Discussion Paper No. 129
Graduate program of Economics and Management,
Tohoku University
Impact of Population Ageing on Japan’s Inter-Prefectural
Migration: A Spatial Econometrics Analysis
SIMON CONSALVO

August, 2022

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANTABLE OF CONTENTS
0.

Abstract

I.

Introduction
1.1.
1.2.
1.3.
1.4.
1.5.

II.

Model Description
2.1.
2.2.

III.

Years of interest
Type of Data

Population Migration Flow Dynamics
4.1.

4.2.

V.

Model Choice
Estimation Methods

Data Description
3.1.
3.2.

IV.

Aim and purposes of the thesis
Descriptive characteristics
Historical Demographic Dynamics
Population Decline’s Main Explanations
Past Inter-prefectural Migration

General Migration Flows
4.1.1. Magnitudinal Net-Migration
4.1.2. Metropolitan Areas’ Net Migration
Age-Group Specific Migration Flows
4.2.1. Japan-wide Inter-Prefectural Migration Flows
4.2.2. Age-Group Specific Metropolitan Areas’ Net-migration
4.2.3. Age-Group Specific Non-Metropolitan Areas’ Net-migration

Regression Analysis Results
5.1.
5.2.

Expected Results
Regression Analysis Results
5.2.1.
Spatial Linear Regression Model Results
5.2.2.
Spatial Autoregressive Model Results

VI. ConclusionVII. Bibliography
VIII. Index

0.

Abstract
This paper examines the impact that the decline in the Japanese population has had on its inter-

prefectural migration flows over the last 15 years. The early 2000s saw the number of internal migrants
consistently decrease, reaching all-time low records in both intra and inter-prefectural migration.
Economic inequalities between cities and other regions began to shrink in the 1970s due to the indirect
effects of the post Second World War’s so-called “economic miracle”. Also, after 2003, such disparities
between metropolitan and rural areas further intensified due to an increase in the income gap, as Japan
recorded the start of an aging society phenomenon. How the prefecture’s socio-economical andhealthcare characteristics are affecting the flow of migration will be examined through spatiotemporal
autoregressive models.

I.

Introduction
The beginning of the 1950’s saw the Japanese economy remarkably rise after being depleted by

the Second World War. Indeed, the real per capita GDP overtook its 1940’s level thanks to an averageannual rate of 7.1% during this recovery period. This rapid-growth era however ended in 1973 after its
expansion first slowed down until 1991, marking the last year of the “bubble economy”. This also marked
the start of the simultaneous decrease in the number of inter-prefectural migrants as well as that of theoverall population growth rate. The fall of the total factor of productivity’s growth rate and of the rate of
return in the 1990s in return increased the steady-state capital-output ratio during this period.
Any decline in population “has important indirect implications for migration, citizenship, health,and retirement policies” as well as “pensions, […] access to medical assistance, and health care provisionin new destinations” [27]. Indeed, an ageing population generally results in a reduced working-age
population. This will in turn, among other things, generate a deficit in global income to a point where it
1tries to cover for the cost of the health and pension systems. Because Japan’s “pay-as-you-go” pension
system is hyper-dependent on the working population’ tax revenues and subsidies, the national social
benefit system is set to rapidly deteriorate if no change is performed. The Japanese pension system is
effectively an unfunded pension system which uses collected taxes and insurance premiums from theworking generations in order to fund the currently retired generations. Potential impacts created by sucha set-up were well identified by the National Institute of Population and Social Security Research
Organization. As such, although it is robust against inflation and rises in wages, it is extremely vulnerable
to any shift in demographics. This disadvantage is further worsened by recent contribution evasion among
younger people [17]. In the private insurance sector, moral hazard, and especially adverse behaviour,seems to be enhanced by high opportunity costs. This prompted the Japanese government to intervene
against such cases by providing long-term care as well as employment insurance. However, one of thedownsides of the social insurance is that individual contribution is proportional to the person’s ability to
pay. This foundation makes the income redistribution scheme heavily based on the insured people’s
economic status.
Rather than economic consequences, the healthcare provision system is obviously the mostheavily affected sector by the ageing population phenomenon. Indeed, direct challenges include a shift in
the health profiles, an increased in the demand for health service (both short and long-term), as well as
rising health costs (extra-costs of at-home long-term services, number of hospitalized patients…). Changes
in health profiles leads to proportional variations in the nature of each age group’ health issues, which
could eventually also cause a possible overhaul in the medical specialization fields’ usage. Suchadjustments may also result in the improved mobility of longer-term healthcare service workers, as wellas the creation of more on-site nursing care for the highly dependent population. This improvement in
the health services necessitated a recalculation and an according adjustment of the health care workforce(in terms of numbers, distribution, and skill sets) towards wherever need occurs.
2Since the beginning of the millennium, in search of solutions to address this newly ageing society
issues, the Japanese government has been diligently implementing policies touching upon the pension
system, its respective health-care benefits and institutional care. As a matter of fact, the disparities
between regions, which were deepened by the economic growth spurt of the second half of the 1900s,
also accentuated the variations in the growth rate of the elderly population in different prefectures. Hence,
due to the fundamental changes in socio-economic conditions, Japan was required to implement costcontaining policies in the health sector. Thus, in order to efficiently distribute healthcare resources
proportional to the region’s needs, a “Regional Medical Care Vision” within the “Medica Care Plan” wasput in place. This was an important part of the 2006 Healthcare Structural Reform Package Act which was
passed with the aim of reforming both the service delivery system and the insurance system into one
package [6]. Further down the line, the 2008 Long-Term Care Insurance Act, among other amendments,
has established ways to reduce the cost of supplies (by restricting the coverage available and tariffs
offered) while expanding financial resources by increasing taxation and co-payment of the 70-years-old
and above bracket from 10% to 20% [14]. This was further emphasized by the 2013 “Proposal for
Promotion of Social Security System Reform” which introduced a reduced burden of social insurance
premiums paid by working people and corporations and enhanced communication networks throughwhich people can obtain crucial information on the social security system reforms changes made in thepast. Moreover, in response to regional discrepancies, the responsibilities of medical care andexpenditure tracking for the elderly shifted from municipal to prefectural governments where they wereasked to prepare a 5-year plan to create a system designed to control medical expenditure. Adjacently,
this plan would be aimed at those suffering from lifestyle-related diseases as well as reduce the average
length of hospital stays.

3

1.1.

Aim and purposes of the thesis

As is further explained later in the paper, problems linked to Japan’s ageing population has not
only worsened but have also become critical between 2010 and 2012 where the overall population
magnitude also started declining. Thus, in an attempt to examine the effects of such an issue on the interprefectural migration flows, spatiotemporal autoregressive models (Spatial Linear Regression Model(SLRM) and Spatial Autoregressive Model (SAR)) are used to find out which prefectural characteristics are
the most important on the population’s decision to migrate. In order to do so, the data will be separated
into three different age groups to have a complete and detailed analysis of the situation.
Research Objective: Inquire about the motivation behind the inter-prefectural migration of threedifferent age groups following the recent decrease in the Japanese population as well as the
response from the government concerning this issue.

