DBJ Discussion Paper Series, No.0801

Income Inequality over Boom, Slump and Recovery: Japan 1963-2005
Jason James(Kings College and Judge Business School,
University of Cambridge)
andPaul A. Kattuman(Corpus Christi College and Judge Business School,
University of Cambridge)

July

2008

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors inorder to protect the tentative characters of these papers. Any opinions, findings, conclusions
or recommendations expressed in these papers are those of the authors and do not reflect
the views of the Institute.

Income Inequality over Boom, Slump and Recovery:
Japan 1963-2005
Jason James 1 and Paul A. Kattuman 2

Summary. Motivated by concerns expressed over a structural increase in Japaneseinequality, we present unobserved-components time series models of the underlying
trends in the Japanese income distribution. The evidence suggests that inequality wassteady and moderate over the 80s, increased over the 90s as the Japanese economy
slumped, and has been declining since 1999, Variations in the real economy haveplayed a significant role in determining inequality, having their greatest effects on thebottom quintile of the income distribution. Recovery in the Japanese economy in the
2000s appears to have reversed a substantial proportion of the preceding rise ininequality.
RUNNING TITLE
Japanese Income Inequality
Key Words: Inequality, Japan, Income Distribution, Structural time series models
JEL Classification: D31, D63, H24, C22

Acknowledgements: We should like to thank Andrew Harvey for useful comments,
but we remain responsible for any errors or omissions. Jason James acknowledges
with gratitude a Shimomura Fellowship from the Research Institute of Capital
Formation, Development Bank of Japan, and valuable help from several researchers
there, particularly Kojiro Sakurai.

1

Kings College and Judge Business School, University of Cambridge, Trumpington Street, Cambridge,
CB2 1AG, United Kingdom, Email: j.james@jbs.cam.ac.uk, Tel: +44 (0) 7968 816495 (corresponding
Author).

2

Corpus Christi College and Judge Business School, University of Cambridge, Trumpington Street,
Cambridge, CB2 1AG, United Kingdom, Email: p.kattuman@jbs.cam.ac.uk, Tel: +44 (0) 1223
764136. Fax: +44 (0) 1223 339701.

1 Introduction
Post-war Japan has traditionally been regarded, and has been happy to regard itself, asa country with a high degree of income equality, a view that largely originated withan OECD study (Sawyer 1976). Over the 80s and 90s, however, the evidence started
to point to widening inequality in Japan. The Gini coefficient, as reported in the
Ministry of Health, Labour and Welfare’s triennial Income Redistribution Survey(IRS) has been rising since 1981, sparking a number of analyses of reasons for thetrend (e.g. Kaneko 2001), and laments of the demise of Japan’s all-embracing middle
class and the growth of an underclass (e.g. Tachibanaki 1998; Satou 2000; Miura
2005). In terms of international comparisons, too, Japan has started to be positioned
along with the US and UK as a country with relatively high inequality (Forster andMira d'Ercole 2005). The conventional view now is that “Japan has shifted from anequal society to a differential society” (Ohtake 2005: 1; Tachibanaki 2006: 103), The
issue is a highly emotive one in Japan, to the extent that the Gini coefficient has
recently been discussed in the national Diet (Ohtake 2006).
As elsewhere, the most commonly cited reasons for rising inequality in Japan are: theimpact of globalisation in reducing demand for unskilled workers in advanced
economies; the impact of technology in allowing highly-skilled workers to leverage
their advantages further; and government policies such as reductions in the tax rates
applied to higher income levels.
Figure 1. Gini coefficient: Japan

0.50

Primary Gini

0.45

0.40

0.35

Redistributed Gini
0.30
62

67

72

75

78

81

84

87

90

93

96

99

02

Source: Income Redistribution Surveys (Ministry of Health, Labour and Welfare)Note: Time scale distorted from 62-72 due to irregular survey dates
Tachibanaki arguably has been the most influential in bringing the issue of rising
Japanese inequality to both academic and popular attention, but his conclusions are2not universally accepted. There can be little doubt about the widening in primary (i.e.
prior to redistribution via tax and benefits) income differentials in the IRS data.
However, as noted by Ohtake (2006: 8), the definition of primary income in the IRS
data excludes income from public pensions, but includes private sector retirement
allowances. Individuals whose only income is their public pension are therefore
recorded as having zero income in this survey, which results in large apparent
inequality3.
Inequality trends in redistributed income (net of tax, benefits and public pensions) areless marked. Forster and Mira D’Ercole (2005: 61-62) show the most recent Gini
coefficient for Japan (in ‘around 2000’) at 0.314, against 0.326 for the United
Kingdom and 0.357 for the United States, placing all three countries in the ‘above
average’ group for inequality. While Tachibanaki characterises the rise in the Gini
coefficient for redistributed income between 1981 and its recent highs as ”alarmingly
large” (2005: 5), this increase (of 6.7%) would be characterised as “small” under theframework used by Forster and Mira D’Ercole (2005: 12). Moriguchi and Saez(2006), using income tax data to estimate top income shares, find recent increases ininequality to be modest, even before the redistributive impact of taxation: “[O]ne ofthe distinct characteristics of Japan today is its low income inequality in the absenceof government redistribution” (p.4). Nishizaki, Yamada et al. (1998) point out thatfailing to adjust the data for household size is misleading, and that inequality on an
adjusted basis remains relatively low in Japan. Their argument is that while inequality
has increased in the preceding decade, this is (i) because of an increase in the numberof retired households, and (ii) because of a rise in the proportion of relatively lowpaid young women in the workforce. They also argue, noting that the middle
quintile’s share of income has not notably changed, that the “hollowing out” of the
middle class (polarisation) being pointed to in several countries does not appear tohave happened in Japan (p. 14). One might add that not everybody thinks Japan’segalitarian society is a good thing; some scholars have argued that too muchredistribution has been a problem for the economy, a view which has been cited infavour of government policies such as reducing top marginal tax rates in recent years(see for instance Kato and Moroi 1996).
In much of this debate, the impact of variations in national economic performance on
inequality is downplayed 4. Our focus in this paper is on the distributive impact ofvariations in the aggregate economy. It is unreasonable to expect households to movein tandem over the business cycle (see for instance Barlevy and Tsiddon 2006).
Which households are most susceptible to decline in economic activity (and to
recovery)? If households in different parts of the income distribution responddifferently to common underlying ups and downs of aggregate economic activity,
then we should expect inequality to be affected by variations in the macro economy.
Deflation is also likely to affect inequality through its effect of redistributing incomes
towards from persons with fixed nominal incomes, such as pensioners.
3

Tachibanaki himself appears to have recanted from the use of the primary income numbers: “I frankly
regret my carelessness” (Tachibanaki 2000)
4

In recent years the focus in business cycle research has been on the micro adjustment behaviour ofhouseholds, as well as firms, to the dynamics of the aggregate economy. This relates to the interactionof cross sectional distributions with aggregate economic variables, and how inequality andheterogeneity propagate changes in the economy, because distributional changes rather than washingout, aggregate up in driving changes in real macro variables.

