Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2017-003
May, 2017

Changes in Household Income Inequality over the Business Cycle:
Husbands’ Earnings and Wives’ Labor Supply in Japan during the Global Financial
CrisisYoshio Higuchi＊
Kayoko Ishii＊＊
Kazuma Sato＊＊＊【Abstract】
This study examines how household income inequality in Japan is impacted by changes
in a couple’s income and employment status after economic crises, using the KeioHousehold Panel Survey (KHPS). Our analysis of the periods before and after the Global
Financial Crisis yields three notable findings. First, during the recession, incomesstagnated for households in the middle- and high-income brackets, with many
experiencing income declines. Conversely, many low-income households saw incomesincrease even during the recession, and the overall income gap may have shrunk. Second,
concerning the impact of changes in the husband’s income on the wife’s employment, weshow that when the husband’s income declines, the added worker effect is observed. Thiseffect was larger in households whose income was originally low. Third, we calculate theGini coefficient both by using only the husband’s labor income, and also with the wife’slabor income added. We find that the wife’s labor income reduced household income
inequality, in particular for several years following the 2008 recession. In conclusion,
when focusing only on married households, income inequality appears to shrink during
economic recessions.
＊

Faculty of Business and Commerce, Keio University＊＊

Faculty of Economics, Keio University＊＊＊

Faculty of Political Science and Economics, Takushoku University

Panel Data Research Center at Keio University
Keio University

Changes in Household Income Inequality over the Business Cycle:
Husbands’ Earnings and Wives’ Labor Supply in Japan during the Global
Financial Crisis *Yoshio Higuchi¶
Kayoko Ishii¶¶
Kazuma Sato¶¶¶SummaryThis study examines how household income inequality in Japan is impacted by changes
in a couple’s income and employment status after economic crises, using the KeioHousehold Panel Survey (KHPS). Our analysis of the periods before and after the Global
Financial Crisis yields three notable findings. First, during the recession, incomesstagnated for households in the middle- and high-income brackets, with many
experiencing income declines. Conversely, many low-income households saw incomesincrease even during the recession, and the overall income gap may have shrunk. Second,
concerning the impact of changes in the husband’s income on the wife’s employment, weshow that when the husband’s income declines, the added worker effect is observed. Thiseffect was larger in households whose income was originally low. Third, we calculate theGini coefficient both by using only the husband’s labor income, and also with the wife’slabor income added. We find that the wife’s labor income reduced household income
inequality, in particular for several years following the 2008 recession. In conclusion,
when focusing only on married households, income inequality appears to shrink during
economic recessions.
J11 Demographic Trends, Macroeconomic Effects, and Forecasts
J30 Wages, Compensation, and Labor Cost General
I31 General Welfare, Well-Being* In conducting this research I received Keio Household Panel Survey microdata produced by the Panel Data
Research Center at Keio University. I would like to take this opportunity to express my appreciation.
¶ Faculty of Business and Commerce, Keio University¶¶

Faculty of Economics, Keio University¶¶¶ Faculty of Political Science and Economics, Takushoku University

1 Introduction
There has been a great deal of societal concern about income inequality since thelengthy recession that followed the bursting of the asset bubble in Japan. There has beena variety of research from an economics perspective in response, investigating the statusof and background to income inequality. To summarize previous research, until the mid2000s, analysis primarily focused on factors behind income inequality. This revealed theaging population and increase in single-person households as main causes (Ohtake 2005).
From the mid-2000s onward, research focused on the question of whether income
inequality was increasing, and found that it was not continuously widening 1 (Oshio2010). For example, Oshio (2010), using the Comprehensive Survey of Living Conditions,found that the Gini coefficient rose from 1997 until 2000, but that it declined in 2003,
and rose again in 2006. The latest data from the Ministry of Health, Labour andWelfare’s 2014 Survey on the Redistribution of Income, released in September 2016,
showed a slight decline in the Gini coefficient compared with the previous survey from2011, based on equivalent income following redistribution in 2014. These results suggestthat there has not been a continuous increase in income inequality since 2000.
What kinds of factors affect changes in income inequality? There is a variety ofconceivable causes. This paper focuses on income inequality among married households
of working age, focusing on the impact of economic fluctuations, which may affecthousehold incomes via two pathways. The first is through the income of the head of thehousehold (typically the husband in Japan), the main breadwinner. During an economic
recession, the household head’s income declines in many cases. If adjustments during arecession are made via reduced bonuses rather than redundancies and other
employment adjustment measures, and there is a large income decline in high-income
brackets, where bonuses are a large share of income, this may reduce income inequality.
When the economy recovers, job creation, which provides major benefits to the lowincome brackets, may indeed contribute to shrinking income inequality. However, whenthose in high-income brackets experience major benefits from rising income, this may
work to widen income inequality.
The second pathway is female employment, particularly changes to the wife’s
employment. As pointed out in the Cabinet Office’s 2014 White Paper on Gender

Equality, in Japan an increasing number of males are in non-regular employment, and1

Sato (2013) conducts surveys on these instances of income inequality.

average incomes are trending downward, so the wife’s income has come to play anincreasingly important role in bolstering household income. If the head of the household’s
income declines in an economic recession, the shortfall may be filled by the wife gettinga job or working longer hours. If the increased supply of labor from the wife is observed
mainly among low household-income brackets, this could be expected to narrow income
inequality. Conversely, if additional labor from the wife is supplied mainly in highhousehold-income brackets, this could be expected to widen income inequality. In thismanner, changes in female employment due to economic fluctuations, in particularchanges in the employment of wives, may also contribute to widening or shrinking
income inequality.
The impact of economic fluctuations on income inequality has not been adequately
investigated, nor has the actual situation been clarified2. However, investigating theseissues would demonstrate the mechanism behind changes in income inequality, a matterof considerable research significance. This paper uses the Keio Household Panel Survey(KHPS) and focuses on working-age married households to investigate the impact on
income inequality of changes in the incomes and employment statuses of husbands andwives due to economic fluctuations, through the following two pathways. The first
analyzes the relationship between economic fluctuations and changes in the husband’s
income. It investigates how the husband’s income changes by quintile, using the KHPS
from 2004 until 2015. It focuses on what sort of impact the recession that followed the
Global Financial Crisis in 2008 had on the incomes of married men. The second focuseson the interrelationship between the incomes and employment of married couples. This
also uses the KHPS to perform quantitative analysis on changes to the wife’s
employment behavior when the husband’s income declines, by income quintile. Further,in order to clarify the impact of the wife’s employment on income inequality, we
calculated the Gini coefficient using only the husband’s income and also using thecouple’s combined incomes, and then compared them.
In consideration of income inequality, it is important to take into account of singleperson, single-mother, and elderly households. However, the main focus of this paper is
to analyze the impact on income inequality of the interrelationship between employment
and incomes for married couples. Therefore, it restricts analysis to working-age marriedhouseholds. To this end, in the next section, we discuss the significance and issues

