GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 344

How does income distribution affect economic growth?
－Evidence from Japanese prefectural data－

Masako Oyama

August 2014

GCOE Secretariat
Graduate School of Economics
OSAKA UNIVERSITY
1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

How does income distribution affect economic growth?
－Evidence from Japanese prefectural data－

Masako Oyama*
Osaka University

Abstract
There have been various empirical researches on the effects of income
distribution on economic growth. Although studies using cross-country
data used to be common, recent researches started to use regional panel-data
within one country. This paper uses Japanese prefectural panel data to
analyze how income distribution affects economic growth.
The measures of the income distribution used in the estimations are
the income share of the third quintile and the Gini indices. In the fixed
effects estimations and the GMM estimations, the income share of the third
quintile has positive and statistically significant effects on five-year
economic growth rates. On the other hand, the Gini indices have positiveand statistically significant effects on both of the five-year and ten-year
economic growth rates in the fixed effects estimations, and negative effect on
the five-year growth rate in the GMM once. These results are the similar to
one of the existing literatures and can be explained with the modified
median voter theory.
JEL Classification Codes: O40, C33, J01* 567-0047

6-1, Mihogaoka, Ibaraki-shi, Osaka, Email: m-oyama@iser.osaka-u.ac.jp
I am grateful to Fumio Otake, Toshiaki Tachibanaki, Takashi Kurosaki, Takayuki
Tsuruga, and the participants at the Japanese Economic Association 2014 Spring
Meeting and the Kansai macroeconomics workshop for their helpful comments andsuggestions. I also appreciate the support at the Institute of Social and Economic
Research, Osaka University and the Kansai labor economics workshop.
11. Introduction
There have been various empirical and theoretical researches on this
topic. According to Weil (2013), income equality affects economic growththrough four channels. These four channels are the accumulation ofphysical capital, the accumulation of human capital, government
redistribution policy, and sociopolitical instability.
In recent Japan, active discussion on the possibility of increase inincome inequality was conducted (Otake 2005, Tachibanaki 2004, 2006), andthe increase of inequality people felt became social problem for several years.
In addition, recent increase of the maximum rate for income taxes andincrease of inheritance taxes can be considered as the increase ofgovernment’s income redistribution. Such increase or decrease in income
inequality can affect economic growth, and that effect is estimated in thisresearch.
In the figure 1, the transition of the Gini index in the Survey on theredistribution of income and the National Survey of family income andexpenditure are shown. The red line shows the Gini index on the income
before redistribution, and it has been increasing sharply. However, theincome after redistribution shown by the blue line increased more slowly
during 1980-2002 and did not show constant increase after 2003. Also, if weFigure１ Gini coefficients in Japan
0.6

Survey on the
Redistribution of Income(Income Before
Redistribution)

0.55
0.5

Survey on the
Redistribution of Income(Income After
Redistribution)