1.2.

Descriptive characteristics

While the second World War had disastrous effects on the Japanese population, the post war
economic era brought about a baby boom (trend that is also present in other countries after both World
Wars). Based on the data gathered from the (1940-2020) Population Censuses’ Total Population Estimates,
this period saw an increase in the population growth rate of 15.6% between 1945 and 1950; yearly ratethat neared 2.5% at its peak and averaging 1.9% between 1945 to 1953 [19]. However, this baby boom
quickly subsided, and the population growth rate started slowing down before once again increasing
throughout the 1960’s.
However, as described by David Frum in his 2000 book How we got Here: The ‘70s, Japan wasamong the countries that were the most severely hit by the 1973 worldwide oil crisis, as it imported 90%
of its oil from the Middle East [4]. Coincidently, 1973 also saw the beginning of the decline in the4population growth rate. From 1.45%, it then consistently shrank until it finally became negative around2006-2010 (depending on the data sources) before hitting an all time low of -0.37% in 2021.

1.3.

Historical Demographic Dynamics

Such changes in the overall population growth rate and magnitude are sure to also bring aboutchanges in the demographics’ dynamics. In a trend that is much more pronounced in Japan that in any
other countries worldwide, generational shares have shifted over time in a way that now shows apredominantly ageing Japanese population. As a direct product of the improvement of the healthcaretechnology in Japan, this demographic shift was seen to be slowly emerging in the 1970s. However, thisageing curve worsened in the 1980s due to the slowing down of the thus far growing economy. Later on,
with the bursting of the aforementioned “bubble economy” in the 1990s, this downward trend
accelerated and is now at its worse.
As we can see on Figure 1 (built on the same data from the Population Census as previously), theupward trend exhibited by the share of the 65 years old and over individuals is clearly visible. In 1950,
Japan’s population share of people aged 65 years old and above was a meager 4.9% which was relatively
low for that time. However, by 1980, this percentage had already almost doubled to reach 9.1%. Although
this increase was also seen in other countries, it continued to rise until it reached 12.1% in 1990 and even17.36% in 2000. However, health and economic variables led this trend to worsen even more, as it reacheda staggering 22.5% when the population growth rate became negative in 2008-2010, before rising evenhigher to 28.44% in 2019. That is to say, the share of Japanese people aged above 65 years old has almost
grown sixfold in the last 70 years.
A similar but opposite trend can also be discerned in the 0-14 year-old population share, as its
decline started slightly earlier on (around 1950). The 0-14 year old population’s share declined from 35.4%
in 1950 to 23.51% in 1980 and then 14.58% in 2000 before ending up at 12.06% in 2019.
5

While these two generation shares demonstrated contradictory trends over the past 70 years, theworking age generation’s share has not changed much, remaining between 60% and 70% but is now
consistently decreasing as shown in Figure 1.

Figure 1 - Japan's Age Structure (1920 - 2020)
80.0

Age group Proportion (%)

70.0
60.0
50.0
40.0
30.0
20.0

0-14

1.4.

15-64

2019

2016

2013

2010

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

1971

1968

1965

1962

1959

1956

1953

1950

1947

1944

1941

1938

1935

1932

1929

1926

1923

0.0

1920

10.0

65+

Main Reasons Behind the Population Decline

Although the issue is not unique to Japan, this country is home to the world’s oldest population
as per their proportion of citizens above 65 years old. The reasons for the emergence of this so-called“super-ageing society” may therefore be most evident in Japan.
We can separate these contributing factors into the following three categories:
1. Increase of life expectancy. The Japanese health technology significantly improved over the past
50 years, leading to Japan’s life expectancy for both men and women becoming the highest in theworld. Highlighted in the World Bank’s data and reports, although marginally decreasing over time,
Japan’s life expectancy came from being the lowest of the G7 countries in 1960 (at 68 years-old)to becoming the highest in the world in 1981 (76 years-old). This can be attributed in part to low
mortality from ischemic heart disease and cancer. It then reached an all-time high in 2022 (84.91
years-old) a 0.14% increase compared to 2021 [13].
62. Continuous worsening of the private economic status of the Japanese population. Industry-based
partitioning along with deteriorating work-life balance are largely responsible for the quick
decrease in the fertility rate. Long working hours, although proven to be minimally efficient, being
culturally recognized as the pinnacle of professional success and devotion, has put a dent in theability of (mainly urban-based) working population to create a family. The slow improvement
towards a more gender-equal society means that more young women now shy away from startinga family early on as it would severely lessen their professional market values in the future. Even
more so than in other countries, the Japanese social norm dictating that wives should take time
off work to focus on childcare decreases the chances of coming back to work following
pregnancies. “Does a Mother’s Early Return to Work after Childbirth Improve Her Future
Employment Status? Evidence Using the Birth Month of Japanese Babies” is an interesting paper
exploring this issue in detail [9]. This factor leads to lower marriage rates, and to an increase in
the average age for a first marriage (now nearing 31 years old). In addition, those who do end up
marrying face low wages, deteriorating labor market segmentation among industries and medical
insurance not covering many of the costs of childbirth and childcare in large cities, leaving close
to no room for family creation [9].
3. Opposition of the Japan’s public to international immigration. This is the last and perhaps one of
the less widely studied reason. Whereas it is vastly sympathetic to influences from the West(baseball becoming the national sport and the increasing number of fast food joints), the Japanese
population, perhaps influenced by a willingness to preserve cultural purity stemmed from pasttraumatic historical events, now imposes gentle but extremely restrictive constraints on
immigrating people.

7

1.5.

Past Inter-prefectural Migration

The following is in part a summary of multiple studies, which have analysed the historical trendsof the Japanese population’s inter-prefectural migration. These include Takashi Yamaguchi’s “Population
Redistribution of Japan – within the context of the national settlement system” and Masatoshi Yorimitsu’s“A review of recent population changes in Japan” [24, 25].
One of the most significant examples of changes in Japan’s demographics is related to the
population concentration in metropolitan areas (MAs) compared to rural areas (non-MAs) since the endof the 1960s. A steady rise of population inflow into metropolitan prefectures was clearly shownaccompanying the Post World War 2 economic growth. This economic growth shifted priorities, which inturn greatly influenced residential selection, in a move resembling Metropolization [24].
However, once the growth rate of productivity started to slowly decrease at the beginning of the
1970s, mainly due to the first oil shock in 1973, a clear positive correlation between the growth rate ofreal GNP and the rate of internal migration can be observed. This relationship is even more prominent
when comparing rural and urban prefectures’ economic status. The national decline in the natural birthrate, as well as a reduction in deviation between rural and urban rates, meant that internal migration
became the major determinant of population growth in each region. As a matter of fact, although thenatural population growth rate of rural prefectures used to be higher than that of urban prefectures
before the 1970s, this relationship was later reversed. The number of prefectures where net-migration
was negative decreased from 25/47 in 1960-1965 to only 1/47 in 1980-1985 [25]. The inter-prefectural
migration rate began to decrease in 1973. Whereas excess in-migration made up more than half of TokyoMA and Osaka-MA population growth rate during the 1960s, it decreased all the way to 3% in Osaka and
30% in Tokyo in the first half of the 1970s, such exodus mainly consisted of 20-24 years-old men.

