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The impact of a state mandated expenditure floor on aggregate property values
Journal of Urban Economics 53 (2003) 531–540 www.elsevier.com/locate/jue
The impact of a state mandated expenditure floor on aggregate property values Laurie J. Bates a and Rexford E. Santerre b,∗ a Bryant College, Department of Economics, Smithfield, RI, USA b University of Connecticut, Department of Finance, 2100 Hillside Road, Unit 1041,
Storrs, CT 06269-1041, USA Received 1 July 2002; revised 27 January 2003
Abstract Using a test of allocative efficiency in the local public sector developed by Brueckner [Journal of Public Economics 19 (1982) 311–331], this study empirically examines the impact of a state minimum education expenditure requirement on aggregate property values in Connecticut communities. The empirical results reveal that the typical community in Connecticut spends less on education and municipal services than the level that maximizes aggregate property values. The results further indicate that spending on education falls further below the property maximization level in those communities constrained by the state expenditure floor. It follows from the analysis that some state expenditure floors can raise aggregate property values and promote efficiency. 2003 Elsevier Science (USA). All rights reserved. JEL classification: H7; R0 Keywords: Mandates; Public goods; Efficiency
1. Introduction For nearly 35 years, public sector analysts have been interested in the economics of the state–local government relationship. The interest began in the 1960s with the development and application of the theory of intergovernmental grants (e.g., Gramlich [9]), evolving more recently into an analysis of state tax and expenditure ceilings like Proposition 13 in California (Mullins and Cox [11], and Bice and Hoyt [2]). Much * Corresponding author.
E-mail addresses: [email protected] (L.J. Bates), [email protected] (R.E. Santerre). 0094-1190/03/$ – see front matter 2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S0094-1190(03)00027-5
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insight has been gained by these studies on the state–local relationship. For example, intergovernmental grant theory suggests and empirical studies confirm that matching grants are more stimulative than equally funded non-matching grants. Furthermore, state tax and expenditure ceilings have been found empirically to reduce the level and growth of local public expenditures. Missing from the discussion on the state–local relationship is the effect of state mandated expenditure floors on the operation and performance of the local public sector. Yet, almost one-half of state governments require a minimum expenditure or effort on the part of local school districts to receive school grants (ECS Information Clearinghouse [8]). Partially funded and unfunded state mandates also exist for other types of local public goods such as town road improvement. Critics essentially offer a “distorted priorities” hypothesis about intergovernmental mandates and complain that state mandates result in both inequitable and inefficient outcomes. Inequities arise when poor communities are forced to incur part of or the entire financial burden from satisfying state mandates. Inefficiencies occur when local priorities fail to match with state “one-size-fits-all” expenditure standards. For example Sofranko and Meuller [13] write: “Higher levels of government are perceived as usurping the right of local officials to decide what small towns need or how local public funds should be spent.” Sofranko and Meuller cite various town and city mayors pointing to state mandates and complaining that “It’s money that could be used best on other purposes” and “I would say all other services are affected because those dollars we could use elsewhere, such as for streets, drainage, fire and police, etc.” In response to the inefficiencies and inequities allegedly caused by state mandates, half of the states by 1994 had implemented some type of constitutional or statutory limitation on the ability to impose various kinds of state mandates on local government (Zimmerman [19]). Given its important impact and the relative lack of economic research on state mandates, this study empirically examines the impact of a state imposed expenditure floor on local aggregate property values. More specifically, using Brueckner’s [3] test of allocative efficiency in the local public sector, this study investigates the impact of a minimum education-spending requirement on aggregate property values in Connecticut communities. Based on the distorted priorities hypothesis, state mandated expenditure floors should lead to efficiency losses (lower aggregate property values) when communities are forced to spend a state imposed rather than community chosen amount of money on education. The empirical results in this paper, however, lend no support for the distorted priorities theory and show that the state-mandated floor on education spending may actually raise aggregate property values and promote efficiency. The paper is organized in the following manner. Section 2 discusses the nature of the minimum expenditure requirement program in Connecticut and develops the empirical model to examine the impact of the state imposed minimum expenditure requirement on efficiency. In Section 3, the empirical results are discussed both in the context of the typical community and the typical community constrained by the state mandate. Concluding comments are offered in Section 4.
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2. The minimum expenditure requirement program and development of the empirical model In 1977, the Connecticut State Supreme Court confirmed, in Horton v. Meskill, that the state is responsible for ensuring that each child has the opportunity to receive a suitable public education and that any reform must eliminate the relationship between per-pupil wealth and per-pupil expenditures (Swaine [14]). In response to the State Supreme Court decision, Connecticut implemented major school finance reform including an education equalization formula and a minimum expenditure requirement (MER). In 1994–1995, the fiscal year of the data used in the empirical analysis below, the Education Cost Sharing (ECS) foundation grant was the state’s major education equalization formula. The MER represents the minimum amount school districts must spend to be eligible for a state ECS grant. In 1994–1995, the MER was calculated by multiplying the ECS foundation amount by the number of need students in each district.1 The ECS foundation level of spending reflects what the state considers a suitable level of educational spending and was set annually based on the per pupil regular program expenditures (largely instructional costs) of the school district at the 80th percentile of spending, three years prior in the state. State law authorizes the State Board of Education to penalize any town that fails to meet its MER. The penalty is withholding ECS aid in an amount equal to twice the amount by which the town failed to meet its MER.2 Consequently, communities are required by state law to spend a minimum amount on education to receive an ECS grant or face a sizeable penalty. If the distorted priorities theory is correct, spending the mandated amount on education may force communities to spend less than the desired amount on other public and private goods and result in allocative inefficiency. This is where Brueckner’s test of allocative efficiency comes into the analysis. Brueckner [3] develops a theoretical model of the local public sector based primarily on the assumption that a given number of identical consumer-voters maximize their utility obtained from consuming units of housing services, two public goods, and a composite commodity subject to the constraints of income and the private prices and tax-prices of these four goods. Besides residential property taxes, business taxes and intergovernmental grants represent additional sources of funding for the local public goods in his model. From his theoretical model, Brueckner derives a bid-rent function that makes consumervoters indifferent with respect to various amounts of public goods and housing as a result of variations in housing rents caused by Tiebout-type [17] sorting. Based upon an aggregated bid-rent function, Brueckner demonstrates theoretically that Samuelson’s condition for the efficient provision of public goods (the sum of the marginal rates of substitution is equal to marginal social cost) is met when the levels of local public 1 Need students refer to the number of a district’s resident students weighted by an extra 25 percent for each student on welfare and by an extra 10 percent for each student with limited English proficiency not in a statemandated bilingual program. 2 The MER program represents a partially funded state mandate. For example, in the fiscal year ending 2000, the ECS grant as a percent of the MER averaged 49 percent, ranging from about 1 to 94 percent across Connecticut communities.
