The Composition of the Property Tax Base and the Exportation of
Municipal Tax Burdens: A Comparison of Sixty-Two Texas Cities
By
Wm. Feagin, Jr.
Submitted for Publication in
The Texas Journal of Political Studies
September 27, 1996
ABSTRACT
This article examines the extent to which municipalities in Texas are able to export their tax
burdens. Exportation indices are calculated for sixty-two Texas cities to indicate their
relative ability to shift the burden of municipal taxes to non-residents. The calculated
indices reveal that about one-half of the cities in the sample are net exporters of taxes
while the others are net importers. A regression model is then specified to explain why
some municipalities are better able to export tax burdens to non-residents than others.
The findings indicate that as the per capita value of commercial and industrial property in a
city’s property tax base increases, the city’s ability to export more of its overall tax burden
increases. It is argued, therefore, that a city’s ability to export taxes occurs mainly through
the shifting of property taxes on commercial and industrial property to consumers outside
the city.
The Composition of the Property Tax Base and the Exportation of
Municipal Tax Burdens: A Comparison of Sixty-Two Texas Cities
Introduction. Former U.S. Supreme Court Justice Oliver Wendall Holmes once remarked
that “taxes are what we pay for a civilized society.” A more precise characterization might
be that taxes are one of the costs we pay for public programs and services. Though there
may be political debate in a given community over the merits of funding specific programs
with tax dollars, there is generally an understanding that tax revenues must be generated if
programs or services are to be provided.
Ultimately, the political debate is over who will bear the burden of taxes that are
imposed. The distribution of tax burdens in a community is a topic of concern for
academics, policy-makers, and citizens. Because municipalities have been forced to meet
increasing or stable demand for public services while resisting or limiting tax increases, the
issue of tax exportation - the degree to which the burden of paying a city’s taxes falls on
non-residents - has become more important.
How much of a city’s tax burden is exported to non-residents? Unfortunately, no
direct measure of tax exportation exists. However, it is possible to compare the extent to
which a municipality exports its tax burden relative to other cities by examining relevant
indicators. The purpose of this paper is to develop and test such indices for sixty-two
Texas cities.1 The exportation indices provide a fairly simple conception of each city’s
ability to export a portion of its tax burden relative to other cities in the sample.
Additionally, this paper tests the hypothesis that a city’s ability to export its tax
burden relative to other cities increases as the per capita appraised value of its commercial
and industrial property tax base increases. This study finds significant support for this
hypothesis, suggesting that the property tax, among the three taxes examined here, may
be the most important in affecting the exportation issue. This, of course, has important
policy implications for cities in designing their revenue structures.
Some Theoretical Considerations Relevant to Tax Exportation. Tax exportation is a
particular type of tax shifting. Depending on the tax in question and on market conditions,
the economic burden of paying a tax (known as economic incidence) may be shifted from
the entity that is legally responsible for transferring tax dollars to the taxing authority
(known as statutory incidence or legal liability).2 In the United States, the legal liability for
many of the taxes imposed by all levels of government falls on businesses. Obvious
exceptions include personal income taxes and property taxes on owner-occupied singlefamily housing.
While businesses may have the legal responsibility for paying a tax, they cannot
bear its economic incidence (Rosen, 1988: 265).3 The economic burden of the tax will be
shifted either to customers on the one hand, or to the employees of the business or the
stockholders in the business on the other. For example, if the tax can be incorporated into
the price structure of a good or service, the incidence of the tax is said to be shifted
forward to the purchaser. Economists refer to this phenomenon as the price-migration
effect (Gade and Adkins, 1992; Phares, 1980).
Whether the economic incidence of a tax will be borne by the consumer depends on
the price elasticity of demand for the taxed good or service. Relatively price elastic
demand means that consumers are sensitive to the price of a good or service and will
adjust downward the quantity that they consume as its price increases. Taxes potentially
increase the prices of goods and services that businesses supply.
If demand is relatively price inelastic, then consumers will not adjust their
consumption of the good or service, regardless of the tax-induced price increase. In such
a situation, we can conclude that the economic incidence of the tax (or at least the largest
portion of it) is borne by consumers. Figure 1(a) illustrates the price-migration effect of an
excise tax placed on some hypothetical good or service for which demand is relatively price
inelastic.
The slope of the demand curve DX indicates that demand for good X is relatively
price inelastic. The pre-tax equilibrium price (PE) is set at the intersection of DX and the
supply curve SX. The price-migration effect means that two new prices are created by the
imposition of the excise tax on good X - the price paid by consumers (PC) and the price
received by suppliers (PS). Assuming that the statutory incidence of the tax is placed on
consumers (demand side), the revenues generated by the excise tax are defined by the
geometric area knhf. The portion of the tax that is paid by consumers is represented by
the difference between PC and PE (the larger shaded rectangle) while the portion of the tax
absorbed by the supplier is the difference between PS and PE (the smaller shaded
rectangle). Thus, while both the consumer and the supplier are negatively affected by the
tax, clearly the consumer bears most of the economic incidence of the tax when demand is
relatively price inelastic.
On the other hand, market conditions may preclude the shifting of the tax to the
consumer. If demand is relatively price elastic, or if reasonable alternatives to the good or
service exist, the tax will be borne by owners of the business in the form of lower return on
investment or by employees in the form of lower wages. Unless demand is perfectly price
elastic, customers will absorb some of the burden of the tax as indicated by the smaller
shaded rectangle. When demand is relatively price elastic, we may say that the greatest
portion of the tax is paid by owners of capital or employees. Figure 1(b) illustrates the
effect of the excise tax placed on good X when demand is relatively price elastic.
