Journal of Urban Economics 52 (2002) 93–108
www.academicpress.com

Fiscal decentralization contributes
to economic growth:evidence from state-level cross-section
data for the United States
Nobuo Akai a,∗ and Masayo Sakata b
a Nobuo Akai, Institute of Economic Research, Kobe University of Commerce,

8-2-1 Gakuen-Nishimachi, Nishiku, Kobe, 651-2197, Japanb Osaka School of International Public Policy, Osaka University, Osaka, Japan

Received 13 February 2001; revised 22 March 2002

Abstract
This paper provides new evidence that fiscal decentralization contributes to economic
growth, in contrast to previous studies that have denied such a contribution. Our new statelevel data for the United States enable us to estimate the effect of fiscal decentralization
more objectively than previously, because the data set exhibits little cultural, historical,
and institutional variation. We also provide the finding that the definition of fiscal
decentralization is important in relation to the effect of fiscal decentralization on economic
growth.  2002 Elsevier Science (USA). All rights reserved.
Keywords: Fiscal decentralization; Economic growth; Fiscal decentralization indicator

1. Introduction
Recently, fiscal decentralization, which involves the devolution of government
fiscal responsibilities to lower levels of government, has been discussed in many
* Corresponding author.

E-mail address: akai@kobeuc.ac.jp (N. Akai).
0094-1190/02/$ – see front matter  2002 Elsevier Science (USA). All rights reserved.
PII: S 0 0 9 4 - 1 1 9 0 ( 0 2 ) 0 0 0 1 8 - 9

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developed countries.1 A reason for this interest is the theoretical prediction that
fiscal decentralization enhances or improves the efficiency of government andpromotes economic development (see, for example, Oates [4]). In particular, theeffect of fiscal decentralization on economic growth is a key issue in conventional
theoretical studies and in recent empirical studies in economics.
Some papers have tried to test the effect of fiscal decentralization on economic
growth. Despite widespread recognition of the contribution of fiscal decentralization to economic growth in theoretical works, none of these studies has beensuccessful in precisely substantiating or verifying the potential contribution of fiscal decentralization to economic growth. The results obtained in these papers arebriefly summarized as follows.
First, Zhang and Zou [7], using panel data for China covering the period
following the reforms of the late 1970s, find that fiscal decentralization reduces
provincial economic growth.
Second, Davoodi and Zou [2], using panel data for 46 developing anddeveloped countries covering the 1970–1989 period, find a negative relationshipbetween fiscal decentralization and economic growth for developing countries,
but no relationship for developed countries.
Finally, Xie et al. [6], using time-series data for the United States covering the
1948–1994 period, show that further fiscal decentralization may be detrimental to
growth.
Given that these studies fail to substantiate the theoretically predicted contribution of fiscal decentralization to economic growth, the methods used by thesestudies to investigate the effect of fiscal decentralization may be inappropriate.
Consideration of this issue reveals problems related to the data chosen for theseempirical studies.
First, Zhang and Zou [7] and Xie et al. [6] use data that cover a period ofhigh economic growth in China and United States, respectively. In such periods,
a relatively high level of government may be required to provide public investmentthat generates large externalities in the early stages of economic development. In
this case, a negative relationship is to be expected from the data sets used by thesestudies.
Second, Davoodi and Zou [2] use cross-country data in which the cultural,
historical, and institutional differences between countries are substantial. It
may be difficult to determine the true effect of fiscal decentralization unlessadjustments are made to the data to account for these differences. In fact, Davoodi
and Zou [2, p. 254] recognize that the cross-country variation among developing
1 As mentioned in Xie et al. [6], many developed countries such as the United States, the United
Kingdom, and Canada are reviving debates on fiscal decentralization or devolution. In addition,
fiscal decentralization has become a key issue in Japan since the law for the promotion of fiscal
decentralization was enacted in 1995.

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95

countries is one of the reasons for the negative relationship found in thesecountries.
Using similar techniques to those used in previous studies, we investigate theeffect of fiscal decentralization by using a more appropriate data set. We use datafrom 50 states of the United States. We use these data for two reasons. First, thisdata set does not cover a period of high economic growth because the data are
drawn from a recent economic survey of the United States. Second, there are no
substantial historical or cultural differences across observations in this data set,since these data are cross-sectional data for a single country. Hence, these data
may be useful for estimating the real effect of fiscal decentralization on economic
growth. Unlike those of other studies, the estimation results of this paper showthat fiscal decentralization does contribute to economic growth, which is a resultthat corresponds to theoretical work.
This paper is organized as follows. Section 2 illustrates four indicators of fiscal
decentralization in order to respond to discussions of fiscal decentralization. The
data and the estimation are discussed in Section 3. Section 4 presents the empiricalresults, and Section 5 concludes the paper.