2 Although Mori (2002), Hamada (2007), and Urakawa (2007) have investigated the impact of the wife's
employment on income inequality, they have not explicitly considered the relationship with economic fluctuations.

involved in setting this restriction. In addition, we assess the impact of the Global
Financial Crisis on the labor market, the subject of this analysis. In the third section, weinvestigate the relationship between economic fluctuations and changes in the husband’s
income. In the fourth section, we investigate changes in the wife’s employment behavior
when the husband’s income declines. In the fifth section, we examine the impact of thewife’s income on household income inequality.
2 Analysis subjects and characteristics
This section considers the significance of limiting analysis to married working-age
households when researching income inequality. It then assesses what sort of impact the
Global Financial Crisis had on the labor market, the focus of this analysis.
Figure 1: Household structure and household types in Japan (2014)

Source: Prepared by the author based on figures from the Ministry of Health, Labor and Welfare, Comprehensive
Survey of Living Conditions 2014.

The subjects of this research are working-age married households—specifically,
married households in the KHPS from 2004 until 2015 where the husband is aged
between 20 and 59 3 in the survey year. Limiting the scope to married households
enabled analysis of not only what sort of impact changes in the household head’s(husband’s) income had on income inequality, but also the impact of changes in the wife’s
3

Analysis confined to married men from couples where subject or spouse were aged from 20 to 59.

employment behavior. In recent years, as employment has become increasingly unstable,
the wife’s income has become more important to the household, so it is meaningful tofocus on the impact of changes to the husband’s and wife’s combined income on income
inequality among households.
However, we should note that limiting the discussion to working-age marriedhouseholds means that households that are at high risk of having low incomes, such as
single-person, single-parent and elderly households are excluded from the analysis.
Figure 1 shows household structures and household types in Japan from the Ministry of
Health, Labor and Welfare’s Comprehensive Survey of Living Conditions 2014. By
structure, the only households excluded from this analysis are single-person households,
which account for 27% of the total, and those with a sole parent and unmarried children,which account for 7%. By type, elderly households4, which account for 24% of the total,
are excluded from the analysis.
Limiting analysis to working-age5 households, Figure 2 from the KHPS shows whereunmarried households (single-person and single-parent households, which are excluded
from this analysis) lie in terms of income distribution. The lower the income bracket, thegreater the share of unmarried households. However, it is necessary to point out that
unmarried households form a very small proportion of working-age households in Japan.
Incidentally, Figure 3 shows the Gini coefficient6 for all households where the head wasaged 20–59 years, including single-person and single-parent households, calculatedusing the household head’s income in the KHPS data. In 2003, when the unemployment
rate reached 5.3%, near its historical peak, the Gini coefficient was high at 0.325, andsubsequently as the unemployment rate dropped, the Gini coefficient also declined,
reaching 0.307 in 2008. In 2009, when the unemployment rate jumped to 5.1% due to the
Global Financial Crisis, the Gini coefficient rose to 0.314. Subsequently, in line with thefalling unemployment rate, the Gini coefficient also fell. In 2013, when theunemployment rate was in the 4.0% range, the Gini coefficient was 0.292. In this manner,income inequality for all working-age households based on the household head’s income
behaves in a countercyclical fashion. It is necessary to reiterate that the research resultsof this paper do not address income inequality for society as a whole. The discussion islimited solely to income inequality among working-age married households.

Comprising households of people aged at least 65 years, or that also contain unmarried members under 18.
Defined here as households where the head (the husband in a couple) is aged 20–59 years.
6 Due to the nature of panel data, inequality may be in a secular decline, but aggregate results show considerable
movement.
4
5

Figure 2: Proportion of married and unmarried households by income quintile
5th quintile
4th quintile
3rd quintile
2nd quintile
1st quintile
0%

20%

40%
Married

60%

80%

100%

Unmarried

Note 1: Estimated by pooling data from the KHPS 2004–2015.
Note 2: Unmarried households are households where the head is not married and aged from 20–59 years, excluding
students.
Note 3: Quintiles calculated every year based on labor income of the household head aged 20–59 years (husband
in case of married households).
Source: Author’s estimates, based on KHPS 2004–2015

Figure 3: Unemployment rate and time-series estimates of Gini coefficient for all
working-age households

Note 1: Gini coefficient calculated using labor income of household head, where the head (husband in case of
married households) is aged 20–59 years. Households where the head is a student were excluded from calculations.
Note 2: Sample limited to cases where data on labor income for the next year available, so data are until 2013

(KHPS 2014).
Source: Author’s estimates, based on Ministry of Health, Labour and Welfare Labor Force Survey and KHPS 2004–
2015.

Features of the impact of the GFC, the focus of the analysis, on the labor market aresummarized here7. The GFC was triggered by the collapse of the American investmentbank Lehman Brothers in September 2008. It had a severe impact on Japan’s labormarket. According to the Labor Force Survey from the Ministry of Internal Affairs andCommunications, the number of unemployed men grew by 440,000 from 2008 to 2009,
and the number of unemployed women grew by 260,000. The unemployment rate formen rose by 1.2 percentage points, from 4.1% to 5.3%, and that for women rose by 1.0
percentage points, from 3.8% to 4.8%. Since the early 2000s, the number of unemployedpeople in Japan had been declining, partly due to economic recovery, so the large rise in
the number of unemployed people illustrates the severity of the shock.
Also, the same survey showed changes in employment status from 2008 to 2009. Amongmen, the number of regular employees shrank by 220,000, and the number of nonregular employees shrank by 330,000. Among non-regular employees, the biggest decline
was for temporary workers, with a fall of 180,000. In contrast, regular female employees
grew in number by 70,000, and there was a decline of just 50,000 in the number of nonregular female employees. In the non-regular category, the number of female temporary
workers declined by 130,000, less than the decline for male workers in this category. For
women, the number of contract, short-term contract, and part-time workers rose from2008 to 2009. This ameliorated the decline in non-regular employees.
Looking at changes in employee numbers by industry from 2008 to 2009 in the Labor