3

Japan offers a particularly interesting test case in this respect, having had a major
economic boom (the “bubble”) in the late 1980s, a prolonged period of decline in the
1990s, and something of an economic recovery so far in the 2000s. In characterising
the Japanese recession on the basis of various measures of the output gap, Kuttner andPosen (2001) concluded that real GDP was below potential real GDP to the order of 3
to 4 percentage points by the end of the 1990s: “No other country in the OECD has
suffered such a lengthy period of unremittingly below potential growth, or even one
half so long or deep” (p. 99-100). There was a clear rise in unemployment andsustained deflation. Beset by structural problems, the conventional business cycle
largely disappeared in Japan in the 1990s, but the recovery that has gathered strength
in the 2000s is likely to lead to a return of the economic cycle. How was income
inequality affected by these variations?
This paper offers three contributions. First, we analyse income distribution in terms ofdecile and quintile 5 data, rather than summary measures. Some of the debate about
Japanese inequality has relied on Gini coefficients estimated from limited data.
Summary measures of inequality lose information about where in the income
distribution changes are taking place, and therefore some of the evidence as to why
the changes might be occurring. The Gini coefficient, in particular, is more sensitive
to differences around mean income than some of the other inequality measures. So if
the hypothesis is that the effects are more pronounced at the extremes of the income
distribution, then the Gini coefficient may not capture this. 6 In this paper, we consider
alternative measures that can be directly calculated from the available data, and easily
decomposed to show which parts of the income distribution are driving changes ininequality.
Secondly, we focus on the long-run evolution of inequality, applying structural timeseries models to the available data. This enables us to measure the link between
inequality and the macroeconomy with more precision. Structural time series models
provide a flexible framework by which changes in the underlying level of a(stochastic) variable can be distinguished from purely random and cyclicalfluctuations (Harvey and Bernstein 2003).
Thirdly, our analysis is focused on the responsiveness of different parts of the income
distribution to the movements in the real economy, to attempt a precise
characterisation of the way in which inequality is affected by macroeconomic
variations.
In the following section we describe the various data sources available and theirstrengths and weaknesses. In section 3, we examine the decile and quintile income
data from the two main data sources, going on to observe patterns after adjusting the5Technically, the ith quintile of a distribution F is the value in the support of that distribution thatsolves the equation F(x) = 0.2i. The data used in this paper contain only average income within eachquintile. We follow Castaneda, Diaz-Gimenez et al. (1998) in using the terms “quintile” and “decile” todenote average incomes within each group. The average income of the poorest 20% of households isreferred to as the ‘bottom quintile’, the average income of the next 20% as the ‘second quintile’ etc.

6

It has been held that the traditionally broad Japanese middle class is disappearing as an increasing
number of households join either the top or bottom end of the distribution.

4

data for household size. In section 4, we outline the unobserved components timeseries approach, and apply it to the Family Income and Expenditure Survey data. In
Section 5, we experiment with adding some cyclical explanatory variables. Section 6
considers the results, and draws some tentative conclusions.
2. The Data
It is useful to outline the sampling frames and income definitions used by the main
statistical sources on the Japanese income distribution. These are:(i)

The Income Redistribution Survey (Shotoku Saibunpai Chousa Houkoku),
published by the Ministry of Health, Labour and Welfare

As argued by Tachibanaki (2005), the Income Redistribution Survey (IRS) is the mostreliable source for inequality measurement. Its primary focus is on income inequality
and the extent to which this is mitigated by the effects of the tax and benefits system.
According to Nishizaki, Yamada et al (1998: 19), however, it is said to have a higher
weighting of low-income households than the National Survey of Family Income andExpenditure (NSFIE). Gini coefficients and income decile information are calculated
directly from the original data for both “primary” and “redistributed” (i.e. after tax
and benefits) income. The sample is drawn from almost all households in Japan, theonly exceptions being single persons living in company or school dormitories, andthose living in social welfare facilities. The survey was first carried out in 1962, andthe latest data relate to 2002 (the survey is published about 18 months after the end of
the year to which it relates). The sample size in 2002 was 10,125 households, with a
response rate of 75.3% (7,623 households). The main drawback is that it is only
published every three years (and the intervals between the first three surveys were offive years). As a result, there are only 13 data points, too few for time series analysis.
(ii)

The Family Income and Expenditure Survey (Kakei Chousa), published by theStatistics Bureau of the Ministry of Internal Affairs and Communications

The Family Income and Expenditure Survey (FIES) appears monthly, but income on
an all-household basis is measured annually. The definition of income is different
from those used in the Income Redistribution Survey, since the FIES definition
includes Social Security Benefits as income, but is stated on a pre-tax basis. In effect,
this income measure is somewhere between the “primary” and “redistributed income”
measures of the IRS data. For years covered by both, the average correlations between
the FIES and IRS income quintile shares are 0.72 and 0.62 for (IRS) primary andredistributed income respectively. By contrast, the correlations for the IRS Gini
coefficients against the FIES ones estimated by Ohtake (2005) are 0.78 for primaryand 0.80 for redistributed income respectively. Annual income quintiles and, in recent
years, income deciles, are included in the published survey, although Gini coefficientsare not. The data series goes back to 1963, with longer time series available forcertain sub-populations such as urban households or “workers’ households”. The
major drawback of this survey is its sampling frame, which for the longer-run timeseries excludes single-person households.
(iii)

The National Survey of Family Income and Expenditure (Zenkoku Shouhi
Jittai Chousa), also from the Statistics Bureau as above

5

The National Survey of Family Income and Expenditure has a much larger sample
size than the FIES, of 60,000 households, but it contains a similar sample bias to the
FIES (Tachibanaki 2005: 3). It is only published at five-year intervals, so it cannot be
used for time series analysis. This makes it less useful than the IRS in terms of both
frequency and sample bias.
(iv)

