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Working P a ~ e r8909

PUBLIC INFRASTRUCTURE AND REGIONAL
ECONOMIC DEVELOPMENT:..'
A SIMULTANEOUS EQUATIONS APPROACH

by Kevin T. Duffy-Deno and Randall W. Eberts

Kevin Duffy-Deno is assistant professor of
economics at Southeastern Massachusetts
University, and Randall W. Eberts is
assistant vice president and'economist at the
Federal Reserve Bank of Cleveland. The
authors wish to thank Brian Cromwell and
Timothy Gronberg for very helpful suggestions
and John Swinton for research assistance.
Estimation of the public capital stock series
was funded in parc by the National Science
Foundation under Grant *SES-8414262-01, to
Eberts, Fogarty, and Garofalo.
Working papers of the Federal Reserve Bank of
Cleveland are preliminary materials
circulated to stimulate discussion and
critical comment. The views stated herein
are those of the authors and not necessarily
those of the Federal Reserve Bank of
Cleveland or of the Board of Governors of the
Federal Reserve System.
August 1989

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I.

Introduction
Interest in the effect of public capital on regional economic development

has increased in recent years in light of numerous reports of the fragile
state of the nation's pablic infrastructure. Estimates of the shortfall
between investment needed to provide "adequate" public infrastructure and
available revenues to fund these projects range from $17.4 billion to $71.7
billion annually over the next several years.

A rnajor concern about the

inability to meet public infrastructure needs is the possible adverse effect
on economic growth.
The importance of public capital for regional growth stems from its effect
on the production and location decisions of private industry.

Following

Meade's (1952) classification of public inputs, public capital, such as
highways, bridges, sewer systems, and water treatment facilities, can be
viewed as inputs in the production process of private industry that contribute
independently to output.

However, unlike private inputs, which are purchased

in a market on a per-unit basis, public capital is provided by the government
sector and is financed to a large extent through taxes.

Since these tax

payments are not necessarily related to the quantity of public capital used by
private industry, public capital is essentially an unpaid input. Moreover,
assuming that firms have no direct control over how much public capital is
supplied to them, public capital is an exogenous input from the firm's
perspective.
Even though public capital is exogenous to the firm, its allocation is to
a large extent endogenous to the local economy, since the level of public

outlays is determined through the political process.

Therefore, assuming a

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median voter model of local collective decision-making, one can posit a
simultaneous relationship between regional income growth and local public
infrastructure investment.
Studies have looked at each side of the relationship between regional
growth, typically measured by personal income, and public investment, but have
not combined the two.

The effect of public infrastructure on regional growth

has received relatively little attention, primarily because of the lack of
reliable measures of local public capital stock.

Regional growth studies that

have considered the effects of public investment typically use capital
expenditures as a proxy for capital stock, instead of estimating capital stock
directly.

*

For example, Helms (1985) and Garcia-Mila and McGuire (1987) find

a positive and statistically significant relationship between highway capital
expenditures and state personal income.
The literature estimating the demand for local public expenditures is much
more extensive, tracing back to the seminal work by Borcherding and Deacon

(1972).

They found, as do more recent studies, large and statistically

significant income elasticities for highway and water-sewer expenditures.
The primary purpose of this paper is to estimate the effect of public
capital stock on regional income.

Our study differs from the few studies that

have examined the relationship between measures of public infrastructure and
personal income in several ways.

First, we construct a model that integrates

three dimensions of the relationship between public infrastructure investment
and regional income: public infrastructure as an input into the production
process, public investment as a construction or "public works" activity, and

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the determination of the level of public infrastructure as a consumption good
in the median household's utility function.
Second, we attempt to improve the measure of public capital stock by
constructing estimates 'based on the perpetual inventory technique for a sample
of metropolitan areas.

This approach provides a much better measure of the

quantity and quality of local public infrastructure than can be obtained by
simply using current capital outlays or adding up a short series of past
expenditures.
Third, in order to avoid possible simultaneity bias arising from ordinary
least squares (OLS) estimation of the personal income and public investment
equations, the relationship between metropolitan personal income and local
public investment is estimated using two-stage least squares (2SLS).

In

addition, the use of 2SLS reduces the possible bias due to measurement errors
of various key variables, such as public capital stock estimates.
For a sample of 28 standard metropolitan statistical areas (SMSAs) during
the first half of the 1980s, we find that both public investment and public
capital stock have a positive and statistically significant effect on per
capita personal income.

We find that the offsetting effects of simultaneity

and errors-in-variables biases cause the OLS and 2SLS estimates to differ
significantly for public investment but not for public capital stock.

11.

Model
This paper attempts to estimate the effect of public investment, both

current outlays and public capital stock, on personal income within

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metropolitan areas.