As in Figure 1(a), we assume that the statutory incidence of the tax is imposed on
consumers. Additionally, the original equilibrium price is denoted PE, the tax-induced price
paid by consumers is PC, and the tax-induced price received by suppliers is PS. In this
scenario (when demand is relatively price elastic), the relative portions of the tax paid by
consumers and suppliers have changed. The economic incidence of the tax borne by
consumers is represented by the smaller shaded rectangle and that absorbed by suppliers
is indicated by the larger.4 Note that it is not possible by examining this graph to determine
whether the incidence (represented by the difference between PE and PS) will be borne by
owners of the business or their employees. Such a determination would depend on
specific knowledge of labor-market conditions.5
Of course, these observations are theoretical. The task of determining exactly how
much of a tax is borne by consumers, owners, or employees depends upon whether we
can obtain reliable empirical estimates of the price elasticity of demand. Estimating the
portion of a city’s taxes that are exported not only requires an reliable assessment of the
elasticity of demand but also requires an accurate determination of which consumers,
owners, and employees reside outside of the city’s jurisdiction.6
Nevertheless, if we accept certain assumptions concerning conditions in the
marketplace, the relative degree of tax exporting can be determined using the exportation
indices developed in this study. No attempt is made to estimate the actual dollar amount
or percentages of taxes that are exported. However, the exportation indices provide a
rough measure of the ability of sixty-two Texas cities to export some of their tax burdens
relative to each other.
Conditions of Tax Exporting. Some taxes seem to be more obviously exportable than
others. Most of us can readily appreciate that a large portion of a city’s hotel/motel room
occupancy tax will be paid by out-of-town visitors. Although the liability rests with the hotel,
we readily assume that the economic incidence of the tax rests with the person renting the
room. After all, when the renter receives his bill at the end of his stay, the tax is indicated
as part of the total. However, what if the tourist sector of a city’s economy is weak or in
recession? Suppose, for example, that on any given night very few of the available hotel
rooms in the city are rented. Under these conditions, the economic incidence of the tax
would fall mainly on the supply-side as hotel operators absorb the burden of the tax in
order to hold rental rates down.
In a similar fashion, it is easy to appreciate that the portion of a city’s sales tax paid
by out-of-town shoppers is exported (provided that the tax can be shifted forward to
consumers). Again, however, it is exceedingly difficult to establish precisely how much of a
city’s sales tax is paid by non-residents. Making an exact determination would require that
retailers identify the city of residence of each of their customers (or at least a
representative sample) at the time that individual transactions are made.
How other taxes are exported is more difficult to appreciate. Taxes imposed on
personal incomes (by states, and, in some parts of the country, local governments) on
personal incomes are a good example. However, a portion of these may be exported to
the federal government (and therefore taxpayers nationally) by virtue of their deductibility
from the federal income tax (Phares, 1980). The tax imposed on personal incomes by the
national government cannot be shifted.
The suggestion that a portion of a city’s property tax burden could be exported might
seem absurd to many. Indeed that portion of the property tax that falls on single-family,
owner-occupied residential property cannot be shifted away from the owner. Neither is it
likely that a substantial portion of the tax on single-family (rental) property is exportable,
although the incidence may be shifted to renters. Where market conditions prevent
property taxes from being incorporated into rental rates, the tax is capitalized and the
incidence falls on the owners of the property. Therefore, whether the tax is exported
depends on where the properties’ owners reside. It is possible that a sizable percentage of
multi-family residential properties could be owned by non-residents and therefore exported,
particularly in cities where a surplus of rental properties exist.
The portion of the property tax that falls on commercial and industrial property is
exportable, however, provided that market conditions allow shifting to consumers and that
businesses that are liable for paying the tax serve regional or national clienteles (Ladd,
1975; McClure, 1967; Gade and Adkins, 1988). Therefore, cities that have relatively high
commercial and industrial property values per capita are likely to be exporting a substantial
amount of their property, and overall, tax burdens. 7
Measuring Tax Exportation. In order to determine the relative degree to which any of the
sixty-two cities in the sample are able to export a portion of their tax burdens, exportation
indices were developed for each city.8 The index for each city in the sample is provided in
Table 5 in the Appendix. It is important to note that all cities export part of their tax
burdens and import a portion of the tax burdens of other cities. The question is whether,
on balance, a city exports more taxes than it imports. Positive indices indicate cities that
export more taxes than they import. Negative indices indicate cities that are net importers
of other cities’ tax burdens.
To be more precise, a negative index indicates a city whose residents pay more of
the tax burdens of other cities than their city is able to export to non-residents. A positive
index suggests that a city’s residents are benefiting from tax shifting to non-residents.
Caution should be exercised in interpreting the indices. The values do not indicate the
percentage of taxes that are imported or exported. The magnitude of a city’s index is
meaningful only as it relates to the indices of the other sixty-one cities.
The exportation index centers on zero (which is the average). An index of 0 would
indicate a city in which exporting and importing is roughly the same. The City of Lubbock
has an index of virtually 0. Note that the indices range from a high of 138.9 for Texas City,
85.5 for Grapevine, and 47.6 for Irving to -58.2 for Missouri City, -54.6 for Euless, and -51.8
for Round Rock. It is important to ask, however, whether each city’s exportation index
makes sense as it relates to the indices of other cities.
Does it seem logical that Texas City, among all 62 cities in the sample, should have
the highest exportation index? Is it reasonable that Grapevine would have the highest
index among cities in the Dallas-Fort Worth Metroplex? Why would the Houston suburb of
Missouri City have the lowest index, followed closely by the Dallas-Fort Worth suburb of
Euless and the Austin suburb of Round Rock? The next section attempts to answer these
questions.
Empirical Findings. What explains whether a city will be a net exporter or importer of
taxes? The key to a city’s ability to export taxes appears to be the property tax - or, to be
more precise, the composition of its property tax base. 9 This seems ironic since few
people appear to appreciate that property taxes can be exported at all. Usually, the
discussion about shifting municipal tax burdens to non-residents centers around the sales
tax.
This study investigates the question of whether a city’s ability to export its tax burden
is related to the per capita appraised value of the commercial and industrial portion of its
property tax base. The following regression model is specified:
Exporti = 1 + 2C/IPCi + 3Valleyi + 4Suburbi
where,
Exporti = the exportation index of a city
C/IPCi = the appraised value of a city’s commercial and industrial
property per capita
Valleyi = a city located in the Rio Grande Valley
Suburbi = a bedroom community (residential suburb of a major
metropolitan city)
Specifically, the following hypotheses are to be tested:
Hypothesis #1: A city’s ability to export its tax burden relative to other cities increases as
the appraised value of the commercial and industrial portion of its property
tax base increases [2 > 0];10
Hypothesis#2: Cities located in the Rio Grande Valley are likely to have higher
exportation indices than other Texas cities [3 > 0];11
Hypothesis#3: Cities that are residential suburbs (bedroom communities) or major
metropolitan cities are likely to have lower exportation indices than other
Texas cities [4 > 0].12
The regression analysis yielded the following estimated equation:
Exporti = -25.0058 + .00207C/IPCi + 23.4111Valleyi - 30.0875Suburbi
(-8.174)
(15.18)
(4.602)
(-8.337)
The estimated equation indicates that for every one thousand dollar increase in the
per capita appraised value of a city’s commercial and industrial property base there is a 2
point increase in its exportation index. The t-statistic for 2 is significant at the 95%
confidence level, indicating support for hypothesis #1. Figure 2 depicts the regression
results; complete regression results are provided in Table 1.