The Basic Survey on Wage Structure (Chingin Kouzou Kihon Toukei Chousa,
also known as Chingin Sensasu), published by the Ministry of Health, Labourand Welfare

The Basic Survey on Wage Structure (BSWS) is immensely detailed, with a sample
size of over one million, and contains data series going as far back as 1948. The
survey unit is individuals, not households. Not only is the sample limited to wageearners, but it only covers wage-earners at private sector companies employing ten or
more people. It is thus not very useful when considering overall inequality inJapanese household incomes. It is however a rich source if the topic is more narrowly
restricted to private sector wage inequality.
(v)

Income tax statistics, published by the National Tax Agency (contained in the
Kokuzeichou Toukei Nenpou)

Finally, the Income Tax data are potentially of some use in measuring inequality, but
as with all tax data sources, they exclude individuals with incomes below the taxable
threshold, and may be more affected than survey data by under-reporting of incomes.
Their strength is that annual income bracket data is available all the way back to1887, when income tax was first introduced in Japan. There are some otherdifficulties with these data: (i) The taxable unit switched in 1950 from the householdto the individual. This not only makes comparisons with the period before 1950
difficult, but also raises problems thereafter, since in the context of inequality studies,
the household is generally considered a more meaningful unit than the individual. (ii)A withholding tax system was introduced in 1950, since when tax data have beencollected separately for individuals filling in tax returns and for withholding tax. The
two sets of data are not strictly comparable, and information about payers ofwithholding tax is sparse, particularly in income categories other than wages, such as
dividends and interest. Nevertheless, these data have been used to produce a credible
series of top income shares in Japan going all the way back to the late 19th century(Moriguchi and Saez 2006).
We follow Tachibanaki in regarding the IRS and the FIES as the best sources for ananalysis of inequality in household incomes. He uses the FIES only as a secondary
source because of its sample bias, but he does note that “from the 1960s to the late
1990s…the time-series trend in income inequality (measured by the estimated Gini
coefficient) from the Family Expenditure Survey is almost parallel with that from theIncome Re-Distribution Survey” (2005: 70). His chart of the estimated Gini
coefficient from the FIES only extends up to 1999, but in fact this was its recent peak.
Several more years of data are available, and they show a sharp drop in 2003 (Figure2). It is also worth noting that differences are apparent between the Gini series
estimated by Tachibanaki and by Otake, even though they both derive from the same6

data. This is no doubt a result of the fact that there are large error margins in Gini
series estimated from income quintile data.
Figure 2: Gini coefficient estimated from FIES data
0.32
0.31
0.30
0.29
0.28
0.27
0.26
0.25
0.24
63 65

67 69 71 73 75 77 79 81

83 85 87 89 91 93 95 97 99

01 03

Source: Ohtake (2005: 7)
2003’s lower inequality level appears to have persisted in the following two years,
judging from the separate inequality measure we calculate below (see Figure 12). We
will devote more attention to the FIES because of its amenability to time-series
analysis, but we accept that it is more useful in showing the direction of the trend
rather than as a reliable indicator of inequality levels themselves.

3. Summary Analysis
Deciles and Quintiles:
Income decile shares are directly available in the IRS data, and they give a morenuanced picture than summary inequality measures as to which parts of the income
distribution are being affected by changing inequality. Although the inequality debate
is primarily about redistributed income, looking at the primary income data helps to
identify the contributors to changes in inequality.
Some of the patterns in the primary income deciles are striking – in particular the
speed of the deterioration at the bottom end of the distribution. The bottom decile’s
share of primary income, which was over 2% in 1972, fell to 0.0% (as rounded in thedata) in 1990, and has remained there ever since (Figure 3). The decline in the second
decile’s share has also been marked. From 4.0% in 1972, it fell below 2% in 1990,
and more than halved just between the 1999 and 2002 surveys, falling from 0.8% to0.3% (Figure 3). It seems likely that the main reason for these trends is the rising
proportion of households with little or no income other than their public pensions,which are not counted in primary income, as noted above. The top decile’s share of7primary income has been rising, but less consistently so. It remained stationary
between 29.7% and 29.3% over the 1990-1996 period, for instance, before risingagain to reach 31.7% in 2002 (Figure 3).
Figure 3: Selected primary income decile shares
35 %
30
25
Decile 1

20

Decile 2

15

Decile 5
Decile 910

Decile 10

5
0
62

67

72

75

78

81

84

87

90

93

96

99

02

Source: Income Redistribution Surveys (Ministry of Health, Labour and Welfare)Note: Time scale distorted from 62-72 due to irregular survey dates
Figure 4: Selected redistributed income decile shares
30
25
Decile 1

20

Decile 2
Decile 515

Decile 910

Decile 10

5
0
62

67

72

75

78

81

84

87

90

93

96

99

02

Source: Income Redistribution Surveys (Ministry of Health, Labour and Welfare)Note: Time scale distorted from 62-72 due to irregular survey dates
It is clear from the much flatter trends in the redistributed income deciles – as fromthe growing gap between the two Gini coefficients in Figure 1 - that the tax andbenefits system is playing a substantial role in cushioning the effects of risinginequalities in primary income. Nevertheless, some widening of differentials has also
been occurring at the redistributed level. The decile trends again reveal some nuances
not apparent from the Gini coefficient. The downtrend in the bottom decile’s share of
redistributed income is in fact not a feature limited to the last two decades, and has
persisted throughout the forty-year span of the data series (Figure 4). Since 1990, thebottom decile share has shown no clear trend in either direction. The low point (1.4%)
was reached in 1999, but the 2002 data point (1.7%) puts it marginally above its early
1990s level. Slippage for the second decile is more persistent, but very gradual8

