Exploring the Influence of Federal Welfare Expenditures on State-Level New Economy Development Performance: Drawing From the Diffusion of Innovation Theory

Abstract

Functional theory suggests that each level of government expands in the arena in which it can best perform, reducing the price of federalism. Focusing on the functional pattern of American federalism, we suggest that increased federal welfare spending increases state government performance in the “new economy” development policy areas by helping states minimize welfare costs and divert more own-source resources into economic development. The central focus is on the direct and indirect empirical relationships between federal welfare spending and state new economy performance. The authors use an index of innovation capacity that reflects the cumulative performance of a myriad of overlapping and mutually dependent state policies intended to bring about new economy development; this index measures state new economy development performance by focusing on the observable outputs of such polices rather than the adoption, implementation, or substance of individual policy choices. Mediating variables, such as state fiscal comfort and administrative capacity, measure the indirect impact of federal welfare spending on state new economy performance. The authors find that federal welfare spending stimulates state new economy development directly, but also indirectly through its positive impact on both state fiscal comfort and administrative capacity. The findings suggest that federal intergovernmental transfers continue to be an important policy mechanism with spillover effects for state economies.

Keywords

federal welfare spending state new economy development performance policy innovation fiscal comfort administrative capacity

Public policy must accommodate diverse political preferences. Sometimes, policy may diverge from political intent as a result of partisan adjustment or delegation to the bureaucracy. The economic development policy landscape is complex from the result of numerous overlapping policies administered by different levels of government, different implementing agencies, and even different legislative committees. Policies may also generate externalities in the form of unintended consequences, particularly in an intergovernmental context. We examine the influence of federal welfare spending on state-level investment in new economy development policies—namely, demand-side policies aimed at growing the regional economic base in knowledge and service-based industries. To that end, we adapt and expand an existing index of state innovation capacity to measure the degree to which states evidence investment in new economy development, and to determine the extent to which these observed state policy impacts can be attributed to the unintended consequences of federal welfare policy choices. Drawing from the diffusion of innovation model, a state’s policies are considered a result of both internal and external determinants, including federal welfare spending and neighboring state performance. Intergovernmental transfers provide a fungible revenue stream that may allow states to adjust their spending priorities. Because of the sheer magnitude of federal welfare investment, there is potential for considerable displacement of fungible state funds away from redistributive purposes toward more competition-driven developmental policies.

Economic Development Policy Change

In the supply-side theory of economic development, which is derived from classical location theory, state and local economic growth is decided by input costs including land, labor, and capital. However, critics argue supply-side inducements have had little impact on state economic growth. As a result of the competitive pressures of globalization, states have become more involved in high-risk enterprises and activities, often under the rubric of entrepreneurial policy (Brace & Mucciaroni, 1990; Spindler & Forrester, 1993). Demand-side economic growth theory suggests that development policies should be designed to discover and improve new markets, produce capacity, and the quality of jobs for indigenous industries (Brace & Mucciaroni, 1990; Mahroum & Al-Saleh, 2013). Demand-side approaches emphasize “support for new and/or risky enterprises and ideas early in the product cycle (entrepreneurs and innovation) and rely on public-private strategic collaboration” (Hanley & Douglass, 2014, p. 223). As a result, U.S. states have invested in emerging markets and existing industries through progrowth innovation policies in which high technology and knowledge are the dominant economic forces (Hall, 2007; Hall & Howell-Moroney, 2012).

Entrepreneurial states develop new economic development strategies by promoting new technologies, providing venture capital, and supporting higher education (Eisinger, 1989; Storm & Feiock, 1999). States have become more involved in innovative high-risk enterprises and activities (Spindler & Forrester, 1993). They lead government toward more people-based strategies and toward production of knowledge necessary to innovate and commercialize new products and services.

Examples of both supply-side and demand-side policies are in place in most jurisdictions, but states vary in the timing and intensity of their adjustment toward demand-oriented strategies. The result is considerable variation in the associated increase of new economy innovation capacity from state to state. Consistent with recent literature on public sector performance, our focus is on the outcomes of such policy to better capture substantive investment rather than symbolic change (Poister, Aristigueta, & Hall, 2014).

Pursuit of economic development policies in the new economy may be driven by factors beyond the changing economic environment and competition. Studies have directly investigated intra- and interstate political and institutional characteristics that drive economic development policy decisions (Kwon, Berry, & Feiock, 2009; Saiz, 2001). These studies focus on internal state factors (political and socioeconomic conditions) and external factors (policy learning and interstate competition (Berry & Berry, 1990; Mohr, 1969). We suggest that federal welfare policy, as an external factor to state policy, influences the way states allocate their own fungible resources. Federal government grants in welfare areas displace state responsibility and provide slack resources that states may use to bolster new economy development.

We draw on the diffusion-of-innovation model as a conceptual framework for investigating state-level economic development performance in an area where the specific effects of a given policy are difficult to establish or attribute. This study informs regional and state economic development policies in the new economy by examining whether the federal government’s welfare apparatus unwittingly invokes state-to-state differences in new economy performance as an externality to federal welfare investment. Federal financial assistance for welfare programs spurs strategic innovation in state economic development efforts by reducing welfare costs and facilitating greater focus on economic development. Organizations are more successful when financial resources are stable and able to engage in strategic, rather than crisis, decision making (LeRoux & Wright, 2010). Federal welfare spending may stimulate states to pursue new economy development through the fiscal stability and administrative capacity it brings about.

Intergovernmental Policy and Externalities

State-level policy innovation has long interested scholars of state policy making (Walker, 1969). An extensive literature in this area has examined the circumstances and conditions that increase the likelihood of state policy adoption. Mostly, these works have explained that state policy adoption is influenced by internal characteristics including the political, economic, or social conditions within states (Sigelman & Smith, 1980; Tolbert, Mossberger, & McNeal, 2008; Walker, 1969). Others dealt with external forces that influence policy adoption (Case, Rosen, & Hines, 1993; Volden, 2002) and argued that state policy adoptions are a result of policy learning and diffusion from other states. Many studies have examined both internal and external determinants of policy innovation (Berry & Berry, 1990; Miller, 2011; Mohr, 1969). Adoption is often symbolic, and actual outcomes may be weakened by tepid effort or weak implementation. Considering these trends, the performance management literature has shifted from a focus on policy inputs to outputs and outcomes to gauge policy success. We look at policy performance through this lens—as a product of a diverse bundle of state laws that are variously implemented by several agencies with varied intensity. We take innovation capacity, therefore, as a proxy for the effectiveness of state policies to promote economic growth in the new economy. Because the relationship between innovation capacity and economic performance has already been established, our research focuses only on the formation of such innovation capacity over time.

Studies that focus on the link between national activities and state-level policy implementation explore the federal government’s ability to direct its cues (hearings, laws, introduction of bills, and financial incentives) to the adoption decision of states, a process sometimes referred to as top–down vertical policy diffusion (e.g., Karch, 2007, 2012; Nicholson-Crotty, 2003; Soss, Schram, Vartania, & O’Brien, 2001). Federal fiscal incentives increase the rate at which a new program spreads across states (Welch & Thompson, 1980). Allen, Pettus, and Haider-Markel (2004) found that the likelihood of state policy adoption increases when the federal government provides fiscal incentives and sends a strong, clear message to states. State ideological proximity to the federal government and other states influences policy adoption (Grossback, Nicholson-Crotty, & Peterson, 2004).

