GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 351

New evidence on income distribution
and economic growth in Japan

Masako Oyama

December 2014

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

New evidence on income distribution and economic growth in Japan

Masako Oyama*
Osaka University

Abstract
There have been many theoretical and empirical researches on theeffects of income distribution on economic growth. This paper uses
Japanese prefectural panel data to empirically analyze how income
distribution affects economic growth.
Four measures of the income distribution are used in the system GMM
estimations. The Gini indices, income share of the third quintile and theratio of the income share of the top decile and the 5th decile show that income
inequality has negative effects on growth. The ratio of the income share of
the bottom decile and the 5th decile does not have statistically significant
effects. Therefore, the estimation results show that the increased income
inequality in recent Japan decreased the economic growth.
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 Ohtake, Toshiaki Tachibanaki, Takashi Kurosaki, Hideo
Akabayashi, Daiji Kawaguchi, and the participants at the Japanese Economic
Association 2014 Spring Meeting, the Kansai macroeconomics workshop, the 17th
Labor economics conference and the Tokyo labor economics workshop for their helpfulcomments and suggestions. I also appreciate the support at the Institute of Social andEconomic Research, Osaka University.

11. Introduction
On the relationship between income distribution and economic growth,
there have been many theoretical and empirical researches. About thetheoretical researches, income inequality decreases economic growththrough the following three channels, according to Weil (2013) and Halter
et.al. (2014). First, income inequality inhibits economic growth by fiscal
policy and less redistribution because more redistribution or higher tax
decreases the efficiency of the economy (Perotti 1993, Alesina and Rodrick
1994, Persson and Tabbelini). Secondly, inequality and capital market
imperfection decreases human capital accumulation, because households
who are liquidity-constrained decrease their spending on educations (Galor
and Zeira 1993, Galor and Moav 2004). Thirdly, inequality decreases thepolitical stability and makes it harder to make expectations on future
economic policies (Benabou 1996).
On the other hand, inequality can affect growth positively by increasing
savings and the accumulation of physical capital, because people withhigher income have higher savings rate. (Weil 2013, Kuznets 1955, Kaldor
1955). In addition, inequality can enhance the realization of high-returnprojects (Rosenzweig and Binswanger 1993) and increase R&D (Foellmi and
Zweimullwe 2006), which enhances economic growth. Therefore, theeffects of income distribution on growth have both signs and the overall
effect is an empirical problem.
In recent Japan since 1980, statistics such as the Gini indices showedthat inequality increased, and active discussion on this possibility of theincrease in income inequality was conducted (Otake 2005, Tachibanaki 2004,
2006). It is indicated that about half of the increase in the Gini indices wascaused by the population aging and the increase of households with only one
or two persons, but consumption inequality within the same generations was
also observed, and it indicates income inequality increased to some extent(Ohtake 2005). Also, the increase of inequality people felt became social
problem for several years, and recent increase of the maximum rate forincome taxes and the increase of inheritance taxes can be considered as theincrease of government’s income redistribution. Such increase or decrease
in income inequality can affect economic growth, and that effect is estimatedin this research with Japanese regional data for the first time, to the best of
my knowledge.
2

Figure１ Gini coefficients in Japan
0.6

Survey on the
Redistribution ofIncome (Income Before
Redistribution)

0.55
0.5

Survey on the
Redistribution ofIncome (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 2011

In figure 1, the transition of the Gini index from two major surveys inJapan are shown. The red line shows the Gini index on the income before
redistribution in the Survey on the redistribution of income, and it has been
increasing sharply.
However, the Gini index on the income after
redistribution in the same Survey shown by the blue line increased more
slowly during 1980-2002 and did not show constant increase after 2003.
Also, if we look at the violet line which shows the Gini index of the pretax
income in the National Survey of family income and expenditure, it is lower
but increasing since 1979.
In the existing empirical researches, the estimated effects of income
distribution on economic growth are different, depending on data and theestimation methods.
Lately, Deininger and Square’s (1996) panel
cross-country dataset and regional panel data within one country are widelyused in the empirical researches. While most cross-country studies found anegative relationship between income inequality and economic growth,Forbes (2000) and Li and Zou (1998) used Deininger and Square’s panel data
and found positive relationship between inequality and 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
3