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goods are chosen to maximize the aggregate property values in a community. In this case, much like the value of a corporation’s stock represents the discounted stream of revenues minus costs, or economic profits, aggregate property values in a community reflect the present value of aggregate property rents less total public sector costs or fiscal surplus. Brueckner explains that aggregate property values exhibit an inverted U relationship with respect to the quantity provided of a local public good. When a local public good is efficiently provided, a marginal adjustment in quantity has no impact on aggregate property values. In contrast, aggregate property values increase (decrease) in response to an increase in a local public good that is initially underprovided (overprovided). From his theoretical model, Brueckner develops a multiple regression model taking on the following general form: P = P (Q, Π, Y, G, Z1 , Z2 ),
(1)
with aggregate property values (P ) as the dependent variable and housing stock (Q), business profits (Π), income (Y ), intergovernmental grants (G), and two local public goods, Z1 and Z2 , which in his empirical work are education and a composite measure of municipal services, as independent variables. According to Brueckner’s theoretical analysis, aggregate property values are expected to rise with increases in Q, Π , and G. The impact of Y on aggregate property values is theoretically unclear. As mentioned previously, an inverted U relationship holds between the level of each local public good and aggregate property values. Several studies have used Brueckner’s model to test for efficiency in the local public sector. Brueckner’s [3] empirical results showed no systematic tendency for under- or over-production in a sample of communities in Massachusetts. Deller [5] employed Brueckner’s test using a data set consisting of Illinois counties and found evidence that police and highway services are underprovided and that education is neither systematically over- nor under-provided. Taylor’s [15] application of Brueckner’s model revealed that local governments in the Hartford, Connecticut metropolitan area do not systematically overprovide any public services and may underprovide highway services. Taken together, these three studies provide little evidence to support the overproduction of local public goods.3 This study uses the same general specification as in Eq. (1) to examine the impact of a state mandated expenditure floor on aggregate property values in Connecticut towns and cities. In the empirical test, aggregate property values are measured by the equalized value of all properties in a community. Similar to Brueckner, the number of housing units measures housing stock4 and the level of employment in a community captures 3 Barrow and Rouse [1] take a different approach than the studies above by examining the impact of additional state aid on aggregate property values. In particular, they test whether a one dollar increase in outside funding generates a properly discounted one dollar increase in housing values. Using a large national sample of independent school districts and an instrumental variables approach, they find that, on net, public school districts do not spend additional state aid for education inefficiently. However, they find that school districts may overspend in large school districts, in primarily rental property districts, and in areas in which residents are poor or less educated. 4 Brueckner also specifies the number of baths as a measure of housing quality. Since a non-census years is used in the empirical analysis, data are unavailable for various measures of housing quality in the different
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business profits. Per capita income represents Y . Total intergovernmental grants, total education expenditures, and total general government expenditures measure G, Z1 , and Z2 , respectively. The mean, standard deviation, and data source for each variable can be found in Appendix A. In terms of empirical strategy, regression results are first obtained for the aggregate property value equation using the entire sample of 169 Connecticut communities. These initial results identify how spending levels compare to the efficient levels in the typical Connecticut community without regard to whether or not the spending floor constrains the behavior of the various communities. Next, by creating a dummy variable indicating if the MER is actually binding or not in each community and specifying interaction terms between the dummy variable and the amount of spending on each local public good, regression results are generated that allow for the possibility that the MER actually impacts aggregate property values in only those communities whose spending would otherwise fall below the state imposed floor. These regression results allow a comparison of how spending compares to the efficient level in communities bound and not bound by the MER.
3. Empirical results Like in the earlier studies by Deller [5] and Taylor [15], experimentation showed that the model fits best when the variables are expressed in log-form. White’s [18] test detected heteroskedasticity so the regression results reflect heteroskedasticity-consistent standard errors. Following Hausman [10], a test was conducted to assess if the levels of education and municipal spending could be treated as being exogenously determined. As a result, good instrumental variables were required to perform the Hausman test. In this particular case, good instruments should be theoretically plausible and correlated with the levels of education or municipal spending but uncorrelated with aggregate property values for reasons beyond their effect on the levels of spending. Public choice theory suggests that people make decisions regarding public spending levels based on their perceived costs and benefits. For this reason, pupils per capita and the fraction of the population 65 years of age and older were chosen as instrumental variables. Education (municipal) spending should be greater (lower) in communities with a greater number of pupils per capita and a lower fraction of elderly persons. Also because of political and bureaucratic inertia, public spending levels often lag behind the growth of population and the number of pupils, resulting in a potentially longrun disequilibrium situation. Consequently, population growth over the last seven years and growth of the number of pupils over the last seven years were selected as two additional instrumental variables. Multiple regression analysis showed that these four instruments were strongly correlated with either school or municipal spending, ceteris paribus, but uncorrelated with aggregate property values when school and municipal spending were also specified in the multiple regression equation. According to the Hausman test, the null Connecticut towns and cities. Since income is specified in the equation, the lack of a housing quality measure should not severely affect the results. Thorson [16] explains that most of the variation in housing quality measures can be explained by income.