Numerous studies indicate that federal action increases the likelihood that states will follow suit; we would expect welfare spending to follow federal investment. Given the potential fungibility of state own-source revenues, however, we might see states divert their own funds toward alternative purposes, including economic development, as federal funds become available to support welfare spending. To this end, we investigate the degree to which the success of state-level new economy policies is attributable to the intergovernmental externalities of federal welfare policy. Variation in federal welfare contributions by state and over time allows us to investigate the influence of such spending on state economic development performance—especially that required for knowledge and high-technology–based industries. Regional economic growth can be achieved not only by transferring income through the tax system and outlays, but by reducing state welfare obligations (Bohte & Meier, 2000; Callen, 2009). Federal welfare spending may allow states to divert fungible own-source revenue to implement more politically popular economic development programs. In addition to this direct effect, federal welfare expenditures may also indirectly affect states’ new economy performance by enhancing state administrative capacity and fiscal comfort, as shown in Figure 1.

Figure 1.

Conceptual linkage between federal redistributive spending and state new economy development performance.

The Effect of Federal Welfare Funds on State Economic Development Performance

Federal and state governments pursue domestic economic policy goals differently. The federal government has traditionally focused on redistributive policy (reallocating economic resources from rich to poor) while states have pursued growth policy through developmental programs (managing physical infrastructure and social infrastructure (Drabenstott, 2005; Markusen & Glasmeier, 2008). The state and local share of welfare spending has steadily declined since 1962 while federal spending has more than doubled.

States compete to increase economic performance while reducing welfare costs. Under such constraint, increases to their welfare budgets create financial obstacles to their developmental goals. States face few critical obstacles in designing and implementing strategic economic development policies. Federal spending in program areas that states do not prioritize (like welfare) may liberate states to focus on progrowth development policies. Increased federal welfare spending would provide resources that reduce obstacles to improved state new economy development. Therefore, we posit the following:

Hypothesis 1: Federal welfare spending will allow states to pursue various new economy policies that will improve new economy development capacity scores.

This framework suggests that federal financial assistance for welfare programs produces state fiscal comfort. Federal welfare funds can help states increase expenditures on welfare policies that are necessary but politically unpopular. Withdrawal of federal aid from welfare programs would dramatically reduce states’ own support for welfare policies as well as state capacity to care for the needs of poor citizens (Chernick, 1998; Peterson, 1995; Powers, 2000). If states want to spend their money on developmental policy but welfare spending becomes a fiscal burden, then federal welfare support can lower the marginal cost of state welfare activity. Taxing businesses to increase welfare spending runs contrary to the logic of state economic development policy. In this sense, state fiscal comfort improves when a state has excess revenue from own sources after fulfilling its welfare obligations. We would expect a measure of comfort based on the proportion of state welfare spending supported by own-source revenues to be more closely correlated with federal welfare spending; however, welfare obligations offer only one piece of the puzzle in explaining state fiscal comfort. Fiscal comfort is the bridge through which federal welfare spending frees up state resources for other purposes. Federal welfare funds help to secure state welfare budgets and thereby increase fiscal comfort. We therefore suggest the following:

Hypothesis 2: Increased federal welfare spending in a state will improve overall state fiscal conditions.

States have difficulty protecting themselves from economic hardships such as increased unemployment, capital plight, and fiscal distress (Hankla, 2008; Oates, 1999). This leaves states more dependent on federal funding (Bartik, 1999; Markusen & Glasmeier, 2008). Federal spending can affect state policy making by increasing dependence on intergovernmental aid or by making federal policy choices more affordable (Allen et al., 2004; Chubb, 1985). States improve their administrative capacity to seek and manage federal aid to obtain more federal grant funding (Hall, 2008). Thus, we suggest the following:

Hypothesis 3: Increased federal welfare spending in a state will increase that state’s administrative capacity.

Federal welfare aid does not just affect program spending, but also enables states to reallocate their overhead—the administrative mechanisms necessary to deliver programs and services. Increased administrative capacity supported by federal welfare spending does not displace economic development administration, but rather frees states to allocate their fungible resources to support administrative capacity for economic development. Following our logic, federal welfare investments would induce spillover effects of improved state administrative capacity into economic development. Thus, federal welfare spending may also indirectly affect a state’s ability to design and implement innovative economic development programs. Increased federal welfare spending not only improves the level of social protection and security, but creates greater scale, leading to efficient administrative organization with fewer societal risks (Ahmad, Dreze, Hills, & Sen, 1991; McCallum & Blais, 1987).

The linkage between federal fiscal assistance and state new economy performance can be better understood by examining systemic models that identify the indirect effects of federal welfare spending through major mediating factors (state fiscal comfort and administrative capacity) that endogenously promote economic development performance by freeing up state resources. The fiscal federalism literature offers little direction in identifying the effects of federal welfare spending on such mediating variables, but there is clear evidence that states have been reluctant to increase welfare spending and manage welfare programs more effectively (Peterson, 1995). It is precisely because of this reluctance that we focus on federal welfare spending rather than total federal spending; the conceptual trade-off between welfare spending and development spending guides our inquiry. We can model these hypothesized effects.

The relationship between mediating variables and state new economy performance is much more convincing because of the large number of works that find state internal factors (including fiscal and administrative capacity) raise the probability of state policy innovations. State pursuit of economic development is affected by fiscal conditions (Saiz, 2001). Regional fiscal conditions almost always motivate decision makers to pursue economic development policies (Oates, 1993; Rubin & Rubin, 1987). Agencies tend to pursue strategic cutbacks during periods of fiscal stress (Levine, Rubin, & Wolohojian, 1981). Fiscal hardship may drive economic development decision makers to support less risky policies designed to strengthen regional economic base and to generate additional resources (Kwon et al., 2009). Fiscal stress likely motivates strategic policy realignment toward economic development. It would be easier for states to draw from their arsenal of traditional economic development programs in search of rapid changes, such as firm locations during periods of fiscal stress. As such, higher fiscal comfort may be understood to increase the opportunity and motivation to pursue new economy development efforts. States with greater administrative capacity develop innovative programs by exercising more expertise and knowledge in the policy-making process (Agranoff & McGuire, 2003). Abundant or slack resources and administrative capacity are critical factors to induce policy development in states (Cyert & March, 1963). Taken as whole then, we expect the following:

Hypothesis 4: Federal welfare spending indirectly improves state new economy development performance, observed through improved innovation capacity scores, by improving state fiscal comfort.

Hypothesis 5: Federal welfare spending indirectly improves state new economy development performance, observed through improved innovation capacity scores, by improving state administrative capacity.

Data and Empirical Analyses

In his seminal 1969 essay, Jack Walker suggested that slack resources—money and personnel—are important determinants of policy innovation. As Walker noted, states may eventually feel pressure to adopt policies that have taken on the appearance of state responsibility by virtue of their adoption in several other states. Walker readily admitted that his focus is on the adoption itself, not on the ultimate performance of the policy, and that the substance of a largely symbolic action can quickly fade. In this sense, policy innovation or adoption is directly related to a government’s substantial capacity to prompt new policies (Bowman & Kearney, 1988; Bozeman & Shusher, 1979). Today, policies and patterns of policy innovation and diffusion are increasingly complex, with a growing focus on the use of evidence-based practice as a core tenet of adoption (Jennings & Hall, 2012). Some policies have a substantive impact and some do not. Moreover, economic development is not a discrete policy, but a complex amalgamation of policies that coalesce to bring about the ultimate development. This presents a unique challenge in the use of economic development policy as a variable for analysis, where the effects of multiple overlapping policies and efforts are difficult to disaggregate or attribute. We confront this challenge by using a single index of policy performance as opposed to the binary decisions associated with policy adoption(s). A reasonable proximate variable for policy choice is observed policy performance, specifically in terms of the desired outcomes of such policies. We focus on observed policy performance rather than potentially symbolic adoption choices.

A well-intentioned policy that is poorly funded and poorly implemented will not generate the desired outcomes. We develop an indicator to measure the degree to which states have realized new economy development outcomes as a proxy for their past policy choices. To test our hypotheses, we compiled panel data comprising 47 states from 1972 to 2009.¹ These tests require a sophisticated measure that captures variation in the extent to which states have demonstrated results of new economy development.

