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National Tax Journal Vol. 46, no. 2, (June, 1993), pp. 135-59

PROPERTY TAXATION, PROPERTY BASE, AND PROPERTY VALUE: AN EMPIRICAL TEST OF THE “NEW VIEW” ROBERT W. WASSMER* Abstract - The “Traditional,” “New,” and “Benefit” Views of property taxation yield different predictions in regard to the effects of property taxation, An empirical test for evidence on the predicted effects of the New View is given. Results support the New View and indicate that (‘7) the greater the positive differential between a city’s rate of property taxation and the nation’s average rate, the smaller the amount of capital in the city and the smaller the per-unit market value of its property tax base; and (2) the higher the average rate of property taxation in the country, the lower the return to all property. Simulations regarding revenue altertax are a/so pronatives to the PweQ vided.

INTRODUCTION Property taxation is controversial in the United States. Reasons for this controversy are numerous. Property taxes are highly visible and usually assessed on a household’s biggest consumption item and a household’s or firm’s biggest investment item. Assessment of the stock of property has inevitably led to horizontal inequities. *Wayne

State Umverslty, Detroit, MI 48202

135

The fact that property taxes in the United States are the primary source of local funding for probably the most valued government service, K-12 public education, also contributes to the debate surrounding them. In addition to the discord caused by the institutional arrangement of property taxes, policymakers believe that the incidence of the property tax is regressive. Many elected officials also contend that an excessive rate of property taxation discourages local economtc development by driving out property, employment, and population. Property taxation is also a controversial political topic because of these two widely accepted beliefs. Economists have responded to this controversy by developing theories on the incidence of property taxation. Unfortunately, the three basic theories or “Views” developed in regard to property taxation yield quite different predictions on the effects of the tax. The Traditional View, popularized by Netzer (1966), finds that owners of the capital portion of taxed property bear no burden of the tax since it is passed fully forward to capital renters. The conclusion that the property tax is regressive follows because the poor generally spend a greater


portion of their income to rent capital than the rich. Alternatively, the New View, presented by Mieszkowski (1972) and popularized by Aaron (1975), finds that the owners of all property bear some burden of the tax. The finding that property taxes are progressive follows, since the rich obtain a greater portion of their income from capital ownership than the poor. The more recent Benefit View, introduced by Hamilton (1975, 1976) and discussed by Fischel (‘I 987, 1992), quite distinctly concludes that property taxes can be a form of efficnent user charges for local public services. If such is the case, the incidence of the property tax is of much less concern. Mieszkowski and Zodrow (1989) have written a comprehensive survey on the three basic theoretical views of the property tax and the many extensions made to them. They conclude that .the Traditional View is (only a variation of the New View and that the Benefit View is entirely valid only if a binding form of local zoning or perfect capitalization exists. Mieszkowski and Zodrow’s support of the New View of property taxation is based on their belief that perfect zoning and capitalization do not exist in the United States. As pointed out in their survey, there is a need for a well-devised investigation to test this opinion and provide some empirical measure of the validity of the New View. The purpose of this paper is to present the derivation and result of an empirical test of the type called for by Mieszkowski and Zodrow. The goal of the empirical test is t.o identify elasticity measures that can, in principle, measure the predicted effects of property taxation obtained from the New View of property taxes. If these elasticity measures are not statistically different from zero or differ in the predicted direction of the effect, then the Benefit View may be the more appropriate way to model the effects of property taxation. To better understand

the denvation

of this

empirical test, a brief discussion of the Traditional, New, and Benefit Views of the property tax is given first. Following this, the conceptual framework behind the empirical test is presented. The next section contains a description of data used in the estimation, econometric issues considered before estimation, and the results of the estimation. Simulations regarding an exogenous increase in property taxation and alternatives to the property tax are then presented. THE TRADITIONAL,

NEW, AND BENEFIT

VIEWS OF THE PROPERTY TAX The Traditional View of the property tax uses a “differential” rnethod of tax incldence analysis. This method ignores the benefits received by those paying taxes, because benefits are (assumed to display little relationship to the amount of taxes paid. The Traditional ‘View of the property tax examines the effect of replacing a “lump-sum” -or nondistortionary form of tax-with a distortionary property tax. The Traditional View originates in the partial-equilibrium framework of one taxing jurisdiction. The analysis considers property taxes to be separate payments on capital and land. The supply of capital to any jurisdiction is modeled as perfectly elastic at a nationally determined rental rate paid to owners of capital (rN). Figure 1 contains the effect in a city that enacts an ad valorem property tax (t) on capital.’ The demand for capital decilines by the percentage amount of the tax (D to D’) and the gross-of-tax rental rate paid by capital renters rises io absorb the entire tax increase (rN to fN(” -t t)). If a city enacts a property tax on its perfectly inelastic supply of land, the result is perfect capitalization-the rental rate paid to owners of land falls by the entire tax increase (rfi4to rN(l - !)). Under these traditional assumptions, the effect of instituting a properly tax in a city (or raising the rate 1 136

