The Road to Weathering the Next Recession:
An Examination of State and Local Expenditures During Recessions
Jeffrey M. Stapleton
Arizona State University
PAF 540 – Final Policy Paper
December 3, 2010
History and Background of the Policy Issue
Budget deficits and unemployment are two issues currently dominating the public agenda. Much attention has been placed on how the U.S. Federal Government will address the $1.3 trillion budget deficit and 9.6% unemployment rate that faces our nation (Associated Press, “Rise in unemployment leaves treasurys mixed,” 2010). However, state and local governments are also facing their own budget deficits and are concurrently attempting to improve the unemployment rate in their own communities. One of the challenges faced by elected leaders is how to cut or rearrange budget priorities during a recession without negatively affecting the unemployment rate. The byproducts of reducing expenditures can result in layoffs, program reductions or eliminations, and reduced purchasing from businesses who consider state and local governments a significant customer. These byproducts all serve to negatively impact a state’s unemployment rate (Sullivan, 2010). This paper hopes to provide significant research demonstrating the relationship between local and state government expenditures and state unemployment rates during a recession. Prior to the analysis, let’s introduce some data on the difficulties local and state governments are currently facing.
While the federal government faces large deficits, its pressure to reduce expenditures can be eased by engaging in deficit spending. This is when the federal government accumulates debt while revenues and expenditures are adjusted until a more sustainable balance is reached. In other words, the issuance of debt allows certain levels of expenditures to continue. Local and state leaders do not enjoy this luxury, many face balanced budget requirements which require that they either raise revenues, cut expenditures, or both in short time frames, usually within one fiscal year (Conant, 2010). The result is that local and state governments have reduced their expenditures by 2.3% and 7.3% respectively during the recent recession. This reduction in spending has been forced not only by the tight timeline to balance the budget, but also the lack of available revenue during a recession. Reduced business activity and job losses during a recession lead to reduced tax collections for state and local governments. The latest recession saw revenues drop by 7.8% for states and 7% for cities (National Association of State Budget Officers [NASBO], 2010)(Hoene & Pagana, 2010).
While facing these fiscal struggles, state and local governments have had to contend with unemployment arising from the recession. According to the Bureau of Labor Statistics, the average state saw a 4.2% increase in the unemployment rate during the recent recession which according to the National Bureau of Economic Research (NBER), began in December 2007 and ended in June 2009 (Bureau of Labor Statistics [BLS], 2010). States have looked at tax policy as a way of addressing the budgetary and unemployment problems. States offering tax cuts or tax incentives are attempting to capture new firms or retain existing ones. The idea is that more firms will generate new economic activity which will drive up revenue, thus limiting budget cuts, and providing necessary jobs (NASBO, 2009). Among other strategies employed during FY ’09 besides tax incentives, states passed stimulus bills for infrastructure and other capital projects. Government stimulus and infrastructure spending policies are more expenditure focused than tax policy (NASBO, 2009). The goal behind these policies is to build valuable infrastructure for private industry, which will attract or retain firms, thus generating more revenue, which will limit further budget cuts. In addition, stimulus and infrastructure spending will put people to work directly by the state’s investments (Congressional Budget Office [CBO], 2008). The research in this paper hopes to act as a guide for policymakers in regards to expenditure focused strategies rather than tax focused policies.
Making economic sense of state and local government expenditure policies is difficult. While it has been noted that diminished expenditures can impact unemployment, the rearrangement of budget priorities are often the result of political decisions which look at budget cuts in terms of who will suffer the most from program reductions. In addition, budget cuts are less likely to be imposed when strong organized resistance will be encountered (Lee, Johnson & Joyce, 2008). Thus, budget cuts are not necessarily made based on economic outcomes, however, this paper hopes to demonstrate the linkages between state and local government expenditure policy and the economic outcomes that result via the unemployment rate during a recession.
Policy Analysis
Data Source and Collection
In order to analyze the relationships between state and local government expenditure policy and the unemployment rate, we require sound datasets. For this paper, data concerning state expenditures was obtained from the Annual Survey of State and Local Government Finances commissioned by the U.S. Census Bureau. Data concerning state unemployment rates was obtained from the U.S. Bureau of Labor Statistics (BLS, 2010)(U.S. Census Bureau, 1993)(U.S. Census Bureau, 2010).
