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The Fiscal Impacts Of Rural Residential Development:
An Econometric Analysis Of The Cost Of Community Services

by Roger H. Coupal
Assistant Professor
Department of Agricultural and Applied Economics
University of Wyoming

Donald M. McLeod
Assistant Professor
Department of Agricultural and Applied Economics
University of Wyoming

and

David T. Taylor
Professor
Department of Agricultural and Applied Economics
University of Wyoming

This research was made possible through funding from the USDA-NRI CGP grant # 99-35401-7742.
The authors would like to express their gratitude for the support.

ABSTRACT

Rural residential development has become an increasingly important issue in many counties around the United States. Empirical tests are provided to determine if rural residential development is a net fiscal loss for county governments, following work by the American Farmland Trust. This study focuses on measuring the net fiscal impacts of rural residential development on Wyoming county governments. An econometric model is developed and used to estimate county revenues, county expenditures, school district revenues, and school district expenditures. The estimated model does not verify that rural residential development is always a net loss to county governments on the margin. However, using a representative scenario, it is shown that such development can be a net fiscal loss on average, the extent to which is a function of assumptions tied to the analysis.


 CONTENTS

  1. Introduction

  2. Background

  3. Previous Work

  4. Method

  5. Results

  6. Conclusions

    References


 

I. Introduction

Rural residential development is a widespread phenomenon in many counties around the United States. Counties located in isolated, but amenity-rich areas are confronted with issues similar to those experienced by counties near growing urban areas (Heimlich and Anderson, 2001). Rural lands are impacted as farm and ranch land is sold and developed into rural residences. A recent study by the American Farmland Trust (2002) estimates that 11 percent of all prime ranchlands (those with rural development densities, located near to public lands, year-round water availability, mixed grass and tree cover, and high variety of vegetation classes) are threatened by conversion to residential development. The development maybe clustered or dispersed, with the latter tending to have a more pronounced effect on the flow of public goods associated with open space.

The debate over farmland has centered on food security, the loss of high quality soils, the cultural character of small communities, wildlife habitat, and county fiscal stability (American Farmland Trust, 1995). However, critiques of the basic premise of farmland preservation question the notion of loss of value (Gordon and Richardson, 1998). The authors argue that proponents of farmland preservation over state their case when it comes to perceived benefits of preservation. Preserving farmland has the potential for restricting the supply of developable land thereby increasing prices and potentially depressing economic development. Daniels (1999 p. 3) in a reply article argues that while fears surrounding threats to U.S. food supply are unwarranted, there are areas where dispersed development can cause fiscal and environmental problems. He argues that planners and policy makers need to be “strategic” and “aim for balanced growth”.

An important player in assisting county planning efforts across the country has been the American Farmland Trust cost of community services methods (COCS), (AFT, 1999). The general results from studies across the country indicate that conversion of agricultural to residential use is a net fiscal loss to county taxpayers. When one accounts for both county revenues and county expenditures, it costs counties more than they receive in revenues, regardless of the higher assessed value for residential property relative to agricultural land. Most states differentiate the assessed valuation of agricultural land and residential land. This study provides an evaluation of the AFT methodology using a set of county fiscal impact models. The AFT housing versus farm use hypothesis is tested both at the margin and on average for Wyoming counties.

II. Background

Dispersed large lot development provides a host of environmental benefits and amenities to the owner. These include quality of life considerations, privacy, and scenery. Growth in dispersed rural residences benefits the landowners that sell property as well as some in the real estate, construction and service industries. Purchasers of rural homes then demand public services and infrastructure investments. Neighboring farms and ranches as well as county taxpayers may experience diminished environmental amenities and an increasingly overburdened local government as dispersed rural residential development occurs. Consequently, local taxes either must be increased or public services decreased, but certainly the mix of services provided is reallocated across users.

The pressure for rural residential development comes from three sources: in-migration, second home development, or intra-county urban to rural relocation. Regionally, the Western US is expected to grow by 54 percent between 1990 and 2020 (Center for the American West, 2001). Wyoming's population already has increased by 8.9 percent between 1990 and 2000. Over 55 percent of the population growth in the state between 1990 and 2000 occurred in rural areas, (Taylor, 2001a). Added to this resident population growth in the state is the number of second homes, which increased by over 30 percent between 1990 and 2000, (Taylor 2001b). American Farmland Trust estimates that over 2.6 million acres are threatened with conversion from ranching and farming to residences in Wyoming (AFT 2002).