I


AN EMPIRICALTEST OF THE “NEW VIEW”

FIGURE 1. The TraditIonal View of a Property Tax on Capital

rN (l+t)

of an existing property tax) is to reduce the amount of assumed perfectly mobile capital in the jurisdiction (K, to K,). The rental rate paid to owners of local capital (or the related market price of local capital) does not change. The rental rate paid to owners of land in the city (or the market price of land) falls.

of a locally provrded service that have separated themselves into N different jurisdictions. Initially, the locally provided service is funded through a head tax. As first described by Tiebout (1956), this results in an efficient allocation of individuals to jurisdictions. The ownership of land and capital is not restricted to the city of residence.

The New View of the property tax distinguishes itself through the use of a general equilibrium analysis of all jurisdictions in a country. A country’s supply of taxable capital is considered fixed and perfectly mobile in the long run. Each jurisdiction also contains a fixed amount of taxable land. The remaining discussion of the New View is based primarily on Zodrow and Mieszkowski’s (1986) reformulation of Mieszkowski’s (1972) original article.

For simplicity, assume that (1) each jurisdiction begins with an equal amount of land and capital, (2) the rental elasticity of capital demand is equivalent in each jurtsdiction, and (3) there are only three types of demanders of the locally provided service. Low types of demanders prefer the local service at 50 percent below the level preferred by medium types, while high types prefer the local service level at 50 percent above the medium service level. Figure 2 contains one city for each type of demander. The head tax in the jurisdiction with

Zodrow and Mieszkowski assume that there are N different types of demanders 137


FIGURE 2. The New View of Property Taxation

'H,3 5J D D'

'H,2 'H,l I?1 PO Hi.gh-Type

pO

Medium-Type

high-type demanders is necessarily 200 jpercent higher than the head tax in the jurisdiction with low-type demanders. Capital mobility Insures that the rental rate paid to owners of capital (rJ is constant across all three jurisdictions in the long run. A head tax has no effect on each city’s property market. Now consider the outcome of an exogenous requirernent that a certain percentage of revenue, formerly raised by the head tax, be raised by an ad-valorem tax on property. The medium-type city would Institute the average rate of property taxation. The rate of property taxation in the high- and lovv-type cities would, respectively, be 50 percent higher and lower. Since capital ownership is not restricted to the jurisdictron in which residents live and there are no local ordinances on the amount of capital that a homeowner or firm must have, this is also a form of drfferential incidence analysis. In Figure 2, the supply of taxable property (the amount of property is represented by a “P”) represents both the supplies of capital and land in the city. Property supplies are fixed in the short run. The result of revenue being partially raised by a property tax is a shift in the property demand curve in each city (D to D’). After the decrease in

po p1

:Low-Type

demand, ihe rental rate paid to owners of property iI7 the medium-type city falls from h to hl. Zodrow and Mieszkowrki refer to this as the “profits tax” effect that depresses the market value of property throughout all cities by the averrjge rate of taxation. In the high-type jurisdiction, the rental rate paid to owners of property falls to an r,,, that is lower than the rM,, in the medium-type city. The negative differential between rH,, 1t-1the high-type city and r,,,,, in mediurn-type city is an “excise tax” effect due to the higher property tax rate in the high.Iype city.2 In the low-type jurisdiction the rental rate paid to owners of property lalls to a higher rL,, than rM,, in the medium-type jurisdrction. This higher rate reflects the sum of the nationwide profits tax effect and a positive excise tax effect. So far, the analysis has been based on the short-run assumption of a fixed supply of property in a community. If owners of taxable capital receive differential rental rates in three cities, there IS an incentive for mobility. In the long run, owners of capital in the high-type city will relocate their capital investments to the low-type city. This shifts the short run property supply curves (S, to S,). Supply-side equilibrium is achieved when the rent paid to owners of property

I



is equalized at rH.2 and rL,2 in the high- and low-type cities, which equals rM,, in the medium-type city. The New View yields empirically testable predictions on the effect of property taxation on a city’s aggregate property base and property value. Holding local capital and land fixed, an increase in the local rate of property taxation-that replaces a nondistortionary form of taxation-depresses a city’s aggregate value of property by the average rate of property taxation. A city’s aggregate value of property will be even lower in the high-type city and higher in the low-type service city. Allowing for capital mobility after an increase in property taxation, the profits tax effect implies that the aggregate value of taxed property will be lower in all cities. Due to excise tax effects, the aggregate property base in the high-type service city will be even lower while the aggregate property base in the low-type service city will be somewhat higher than implied by the profits tax alone. At this point in the analysis, the rental rate paid by renters of taxable property is highest in the high-type city (at r,,J , and lowest in the low-type city (near rL,,). If the sorting of individuals to city types does not result in perfectly homogenous communities, residents may now be able to increase their utility by moving between cities.3 Residents for which the higher rental rate of T,,~ is not offset by higher service levels would migrate to other cities, causing the rental rates to move closer together but not necessarily converge. The convergence of rental rates paid by demanders would cause another round of the supply shifts just described. Since all high service demanders do not wish to move to lower service cities, a long-run equilibrium with excise tax rent differentials on local property demanders is likely to persist.4 The final view of property taxation, Benefit View, uses the assumptions