(Figure 4). The data for the top decile mirror the Gini statistic more closely, with adecline to the 1981 low followed by a steady uptrend to new highs thereafter, andsome decline between 1999 and 2002 (Figure 4).
If we divide the income of the top and bottom quintiles, respectively, by the incomeof the middle quintile, we can see that the primary income of the bottom quintile has
fallen from around one-third of middle quintile income in 1978 to just 2% of it in2002 (Figure 5). On a redistributed basis, the percentage drops from 38% to 31% overthe same period, with a slight increase between the most recent two surveys, since thefigure in 1999 was 29%. (This change in direction is entirely due to taxes andtransfers; on a primary income basis the figure more than halved over the same three
years, from 5% to 2%.)At the upper end of the income scale, the top quintile was earning 2.8 times as muchin primary income as the middle quintile in 1962. After dropping to a low of 2.4 times
in 1981, this figure has now recovered to a new high of 3.1 times. After redistribution,
the same trends are apparent, but they are relatively mild; the top quintile earned 2.6
times as much as the middle quintile in 1962, fell to 2.3 times in 1981 (and also in1975), and was back to 2.7 times in the 2002 data. (Even isolating the top decile ofthe data only, dividing this by the middle quintile, and doubling to put it on acomparable basis, gives a similar set of figures, starting at 3.3 times, falling to lows at
3.0 times, and rising to 3.4 times currently.)
Figure 5: Top and bottom quintile incomes as ratios of middle quintile
3.5

Top/middle quintile ratios

3

Primary

2.5
Redistributed
2
1.5

Bottom/middle quintile ratios

1

Redistributed

0.5
Primary
0
62

67

72

75

78

81

84

87

90

93

96

99

02

Source: Income Redistribution Surveys (Ministry of Health, Labour and Welfare)Note: Time scale distorted from 62-72 due to irregular survey dates
Overall, therefore, the decile and quintile data suggest some aspects not apparent fromthe Gini statistics. Firstly, in terms of primary income, the most striking feature is thecollapse in income share of the poorest households. As discussed above, the main
reason for this appears to be that public pension income is excluded from thedefinition of primary income.

9Secondly, the tax and transfer system has acted, as it should, as a significant brake on
the decline in redistributed income share among the poorest deciles. Again, the public
pension system is a large part of the reason for this. Thirdly, the bottom decile’s shareof redistributed income has been trendless since the beginning of the 1990s, so there
is little evidence here that the very poor are getting poorer.
Many of these phenomena can, to a large extent, be explained by the ageing of the
population and the generosity of the state pension system. Many retired households
have little or no “primary” income, relying heavily on the state pension, so as anincreasing proportion of households move into retirement, the primary income of thelowest brackets has collapsed. Trends in redistributed income are much less marked
because of the growing role of state pensions. Public spending on the aged accounted
for only 25% of total welfare spending in 1973, but 69% in 2001, with pension
spending accounting for the bulk of this. The growth is driven not only by the rising
proportion of elderly in the population; per capita old age pensions have risen twice as
fast as national income between 1975 and 2000 (Chopel, Kuno et al. 2005).
Adjusting for Household Size
Although income inequality has tended to increase since the 1980s, there has also
been an increase in the diversity of households in terms of size, and in particular there
has been a rise in the number of single-person households. Furthermore, there is aclear link between household income and household size; for every year in the FIES
data (which exclude single-person households), the average household size of any
income quintile is smaller than the average household size of all income quintiles
above it. It is necessary to adjust for household size; the average Japanese householdin the IRS data has 2.4 members, so a one-person household with below average
income would not necessarily have a lower than average income in any meaningful
sense.
Atkinson, Rainwater et al. (1995) offer a detailed discussion of equivalence scales
that adjust for household size. One relatively simple scaling method is to divide
household income by the square root of the number of members of the household.
Using this method, Nishizaki, Yamada et al. (1998) found that Japanese inequalities
were substantially lower after adjustment was made for household size, and also thatJapan remains a relatively “equal” country by international standards. The same
method is also used by Ohtake (2005).
This adjustment is made in the IRS data to calculate equivalised Gini coefficients andincome deciles, but these adjusted data are only available for the last two surveys. On
the equivalised basis, the most recent IRS Gini coefficients for primary andredistributed income are 0.419 and 0.322 respectively, substantially lower than the
0.498 and 0.381 respectively calculated from the unadjusted data. Households withlow primary income tend to be those headed by elderly people, but these households
often have relatively few members, since the children have generally set up their own
households by this stage. In 2002, average primary incomes for households headed bypeople of 75 or over were ¥2,172,000, less than half the average of ¥5,108,000 for all
households. But on an equivalised basis, this figure rises to 63% of average householdincome, because, calculating back from the IRS data, the average household has 2.4
members, while the average household headed by someone 75 years or over has only10

1.1 member - the majority of these households are single-person households. Other
household types which tend to have low incomes even after adjustment for size, andtherefore certainly before as well, (see Nishizaki, Yamada et al. 1998) are single
households, and single-parent households (the latter are of course larger than average
where there is more than one child).
Because the equivalised data from the IRS are only available for the last two surveys,
it is impossible to draw any conclusions from them about inequality trends. But since
the Gini coefficient for equivalised redistributed income shows a drop between 1999
and 2002, from 0.3326 to 0.3217, it certainly is not indicative of a rising trend.
One way to examine this issue is to use the FIES data, which have consistently
contained the relevant information on household size since the survey began. (Asnoted above, however, these data exclude single-person households, and to the extent
that the proportion of single-person households is increasing, they may underestimate
the increasing dispersion of household sizes.) It can be seen from Figure 6 that there
has been an increasing dispersion in household size between the top and bottom
income quintiles since around the early 1980s, suggesting that this may explain partof the rise in inequality since then.
Any inequality measures calculated from this data will be “pseudo-“measures; in theabsence of the full micro data the households cannot be reordered in terms ofequivalised income. Since Gini coefficients based on the FIES data are already only
estimates, the value of pseudo-Gini coefficients adjusted for household size would be
limited. We therefore prefer to work directly with the quintile data. Again, adjustingthem for household size leaves them as “pseudo-equivalised quintiles”, but they
nevertheless give us some information about the extent to which apparent increases ininequality are driven by changes in household size.
Figure 6: Trends in relative household size: top and bottom income quintiles
120%
115%
110%
105%

Top income quintile: household size as % of mean

100%
95%

Bottom income quintile: household size as % of mean

90%
85%
80%
75%
63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05

Source: FIES

11

Figure 7 shows pseudo-equivalised income quintiles from the FIES data, where thescaling is by dividing each quintile’s income share by the square root of its mean
number of household members. To make any trend in inequality more visible, in
Figure 8 we show the top (pseudo-)quintile’s equivalised income share as a multipleof the bottom quintile’s share. Here we can see some increase in inequality taking
place between the 1970s and 1990s, roughly in line with the IRS data. However, thetrend appears to have reversed since the end of the 1990s, and the figures for the last
few years put inequality fully back to early 1980s levels.
Figure 7: Equivalised income: pseudo-quintiles
45
40
35
30
25
20
15
10
5
0
63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03