2. Measures of fiscal decentralization
Although fiscal decentralization is discussed as a political issue in many
countries, the term is not sufficiently clear even in the fields of political scienceor public administration.2 To investigate statistically the potential contributionof fiscal decentralization to economic growth, a quantitative measure of fiscal
decentralization must be constructed.
Generally, fiscal decentralization is interpreted as devolution of the authority
associated with decision making to a lower-level government. To measure fiscal
decentralization, we have to know the degree of devolution or the level ofauthority of the lower-level government. Authority associated with decisionmaking has been allocated on the basis of legal relationships between higherand lower-levels government. However, it is difficult to measure quantitatively
the allocation of authority.
The standard approach to measuring the allocation of authority is to make useof accounting measures such as revenue or expenditure. However, there are somereservations about using accounting information to obtain accurate measures of
the allocation of authority.
First, expenditure by lower-levels government may be financed by intergovernmental grants from higher-levels government. Hence, the share of expenditure in the total budget does not necessarily reflect the level of authority allocated
2 For example, Bird and Vaillancout [1] discuss various measures of fiscal decentralization.

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to a lower-level government because, to some extent, its grant relates to expenditure authorized by a higher-level government. Therefore, it is inappropriate toregard expenditure shares as necessarily an accurate measure of shares of authority. Given the allocation of lump-sum grants, neither do revenue shares necessarily reflect shares of authority. This is because the authority associated with thespending of the lump-sum grant is attributed to the lower-level government.
Second, even if expenditure shares or revenue shares are small, authority
is considered to be fiscally decentralized provided that sufficient resources for
public spending are originally allocated to the lower-level government; that is, if
autonomy is achieved. Therefore, autonomy should be considered as one of theindicators of fiscal decentralization.
Despite these reservations, previous studies have used an expenditure shareas an indicator of fiscal decentralization. However, it is doubtful that only one
indicator (for example, an expenditure share) fully captures the various dimensionof fiscal decentralization. To derive a convincing general result and to respondto discussions outside the economic field (for example, political science andlaw), it is necessary to construct indicators of fiscal decentralization that reflectvarious viewpoints. Since it is difficult to develop a single measure that iscompletely satisfactory, we consider four decentralization indicators that measure
the allocation of authority from various standpoints and, hence, deal with the tworeservations mentioned above.
We deal with the first reservation by considering both the revenue andexpenditure shares of governments. Simply assume that differences between
revenue and expenditure correspond to inter-governmental grants. As Oates [3]suggests, when the grantor directs in some detail the purposes for which thefunds are to be used, the grants should be attributed to the level of governmentthat collects the revenues. This is because the authority relating to the use of the
grant may belong to the grantor. Then the share of revenue in the total budget(indicator 1) may approximately measure the degree of authority. By contrast,
lump-sum grants or grants that are unconditional should be attributed to the levelof government that undertakes the expenditure. Then the share of expenditure in
the total budget (indicator 2) may approximately measure the degree of authority.
These two indicators are regarded as two extreme cases relating to the allocationof authority. For the case in the middle, the indicator (indicator 4) that combinesthese two indicators is considered.
To deal with the second reservation, we introduce a new indicator, whichreflects the fiscal autonomy of local government (indicator 3). This indicatorconsiders how public spending at lower-level government is maintained on thebasis of its revenue.
Detailed explanations of these four types of indicators follow. (Statisticalcharacteristics of the computed indicators are presented in Appendix A.)

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97

2.1. Revenue indicator (indicator 1)
The revenue indicator (RI) is defined for each state as the ratio of local
government revenue to combined state and local government revenue.3 This
indicator corresponds to the most approximate measure of the allocation of
authority when the government that collects revenue has authority associatedwith its own revenue (the tax to be collected and the type of expenditure to bemade), but all inter-governmental grants are conditional or matching grants. In
calculating a revenue share, we use government revenue excluding grants fromother governments.
2.2. Production indicator (indicator 2)
The production indicator (PI) is defined for each state as the ratio of local
government expenditure to combined state and local government expenditure.4
This indicator corresponds to the most approximate measure of the allocation of
authority when a local government has authority associated with its expenditure(the tax to be collected and the type of expenditure to be made) implicitly
considering that all inter-governmental grants are non-matching or lump-sum
grants. In calculating an expenditure share, we use government expenditure
including grants from other governments.
2.3. Autonomy indicator (indicator 3)
This indicator measures the autonomy (degree of fiscal independence) of a
local government in a state. For instance, even if the revenue or expenditure shareof local government is small in relation to total revenue or expenditures withina state, local government autonomy is high if all fiscal needs are financed in
the local government region, in which authority may be fiscally decentralized.
Therefore, autonomy should be considered as one of the measures of fiscal
decentralization. The autonomy indicator (AI) is defined as the local government’s
own revenue share of its total revenue. This indicator equates to the grants share(the share of grant revenue in total revenue) subtracted from unity. In calculating
the autonomy of the local government in a state, the federal grant provided fromoutside the state should be considered. Hence, we consider two indicators for AI
to account for the potential impact of the federal grant. AII represents the local
government’s real fiscal independence and is based on the local government’s
own revenue and total revenue, with both revenues excluding federal grants.
3 In order to grasp authority allocation accurately, we exclude revenues financed by public debt