Inclusion in the model of the two dummy variables makes it possible to test whether
there are three separate regression lines (one for all Texas cities with populations over
25,000, a second for Rio Grande Valley cities with populations over 25,000, and a third for
suburban cities with populations over 25,000). The center regression line represents the
relationship between the per capita commercial and industrial property values for all Texas
cities with populations over 25,000 and their exportation indices (Y-intercept = -25.0058).
However, the estimated equation also indicates that the average autonomous
exportation index for Rio Grande Valley cities is 23.41 points greater than other Texas
cities. By adding the regression coefficient for Rio Grande Valley cities to the Y-intercept,
we obtain the Y-intercept for the top regression line. This line represents the relationship
between the exportation indices of Rio Grande Valley cities and their per capita commercial
and industrial property values. The average autonomous exportation index (Y-intercept)
for Rio Grande Valley cities, therefore, is approximately -2. The t-statistic for 3 is
statistically significant, indicating support for hypothesis #2.
The bottom line is similarly obtained by adding the regression coefficient for
suburban cities to the Y-intercept.
The average autonomous index for bedroom
communities is -55.0933, 30.09 points lower than other Texas cities. A significant t-statistic
for 4 indicates support for hypothesis #3.13 In other words, bedroom communities do have
lower exportation indices, holding per capita commercial and industrial property values
constant.
Table 1. Regression Results
Ordinary Least Squares
Dependent Variable: Exportation Index
Mean of Dependent Variable:
0
Std. Error of the Regression:
12.0888
Total Variation:
65031.
Regression d.f.:
3
2
R:
.86966
F3,58:
128.9997
Variable
Coefficient
Y-intercept -25.0058
C/IPC
.0020701
Valley 23.4111
.2288
Suburb
-30.0875
Beta
-.7466
5.087
.4145
Std. Error
3.059
.000136
4.602
3.609
Number of Observations
Std. Dev. of Dep. Variable: 32.65
Sum of Squared Residuals:
Regression Variation:
Residual d.f.:
58
2
Adjusted R :
t-stat
Mean of X
-8.174
-15.177
14789.76
.11290
-8.337 .27419
62
8476.01
56555.
.86292
Std. Dev. of X
-11774.92078
.31906
.44975
Finally, note that the general F-statistic (F3,58) is significant at the 95% confidence
level. The adjusted R2 is very high (.8692), indicating that 87% of the variation in
exportation indices among the sample cities is explained by the specified model.
These findings provide some empirical verification of the theoretical assumptions on
which the exportation index is based - specifically, that property taxes can be exported and
that a city’s ability to export its overall tax burden depends in large part on the composition
of its property tax base. With respect to this latter point, we may note that Texas City’s
apparent ability to export its tax burden results from the fact that much of its property value
lies primarily in the petrochemical industries located there. The presence of the Dallas-Fort
Worth International Airport and concomitant hospitality industry located in Grapevine
provide that city with a strong commercial and industrial property tax base and high
exportation ability. On the other hand, the absence of extensive commercial and industrial
properties in Missouri City, Euless, and Round Rock provides those cities with limited
ability to export taxes.
Policy Implications.
While it is understandable that policy-makers may be eager to
embrace taxes that are more easily exportable than others, the desire to shift the tax
burden of a municipality to non-residents probably should not be the sole criterion for
adopting or increasing a particular tax. Nor should it be the goal of a revenue structure.
Taxes that are frequently identified as candidates for exportation (i.e., sales tax and
hotel/motel taxes) generate revenues that can be highly cyclical, unstable, or seasonal
(Bland, 1989: 6-8, 15, 17-20, 43, 55, 62).
Additionally, some economists argue, as a philosophical issue, that exportation of
tax burdens leads to inefficiencies in the public sector because citizens are less aware of
the costs of providing public services (Wildasin, 1987). However, a city does have a
legitimate interest in exporting a portion of its tax burden as a means of re-capturing some
of the costs of providing services to non-residents (public safety at events and the
workplace, well-maintained streets, etc.). Consequently, the findings of this study indicate
that a city’s best strategy to accomplish that goal would be to increase the commercial and
industrial component of its property tax base. Of course, depending on the type of
businesses attracted by a city (i.e., “clean” or “dirty” industry), a municipality also risks
incurring increased costs when its expands its commercial and industrial property tax
base.14
Finally, the issue of exportation (and importation) of municipal tax burdens deserves
more scholarly attention. While this analysis provides some interesting insights into the
issue, it admittedly suffers from a lack of methodological rigor. The computation of
exportation indices in the manner performed here poses some concerns about construct
validity. A direct measure of exportation would clearly be preferable. Additional analyses
designed to verify the theoretical assumptions associated with municipal tax exportation
are needed. A study of municipal tax exportation nationally would be an important
contribution.
Furthermore, future studies should attempt to construct more comprehensive
models to address a number of questions related to exportation. For example, do the type
of businesses or industries which make up a city’s commercial and industrial property tax
base affect the exportation issue? This study is an initial step in verfiying the importance
of the property tax in exporting municipal tax burdens. Additional analyses are needed.
Are the decisions of individuals and households with respect to establishing
residence in one city versus another affected by the exportation issue? The Tiebout
hypothesis would lead us to expect people to establish residence in cities that are net
exporters. As previously mentioned, Wildasin’s findings (1987) suggest that exportation of
tax burdens leads to inefficiencies in the public sector. Do individuals and households
choose to live in cities where the costs of providing higher levels of public services are
shifted to non-residents?