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Table 1 Regression results Variable
Coefficient estimate (t-statistic) Basic model
Constant Housing units Employment Per capita income Intergovernmental grants Education expenditures Municipal expenditures
−3.69 (1.02) 0.43* (3.77) 0.10* (3.32) 0.69* (3.97) −0.15* (2.68) 0.43* (3.64) 0.11 (1.52)
Bound ∗ education expenditures Bound ∗ municipal expenditures Adjusted R 2
0.974
Model with interaction terms −0.87 (0.55) 0.44* (3.96) 0.08* (2.91) 0.61* (3.64) −0.16* (2.96) 0.31* (3.06) 0.26* (2.81) 0.27* (3.85) −0.29* (3.88) 0.976
Notes. All variables but Bound expressed in log form. Standard errors have been corrected for heteroskedasticity. * Significant at the 5 percent level or better.
hypothesis of exogeneity could not be rejected for either the level of school or municipal spending.5 Ordinary least square results from estimating the aggregate property value equation can be found in column 2 of Table 1. As Brueckner’s theoretical model predicts, the findings suggest that aggregate property values increase with the number of housing units and business profits, as proxied by employment. Per capita income is found to directly impact aggregate property values as one might expect given that this variable also proxies for the quality of the housing stock (see footnote 4). An unexpected result is that intergovernmental grants are found to inversely affect aggregate property values. However, Brueckner [3], Deller [5] and Taylor [15] all find a similar negative relation in their studies, most likely indicating that intergovernmental aid tends to be lower in wealthier communities. 5 The percentage of the population with at least a high school degree was also specified as an additional public
choice variable but its estimated coefficient was weakly negative in the first stage education equation. It should also be noted that the amount of state aid in Connecticut depends largely on predetermined formulas and factors outside the control of the individual community. For example, the size of the above-mentioned ECS grant received by a school district depends on three factors: number of “need students,” level of the foundation expenditure, and the state’s aid percentage determined by a district’s wealth. Hence the size of G is treated as being exogenously determined given that it is formula driven and outside the control of the individual community.
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Of more importance to this study are the coefficient estimates on the variables capturing the amount of spending on local public education and municipal services. Recall that positive (negative) coefficient estimates reflect under (over) production of local public services. An insignificant coefficient estimate indicates no systematic pattern of under- or over-production of local public services. According to the distorted priorities hypothesis, the estimated coefficient on education expenditures should be found negative, reflecting that the state MER program forces communities to spend beyond their efficient level on education. The positive and statistically significant coefficient estimate on education expenditures lends no support for the distorted priorities hypothesis. In fact, despite the existence of the state MER program, the positive coefficient estimate suggests that local public education remains underprovided in the typical Connecticut community. Since the estimated coefficients can be treated as elasticities, the results imply that a 10 percent increase in education expenditures would result in a 4.3 percent increase in aggregate property values, assuming all other factors remain constant. When interpreted at the mean values of the data in the sample (see Appendix A), the ten-percentage increase implies that a $2.1 million increase in education spending would raise aggregate property values in the typical community by approximately $149 million. In contrast, the statistically insignificant coefficient estimate on municipal expenditures in column 2 indicates no systematic under- or over-provision of municipal services in the typical Connecticut community. Consequently, the evidence implies that the Connecticut MER program has the capacity to raise aggregate property values and improve efficiency in Connecticut communities, at least in 1995 the fiscal year of the data. One problem with the empirical results is that not all Connecticut communities may be constrained by the MER. As logic suggests, property values should be affected only in those communities where education spending would otherwise fall below the state expenditure floor. As a result, interaction terms are specified in the regression equation to capture the isolated impact of the state imposed spending floor in those communities that were more likely to be bound by the MER program. To obtain the interaction terms, a dummy variable indicating constrained status (Bound) was multiplied by the amount of spending on each of the two local public goods and specified as additional independent variables in the multiple regression equation. Those communities that spent less than 5 percent above the MER are considered as being bound by the expenditure floor. The 5 percent figure is chosen because very few towns actually spend less than the MER because of the sizeable penalty and because the Department of Education in Connecticut monitors communities that spend less than 5 percent above the MER. The concern of the Department of Education is that “the projected margin of compliance is small enough that any significant reduction in spending or movement of funds to a non-MER expenditure category is likely to create an MER shortfall” (Department of Education [6]). Fifty-one of the 169 towns and cities in Connecticut spent less than 5 percent above the MER in 1995. Since these communities were more closely constrained by the state expenditure floor, the distortion of priorities hypothesis predicts that the coefficient estimate will be negative on the interaction term involving education spending. That is, the state mandated expenditure floor should increase
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local public spending on education beyond the efficient level and thereby reduce property values in the constrained communities. The regression results allowing for interaction terms are shown in the third column of Table 1. According to the findings, the typical community in Connecticut not bound by the MER underprovided both education and municipal services given the positive and statistically significant coefficient estimates on those two variables. The positive coefficient estimate on the education interaction term indicates that those communities bound by the MER spent considerably less on education relative to their efficient levels than those not bound by the MER. Comparing estimated elasticities, aggregate property values would increase by 5.8 percent in the typical community bound by the MER compared to only 3.1 percent in the typical community not bound by the MER given a 10 percent increase in spending on education. Interestingly, the typical community bound by the MER does not systematically underor overprovide municipal services as suggested by the elasticity estimate obtained when summing the corresponding coefficients on municipal expenditures and its interaction term (−0.03). However, a tendency towards the underprovision of municipal services can be observed in the typical community not bound by the MER given the estimated elasticity of 0.26. All in all, the empirical examination offers a picture of two types of communities in Connecticut. One type of community tends on average to underprovide both education and municipal services to a comparable degree (i.e., elasticities of 0.31 and 0.26, respectively). The other type of community does not systematically under- or over-provide municipal services but provides education services that fall further short of the efficient level. In both types of communities, however, the results indicate that education spending falls below the efficient level. The finding that the state MER program in Connecticut can raise aggregate property values and improve efficiency may come with some surprise especially to those who have accepted the distorted priorities view of state mandates even though empirical support for the theory has been absent. But of course, the distorted view of state mandates presupposes that communities would otherwise choose the efficient levels of public spending. However, local public institutions could bias the results above or below the efficient level. For example, the public choice process may fail if special interest groups dominate in the community. As a result spending floors may be designed to constrain the tendencies of some types of communities to spend too little on public goods. If this is the case, and if the floors are not fully effective, these communities may remain below the efficient level as indicated by the positive coefficient estimates on education spending in Table 1.