open to capital flows from abroad. Actually, Barro (2000) found that
inequality increases growth within rich countries, but inhibits it in poorer
countries.
Recently, Panizza (2002) and Partridge (1997) conducted empiricalresearches with U.S. states panel data, Simoes et. al. (2013) used Portuguese
regional panel data, and Kurita and Kurosaki (2011) used Thai and thePhilippine regional panel data. The research in Panizza (2002) foundevidence in support of a negative relationship between inequality and growth,using a data of the 48 states of the continental US for the 1940-1980 period.
In the research using panel data of U.S. states, Partridge (1997) foundout that inequality measured with the Gini index has positive andsignificant effect on growth, and that inequality measured with the income
share of the third quintile has negative and significant effect on growth.
Partridge (1997) and Panizza (2002) both used the same two measures ofincome distribution, the Gini indices and the income share of the third
quintile. This research used four measures of inequality including thesesame two measures on the Japanese prefectural panel data and found that
inequality had negative effects on growth. Partridge (1997) explains his
result the median voter theory, and this theory can also be applied to some of
the results from Japanese data.
In addition, Simoes et. al. (2013) and Voitchovsky (2005) analyzed theeffects of different distribution measures on growth with cross-country panel
data. In addition to the general Gini indices, they used income percentile
data of the top income group and the bottom income group to analyze theireffects on income, and found different effects from different measures ofinequality. Thus, in this research, I analyzed the effects of the Gini indicesand the income share of the third quintile at first, then, I also investigatedthe effects of the income share of the top 10% income group and the bottom
10% income group.
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 open
to capital flows from abroad, and the measurement method of inequality are
the same in the data. Therefore, in this paper, prefectural panel data fromJapan is used, following the recent empirical researches. Since suchresearch using Japanese panel data has been conducted for the first time, it
is important to find out what kind of effects this data shows.
4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. The summary
statistics is shown in table 1, and the correlation matrix is shown in table 2.
Data is a panel for 47 prefectures for the 1980 (1979 for the distribution
variables) – 2010 (2009), every 5 years for 6 periods. growth5 is the
five-year average annual growth rate from the base year. LogIncome is thenatural log of the average per capita income in prefectures. These data are
obtained or calculated from “the Annual Report on Prefectural Accounts”
released by the Cabinet Office.
Gini is the Gini index about the yearly income and Q3 is the income
share of the third quintile in 47 prefectures. 90/50 is the ratio of the income
Table1 Summary Statistics
No.of obs.

Average

S.E.

