Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Labor supply response to house price shocks: Evidence from Japan
Junya Hamaaki, Shinichiro Iwata
29 September, 2023
DP2023-001
https://www.pdrc.keio.ac.jp/en/publications/dp/8792/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
29 September, 2023

Labor supply response to house price shocks: Evidence from Japan

Junya Hamaaki, Shinichiro Iwata
PDRC Keio DP2023-001
29 September, 2023
JEL Classification: D12; E21; J14; R21
Keywords: Elderly; Housing Wealth; Labor Force Participation; Labor Hours; Japan

Abstract
This paper examines to which extent the labor supply of homeowners aged 40 years or over responds
to unanticipated shocks to house prices in Japan. Our findings suggest that only people in their 60s
or over respond to house price shocks. They try to remain in the labor force but tend to reduce theirworking hours. Specifically, we find that a 10 percent increase in house prices leads to a 14 minutereduction in weekly working hours for elderly women and a 21 minute reduction for elderly men. We
also find that women respond to house price shocks at a younger age than men. Women decrease theirweekly working hours from the age of 60, while men start to reduce their working hours from the ageof 65. The small and late response to a positive house price shock among the elderly may reflect thepeculiarities of Japan's housing and labor markets.

Junya Hamaaki
Faculty of Economics, Hosei University
4342 Aiharamachi, Machida, Tokyohamaaki@hosei.ac.jp
Shinichiro Iwata
Faculty of Economics, Kanagawa University
3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, Kanagawa
siwata@kanagawa-u.ac.jp

Acknowledgement: We would like to thank Shinya Kajitani, Daiji Kawaguchi, Tsunao Okumura,
Kazuto Sumita, participants of workshops at the University of Bristol, Musashino University, andToyo University, participants of the 2022 Japanese Economic Association Spring Meeting and the26th Asian Real Estate Society Annual Conference for their valuable comments and constructive
suggestions. We would also like to thank Ralph Paprzycki for his excellent English editing service.
We are grateful to Misa Izuhara for encouraging us to write this paper. Finally, we would like tothank the Panel Data Research Center at Keio University for providing us with access to its microdata and Norifumi Yukutake for his useful advice on the analysis of this data. This research was
supported by JSPS KAKENHI Grant Numbers JP19K01603 and JP19K01703 and ESRC-AHRC UKJapan SSH Connections Grant ES/S013695/1.

Labor supply response to house price shocks:
Evidence from Japan
Junya Hamaaki
Faculty of Economics, Hosei University
4342 Aiharamachi, Machida, Tokyo 194-0298, Japanhamaaki@hosei.ac.jp

Shinichiro Iwata
Faculty of Economics, Kanagawa University
3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, Kanagawa 221-0802, Japansiwata@kanagawa-u.ac.jp

29 September, 2023

Abstract
This paper examines to which extent the labor supply of homeowners aged 40 years orover responds to unanticipated shocks to house prices in Japan. Our …ndings suggest that
only people in their 60s or over respond to house price shocks. They try to remain in thelabor force but tend to reduce their working hours. Speci…cally, we …nd that a 10 percent
increase in house prices leads to a 14 minute reduction in weekly working hours for elderly
women and a 21 minute reduction for elderly men. We also …nd that women respond tohouse price shocks at a younger age than men. Women decrease their weekly working hours
from the age of 60, while men start to reduce their working hours from the age of 65. The
small and late response to a positive house price shock among the elderly may re‡ect thepeculiarities of Japan’s housing and labor markets.
Keywords: elderly; housing wealth; labor force participation; labor hours; JapanJEL classi…cation: D12, E21, J14, R21

1

1

Introduction

The impact of positive house price shocks on labor supply decisions is an issue that has received
considerable attention, since housing represents an important form of asset holdings for households. There are at least two channels through which an increase in house prices a¤ects labor
supply. The …rst channel is through the higher price households receive when they sell theirhome, which increases the lifetime/permanent income of those who sell their home and downsize
and may lead them to reduce their labor supply. Second, even if households do not sell theirhome, the increase in value can still have an income e¤ect on their labor supply: if homeowners have a certain amount in mind that they want to bequeath to their children, an increasein the value of their home allows them to use more of their other assets for consumption andreduce their labor supply while maintaining the same level of consumption. According to thelife-cycle/permanent income hypothesis (LC-PIH), if leisure is a normal good and house pricechanges are unanticipated, an increase in house prices should tend to reduce the labor supply of
homeowners through these two channels.1 Moreover, the LC-PIH suggests that elderly people
are more likely to respond to house price shocks because of their shorter expected time horizon
before death.
Consistent with these theoretical predictions, a number of previous studies for the UK and
the US have found that house price shocks tend to decrease the labor supply of homeowners (e.g.,
Henley, 2004; Disney & Gathergood, 2018 for the UK, Ondrich & Falevich, 2014; Farnham &Sevak, 2016; Zhao & Burge, 2017; Begley & Chan, 2018 for the US). Especially for the elderly, a
few previous studies that estimated the e¤ects both on the probability that individuals continued
work and on their working hours found that they tend to decrease labor supply by leaving thelabor market rather than by reducing their working hours (Zhao & Burge, 2017; Disney &Gathergood, 2018).
However, these results may not necessarily hold for other countries, because the nature of
the housing market di¤ers across countries. In Japan, for example, the …rst channel likely to
1

A third channel through which an increase in house prices potentially a¤ects labor supply, even when theprice increase is anticipated, is through the relaxation of liquidity constraints on liquidity-constrained households
due to the increase in collateral value. We do not consider this case, since we solely focus on unanticipated house
price shocks.