State and local expenditure data collected by the U.S. Census Bureau is collected via a survey from 50 state governments, the District of Columbia, and 89,476 local governments. These local governments include counties, municipalities, townships, special districts, and school districts. In some years, the bureau surveys every government for data via a questionnaire asking for information concerning revenue, expenditures, and debt. A full canvass is performed for years ending in ‘2’ and ‘7’. As will be explained in the following section, this analysis collected data from surveys performed in fiscal years during recessions, thus for the 2001 recession, FY ’01 expenditure data was used from the 2002 survey which was a full canvass. For the 1990-91 and 2008-09 recessions, data was collected from the 1990-91 (FY ’91) and 2008 (FY ’09) surveys. These surveys were collected from all states, counties over 100,000 in population, cities over 75,000 population, and special districts with over 1,000 employees, or $20 million or more in revenues or expenditures. Local governments falling outside this sphere were surveyed via a sample. The overall response rate was 91.5% for the 2008 survey. All 50 states participated.
The Census Bureau assures that every effort was made to minimize errors, but they acknowledge that the data is subject to levels of non-sampling error. In addition, some surveys were not fully completed. In some cases the Census Bureau imputed data based on historical baselines and estimated growth rates. Standard errors can be calculated via a coefficient of variation for data from the 1990-91 and 2008 surveys.
BLS data concerning unemployment are collected by state agencies. However, through a federal-state cooperative program, BLS develops concepts, definitions, and technical procedures employed by states to collect the data. This has the effect of standardizing the data collection over the 50 states. States derive the unemployment rate based on the amount of people collecting unemployment insurance (UI), those who have exhausted UI benefits, and a formula estimating who composes the unemployed who are part of the labor force, but not receiving UI benefits. Similar to the Census Bureau, BLS offers a coefficient of variance for their estimates in order to determine the standard error (BLS, 2010).
Quantitative Policy Analysis
Using U.S. Census Bureau and BLS data, a pearson’s correlation was calculated between the change in state unemployment rates and categories of state and local government expenditures during recessionary periods (see Table 1). In order to demonstrate patterns over multiple recessions, correlations were run on data from the past 3 recessions. Each recessionary period was paired with expenditure data from the closest corresponding fiscal year available (see Table 2). State by state data for FY’01 expenditures was not available for the 2001 Survey of State and Local Government Finances which covered the first three months of the 2001 recession. FY ’09 data for the latest recession was not yet available, thus only FY ’08 data from the survey was used.
The state-by-state BLS unemployment rate data was then selected for each of the recessionary periods in Table 1. For each state, the unemployment rate at the beginning of the recession was subtracted by the rate from the concluding month of the recession (See Table 3). The result was a negative number reflecting the increase in the state’s unemployment rate for that period. This figure was then correlated against 25 different expenditure categories for the corresponding recessionary fiscal year.
Expenditure data provided in dollars was then divided by the state’s population for that year to provide per-capita spending for the expenditure category. The 25 expenditure categories evaluated are described in Table 4 (See Table 4). These categories included big-ticket budget items such as education, wages and salaries, and public welfare spending. Other measures included per-capita spending on police, fire, corrections, and parks and recreation.
The pearson’s correlation coefficient produced on the variables produced some moderately significant correlations (See Table 5). Of the 25 expenditure categories evaluated, 9 expenditure categories saw a significant relationship with state changes in the unemployment rate during at least one recession. We will first look at categories that achieved correlations in only one recession (5 categories), then categories achieving significance in two recessions (2 categories), and categories that were significant in all three past recessions (2 categories).
Significance in only one of the past three recessions
Public Welfare Expenditures
The first category achieving significance was public welfare (PW) expenditures during the 1990-91 recession (r = -.416, p<.01). This negative relationship demonstrated that states with higher per-capita expenditures on public welfare also saw higher increases in the unemployment rate during the recession. Public Welfare was classified as support and assistance based on need. Some economists have long held that higher public welfare spending will retard necessary economic growth (Vedder, 1993). However, we did not see a significant relationship for the 2001 or 2008-09 recessions. It should be noted that the federal welfare reform enacted in 1996 had the effect of reducing public welfare expenditures made by state governments (O’Sullivan, 2009). This had a likely impact on the relationship obtained. Education and Higher Education Expenditures
The next two categories achieving significance was overall education (EDU) expenditures during the 2001 recession (r = .323, p<.05), and higher education (HED) expenditures during the 2008-09 recession (r = .297, p<.05). These relationships were not as strong as the public welfare measure, but the positive correlations demonstrated that states spending more per-capita on education and higher education saw lower increases in the unemployment rate. Some leading theories are that education not only employs a large workforce, but it also increases the human capital of the community, thus improving productivity and economic growth (O’Sullivan, 2009). In addition, recessions see those with less than a high school education impacted the most, with current unemployment rates for this cohort exceeding 15% (BLS, 2010). Despite this evidence we did not see education (EDU) and higher education (HED) expenditures have a significant impact in other recessions. More specifically, elementary and secondary education (ELEM_SEC) expenditures were not significant in any of the three recessions. While education improves skills, its expenditures alone have an unclear direct impact on economic growth (Blankenau & Simpson, 2004). Utilities and Financial Administration Expenditures
The final two categories achieving significance at least once were utility expenditures (UTIL) for the 2001 recession (r = -.319, p<.05) and financial administration (F_ADMIN) for the 2001 recession (r = .405, p<.01). For the 2001 recession, states with higher utility expenditures experienced higher increases in unemployment, while states with higher financial administration expenditures saw lower increases in unemployment. In regards to utilities, it is theorized that deregulation or privatization reduces the cost of utilities for government and results in more efficient management of resources, thus increasing productivity and economic activity (Coulson, 2005). This is a possible explanation of the relationship. As for financial administration, it is defined as the activities of funding central agencies performing accounting, auditing, and budgeting for state and local governments. No theories could be found concerning the nature of the relationship between these expenditures and unemployment, or even general economic activity.