from both state and local revenue data.
4 Similar to Davoodi and Zou [2] and Xie et al. [6], expenditure for redeeming public debt isincluded in both state and local expenditure data.

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AIII represents actual independence from the state government, and is based on
the local government’s own revenue and the total revenue, with both revenues
including federal grants.
2.4. Production-revenue indicator (indicator 4)5
The production-revenue indicator (PRI) represents a decentralization measurethat incorporates both revenue and expenditure shares. The normalized indicator
is defined as the mean of indicators 1 and 2; that is, PRI = (PI + RI)/2.

3. Empirical analysis
3.1. Empirical model
Following Xie et al. [6], the growth regression is written as
GSPi = α0 + α1 Decentralizationi + Xi β + εi ,i = 1, . . . , 50,(1)where i refers to state i;  GSPi represents the average annual growth rate of
per capita gross state product (GSP) between 1992 and 1996; Decentralizationi
represents indicators of fiscal decentralization in state i; Xi is control variables
comprising state characteristics. The parameters α0 and α1 are scalars, β represents a parameter vector, and εi is an error term, which is assumed to be normally
distributed, homoscedastic, and independent across observations. Eq. (1) is estimated by the method of ordinary least squares (OLS) on cross-section state-level
data.63.2. Variables
Characteristics of the data used are summarized in Table 1. First, thefour indicators of fiscal decentralization described in Section 2 are introduced
into the regression as alternate independent variables to test the effect offiscal decentralization on economic growth. The economic theory developed by
Oates [4] predicts that fiscal decentralization contributes to economic growth, thatis, α1 > 0.
5 Steunenberg and Mol [5] suggest a similar decentralization measure that incorporates revenue√and expenditure shares, which is computed as PI2 + RI2 / 2. However, the problem with thisindicator is that degrees of both fiscal decentralization and fiscal centralization may increase at thesame time following reforms, since the degree of decentralization and the degree of centralization do
not necessarily sum to unity, as is the case for other indicators.
6 Panel regression with similar results is also performed. These results are reported in Appendix B.

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99

Table 1
Variable definitions, means, and standard deviations
Variable

MeanStandard
deviation

Definition

 GSP

0.27710.0136POP

0.26030.0106

 GSP(−1)

0.25720.0146EDUC

0.09140.0136

LIB vs. CON

0.52130.1554

GINI

0.09090.0230SOUTHERN

0.32

0.4712PATENTSOPENNESS

0.02
0.05540.0276
0.0386

Average annual growth rate of real GSP per capita over the
1992–1996 period
Average annual growth rate of state population over the
1988–1992 period
Average annual growth rate of per capita real GSP over the
1988–1992 period
Percentage of high school graduates in total population
aged 18–24 years in 1992
The share of seats in state legislature held by Democrats
in 1992
Gini coefficient calculated from pre-tax income differences between counties within a state in 1992
Dummy variable indicating state’s location in the southern
region; Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North
Carolina, Oklahoma, South Carolina, Tennessee, Texas,
Virginia, and West Virginia.
The state’s share of total US patents issued in 1992
Ratio of state’s exports to other countries and other states
to nominal GSP in 1992

Indicators of fiscal decentralization
RI

0.38680.0858PI

0.46130.0825AII

0.68840.0783AIII

0.66610.0754PRI

0.42410.0813

Ratio of local government revenue to state and local
government revenue in 1992
Ratio of local government expenditure to state and local
government expenditure in 1992
Ratio of local government’s own revenue to total revenue,
with revenues excluding federal grants in 1992
Ratio of local government’s own revenue to total revenue,
with revenues including federal grants in 1992
(PI + RI)/2, which reflects both revenue and expenditureaspects of fiscal decentralization in 1992

USA COUNTIES 1998, Statistical Abstract of the United States1998, State and Metropolitan Area
Data Book 1997–1998.