Minimum Maximum

growth5282

0.01170.0245-0.0375

0.0654growth101410.01450.0253-0.0200

0.0627LogIncome329

3.37300.11103.07903.6646Gini

282

0.25230.08500.05900.3800Q3282

0.17690.00450.15650.189290/50

282

2.71510.24992.16664.081610/50

282

0.40240.03440.30670.5091HighSchool282

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

6.7096

5

6

0.707

-0.254-0.237

0.308

0.379

0.780

-0.741

0.382

0.251

0.197

0.424

-0.403

Q3

10/50

90/50

HighSchool

College

Agriculture

Urban

Old

Manufacturing

FinanInsRealEst

Government-0.879Gini-0.759growth10

1

growth5LogIncome

0.030-0.1380.217-0.382-0.1480.476-0.738-0.384-0.422

0.474

0.332

-0.601

0.948

1.000

0.060

-0.2240.116-0.732-0.1530.510-0.728-0.742-0.391

0.555

0.258

-0.9441.000

Table2. Correlation Matrix
LogIncome growth5 growth10

0.017

0.159

-0.174

0.636

0.103

-0.366

0.512

0.337

0.470

-0.525-0.378

1.000

Gini

-0.119-0.2860.303-0.142-0.3460.326-0.401

0.089-0.940

0.230

1.000

Q3

-0.250

0.093

0.472

-0.475

0.074

0.001

-0.312-0.157-0.427

1.000

10/50

0.124

0.246

-0.353

0.201

0.311

-0.3000.426-0.039

1.000

90/50

0.022

-0.044

0.064

0.172

-0.094-0.157

0.224

1.000

-0.1780.554-0.098

0.234

0.537

-0.7601.000

HighSchool College

0.373-0.645-0.3100.053-0.6971.000

Agriculture-0.1570.835-0.018-0.348

1.000

Urban

0.119

-0.350-0.2881.000

Old-0.785-0.0151.000-0.138

1.000
1.000

Manufacturing FinanInsRealEst Government

share of the top income decile and the 5th income decile, and 10/50 is theratio of the income share of the bottom income decile and the 5tn income
decile in prefectures.
The Gini indices data is obtained from “the National Survey of Family
Income and Expenditure.” The data on the income share of the third
quintile, 90/50 and 10/50 are calculated from the yearly household pretax
income share by deciles in “the National Survey of Family Income andExpenditure.1”
Table 2 shows that the correlation between the Gini index and Q3 is-0.378. The Gini index is the established measure of income distribution,and the negative correlation with the Gini index shows that Q3 is themeasure 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 + Q2) and that of the richer fourth and fifth
quintiles (Q4 + Q5) at the vertical axis. This figure shows that when theincome share of the middle class increases, income share of the poorer twoquintiles tend to increase and the income share of the richer two quintiles
tend to decrease. Therefore, we can interpret that the overall income
inequality tends to decrease when Q3 increases.
In Figure 3, the correlation between the change of Q3 and the change ofthe ratio of income share of the top decile and the 5th decile (90/50), and thecorrelation between the change of Q3 and the change of the ratio of income
share of the bottom decile and the 5th decile (10/50) are shown. Table 2
shows us that the correlation between Q3 and 90/50 is -0.940 and thecorrelation between Q3 and 10/50 is 0.230. Therefore, the income share of
the middle quintile has strong negative correlation with the income share of
the top decile, and has week positive correlation with the income share of thebottom decile. Figure 3 also shows that the Q3 and the 90/50 has negative
correlation, and the Q3 and the 10/50 has week positive correlation. This
also indicates that the Q3 is the measure of equality.

The data of the Gini indices and the yearly household pretax income share by deciles
in “the National Survey of Family Income and Expenditure” are data about thehouseholds who have two persons or more. The data on the number of household
members in each prefecture is not available, so the household income is used in thisresearch instead of per-capita income adjusted by the equivalence scale.
17Figure3. Change of Q3 versus 10/50, 90/50 during 1979-2004
1
0.8

Change of 10/50, 90/50

0.6

-0.02

0.4
0.2
10/50

0
-0.015-0.01-0.005

-0.2

0

0.005

-0.4
-0.6
-0.8
-1

Change of Q3

8

0.01

0.015

0.02

90/50

As for the other variables, following Panizza (2002), Partridge (1997)
and Perotti (1996), the first one is the average skills of the labor force(HighSchool is the percentage of the population over 15 years old that have
graduated from high school, but not a college, and College is the percentagethat graduated from two- or four-year college or graduate school) and they
are from “the employment status survey.” The next variables are the degreeof urbanization (Urban measures the fraction of the population that lives inurban areas), age structure (Old measures the percentage of the population
above 65 years of age), and industrial structure (Agriculture, Manufacturing,
FinanInsRealEst, Government measure the percentage of the population
employed in agriculture; construction; manufacturing; finance, insurance,and real estate; and government). Agriculture and Urban are the data fromthe “Statistical Indicator of Social Life –Prefectural Indicator－” by theStatistics Bureau, Ministry of Internal Affairs and Communications. Old,
Construction, Manufacturing, FinanInsRealEst, Government are from “thePopulation Census.”
3. Estimations
In this section, the estimation results are shown.
model is the following:ℎ( ,In this equation,

),

=

,

ℎ( ,

+

,
)

+

,

+

+

,

The estimated(1)

is the average annual growth rate ofprefectural income from year t to t+5,
is prefecture i’s natural log ofincome per capita,, is a variable capturing income distribution(measured using the Gini index, the income share of the third quintile, 90/50,
and 10/50) in year t-1 andis the prefecture i’s matrix of controls.
As the Kuznets curve argues, the growth or income level also affects
income distribution, so there is an inverse causality from growth to income
inequality. However, in this research, only the effect of inequality on
growth is estimated as the first step. In order to clarify this causality, thevariables on income distribution are used with one-year lag.
The matrix
includes stock of human capital (HighSchool andCollege), the degree of urbanization (Urban), age structure (Old) and theinitial industrial mix of the prefecture (Agriculture, Manufacturing
9