2

be less important, because the selling and buying of used houses in the second-hand market is
not very common (Kanemoto, 1997; Kobayashi, 2016).2 According to the Ministry of Land,
Infrastructure, Transport and Tourism (2019), sales of existing homes account for only 14.7
percent of total home sales in Japan, compared with 88.2 percent in the UK and 83.1 percent
in the US. Moreover, selling existing homes is becoming increasingly di¢ cult in municipalities
whose population is shrinking. In such municipalities, o¤spring often refuse inheriting houses
left by their parents. This means that the second channel –a reduction in labor supply throughthe increase in the size of the assets to be bequeathed to o¤spring – is also less likely to work.
In sum, because of the nature of the housing market in Japan, the bene…ts of a positive house
price shock are likely to be smaller than in the UK and the US.
Another factor is that the nature of the Japanese labor market also di¤ers from that of theother two countries. Japanese …rms generally pay wages based on tenure rather than productivity, especially for full time workers. Tenures therefore tend to be long. According to theJapan Institute for Labour Policy and Training (2023), the share of employees that have worked
for the same company for 10 years or more is relatively high at 46.8 percent, exceeding thecorresponding shares in the UK (30.6 percent) and the US (26.9 percent). The nature of thelabor market in Japan means that Japanese employees likely are more hesitant to quit their job
even if house prices rise substantially, since re-entering the labor market if things do not workout in all probability would involve a substantial wage cut.
Against this background, our study contributes to the literature in the following two respects.
First, to the best of our knowledge, this study is the …rst analysis of the impact of house priceshocks on labor supply in Japan taking the lack of a liquid housing market and of a rigid labormarket into account.
Second, this study further contributes to the literature on the impact of house price shocks
on labor supply by dividing individuals into detailed age groups in order to examine whether the2

Against this background, another way in which higher house prices theoretically could allow homeowners to
reduce their labor supply is through equity loans. That is, homeowners could borrow against the equity in theirhome and reduce their labor supply. However, Japanese banks generally do not provide equity loans against
property of uncertain value (Mitchell & Piggott, 2004), and …gures by the Ministry of Land, Infrastructure,
Transport and Tourism (2021) suggest that only 1.4 percent of commercial banks (out of a total of 1,105 banks)
provide this kind of loans.

3impact of house price shocks di¤ers by age. While most studies divide individuals into two or
three age groups (e.g., the young, middle-aged, and elderly), we create seven groups from 40 to
70+ in 5-year increments. This allows us to examine in greater detail whether age heterogeneity
in the labor response to house price shocks is in line with the LC-PIH.3
For our analysis, we use the Japan Household Panel Survey, a longitudinal survey of Japanesehouseholds covering the period from 2005 to 2019. The data is ideal for our analysis because it
contains respondents’self-reported value of their home as well as a variety of measures for labor
supply outcomes such as information on whether respondents participate in the labor force, thenumber of days worked per month, and the hours worked per week. To identify the impact ofhouse price shocks, we extract unanticipated changes in house prices, since, according to the LCPIH, people change their behavior as soon as they expect a change in their income, so that there
should be no change in labor supply when that change actually occurs. Speci…cally, following
previous studies such as Farnham & Sevak (2016) and Begley & Chan (2018), we estimate an
equation with self-reported house prices as the dependent variable and their lag as well as year
dummies as explanatory variables. We then use the residuals as the unanticipated components
of house prices (house price shocks) in the estimation of the labor supply function.
House price shocks likely re‡ect a variety of events that occurred during our observation
period from 2005 to 2019. For example, Japan won the race to host the 2019 Rugby World Cup,the 2020 Olympic and Paralympic Games, and the 2025 World Exposition, which may have
led to positive house price shocks in certain local housing markets (Kontokosta, 2012). On theother hand, Japan is a country that is prone to major natural disasters, which generally in‡ict
a negative shock to house prices in certain geographical areas (Naoi et al., 2009). Therefore,house price shocks contain su¢ cient variation to estimate their impact on labor supply.
We obtain three main results. First, when we estimate the e¤ect of house price shocks
without taking respondents’age into account, we …nd no signi…cant e¤ect for any of the labor
supply outcomes. This result is highly plausible given the nature of Japan’s housing and labor
3
Relatedly, studies focusing on the impact of house price shocks on household consumption rather than labor
supply in Japan have found a small but signi…cant e¤ect for the middle-aged and the elderly but no e¤ect for the
young (Naoi, 2014; Hori & Niizeki, 2019). This …nding is consistent with the LC-PIH and the argument regarding
the role played by the low liquidity in Japan’s housing market.

4markets.
Second, when taking age into account, we …nd that only the working hours of people in their
60s or over show a signi…cant response to house price shocks. Speci…cally, a positive house priceshock of 10 percent leads to a reduction in weekly working time of 14 minutes for elderly womenand 21 minutes for elderly men. The results are in line with the LC-PIH, which suggests thatthe elderly are more likely to respond to a house price shock, although they contrast with theaforementioned …ndings for other countries that the elderly are more likely to leave the labormarket than reduce their working hours.
There are at least two likely explanations why our …ndings di¤er from those for the UK and
US. One, as discussed above, is that due to the low liquidity in the market for existing houses inJapan, it is di¢ cult for the elderly to sell their home and downsize when the value of their houserises. Another is that, as mentioned too, people hesitate to fully retire, presumably because thecost of reversing that decision in Japan is much higher than in the UK and US.
Our main …nding is that women respond to house price shocks at younger age than men.
Women decrease their weekly working hours from the age of 60, while men start to reduce theirweekly working hours only from the age of 65. This suggests that women face lower opportunity
costs of reducing working hours than men.
The remainder of the paper is organized as follows. Section 2 provides a review of theliterature. Sections 3, 4, and 5 then respectively present the empirical models, datasets, andempirical …ndings. Finally, Section 6 concludes the paper.