Population has grown rapidly in the intermountain west from 1990 to 2000, compared to other regions. Idaho, Utah and Colorado, which border Wyoming, grew at rates at least double the national average during that period (Taylor, 2001a). Twenty-six percent of Wyoming's counties grew at, or faster than, the national average from 1990 to 2000. Rural areas in the western and northern portion of Wyoming near Yellowstone and Grand Teton National Parks, grew in excess of 60 percent in the same period.

Residential land use increased dramatically in Wyoming from 1960 to 1990, Figure 1. Total acres of residential development increased by 2.4 times (Theobald, 2001). Nearly 70 percent of the converted acreage was for "exurban" development of one acre per 10 to 40 acres. Only 3 percent was "urban / suburban" development of more than 2 units per acre.

County commissioners and planners are left attempting to determine the consequences of rural residential growth. They require analyses indicating the impact of rural growth on county operations and budgets. American Farmland Trust cost of community services studies are a tool widely used by planners and local policy makers.

Source: Source: Theobald (2001).
FIGURE 1: Increase in Residential Development Acres for Wyoming 1960-90 (Increase = 417,000 acres)

FIGURE 1: Increase in Residential Development Acres for Wyoming 1960-90 (Increase = 417,000 acres)

III. Previous Work

The literature review covers the modeling of fiscal impacts due to rural residential development. Burchnell and Listokin (1978) identify two broad types of fiscal impact methodologies: average cost and marginal cost analyses. Average cost approaches involve the use of ratios or multipliers per unit of service extended. These approaches assume future costs are approximately the same as current costs. They do not account for deficient or excess service capacity, AFT (1999). Marginal cost approaches typically involve calculating specific impacts through a case study or the use of statistical models, (Smith, Propst, and Abberger 1991).

Average cost approaches

The American Farmland Trust COCS is an average cost approach to address the fiscal impact of residential development, AFT (1999). The procedure for implementing a COCS study is straightforward. First, the analyst decides on the land use categories that are relevant for the policy issue at hand. This means residential and agricultural among others in the case of farmland conversion. Typical AFT type studies include three to as many as five types of land uses: residential, agricultural, forestland, commercial, and industrial. Then using what information is available at the local level, expenditures and revenues are allocated to each land use category. This may be done either through records where land use categories can be identified or on a simple proportional basis. A ratio of expenditures to revenues is then calculated for each category. A ratio of expenditures to revenues of less than one suggests the land use is a net fiscal benefit to county government. A ratio of expenditures to revenues of greater than one suggests that the particular land use is a net fiscal loss to county government.

A summary by Heimlich and Anderson (2001) identified 88 cost of community service studies. The studies indicated expenditure to revenue ratios for residential development that exceeded one while those for farm and timberland fell below one across all studies. The average ratio of expenditures to revenues for rural residential development equaled 1.24 compared to 0.38 for farmland and open space. It is important to point out that AFT cost of community services studies generally do not distinguish between urban and rural residential development.

The AFT methodology clearly implicates residential development as a net fiscal loss to local governments. Yet, cautions have been voiced concerning the use of the ratios developed by this approach. The ratio of fiscal expenditures to fiscal revenues generated for each land use is a snapshot of the financial relationships between users and providers at a point in time. The net fiscal impacts of a specific change in land use may not necessarily change in the proportion as indicated by the ratio calculated. Both numerator and denominator are endogenously determined in this case.

The problem with average cost approaches is that they are poor indicators of general results. Taylor (1999) used the AFT approach for Sublette County, Wyoming. A ratio of expenditures-to-revenues was estimated at $2.35 for residential development in the case where there is a resident who is consuming services but is not employed locally. The ratio falls to 1.27 if the resident is employed. This revised ratio takes into account taxes paid by the local employer (commercial property taxes and use taxes). The estimated ratio from an average cost approach then is driven by the assumptions concerning the resident.

Kelsey (1996) critiqued the AFT methodology as used for six Pennsylvania counties. He concluded that cost of community services studies do provide useful information to communities. The author identified a series of limitations in interpretation that local policy makers must be aware of to use the ratios correctly. The first is that the ratio is primarily a reflection of the proportion of local spending for schools. The second is that the ratio is averaged across land types and therefore differences among uses within the same category are lost. The third is that the starting point or unit basis used to measure the ratio can affect the fiscal ordering of land uses. Deller (1999) added another assumption being that different land uses are independent, e.g. a rural resident may be employed by a local business paying the commercial tax rate. That employee then can be viewed as contributing to the commercial tax revenue of the business. An AFT ratio does not capture this relationship.