Force Survey, the number of male employees declined by 570,000 overall8. The decline
was mainly in manufacturing, with about 340,000 fewer employees9 . The number offemale workers rose by 10,000. By industry, the number of women in manufacturing fellby 250,000, similar to the number for men, but there was an increase of 170,000 womenin the medical and welfare sectors, as well as gains in other service industries, which
offset the decline in manufacturing.
See Appendices 1-4 for detailed figures regarding impact of global financial crisis on labor market.
Changes in employee numbers by industry include changes in the numbers of executives and regular and nonregular employees.
9 Sectors with large drops in employment for males outside the manufacturing sector included construction(120,000) and services not elsewhere classified (140,000)
78What sort of relationship was there between unmarried working-age households, which
are excluded from this analysis, and the GFC? According to the Labor Force Survey,
prior to the GFC (in 2007)10, married men accounted for 69% of the total among male
employees overall11 over age 15 years. Only 40% of male temporary workers, for whom
the global financial crisis resulted in the most unemployment, were married. That is,more than half of the male temporary employees, who were greatly affected by the shockof the GFC, were unmarried at the time and so not included in this analysis.
Nevertheless, it is important to note that they were strongly affected by the GFC.
To summarize the above results, the number of employees fell sharply during the GFC,
with the effects particularly notable among male temporary workers. As pointed out by
Nagahama (2012), the background to this was a strong impact from the GFC on the
manufacturing sector, where men are heavily employed, due to a slump in global
consumption. In contrast, female employment increased in some cases during the GFC,
so the impact was the opposite. Female employment patterns and the industries they
worked in showed employment growth even during the GFC, so it may have been easier
for women to find work during this time.
Finally, we touch upon data used. The KHPS, which provided data used in the analysis
in this paper, is a survey conducted in February every year. Questions on income ask
about income in the previous year and those on employment status ask about
employment status in the previous month (January). As a result, when data from KHPS
from 2004 until 2015 are used, information on income covers the period from 2003 until
2014. In this paper, we refer to years according to the year the data are from, not thesurvey year.
3 Economic fluctuations and changes in husband’s income
The section uses data from KHPS from 2004 to 2015 to investigate the relationshipbetween economic fluctuations and changes in income, limiting analysis to married men.
Specifically, we assess changes in husband’s income from the current year (period t) to
the next year (period t+1), by husband’s income bracket. We examine the descriptive data
to see whether there are differences between developments at the time of the GFC and

10
11

Refer to Appendix 4.
Including agricultural workers.

other periods.
Before discussing analysis, Table 1 shows characteristics of the sample used in analysis
in this section. They cover married men12 aged from 20–59 years for each period t who
were employed in period t. Table 1 shows aggregate values for pooled data from KHPS
from 2004 to 2015, by income quintile. The income quintiles were calculated for eachyear based on the husband’s income from work over the year (before deducting taxes andinsurance premiums). The sample contains self-employed respondents, so there is apossibility that business income is included. Looking at husband’s age by income quintile
shows that in the fifth quintile, the highest, the husband’s age is greater; in the first, thelowest income quintile, the husband’s age is on average around the middle of the range.
The husband’s employment status varies markedly between the lower quintiles and theothers. For the lower income brackets, and for the first quintile in particular, the shareof regular employment is notably low while that of non-regular employment is high.
Furthermore, the share of self-employed and family-business workers is larger than thatin the middle- and high-income brackets13.
Table 1: Characteristics of analysis subjects

N
1st quintile
2nd quintile
3rd quintile
4th quintile
5th quintile

2,485
2,704
2,911
2,720
2,624

Husband’s average age(years)
44.9
42.7
43.9
46.6
49.3

Husband’s average income(million JPY)
250
414
543
709
1,049

RegularHusband’s employment status (%)
Non-regular Self-employed Family business worker
46
19
30
4
76
5
17
2
82
3
14
1
91
1
8
0
90
1
9
0

Note 1: Pooled data from KHPS from 2004 to 2015.
Note 2: Subjects were married men aged 20–59 years employed during period t. Income brackets aggregated every
year based on income (annual income before tax and social security deductions) of married men aged 20-59.
Source: Author’s estimates using KHPS from 2004 to 2015.

Figure 4 shows to what extent income increased (resp., declined) from period t to period
t+1 by husband’s income quintile in period t, using pooled KHPS data from 2004 to 2015.
As discussed previously, the KHPS asks about the previous year’s income, so the years
shown in the figure do not refer to the survey year but the year in which income was
Restricted to cases where the survey subject and partner are in that age range
Here we examine to what extent the first income quintile in this section's analysis overlaps with the poor
demographic in society overall. Comparing equivalent gross income of the subjects in this section with the relative
poverty line (half of the median equivalent disposable income) published by the MHLW’s Comprehensive Survey of
Living Conditions, 11.5% of the subjects in the first quintile are classified as poor under the relative poverty
concept. The relative poverty line referenced in the Comprehensive Survey of Living Conditions covers 2003 to
2012, the closest corresponding periods to the KHPS survey years.
1213

earned. As the figure clearly shows, the lower the income of the married man, the greater
proportion of rising incomes the following year. Around half of those in the first quintile
experienced income gains of more than 5% the next year (an increase of 120,000 JPY on
average), and around 15% experienced income gains of more than 50% (1.25 million JPY
on average). Wherever the cutoff point is set, the lower the income in the earlier period,
the greater the share of respondents whose income grew in period t+1.
Looking at the share of respondents whose income declined a year later, there is not a
great difference among income brackets, with roughly 20–30% of households in eachexperiencing declines of more than 5% in the husband’s income (120,000 JPY on averagefor the lowest quintile, and 500,000 JPY for the highest). The proportion of respondents
who saw declines of more than 10% (250,000 JPY on average in the lowest quintile andmore than 1 million JPY for the highest quintile) were generally higher for the lower
income brackets.
Figure 4: Changes in husband’s income from t to t+1 by income classes(Pooled data for income changes covering 2003/2004 to 2013/2014)100%
Up more than 50%

90%

Up 40-50%

80%

Up 30-40%

70%

Up 20-30%

60%

Up 10-20%

50%

Up 5-10%40%

Up/down less than 5%30%

Down 5-10%

20%

Down 10-20%

10%Down 20-30%

0%
1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile

Husband's income bracket in period t

Down 30-40%
Down 40-50%

Note 1: Estimates using pooled KHPS data from 2004 to 2015.
Note 2: Subjects were married men aged 20–59 years employed during period t. Income brackets aggregated every
year based on income (annual income before tax and social security deductions) of married men aged 20–59 years.
Source: Author’s estimates using KHPS data from 2004 to 2015.