Dependent Variable

Our key dependent variable is observed new economy readiness; that is, state performance in laying up the kinds of capacity necessary to generate development from within through innovation and commercialization (Hall, 2007). This variable is a proxy for the performance of a state’s collective basket of development policies and strategies. While studies of state policy implementation often use a binary variable that indicates the success or failure of government policy in a specific year, ours are ratio variables that reflect the relative performance in building new economy development capacity.

We base our dependent variable on Hall’s (2007) index of state innovation capacity, as a proxy for state new economy development performance. Hall used factor analysis to create a measure of state innovation capacity. We replicate and extend his factor analysis from 1972 to 2009, which is the almost double of the timeframe used for Hall’s index, to better observe the hypothesized effects over time.² We collect 18 variables, excluding one variable from Hall’s analysis: the number of persons employed in high-tech Standard Industrial Classification (SIC) code industries. This variable was dropped because of reliability concerns associated with inconsistent data collection and classification by the U.S. Bureau of Economic Analysis.³ Furthermore, we were also forced to exclude two other variables—federal obligations for R&D plant and federal obligations for science and engineering facilities and equipment—because values were missing for more than 300 observations. The variable state and local government funded academic R&D expenditures was dropped during the factor analysis because it was an inconsequential factor.⁴

In the final iteration of our factor analysis, 15 population-adjusted variables were included; their shared variance was collapsed into three common factors reflecting unique dimensions of state new economy development performance. Table 1 reports the result of the rotated component matrix. Our results show no substantial change compared with Hall’s original findings. As in that analysis, three common factors are extracted: human capacity, federal financial capacity for R&D, and state financial capacity for R&D; they collectively represent approximately 77% of the variance among the original 15 variables. We next generate factor scores for each state year using the Anderson–Rubin method. We use state human capacity and state financial capacity as our dependent variables, as they reflect the potential capacity to drive state performance in the new economy. States with higher capacity scores on either factor exhibit greater performance toward reaching new economy policy goals, other things being equal.

Table 1.

Factor Analysis of State New Economy Development Performance.

Variable	Component
Variable	Human-based capacity	Financial capacity (federal)	Financial capacity (state)
Number of full-time science & engineering graduate students	0.878
Science & engineering doctorates awarded	0.875
Total number of science & engineering graduate students	0.863
Percentage of the population with a degree awarded in higher education	0.751
Number of science & engineering postdoctoral fellows	0.747
Academic R&D, funded by other sources	0.621
Federal obligations for science and engineering		0.779
Total federal obligations for R&D, all performers		0.757
Academic R&D, federally funded		0.743
Federal obligations for general science		0.677
Total federal obligations to universities and nonprofit organizations for R&D		0.656
Federal obligations for fellowships and traineeships		0.618
Academic R&D, self-funded by institution			0.843
Total higher education expenditure			0.816
Academic R&D, industry funded			0.523

Model Specification and Major Independent Variables

We seek to identify direct and indirect impact of federal welfare spending in influencing state-level new economy development performance. To do this, we utilize the set of linear equation models to investigate the causal mechanisms among independent variables, mediating variables, and dependent variables. All variables and data sources are identified in Table 2. Summary statistics are presented in Table 3. We first examine the direct effect of federal welfare spending on state new economy performance while including two mediating variables as predictors in the model. For this purpose, we estimate the following basic model:

\begin{matrix} I N N O V A T I O N_{s t} = α F E D W E L E X P_{s t - 1} + β A D M C A P_{s t - 1} \\ + δ F I S C O M F O R T_{s t - 1} + γ X_{s t - 1} + μ_{s} + ε_{s t} \end{matrix}

where subscript s denotes state and t denotes time. A positive and significant correlation between federal welfare spending (FEDWELEXP) and state new economy performance (INNOVATION) in the first model would indicate a need to then test the indirect effect of federal welfare spending (FEDWELEXP) on state new economy performance through additional models that include our two mediating variables—state administrative capacity (ADMCAP) and fiscal comfort (FISCOMFORT)—as dependent variables. We specify the following models:

\begin{matrix} A D M C A P_{s t} = α F E D W E L E X P_{s t - 1} \\ + γ X_{s t - 1} + μ_{s} + ε_{s t} \end{matrix}

\begin{matrix} F I S C O M F O R T_{s t} = α F E D W E L E X P_{s t - 1} \\ + γ X_{s t - 1} + μ_{s} + ε_{s t} \end{matrix}

Table 2.

Variable Descriptions and Sources.

Variable	Description	Source
Federal welfare expenditures	Per capita aggregate federal welfare expenditures including grants, direct payments, procurement contracts, salaries and wages of federal employees (in 2005 constant dollars)	State Government Finances, U.S. Census Bureau
Human innovation	Index of states’ human-related new economy development performance, which is extracted by factor analysis based on 15 population-adjusted key variables	National Science Foundation, U.S. Bureau of Economic Analysis, and U.S. Department of Education
Financial innovation	Index of states’ finance-related new economy development performance, which is extracted by factor analysis based on 15 population-adjusted key variables	National Science Foundation, U.S. Bureau of Economic Analysis
Human innovation of neighbors	Average index score of neighboring states’ human-related new economy development performance	National Science Foundation, U.S. Bureau of Economic Analysis, and U.S. Department of Education
Financial innovation of neighbors	Average index score of neighboring states’ finance-related new economy development performance	National Science Foundation, U.S. Bureau of Economic Analysis
Fiscal comfort	Total state revenue by total state spending	State Government Finances, U.S. Census Bureau
Administrative power	Bureaucratic capacity measured using the number of full-time-equivalent state government employees per 10,000 population	Public Employment Historical Data, U.S. Census Bureau
Ln(Per capita GSP)	Log of per capita gross state product	Bureau of Economic Analysis of U.S. Department of Commerce
Unemployment rate	Annual state unemployment rate	U.S. Bureau of Labor Statistics
Poverty rate	Annual state poverty rate	Statistical Abstract, U.S. Census Bureau of U.S. Department of Commerce
Ln(Population)	Log of the total state population	Statistical Abstract, U.S. Census Bureau of U.S. Department of Commerce
Tax & expenditure limitation	Index of stringency of state tax and expenditure limitations	Amiel, Deller, and Stallmann (2009)
Institutional ideology	Index of state government ideology	Berry et al. (1998) (http://rcfording.wordpress.com/state-ideology-data/)
Democratic governor	Democratic control of governor, but neither house of legislature (1 = Democratic control)	The National Governors Association and Klarner Politics (http://klarnerpolitics.com)
Democratic legislators	Unified Democratic control of both houses (upper and lower house) of state legislators (1 = Democratic control	The Council of State Governments and Klarner Politics
Unified government	Unified party control over the governorship and both chambers of the legislature (1 = unified Democratic or Republican control of both the legislature and governor’s office)	The Council of State Governments and Klarner Politics
Gubernatorial election	The gubernatorial election year into the calendar year in which it was held	The National Governors Association and Klarner Politics
Gubernatorial power	Index of governor’s institutional power	Beyle (2003) (https://www.unc.edu/polisci_old/beyle/gubnewpwr.html)
Legislative professionalism	Index of legislative professionalism	Legislative Studies Quarterly data from King & Squire
National GDP growth rate	Annual growth rate of real gross domestic product	U.S. Bureau of Economic Analysis of U.S. Department of Commerce

Note. All financial variables are adjusted in constant 2005 dollars.

Table 3.

Descriptive Statistics.

