THE POLITICS OF BUDGETING: EVALUATING THE EFFECTS OF THE POLITICAL ELECTION CYCLE ON STATE-LEVEL BUDGET FORECAST ERRORS MICHAEL BROGAN Rider University

ABSTRACT Budget forecasts are used by incumbent governments as a political tool to manage the electorparticularly during an election cycle (Bruck and Stephan 2006). Incumbents apply budget forecasts strategically. They use short-term forecasts, which are more likely to be unbiased by political factors, annually. They rely on long-term forecasts, which are likely to be biased by political calculations, to set their political agenda (Corder 2005). Incumbents attempt to maintain stability and popularity through short-term forecasts, and retain political power as well as manage the interests and demands of their constituents through long-term forecasts. Regardless of the type of forecast applied, incumbents will typically underestimate forecasts in order to downplay overall spending increases and demonstrate that they are responsible managers of the public purse (Rogers and Joyce 1996). Could there be an ulterior motive for underestimating surpluses and deficits? My research that incumbents downplay forecasts so they can claim credit for properly managing public finances and provide themselves with enough leverage to increase spending during non-election years to win votes. This paper tests this hypothesis at the state level by developing three differing approaches to estimating budget forecasts. First, the naïve model simply estimates budget forecast errors based on economic data (e.g. tax receipts, expenditures). Second, the strategic model, which builds on the naïve model, incorporates political variables into estimating forecast errors (e.g. party control of the legislature and governor, national political factors). Last, the incentive model, which builds on both previous models, integrates the political cycle with budget forecasts (e.g. terms in office, proximity to the next election). By using these three approaches, the model utilizes state-level and national-level political variables to explain the errors generated by budget projections in order to provide a deeper understanding of how states manage fiscal policy by addressing how the political-business

PAQ SPRING 2012 85

cycle effects budget projections. The data used to test this hypothesis will come from state-level budget submission for the beginning of the fiscal year (1992-2008).

INTRODUCTION

Budget forecasts tend to be wrong. Random error, an essential part of generating future budget projections, guarantees that public budgeting is an uncertain business. Uncertainty, therefore, ensures a strong connection between economics and politics when setting and implementing

The inability of states to forecast exactly the rate of revenues and expenditures plays a critical role in their fiscal health (Wagner and Garrett 2004). In the short term, forecasts are generated to reduce uncertainty in the

ations of public resources are subject to greater levels of uncertainty. This results in political pressures intervening in the budget

commitments. So, regardless of how technically competent or correct specific forecast models tend to be in determining future estimates, the budget process is, nevertheless, influenced by competing actors attempting to shape the budget into a form that is most amenable to their needs. Incumbent politicians seek to ensure certainty in the process in order to convey to the public a high level of competence in managing the public purse, while opponent politicians do so by capitalizing on the uncertain.

Research to date on budgeting, has not addressed how the political-business cycle effects budget forecast errors. Recent work by Dohan and Thompson (2007) finds that policymakers attempt to stabilize the rate of spending growth when they must do so to keep a state solvent (for a review of this constraint when there is no-Ponzi scheme, see Buiter 1990) given current information on revenue

86 PAQ SPRING 2012

growth and volatility (Dohan and Thompson 2007). By applying a buffer stock model to budgeting, they predict that spending growth will be more stable than revenue growth. This approach, however, ignores the fact that public spending tends to be more sensitive to lagged revenue growth than their modeling allows. Stability in spending growth is not based on fixed time preferences but rather can be endogenous to the political-business cycle. I suggest that spending preferences depend upon the timing of the electoral cycle.

This article builds a forecasting model of state-level expenditures, and revenues that incorporates economic conditions, as well as incremental spending and revenue patterns. The model utilizes state-level and national-level political variables to explain the errors generated by budget forecasts. The research will provide a deeper understanding of how states manage fiscal policy by addressing how the political-business cycle effects budget projections.

A BRIEF REVIEW OF RESEARCH ON STATE

BUDGET FORECASTS Public budgeting sets the priorities for government

by not only determining how much money is available to spend, but also which policies and programs would be implemented (Clynch and Lauth 1991). Typically the budget process is defined by revenue forecasts and spending budgets that are enacted into law (Buiter 1990). The process of developing revenue projections a heavy influence on budget forecasts can, at times, restrict current and future disbursements (Hale and Douglass 1977, p. 370). From this perspective,

states, incumbent governments use varying strategies to develop short-term and long-term projections to set the

PAQ SPRING 2012 87

political agenda (Corder 2005). In the short-term, annual forecasts of revenue and spending budgets tend to be unbiased by political factors, while long-term forecasts are likely to be biased by political calculations. Therefore the process of developing revenue forecasts and spending budgets serve as a political tool by incumbents who seek to

performance, particularly during an election cycle (Bruck and Stephan 2006).

Typically the budget process is incremental. Any political conflict over financial resources tends to deal with discretionary funding (Lindblom 1959; Wildavsky and Caiden 1988). Such an approach allows for moderation of political conflict over budgeting because incrementalism tends to stabilize budget functions and expectations (Wildavsky and Caiden 1988; Schick 1990). An incremental approach allows for policymakers to focus only on proposed increases; all other existing expenditures

Yet even in light of incrementalism, the process of

setting revenue forecasts is subject to uncertainty. This presents policymakers with various levels of financial and political risk when developing budgets (Corina and Nelson 2003). In extreme cases, when major shortfalls are realized, this has caused state governments to implement contingency plans (either tax increases and/or spending cuts) to close the budget gap (Barro 1979; Alt and Lowery, 1994). Typically, state governments will, over the long run, engage in strategies designed to reduce risk, as well as limit their chances of having to implement contingency plans, by tending to underestimate revenue estimates (Rodgers and Joyce 1996). This allows them not only to downplay overall cost increases but also to demonstrate to voters that they are responsible managers of public finances (Rogers and Joyce 1996).