8Ultimately, any positive changes in population change between 1975 and 1980 can exclusively be
attributed to natural means.
Ever since the beginning of the 1970s, economic inequalities between cities and rural regions havebeen shrinking. However, the emergence of the ageing-society phenomenon sparked a shift in eachprefectures’ share of responsibilities concerning healthcare activities. This, although meant to decrease
the burden of the ageing society put on the national healthcare system, is another way prefecturaldifferences are increased. As is generally true in most countries, wages are disproportionally higher for
full-time employees of major Japanese firms compared to other types of sectors. Therefore, most jobseekers head to densely inhabited urban areas where major companies are located, far from ruralprefectures. This is particularly true for the 25-29 years old demographics who represents the vast
majority of the job seeking population, also making up almost 50% of the total inter-prefectural migrants
between 1991 and 1996.
However, the main contributing factors of metropolitan out-migration after the rapid economic
expansion include the ensuing deterioration of living conditions in under-prepared over-populated
metropolitan areas, an astonishing rise in land and housing costs, as well as undesirable commuting
distances [24].
While Koji Murayama and Jun Nagayasu affirm that “the inter-prefectural differential factor of
per capita income is less valid for explaining any type of migration”, they also notice that the increase inemployment opportunities still plays an important role in out-migration [8]. Result highlighted in “Keyfactors in determining internal migration to rural areas and its promoting measures - A case study ofHirosaki City, Aomori Prefecture”, relocation to rural areas simply came from the desire of people (mostly
unmarried or self-employed) to return to their hometown due to the presence of their parents’ homes[10]. Due to the strong correlation between job-to-applicant ratio and population migration, it is natural9to also notice a lack of educational institutes and of opportunity costs in job hunting activities for those
who migrate to more rural prefectures. The relationship between distance and age with the interprefectural migration flow is also observed to be negative.

II.

Model Description

2.1.

Model Choice

Originally derived from Newton’s theory of gravitation, the standard gravity model put forth firstby Jan Tinbergen in 1969 was one of the first utilizing spatial dependence to explore international trade
movements [22]. This gravity model also recently helped provide or theoretical framework to forecast theeffects of Brexit onto international trade flows. However, my work’s interest lies in population migration
flow, rather than trade flows. Thus, to transform this model into one that can take into account spatial
dependence in relation to internal migration flows rather than trade flows between regions, we consider
the unique characteristics of both regions, parameters introduced into the X variables below. Equation (1)represents the transition from the international trade gravity model to one that considers migration, while
equation (2) is its extension into a spatial linear regression model.

ܻ௢ௗ = ‫ܩ‬ܺ଴ ܺௗ1݀݅‫ݐݏ‬௜௝ܻ௢ௗ = ߙ + ߚ௢ ܺ௢ + ߚௗ ܺௗ + ߛ݀݅‫ݐݏ‬௢ௗ + ߳

(1)

(2)

[For reference, please let n be the number of regions studied in each section, i and j two arbitrary
regions/prefectures where ݅, ݆ ‫[ א‬1, ݊], ߙ a constant intercept term and ‫ א‬the error term] .

1‫ܨ‬௜௝ = ‫ܩ‬

ெ೔ ெೕ
஽೔ೕ

where G is a spatial constant, ‫ܨ‬௜௝ represent the trade flow from region i to region j, Mi and Mj standfor economic dimensions of regions i and j respectively and finally Dij represents the distance between the tworegions

10

The above Yod is a n2 x 1 matrix representing the migration flow from the prefecture of origin o tothe destination d prefecture. Its elements equal 0 when the origin prefecture is the same as thedestination prefecture. While K is a spatial constant, ݀݅‫ ݆݅ݐݏ‬denotes the n2 x 1 vectorized origin-destination
log geographical distances, which acts as a proxy for the transaction costs of migration. Similar to theinter-prefectural migration flow, the distance variable’s elements equal to 0 if i = j. Finally, ܺ‫ ݋‬and ܺ݀
characterize the k prefectural characteristics we have chosen to interpret the effects in sectors that weredeemed the most significant in the migration decisions based on past studies. The nature of thesevariables will be explained later on in the “Data” section of the paper.
Next, spatial autocorrelation between migration flows can be added to the standard spatial model.
Such spatial interaction models have been applied, notably by E. Marrocu and R. Paci, to examine thedestination and routes tourists choose depending on variables such as their income, received amenities
and the destination region’s accessibility, distance and population [15]. Moreover, James J. Lepage and R.
Kelly Pace have, in their paper “Spatial Econometric Modeling of Origin-Destination Flows”, furthered thismodel by introducing origin and destination-based spatial weight matrices to turn it into a Spatial
Autoregressive Model resulting in the following equation (3) [12]:ܻ௢ௗ = ߩ௢ ܹ௢ ܻ௢ௗ + ߩௗ ܹௗ ܻ௢ௗ + ߙߡ + ߚ௢ ܺ௢ + ߚௗ ܺௗ + ߛ݀݅‫ݐݏ‬௢ௗ + ߳(3)

The two spatial weight matrices ܹ௢ and ܹௗ are respectively the origin and destination-based
matrices. Firstly, the spatial lag interaction term ܹ௢ ܻ௢ௗ represents the average flow from the neighborsof the origin prefectures to the destination prefectures. Similarly, the second type of spatial dependence
arises from the destination-based dependence and takes the form of the spatial lag interaction termܹௗ ܻ௢ௗ , which captures the spatial dependence of the migration flows from origin prefectures to theneighbors of the destination prefectures.

11One of the most important parts of the equation is the multiplication of each weight matrix by
the response variable ܻ௢ௗ to get rid of the potential collinearity present in the previous gravity model. Thismethod allows the model to remove endogeneity caused by the migration flow on the variables of thetwo prefectures involved in the inhabitant exchange.
The two aforementioned spatial weight matrices are however first and foremost based on a
typical row-standardized inverse distance squared weighted interpolation. This spatial matrix indicatesthe spatial relationships between regions, where the distance between each prefecture’s capital iscalculated. Each element ‫ݓ‬௜௝ of this n x n matrix are spatial weights whose value take the form of:షమௗ௜௦௧೔ೕ
షమ‫ݓ‬௜௝ = ൝ σరళೕసభ ௗ௜௦௧೔ೕ
0,,݂݅ ݅ ് ݆(4)‫݁ݏ݅ݓݎ݄݁ݐ݋‬As shown in equation (4), the choice of power used is arbitrary, here p = 2 being the default value.
An increase in p would imply the rapid decrease in the importance of distant points in the regression, andonly the immediate neighbors would end up influencing the analysis. Moreover, because distances
between prefecture’s capitals are not always representative of the distance of moves performed,
proportional weights are created by row standardization. Row standardization is a technique in which we
divide each element of a row by the sum of all elements in the same row, making the sum of each row
equal to 1.
The distances the W matrix is based on were calculated using each prefectural capitals
longitudinal (long) and latitudinal (lat) data found in the Final Report of the 2015 Population Census of
the Population and Households of Japan section [19]. Considering all 47 Japanese prefectures in thisanalysis means that the W matrix’s dimensions are 47x47.