or the of the 139

New View but places additional restrictions on property ownership and pricing. As given by Hamilton (1975, 1976), these restrictions include “perfect fiscal zoning” or “perfect fiscal capitalization.” Perfect fiscal zoning allows a city to restrict residential capital (housing units) to some minimum value. In a Tiebout world, homes greater than this value would not be built in the city because through property tax financing, they would pay a greater price for city services. Perfect fiscal capitalization occurs in a fully developed heterogeneous housing community when the fiscal surplusthe excess of local service benefits over local property tax payments for a small house in a typically large house community-is capitalized into a higher rental rate (or market price) than the same small house would earn in a homogenous small house community. Fiscal deficits for relatively large houses are also negatively capitalized in the heterogeneous community. In essence, binding fiscal zoning and/or perfect capitalization turns the property tax into the head tax or nondistortionary user charge that Tiebout (1956) imagined for the provision of local residential services. If either occurs, all jurisdictions in a Tiebout world would be equal in regard to the perunit, after-tax cost of residential capital. Jurisdictions would also be homogenous in regard to residents’ demand for the locally provided service. White (1975) and Fischel (1975) have extended Hamilton’s line of reasoning to the taxation of firm property . Assuming perfect fiscal zoning in regard to all types of property, a pure Benefit View of property taxation performs a “balancedbudget” incidence analysis of the property tax by considering that property taxes paid by demanders of residential and firm property are equivalent to the benefits they receive through the city’s expenditures. If this is the case, the exogenous adoption of a partial property tax by the three communities shown in Figure 2 should have no ef-

National Tax Journal Vol. 46, no. 2, (June, 1993), pp. 135-59 feet on the demand for property community.

in each

Under perfect fiscal capitalization in fully developed heterogeneous cities, the Benefit View of property taxation implies that local property tax dbfferentials will be capitalized tnto individual local property values. Relatively large (small) homes in a heterogeneous city would sell at a discount (premium). As given by Hamllton (19’76), in the aggregate, these individual capitalization effects cancel so that the net effect on total city property tax base from shifting to a property tax should be zero, despite a multitude Iof negative and positive capitalization effects on individual properties. Therefore, the Benefit View also yields empirically testable predictions on the effect of property taxation on a city’s aggregate property value and amount. A shift to property taxation (or alternatively a revenue neutral increase in the rate of property taxation that lowers an alternative local tax) should have no effect on a city’s aggregate unit value of property and no effect on a city’s aggregate property amount. Note that this absence of aggregate effects IS in stark contrast to the profits and excise tax effects predicted by the New View of property taxation. Local service levels are reflected in higher aggregate local property values in both the New and Eenefit Views But, only under the New Vew should tlhe method or rate of property taxation affect aggregate property valUf!S.

In the past, economists have evaluated the merits of the 13enefit View based on evidence of perfect fiscal zoning or capitalization. Mieszkovvski and Zodrow (1989) have argued that there is little evidence to support perfect fiscal zoning and capttalizaton.’ Flschel (1987, 1992) has responded with a long and impressive list of institutional evidence demonstrating the pervasiveness of fiscal zoning in the United States. Beginning with Oates (1969), capitalization studies have shown that, ceteris

pdribus, Increased local expenditures increase individual and aggregate local property values, while increased local property taxation decreases individual and aggregate local property values.” The capitalization of local property tax rates into indiv~~dual local property values can conceivably occur under both I he New View and the Benefit View of the property tax that relies on perfect fiscal capitalization within heterogeneous jurisdictions. The validity of the New View of property taxation remains to be shown through an empirical test for evidence on the aggregate excise and profits tax effects predicted specifically by the New View and not expected to occur under the Benefit View. THE CONCEPTUAL

FRAMEWORK

OF

THE EMPiRICAL TEST It would be difficult to prove that only the New View of property taxation is valid and to consequently disprove the Benefit View entirely. E!ven if evidence in support of the New View is found, the magnitude of this evidence rnay have been mitigated by offsetting effects predicted by the Benefit View. Therefore, a reasonable null hypothesis to test is that the New View is entirely Invalid. Rejecting this null hypothesis requires proof of the existence of the aggregate effects predicted specifically by the New View A rejection of this null hypothesis does not dismiss the possibility of effects predicted by the Benefit View. As Wildasin (‘1986) has suggested, under irnperfect zoning and imperfect capitalization, the choice between the New and Benefit Views may not be absolute and the property tax may best be c:onsidered a combination capital tax and user charge. The objective here is to look for empirical proof to support the claim that the property tax is, in part, a capital tax. As shown earlier, under the national system of property ferent local rates exerts two Ing the amount of property 140