Source: FIES

12

Figure 8: Equivalised pseudo-income quintile shares – Top quintile as multipleof bottom quintile
5.0
4.8
4.6
4.4
4.2
4.0
3.8
3.6
3.4
3.2

03

01

99

97

95

93

91

89

87

85

83

81

79

77

75

73

71

69

67

65

63

3.0

5. Unobserved components time series analyses
The fact that income quintiles in the FIES series are available annually since 1963
allows for a more systematic time series analysis. In this section we estimate
unobserved-components time series models for income quintiles, following theapproach taken by Harvey and Bernstein (2003) in modelling US real wage deciles.
Figure 9 shows the logs of average real annual household incomes by quintile since1963. We use log income to focus attention on relative differences. In the Japanesecase, the dominant feature is the rise in real incomes from which all quintiles
benefited over the period until 1990. All quintiles have seen some weakening over thelast decade.
Figure 9. – Log real annual household incomes (¥000, 2002 yen )
10.0
9.5
9.0

Quintile 1
Quintile 2
Quintile 3
Quintile 4
Quintile 5

8.5
8.0
7.5
7.0
6.5
63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05

13

For each quintile, we estimate a univariate time series model, to decompose the data
into components. 7
In Structural Time Series models, the value of the variable of interest, y, is conceivedof as consisting of its actual underlying level μ, plus a normally distributed error
component ε, which represents sources of error such as sampling or measurement
error. In the local level model, the underlying level at time t μ t is equal to theunderlying level in the previous period μ t-1 plus a normally-distributed shockη t . η
represents the various factors which cause the actual level of the variable to change,
which are not specifically identified. In the local level model the variable is as likely
to move up as to move down at any given time-point; there is no trend.
Local level model:
y t = μ t + ε t ~ NID (0,σ ε 2), t = 1,...., T,
μ t = μ t-1 + η t , η t ~ NID (0, σ η 2)
In the local linear trend model a trend component β is added in the determination of
the level. This too is subject to a normally distributed shock ζ in each period,
representing the unobserved fundamental factors which cause the trend in the variable
to change, but this version of the model allows for trends which persist over time. It isalso possible to assume that the variable is influenced by one or more cycles, and wehave generally included a cyclical component in the models discussed below. Details
regarding the modelling procedures can be found in Koopman, Harvey et al. (2000).
Local linear trend model:
y t = μ t + ε t ~ NID (0,σ ε 2), t = 1,...., T,
μ t = μ t-1 + β t + η t , η t ~ NID (0, σ η 2),
β t = β t-1 + ζ t , ζ t ~ NID(0, σ ζ 2)
Once the model has been specified in this way, parameter values are estimated by
maximum likelihood. The main diagnostic statistics used in evaluating models arer(1), the first-order residual autocorrelation, and Q(P,d), the Box-Ljung statistic basedon the first P residual autocorrelations, and assumed to have a χ2 d distribution in acorrectly specified model.
We have followed Harvey and Bernstein in preferring to specify a non-stochastic
level, which gives a more easily identifiable trend. We have differed, however, inallowing for the inclusion of cyclical effects in real income and/or inequality
measures, guided by the empirical evidence that these exist.

7

These models were estimated using the STAMP software.

14

Table 1: Univariate Time Series Models Diagnostics
Quintile
S.E.
Q (P†, 6)
R(1)
Log L
Cycle Period
10.0327
3.44
-0.02
138.7
6.6 years
20.0239
6.34
-0.07
151.6
6.5 years
30.0216
8.84
-0.05
155.7
6.0 years
40.0206
6.06
-0.06
157.6
5.9 years
50.0316
6.25
0.00
140.1
4.9 years† The STAMP output selects P as the lag length with highest residual autocorrelation;
the value of P therefore varies by quintile.
Table 1 shows some diagnostics on the univariate time series models generated foreach quintile’s logged real mean income. All of the models have acceptable fits. In nocase does the Box-Ljung Q statistic suggest a problem with model specification, andthe estimated cycle periods are in the range of 4.9-6.6 years.
These models enable us to separate out cyclical and random effects so that we can see
the underlying level of real mean income in each quintile clearly. Since the slope is an
explicit component of the model, we can also see whether the real income level in any
given quintile is trending upward or downward, and at what points the trend has
switched in the past. Exponentiating the log variables shows, in Table 2 below,
underlying real income levels and trends for each quintile as at the latest data point in2005. Real incomes are expressed in 2002 yen.
In the case of the bottom income quintile, for instance, the underlying level of realmean income in 2005, after removing cyclical and random components, was¥2.790mn per annum, 0.2% below the actual level of ¥2.796mn. The slope coefficientis -0.0013, suggesting that real income is falling by 0.1% per annum for this income
group, though the negative slope is not statistically significant. Similar results for theother quintiles are shown in Table 2. While none of the slope coefficients are
significantly different from zero, it is nevertheless suggestive that the slope
coefficients are all negative, and that the rates of decline are higher for the higher
quintiles. This analysis therefore tentatively suggests that (i) underlying real incomesin 2005 were trending downward for all income quintiles, and (ii) underlying income
inequalities were tending to decrease.
Table 2: FIES data 2005 real income trends by quintile
Quintile
Average
Underlying Underlying Slopereal income real income rel. to actual per annum,
(¥mn)
(¥mn)
(%)
(%)
1
2.796
2.790
-0.2%
-0.1%
2
4.234
4.221
-0.3%
-1.1%
3
5.612
5.603
-0.2%
-1.6%
4
7.462
7.460
-0.0%
-1.7%
5
11.977
11.934-0.4%-2.3%

Slope T-stat

-0.09
-0.91
-1.44
-1.65
-1.50

Table 3 presents models for the logarithms of the ratios of each quintile’s income tothe median income. Because the variations in these ratios are smaller than those inabsolute real income levels, it is more difficult to generate acceptable models, and wehave dropped the fixed level constraint to make this easier.