Figure 4 shows pooled movements in husband’s income over two-year periods from 2003

to 2014. If these movements are shown as a time series, will there be any differences
around the time of the GFC? That is, is it possible to observe an impact of the GFC thatvaries by income bracket? Figure 5 shows the share of households in which the husband’s
income rose more than 10% from period t to period t+1, by husband’s income bracket. As
shown in Figure 4, there was a markedly high proportion of income gains in the firstquintile, up to 60% in some years, and even in years when the proportion was less, 30%
saw income gains of more than 10%. Of particular interest is that in income brackets
outside the first quintile, there is a sharp decline in the number of households thatexperienced income gains of more than 10% during the GFC (2008–2009). However, inthe first quintile, this phenomenon was not observed. A similar phenomenon wasobserved when we changed the cutoff point for income gains from 10% to 5%. That is, thelower the income bracket, the greater the share of households with income gains. Except
for the first quintile, there was a large increase in the number of households whose
income declined at the time of the GFC.
Figure 5: Share of respondents where husband’s income increased more than 10% fromperiod t to period t+1, by income bracket
70%
60%
50%
40%
30%
20%
10%
0%

1st quintile

2nd quintile

4th quintile

5th quintile

3rd quintile

Note 1: The values in the figure show a time series of the percentage of households where the husband’s income rose
more than 10% from period t to period t+1 by husband’s income bracket in period t.
Note 2: Subjects were married men aged 20-59 employed during period t. Income brackets aggregated every year
based on income (annual income before tax and social security deductions) of married men aged 20-59.
Source: Author’s estimates using KHPS from 2004 to 2015

Figure 6: Share of respondents where husband’s income declined more than 10% fromperiod t to period t+1, by income bracket
40%
30%
20%
10%
0%

1st quintile

2nd quintile

4th quintile

5th quintile

3rd quintile

Note 1: The values in the figure show a time series of the percentage of households where the husband’s income
declined more than 10% from period t to period t+1 by husband’s income bracket in period t.
Note 2: Subjects were married men aged 20–59 years employed during period t. Income brackets aggregated every
year based on income (annual income before tax and social security deductions) of married men aged 20–59 years.
Source: Author’s estimates using KHPS data from 2004 to 2015.

Figure 6 shows the share of households where the husband’s income declined more than
10% from period t to period t+1, by income bracket. It is apparent that in each income
bracket, a higher proportion of households experienced lower income from 2008 to 2009.
While there is not a major difference among the income brackets in the share ofhouseholds where income declined more than 10%, if the cutoff point is set at a relatively
small income drop of 5%, the lower the income bracket, the smaller the shock.
Surprisingly, incomes in the middle- and high-income brackets were particularly
stagnant during the GFC, and not just low-income brackets but medium- and highincome brackets were subject to the shock of lower incomes, suggesting that the GFC
may have reduced income inequality.
As a check, we review data on why married working males experienced a decline inincome. Figure 7 shows the share of respondents who lost or changed jobs by income
bracket among respondents who experienced income declines of more than 10%. On theleft side, it shows the aggregate results pooled from 2003 to 2014. On the right side, itshows aggregates for the GFC period alone. Married males in the first quintile were most
likely to experience job loss: around 10% over the entire time frame and 20% at the timeof the GFC. There is also a difference among income brackets regarding changing jobs.
Similarly, a greater proportion of those in the lower-income brackets tend to change jobs.
In the lower-income bracket, job loss or changing jobs is one factor in income decline, but
it is not the primary cause.
Figure 7: Proportion who lost or changed jobs among those whose income declined bymore than 10% from period t to period t+1 (by husband’s income bracket)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Changed job

Changed job

Lost job

Lost job

Continued employment

Continued employment

Note 1: Subjects were married men aged 20–59 years employed during period t, whose income declined
more than 10% from period t to period t+1. Income brackets aggregated every year based on income(annual income before tax and social security deductions) of married men aged 20-59.
Source: Author’s estimates using KHPS from 2004 to 2015.

What then, is the cause of income decline among high-income brackets? Figure 8 shows
aggregate figures on whether respondents who experienced income declines of more than
10% while continuing employment at the same company experienced decreased bonuses.
Similar to before, the left-hand side shows aggregate data pooled for 2003 through 2014,
while the right-hand side shows aggregate data for the time of the GFC alone. This showsthat the higher the income bracket, the greater the proportion of respondents who
experienced decreased bonuses. Further, this proportion is particularly large at the timeof the GFC14.
14 In the low-income brackets, there are more workers in non-regular employment and other formats with noFigure 8: Share of employees who experienced decreased bonuses while continuing
employment at the same company among employees whose income fell more than 10%
from period t to period t+1
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Bonus not cut

Bonus cut

Bonus not cut

Bonus cut

Note 1: Subjects were married men aged 20–59 years employed during period t, whose income declined
more than 10% from period t to period t+1. Income brackets aggregated every year based on income(annual income before tax and social security deductions) of married men aged 20–59 years.
Source: Author’s estimates using KHPS data from 2004 to 2015.

From the above results, the following two points are clear. First, incomes stagnate for
middle- and high-income brackets in particular when there is a major economic recession.
Also, not just low-income brackets, but middle- and high-income brackets experience therisk of decreasing incomes, so the GFC may have reduced income inequality. The second
point is that in a recession, while there is a higher risk of job loss or unemployment forlower-income brackets, other changes, such as adjustments via bonuses and other wage
measures, are common, particularly for high-income brackets. It is not the case thatduring recessions, only low-income brackets experience damage from unemployment. No
matter what the bracket, there are widespread declines in income, suggesting this may
also reduce income inequality during recessions.
4 Relationship between decline in husband’s income and changes in wife’s employment

bonuses than in other income brackets.

This section focuses on the impact of changes in the husband’s income, declines inparticular, on the wife’s employment status. When the husband’s income declines due toan economic recession or unemployment, it is possible that a working wife will increase
her overtime hours or one who has not been in the workforce will start working, thusincreasing the supply of labor, in order to maintain a certain living standard. This iscalled the “added worker effect”15 (Higuchi 2001). This added worker effect is thought tobe related to household income inequality. Specifically, when a household’s income levelis originally high, the impact of lowered income from the husband is negligible, and theadded worker effect from the wife is also small. However, if the household income is low
to start with, the impact of a decline in the husband’s income is severe, so the added
worker effect from the wife is expected to be larger. As such, even if the husband’s income
declines, it is possible that there will be a strong added worker effect among brackets
with low household-income levels, which will work to suppress the expansion ofhousehold income inequality. This section examines the mechanisms by which the wife’s
employment reduces household income inequality. The data used are from the KHPS
from 2004 to 2015. The subjects are married couples where the husband is younger than
60 years.

4.1 Estimation methodology
We estimate three models to analyze the added worker effect attributable to the wife.
The first analyzes whether a wife who had withdrawn from the labor market re-enters
due to a decline in the husband’s income. The second examines whether a working wife
continues to work at her current job due to a decline in the husband’s income. The third
analyzes whether a working wife increases her employment hours due to a decline in thehusband’s income.
The discussion below starts with the first estimated model. Analysis of the wife reentering the labor market uses the logit model below.

15 There has been a great deal of research into the added worker effect in Japan and abroad. Much non-Japanese

research points to the existence of the added worker effect (Heckman and MaCurdy 1980, 1982; Lundberg 1985;
Stephens 2002; Skoufias and Parker 2006). One representative study is by Stephens (2002). It analyzes the wife'slabor supply before and after the husband's unemployment. This showed that the wife's added worker effect is notapparent just at the time of the husband's unemployment, but subsequently as well. Much Japanese research
points out the presence of the added worker effect, similar to outside Japan (Higuchi and Abe 1999, Kuroda andYamamoto 2007; Kohara 2007; Kohara 2010; and Sato 2012). One representative study by Kohara (2007) analyzes
the impact of the husband's unemployment on the wife's labor supply. This shows that the husband's
unemployment increases the wife's working hours and that the fewer financial assets in a household, the larger
the impact.