New View, a taxation at difeffects. Holdin the country

I


AN EMPIRICALTESTOF THE “NEW VIEW”

constant, the profits tax effect depresses any owner’s per-unit rental rate (or perunit market price) of property by the average effective rate of property taxation in the country. Holding the amount of property constant in each jurisdiction, the predicted excise tax effects are that local property tax rates above the national average depress the per-unit market price of property further while local rates below the national average raise the per-unit market price above that indicated by the profits tax effect. Under the Benefit View of property taxation, the national average rate of property taxation and local deviations from this rate should have no effect on the perunit market price of aggregate local taxable property. Proof of an excise or profits tax effect is sufficient evidence to reject the null hypothesis that the New View of property taxation is invalid. The prescribed empirical test is to seek evidence of a profits and/or excise tax effect on the per-unit market price of property and the amount of property in a city. The test presented here is motivated by Brueckner’s suggestion included in Mieszkowski and Zodrow (1989, p. 1131). Brueckner suggests that the tax effects predicted in the New View, and not the Benefit View, can be sorted out by focusing on intermetropolitan rent differences. At an intermetropolitan level, the Benefit View predicts that local property rents depend on construction costs, local expenditures per household, and other demand characteristics.’ As discussed earlier, with a local budget identity and assuming efficient production, how local revenue is raised, or the rate of local property taxation, should exert no influence on aggregate property value.* On the other hand, the New View indicates that the rate of property taxation exerts distinct profits and excise tax effects. A problem arises in finding an appropriate measure of the per-unit aggregate price of property. The solution is that the market value of a community’s property, holding 141

the amount of property constant, is directly related to the per-unit aggregate market price of property. As given in equation 1, the aggregate market value of city i’s property tax base (PTB) is expected to be a positive function of the quantity (supply) of property-capital (K) and land (L)-in the city and a positive function of a J-length vector of variables (0,; where i = 1, 2, 3, J) that determines the demand for property in the city:

PTB, = PTB(K,, L,, O,,,, As shown using Figure 2, holding property constant, the New View predicts that the national average rate of property taxation (APT) and city i’s rate of property taxation less APT (DAPT) have a negative effect on the demand for property and hence on the market value of the property tax base.’ Under both the New View and Benefit View of property taxation, the level of local expenditure levels per unit of property (EXP) should increase property demand and PTB.” Assuming that property is a normal good, the level of individual income (Y) in the city would also increase property demand and PTB. If there is any shifting of tax incidence to producers, the existence of other forms of local taxation-a local sales tax (STAX) and/or a local income tax (ITAX)-could reduce property demand and PTB. Ladd and Bradbury (1988) point out that city property owners receive services in return for paying taxes to overlapping special districts, school districts, county governments, and state governments. Ceteris paribus, this rate of overlapping government (OVTAX) should exert an additional influence on PTB.” A high rate of crime (CM) would reduce property demand and PTB. The city’s population (POP) represents the number of aggregate residential property demanders. Replacing 0, in equation 1 with the just described vector of demand variables results in the following:


PTB, = PTB(K,, L,, DAPT,, EXP,, Y,, STAX,, ITAX,, OVTAX,, CM,, POP,). E.quatilon 2 does not include APT, because, in a cross section of cities, it is constant.lZ The prescribed test of the null hypothesis is to estimate equation 2 using a national sample of cities and to check the derived relationship between PTB and DAPT. If PTB is negatively Irelated to DAPT, the null hypothesis that the New View is entirely inappropriate can be rejected.13 An additional problem to estimating equation 2 is the ,ssmultanerty that exists among some of the explanatory variables. Capital (K) is endogenous because capital mrgration into a city may be negatively related to the endogenously determined excise tax effect (DAPT). Controlling for local taxes, residential and business capital may also be attracted to a high service city (EXP). Holdng all else constant, residential capital is (also expected to be larger in a high income (Y) city The difference between a city’s effective property tax rate and the average rate Iof national property taxation I(II) is expected to be greater the lower the city’s property tax base (PTB) and the Ihigher the city’s expenditure per unit of property (EXP) A city’s expenditure per unit of property (EXP) is positively determined, in part, by its level of indrvidual income (Y). EXP may also be positively or negatively related to DAPT.14 Accounting for capital and labor complementanty in production, local personal ilqcome (Y) is related to the amount and type of business capital (K) in the city. The marginal product of labor, and hence person,al income, should be larger the greater the arnount of capital in the city. If equation 2 is estimated without taking 1hese endogenous relationships into consideration, the regression coefficients on the endogenous variables will be inconsis-

tent. A silnultaneous equation therefore necessary.“’

estimation

is

The first r>tep to estimate the system of simultaneous equations is to specrfy, in general functional form, the four rernaining equations that represent the determination of the endogenous variables. This is done in equations 3-6:

