly income, and females are also 

1.6 times (odds ratio = 1.63) more likely to have their case closed due to sanction for 

non-compliance (see Table 5). 

Once again, the Latino variable proves insignificant to the estimations, but race 

continues to be a significant predictor in the models. The odds that African American 

44 
clients exit TANF due to employment are 21.5 percent less than for non-African 

Americans in this sample. Put another way, for every five non-African American 

TANF clients exiting due to employment or earnings, there are fewer than four African 

American clients exiting due to employment. Furthermore, African American identity 

is associated with a 16 percent increase in the odds of being sanctioned off the welfare 

rolls. This relationship is not as strong as the one observed among open TANF cases 

yet remains consistent with the initial open cases findings and with previous research 

(See Fording et. al., 2006). 

Table 4. Pooled HLM Analysis of TANF Privatization and 

Client Outcomes for Closed Cases, 2000-2005 

IVs CloseEmpl CloseSanct Earnings 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

-.079 .489** -43.77** 

(.145) (.124) (19.08) 

-.247*** .018 -36.61*** 

(.086) (.063) (9.84) 

-.011 .014 -17.97 

(.087) (.073) (11.44) 

-.241** .146** -3.76 

(.087) (.074) (11.59) 

-.062 .039 -7.02 

(.105) (.091) (14.43) 

Contextual Level 

ForProfit 

NonProfit 

 -.025 .279* -24.57**

(.093) (.083) (8.55) 

-.081 .104 -7.02 

 (.101) (.078) (13.68) 

N 4341, 67 4341 4341 

Note: Odds Ratios in bold, with robust standard errors clustered by county in 

parentheses. ***p<.001, **p<.05, *p<.10.

 

45 
Table 5. Pooled HLM Analysis Odds Ratios for 

Closed Cases, 2000-2005 

IVs CloseEmploy CloseSanct 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

.999 1.63*** 

(.145) (.202) 

.781** 1.02 

(.058) (.064) 

1.11 1.01 

(.097) (.074) 

 .785** 1.16** 

(.068) (.085) 

 .941 1.04 

(.099) (.093) 

Contextual Level 

ForProfit 

NonProfit 

.975 1.32** 

(.091) (.110) 

.922 1.11 

(.092) (.086) 

N 4341; 67 4341 

Note: Odds Ratios in bold, with robust standard errors clustered by county 

in parentheses. ***p<.001, **p<.05, *p<.10. 

Directing attention to the county level provider type (level-2) variables in Tables 

4 and 5, the administrative environment does not seemingly impact TANF clients in 

consistently superior ways. Clients served by profit seekers and non-profits are no more 

likely to exit TANF due to employment or earnings, and clients in for-profit settings 

have a 32 percent greater odds of having their case closed due to sanction. Once again, 

It is unclear if this is due to underperformance among for-profit firms, or the notion that 

for-profits are more likely to appear in disadvantaged settings, but the findings should 

give the reader pause26

 

26 For the sake of brevity and parsimony, control variables including county unemployment, ideology, 

and year dummy variables are not included in the tables. The ideology measure (county vote for Gore) is 

insignificant to the estimations. Future research should develop different and more valid measures of 

political ideology that get beyond mere electoral returns. The unemployment variable is only significant 

in the closed case models. Being from a high unemployment county is inversely associated with case 

closure due to employment and monthly earnings from employment. 

46 
Analyses of Interactive Privatization Effects across Client Groupings

There is a fear among opponents of social policy privatization that certain 

disadvantaged clientele groups will be treated inequitably by private (especially forprofit) vendors leading to inferior program outputs. This is simply not the case for loweducated clients that lack a high school education. No significant interactions were 

found for low-educated clients and thus these estimations are not discussed further in 

the paper. The employment, earnings, and sanction outcomes of low-educated clients 

do not vary significantly across differing administrative arrangements. Low-educated 

clients are seemingly no worse off when served by profit-seekers and no better off when 

served by non-profits and public agencies. 

Program outcomes of African American clients are found to differ significantly 

across administrative arrangements, but there is little evidence to suggest that 

privatizing welfare services inevitably leads to systematically inferior outcomes for this 

historically disadvantaged group of clients. The interaction terms for open cases found 

in Table 6 are largely insignificant to the estimations, but there remain some interesting 

findings in need of discussion. The Profit*Black coefficient is negative in column 1 

indicating that African American clients served by profit-seekers are less likely to exit 

TANF due to employment, but the coefficient does not achieve accepted levels of 

statistical significance, so the null hypotheses (i.e., administrative arrangements have 

zero effect on client outcomes) cannot be rejected27. 