∗

(1)

∙

is a dummy variable that takes a value of 1 when the wife changes her status toemployed or looking for work from not in the labor force, and 0 if she remains out of thelabor force.

is a vector of individual attributes, including educational history

dummies for the husband and wife, wife’s age, a dummy for presence of a child under
three years old, number of children, savings (in millions of JPY 10,000, debts (in millionsof JPY), job-offers-to-applicants ratio by prefecture, and a year dummy.

is a dummy

variable that takes a value of 1 when the husband’s income declines more than 10% fromperiod t-1 to period t and 0 otherwise.

is a household income quintile dummy, andindicates equivalent household income at period t-1. In this analysis we prepared byquintile dummies (for the first, second, third, fourth, and fifth quintiles). The firstquintile dummy (for the lowest household income quintile) and the fifth quintile dummy(for the highest household income quintile) were used as explanatory variables.
Reference group are second, third, and fourth quantiles.

∙

is the cross term

between whether the husband’s income declines more than 10% dummy and thehousehold-income fifth-quintile dummy.

are unobservable fixed effects andindicates error terms.
Of these variables, those of most interest in this analysis are the estimation results forthe cross term

for the husband’s income declines more than 10% dummy and∙the household-income fifth-quintile dummy. This cross term shows for which householdincome brackets the impact of declining husband’s income is greatest. If the sign ispositive, the wife’s added worker effect is stimulated; if negative or found to be
insignificant, it means there is no added worker effect from the wife. We examine thisusing pooled logit and random effect logit models16. In the estimates, we incorporated
analysis using the cross term for the job-offers-to-applicants ratio by prefecture and theyear-ago household-income dummy as an additional independent variable to investigate
whether changed labor market supply-and-demand conditions affect the wife’s reentering the workforce.
The second analysis regarding continued employment is estimated using the logit
model below.
∗∙

We investigated using a fixed-effect model, but there was not enough variation in the dependent variable,
making it impossible to obtain estimates, so we abandoned this approach.

16

(2)

The dependent variable

is a dummy variable set to 1 if a wife who was working at

period t-1 continued working at the same company at period t and 0 if not. This analysis
applies to only employed workers. Just as in Formula (1), it uses () for individual

attributes and a dummy for when the husband’s income declines by more than 10% ().

Whereas Formula (1) used a dummy for household income at t-1, Formula (2) uses ()

as a dummy for husband’s income quintile at period t-1. This is because in the case of aworking wife, her labor income is reflected in household income at period t-1, becoming
an endogenous variable and potentially causing a bias in the estimation results. The useof a dummy for the husband’s income quintile deals with this bias. Formula (2) uses the
cross term () for the dummy of whether husband’s income declines by more than∙

10% against husband’s income quintile dummy, and we focus on the estimation results.
Estimations are made using the pooled logit and random-effect logit models.
Third, analysis regarding an increase in the wife’s working hours is estimated using
ordinary-least-squares minimization in the model below.
∙

∆
∆

(3)

is the difference in the wife’s weekly working hours between period t-1 and periodt. The analysis covered only workers with jobs and the explanatory variables include a
regular employment dummy added to Formula (3). Note that the estimates use pooled
OLS, fixed-effect OLS, and random-effect OLS.
Using the above estimation methodology, we investigated the impact of a decline inhusband’s income on wife’s employment behavior. An examination of previous research,
including that by Kuroda and Yamamoto (2007) and Kohara (2010), shows that the wife’s
added worker effect primarily takes the form of an unemployed wife entering theworkforce (by a wide margin). In this research as well, we focus on whether similar
trends are apparent. Basic statistics regarding the variables used in the estimations areshown in Table 2.
Table 2: Basic statistics

Analysis of wife’s new
Analysis of wife’s continued Analysis of changes to wife’s
employment
employment
work hours
Average Standard deviation Average Standard deviation Average Standard deviation
Wife’s new employment dummy
0.13
0.34
Wife’s continued employment dummy
0.95
0.21
Difference in wife’s weekly work hours
0.51
15.02
Husband’s income declined more than 10% dummy
0.15
0.36
0.18
0.38
0.17
0.38
Year-ago household-income quintile 5th quintile
0.15
0.36
2nd-4th quintile
0.61
0.49
1st quintile
0.24
0.43
Year-ago husband’s income quintile 5th quintile
0.19
0.39
0.19
0.39
2nd-4th quintile
0.62
0.49
0.62
0.48
1st quintile
0.19
0.39
0.19
0.39
Job-offers-to-applicants ratio by prefecture
0.87
0.33
0.89
0.34
0.89
0.35
0.50
0.54
0.50
0.53
0.50
Husband’s education dummy
Junior/senior high 0.44
0.24
0.07
0.26
0.08
0.27
Technical/junior c 0.06
0.50
0.34
0.47
0.34
0.47
University gradua 0.45
0.50
0.50
0.50
0.50
0.50
Wife’s education dummy
Junior/senior high 0.45
0.47
0.29
0.45
0.29
0.45
Technical/junior c 0.32
University gradua 0.16
0.37
0.13
0.34
0.13
0.34
Wife’s age
42.47
8.36
45.12
7.29
44.75
7.28
Wife’s employment status dummy Regular employment
0.28
0.45
Non-regular employment
0.72
0.45
Child under three dummy
0.23
0.42
0.05
0.22
0.06
0.24
Number of children
1.83
0.98
1.88
0.92
1.88
0.92
Savings millions of JPY
6.42
10.93
5.26
8.45
5.07
8.23
Debt millions of JPY
8.63
11.73
8.17
11.95
8.17
11.78
Sample size
3,185
5,926
5,742
Variable

Source: Author’s estimates using KHPS data from 2004 to 2015.