Significance in at least two of the past three recessions
Sewerage Expenditures
The next expenditure category demonstrating a significant relationship with unemployment is sewerage. The Census defines this category as the, “provision, maintenance, and operation of sanitary and storm sewer systems and sewage disposal and treatment facilities, as well as all intergovernmental payments for such activities.” Statistically significant relationships were found in both the 1990-91 recession (r = -.342, p<.05) and the 2001 recession (r = -.374, p<.01). The negative relationship demonstrates that the higher per-capita state expenditures were for sewerage, the higher the change experienced in the state unemployment rate. This relationship appears confusing at first, considering that spending on sewerage would be considered infrastructure spending which commonly generates construction jobs and improves infrastructure for private investment (Congressional Research Service [CRS], 2010). However, a relationship noticed in reviewing the data was that states with predominantly rural populations tended to have low per-capita sewerage expenditures. Additional analysis was conducted on the relationship. U.S. Census Bureau data of urban versus rural population share of each state from 1990 were collected. A pearson’s correlation was conducted between the rural population percentage and FY ’91 sewerage expenditures. The result was a statistically significant moderately negative relationship (r = -.547, p<.01). The relationship confirmed that states with higher rural populations often had lower sewerage expenditures. A partial correlation was then performed between the 1991 recession unemployment rates and FY ’91 sewerage expenditures partialling out for rural population. A p-value of less than .05 was required for significance which was achieved (r = -.499, p=.000). Thus, rural population did not explain the negative relationship between sewerage expenditures and increases in the unemployment rate.
Expenditures for Natural Resources
This is the first expenditure category that was statistically significant in all three recessions. Natural resource expenditures are defined as spending related to water and mineral resources, agriculture, and the regulation or development of agricultural or natural resource products. The relationship was positive in all three recessions examined, 1990-91 (r = .313, p<.05), 2001 (r = .486, p<.01), and 2008-09 (r = .366, p<.01). The relationship demonstrates that higher natural resources expenditures correlated with smaller overall changes to a state’s unemployment rate.
It is important to note that this category includes agriculture expenditures. A careful examination of states with low unemployment rates in each of the past three recessions reveal that a geographically contiguous region with low unemployment in the Upper-Mountain West/Midwest consists of states with strong agricultural economies. According to the U.S. Bureau of Economic Analysis (BEA), these states, Idaho, Montana, Wyoming, North Dakota, South Dakota, Nebraska, Kansas, and Iowa each had a higher than average GDP per-capita in agriculture (U.S. Bureau of Economic Analysis [BEA], 2010). This observation resulted in additional statistical tests to determine if agriculture per-capita GDP was a common cause variable explaining the relationship between natural resource expenditures and unemployment.
A partial correlation was performed between natural resource expenditures and unemployment rate changes, but with by-state agriculture per-capita GDP used as a control variable. A p-value of less than .05 was required for significance, which was not achieved for the 1990-91 recession (r = .127, p=.378), and the 2008-09 recession (r = .207, p=.149). These results indicate that a partial correlation confirmed twice that agriculture per-capita GDP was a common cause variable explaining the relationship between natural resource expenditures and changes in the unemployment rate (See Table 6).