2

Literature review

2.1

Endogeneity issues related to house prices

Previous studies on the UK and the US have found that housing windfalls tend to reduce thelabor supply of homeowners. Because of the endogeneity issues associated with house prices,a simple OLS estimation with labor supply as the dependent variable and (self-reported or
regional) house prices as the independent variable is unlikely to identify the impact of house
prices on labor supply. This section, therefore, explains how previous studies have addressed5the following three types of endogeneity: (i) measurement errors in (self-reported) house prices,(ii) the correlation between house prices and the local labor market, and (iii) the correlation ofhouse prices with respondents’income expectations.
The …rst type of endogeneity potentially leads to attenuation bias of the e¤ect of house priceshocks. In fact, Tur-Sinai et al. (2020) showed that homeowners are likely to overestimate theprice of their residence. To avoid the measurement error in self-reported house prices, several
studies have estimated house price shocks using regional house price indices (Farnham & Sevak,
2016; Zhao & Burge, 2017; Begley & Chan, 2018). Farnham & Sevak (2016), for example, usedregional house price indices from the US Federal Housing Finance Agency as a proxy variable
for house prices. A di¤erent approach was taken by Fu et al. (2016) to deal with this issue,
which will be discussed shortly.
Moving on the second type of endogeneity issue, (self-reported) house prices (or changes
therein) in a particular area may be correlated with the local labor market, since where people
live is not randomly assigned. For example, individuals who want to work long hours and/or for
longer years may decide to live in areas with abundant employment opportunities, giving rise toa situation in which both house prices and labor supply outcomes are correlated with regional
characteristics (e.g., local economic conditions). Begley & Chan (2018) in their study therefore
add US Zip code-level region-with-year …xed e¤ects and the local unemployment rate as control
variables.
The third type of endogeneity is the possible correlation between individuals’income expectations and house prices. If income expectations are not controlled for, they will be included in
the error term, leading to omitted variable bias, since income expectations a¤ect both current
labor supply and current house prices through housing demand. Therefore, if we fail to control
for income expectations, this will lead to an overestimation of the impact of house price shocks
on labor supply. Fu et al. (2016) estimated the impact of housing capital gain, measured as thedi¤erence between the purchase price and the current price of a house, on labor supply in China.
To deal with the correlation of house prices with income expectations as well as the possible
measurement error in self-reported house prices, they use the average housing capital gain of all

6

respondents other than the respondent in a given community as an instrumental variable.
To address these endogeneities, some previous studies have estimated the di¤erences in responses to house price shocks between homeowners (as the treatment group) and renters (asthe control group) using di¤erence-in-di¤erences estimation (Farnham & Sevak, 2016; Zhao &Burge, 2017; Disney & Gathergood, 2018). However, renters may also change their current labor
supply in response to house price changes due to concerns that higher house prices will increase
the cost of purchasing their own home in the future (Begley & Chan, 2018). Therefore, there isno consensus on how to address these endogeneity issues.4

2.2

House price shocks and labor supply

Next, studies examining the association between house price shocks and labor supply – taking
the mentioned endogeneity issues into account –are reviewed. Starting with studies for the UK,
Henley (2004) found that a 10 percent increase in house prices leads to a 28 minute decline inweekly hours of work for women aged 18-65, while a 10 percent fall in house prices is associatedwith an 18 minute increase for men in the same age bracket. Meanwhile, focusing not only
on hours of work but also on the likelihood of labor force participation, Disney & Gathergood(2018) examine the labor supply response distinguishing the following three age groups: theyoung (under 40), the middle-aged (age 40-54 years old), and the elderly (over 54). Their
empirical results indicate that an increase in house prices reduces both working hours and thelikelihood of participation for young female homeowners. They also …nd that an increase inhouse prices lowers the likelihood of participation for elderly men. Elderly men’s working hours,
however, do not respond to house price shocks. These results are similar to those obtained by
Zhao & Burge (2017) for the elderly in the US. They showed that a decline in house prices
increased their likelihood of participation, while it did not have any e¤ect on their hours of workwhen house prices fell.
Continuing with studies on the US and the link between house price shocks and labor supply,
a number of studies have focused on the impact on the the retirement decisions of the elderly.
4

One possible solution is to employ a quasi-experimental design comprising exogenous house price shocks. See,for example, Li et al. (2020)7

Ondrich & Falevich (2014), for instance, found that negative house price shocks lowered retirement probabilities of elderly males, while Farnham & Sevak (2016) showed that an increase inhouse prices led elderly men to retire sooner. Conversely, Begley & Chan (2018) found thatelderly men who experienced a negative house price shock were less likely to retire and more
likely to reverse retirement.

3

Empirical models

3.1

House price function and house price shocks

Previous studies have tried to capture the unanticipated component of house price changes,
i.e., house price shocks, by calculating the di¤erence between actual house prices and house
prices predicted from an autoregressive house price model (Farnham & Sevak, 2016; Begley &Chan, 2018; Burrows, 2018). In this paper, we assume that individuals form expectations about
current house prices on the basis of an autoregressive lag (AR2) model. Based on this model,
the predicted natural log of individual house prices is given byln hpij;t =0+

1 ln hpij;t 1 +2 ln hpij;t 2 +3 ln bit +i+t + j + "ij;t ;(1)where hpij;t is the price of individual i’s house in location j in year t, bit is the age of the building,i represents unobserved housing unit …xed e¤ects,

t represents year …xed e¤ects to control for

aggregate economic conditions in a particular year,

j represents location …xed e¤ects, and "ij;tis the error term. Using the estimated parameters, we calculate house price shocks ln hpuij;t as
the di¤erence between the actual house price (ln hpij;t ) and the house price predicted based onc ). Note that we focus on individuals that have lived in the same house for atEq.(1) (ln hp
ij;tleast three consecutive years when estimating Eq. (1). This is becuase, ln hpuij;t seeks to capture
house price ‡uctuations due to exogenous shocks rather than ‡uctuations due to individuals’
own decisions such as moving to a di¤erent house or renovating their house.5
The signs of the coe¢ cients

1 and

2 depend on homeowners’forecasts of the price of theirhome and are therefore di¢ cult to know a priori. The signs will be positive if homeowners expect5
Note that our house price estimates may not fully re‡ect renovation spending because of the thin second-handmarket in Japan.

8the price of their home to increase as the lagged value of the price of their home increases andvice versa.
We estimate Eq. (1) using Arellano-Bond dynamic panel GMM estimators (Arellano &Bond, 1991), which generate consistent and e¢ cient parameter estimates in lagged dependent
variable models.

3.2

Labor supply function

Using the information on house price shocks (ln hpuij;t ), we next explore the relationship between
house price shocks and homeowners’labor supply. We estimate two speci…cations of the labor
supply function. The …rst speci…cation is as follows:
lsij;t =0+u
1 ln hpij;t + Xit+

i+t+ j + jt + uij;t ;(2)where lsij;t is the labor supply outcome of individual i in location j in year t, Xit representssocio-economic characteristics of the individual,t for year …xed e¤ects,

i stands for unobserved individual …xed e¤ects,

j for location …xed e¤ects,jt for location-with-year …xed e¤ects, anduij;t is the error term.6 Year …xed e¤ects control for the impact of macroeconomic ‡uctuations on
the labor supply, while location …xed e¤ects control for regional di¤erences in the labor supply.
Following Begley & Chan (2018), the interaction terms between location and year …xed e¤ects in
Eq. (2) are added to control for time series variations in region-speci…c macroeconomic shocks.
In addition, following Farnham & Sevak (2016) and Begley & Chan (2018), we also include theregional employment rate, which we obtain from the Labour Force Survey. When we estimate
Eq. (2), we omit individuals under 40, since the majority of those under 40 are renters rather
than homeowners.
The parameter of primary interest is

1 , which captures the extent to which the labor supply

of individual i reacts to a house price shock. The LC-PIH implies that the sign of

1 should be

negative.
The second speci…cation aims to capture the heterogeneous impact of house price shocks
across age groups. The sample is the same as that when estimating Eq. (2), i.e., we focus on
6

In our analysis, we try to reduce the e¤ect of measurement error in self-reported house prices/house priceshocks driven by time-invariant respondent characters (such as an optimistic personality) by controlling for individual …xed e¤ects.