Marginal cost approaches

Other work in fiscal impact modeling has stronger ties to concepts derived from economic theory. Grosskopf, Hayes, and Hirshberg (1995) use an economic distance function approach to estimate efficiencies in the provision of public law enforcement services.1 The distance function approach has the advantage that it allows for multiple outputs and completely describes the technology. Coefficients then become a direct measure of efficiency changes for a particular policy scenario.

Heikkila and Craig (1991) and Heikkila (2000) present an approach to fiscal impact modeling that draws upon the economic theory of the firm. Levels of government service are a function of inputs (mostly labor and capital) and neighborhood or community characteristics. Once public service impacts are estimated, a welfare analysis is used to measure the change in benefits associated with population changes.

Marcoullier, Deller, and Green (2000) estimate a fiscal impact model using a generalized econometric approach to evaluate the fiscal effects of second home development. The fiscal impact of recreational housing development, on a variety of public services, is analyzed. Public service expenditures are regressed against tax, demographic variables, and recreational houses per capita. The authors report results suggesting that recreational housing just pays for itself.

The study summarized here focuses on measuring the net impacts of rural residential development on the fiscal structure of Wyoming county governments and school districts. The model departs from the approach used by American Farmland Trust (AFT), which is primarily a categorization of rural and urban residents (AFT, 1999). The analysis provided below presents estimates of the fiscal impacts of rural residential development using an econometric model of county revenues, county expenditures, school district revenues, and school district expenditures. This modeling approach reveals marginal as well as average costs, and can make possible projections about cost and revenues of future development. The modeling framework allows for analyzing specific scenarios and can be used to test specific assumptions that are implicit in the AFT approach. This analysis is useful for evaluating the fiscal impacts of rural residential development in the aggregate. The model is used to derive AFT type ratios for specific Wyoming counties.

IV. Method

Model development

The conceptual framework for this research is motivated in part from Heikkila (2000). Government is modeled as a firm providing a vector of services to the public. These services can be likened to outputs paid for by county revenues. The analysis consists of changes in the distribution of revenues and expenses arising from a change in land use. The model given here cannot replicate Heikkila's approach exactly because he uses a proxy for a specific government services (e.g. inverse of the number of crimes in a community). The proposed model focuses on the entire array of services. This analysis begins with a total operating revenue function and a total variable cost function for a particular county.

Formula 1

(1)

Total operating revenues for a county (TRc) are defined in (1) where "mri" is the marginal revenue for tax instrument "i" and "u" is the taxpayer type. "S" is the number of users in class "u". There is no actual market for the public services. The services are nonexclusive, congestible, and lack private sector substitutes. Government service providers then are by law public monopolies, thus marginal revenue is used in place of price. This analysis does not distinguish between types of tax instruments, i.e. property taxes, sales taxes, or use taxes. The overall tax burden charged by the local government is the value of interest.

The total operating revenue function then is a marginal revenue burden "mbu" multiplied by the number of users in the county "S" where mbu = Si * mri. This relation is represented in (2). The difference here is that each user group is charged for the entire array of government services.

Formula 2

(2)

Total variable cost is represented in (3). Total variable cost for county “c” is the marginal service unit service cost multiplied by the number of service units, where “mucju” is the marginal service cost for user “u” using service “j”.

Formula 3

(3)

Generally all services in government are provided without a unique service price. It is assumed that changes in the number of service units, expand the demand for the entire array of government services, and thus total variable cost. Equation (3) collapses into cost as a function of service units (4). Total variable cost then is the sum of each marginal unit cost of service multiplied by the number of service users.2 “TVCc” then is a function of the service users where the coefficient “usercst” represents the unit demand for government expenditures by user type Su.

Formula 4

(4)

The goal of the policy maker is to allocate expenditures across different groups and interests in a county subject to a revenue constraint. If the service burden of each user group is growing proportionally, then the job of the policy maker is simplified. The problem occurs when various groups' burdens change differentially. The array of services required by user types may differ. One user group may view another as taking a disproportionate level of public resources. It is assumed in this analysis that access and availability to public services across different geographic groups remains the same. There may be public choice aspects to rural versus urban residents, but the data cannot be used to adequately address it.

If county expenditure requirements increase as dispersed rural residential development increases, then this would impose a higher tax burden on the rest of the user groups. Government must either increase tax revenues and shift resources away from traditional users to the newcomers (rural homeowners) or reduce services to at least some users.