Highway Spending and Capital Outlays for Highways
The most relevant findings were in relation to highway expenditures and highway capital outlays. Capital outlays (HCAP) are considered part of overall highway spending and are defined as direct expenditures for the construction of highways, streets, roads, alleys, sidewalks, bridges, tunnels, ferry boats, and viaducts. This also includes purchase of land, existing structures, and equipment. The larger category of Highways (HIGHWAY) includes capital outlays and the maintenance, operation, and repair of highways, streets, roads, etc.
Overall highway spending (HIGHWAY) was statistically significant during two recessions, 2001 (r = .480, p<.01), and 2008-09 (r = .490, p<.01). In addition, expenditures for capital outlays on highways were statistically significant for all 3 recessions analyzed, 1990-91 (r = .316, P<.05), 2001 (r = .464, p<.01), and 2008-09 (r = .455, p<.01). These results suggest that higher per-capita expenditures by state and local governments on highways will lead to smaller changes in a state’s unemployment rate during a recession (See Figures 1, 2, and 3).
Before declaring this correlation significant it is important to test for common cause variables. A bi-variate correlation performed between HIGHWAY, HCAP, and natural resources (NR) expenditures revealed a strong relationship between each variable in each recession. In four of the six pearson’s correlations performed, the correlation exceeded .775 with p<.01. Having seen that agriculture GDP explained the relationship between natural resource expenditures and the unemployment rate, partial correlations were performed on the HIGHWAY and HCAP variables for each recession where a statistically significant result was obtained.
The first partial-correlation correlated overall highway spending (HIGHWAY) and unemployment partialling out for agriculture per-capita GDP. The result were p-values < .05, thus establishing that agriculture per-capita GDP spending was not a common cause variable for overall highway spending. The second partial-correlation correlated highway capital outlays (HCAP) and unemployment partialling out for agriculture per-capita GDP. The result for the 1990-91 recession was p>.05, thus establishing agriculture GDP as a common-cause variable in that one instance (See Table 7). For the 2001 and 2008-09 recessions, p<.05, thus maintaining the statistical significance of the original pearson’s correlation between highway capital outlays and state unemployment rates. Policy Analysis Impacts
The pearson’s correlations obtained demonstrate that except for a few categories, most state and local government expenditures do not have a statistically significant impact on the change experienced in the state’s unemployment rate during a recession. The purpose of analyzing multiple recessions was to establish patterns among the relationship between expenditure categories and unemployment. The statistically significant relationships obtained fell into three categories, 1) categories achieving significance with unemployment in only one recession, 2) categories achieving significance with unemployment in more than one recession, but explained by a common cause variable, and 3) categories achieving significance with unemployment in more than one recession. Category 3 should be the focus for policymakers in the future. When the next recession nears and budgets become squeezed from declining revenues it is important for states to examine highway, highway capital outlay, and sewerage expenditures.
Policy Recommendations
The analysis in this paper demonstrates that states should encourage greater highway spending during a recession. It will have the effect of mitigating a rising state unemployment rate. There is sound economic theory that supports this recommendation. According to the Congressional Research Service (2010), public works expenditures on construction activities such as highways, bridges, dams, flood control help stimulate construction jobs and economic activity of industries that support construction activity. Furthermore, the infrastructure benefits help reduce the costs of private business transactions, which spurs greater economic activity (CBO, 2010). Highway spending would also provide work for a construction industry that is currently experiencing a national unemployment rate of 19.0%, about twice the standard unemployment rate of 9.6% for November 2010 (BLS, 2010).
Some policy alternatives being considered encourage policymakers to undertake stimulus programs that invest in general public works projects (Alliance for American Manufacturing [AAM], 2009). While investment in public works can stimulate growth, it also includes investments in sewerage, which was seen to have a negative impact on state unemployment rates. We recommend state policymakers carefully evaluate the impacts of sewerage expenditures and instead prioritize highway spending over other categories.
This year, the American Association of State Highway and Transportation Officials (AASHTO) (2010) released a report encouraging policy makers to build and invest in new highway infrastructure. This includes widening or reconfiguring existing highways and modernization of transportation systems. The U.S. Public Interest Research Group (U.S. PIRG) (2010), however, advocated for more maintenance of existing infrastructure rather than new investments in new highways. The research conducted in this report demonstrates that AASHTO and U.S. PIRG’s goals are not mutually exclusive to influencing unemployment rates. In fact, investments in new highway projects and maintenance will have the effect of lowering a state’s unemployment rate.