88 PAQ SPRING 2012

gubernatorial recommendations and legislative

facilities when they are too conservative in their revenue estimates (Lauth 1992, p. 57). Practical issues also challenge the claim that governments systematically under forecast budgets (Rodgers and Joyce 1996). In contrast, forecasters typically engage in a process of developing their forecasts based on unbiasedness and forecast rationality (UFR) (Blackley and DeBoer 1993; Corder 2005; Kamlet et al. 1987; Krause and Douglas 2005; McNees 1995). State governments engage in this behavior when developing forecasts to avoid having to rebalance their

above projected expenditures to avoid legal or political pressures to close a fiscal year with a balanced budget (Corrina et al. 2004; Poterba 1994). Underforecasting also creates political problems. It minimizes a state

moral hazard among policymakers in managing public finances; and can cause an expected surplus to occur during the fiscal year, which would create pressure on incumbents to either increase the budget or cut taxes (Corrina et al. 2004; Jernberg 1992). When under forecasting does occur, it is more likely a result of macro-economic volatility that causes policymakers to overcompensate in their budget projections (Khaneman and Tversky 1992; Krause 2008).

Incremental budgeting also tends to be constrained by the levels of expenditure interdependence across budget categories. Spending levels in one category during a given year are influenced by external fiscal pressures, the expectations and availability of tax revenues, and/or expenditure pressures that are generated by other spending categories (Su, Kamlet, and Mowery 1993). Interdependence of spending categories therefore makes it difficult for policymakers to accurately gauge future

PAQ SPRING 2012 89

revenue and spending patterns. On the one hand, budget variation

in the macro-economy (e.g. unemployment). On the other,

items (e.g. middle class entitlements based on cost-of-living adjustments) (Su, Kamlet, and Mowery 1990, p. 237). In either event, simply making adjustments for incremental revenue and spending patterns may at times under or over allocate the necessary resources across budget categories, hindering the flexibility of policymakers to cope with fluctuations in the fiscal environment. Scholars have also found significant evidence that political institutions shape and constrain the budget process. These factors tend to influence the way expected revenue and expenditures are set. From this perspective, divided and unified control of a sas well as systemic differences between the two major parties (Democrats and Republicans), influence the budget process (Alt and Lowry 1993). State-level fiscal problems

onal control on the one hand and legal restrictions on fiscal

which of course, limits the financial options available to state governments in developing future budgets (Alt and Lowry 1993, p. 823). Corina et al. (2004) contend that competition over political institutions has a significant impact on the accuracy of revenue forecasts after accounting for economic changes and fiscal stress. Research conducted by Poterba (1994) supports this view. He found that state balanced budget rules have a significant impact in how the state responds to unanticipated deficits or surpluses (Poterba 1994). Lastly, research conducted by Garand (1988) found strong empirical evidence that spending patterns of government employees, who in turn vote for the incumbent government, have more of an impact on future revenue growth than do the preferences of the public over revenue and expenditures.

90 PAQ SPRING 2012

Research on state budgeting shows that state legislatures have a prominent role in the development of future budgets. Stanford (1992) found that legislatures

budgets (Stanford 1992, p. 24). She concluded that legislatures were more likely to monitor spending on programs when budgets were tight and more lax in their oversight of programs in years of economic growth (Stanford 1992). Even though the potential benefits of trying to boost the state economy are quite limited and the outcomes of these efforts are uncertain, members have nevertheless successfully insulated themselves from electoral swings when the economy is thriving, thus giving them increased flexibility and latitude in how they engage in the budget process (Chubb 1988). These findings indicate that, in combination with incremental budget conditions, fiscal stress, national economic conditions, and state budget rules, political institutions have real effects on fiscal policy outcomes, (Poterba 1996).

There is a problem, however, in presuming that incumbents are going to be strategic in setting budget projections because they are insulated from exogenous electoral pressures, such as changes in national or state

the best budget forecast to suit their needs. Competing models, prepared by qualified and well-meaning individuals, present multiple scenarios to governors and

discern which model is correct and which should be ignored (Aaron 2000, p. 194). Strategic behavior among incumbents is limited not only by the competence of the forecast model, but also by luck. This leads policymakers to ask what circumstances, independent of forecasts, have caused projections to be wrong, and what assumptions made in the models were not realized. In these instances, economic forces push budgets to either be in the black

PAQ SPRING 2012 91

based upon economic growth or in the red based on a decline, diminishing the likelihood that politicians can be strategic in developing budget forecasts. Further, national economic conditions alegislatures (Garand 1988). Therefore, the fate of incumbent governors and state legislators may not necessarily be based on their ability to manage the state

ability to manage the national economy (Klarner 2010).

DEFINING THE MODEL My model builds on current research by testing

three approaches to analyzing patterns of errors in state-level budget forecasts. It relies on a forecasting model of state-level expenditures and revenues that incorporates economic conditions, as well as incremental spending and revenue patterns. It analyzes the errors generated from these forecasts by utilizing state-level and national-level political variables to explain uncertainty within these predictions. This combined approach allows for a more comprehensive understanding of the economic and political factors that drive forecasts, and provides a means to

interactions between economics and politics in developing

factors influence this process. Prior studies of state-level forecasting practices

have found that state governors are likely to provide themselves with cushions in order to make adjustments as a result of shifts in the economy (Frank and Gianakis, 1990; Klay, 1983). This research has been successful in explaining why states are likely to under forecast to contend with future economic uncertainty (Rodgers and Joyce 1996). I build on these research findings in order to estimate the baseline for budget forecasts, evaluate the

92 PAQ SPRING 2012

effects of political variables in explaining errors generated by budget projections, and determine whether the political-business cycle influences if budget forecasts are periodically underestimated during election years. To accomplish this, I have incorporated three approaches, with each approach building upon the previous.

The first approach is defined as the naïve model, which simply estimates budget forecast errors based on economic data. It develops forecasts for both revenues (based on the following variables: lagged state gross product unemployment (GSP), previous year tax revenue and expenditures (based on the following variables: lagged state gross product (GSP), unemployment, and previous year tax revenue) for a given year. The model picks up trends within the budgeting processes because prior year revenues influence current year levels in both revenue and spending categories. These estimates are designed to closely model the current budget process to generate forecast errors that will serve as the dependent variables in the next two approaches. The intent is to obtain the smallest forecast error possible, in order to minimize unexplained effects for estimating the strategic and incentive approaches.