FinanInsRealEst, Government).
denotes the prefecture i’s unobservable
prefecture-specific effect, and , is the remainder stochastic disturbanceterm.
The independent variables of equation (1) contain the lagged dependent
variable (prefectural income) and this dynamic panel data structure may
make the fixed effects estimators biased2 (Panizza 2002; Caselli et al. 1996;
Judson and Owen 1999). Also, we have data of 6 periods for 5 years each,and this small number of samples makes the system GMM estimation
developed by Arellano and Bover (1995) and Blundell and Bond (1998) moredesirable than the first-difference GMM developed by Arellano andBond(1991). Therefore, in this research, the system GMM estimation isconducted as in the many recent literatures (Voitchovsky 2005, Kurita andKurosaki 2011, Castello-Climent, A., 2010 etc.)
The system GMM estimation results with Q3 and the Gini indices areshown in table3. In table 3, the estimation results without the control
variables are shown in the first two columns, and the results with the control
variables are in the next two columns, and the results with the control
variables and the period dummies are shown in the last two columns. In all
estimation results, the changes of Q3 have positive effects on changes ofgrowth when they are statistically significant, and changes in the Gini
indices have negative effects on changes in growth when they arestatistically significant.
Therefore, both of the income of the third quintile and the Gini indicesindicate that income inequality decreases the economic growth. The
difference between the two measures is that the Gini indices measure theoverall income distribution, although the income share of the third quintile
measures the income or well-being of the middle class of the economy. In
addition, we should note that in these estimations the population aging iscontrolled by the variable Old (the share of the residents who are older than
65 years), and Old does not have statistically significant effects on growth3.
The OLS, the random effects and the fixed effects estimations are also made, and theF-tests and Hausman tests results show that the fixed effects estimation is thedesirable among these three estimation methods. The fixed effects estimation results
are biased and are not reported in this paper.
3 Ohtake and Sano (2009) used prefectural panel data and median voter theory andfound out that population aging has negative effects on public spending on education.
Therefore, the higher share of old people can affect education (College) negatively andresults in lower economic growth.
210As for Q3, Partridge (1997) used the U.S. state panel data and obtained
the same positive effects. Partridge explained this result with a positive
relationship between the median voter’s relative well-being and economic
growth as suggested by the Persson and Tabellini (1994) and Alesina andRodrik (1994).
The Q3 results from Japanese data can also be explained with themedian voter theory. According to the median voter theory, the decision
over the tax rate is reached under simple majority rule in voting. Then, thetax rate or the policy chosen will be the one preferred by the person with themedian level of pretax income, who is often referred to as the median voter.
(Alesina and Rodrik 1994, Weil 2013)
Under this median voter theory, if the income share of the median voter
who is included in the third quintile increases, s/he demands lessredistribution. Then, the tax rate will be lower and there will be lessinefficiency caused by tax and redistribution, which leads to higher economic
growth rate.
Although Japanese prefectural governments are more centralized than
U.S. state governments, Doi (1999) empirically showed that the median voter
theory also applies to Japanese prefectural governments. In Japan,
prefecture revenues are almost entirely controlled by the central government,with the rates and sources of Local Taxes being basically determined bynational laws such that prefectural governments have limited discretion overthem. However, governors petition the central government as the agents ofthe median voters and that the central government accordingly distributes
inter-regional grants to each prefectural government in a manner reflecting
prefectural election results, i.e., the jurisdictional preference of the medianvoter. The probability of reelection for an incumbent governor increased as
the difference between the actual level of expenditure and the estimatedlevel desired by the median voter decreased ; a finding which supports theinterpretation of the median voter hypothesis in Japanese prefectures.
As for the Gini indices, the negative effects on growth can be caused by
the lower investment in human capital such as education, moreredistribution and more inefficiency, and political instability in Japan.
About the political instability, Japan had five short-lived cabinets, each ofwhich lasted for less than one year since 2006. These often changed
cabinets make the government policies unstable and make it harder for11

private agents to invest aggressively.
About 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 college
graduates among residents has positive effects on growth, which is the
expected positive effect of human capital. In addition, larger share of
Table3 System GMM Estimations
No controls
LogIncome-0.258-0.331-0.596-0.598-0.599-0.574-0.582-0.575(.0440)***(.0240)***(.0442)***(.0632)***(.0635)***(.0651)***(.0676)***(.0678)***(.0684)***0.386-0.382