4.2 Estimation results
Table 3 shows the results of investigating the impact of the decline in husband’s income
on wife’s entry into the labor market. (A1) and (A3) in the table are pooled logit
estimation results, and (A2) and (A4) are estimation results from the random-effect logit
model. The values in the table are marginal effects.
Looking at the husband’s income decline dummy and year-ago household income
dummy cross terms for (A1) and (A2) shows that, in both cases, the cross terms for the
decline of 10% or more dummy and first-quintile household income dummy are
significant and positive. This result means that when the husband’s income declines, thepercentage of wives newly entering the labor market in the low household-income
bracket increases. In contrast, the cross term for the husband’s income declines morethan 10% dummy and the fifth-quintile household income dummy was not significant.
This means that even if the husband’s income declines by more than 10%, in households
that were originally in a high-income bracket, there is no impact on the wife’s
employment behavior. The above results mean that the lower the household’s householdincome level, the more sensitive to a decline in husband’s income, and the more labor
supply is increased. Because the added worker effect from the wife is present, there is apossibility that the wife’s employment suppresses the widening of income inequalityamong households17.
In other variables, the coefficient for the job-offers-to-applicants ratio by prefecture,
which shows changes to supply-and-demand conditions in the labor market due toeconomic fluctuations, shows positive and significant results for (A1) and (A2). These
results mean that new employment by the wife increases when the job-offers-toapplicants ratio rises in an economic recovery. Further, these results may also show thatnew job creation is suppressed during an economic downturn and there is a discouraged
worker effect, whereby an increasing number of wives remain outside the labor force. If
the discouraged worker effect has a different impact on different household income
brackets, the impact of the added worker effect on reducing household income inequality
may vary. In order to assess this point, we conducted estimations with the addition ofcross terms between the job-offers-to-applicants ratio by prefecture and year-ago
household income dummy in (A3) and (A4). The results (A3) and (A4) showed that crossterms between the job-offers-to-applicants ratio by prefecture and year-ago householdincome dummy were not significant. This implied that there were no variations in theimpact of the discouraged worker effect among different household income brackets.
Table 3: Impact of decline in husband’s income on wife’s entry into labor market

This research focuses on households where there is an unemployed wife during recessions, but it is possible that
there are households in which a wife who had already been working loses income due to a recession. In this case,particularly in low-income households, if the wife loses her job and the household faces a decline in income, an
economic recession will not necessarily reduce household income inequality. In order to assess this, we carried out
logit analysis using an unemployment dummy a dependent variable with a value of 1 when the working wife losther job and 0 when she continued working. This analysis focused on the cross terms of the job-offers-to-applicantsratio by prefecture and year ago household income dummy. The analysis showed that neither of the cross terms forthe job-offers-to-applicants ratio by prefecture or year ago household income dummy was significant. This meansthat wives in low-income households do not necessarily lose their jobs and face a decline in income.

17

Explanatory variables
Husband’s income declined more than 10% dummyYear-ago household-income quintile dumm5th quintile
ref: 2nd-4th quintile
1st quintile
Year-ago household-income quintile dumm5th quintile
Husband’s income declined more than 10% dummy
1st quintile
Job-offers-to-applicants ratio by prefecture(A1)-0.018(0.022)-0.034(0.025)0.019(0.016)0.043(0.057)0.059*(0.034)0.094***(0.025)(A2)-0.027(0.023)-0.026(0.029)0.025(0.019)0.044(0.054)0.064*(0.038)0.093***(0.032)Pooled Probit-1166.851
3,185

RE Probit-1144.097
3,185

Year-ago household-income quintile dumm5th quintile
Job-offers-to-applicants ratio by prefecture
1st quintile
Estimation methodology
Log-likelihood
Sample size

(A3)
-0.018(0.022)-0.076(0.058)-0.004(0.038)0.042(0.057)
0.061*(0.035)0.083***(0.028)0.046(0.058)0.025(0.039)Pooled Probit-1166.424
3,185

(A4)
-0.027(0.023)-0.034(0.062)0.013(0.043)0.044(0.054)0.065*(0.038)0.088**(0.035)0.009(0.057)0.013(0.043)
RE Probit-1144.053
3,185

Note 1: ***, **, and * indicate that the respective estimated coefficients are significant at the 1%, 5% and 10% levels.
Note 2: Figures in parentheses ( ) are standard errors robust to non-uniform distribution.
Note 3: Table figures show marginal effects.
Note 4: In the estimates, educational history dummies for the husband and wife, wife’s age, number of children,savings in millions of JPY, debts in millions of JPY, and a year dummy are used as explanatory variables.
Source: Author’s estimates using KHPS from 2004 to 2015.

Table 4 shows the results of investigating the impact of a decline in husband’s income on
wife’s continued employment. Among the estimation results, looking at the cross terms
between the dummy for decline in husband’s income and husband’s year-ago income
quintile dummy, no cross terms were significant. These results mean that even if income
levels were different, there was no difference in the probability that the wife would
continue working due to a decline in husband’s income. We considered that there may be
a higher probability of the wife remaining employed if a decline in the husband’s income
prevented her from leaving her job, but we found no impact on this from changes in thehusband’s income18.
Table 4： Impact of decline in husband’s income on wife’s continued employment

We also conducted analysis dividing wife's employment status one year prior into regular and non-regular
employment, but the cross terms with the husband's income decline dummy and year-ago household income
dummy were not significant. Further, we also conducted analysis taking into consideration not just the wife
leaving employment, but whether or not she changed jobs, but none of the cross terms was significant.

18

Explanatory variables
Husband’s income declined more than 10% dummyYear-ago husband’s income quintile dumm5th quintile
ref: 2nd-4th quintile
1st quintile
Year-ago husband’s income quintile dumm5th quintile
Husband’s income declined more than 10% dummy
1st quintile
Job-offers-to-applicants ratio by prefecture
Estimation methodology
Log-likelihood
Sample size

(B1)
-0.011(0.009)-0.013*(0.008)-0.003(0.008)-0.006(0.017)0.006(0.017)0.000(0.010)Pooled Probit-1073.519
5,926

(B2)
-0.284(0.222)-0.357(0.220)-0.082(0.219)-0.118(0.410)0.221(0.435)0.022(0.288)RE Probit-1065.804
5,926

Note 1: ***, **, and * indicate that the respective estimated coefficients are significant at the 1%, 5% and 10% levels.
Note 2: Figures in parentheses ( ) are standard errors robust to non-uniform distribution.
Note 3: Table figures show marginal effects.
Note 4: In the estimates, educational history dummies for the husband and wife, wife’s age, number of children,savings in millions of JPY, debts in millions of JPY, and a year dummy are used as explanatory variables.
Source: Author’s estimates using KHPS data from 2004 to 2015

Table 5 shows the results of investigating the impact of a decline in husband’s income
on wife’s work hours. Looking at the cross terms for (C3) husband’s income decline
dummy adopted using the Hausman test and husband’s year-ago income quintile dummy,none of the cross terms was significant. This means that even at different income levels,
there is no impact on the wife’s working hours from a decline in the husband’s income.
Just as in previous research by Kuroda and Yamamoto (2007) and Kohara (2010), there
is no observable added worker effect from changes in the working wife’s work hours.
Table 5： Impact of decline in husband’s income on working wife’s work hours

Explanatory variables
Husband’s income declined more than 10% dummyYear-ago husband’s income quintile dummy
ref: 2nd-4th quintile

5th quintile
1st quintile

Year-ago husband’s income quintile dummy x
Husband’s income declined more than 10% dummy