To estimate expenditure and revenue forecasts for the states, I utilize a weighted least squares regression method. The reason for choosing this technique is to correct for heteroskedasticity. The technique does so by taking the

independent variables and then adjusts them based on the

revenues and expenditures estimates are based on each the natural log of each variable. This is to reduce skewness in the variables. Population is used as a weight, as are lagged revenues, lagged gross state product (GSP), and unemployment. In addition, a dummy variable for each state except Nebraska (which has unicameral system) and

PAQ SPRING 2012 93

Wyoming (which is treated as a reference category) are included as direct effects in each equation. The forecasts allow for state differences in methods for forecasting revenues and expenses. I have specified a disturbance term that is divided into short-term forces and random observations (Green et al. 2002). Short-term forces are

instance, when estimating changes in current expenditures, I include short-term changes in lagged revenues, as well as lagged gross state product. The multivariate model for estimating party volatility is summarized in equation 1.

(Eq. 1) (State Level Revenue [Expenditures])k =constantk + 1,i + k-1 + k-1k,+ + 1 49 + 2k-i + k,1

coefficients is positive for lagged GSP and lagged revenues and negative for unemployment. The rationale for these expectations is that prior year growth is likely to result in current year growth and, during periods of economic downturns, revenues are likely to drop. For the expenditure

positive for lagged GSP, lagged revenue, and unemployment. Expectations suggest that pressures for increased expenditures come from both periods of economic growth and also economic downturns, as demand increases for state services (e.g. unemployment benefits, food stamps, insurance, etc.).

The second approach is defined as the strategic model. This approach builds on the naïve model by incorporating political variables into estimating forecast errors (e.g. party control of the legislature and governorship, national political factors). The dependent variable in this approach is based on the forecast errors

94 PAQ SPRING 2012

from the naïve approach. By calculating forecasts based on economic conditions, forecast errors can now be analyzed by political variables. This allows for the analysis to control for the direct effects of political variables in determining if states overestimate or underestimate their forecasts after accounting for economic conditions and incremental spending commitments.

The theoretical expectation of this model is that differences persist between the political parties in budget forecast errors. State legislatures controlled by the Democratic Party, in very politically liberal states (as determined by Americans for Democratic Action (ADA) scores, vote for the Democratic presidential candidates in each state, etc.), are more likely to over forecast revenue and expenditures. Implicit within this assumption is the parties have two clear approaches to public policy, resulting in Democrats and Republicans having different positions on how to fund them (Garand 1985; Alt and Lowry 1993). The strategic benefit for the political parties is that they provide voters with distinct alternatives by taking competing views about projected funding levels and revenue estimates. Lastly, this approach should also show differences based on the branch of government. Democratic governors are more likely to under forecast revenues and expenditures when compared to a Democratic legislature. Governors tend to be held more accountable for budget management than members of the state legislatures by state voters. State voters tend to use national economic

managing it; thus, a connection is made first between governors and the president for these conditions and then to state legislatures (Chubb 1988, p. 149). State legislatures, therefore, insulate themselves from direct evaluations by state voters for state economic conditions. Rather, it is the

held

PAQ SPRING 2012 95

accountable by voters, who tend to be focused more on national conditions than on state conditions (Chubb 1988).

The technique for estimating the strategic model is based on a multi-level design (with fixed and random effects). This estimation measures differences in revenue and expense budget forecast errors within and between states and allows the ability to account for random effects

elections. Errors within each of the two levels are likely correlated, thus requiring a random effects model in order to make more accurate inferences about the fixed effects of state level budget forecast errors (Greene 2001). This model is defined by equation 2:

(Eq. 2): Y(Forecast Error)ij = 00 + 01

(Democratic Control of Legislature Factor) + 02

(Governor Electoral Change) + 03 (Previous Budget Deficit) + 04 (Divided Government) + 05

(Annual State Unemployment Rate) + 05 (Annual State Gross Domestic Product) + 06

55(State) + u0j + u1j (Gov. Percent Electionij) + rij

The fixed effects model element of Equation 2

forecast errors. The random effects elements in Equation 2

Telectoral margin on the budget cycle.

The incentive model, which builds on both previous models, integrates the political-electoral cycle into estimating variation in budget forecast errors. This model also uses a mixed-level design. The intent of these estimates is to assess the effects of the timing of the next statewide election on budget forecasts. The model tests whether incumbents change their behavior regarding

96 PAQ SPRING 2012

budget forecast errors the closer they are to the next election. It is expected there will be a decrease in forecast errors for both revenues and expenditures during a gubernatorial election year. The motivation for incumbents to do this is based on illustrating to the electorate that they are effective managers of the public purse while, during off-election years, forecast errors are likely to be larger as a means to reward supporters by

forecast errors results in incumbents funding these types of projects during off-election years than what one would expect if projections were based solely on macro-economic conditions and prior year revenue.

The random effects elements in Equation 2 estimate ors, which can

as well as the effects of an election year on the budget cycle.

The parameters used to estimate the incentive model are defined in Equation 3.

(Eq. 3): Y(Forecast Error)ij = 00 + 01

(Democratic Control of Legislature Factor) + 02

(Governor Electoral Change) + 03 (Previous Budget Deficit) + 04 (Divided Government) + 05

(Annual State Unemployment Rate) + 05 (Annual State Gross Domestic Product) + 06

55(State) + 55(Years Until Next Election)56 + u0j + u1j (Gov. Percent Electionij) + u2j (Election) + rij

definitions, please refer to Table 5 in the appendix.

PAQ SPRING 2012 97

RESULTS The overall results from the forecast models of

state expenditures and revenues are summarized in Table 1. The model treats the states as separate factors in generating budget projections, because states have separate methodologies for generating estimates; this allows the model to capture these differences within the estimates. Overall, the forecast models for expenditures and revenues are statistically significant. For the expenditure model, approximately 95% of the variation in total expenditures can be explained by controlling for the

independent variables. The means square error of the overall expenditure forecast model is ± .14. For the revenue model, approximately 97% of the variation in total receipts can be explained. The mean square error of the model is ± .16.