Second, we include a large number of state economic characteristics in theempirical model to capture all the relevant economic effects on growth after
controlling for differences across states. Most of these explanatory variables used
are similar to those used in the regression model of Xie et al. [6].7 However, in
7 However, due to limited data availability, we were unable to use all of the variables included bythese authors.

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addition to the variables used by these authors, we include several others justified
by regional growth theory. A list of the variables used follows. We use explanatory
variables measured at 1992 data (the initial year in our sample) to deal withpotential endogeneity problems, with the exception of POP and GSP(−1) (seeTable 1 for definitions, means, and standard deviations).
• Population growth rate over the 1988–1992 period (POP);• Average annual growth rate of per capita GSP over the 1988–1992 period( GSP(−1));• Education level and labor quality, measured by the level of human capital(EDUC);• Liberal vs. Conservative tendencies, measured by the share of the seats in thestate legislature held by Democrats (LIB vs. CON);• The effect of income distribution, measured by Gini coefficients (GINI);• Quality of regional human capital, measured by patents (PATENTS);• Economic structure, measured by trade (OPENNESS);• Regional-specific effect (SOUTHERN).
These variables (except for SOUTHERN) are included in the regression in thelogarithmic form after transformed into natural logarithms.
Predictions can be stated for the effects of some of these variables. First, sincemodels often suggest that population increases enhance economic growth, theeffect of POP on GSP growth is expected to be positive. Second, since a higher
education level may increase economic activity, the effect of EDUC on GSP
growth is expected to be positive. Third, since a high level of income inequality
requires income redistribution within the region and may reduce incentives towork, one would expect the effect of GINI on GSP growth to be negative.
Fourth, since the quality of human capital in a state (PATENTS) develops theregional economy, the effect of PATENTS on GSP growth is expected to bepositive. Although it is difficult to predict the effects of the remaining variables
on GSP growth, they are included in the model to capture unexpected influences.
All data used in this paper are from the following sources: USA COUNTIES
1998; Statistical Abstract of the United States 1998; and State and Metropolitan
Area Data Book 1997–1998.

4. Regression results
Results based on four different types of indicator for fiscal decentralization
are presented in Table 2. (We added models that omitted OPENNESS andPATENTS to test the robustness of the regression results from the basic specification.)Table 2
Regression results
Indep. var.
RI

AII
AIII
PRI
POP
 GSP(−1)
EDUC
LIB vs. CON
GINI
PATENTSOPENNESSSOUTHERN
Constant
Adjusted R 2

(2.2)
0.14
[1.86]∗
–
–
–
–
–
–
–
–
−0.82
[2.55]∗∗
1.03[3.03]∗∗∗−0.02[0.44]−0.08[1.15]−0.07[3.05]∗∗∗
–
–
–
–
0.01
[0.70]−1.30[4.66]∗∗∗

(2.3)
–
–
0.16
[1.73]∗
–
–
–
–
–
–
−0.88
[2.86]∗∗∗
1.04[3.04]∗∗∗−0.01[0.19]−0.07[1.00]−0.08[3.50]∗∗∗
0.007[1.04]−0.003[0.32]
0.01[0.84]−1.36[3.96]∗∗∗

(2.4)
–
–
0.22
[3.00]∗∗∗
–
–
–
–
–
−0.87
[2.89]∗∗∗
0.96[2.98]∗∗∗−0.03[0.63]−0.07[1.03]−0.07[3.42]∗∗∗
–
–
–
–
0.01
[0.69]−1.54[5.63]∗∗∗

(2.5)
–
–
–
–
−0.07
[0.85]
–
–
–
–
−0.89
[2.80]∗∗∗
1.17[3.42]∗∗∗−0.01[0.16]−0.10[1.43]−0.08[3.68]∗∗∗
0.01[2.44]∗∗−0.004[0.43]
0.02[1.06]−1.08[3.57]∗∗∗

(2.6)
–
–
–
–
−0.08
[0.99]
–
–
–
–
−0.86
[2.62]∗∗
1.05[3.02]∗∗∗−0.06[1.14]−0.13[1.89]∗−0.07[3.06]∗∗∗
–
–
–
–
0.01
[0.85]−1.33[4.65]∗∗∗

(2.7)
–
–
–
–
–
–
−0.06
[0.79]––−0.89[2.81]∗∗∗
1.17[3.41]∗∗∗−0.01[0.13]−0.10[1.43]−0.08[3.67]∗∗∗
0.01[2.46]∗∗−0.004[0.40]
0.02[1.07]−1.07[3.55]∗∗∗