12While the intra-prefectural distance is set to 0, equation (4)’s dist variable is computed thefollowing way for any two prefectures i and j (such that ݅, ݆ ߳[1,47]):݀݅‫ݐݏ‬௜௝ = ܽ‫(ݏ݋ܿݎ‬cos(90 െ ݈ܽ‫ݐ‬௜ )) ‫ כ‬cos(90 െ ݈ܽ‫ݐ‬௝ ))+ sin(90 െ ݈ܽ‫ݐ‬௜ )) ‫ כ‬sin(90 െ ݈ܽ‫ݐ‬௝ )) ‫ כ‬cos((݈‫݃݊݋‬௜ െ ݈‫݃݊݋‬௝ ))) ‫ כ‬6371

Thereafter, we can build the two spatial weight matrices by multiplying the W matrix describedabove from both sides by the identity matrix ‫ܫ‬௡ (where n = 47 in our case) using an outer product such as
to get:a. ܹ௢ = ‫ܫ‬௡ ٔ ܹ

Because the same W matrix was transcribed 47 times onto the diagonal once for each prefecture
as pictured above, a 2209 x 2209 ܹ௢ matrix was formed.
b. ܹௗ = ܹ ٔ ‫ܫ‬௡ (In the case of n = 3, the ܹௗ matrix would look such as below)ܹௗ =0‫ݓ‬ଵଶ ‫ܫ כ‬ଷ‫ݓ‬ଵଷ ‫ܫ כ‬ଷ‫ݓ‬ଶଵ ‫ܫ כ‬ଷ0‫ݓ‬ଶଷ ‫ܫ כ‬ଷ‫ݓ‬ଷଵ ‫ܫ כ‬ଷ‫ݓ‬ଷଶ ‫ܫ כ‬ଷ0

The diagonal elements of ܹௗ are n x n matrices with elements 0, and the non-diagonal elements(where ݅ ് ݆) are equal to ‫ݓ‬௜௝ ‫ܫ כ‬௡ . Hence, for this paper, a 47 x 47 identity matrix was thus subsequently
multiplied by each element of the W matrix.

13

2.2.

Estimation Methods

Although very computational heavy, standard Maximum Likelihood Estimation (MLE) can be
efficiently applied to origin-destination flow-based modeling estimations. Thoroughly investigated by K.
Ord in his 1975 paper entitled “Estimation Methods for Models of Spatial Interaction”, evaluating a Spatial
Autoregressive Model involving two spatial weight matrices provides greater efficiency and stability byoffering flexibility when it comes to evaluating a wide variety of functions [18]. Moreover, more so thanany other methods, unbiased estimations for larger samples can be obtained. Methods such as theBayesian Markov Chain Monte Carlo procedure put forward by LeSage (1997) as well as the MarginalMaximum Likelihood (MML) approach proposed by Thomas Suesse (2017) in the case of missing data canalso be employed to evaluate such spatial interaction models [11, 21].
Calculating the log-determinant values for cases where n is large may be quite difficult. As isexamined by LeSage, the “log-determinant ensures that the transformed continuous random variable has
a proper density” [12]. Neglecting this calculation in any way would “reduce the magnitude of theestimation residuals to a negligible level” [12]. This log-determinant can be measured by the trace of thematrix logarithm of the transformation: ‫(ݎݐ‬ln(‫ܫ‬௡మ െ ߩ௢ ܹ௢ െ ߩௗ ܹௗ )).
The analyses methods discussed above were all performed in R Studio.

III.

Data Description

3.1.

Years of interest

As past papers (such as “International and Interprefectural migration in Japan: Implications for
the Spatial Assimilation Theory”) have principally featured the period between 2005 and 2010, this study
emphases the results for the subsequent period (2010-2015) [8]. There are two main reasons for it. The
first is that the last available set of prefectural variables (ܺ‫ ݋‬and ܺ݀ ) data is that of 2015. Although the14

2015-2020 period is officially over, some time is always required to provide such detailed information anddisclose it to the public, and the data is still not fully available. The COVID-19 pandemic has also had adetrimental effect on data release times. The second reason is that this period is the first full 5-year period
following the start of the population decline, which started at the end of the 2000s.

3.2.

Type of Data

Among all prefectural characteristics on hand, the following independent variables are chosen inorder to fully capture the prefectures’ socio-economic conditions as well as the main healthcare aspects.
They were chosen as a result of gathering the most crucial results which have been deemed to be ofimportance in past papers.
First of all, the variable of interest, migration flow data (variable ܻ‫ ) ݀݋‬is directly obtained from thedatabase of the Statistics Bureau of Japan, under the charge of the Ministry of Internal Affairs andCommunications. 5-year periods are analysed because the Population Census database (especially thesection “Tabulation on Internal Migration for Population”) captures the current location of eachprefecture’s resident and compares it to their respective “place of usual residence five years ago” [19].
Later graphs found in Section 4 are based on data found in the 2005, 2010, 2015 and 2020 Censuses.
There, the 2005 and 2020 data, out of the official of the study, are given for information purposes only inorder for the reader to get a feeling of the trends.
Data related to prefectural characteristics (ܺ‫ ݋‬and ܺ݀ ) was taken from the Japanese Governmental
Statistics’ website, under the System of Social and Demographic Statistics (SSDS) section. For the mostpart, yearly data for each variable is available (unemployment rate being the main exception). However,
because the migration flow data is only available every 5 years, prefectural characteristics’ data is chosen
according to the same 5-year span. Hence, the origin characteristics ܺ௢ correspond to the 2010 data, theparameters of the “place of residence five years ago” [20]. Likewise, the destination characteristics ܺௗ15

represent the 2015 data of the “current” location. As we consider the 7 variables described thereafter inTable 1, the ܺ௢ and ܺௗ are both 47 x 7 dimensional matrices.
Gravity characteristics: The prefectures’ “total population” is introduced due to its role as a proxy to thesize of the labor market, as it was found that larger population usually tends to encourage in-migration(see 1.3 Historical Demographic Dynamics for more details). Geomorphologically, due to massive
differences in prefectural size, the variable “area” is also added in order to regulate the effects of suchdisparities in the migration flows.
Economic variables: Generally speaking, “Per capita Gross Prefectural Product” is always being
considered as an important determinant in the migration process. As in past literature, real per capita GPP
is also used here as a proxy for income, in an attempt to reflect each prefecture’s basic-yet-fluctuating
economic characteristics. While GPP somewhat displays the prefectures’ labor markets impact on
migration, the addition of “unemployment rates” would however help assess the importance of job
opportunities in migration flows as well.
Healthcare: As is commonly the case, the impact of ageing can generally be seen through shifts in thehealthcare system structure. Because prefectures have gradually been given more responsibilities related
to medical care and expenditure tracking over the years, healthcare variables such as “prefectural medicalcare expenditure for the elderly”, “the average length of stay in hospital” and the “percentage of death
attributed to lifestyle diseases” (malignant neoplasm, diabetes, hypertension indued diseases, cardiac
diseases, and Cerebrovascular diseases) are used in order to examine the effects of fluctuations in thehealthcare system onto the migration flow.
The effects of the variables “unemployment rate” and “medical expenditure” are especially vital as “goodinfrastructure, environmental amenities, and lower costs of living and housing income become important16

in older age and acts as a pull-factor for migration of the elderly and retirement migration, including return
migration” [27].
Table 1: Variables’ Units
Variable
Migration Flow
Population
Area
Real per capita Gross Prefectural Product
Unemployment rate
Medical care expenditure for elderly per insured person
Average length of stay in hospital
Death per lifestyle disease