4. Conclusion This is the first study to examine the impact of a state mandated expenditure floor on aggregate property values. Many individuals claim that state mandates cause inefficiencies by distorting local priorities and also creates inequities if mandates are not fully funded. While this paper does not address the equity issue, it does show that the minimum expenditure requirement on education (MER) program in Connecticut has the potential of raising aggregate property values and promoting efficiency because communities in
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Connecticut tend to spend below the level that maximizes aggregate property values. Thus the existence of the expenditure floor program is not surprising and can be justified on efficiency grounds. Consequently, some state mandates may be well intended and have the ability to promote efficiency. If future studies find similar results for communities in other states subject to expenditure floors, proponents of the distorted priorities hypothesis may want to reexamine their theory.
Acknowledgments We thank the participants at the Bentley College and the University of Connecticut Department of Economics seminar series and those at the 71st annual meeting of the Southern Economic Association for their many helpful comments and suggestions. We also thank Jan Brueckner and the anonymous referee for their comments.
Appendix A. Means, standard deviations, and data sources
Table A.1 Variable Aggregate property values ($000) Number of housing units Employment Per capita income Intergovernmental aid ($000) Total education expenditures ($000) Total municipal expenditures ($000) Fraction elderly persons Fraction pupils per person Population growth from 87 to 95 Growth of the number of pupils from 87 to 95
Mean
Standard deviation
1,486,315 8055 9072 31,466 10,799 20,988 17,537 0.13 0.17 0.06 0.10
1,921,102 10,247 14,990 11,394 24,221 24,616 31,910 0.04 0.07 0.12 0.10
Data source OPM [12] ECD [7] ECD [7] ECD [7], CPEC [4] OPM [12] OPM [12] OPM [12] ECD [7] OPM [12] OPM [12], CPEC [4] OPM [12], CPEC [4]
References [1] L. Barrow, C.E. Rouse, Using market valuation to assess public school spending, NBER Working Paper No. 9054, July 2002. [2] D.C. Bice, W.H. Hoyt, The impact of mandates and tax limits on voluntary contributions to local public services: An application to fire-protection services, National Tax Journal 53 (2000) 79–104. [3] J.K. Brueckner, A test for allocative efficiency in the local public sector, Journal of Public Economics 19 (1982) 311–331. [4] Connecticut Policy and Economic Council (CPEC), http://www.cpec.org. [5] S.C. Deller, An application of a test for allocative efficiency in the local public sector, Regional Science and Urban Economics 20 (1990) 395–406.
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[6] Department of Education, State of Connecticut, Letter to Edward Favolise, Superintendent of Schools, Thompson Public Schools, dated October 25, 1999, from Robert A. Brewer, Director, Division of Grants Management. [7] Department of Economic and Community Development (ECD), State of Connecticut, http://www.state.ct.us/ ecd/research/ceis. [8] ECS Information Clearinghouse, State School Finance Systems, 1993–1994, http://www.ecs.org/ecs. [9] E.M. Gramlich, Intergovernmental grants: A review of the empirical literature, in: W.E. Oates (Ed.), The Political Economy of Fiscal Federalism, Lexington Books, Lexington, MA, 1977, pp. 219–240. [10] J.A. Hausman, Specification tests in econometrics, Econometrics 46 (1978) 1251–1271. [11] D. Mullins, K. Cox, State tax and expenditure limits on local governments, Advisory Commission on Intergovernmental Relations, Washington, DC, 1995. [12] Office of Policy and Management (OPM), State of Connecticut, Fiscal Indicators in Connecticut Municipalities, 1994–1998, http://www.opm.state.ct.us/igp/MUNFINSR/fi95str2.HTM. [13] A.J. Sofranko, B.E. Meuller, Mayors and mandates: What is the problem? Spectrum: the Journal of State Governments 74 (2001) 8. [14] D.G. Swaine, Are foundation grant programs a panacea or problem? Federal Reserve Bank of Boston, Fall 1999, http://www.bos.frb.org/economic/neff/neff22/neff22.htm. [15] L.L. Taylor, Allocative efficiency and local government, Journal of Urban Economics 37 (1995) 201–211. [16] J.A. Thorson, An examination of the monopoly zoning hypothesis, Land Economics 72 (1996) 43–55. [17] C. Tiebout, A pure theory of local expenditures, Journal of Political Economy 64 (1956) 416–424. [18] H. White, A heteroskedasticity consistent covariance matrix estimator and a direct test of heteroskedasticity, Econometrica 48 (1980) 817–838. [19] J.F. Zimmerman, State mandate relief: A quick look, Intergovernmental Perspective (1994) 28–30.