However, the linkage between public investment and

personal income works in two directions. Public investment influences
personal income through its effect on the marginal product of labor.

Personal

income in part determines the' level of public investment, as described by the
median voter model.

For purposes of our simple model, we assume that the

source of personal income is wage and salary disbursement. Under this
assumption, public investment can affect personal income through two channels:
wages and employment.

Wages
By considering a neoclassical production function, wages can be equated
with the value of the marginal product of labor,

where wt is the wage level and pt is the price level.

Labor (L), private

capital stock (K), energy (E), and public capital stock (G) contribute
positively to production. We assume that the rents gained by the firm in the
short run through the contribution to output of public capital stock, an
unpaid factor, are returned to workers through higher wages (see Negishi,
1973).
Several studies have found that public capital has a significant effect on
production decisions at the regional level. Eberts (1986) estimates a
production function with public capital stock, private capital stock, and

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labor as inputs for manufacturing within 38 metropolitan areas. He finds that
the marginal product of public capital is positive and statistically
significant. Deno (1988), estimating a profit function, also finds a positive
relationship between public capital and manufacturing output.
The effect of public capital stock on wages may be mitigated to some
extent if either of two cases occurs. First, if labor and public capital are
substitutes and labor supply is upward sloping, then an increase in public
capital stock could decrease wages.'

Second, some rents may accrue to factors

other than labor, such as capital or entrepreneurship. However, wages could
still be positively affected by an increase in public capital in the long run.
If rents accrue to capital or entrepreneurship, then the higher returns due to
the unpaid public capital input would attract additional firms into the area,
increasing the demand for both labor and private capital.

Additional firms

move into the region until the rents are dissipated and capital earns a
competitive rate of return.4

Em~lovment
Local labor market employment is determined by equating labor supply with
demand, assuming the labor market clears each period. We also take the
long-run view that private capital and energy consumption vary.

Consequently,

we enter the prices of these factors, rather than the levels, into the wage
equation (equation

[I]):

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where rt is the private capital price and et is the energy price.
capital stock is considered to be quasi-fixed.

Public

The determination of the level

of public investment wi'li be =onsidered in the following section.
Rearranging equation (1) yields the demand for labor (n d t):

where e denotes technical production parameters.
The local labor supply depends on the real net wage (w/p) and the size of
the local population (S).

Higher wages, resulting from a larger-than-average

public capital stock, may attract additional workers into the local labor
market, until the rents accrued from the public capital stock are dissipated
and the wages return to some equilibrium level across regions.

Public capital

stock also enters the labor supply function through the household's utility
func~ion. Although the labor market clears at the current wage, unemployment

(U) may exist due to frictional aspects of the job-search process and
intertemporal labor supply substitution.

We therefore add the unemployment

rate (Ut) to the labor supply equacion (nst):

where

T

represents household preferences.

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Equating the real wages in the demand and supply equations yields the
long-run market-clearing employment level (n*t):

Combining the wage (equation

[l']) and employment (equation

[ 4 ] ) equations

yields a real personal income equation:

which is expressed in per capita terms to be consistent with the median voter
model.5

Determination of Public Investment
Determination of the level of public investment follows the conventional
median-voter model with the additional feature that public goods enter not
only the utility function as a consumption good, but also the production
function as an unpaid input.

Consequently, public capital affects the

household directly through the utility function and indirectly through its
effect on the household's income.
Consider a representative consumer who lives and works in an urban labor
market and who derives utility from consuming a private consumption good, X,
and public capital stock, G.

We consider public capital stock to be a rival

good, in the sense that local public services, such as transportation and

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highways, and water treatment and distribution systems, are subject to
congestion. Therefore, we express public capital in per capita terms, under
the assumption that individual household utility and firm production depend on
the amount of public capital services each receives.6
In each time period t, an individual chooses the consumption good (X) and
the per capita public capital stock (G/S) by maximizing utility subject to a
budget constraint:

max U(Xt,G/St)

s.t. Yt-ptXt

where Yt is the individual's income,
price of X.

u

+

oYt,

is the local tax rate, and p t i s the

Public capital stock, G t , is supplied by a single local

government, which encompasses each metropolitan area.

Although total public

capital stock affects production and utility, only a portion of it is
allocated each year.

Therefore, the decision variable of the median voter is

gross public investment. The amount of capital stock present in year t
depends on the gross investment in year t (gt), the amount of capital stock in
the previous year (Gt-I), and the rate of depreciation and discard (6) of the
capital stock:

Substituting equation (7) for Gt in the utility function in equation (6),
recognizing that public investment is funded by taxing a portion of the

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household's income (oYt-gt), and then solving the median voter's maximization
problem yields the demand for gross public investment:7

where

7

denotes household tastes.