Variable	Observations	Mean	Standard deviation	Minimum	Maximum
Federal welfare spending	1,786	440.643	255.173	50.462	1526.35
Human new economy performance	1,786	0.061	0.952	−1.882	6.170
Financial new economy performance	1,786	−0.075	0.914	−3.104	4.754
Human performance of neighbors	1,786	0.097	0.721	−1.452	4.161
Financial performance of neighbors	1,786	−0.096	0.794	−2.899	3.054
Fiscal comfort	1,786	2378.52	750.29	1013.87	6031.47
Administrative power	1,786	164.738	40.258	88.146	313.874
Ln(Per capita GSP)	1,786	10.371	0.220	9.831	11.075
Unemployment rate	1,786	5.916	2.001	2.1	17.4
Poverty rate	1,786	12.977	3.933	2.9	27.2
Ln(Population)	1,786	15.029	0.998	12.757	17.425
TELs	1,786	5.819	7.466	0	30
Institutional ideology	1,786	54.210	20.782	4.051	96.257
Democratic governor	1,786	0.123	0.329	0	1
Democratic legislators	1,786	0.520	0.499	0	1
Unified government	1,786	0.462	0.499	0	1
Gubernatorial election	1,786	0.264	0.441	0	1
Gubernatorial power	1,786	3.545	0.516	1.667	4.833
Legislative professionalism	1,786	0.201	0.123	0.027	0.659
National GDP growth rate	1,786	2.859	2.211	−3.069	7.187

As a preliminary test, we first employ standard fixed effects for all three models since many unobserved factors that affect state policy innovation and vary across states are not captured in the model.⁵ All major independent and control variables are lagged by 1 year to control for possible reverse causality. In the equations, µ_s represents the state fixed effects, ε_st is a stochastic error term, and X_st₋₁ represents the set of control variables including institutional, political, and socioeconomic variables of state s at time t − 1. However, the fixed effects model does not adequately deal with such critical problems as the presence of heteroscedasticity and the autoregressive nature of our dependent variables (Beck & Katz, 1995). Thus, we estimate each of the models with panel corrected standard errors (PCSE) and dummy variables for the states by applying first-order autocorrelation. The major independent variable, as shown in each equation, is the total federal welfare transfer to the states, which is divided by state population. This is based on the U.S. Census Bureau’s State Government Finance data, which aggregates amounts spent for the support of and assistance to needy persons contingent on their need.⁶ Aggregate federal welfare spending data are available from 1972 to 2009.⁷ The variable is inflation adjusted to real 2005 dollars using the Consumer Price Index from the U.S. Bureau of Labor Statistics. Referring to Equation (1), as already described above, $I N N O V A T I O N_{s t}$ is a state’s new economy development performance, which state s exhibits at time t. Equation (1) also includes two mediating variables: $F I S C O M F O R T_{s t}$ is the fiscal condition of state s at time t, and $A D M C A P_{s t}$ denotes the administrative capacity of a state s at time t.

Tannenwald’s (1998) index of fiscal comfort is calculated by dividing the state’s tax capacity by its expenditure need. States with higher tax capacity and lower need enjoy the most fiscal comfort, while lower tax capacity and higher need forebear fiscal crisis. While this measure fits our conceptual definition, neither the index of fiscal capacity nor the index of expenditure need has been consistently measured. Instead, for the fiscal comfort variable, our models use state-level own-source revenue per capita in 2005 dollars, which is free from any intergovernmental monies.⁸ This alternative measure is an improvement over studies that use raw total state revenue (Berry & Berry, 1990; Kwon et al., 2009). Because a state’s fiscal capacity is “its ability to raise own-source revenues through state and local government taxes, fees and charges relative to its need for public services,” analyzing direct revenues is a more straightforward way to observe fiscal comfort (Yilmaz, Hoo, Nagowski, Rueben, & Tannenwald, 2006, p. 18). To measure state administrative capacity, we adopt the popular method of taking the number of full-time equivalent state employees per 10,000 population, available from the U.S. Census Bureau’s historical public employment historical data.⁹ A measure that represents qualitative characteristics of government employees is desirable but not available (Barrilleaux, Feiock, & Crew, 1992).

Equations (2) and (3) test the indirect effects of federal welfare expenditures on state new economy performance using the mediating variables (state fiscal comfort and administrative capacity) from Equation (1) as dependent variables. We expect that federal welfare spending will positively and significantly affect a state’s administrative capacity and fiscal comfort. If this proposition is statistically supported, the effect of federal welfare expenditures is indirectly related to state new economy performance by way of improved state administrative capacity and fiscal comfort.

Control Variables

We control for several internal and external factors that may affect state decisions to pursue new economy development. This includes the influence of regionally proximal states and states’ political and socioeconomic characteristics. To capture the degree to which a state’s neighbors exhibit capacity to pursue new economy development, we calculate the average human and financial capacity scores for all contiguous states. If states are influenced by their neighbors’ performance, there should be a positive association between each state’s potential capacity for new economy performance and the average capacity of its neighbors.

Among internal state characteristics, political and institutional factors must be considered. We include three dichotomous variables that measure partisan control of state institutions: (a) Democratic control of the governor’s office but neither house of the legislature, (b) unified Democratic control of both houses of the legislature, and (c) unified party control over the governorship and both houses of the legislature. Democrats are more likely to increase the tax burden on firms (Alt & Lowry, 2000; Quinn & Shapiro, 1991). Republicans proffer policies more beneficial to business (Hibbs, 1987; Inclan et al., 2001). Given these arguments, Democratic control is less likely to stimulate development policies in the new economy. Divided government produces hardships in making fiscal or economic policy, while unified government makes it easier to implement policies, generating fiscal stability (Alt & Lowry, 2000; Poterba, 1994). We expect a positive relationship between unified party control and state new economy performance.¹⁰

The effect of election cycles on policy implementation has also been recognized (Berry & Berry, 1990; Kiewiet & McCubbins, 1985). Politicians increase the use of policies to induce economic upswing when it is most politically advantageous to their election cycle. We expect that the probability of new economy performance is high in a year of gubernatorial elections. Election cycle data also come from Klarner’s State Partisan Balance Data.

We use two indicators of governing capacity: legislative professionalism and gubernatorial power. Professional state legislatures with longer sessions and greater resources are more likely to engage in policy innovations (Miller, 2006; Shipan & Volden, 2006). Legislative professionalism data are obtained from Squire’s (2007) measure. Governors with greater statutory and constitutional authority are more likely to increase policy innovation (Ambrosius, 1989; Miller, 2006). We use Beyle’s (2003) index of gubernatorial power, based on formal powers of tenure, veto, budget, appointment, party control, and organization.¹¹

In addition to political and institutional considerations, we control for state ideology and tax and expenditure limitation (TEL). Elected officials are expected to pursue economic development policies in accord with ideological dispositions of state constituencies. It is unclear whether liberal or conservative states will be more likely to pursue new economy development policies. To measure state ideology, we adopt the measure developed by Berry, Ringquist, Fording, and Hanson (1998), from most conservative (0) to most liberal (100).

States’ TEL status may limit policy innovation. Based on evidence that suggests TEL is effective in reducing state-level expenditures (Archibald & Feldman, 2006; Kousser, McCubbins, & Moule, 2008), we expect that states with more stringent TEL are less likely to exhibit strong new economy performance. TEL data were obtained from Amiel, Deller, and Stallmann (2009).¹²

The remaining control variables represent state socioeconomic characteristics. The models include population, poverty rate, unemployment rate, gross state product, and the national gross domestic product (GDP). In general, studies indicate that states with large population take more competitive advantages in pursuing development by being more capable of managing government and learning from others (Kwon et al., 2009; Shipan & Volden, 2008). States with higher population will be more likely to choose new economy development policies. The variable is the natural log of the total state population, obtained from the U.S. Bureau of Economic Analysis. We expect increased poverty and unemployment to justify economic development policy action, thereby inducing new economy investment. Poverty is measured as the percentage of families below the poverty income level (obtained from the U.S. Census Bureau’s Population Estimates Program). The unemployment rate is measured as a percentage of total population of individuals age 16 years and older (Bureau of Labor Statistics local area unemployment statistics). Because greater financial capacity is expected to increase new economy performance, the natural log of the per capita real gross state product (U.S. Census Bureau) and the growth rate of national GDP are included.