Ultimately, municipal tax exportation is a political issue. Municipalities have been in
competition for years over economic development. If competition among the political
leaders of cities to bring economic benefits to their residents is to be expected, it is equally
likely they would attempt to shift economic costs away from their residents. For political
scientists, particularly game theorists, municipal tax exportation is an issue that deserves
greater attention. In a sense, the competition among cities to shift tax burdens is like the
child’s game of musical chairs - except in this version, the winners are the residents of
cities that are able to have their tax shares (sic) sat in (paid by) non-residents.
Bibliography
Sources Cited:
Bland, Robert L. 1989. A Revenue Guide for Local Government. Washington, D.C.:
ICMA
Bland, Robert L. and Phanit Laosirirat. 1995 “The Effect of Truth-in-Taxation on Property
Tax Yields.” Unpublished.
Ebel, Robert. 1990. A Fiscal Agenda for Nevada. Reno: University of Nevada Press.
Gade, Mary N. and Lee C. Adkins. 1992. “Tax Exporting and State Revenue Structures,”
National Tax Journal, vol. XLIII, pp. 39-52.
Ladd, Helen. June 1975. “Local Education Expenditures, Fiscal Capacity, and the
Composition of the Property Tax Base,” National Tax Journal, pp. 145-158.
McClure, Jr., Charles E. 1967. “The Interstate Exporting of State and Local Taxes:
Estimates for 1962,” National Tax Journal, vol. XX, no. 1, pp. 49-77.
Phares, Donald. 1980. Who Pays State and Local Taxes?
Oelgeschlager, Gunn, and Hain Publishers, Inc.
Cambridge, MA:
Rosen, Harvey S. 1988. Public Finance. 2nd edition. Homewood, IL: Richard D. Irwin,
Inc.
Wildasin, David. 1987. “The Demand for Public Goods in the Presence of Tax Exporting,”
National Tax Journal, vol. XL, pp. 591-601.
Other Relevant Sources:
Aaron, Henry J. and Joseph A. Pechman, eds. 1981. How Taxes Affect Economic
Behavior. Washington, D.C.: the Brookings Institution
Bowman, John H. 1974. “Tax Exportability, Intergovernmental Aid, and School Finance
Reform,” National Tax Journal, pp. 163-173.
Davies, David G. 1986. United States Taxes and Tax Policy. Cambridge: Cambridge
University Press
Fuji, Edwin, Mohammed Khaled, and James Mak. 1985. “The Exportability of Hotel
Occupancy and Other Tourist Taxes,” National Tax Journal, vol. XXXVIII, pp. 169177.
Greene, Kenneth V. and Vincent G. Munley. 1984. “Perceptions of the Ability to Export
Nonresidential Property Tax Burdens,” Public Finance Quarterly, vol 12, no. 1, pp.
117-127.
Pechman, Joseph A. 1987. Federal Tax Policy. 5th edition. Washington, D.C.: the
Brookings Institution
Appendix
Calculation of the Exportation Indices. Data relative to three taxes - the general sales
tax, the property tax, and the hotel/motel room occupancy tax - for sixty-two Texas cities
were collected and are provided in Tables 1, 2, and 3 respectively. Each table indicates
the nominal rates imposed on the tax base by each city and the levy or revenue generated
by the tax. All figures are for FY 1994. A number of basic statistics relative to each of
these three taxes were calculated for each of the sixty-two cities in order to determine the
exportation indices.
Revenue or Levy Per Capita. The revenue generated by applying a city’s local rate to its
tax base is divided by the city’s population. Revenue per capita provides a rough measure
of a city’s tax effort relative to that of other cities.
Tax Capacity. Sales, property, and hotel/motel room occupancy tax capacity measures are
calculated to answer the question, “If each of the sixty-two cities in the sample applied a
uniform tax rate to its tax base, how much revenue would each city raise from each tax?”
For each of the three taxes, tax capacity is calculated by dividing the taxable value of a
city’s tax base by the local rate, multiplying that figure by the average local rate for the tax
(the uniform rate), and dividing by the population for the city. Tax capacity provides a
rough measure of the potential revenue that a city could raise from a particular tax if it
applied the average rate to its base.
Tax Capacity Index. The tax capacity figure is divided by the average tax levy per capita
for the sixty-two cities. Capacity indices are calculated for each city for each of the three
taxes. The tax capacity indices are expressed as percentages. A tax capacity index of
100 indicates a city whose tax capacity is equal to the average levy per capita of the sixtytwo cities. An index of 90 indicates a city whose tax capacity is 90% of the average levy
per capita while an index of 110 indicates a city whose tax capacity is 10% greater than the
average.
Total Tax Capacity. The total tax capacity for a city is calculated by summing its sales,
property, and hotel tax capacity measures. This figure represents the total revenue per
capita that would be generated by the three taxes if a city applied the average rate for each
tax. The average levies per capita for the three taxes can be summed to obtain the
average total tax levy per capita for the sixty-two cities.
Total Tax Capacity Index. Total tax capacity indices for each city are obtained in the same
manner as tax capacity indices for sales, property, and hotel taxes except that this index
expresses the combined tax capacity of a city as a percentage of the average combined
revenue per capita. Therefore, a total tax capacity index of 100 indicates a city whose total
tax capacity (for all three taxes) is equal to the average levy per capita (for all three taxes).
Per Capita Income Index. Income per capita for each city is divided by the average income
per capita for the sixty-two cities to obtain its per capita income index. An index of 100
would indicate a city whose income per capita is equal to the sixty-two city average. The
index provides a rough measure of the ability of the city’s residents to pay taxes. Again,
the index is centered on 100 and suggests that, if the residents of each of the sixty-two
cities paid an equal percentage of their incomes in taxes to their respective municipal
governments, the residents of a city with an index of 100 would pay the average amount of
their incomes in taxes. Residents of a city with an index of 90 would pay 90% of the sixtytwo city average while residents of a city with an index of 110 would pay 10% more dollars
in taxes.
The Exportation Index. In order to determine the relative degree of tax exportation (or
importation) that occurs in a city, its total tax capacity index is compared to its per capita
income index (total tax capacity index is subtracted from the per capita income index).