5th quintile
1st quintile

Job-offers-to-applicants ratio by prefecture
Estimation method
Hausman test
R2
Sample size

(C1)
0.385
(0.676)0.514(0.606)-0.706(0.595)-0.724(1.418)0.607(1.328)0.411(0.823)Pooled OLS

(C2)
0.513
(0.860)0.160(1.332)-0.554(1.161)-0.959(1.850)0.905(1.632)1.457(2.106)FE OLS(C3)
0.385(0.676)0.514(0.606)-0.706(0.595)-0.724(1.418)0.607(1.328)0.411(0.823)RE OLS
0.999

0.004
5,742

0.004
5,742

0.004
5,742

Note 1: ***, **, and * indicate that the respective estimated coefficients are significant at the 1%, 5% and 10% levels.
Note 2: Figures in parentheses ( ) are standard errors.
Note 3: In the estimates, educational history dummies for the husband and wife, wife’s age, a dummy for wife’s
regular employment, number of children, savings (JPY10,000)/100, debts (JPY10,000)/100, and a year dummy areused as explanatory variables.
Source: Author’s estimates using KHPS data from 2004 to 2015.

Summarizing the above results, two points have become clear. First, when thehusband’s income declines, there is an added worker effect observed, whereby a wife who
had not been previously working supplies additional labor. This effect was observed
mainly in households that originally had low incomes. Because the added worker effectis stronger in the lower income households, it is possible that the wife’s employment
helps to constrain the expansion of household income inequality when the husband’s
income declines. The second point is that when the husband’s income declines there isno observable change in the supply of labor from working wives19.
5Impact of wife’s income on household income inequality
Regarding income inequalities in society as a whole, publications from the Ministry of

Health, Labor and Welfare (Survey on the Redistribution of Income) and Ministry ofInternal Affairs and Communications (Comprehensive Survey of Living Conditions) bothshow that from the late 2000s through the 2010s, the representative indicator of income
In Table 4 and Table 5, we added cross terms for the job-offers-to-applicants ratio by prefecture and husband's
year-ago income quintile dummy to analyze the impact of economic fluctuations on working hours and continued
employment, but virtually all of the cross terms were insignificant. This result suggests that even if there aredifferences in the husband's income, economic fluctuations do not result in changes to the wife's working hours orcontinued employment.

19inequality, the Gini coefficient, was declining 20 . It is impossible to grasp yearlyfluctuations in the Gini coefficient or the impact of income source on the Gini coefficientfrom official published statistics. Therefore, in this section, we look at a time series of
the Gini coefficient estimated using only the husband’s income, versus one that includes
the wife’s income as well, to see the impact of wife’s income on reducing household income
inequality21.
Figure 9 shows the time series for the Gini coefficient calculated using only thehusband’s income from work and that using the husband’s and wife’s combined income
from work. From the figure, it is clear that the wife’s income has a leveling effect onhousehold income inequality. There is a secular downtrend in the Gini coefficient22. The
most salient feature is that there is a watershed around the GFC in 2008 andsubsequently until 2011: there is a major impact on lessening the Gini coefficient due tothe wife’s income. As shown in the previous section, in particular for low-income brackets,
disparities among households improved as wives began to work to compensate a decline
in the husband’s income due to the GFC.
Figure 9： Gini coefficient calculated using only husband’s income and including
wife’s income

20 According to the Ministry of Health, Labour and Welfare’s Survey on the Redistribution of Income, the Gini
coefficient measured using equivalent disposable income was 0.322 in 2004 (from survey year 2005, here andafter), 0.327 in 2007, 0.322 in 2010, and 0.316 in 2013. According to the Ministry of Internal Affairs andCommunications’ Comprehensive Survey of Living Conditions, the Gini coefficient measured using equivalentdisposable income was 0.278 in 2004, 0.283 in 2009, and 0.281 in 2014.
21 In Europe and the US there is much reported research into what sort of impact the wife's income has onhousehold income inequality against the backdrop of rising wives’ employment. Researchers have come to different
conclusions. Lerman and Yitzhaki (1985) analyze the Gini coefficient in the US and report that since 1979, female
incomes have widened household income inequality. Using the same methodology, Urakawa comments that fromthe early 1990s to the beginning of the 2000s in Japan, the wife's income has contributed to widening thehousehold income inequality in working-age households. Conversely, Harkness, Machin and Waldfogel (1997) and
Cancinan and Reed (1999) conclude from their analysis that wives’ income reduces household income inequality or,
if it does widen it, the impact is negligible.
22 Changes in the Gini coefficient time series may be affected by problems such as sample dropouts in the panel
data and aging of the sample.

Note 1: Analysis subjects are the same as used in the analysis in Section 3, married males aged 20–59 years in
employment in period t for whom information was available through period t+1, so aggregate results are until 2013.
Income (before deducting tax and social security payments) for husband and wife obtained through their respective
work over the period of a year was used for income.
Source: Author’s estimates using KHPS data from 2004 to 2015.

6 Conclusions
This study examines how household income inequality in Japan is impacted by changes
in a couple’s income and employment status after economic crises, using the KeioHousehold Panel Survey (KHPS). Our analysis of the periods before and after the Global
Financial Crisis yields three notable findings. First, during the recession, incomesstagnated for households in the middle- and high-income brackets, with many
experiencing income declines, typically bonus declines. Conversely, many low-income
households saw incomes increase even during the recession, and this suggests thatduring economic downturns, income inequality between low-income and high-brackets
shrinks. Second, concerning the impact of changes in the husband’s income on the wife’s
employment, we show that when the husband’s income declines, the added worker effectis observed. This effect was larger in households whose income was originally low. Third,
we calculate the Gini coefficient both by using only the husband’s labor income, and alsowith the wife’s labor income added. We find that the wife’s labor income reduced
household income inequality, in particular for several years following the 2008 recession.