Units
Person (1,000 / 10,000 people depending on model)
Number of people (natural log)
Hectares (natural log)
2011 Base Million yen (natural log)
Percentage of total working age population
Yen (natural log)
Days (natural log)
Percentage of total number of deaths

17

IV.

Population Migration Flow Dynamics
As presented in section 3.1, due to the unavailability of some necessary data for the 2015-2020

period, the period of interest of the paper’s regression analysis is the 2010-2015 period. However, themigration flow data of the 2015-2020 period having been made accessible on the Japanese government
official website in February 2022, it is included in as to give an idea of the comparison of the migratio
flows. Thus, examining the evolutionary net-migration trends exhibited in this section’s graphs give acomplete overview of national trends surrounding the period of interest.

4.1.

General Migration Flows

4.1.1.

Magnitudinal Net-migration
Figure 2: Evolution of Prefectural Net-Migration (2005-2020)

400
300
200
100
0
-100
-200

Hokkaido
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
IbarakiTochigi
Gumma
Saitama
Chiba
Tokyo
Kanagawa
Niigata
ToyomaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

Prefectural Net-migration (1,000 persons)500

2005-20102010-2015

2015-2020

Figure 2 above uses the “Japanese Government Statistics’ Report” on Internal Migration in Japan,
which features yearly prefectural net-migration. For each prefecture, data was then grouped into 5 year
long periods, including in addition to the 2010-2015 period of interest, earlier 2005-2010 and later 2010-18

2015 (i.e. 2005, 2006, 2007, 2008, 2009 and 2010 formed the 2005-2010 group), producing the above
three curves in Figure 2 [19].
Although remaining negative, the net-migration of three of the prefectures containing some of
the most populated cities in Japan (namely Hokkaido (Sapporo), Miyagi (Sendai) and Hiroshima) have
gradually been rising. Similar tendencies can also be observed in Fukuoka, Kyoto and Osaka, whose
original net-migration was negative during the 2005-2010 period and then became positive later on.
Conversely, among the last three other large cities, Shizuoka is seeing its population decrease at an evengreater rate as the years pass. Displaying a comparable trend as Shizuoka, Aichi’s (Nagoya) inorganic
population growth rate (yet still positive today) has been decreasing rapidly and is recently even nearing
0, threshold surpassed by its neighboring prefecture Mie (Tsu).
Tokyo has, however, always been somewhat of exception as it is both the economic and political
capital. Indeed, even though both Japan’s and Tokyo’s populations have steadily been decreasing for the
past 12 years, Tokyo prefectural net-migration on the other hand continues to be positive. Still, it had firstdecreased between 2010 and 2015 compared to the five previous years, before increasing again between
2015 and 2020 without nonetheless returning to the 2005 rate.
All the same, (with the exception of Okinawa’s which became positive between 2015 and 2020),
net-migrations of non-metropolitean prefectures are mainly negative while no clear unified uplifiting
trends is yet to be observed.
As we compare the difference in trends between 2010-2015 and 2015-2020 time periods, weshould be aware of the fact that the impact of the COVID-19 pandemic is likely to account for a somewhat
big part of the overall trend exhibited by some prefectures in the 2015-2020 period’s data. This is highly
probable due to the fact that the 2020 Popualtion Census’ survey date is October 2020 rather than19

December 2019 (as is often the case globally). The gap between the beginning of the COVID-19 pandemic(in March 2020) and October 2020 leaves room for fluctuations in popualtion movement.

4.1.2. Metropolitan Areas’ Net-migration
First and foremost, to form the 8 metropolitan areas, 22 of the 47 prefectures were divided into
the following groups:
1. Sapporo MA: Hokkaido
2. Sendai MA: Miyagi
3. Tokyo MA: Ibaraki, Tochigi, Gumma, Saitama, Chiba, Tokyo, Kanagawa and Yamanashi
4. Shizuoka-Hamamatsu MA: Shizuoka
5. Chukyo MA: Aichi, Mie and Shiga
6. Keihanshin MA: Kyoto, Osaka and Hyogo
7. Hiroshima MA : Hiroshima
8. Fukuoka-Kitakyushu MA: Yamaguchi, Fukuoka and Saga

Figure 3 - Metropolitan Areas' Net-Migration (2010-2020)
300
250

1000

200
800

150
100

600

50

400

0
200
0

Net-migration (1,000 people)

In and Out migration (1,000 people)

1200

-50
Sapporo MMA

Sendai MMA

Tokyo MMA

Sizuoka-Hamamatsu-MMA

Chukyo MMA

Keihanshin MMA

Hiroshima MMA

Fukuoka Kitakyusgu MMA

In-migration (2010-2015)

In-migration (2015-2020)

Out-migration (2010-2015)

Out-migration (2015-2020)

Net-migration (2010-2015)

Net-migration (2015-2020)

-100

20

While still acknowledging the prefectural capitals’ significant influence on their own prefectural
net-migration flow, it cannot systematically account for that of the multi-prefectural metroplitean areas
they belong to. As they represent the majority of Japan’s total population (73-74%), understanding thetraffic flow both from and to Metropolitan Areas (MA) and non-Metropolitan Areas (non-MAs) before
performing a model analysis enlightens us on the veractiy fo our hypothetical statement.
In-migration data introduced in Figure 3 below was obtained by adding the migration in the
direction of each individual metropolitan area from all other prefectures that are not part of it.
Correspondingly, the out-migration corresponds to the migration out of the prefecture(s) within themetropolitan area to all other prefectures combined. Mathematically speaking, the sample size for bothin-migration and out-migration is: 47 – ݊ெ஺௣௥௘௙_௜ (where ݊ெ஺௣௥௘௙_௜ is the number of prefecture withineach metropolitan area i). The difference in magntidude between the two (net-migration) is thencalculated during two consecutive 5-year time periods: 2010-2015 and 2015-2020.
While Sapporo-MA, Sendai-MA, Shizuoka-Hamamatsu-MA and Hiroshima-MA are monoprefectural metropolitean areas, different migration flow are exhibited by the remaining 4 metropoliteanareas. Unsurprisingly, Tokyo-MA’s overall net migration is the largest by far. Tokyo’s continuous supply of
job opportunities and somewhat constant economic prosperity over the years have also influenced its
neighboring prefectures’ net migration flows as well. As it is, with the exception of Ibaraki, all prefecturesin the Tokyo-MA show positive net-migration. In addition, Tokyo-MA’s net-migration has risen
significantly between the two periods (2010-2015 and 2015-2020) mainly due to the decrease in themetropolitan area’s out-migration (from 952,953 people in 2010-2015 to 881,832 in 2015-2020).
Although on another magnitude, Sendai-MA and Keihanshin-MA also both saw their respective
net-migration decrease compared to the earlier time period. Indeed, while the fault in Sendai-MMA falls