Table 1 summarizes the reduced form of forecast estimates for the revenue and expenditure models. For the revenue model, all of the variables are statistically significant except for the parameters that specify Delaware, New Mexico, New Jersey, Montana, Hawaii, Delaware, and Massachusetts.1 For the expenditure model, all of the

1 State parameters and their standard errors, in parentheses, from the revenue model are: Alabama 0.27 (0.06), Alaska 0.24 (0.07), Arizona 0.21 (0.06), Arkansas 0.32 (0.07), California 0.44 (0.14), Colorado 0.34 (0.07), Connecticut 0.26 (0.07), Delaware 0.02 (0.06), Florida 0.49 (0.09), Georgia 0.38 (0.08), Hawaii 0.02 (0.06), Idaho 0.16 (0.07), Illinois 0.06 (0.06), Indiana 0.45 (0.09), Iowa 0.31 (0.07), Kansas 0.16 (0.06), Kentucky 0.29 (0.07), Louisiana 0.33 (0.07), Maine 0.38 (0.08), Maryland 0.33 (0.07), Massachusetts 0.02 (0.06), Michigan 0.46 (0.09), Minnesota 0.37 (0.07), Mississippi 0.4 (0.07), Missouri 0.23 (0.07), Montana -0.05 (0.06), Nevada 0.47 (0.08), New Hampshire -0.14 (0.06), New Jersey -0.02 (0.06), New Mexico 0.46 (0.08), New York 0.18 (0.06), North Carolina 0.22 (0.06), North Dakota 0.53 (0.11), Ohio 0.56 (0.09), Oklahoma 0.24 (0.07), Oregon 0.39 (0.07), Pennsylvania 0.51 (0.09), Rhode Island 0.01 (0.06), South Carolina 0.27 (0.07), South Dakota -0.12 (0.06), Tennessee 0.34 (0.07), Texas 0.54 (0.1), Utah 0.18 (0.06), Vermont 0.41 (0.08), Virginia -0.18 (0.05), Washington 0.45 (0.08), West Virginia 0.44 (0.08), and Wisconsin 0.14 (0.06).

98 PAQ SPRING 2012

parameters are significant except for unemployment, previous year expenditures, Idaho, Delaware, Colorado, California, Hawaii, Illinois, Indiana, Kansas, Massachusetts, Montana, New Hampshire, New Jersey, North Carolina, Rhode Island, Utah, South Dakota, Texas and Wisconsin.2 Table 1 Weighted Least Squares Regression Estimates of State-Level Budget Forecasts (Revenues and Expenses (1992-2008): Reduced Form Expenditures Revenues Variables Coef

. Std. Err.

t Coef. Std. Err.

t

Lag GSP 0.00 0.00 22.5 0.00 0.00 3.9 State Unemployment Rate

0.03 0.01 3.4 -0.02 0.01 -3.7

Lagged State Revenue

0.47 0.04 10.6 0.74 0.03 28.0

Constant 8.53 0.72 11.8 4.53 0.42 2.2 Model Fit R2 MSE n R2 MSE n 0.95 0.14 850 0.97 0.16 850

2 State parameters, and their standard errors in parentheses, from the expenditure model are: Alabama 0.23 (0.29), Alaska 0.61 (0.26), Arizona 0.43 (0.27), Arkansas 0.6 (0.26), California -0.21 (0.3), Colorado 0.48 (0.26), Connecticut 0.54 (0.25), Delaware 0.15 (0.3), Florida 0.51 (0.27), Georgia 0.56 (0.26), Hawaii 0.33 (0.28), Idaho 0.48 (0.26), Illinois 0.16 (0.3), Indiana 0.48 (0.26), Iowa 0.59 (0.26), Kansas 0.4 (0.27), Kentucky 0.58 (0.26), Louisiana 0.69 (0.26), Maine 0.7 (0.25), Maryland 0.66 (0.25), Massachusetts 0.22 (0.29), Michigan 0.7 (0.26), Minnesota 0.68 (0.25), Mississippi 0.54 (0.26), Missouri 0.47 (0.27), Montana 0.1 (0.31), Nevada 0.64 (0.26), New Hampshire 0.24 (0.28), New Jersey 0.13 (0.3), New Mexico 0.68 (0.26), New York 0.47 (0.27), North Carolina 0.23 (0.28), North Dakota 0.41 (0.28), Ohio 0.71 (0.26), Oklahoma 0.52 (0.26), Oregon 0.54 (0.26), Pennsylvania 0.69 (0.26), Rhode Island 0.14 (0.3), South Carolina 0.59 (0.26), South Dakota -0.06 (0.33), Tennessee 0.51 (0.26), Texas 0.25 (0.27), Utah 0.41 (0.27), Vermont 0.62 (0.25), Virginia 0.04 (0.32), Washington 0.65 (0.25), West Virginia 0.67 (0.25), and Wisconsin 0.33 (0.28).

PAQ SPRING 2012 99

To assess overall accuracy of the forecasts, I have summarized the mean absolute percent error (MAPE) by state in Table 2. Overall the MAPE is 2.87% for the expenditure model and 2.75% for the revenue model. The median for the revenue model is 2.75% and for the expenditure model, 2.88%. The minimum MAPE for the revenue model is 2.72% for California and the maximum is 2.81% for Wyoming. For the expenditure model, the minimum is 2.53% for California and the maximum is 3.19% for Louisiana.

