Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Spatial dimensions of intra-metropolitan disparities in commuting timeand female labor force participation
Mizuki Kawabata, Yukiko Abe
11 November, 2016
DP2016-024
http://ies.keio.ac.jp/en/publications/6861

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
11 November, 2016

Spatial dimensions of intra-metropolitan disparities in commuting time and female labor force
participation
Mizuki Kawabata, Yukiko Abe
Keio-IES DP2016-024
11 November, 2016
JEL classification: J21; R12; C30
Keywords: female labor force participation; spatial patterns; geographic information systems (GIS);
spatial statistics; Tokyo metropolitan area

Abstract
We examine intra-metropolitan patterns of geographic disparities in female participation in the labormarket and their associations with commuting time in the Tokyo metropolitan area. Our analysis
based on the Global Moran’s I and Getis-Ord Gi* statistics reveals that the spatial patterns of labor
force participation and regular employment rates differ markedly by marital status and the presenceof children. Compared with unmarried women and married women without children, married
women with children exhibit more significant spatial clustering of high and low values of labor force
participation and regular employment rates, and these rates are negatively correlated with malecommuting time. The non-spatial and spatial regression results show that for married women withchildren, longer commuting time is significantly associated with lower participation and regular
employment rates, while for unmarried women and married women without children, theassociations are mostly insignificant. These results are robust to different model specifications andspatial weights. Our findings suggest that policies alleviating commuting constraints help womenwith children in dual-earner couples more actively participate in the labor market.

Mizuki Kawabata
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japanmizuki@econ.keio.ac.jp

Yukiko Abe
Graduate School of Economics and Business Administration, Hokkaido University
Kita 9 Nishi 7, Kita-ku, Sapporo, Hokkaido 060-0809, Japan
abey@econ.hokudai.ac.jp

Acknowledgement: Kawabata gratefully acknowledges financial support received from JSPS
KAKENHI Grand Number JP16K13363.

Spatial dimensions of intra-metropolitan disparities in commuting time andfemale labor force participation *Mizuki Kawabata † and Yukiko Abe ‡November 11, 2016

Abstract
We examine intra-metropolitan patterns of geographic disparities in female participation in the labormarket and their associations with commuting time in the Tokyo metropolitan area. Our analysis basedon the Global Moran’s I and Getis-Ord Gi* statistics reveals that the spatial patterns of labor force
participation and regular employment rates differ markedly by marital status and the presence ofchildren. Compared with unmarried women and married women without children, married womenwith children exhibit more significant spatial clustering of high and low values of labor force
participation and regular employment rates, and these rates are negatively correlated with malecommuting time. The non-spatial and spatial regression results show that for married women withchildren, longer commuting time is significantly associated with lower participation and regular
employment rates, while for unmarried women and married women without children, the associations
are mostly insignificant. These results are robust to different model specifications and spatial weights.
Our findings suggest that policies alleviating commuting constraints help women with children indual-earner couples more actively participate in the labor market.

Keywords: female labor force participation, spatial patterns, geographic information systems (GIS),
spatial statistics, Tokyo metropolitan area
JEL codes: J21, R12, C30*

This research was supported by JSPS KAKENHI Grand Number JP16K13363.
Faculty of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan.
‡ Graduate School of Economics and Business Administration, Hokkaido University.
†

11. Introduction
Making a long commute to one’s job is one of the most stressful urban activities around the world. Ina large metropolitan area like Tokyo, many people spend extended time commuting via heavily
congested public transportation. It is conceivable that women in particular dislike spending timemaking such a commute. Besides, many parents with young children must take them to childcare
facilities, adding extra time spent commuting between home and childcare facilities before going towork in the morning and after leaving work in the evening. Although greater participation by womenin the labor market has been an important policy agenda in many countries including Japan (OECD,
2012; CAO, 2016), spatial dimensions of the difficulties in female participation have not been
understood well. In this paper, we ask: (1) to what degree intra-metropolitan geographic disparities
exist in female participation in the labor market; (2) whether any spatial regularity exists in such
geographical disparities, and (3) whether the disparities are related to male commuting time. We
examine these questions for women aged 25-54 years in Tokyo, the world’s most populated
metropolitan area.
Geographic disparities in female participation in the labor market have attracted increasing
attention. Recent studies show that female participation differs considerably across regions, such as
metropolitan areas, counties, and prefectures (Abe, 2011; Fogli and Veldkamp, 2011; Black et al.,2014). Several argue that commuting time is an important factor explaining the large geographic
variations in female participation (Abe, 2011; Black et al., 2014). Empirical evidence on the influenceof commuting time on female participation is limited, however. Using commuting time at the level ofUS metropolitan areas, Black et al. (2014) find negative associations between commuting time andlabor force participation of married women. The intra-metropolitan spatial patterns of female
participation and their associations with commuting time, however, have not been well understood.
The level of female participation is unlikely to be evenly distributed within a metropolitan area. For
example, married women with children who need more housing space may live disproportionately inthe suburbs, which offer lower housing costs; such women are more likely to be housewives or workpart time, rather than work full time and commute to the central business district (CBD), assumingthat full-time jobs are more abundant in the CBD. In contrast, households in which both spouses workin the CBD have greater incentives to reside close to the CBD and perhaps endure smaller dwellingsize.
The purpose of this research is to shed new light on the geographic disparities in female
participation by examining intra-metropolitan spatial patterns and studying whether they are related
to male commuting time. It is known that female commuting is shorter than male commuting (thispoint is reviewed in the next section). The shorter commuting time for women suggests that making along commute is not feasible for women, either because they face greater spatial constraints or they
have stronger preferences for short commutes. It is conceivable that long commutes in large

2metropolitan areas impede many women from participating in the labor market.
The contribution of this paper is three-fold. First, we examine the three groups of women
aged 25-54 years—unmarried women, married women with children, and married women withoutchildren, and the rates for the three types of female participation in the labor market—labor force
participation, regular employment, and part-time employment. A comparison of women in the threedemographic groups and the three types of female participation is rare in the literature. Second, we
use data at the municipal level, a more disaggregated spatial unit than inter-metropolitan data used in
the previous research (Black et al., 2014). The use of the smaller geographical unit is important forthis study, as commuting time could differ significantly across locations within the metropolitan area.
We are unaware of research that examines municipal-level female participation vis-à-vis commuting
time using Japanese data. Examining municipal data within a metropolitan area allows us to examine
the role of commuting time in an environment where relocation (sorting by choosing residential
location) is more realistic. It may be unlikely that people migrate to Minneapolis from New York City,
for example, because commuting cost is lower in the former. However, it is likely that people choose
from locations within the Tokyo metropolitan area (e.g., Kamakura versus central Tokyo) when
considering commuting distance and housing costs of each. Finally, we employ geographic
information systems (GIS) to visualize the spatial patterns on the maps and use spatial statistics—the
Global Moran’s I and Getis-Ord Gi* statistics, as well as spatial regression models—to examine thespatial patterns of female participation and their associations with commuting time. GIS and spatial
statistics have not been applied much in previous research on female participation.
We find that considerable geographic disparities exist in female participation. We also findthat the spatial patterns of labor force participation and regular employment rates differ markedly by
marital status and the presence of children. Compared with unmarried women and married women
without children, married women with children have more significant spatial clustering of high andlow values of labor force participation and regular employment rates, and these rates are morenegatively and significantly associated with male commuting time.
The next section provides a review of the related literature. Section 3 explains the methods
including a description of the study area and data. Section 4 presents the results, and Section 5
concludes.