Positive differences indicate cities that, on balance, export more of their tax burdens to
nonresidents than import the tax burdens of other cities. Negative values indicate cities
whose residents pay more of the tax burdens of other municipalities than the city is able to
export. An exportation index of 0 would indicate a city that exports taxes that are roughly
equal to the taxes it imports. As the values of the exportation index increase from zero, the
ability of a city to export taxes to nonresidents increases. Conversely, as the values of the
exportation index decrease from zero (negative values), the city increasingly imports the
tax burdens of other cities.
Table 1. Sales Tax Rates, Levies, Levies Per Capita, Sales Tax Capacities, and Sales Tax Capacity Indices for Sixty-Two
Texas Cities With Populations Over 25,000, FY 1994
Sales Tax
Sales Tax
Sales Tax Levy
Sales Tax
Sales Tax
City
Rate
Levy
Per Capita
Capacity
Capacity Index
Abilene
0.02
$ 19,348,000
$181.41
$118.36
85.9%
Amarillo
0.02
33,162,000
210.40
137.26
99.6
Arlington
0.02
48,029,000
183.51
159.63 115.8
Baytown
0.01
5,232,600
81.95
106.93
77.6
Beaumont
0.02
20,628,000
180.44
156.96 113.9
Bedford
0.44
3,324,400
75.97
99.12
71.9
Brownsville
0.65
15,456,000
156.18
101.89
73.9
Carrollton
0.58
13,041,000
158.70
207.08 150.3
Corpus Christi
0.62
25,397,000
98.65
128.72
93.4
Dallas
0.67
145,920,000
144.92
189.09 137.2
Deer Park
0.68
1,472,500
53.25
69.48
50.4
Denton
0.75
7,242,800
109.29
95.07
69.0
DeSoto
0.65
1,927,700
63.11
82.35
59.8
Duncanville
0.86
2,791,500
78.09
101.89
73.9
Edinburg
0.65
3,850,000
128.83
84.05
61.0
Euless
0.62
4,439,100
110.24
95.90
69.6
Fort Worth
0.95
48,854,000
109.14
142.41 103.3
Galveston
0.56
7,387,600
125.07
95.99
69.7
Garland
0.62
12,744,000
70.55
92.05
66.8
Grand Prairie
0.70
13,346,000
133.98
116.54
84.6
Grapevine
0.43
4,533,600
155.25
202.57 147.0
Haltom City
0.47
2,826,900
86.04
112.26
81.5
Harlingen
0.45
11,325,000
232.38
151.60 110.0
Houston
0.63
229,470,000
140.73
183.62 133.3
Huntsville
0.38
3,197,600
114.51
99.61
72.3
Hurst
0.60
7,792,900
232.11
201.91 146.5
Irving
0.52
28,328,000
182.72
238.41 173.0
Killeen
0.61
7,766,100
122.23
106.33
77.2
Kingsville
0.70
2,048,900
81.06
70.51
51.2
LaPorte
0.71
1,248,100
44.72
58.35
42.3
Laredo
0.51
12,520,000
101.87
132.92
96.6
League City
0.80
1,767,600
58.61
44.98
32.7
Lewisville
0.59
8,407,600
180.73
235.81 171.1
Longview
0.52
15,292,000
217.49
189.19 137.3
Lubbock
0.64
21,492,000
115.42
150.60 109.3
Lufkin
0.44
6,518,000
215.79
187.70 136.2
McAllen
0.49
20,780,000
247.32
215.13 156.1
Mesquite
0.51
18,952,000
186.75
162.45 117.9
Midland
0.68
10,057,000
112.44
146.71 106.5
Mission
0.63
4,028,900
140.61
91.73
66.6
Missouri City
0.54
1,443,300
39.90
52.06
37.8
Nacogdoches
0.67
3,145,800
101.90
132.96
96.5
New Braunfels
0.41
3,500,500
128.06
167.10 121.3
North Richland Hills 0.57
8,828,200
192.36
167.32 121.4
Odessa
0.61
8,896,100
99.18
129.41
93.9
Pasadena
0.65
8,787,300
73.62
96.06
69.7
Pharr
0.57
4,171,900
126.72
82.68
60.0
Plano
0.51
24,775,000
192.48
251.16 182.3
Port Arthur
0.78
3,502,000
59.64
77.81
56.5
Richardson
0.45
14,278,000
187.47
244.60 177.5
Round Rock
0.57
5,610,700
74.97
65.21
47.3
San Angelo
0.83
7,072,800
83.73
109.25
79.3
San Antonio
0.60
91,874,000
98.16
128.08
92.9
San Marcos
0.46
6,208,500
216.00
187.89 136.4
Sherman
0.59
4,974,100
157.40
205.38 149.0
Temple
0.59
9,102,300
197.41
171.72 124.6
Texarkana
0.50
7,496,500
236.81
205.99 149.5
Texas City
0.25
12,715,000
311.47
203.21 147.5
Tyler
0.53
12,248,000
164.72
214.92 155.9
Victoria
0.71
6,885,900
125.03
163.13 118.4
Waco
0.67
17,382,000
167.80
145.96 105.9
Wichita Falls
0.67
8,290,900
86.13 112.38 81.6
62 city averages
.013
18,118,000
137.80
139.96
101.5
std. deviations
.004
35,138,000
58.80
53.01 38.5
Table 2. Property Tax Rates, Levies, Levies Per Capita, Property Tax Capacities, and Property Tax Capacity Indices for Sixty-Two
Texas Cities With Populations Over 25,000, FY 1994
Property
Property
Levy
Property Tax
Property Tax
City
Tax Rate
Tax Levy
Per Capita
Capacity
Capacity Index
Abilene
0.58750
$ 13,199,000
$123.75
$125.26
69.3%
Amarillo
0.27890
11,533,000
73.17
156.01
86.3
Arlington
0.64170
53,557,000
204.63
189.63
104.9
Baytown
0.73703
12,090,000
189.36
152.77
84.5
Beaumont
0.61500
19,869,000
173.80
168.05
93.0
Bedford
0.43594
6,915,300
158.02
215.55
119.3
Brownsville
0.65450
10,469,000
105.79
96.12
53.2
Carrollton
0.57830
26,458,000
322.00
331.09
183.2
Corpus Christi
0.61796
37,232,000
144.62
139.16
77.0
Dallas
0.67440
284,690,000
282.74
249.30
138.0
Deer Park
0.68000
9,212,300
333.15
291.33
161.2
Denton
0.74790
13,470,000
203.27
161.61
89.4
DeSoto
0.64850
8,379,800
274.35
251.57
139.2