Population changes in rural areas contribute both to revenues and expenditures. More households located further away from urban public service centers increases the cost of providing public services and spreads out available maintenance operating resources to a larger area. Land used in agricultural or forestland is hypothesized to require a lower level of public services than land used for residential purposes. The expectation then is that rural residential development exacts a higher cost to the taxpayer as land is moved from agriculture or forest to residential uses.

The conversion of private ranch and farmland to rural residential use however is expected to increase revenue generation, which generates some of the skepticism for rural land use controls. Residential land is generally taxed at a higher rate than agricultural land, so conversion is viewed as a revenue enhancement to taxpayers. What matters to public services providers and taxpayers alike is ultimately the net, not the gross, revenue generated. Empirical studies using AFT methodology indicate that, on average, residential development is a net loss to a county's tax base. However, the issue is framed as a general result and not a case-by-case basis. The hypothesized relationship can be stated as in (5).

Formula 5

(5)

This statement implies that rural residential development is a breakeven or net loss prospect to county governments. Many AFT type studies suggest that the ratio of expenditures to revenues for rural residential development is greater than unity. However, there are other factors that can account for changes in the fiscal impacts of residential development that AFT methodology cannot effectively incorporate. Income, wealth, and unique aspects of the land base itself can all affect the kind of ratios the AFT methodology generates. Higher assessed valuation or higher income generally means higher taxes paid out.

The approach utilized here first tests the general hypothesis formulated in (5) with a complete model for both county and school revenues and expenditures. The AFT-type ratios are calculated using the estimated model for a representative scenario of ranch land conversion common in Wyoming (and throughout the inter-mountain west): replacement of 35 acres of ranchland with a one unit residential parcel. This latter procedure first calculates the net fiscal impacts on county governments and schools from the representative scenario. Predicted changes in revenues and expenditures are then converted to percent changes from the baseline. The percent changes from the baseline are then used to calculate actual average revenue and expenditure changes for each county. The aforementioned procedure permits AFT-type expenditure-to- revenue ratios to be calculated.

The fiscal impact model developed for this analysis predicts total county government operating revenues and expenditures as well as school district revenues and expenditures. Municipal government is not considered in this modeling framework since the issue relates to policies in unincorporated areas of counties.3 School districts and county governments have jurisdictional control in rural areas.

Four equations are estimated for each county: total county government operating revenues, total county government operating expenditures, total school operating revenues, and total school operating expenditures. Revenues come from the following categories of sources: property taxes, sales tax recapture, and intergovernmental transfers (Wyoming Statutes §39-1-21). Intergovernmental transfers and sales tax recapture are for the most part a function of population (e.g. Wyoming Statutes §39-15-111,§39-15-112, §39-14-211). Changes in the level of all types of user groups affect the revenues that are received by a county. Fluctuations in mineral activities (coal, oil, gas, trona, and other minerals) and the resulting taxes (severance and federal mineral royalties) are collected but are not repatriated to the county of origin. They are distributed based upon changes in population. The larger urban population areas then receive most of these sources of funds.

Property taxes are the largest source of revenue for counties . Land uses are assessed at four tiers: mineral, commercial and industrial, residential, and agricultural. Minerals are taxed at 100 percent of the value of production. Commercial and industrial lands are taxed at 11 percent of assessed valuation. Residential land is taxed at 9 percent of assessed valuation. Finally agricultural land is taxed at 9 percent of the value of production as determined by the State. The local mill levy is then applied to the assessed value.

The State of Wyoming preempts all local jurisdictions in levying income or earnings taxes (Wyoming Statutes §39-12-101). State personal or business income taxes do not currently exist in Wyoming.

Statistical procedure

Total revenue and expenditure equations were estimated for each county governmental unit in Wyoming using time series cross-sectional models. The four dependent variables are county government operating revenue (CREV), county government operating expenses (CEXP), school operating revenues (SCHREV), and school operating expenses (SCHEXP). All variables, in dollar terms, both dependent and explanatory variables were represented as real 1998 dollars.

County revenues and expenditure equations are estimated as a function where "CREV" and "CXPE" for county "i" and time "t" is a function of rural personal income, urban personal income, acres of agricultural land, residential and commercial assessed valuation, and mineral assessed valuation, (6) and (7). Similarly, "SCHREV" and "SCHEXP" county "i" and time "t" are a function of rural personal income, urban personal income, and total assessed valuation. The arguments in each function are proxies that represent the user groups who contribute to revenues and exact a demand for services.