The Alternative Estimation Method

Although the aforementioned models describe both direct and indirect effects of federal welfare spending on state new economy performance, they do not capture bias due to endogeneity. The key independent variable, federal welfare spending, is obviously highly endogenous as in general these funds are distributed through formulas based on state economic conditions. Thus, omitted variable bias resulting from federal adjustment to the level of welfare funds is a major concern. We also suspect that all other explanatory variables in these regressions are not strictly exogenous due to the complex political and policy environment in the American states. To address potential endogeneity of the estimators, we consider a model that captures dynamic and time effects. For example, our dependent variables (new economy performance, administrative capacity, and fiscal comfort) may correlate over time. The dynamic panel model can ameliorate the problem of temporal dependence by including a lagged dependent variable. The lagged dependent variable on the right-hand side of the equation is included to control for time-invariant heterogeneity, which, especially in the fixed effects model, yields the transformed lag to be correlated with the error term (Nickell, 1981). It is also important to capture both observed and unobserved time fixed effects by including a set of year dummies.

The “difference” generalized methods of moments (GMM) estimator introduced by Arellano and Bond (1991)¹³ is an efficient model to ameliorate problems typical of panel data, such as endogeneity, autocorrelation, and heteroscedasticity. Difference GMM controls for any time invariant factors within each state by first differencing the original equation. The equation of this estimator is first differenced and includes a lagged dependent variable, eliminating the unobserved state-level fixed effects and the issue of stationarity. It also uses lagged and differenced right-hand-side variables as instruments to deal with issues of endogeneity and collinearity.¹⁴ We specify all potential endogenous explanatory variables and the lagged dependent variable by instrumenting with two lags to avoid the inclusion of excessive instruments.¹⁵ Our model uses the one-step standard error estimation and includes yearly dummies to control for common time-specific effects.¹⁶

Findings

Table 4 reports the findings of the fixed effects, PCSE, and the difference GMM models that estimate the effect of aggregate federal welfare spending on a state’s new economy development performance across two different types of dependent variables. The first three columns consider state human-based capacity while columns 4 through 6 consider finance-based capacity. Again, these two capacity variables are used as proxy variables to indicate state new economy development performance through its two primary outputs.

Table 4.

The Effect of Federal Welfare Spending on State New Economy Performance.

Variables	Human-based capacity			Financial capacity
Variables	(1) FE	(2) PCSE	(3) GMM	(4) FE	(5) PCSE	(6) GMM
Federal welfare spending	0.0002*** (0.0001)	0.0004*** (0.0001)	0.0002* (0.0001)	0.0005*** (0.0001)	0.0006*** (0.000231)	0.0004*** (0.0001)
Fiscal comfort	0.0001*** (0.00003)	0.0001* (0.0001)	0.0001** (0.00004)	0.0004*** (0.00004)	0.0004*** (0.0001)	0.0002*** (0.00004)
Administrative capacity	0.004*** (0.001)	0.002^{^} (0.001)	0.001^{^} (0.001)	0.004*** (0.001)	0.002 (0.002)	−0.001 (0.001)
Neighbors’ capacity	0.333*** (0.035)	0.164** (0.070)	−0.014 (0.061)	0.505*** (0.037)	0.399*** (0.104)	0.164** (0.068)
Ln(Per capita GSP)	0.460*** (0.127)	0.529** (0.221)	−0.230 (0.155)	−0.039 (0.156)	0.351 (0.321)	−0.061 (0.141)
Unemployment rate	0.018*** (0.006)	0.013 (0.011)	0.032*** (0.007)	0.003 (0.007)	0.008 (0.020)	−0.015** (0.006)
Poverty rate	0.011** (0.005)	0.005 (0.004)	0.004 (0.004)	0.018*** (0.005)	0.012* (0.007)	0.002 (0.004)
Ln(Population)	−0.248*** (0.085)	−0.349** (0.142)	−0.299** (0.145)	0.140^{^} (0.099)	0.389** (0.186)	−0.289** (0.121)
TELs	−0.013*** (0.002)	−0.006** (0.003)	−0.004* (0.002)	−0.003^{^} (0.002)	−0.001 (0.003)	0.002 (0.002)
Institutional ideology	−0.001* (0.001)	−0.002*** (0.001)	−0.0001 (0.001)	0.002*** (0.001)	0.001^{^} (0.001)	0.001** (0.001)
Democratic governor	−0.047^{^} (0.032)	0.007 (0.027)	0.022 (0.028)	0.006 (0.038)	−0.023 (0.037)	−0.025 (0.026)
Democratic legislators	−0.055** (0.026)	0.014 (0.023)	0.028 (0.025)	0.038 (0.030)	0.028 (0.031)	0.002 (0.024)
Unified government	−0.004 (0.019)	0.023^{^} (0.015)	0.001 (0.017)	0.027 (0.022)	−0.004 (0.020)	0.008 (0.016)
Gubernatorial election	−0.027^{^} (0.017)	−0.020 (0.017)	−0.007 (0.015)	−0.002 (0.020)	0.010 (0.029)	0.015 (0.014)
Gubernatorial power	−0.115*** (0.028)	−0.014 (0.043)	−0.036 (0.034)	−0.029 (0.034)	−0.005 (0.067)	0.014 (0.031)
Legislative professionalism	−0.268^{^} (0.197)	−0.188 (0.357)	0.271^{^} (0.208)	0.048 (0.231)	−0.404 (0.523)	0.014 (0.196)
National GDP growth rate	−0.019*** (0.004)	−0.011* (0.007)	0.046*** (0.015)	−0.031*** (0.005)	−0.027** (0.012)	−0.385*** (0.014)
Lagged DV			0.496*** (0.037)			0.437*** (0.035)
Constant	−1.519 (1.367)	−1.017 (2.565)		−3.645* (1.958)	−10.66*** (3.777)
R ²	0.441	0.708		0.782	0.623
Observations	1,739	1,739	1,692	1,739	1,739	1,692
Wald test			5241.35***			42697.82***
Arellano–Bond AR(1) test			−15.40***			−12.49***
Arellano–Bond AR(2) test			1.64			−0.76

Note. Fixed effects and GMM models reports regression coefficients with standard errors in parentheses. PCSE models report regression coefficients with panel-corrected standard errors and AR(1) correction.

p < .10. **p < .05. ***p < .01 (2-tailed tests). ^{^}p < .10 (1-tailed test).

In the fixed effects estimation (Column 1), the effect of federal welfare spending is positively and significantly correlated with human-capital–based new economy performance. A 1 standard- eviation increase in federal welfare expenditures per capita within a state would result in a 0.06 standard deviation increase in that state’s predicted human capital accumulation.¹⁷ This result suggests that states with more federal welfare money exhibit stronger new economy performance. Both state fiscal comfort and administrative capacity are positively associated with human-based new economy performance and is statistically significant at the .01 level. These results suggest that a 1 standard deviation increase in the level of fiscal comfort and in the degree of administrative capacity would result in a 0.08 and 0.16 standard deviation increase, respectively, of state human-capital–based new economy performance. To address concerns regarding heteroscedasticity and the likely autoregressive nature of state innovation efforts, Column 2 reports the PCSE estimate and confirms that federal welfare spending, state fiscal comfort, and administrative capacity continue to show significant and positive impact on human–capital-based new economy performance.