Table 3 presents the reduced form results from the mixed level models for estimating the strategic and incentive models for expenditure errors. All of the covariates for the fixed effects of estimating the strategic model of expenditure forecast errors are significant except for the parameters for Illinois, Kansas, divided government, and governor change.3

Within the strategic model there are notable differences among the political variables. Namely, the interactions between divided government and the Democratic Party Factor (Democratic control of the legislature, liberal ADA scores, and strong historic voting

3 State-level parameters and their standard errors, in parentheses used in the strategic expenditure model are: Alabama -0.3 (0.11), Alaska -0.39 (0.13), Arizona -0.58 (0.14), Arkansas -0.3 (0.12), California -1.23 (0.24), Colorado -0.34 (0.12), Connecticut -0.51 (0.14), Delaware -0.31 (0.12), Florida -0.56 (0.14), Georgia -0.42 (0.13), Hawaii -0.61 (0.16), Idaho -0.27 (0.12), Illinois 0.03 (0.11), Indiana -0.58 (0.14), Iowa -0.41 (0.11), Kansas -0.18 (0.11), Kentucky -0.34 (0.12), Louisiana -0.39 (0.14), Maine -0.68 (0.17), Maryland -0.5 (0.14), Massachusetts -0.4 (0.13), Michigan -0.51 (0.13), Minnesota -0.45 (0.13), Mississippi -0.45 (0.12), Missouri -0.39 (0.12), Montana -0.34 (0.11), Nevada -0.46 (0.12), New Hampshire -0.28 (0.11), New Jersey -0.26 (0.11), New Mexico -0.49 (0.14), New York -0.39 (0.13), North Carolina -0.47 (0.12), North Dakota -0.96 (0.19), Ohio -0.49 (0.13), Oklahoma -0.34 (0.12), Oregon -0.46 (0.13), Pennsylvania -0.56 (0.13), Rhode Island -0.61 (0.17), South Carolina -0.4 (0.12), South Dakota -0.28 (0.11), Tennessee -0.5 (0.13), Texas -0.77 (0.17), Utah -0.17 (0.11), Vermont -0.39 (0.12), Virginia -0.37 (0.13), Washington -0.48 (0.13), West Virginia -0.44 (0.12), and Wisconsin -0.57 (0.14).

100 PAQ SPRING 2012

for Democratic presidential candidates) variable and the parameter for a Democratic governor indicate that inter-institutional differences reduce forecast errors. Also, the direct effects of the Democratic Party Factor and Democratic governor are significant. These results point to partisan differences as well as variation between branches of government in estimating budget forecasts annually.

This suggests that budget forecasts are not only determined by economic factors and past spending but also by current political pressures applied to the budget process as a result of differences between state political institutions.

PAQ SPRING 2012 101

Table 2 Summary of Mean Absolute Percent Errors State-Level Forecasts 1992-2008 Mean Absolute Percent Errors Mean Absolute

Percent Errors States Exp. Rev. States Exp. Rev. Alabama 2.97 2.73 Montana 2.98 2.76 Alaska 2.88 2.80 Nebraska 2.78 2.79 Arizona 3.02 2.73 Nevada 2.85 2.76 Arkansas 2.98 2.73 New

Hampshire 2.91 2.76

California 2.53 2.72 New Jersey 2.89 2.74 Colorado 2.88 2.74 New Mexico 3.09 2.75 Connecticut 2.84 2.73 New York 2.57 2.76 Delaware 3.08 2.72 North Carolina 2.72 2.75 Florida 2.68 2.73 North Dakota 2.98 2.76 Georgia 2.80 2.73 Ohio 2.75 2.75 Hawaii 2.94 2.73 Oklahoma 2.88 2.75 Idaho 3.05 2.73 Oregon 2.76 2.77 Illinois 2.74 2.73 Pennsylvania 2.75 2.75 Indiana 2.81 2.74 Rhode Island 2.89 2.74 Iowa 2.86 2.73 South Carolina 2.82 2.75 Kansas 2.92 2.73 South Dakota 2.97 2.77 Kentucky 2.92 2.73 Tennessee 2.72 2.79 Louisiana 3.19 2.73 Texas 2.67 2.78 Maine 2.99 2.74 Utah 2.85 2.76 Maryland 3.03 2.73 Vermont 3.10 2.76 Massachusetts 2.89 2.74 Virginia 2.79 2.76 Michigan 2.76 2.73 Washington 2.76 2.75 Minnesota 2.86 2.73 West Virginia 2.90 2.75 Mississippi 3.01 2.74 Wisconsin 2.73 2.78 Missouri 2.77 2.75 Wyoming 3.12 2.81 Overall Average

2.87 2.75

102 PAQ SPRING 2012

Table 3 Results from Strategic and Incentive Models for State-Level Forecast Expenditure Errors (1992-2008): Reduced Form

Strategic Model Incentive Model Fixed Effects

Coef. Std. Err.

t Coef.

Std. Err.

t

Gross State Product 0.00 0.00 3.1 0.00 0.00 3.3 Unemployment 0.03 0.01 2.8 0.03 0.01 3.0

Term Year -0.03 0.01 -3.9 Governor Change 0.00 0.03 -0.1 -0.03 0.03 -1.1 Democratic Factor: (ADA, Legislature, State Percent Presidential Vote for Democratic Candidate)

0.20 0.04 4.9 0.20 0.04 4.9

Democratic Governor -0.18 0.05 -3.6 -0.18 0.05 -3.7

Divided Government (DG)

-0.07 0.04 -1.9 -0.06 0.04 -1.7

Democratic Governor by DG

0.15 0.06 2.5 0.15 0.06 2.5

Democratic Factor by DG

-0.09 0.03 -2.6 -0.09 0.03 -2.7

Constant 0.33 0.11 3.1 0.40 0.11 3.7 Random Effects Winning Governor Election Percent of Vote:

Est. Std. Err.

Est. Std. Err.

Constant 0.12 0.02 0.12 0.02 Governor Election Year:

Constant 0.05 0.09 Residual 0.21 0.01 0.20 0.01 Model Fit AIC 236.5 230.9 BIC 503.5 506.98 % Variance Explained By Forecast Errors by Governors' Electoral Margins

37.0% 32.4%

% Variance Explained By Forecast Errors by Election Years

13.8%

PAQ SPRING 2012 103

Within the incentive model for estimating

expenditure errors, all of the parameters are significant except Illinois, Kansas, Utah, change in governor, and the variable for indicating divided government.4 Estimates from the incentive model point to incumbents acting strategically to reduce forecast errors the closer they are to the next election, in order to demonstrate economic competence to the electorate. Therefore, as each year of a

-3% decrease in forecast errors, other things being equal. This leaves room for incumbent governors to spend more during early years of a term because the legislature is more likely than the governor to push for greater spending, which results in higher forecasts. For a Democratic legislature, it is expected to yield a forecast of 20% above the baseline. Yet institutional effects also exist in the incentive model. A Democratic governor is likely to under forecast budgets when compared to the legislature; a Democratic governor will yield a -18% decrease in forecasts, other things being equal. The magnitude and direction of these variables are similar to the effects of the strategic model.