Formula 6

(6)

Formula 7

(7)

Formula 8

(8)

Formula 9

(9)

The explanatory variables are defined in Table 1. Two population base variables are "rupi" or rural personal income and "urpi", the incorporated area personal income. The other three variables represent the important land uses: agriculture, commercial, and mineral. The number of acres of agricultural land is "agland"; "asval" is total assessed valuation excluding mineral valuation; and "mval" is the assessed valuation of minerals. Rural and urban personal income is used instead of rural and urban population in order to capture both income and population effects without incurring statistical problems. Urban population and personal income exhibit multi-collinearity when they are used as separate arguments in the equations.

Table 1: Explanatory variables for the estimated equations

Variable Definition Expected Sign
CREV County operating revenue Endog.
CXPE County operating expenditure Endog.
SCHREV School district revenues Endog.
SCHXPE School district expenditures Endog.
RUPI Rural personal income +
URPI Urban personal income +
AGLAND Acres of agricultural land +
ASAL Assessed valuation of commercial and residential property +
MIN Assessed valuation of mineral property +
TOTVAL Total assessed valuation in county +

Mineral assessed valuation was separated out from the other components of assessed valuation because of the large contribution of minerals to most county budgets in the State. County government revenue and expenditure equations separated the effects of mineral assessed valuation while for school districts a total assessed valuation was used. Ad-valorem taxes are levied against both structures and on the value of the mineral. The mill levy charged to structures and improvements are set and collected by the county. The mill levy on the value of production are set by the State and collected by the county.

The log-log structure is used to account for governmental economies of scale between large communities and small communities. Natrona County and Laramie County, with the largest populations, have 66,000 and 81,000 respectively. Niobrara County conversely has approximately 2,400 in population. This structure was compared with linear and semi-log forms. The log-log format performed best based on a comparison of F-statistics for each specification.

Data Sources

Data sources come from information collected and summarized by the State Department of Audit (Wyoming Dept. of Audit; 2000) from 1993 to 1998. Total expenditures are solely operating expenditures. Urban and rural personal income variables are estimated based upon the 1990 Census estimates of per capita income in rural versus urban census tracts. The difference between per capita income in rural census tracts and urban census tracts was less than one percent. School district variables are collected from the State Department of Education (Wyoming Department of Education; 1993-1998).

V. Results

Results of the estimation procedure are presented in Table 2 and 3. Time series cross sectional model structure was evaluated using a fixed effects model, a constrained OLS estimator, and a random effects estimator using generalized least squares. The fixed effects model was inferior to either the constrained OLS estimator or the random effects model based on F-tests calculated for all equations. The constrained OLS model was tested against the random effects model using a Hausman (1978) test where the null hypothesis was that the random effects model was the best model. The preferred specification is presented in Table 3. The random effects specification was chosen because it performed better than the others.

Table 2: Statistical results of the choice of model

Lagrange multiplier test
Chi-square statistic (p value)
Null Hypothesis: Ho County Revenue County Expenditures School Revenue School Expenditures
Error components do not exist
127.0399
75.2547
155.8269
95.2267
(0.000)
(0.000)
(0.000)
(0.000)
Error components model is the correct specification
36.6892
95.182
28.172
14.567
(0.000)
(0.000)
(0.000)
-0.0022

Table 3: Fiscal impact model results

Coef. Std. Error t-Stat P-Value R2 F df
County Revenues
0.000
0.941
398.83
125
Constant
0.9062
0.6929
1.3077
0.193
Rural Personal Inc ($000)
0.2333
0.0502
4.645
0.000
City Personal Inc ($000)
0.2948
0.0482
6.1182
0.000
Agric. Land (acres)
0.1006
0.0332
3.0283
0.003
Non-min assessed val.
0.1084
0.071
1.5273
0.129
Mineral assessed val.
0.2837
0.0149
19.017
0.000
County Expenses
0.000
0.928
324.31
125
Constant
1.1294
0.7674
1.4717
0.144
Rural Personal Inc ($000)
0.257
0.0556
4.6195
0.000
City Personal Inc ($000)
0.2998
0.0534
5.6185
0.000
Agric. Land (acres)
0.1011
0.0368
2.7502
0.007
Non-min assessed valuation
0.0835
0.0786
1.0628
0.29
Mineral assessed valuation
0.2774
0.0165
16.792
0.000
School Revenues
0.000
0.94
661.02
127
Constant
4.1856
0.364
11.5
0.000
Rural Personal Inc ($000)
0.3891
0.0311
12.513
0.000
City Personal Inc ($000)
0.3449
0.0372
9.2653
0.000
Total assessed valuation
0.2082
0.0256
8.1432
0.000
School Expenses
0.000
0.933
588.02
127
Constant
4.0645
0.3908
10.401
0.000
Rural Personal Inc ($000)
0.4203
0.0334
12.589
0.000
City Personal Inc ($000)
0.3048
0.04
7.6258
0.000
Total assessed valuation
0.2211
0.0274
8.0568
0.000