0.45
0.4
0.35

the National Survey of
Family Income andExpenditure

0.3
0.25

1972 1978 1981 1987 1990 1994 1999 2004 2008 20112

look at the violet line which shows the Gini index of the National Survey offamily income and expenditure, it is low but increasing since 1979.
In the existing empirical researches, the effects of income distribution
on economic growth are different, depending on data and estimation methods.
Persson and Tabellini (1994) found that equality has positive and significant
effects on growth, using historical panel data and post-war cross-country
data. Perotti (1996) used cross-country data and also found that equality
has positive effect on growth. Forbes (2000) and Li and Zou (1998) usedcross-country panel data and found opposite evidence that equality decreases
growth.
Weil (2013) explains the reason why it is difficult to find out the effectof income distribution on economic growth is that the effect may depend on acounty’s stage of growth, as well as other factors such as whether a country is
open to capital flows from abroad.
On the other hand, recently, Panizza (2002), Partridge (1997), Atems(2013) etc. conducted researches using panel data of U.S. states. According
to Panizza (2002), while most cross-country studies find a positive
relationship between income equality and economic growth, most existingstudies that use panel data suggest the presence of a negative relationshipbetween income equality and growth. The research in Panizza (2002),
however, found some evidence in support of a positive relationship between
equality and growth, using a panel data of the 48 states of the continental
US for the 1940-1980 period. Atems (2013) also used U.S. county panel
data to estimate dynamic spatial Durbin model, and found that the direct
effect of a one-point increase in a county’s inequality is associated with a
3.3% decrease in its growth, while one-point increase in inequality in acounty’s neighbors decrease its growth by 4.8%.
Partridge (1997) also used panel data of U.S. states and found out that
equality measured with the Gini index has negative and significant effect on
growth, and that equality measured with the income share of the third
quintile has positive and significant effect on growth. This research used
Japanese prefectural panel data in estimation and found the same effects as
Partridge (1997). Partridge explain the result the median voter theory, andthis can also be applied to Japanese results.
Using a regional panel data within one country has an advantage thatthe county’s stage of growth, other factors such as whether a country is open3

to capital flows from abroad, and the measurement method of equality are
the same.
Therefore, in this paper, empirical investigation using
prefectural panel data in Japan is conducted, following the recent empiricalresearches. Since such research using Japanese panel data has not been
conducted yet, it is important to find out what kind of effects such datashows.
This paper is organized as follows. Section 2 illustrates data set;
Section 3 presents the results of estimation; Section 4 concludes.
2. DataIn this paper, Japanese prefectural panel date is used in estimation.
The summary statistics is shown in table 1, and the correlation matrix isshown in table 2.
Data is a panel for 47 prefectures for the 1980 (1979 for Gini
coefficients) - 2010, every 5 years for 6 periods. Growth 5 is the five-year
average annual growth rate from the base year, growth10 is the ten-year
average annual growth rate of each prefecture. LogIncome is the naturallog of the average per capita income in prefectures. These data are obtained
Table 1.
No.of
obs.

Summary Statistics
Average

S.E.

Minimum Maximum

growth5282

0.01170.0245-0.0375

0.0654growth101410.01450.0253-0.0200

0.0627LogIncome329

3.37300.110997

3.07903.6646Gini

282

0.25230.08500.05900.3800Q3282

0.17690.00450.15650.1892HighSchool282

41.1663

5.843125.0151

56.8238

College282

20.17458.25187.339147.6881

Agriculture282

10.2585

6.00170.400026.6000

Urban

282

48.599318.5704

23.4000

98.0000

Old

282

16.7283

4.6685

6.163627.1352

Manufacturing282

20.8058

6.50054.917834.6487

FinanInsRealEst

282

3.32910.90382.0771

7.0241Government

282

3.70170.80642.2581

6.70964

5

or calculated from “the Annual Report on Prefectural Accounts” released by
the Cabinet Office.
Gini is the Gini index and Q3 is the income share of the third quintile
in 47 prefectures, and both data are obtained or calculated from “theNational Survey of Family Income and Expenditure.” Table 2 shows thatthe correlation between the Gini index and Q3 is -0.378. The Gini index isthe established measure of income distribution, and the negative correlation
with the Gini index shows that Q3 is the measure of income equality.
Also, the figure 2 shows the change of the income share of the third
quintile (Q3) at the horizontal axis, and the change of the income share of thefirst and second quintiles (Q1 and Q2) and that of the richer fourth and fifth
quintiles (Q4 and Q5) at the vertical axis. This figure shows that when theincome share of the middle class increases, income share of the poorer
quintiles tend to increase and the income share of the richer quintiles tend to
decrease. Therefore, we can interpret that the overall income equality
tends to increase when Q3 increases.