Collecting the real personal income equation (equation [5]), the public
investment equation (equation [ 8 ] ) and the investment relationship (equation
[ 7 ] ) yields the following system of equations:

Thus, from these equations, one can recognize the simultaneous relationship
between public capital and personal income.

Estimatine Eauations
As stated earlier, the primary purpose of this study is to estimate the
effect of public capital stock on regional income. Substituting equation (7)

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into equation (5) and linearizing both functions yields the following two
equations, which are estimated simultaneously:

where

Zit and

Z2t are vectors of exogenous variables described in the next

section.
Previous studies have estimated the effect of public capital on personal
income using single-equation OLS estimation.

It is obvious from equations (9)

and'(10) that OLS estimates may be biased upward if the effect of income on
pub1,ic capital stock investment (bl) is significant and positive.

On the

other hand, measurement error in public capital stock estimates could bias the
estimates downward.8 Therefore, the net direction of the bias is ambiguous
and depends on the relative magnitudes of the two biases.
Since both public capital expenditures and public capital stock appear in
equation ( 9 ) , this framework also allows us to compare the separate effects of
expenditures and stock on personal income.

Expenditures affect personal

income as construction dollars are spent in the local economy (al).

Capital

stock affects personal income as an input in the production process, which
enhances the marginal product of labor (a2).

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111. Data
Equations (9) and (10)

are estimated using annual data from 28 SMSAs for

the years 1980 through 1984.

The sample of SMSAs is constrained primarily by

the availability of public capital stock estimates.
both an economic recession and an expansion.

The time span includes

A list of the SMSAs used in this

study is presented in appendix A , and a summary of data sources is provided in
appendix B.

Personal Income Eauation
Personal income for each SMSA was obtained from the Bureau of Economic
Analysis and measured in per capita terms. The income series is deflated
using the national Consumer Price Index (CPI).

Time dummy variables are also

included in the equation, since nationwide price shocks may occur to real
personal income that are not fully reflected in the CPI. Prices also vary
among regions.

Although CPIs are available for selected SMSAs, they are not

available for all of the metropolitan areas for which public capital stock
estimates are available. Using the available CPIs would reduce the sample to
a prohibitively small number of observations. Instead, we entered into the
personal income equation the median house value for each of the 28
metropolitan areas.

Since most of the regional variation in prices comes from

housing costs, we considered it to be a reasonable measure of regional price
differences.9
Public capital stock (Gt), expressed in per capita terms, is defined as
the dollar value of the total stock of public capital in the SMSA.

Public

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capital includes: (a) sanitary and storm sewers and sewage disposal
facilities, (b)

roadways, sidewalks, bridges and tunnels, (c)

distribution systems, (d) public hospitals, and (e)
such as airports and ports.

water supply and

public service enterprises

These estimates are constructed using the

perpetual inventory method, which adjusts accumulated gross investment for
retirement and depreciation.

This method is based on the assumption that

capital stock at any given time is a function of past investments in public
structures and equipment.

Over time, vintages of capital lose efficiency, and

a portion are discarded each year.10

The annual capital outlay series, used

to estimate stock and to measure gross investment (gt), was obtained from the
Government Finance Series compiled by the Census Bureau.
The remaining variables in the personal income equation fall within two
categories.
process.

The first group contains variables related to the production

Firms use various types of energy in the production process (e.g.,

electricity, natural gas).

Following Carlton (1983), we use the price of

electricity for the 300 KWH to 120,000 KWH industrial classification as a
proxy for energy costs.

In particular, we use the rate in the highest

continuously listed rate schedule of the largest city in the SMSA, as listed
in the rate schedules found in Tvpical Electric Bills.11
The remaining independent variables include factors that may affect the
private sector demand for and/or supply of labor.

Many factors affect both

firms and households, which makes a priori interpretations of the signs of the
coefficients difficult.

Several measures of business climate are used.

Presumably, firms will be less attracted to SMSAs 1ocate.d in states with a

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relatively high percentage of union members (UNION), because of a perception
by managers of less flexibility in personnel matters and higher associated
labor costs.

Thus, regions with high union representation may have lower

personal income because of the negative effect of unions on labor demand.

On

the other hand, the wage component of personal income may be higher in highly
unionized regions because of the union-wage premium.
Another business-climate factor is the Right-To-Work Law (RTW), which may
provide potential entrants with information on the business climate of the
region and on future wage levels while reducing the probability of union
involvement.

Thus, firms may be attracted to SMSAs that are located in states

with right-to-work laws.

This variable may also affect the migration

decisions of workers, but in a direction that reinforces the effect on labor
demand.