Duncanville
0.85800
9,539,200
266.85
184.94
102.3
Edinburg
0.65208
3,563,200
119.23
108.73
60.2
Euless
0.61862
6,006,400
149.16
143.38
79.3
Fort Worth
0.95000
126,090,000
281.68
176.31
97.6
Galveston
0.55590
8,777,100
148.59
158.94
88.0
Garland
0.61910
34,262,000
189.66
182.16
100.8
Grand Prairie
0.69559
25,300,000
253.98
217.12
120.1
Grapevine
0.42500
10,476,000
358.75
501.95
277.8
Haltom City
0.47243
3,326,200
101.23
127.42
70.6
Harlingen
0.45470
4,723,500
96.92
126.75
70.1
Houston
0.63000
370,710,000
227.35
214.58
118.7
Huntsville
0.38440
1,410,400
50.51
78.13
43.2
Hurst
0.59600
6,171,800
183.83
183.41
101.5
Irving
0.52210
41,324,000
266.54
303.57
168.0
Killeen
0.61240
7,173,300
112.90
109.63
60.7
Kingsville
0.69917
2,476,900
97.99
83.34
46.1
LaPorte
0.71000
7,394,800
264.99
221.90
122.8
Laredo
0.51010
14,004,000
113.95
132.83
73.5
League City
0.79500
10,172,000
337.29
252.29
139.6
Lewisville
0.58740
10,865,000
233.55
236.42
130.8
Longview
0.52340
14,629,000
208.05
236.37
130.8
Lubbock
0.64000
31,429,000
168.79
156.82
86.8
Lufkin
0.43520
3,657,000
121.07
165.42
91.5
McAllen
0.48510
12,375,000
147.29
180.54
99.9
Mesquite
0.51000
16,049,000
158.15
184.40
102.0
Midland
0.67930
16,780,000
187.61
164.23
90.9
Mission
0.63480
3,193,700
111.46
104.41
57.8
Missouri City
0.54000
7,641,200
211.22
232.59
128.7
Nacogdoches
0.66750
4,130,500
133.79
119.19
66.0
New Braunfels
0.41000
3,551,300
129.92
188.43
104.3
North Richland Hills
0.57000
8,150,800
177.60
185.27
102.5
Odessa
0.61300
9,856,900
109.89
106.60
59.0
Pasadena
0.64750
17,235,000
144.39
132.60
73.4
Pharr
0.57000
2,731,500
82.97
86.56
47.9
Plano
0.51020
41,256,000
320.53
373.59
206.7
Port Arthur
0.77500
6,850,600
116.66
89.51
49.5
Richardson
0.45385
22,125,000
290.49
380.61
210.6
Round Rock
0.56924
6,137,800
82.01
85.67
47.4
San Angelo
0.82850
14,404,000
170.51
122.38
67.7
San Antonio
0.59557
131,960,000
140.99
140.67
77.8
San Marcos
0.46000
2,993,800
104.16
134.65
74.5
Sherman
0.58730
6,342,000
200.69
203.20
112.4
Temple
0.58620
7,940,700
172.22
174.70
96.7
Texarkana
0.50000
4,621,800
145.97
173.64
96.1
Texas City
0.24500
9,482,900
232.29
563.81
312.0
Tyler
0.53360
13,159,000
174.40
194.35
107.5
Victoria
0.71000
10,785,000
195.81
164.00
90.8
Waco
0.66764
16,989,000
164.01
146.07
80.8
Wichita Falls
0.67360
15,227,000
158.19
139.64
77.3
62 city averages
0.59464
26,976,000
180.72
187.12
103.5
std. deviations
0.12740
60,518,000
73.27
91.77
50.8
Table 3. Hotel Tax Rates, Levies, Levies Per Capita, Hotel Tax Capacities, and Hotel Tax Capacity Indices for Sixty-Two Texas Cities
With Populations Over 25,000, FY 1994
Hotel Tax
Hotel Tax
Hotel Tax Levy
Hotel Tax
Hotel Tax
City
Rate
Levy
Per Capita
Capacity
Capacity Index
Abilene
0.07
$ 933,365
$ 8.75
$ 7.82
79.6%
Amarillo
0.07
2,211,045
14.03
12.54
Arlington
0.07
2,670,000
10.2
9.12
Baytown
----Beaumont
0.07
1,404,800
12.29
10.99
Bedford
0.07
437,852
10.00
8.95
Brownsville
0.07
831,074
8.40
7.51
Carrollton
0.03
56,606
.69
1.44
14.6
Corpus Christi
0.07
3,642,320
14.15
12.65
Dallas
0.07
20,159,140
20.02
17.90
Deer Park
-----Denton
0.07
485,487
7.33
6.55
DeSoto
0.07
176,280
5.77
5.16
Duncanville
0.07
136,774
3.83
3.42
Edinburg
0.07
140,595
4.70
4.21
Euless
0.07
129,451
3.22
2.87
Fort Worth
0.07
3,306,419
7.39
6.60
Galveston
0.07
2,461,200
41.67
37.25
377.8
Garland
0.07
347,816
1.93
1.72
Grand Prairie
0.07
332,769
3.34
2.99
Grapevine
0.06
2,349,700
80.46
83.93
Haltom City
0.04
34,428
1.05
1.64
Harlingen
0.07
483,446
9.92
8.87
90.2
Houston
0.07
25,069,000
15.38
13.75
Huntsville
0.04
121,427
4.35
6.80
69.2
Hurst
0.07
18,542
.55
.49
Irving
0.05
4,771,449
30.78
38.52
Killeen
0.07
425,467
6.70
5.99
Kingsville
0.07
152,000
6.01
5.38
54.7
LaPorte
0.04
75,162
2.69
4.22
Laredo
0.07
1,770,078
14.40
12.88
League City
0.06
281,720
9.34
9.74
Lewisville
0.07
234,147
5.03
4.50
45.8
Longview
0.07
655,246
9.32
8.33
84.8
Lubbock
0.07
1,637,504
8.79
7.86
Lufkin
0.07
335,544
11.11
9.93
McAllen
0.07
1,965,673
23.40
20.92
Mesquite
0.07
271,174
2.67
2.39
Midland
0.07
663,113
7.41
6.63
Mission
0.04
16,234
.57
.89
Missouri City
----Nacogdoches
0.07
327,646
10.61
9.49
New Braunfels
0.07
671,079
24.55
21.95
North Richland Hills 0.07
156,782
3.42
3.05
31.1
Odessa
0.07
596,424
6.65
5.95
Pasadena
0.07
178,549
1.50
1.34
Pharr
0.07
106,908
3.25
2.90
Plano
0.07
851,256
6.61
5.91
Port Arthur
0.07
290,402
4.95
4.42
Richardson
0.07
1,170,853
15.37
13.74
Round Rock
0.07
143,709
1.92
1.72
San Angelo
0.07
614,293
7.27
6.50
San Antonio
0.07
21,323,974
22.78
20.37
San Marcos
0.07
399,526
13.90
12.43
Sherman
0.05
147,131
4.66
5.83
Temple
0.07
461,765
10.01
8.95
Texarkana
0.04
247,985
7.83
12.26
Texas City
0.07
104,398
2.56
2.29
Tyler
0.07
665,080
8.81
7.88
Victoria