15

Table 3: Final state (2005) data for quintile income ratios to median income
Quintile
Ratio toUnderlying Underlying Slope
Slope T-stat
medianratio torel. to actual per annum,
income (x)median (x)ratio (%)
(%)
1
0.498
0.50
+0.5%
1.17%
1.24
2
0.754
0.756
+0.2%
0.68%
1.80
4
1.330
1.330
-0.0%
0.06%
0.31
5
2.134
2.131
-0.2%
-0.29%-0.35
The conclusions are broadly consistent with those given above for the income
quintiles themselves. An interesting feature of these models is that they suggest that
inequality measures calculated from these 2005 data would be overstated relative tothe underlying level, with the ratio of the bottom quintile to the median pushed below
its underlying level by random and/or cyclical factors, and the reverse being the casefor the top quintile. None of the slope coefficients individually is statisticallysignificant, so one should probably accept the null hypothesis in each case that there
is no trend in relative income share. It is nevertheless suggestive that the 2005 slope
coefficients decrease as income increases, turning negative for the top income
bracket. Although one cannot draw a firm conclusion, there is certainly no evidence
here that inequalities are currently on an increasing trend, and if anything the reverse
seems to be the case.
Multivariate analysis
Harvey and Bernstein (2003) use Seemingly Unrelated Time Series Equations(SUTSE) models to model all of the dependent variables jointly. Here the vector of
dependent variables consists of the same four logged ratios, of different quintiles’
income to the median quintile, modelled individually in the section above. The
SUTSE model takes account of interactions between the variables, and since in thiscase the variables are the quintiles of the income distribution, one would expect
intercorrelations. Results in Table 4 have low standard errors and r(1) statistics, andno evidence of mis-specification in the Box-Ljung statistics. The cycle period is
estimated at 6.3 years in the combined model.
Table 4: SUTSE model diagnostics
Ratio 1
Ratio 2S.E.
0.018
0.0064
R(1)
-0.03
0.03
Q(11,6)
7.03
7.93

Ratio 40.0062-0.05
7.71

Ratio 50.02420.02
12.31

The results are again consistent with the earlier analysis. Inequalities in 2005 areslightly overstated compared with the underlying trend, particularly at the top end of
the income distribution. Again, the slope coefficients suggest that underlying
inequalities are declining, with the first quintile ratio in particular showing an uptrend,
significant at the 10% level.

16

Table 5: SUTSE model results
Quintile
Ratio toUnderlying
medianratio toincome (X) median (X)
1
0.498
0.499
2
0.754
0.754
4
1.330
1.329
5
2.134
2.120

Underlying
rel. to actual
ratio (%)
0.1%
-0.1%
-0.0%
-0.7%

Slope
per annum,
(%)
1.5%
0.4%
-0.1%
-1.1%

Slope T-stat

1.94
1.37
-0.46
-1.38

Equivalised household income trends
It is also interesting to look at the trends in the equivalised household income sharesdiscussed above, i.e. adjusting household income for household size. We were notable to generate a convincing SUTSE model to explain all five of these variables
jointly, but we developed models for them individually. Because the movements inthese variables over time have been smaller than the movements in the raw data, weagain found it easier to model them if we allowed a stochastic rather than a fixed
level. We generally used a cyclical component, but for Quintile 3 the results
suggested that there is no cyclicality, so we dropped the cycle from our eventualmodel. Although again none of the slope coefficients is statistically significant, weagain find that the income shares of the bottom quintiles are trending up, while that of
the top quintile is trending down, suggesting that inequality is currently declining.
Table 6: Recent trends in equivalised quasi-income quintile shares
Quasi-income quintile
2005 slope
T-stat on slope
1
+0.7%
0.81
2
+0.4%
0.67
3 (no cycle)
+0.1%
0.52
4
+0.1%
1.08
5
-0.4%
-0.72
Overall inequality measure
A single inequality measure calculated directly from the distributional data would be
useful. We estimate a measure from the quintiles equivalent to the decile-based
measure used by Harvey and Bernstein (2003), defined simply as the sum of the logsof the ratios of quintiles 4 and 5 to quintile 3, minus the sum of the logs of theequivalent ratios for quintiles 1 and 2. As any quintile diverges from the medianquintile, this ratio will rise, and vice versa. This measure is calculated directly fromthe available data and is simple to decompose into the contributions from individual
income quintiles.

17Figure 10: Trends in overall inequality measure I
2.30
2.25
2.20

Inequality measure I

T rend component of I

2.15
2.10
2.05
2.00
1.95
1.90
1965

1970

1975

1980

1985

1990

1995

2000

2005

Sources: FIES, STAMP Output
The chart of this inequality measure (shown with its modelled trend component in
Figure 10) has some broad similarities with the IRS Gini coefficients (Figure 1), andlooks closer still to the chart of the Gini coefficient estimated from the same FIES
data (Figure 2). Inequality, on this measure, rose steadily through the 1980s and1990s. But it peaked in 1999, and its recent fall has undone about half of its rise overthe last two decades, returning it to around the levels of 1990. Figure 11 shows theestimated slope coefficient for this inequality measure. It is true that there was asustained period of rising inequality which persisted through the 1980s and 1990s.
But the current direction of the inequality trend is downward (-3.2% per annum)
rather than upward. Once again, however, the t-statistic on this slope coefficient is toolow (-1.24) for us to reject the null hypothesis that the inequality trend is currently
flat. This is equally true of the whole of the last 20 years – while inequality trended up
over that period, the trend was at no point strong enough to be significant in thestatistical sense. However, it is probably not necessary to insist on strict statisticalsignificance in this context.
Figure 11: Slope coefficient on modelled inequality measure I

0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
-0.07
1965

1970

1975

1980

1985

1990

18

1995

2000

2005

Overall inequality measure – equivalised version
We can obtain a more meaningful indicator of inequality by recalculating the abovemeasure based on equivalised data adjusted for household size, as discussed in
Section 4. The broad picture (Figure 12) is similar to that shown by the unadjusted
measure, but in this case the trend towards rising inequality appears to have been inplace since the beginning of the 1970s, rather than just the beginning of the 1980s.
Again, we see a reversal since 1999, and the most recent figure, for 2005, is thelowest since 1980. As expected, inequality is lower when measured on the equivalised
measure than on the unadjusted measure. The most recent figure for the unadjusted
measure is 2.02, while that for the adjusted measure is 1.76. Similarly, the rise inrecent decades is smaller when measured on an equivalised basis; the rise in theunequivalised measure between the 1979 low and the 1999 high was 0.26 points,
while it was only 0.16 points on an equivalised basis. It appears that failure to adjust
for household size gives an exaggerated impression of the rise in Japanese inequality
since 1979.
Figure 12: Equivalised household income inequality measure EI
2.2
2.1
2.0
1.9
1.8
1.7
1.6
1.5
63

65

67

69

71

73

75

77

79

81

83

85

87

89

91

93

95

97

99

01

03

05

Univariate time-series modeling of this inequality measure suggests some interesting
conclusions. (We used our standard model with a fixed level and a cycle here.) Figure
13 shows the slope coefficient. While it was positive for most of the 1980s and 90s, itsuggests that inequality recently has been falling at the fastest rate since the 1960s.
The same is true for the slope on the unadjusted measure (Figure 11).