(2.8)
–
–
–
–
–
–
−0.07
[0.86]––−0.86[2.62]∗∗
1.05[3.00]∗∗∗−0.06[1.11]−0.13[1.89]∗−0.07[3.04]∗∗∗
–
–
–
–
0.01
[0.89]−1.34[4.64]∗∗∗

(2.9)
–
–
–
–
–
–
–
–
0.11
[1.29]−0.87[2.77]∗∗∗
1.11[3.23]∗∗∗
0.003[0.04]−0.07[0.98]−0.08[3.47]∗∗∗
0.009[1.49]−0.003[0.27]
0.01[0.86]−1.19[3.76]∗∗∗(2.10)
–
–
–
–
–
–
–
–
0.19
[2.46]∗∗−0.83[2.68]∗∗∗
0.99[3.00]∗∗∗−0.02[0.47]−0.07[1.05]−0.07[3.22]∗∗∗
–
–
–
–
0.01
[0.66]−1.40[5.12]∗∗∗

0.37

0.34

0.40

0.41

0.37

0.31

0.37

0.30

0.38

0.38
101

Dep. var: average annual growth (1992–1996). Figures in parentheses are the absolute values of t-statistics. Asterisks indicate variables whose coefficients are
significant at the 10(∗ ), 5(∗∗ ), and 1%(∗∗∗ ) levels. The total number of observations are 50.

N. Akai, M. Sakata / Journal of Urban Economics 52 (2002) 93–108

PI

(2.1)
0.07
[0.90]
–
–
–
–
–
–
–
–
−0.87
[2.74]∗∗∗
1.16[3.38]∗∗∗
0.01[0.16]−0.08[1.02]−0.08[3.48]∗∗∗
0.01[1.88]∗−0.003[0.25]
0.01[0.91]−1.09[3.58]∗∗∗

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The empirical findings can be stated as follows. The primary finding is thatthe estimated coefficient on fiscal decentralization (α1 in (1)), is positive andstatistically significant at the 1, 5, and 10% levels in regressions that include
indicator 1 (RI), indicator 2 (PI), or indicator 4 (PRI) (see columns (2.2)–(2.4)
and (2.10) in Table 2). This finding provides evidence that fiscal decentralization
contributes to economic growth. It is important to note that this finding is notconsistent with the results of previous papers, but is consistent with the theoreticalpredictions of Oates [4].8 Moreover, it should be emphasized that indicator
2 (PI), which is similar to those of previous studies, is positively signed andstatistically significant at the 1% level in a regression with a limited set ofvariables.
These new findings are due to the use of a new, more appropriate, data set.
These data have several desirable features. First, the data used here exhibit little
historical or cultural variation, unlike the cross-country data set used by Davoodi
and Zou [2], in which such variations between observations are substantial.
Second, the period covered by the data does not include an economic development
stage because the data are recent United States data. By contrast, the timeseries and panel data used by Zhang and Zou [7] and Xie et al. [6] coverperiods that include economic development stages in the countries under study.
Thus, the results obtained here may describe the likely effects of future fiscal
decentralization in developed countries.
Insignificant effects of AII and AIII are found in regressions that include fiscal
decentralization indicator 3 (see columns (2.5)–(2.8) in Table 2). This meansthat it is difficult to determine the effect of fiscal decentralization on economic
growth when autonomy is used as an indicator of fiscal decentralization. It should
be apparent from this result that the definition of fiscal decentralization is an
important issue in the context of the effect of fiscal decentralization on economic
growth.
Concerning the estimated coefficients of other state characteristics, some conclusions emerge from the results in Table 2. While some variables significantly
affect economic growth, others for which an effect was expected have insignificant coefficients. Detailed discussion follows.
First, we have controlled for the quality of regional human capital by using
two variables (PATENTS and EDUC). PATENTS has the expected positive effect,
which is statistically significant in regressions that include indicator 1 (RI) and
8 As a referee has pointed out, it is important to explain intuitively why Davoodi and Zou [2] find noevidence of such a relationship from a similar cross-section regression for a similar group of developed
countries. The explanation concerns country-specific effects. It may be difficult to adjust adequately
enough for country-specific effects where there are historical and cultural differences. Analysis will
fail to reveal the true effect of fiscal decentralization on economic growth if adjustment for countryspecific effects is inadequate. Our data set has the advantage that variations between observations are
relatively small, which facilitates adjustment for region-specific effects.