Newman (1983) finds that UNION and RTW have a statistically

significant effect on the growth of state manufacturing employment. The
growth rate is higher in states with right-to-work laws and lower in states
with a high percentage of unionized workers.
High tax rates may deter firms and households from entering a region,
given equal levels and quality of public services. Taxes are measured as the
metropolitan area's tax liabiliry (STAX) of the median-income family.

STAX is

the ratio of state tax revenue to tax capacity as derived by the Advisory
Commission on Intergovernmental Relations (ACIR).

Since personal and firm tax

liabilities are likely to be highly correlated, STAX may also capture the
effect of the overall tax structure.

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Firms' location decisions may also be influenced by the availability of
labor.

The population of the SMSA is used to measure the size of the labor

pool. The SMSA unemployment rate measures the tightness of the labor force

(UNEMP).

Wages and thus personal income may also be higher in regions with

higher-than-average concentration of manufacturing employment, since
manufacturing wages are typically higher than wages in other sectors. The
percentage of manufacturing workers (RMFG) accounts for this effect.

Also,

human capital has a large influence on wages and thus personal income.

A

variable measuring the average years of education of workers in each SMSA is
included to reflect the level of human capital.
Workers and firms may find regions with favorable climates more attractive
and migrate there. The average number of days with temperatures below
freezing (FRZDAY) and above 90 degrees (T90DAY) per year are used to measure
climatic effects on firm location. However, the sign of the coefficient is
ambiguous since demand and supply effects are commingled.
Finally, three regional dummy variables are included to account for any
unspecified regional factors that may affect per capita personal income
(SOUTH, WEST, and MIDWEST); the Northeastern region is omitted.

Public Investment Equation
The dependent variable for equation (10) is real public gross investment
per capita estimated for each SMSA. The explanatory variable of primary

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interest is real personal income per capita (LYPN).

Intergovernmental

revenue per capita is also included to account for the income effect of state
and federal revenue to local governments (LFINT).

The median income family's

tax liability (LSTAX) is included to measure local tax effort.

Since property

taxes constitute a large source of local government revenue, property tax
rates (PROPRATE) are also entered into the public investment equation.
The remaining explanatory variables reflect differences in the preferences
of median voters among SMSAs. These variables include median house value
(LMEDVAL), percentage of owner-occupied housing (OWNOC), and percentage of the
population below the poverty level (POVERTY).

Median house value is included

to capture variations in metropolitan price levels.

Regional dummy variables

and time dummy variables are also included.

IV.

Estimation
Each equation is estimated using pooled data for 28 SMSAs from 1980

through 1984.

Following Plaut and Pluta (1983), all coefficients except the

intercept are constrained to be equal over the time period. The variables are
entered in log-log form, except when the variables are expressed as
percentages.12 OLS and 2SLS estimates of the personal income and public
investment equations are shown in table 1.

Personal Income Eauation
Results in table 1 show that both public investment and public capital
stock have positive and statistically significant effects on real per capita

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personal income.

Hausman's (1978)

test of significance shows that the OLS and

2SLS estimates are not statistically different for public capital stock but

they are for public investment .I3 Consequently, the OLS bias and the
errors-in-variable bias are either each negligible or they are offsetting in
the former case.
The coefficients of public investment and public capital stock reveal two
separate effects of public infrastructure on personal income.

The effect of

public investment on personal income results primarily from the construction
of public capital stock, either replacement or net additions. Public
investment increases personal income by increasing employment and wages in the
construction industry. The coefficient may also account for the multiplier
effect throughout other sectors of the local economy, if the response is quick
enough to occur within a year.

A 10 percent increase in public outlays

increases personal income per capita by 0.37 percent using OLS estimates and
1.1 percent using 2SLS.

The coefficient-on public investment lagged one year

was insignificant (not shown), suggesting that most construction projects last
less than a year and the multiplier effect dampens very quickly.
The public capital stock coefficient reflects the effect of public
investment as a production input and as a household's consumption good, since
the "public works" aspect of public investment appears to last less than a
year and the public capital stock variable is lagged one period. A 10 percent
increase in public capital stock is associated with a 0 . 9 4 percent increase in
per capita personal income using OLS and a 0.81 percent increase using 2SLS.
The OLS point estimates, which can be read as elasticities, suggest that the

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effect of public capital as an input has nearly twice the effect on personal
income as does public capital as a construction activity.

When 2SLS is used,

the effect of public capital stock on personal income is much smaller relative
to the effect of gross investment.

However, one can conclude that the

contribution of public capital stock to economic growth clearly outlasts its
initial construction phase.
The remaining variables, which were statistically significant at the 95
percent level using either OLS or 2SLS, have the expected signs.

High tax

liability is associated with low per capita personal income, presumably due to
its deterrent effect on firm entry, which lowers labor demand.