0.413

0.277

0.387

0.274

(.2193)*(.3508)(.1900)**(.3269)(.1964)**(.3316)-0.177-0.270-0.125-0.055-0.114-0.048(.0692)**(.1143)**(.0610)**(.1074)(.0641)*(.1102)HighSchool

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

N. obs.

Controls and Period Dummies-0.314

Q3

Gini

Controls-0.001-0.001-0.001-0.001-0.001-0.001(.0005)(.0005)(.0005)(.0007)(.0007)(.0007)

0.002

0.002

0.002

0.002

0.002

0.002

(.0009)**(.0008)**(.0009)**(.0011)*(.0011)*(.0011)*-0.001-0.001-0.001-0.001-0.001-0.001(.0009)(.0008)(.0009)(.0009)(.0009)(.0009)

0.000

0.000

0.000

0.001

0.001

0.001

(.0014)(.0014)(.0015)(.0020)(.0020)(.0020)

0.002

0.001

0.001

0.002

0.001

0.002

(.0020)(.0020)(.0021)(.0022)(.0022)(.0022)

0.003

0.003

0.003

0.002

0.002

0.002

(.0016)*(.0016)(.0016)(.0017)(.0016)(.0018)

0.021

0.022

0.021

0.021

0.022

0.021

(.0064)***(.0060)***(.0066)***(.0067)***(.0066)***(.0068)**

0.009

0.010

0.009

0.010

0.011

0.010

(.0093)(.0093)(.0095)(.0098)(.0098)(.0100)

1.164

0.739

1.421

1.881

1.743

1.809

1.833

1.755

1.735

(.1528)***(.1105)***(.2023)***(.2380)***(.2353)***(.2711)***(.2480)***(.2402)***(.2874)***

188

188

188

188

188

188

188

188

188

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

12

employment in manufacturing, finance, insurance and real estate raised thegrowth rates. This may mean that these industries had higher growth
rates of income or productivity.
Next, I used the different income distribution measures to estimate
their effects on growth. Specifically, I used the ratio of the income share of
the top decile and the 5th decile (90/10) and the ratio of the income share of
the bottom decile and the 5th decile (10/50) to analyze how the distributionchange in the top income and the bottom income affect the growth. This isbecause the existing researches such as Halter, et. al. (2014),
Castello-Climent (2010) and Voitchovsky (2005) have shown that thedifferent parts of income distribution such as the income share of the top andbottom can have different effects on growth from the general distribution
shown by the Gini and the income share of the middle class shown by Q3.
The system GMM estimation results are in table 4 to 6. Table 4 showsthe estimation results without the control variables, table 5 shows theresults with the control variables, and the table 6 shows the results with thecontrol variables and the period dummies. In all tables, we find that theincome share of the bottom decile does not have statistically significant
effects, although the income share of the top decile mainly has negative
Table4 System GMM Estimations: No Controls
10/50

LogIncome

10/50

90/50

10/50 andGini, 10/50

10/50

90/50

90/50

and 90/50-0.262-0.335-0.346-0.287-0.348(.025)***(.0238)***(.0457)***(.0446)***(.0250)***(.0473)***

0.031

0.016

0.027

-0.036(.0344)(.0352)(.0342)(.0447)-0.0070.014-0.0060.020(.0043)(.0090)(.0043)(.0117)*Gini

N. obs.

Gini and-0.28790/50

Constant

Gini and-0.143-0.354-0.461(.0718)**(.1493)**(.1990)**

0.912

0.956

1.142

1.329

0.971

1.323

(.0841)***(.0893)***(.1534)***(.1570)***(.0904)***(.1683)***

141

141

141

141

141

141

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

13

Table5. System GMM Estimations: With Controls
Gini

10/50

90/50

Gini andGini and

10/50 and

Gini,
10/50

LogIncomeCollege

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

N. obs.

90/50

and 90/50-0.586-0.605-0.596-0.606-0.605-0.607(.0632)***(.0638)***(.0632)***(.0641)***(.0653)***(.0644)***(.0670)***0.003-0.013