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103

indicator 3 (AI) (see columns (2.5) and (2.7) in Table 2), while the effectof EDUC is not significant in all regression models. This result means thatPATENTS represents a real benefit of accumulated human intelligence, while
EDUC (which indicates education levels) may serve as a poor proxy for the effectof the quality of regional human capital, perhaps because of migration to otherregions.
Second, the estimated coefficient of  GSP(−1) is positive and significant at
the 1% level in all regression models. This means that economic growth in theprevious period is an important determinant of economic growth in the current
period.
Third, the estimated coefficient of GINI is negative, as expected, andsignificant at the 1% level in all regression models. This result suggests that
income inequality reduces economic growth and confirms the findings of Xie
et al. [6]. It may be that incentives to work are lower in regions needing a
redistribution policy to reduce income inequality.
Fourth, the estimated coefficient of LIB vs. CON is negative and significant at
the 10% level in some regressions (see columns (2.6) and (2.8) in Table 2).
Finally, insignificant coefficients were observed for SOUTHERN and OPENNESS, both of which were expected to have an effect. An explanation for theinsignificance of the SOUTHERN dummy is that it is difficult to determine
the region-specific effect in a cross-section regression by using only a regionaldummy. Panel data, which have both a cross-section and a time-series dimension,
are useful for controlling for the regional-specific effect (see Appendix B for a
trial panel regression.) Since OPENNESS is based on all types of trade, it may
represent an imperfect measure of regional economic structure.
The empirical findings can be summarized as follows. Most importantly, theresults provide convincing evidence that fiscal decentralization contributes to economic growth. Unlike previous papers, this paper finds that fiscal decentralization
does indeed play an important role in economic growth. As expected, the results
also indicate that several other factors affect economic growth. To see why our
results on the effect of decentralization differ from those of other studies, supposethat the effect of fiscal decentralization changes according to history, culture, andstage of economic development. To determine the true effect of fiscal decentralization in the developed countries, it is necessary to use data for those countries
from which historical differences are absent. The data used in this paper satisfy
this requirement.

5. Conclusion
The effect of fiscal decentralization on economic growth has been a major
focus of debate and discussion in the context of recent public reforms. This
paper has presented new empirical evidence on this important issue. Having

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provided evidence that fiscal decentralization contributes to economic growth,
this paper suggests that recent moves toward fiscal decentralization by developed
countries may stimulate their economic growth. This finding is consistent withexisting theoretical results, but contradicts the empirical results of previous
studies.
The nature of the data set used in the regression analysis of this paper explains
why our results substantiate existing theoretical results and why those of otherstudies do not. As already mentioned, our data set is one in which differences
relating to history, culture, and the stage of economic development are minimal,and hence admirably suited to determining the true effect of fiscal decentralization
on economic growth. This distortion-free data set has revealed the true positiveeffect of fiscal decentralization.
To investigate the contribution of fiscal decentralization more thoroughly, it isnecessary to construct accurate decentralization indicators that reflect the politicaland institutional processes that assign the authority to raise taxes and undertake
public spending.
Finally, it is important to note that, although the results of this paper provide
evidence of a contribution to economic growth, they do not demonstrate how
fiscal decentralization contributes to economic growth. Further research based ontheoretical models is required to explain the mechanisms involved.
Acknowledgments
We thank Jan Brueckner, Masatomi Funaba, Masayoshi Hayashi, Colin
McKenzie, Toshihide Mizuno, referees, and seminar participants at several
universities for helpful comments and discussion. However, any errors or
shortcomings in the paper are our responsibility.
Appendix A. Data characteristics
Table A.1
Correlation coefficients between fiscal decentralization indicators correlation matrix
1992

RI

PI

AII

AIII

PRI

RI
PI
AII
AIII
PRI

1.000
0.865
0.506
0.560
0.967

1.000
0.173
0.222
0.964

1.000
0.993
0.355

1.000
0.408

1.000

This table shows correlations between five decentralization indicators. It is apparent that RI, PI,
and PRI are highly correlated with each other. However, AII and AIII are less correlated with otherindicators.

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105

Table A.2
Degree of fiscal decentralization by state and indicator (1992)
1992

RI

PI

AII

AIII

PRI

Alabama
AlaskaArizona
Arkansas
California
Colorado
Connecticut
Delaware
FloridaGeorgia
Hawaii
Idaho
Illinois
Indiana
Iowa
KansasKentuckyLouisiana
Maine
Maryland
Massachusetts
MichiganMinnesota
Mississippi
MissouriMontana
Nebraska
NevadaNew Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
OregonPennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington

0.396
0.214
0.468
0.316
0.439
0.485
0.358
0.216
0.529
0.513
0.203
0.310
0.464
0.398
0.404
0.480
0.299
0.411
0.315
0.403
0.373
0.439
0.406
0.397
0.427
0.286
0.583
0.371
0.467
0.364
0.247
0.509
0.398
0.317
0.340
0.348
0.384
0.374
0.308
0.358
0.413
0.537
0.515
0.421
0.348
0.422
0.383