Evaluating the other sets of coefficients across equations indicates some expected and some interesting relationships. The coefficient on county revenues for urban residents is not significantly different than the coefficient on the expenditure equations. This suggests that city dwellers payment to county tax rolls is not an unencumbered source of revenues. Urbanites pay taxes to counties but receive services from both the county and cities (and pay taxes to cities also). The implication is that city population increases should benefit county government. Counties do indeed view city population growth as a draw on their resources. The county sometimes can end up providing law enforcement, health, and other public services that very small communities cannot.

The results suggest that at face value the marginal contribution of rural residential population to county revenue (revenue equation for both county government and schools) is less than the marginal contribution to county expenditures. The results also show that for agricultural and rangelands, the marginal contributions to expenditures are practically equal than those to revenues. It remains to be determined if the relationship between the two sets of parameters is statistically significant. This would validate the supposition that rural residential development is always a net fiscal loss to the county government and schools while agricultural land is a net fiscal gain.

The following statistic was used to test the hypothesis that rural residential development costs county taxpayers more than it contributes to county revenues at the margin, (10), as suggested by Mittelhammer (2001). The estimated parameter for rural personal income from the revenue equation is subtracted from the estimated parameter for rural personal income from the expenditure equation. The difference then is divided by a weighted average of the standard errors of the coefficients and the covariance between the two coefficients (calculated by estimating the equations as a seemingly unrelated regression). A test statistic greater than the student t distribution for the number of degrees of freedom would suggest that expenditure coefficients are significantly higher than revenue the coefficients. A similar test statistic was developed to test the hypothesis that agricultural land contributes more in revenues than in expenditures (11).

Formula 10

(10)

Formula 11

(11)

The results indicate that the differences between both pairs of coefficients are not significant, Table 4. The null hypothesis and alternative hypothesis for each test is presented in Table 4. The null hypothesis stating that the revenue generation of rural populations is equal to or greater than the expenditure generation of rural populations could not be rejected at a 95 percent confidence level. Likewise, the null hypothesis for agricultural land coefficients could not be rejected either at a 95 percent confidence level. The notion that rural residential development does not pay while agricultural land does pay is not corroborated with any degree of confidence by the statistical relationship from the model estimated. An AFT-type ratio cannot be validated as a general planning concept at the margin. One cannot claim, as a general rule, that rural residential development is a net fiscal loss to counties.

Table 4: Fiscal impact model results

Hypothesis test t-test
Hypothesis test 1
0.864899
Hypothesis test 2
0.049849

The inability to show a general result as presented above does not imply the opposite, i.e., rural residential development always pays for itself. It simply suggests that there may be scenarios where it might pay for itself. The outcomes then are a function of the specified scenario. A particular scenario is identified and used with the estimated equations to calculate AFT-type ratios. Thirty-five acres of agricultural land are replaced by one new rural household in the county to evaluate the relative role that rural residential development plays in a county fiscal structure.

The addition of one rural household is assumed to earn the county-wide average income and possess a house with a county-wide average assessed valuation. Thirty-five acres are used for two reasons. First, a smaller acreage expansion (e.g. one or even five acre expansions) is usually connected with subdivision development which, while fragmentation nonetheless, can begin to approximate cluster development. This can allow for population growth without the more egregious consequences of fragmentation. The second reason for using a 35-acre size is that anything under 35 acres in Wyoming has to be designated as a subdivision. This level of fragmentation then is a less regulated development (Wyoming State Statutes, 1999). Baseline analysis uses family sizes for rural populations equal to the average family size specific to the county. Likewise, county-wide average incomes are used. The scenario assumes a new rural resident that is approximately the same size and generating the same income as the average household in the specific county.