0.005

0.020

(.0321)(.0330)(.0319)(.0404)90/50

HighSchool

90/50-0.596

10/50

Gini

10/50

-0.009-0.012-0.009-0.016(.0038)**(.0090)(.0039)**(.01116)-0.125-0.129

0.045

0.114

(.0610)**(.0641)**(.1454)(.1834)-0.001-0.001-0.001-0.001-0.001-0.001-0.001(.0005)(.0005)(.0005)(.0005)(.00058)(.00058)(.00060)

0.002

0.002

0.002

0.002

0.002

0.002

0.002

(.0009)**(.0009)**(.0008)**(.0009)**(.0009)**(.0009)**(.00098)-0.001-0.001-0.001-0.001-0.001-0.001-0.001(.0009)(.00092)(.0008)(.0009)(.00092)(.00091)(.00094)

0.000

0.000

0.000

0.000

0.000

0.001

0.000

(.0014)(.0014)(.0014)(.0015)(.00157)(.00147)(.00166)

0.002

0.002

0.002

0.002

0.002

0.002

0.002

(.0020)(.00212)(.0020)(.0021)(.0021)(.0021)(.00212)

0.003

0.003

0.003

0.003

0.003

0.003

0.003

(.0016)*(.0016)*(.0015)*(.0016)*(.0016)*(.0016)*(.0016)*

0.021

0.024

0.022

0.021

0.022

0.023

0.024

(.0064)***(.0062)***(.0061)***(.0065)***(.0066)***(.00623)***(.0066)***

0.009

0.007

0.011

0.010

0.011

0.010

0.010

(.0093)(.0095)(.0093)(.0095)(.0095)(0.0095)(.0096)

1.881

1.798

1.881

1.893

1.886

1.877

1.872

(.2380)***(.2389)***(.2355)***(.2442)***(.2423)***(.2409)***(.2459)***

188

188

188

188

188

188

188

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

14

Table6. System GMM Estimations:
Gini

With Controls and Period Dummies
10/50

90/50

Gini and

10/50

Gini

10/50

Gini,

and

and

10/50

90/50

90/50
and 90/50

LogIncome-0.574-0.569-0.592-0.570-0.580-0.591-0.573(.0676)***(.0671)***(.0679)***(.0689)***(.0679)***(.0694)***(.0696)***

10/50

0.002

-0.013

0.004

0.027

(.0329)(.0339)(.0330)(.0411)

90/50

Gini

HighSchool

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

N. obs.

-0.009-0.013-0.009-0.019(.0039)**(.0092)(.0040)**(.0114)*-0.114-0.117

0.072

0.176

(.0641)*(.0670)*(.1493)(.1890)-0.001-0.001-0.001-0.001-0.001-0.001-0.001(.0007)(.0007)(.0007)(.0007)(.0007)(.0007)(.0007)

0.002

0.002

0.002

0.002

0.002

0.002

0.002

(.0011)*(.0011)(.0011)*(.0011)*(.0011)*(.0011)*(.0012)*-0.001-0.001-0.001-0.001-0.001-0.001-0.001(.0009)(.0009)(.0009)(.0009)(.0009)(.0009)(.00096)

0.001

0.001

0.001

0.001

0.001

0.001

0.001

(.0020)(.0020)(.0020)(.0020)(.0020)(.0020)(.0021)

0.002

0.001

0.001

0.002

0.002

0.001

0.002

(.0022)(.0022)(.0022)(.0022)(.0022)(.0022)(.0022)

0.002

0.003

0.003

0.002

0.002

0.003

0.002

(.0017)(.0017)(.0016)(.0018)(.0017)(.0017)(.0018)

0.021

0.023

0.022

0.022

0.022

0.023

0.023

(.0067)***(.0067)***(.0066)***(.0068)***(.0068)***(.0066)***(.0068)***

0.010

0.008

0.012

0.011

0.010

0.012

0.011

(.00983)(.0101)(.0098)(.0102)(.0100)(.0102)(.0102)