0.466
0.323
0.561
0.389
0.543
0.584
0.407
0.329
0.587
0.557
0.221
0.441
0.515
0.507
0.492
0.551
0.377
0.430
0.378
0.455
0.432
0.551
0.532
0.474
0.494
0.401
0.595
0.516
0.427
0.424
0.399
0.527
0.514
0.360
0.457
0.427
0.529
0.447
0.301
0.425
0.451
0.560
0.558
0.493
0.366
0.503
0.475

0.707
0.654
0.686
0.590
0.606
0.751
0.724
0.581
0.742
0.754
0.892
0.594
0.736
0.657
0.677
0.756
0.604
0.693
0.673
0.749
0.705
0.709
0.634
0.620
0.717
0.643
0.825
0.616
0.875
0.647
0.498
0.700
0.649
0.632
0.682
0.649
0.746
0.682
0.746
0.687
0.783
0.812
0.754
0.715
0.730
0.720
0.668

0.686
0.630
0.665
0.575
0.591
0.731
0.705
0.561
0.723
0.730
0.831
0.577
0.704
0.643
0.660
0.745
0.584
0.665
0.651
0.720
0.674
0.693
0.614
0.597
0.697
0.595
0.806
0.582
0.854
0.639
0.478
0.684
0.630
0.606
0.658
0.631
0.705
0.654
0.715
0.666
0.745
0.791
0.738
0.700
0.720
0.699
0.649

0.431
0.269
0.515
0.353
0.491
0.534
0.382
0.272
0.558
0.535
0.212
0.376
0.489
0.453
0.448
0.516
0.338
0.421
0.347
0.429
0.402
0.495
0.469
0.436
0.460
0.344
0.589
0.443
0.447
0.394
0.323
0.518
0.456
0.338
0.399
0.388
0.456
0.411
0.304
0.391
0.432
0.549
0.537
0.457
0.357
0.462
0.429

106

N. Akai, M. Sakata / Journal of Urban Economics 52 (2002) 93–108

Table A.2 (continued)
1992

RI

PI

AII

AIII

PRI

West Virginia
Wisconsin
Wyoming

0.268
0.328
0.388

0.346
0.519
0.449

0.547
0.590
0.611

0.534
0.576
0.598

0.307
0.423
0.419

Appendix B. Panel data analysis
B.1. Panel regression model
The use of data with time-series and cross-section dimensions enablesconsideration of state-specific characteristics. Therefore, we estimate the panel
regression model below in order to test the robustness of the clear-cut results
obtained from our simple cross-section regression analysis of Section 4.
Our panel data cover the periods 1992–1994 and 1994–1996 in the UnitedStates. Eq. (B.1) is the panel regression model to be estimated by OLS. We
consider the one-way fixed-effects model.9
 GSPit = α0 + α1 Decentralizationit + Xit β + δ1 Si + εit ,i = 1, . . . , 50, t = 1, 2,(B.1)

where i and t refer to country i and time t, respectively; Xit is state characteristics; Si is a vector of I − 1 (= 49) state fixed effects (i.e., state dummies); and GSP represents average annual growth over the two-year period from 1992 and1994. Therefore, there are 100 observations for the panel regression. All otherexplanatory variables used here are measured as of the initial year in each crosssection sample; that is, 1992 and 1994.
B.2. Results from panel regression
The results of estimating (B.1) are presented in Table B.1. (With the exceptionof the decentralization indicators, all variables were transformed into natural
logarithms.) Focusing on the effect of decentralization, it is found that even afterusing panel data analysis to account for state-specific effects, the main result
concerning the effect of fiscal decentralization survives; that is, the main results
obtained from the panel regression are similar to those obtained in Section 4.
Focusing on the regression that includes the most important coefficient, that forindicator 2 (PI) and 4 (PRI), Table B.1 shows that the estimated coefficient on
fiscal decentralization (α1 in (B.1)), is positive and statistically significant at the
9 F -tests and Hausman tests reject two-way and random-effects specifications in all regressions.