21

onto the greater decrease in in-migration, the opposite is true in Keihanshin-MMA and its net-migration
continues to fall much further than below 0.
On the contrary, the stagnation of out-migration combined with a decrease in in-migration
resulted in a net decline in Chukyo-MA’s net migration, going as far as becoming negative between 2015
and 2020 (from 44,195 people to -1,573 people).
While biding farewell to more people than it welcomed between 2010 and 2015 (-3,979 people),
Hiroshima-MA’s net-migration has only become slightly positive (1,514 people) in the following period
althought its overall levels of migration stayed quite similar.
In total, the non-metropolitan areas documented an overall net-migration loss of 253,851 persons
between 2010 and 2015 (most of them moving to Tokyo (14.4%), Fukuoka (11%) and Aichi (9.3%)
prefectures). On the opposite, all metropolitan areas welcomed a net-migration increase of 181,568
persons in the second time period (the majority due to Tokyo’s massive increase from 150,568 persons to237,944 persons).

4.2.

Age-Group Specific Migration FlowsAs they continue to make up an increasing percentage of the Japanese population, the likelihoodof the elderly population migrating is generally thought to be heavily reduced due to the general“intensification of globalization process, development of technologies, and telecommunications” [7].
However, only few papers investigate the connection between the population ageing phenomenon andthe factors affecting the older population’s migration decisions in particular. Although reasons for
migration differ per age groups, such motivations most likely always stem from a desire to improve their
way of live one way or another. As people get older, their center of attention is thought to shift towards

22

factors which may influence their health conditions as well as their ability to live out the rest of their lives
with relative ease.
In addition to total migration information, tables accessed from the “Tabulation on Internal
Migration for Population” report displays separate data for all ages (from 1 year old babies to 87+ elderly
individuals) [19]. Hence, in order to form age groups which would represent each a specific social andeconomic situation in one’s life, yearly data is then rearranged manually in the following three groups:
1. 18-25 years old (refered to as “youngest generation” or “y”)
2. 26-64 years old (representing the “working individuals” or “w”)
3. 65+ (symbolizing the “retired population” or “r”)
Because each age group has a distinct set of inherent characteristics, section 5.1 will later discuss thedifference in the regression analysis’ expected results from each age group’s perspective.

4.2.1. Japan-wide Inter-Prefectural Migration Flows
New moves are generally performed by people between 18 years old and their late 30s, with theconcentration of people in their 20s being the largest because it is the first time they get the opportunity
to do so on their own. Most of younger adults’ first move occurs upon entering university, getting theirfirst job or even when moving in their first house after deciding on where they settle down. On thecontrary, even if they still consist of the majority of the migrating population, the people between 26 and64 years old usually are largely motivated by changes in their professional or familial statuses. Further
down the line, the migration rate of people having reached their late 50s drops considerably, far lower
than that of 18-25 year olds despite their population being much greater.

23

For the two most recent consecutive periods (2020-2015 and 2015-2020), Figure 4’s age-specificproportions are calculated by taking the percentage taken up by each age group out of the total outmigrating population’s magnitude, i.e.:ܽ݃݁_‫ܿݎ݁݌‬௜ =ܽ݃݁_݉ܽ݃݊௜ ‫ כ‬100,‫݊݃ܽ݉_݈ܽݐ݋ݐ‬‫ݕ[ ߳ ݅ ݁ݎ݄݁ݓ‬, ‫ݓ‬, ‫]ݎ‬

(Note: Of course, because we do not take into account the 0-17 years-old share, the total percentage does
not sum up to 100%)
The difference in the the aforementioned percentages of each age group’s inter-prefectural outmigration in the two consecutive periods 2010-2015 and 2015-2020 can be seen in Table 2. The
corresponding actual percentages are visually represented in Figure 4. Prefectures whose names are
written in red are prefectures located in high density metropolitan areas. Moreover, the magnitude ofeach difference is displayed as a coloured gradient (from green being highly positive to red representing

80

Figure 4 - Proportion of the Three Age Groups in Japan-wide Inter-Prefectural
Out-Migration (2010-2020)

70
60
50
40
30
20
10
0

Hokkaido
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
IbarakiTochigi
Gumma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
OkinawaAge group-specific proportion in out-migration (%)

highly negative).

18-25 (2010-2015)26-64 (2010-2015)

65- (2010-2015)18-25 (2015-2020)

26-64 (2015-2020)

65- (2015-2020)24

Table 2 - Difference in the Age Structure’s Proportions (2010-2015 / 2015-2020)

What we first notice is that the percentage of 26-64 year-olds’ out-migrants in Figure 4 obviously
twarts those of the other two. Although the proportion taken by the older generation has not changed at
all between the two time periods, a decrease of 2.0262% in the percentage of the working population canbe matched by a proportional increase in the relative fraction taken up by the younger generation (1.913%).
Unsurprisingly, this ratio of working population out-migrating from Tokyo-MA’s prefectures is thehighest (64-70%), while it is at the lowest in Wakayama (47-50%), Shimane (50-51%), Nagasaki (49-51%),
Miyazaki (47-48%) and Kagoshima (48-49%). Naturally, these fluctuations are almost systematicallycombined with an opposite trend among the youngest generation in the same prefecture (8-10% in Tokyo
compared to 29-31% in Wakayama, 32-34% in Shimane, 31-32% in Nagasaki, 31-34% in Miyasaki, and 2931% in Kagoshima).
Interestingly enough, while the percentage of elderly people does not vary excessively between
prefectures, it is around 8-10% in Tokyo (which is the only prefecture with almost the same amount of 1825 years old moving out as 65+ years-olds) and Toyama, 9% in Ishikawa as well as 8.5% in Osaka and 8.4%

25

in Nara. Moreover, such trend has not changed between the two time periods, and has remained low,
only exhibiting an overall average difference of 0.0545%.
Three prefectures’ trends however stand out among them.
1. Obviously, Okinawa’s age group evolutionary gaps are by far the largest. As a matter of fact, theproportion taken up by the working population drastically decreased from being the second
largest (at 63.2%) all the way down to 50.7% and becoming one of the lowest, dropping by
12.508%. Because the elderly population’s percentage only decreased by 2.327%, the younger
population moved a lot more in comparison to the previous five years (from 14.5% in 2010-2015
to 28.5% between 2015 and 2020). As a matter of fact, Okinawa’s effort in twarting thecoronavirus’ infection in the prefecture was compromised by the regrettably low standard of
infection protection enforced by the US military. Potential indirect exposition to the military
personel (both Japanese and American) has induced the government to impose a stricter
lockdown on Okinawa where vacation-related trips account for most of the migration.
Although much more weakly, the other prefecture which followed a similar trend was Ishikawa.
In fact, 8.3% more people aged 18-25 y.o moved out of Ishiawa while the retired population
exhibited a 5.087% increase. In the countrary, 5.363% less working age persons moved out
compared to 2010-2015.
2. Saitama, on the other hand, displayed the opposite tendency, losing 10.825% more young people
than in the last time period, while gaining 4.783% more work capable people.