 

27 Although African American welfare clients vary throughout the state and are found in every Florida 

county, they are disproportionately represented in urban centers. Nearly a quarter of African American 

clients live in Miami-Dade County. Almost eleven percent live in Orange County (Orlando), nine percent 

in Broward County (Fort Lauderdale), and approximately seven percent reside in Duval (Jacksonville) 

and Hillsborough (Tampa) Counties respectively. 

47 
Table 6. Pooled Interactive HLM Analysis of TANF 

Privatization and Client Outcomes for Open Cases, 2000-

2005 

IVs WorkPart Earnings Sanction 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

-.615*** -15.07* .073 

(.028) (8.94) (.472) 

-.041*** -52.31*** .093* 

(.005) (15.07) (.053)

-.817*** -4.71 .065 

(.015) (4.79) (.241) 

-.317** 14.34 -.941* 

(.144) (10.12) (.049) 

 .105 10.01 .107 

(.021) (6.63) (.406) 

Contextual Level 

ForProfit 

NonProfit 

-.163 -9.20 .075 

(.126) (8.94) (.054) 

 -.102 12.01 -.321 

(.123) (8.71) (.504) 

Interaction 

Prof * Black 

NonPr *Black 

-.212 -7.27 .694 

(.172) (12.14) (.605) 

 .347** 9.46 -.071 

(.171) (12.04) (.061) 

N 4624 ; 67 4624 4624 

Note: Multi-level logit and regression (Earnings) coefficients in bold, with robust 

standard errors clustered by county in parentheses. ***p<.001, **p<.05, *p<.10. 

48 
Table 7. Pooled Interactive HLM Analysis 

Odds Ratios for Open Cases, 2000-2005 

IVs WorkPart Sanction 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

.541*** 1.07 

(.063) (.508) 

.921*** 1.01* 

(.114) (.005)

.442*** .937 

(.031) (.226) 

.729** .392* 

(.105) (.004) 

 1.13 .898 

(.057) (.365) 

Contextual Level

ForProfit 

NonProfit 

.849 1.08 

(.107) (.543) 

 .903 .725 

(.111) (.381) 

Interaction 

Prof * Black 

NonPr *Black 

1.24 .725 

(.213) (.381) 

1.42** .499 

(.241) (.302) 

N 4624 ; 67 4624 

Note: Odds Ratios in bold, with robust standard errors 

clustered by county in parentheses. ***p<.001, **p<.05, 

*p<.10. 

On the other hand, the NonProfit*Black variable is positive and statistically 

significant suggesting that African Americans served under non-profit administration 

are more likely to exit TANF due to employment or earnings. The positive and 

significant Black coefficient suggests that African Americans are also more likely to 

exit TANF due to employment when served by public agencies (the reference category). 

Similarly, African American clients served by public agencies are less likely to be in 

sanction for non-compliance with program rules. The open case interactive estimations 

highlight that African American clients do fair well within government agencies, but 

49 
clients do not necessarily experience inferior outcomes when served by privatized 

welfare bureaucracies. Indeed, there are no significant negative racial effects among 

profit-seekers, and the odds of African American TANF clients participating in fulltime work activities is 42 percent (1.42 – 1.00) greater when served by non-profit 

organizations (see Table 7). 

 The closed case interactive coefficients and calculated odds ratios are found in 

Tables 8 and 9 further corroborate the notion that African Americans outcomes differ 

significantly across administrative arrangements but not in any systematically inferior 

manner. The positive and significant NonProfit*Black interaction terms in Table 8 

indicate that African American identity in non-profit settings is associated with an 

increased likelihood of exiting TANF due to employment and increased monthly 

income vis-à-vis African Americans served by public agencies (the reference category). 

Indeed, African American clients served by non-profits are 1.45 times more 

likely (45 percent increased odds) of exiting TANF due to employment relative to their 

for-profit and public peers. Next, the insignificant ForProfit*Black coefficients in the 

first two columns of Table 8 suggest that for-profit African American clients are no 

more or less likely to exit due to employment or be sanctioned off the rolls than their 

public counterparts. The positive and significant coefficient in the earnings model 

(column 3 in Table 8) unexpectedly suggests that African Americans served by profitseekers earn more monthly income after they exit the TANF program. Even as 

privatized administration is not found to consistently induce superior program 

outcomes, disadvantaged African American clients are oftentimes working and earning 

more when served by private, especially non-profit administration. 