9individuals aged 40 years or over. The second speci…cation is as follows:
lsij;t =

0+

7
X

u
a (ln hpij;ta=1dait ) +7Xaa dit + Xit+

i+t+ j + jt + uij;t ;

(3)

a=2

where dait is a binary variable, which takes 1 if individual i falls into age group a and 0 otherwise.
There are seven 5-year age groups ranging from a = 1 (for those aged 40-44 years old) to a = 7(70 years or over).
The parameteracaptures to which extent the labor supply of individual i in group a

responds to a house price shock. As previously noted, according to the LC-PIH, the impact of ahouse price shock should become larger with age, because the elderly have a shorter remaining
life expectancy. The parametera aims to control for inherent di¤erences in the level of thelabor supply across age groups (the reference group is those aged 40-44).
We estimate both Eqs. (2) and (3) separately for each sex. Moreover, we estimate theequations using a …xed-e¤ects model to allow for unobserved individual …xed e¤ects. Since we
use generated regressors (ln hpuij;t ), we calculate the standard errors of the estimates employing
a bootstrap procedure using 500 bootstrapped resamples clustered at the household level.

4

Data

To estimate Eqs. (1), (2), and (3), we use information from the Japan Household Panel Survey(JHPS), which is compiled by the Panel Data Research Center at Keio University. The JHPS
is a nationally representative, large-scale longitudinal survey of Japanese households conductedannually in January. The JHPS comprises two sets of surveys: one that was started in 2004
(originally called the Keio Household Panel Survey, KHPS) and another started in 2009 (theinitial JHPS sample). Both surveys initially started with a sample of approximately 4,000 respondents. To address sample attrition, the KHPS added approximately 1,400 new respondents
in 2007 and another 1,000 in 2012. The KHPS was integrated into the JHPS in 2014. In thefollowing analysis, we use 15 waves of the JHPS (integrated with the KHPS after 2014) from2005 to 2019. The JHPS is particularly suited to address the research questions of this study
since it contains detailed information on self-reported house prices for homeowners, individuallabor supply, and a rich set of data on respondents’socio-economic characteristics.
10

4.1

House prices

Self-reported house prices are used to estimate Eq. (1). The survey asks “How much do you think
the house and lot would sell for in today’s market?”. Self-reported house prices are converted to2005 prices using the national consumer price index.
The number of homeowners who answered this question with a value larger than 0 is 42,853.
Of these 42,853 observations, we can use 18,276 observations to estimate Eq. (1). The reason forthe large reduction in the number of observations is that many of the households did not provide
self-reported values for the same house for at least three consecutive years. The mean house
price for hpij;t is 22.2 million JPY, and the standard deviation of house prices is 21.1 million
JPY. The minimum and maximum values for hpij;t are 0.1 million JPY and 650.0 million JPY.

4.2

Labor supply

To examine the labor supply response to house price shocks, we focus only on individuals thatworked in the previous year. We also restrict our sample to non-regular employees for women andregular employees for men because the Japanese labor market is characterized by a high parttime employment rate for females and a high full-time employment rate for males. According
to the Ministry of Health, Labour and Welfare (2016), the share of male employees hired by abusiness establishment that were in full-time employment was 77.4 percent, while that of female
employees hired by a business establishment was 45.2 percent.
The dependent variables in Eqs. (2) and (3), i.e. lsij;t , are: (i) a dummy variable for labor
force participation (LFP) and (ii) two continuous variables, namely, one for the days of work permonth (Monthly Days of Work) and one for the hours of work per week (Weekly Hours of Work).
The JHPS asks respondents (and their spouse if they are married) to report whether they did
any paid work in the survey month. Thus, our LFP dummy for women takes a value of 1 if an
individual was working as a non-regular (part-time) employee in the previous year and continued
working as a non-regular employee in the current year and 0 if she quite working. Similarly, the
LFP dummy for men takes a value of 1 if an individual worked as a regular (full-time) employee
in the previous year and continued to do so in the current year and 0 if he stopped working.
If respondents participate in the labor market, they are also asked, “On average, how many11

days of paid work do you perform each month?”, and “On average, how many hours of paid
work do you perform each week (including overtime)?”. For respondents (or their spouses) who
were not working on the survey date, the number of monthly days of work and weekly hours ofwork were set to zero.

4.3

Control variables

Location …xed e¤ects ( j in Eq. 1, andj in Eqs.

2 and 3) are based on respondents’place ofresidence. The JHPS classi…es respondents’place of residence into 8 regions (Hokkaido, Tohoku,
Kanto, Chubu, Kinki, Chugoku, Shikoku, and Kyushu).
In Eqs. (2) and (3), we include the regional unemployment rate (Regional Unemployment
Rate) from the Labour Force Survey (Statistics Bureau of Japan). Regional unemployment rates
are calculated by the authors based on the above 8 regions.
To control for respondents’ socio-economic characteristics, Xit in Eqs. (2) and (3), we use
the following four variables. The …rst is a dummy variable that takes value 1 if respondent
i’s household has a mortgage and 0 otherwise (Mortgage). Generally, households pay o¤ theirmortgage in monthly payments, and having a mortgage may a¤ect individuals’labor supply. The
survey asks both whether respondents have a mortgage and, if so, what their yearly mortgage
payments are and what the outstanding mortgage amount is. However, information on mortgage
payments and the outstanding mortgage amount is missing for many observations, so that
we only construct a dummy variable re‡ecting whether respondents have a mortgage or not.
The second control variable tries to capture whether households are …nancially constrained.
Speci…cally, we construct a dummy (No Financial Assets) that takes 1 for households that haveno …nancial assets and 0 otherwise. Households with no …nancial assets are more likely to beliquidity constrained, which may force them to work. Third, we control for the number ofhousehold members (Household Size). On the one hand, a larger household may have more
dependents and some household member may decide to reduce their labor supply to take care ofdependents. On the other hand, a larger household has more mouths to feed, so that household
members maybe more inclined to to work to contribute to living expenses. Fourth, we include
the number of children under the age of 7 in the household (Children Aged 0-6), since parents12(especially mothers) may reduce their labor supply when they have preschool-aged children.