It should be noted that there is rising interest in making investments in alternative forms of transport besides highways and roads. State and local government and special purpose governments invested $8.7 billion into public transportation in FY’ 2002 (Shapiro & Hassett, 2005). AASHTO notes that public transportation funding should continue to increase, but not at the expense of highway spending during tight budgetary times. If state leaders wish to impact the local unemployment rate, they should heed this advice that highway spending should be prioritized. Highways not only account for the majority of personal and freight trips made, but it is also a much larger share of state and local government expenditures, with $115 billion being spent in FY’ 02 versus $8.7 billion for public transportation (U.S. Census Bureau, 2010).
An example of a policy that states could pursue would be the proposed TIME Imitative championed by former Arizona Governor Janet Napolitano. TIME would have raised the state sales tax to invest $42 billion over 30 years into transportation projects, primarily highways. This would have dramatically increased highway expenditures and decreased unemployment. Unfortunately, the TIME Initiative failed to make the Arizona ballot in 2008 (Associated Press, “Group launches initiative drive for transportation funding,” 2008).
Conclusion
While it is recommended that highway expenditures by state and local government per-capita are increased during recessions in order to mitigate rising unemployment, the issue deserves further study. Other factors such as population growth in states should be tested as common cause variable for the relationship. In addition, detailed by-state expenditures on public transportation should be added to the U.S. Census Bureau’s Annual Survey of State and Local Government Finances. While the level of public transportation expenditure pales in comparison to highway expenditures, it would help to further clarify why highway spending should be prioritized in order to aid job creation.
It should be noted that highway projects come in different shapes and sizes. Research on the relationships observed would thus be aided by more detailed by-state categories on what different classifications of projects constituted highway spending. Perhaps spending on widening projects has a stronger correlation with unemployment instead of new freeways. A greater level detail by-state would aid policymakers in further maximizing highway spending in order to generate maximum employment benefit.
In addition to transportation data, more data on sewerage expenditures is needed to further understand its negative relationship with unemployment. Literature on sewerage expenditures and finance did not addresses specific economic impacts on the labor market. In addition, it failed to point toward potential common cause variables that would explain the relationship.
As for variables like public welfare, education, higher education, financial administration, and utilities expenditures, which achieved statistical significance only once, more user-friendly data from the U.S. Census Bureau survey from previous years would help provide more supporting evidence on whether relationships existed in recessions prior to 1990-91, particularly the lengthy recession lasting from July 1981 – November 1982. Data from the 1990-91 Local Government Finance survey was obtained via a .pdf and manually entered into the master data set.
Since this research was designed to help inform policymakers to prepare for future recessions, it is difficult to predict when there will be another recession and what will be the fiscal health of state and local governments when that period of negative growth arrives. What can be predicted for the future of government expenditures is that there will only be more fierce competition for limited government revenues. Increases in health care, corrections, and education spending can only serve to crowd out other categories of expenditures. In addition, highway spending is also dependent on state gasoline taxes which have remained stagnant (Wachs, 2003). These factors serve to potentially depress future highway spending. This would not be unprecedented as transportation infrastructure spending lagged during the mid-1970s through the early 1980s, an era noted for its slow economic growth and stubbornly high unemployment rates (CBO, 2010). It is possible that in the years leading up to the next recession leaders will face downward pressures on highway expenditures which serve to create jobs.
Charts, Table, and Figures
Table 1 –Independent Variable and Dependent Variable
| | IV | DV |
| | Per-Capita Expenditures by State and by expenditure category | Change in State’s Unemployment Rate During Recession |
| 1990-91 Recession | FY ’91 Per-Capita Expenditures by state | ([State Unemployment Rate in July 1990] – [State Unemployment Rate in March 1991] |
| 2001 Recession Start: 3/2001 | FY ’02 Per-Capita Expenditures by state | ([State Unemployment Rate in March 2001] – [State Unemployment Rate in November 2001] |
| 2008-09 Recession Start: 12/2007 | FY ’08 Per-Capita Expenditures by state | ([State Unemployment Rate in December 2007] – [State Unemployment Rate in June 2009] |
Table 2 - Recessionary Period and Corresponding Fiscal Year
| Recessionary Period* | Fiscal Year Data Used | Fiscal Year Data Desired but Not Available |
| 1990-91 Recession | FY ‘91 | |
| 2001 Recession | FY ‘02 | FY ‘01 |
| 2008-09 Recession | FY ‘08 | FY ‘09 |
*Recessionary Periods are defined by the National Bureau of Economic Research (NBER)
Table 3 – Descriptive Statistics for State Change in Unemployment Rates
| Descriptive Statistics | |||||
| | N | Minimum | Maximum | Mean | Std. Deviation |
| UNEMP_CNG_1990_91 | 51 | -3.4 | .2 | -1.110 | .7643 |
| UNEMP_CNG_2001 | 51 | -2.5 | .0 | -.982 | .5417 |
| UNEMP_CNG_2008_09 | 51 | -6.8 | -1.4 | -4.159 | 1.3411 |
| Valid N (listwise) | 51 | | | | |
Table 3. The 1990-91 and 2001 recessions were considered mild versus the recent severe recession experienced from 2008-09. This is reflected by the much higher average change in the unemployment rate for the most recent recession, which saw the average state’s unemployment rate rise by 4.2%.