The models are used to calculate changes in revenues and expenditures for both county government and schools. County population, personal income, and assessed valuation rise as a result of the new household. Agriculture's contribution through total assessed valuation declines by a small amount. The predicted net changes in both revenues and expenditures are used to calculate average ratios of total county expenditure changes to total county revenue changes. The results are displayed in Table 5. Residential development at the expense of agricultural land costs county government and schools on average across counties 1.14 of expenditures for every dollar of new revenue received when incorporating both general government and schools into the equation. The average ratio is $1.08 when looking at only general government and excluding schools. This is generally consistent with the AFT (1999) findings. The results for the overall ratio vary considerably from county to county: Hot Springs County has the highest ratio at 1.45 and Weston County the lowest with 1.03.

The results suggest the AFT claims are provisionally valid, being highly dependent upon the characteristics of the scenario chosen. Changing those assumptions (county average household income, county average residential assessed valuation, county average family size, and inclusion of schools) can change the ratios. Policy makers are right to be concerned about rural residential development. The abundance of AFT-type studies and this research also, suggest that rural residential development in the aggregate is a net fiscal loss to county governments. What these results suggest though is that the character and type of development should be studied before one can say that a particular development is itself a net fiscal loss.

Table 5: Population mix, revenue change, and ratios of public expenditures to revenues for Wyoming Counties with a replacement of 35 acres of agricultural land with one average size family.

Wyoming Counties Urban population Rural Population Household size Ave. Assessed Valuation per person Revenue Change Net Expense Change County Government Ratio (excluding schools) Total Government Ratio (including schools)
Albany Co.
26,526
3,849
2.23
1,889
809
992
1.10
1.21
Big Horn Co.
7,049
4,007
2.60
2,086
2,429
2,779
1.04
1.13
Campbell Co.
20,249
11,293
2.73
2,219
2,344
2,779
1.07
1.17
Carbon Co.
13,755
2,174
2.39
2,559
1,620
1,717
1.06
1.04
Converse Co.
8,151
3,844
2.55
2,801
1,989
2,257
1.06
1.12
Crook Co.
2,653
3,030
2.51
3,067
3,426
3,730
1.09
1.08
Fremont Co.
19,273
16,269
2.58
1,179
2,774
3,116
1.11
1.11
Goshen Co.
7,014
5,685
2.38
1,250
2,039
2,301
1.07
1.12
Hot Springs Co.
3,406
1,253
2.25
1,264
1,807
2,644
1.05
1.45
Johnson Co.
3,828
2,797
2.36
1,623
2,153
2,409
1.05
1.11
Laramie Co.
54,922
23,271
2.45
1,839
1,439
1,626
1.08
1.12
Lincoln Co.
7,233
6,500
2.75
1,905
3,359
3,919
1.05
1.15
Natrona Co.
53,432
10,082
2.42
1,509
1,245
1,429
1.09
1.13
Niobrara Co.
1,684
923
2.28
1,594
2,217
2,440
1.10
1.09
Park Co.
14,751
10,459
2.42
1,724
1,667
1,908
1.09
1.13
Platte Co.
5,074
3,320
2.40
2,007
2,255
2,551
1.08
1.12
Sheridan Co.
16,098
8,838
2.31
2,197
1,670
1,872
1.10
1.11
Sublette Co.
2,461
3,041
2.47
2,222
3,965
4,252
1.10
1.06
Sweetwater Co.
33,929
6,313
2.62
1,341
1,534
1,874
1.04
1.20
Uinta Co.
15,020
5,148
2.84
1,393
2,062
2,313
1.07
1.11
Washakie Co.
6,222
2,349
2.47
1,454
1,647
1,831
1.10
1.10
Weston Co.
4,139
2,395
2.42
1,690
2,188
2,268
1.10
1.03

The implications of this change can be seen using the AFT estimates of ranchland at risk for one county. Fremont County had an annual budget of $10 million. AFT estimates that there are 296,960 acres of ranch and farmland threatened by development. Following the results of Table 5, conversion of this acreage to 35-acre ranchettes with households earning average incomes in the county and average sizes would generate almost 8,500 dispersed family households. This would mean a $2.9 million net increase in cost to county residents with the same level of service.

VI. Conclusions

Rural residential development poses several policy questions for state and local policymakers. Rural residential development affects wildlife, public land access, open spaces, and ultimately fiscal structure of the county. The fiscal impact model developed in this research partially validates the AFT results that rural residential development costs taxpayers more than it contributes in revenues; and conversely, that agricultural land contributes more to county coffers than it asks for in services. However, relying on simply averages to make the case is risky. County land use and planning policy should encourage agricultural land protection in order to capture the fiscal savings as well as the attending flows of public goods associated with non-fragmented lands.