4.4

Summary statistics

We restrict the sample to respondents for whom information necessary to estimate Eqs. (2) and(3) is available.7 The number of person-year observations is 4,934 (1,216 persons) for womenand 7,164 (1,640 persons) for men.
Table 1 displays summary statistics for our sample. LFP takes a high value for both men
and women, since we limit the sample to those who worked in the previous year. Looking atmonthly days of work and weekly hours of work, we …nd that men tend to work more days amonth and hours a week than women. This re‡ects the fact that for women we focus on nonregular workers, while for men we concentrate on regular workers. The average age of women in
our sample is 0.6 years higher than that of men. Meanwhile, at least half of all respondents havea mortgage, and 1 in 5 have no …nancial assets. The average number of household members isaround 3.5, which may re‡ect the fact that remaining observations consist mainly of married
couples.8 Finally, the number of preschool children in the household is quite small for both
women and men since we focus on individuals aged 40 years or over.

5

Results

5.1

House price function

The estimation results for Eq. (1) are presented in Table 2. We …nd that while the coe¢ cient onln(House Price t2) is not signi…cant, that on ln(House Price t

The negative sign of the coe¢ cient on ln(House Price t

1) is negative and signi…cant.

1) suggests that homeowners tend to

expect that when the price of their house rises in a particular year, it will decrease in the next
year.9 The coe¢ cient on Building Age is negative and signi…cant, indicating that self-reported
7

The JHPS includes information on individuals’annual income from their main job in the previous year. In theestimation stage, we discard the top 1 percent observations in terms of labor income to exclude extreme outliers.
We also restrict our sample to persons who worked not more than 100 hours per week.
8
As highlighted by Disney & Gathergood (2018), individuals’ marital status (i.e., whether they are single or
married/cohabiting) matters for their labor supply decision. However, in our sample, only 5.9 percent of womenand 5.6 percent of men are single.
9
As mentioned in Section 3.1, from a theoretical perspective, the sign is undetermined. Therefore, whether
it is positive or negative is an empirical question. In fact, the sign here di¤ers from that obtained by Burrows(2018) for the UK.

13

house prices are likely to fall with building age. The coe¢ cients on the year dummies (thereference year is 2007) are signi…cantly negative and increase in absolute value over time.
Using the estimated parameters in Table 2, we calculate the natural log of house price shocks,
ln hpuij;t (= ln hpij;tc ). Fig. 1 shows the density function for ln hpu . House price shocks
ln hp
ij;t
ij;t

are distributed in the range from

8:1 percent to 4:5 percent, which means that there is su¢ cientvariation to estimate the impact of house price shocks on labor supply.

5.2

Scatter plots between house price shocks and labor supply

Fig. 2 displays the association between ln hpuij;t (horizontal axis) and labor supply (vertical axis)
by sex together with …tted lines. Panel A of Fig. 2 suggests that men’s LFP is negatively associated with house price shocks, while women’s LFP does not show such a negative association.
Meanwhile, Panels B and C indicate that days of work per month and hours of work per week
decrease in response to positive house price shocks both for men and for women. The negative
slope of the …tted line in these panels is steeper for women than for men, suggesting that womenare more sensitive to house price shocks than men in terms of how much they work.
Because we are interested in the heterogeneous e¤ects with respect to age, Fig. 3 presents
scatter diagrams for 5-year age groups. For women, monthly days of work and weekly hours
of work appear to be negatively correlated with house price shocks across all age groups. For
men, Fig. 3 suggests that labor supply outcomes are more consistent with the LC-PIH thanfor women, i.e., the negative association between labor supply and house price shocks becomesstronger with age.

5.3

Benchmark results for the labor supply function

Table 3 shows the estimation results of Eq. (2). Column A displays the results for the LFP of
women and men separately, based on the sample of women who worked as non-regular employees
and men who worked as regular employees in the preceding year. Similarly, column B presents
the results for monthly days of work, while column C shows the results for weekly hours of work.
The estimated coe¢ cients on the variable of primary interest, ln(House Price Shock), have –except for the LFP for men –the expected negative sign, although none of them are signi…cant14

at conventional levels.10
Next, let us look at the result for the other control variables. The coe¢ cients on Mortgage
are positive and signi…cant in the equations for men’s LFP and monthly days of work, suggestingthat men are more likely to work and/or work more if they have a mortgage. The coe¢ cienton No Financial Assets is positive and signi…cant in the equation for women’s weekly hours
of work, implying that women tend to work longer hours when they lack …nancial resources.
The coe¢ cient on Household Size is negative and signi…cant in the equation for women’s LFP,
indicating that women are less likely to participate in the labor force the larger the numberof household members. This likely re‡ects that a larger household increases the time spenton domestic chores such as preparing food, general cleaning, and clothing maintenance. The
coe¢ cient on Children Aged 0-6 is negative and signi…cant in all equations for women, indicatingthat women with a child or children of preschool age tend to reduce their labor supply due tochild-rearing demands. For men, the coe¢ cient on Children Aged 0-6 is negative and signi…cantonly in the case of weekly hours of work. Moreover, the size of the coe¢ cient is less than half ofthat for women. Overall, the number of household members (Household Size) and having a smallchild or children (Children Aged 0-6) matters especially for women. The coe¢ cient of Regional
Unemployment Rate is negative and signi…cant only in the men’s LFP equation, suggesting that
men are less likely to be in work when the unemployment rate is higher. On the other hand, forthose that have a job, the unemployment rate does not a¤ect how much they work.
Fig. 4 presents the coe¢ cient estimates for the interaction terms between house price shocks
and 5-year age group dummies, together with 90 and 95 percent con…dence intervals. Looking at
the impact of house price shocks on LFP (Panel A), for women the coe¢ cients on the interaction
term are close to 0 and insigni…cant, suggesting that women’s LFP does not respond to house
price shocks for any age group. On the other hand, for men the coe¢ cient on the interaction
term is negative for those aged 65-69 and 70+, and the coe¢ cient is larger in absolute value forthose aged 70+ than for those aged 65-69. Therefore, men’s LFP appears to behave in line with
10
A possible reason for the insigni…cant results could be that some individuals take a while to change theirlabor supply after experiencing a house price shock. To examine whether this is the case, instead of using
contemporaneous shocks hpuij;t , we use one- and two-year lagged house price shocks, hpuij;t 1 and hpuij;t 2 , asalternative measures. However, we still …nd no signi…cant response in individuals’labor supply.