4 State parameters and their standard errors, in parentheses for the incentive expenditure model are: Alabama -0.3 (0.11), Alaska -0.39 (0.13), Arizona -0.58 (0.14), Arkansas -0.31 (0.12), California -1.25 (0.24), Colorado -0.33 (0.12), Connecticut -0.51 (0.14), Delaware -0.31 (0.12), Florida -0.56 (0.14), Georgia -0.42 (0.13), Hawaii -0.59 (0.15), Idaho -0.26 (0.12), Illinois 0.04 (0.11), Indiana -0.58 (0.14), Iowa -0.42 (0.11), Kansas -0.19 (0.1), Kentucky -0.35 (0.12), Louisiana -0.41 (0.14), Maine -0.68 (0.17), Maryland -0.49 (0.14), Massachusetts -0.38 (0.13), Michigan -0.51 (0.13), Minnesota -0.46 (0.13), Mississippi -0.45 (0.12), Missouri -0.41 (0.12), Montana -0.34 (0.11), Nevada -0.45 (0.12), New Hampshire -0.28 (0.11), New Jersey -0.27 (0.11), New Mexico -0.51 (0.14), New York -0.38 (0.13), North Carolina -0.48 (0.12), North Dakota -0.98 (0.19), Ohio -0.5 (0.12), Oklahoma -0.32 (0.12), Oregon -0.46 (0.13), Pennsylvania -0.56 (0.13), Rhode Island -0.6 (0.17), South Carolina -0.41 (0.12), South Dakota -0.27 (0.11), Tennessee -0.5 (0.13), Texas -0.79 (0.17), Utah -0.18 (0.11),Vermont -0.41 (0.12), Virginia -0.39 (0.13), Washington -0.48 (0.13), West Virginia -0.45 (0.12), and Wisconsin -0.56 (0.14).

104 PAQ SPRING 2012

Rahave significant effects between states. The random effects of this model explain approximately 37% of the variation in expenditure forecast errors for the strategic model. In the incentive model 38% of the variation in expenditure forecast errors can be attributed to these random effects. These results suggest that incumbents are likely to act strategically when generating budget forecasts that differ from incremental budgeting. Theoretically, national factors that affect gubernatorial elections will also have an impact on forecast errors across all states. The pooled electoral fortunes of governors across states may be a result of economic prosperity or economic downturns, which from an extensive literature on economic voting (see Kinder and Kiewiet 1981; Lewis-Beck et al. 2008) indicates that electoral fortunes of incumbents are tied to the economy.

Furthermore, elections matter in understanding the impact of budget forecasts. In the incentive model, roughly 13.8% of all variation in forecast errors can be explained by elections. Again, random effects on variation in expenditure forecast errors tie back to elements of economic voting behavior among the electorate, namely that incumbent politicians are punished for economic downturns and rewarded for periods of prosperity (Lewis-Beck and Stagmeier 2007).

Table 4 summarizes parameter results from the reduced form revenue model. The findings indicate that, for the strategic and incentive models, the Democratic Factor is significant, while the variable that estimates gubernatorial term year is significant in the incentive model.5 These results confirm that, based on political

5 State-level parameters and their standard errors, in parentheses, for the strategic revenue model are: Alabama -0.03 (0.11), Alaska -0.27 (0.11), Arizona -0.51 (0.13), Arkansas -0.14 (0.12), California -0.48 (0.22), Colorado -0.23 (0.11), Connecticut -0.44 (0.12), Delaware -0.28 (0.11), Florida -0.26

PAQ SPRING 2012 105

institutions, annualized revenue estimates appear to be primarily driven by state legislatures and the electoral cycle tends to minimize revenue errors. These findings show a tendency for the legislature to over-forecast revenues in states where one party controls the legislature

delegation. Other things being equal, a one unit change in the Democratic Factor variable yields a 12% increase in the strategic model and a 11.5% increase in the incentive model.

Random effects (between states), have a significant effects on explaining variation. In the strategic model, approximately 57.4% of the variation in the dependent variable (revenue forecast errors) is explained by random

margins explain about 55% of variation in the dependent

scheduled to be held explain approximately 8.7% of variation in revenue forecast errors.

(0.13), Georgia -0.11 (0.12), Hawaii -0.42 (0.13), Idaho -0.04 (0.11), Illinois 0.23 (0.09), Indiana -0.42 (0.13), Iowa -0.37 (0.1), Kansas -0.02 (0.1), Kentucky -0.21 (0.1), Louisiana -0.21 (0.12), Maine -0.59 (0.15), Maryland -0.32 (0.12), Massachusetts -0.28 (0.13), Michigan -0.11 (0.12), Minnesota -0.22 (0.11), Mississippi -0.39 (0.1), Missouri -0.12 (0.11), Montana -0.05 (0.11), Nevada -0.24 (0.1), New Hampshire 0.05 (0.11), New Jersey -0.17 (0.1), New Mexico -0.2 (0.13), New York -0.23 (0.12), North Carolina -0.19 (0.1), North Dakota -0.53 (0.18), Ohio -0.33 (0.11), Oklahoma 0.02 (0.11), Oregon -0.19 (0.13), Pennsylvania -0.12 (0.12), Rhode Island -0.5 (0.15), South Carolina -0.19 (0.1), South Dakota -0.15 (0.1),Tennessee -0.24 (0.14), Texas -0.2 (0.16), Utah -0.12 (0.1), Vermont -0.17 (0.1),Virginia -0.29 (0.11),Washington -0.21 (0.12), West Virginia -0.1 (0.11), and Wisconsin -0.3 (0.12).

106 PAQ SPRING 2012

Table 4 Results from Strategic and Incentive Models for State-Level Forecast Revenue Errors (1992-2008): Reduced Form Strategic Model Incentive Model Fixed Effects Coef. Std.