1.833

1.783

1.867

1.834

1.836

1.860

1.780

(.2480)***(.2443)***(.2456)***(.2546)***(.2469)***(.2505)***(.2505)***

188

188

188

188

188

188

188

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

15

effects on growth when they are statistically significant. The Gini indiceshave negative effects on growth when they are significant, as in the previous
estimations. Therefore, in these estimations, we find that the inequality at
the top income and the overall income inhibits economic growth. This
result has the opposite sign from the existing literature which uses the
cross-country panel data (Castello-Climent 2010, Voitchovsky 2005).
One of the explanations of this negative effect could be that under low
growth rate and low rate of wage increase, increase in the income share of
top 10% makes people feel more inequality than the actual level, which maylead to demand for more redistribution. The second possibility is thatrichest 10% people have more political power than others and they may beless willing to pay for the government expenditure on public educations,because they tend to use more private schools. The third possibility is thathigher income share of top 10% people may make these rich individuals or
firms to move their residents to foreign tax haven such as Singapore or Hong
Kong, which decreases the efficiency of the economy and the tax revenue ofthe government.
Finally, the results of the first-difference GMM are shown in table 7 to
9 in order to see the sensitivity to changes in the estimation methods andinstrument set. In these two tables, the estimated coefficients on the four
distribution variables such as the Gini indices, Q3, 90/50, 10/50 have thesame sign as the results in the system GMM estimation. Although thecoefficients estimates on some control variables are different, the main
results about the effects of inequality on growth are unchanged, and it
suggests the estimate results in this research are robust.
Although the estimation results in this research show that income
inequality decreased economic growth in Japan, how income inequality
affected growth need to be investigated further. For example, the effects ofinequality through public spending on education or college enrolment rates
are planned to be estimated as the next step.
4. Conclusion
In this paper, the prefectural panel data of Japan from 1979 to 2010 isused to investigate how income inequality affects economic growth. In thesystem GMM estimations, income inequality affects five-year growth
negatively and statistically significantly, if inequality is measured with the16Gini indices and the income share of the third quintile.
The estimation results with Q3 can be explained with the median voter
theory, because if the income share of the third quintile increases, the incomeof the median voter also increases and less redistribution will be chosen,
which decrease inefficiency and enhances growth. The negative effects of
the Gini indices can be explained with less investment in human capital,
more redistribution and more inefficiency, and political instability. As forthe estimations with the income share of the top decile, we find that
inequality decreases growth, and the income share of the bottom decile does
not affect growth rate. The effect of inequality through education isplanned to be estimated in future research,

17

Table7. Sensitivity analysis: First-difference GMM Estimations
No controls
LogIncomeQ3

Controls-0.343-0.421-0.440-0.515-0.552-0.584-0.740-0.739-0.741(.0085)***(.0191)***(.0210)***(.038)***(.041)***(.048)***(.050)***(.047)***(.048)***

0.497

0.556

0.208

0.396

0.297

0.158

(.2145)**(.2621)**(.151)(.2284)*(.1592)*(.2227)Gini-0.154-0.031-0.0130.067-0.094-0.057(.0562)***(.0759)(.0388)(.0606)(.0471)**(.06631)

0.000

0.000

-0.001-0.0013-0.0011-0.0011(.0003)(.0003)(.0002)*(.00061)**(.00061)*(.00059)*

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

HighSchool

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

p-value¹
N. obs.

Controls and Period dummies

0.006***
188

188

0.214

188

188

188

Notes: Robust standard errors in parentheses* Denotes a parameter which is significant at 10%;、** at 5%, and *** at 1%.
¹ Wald joint test on the inequality variable coefficients in the regression

18

188

Table8. Sensitivity analysis: First-difference GMM Estimations with Controls
Gini

10/50

90/50

Gini andGini and

10/50 and

Gini,
10/50

LogIncomeCollege

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

and 90/50-0.618-0.629-0.616-0.616-0.628(.0631)***(.0633)***(.062)***(.0654)***(.064)***(.064)***(.066)***-0.008-0.021-0.0030.004(.0311)(.0319)(.0311)(.0401)-0.009-0.010-0.009-0.011(.0039)**(.0088)(.004)**(.0112)-0.1300.004-0.1350.032(.0637)**(.1440)(.066)**(.18627)0.000-0.0010.000-0.001-0.001-0.001-0.001(.0006)(.0007)(.0006)(.0006)(.0007)(.0007)(.0007)