The impact of a state mandated expenditure floor on aggregate property values Laurie J. Bates a and Rexford E. Santerre b,∗ a Bryant College, Department of Economics, Smithfield, RI, USA b University of Connecticut, Department of Finance, 2100 Hillside Road, Unit 1041,
Storrs, CT 06269-1041, USA Received 1 July 2002; revised 27 January 2003
Abstract Using a test of allocative efficiency in the local public sector developed by Brueckner [Journal of Public Economics 19 (1982) 311–331], this study empirically examines the impact of a state minimum education expenditure requirement on aggregate property values in Connecticut communities. The empirical results reveal that the typical community in Connecticut spends less on education and municipal services than the level that maximizes aggregate property values. The results further indicate that spending on education falls further below the property maximization level in those communities constrained by the state expenditure floor. It follows from the analysis that some state expenditure floors can raise aggregate property values and promote efficiency. 2003 Elsevier Science (USA). All rights reserved. JEL classification: H7; R0 Keywords: Mandates; Public goods; Efficiency
1. Introduction For nearly 35 years, public sector analysts have been interested in the economics of the state–local government relationship. The interest began in the 1960s with the development and application of the theory of intergovernmental grants (e.g., Gramlich [9]), evolving more recently into an analysis of state tax and expenditure ceilings like Proposition 13 in California (Mullins and Cox [11], and Bice and Hoyt [2]). Much * Corresponding author.
E-mail addresses: [email protected] (L.J. Bates), [email protected] (R.E. Santerre). 0094-1190/03/$ – see front matter 2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S0094-1190(03)00027-5
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insight has been gained by these studies on the state–local relationship. For example, intergovernmental grant theory suggests and empirical studies confirm that matching grants are more stimulative than equally funded non-matching grants. Furthermore, state tax and expenditure ceilings have been found empirically to reduce the level and growth of local public expenditures. Missing from the discussion on the state–local relationship is the effect of state mandated expenditure floors on the operation and performance of the local public sector. Yet, almost one-half of state governments require a minimum expenditure or effort on the part of local school districts to receive school grants (ECS Information Clearinghouse [8]). Partially funded and unfunded state mandates also exist for other types of local public goods such as town road improvement. Critics essentially offer a “distorted priorities” hypothesis about intergovernmental mandates and complain that state mandates result in both inequitable and inefficient outcomes. Inequities arise when poor communities are forced to incur part of or the entire financial burden from satisfying state mandates. Inefficiencies occur when local priorities fail to match with state “one-size-fits-all” expenditure standards. For example Sofranko and Meuller [13] write: “Higher levels of government are perceived as usurping the right of local officials to decide what small towns need or how local public funds should be spent.” Sofranko and Meuller cite various town and city mayors pointing to state mandates and complaining that “It’s money that could be used best on other purposes” and “I would say all other services are affected because those dollars we could use elsewhere, such as for streets, drainage, fire and police, etc.” In response to the inefficiencies and inequities allegedly caused by state mandates, half of the states by 1994 had implemented some type of constitutional or statutory limitation on the ability to impose various kinds of state mandates on local government (Zimmerman [19]). Given its important impact and the relative lack of economic research on state mandates, this study empirically examines the impact of a state imposed expenditure floor on local aggregate property values. More specifically, using Brueckner’s [3] test of allocative efficiency in the local public sector, this study investigates the impact of a minimum education-spending requirement on aggregate property values in Connecticut communities. Based on the distorted priorities hypothesis, state mandated expenditure floors should lead to efficiency losses (lower aggregate property values) when communities are forced to spend a state imposed rather than community chosen amount of money on education. The empirical results in this paper, however, lend no support for the distorted priorities theory and show that the state-mandated floor on education spending may actually raise aggregate property values and promote efficiency. The paper is organized in the following manner. Section 2 discusses the nature of the minimum expenditure requirement program in Connecticut and develops the empirical model to examine the impact of the state imposed minimum expenditure requirement on efficiency. In Section 3, the empirical results are discussed both in the context of the typical community and the typical community constrained by the state mandate. Concluding comments are offered in Section 4.
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2. The minimum expenditure requirement program and development of the empirical model In 1977, the Connecticut State Supreme Court confirmed, in Horton v. Meskill, that the state is responsible for ensuring that each child has the opportunity to receive a suitable public education and that any reform must eliminate the relationship between per-pupil wealth and per-pupil expenditures (Swaine [14]). In response to the State Supreme Court decision, Connecticut implemented major school finance reform including an education equalization formula and a minimum expenditure requirement (MER). In 1994–1995, the fiscal year of the data used in the empirical analysis below, the Education Cost Sharing (ECS) foundation grant was the state’s major education equalization formula. The MER represents the minimum amount school districts must spend to be eligible for a state ECS grant. In 1994–1995, the MER was calculated by multiplying the ECS foundation amount by the number of need students in each district.1 The ECS foundation level of spending reflects what the state considers a suitable level of educational spending and was set annually based on the per pupil regular program expenditures (largely instructional costs) of the school district at the 80th percentile of spending, three years prior in the state. State law authorizes the State Board of Education to penalize any town that fails to meet its MER. The penalty is withholding ECS aid in an amount equal to twice the amount by which the town failed to meet its MER.2 Consequently, communities are required by state law to spend a minimum amount on education to receive an ECS grant or face a sizeable penalty. If the distorted priorities theory is correct, spending the mandated amount on education may force communities to spend less than the desired amount on other public and private goods and result in allocative inefficiency. This is where Brueckner’s test of allocative efficiency comes into the analysis. Brueckner [3] develops a theoretical model of the local public sector based primarily on the assumption that a given number of identical consumer-voters maximize their utility obtained from consuming units of housing services, two public goods, and a composite commodity subject to the constraints of income and the private prices and tax-prices of these four goods. Besides residential property taxes, business taxes and intergovernmental grants represent additional sources of funding for the local public goods in his model. From his theoretical model, Brueckner derives a bid-rent function that makes consumervoters indifferent with respect to various amounts of public goods and housing as a result of variations in housing rents caused by Tiebout-type [17] sorting. Based upon an aggregated bid-rent function, Brueckner demonstrates theoretically that Samuelson’s condition for the efficient provision of public goods (the sum of the marginal rates of substitution is equal to marginal social cost) is met when the levels of local public 1 Need students refer to the number of a district’s resident students weighted by an extra 25 percent for each student on welfare and by an extra 10 percent for each student with limited English proficiency not in a statemandated bilingual program. 2 The MER program represents a partially funded state mandate. For example, in the fiscal year ending 2000, the ECS grant as a percent of the MER averaged 49 percent, ranging from about 1 to 94 percent across Connecticut communities.