6Following Panizza (2002), Partridge (1997) and Perotti (1996), othervariables are the average skills of the labor force (HighSchool is thepercentage of the population over 15 years old that have graduated fromhigh school, but not a college, and College is the percentage that graduatedfrom two- or four-year college or graduate school) and they are from “theemployment status survey.” The degree of urbanization (Urban measures
the fraction of the population that lives in urban areas), age structure (Oldmeasures the percentage of the population above 65 years of age), andindustrial structure (Agriculture, Manufacturing, FinanInsRealEst,
Government measure the percentage of the population employed inagriculture; construction; manufacturing; finance, insurance, and realestate; and government) are also used. Agriculture, Urban are data from
“Statistical Indicator of Social Life –Prefectural Indicator－” by the Statistics
Bureau, Ministry of Internal Affairs and Communications.
Old,
Construction, Manufacturing, FinanInsRealEst, Government are from “thePopulation Census.”
3. Estimations
In this section, the results of OLS and fixed effects estimations areshown. First, the basic simple regression of the following equation isconducted:
ℎ =

+

+

+

(1)

where
ℎ is the prefecture i’s annual growth rate of income per capita,
is prefecture i’s natural log of income per capita,, is a variable

capturing income distribution (measured using the income share of the third
quintile or the Gini index) andis the prefecture i’s matrix of controls.
The matrix
includes stock of human capital (HighSchool and College), thedegree of urbanization (Urban), age structure (Old) and the initial industrial
mix of the prefecture (Agriculture, Manufacturing FinanInsRealEst,
Government).
First, I estimated equation (1) with pooled OLS and the result is shownin table 3. They suggest that income share of the 3rd quintile has positive or
negative significant effects on growth, depending on the length of growth
episodes. As for the Gini indexes, they have positive effects on five-year7

Table 3. Pooled OLS Estimation: Five and ten-year growth episodes
Length of Growth Episodes
5 years

10 years

LogIncome-0.160-0.181-0.189-0.182-0.082(-13.28)*** (-10.67)*** (-10.78)*** (-18.06)*** ( -4.93)***
Q3
0.160
0.319
-0.121( 0.92)(1.70)*(-0.76)
Gini
0.032
0.044
-0.115(1.74 )*
(2.26)**(-7.00)***HighSchool-0.0003-0.0003-0.00030.0001-0.0002(-1.99)**(-1.89)*(-2.14)**(0.67)(-0.85)
College-0.0011-0.0010-0.0009
0.0000-0.0004(-5.85)*** (-4.95)*** (-4.73)***( -0.21)( -2.80)**
Urban
0.00040.00030.00030.00010.0000(4.36)***(4.14)***( 4.14)***(0.64)( 0.20)Old
0.00140.00110.0010-0.0010-0.0002(6.79)***(3.90)***(3.39)***(-2.42)**(-0.51)Agriculture
0.00120.00120.00110.00030.0001(3.83)***(3.94)***(3.41)***(0.98)(0.44)
Manufacturing0.00240.00250.00230.00100.0006(8.88)***(9.35)***(8.58)***(3.51)***(2.40)**
FinanInsRealEst
0.013
0.012
0.012
0.004
0.004
(8.79)***(8.29)***(7.98)***(2.96)***(3.35)***
Government
0.00410.00320.0027-0.00210.0003(2.34)**(1.79)*(1.45)(-1.25)(0.22)
Constant
0.400
0.499
0.476
0.626
0.306
(7.58)***(8.20)***(7.66)*** (13.37)*** (5.73)***
Adj. R2
N.obs.

0.844
282

0.845
282

0.846
282

0.926
141

0.946
141

Notes: t statistics in parentheses* Denotes a parameter which is significant at 10%, ** at 5%, and *** at 1%.