26

4.2.2. Age-Group Specific Metropolitan Areas’ Net-migration
Figure 5 - Proportion by Age group of migration from Metropolitan Areas to non-Metropolitan Areas(2010-2015 / 2015-2020)

Age group specific proportion in out-migration (%)

70
60
50
40
30
20
10
0

Sapporo MMA

18-25 (2010-2015)

Sendai MMA

Tokyo MMA

18-25 (2015-2020)

Sizuoka-Hamamatsu-MMA

26-64 (2010-2015)

Chukyo MMA

Keihanshin MMA

26-64 (2015-2020)

Hiroshima MMA

65- (2010-2015)

Fukuoka Kitakyusgu MMA

65- (2015-2020)

Whereas previous graphs focused on Japan-wide out-migration (i.e. out-migration to all otherprefectures other than itself), Figure 5 shows the results of out-migration from each metropolitan areas
to all Non-Metropolitan areas’ prefectures only. Population Census’ migration flow data during the sametwo time periods (2010-2015 and 2015-2020) were divided in two simultaneous ways [19]:1. Division between Metropolitan Areas’ and Non-Metropolitan Areas’ prefectures
2. Segmentation into three age groups (18-25, 26-64 and 65+)
Similarly to before, the proportion of younger individual moving out of almost all prefectures (andhence whole metropolitan areas too) rose while the working popualtion’s share decreased by anapproximately similar amount.
With the rise of Tohoku Unviersity as one of the leading universities in the country, it is not
surprising to see than Miyagi-MA has the lowest ratio of 18-25 years olds moving out (14%), coupled with27

the highest ratio of 26-64 year olds (64%). A similar thinking could be applied to Tokyo-MA, which has
always been the most important hub for education, economic and entertainment purposes.
On the contrary, Shizuoka-MA and Fukuoka-Kitakyushuu-MA exhibit opposing tendencies, with a
lot of younger individuals moving out of the prefectures (21%), coupled with low working population outmigration (57% and 59% respectively). Among all metropolitan areas, Shizuoka-MA and Hiroshima-MA
demonstrated the biggest change in the proportion of young individual moving out (3.8% in Shizuoka and3.5% in Hiroshima compared to 2% on average).
On a similar note, while metropolitan areas with some of the lowest population (Sendai-MA,
Hiroshima-MA and Fukuoka-MA) had the lowest percentage of retired people moving to other prefectures,
Keihanshin-MA’s elderly population seems to be the least willing to move out. Its 8.5% proportion
overshadows the other metropolitan areas by an average of 3%.

4.2.3. Age-Group Specific Non-Metropolitan Areas’ Net-migration

Age group specific Proportion in out-migration (%)

70

Figure 6 - Proportion by Age group of migration from Non-Metropolitan Areas to Metropolitan
Areas (2010-2015 / 2015-2020)

60
50
40
30
20
10
0

18-25 (2010-2015)18-25 (2015-2020)

26-64 (2010-2015)26-64 (2015-2020)

65- (2010-2015)

65- (2015-2020)28

Similar to Figure 5, the above Figure 6 considers the migration from non-metropolitan prefecturesin direction of all prefectures constituing metropolitan areas only. Just like previous assessements, theproportion of elderly retired people does not vary much while the the yougest generation also increased
their out-migration to metropolitan areas at the expense of the working population. While the difference
between time period is mostly minimal, some rurual prefectures showcased some more drastic changes.
Indeed, the proportion of younger individuals migrating from Fukushima, Ishikawa, Okayama andespecially Okinawa (for similar reasons as previously discussed) had greatly decreased. Comparably,
Yamagata, Okayama and Okinawa saw their working population decrease more than the average.

V.

Regression Analysis Results

5.1.

Expected Results

The main focus of this thesis concerns the population migration flow’s relationship to changes inspatial characteristics, with an emphasis on the connection between population movement and the now
not-so-recent ageing population.
1. Based on previous papers reviewing past trends of similar variables, there are reasons to believethat the most significant factors impacting spatial migration flows for the years of interest arelinked to the location’s population and the economic factors: prefectural GPP and unemployment
rates (proxies for potential future remunerations and job opportunities respectively). This trend,
I believe, will be more impactful among the younger generation (18-25 years-old) who are insearch of their first professional experience. This chief motivation will most likely lead them tomove in the direction of metropolitan areas, which will have an ambiguous impact on the distancecovered during a move.
2. The majority of migration performed by 25-64 year-olds are hypothetically motivated primarily
by either professional intent or family related business. Such moves (albeit intra-prefectural
29

moves) are thought to rather be long-distance travels. Whereas couples with kids might also
attach greater importance to the health care offers (especially pediatric specializations), childlesscouples and single people’s interests are instead focused on career-affecting variables. Therefore,
which coefficients end up the most significant is left to be determined.
3. Furthermore, it is hypothesized that indirect changes in variables in the medical field precedented
by the healthcare system’s revamp of the late 2000s may have a greater impact on the 65+
migration flow. Additionally, instead of presenting a desire to move to prefectures with highpopulation density, it is more likely plausible that the older population would instead seek quieter
and less densely inhabited areas. While it is expected that a ruralisation-led exodus on the part ofthe elder population is likely to take place, they would instead predominantly perform shortdistance moves.
4. Finally, upon analysing the results of the spatial autoregressive model, the weight matrices’coefficients are expected to be statistically significant and positive for all following trends
observed previously.

5.2.