2. Related literature
2.1 Female commuting
Female commuting is shorter than male commuting. Studies consistently find this gender difference
across regions and countries (e.g., Madden, 1981; Gordon et al., 1989; Hjorthol, 2000; Lee and
McDonald, 2003; Crane, 2007; Roberts et al., 2011; Neto et al., 2015). Commuting time is short for
married women, especially when they have children, whereas it is long for married men, even when3

they have children (Madden, 1981; McLafferty and Preston, 1997; Hjorthol, 2000; Rapino and Cooke,2011).
Researchers examine various explanations of why women work closer to home than do men.
Many scholars argue that the gender disparity in commuting reflects the household division of laborin which women shoulder more housework and childcare than do men (e.g., Maddden, 1981;
McLafferty and Preston, 1997). This argument is often referred to as the household responsibility
hypothesis (HRH) (e.g., Johnston-Anumonwo 1992; Turner and Neimeier, 1997). Indeed, evidence
suggests that although the gender gap is narrowing, women do more domestic work than men do(Shelton and John 1993; Lennon and Rosenfield, 1994: Bianchi et al., 2012), even within dual-earnerhouseholds in which both men and women work full-time (Hersch and Stratton, 1994). Empiricalresults of the HRH are mixed, however. Some support the HRH (Hanson and Hanson, 1980; Turnerand Niemeier, 1997; Neto et al., 2015), while others show little or mixed evidence supporting the HRH(Hanson and Johnston, 1985; Gordon et al., 1989; Shingell and Lillydahl, 1986). Turner and Neimeier(1997) provide a critical review of the HRH.
Other explanations also exist for women’s shorter commutes. Women tend to have lower
wages, higher job turnover rates, and shorter work hours, which reduce economic incentives for longcommuting (Madden, 1981; White, 1986; MacDonald, 1999). Differences in the spatial distributionsof jobs suitable for women and men may partly explain the gender disparities in commuting (Hanson
and Johnston, 1985; Hanson and Pratt, 1995; MacDonald, 1999). Women make more family support
trips than do men, and women are more likely to combine non-work trips with work trips (Hanson and
Hanson, 1981; Hanson and Johnston, 1985; Rosenbloom, 1987; McGuckin and Murakami, 1999;
Hjorthol, 2000). The gender difference in the trip-chaining behavior is particularly noticeable whenchildren are present. McGuckin and Murakami (1999) show that women with young children are far
more likely than men (and women without children) to make multiple stops linked to their commutes.
Boarnet and Hsu (2015) find that within households with children, women make considerably morechauffeuring trips than do men, while non-work trips of men and women do not differ much when
they do not have children. These studies suggest that mothers are more sensitive to commuting timethan men and women without children.
The sensitivity to commuting time is likely to be particularly severe for women with young
children. Traveling with young children is not the same as traveling alone or traveling with adults.
When commuting involves travels with infants and toddlers, mobility becomes limited, and spatialconstraint increases in severity. Commuting may even involve unexpected trips to and from a hospitalwhen a child falls sick. Roberts et al. (2011) show that commuting time has a negative influence on
the psychological health of women, particularly for those who have preschool-aged children. Theyfind no such negative effect for women and men who are single without children, working with flexible
hours, or with partners performing most of childcare. Their study suggests that women who do more

4child rearing and household work have a greater psychological barrier to long commuting than those
who have less domestic work.

2.2 Commuting time, female labor force participation, and urban spatial structure
Basic urban models indicate that housing and commuting costs are closely related with urban spatial
structure. In the monocentric city model formulated by Alonso (1964), Muth (1969), and Mills (1972),
there is a tradeoff between housing and commuting costs. The further the distance from a CBD, thelower the housing prices and the higher the commuting cost. This pattern is found in metropolitan
areas around the world, although actual specific patterns are more complex.
The intra-metropolitan differences in housing and commuting costs may be significantly
associated with intra-metropolitan differences in female participation. Madden (1981) finds thatcompared with unmarried women, married women reside in larger houses and in locations further
from the city center. She argues that married women are more residentially immobile than unmarried
women. Married women consider not only their preferences but also the opinions of their husbands
when selecting residential location. Those who have children also take into account residential
environments beneficial for their children, such as schools and neighborhoods (Gamsu, 2016). Most
women are secondary wage earners who have less bargaining power within households. Kain (1962)
notes that such women are more likely to select their employment locations conditioned by selecting
their residential location than are primary wage earners (i.e., men). Several studies suggest that dualearner couples select their residential location based more on men’s workplaces than women’s(Madden, 1981; Singell and Lillydahl, 1986).
Hjorthol (2000) shows that in Oslo, commuting of married women and men is the shortest
in the central parts of the region. In the Tokyo metropolitan area, many suburban workers commute to
the central city, and their commuting time is especially lengthy. Of persons who commute to the Tokyo
ward area (referred to as the CBD in this study), half spend 60 minutes or longer, and 15% spend 90
minutes or longer, for a one-way commute (TMRTPC, 2010). The great majority (80%) of the suburbsto-CBD commuters use public transportation (TMRTPC, 2010). Trains and buses are heavily
congested during rush hours. Long commuting in heavily congested trains and buses may inhibit many
women from fully participating in the labor market.

3. Methods
3.1 Intra-metropolitan patterns of female participation and commuting time
The spatial patterns of female participation in the labor market and commuting time are examined by
calculating Global Moran’s I and Getis-Ord Gi* statistics. The Global Moran’s I statistic (Moran, 1950)
is a global measure of spatial autocorrelation. In this study, the Moran’s I statistics are used to evaluate
whether the spatial patterns of commuting time and the three participation measures (female labor5force participation, regular employment, and part-time employment rates) are random, clustered, ordispersed. The Moran’s I value (I) is calculated as:𝑛𝑛

I = ∑𝑛𝑛 ∑𝑛𝑛

𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗

𝑖𝑖=1

𝑛𝑛
�
�
∑𝑛𝑛
𝑖𝑖=1 ∑𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 (𝑥𝑥𝑖𝑖 −𝑋𝑋)(𝑥𝑥𝑗𝑗 −𝑋𝑋)

� 2∑𝑛𝑛𝑖𝑖=1(𝑥𝑥𝑖𝑖 −𝑋𝑋),(1)

where n is the number of spatial units indexed by i and j, x denotes the variable of interest, 𝑋𝑋� is themean of x, and 𝑤𝑤𝑖𝑖,𝑗𝑗 indicates the spatial weight between i and j. The z-score for the Moran’s I is given

as:

where

𝑧𝑧𝐼𝐼 =

𝐼𝐼−𝐸𝐸[𝐼𝐼]�𝐸𝐸[𝐼𝐼 2 ]−𝐸𝐸[𝐼𝐼]2−1

E[I] =

,

(2)

,

(3)

𝑛𝑛−1

∑𝑛𝑛 𝑧𝑧4𝑛𝑛�(𝑛𝑛2 −3𝑛𝑛+3)𝑆𝑆1 −𝑛𝑛𝑆𝑆2 +3𝑆𝑆02 �− 𝑖𝑖=1 𝑖𝑖 2 �(𝑛𝑛2 −𝑛𝑛)𝑆𝑆1 −2𝑛𝑛𝑆𝑆2 +6𝑆𝑆02 �2�∑𝑛𝑛
𝑖𝑖=1 𝑧𝑧𝑖𝑖 �(𝑛𝑛−1)(𝑛𝑛−2)(𝑛𝑛−3)𝑆𝑆02

E[𝐼𝐼2 ] =,𝑆𝑆0 = ∑𝑛𝑛𝑖𝑖=1 ∑𝑛𝑛𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 ,

(4)
(5)

2

1

𝑆𝑆1 = � � ∑𝑛𝑛𝑖𝑖=1 ∑𝑛𝑛𝑗𝑗=1�𝑤𝑤𝑖𝑖,𝑗𝑗 + 𝑤𝑤𝑗𝑗,𝑖𝑖 � ,
2

(6)

2

𝑆𝑆2 = ∑𝑛𝑛𝑖𝑖=1�∑𝑛𝑛𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 + ∑𝑛𝑛𝑗𝑗=1 𝑤𝑤𝑗𝑗,𝑖𝑖 � .