As we noted earlier, there is a well-established conceptual trade-off between state investment in welfare versus economic development. As the federal government fulfills its redistributive role, some states will be net losers of federal revenue while others are net gainers. It is not necessarily the case that the pattern of federal welfare investment would mirror cumulative federal intergovernmental transfers in a state over time. Moreover, welfare spending is unique from other forms of spending because of its entitlement status; any federal investment would simply reduce a state’s entitlement obligation, thereby freeing up general fund revenue for other purposes. Put differently, a state experiencing economic difficulty is likely to see a reduction in own-source revenue while its welfare obligations increase; such a state is also likely to be a net recipient of federal redistributive welfare spending. To ensure that our results are not simply capturing the effects of increased total federal intergovernmental expenditures and that the effect of welfare spending is distinct from total spending, we repeat our analysis first substituting federal intergovernmental expenditures per capita as the independent variable in place of federal welfare spending, and again using both measures. The results of that analysis reveal inconsistent and/or insignificant effects of total federal expenditures on capacity while welfare expenditures perform consistently as expected.¹⁸

We examine the robustness of these preliminary tests by running the Arellano–Bond GMM estimate, which effectively controls for the issues of endogeneity and Nickell bias. The results (Column 3) continue to endorse a significant effect of federal welfare funds on state human-capital–based new economy performance at the .10 level. The effects of both state fiscal comfort and administrative capacity variables, compared with the basic fixed-effects model, are still significant at the .05 level using a two-tailed test and at the .10 level using a one-tailed test. Tests confirm that the results of the Arellano–Bond GMM estimate are valid. The p value of first-order and second-order tests present consistent estimates: The test of AR(1) rejects the null hypothesis of no serial correlation, while the test of AR(2) fails to reject it. Furthermore, the difference-in-Sargan tests (not reported to save space) do not reject the null hypothesis of exogeneity of instruments.

Columns 4 through 6 present the findings using state finance-based new economy performance as the dependent variable. In all three estimators, both federal welfare spending and state fiscal comfort variables are positively and significantly correlated with this measure of state finance-based new economy performance. As seen from the Arellano–Bond GMM estimate (Column 6), the coefficient value of federal welfare spending indicates that a 1 standard deviation increase would yield a 0.12 standard deviation increase in a state’s finance-based new economy performance. The result also indicates that a 1 standard deviation increase in the level of fiscal comfort results in a 0.14 standard deviation increase in state finance-based new economy performance, while the degree of state administrative capacity is negatively (but insignificantly) correlated with the dependent variable. State administrative capacity is positively related to state finance-based new economy performance in both the fixed effects and PCSE models but its coefficient is statistically confirmed only in the fixed effects model.

The control variables in Table 4 generally produce as expected, although the coefficients of some variables are not consistently significant across the models. Particularly, the degree to which neighbor states perform well in both human- and finance-based performance measures positively and significantly increases the likelihood that a state will exhibit stronger new economy development performance. Although the effect of the new economy performance of neighbor states on human-capital–based new economy performance does not appear to be statistically significant in the Arellano–Bond GMM estimate, the results generally reaffirm that a state is significantly influenced by its neighbors in choosing new economy policies. These findings suggest that a state’s new economy performance is a result of learning from other states’ adaptation and competition for economic resources. The TEL variable is negatively and significantly associated with state human-capital–based new economy performance, suggesting that restrictive rules limit new economy development potential. Additionally, the coefficient of the institutional ideology variable is positive and significant, suggesting that more liberal states exhibit stronger finance-based new economy performance. Because of the extensive controls for state and year fixed effects and time series dynamics, these results are not robust across the model specifications.

Table 5 reports findings from the fixed effects, PCSE, and the Arellano–Bond GMM models that investigate the linkage between all federal welfare funds and the two mediating variables—state fiscal comfort and administrative capacity. First, the fixed effects models show that aggregate federal welfare spending is positively and significantly correlated with both state fiscal comfort and administrative capacity, as shown in Columns 1 and 4, respectively. Columns 2 and 5 report the alternative estimates with panel-corrected standard errors and first-order autocorrelation and confirm that federal welfare spending continues to show significant and strong positive impact on two mediating variables.

Table 5.

The Effect of Federal Welfare Spending on Mediating Variables.

Variables	Fiscal comfort			Administrative capacity
Variables	(1) FE	(2) PCSE	(3) GMM	(4) FE	(5) PCSE	(6) GMM
Federal welfare spending	0.754*** (0.043)	0.580*** (0.095)	0.183** (0.090)	0.018*** (0.002)	0.009** (0.004)	0.012** (0.005)
Ln(Per capita GSP)	2583.637*** (68.514)	2273.349*** (159.636)	790.874*** (104.712)	42.412*** (3.748)	40.418*** (7.204)	−16.291*** (5.430)
Unemployment rate	2.857 (4.479)	16.114* (8.347)	3.453 (4.225)	−0.474* (0.245)	−0.315 (0.366)	−0.285 (0.227)
Poverty rate	22.119*** (3.404)	2.086 (2.963)	5.372* (3.044)	0.220 (0.186)	−0.008 (0.137)	−0.103 (0.165)
Ln(Population)	−263.733*** (59.507)	79.261 (125.452)	−175.811** (83.438)	−43.10*** (3.255)	−29.929*** (7.741)	−17.512*** (4.767)
TELs	5.327*** (1.391)	2.079^{^} (1.542)	−0.418 (1.479)	0.566*** (0.0761)	0.263*** (0.088)	0.109 (0.080)
Institutional ideology	−0.088 (0.420)	0.144 (0.461)	−0.068 (0.370)	0.080*** (0.023)	0.036* (0.021)	−0.015 (0.020)
Democratic governor	−63.190*** (24.065)	−23.892^{^} (18.663)	−9.154 (18.865)	−9.752*** (1.316)	−1.819* (0.941)	−1.773* (1.000)
Democratic legislators	51.175*** (19.694)	30.414* (16.503)	30.153* (17.239)	2.293** (1.077)	0.838 (0.788)	0.951 (0.921)
Unified government	−49.193*** (14.108)	−22.773** (11.261)	−22.291** (11.355)	−4.303*** (0.772)	−0.957* (0.542)	0.130 (0.612)
Gubernatorial election	−8.159 (12.846)	−14.691^{^} (11.079)	−13.728^{^} (10.174)	0.128 (0.703)	−0.061 (0.471)	−0.555 (0.538)
Gubernatorial power	22.809 (21.123)	−2.998 (28.222)	7.792 (22.696)	7.905*** (1.155)	1.667 (1.304)	−0.536 (1.237)
Legislative professionalism	223.924^{^} (148.743)	7.580 (228.769)	−29.167 (140.455)	44.925*** (8.137)	11.432 (9.573)	−14.278* (7.480)
National GDP growth rate	2.309 (2.921)	−1.085 (4.765)	58.867*** (9.104)	−0.048 (0.160)	−0.321^{^} (0.198)	0.637 (0.490)
Lagged DV			0.650*** (0.029)			0.494*** (0.038)
Constant	−2119.93*** (853.370)	−2272.03*** (2083.624)		322.8*** (46.68)	208.2* (124.9)
R ²	0.837	0.713		0.303	0.772
Observations	1,739	1,739	1,692	1,739	1,739	1,692
Wald test			50962.75***			5808.32***
Arellano–Bond AR(1) test			−15.44***			−13.33***
Arellano–Bond AR(2) test			−1.13			0.23

Note. Fixed effects and GMM models reports regression coefficients with standard errors in parentheses. PCSE models report regression coefficients with panel-corrected standard errors and AR(1) correction.

p < .10. **p < .05. ***p < .01 (2-tailed tests). ^{^}p < .10 (1-tailed test).