50 
Table 8. Pooled Interactive HLM Analysis of TANF 

Privatization and Client Outcomes for Closed Cases, 2000-

2005 

IVs CloseEmploy CloseSanct Earnings 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

-.011 .486*** -43.61** 

(.146) (.124) (19.09) 

-.241*** .019 -37.56*** 

(.074) (.063) (11.14) 

-.087 .014 18.51 

(.088) (.073) (11.44) 

-.341** .221 -43.87** 

(.039) (.151) (23.67) 

 .024 .026 -9.93 

(.119) (.092) (14.38) 

Contextual Level 

ForProfit 

NonProfit 

-.171 .363** -39.26* 

(.149) (.143) (20.67) 

-.174* .144 -28.59 

(.162) (.132) (22.29) 

Interaction 

Prof * Black 

NonPr *Black 

 .151 -.192 61.78** 

(.207) (.167) (24.52) 

.374* -.092 51.82** 

(.042) (.156) (26.35) 

N 4341; 67 4341 4341 

Note: Multi-level logit and regression (Earnings) coefficients in bold, with robust 

standard errors clustered by county in parentheses. ***p<.001, **p<.05, *p<.10. 

51 
Table 9. Pooled Interactive HLM Analysis 

Odds Ratios for Closed Cases, 2000-2005 

IVs CloseEmploy CloseSanct 

Individual Level

Female 

LTHS 

Single 

Black 

Latino 

1.01 1.63*** 

(.146) (.202) 

.786*** 1.02

(.058) (.064)

1.11 1.01 

(.097) (.074) 

.711** 1.25 

(.111) (.188) 

 .966 1.02 

(.103) (.094) 

Contextual Level

ForProfit 

NonProfit 

.843 1.44** 

(.126) (.205) 

 .841 1.15 

(.136) (.153) 

Interaction 

Prof * Black 

NonPr *Black 

 1.16 .789 

(.241) (.144) 

 1.45* .918 

(.315) (.161) 

N 4341 ; 67 4341 

Note: Odds ratios in bold, with robust standard errors 

clustered by Workforce Region in parentheses. ***p<.001, 

**p<.05, *p<.10. 

Summary

Privatizing the administration of public policies through contracting with forprofit and non-profit service providers is a popular tool of governance, yet the 

implications of contracting decisions across human support policy remain understudied 

and relatively unknown. One recent case in need of study involves welfare policy in 

America. The latest wave of reform legislation devolved administrative authority to 

states and localities that have instituted privatized bureaucratic approaches at the streetlevel, in the hopes of achieving improvement in service quality (GAO, 2002 and 

52 
Winston, 2002). It is well documented that sub-national jurisdictions are privatizing 

welfare implementation under PRWORA, yet researchers have accumulated very 

modest knowledge about how variation in privatized administration affects the TANF 

program outputs experienced by clients. 

This analysis principally seeks to empirically connect administrative 

arrangements to TANF program outcomes through utilizing methodologically 

appropriate multi-level models that simultaneously incorporate and estimate both 

individual-level and contextual factors within the state of Florida. The primary 

expectation is to observe that both client-level factors and administrative arrangements 

affect TANF outcomes and to observe superior program outcomes among clients served 

under privatized administration vis-à-vis public agencies. On one hand, the individuallevel variables do display consistent and expected relationships with TANF outcomes. 

Clients that are female, single, low-educated, and African American are found to 

generally participate in work activities at lower rates, earn less in monthly earnings 

from employment, and are sanctioned at higher rates. Unlike level-1 predictors, there is 

little consistency among level-2 provider type variables and little evidence of privatized 

superiority in the initial pooled estimations. 

More often than not, the provider type variables are insignificant to the pooled 

open and closed case models (Tables 2 and 4), meaning that the likelihood of 

participating in full-time work activities or the likelihood of leaving TANF due to 

employment is no more likely in for-profit and non-profit settings than in public 

settings. When variables are statistically insignificant, the null hypothesis that 

privatization has no appreciable effect on TANF outcomes cannot be rejected. In this 

53 
case, nine of the twelve ownership coefficients found in Tables 2 and 4 are statistically 

insignificant, and the significant coefficients indicate unexpectedly that clients served in 

for-profit environs are earning less monthly income and are being sanctioned at higher 

rates. In short, there is little empirical evidence of privatized superiority in these 

models. 