(7)

The null hypothesis is that the values being analyzed are randomly distributed across space (or no
spatial autocorrelation). If the Moran’s I statistics are significant, a negative Moran’s I value indicatesspatial dispersion, and a positive Moran’s I value denotes spatial clustering. Note that the Moran’s I
value is a global measure, i.e., a single measure for a study area as a whole (the Tokyo metropolitan
area in this study) and does not evince the locations of spatial clustering within the study area.
The Getis-Ord Gi* statistic (Getis and Ord, 1992; Ord and Getis, 1995), on the other hand,is a local measure of spatial autocorrelation, each of which is calculated for each spatial unit (themunicipality in this study) within the study area. Therefore, the Gi* statistics can identify the locationsof spatial clusters of high values (hot spots) and low values (cold spots), if such spatial clusters exist.
The Gi* statistic is calculated as:
𝐺𝐺𝑖𝑖∗ =
where
𝑋𝑋� =

� 𝑛𝑛
∑𝑛𝑛
𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 𝑥𝑥𝑗𝑗 −𝑋𝑋 ∑𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗

𝑆𝑆

2
𝑛𝑛
2
�𝑛𝑛 ∑𝑛𝑛
𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 −�∑𝑗𝑗=1 𝑤𝑤𝑖𝑖,𝑗𝑗 � ��∑𝑛𝑛
𝑗𝑗=1 𝑥𝑥𝑗𝑗
𝑛𝑛

,

(8)

𝑛𝑛−1

,

(9)

6

S=�

2
∑𝑛𝑛
𝑗𝑗=1 𝑥𝑥𝑗𝑗

𝑛𝑛− (𝑋𝑋�)2 .

(10)

n is the number of spatial units indexed by i and j, xj is the value for j, and wi,j is the spatial weight
between i and j.
The Gi* statistic is essentially a z-score. When the Gi* statistic is positive and significant, alarger statistic indicates more intense clustering of high values (hot spots). When the Gi* statistic is
negative and significant, a smaller statistic denotes more intense clustering of low values (cold spots).
In this study, the conventional 5% significance level is used to determine the hot spots and cold spots.
(Municipalities with Gi*statistics greater than 1.96 are regarded as hot spots, and municipalities with
Gi*statistics less than -1.96 are referred to as cold spots.) Using GIS, we plot the Gi* statistics on maps
to examine the spatial patterns of hot and cold spots visually.
The spatial weight for the Moran’s I and Gi* statistics is specified based on the first-orderbinary contiguity matrix (often called queen contiguity), where two spatial units are defined asneighbors when they share a common border or a common vertex. The contiguity matrix is acommonly used spatial weights matrix for data represented by areal units (polygons) that vary in size.
Other spatial weights are also used to examine whether the results are sensitive to the choice of weights.
In calculating the Moran’s I statistics, the row elements of the spatial weights matrix are standardized
so that their sum equals one. For the Gi* statistics, the row standardization does not matter; resultant
statistics with and without the row standardization are the same.

3.2 Non-spatial and spatial models
Regression models are estimated to examine the relationships between commuting time and the threeparticipation measures. First, we estimate the regression models with ordinary least squares (OLS),
which is a non-spatial specification. If diagnostic tests suggest the presence of spatial dependence,then we also estimate spatial lag or spatial error models (spatial models), each of which is explained
below.
The spatial lag model, also known as the mixed regressive spatial autoregressive model,
incorporates a spatially lagged dependent variable in addition to exogenous explanatory variables on
the right hand of regression equation (Ord, 1975; Anselin, 1988). The specification is as follows:
𝐲𝐲 = 𝜌𝜌𝐖𝐖𝐖𝐖 + 𝐗𝐗𝛽𝛽 + 𝐮𝐮,(11)

where y is a vector of observations on the dependent variable, W represents a spatial weights matrix,
Wy is the spatial lag term (spatially lagged dependent variable) with the spatial autoregressive
parameter ρ, X indicates a matrix of observations on exogenous explanatory variables with acoefficient vector β, and u is a vector of error terms.
The spatial lag models are estimated with the spatial two stage least squares (S2SLS), whichapplies the concepts of two stage least squares (2SLS) to the spatial lag model (Anselin, 1988, 2014;7

Kelejian and Robinson, 1993) and also with the maximum-likelihood (ML) estimation (Ord, 1975;
Anselin, 1988). The S2SLS estimation uses the spatially lagged explanatory variables as instruments
to correct for the endogeneity of a spatial lag term and uses first order spatial lags for the instruments.
Standard errors of coefficients are computed with robust variance estimates that take into accountheteroscedasticity (the White variance).
The spatial error model incorporates spatial dependence in error terms (Ord, 1975; Anselin,1988). The model can be specified as follows:
𝐲𝐲 = 𝐗𝐗𝛽𝛽 + 𝐮𝐮,

𝐮𝐮 = λ𝐖𝐖𝐖𝐖 + ϵ,

(12)
(13)

where u is the vector of error terms that follows a spatial autoregressive process, λ denotes the spatial
autoregressive parameter, W is the spatial weights matrix, and є presents a vector of error terms. The
spatial error models are estimated using the generalized method of moments (GMM) withheteroscedastic errors (Arraiz et al., 2010; Kelejian and Prucha, 2010; Drukker et al., 2013) and using
the ML estimation (Anselin, 1988).
The spatial weights matrix W is specified based on the first-order binary contiguity matrix.
The row elements of the spatial weights matrix are standardized so that their sum equals one. We also
experiment with different spatial weights to see whether the results are robust.
Whether to estimate the spatial lag or spatial error model is determined based on the
Lagrange Multiplier (LM) test statistics (Anselin, 1988, 2014; Anselin and Rey, 1991). The null
hypothesis of the LM test for spatial lag is no spatially lagged dependent variable. The null hypothesisof the LM test for spatial error is no spatially autocorrelated error term. We follow the specification
search suggested by Anselin (2014, pp. 109-111). If the LM statistics for spatial lag (LMρ) and spatial
error (LMλ) are both insignificant, neither the spatial lag nor the spatial error model is estimated. If
LMρ is significant but LMλ is insignificant, the spatial lag model is estimated. Conversely, if LMλ issignificant but LMρ is insignificant, the spatial error model is estimated. If both LMρ and LMλ are
significant, we see the robust LM statistics for lag (LMρ*) and error (LMλ*). If LMρ* is more significant
than LMλ*, the spatial lag model is estimated. Conversely, if LMλ* is more significant than LMρ*, thespatial error model is estimated. The conventional p-value of 0.05 is used to determine whether thestatistics are significant.
In the spatial error model, the average marginal effect of an explanatory variable equals thecoefficient estimate of that variable. For the spatial lag models, on the other hand, the marginal effectdoes not equal the total effect, due to the presence of indirect effect. In the spatial lag models, the value
for the dependent variable at a given location is associated not only with the values for the explanatory
variables at that location (direct effect) but also with the values for the explanatory variables atneighboring locations (indirect effect). The total effect is the sum of the direct and indirect effects. The
coefficient estimate in a spatial lag model represents the direct effect. The total effect of a unit change

8in an explanatory variable is computed as 𝛽𝛽̂ /(1 − 𝜌𝜌�), and the indirect effect is the difference between
the total effect and direct effect (Kim, 2003; Anselin, 2014).

3.3 The study area and data
The study area is the Tokyo metropolitan area comprised of Tokyo Metropolis (the metropolitan
prefecture) and its three neighboring prefectures—Chiba, Kanagawa, and Saitama. In terms of
population, the Tokyo metropolitan area is the largest in the world, inhabited by 35.6 million people
in 2010. Although several sub centers do exist, Tokyo’s urban spatial structure has features of a
monocentric city. The metropolitan area has a core, the Tokyo ward area, which is comprised of 23
special wards with densely concentrated population and business districts. In this study, the Tokyo
ward area is regarded as the CBD.
The spatial unit of the analysis is the municipality (Shi, Ku, Mura, and Machi) in 2010,
which is the smallest level of spatial detail available for the data used in this study. In 2010, the Tokyo
metropolitan area contained 243 municipalities. We restrict the sample to municipalities with apopulation of 50 or more in order to minimize sampling errors in participation statistics. As a result,the number of observations (municipalities) is 243 or less, depending on the sample.
Commuting time in this study is the average one-way travel time from home to work bymunicipality of residence, in minutes. Data on commuting time for men and women aged 25-54 years
are from the special tabulations of the Tokyo Metropolitan Region Person Trip Survey in 2008. The
survey is conducted every ten years, and the year 2008 is closest to 2010, the year of the labor force
data. The special tabulations are obtained from the Tokyo Metropolitan Region Transportation
Planning Commission (TMRTPC).
Data on labor force participation and regular and part-time employment for women aged 2554 years by marital status, the presence of children, and education are from both publicly-available
and order-made tabulations of the 2010 Population Census. The order-made tabulations are provided
by the National Statistics Center of Japan. The regular employment rate is the proportion of those whowork as regular employees among the population, and the part-time employment rate is the similarrate of part-time employees. The rates are in percentages.
In the regression analysis, the dependent variable is one of the labor force participation,
regular employment, or part-time employment rates. The independent variables are commuting time,the variable of our interest, and control variables. Commuting time is the average one-way commuting
time for men (in minutes), as explained above. Since most men work, the use of male commuting time
is likely to alleviate selection bias arising from the use of female commuting time. 1 Men and womenin our data are 25 to 54 years old, unless otherwise noted.