R K, = K(DAPT,, EXP,, Y,,, STAX,, ITAX,, OVTAX,, AREA,, CM,, POP,)

R DAPT, = DAPT(PTB,, EXP,, STAX,, I-TAX,, IGOV,, OVTAX,, SENR,, OWN,, MAN&)

R EXP, = EXP(DAPT,, Y,, STAX,, ITAX,, OVTAX,, IGOV,, CM,, SENR,, CHILD,, EDUC,, MIANUF,)

R0 Y, = Y(K,, SENR,, CHILD,, EDUC,, MANUF,) As given In equation 3, besides the endogenous relationships just described, local and state taxes could also drive out capital and cause it to be negatively related to a local sales tax (STAX), local income tax (IlAX), and the rate of overlappirlg taxes (OVTAX). The amount of residential and business capital in a city is likely to be positively related to its square miles (AREA) and negatively rela ted to its crime rate (CM). The amount of residential capital is also positively related to the city’s population (POP) As given I~I equation 4, the difference between a city’s local property tax rate and the natiorl’s average rate of property taxation is expected to be negatively related to the availability of alternative tax ii-rstru142

I



ments (STAX and ITAX), the percentage of a city’s revenue garnered from intergovernmental revenue sharing (IGOV), and the degree of services (proxied by the degree of taxation) offered by overlapping jurisdictions (OVTAX). A city’s degree of property tax reliance should also be related to the percentage of residents over 65 (SENR) and the percentage of owner-occupied homes (OWN). Due to relatively larger property holdings, fixed incomes, and the lack of school-age children, senior citizens are more likely to direct city officials to limit property taxation. Homeowners, as opposed to renters, may also be more likely to demand limited property taxation due to their perception of a direct tax burden. As Ladd (1975) and others have shown, the composition of the local property tax base should also have an impact on the rate of property taxation. Controlling for the size of the property tax base, a higher percentage of people employed in manufacturing (MANUF) would indicate a larger manufacturing property tax base and perhaps a tendency to rely less on property taxes due to the fear of driving more mobile manufacturing firms-as opposed to less mobile commercial firms-out of the community.

Using the standard model of a decisive voter, city expenditure would also be related to the characteristics of this voter. These are proxied for by the percentage of the population greater than age 65 (SENR), the percentage of the population less than age 18 (CHILD), and the percentage of population with greater than a high school education (EDUC). The influences that these variables exert could take many avenues and are considered unpredictable. Though also unpredictable in direction, MANUF should also influence a city’s expenditure.

As given in equation 5, a city’s expenditure per unit of property is also expected to be related to other local revenue alternatives (STAX and ITAX). Due to the general form of the EXP function, these relationships could be either positive or negative. Local expenditure per unit of capital should also be negatively related to the degree of substitute expenditure offered by overlapping jurisdictions (OVTAX). EXP is expected to be positively related to the percentage of total revenue coming from outside grants (IGOV). The greater the value of IGOV, the greater the percentage of revenue raised through tax exportation to nonresidents. Though the direction of the influence is unpredictable, expenditure should also be influenced by exogenous forces that can be measured by the rate of crime (CM).

In the system of simultaneous equations, there are five endogenous variables (PTB, K, DAPT, EXP, and Y) and 13 exogenous variables (STAX, ITAX, OVTAX, IGOV, AREA, CM, SENR, OWN, CHILD, EDUC, HPLUM, HROOM, and MANUF).16 Each of the endogenous variable equations (2-6) is overidentified for both the rank and order conditions. The system can be estimated using the method of two-stage least squares (TSLS) and U.S. cities as the unit of observation.

143

Equation 6 is a production function for local family income (Y). A predominantly older (SENR) or younger (CHILD) population should produce lower Y, while a more educated (EDUC) population should produce greater Y. Considering the greater value added in manufacturing, a larger MANUF could produce a higher Y. Alternatrvely, considering the economic decline in primarily manufacturing cities, a larger MANUF could predict a lower Y. DATA, ECONOMETRIC

ISSUES, AND

REGRESSION RESULTS

Due to jurisdictronal differences in the rate of property assessment and variance in the accuracy of cities achieving their specific assessment ratios, it is difficult to get a property base measure that is comparable across U.S. cities.17 The quinquennral Census of Governments contains the solution