0.001

0.001

0.002

0.001

0.001

0.002

0.001

(.0009)(.0009)(.0009)*(.0009)(.0009)(.0009)*(.0009)-0.001-0.001-0.001-0.001-0.001-0.001-0.001(.0009)(.0009)(.0008)(.0009)(.0009)(.0009)(.0009)

0.000

0.000

0.000

0.000

0.000

0.000

0.000

(.0014)(.0014)(.0014)(.0015)(.0015)(.0014)(.0016)

0.003

0.002

0.003

0.003

0.003

0.003

0.003

(.0021)(.0021)(.0020)(.0021)(.0021)(.0021)(.0021)

0.003

0.003

0.003

0.003

0.003

0.003

0.003

(.0015)*(.0016)*(.0015)*(.0015)(.0016)(.0016)*(.0016)*

0.011

0.011

0.013

0.013

0.012

0.013

0.013

(.0082)(.0082)(.0082)(.0083)(.0083)(.0082)(.0083)

0.015

0.015

0.016

0.015

0.016

0.017

0.016

(.0091)(.0094)(.0091)*(.0092)*(.0093)*(.0093)*(.0094)*

2.015

1.994

1.977

2.033

2.025

1.968

2.031

(.254)***(.2593)***(.2503)***(.2580)***(.260)***(.256)***(.2619)***0.054*

0.128

0.082*

0.144

141

141

141

141

p-value¹
N. obs.

90/50-0.601

90/50

HighSchool

10/50-0.618

10/50

Gini

90/50

141

141

141

Notes: Robust standard errors in parentheses* Denotes a parameter which is significant at 10%;、** at 5%, and *** at 1%.
¹ Wald joint test on the inequality variable coefficients in the regression

19

Table9. First-difference GMM Estimations With Controls and Period Dummies
Gini

LogIncome

10/50

College

Urban

Old

Agriculture

Manufacturing

FinanInsRealEst

Government

Constant

N. obs.

Gini and

10/50

Gini,

and

10/50

90/50

10/50

90/50

and 90/50-0.732-0.738-0.740-0.741-0.737-0.742(.0479)***(.0496)***(.0497)***(.0484)***(.0491)***(.0499)***(.0493)***0.002-0.0100.003-0.007(.0262)(.0260)(.0254)(.0353)

90/50

HighSchool

Gini and-0.739

10/50

Gini

90/50

-0.005-0.001-0.0050.000(.0029)*(.0061)(.0029)*(.0082)-0.095-0.082-0.100-0.096(.0471)**(.0993)(.0496)**(.1406)-0.0011-0.0013-0.0012-0.0011-0.0011-0.0012-0.0011(.00061)*(.00061)**(.00062)**(.00061)*(.0006)*(.0006)**(.00059)*-0.001-0.001-0.001-0.0010.000-0.001-0.001(.0006)(.0006)(.0006)(.00065)(.0005)(.0006)(.00065)

0.000

0.000

0.000

0.000

0.000

0.000

0.000

(.0005)(.0005)(.00058)(.0005)(.0005)(.0005)(.0005)-0.003-0.004-0.003-0.004-0.003-0.003-0.003(.0014)**(.0015)**(.0015)**(.0015)**(.0015)**(.0015)**(.0015)**

0.001

0.001

0.001

0.001

0.001

0.001

0.001

(.0011)(.0012)(.0011)(.0011)(.0011)(.0011)(.00115)

0.002

0.002

0.002

0.002

0.002

0.002

0.002

(.0009)**(.0009)**(.0009)**(.0010)*(.00096)**(.00093)**(.00099)*

0.006

0.006

0.007

0.006

0.007

0.007

0.006

(.0051)(.0054)(.0053)(.0054)(.0051)(.0053)(.00529)

0.007

0.005

0.007

0.007

0.007

0.007

0.007

(.0059)(.0054)(.0059)(.0059)(.0058)(.0059)(.0058)

2.560

2.534

2.541

2.571

2.573

2.535

2.587

(.2213)***(.2214)***(.2235)***(.2217)***(.2229)***(.2245)***(.2241)***

188

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%.

20

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