Areas with

high unemployment rates, indicating a slack labor market, have low per capita
personal income, primarily through the depressing effect on wages. The
population of the metropolitan area is positively correlated with real per
capita personal income.

One explanation of this relationship could be the

beneficial effects of agglomeration economies on firm location. The
proportion of manufacturing employment in a metropolitan area is also
positively correlated with per capita personal income, presumably due to wage
premiums enjoyed by manufacturing workers over comparable workers in other
industries.

Education also positively affects earnings, as evidenced by the

positive coefficient on average years of education.

Public Investment E~uation
Although this equation is included only to control for possible
simultaneity between per capita income and public investmen,t,some of the

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results are interesting to highlight.

For instance, as shown in table 2, per

-.

..=I:

capita real personal income has a positive and statistically significant
contemporaneous effect on local public investment.

The income elasticity

estimate differs considerably bepending on the estimation technique.
OLS, the estimate is close to unity;

Using

using 2SLS, the estimate is close to 2.

The second elasticity estimate is still consistent with results found by
Borcherding and Deacon (1972) for some forms of infrastructure.

We also find

that federal and state grants have a positive effect on public investment
The 2SLS estimates reveal that a 10 percent increase in intergovernmental
revenues per capita raises public investment expenditures by 0.25 percent.
The OLS estimate is virtually identical.

Both OLS and 2SLS estimates are

statistically significant at the 10 percent level.
The other variables are included to account for differences in preferences
across metropolitan areas.

For instance, areas with higher-than-average

poverty rates spend a lower-than-average amount on public investment,
presumably using their tax dollars to fund social programs instead of economic
development.

The negative relationship between the percentage of

owner-occupied housing and public investment may also reflect preferences for
other local government programs. However, one can only speculate on the
tradeoff within the local government budget, without expanding the system of
equations to include other government expenditures.

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V.

Summarv and Concluding Remarks
The purpose of this study is to estimate the effect of public

infrastructure on regional economic development, as measured by per capita
personal income.

The paper makes two contributions. First, we use public

capital stock estimates instead of simply using expenditures. Second, we
construct a simple model of both the effects of local public infrastructure on
personal income and the effect of personal income on the allocation of local
public outlays. The resulting system of equations highlights the potential
single-equation estimation bias if public investment is considered exogenous,
as is the case with other studies.
Results derived from annual data for 28 metropolitan areas from 1980
through 1984 reveal that public capital stock has positive and statistically
significant effects on per capita personal income. The effects come through
two channels. The first is through the actual construction of the public
capital stock.

The second effect comes through public capital stock as an

unpaid factor in the production process and a consumption good of households.
This second effect is twice as large as the first effect using OLS, but the
relative magnitudes of the two effects are roughly reversed using 2SLS.
Although single-equation estimation bias is a potential problem when
estimating the effect of public capital stock on personal income, it is not
possible to determine the magnitude of the problem because of the potential
errors - in-variables bias.
Recent studies have concluded that the nation's public infrastructure is
in serious disrepair. These findings take on added importance when considered

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together with the findings of this study.

Decaying public capital appears to

be one factor that can retard regional economic development, as measured by
per capita personal income. Our results show that the positive effect of
public capital on a region's economy comes from more than simply a surge in
construction activity. Public capital stock is shown to be an important input
into the regional production process, which has long-run consequences for
enhancing a region's productivity, and thus its competitive advantage.
Therefore, well-maintained public infrastructure should be an important
component of any policy package designed to promote regional economic
development.

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FOOTNOTES

1. The study by the Associated General Contractors of America estimates the
largest gap, while the Congressional Budget Office comes in with the lowest
estimate.

2. One notable exception is the series of studies done by Mera (1975)
considering the effect of public infrastructure on regional development in
both the United States and Japan. Mera develops a capital stock measure for
the nine census regions and four prefectures in Japan. In the U.S. study,
Mera concentrated primarily on the effect of infrastructure on manufacturing.
Costa, Ellson, and Martin (1987) also construct capital stock measures for
states and use these to examine effects on manufacturing.
3. Estimates of the relationship between public capital stock and labor
depend on whether or not output is held constant. Eberts (1986), estimating a
production function, and Dalenberg (1987), estimating a cost function, find
public capital and labor to be weak conditional substitutes. On the other
hand, Deno (1988), estimating a profit function, finds public capital stock
and labor to be unconditional complements. Costa, Ellson, and Martin (1987)
construct estimates of public capital stock for the state level. Using a
three-input translog production function, they find that public capital and
labor are conditional complements.
4. Eberts (1989) shows that local public capital stock has a positive and
significant effect on the openings of firms in metropolitan areas. Other
studies, including Charney (1983) and Bartik (1985), which use public outlays
rather than public capital stock, find similar results.