Err. t

Coef. Std. Err.

t

Gross State Product 0.00 0.00 2.0 0.00 0.00 2.3 Unemployment 0.01 0.01 1.1 0.01 0.01 1.0 Term Year -0.02 0.01 -2.8 Governor Change 0.00 0.02 0.1 -0.02 0.02 -0.9 Democratic Factor: (ADA, Legislature, State Percent Presidential Vote for Democratic Candidate)

0.12 0.03 3.7 0.12 0.03 3.5

Democratic Governor -0.05 0.04 -1.3 -0.05 0.04 -1.2

Divided Government (DG)

-0.01 0.03 -0.3 -0.01 0.03 -0.2

Democratic Governor by DG

0.00 0.05 0.1 0.00 0.05 0.0

Democratic Factor by DG

0.00 0.03 0.0 0.01 0.03 0.2

Constant 0.16 0.09 1.7 0.20 0.10 2.1 Random Effects

Winning Governor Election Percent of Vote

Est. Std. Err.

Est. Std. Err.

Constant 0.21 0.02 0.22 0.02

Governor Election Year: Constant 0.04 0.02

Residual 0.16 0.00 0.16 0.01 Model Fit AIC 235 229.2 BIC 502 505.2 % Variance Explained By Forecast Errors by Governors' Electoral Margins

57.4% 53.5%

% Variance Explained By Forecast Errors by Election Years

8.7%

PAQ SPRING 2012 107

DISCUSSION

The estimates reveal a pattern in which political factors, the timing of the electoral cycle, and political institutions affect state-level budget forecasts. Incumbents tend to underestimate budget forecasts. They do this because politicians have a propensity to be risk averse when managing the budget (Kahenman and Tversky 1992; Krause 2008; Rogers and Joyce 1996). This type of behavior causes incumbents to downplay forecasts to claim credit for properly managing public finances and also to provide themselves with enough leverage to increase spending during election years to win votes. Therefore, incumbent governors are periodically prone to underestimating budgetary forecasts so that spending can be increased during off-election years. In these instances, governors need to claim economic competence when managing public finances while at the same time reward voters for keeping them in power.

These findings confirm that budget forecast errors tend not to be randomly distributed across the states. The presumption that government forecasters engage in the practice of unbiased forecast rationality (UFR) (Corrina et al. 2004; Krause 2008), in which the stochastic element of forecasting models are random and independent, reveal that projections tend to be subject to the political-business cycle. In these instances, errors can therefore be biased upwards or downwards, not only based on economic conditions but also on institutional and political factors.

Moreover, this research indicates that not all incumbents engage in under forecasting equally. Incumbent legislatures and governors, even when they are from the same party, tend to have conflicting budgetary priorities. Forecast errors depend on the balance of power within a state between these two branches of government. In addition, this paper confirms that forecasting errors tend

108 PAQ SPRING 2012

to be subject to intertemporal variations. Earlier work by Brettschneider et al. (1986) and Rodgers and Joyce (1996) found systemic under forecasting in revenue forecasting, controlling for economic conditions and public policy considerations. This research confirms their findings but also demonstrates that fluctuations in the accuracy of revenue and expenditure budget projections tend to be based on the proximity to the next election cycle.

In order to remedy this problem, states should consider developing binding targets regarding the degree of error policymakers, and the public, are willing to tolerate in future year forecasts. For instance, holding states to a maximum annual error of 2.8 for both revenue and expenditure forecasts. Ideally, this target would dampen political considerations within the process, improve forecasting accuracy, and would provide the public with more transparency for understanding uncertainty within the budgeting process.

work on the budget process between budget forecasting researchers and political economy scholars who focus on the role of political institutions in shaping the budget process at the state level. (Corrina and Nelson 2002; Alt and Lowry 1993). It confirms that the budget process, in which incumbent governments engage in either under or over forecasting, is substantially conditioned by institutional constraints (e.g. balanced budget amendments and/or rainy day funds), divided government at the state level, as well as the proximity of the next election (Rodgers and Joyce 1996; Corrina and Nelson 2002; Potreba 1994). This work also reveals how this conditional relationship mediates budget projections, primarily through the direct and interactive effects that divided government have on governors and state legislatures in expenditure forecast errors. Revenue forecast errors tend to be affected primarily by state legislatures, as well as by the electoral cycle. This

PAQ SPRING 2012 109

work suggests the reason for this outcome can be attributed to the ability of legislatures to insulate themselves from both the governor as well as from direct electoral pressures

also confirm that electoral pressures stem from national

president, his party, and the governor; all of these factors condition the accuracy of budget projections.

Future research will focus on how budget forecast errors can serve as a governing tool by state governments. It will look at how promptly, and how correctly, mid-year corrections improve upon earlier forecasts as a tool to manage expectations about the state of public resources. This work will examine differences in the electoral consequences of revenue and expenditure projection errors, in order to analyze the process and factors that affect overall projection errors and adjustments that follow elections. Utilizing both quantitative and qualitative approaches provides a richer understanding of the budget process and the politics concerning budget forecast errors.

110 PAQ SPRING 2012

Appendix Table 5 Defining the Variables

Dependent Variables

Comments

State Annual Expenditures

All state governmental spending for a given year. The natural log of this variable is used to estimate the forecast model. Forecast errors from these projections are used to estimate the strategic and incentive models. Data for this variable come from the U.S. Census Annual Survey of State Government Finances from 1992 to 2008.6

State Annual Revenues

All state government spending for a given year. The natural log of this variable is used to estimate the forecast model. Forecast errors from these projections are used to estimate the strategic and incentive models. Data for this variable also come from the U.S. Census Annual Survey of State Government Finances from 1992 to 2008.

Independent Variables State-Level Variables

A binary variable is used for each state in estimating the forecasting model and mixed effects models. State variables are not included for Nebraska and Wyoming.

Lagged State Annual Revenues

The natural log of previous year revenues in a given state.

Lagged State Gross State Product

U.S. Department of Commerce's annual estimates, of the Gross Domestic Product (GDP) for State and Metropolitan Areas series.