15

the LC-PIH in the sense that the elderly respond more strongly to house price shocks, althoughboth coe¢ cients are insigni…cant. The results for monthly days of work (Panel B) show broadly
similar patterns to those for LFP.
Turning to the results for weekly hours of work (Panel C), we …nd that the coe¢ cients on theinteraction terms for women aged 40-44, 45-49, 50-54, and 50-59 are not signi…cant, indicatingthat weekly working hours among women in their 40s and 50s do not respond to unexpectedhousing gains. However, house price shocks do have a large impact on the labor supply of womenin their 60s or over. Although the coe¢ cient on the interaction term for women aged 65-69 is
insigni…cant, those for the 60-64 and 70+ age groups are negative and signi…cant, indicating thatwomen in these age groups do reduce their hours of work per week. Therefore, the estimation
results for women’s weekly hours of work are consistent with the predictions of the LC-PIH.
Meanwhile, the response of men’s weekly hours of work also di¤ers across age groups in linewith the LC-PIH: the coe¢ cients on the interaction terms for men aged 40-44, 45-49, 50-54,
50-59, 60-64 are not signi…cantly di¤erent from 0, while that for men aged 65-69 is negative andsigni…cant and the signi…cant negative impact is even larger for men aged 70+.
Our main …ndings from Table 3 and Fig. 4 can be summarized as follows. First, as expected,
Table 3 suggests that Japanese individuals’labor supply is hardly a¤ected by house price shocks.
Second, when we examine the impact by age group, we …nd that house price shocks lead to a
reduction in weekly hours worked only for the elderly in their 60s or over, in a manner consistent
with the LC-PIH. As mentioned in the introduction, in Japan it is di¢ cult for individuals to
fully take advantage of house price shocks because of the peculiarities of the housing and labormarkets. Under these circumstances, it is di¢ cult for those close to retirement to substantiallydecrease their labor supply by withdrawing from the labor force or reducing their monthly daysof work. Therefore, they moderately adjust their labor supply by reducing weekly hours of work.
Third, we …nd that women reduce their weekly hours of work in response to a house price shock
at a younger age than men. In comparison with men, who predominantly work as full-timeworkers, women, who predominantly work as part-time workers, are more likely to adjust theirworking hours.

16

5.4

Robustness checks for the labor supply function

5.4.1

Alternative de…nition of age dummies

This section reports the results of additional speci…cations to assess the robustness of our main…ndings from Fig. 4. Eq. (2) divides individuals into seven 5-year age groups. The disadvantageof this disaggregation is that the number of observations for each age group becomes relatively
small, resulting in a larger standard error of the coe¢ cient estimates. In order to examine
whether this substantially a¤ects our …ndings, we divide the sample into two age groups only,
the middle-aged and the elderly.
To de…ne the elderly group, we use the following two age cuto¤s. First, we de…ne the elderly
as persons aged 60 years or over (Aged 60+), since Japanese …rms generally set the mandatory
retirement age at 60 years. Second, we de…ne the elderly as those 65 years or over (Aged 65+),
since in 2013 the government stipulated that employers need to continue to employ those whowish to keep working until the age of 65.
In order to use the alternative de…nitions of the age dummies, we employ the following
speci…cation of the labor supply function:lsij;t =u
0 + 1 (ln hpij;tdmit ) +u
2 (ln hpij;tdeit ) + deit + Xit + i +t+ j+ jt + wij;t ; (4)ewhere dm
it and dit are binary variables that respectively take 1 if individual i was middle-aged(m) or elderly (e) in year t, and zero otherwise. The parameters

1 and

2 capture the extent towhich the labor supply of individuals in the middle-aged or elderly group reacts to house priceshocks.
Fig. 5 shows the estimates of

1 and

2 from Eq.

(4), together with 90 and 95 percent

con…dence intervals. The charts on the left show the results for when the elderly are de…ned as
those aged 60+, while those on the right show the results for when the elderly are de…ned as those
aged 65+. Starting with the right-hand chart of Panel A, this shows that only the coe¢ cientfor elderly males (Aged 65+) is negative and signi…cant at the 10 percent level, although it is
economically insigni…cant. That is, the coe¢ cient estimate implies that a 10 percent increase inhouse prices leads to a 0.005 percentage point reduction in the likelihood of being in employment,

17which is almost zero.11
Panel B shows the results for monthly days of work. Both charts indicate that women’smonthly days of work do not change, which con‡icts with the LC-PIH. On the other hand, theright-hand chart of Panel B suggests that men’s monthly days of work respond negatively to ahouse price shock and the response of the elderly group is larger than that of the middle-aged
group, although the coe¢ cients are not statistically signi…cant. Overall, the results for LFP andmonthly days of work do not provide evidence supporting the LC-PIH, which is consistent withthe previous section.
In contrast, for weekly hours of work we again …nd that the results are in line with the LCPIH. Both charts of Panel C show that the response of the elderly to a house price shock is larger
and more signi…cant than that of the middle-aged. The empirical results for women in the left
chart of Panel C show that the coe¢ cient on the interaction term ln hpuij;twhile the coe¢ cient on ln hpuij;tdm
it is insigni…cant,deit is negative and signi…cant. The coe¢ cient estimate for thelatter implies that a 10 percent increase in house prices is associated with a reduction of 0.23
hours (14 minutes) per week for elderly women who work as a non-regular employee. Given
that the average weekly hours of work for the women in our sample is 21.9 hours (Table 2), a0.23 hour decrease translates into a 1.1 percent reduction. According to the 2019 Labour Force
Survey (Statistics Bureau of Japan, 2019), 3.25 million women aged 60+ worked as non-regular
employee. Therefore, a 1.1 percent reduction is equivalent to 34,000 women withdrawing from
non-regular employment.
Turning to men, the right chart of Panel C shows that the coe¢ cient on ln hpuij;tdm
it