5. For convenience, we assume that the income distribution is such that
median income equals mean income.

6. One could also follow the approach used by Borcherding and Deacon (1972)
to specify and estimate a congestion parameter, such that GI-G/N~. Estimating
the congestion parameter, a, would be an interesting extension. However, we
feel that the assumption that a=l will not alter the main thrust of the paper.
7. By reformulating the maximization problem in terms of output, one can
derive the standard result that the sum of the marginal rates of substitution
equals the marginal rate of transformation, but in this case the latter is
adjusted for public capital stock's contribution to output. Pestieau (1976)
provides the optimality conditions within an median voter framework for
allocating public inputs, when public goods enter the production function but
not the utility function. Furthermore, as shown by Atkinson and Stiglitz
(1980), the condition for the existence of an interior solution when the

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number of workers varies depends upon the elasticities of consumption and
production. We assume that the elasticities are appropriate to achieve an
interior solution.
8. At least two possible sources of errors in variables are pertinent.
First, the capital stock estimates may not include all the public capital
stock in place in each SMSA. .We tried to include public outlays from all
levels of government that were spent in the SMSA. Nonetheless, some sources
could have been missed. Second, our assumptions about depreciation and
discard rates could introduce some bias.

9. It could be argued that house values are also endogenous, since movement
of firms into the area in order to capture the rents from the public capital
stock could bid up land prices. We abstract from this possibility at this
time.
10. Faucett (1977) discusses the perpetual inventory method in detail.
Construction of the public capital stock estimates is discussed by Eberts,
Dalenberg, and Park (1986).
11. Property tax rates were also included in the equation to capture their
effect on the price of private capital. However, the estimates were
statistically insignificant and omitted from the equation so they might be
used in the public investment equation to help identify the personal income
equation.
12. The log-log form appears to fit the data better than other functional
forms. Moreover, this functional form reduces the likelihood of
heteroscedasticity (Theil, 1971). The procedure described in Krnenta (1986)
was used to correct for possible heteroscedasticity and autocorrelation.
Estimates using this procedure were very similar to the estimates using OLS.
Unfortunately, a similar correction procedure is not available for 2SLS, so
the reported estimates do not correct for heteroscedasticity and
autocorrelation. We used Hausman and Taylor's (1981) methodology to test
whether the system of equations is properly identified. We found that the
exogenous variables excluded from the personal income equation and entered in
the public investment equation were not correlated with the 2SLS residuals of
the personal income equation, which satisfied their test.
22SLS-02OLS) - 1
13. Hausman (1978) shows that if (D2SLS-BOLS)(~
(A2SLS-BOLS) is
greater than chi-square with one degree of freedom, then one can reject the
hypothesis of no statistically significant specification bias. For public
investment, the test statistic is 7.46, which is greater than the 95 percent
chi-square value of 3.84. For public capital stock, the test statistic is
1.17, which is less than 3.84. Thus, we can reject the null hypothesis of no
significant bias for the public investment estimate, but we cannot reject the
hypothesis of no bias for public capital stock.

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REFERENCES
Associated General Contractors of America, America's Infrastructure: A
Plan to Rebuild, Washington, D.C., 1983.
Atkinson, Anthony B.,.and Joseph E. Stiglitz, Lectures on Public Economics,
New York: McGraw Hill, 1980.
Bartik, T. J., "Business Decisions in the United States: Estimates of
the Effects of Unionization, Taxes, and Other Characteristics of
States," Journal of Business and Economic Statistics, 3 (1985), 14-22.
Borcherding, T. E., and R. T. Deacon, "The Demand for the Services of NonFederal Governments," American Economic Review, 62, (1972), 842-853.
Carlton, D. W., "The Location and Employment Choices of New Firms: An
Econometric Model with Discrete and Continuous Variables," Review of
Economics and Statistics, 65 (1983), 440-449.
Charney, A. H., "Intraurban Manufacturing Location Decisions and Local Tax
Differentials," Journal of Urban Economics, 14 (1983). 184-205.
Congressional Budget Office, Public Works ~nirastructure
: Policy
Considerations for the 19801s,April 1983.
Costa, J. da Silva, R. W. Ellson and R. C. Martin, "Public Capital,
Regional Output, and Development: Some Empirical Evidence," Journal of
Regional Science, 27 (1987), 419-437.
Dalenberg, D., "Estimates of Elasticities of Substitution Between Public
and Private Inputs in the Manufacturing Sector of Metropolitan Areas,"
Ph.D. dissertation, University of Oregon, 1987.
Deno, K. T., "The Effect of Public Capital on U.S. Manufacturing Activity:
1970 to 1978," Southern Economic Journal, 53 (1988), 400-411.
Eberts, R. W., "Estimating the Contribution of Urban Public Infrastructure
to Regional Growth," Federal Reserve Bank of Cleveland Working Paper
8610, 1986.
, "Some Empirical Evidence on the Linkage between Public
Infrastructure and Local Economic Development," in Henry W. Herzog, Jr.
and Alan M. Schottmann (eds.) Industrv Location and Public Policy,
University of Tennessee Press, forthcoming.
, D. Dalenberg, and C. S. Park, "Public Infrastructure Data
Development for NSF," mimeo, University of Oregon, 1986.