Table 4 – Definition of State and Local Government Expenditure Categories Examined
| Expenditure Category | Variable Name(s) | Definition* |
| Overall Expenditures | EXP_1990_91, EXP_2001, EXP_2008_09 | Expenditure includes all amounts of money paid out by a government during its fiscal year |
| Overall Capital Outlays | CAPOUT_1990_91, CAPOUT 2001, CAPOUT_2008_09 | Direct expenditure for purchase or construction, by contract or government employee, construction of buildings and other improvements; for purchase of land, equipment, and existing structures; and for payments on capital leases. |
| Construction | CON_1990_91, CON_2001, CON_2008_09 | Production, additions, replacements, or major structural alterations to fixed works, undertaken either on a contractual basis by private contractors or through a government's own staff. |
| Interest on Debt | IDEBT_1990_91, IDEBT_2001, IDEBT_2008_09 | Amounts paid for the use of borrowed money. |
| Salaries and Wages | SW_1990_91, SW_2001, SW_2008_09 | Total expenditure during fiscal year for salaries and wages, covering all functions and activities of the government and its dependent agencies. |
| Education - Total | EDU_1990_91, EDU_2001, EDU_2008_09 | Expenditures on Schools, Colleges, and other educational institutions and educational programs for adults, veterans, and other special classes. (Note: Includes HED and ELEM_SEC) |
| Higher Education | HED_1990_91, HED_2001, HED_2008_09 | Operation, maintenance, and construction of Colleges and Universities |
| Elementary and Secondary Education | ELEM_SEC_1990_91, ELEM_SEC _2001, ELEM_SEC _2008_09 | The operation, maintenance, and construction of public schools and facilities for elementary and secondary education (kindergarten through high school) |
| Public Welfare | PW_1990_91, PW_2001, PW_2008_09 | This function covers expenditures associated with only three Federal programs, SSI, TANF, and Medicaid |
| Hospitals | HOSP_1990_91, HOSP_2001, HOSP_2008_09 | Expenditures related to a government's own hospitals as well as expenditures for the provision of care in other hospitals (public or private) |
| Health | HLTH_1990_91, HLTH_2001, HLTH_2008_09 | Outpatient health services, other than hospital care, including; public health administration; research and education; categorical health programs; treatment and immunization clinics; nursing; and environmental health activities. |
| Highway | HIGHWAY_1990_91, HIGHWAY_2001, HIGHWAY_2008_09 | Maintenance, operation, repair, and construction of highways, streets, roads, alleys, sidewalks, bridges, tunnels, ferry boats, and viaducts |
| Highway Capital Outlays | HCAP_1990_91, HCAP_2001, HCAP_2008_09 | Direct expenditure for purchase or construction, by contract or government employee, for construction of highways, streets, roads, alleys, sidewalks, bridges, tunnels, ferry boats, and viaducts |
| Airports | AIR_1990_91, AIR_2001, AIR_2008_09 | Provision, operation, construction, and support of airport facilities serving the public at-large on a scheduled or unscheduled basis |
| Police | POLICE_1990_91, POLICE_2001, POLICE_2008_09 | Expenditures for general police, sheriff, state police, and other governmental departments that preserve law and order, protect persons and property from illegal acts, and work to prevent, control, investigate, and reduce crime. |
| Fire | FIRE_1990_91, FIRE_2001, FIRE_2008_09 | Prevention, avoidance, and suppression of fires and provision of ambulance, medical, rescue, or auxiliary services provided by fire protection agencies. |
