Modeling grant implementation

To test the hypothesis, the analytical model explains variation in the implementation rate of GC and other control variables. Specifically, the study models implementation pace—as affected by government capacity, state politics, state needs, and fiscal institution. The basic specification is expressed as:

Implementation pace

= f (government capacity, state politics, state needs, fiscal institution)

The equation is composed under the assumption of linear combination, and the function is expressed in the mathematical form,

Grant implementation rate𝑖𝑖𝑖𝑖 = α + βi𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖−1+ γi𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖−1+ δi𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖−1+ θ𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖−1+ ε,

where GC represents government capacity, SP indicates state politics, SN means state needs, FI represents fiscal institutions.

4.2.3. Data and variables

The unit of analysis is the Department of Transportation in the 50 U.S. states.³⁵ The time range of data is 2009 to 2012. ARRA funds were initially designed to be completely spent no later than 2011 (Carley et al., 2015, p. 114); however, as described above, many states failed to do so by 2011 and were allowed to use the funds until 2013.

So, the present study tests the model using available data of 2009-2012.

The dependent variable is implementation pace, measured by the percentage of a state’s outlays in the amount of obligated funds. I set the implementation pace separately in formula grants, competitive grants, and total grants (combining the two grants). Data were obtained from DOT weekly financial and activity reports in Recovery.gov. Table 12 presents the detailed information of variables.

35 In the real analysis, Nebraska was omitted because it does not have partisanship in the legislature.

Table 12 Dependent variable of research question 2

Variables Measurement

Implementation pace of formula grants (formula grants outlays / obligations)x100 Implementation pace of competitive

grants (competitive grants outlays / obligations)x100 Implementation pace of total grants (total grants outlays / obligations)x100

Following prior studies (e.g., Carley et al., 2015; Hou et al., 2003; McDermott, 2006)—in which GC was essential to policy implementation and performance—I assert that independent variables are components of GC. In terms of ARRA grants, quickly spending grant funds is an indicator of success in grant implementation. For speedy implementation, governments need sufficient human resources, money, and relevant experience. Thus, the present study assumes that higher capacity governments will spend grant funds faster than lower capacity governments. The operationalization methods for GC are the same as in the first question’s model, as described in Table 13.

Table 13 Government capacity measurement

Variable Measurement

Human resource capacity1: size of human resource

Number of DOT employees per 1000 residents Human resource capacity2: quality of

human resource

Total amount of DOT Payrolls / number of DOT employees / state median income x 100

Financial resource capacity Transportation revenue (motor fuel taxes, motor vehicle taxes, and charges)/ residents

General management capacity GPP (government performance project) infrastructure scores

Previous experience Amount of federal grants

In the scholarly research, determinants of expenditure include: state needs, political variables (Alt & Lowry, 1994; Hou & Smith, 2010), and fiscal institution (Amiel et al., 2009; Deller et al., 2012; Mullins & Wallin, 2004). Indicators of state needs are the same as those in the first research question’s model, as seen in Table 14. Urbanization, economic condition, and median voters are assumed to affect government expenditure or policies on government spending.

Table 14 State needs measurement

Variable Measurement

Urbanization Population density/100

Economic condition Unemployment rate

Median voters Median income/1000

Following prior research, this present study adopts political variables and fiscal institutions as explanatory variables for grant implementation, as described in Table 15.

Political variables include each governor’s political propensity, divided government, and party control in the legislature. They are all coded as dummy variables. Governor’s political propensity is coded as the political party affiliation of governor; I assigned a value of 1 to Republican governors, and zero otherwise. Divided government has a value of 1 if the governor’s political party controls both houses in the legislature, and zero otherwise. For party control of legislature, I coded a value of 1 when the Republican Party occupies the majority in legislature, and zero otherwise.

Table 15 State politics measurement

Variable Measurement

Governor’s propensity Republican=1, otherwise=0

Divided government 1 = governor’s party controls both houses, 0 = otherwise Party control of legislature Republican is majority party=1, otherwise= 0

The study uses tax and expenditure limitation (TEL) and balanced budget requirement as proxies for fiscal institutions as described in Table 16. First, data of TELs originate from previous research: Amiel et al. (2009). The authors developed the TEL stringency index with six categories: “1) the type of TEL; 2) if the TEL is statutory or constitutional; 3) growth restrictions; 4) method of TEL approval; 5) override provisions;

and 6) exemptions” (p.5). Its values were coded as an interval scale, so readers can interpret it as State A is more stringent than State B, but cannot interpret it as State A is “many times”

stricter than State B. Maher and Deller (2012) reported that TELs positively affect fund balance, but negatively affect own-source revenue and general fund expenditure. Second, generally, the balanced budget requirement (BBR) is also used as a proxy for fiscal institution in expenditure studies. However, BBR is not directly applied to capital investment or transportation, because it is funded mostly from other funds beyond general funds. Nevertheless, BBR is controlled in the analytic models since it influences the entire budget allocation and could affect transportation expenditure.

Table 16 Stringency of Tax and Expenditure Limit and of Balanced Budget Requirements Variable Measurement

Stringency of TELs

Summation of values of TELs: 1) the type of TEL; 2) if the TEL is statutory or constitutional; 3) growth restrictions; 4) method of TEL approval; 5) override provisions; and 6) exemptions

Stringency of BBRs

Sum of BBRs (Governor Must Submit Balanced Budget, Legislature Must Pass Balanced Budget, and Cannot Carry Over Deficit)

The present study utilizes multiple sources for data collection, including Recovery.gov, Annual Survey of Public Employment & Payroll, National Conference of State Legislatures, USAspending.gov, the Book of States, U.S. Census, etc. Detailed information of each variable is described in Table 17 below. All explanatory variables reflect values in 2008-2011.

Table 17 Data source

Variable Source

Grant

Implementation

Implementation pace of formula and competitive grants

Recovery.gov Government

Capacity (GC)

Human resource capacity1: size of human resource

Annual Survey of Public Employment

& Payroll Human resource capacity2: quality of human

resource Previous experience: policy experience for grant

USAspending.gov

State Needs Urbanization Census Bureau

Bureau of Labor Statistics Economic condition

Median voters Census Bureau

State Politics Governor’s propensity National Conference of State Legislatures