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goods are chosen to maximize the aggregate property values in a community. In this case, much like the value of a corporation’s stock represents the discounted stream of revenues minus costs, or economic profits, aggregate property values in a community reflect the present value of aggregate property rents less total public sector costs or fiscal surplus. Brueckner explains that aggregate property values exhibit an inverted U relationship with respect to the quantity provided of a local public good. When a local public good is efficiently provided, a marginal adjustment in quantity has no impact on aggregate property values. In contrast, aggregate property values increase (decrease) in response to an increase in a local public good that is initially underprovided (overprovided). From his theoretical model, Brueckner develops a multiple regression model taking on the following general form: P = P (Q, Π, Y, G, Z1 , Z2 ),
(1)
with aggregate property values (P ) as the dependent variable and housing stock (Q), business profits (Π), income (Y ), intergovernmental grants (G), and two local public goods, Z1 and Z2 , which in his empirical work are education and a composite measure of municipal services, as independent variables. According to Brueckner’s theoretical analysis, aggregate property values are expected to rise with increases in Q, Π , and G. The impact of Y on aggregate property values is theoretically unclear. As mentioned previously, an inverted U relationship holds between the level of each local public good and aggregate property values. Several studies have used Brueckner’s model to test for efficiency in the local public sector. Brueckner’s [3] empirical results showed no systematic tendency for under- or over-production in a sample of communities in Massachusetts. Deller [5] employed Brueckner’s test using a data set consisting of Illinois counties and found evidence that police and highway services are underprovided and that education is neither systematically over- nor under-provided. Taylor’s [15] application of Brueckner’s model revealed that local governments in the Hartford, Connecticut metropolitan area do not systematically overprovide any public services and may underprovide highway services. Taken together, these three studies provide little evidence to support the overproduction of local public goods.3 This study uses the same general specification as in Eq. (1) to examine the impact of a state mandated expenditure floor on aggregate property values in Connecticut towns and cities. In the empirical test, aggregate property values are measured by the equalized value of all properties in a community. Similar to Brueckner, the number of housing units measures housing stock4 and the level of employment in a community captures 3 Barrow and Rouse [1] take a different approach than the studies above by examining the impact of additional state aid on aggregate property values. In particular, they test whether a one dollar increase in outside funding generates a properly discounted one dollar increase in housing values. Using a large national sample of independent school districts and an instrumental variables approach, they find that, on net, public school districts do not spend additional state aid for education inefficiently. However, they find that school districts may overspend in large school districts, in primarily rental property districts, and in areas in which residents are poor or less educated. 4 Brueckner also specifies the number of baths as a measure of housing quality. Since a non-census years is used in the empirical analysis, data are unavailable for various measures of housing quality in the different
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business profits. Per capita income represents Y . Total intergovernmental grants, total education expenditures, and total general government expenditures measure G, Z1 , and Z2 , respectively. The mean, standard deviation, and data source for each variable can be found in Appendix A. In terms of empirical strategy, regression results are first obtained for the aggregate property value equation using the entire sample of 169 Connecticut communities. These initial results identify how spending levels compare to the efficient levels in the typical Connecticut community without regard to whether or not the spending floor constrains the behavior of the various communities. Next, by creating a dummy variable indicating if the MER is actually binding or not in each community and specifying interaction terms between the dummy variable and the amount of spending on each local public good, regression results are generated that allow for the possibility that the MER actually impacts aggregate property values in only those communities whose spending would otherwise fall below the state imposed floor. These regression results allow a comparison of how spending compares to the efficient level in communities bound and not bound by the MER.
3. Empirical results Like in the earlier studies by Deller [5] and Taylor [15], experimentation showed that the model fits best when the variables are expressed in log-form. White’s [18] test detected heteroskedasticity so the regression results reflect heteroskedasticity-consistent standard errors. Following Hausman [10], a test was conducted to assess if the levels of education and municipal spending could be treated as being exogenously determined. As a result, good instrumental variables were required to perform the Hausman test. In this particular case, good instruments should be theoretically plausible and correlated with the levels of education or municipal spending but uncorrelated with aggregate property values for reasons beyond their effect on the levels of spending. Public choice theory suggests that people make decisions regarding public spending levels based on their perceived costs and benefits. For this reason, pupils per capita and the fraction of the population 65 years of age and older were chosen as instrumental variables. Education (municipal) spending should be greater (lower) in communities with a greater number of pupils per capita and a lower fraction of elderly persons. Also because of political and bureaucratic inertia, public spending levels often lag behind the growth of population and the number of pupils, resulting in a potentially longrun disequilibrium situation. Consequently, population growth over the last seven years and growth of the number of pupils over the last seven years were selected as two additional instrumental variables. Multiple regression analysis showed that these four instruments were strongly correlated with either school or municipal spending, ceteris paribus, but uncorrelated with aggregate property values when school and municipal spending were also specified in the multiple regression equation. According to the Hausman test, the null Connecticut towns and cities. Since income is specified in the equation, the lack of a housing quality measure should not severely affect the results. Thorson [16] explains that most of the variation in housing quality measures can be explained by income.