| Corrections | CORR_1990_91, CORR_2001, CORR_2008_09 | Residential institutions or facilities for the confinement, correction, and rehabilitation of convicted adults, or juveniles adjudicated, delinquent or in need of supervision, and for the detention of adults and juveniles charged with a crime and awaiting trial. |
| Natural Resources | NR_1990_91, NR_2001, NR_2008_09 | Expenditures related to water and mineral resources, agriculture, and the regulation or development of agricultural or natural resource products. |
| Parks and Recreation | PREC_1990_91, PREC_2001, PREC_2008_09 | Provision and support of recreational and cultural-scientific facilities maintained for the benefit of residents and visitors. |
| Housing and Community Development | HCDEV_1990_91, HCDEV_2001, HCDEV_2008_09 | Construction, operation, and support of housing and redevelopment projects and other activities to promote or aid public and private housing and community development. |
| Sewerage | SEW_1990_91, SEW_2001, SEW_2008_09 | Provision, maintenance, and operation of sanitary and storm sewer systems and sewage disposal and treatment facilities. |
| Financial Administration | F_ADMIN_1990_91, F_ADMIN _2001, F_ADMIN _2008_09 | Officials and central staff agencies concerned with tax assessment and collection, accounting, auditing, budgeting, purchasing, custody of funds, and other finance activities. |
| Judicial and Legal Administration | JL_ADMIN_1990_91, JL_ADMIN _2001, JL_ADMIN _2008_09 | Courts (criminal and civil) and activities associated with courts, legal services, and legal counseling of indigent or other needy persons. |
| Utilities | UTIL_1990_91, UTIL_2001, UTIL_2008_09 | Utility expenditure comprises outlays for the purchase or construction of utility facilities, interest on utility, and production or acquisition and distribution of utility commodities and services for sale to the general public. |
| Employee Retirement | EMPR_1990_91, EMPR_2001, EMPR_2008_09 | Distribution of cash benefits to, or withdrawals by, eligible persons under government-administered employee retirement systems covering public employees. |
*Definitions provided by the U.S. Census Bureau Annual Survey of State and Local Government Finances.
Table 5 – Pearson’s Correlations for State Expenditure Categories and State Unemployment Rate Changes
| UNEMP_CNG_1990_91 | | UNEMP_CNG_2001 | | UNEMP_CNG_2008_09 | |||||
| UNEMP_CNG_1990_91 | 1 | UNEMP_CNG_2001 | 1 | UNEMP_CNG_2008_09 | 1 | ||||
| EXP_1990_91 | -.046 | EXP_2001 | .117 | EXP_2008_09 | .110 | ||||
| CAPOUT_1990_91 | .234 | CAPOUT_2001 | .059 | CAPOUT_2008_09 | .109 | ||||
| CON_1990_91 | .191 | CON_2001 | .099 | CON_2008_09 | .157 | ||||
| IDEBT_1990_91 | .071 | IDEBT_2001 | .017 | IDEBT_2008_09 | .020 | ||||
| SW_1990_91 | -.029 | SW_2001 | .223 | SW_2008_09 | .147 | ||||
| EDU_1990_91 | -.024 | EDU_2001 | .323* | EDU_2008_09 | .247 | ||||
| HED_1990_91 | .187 | HED_2001 | .196 | HED_2008_09 | .297* | ||||
| ELEM_SEC_1990_91 | -.109 | ELEM_SEC_2001 | .198 | ELEM_SEC_2008_09 | .124 | ||||
| PW_1990_91 | -.416** | PW_2001 | .178 | PW_2008_09 | .104 | ||||
| HOSP_1990_91 | .064 | HOSP_2001 | -.203 | HOSP_2008_09 | -.174 | ||||
| HLTH_1990_91 | -.182 | HLTH_2001 | -.053 | HLTH_2008_09 | -.102 | ||||
| HIGHWAY_1990_91 | .224 | HIGHWAY_2001 | .480** | HIGHWAY_2008_09 | .490** | ||||
| HCAP_1990_91 | .316* | HCAP_2001 | .464** | HCAP_2008_09 | .455** | ||||
| AIR_1990_91 | .233 | AIR_2001 | -.026 | AIR_2008_09 | -.098 | ||||
| POLICE_1990_91 | -.086 | POLICE_2001 | -.087 | POLICE_2008_09 | -.151 | ||||
| FIRE_1990_91 | -.110 | FIRE_2001 | -.232 | FIRE_2008_09 | -.273 | ||||
| CORR_1990_91 | -.114 | CORR_2001 | -.112 | CORR_2008_09 | -.120 | ||||