8-0.060(-3.64)***-0.576(-4.12)***-0.142(-8.43)***-0.0001(-0.70)-0.0005(-3.45)***0.0000(-0.27)-0.0001(-0.39)0.0002(0.90)
0.0006( 2.73)***0.004(3.58)***
0.0009(0.64)
0.337(6.62)***
0.952
141

growth rate and negative effects on ten-year growth rate. Therefore, in the
OLS estimations, the effects of the income distribution on economic growth
are mixed and not very clear.
In addition, in the following fixed effects estimations, F-tests which test
the null hypothesis that all prefecture-specific fixed effects
are zero wereconducted. As the table 4 and table 5 show, The test results always reject
the null hypothesis, so there exist unobservable prefecture-specific fixed
effects and therefore the fixed effects estimations are preferred over pooled
OLS estimations.
Therefore, this paper shows the results of estimating the following
fixed effects model:
ℎ( ,

),

=

In this equation,

,

+

ℎ( ,

,
)

+

,

+

+

+

,

(2)

is the average annual growth rate ofprefectural income from year t to t+n,denotes the prefecture i’sunobservable prefecture-specific effect,
denotes a period-specific intercept,
and , is the remainder stochastic disturbance term. The estimation
results are shown in table4 and 5.
In order to decide which of the random effects and fixed effects
estimation should be adopted, the Hausman test results are shown in thebottom of these two tables. When Hausman test is effective, it always reject
the null that explanatory variables , and the prefecture-specific effectis not correlated. This means that random effects estimates are notconsistent and fixed effects estimations should be adopted rather thanrandom effects estimations. Therefore, the estimation results of fixed
effects are shown.
In the fixed effects estimation with five-year growth episodes in table 4,
the income share of the third quintile (Q3) have positive and significant
effects on growth most of the time, and the Gini index also has positive andstatistically significant effects on growth in most estimations.
In the table 5, the fixed effects estimation with ten-year growth
episodes are reported. In this table, the income share of the third quintile(Q3) has negative but insignificant effects, and the Gini index have positiveand mostly statistically significant effects on the economic growth.
These fixed effects estimation results of table 4 and 5 may be biased by9

Table 4. Fixed effects estimations: Five-year growth episodes.
No controls
LogIncomeQ3

Controls-0.234-0.388-0.383-0.183-0.284-0.313-0.381-0.383-0.384(-26.02)***(-30.00)***(-32.42)***(-10.30)***(-12.35)***(-13.45)***(-12.73)***(-12.83)***(-12.81)***

0.829

1.297

0.250

0.675

0.280

0.098

(3.21)***(6.93)***(1.47)(4.17)***(1.92)*(0.50)

Gini

0.196

0.212

0.167

0.206

-0.110-0.089(12.79)***(14.99)***(6.37)***(7.63)***(-2.33)**(-1.40)

0.000

0.000

0.000

0.000

0.000

0.000

( 0.34)(0.48)(-0.14 )(-0.26)(0.00 )(-0.04)-0.001-0.001-0.001-0.001-0.001-0.001(-2.77)***(-2.15)**(-1.83)*(-1.00)(-0.89)(-0.91)

0.000

0.000

0.000

0.000

0.000

0.000

(0.18)(-0.82)(-0.54)(-0.96)(-0.91)(-0.89)

0.002

0.000

0.000

-0.002-0.002-0.002(12.34)***(-0.02)(-1.18)( -2.08)**(-1.98)**(-2.00)**

0.002

0.001

0.000

0.001

0.001

0.001

(4.52)***(1.74)*( 0.76)(0.96)(1.22)( 1.18)

0.003

0.002

0.002

0.002

0.002

0.002

(4.69)***(3.38)(2.94)***(2.88)***( 2.81)***( 2.83)***0.013-0.001-0.001

0.001

0.002

0.002

(2.88)***(-0.11)(-0.22)(0.18)(0.37)(0.37 )

-0.002-0.001

0.001

0.005

0.005

0.005

(-0.53)(-0.28)(0.34)(1.15)(1.14)(1.17)HighSchool

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

Controls and period dummies

0.662

1.276

1.026

0.450

0.917

0.892

1.189

1.241

1.224

(10.41)***(31.67)***( 19.92)***(6.13)***(10.15)***(10.20)***(11.16)***(11.92)***(11.20)***0.59510.47090.49360.71920.5554

0.5881

0.52130.52780.5282

4.67***

11.29***

13.90***

5.21***

6.82***

7.56***

3.28***

3.86***

3.85***

Hausman Test

-214.97-32105.16-2165.86-103.31-217.32-147.32183.59***187.71***

185.77***

N.obs.