1

To mitigate selection bias, Black et al. (2014) use commuting time for white married men. Commuting time data by
marital status are not available in Japan.

9The control variables include local housing prices, local income, and local unemployment
rates for men, which are suggested as possible explanations for the large spatial variations in labor
force participation of married women by Black et al. (2014); and two additional variables: proportionof households with two or more children and availability of childcare centers. Including the availabilityof childcare centers is important for this study since it is found to be a significant factor associatedwith female participation in Tokyo (Kawabata, 2014). In Japan, eliminating long waiting lists forchildcare centers has been an urgent policy issue. Since most childcare centers are publicly subsidized,
availability is more likely to be an issue than cost, as in the cases of Italy and Germany (Del Boca andVuri, 2007; Kreyenfeld and Hank, 2000).
Local housing prices come from the average residential land price in the 2010 Nikkei Needs
database. 2 The local unemployment rates for men are from the 2010 Population Census. The
proportion of households with two or more children represents the fraction of households with two or
more children among all households (in percentage), from the 2010 Population Census. The
availability of childcare centers is represented by the ratio (in percentage) of the capacity of licensed
childcare centers to the population of preschool-aged children (under 6 years old) in 2010. The dataon childcare centers come from prefectural governments and municipalities, and the data on thepreschool-aged population are from the 2010 Population Census.

4. Empirical results
4.1 Spatial disparities in commuting time and female participation
Table 1 presents summary statistics of commuting time (average one-way commuting time) and thelabor force participation, regular employment, and part-time employment rates.
Commuting time is shorter for women than for men: the median of commuting time is 38.8
minutes for women and 49.9 minutes for men. Shorter commuting time for women is observed
throughout the world, but commuting time in Tokyo is considerably lengthy, even for women. For
both men and women, commuting time has marked spatial dispersion. Commuting time varies from
11.7 to 59.7 minutes for women and from 21.4 to 67.6 minutes for men, with a standard deviation of
9.2 for women and 10.4 for men.
Female labor force participation, regular employment, and part-time employment rates
differ by marital status and by the presence of children. Compared with unmarried women, married
women without children are less likely to participate in the labor market and work as regular
employees, and married women with children are even less likely to participate in the labor market
2

In the regression models, the log of the average residential land price is used. As a measure for local income, we
could use average annual income per person, which is calculated as total taxable annual income divided by number of
taxpayers. The data are from the 2010 municipality taxation status and others [Shichoson Kazei Joukyoutou no
Shirabe], available from the Ministry of Internal Affairs and Communications of Japan. Average annual income perperson, is not included in regressions, however, since it is highly correlated with average residential land price (with acorrelation coefficient of 0.89).

10

and work as regular employees. The median labor force participation and regular employment rates
for married women with children are 55.1% and 14.9%, respectively, which are notably lower thanthose for unmarried women (84.0% and 45.8%, respectively) and for married women without children(64.8% and 26.0%, respectively). The median part-time employment rate, on the other hand, is higher
for married women with children (31.6%) than for unmarried women (17.3%) and married women
without children (23.9%).
The spatial dispersion of the labor force participation rate of married women with children
is larger than that of unmarried women or that of married women without children, as indicated by thewider range and greater standard deviation for married women with children. For the regular
employment and part-time employment rates, differences in the spatial dispersion by marital status are
relatively large but the differences by the presence of children among married women are small.
Table 2 reports the Global Moran’s I statistics. The statistics are all significant at the 1%
significance level, rejecting the null hypothesis of no spatial autocorrelation. In other words,
commuting time and the labor force participation, regular employment, and part-time employment
rates are not evenly distributed across municipalities within the metropolitan area. The Moran’s I
values are all positive and highly significant, indicating the existence of spatial clustering of high andlow values.
As suggested by the Moran’s I values and associated z scores, the magnitude of spatial
clustering of the labor force participation, regular employment, and part-time employment rates isgreater for married women with children than for unmarried women and married women withoutchildren. In addition to the first-order contiguity, we have experimented the following three spatial
weights: (1) inverse distance; (2) inverse distance squared, and (3) fixed distance band (determined toinclude at least one municipality as a neighbor). The results above do not change by the use of thedifferent spatial weights.
Figure 1 depicts commuting time for men and women and its hot spots (spatial clusters ofhigh values) and cold spots (spatial clusters of low values) based on the Getis-Ord Gi* statistics. For
both men and women, considerable spatial disparities in commuting time exist. As the maps show, formen, most hot spots are located around the inner-suburban areas (within approximately 30 kilometersof the Tokyo ward area), suggesting that many men residing in the inner suburbs commute to the CBD,
enduring a long commuting time. The cold spots of male commuting time are located around the core
parts of the CBD and in the outer suburbs around the peripheries of the metropolitan area, suggestingthat men residing in the outer suburbs work in the suburbs rather than commute to the central city. The
spatial pattern of commuting time for women is similar to that for men but less conspicuous; forwomen, a lesser number of hot and cold spots exists. Most hot spots for women are located around theinner-suburban areas of Chiba, Kanagawa, and Tokyo prefectures.
Figure 2 shows the labor force participation rates of women and their hot and cold spots

11

based on the Getis-Ord Gi* statistics. It is visually apparent that the spatial pattern differs by marital
status and the presence of children. In particular, striking differences exist in the spatial patterns
between married women with and without children. For married women with children, the labor force
participation rate exhibits a more distinctive spatial pattern. Most cold spots are located around thewestern parts of the Tokyo ward area and the inner suburbs, while most hot spots are located aroundthe peripheries. For married women without children, on the other hand, the participation rate is more
evenly distributed across municipalities, in accordance with their smaller and less significant Moran’s
I statistics (see Table 2). Comparing Figures 1 and 2, we see that for married women with children,
many of the cold spots of the participation rate overlap with the hot spots of commuting time and viceversa (i.e., many of the hot spots of the participation rate overlap with the cold spots of commutingtime). The visual spatial patterns suggest that the labor force participation of married women withchildren is more strongly related to commuting time than that of unmarried or married women withoutchildren.
Figure 3 portrays the hot and cold spots of the regular employment and part-time
employment rates of women. For the regular employment rates, the spatial pattern differs remarkably
by marital status and the presence of children, as in the case of the labor force participation rates. For
married women with children, cold spots of the regular employment rate are mostly located in the
inner-suburban areas, while hot spots are located in the outer suburbs; this spatial pattern is thereversed spatial pattern of commuting time (see Figure 1); that is, for these married women withchildren, many cold spots of the regular employment rate overlap with the hot spots of commuting
time and vice versa. For unmarried women and married women without children, no clear spatial
relationships exist. The spatial patterns of the part-time employment rates do not differ much by
marital status and the presence of children, and they have no obvious spatial relationships with
commuting time.
We examine correlations between the labor force participation, regular employment, andpart-time employment rates and commuting time for men (Table 3). Here, male commuting time isused: since most men work, male commuting time is an accurate proxy that reflects the true cost ofcommuting. 3 The results support the visual impression of the spatial relationships from the maps
(Figures 1-3). For married women with children, the labor force participation and regular-employment
rates are highly and negatively correlated with male commuting time (with correlation coefficients of-0.71 and -0.78, respectively). For married women without children, the correlations are also negative
but weaker, with correlation coefficients of -0.59 and -0.20, respectively. For unmarried women, thecorrelations are smaller, and the sign is positive (with correlation coefficients of 0.02 and 0.22,
respectively). For the part-time employment rates, the correlations do not differ much by marital status
3

If a commute impedes participation (i.e., people choose not to work because of costly commuting),
then latent commuting time for them does not show up in the data.

12

and the presence of children.