| NR_1990_91 | .313* | NR_2001 | .486** | NR_2008_09 | .366** | ||||
| PREC_1990_91 | .265 | PREC_2001 | -.133 | PREC_2008_09 | -.050 | ||||
| HCDEV_1990_91 | -.173 | HCDEV_2001 | -.053 | HCDEV_2008_09 | .074 | ||||
| SEW_1990_91 | -.342* | SEW_2001 | -.374** | SEW_2008_09 | -.221 | ||||
| F_ADMIN_1990_91 | .034 | F_ADMIN_2001 | .405** | F_ADMIN_2008_09 | .192 | ||||
| JL_ADMIN_1990_91 | -.021 | JL_ADMIN_2001 | .047 | JL_ADMIN_2008_09 | .046 | ||||
| UTIL_1990_91 | .068 | UTIL_2001 | -.319* | UTIL_2008_09 | -.092 | ||||
| EMPR_1990_91 | -.171 | | EMPR_2001 | -.028 | | EMPR_2008_09 | -.064 | ||
| *. Correlation is significant at the 0.05 level (2-tailed). |
| |||||||
| **. Correlation is significant at the 0.01 level (2-tailed). |
| |||||||
Table 6 – Unemployment and Natural Resource Expenditure Partial Correlation, controlling for agriculture per-capita GDP by state
| Correlations | ||||||||||||
| Control Variables | UNEMP_CNG_1990_91 | NR_1990_91 | ||||||||||
| AG_GDP_PerCapita_1991 | UNEMP_CNG_1990_91 | Correlation | 1.000 | .127 | ||||||||
| Significance (2-tailed) | . | .378 | ||||||||||
| df | 0 | 48 | ||||||||||
| NR_1990_91 | Correlation | .127 | 1.000 | |||||||||
| Significance (2-tailed) | .378 | . | ||||||||||
| df | 48 | 0 | ||||||||||
| Correlations |
| |||||||||||
| Control Variables | UNEMP_CNG_2001 | NR_2001 |
| |||||||||
| AG_GDP_PerCapita_2002 | UNEMP_CNG_2001 | Correlation | 1.000 | .397 |
| |||||||
| Significance (2-tailed) | . | .005 |
| |||||||||
| df | 0 | 47 |
| |||||||||
| NR_2001 | Correlation | .397 | 1.000 |
| ||||||||
| Significance (2-tailed) | .005 | . |
| |||||||||
| df | 47 | 0 |
| |||||||||
| Correlations | ||||||||||||
| Control Variables | UNEMP_CNG_2008_09 | NR_2008_09 | ||||||||||
| AG_GDP_PerCapita_2008 | UNEMP_CNG_2008_09 | Correlation | 1.000 | .207 | ||||||||
| Significance (2-tailed) | . | .149 | ||||||||||
| df | 0 | 48 | ||||||||||
| NR_2008_09 | Correlation | .207 | 1.000 | |||||||||
| Significance (2-tailed) | .149 | . | ||||||||||
| df | 48 | 0 | ||||||||||
Table 6 – These results demonstrate that agriculture per-capita GDP by state was a common cause variable (p>.05) for unemployment rate change and natural resource expenditures in two of the last three recessions.
Table 7 – Unemployment and Highway Capital Outlay Expenditure Partial Correlation, controlling for agriculture per-capita GDP by state
| Correlations | ||||
| Control Variables | UNEMP_CNG_1990_91 | HCAP_1990_91 | ||
| AG_GDP_PerCapita_1991 | UNEMP_CNG_1990_91 | Correlation | 1.000 | .177 |
| Significance (2-tailed) | . | .219 | ||
| df | 0 | 48 | ||
| HCAP_1990_91 | Correlation | .177 | 1.000 | |
| Significance (2-tailed) | .219 | . | ||
| df | 48 | 0 | ||
Table 7 – These results demonstrate that agriculture per-capita GDP by state was a common cause variable (p>.05) for unemployment rate change and highway capital outlay expenditures for only the 1990-91 recession.
Figure 1 – Bivariate Scatterplot of Highway Expenditures and Unemployment Change*
*Figure 1 – The tables have been re-sized to omit Alaska, a consistent outlier.
2001 Alaska: (HIGHWAY_2001 = 1,422, UNEMP_CNG_2001 = -.2)
2008-09 Alaska (HIGHWAY_2008_09 = 2,217, UNEMP_CNG_2008_09 = -1.8)
Figure 2 – Bivariate Scatterplot of Highway Capital Outlay Expenditures and Unemployment Change**
Figure 3 – Bivariate Scatterplot of Highway Capital Outlay Expenditures and Unemployment Change**
**Figure 2 and 3 – The tables have been re-sized to omit Alaska, a consistent outlier.
2001 Alaska: (HCAP_2001 = 750, UNEMP_CNG_2001 = -.2)
2008-09 Alaska (HCAP_2008_09 = 1210, UNEMP_CNG_2008_09 = -1.8)
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