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Faucett, J., "Capital Stock Estimates for Input-Output Industries: Methods
and Data," U. S. Department of Labor, Bulletin 2034, 1977.
Garcia-Mila, Teresa, and Therese J. McGuire, "The Contribution of Publicly
Provided Inputs to States' Economies," Working Paper No. 292,
Department of Economics, State University of New York at Stony Brook,
July 1987.
Hausman, J. "Specification Tests in Econometrics," Econometrica, 46
(1978), 1251-1272.
Hausman, J., and W. Taylor, "A Generalized Specification Test," Economic
Letters, 8 (1981), 239-245.
Helms, L. J., "The Effect of State and Local Taxes on Economic Growth: A
Time Series-Cross Section Approach," Review of Economics and
Statistics, 67 (1985), 574-582.
Kmenta, Jan, Elements of Econometrics, Second Edition, New York: MacMillan,
1986.
Meade, J . E., "External Economies and Diseconomies in a Competitive
Situation," Economic Journal, 62 (1952), 54-67.
Mera, K., Income Distribution and Regional Develo~ment,Tokyo:
of Tokyo Press, 1975.

University

Negishi, Takashi, "The Excess of Public Expenditures on Industries,"
Journal of Public Economics, 2 (1973), 231-240.
Newman, R. J . , "Industry Migration and Growth in the South," Review of
Economics and Statistics, 65 (1983), 76-86.
Pestieau, Pierre, "Public Intermediate Goods and Majority Voting," Public
Finance, 31 (1976), 209-217.
Plaut, T. R. and J. E. Pluta, "Business Climate, Taxes and Expenditures,
and State Industrial Growth in the United States," Southern Economic
Journal, 49 (1983), 99-119.
Theil, H . , Principles of Econometrics, John Wiley and Sons, New York, 1971.

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Table 1: OLS and 2SLS Estimates of Personal Income Equation
Variables

MEAN

LCAPINV: log(pub1ic investment
per capita).

4.06

LCAPTOT: log(pub1ic capital stock
per capita; lagged)

7.63

RMFG :
UNION:

LPE :
LSTAX:
LFRZDAY:

percentage of workers
unionized

-22

4.62
3.96

LT90DAY: log(number of above 90
degree days)

2.59

.081*
.031)

.480*
.118)

(

.I90
.179)

(

.I70
.190)

(

.003
.016)

(

.010
.018)

- .277*

(

.508*
.126)

(

.042)

- .274*
( .044)

(

.017*
.008)

( .009)

- .013
( .012)

- .003
( .013)

7.63

.008

.044*
.009)

(

.033*
.011)

log(average years of
education)
log(median house value)

2.56

1.026*
( .338)

(

.953*
.360)

10.92
(

RTW :

-1 if right-to-work
state

SOUTH :

-1 if SMSA in South

.21

-1 if SMSA in West

.25

.255*
.032)

- .017
(

WEST :

(

(

(

LMEDVAL:

.113*
.034)

unemployment rate

LSMSAPOP: log(SMSA population)
LYEANED:

(

.094*

7.51

log(number of freezing
days)

UNLYP:

.037*

( .029)

.21

log(state tax liability)

2SLS

( .019)

ratio of manufacturing
to total employment

log(e1ectricity price)

OLS

.030)

.256*
( .034)

- .051
(

.034)

- .099*

- .123*

.025)

( .027)

(

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MIDWEST:

-1 if SMSA in Midwest

.36

Y81:

-1 if year-1981

-20

Y82 :

-1 if year=19'82

.20

Y83:

-1 if year-1983

.20

Y84 :

-1 if year91984

.20

Intercept
Adjusted R'

.82

.81

Note: Dependent variable is the log of real per capita personal income,
deflated by CPI. Standard errors are in parentheses. Asterisk (*) denotes
statistical significance at the 95 percent confidence level. The omitted
regional dummy variable is the Northeast, and the omitted time variable is
1980. See text for data sources.