4.2 Regression results
Table 4 reports regression results for the labor force participation rates. The results for married men
are presented for comparison.
The results of commuting time, the variable of our interest, indicate striking differences by
marital status and the presence of children. For married men, unmarried women, and married women
without children, commuting time is not significantly associated with the labor force participation rate.
In contrast, for married women with children, an increase in commuting time is significantly
associated with a decrease in the participation rate. The negative association between commuting timeand labor force participation for married women with children (both under 6 years old and 6 years old
or over) is greater for college graduates than for those with high school education or less, indicatingthat highly educated mothers are more sensitive to commuting time when participating in the labormarket. For college-graduated married women with children (both under 6 years old and 6 years old
or over), a one-minute increase in commuting time is associated with a 0.31 percentage point decreasein the labor force participation rate. Since the range of commuting time across municipalities is 46.2
minutes (see Table 1), the commuting time difference results in a 14.3 percentage point difference inthe labor force participation rate for college-graduated married women with children. This is a large
difference, given that the range of the participation rate for these women is 29.6 percent (the maximumof 40.2 percent minus the minimum of 10.6 percent). Besides the OLS estimates, Table 4 shows theestimates from the spatial lag and spatial error models when the LM statistics suggest the spatialmodels as preferred specifications. The sign and significance of the estimates on commuting time areconsistent with those from the OLS models, and differences in the marginal effects of commuting time
across the non-spatial and spatial models are small. 4
The results of the control variables for women are as follows. Residential land price isnegatively and mostly significantly associated with labor force participation. This result is similar to
the finding by Black et al.’s (2014). a negative association between housing cost and labor force
participation for married women. The coefficients of the unemployment rate are negative andsignificant for childless married women with high school education or less; for the other women, thecoefficients are insignificant. The coefficients of the proportion of households with two or morechildren are positive and significant for unmarried women, but they are negative and significant forchildless married women and college-graduated married women with children under 6 years old. The
coefficients of the availability of childcare centers are positive and significant for married women withpreschool-aged children and also for married women with high school education or less who havechildren aged 6 years or over.
4

The marginal effects for the spatial lag models are the total effects, as explained in Section 3.2.

13

Table 5 presents regression results for the regular employment rates. Results for the parttime employment rates are not presented since their associations with commuting time are mostly
insignificant. As in the case of labor force participation, the results differ markedly by marital status
and the presence of children. The coefficients of commuting time are insignificant for married women
without children. The coefficients are positive and mostly significant for unmarried women, whereas
they are negative and significant for married women with children. Among married women withchildren, the negative coefficients are greater in absolute values for college graduates than for thosewith high school education or less, as in the case of labor force participation. The association between
commuting time and the regular employment rate is particularly strong for college-educated married
women with children aged 6 years or over. For these women, a one-minute increase in commuting
time is associated with a 0.32 percentage point decrease in the regular employment rate. Incorporating
the range of commuting time (46.2 minutes) results in a 14.8 percentage point difference in the regular
employment rate.
Table 5 also reports estimates from the spatial lag and spatial error models when the LM
statistics suggest them as the preferred alternative to the non-spatial models. The spatial autoregressive
coefficients (ρ and λ) are all significant. The sign and significance of our key variable, commuting
time, are mostly the same across the non-spatial and spatial models, and differences in the marginal
effects of commuting time are small. 5
The results for the control variables are as follows. The coefficients of residential land priceare negative and significant for married women with children. The coefficients of the unemployment
rate are negative and mostly significant for unmarried women and married women with high school
education or less. The coefficients of the proportion of households with two or more children are
positive and mostly significant for unmarried women, whereas they are negative and mostly significant
for married women. The availability of childcare centers is positively and significantly associated withregular employment for married women with children (except for college-educated women withpreschool-aged children, for whom the coefficient is insignificant).
As robustness checks, we have estimated the models with three alternative specifications.
First, we estimated the OLS models with the weighted least squares. Second, instead of using the firstorder contiguity for the spatial weights matrix, we experimented the following spatial weights: (1) the
inverse distance weights (with the power = 1 and 2) and (2) k-nearest neighbors (with a value of k =4, 6, and 8). Finally, we estimated the non-spatial and spatial models for municipalities in which 10
percent or more male commuters travel to the Tokyo ward area to work. Appendices A1 and A2 contain5

For unmarried women, the coefficient of commuting time is insignificant in the spatial lag model. This result may
be due to the fact that the spatial lag coefficient (ρ) for S2SLS is 1.004, which exceeds the general upper bound ofone. The marginal effect (total effect) is not computed for this spatial lag model. The Anselin-Kelejian test statistic(Anselin and Kelejian, 1997; Anselin, 2014), a diagnostic for spatial autocorrelation, is 15.35 with a p-value of 0.00,
which indicates the presence of remaining spatial dependence. For the other spatial lag models in Tables 4 and 5, theAnselin-Kelejian test statistics are all insignificant, indicating no remaining spatial dependence.

14

the corresponding maps for male commuting time and the labor force participation and regular
employment rates for women, respectively. The regression estimates for the labor force participation
and regular employment rates are reported in Appendices B1 and B2, respectively. These different
specifications and spatial weights did not change the result that the associations between commuting
time and the labor force participation and regular employment rates are negative and significant for
married women with children, while those associations are mostly insignificant for unmarried andmarried women without children. 6

5. Conclusions
We use municipal-level data of the Tokyo metropolitan area to study the spatial patterns of female
participation in the labor market and their associations with commuting time. We find that considerable
intra-metropolitan disparities exist in the female labor force participation, regular employment, andpart-time employment rates. As implied by the Global Moran’s I statistics, these rates are not evenly
distributed within the metropolitan area; there are spatial clusters of high and low rates. The hot andcold spot maps based on the Getis-Ord Gi* statistics reveal that the spatial patterns of the labor force
participation and regular employment rates differ markedly by marital status and the presence ofchildren. Compared with unmarried women and married women without children, married womenwith children exhibit more significant spatial clustering of the participation and regular employment
rates, and these rates are negatively correlated with male commuting time. For married women withchildren, the spatial clusters of low participation and regular employment rates are largely located in
the inner-suburbs, many of which overlap with the spatial clusters of long male commuting time.
The non-spatial and spatial regression results show that for married women with children,
longer commuting time is significantly associated with lower labor force participation and regular
employment rates, while for unmarried women and married women without children, the associations
are mostly insignificant. These results are robust to different regression specifications and spatial
weights. The findings support the view that labor market participation by mothers has salient
sensitivity to commuting time. Since residential decisions are endogenous, the effect of commuting
time on participation is not causal. Rather, the circumstances in the Tokyo metropolitan area induce
households to decide location and labor market participation by both spouses simultaneously: thetypical choices are (1) living in the suburbs, the husband commutes to the CBD, and the wife stayshome or works locally, or (2) living close to the CBD and both spouses work at the CBD. Naturally,
these choices result in sorting in residential location.
The regression results also show that among married women with children, the negative
6

In Appendix B2, the associations between commuting time and the regular employment rates become insignificant
for married women with children under 6 years old, while the associations remain significant for married women withchildren aged 6 years and older. Therefore, we estimated the additional models for married women with children (ofall ages); the results indicate that the association for these women is negative and significant.