To summarize the above, when focusing only on married households, income inequality
appears to shrink during economic recessions. The background to this is income declines
by the husband in medium- and high-income brackets and assumption of work by wives
in low-income brackets. These results show that a couple’s employment and income affectincome inequality at times of economic fluctuation, a new finding not pointed out inprevious research.
We showed that the Gini coefficient for all households by considering household head’s
income, including that of unmarried households (single-person and single-parent
households) shows countercyclical movements. During economic recoveries, income
inequality shrinks. When the economy worsens, inequality expands. Single-person andsingle-parent households suffer a strong impact from increased unemployment when theeconomy worsens, but the effect is smaller on household heads in married households,
and indeed there is a strong impact from bonuses and overtime pay. Furthermore, in
addition, increased employment opportunities for women encourage wives to startworking, particularly in low-income households, increasing income and reducing income
inequality during recessions. In the future, it will be necessary to ascertain whether
these developments are specific to the Global Financial Crisis, or whether they may beobserved as general trends during other processes in the economic cycle. It is nonethelessundeniable that changes in labor supply by household and attribute have a major impact
on household income inequality.
Appendix 1

Number of unemployed persons and changes in unemployment rate bygender
Unemployed males

2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

Numbers
Year-on-year
Year-on-year(10,000 persons)changeUnemployment ratechange
219
5.5
215
-4
5.5
0
192
-23
4.9
-0.6
178
-14
4.6
-0.3
168
-10
4.3
-0.3
154
-14
3.9
-0.4
159
5
4.1
0.2
203
44
5.3
1.2
207
4
5.4
0.1
187
-20
4.9
-0.5
173
-14
4.6
-0.3
162
-11
4.3
-0.3
141
-21
3.7
-0.6
134
-7
3.6
-0.1

Unemployed females
Numbers(10,000
persons)
140
135
121
116
107
104
107
133
127
115
112
103
95
88

Year-on-year
Year-on-yearchange Unemployment rate change
5.1
-5
4.9
-0.2
-14
4.4
-0.5
-5
4.2
-0.2
-9
3.9
-0.3
-3
3.7
-0.2
3
3.8
0.1
26
4.8
1
-6
4.6
-0.2
-12
4.2
-0.4
-3
4
-0.2
-9
3.7
-0.3
-8
3.4
-0.3
-7
3.1
-0.3

Source: Labor Force Survey, Statistics Bureau, Ministry of Internal Affairs and Communications

Appendix 2

Number of workers by employment status and gender(A) males(10,000 persons)

Employees

2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Change from 2008 to 2009

Employees
excludingexecutives

Regularemployee

2867
2853
2851
2864
2897
2947
2928
2874
2865
2885
2865
2878
2889
2896
-54

2437
2410
2385
2357
2378
2408
2367
2345
2324
2313
2300
2267
2259
2261
-22

3165
3152
3152
3165
3194
3240
3220
3162
3148
3163
3147
3140
3151
3158
-58

PartNon-regular
employee time/tempora
ry worker
431
444
466
507
519
539
560
527
540
571
566
610
630
634
-33

Part-time
workerTemporaryworker

Dispatchedworker from
temporary laboragency

63
63
70
77
79
83
82
84
87
94
97
101
103
108
2

166
171
166
171
168
172
166
166
172
182
175
200
201
204
0

10
13
28
42
49
54
55
37
35
39
36
48
48
50
-18

229
235
236
247
247
255
248
250
259
276
272
301
304
312
2

Contract
employee

Entrusted
employee

Other

122
125
136
149
151
162
180
174
181
197
197
147
159
154

70
71
66
69
71
69
77
67
66
62
61
42
43
42
-10

72
76
75
-6

(B) females(10,000 persons)

Employees

2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Change from 2008 to 2009

Employees
excludingexecutives

Regularemployee

2073
2095
2124
2144
2195
2237
2248
2250
2273
2279
2288
2323
2351
2388
2

1052
1034
1025
1018
1036
1041
1043
1050
1051
1039
1041
1027
1019
1042
7

2172
2191
2220
2243
2292
2332
2337
2341
2361
2369
2375
2405
2436
2473
4

PartNon-regular
employee time/tempora
ry worker
1021
1061
1098
1126
1159
1196
1205
1200
1223
1241
1247
1296
1332
1345
-5

Part-time
workerTemporaryworker

Dispatchedworker from
temporary laboragency

655
685
693
703
713
741
741
733
764
779
792
826
840
852
-8

170
170
166
169
165
170
165
173
172
173
177
192
202
201
8

33
37
57
64
78
81
85
72
62
59
55
68
71
76
-13

825
855
860
872
878
911
906
906
937
954
969
1019
1042
1053
0

Contract
employee

Entrusted
employee

Other

108
111
119
130
133
137
142
149
152
163
157
126
133
133

55
58
62
60
70
68
71
73
73
66
67
40
42
41
2

43
44
43
7

Source: Labor Force Survey, Statistics Bureau, Ministry of Internal Affairs and Communications

Appendix 3

Number of male and female employees by industry

Source: Labor Force Survey, Statistics Bureau, Ministry of Internal Affairs and Communications(A) males(10,000 persons)Total

culture and forgricultural industries
Fisheries

2008
2009
Change from 2008 to 2009

3226
3169
-57

25
27
2

3201
3142
-59

6
5
-1

Mining and Construction Manufacturi Electricity,
quarrying of
nggas, heatstone andsupply andgravel
water

2
3
1

372
360
-12

760
726
-34

29
31
2

Information
andcommunicati
onsTransport
and postal
activities

Wholesaleand retail
trade

Finance andinsurance

137
140
3

268
272
4

470
462
-8

76
77
1

Information
andcommunicati
onsTransport
and postal
activities

Wholesaleand retail
trade

Finance andinsurance

45
46
1

60
63
3

474
476
2

82
84
2

Real estate
Scientific Accommodat
Livingand goods
research,ions, eating related andrental and professional and drinking
personal
leasing
and technical services
services andservices
amusement
services

63
62
-1

97
97
0

113
114
1

73
74
1

Education,
learning
support

Medical,health careand welfare

123
124
1

123
129
6

Education,
learning
support

Medical,health careand welfare

136
138
2

444
461
17

Compound Services, not Government,
services
elsewhere
exceptclassified
elsewhereclassified

34
31
-3

255
241
-14

(B) females(10,000 persons)
Total

2008
2009
Change from 2008 to 2009

173
172
-1

2320
2321
1

Appendix 4

culture and forgricultural industries
Fisheries

21
24
3

2298
2297
-1

2
3
1

Mining and Construction Manufacturi Electricity,
quarrying of
nggas, heatstone andsupply andgravel
water

0
0
0

67
66
-1

324
299
-25

3
4
1

Real estate
Scientific Accommodat
Livingand goods
research,ions, eating related andrental and professional and drinking
personal
leasing
and technical services
services andservices
amusement
services

35
35
0

53
51
-2

187
193
6

105
109
4

Compound Services, not Government,
services
elsewhere
exceptclassified
elsewhereclassified

22
20
-2

188
180
-8

51
52
2

Marital status of male employees over 15-years-old by employment status

(2007 average)
Source: Labor Force Survey, Statistics Bureau, Ministry of Internal Affairs and Communications

NevermarriedMarriedWidowed or divorced

Total employees

27%

69%

4%

Agriculture

12%

82%

6%

Non-agriculture

27%

69%

4%

Self-employed

12%

82%

6%

Piece worker

0%

100%

0%

Family worker

52%

43%

4%

Employee

29%

67%

4%

Executive

7%

90%

4%

Regular employee

28%

68%

3%

Part-time/temporary worker

58%

37%

5%

55%

40%

6%

Contract/entrusted employee

26%

68%

6%

Other

30%

64%

6%

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