0.07
472,561
8.58
7.67
Waco
0.07
972,785
9.39
8.40
Wichita Falls
0.07
750,211
7.79
6.97
62 city averages
0.06
1,804,000
9.84
9.41
std. deviations
0.02
4,756,600
11.92
12.16
Table 4. Total Tax Capacities, Total Tax Capacity Indices, Per Capita Income, and Per Capita Income
Indices for Sixty-Two Texas Cities With Populations Over 25,000, FY 1994
Total Tax
Total Tax
Per Capita
Per Capita
City
Capacity
Capacity Index Income
Income Index
Abilene
$251.44
74.7%
$11,857
90.2%
Amarillo
305.81
90.9
12,744
97.0
Arlington
358.38
106.5
16,239
123.6
127.6
92.8
-111.8
91.0
76.4
128.7
182.1
67.7
52.5
34.8
42.8
29.2
67.2
17.5
30.4
853.8
16.7
139.8
5.0
391.9
60.9
42.9
131.0
99.1
80.0
101.0
21.3
24.3
67.4
9.0
-96.5
223.3
60.5
13.6
29.5
60.1
45.0
139.8
17.5
66.1
207.2
126.4
59.3
91.1
124.7
23.3
80.2
78.0
85.4
70.9
96.0
124.0
Baytown
259.70
77.2
12,963
98.6
Beaumont
336.00
99.9
12,751
97.0
Bedford
323.61
96.2
19,847
151.0
Brownsville
205.51
61.1
6,284
47.8
Carrollton
539.61
160.4
19,065
145.0
Corpus Christi
280.53
83.4
11,755
89.4
Dallas
456.29
135.6
16,300
124.0
Deer Park
360.81
107.2
15,645
119.0
Denton
263.23
78.2
12,013
91.4
DeSoto
339.08
100.8
18,093
137.7
Duncanville
290.25
86.3
17,060
129.8
Edinburg
196.98
58.5
7,474
56.9
Euless
242.15
72.0
16,635
126.6
Fort Worth
325.32
96.7
13,162
100.1
Galveston
292.18
86.8
12,399
94.3
Garland
275.93
82.0
15,056
114.6
Grand Prairie
336.65
100.1
13,752
104.6
Grapevine
788.45
234.3
19,526
148.6
Haltom City
241.32
71.7
11,764
89.5
Harlingen
287.22
85.4
9,183
69.9
Houston
411.95
122.4
14,261
108.5
Huntsville
184.54
54.9
9,273
70.6
Hurst
385.81
114.7
16,621
126.5
Irving
580.50
172.5
16,424
125.0
Killeen
221.95
66.0
9,582
72.9
Kingsville
159.23
47.3
9,338
71.0
LaPorte
284.46
84.5
14,439
109.2
Laredo
278.63
82.8
6,981
53.1
League City
307.02
91.2
17,932
136.4
Lewisville
476.73
141.7
15,316
116.5
Longview
433.89
129.0
12,761
97.1
Lubbock
315.28
93.7
12,322
93.8
Lufkin
363.05
107.9
12,527
95.3
McAllen
416.58
123.8
9,814
74.7
Mesquite
349.24
103.8
14,115
107.4
Midland
317.57
94.4
16,201
123.3
Mission
197.03
58.6
6,887
52.4
Missouri City
284.65
84.6
18,764
142.8
Nacogdoches
261.64
77.8
9,478
72.1
New Braunfels
377.48
112.2
11,777
89.6
North Richland Hills 355.64
105.7
15,912
121.1
Odessa
241.95
71.9
11,588
88.2
Pasadena
230.00
68.4
12,402
94.4
Pharr
172.14
51.2
5,561
42.3
Plano
630.66
187.4
21,820
166.0
Port Arthur
171.74
51.0
9,706
73.8
Richardson
638.95
189.9
21,335
162.3
Round Rock
152.60
45.4
12,764
97.2
San Angelo
238.13
70.8
11,353
86.4
San Antonio
289.12
85.9
10,884
82.8
San Marcos
334.97
99.6
8,103
61.7
Sherman
414.41
123.2
12,929
98.4
Temple
355.37
105.6
12,914
98.3
Texarkana
391.89
116.5
11,931
90.8
Texas City
769.31
228.6
11,794
89.8
Tyler
417.15
124.0
13,400
102.0
Victoria
334.80
99.5
12,332
93.8
Waco
300.43
89.3
10,195
77.6
Wichita Falls
258.99
77.0
11,686
88.9
62 city averages
336.48
100.0
13,144
100.0
std. deviations
132.24
39.3
3,662
27.8
Table 5. Commercial/Industrial Property Per Capita and Exportation Indices for Sixty-Two Texas Cities
With Populations Over 25,000, FY 1994
Commercial/Industrial
Exportation
City
Property Per Capita
Index
Abilene
$ 9,549
- 15.1
Amarillo
10,300
- 6.1
Arlington
13,699
- 17.0
Baytown
13,712
- 21.4
Beaumont
12,621
2.9
Bedford
9,265
- 54.8
Brownsville
Carrollton
Corpus Christi
Dallas
Deer Park
Denton
DeSoto
Duncanville
Edinburg
Euless
Fort Worth
Galveston
Garland
Grand Prairie
Grapevine
Haltom City
Harlingen
Houston
Huntsville
Hurst
Irving
Killeen
Kingsville
LaPorte
Laredo
League City
Lewisville
Longview
Lubbock
Lufkin
McAllen
Mesquite
Midland
Mission
Missouri City
Nacogdoches
New Braunfels
North Richland Hills
Odessa
Pasadena
Pharr
Plano
Port Arthur
Richardson
Round Rock
San Angelo
San Antonio
San Marcos
Sherman
Temple
Texarkana
Texas City
Tyler
Victoria
Waco
Wichita Falls
62 city averages
std. deviations
7,557
28,624
13.3
15.3
9,888
20,886
27,373
- 6.1
11.6
- 11.8
10,869
11,728
8,085
7,165
7,328
17,396
9,793
- 13.2
- 36.9
- 43.5
1.7
- 54.6
- 3.5
- 7.5
11,007
17,756
55,565
10,539
11,202
- 32.6
- 4.6
85.8
- 17.8
15.5
12,890
5,656
13.9
- 15.7
12,424
30,031
5,680
4,342
- 11.8
47.6
- 6.9
- 23.7
17,898
11,460
9,559
15,821
23,277
- 24.6
29.7
- 45.2
25.2
31.9
13,681
17,684
15,806
11,637
10,460
6,006
5,549
11,503
13,436
11,209
- 0.1
12.6
49.2
- 3.6
- 28.9
6.2
- 58.2
5.7
22.6
- 15.4
8,845
5,283
6,238
22,912
8,561
26,071
5,038
9,080
11,535
13,002
20,837
19,121
14,780
81,538
17,566
13,318
14,879
10,475
14,790
11,775
- 16.3
- 26.0
8.9
21.4
- 22.8
27.6
- 51.8
- 15.6
3.1
37.9
24.8
7.4
25.7
138.9
22.0
5.7
11.7
- 11.9
0.0
32.7
ENDNOTES
1
A discussion of how the exportation indices were developed for the sixty-two Texas