15

associations between commuting time and the labor force participation or regular employment rates
are greater for college graduates than for those with high school education or less. This result differsfrom those in Black et al. (2014), who find greater associations for high school graduates than forcollege graduates among married women. 7 These contradictory results may arise partly because ourstudy is based on intra-metropolitan data, while the study by Black et al. (2014) is based on intermetropolitan data. In the inter-metropolitan analysis, highly educated women are perhaps more likely
to live and work in larger metropolitan areas that have longer commuting time. In our intrametropolitan analysis of Tokyo, on the other hand, highly educated women are more likely to live andwork closer to the CBD, with shorter commuting time.
Our findings suggest that for married women with children, intra-metropolitan disparities incommuting time play an important role in their participation in the labor market. The inner suburbs,
which are commuting distance for men but farther away from the CBD, have high concentrations oflower rates of labor force participation and regular employment for married mothers and also havehigh concentrations of long male commuting time. Given that commuting time is not significantly
associated with the labor force participation rate for married men (see Table 4), suburban living thatentails long commuting for the husband intensifies the household division of labor, in which thehusband commute to the CBD and the wife either stays home or works locally.
Perhaps this propensity is particularly conspicuous for Tokyo. Of 243 municipalities in theTokyo metropolitan area, half have an average daily commuting time (for men) of over 100 minutes,
and 13 municipalities have daily commuting time exceeding 120 minutes. Most of these municipalities
are located in the inner suburbs. Among the 26 OECD countries, Japanese men do the least housework
and related unpaid work; Japanese men on average spend 62 minutes on unpaid work while theirspouses dedicate 299 minutes per day to unpaid work (OECD, 2016). It is conceivable that men who
spend such lengthy time on commuting (often in heavily congested trains) are even less likely to dohousework and perform childcare. It is also imaginable that women who bear most housework andchildcare responsibilities are unable to cope with long commuting.
Among women with children in many countries, preferences for participation in the labormarket are much higher than actual participation rates (Jaumotte, 2003; Gender Equality Bureau
Cabinet Office of Japan, 2007). A national survey in Japan shows that among couples with children
under 15 years old, the great majority (86%) of women who are not currently working actually wish
to work (National Institute of Population and Social Security Research, 2016). Our results suggest thatpolicies alleviating commuting constraints help women in dual-earner couples with children more
actively participate in the labor market. Examples of such policies are improving accessibility toemployment, reducing congestion, promoting flexible working hours, increasing housing supply
7

In a model for women with children under 5 years old in Black et al. (2014, Panel B2 in Table 6, p. 68), theassociation is greater for college graduates than for high school graduates, but in all other models, including married
women in general, the associations are greater for high school graduates than for college graduates.

16

around employment centers, and encouraging male commitment to housework and childcare.
In recent years, the number of dual-earner couples in Japan has dramatically increased (TheJapan Institute for Labour Policy and Training, 2016). Spatio-temporal analysis using data after 2010is a task for further research. Geographic disparities in female participation are examined mostly at
the national, metropolitan, and prefectural levels. Our research shows that within a metropolitan area,
the levels of female participation also differ by location, and the intra-metropolitan disparities haveunique spatial patterns. Spatially disaggregated analysis potentially unveils important dimensions of
the urban labor market that deserve more attention.

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20

Table 1. Summary statistics.
MedianMean

Std. Dev.

Min.

Max.

Men

49.9

47.7

10.4

21.4

67.6

Women

38.8

37.0

9.2

11.7

59.7

84.0

82.9

5.2

61.7

94.0

No children

64.8

65.5

5.2

56.1

85.7

With children

55.1

57.4

8.7

40.5

79.6

45.8

45.5

5.0

32.5

66.7

No children

26.0

26.2

4.4

14.3

40.0

With children

14.9

16.2

4.5

10.4

37.9

17.3

17.3

4.2

6.0

32.1

23.9

24.3

6.3

8.5

46.2

Commuting time (min.)Labor force participartion rate (%)
Unmarried women
Married women

Regular employment rate (%)
Unmarried women
Married womenPart-time employment rate (%)
Unmarried women
Married women
No children

With children
31.6
31.0
6.6
9.2
47.5
Note : Men and women are 25-54 years old. The number of observations (municipalities) is 243
except that the number for married women without children is 242. Commuting time is theaverage one-way trave time to work.

21

Table 2. Global Moran's I statistics.
Moran's I

z-scorep-value

0.66
0.60

16.22
14.72

0.00
0.00

0.48

11.77

0.00

0.33
0.76

8.13
18.77

0.00
0.00

Regular employment rates
Unmarried women

0.31

7.72

0.00

Married women
No children
With children

0.15
0.70

3.82
17.46

0.00
0.00

Commuting time
Men
Women
Labor force participartion rates
Unmarried women
Married women
No children
With children

Part-time employment rates
Unmarried women
0.49
12.01
0.00
Married women
No children
0.56
13.76
0.00
With children
0.76
18.81
0.00
Note : Men and women are 25-54 years old. The number of observations(municipalities) is 243 except that the number for married women withoutchildren is 242. Commuting time is the average one-way travel time to
work.

22

Tokyo ward area
SaitamaPrefecture
Municipality

Tokyo

One-way
Commuting time

Kanagawa<= 20 (min.)20 – 30
30 – 40
40 – 50
50 – 60
> 60
0

20

Chiba

(a) Commuting time for men(b) Commuting time for women

40 km

Getis-Ord Gi*
Cold spot (< -2.58)
Cold spot (-2.58 – -1.96)
Cold spot (-1.96 – -1.65)
Not significant (-1.65 – 1.65)
Hot spot (1.65 – 1.96)(c) Hot spots and cold spots of
Hot spot (1.96 – 2.58)
commuting time for men
Hot spot (> 2.58)(d) Hot spots and cold spots ofcommuting time for women

Figure 1. Commuting time for men and women aged 25-54 years.

23Prefecture
Municipality
Labor force
participation rate<= 50 (%)
51 – 60
61 – 70
71 – 80>= 80
No data

Saitama

Tokyo ward areaTokyo
Kanagawa
0

20

Chiba

40 km

(a) Unmarried women(b) Married women with no children(c) Married women with children

Labor force participation rates

Getis-Ord Gi*
Cold spot (< -2.58)
Cold spot (-2.58 – -1.96)
Cold spot (-1.96 – -1.65)
Not significant (-1.65 – 1.65)
Hot spot (1.65 – 1.96)
Hot spot (1.96 – 2.58)
Hot spot (> 2.58)
No data(d) Unmarried women(e) Married women with no children(f) Married women with children

Hot spots and cold spots of labor force participation rates

Figure 2. Labor force participation rates for women aged 25-54 years.

24Tokyo ward area

Prefecture

Saitama

Municipality

Tokyo

Getis-Ord Gi*
Cold spot (< -2.58)
Cold spot (-2.58 – -1.96)
Cold spot (-1.96 – -1.65)
Not significant (-1.65 – 1.65)
Hot spot (1.65 – 1.96)
Hot spot (1.96 – 2.58)
Hot spot (> 2.58)
No data020

Kanagawa

40 km

Chiba

(a) Unmarried women(b) Married women with no children(c) Married women with children

Hot spots and cold spots of regular employment rates(d) Unmarried women(e) Married women with no children(f) Married women with children

Hot spots and cold spots of part-time employment rates

Figure 3. Regular and part-time employment rates for women aged 25-54 years.

25

Table 3. Getis-Ord Gi*: correlations with commuting time for men.
Labor force participartion rates
Unmarried women
Married women
No children
With children
Regular employment rates
Unmarried women
Married women
No children
With children

0.02
-0.59
-0.71
0.22
-0.20
-0.78

Part-time employment rates
Unmarried women-0.22
Married women
No children-0.27
With children-0.25
Note : Men and women are 25-54 years old. The number ofobservations (municipalities) is 243 except that the number is 242
for married women without children.

26

Table 4. Regression of labor force participation.
Married men

Unmarried women

Married women
No children
HS or less

OLS
Commuting time

0.02
(0.020)Log of residential land price

Lag-S2SLS Lag-ML

HS or lessCollegeHS or lessCollege

Err-GMM

Err-ML

OLS

OLS

OLS

OLS

OLS

-0.06
(0.05)-0.06(0.05)

0.00(0.07)-0.19 **
(0.05)-0.31 **
(0.07)-0.14 **
(0.03)-0.31 **
(0.06)-0.08(0.05)-1.98 ** -0.87 * -0.81 **(0.314) (0.417) (0.312)-1.36 ** -0.04(0.50)(0.66)-0.33(0.48)-2.77 ** -2.69 **(0.77)(0.98)-2.67 ** -0.87(0.88)(1.14)-2.66 **
(0.85)-2.68 *
(1.15)-1.63 **
(0.52)-7.62 **
(0.95)

Unemployment rate

0.35 * 0.32 ** 0.32 **(0.144) (0.115) (0.125)

0.35(0.23)

0.37(0.21)-0.91 *
(0.35)-0.76 *
(0.36)-0.36(0.40)-0.36(0.54)

0.09(0.25)

0.51(0.45)

Households with two or more children

0.29 ** 0.22 ** 0.21 **(0.052) (0.047) (0.047)