to the problem of property tax base comparability. In the volume titled Taxable Property Valwes and Assessment--Sales Price Ratios, the U.S. Census Bureau reports the results of a survey that measures the average assessment to sales price ratio for real business and residential property in a select groulp of cities.18 The survey’s purpose was to Icorrect the comparability problem discussed earlier. Unfortunately, the survey was terminated after 1981 and real business property was excluded before 1966. The desired statistics are only available for limited cities for periods 5 years apart between 1966 and 1981. I9 A pooled data set is necessary to obtain the necessary degrees of freedom. T’he initial objective was to gather data from the 200 most populated U.S. cities in 1986.*’ It was only possible to gather the appropriate sales-assessment ratios for some of these cities To calculate PTB, it was then necessary to multiply each city’s sales-assessment ratio by its gross value of all locally assessed real property 21 NonreIporting of this variable further reduced the original sample of 2100 cities. Table 1 contains the final sample of 62 cities included in one of the four cross sections:” The effective rate of real property taxation in each city is equal to the property tax payments made to the city divided by the calculated PTB and rnultiplied by 1OO.23 The average rate of property taxation is measured as the mean effective property tax rate across all cilies in a cross section. Table 1 contains the calculated effective property tax rates for each city in each cross section.“4 Table 1 also includes the mean and standard deviation of effective property taxation in a given year. Notice the general decline in both of these statistics. DAPT equals the city’s effective property tax rate rninus the mean for that cross section .25 A further issue to consider is the appropriate proxy measure for the amount of city

capital. Two possibilities come to mind. The number of homes could roughly measure the city’s residential capital, while employment could approximate the city’s businesi capital. In the sample of cities, homes and employment exhibit a simple correlation coefficient of 0.982. Since both essentially measure the same vanation, the number of homes was chosen as the appropriate proxy for K. HOMES is a more direct measure and avoids the substitutability issue that arises if employment is used to proxy for business capital. If this measure is to be used effectively, local capital differences that are not picked up by HOMES need to be controlled for. This is done by including the percentage of a city’s housing that is owner occupied (OWN), the percentage of a city’s housing that lacks complete plumbing (HPLUM), the median number of rooms in a year-round housing unit in the city (HROOM), and a proxy for the percentage of a c:ity’s nonresidential capital that is used in manufacturing (MANUF). HROOM and MANUF should be related to a more valuable local capital stock. HPLUM should be related to a less valuable capital stock The effect of OWN on PTB is uncertain. HPLUM is also included in the HOMES regressions as an approximation of the age and quality of the residential housing stock. An inferior housing stock (high HPLUM) may require, everything else constant, a greater number of housing units to serve a gtven population. The greater existence Iof inferior housing and the number of rooms in the typical houslng unit may also provide information on residential taste for local expenditure. HPLUM and HROOM are therefore included in the EXP regression An additional issue is the inclusion of population (POP) as an explanatory variable in equations 2 and 3. POP and HOMES exhibit a sirnple correlation coefficient of 0.997. A city’s population and the chosen proxy for its capital base essentially mea-

I



TABLE 1 CITIES IN EACH CROSS SECTION (GIVEN BY EFFECTIVE PROPERTY TAX RATE) City 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62.

Little Rock, Birmingham, Mobile, Montgomery, Phoenix, Tucson, Berkeley, Fresno, Glendale, Long Beach, Los Angeles, Oakland, Pasadena, Sacramento, San Diego, San Jose, San Francisco, Torrance, Denver, Bridgeport, Washington, Jacksonville, Miami, St. Petersburg, Tampa, Honolulu, Des Moines, Chicago, Wichita, Louisville, New Orleans, Shreveport, Baltimore, Detroit, Minneapolis, St. Paul, Kansas City, St. Louis, Omaha, Charlotte, Jersey City, Newark, Albuquerque, Buffalo, New York, Rochester, Cleveland, Columbus, Dayton, Toledo, Oklahoma City, Tulsa, Portland, Philadelphia, Pittsburgh, Memphis, Nashville, Salt Lake City, Norfolk, Richmond, Seattle, Milwaukee

Mean Standard

deviation

AR AL AL AL AZ AZ CA CA CA CA CA CA CA CA CA CA CA CA co CT DC FL FL FL FL HI IA IL KS KY LA LA MD Ml MN MN MO MO NE NC NJ NJ NM NY NY NY OH OH OH OH OK OK OR PA PA TN TN UT VA VA WA WI

1966

1971

1976

1981

0.205 0.287 0.125 0.364 0.334 0.329 0.461 0.195 0.265 0.352 0.434 0.339 0.373 0.328 0.305 0.747 0.187 0.714 1.406 1.064 0.491 0.650 0.727 1.064 0.812 0.695 0.538 0.489

0.270 0.147a 0.161' 0.562 0.419 0.187 0.305 0.416 0.505 0.372 0.454 0.263 0.261 0.846 0.163 0.447 2.442 1.162 0.482 0.690 0.813a 0.563a 0.383 0.405 0.301 2.013 1.570 0.881 0.870 0.758 0.532a 4.291 2.397 0.407" 1.721 1.809 1.974 0.543" 0.086a 0.115" 0.351 0.148 0.555' 0.701 1.068 0.683" 0.853 0.154" 1.036 1.630 1.111

0.124 0.089 0.071 0.258 0.150 0.083 0.127 0.216 0.190 0.127 0.247 0.127 0.129 0.412 0.070 0.236 2.310 1.196 0.467 -