Table B.1
Panel regression results
Indep. var.
RI

AII
AIII
PRI
POP
 GSP(−1)
EDUC
LIB vs. CON
GINI
PATENTSOPENNESS
Adjusted R 2
Hansman tests
P -value of Hansman tests

(3.2)

(3.3)

(3.4)

(3.5)

(3.6)

(3.7)

(3.8)

(3.9)

(3.10)

0.33[1..05]
–
–
–
–
–
–
–
–
−0.02
[0.06]−0.32[3.72]∗∗∗
0.11[1.10]
0.16[1.96]∗
0.18[1.50]
0.01[0.31]
0.0002[0.005]0.33[1.08]
–
–
–
–
–
–
–
–
−0.02
[0.06]−0.32[4.28]∗∗∗
0.11[1.11]
0.15[2.00]∗∗
0.18[1.63]
–
–
–
–

–
–
0.32
[2.29]∗∗
–
–
–
–
–
–
−0.01
[0.03]−0.27[3.22]∗∗∗
0.12[1.28]
0.01[0.06]
0.20[1.70]∗
0.02[0.42]−0.0004[0.009]––0.32[2.32]∗∗
–
–
–
–
–
–
−0.01
[0.04]−0.27[3.73]∗∗∗
0.12[1.28]
0.002[0.02]
0.19[1.85]∗
–
–
–
–

–
–
–
–
−0.16
[0.60]
–
–
–
–
−0.17
[0.54]−0.30[3.34]∗∗∗
0.09[0.96]
0.13[1.69]∗
0.17[1.41]
0.02[0.38]−0.001[0.02]

–
–
–
–
−0.15
[0.59]
–
–
–
–
−0.17
[0.55]−0.30[3.88]∗∗∗
0.09[0.96]
0.13[1.72]∗
0.17[1.53]
–
–
–
–

–
–
–
–
–
–
−0.33
[1.17]––−0.22[0.68]−0.28[3.24]∗∗∗
0.09[0.90]
0.13[1.65]
0.18[1.49]
0.02[0.45]−0.004[0.08]

–
–
–
–
–
–
−0.31
[1.15]––−0.21[0.68]−0.29[3.82]∗∗∗
0.09[0.90]
0.12[1.68]∗
0.18[1.63]
–
–
–
–

–
–
–
–
–
–
–
–
0.48
[2.27]∗∗
0.04[0.14]−0.28[3.45]∗∗∗
0.12[1.27]
0.05[0.64]
0.20[1.71]∗
0.02[0.39]−0.001[0.02]

–
–
–
–
–
–
–
–
0.47
[2.31]∗∗
0.04[0.15]−0.29[4.00]∗∗∗
0.12[1.28]
0.05[0.62]
0.20[1.87]∗
–
–
–
–

0.41
63.45
0.00

0.44
61.86
0.00

0.46
54.23
0.00

0.49
54.22
0.00

0.40
64.47
0.00

0.43
63.81
0.00

0.42
66.40
0.00

0.44
65.65
0.00

0.46
61.51
0.00

0.49
60.38
0.00

107

Dep. var: average annual growth (92–94 and 94–96). Figures in parentheses are the absolute values of t-statistics. Asterisks indicate variables whose coefficientsare significant at the 10(∗ ), 5(∗∗ ), and 1%(∗∗∗ ) levels. Number of observations are 100. Due to limits on space, we do not report the results for the estimates of
the individual dummy variables in the table. The Hausman test tests the null hypothesis of a random effects model against a fixed effects model.

N. Akai, M. Sakata / Journal of Urban Economics 52 (2002) 93–108

PI

(3.1)

108

N. Akai, M. Sakata / Journal of Urban Economics 52 (2002) 93–108

5% level. (See columns (3.3), (3.4), (3.9), and (3.10) in Table B.1.) This finding
confirms the robustness of the evidence that fiscal decentralization contributes toeconomic growth.

References[1] R.M. Bird, F. Vaillancout, in: R.M. Bird, F. Vaillancout (Eds.), Fiscal Decentralization in
Developing Countries, Cambridge University Press, Cambridge, UK, 1998.
[2] H. Davoodi, H. Zou, Fiscal decentralization and economic growth: A cross-country study, Journalof Urban Economics 43 (1998) 224–257.
[3] W.E. Oates, Fiscal Federalism, Harcourt Brace Jovanovic, New York, 1972.
[4] W.E. Oates, Fiscal decentralization and economic development, National Tax Journal 46 (1993)
237–243.
[5] B. Steunenberg, N. Mol, Fiscal and financial decentralization: A comparative analysis of six West
European countries, in: J.-E. Lane (Ed.), Public Sector Reform, SAGE, 1998, pp. 235–256.
[6] D. Xie, H. Zou, H. Davoodi, Fiscal decentralization and economic growth in the United States,
Journal of Urban Economics 45 (1999) 228–239.
[7] T. Zhang, H. Zou, Fiscal decentralization, public spending, and economic growth in China, Journalof Public Economics 67 (1998) 221–240.