282

282

282

282

282

282

282

282

282

Overall R2
F rest that all=0

Notes: t statistics in parentheses* Denotes a parameter which is significant at 10%, ** at 5%, and *** at 1%.

10

Table 5. Fixed effects estimations: Ten-year growth episodes.
No controls
LogIncomeQ3

Controls-0.219-0.296-0.307-0.199-0.246-0.246-0.260-0.261-0.260(-46.73)***(-13.69)***(-13.39)***(-13.80)***(-12.41)***(-11.05)***(-10.69 )***(-10.70)***(-10.58)***-0.0500.265-0.197-0.001-0.122-0.143(-0.26)(1.37)(-1.40)( -0.01)(-0.88)(-0.74)

0.088

0.103

0.000

0.062

0.062

0.021

-0.009(3.64)***(3.90)***(0.08)(3.07)***(2.69)***(0.50)(-0.16)

0.000

0.000

0.000

0.000

0.000

0.000

(-1.04)(0.41)(0.41)(0.79)(0.82)(0.80)

0.000

0.000

0.000

0.000

0.000

0.000

(-0.39)( -1.11)(-1.09)( -0.08)(-0.02)(-0.06)

0.000

0.000

0.000

0.000

0.000

0.000

(0.36)(-0.51)(-0.51)(-0.46)(-0.40)(-0.46)

0.000

0.000

0.000

0.000

0.000

0.000

(0.36)(-0.14)( -0.14)( 0.03)(0.10)(0.02)

0.000

0.000

0.000

0.000

0.000

0.000

( 0.49)(0.13)(0.13)(0.25)(0.21)(0.28)

0.001

0.001

0.001

0.001

0.001

0.001

(1.93)*( 1.65)(1.64)(1.31)(1.28)(1.31)

0.002-0.001-0.001-0.001-0.001-0.001(0.56)( -0.26)(-0.26)(-0.22)(-0.20)(-0.18)

0.002

0.002

0.002

0.003

0.003

0.003

(0.45)(0.61)(0.59)(0.68)(0.74)( 0.69)Gini

HighSchool

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

Controls and period dummies

0.761

0.991

0.977

0.688

0.815

0.815

0.872

0.852

0.876

(17.99)***(14.72)***(14.39)***(11.60)***(11.06)***(10.98)***(10.24)***(10.23)***(9.75)***

0.772

0.662

0.646

0.815

0.701

0.701

0.699

0.700

0.700

11.16***

4.09***

3.82***

6.52***

4.79***

3.97***

3.30***

3.28***

3.25***

Hausman Test

-44.86163.57***157.56***-5.94

467.37***1396.81***179.34***118.18***173.72***

N.obs.

141

141

141

141

141

141

141

141

141

Overall R2
F rest that all=0

Notes: t statistics in parentheses* Denotes a parameter which is significant at 10%, ** at 5%, and *** at 1%.