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Table 1 Regression results Variable
Coefficient estimate (t-statistic) Basic model
Constant Housing units Employment Per capita income Intergovernmental grants Education expenditures Municipal expenditures
−3.69 (1.02) 0.43* (3.77) 0.10* (3.32) 0.69* (3.97) −0.15* (2.68) 0.43* (3.64) 0.11 (1.52)
Bound ∗ education expenditures Bound ∗ municipal expenditures Adjusted R 2
0.974
Model with interaction terms −0.87 (0.55) 0.44* (3.96) 0.08* (2.91) 0.61* (3.64) −0.16* (2.96) 0.31* (3.06) 0.26* (2.81) 0.27* (3.85) −0.29* (3.88) 0.976
Notes. All variables but Bound expressed in log form. Standard errors have been corrected for heteroskedasticity. * Significant at the 5 percent level or better.
hypothesis of exogeneity could not be rejected for either the level of school or municipal spending.5 Ordinary least square results from estimating the aggregate property value equation can be found in column 2 of Table 1. As Brueckner’s theoretical model predicts, the findings suggest that aggregate property values increase with the number of housing units and business profits, as proxied by employment. Per capita income is found to directly impact aggregate property values as one might expect given that this variable also proxies for the quality of the housing stock (see footnote 4). An unexpected result is that intergovernmental grants are found to inversely affect aggregate property values. However, Brueckner [3], Deller [5] and Taylor [15] all find a similar negative relation in their studies, most likely indicating that intergovernmental aid tends to be lower in wealthier communities. 5 The percentage of the population with at least a high school degree was also specified as an additional public
choice variable but its estimated coefficient was weakly negative in the first stage education equation. It should also be noted that the amount of state aid in Connecticut depends largely on predetermined formulas and factors outside the control of the individual community. For example, the size of the above-mentioned ECS grant received by a school district depends on three factors: number of “need students,” level of the foundation expenditure, and the state’s aid percentage determined by a district’s wealth. Hence the size of G is treated as being exogenously determined given that it is formula driven and outside the control of the individual community.
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Of more importance to this study are the coefficient estimates on the variables capturing the amount of spending on local public education and municipal services. Recall that positive (negative) coefficient estimates reflect under (over) production of local public services. An insignificant coefficient estimate indicates no systematic pattern of under- or over-production of local public services. According to the distorted priorities hypothesis, the estimated coefficient on education expenditures should be found negative, reflecting that the state MER program forces communities to spend beyond their efficient level on education. The positive and statistically significant coefficient estimate on education expenditures lends no support for the distorted priorities hypothesis. In fact, despite the existence of the state MER program, the positive coefficient estimate suggests that local public education remains underprovided in the typical Connecticut community. Since the estimated coefficients can be treated as elasticities, the results imply that a 10 percent increase in education expenditures would result in a 4.3 percent increase in aggregate property values, assuming all other factors remain constant. When interpreted at the mean values of the data in the sample (see Appendix A), the ten-percentage increase implies that a $2.1 million increase in education spending would raise aggregate property values in the typical community by approximately $149 million. In contrast, the statistically insignificant coefficient estimate on municipal expenditures in column 2 indicates no systematic under- or over-provision of municipal services in the typical Connecticut community. Consequently, the evidence implies that the Connecticut MER program has the capacity to raise aggregate property values and improve efficiency in Connecticut communities, at least in 1995 the fiscal year of the data. One problem with the empirical results is that not all Connecticut communities may be constrained by the MER. As logic suggests, property values should be affected only in those communities where education spending would otherwise fall below the state expenditure floor. As a result, interaction terms are specified in the regression equation to capture the isolated impact of the state imposed spending floor in those communities that were more likely to be bound by the MER program. To obtain the interaction terms, a dummy variable indicating constrained status (Bound) was multiplied by the amount of spending on each of the two local public goods and specified as additional independent variables in the multiple regression equation. Those communities that spent less than 5 percent above the MER are considered as being bound by the expenditure floor. The 5 percent figure is chosen because very few towns actually spend less than the MER because of the sizeable penalty and because the Department of Education in Connecticut monitors communities that spend less than 5 percent above the MER. The concern of the Department of Education is that “the projected margin of compliance is small enough that any significant reduction in spending or movement of funds to a non-MER expenditure category is likely to create an MER shortfall” (Department of Education [6]). Fifty-one of the 169 towns and cities in Connecticut spent less than 5 percent above the MER in 1995. Since these communities were more closely constrained by the state expenditure floor, the distortion of priorities hypothesis predicts that the coefficient estimate will be negative on the interaction term involving education spending. That is, the state mandated expenditure floor should increase
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local public spending on education beyond the efficient level and thereby reduce property values in the constrained communities. The regression results allowing for interaction terms are shown in the third column of Table 1. According to the findings, the typical community in Connecticut not bound by the MER underprovided both education and municipal services given the positive and statistically significant coefficient estimates on those two variables. The positive coefficient estimate on the education interaction term indicates that those communities bound by the MER spent considerably less on education relative to their efficient levels than those not bound by the MER. Comparing estimated elasticities, aggregate property values would increase by 5.8 percent in the typical community bound by the MER compared to only 3.1 percent in the typical community not bound by the MER given a 10 percent increase in spending on education. Interestingly, the typical community bound by the MER does not systematically underor overprovide municipal services as suggested by the elasticity estimate obtained when summing the corresponding coefficients on municipal expenditures and its interaction term (−0.03). However, a tendency towards the underprovision of municipal services can be observed in the typical community not bound by the MER given the estimated elasticity of 0.26. All in all, the empirical examination offers a picture of two types of communities in Connecticut. One type of community tends on average to underprovide both education and municipal services to a comparable degree (i.e., elasticities of 0.31 and 0.26, respectively). The other type of community does not systematically under- or over-provide municipal services but provides education services that fall further short of the efficient level. In both types of communities, however, the results indicate that education spending falls below the efficient level. The finding that the state MER program in Connecticut can raise aggregate property values and improve efficiency may come with some surprise especially to those who have accepted the distorted priorities view of state mandates even though empirical support for the theory has been absent. But of course, the distorted view of state mandates presupposes that communities would otherwise choose the efficient levels of public spending. However, local public institutions could bias the results above or below the efficient level. For example, the public choice process may fail if special interest groups dominate in the community. As a result spending floors may be designed to constrain the tendencies of some types of communities to spend too little on public goods. If this is the case, and if the floors are not fully effective, these communities may remain below the efficient level as indicated by the positive coefficient estimates on education spending in Table 1.