0.463 3.033 1.022 0.787 0.830 0.394 0.735 0.555 0.670 3.785 4.237 0.434 3.236 2.066 2.058 0.676 0.128 0.369 0.214 0.367 0.277 0.680 1.319 1.092 0.647 1.439 0.369 1.155 1.625 0.318 1.159

0.213 0.254 0.134 0.189 0.205 0.247" 0.601 0.466 0.218 0.306 0.449 0.540 0.393 0.554 0.332 0.336 1.541 0.211 0.527 1.705 1.431 0.638 0.704 0.449 0.606 0.831 0.886 0.680 0.770 0.407 0.345 3.058 1.163 0.784 0.717 0.352 0.638 0.601 0.641 3.881 5.069 0.548 1.710 1.677 1.720 0.607 0.099 0.318 0.120 0.371 0.324 0.617 1.086 1.300 0.755 1.014 0.130 1.230 1.794 0.273 1.107

0.855 0.860

0.848 0.870

0.808 0.774

0.644 0.698

?mputed value: cross-sectional data not available for regression.

145

0.483 0.740 0.447 0.324 0.338 0.245 1.779 1.777 0.386 0.321 0.275 0.424 3.018 2.011 0.266 2.123 1.439 1.901 0.480 0.072 0.109 0.158 0.058 0.492 0.903 0.912 0.611 0.682 0.178 1.140 1.494 0.662


sure the same thing In equation 2, POP was dropped as an explanatory variable because of the collinearity problem its inclusion would create with HOMES. In equation 3, POP IS not included as an explanatory variable of HOMES because of collinearity with lagged (HOMES) whose inclusion will be discussed.“6 Land (L) is measured in square miles (AREA). A city’s cnme rate (CM) is calculated as the number of crimes per home. A city’s level of income (Y) is measured by the city’s median family income. Substituting the variables just discussed into the original system of equations (2-6) and dropping POP yields the following revised system :

P-I-B,= PTB(HOMES,, DAPT,, EXP,, Y,, ST,4X,, ITAX,, OVTAX,, AREA,, CM,, OWN,, HPLUM,, HROOM,, MANUF,)

HOMES, == HOMES(DAPT,, EXP,, Y,, ST,4X,, ITAX,, OVTAX,, AREA,, CM,, HPLIJM,)

DAPT, == DAPT(PTB,, EIXP,,STAX,, ITAX,, OVTAX,, lGOV,, SENR,, OWN,, MANUF,)

m EXP, = EXP(DAPT,, Y,, STAX,, ITAX,, OVTAX,, IGOV,, CM,, SENR,, CHILD,, EDUC,, HPLUM,, HROOM,, MANUF,)

Y, = Y(HOMES,, SENR,, CHILD,, EDUC,, MANUF,).

Table 2 contains a description of all variables and a list of sources. Table 3 contalns descriptive statistics for all variables. There are a few econometric issues to consider before equations (7-l 1) can be estimated. Along with the right-side variables specified previously, city or regional specific influences that do noit change over time (flxed effects) are also irnportant City property values, property bases, reliance on the property tax, and expenditure levels can all be influenced by time-invariant politics, laws, institutions, historical factors, climate, location in country, etc. City income could also be influenced by compensating differentials, labor market peculiarities, and some of the same timle-consistent factors just mentioned. In a pooled regression with many cross-sectional units and a few time series performed on stock-dependent variables, sue h city-specific effects could be crudely p:oxred for by regional dLlmmies and climate variables Ibut never fully controlled for.27 As suggested by Holtz-Eakin (1986), a solution is to estimate the regressions using the first differences of each variable’s observatlons.2E’ This was accomplished by restricting the 62 city clata set to cities in which observations were available for contiguous cross sections. Subtracting 1966 values from 1971 values, 1971 from 1976 values, and 1976 from 1981 values yielded a new poolecl data series of three cross sections consisting of 134 observations. A “difference” variable is delineated from the previous stock variables by a “I)” after the stock variable’s name. The interpretation of the differenced regression coefficients is no different than if they were stock regression coefficients. A second econometric issue is that equation 8 represents a housing stock equation. A partial-adjustment model of housing was used to account for the inertia in a city’s housing stock.*’ A third econometric issue relates to Ihe two-stage estimation process. To derive consistent estimators for the coefficient!, on the endogenous variables in

I


AN EMPIRICALTESTOF THE “NEW VIEW”

TABLE 2 VARIABLE DESCRIPTION AND SOURCE Description

Name

Sourcea

Endogenous: PTB HOMES DAPT EXP Y

market value of all real property number of year-round housing units (effective real property tax rate-“APT”) total city general expenditure/“HOMES” median family income

CENGOV CCDB* CENGOV CGF CCDB* and DCEN*

in percent

Exogenous: APT STAX ITAX OVTAX IGOV AREA CM OWN SENR CHILD EDUC HPLUM HROOM MANUF D8176, etc.

city’s average effective property tax rate in percent dummy if city general or selective sales tax dummy if city income tax overlapping state, county, school taxes/“HOMES” (city’s intergovernmental revenue/general revenue) in percent square miles (serious crimes known to police/“HOMES”) in percent housing units owner occupied in percent population greater than age 65 in percent population less than age 18 in percent population high school graduates in percent housing lacking adequate plumbing in percent median number of rooms in year-round housing manufacturing employment in percent dummy for 1981-1976 cross section, etc.