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Table 2: OLS and 2SLS Estimates of the Public Investment Equation
Variables

MEAN

LYPN:

log(rea1 per capita
personal income)

2.40

LFINT:

log(intergovernmenta1
revenue )

6.24

LMEDVAL: log(median house value)

OLS
1.197*
( .300)

- .727*

LSTAX:

log(state tax liability)

(

.008
.006)

.084
.169)

(

.I31
.174)

.007

(

60.92

POVERTY: percentage below poverty

8.34

SOUTH: -1 if SMSA in South
WEST:

- .962*

( .005)

4.62

OWNOC: percentage owner occupied
housing

.248
.131)
.176)

3.45

.157)

(

(

(

PROPRATE: property tax rate

1.976*.
( .387)

.236
( .128)

10.92

2SLS

- .029*
(

.004)

- .117*

- .027*
(

.004)

- .099*

(

.015)

(

.017)

(

.643*
.085)

(

.687*
.088)

(

.205*
.077)

.21

-1 if SMSA in West

MIDWEST: -1 if SMSA in Midwest

.36

.I25
( .071)

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intercept

Adjusted R~

.59

.59

Note: Dependent variab1.e.i~the log of real gross public investment per
capita. Standard errors are 'in parentheses. Asterisk (*) denotes statistical
significance at the 95 percent confidence level. The omitted regional dummy
variable is the Northeast, and the omitted time variable is 1980. See text
for data sources.

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APPENDIX A:

List of SMSAs

EAST REGION

SOUTH REGION

Buffalo, NY
New York, NY
Newark, NJ
Philadelphia, PA
Pittsburgh, PA

Atlanta, GA
Birmingham, AL
Baltimore, MD
Dallas, TX
Houston, TX
New Orleans, LA

MIDWEST REGION
Akron, OH
Chicago. IL
Cincinnati, OH
Columbus, OH
Cleveland, OH
Detroit, MI
Indianapolis, IN
Kansas City, MO
Milwaukee, WI
Minneapolis, MN

WEST REGION
Denver, CO
Los Angeles, CA
Portland, OR
San Diego, CA
San Francisco, CA
Seattle, WA

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APPENDIX B:

Variable Sources

LYPN: LOG(PER CAPITA PERSONAL INCOME), U.S. Department of Commerce, Bureau of
Economic Analysis, "Survey of Current Business," advance
tables and unpublished material.
LFINT: LOG(REAL INTERGOVERNEMTAL REVENUE), U.S. Department of Commerce, Bureau
of Census, Survev of Government Finance and Census of
Government Finance, books and tape files.
LMEDVAL: LOG(MED1AN HOUSE VALUE), U.S. Department of Commerce, Bureau of the
Census, County and City Data Book, various years.
LSTAX: LOG(STATE TAX LIABILITY), Tax revenue divided by tax capacity, Advisory
Commission on Intergovernmental Relations, Measurin~
State Fiscal Cavacitv, 1987.
OWNOC: PERCENT OWNER OCCUPIED HOUSING, U.S. Department of Commerce, Bureau of
the Census, County and Citv Data Book, various years.
LMEANED: LOG(AVERAGE EDUCATIONAL ATTAINMENT), U.S. Department of Commerce,
Bureau of the Census, Current Population Survey tapes
LCAPINV: LOG(PUBL1C REAL GROSS INVESTMENT PER CAPITA), Unpublished data series
derived from U.S. Department of Commerce, Bureau of the
Census, Current Population Reports, various years.
LCAPTOT: LOG(PUBL1C CAPITAL STOCK PER CAPITA; LAGGED), Unpublished data
series, see text.
RMFG: RATIO OF MANUFACTURING TO TOTAL EMPLOYMENT, U.S. Department of Labor,
Bureau of Labor Statistics, Emplovment and Earnings.
UNION: PERCENT OF WORKERS UNIONIZED, U.S. Department of Commerce, Bureau of
the Census, Current Population Survey tapes.
LPE: LOG(ELECTRICIT3' PRICES), U.S. Department of Energy, Tvpical Electric
Bills.
UNEMP: UNEMPLOYMENT RATE, U.S. Department of Commerce, Bureau of Labor
Statistics, COSTAT and Local Area Unemployment Statistics
tapes.
LTFRZDAY and LTgODAY, average number of days with below freezing
temperatures and with temperatures above 90 degrees,
Boyer, Richard and David Savageau, Places Rated Almanac,
Rand McNally, 1985.

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RTW: RIGHT-TO-WORK STATE (-I), U.S. Department of Commerce, Bureau of the
Census, Statistical Abstract, various years.
POVERTY

Percentage of population below the poverty level, 1980,
Bureau of the Census

LSMSAPOP: Log(SMSA POPUI;ATION), Bureau of the Census, various years.
PROPRATE:

Property tax rate, computed by dividing total property
tax revenue by true assessed value (assessed value times
the assessment rate), Census of Governments, 1982.