19

Figure 13: Slope coefficient on modelled equivalised inequality measure EI
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
1965

1970

1975

1980

1985

1990

1995

2000

2005

It is a puzzle that these data suggest such a rapid fall in equality at precisely the time
when rising inequality has become a major topic of discussion. But it isstraightforward to decompose these inequality measures to establish which parts of
the income distribution are driving recent trends. If we focus on the uptrend since the1970s and its recent reversal, we obtain the results shown in Table 7:
Table 7: Quintile contributions to changes in equivalised inequality measure EI
PeriodQuintile 1
Quintile 2
Quintile 4
Quintile 5
Total
1970-1999
+0.03
+0.05
+0.04
+0.10
+0.22
1999-2005
-0.06
-0.05
-0.00
-0.09
-0.16
Looking at the whole three-decade rise in the equivalised inequality measure since its
1970 low, we can see that the relative income gains of the top quintile had twice aslarge an impact as any other quintile, accounting for almost half of the total increase
in inequality. The recent decline, however, has been driven more by improving
relative income for the bottom two quintiles.
Indeed, since we have not only quintile but also decile information since 1979, we candecompose the contributions to rising and falling inequality further for the period
since then. Using a similar inequality measure based on equivalised decile income
data (calculated from logged ratios to median quintile) gives the following
contributions by decile to the rise and fall in income inequality over the period. (Therise in this case is dated from 1980, since this marks the low for this inequality
measure over the period for which data are available.)20

Table 8: Decile contributions to changes in equivalised inequality measure EI
DecileContribution to 1980-1999 riseContribution to 1999-2005 fall
1
0.07
-0.10
2
0.04
-0.04
3
0.05
-0.06
4
0.04
-0.04
5
0.00
0.00
6
0.00
0.00
7
0.01
0.00
8
0.04
-0.01
9
0.03
-0.01
10
0.04
-0.06
Total
0.32
-0.32
It is apparent from this breakdown that both during the period when inequality wasincreasing, and during the reversal over the last six years, it has been the bottom endof the income distribution which has been the more important in driving the trend. On
these figures, the trends at the bottom end of the income distribution have beenroughly twice as important as those at the top in determining the overall changes in
the inequality measure in recent years.
We referred in Section 1 to three commonly cited reasons for rising Japanese income
inequality: (i) globalisation (impacting unskilled workers at the bottom end of theincome distribution, (ii) technology (benefiting the top end), and (iii) government
policies such as lowering top marginal tax rates (which would benefit the top end).
Our finding that inequality changes have been primarily driven by declines in relative
income at the bottom end of the income distribution would be consistent with the firstof these explanations, but the conclusions from direct studies of this point are mixed,with little evidence that the premium paid to skilled workers has increased because ofrising levels of trade (Yamamoto 2004). Furthermore, the globalisation explanation
seems inconsistent with the reversal in the inequality trend since 1999. Given theevidence from our times series modelling, macro-economic effects appear to be apossible alternative explanation. There could potentially be a cycle-based explanation
for the recent decline in inequality, for instance, given the time period over which it
has occurred. Late 1998 was a low point for the Japanese economy, marked by thecollapse of several major financial institutions. The accompanying economic
retrenchment could well explain a reduction in relative incomes for lower income
deciles, which may have been reversed by the subsequent economic recovery.
The cyclical component in our time series model of EI is charted in Figure 14. It has aperiod of 4.0 years, a little shorter than those identified in other models above, but
still roughly consistent with a business cycle impact. What is immediately evident isthat the impact of the cyclical component has in fact diminished rather than increased
in recent years. This suggests that, while cyclical factors were a significant influence
on inequality during the 1960s and 1970s, their impact has faded during the 1990s.
This may well be because the normal business cycle largely disappeared in Japan in
the 1990s as the economy became dominated by structural problems, including
deflation and a long-running banking crisis. But whether we characterise them as
cyclical or structural, problems like weak economic growth and rising unemployment

21

are generally expected to have their largest impact at the bottom end of the income
distribution.
Figure 14: Cycle in modelled equivalised inequality measure (EI)
0.04
0.03
0.02
0.01
0.00
-0.01
-0.02
-0.03

1965

1970

1975

1980

1985

1990

1995

2000

2005

6. Adding explanatory variables
The discussion above suggests that the aggregate economy, in the shape of factors
like economic growth and the strength of the labour market, is likely to influence
income inequality in Japan. With the exception of the third (median) quintile, we
found that allowing for a cycle gave better-fitting models for quintile income sharesand the inequality measures we derived from them. We also found that the biggest
influence on our overall inequality measures was from changes at the bottom end of
the income distribution. This is consistent with the hypothesis that it is changes inunemployment which are the largest driver of changes in inequality in Japan. It is also
consistent with findings from the US, where “the income share earned by the lowest
quintile is both the most volatile and the most procyclical” (Castaneda, Diaz-Gimenez
et al. 1998: 94)
We tested these hypotheses by explicitly considering economic indicators relating to
output, inflation, and employment. The variables used were the log of real GDP ineach year, the log of the Consumer Price Index, and the offers/applicants ratio, which
we preferred to the unemployment rate, since the unemployment rate in Japan
typically lags overall economic performance. None of the models used in this section
contained an unobserved cyclical component, since the intention is that the cyclicalinfluence is now being observed through the explanatory variable; indeed, adding anunobserved cyclical component typically resulted in deterioration in model fit.