cities is provided in the Appendix. Originally, sixty-eight Texas cities with populations
over 25,000 were selected for this study. The unavailability of data pertaining to the
appraised value of commercial and industrial property for six cities (Austin, Bryan,
College Station, Conroe, Del Rio, and El Paso) prevented their inclusion. Please see
tables provided in the Appendix for a list of the sixty-two cities included in the sample.
The appraised value of a city’s commercial and industrial properties is used as
an exogenous variable in a regression model to explain the city’s relative ability to
export its tax burden (see section titled “Empirical Findings”).
2
In some of the literature on tax incidence, statutory incidence is also referred to as tax
impact. See, for example, Ebel (1990: 287).
3
I speak of a business as a legal entity. The legal entity, however, cannot bear the
economic burden of a tax. The economic burden of a tax on business will be borne by
consumers in the form of higher prices, employees in the form of lower wages, or
owners of the business in the form of lower return on investment. Ultimately, people
bear the economic burden of paying taxes.
4
Figures 1(a) and 1(b) are based largely on the analysis of partial equilibrium models in
Rosen (1988: 269-273).
5
In-depth analysis of labor-market conditions is beyond the scope of this paper.
McClure (1967: 56) observes that “(o)ne prime determinant of the price effects of any
business tax....is the dominance of taxed firms in their respective markets. Any tax on
business is clearly likely to be shifted forward (to consumers) if all firms in a market pay
than if only a few do. In the latter case, the tax is likely to be absorbed by profits on
capital or shifted backward to the less mobile factors, land and labor.”
6
Again, an in-depth analysis of these issues are beyond the scope of this paper. The
exportation indices developed here are designed to be relative, rather than exact,
measures of the ability of the sixty-two cities in the sample to export taxes.
7
This hypothesis is discussed and tested in the section titled “Empirical Findings.”
8
The methodology used to develop the indices are discussed in detail in the Appendix.
This approach has been employed in at least one other tax exportation study; however,
the indices in that study were calculated for states rather than municipalities (see
Robert D. Ebel, A Fiscal Agenda for Nevada, 1990, pp. 137-142.)
Bland and Laosiriat found empirical support for the hypothesis that “as the businessowned portion of the property tax base increases, local governments have an
opportunity to export more of their tax burden (sic) to non-residents” (1995: 13, 17).
9
10
Cities that have much of their appraised property value in commercial and industrial
property likely have businesses that serve national or regional clienteles. If market
conditions allow property taxes to be shifted to consumers, then much of these cities’
property taxes would be exported to nonresidents.
11
Cities in the Rio Grande Valley have relatively high exportation indices because the
average income per capita in these cities is only 57% of the sixty-two city average
($13,144) while their average total tax capacity is 78% of the sixty-two city average
($336.48). In other words, while taxes collected by Rio Grande Valley cities are, on
average, about three-quarters of those collected by other Texas cities, average
incomes in these cities are a little over one-half of average incomes in other Texas
cities. This suggests that these cities collect more taxes than their residents have the
ability to pay. Consistent with the assumptions associated with the methodology used
to calculate the exportation indices (see the Appendix), the difference is likely exported
to non-residents. Thus, we would expect these cities to have higher autonomous
exportation indices than other Texas cities, regardless of the per capita values of their
respective commercial and property bases.
12
Bedroom communities are likely to have lower exportation indices for two reasons:
(a) the percentage of their property tax bases that is composed of residential properties
(non-exportable) is much higher than the average of the sixty-two city sample and (b)
residents of these communities typically work, shop, conduct their business, consume
entertainment and leisure services in municipal jurisdictions other than those they
reside in.
13
14
t-statistics for 3 and4 are significant at the 95% confidence level.
The model developed in this study is not intended to be a comprehensive model to
measure all financial costs and benefits that might be associated with such a strategy,
much less social costs and benefits.