is insigni…cant, suggesting that weekly hours of work for middle-aged men is una¤ected by
unexpected housing gains. However, we do …nd a negative and signi…cant coe¢ cient on ln hpuij;tdeit , with the size of coe¢ cient suggesting that for elderly men a 10 percent increase in house
prices is associated with a work reduction of 0.35 hours (21 minutes) per week. Given that the
average weekly hours of work for men in the sample is 45.2 hours (Table 2), a 0.35 hour decrease
corresponds to a 0.8 percent reduction. The 2019 Labour Force Survey (Statistics Bureau of
11This result di¤ers from Disney & Gathergood’s (2018) …nding for the UK, where a 10 percent increase inhouse prices lowered the likelihood of labor force participation of elderly men by 1.3 to 1.5 percentage points.

18

Japan, 2019) shows that 75,000 men aged 65 or over worked as regular employees. A 0.8 percent
reduction, therefore, is equivalent to 5,800 men withdrawing from regular employment.
5.4.2

Alternative de…nition of …xed e¤ects

Previous studies have carefully examined the endogeneity that arises from the correlation between house price shocks and local labor market. So far, we took into account this endogeneity
by incorporating location-with-year …xed e¤ects into the estimation models. In this section,
we instead incorporate industry, occupation, or …rm size dummies as well as interaction terms
between industry, occupation, or …rm size dummies and year …xed e¤ects into Eq. (2). Specifically, we replacej + jt withit + it t , where

it represents industry/occupation/…rm size

…xed e¤ects andit t represents industry/occupation/…rm size-with year …xed e¤ects.

Simi-

lar industries, occupations, and …rm sizes tend to cluster in certain regions rather than being
randomly located, so these variables may proxy for regions.
Panel A of Fig. 6 shows the results for the impact of house price shocks on weekly hours
worked with industry …xed e¤ects and industry-with year …xed e¤ects. Based on their work,
the JHPS divides respondents as belonging to one of 18 industry categories.12 Next, Panel B
of Fig. 6 shows the results when we use occupation …xed e¤ects and occupation-with year …xede¤ects. The JHPS asks respondents for their occupation and classi…es these into 12 categories.13
Finally, Panel C of Fig. 6 presents the results when we control for …rm size …xed e¤ects and…rm size-with year …xed e¤ects. The JHPS asks respondents about the number of employees ofthe …rm they usually work for. Firms are categorized into 6 size groups.14
As shown in Fig. 6, these alternative speci…cations yield very similar patterns to our benchmark results for weekly hours of work shown in Fig. 4, suggesting that our main empirical12

The 18 industries are 1. Agriculture; 2. Fishery, forestry, marine products; 3. Mining; 4. Construction; 5.
Manufacturing; 6. Wholesale, retail; 7. Restaurants, accommodations; 8. Finance, insurance; 9. Real estate;
10. Transportation; 11. Information services and surveys; 12. Information & telecommunications other thaninformation services and surveys; 13. Utilities; 14. Medicine, welfare; 15. Education, learning support; 16. Other
services; 17. Public service; and 18. Other.
13
The 12 occupation categories are 1. Agriculture, forestry, or …shery worker; 2. Mine worker; 3. Salesperson; 4.
Service worker; 5. Manager; 6. Clerical worker; 7. Transportation or communications worker; 8. Manufacturing,
construction, maintenance, or freight worker; 9. Information technology engineer; 10. Specialized or technicalworker; 11. Public safety employee; 12. Other.
14
The six …rm size groups are 1. 1-4 persons; 2. 5-29 persons; 3. 30-99 persons; 4. 100-499 persons; 5. 500
persons or more; 6. Government.

19

…ndings are not unduly in‡uenced by the correlation between house prices and the local labormarket.

6

Conclusion

Focusing on Japan, this study examined individuals’labor supply response to a house price shock
against the background of Japan’s housing and labor market idiosyncrasies. Speci…cally, we
argued that while the life-cycle/permanent income hypothesis suggests that elderly individuals
are likely to reduce their labor supply in response to unanticipated housing gains, the low
liquidity in the market for second-hand homes as well as low labor market mobility make itdi¢ cult for individuals to ‡exibly adjust their labor supply.
Our empirical results indicate that neither labor force participation, monthly days of work,
nor weekly hours of work show a signi…cant response to house price shocks when respondents’age is ignored. However, when respondents’age is taken into account, we …nd that house priceshocks do have a signi…cant e¤ect in some cases. Speci…cally, while we generally …nd no e¤ect onlabor force participation or monthly days of work, we do …nd a signi…cant reduction in weekly
hours of work for people in their 60s or over. Our estimation results suggest that a 10 percent
increase in house prices leads to a reduction of 14 minutes of work per week for elderly womenand 21 minutes per week for elderly men. Our empirical results also suggest that women respondto house price shocks at an earlier age than men.
Due to Japan’s housing and labor market idiosyncrasies, it is di¢ cult for individuals to
respond to a positive house price shock: they cannot sell their house to downsize, quit their job,and live o¤ the capital gains. They also cannot reduce their monthly days of work, so all they
can do is reduce their weekly hours of work a bit as they get older. In sum, Japan’s institutionalsetting tends to prevent individuals from ‡exibly adjusting their labor supply in response to a
positive house price shock.
Although it is still di¢ cult for elderly homeowners to liquidize housing assets, reverse mortgages for the elderly are becoming increasingly popular in Japan because of innovations in the…nancial industry. Our results suggest that more widespread use of reverse mortgages will allow

20

homeowner to bene…t to a greater extent from house price gains by adjusting their labor supply.
At the same time, however, it may also have an e¤ect on the labor market by exacerbating thelabor shortages Japan is experiencing as a result of demographic trends.15
Acknowledgments
We would like to thank Shinya Kajitani, Daiji Kawaguchi, Tsunao Okumura, Kazuto Sumita, participants of workshops at the University of Bristol, Musashino University, and Toyo University, participantsof the 2022 Japanese Economic Association Spring Meeting and the 26th Asian Real Estate Society Annual Conference for their valuable comments and constructive suggestions. We would also like to thankRalph Paprzycki for his excellent English editing service. We are grateful to Misa Izuhara for encouraging
us to write this paper. Finally, we would like to thank the Panel Data Research Center at Keio University
for providing us with access to its micro-data and Norifumi Yukutake for his useful advice on the analysisof this data.