22

Table 8: Alternative models of EI with explanatory economic variables
VariableS.E.
Q (P†, 6) r(1)Log L
Coefficient
Univariate models
Ln Real GDP
0.0464
8.6
-0.00
122.3
-0.65359**
Ln CPI
0.0469
11.6
0.00
121.9
0.58544**
Off./Appl. Ratio
0.0471
7.4
-0.03
119.7
-0.074638**
Model for EI with Ln CPI and Ln Off/Appl. Ratio
Ln CPI
0.426*
0.04587.6-0.03
0.0704
Off./Appl. Ratio-0.049† The STAMP output selects P as the lag length with highest residual autocorrelation;
the value of P therefore differs by variable.
** Significant at 5% level. * Significant at 10% level.
Table 8 shows the results of this analysis. Both the offers/applicant ratio and the logof the real GDP level have statistically significant negative effects on inequality at the
5% confidence level. Inflation, measured by the log of the CPI, has an inequality
enhancing effect. This is consistent with price rises redistributing income away frompersons with fixed nominal incomes. Deflation thus tends to reduce inequality. All of
the explanatory variables had the expected signs. As seen in the bottom panel, amodel that analyses the impact of the labour market and inflation on income inquality
shows that inflation/deflation has a stronger and more significant impact in inequality
In the light of the discussion above, we would expect to find that these macroeconomic explanatory variables have relatively large impacts on the bottom quintile.
Since EI can be easily decomposed into its quintile contributions, we tested this byusing SUTSE models of all four equivalised income pseudo-quintile ratios (logged)
together, using one explanatory variable at a time.
Table 9: Coefficients of explanatory variables for each quintile (relative to third)
Variable
Quintile 1
Quintile 2
Quintile 4
Quintile 5
Univariate models
Ln Real GDP
0.2752***0.0996**-0.0876***-0.2196
Ln CPI-0.348***-0.097*** 0.047**
0.191*
Off.-Appl. Ratio
0.0382***0.0077-0.0128***-0.0238
Model with Ln CPI and Ln Off/Appl. Ratio
Ln CPI-0.236***-0.115*** 0.005
0.212*
Off.-Appl. Ratio
0.024*-0.002-0.013**-0.004***Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.
Again, the signs were consistent with the hypothesis that economic decline tends tocause an increase in inequality, by reducing relative incomes for the bottom quintiles.
Inflation tends to reduce relative incomes in the bottom two quintiles and increase
them at the top of the income distribution. Looking at the relative size of thecoefficients, we see that in general the magnitudes of the economy-wide effects arelargest for quintile 1. It was notable and surprising that economic expansion and astronger labour market reduces the relative income of the 4th quintile with such highstatistical significance. The magnitudes of these effects are quite small.

23

In general, therefore, this analysis supports the view that the structural wave in theeconomy has had statistically significant effects on Japanese inequality, and that theseeffects are primarily felt through their impact on the relative income of the bottom
quintile. The way in which real GDP and the offers/applicants ratio have significant
effects on inequality is primarily through the bottom quintile, which is the mostaffected by variation of the aggregate economy.
7. Conclusions
In this paper we have focussed on income deciles and quintiles, and inequality
measures derived directly from them, in order to clarify the patterns in Japaneseincome inequality trends. We have applied structural time series modelling techniques
to one of the main data series, allowing a more rigorous analysis of both the long-run
evolution of income inequality and recent trends.
There can be little doubt that inequality in primary income has widened sharply overthe last two decades. This is primarily the result of a collapse in the income share of
the bottom two deciles of Japanese households. Since the state pension is excluded
from the definition of primary income, this collapse is a natural consequence of theageing population, and hence the rising proportion of the Japanese population which
has little or no “primary income” as a result of retirement. While there has also beensome rise in the inequality of redistributed income, the trends here are much lessclear, and what is immediately apparent is that redistribution through the tax andwelfare system is playing a large and increasing role in reducing inequalities at thisincome level.
Focussing on the Family Income and Expenditure Data since 1963 allowed us tomake some adjustment of household income for household size. Inequality is
substantially lower when income is equivalised in this way, and the rise in inequality
in recent decades is less marked. Secondly, the FIES’s longer data series allows
structural time series analyses, enabling us to distinguish the underlying trends fromcyclical and random factors, and to establish statistical significance. This analysis
suggests that at the most recent data point (2005), (i) average real incomes are fallingfor all income quintiles, (ii) real incomes are declining faster for higher earners thanfor lower earners, (iii) inequality is declining at the fastest rate since the 1960s, and(iv) this decline in inequality has already unwound a large part of the previous rise.
The downtrend in inequality is, however, still not strong enough to be statisticallysignificant (the same is true of the uptrend in inequality in preceding decades).
Nevertheless, this evidence suggests that current concerns about rising inequality inJapanese incomes are overdone.
Looking at the drivers of change, we found that both the rise in inequality since the1980s and the decline since the 1999 peak have been primarily driven by changes inrelative income shares for the bottom two quintiles of the income distribution. Falling
relative incomes at the bottom end of the distribution are consistent with the
hypothesis that globalisation is causing an increase in inequalities by reducing thedemand for skilled labour in advanced countries. But the reversal since 1999 isdifficult to square with this explanation. We find instead that aggregate economic
indices such as GDP and the offers/applicants ratio have statistically significant
effects on inequality, and that these effects are largest for the bottom income quintile.

24

The implication is that the role in increasing inequality in Japan in recent years offactors such as globalisation, increasing use of technology and a trend towards lower
top marginal tax rates has been overstated. Rather, a significant proportion of the rise
was the result of the poor state of the economy during the 1990s.
If the Japanese economy has now emerged from the structural difficulties which beset
it during the 1990s, the decline in inequality seen between 1999 and 2005 should turnout to be sustainable, and indeed is likely to extend further, given further
improvement in the economy and labour market. As the economy returns to normal,
we may also start to see the return of a cyclical component in inequality, relative tothe pattern during the 1990s, dominated as they were by structural problems.
While we find that macroeconomic effects on Japanese inequality are felt moststrongly at the bottom end of the income distribution, the strength of such an effectmay well depend on the type of welfare system in the country concerned. As noted
above, a similar pattern to the Japanese one is found to hold in the US (Castaneda,
Diaz-Gimenez et al. 1998). In the UK, however, the opposite pattern, that “inequality
leveled off as the recession bit” was noted by Jenkins (1996: 37), who ascribes this tothe effect of the welfare system in limiting the decline in income for the unemployed.
In the Japanese case, the bulk of welfare spending is directed to public pensions,which do not act as cyclical stabilisers. Indeed, Chopel, Kuno et al (2005: abstract)conclude that “the system today appears to redistribute income from people who on
average have lower incomes to the aged population, which today have higher
incomes.” There is scope for further research to establish what type of welfare system
is most effective in limiting the cycle-related variations in inequality.

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