11

the fact that equation (2) contains a lag of the dependent variable (Panizza
2002; Caselli et al. 1996; Judson and Owen 1999). In order to address thispoint, I re-estimated the equation (2) with the robust GMM estimatorsdeveloped by Arellano and Bond (1991). The estimation results are shownin table 6.
In table 6, changes of Q3 sometimes have positive and significant
effects on growth, and changes in the Gini indices have negative andsignificant effect on changes in growth once. The positive effects of changesof Q3 are the same as those in fixed effects estimations. However, thenegative effect of changes in the Gini is the opposite from the fixed effects
estimations although the coefficient is statistically significant only in theestimation without Q3 and with period dummies.
Therefore, if the income of the third quintile is used as the equality
measure, (changes in) income equality enhances the (changes in) economic
growth. However, when (changes in) the Gini indices are used, (changes in)
the income equality decreases (changes in) economic growth most of the time.
Thus, the results are the same as those in Partridge (1997).
Partridge (1997) used the U.S. state panel data and explained the Q3
results with a positive relationship between the median voter’s relativewell-being and economic growth as suggested by the Persson and Tabellini(1994) and Alesina and Rodrik (1994). In addition, Partridge (1997)
explained the result of the Gini index also with the median voter theory aftermodest modification.
That is, by assuming that the likelihood of
growth-enhancing policies increases as higher-income groups gain politicalpower relative to lower-income groups.
That is what the positive
Gini-economic growth relationship may reflect.
The results from Japanese data can also be explained with thismodified median voter theory. Although Japanese prefectural governments
are more centralized than U.S. state governments, Doi (1999) empiricallyshowed that the median voter theory also applies to Japanese prefectural
governments.
As for other independent variables, if the initial income level is higher,
growth rate is lower, which means that prefectural per capita incomes tend
to converge. The human capital measured by the shares of high school
graduates and college graduates have negative effects on growth, which areopposite signs from the expected. In addition, larger share of employment
12Table 6. GMM Estimations(Dependent variables: growth rate in 5 years)
No period dummies
LogIncomeQ3-0.515-0.552-0.584-0.740-0.739-0.741(.038)***(.041)***(.048)***(.050)***(.047)***(.048)***

0.208

0.396

0.297

0.158

(.151)(.2284)*(.1592)*(.2227)

Gini

HighSchool

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

N. obs.

Period dummies-0.0130.067-0.094-0.057(.0388)(.0606)(.0471)**(.06631)

0.000

0.000

-0.001-0.001-0.001-0.001(.0003)(.0003)(.0002)*(.0006)**(.0006)*(.0005)*

0.000

0.001

0.001

-0.001-0.001-0.001(.0004)(.0004)(.0004)(.0006)(.0006)(.0006)

0.000

0.000

0.000

0.000

0.000

0.000

(.0006)(.0005)(.0005)(.0005)(0.0005)(.0005)-0.005-0.004-0.003-0.003-0.003-0.003(.001)***(.001)***(.001)**(.001)**(.001)**(.001)**-0.0010.000-0.001

0.001

0.001

0.001

(.0013)(.0014)(.0013)(.0011)(.0011)(.0011)

0.000

0.001

0.001

0.002

0.002

0.002

(.0010)(.0009)(.0009)(.0009)**(.0009)**(.0009)*

0.002

0.002

0.002

0.007

0.006

0.006

(.0060)(.0059)(.0058)(.0053)(.0051)(.0054)

0.002

0.004

0.006

0.007

0.007

0.007

( .0073)(.0068)(.0067)(.0058)(.0059)(.005)

1.730

1.856

1.776

2.476

2.560

2.550

(.173)***(.165)***(.178)***(.227)***(.221)***(.241)***

188

188

188

188

188

188

Notes: Robust standard errors in parentheses* Denotes a parameter which is significant at 10%, ** at 5%, and *** at 1%.

13

in agriculture, manufacturing, finance, insurance and real estate andgovernment sometimes raise the growth rate, and larger share of old people
increases or decreases the growth rate.
4. Conclusion
In this paper, prefectural panel data of Japan is used for the first timeto investigate how income equality affects economic growth. As a result of
estimations and tests using data from 1979 to 2010, the fixed effectestimation and GMM estimation results are chosen. In the fixed effectestimations, income equality affects five-year growth positively andstatistically significantly, if equality is measured with the income share of
the third quintile. However, income equality affects growth negatively andstatistically significantly if equality is measured with the Gini index. In the
GMM estimations with the Q3, equality enhances growth again. Theseestimation results are the very similar to those with Partridge (1997), andcan be explained with the modified median voter theory.
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