4. Conclusion This is the first study to examine the impact of a state mandated expenditure floor on aggregate property values. Many individuals claim that state mandates cause inefficiencies by distorting local priorities and also creates inequities if mandates are not fully funded. While this paper does not address the equity issue, it does show that the minimum expenditure requirement on education (MER) program in Connecticut has the potential of raising aggregate property values and promoting efficiency because communities in
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Connecticut tend to spend below the level that maximizes aggregate property values. Thus the existence of the expenditure floor program is not surprising and can be justified on efficiency grounds. Consequently, some state mandates may be well intended and have the ability to promote efficiency. If future studies find similar results for communities in other states subject to expenditure floors, proponents of the distorted priorities hypothesis may want to reexamine their theory.
Acknowledgments We thank the participants at the Bentley College and the University of Connecticut Department of Economics seminar series and those at the 71st annual meeting of the Southern Economic Association for their many helpful comments and suggestions. We also thank Jan Brueckner and the anonymous referee for their comments.
Appendix A. Means, standard deviations, and data sources
Table A.1 Variable Aggregate property values ($000) Number of housing units Employment Per capita income Intergovernmental aid ($000) Total education expenditures ($000) Total municipal expenditures ($000) Fraction elderly persons Fraction pupils per person Population growth from 87 to 95 Growth of the number of pupils from 87 to 95
Mean
Standard deviation
1,486,315 8055 9072 31,466 10,799 20,988 17,537 0.13 0.17 0.06 0.10
1,921,102 10,247 14,990 11,394 24,221 24,616 31,910 0.04 0.07 0.12 0.10
Data source OPM [12] ECD [7] ECD [7] ECD [7], CPEC [4] OPM [12] OPM [12] OPM [12] ECD [7] OPM [12] OPM [12], CPEC [4] OPM [12], CPEC [4]
References [1] L. Barrow, C.E. Rouse, Using market valuation to assess public school spending, NBER Working Paper No. 9054, July 2002. [2] D.C. Bice, W.H. Hoyt, The impact of mandates and tax limits on voluntary contributions to local public services: An application to fire-protection services, National Tax Journal 53 (2000) 79–104. [3] J.K. Brueckner, A test for allocative efficiency in the local public sector, Journal of Public Economics 19 (1982) 311–331. [4] Connecticut Policy and Economic Council (CPEC), http://www.cpec.org. [5] S.C. Deller, An application of a test for allocative efficiency in the local public sector, Regional Science and Urban Economics 20 (1990) 395–406.
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[6] Department of Education, State of Connecticut, Letter to Edward Favolise, Superintendent of Schools, Thompson Public Schools, dated October 25, 1999, from Robert A. Brewer, Director, Division of Grants Management. [7] Department of Economic and Community Development (ECD), State of Connecticut, http://www.state.ct.us/ ecd/research/ceis. [8] ECS Information Clearinghouse, State School Finance Systems, 1993–1994, http://www.ecs.org/ecs. [9] E.M. Gramlich, Intergovernmental grants: A review of the empirical literature, in: W.E. Oates (Ed.), The Political Economy of Fiscal Federalism, Lexington Books, Lexington, MA, 1977, pp. 219–240. [10] J.A. Hausman, Specification tests in econometrics, Econometrics 46 (1978) 1251–1271. [11] D. Mullins, K. Cox, State tax and expenditure limits on local governments, Advisory Commission on Intergovernmental Relations, Washington, DC, 1995. [12] Office of Policy and Management (OPM), State of Connecticut, Fiscal Indicators in Connecticut Municipalities, 1994–1998, http://www.opm.state.ct.us/igp/MUNFINSR/fi95str2.HTM. [13] A.J. Sofranko, B.E. Meuller, Mayors and mandates: What is the problem? Spectrum: the Journal of State Governments 74 (2001) 8. [14] D.G. Swaine, Are foundation grant programs a panacea or problem? Federal Reserve Bank of Boston, Fall 1999, http://www.bos.frb.org/economic/neff/neff22/neff22.htm. [15] L.L. Taylor, Allocative efficiency and local government, Journal of Urban Economics 37 (1995) 201–211. [16] J.A. Thorson, An examination of the monopoly zoning hypothesis, Land Economics 72 (1996) 43–55. [17] C. Tiebout, A pure theory of local expenditures, Journal of Political Economy 64 (1956) 416–424. [18] H. White, A heteroskedasticity consistent covariance matrix estimator and a direct test of heteroskedasticity, Econometrica 48 (1980) 817–838. [19] J.F. Zimmerman, State mandate relief: A quick look, Intergovernmental Perspective (1994) 28–30.