One possible reason for the prevalence of null findings could likely pertain to 

the system of transparent “performance based” contracting that exists in the state of 

Florida. Although workforce regions within Florida are at liberty to contract-out 

welfare administration to private sector stakeholders, they are not at liberty to design 

unique contracts that outline desired performance objectives. Performance benchmarks 

related to work-participation rates and wage ratios, for instance, are centrally and 

uniformly developed at the state-level by the Florida Department of Children and 

Families. 

 Regardless of administrative ownership, all welfare providers in Florida are 

held to uniform performance standards, and performance outcomes are publicly 

scrutinized in quarterly “red” and “green” reports that rank how various regions are 

performing in terms of work participation and the like28. The uniformity of 

performance pressures inherent in the Florida contracting system likely mitigates any 

main effects of administrative ownership. When public agencies and non-profits must 

compete alongside profit seekers for similar performance-based contracts, the efficacy 

of privatization is seemingly decreased. Privatization is not an administrative panacea. 

 

28 Top performing regions are put in the “green” while underperforming regions are put in the “red”, and 

these summary TANF performance reports are distributed throughout the state. Having performance 

publicly scrutinized likely incentivizes welfare providers to optimize program outputs, irrespective of 

organizational form and ownership. 

54 
Future research should proceed in several directions. This analysis presented 

here utilized dummy variables (0 to 1 coding) to denote administrative ownership but 

there are likely other agency-level variables that matter to TANF employment 

outcomes. Factors found within different welfare agencies, such as organizational 

culture or managerial expertise, could theoretically impact TANF program outcomes 

and need to be explored in future research. In addition to agency-specific variables, 

other contextual variables, such as county-level business activity and other economic 

measures (poverty rates, county wealth, etc.) should also be explored. 

Another possible avenue for future research involves the incorporation of 

qualitative or descriptive evidence that augments statistical analyses. The research 

presented here collected and coded administrative ownership in Florida across several 

years, but did not flesh out what exactly administrative ownership entails. Through 

interviewing local TANF administrators in various privatized settings or observing the 

actual daily operations taking place, researchers can develop a more complete account 

of the contracting process and innovative operations taking place across administrative 

settings . 

Future research would also benefit from moving beyond the state of Florida. 

All current systematic quantitative endeavors connecting TANF administration to 

employment outputs are undertaken in Florida, thus the generalizability of any findings 

is incomplete until other states are examined. This research expands and largely 

confirms Crew and Lamothe’s previous assertions about the inadequacies of TANF 

privatization in Florida, but researchers and policymakers require a more general 

55 
understanding of the relationship between contracting and outcomes. This can only be 

accomplished by extending multi-level analyses to other state-level contexts. 

Although privatized welfare providers are not found to consistently induce 

superior employment outcomes among the general TANF population in Florida, the 

evidence in the final section of this report suggests that privatizing welfare 

administration also does not have deleterious effects on disadvantaged clientele. On the 

contrary, the outcomes of clients lacking a high school education were not found to 

differ significantly across administrative arrangements. In the case of another 

disadvantaged group, African American clients, it appears that being served by profitseekers does not decrease the likelihood that these clients will be working full-time and 

exiting due to employment. 

Furthermore, there is evidence that opening up welfare administration to private, 

non-profit organizations can assist African American clients. African American clients 

served in non-profit organizations are more likely to be participating in full-time work 

activities, more likely to exit TANF due to employment, and also earn more in monthly 

income than African American peers served by for-profits and public agencies. 

Although no steadfast causal claims can be put forth at this juncture, the exploratory 

findings presented in this study set the stage for continued research into the 

consequences of welfare contracting. 

56 
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Appendix 

Summary of Variables

State-Level Dependent Variables: 

TVPrivate – Range = 0 to 74, Total-value of TANF contracts to private service providers 

as a percentage of total TANF spending. Source: The General Accounting Office, 2002. 

Mean = 15.16, standard deviation = 16.31 

NonProfit - Range = 0 to 100, The percentage of privatized TANF funding afforded to 

non-profit organizations, weighted by the total value of TANF contracts. Source: The 

General Accounting Office, 2002. Mean = 74.45, standard deviation = 27.94 

ForProfit – Range = 0 to 100, The percentage of privatized TANF funding afforded to 

private organizations, weighted by the total value of TANF contracts. Source: The 

General Accounting Office, 2002. Mean = 25.55, standard deviation = 26.06 

State-Level Independent Variables: 

LogPop – Range = 5.69 to 7.54, The log (base10) of state population. Source: The 

University of Kentucky Center for Poverty Research State-Level Database, available at 

www.ukcpr.edu, 2000. Mean = 6.53, standard deviation = .453 

LogBus – Range = 1.44 to 2.94, The log (base 10) of service business establishments. 