The Arellano–Bond GMM results, presented in Columns 3 and 6, are again supported by the p value of the AR(1) and AR(2) tests and the difference-in-Sargan tests. The results continue to show a strong positive and significant impact of federal welfare spending on both state fiscal comfort and administrative capacity. A one-dollar increase in aggregate federal welfare spending per capita leads to an increase of a state own-source revenue per capita by $0.18, and an increase of the number of full-time-equivalent state employees by 120. To provide a more substantively meaningful interpretation, a 1 standard deviation increase in federal welfare spending would increase the level of fiscal comfort by a 0.06 standard deviation and increase the level of administrative capacity by a 0.08 standard deviation. The impact of federal welfare aid on administrative capacity is slightly larger than its impact on fiscal comfort.

Given our concern for endogeneity, the most conservative interpretation of these results confirms that the direct effects of federal welfare spending on both state human-capital- and finance-based new economy performance are evident. Our empirical analyses also underline the indirect impact of federal welfare funds that positively influence states to increase new economy development performance in a way that improves state fiscal comfort.

Conclusion

Our approach investigated the interaction between federal redistribution and state new economy performance. Federal welfare expenditure was determined to affect two key performance outcomes of state new economy development: human-based capacity and financial capacity for development in the knowledge economy. We have clarified the direct and indirect relationship between federal welfare spending and state new economy development performance by examining the mediating effects of welfare spending on state administrative capacity and fiscal comfort. Robust empirical analyses support the use of a continuous qualitative measure of new economy development performance rather than a complex series of binary indicators of policy choice in estimating the degree to which states have embraced a new economy posture. The results characterize the outputs of a complex set of overlapping and interdependent state policy choices that better capture substantive state policy effectiveness than would individual policy analyses.

Our models suggest that aggregate federal welfare spending spills over into state new economy development both directly and indirectly. Direct effects of federal welfare spending on state new economy development performance are substantial, although federal welfare funds have a larger magnitude for influencing states’ finance-based new economy performance than for influencing states’ human-based new economy performance. This finding supports our argument that federal welfare money is more effective to lower state financial burdens, and thus state governments are more likely to be driven to pursue development through financial mechanisms than through human capacity approaches. The direct positive association between fiscal comfort and both human and knowledge dimensions of new economy performance is also robust and straightforward.

Federal financial assistance for welfare programs indirectly increases state new economy performance by enhancing state fiscal and administrative capacities. The preceding analyses suggest, however, that the indirect effect of federal spending in welfare programs is more distinctively exercised through state fiscal comfort than through state administrative capacity. Our results suggest that states are encouraged to design and implement progressive economic development programs as a result of federal welfare spending, demonstrating a powerful intergovernmental policy externality observed through state performance in building knowledge-based development capacity.

Overall, our findings suggest a few key policy implications. Federal intergovernmental transfers continue to be an important policy mechanism. To the extent state revenue is fungible, it is possible to pursue both federal welfare goals and state economic development goals simultaneously. In light of frequent policy debates over federal spending, and threats of reduced spending in particular, it is relevant that cuts to federal welfare programs may also have unintended direct and indirect effects on state economic performance by reducing the investment in the kinds of capacity necessary to produce economic development and growth in the knowledge economy. We also know that fiscal comfort supports the development of new economy development and that federal welfare investment contributes to state fiscal comfort. Withdrawal of federal welfare program support to the states would result in a crisis mode of decision making that is likely to see a decline in administrative capacity alongside a reallocation of economic development funds to other purposes, or at least to more traditional approaches. States faced with TEL have less flexibility around which to work and suffer in new economy performance relative to those that do not, suggesting fewer constraints on government action would lead to improved economic performance. Finally, competition among states seems to be alive and well, even with respect to new economy development performance. States learn from their neighbors, suggesting that more aggressive policy innovation will give states a lead in building the kinds of capacity—human and financial—necessary to outperform their neighbors in the new economy.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Geiguen Shin is a postdoctoral research associate in the School of Public Affairs and Administration at Rutgers University–Newark. His research focuses on federalism and intergovernmental relations, public management and performance, entrepreneurship, public sector innovation, regional economic development, and public finance.

Jeremy L. Hall is a professor of public administration and MPA director at the University of Central Florida, and co-editor in chief of Public Administration Review. His research focuses on the nexus of public sector performance, public policy processes, and intergovernmental relations, often addressing questions of performance management and evidence-based practice.

References

Agranoff

McGuire

(2003). Collaborative public management: New strategies for local governments. Washington, DC: Georgetown University Press.

Ahmad

Dreze

Hills

Sen

(1991). Social security in developing countries. Oxford, England: Clarendon Press.

Allen

M. D.

Pettus

Haider-Markel

D. P.

(2004). Making the national local: Specifying the conditions for national government influence on state policymaking. State Politics & Policy Quarterly, 4, 318-344.

Alt

J. E.

Lowry

R. C.

(2000). A dynamic model of state budget outcomes under divided partisan government. Journal of Politics, 62, 1035-1069.

Ambrosius

M. M.

(1989). The role of occupational interests in state economic development policy-making. Western Political Quarterly, 42, 53-68.

Amiel

Deller

Stallmann

(2009). The construction of a tax and expenditure limitation index for the U.S. Department of Agricultural and Applied Economics (Staff Paper No. 536). Madison, WI: University of Wisconsin-Madison.

Archibald

R. B.

Feldman

D. H.

(2006). State higher education spending and the tax revolt. Journal of Higher Education, 77, 618-644.

Arellano

Bond

(1991). Some tests of specification for panel data: Monte Carlo evidence and application to employment equations. Review of Economic Studies, 58, 277-297.

Barrilleaux

Feiock

Crew

R. E.

Jr. (1992). Measuring and comparing American states’ administrative characteristics. State and Local Government Review, 24(1), 12-18.

10.

Bartik

(1999). Federal policy toward state and local economic development in the 1990s. In Norton

R. D.

(Ed.), The millennial city: Classic readings on U.S. urban policy (pp. 235-251). Stamford, CT: JAI Press.

11.

Beck

Katz

(1995). What to do (and not to do) with time-series cross-section data. American Political Science Review, 89, 634-647.

12.

Berry

F. S.

Berry

W. D.

(1990). State lottery adoptions as policy innovations: An event history analysis. American Political Science Review, 84, 395-415.

13.

Berry

W. D.

Ringquist

E. J.

Fording

R. C.

Hanson

R. L.

(1998). Measuring citizen and government ideology in the American states, 1960-93. American Journal of Political Science, 42, 327-348.

14.

Beyle

(2003). The governors. In Gray

Hanson

R. L.

(Eds.), Politics in the American states: A comparative analysis (pp. 194-231). Washington, DC: CQ Press.

15.

Bohte

Meier

K. J.

(2000). The marble cake: Introducing federalism to the government growth equation. Publius, 30(3), 35-46.

16.

Bowman

A. O’M.

Kearney

R. C.

(1988). Dimensions of state government capability. Western Political Quarterly, 41, 341-362.

17.

Bozeman

Shusher

E. A.

(1979). Scarcity and environmental stress in public organizations. Administration & Society, 11, 335-355.

18.

Brace

Mucciaroni

(1990). The American states and the shifting locus of positive economic intervention. Policy Studies Review, 10, 151-173.

19.

Callen

Z. A.

(2009). The seams of the state: Infrastructure and intergovernmental relations in American state building (Doctoral dissertation). University of Chicago, Chicago, IL.

20.

Case

A. C.

Rosen

H. S.

Hines

J. R.

(1993). Budget spillovers and fiscal policy interdependence: Evidence from the states. Journal of Public Economics, 52(3), 285-307.

21.

Chernick

H. A.

(1998). Fiscal effects of block grants for the needy: An interpretation of the evidence. International Tax and Public Finance, 5, 205-233.

22.

Chubb

J. E.

(1985). The political economy of federalism. American Political Science Review, 79, 994-1015.

23.

Cyert

R. M.

March

J. G

. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall.

24.

Drabenstott

(2005). A review of the federal role in regional economic development. Kansas City, MO: Federal Reserve Bank of Kansas City.