0.48 ** 0.36 ** 0.39 **(0.08)(0.11)(0.08)-0.54 ** -0.53 **(0.13)(0.15)-0.53 ** -0.47 *(0.14)(0.19)-0.02(0.14)-0.39 *
(0.19)

0.14(0.09)-0.23(0.16)

Availability of childcare centerｓ-0.01(0.012)-0.01(0.02)

0.03(0.04)

0.02(0.05)

0.02(0.05)

0.34 **(0.05)

0.24 **(0.07)

0.12 **(0.03)-0.04(0.05)

0.25 **(0.08)

0.27 **(0.09)
70.12 **
(13.13)94.30 **
(17.88)87.24 **
(8.08)

174.35 **(14.70)0.00(0.010)0.04(0.03)

CollegeOLS

0.01(0.03)

0.00(0.011)0.00(0.017)Lag-S2SLS Lag-ML

With children, none under 6

0.00(0.04)

Spatial lag (ρ)

0.01(0.017)

OLS

With children under 6

0.45 ** 0.47 **(0.114) (0.067)

0.38(0.28)

0.00(0.02)

0.00(0.02)

0.53 *(0.21)

0.41 **(0.07)

λConstant

N
R

2

OLS

109.08 ** 55.57 ** 52.96 **(4.705) (14.524) (8.800)

85.83 ** 30.73(7.527)(21.94)-0.78 *
(0.38)-0.06(0.53)

0.00(0.07)

42.63 ** 114.89 ** 112.33 ** 111.98 ** 94.52 **(9.88)(11.78)(14.62)(13.34)(17.56)

243

243

243

243

243

243

206

206

206

206

209

209

218

218

0.58

0.68

0.68

0.37

0.39

0.47

0.15

0.17

0.17

0.02

0.47

0.32

0.49

0.55

Value

p

Value

p

Value

p

Value

LM spatial lag
LM spatial error

69.71
60.86

0.00
0.00

30.78
23.91

0.00
0.00

2.87
8.11

0.09
0.00

2.55 0.11
1.92 0.17

Robust LM spatial lag
Robust LM spatial error

10.11
1.26

0.00
0.26

7.13
0.27

0.01
0.60

p

Value

p

Value

p

2.62 0.11 0.04 0.84
3.06 0.08 0.27 0.60

Value

p

Value

p

2.66
0.17

0.10
0.68

1.13
0.17

0.29
0.68

2
2
2
Note : Men and women are 25-54 years old. Standard errors are in parentheses. R is adjusted R for OLS and pseudo R for spatial lag and spatial error models. p denotes p value.
Robust Lagrange Multiplier (LM) test statistics are reported when the LM test statistics for spatial lag and spatial error are both significant.
Municipalities with population less than 50 for each category of the presence of children and education are excluded from the sample.
**Significant at 1%; *Significant at 5%.

27

Table 5. Regression of regular employment.
Unmarried women

Married women
No children

OLS

Lag-S2SLS Lag-ML

HS or lessCollege

OLS

OLS

Err-GMM Err-ML

Lag-S2SLS Lag-ML

With children under 6With children, none under 6

HS or lessCollegeHS or less

OLS

OLS

OLS

OLS

CollegeCommuting time

0.11 ** 0.07
(0.04)(0.04)

0.09 **(0.03)-0.07(0.04)-0.07(0.05)-0.06(0.05)-0.02(0.09)-0.18(0.12)-0.11(0.09)-0.12 **
(0.03)-0.16 *
(0.07)-0.09 **
(0.02)-0.32 **
(0.07)

Log of residential land price-0.44(0.55)-0.07(0.51)-0.21(0.66)-0.31(0.71)-0.32(0.74)-0.75(1.43)-0.44(1.58)-0.58(1.36)-1.40 **
(0.51)-2.31 *
(1.12)-1.29 **
(0.35)-6.79 **
(1.09)

Unemployment rate

-0.97 ** -0.54(0.26)(0.28)-0.80 **
(0.24)-0.74 * -0.70 *(0.30)(0.33)-0.70 *
(0.31)-1.06(0.66)-1.00(0.89)-1.03(0.63)-0.78 **
(0.24)-0.43(0.53)-0.11(0.16)-0.34(0.51)

Households with two or more children

0.36 ** 0.16
(0.09)(0.12)

0.28 **(0.09)-0.26 * -0.27 *(0.11)(0.12)-0.27 *
(0.12)-0.60 *
(0.24)-0.81 ** -0.72 ** -0.31 **(0.25)(0.23)(0.08)-0.72 **
(0.19)-0.23 **
(0.06)-0.27(0.18)

Availability of childcare centerｓ

0.03(0.02)

0.05 *(0.02)

0.01(0.04)

0.01(0.04)

0.02(0.08)-0.10(0.12)

0.04(0.07)

0.14 **(0.02)

0.19 **(0.06)

76.89 **
(17.34)33.66 **
(5.33)

122.50 **(16.73)

Spatial lag (ρ)

0.47(0.72)

0.07 *(0.04)

0.01(0.04)

1.00 ** 0.40 **(0.23)(0.08)
0.22 *(0.10)
44.15 ** -10.65(8.32) (17.61)

0.06 *(0.03)-0.60 ** -0.32 **(0.21)(0.10)

λConstant-0.05(0.08)

0.22 *(0.09)

22.15 ** 34.62 ** 35.41 ** 35.49 ** 69.25 ** 105.34 ** 88.69 ** 41.64 **(8.23)(10.10) (10.59)(11.21)(22.02)(24.78) (21.44)(7.84)

243

243

243

206

206

206

206

206

206

209

209

218

218

2

0.17

0.29

0.20

0.10

0.12

0.12

0.05

0.15

0.14

0.25

0.14

0.59

0.54

OLS

Value

p

Value

p

Value

p

Value

p

30.26
25.18
5.45
0.38

0.00
0.00
0.02
0.54

2.30
4.29

0.13
0.04

10.64
5.95
6.87
2.18

0.00
0.01
0.01
0.14

3.06
2.59

0.08 0.00 1.00
0.11 0.18 0.67

NR

LM spatial lag
LM spatial error
Robust LM spatial lag
Robust LM spatial error

Value

p

Value

p

Value

p

2.58 0.11
0.31 0.58

0.01
1.39

0.94
0.24

Note : Women are 25-54 years old. Standard errors are in parentheses. R 2 is adjusted R 2 for OLS and pseudo R 2 for spatial lag and spatial error models. p denotes p value.
Robust Lagrange Multiplier (LM) test statistics are reported when the LM test statistics for spatial lag and spatial error are both significant.
Municipalities with population less than 50 for each category of the presence of children and education are excluded from the sample.
**Significant at 1%; *Significant at 5%.
28

Saitama
Prefecture
Municipality

Tokyo

One-way
commuting time<= 30 (min)30 – 40
40 – 50
50 – 60> 60
0Tokyo ward area

Kana
gawa

Chiba
Getis‐Ord Gi*

20

40 km

Cold spot (< ‐2.58)
Cold spot (‐2.58 – ‐1.96)
Cold spot (‐1.96 – ‐1.65)
Not significant (‐1.65 – 1.65)
Hot spot (1.65 – 1.96)
Hot spot (1.96 – 2.58)
Hot spot (> 2.58)
No dataAppendix A1. Commuting time for men aged 25-54 years for municipalities inwhich 10 percent or more male commuters travel to the Tokyo ward area to work.