Source: Statistical Abstract of the United States, 2000. Mean = 2.27, standard deviation 

= .491 

FTE PerCap – Range = 420 to 806, State government’s full-time equivalent (FTE) 

employment per 10,000 population. Source: Statistical Abstract of the United States, 

2000. Mean = 558.62, standard deviation = 68.06 

GovtID – Range = 5.38 to 97.5, Ideological score of state governments constructed by 

Berry, et. al., 1996. Higher scores indicate greater amounts of state government 

liberalism. Mean = 44.45, standard deviation = 27.06 

Capacity – Range = 54 to 149, Fiscal capacity index developed by Yatiz et al. Mean = 

101.96, standard deviation = 20.56 

CasePop – Range = .11 to 2.87, Percentage of total state population that is receiving 

welfare benefits. Source: The University of Kentucky Center for Poverty Research 

State-Level Database, 2000. Mean = .681, standard deviation = .421 

Poverty – Range = 6.1 to 20.3, Percentage of total state population that has incomes 

below the federal poverty line. Source: Statistical Abstract of the United States, 2000. 

Mean = 12.11, standard deviation = 3.22 

UC-Turnout – Range = 129.09 to 216.87, Percentage of the upper class that voted in a 

state divided by the percentage of lower class that did so, multiplied by 100. Constructed 

by Avery and Peffley, 1996. Higher scores indicate greater upper-class turnout vis-à-vis 

lower class voters. Mean = 172.23, standard deviation = 20.04 

AA-Caseload – Range = 0.3 to 83.8, Percentage of state welfare caseload that is headed 

by African Americans. Source: The University of Kentucky Center for Poverty 

Research State-Level Database, 2000. Mean = 34.57, standard deviation = 27.12 
Individual-Level Dependent Variables: 

Work Participation – Range = 0 to 1, Coded 1 if meeting federal TANF work 

participation requirements. Mean = .256, standard deviation = .483 (open cases) 

Sanction – Range = 0 to 1, Coded 1 if case has previously received or is currently 

sanctioned for non-compliance with program rules. Mean = .187; standard deviation = 

.315 (open cases) 

Earnings from Employment – Range = 0 to 2,416, Reported monthly earnings from 

employment. Mean = 83.05, standard deviation = 249.67 (open cases); Mean = 94.67, 

standard deviation = 293.68 (closed cases)

Reason for Closure Employment/Earnings – Range = 0 to 1, Coded 1 if exit TANF due 

to employment or earnings. Mean = .205; standard deviation = .404 (closed cases) 

Reason for Closure Sanction - Range = 0 to 1, Coded 1if closed due to sanction for noncompliance with program rules. Mean = .283, standard deviation = .451 (closed cases) 

Individual-Level Independent Variables: 

Black – Range = 0 to 1, Coded 1 if African American. Mean = .518, standard deviation 

= .499 (open cases); Mean = .513, standard deviation = .499 (closed cases) 

Latino – Range = 0 to 1, Coded 1 if Hispanic/Latino. Mean = .191, standard deviation = 

.393 (open cases); Mean = .208, standard deviation = .406 (closed cases) 

Female – Range = 0 to 1, Coded 1 if female. Mean = .933, standard deviation = .249 

(open cases); Mean = .926, standard deviation = .262 (closed cases) 

Single – Range = 0 to 1, Coded 1 if single. Mean = .747, standard deviation = .471 (open 

cases); Mean = .731, standard deviation = .444 (closed cases) 

LTHS – Range = 0 to 1, Coded 1 if have less than 12 years of formal education. Mean = 

.529, standard deviation = .474 (open cases); Mean = .599, standard deviation = .491 

(closed cases) 

County-Level Independent Variables: 

Public – Range = 0 to 1, Coded 1 if TANF is administered by government agencies. 

Mean = .103, standard deviation = .304 

ForProfit – Range = 0 to 1, Coded 1 if administered by for-profit firms. Mean = .411, 

standard deviation = .492 

NonProfit – Range = 0 to 1, Coded 1 if administered by non-profit organizations. Mean 

= .391, standard deviation = .488 

Unemployment – Range = 1.9 to 11.1, County unemployment rate. Mean = 3.89, 

standard deviation = 1.36 

Ideology – Range = .239 to .674, County percentage of votes for Al Gore in the 2000 

Presidential Election. Mean = .495, standard deviation = .086