The two sets of results suggest that the type of rural residential development may affect the fiscal impact to the county. Development distance from public service nodes, the composition of the in-migrating households, the density of development and the natural resource land base all may be important factors to integrate into a fiscal impacts model. Such data should be obtained and analyzed in order to assist county officials with planning strategies.

The AFT cost of community service methodology provides a simple way of calculating ratios that can be used in public policy formation that protects open spaces. It is important that the community leaders and policy makers use the ratios with caution. The results of the general test suggest that there is not a significant difference between rural residential revenues and public expenditures attributed to rural residents. However the results of the simulation indicate that rural residential development costs taxpayers more than it contributes on average but not necessarily at the margin. The mix of services and service recipients in this case are simply re-allocated in order for county budgets to balance.

It is important here to point out that this estimate does not include the broad array of other public good values associated with agricultural land which includes wildlife habitat, water quality, and viewshed. Thus this fiscal value estimate is a conservative measure of the cost and benefit disparity resulting from dispersed rural residential development.

Endnotes

  1. Here the distance function is defined by Shephard (1953).

  2. The coefficients on the expenditure function are similar to a conditional factor demand if TVC is considered as the indirect cost function.

  3. Urban school districts are included because it was impossible to separate out urban versus rural attendance.

References

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American Farmland Trust. 1999. “Cost of Community Services Studies”, http://www.farmlandinfo.org.

American Farmland Trust. 2002. “Strategic Ranchland in the Rocky Mountain West: Mapping the Threats to Prime Ranchland in Seven Western States,” http://www.farmlandinfo.org.

Burchnell and Listokin. 1978. The Fiscal Impact Handbook. New Brunswick, NJ: Center for Urban Policy Research.

Collinge, Sharon K. 1996. “Ecological Consequences of Habitat Fragmentation Implications for Landscape Architecture and Planning,” Landscape and Urban Planning 36, 1:59-77.

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Daniels, Tom. 1999. “A Cautionary Reply for Farmland Preservation,” http://www-pam.usc.edu.

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Judge, G. G., R. C. Carter, William E. Griffiths, Helmut Lutkepohl, and Tsoung-Chao Lee. 1982. Introduction to the Theory and Practice of Econometrics. New York: John Wiley and Sons.

Kelsey, T. W. 1996. “The Fiscal Impacts of Alternative Land Uses: What Do Cost of Community Service Studies Really Tell Us?” Journal of the Community Development Society, 27, 1:79-89.

Marcoullier, D., S. Deller, and G. Green. Forthcoming. “The influence of recreational housing development on local government finances,” Annals of Tourism Research.

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Shephard, R. 1953. Cost and Production Functions. Princeton, NJ: Princeton University Press.

Smith, Probst, L. and Abberger. 1991. “Local Land Acquisition for Conservation: Trends and Facts to Consider,” World Wildlife Fund, Washington D.C.

Taylor, David T. 1999. “Cost of Community Services Study for Sublette County, Wyoming,” Presentation to Sublette County Commissioners, Cooperative Extension Service, University of Wyoming, Laramie Wyoming (unpublished manuscript).

Taylor, David T. 2001a. “Population Growth in Wyoming, 1990-2000,” Information Bulletin for the Open Spaces Partnership, Institute for the Environment and Natural Resources and Cooperative Extension Services, University of Wyoming.

Taylor, David T. 2001b. “Second Home Growth in Wyoming, 1990-2000,” Information Bulletin for the Open Spaces Partnership, Institute for the Environment and Natural Resources and Cooperative Extension Services, University of Wyoming.

Theobald, D.M. 2001. “Technical description of mapping historical, current, and future housing densities in the US using Census block-groups,” Natural Resource Ecology Lab, Colorado State University. http://www.ndis.nrel.colostate.edu/davet/dev_patterns.htm.

Wyoming State Department of Audit. 2000. Tax Reform 2000 Data, Cheyenne, Wyoming.

Wyoming State Department of Education. 1993-1998. “Statistical Bulletin No. 3," Cheyenne Wyoming.

Wyoming Statutes. 1999. §39-1-21.

Wyoming Statutes. 1999. §39-15-111.

Wyoming Statutes. 1999. §39-15-112.

Wyoming Statutes. 1999. §39-14-211.

Wyoming Statutes. 1999. §39-12-101.

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