29

Prefecture
Municipality

Tokyo ward area
Saitama

Getis-Ord Gi*
Cold spot (< -2.58)
Cold spot (-2.58 – -1.96)
Cold spot (-1.96 – -1.65)
Not significant (-1.65 – 1.65)
Hot spot (1.65 – 1.96)
Hot spot (1.96 – 2.58)
Hot spot (> 2.58)
No data
0

20

Tokyo
Kanagawa

Chiba

40 km

(a) Unmarried women(b) Married women with no children(c) Married women with children

Hot spots and cold spots of labor force participation rates(d) Unmarried women(e) Married women with no children(f) Married women with children

Hot spots and cold spots of regular employment rates

Appendix A2. Labor force participation and regular employment rates for women aged 25-54 years formunicipalities in which 10 percent or more male commuters travel to the Tokyo ward area to work.
30

Appendix B1. Regression of labor force participation for municipalities in which 10% or more male commuters travel to the Tokyo ward area to work.
Married men

Unmarried women

Married women
No children

Err-GMM Err-ML

With children under 6With children, none under 6
HS or lessCollege

OLS

OLS

HS or lessCollegeHS or lessCollege

OLS

OLS

OLS

OLS

OLS

Err-GMM

Err-MLOLS

Commuting time

0.03
(0.03)

0.04(0.04)

0.04(0.04)

0.02(0.05)

0.05(0.05)

0.05(0.05)-0.05(0.07)-0.10(0.08)-0.16 *
(0.07)-0.32 **
(0.08)-0.41 ** -0.38 **(0.08)(0.08)-0.20 **
(0.05)-0.21 **
(0.08)

Log of residential land price-1.98 **
(0.63)-2.34 **
(0.65)-2.36 **
(0.68)-1.00(0.89)

-1.04
(1.1)

-1.02
(0.97)-1.11(1.33)

0.04(1.52)-2.18(1.42)-2.70(1.65)-2.13(1.86)-2.34(1.61)-1.39(0.93)-5.97 **
(1.48)

Unemployment rate

0.44
(0.24)

0.15(0.24)

0.12(0.23)

0.41(0.34)

0.26
(0.4)

0.25
(0.34)

-0.53
(0.5)

0.08
(0.55)

0.36(0.53)

0.35(0.61)

0.49
(0.7)

0.44
(0.59)

0.23(0.35)

0.57(0.56)

Households with two or more children

0.35 **(0.08)

0.29 **(0.09)

0.28 **(0.08)

0.66 ** 0.62 ** 0.61 **(0.12)(0.13)(0.12)-0.42 *
(0.18)

-0.22
(0.2)

-0.01
(0.19)-0.45 *
(0.22)-0.49 *
(0.24)-0.48 *
(0.21)

0.26 *(0.13)

-0.14
(0.2)

Availability of childcare centerｓ-0.04(0.03)-0.01(0.03)

0.00(0.03)-0.06(0.05)-0.07(0.07)

0.02(0.08)

0.40 **(0.07)

0.25 **(0.09)

0.22 *(0.09)

0.24 **(0.08)

0.14 **(0.05)

0.01(0.08)-0.31 *
(0.15)-0.20 *
(0.09)

0.00(0.04)

0.00(0.05)

Spatial lag (ρ)λ

0.47 **
0.48 **(0.09)(0.09)

Constant

N
R

2

OLS

107.65 ** 113.83 ** 114.30 **(9.97)(9.87)(10.44)Lag-S2SLS Lag-ML

0.41 ** 0.43 **(0.1)(0.09)
80.07 ** 79.57 ** 79.06 ** 90.98 **(14.23) (16.47)(15.02)(21.13)83.43 **
(24.08)57.23 *
(22.51)92.04 ** 103.54 ** 99.36 ** 83.93 **(26.29)(25.17)(25.38)(14.78)146.04 **(23.53)

163

163

163

163

163

163

163

161

163

162

162

162

163

163

0.59

0.60

0.59

0.52

0.53

0.53

0.05

0.03

0.28

0.27

0.32

0.32

0.26

0.26

Value

p

Value

p

Value

p

Value

p

Value

p

Value

p

LM spatial lag

0.66

0.42

0.79

0.37

0.02

0.88

1.27 0.26

3.48 0.06 4.19

0.04

0.39

0.53

0.16

0.69

LM spatial error

21.69

0.00

14.88

0.00

0.39

0.53

0.43 0.51

2.50 0.11 1.10

0.29

0.08

0.78

2.27

0.13

p

Value

p

Value

2
2
2
Note : Men and women are 25-54 years old. Standard errors are in parentheses. R is adjusted R for OLS and pseudo R for spatial lag and spatial error models. p denotes p value.
Municipalities with population less than 50 are excluded. Robust Lagrange Multiplier (LM) test statistics are not reported since none of the LM test statistics for spatial lag and spatial error are both significant.
**Significant at 1%; *Significant at 5%.

31

Appendix B2. Regression of regular employment for municipalities in which 10% or more male commuters travel to the Tokyo ward area to work.
Unmarried women

Married women
No children

OLS

With children under 6With children, none under 6

HS or lessCollegeHS or lessCollege

OLS

OLS

OLS

OLS

HS or lessCollegeHS or lessCollege

OLS Lag-S2SLS Lag-ML

OLS

OLS

OLS

0.06
(0.05)-0.01(0.06)-0.02(0.06)-0.02(0.06)-0.23 *
(0.09)-0.10 **
(0.03)-0.22 ** -0.27 ** -0.27 **(0.05) (0.04)(0.04)-0.05(0.04)-0.14(0.08)-0.12 **
(0.03)-0.22 **
(0.06)

Err-GMM Err-ML
0.05(0.05)

With children

Err-GMM Err-ML

Commuting time

0.01
(0.05)

Log of residential land price-2.52 ** -2.29(0.92)(1.25)-2.23 *
(0.98)

0.84
(1.1)

-0.96
(1.18)

0.10(1.08)

2.82(1.83)-0.22(0.49)-3.44 ** -3.39 ** -3.37 **(1.01) (1.08)(0.83)

0.76
(0.8)

-2.12
(1.53)-0.50(0.56)-3.38 **
(1.18)

Unemployment rate

-1.03 ** -1.00 *(0.35)(0.41)-0.99 **
(0.36)-0.54(0.42)-0.53(0.44)

-0.54
(0.4)

-0.11
(0.66)-0.08(0.18)-0.06(0.38)

-0.28
(0.3)

-0.59
(0.56)

0.02(0.21)

0.73(0.45)

Households with two or more children

0.19(0.12)

0.21(0.15)

0.21(0.12)-0.29 * -0.32 *(0.15)(0.16)-0.30 *
(0.14)-0.12(0.24)-0.13(0.07)-0.50 ** -0.47 ** -0.47 **(0.14) (0.15)(0.12)-0.10(0.11)-0.66 **
(0.21)-0.13(0.08)-0.21(0.16)

Availability of childcare centerｓ-0.10 *
(0.05)-0.05(0.04)-0.04(0.05)-0.12 * -0.08(0.06)(0.05)-0.10(0.06)-0.16(0.09)

0.11 **(0.03)-0.02(0.05)

0.10 *(0.04)

0.06(0.08)

0.11 **(0.03)-0.04(0.06)

4.48(12.72)73.12 **
(24.41)23.85 **
(8.87)

73.80 **
(18.81)

Spatial lag (ρ)λ

0.60 **(0.17)
With childre

0.33 **(0.08)

Constant-0.16(0.31)-0.02(0.05)-0.17(0.32)-0.02(0.04)

0.25 *(0.1)

0.31 **-0.40 ** -0.40 **(0.1)(0.12)

80.51 ** 74.24 ** 72.64 ** 21.15(14.69)(18.6)(15.44)(17.56)31.07(17.66)25.24(16.84)26.95(29.01)19.06 *
(7.81)(0.12)

86.60 ** 88.45 ** 88.47 **(16.11) (17.45) (13.19)

N

163

163

163

163

163

163

161

163

163

163

163

163

162

163

163

R2

0.31

0.33

0.32

0.16

0.23

0.22

0.14

0.36

0.19

0.22

0.21

0.16

0.15

0.32

0.18

OLS

Value

p

Value

p

Value

Value

p

Value

p

p

Value

p

Value

p

LM spatial lag

1.86

0.17

4.98

0.03

0.98 0.32

1.50 0.22 0.08

0.78

0.09

0.76 0.08 0.78

1.06

0.30

0.04

0.85

LM spatial error

7.68

0.01

2.36

0.12

0.66 0.42

0.48 0.49 6.36

0.01

0.00

0.96 1.34 0.25

0.50

0.48

1.12

0.29

p

Value

p

Value

Note : Women are 25-54 years old. Standard errors are in parentheses. R 2 is adjusted R 2 for OLS and pseudo R 2 for spatial lag and spatial error models. p denotes p value.
Municipalities with population less than 50 are excluded. Robust Lagrange Multiplier (LM) test statistics are not reported since none of the LM test statistics for spatial lag and spatial error are both significant.
**Significant at 1%; *Significant at 5%.

32