25.

Eisinger

P. K.

(1989). The rise of the entrepreneurial state. Madison: University of Wisconsin Press.

26.

Grossback

L. J.

Nicholson-Crotty

Peterson

D. A. M.

(2004). Ideology and learning in policy diffusion. American Politics Research, 32, 521-545.

27.

Hall

J. L.

(2007). Developing historical 50-state indices of innovation capacity and commercialization capacity. Economic Development Quarterly, 21, 107-123.

28.

Hall

J. L.

(2008). The forgotten regional organizations: Creating capacity for economic development. Public Administration Review, 68, 110-125.

29.

Hall

J. L.

Howell-Moroney

M. E.

(2012). Poverty, innovation capacity, and state economic development in the knowledge economy: Evidence from the U.S. Growth and Change, 43, 228-251.

30.

Hanley

Douglass

M. T.

(2014). High road, low road, or off road? Economic development strategies in the American states. Economic Development Quarterly, 28, 220-229.

31.

Hankla

C. R.

(2008). When is fiscal decentralization good for governance? Publius, 39, 632-650.

32.

Hibbs

D. A.

(1987). The American political economy. Cambridge: Harvad University Press.

33.

Inclan

Quinn

D. P.

Shapiro

R. Y.

(2001). Origins and consequences of changes in U.S. corporate taxation, 1981-1998. American Journal of Political Science, 45(1), 179-201.

34.

Jennings

E. T.

Hall

J. L.

(2012). Evidence-based practice and the use of information in state agency decision-making. Journal of Public Administration Research and Theory, 22, 245-266.

35.

Karch

(2007). Democratic laboratories: Policy diffusion among the American states. Ann Arbor: University of Michigan Press.

36.

Karch

(2012). Vertical diffusion and the policy-making process: The politics of embryonic stem cell research. Political Research Quarterly, 65, 48-61.

37.

Kiewiet

R. D.

McCubbins

M. D.

(1985). Congressional appropriations and the electoral connection. Journal of Politics, 47(1), 59-82.

38.

Kousser

McCubbins

M. D.

Moule

(2008). For whom the TEL tolls: Can state tax and expenditure limits effectively reduce spending? State Politics & Policy Quarterly, 8, 331-361.

39.

Kwon

Berry

F. S.

Feiock

R. C.

(2009). Understanding the adoption and timing of economic development strategies in U.S. cities using innovation and institutional analysis. Journal of Public Administration Research and Theory, 19, 967-988.

40.

LeRoux

Wright

N. S.

(2010). Does performance measurement improve strategic decision making? Findings from a national survey of nonprofit social service agencies. Nonprofit and Voluntary Sector Quarterly, 39, 571-587.

41.

Levin

Lin

C-F.

Chu

C-S. J.

(2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24.

42.

Levine

C. H.

Rubin

I. S.

Wolohojian

G. G.

(1981). Resource scarcity and the reform model: The management of retrenchment in Cincinnati and Oakland. Public Administration Review, 41, 619-628.

43.

Mahroum

Al-Saleh

(2013). Demand-led related diversification: An innovation policy approach to economic diversification and development. Science and Public Policy, 40, 406-418.

44.

Markusen

Glasmeier

(2008). Overhauling and revitalizing federal economic development programs. Economic Development Quarterly, 22, 83-91.

45.

McCallum

Blais

(1987). Government, special interest groups and economic growth. Public Choice, 54(1), 3-18.

46.

Miller

E. A.

(2006). Explaining incremental and non-incremental change: Medicaid nursing facility reimbursement policy, 1980-98. State Politics & Policy Quarterly, 6, 117-150.

47.

Miller

E. A.

(2011). Conditioning subnational responses to external influences: The role of internal moderators in public policy adoption in the American states. In Ongaro

Massey

Holzer

Wayenberg

(Eds.), Policy, performance, and management in governance in intergovernmental relations (pp. 299-322). Cheltenham, England: Edward Elgar.

48.

Mohr

L. B.

(1969). Determinants of innovation in organizations. American Political Science Review, 63, 111-126.

49.

Nicholson-Crotty

(2003). Punishment from the top down? Federal influence on state-level criminal justice policy (Doctoral dissertation). Texas A&M University, College Station.

50.

Nickell

(1981). Biases in dynamic models with fixed effects. Econometrica, 49, 1417-1426.

51.

Oates

W. E.

(1993). Fiscan decentralization and economic development. National Tax Journal, 46, 237-243.

52.

Oates

W. E.

(1999). An essay on fiscal federalism. Journal of Economic Literature, 37, 1120-1149.

53.

Peterson

P. E.

(1995). The price of federalism. Chicago, IL: University of Chicago Press.

54.

Poister

T. H.

Aristigueta

M. P.

Hall

J. L.

(2014). Managing and measuring performance in public and nonprofit organizations: An integrated approach. San Francisco, CA: Wiley.

55.

Poterba

(1994). State responses to fiscal crisis: The effects of budgetary institutions and politics. Journal of Political Economy, 102, 799-821.

56.

Powers

E. T.

(2000). Block granting welfare: Fiscal impact on the states. Economic Development Quarterly, 14, 323-339.

57.

Quinn

D. P.

Shapiro

R. Y.

(1991). Business political power: The case of taxation. American Political Science Review, 85, 851-874.

58.

Roodman

(2006). How to do xtabond2: An introduction to “Difference” and “System” GMM in Stata. Stata Journal, 9, 86-136.

59.

Rubin

I. S.

Rubin

H. J.

(1987). Ecnoomic development incentives: The poor (cities) pay more. Urban Affairs Quarterly, 23(1), 37-62.

60.

Saiz

(2001). Politics and economic development: Why governments adopt different strategies to induce economic growth. Policy Studies Journal, 29, 203-214.

61.

Shipan

C. R.

Volden

(2006). Bottom-up federalism: The diffusion of antismoking policies from U.S. cities to states. American Journal of Political Science, 50, 825-843.

62.

Shipan

C. R.

Volden

(2008). The mechanisms of policy diffusion. American Journal of Political Science, 52, 840-857.

63.

Sigelman

Smith

(1980). Consumer legislation and the American states. Social Science Quarterly, 61, 58-70.

64.

Soss

Schram

S. F.

Vartania

T. P.

O’Brien

(2001). Setting the terms of relief: Explaining state policy choices in the devolution revolution. American Journal of Political Science, 45, 378-395.

65.

Spindler

C. J.

Forrester

J. P

. (1993). Economic development policy: Explaining policy preferences among competing models. Urban Affairs Quarterly, 29(1), 28-53.

66.

Squire

(2007). Measuring state legislative professionalism: The Squire index revisited. State Politics & Policy Quarterly, 7, 211-227.

67.

Storm

Feiock

R. C.

(1999). State support for higher education and economic development. State and Local Government Review, 31(2), 97-105.

68.

Tannenwald

(1998). Come the devolution, will states be able to respond? New England Economic Review, May/June, 53-73.

69.

Tolbert

C. J.

Mossberger

McNeal

(2008). Institutions, policy innovation, and e-government in the American states. Public Administration Review, 68, 549-563.

70.

Volden

(2002). The politics of competitive federalism: A race to the bottom in welfare benefits? American Journal of Political Science, 46, 352-363.

71.

Walker

(1969). The diffusion of innovations among the American states. American Political Science Review, 63, 880-899.

72.

Welch

Thompson

(1980). The impact of federal incentives on state policy innovation. American Journal of Political Science, 24, 715-729.

73.

Yilmaz

Hoo

Nagowski

Rueben

Tannenwald

(2006). Measuring fiscal disparities across the U.S. states: A representative revenue system/representative expenditure system approach, fiscal year 2002 (Joint Working Paper No. 06-2). Boston, MA: New England Public Policy Center at the Federal Reserve Bank of Boston.