Annual State Unemployment Rate

Unemployment rate per state per year compiled by the U.S. Department of Labor and is part of the Local Area Unemployment Statistics (LAUS) data series.

Gross State Product

Current gross state product for a state per year as estimated by the U.S. Department of Commerce.

Divided Government (where party control is split in the upper/lower chamber in

6 The years included in this study are based on the current availability of data. The data series provides comprehensive information on state and local governmental revenue, expenditures, debt and assets (cash and security holdings).

PAQ SPRING 2012 111

Governor Change

A

Democratic Party Factor

A principal component factor of the following variables: the percent of the lower and upper houses held by the Democratic Party; the percent of votes the democratic party received in the state in the previous presidential election; and the average Americans for Democratic Action (ADA)7 scores of the members of Congress per state.8

Democratic Governor

c governor

Term in Office

Democratic Party Factor by Divided Government

The cross product between Democratic Party Factor variable and the variable for Divided Government.

Democratic Governor by Divided Government

The cross product between Democratic Governor variable and the variable for Divided Government.

Random Effects Variables

Governor Electoral Percent

The percent of vote the governor received when elected and is held cons

Election

7 ADA scores are defined as the 20 most important annual votes, ranging from social and economic issues both domestically and internationally, deemed by

ttee during a legislative session. The index measures political liberalism of members of Congress (House and Senate) by combining the 20 key votes into the Liberal Quotient (LQ) which gives each member 5 points if he/she voted with ADA, and 0 points if he/she voted against us or was absent. The total possible score per member, per session, is 100. 8 The Eigen value for this estimate is 2.47 and explains 62% of total variation. Principal Components for this factor are: Percent Democratic Lower House .546; Percent Democratic Upper House, .544; ADA scores .43; and State Presidential Vote for Democratic Candidates is .48.

112 PAQ SPRING 2012

REFERENCES

Government, Fiscal Institutions, and Budget Deficits: E American Political Science Review, 88(4), 811-828

Journal of Policy Analysis and Management, 19 (2), 193-206.

Behn, Robert, ed. (1991). Governors on Governing.

Lanham, MD: University Press of America. Beyle, Thad. (1983). "Governors." In Politics in the

American States. Ed. Virginia Gray, Herbert Jacob, and Kenneth N. Vines. Boston: Little, Brown.

Buiter, William. (1990). Principles of Budgeting and

Financial Policy. Cambridge MA: MIT Press. Brück , Tilman and Stephan, Andreas (2006). "Do

Eurozone Countries Cheat with their Budget Deficit Kyklos, Blackwell Publishing, 59(1), 3-

15. Chubb, John. (1985). "The Political Economy of

Federalism." American Political Science Review 79, 994-1015.

Chubb, John. (1988). "Institutions, the Economy, and the

Dynamics of State Elections." American Political Science Review 82, 133-54.

PAQ SPRING 2012 113

Clynch, Edward, and Thomas Lauth. (1991). Governors, Legislators, and Budgets: Diversity across the American States. New York: Greenwood.

Cornia, Gary C. and Ray D. Nelson. (2003).

Tax Analysis State Tax Notes, August 25, 2003, 563-567

Corina, Gary C., Ray Nelson, and Andrea Wilko. (2004).

Public Administration Review, 64 (2), 164-179.

Downs, Anthony. (1957). Economic Theory of Democracy. Erikson, Robert S., Gerald C. Wright, Jr., and John McIver.

(1989). "Political Parties, Public Opinion, and State Policy in the United States." American Political Science Review 83, 729- 50.

Frank, Howard A. and Gerasimos A. Gianakis, (1990).

"Raising the Bridge Using Time Series Forecasting Models." Public Productivity Management Review, 14(2), 171-188

Garand, James. (1988). "Explaining Government Growth in

the U.S. States." American Political Science Review 82, 837-49.

Gramlich, Edward. (1991). "The 1991 State and Local

Fiscal Crisis." Brookings Papers on Economic Activity 1991, p. 249-85.

114 PAQ SPRING 2012

Government Finances: World War II to the Current Federal Reserve Bank of St. Louis Review,

March/April, 86(2), 9-25. Hansan, Susan. (1983). The Politics of Taxation: Revenue

Without Representation. New York: Praeger Publishers

Hutchison, Tony, and Kathy James. (1988). "Legislative

Budget Procedures in the Fifty States." Denver: National Conference of State Legislatures Fiscal Affairs Program.

Kinder, Donald R. and D. Roderick Kiewiet. (1981).

British Journal of Political Science, 11(2), 129-161.

Governments and Redistricting Authority After the The Forum, Vol. 8, (3) Article 14.

Klay, William Earle, (1983). "Revenue Forecasting: An

Administrative Perspective." In Jack Rabin and Thomas D. Lynch, eds., Handbook on Public Budgeting and Financial Management. New York: Marcel Dekker, 287-315.

Lindblom, Charles. (1

Public Administration Review, 19, 78-88. Lewis-Beck, Michael S. and Mary Stegmaier. 2007.

The Oxford Handbook of Political Behaviour ed. Russel J. Dalton and Hans-Dieter Klingemann. Oxford: Oxford University Press

PAQ SPRING 2012 115

Lewis-Beck, Michael S., Jacoby, William G., Norpoth, Helmut, Weisburg, Herbert F. (2008) The American Voter Revisited. Ann Arbor: University of Michigan Press.

Electoral Stress and Revenue

Structures in the American States : Searching for the Elusive Fiscal IllusionResearch , 15 (1), 5-46.

Mayhew, David R. (1991). Divided We Govern Poterba, James. (1994). "State Responses to Fiscal Crisis:

The Effects of Budgetary Institutions and Politics." Journal of Political Economy 102, 799-821.

The American Economic Review, 86 (2), 395-400.

Underforecasting on the Accuracy of Revenue Public

Administration Review, 56 (1), 48-56. Schick, Allen. (1990). The Capacity to Budget. Urban

Institute: Washington D.C.

Do L Public Administration Review, 52 (1),16-26.

Tullock, Gordon. (1967). The Politics of Bureaucracy. Wildavsky, Aaron and Caiden, Naomi. (1988). The New

Politics of the Budgetary Process.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.