Funding
This research was supported by JSPS KAKENHI Grant Numbers JP19K01603 and JP19K01703 and
ESRC-AHRC UK-Japan SSH Connections Grant ES/S013695/1.

15

Favilukis and Li (2023), for example, focusing on the "Great Resignation" in the US –the substantial decreasein labor force participation among the elderly beginning in early 2021 in the wake of the COVID-19 pandemic –show that this can almost entirely be explained by the increase in house prices.

21

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24

Table 1

Descriptive statistics.
Variables
LFP dummy
Monthly Days of Work
Weekly Hours of WorkAge
Age 40-44 dummy
Age 45-49 dummy
Age 50-54 dummy
Age 55-59 dummy
Age 60-64 dummy
Age 65-69 dummy
Age 70 dummy
Mortgage dummy
No Financial Assets dummyHousehold Size #Children Aged 0-6 #Regional Unemployment Rate

Women
N
Mean
SD
4,678
0.9
0.3
4,934
16.0
6.6
4,896
21.9
13.2
4,934
52.1
8.3
0.2
0.4
4,934
0.2
0.4
4,934
0.2
0.4
4,934
0.2
0.4
4,934
0.1
0.3
4,934
0.1
0.2
4,934
0.1
4,934 2.E-02
4,934
0.5
0.5
4,934
0.2
0.4
4,934
3.5
1.3
4,934 3.E-02
0.2
4,934
3.4
1.0

Men
N
Mean
SD
7,431
1.0
0.2
7,164
21.0
4.6
7,067
45.2
15.5
7,164
51.4
7.5
7,164
0.2
0.4
7,164
0.2
0.4
7,164
0.2
0.4
7,164
0.2
0.4
7,164
0.1
0.3
7,164
4.E-020.2
7,164
1.E-02
0.1
7,164
0.6
0.5
7,164
0.2
0.4
7,164
3.6
1.3
7,164
0.1
0.4
7,164
3.5
1.0

Note: Descriptive statistics for the year, the location, and the location-with-year dummies not shown.

Table 2
Estimation results for the natural log of house prices.
Variablesln House Price 𝑡 1
ln House Price 𝑡 2
ln Building Age
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Constant
First-order Autocorrelation
Second-order Autocorrelation
Observations

Coefficient–0.105*–0.043–0.218***–0.030**–0.052***–0.080***–0.109***–0.133***–0.153***–0.184***–0.205***–0.227***–0.263***–0.267***–0.281***
4.064***–7.480–0.97918,276

Robust SE
0.057
0.029
0.030
0.015
0.018
0.021
0.023
0.025
0.028
0.031
0.035
0.038
0.040
0.044
0.047
0.270
[0.000][0.328]Notes: The model is estimated using the Arellano-Bond dynamic panel GMM estimator.
Arellano-Bond tests for zero autocorrelation in first-differenced errors are reported.
z-values are reported in parentheses and Prob𝑧 is reported in square brackets.
***, **, * denote significance at the 1 percent, 5 percent, 10 percent levels, respectively.

Table 3
Estimation results for the labor supply function.
A: LFP
Women
ln House Price Shock–0.0100.012
Mortgage–0.0090.021
No Financial Assets
0.016
0.020
Household Size
–0.016*
0.009
Children Aged 0-6–0.077*0.042
Regional Unemployment Rate 0.009
0.340
0.048
Within R2
Observations
4,678

Men
0.007
0.007
0.019**
0.009
0.006
0.007
0.002
0.004
–0.0050.005–0.186*
0.098
0.028
7,431

B: Monthly Days of Work
Women
Men
–0.010–0.109
0.283
0.213
0.078
0.585*
0.417
0.302
0.664
0.153
0.427
0.196
–0.195
0.066
0.177
0.109
–1.736**–0.1610.708
0.166–3.753
2.158
9.874
4.289
0.035
0.024
4,934
7,164

C: Weekly Hours of Work
Women
Men
–0.691–0.429
0.591
0.683
0.094
0.680
0.945
0.957
1.471*–0.264
0.888
0.842
0.363
0.119
0.417
0.444
–2.421*–1.189*1.409
0.676–18.135
16.78718.14612.962
0.027
0.036
4,896
7,067

Notes: Bootstrap standard errors obtained by bootstrap approximation using 500 resamples clustered at the household
level are shown in parentheses.
Additional controls include individual-level fixed effects, year fixed effects, location fixed effects, and location-with-year
fixed effects.
** and * denote significance at the 5 percent and 10 percent levels, respectively.

Fig. 1. Density function for house price shocks.

Fig. 2. Association between labor supply and house price shocks.

Fig. 3. Association between labor supply and house price shocks by age group.

Fig. 4. Estimated coefficients of ln(House Price Shocks) × age group dummies.

Notes: Additional controls include age group dummies, the mortgage dummy, the dummy for households with no financial assets,household size, and the number of children aged 0-6. Individual-level fixed effects, year fixed effects, location fixed effects, and location-

with-year fixed effects are also controlled for. Bootstrap standard errors are obtained by bootstrap approximation using 500 resamples
clustered at the household level.

Fig. 5. Changing age group dummies.

Notes: Additional controls include age group dummies, the mortgage dummy, the dummy for households with no financial assets,household size, and the number of children aged 0-6. Individual-level fixed effects, year fixed effects, location fixed effects, and location-

with-year fixed effects are also controlled for. Bootstrap standard errors are obtained by bootstrap approximation using 500 resamples
clustered at the household level.

Fig. 6. Estimation results for weekly hours of work with industry/occupation/firm size fixed effects.

Notes: Additional controls include age group dummies, the mortgage dummy, the dummy for households with no financial assets,household size, the number of children aged 0-6, individual-level fixed effects, and year fixed effects. The number of observations is 4,888

for women and 7,056 for men in Panel A, 4,865 for women and 7,046 for men in Panel B, and 4,855 for women and 7,041 for men in Panel

C. Bootstrap standard errors are obtained by bootstrap approximation using 500 resamples clustered at the household level.