CENGOV CGF CGF CENGOV, SGF CGF

population

DCEN*

CCDB* UCR, CCDB* CCDB* CCDB* CCDB* CCDB* CCDB* DCEN* CCDB*

Weighting: POP

%ENGOV = Census of Governments, 1966, 1971, 1976, and 1981; CCD8 = City and County Databook, 1968, 1978, and 1988; CGF = City Government Finances, 1966, 1971, 1976, and 1981; SGF = State Government Finances. 1966, 1971, 1976, and 1981; DCEN = U.S. Decennial Census (various volumes), 1960, 1970, and 1980; and UCR = F.B./.‘s Uniform Crime Report, 1966, 1971, 1976, and 1981. All variables measured in dollars have been deflated by the appropriate U.S. GNP deflator. A local CPI deflator was not used, because part of the variation in local consumer price; is.due to the housing component of the CPI. Deflating by CPI would rem&e much of the variation that the empirical work is attempting to-explain. An asterisk (*) indicatesthat the variable was riot available for the desired years of 1966, 1971, 1976, and 1981 and was calculated from 1960, 1970, and 1980 census values by interpolation and extrapolation. A “D” follows variable of the nondifferenced

names to indicate the 5 year difference variable for the particular cross section.

the second stage, the first-stage instruments must each be uncorrelated with the second-stage error term. Candidates for the required instruments are all 13 exogenous difference variables and the initial values of all endogenous and exogenous variables. To test the requirement that the candidates for instruments are uncorrelated with the second-stage error term, a form of the Hausman (1978) test was used.30 To estimate the system, the three differenced cross sections were pooled together. The regressions were initially run without a constant and with a set of three dummies 147

in value.

An “L” follows

to indicate

the lagged

value

(D8176, D7671, and D7166). If a set of dummies was jointly significant, they remained in the regression. These dummies are intended to control for time-specific conditions, such as the average rate of property taxation in the country (APT) and macroeconomic effects, that would be common to all jurisdictions but not controlled for by dlfferencing.3’ The final econometric issue is heteroskedasticity. In studies of this sort, residual variance is often related to some measure of relative size. If this is not corrected, calculated standard errors are biased and t-


.----__

-----

Variablle _-~---Endogenous: PTB HOME!; DAPT absolute EXP Y

(DAPT)

Mean

TABLE 3 VARIABLE DESCRIPTIVE STATISTICS .--Coefficient of Variation Maximum .---

1.579E+ 10 246005.00 0.02 0.57 1869.00 23942.09

2.15 I .74 36.75 I .06 0.70 0.15

2.575E-t ‘I 1 .2943 I 5 I .oo 4.22 4.22 8799.43 36882.50

--.-

Minimum

Value where DAPT is Maxa

1.822E+9 19723.00 -0.75 0.02 611.88 14417.38

4.04E+9 126776.00 4.22 4.22 3641.77 19730.44

0.65 0.00 0.00 0.00 0.61 IO.36 4.47 20.56 4.38 15.02 21.10 0.50 3.48 5.07

0.85 1.oo 0.00 3464.10 33.31 23.56 27.42 20.56 8.08 37.07 34.34 5.14 4.20 35.19

Exogenous: APT STAX ITAX OVTAX IGOV AREA CM OWN SENR CHILD E.DUC HPLUM HROOM MANUIF

0.79 0.98 0.20 3202.49 30.14 ‘I 25.50 17.35 50.Q3 1 I .S6 29.02 57.46 4.28 4.78 26.013

0.10 0.29 2.00 0.41 0.49 1.15 I .02 0.22 0.27 0.16 0.23 0.92 0.09 0.55

649105.32 ---

1.71

0.86 1.oo I .oo 6187.23 76.88 765.13 247.89 71.22 30.21 38.36 87.46 17.82 5.68 91.44

Weighting: POP ----

“DAPT was a maximum 01 4.22 in the city of Newark, The descriptive statistics are based on 209 observations

‘7850132.00 .------.--

101901 .oo

313547.00

NJ in 1976. from 1966, 1971, 1976, and 1981.

tests of statistical significance are invalid. Using different forms of the Glesjer test, heteroskedasticity in relation to HOMES or POP was detected in all regressions and they were re-estimated using the appropri.ate weight.32 The final results of tihe estimation of equations (7-1 1) are reczrded in Table 4.33 The