Regression Analysis Results

Two different analyses are performed below using data gathered from all 47 prefectures.
1. By applying Spatial Gravity Linear Regression and the Spatial Autoregressive Models (SAR), interprefectural migration flows are evaluated using data from all 47 prefectures at once, regardlessof age.
2. After manually segmenting the data into the three different age classes (namely 18-25y.o., 2664y.o. and 65+y.o.), the significance of each age group’s motivating factors is compared in moredetails. When comparing the results for all 3 separate age groups, the coefficients (as well as theirstandard errors and significance levels) are all entered in Table 530

5.2.1. Spatial Linear Regression Model Results
In order to examine the influence that the spatial weight matrices have within migration
econometrics, we first observe the results of the spatial weight-less spatial (gravity) linear regression
model which uses equation: ܻ௢ௗ = ߙ + ߚ௢ ܺ௢ + ߚௗ ܺௗ + ߛ݀݅‫ݐݏ‬௜௝ + ߳.
Table 3 below shows a summary of the results obtained by applying the aforementioned gravity
model onto the data from all 47 prefectures. It includes analysis by Ordinary Least Squares robust
standard errors) as well as Maximum Likelihood Estimation.
According to Table 3 results, there seems to be a strong tendency for people to move toprefectures with a large population (0.82 (0.074) ***). Such inclination is highly statistically significant
even to the 1% significance level in both robust OLS and when MLE (0.82 (0.074) ***). If we couple thisresult with the fact that people also seem to want to migrate to smaller prefectures (- 0.34 (0.066) ***),
then prefectures’ population density seems to have a predominant positive effect.
Among included economic variables, per capita Gross Prefectural Product (GPP) at both the origin
and destination prefectures are found to be positive. However, the surprising result that only the origin
coefficient (0.89 (0.30) **) is statistically significant at the 5% level means that the population tends toleave prefectures with higher per capita GPP more substantially rather than moving towards those withhigh pcGPP (0.50 (0.29)). However, we may think of this occurrence as a reason as to why, althoughimmigration tends to increase the average income of workers, it may also lead to a decrease in the wagesof low-paid workers, especially in richer high-density populated areas.
Although neither is significant, it is important to notice the signs of the coefficients attributed to
the respective unemployment rates’ coefficients. Indeed, while the origin’s positive coefficient (0.034(0.018)) reflects a greater emigration out of prefectures with lower unemployment rates, the negative31coefficient at the destination (0.0070 (0.031)) means that people are more inclined to move to prefectureswith higher unemployment rates.
Remarkably, none of the healthcare variables are significant neither under the robust standard
errors nor MLE. Only the destination’s medical expenses’ coefficient (- 0.20 (0.52)) tends to decrease thecoming of new population. This is rather counter-intuitive as we may think that people, especially theolder generation, may be more tempted to move to prefectures whose more budget is attributed to
medical expenses.
Finally, the negative and highly statistically significant coefficient of the distance variable (- 0.31
(0.035) ***) concords with what is expected from general understanding of migration principles, statingthat the further away the destination, the less likely you are to perform the relocation.
Table 3 – Spatial Linear (Gravity) Model Results
Intercept
Pop_o
Area_opcGPP_o
Unemployment rate_oMedical expenses_o
Lifestyle disease deaths_oLength of hospital stays_o
Pop_d
Area_dpcGPP_d
Unemployment rate_d
Medical expenses_d
Lifestyle disease deaths_dLength of hospital stays_dDistance_od

Robust Standard errors

MLE-16.17 (3.83) ***- 16.17 (5.11) **0.73 (0.12) ***0.73 (0.075) ***-0.29 (0.086) ***- 0.29 (0.066) ***0.89 (0.97)

0.89 (0.30) **0.034 (0.026)

0.034 (0.018)

0.027 (0.47)

0.027 (0.53)

0.027 (0.014) .

0.027 (0.012) .

0.13 (0.29)

0.13 (0.37)

0.82 (0.16) ***0.82 (0.074) ***-0.34 (0.094) ***- 0.34 (0.066) ***0.50 (0.80)

0.50 (0.29)

0.0070 (0.031)

0.0070 (0.032)-0.20 (0.53)- 0.20 (0.52)

0.013 (0.0081)

0.013 (0.011)

0.29 (0.29)

0.29 (0.39)-0.31 (0.056) ***- 0.31 (0.035) ***32

5.2.2. Spatial Autoregressive Modelܻ௢ௗ = ߩ௢ ܹ௢ ܻ௢ௗ + ߩௗ ܹௗ ܻ௢ௗ + ߙߡ + ߚ௢ ܺ௢ + ߚௗ ܺௗ + ߛ݀݅‫ݐݏ‬௢ௗ + ߳
Studying the effects of spatial inference and spatial autocorrelation necessarily involves theintroduction of interaction terms in the form of weight matrices, which will provide support in analysing
the impact of local flows from nearby prefectures. Indeed, an OLS analysis assumes that the explanatory
variables’ coefficients are constant across any and all spatial dimensions. Hence, inevitably, if any spatial
variations exist, it would be introduced in the error term ߳.
A regression that is similar in all aspect to the one described above was first performed using acontiguity matrix instead of the aforementioned usual inverse distance matrix as base for the weightmatrices. This spatial “nearest neighbors” weight matrix indicates the spatial relationships between
regions, where the regions are considered “contiguous” if they share a common frontier. Each element‫ݓ‬௜௝ of this n x n matrix are spatial weights taking on non-zero values when regions i and j are contiguous
neighbors, and zero otherwise. Although the issue only rarely occurred, it was decided that the “queen
contiguity” method should be used (method where neighbors are defined as two regions share any typeof frontier, no matter how small). The results to this regression are shown in Table Ap. 2, while its
respective Moran’s I statistics are present in Table Ap. 1.
Before going forward with the Spatial Autoregressive Model, the presence of implied spatial
autocorrelation must first be tested through a Moran’s I test. The p-values (all smaller than 0.0001) in
Table 4 below confirm that, in all cases, the common assumption arguing against spatial correlation canbe rejected. Looking in more details, the Moran I statistic for Wd is greater than that of Wo overall as well
as for the youngest generation. However, the opposite is found when it comes to analysing the whole
population at once. This means that there is convincing spatial autocorrelation based on destination
among each age group’s individual migration flow, which is not found for Japan as a whole.
33

Table 4 – Moran’s I Test
Spatial Weight Matrix

Moran’s I statistics

ExpectationSD

p-valueܹ௢

0.338-0.000453

0.0111

0.000

ܹௗ

0.337

-0.000453

0.01110.000

ܹ௢ (18-25)

0.327-0.000452

0.01150.000

ܹௗ (18-25)

0.486-0.000453

0.01150.000

ܹ௢ (26-64)

0.323-0.000452

0.01150.000

ܹௗ (26-64)

0.483-0.000452

0.01150.000

ܹ௢ (65+)0.312-0.000453

0.01150.000

ܹௗ (65+)0.489-0.000452

0.01150.000

Results summarized in Table 5 below show the results of analysing inter-prefectural migration
flow by Maximum Likelihood Estimation. The first column displays the results for all migrants, irrespectiveof age, while the next three column summarize those of the three aforementioned age groups.
Table 5 – Spatial Autoregressive Model Results

Intercept
Pop_o
Area_opcGPP_o
Unemployment rate_oMedical expenses_o
Lifestyle disease deaths_oLength of hospital stays_o
Pop_d
Area_dpcGPP_d
Unemployment rate_d
Medical expenses_d
Lifestyle disease deaths_dLength of hospital stays_d
WoYod
WdYod
Distance_od

All Age Groups

18-25

26-64

65+

-20.67 (5.36) ***-34.78 (8.36) ***-12.64 (3.54) ***-5.79(4.71)

0.50 (0.077) ***0.54 (0.12) ***0.26 (0.051) ***0.25 (0.068) ***-0.19 (0.68) **0.11 (0.11)- 0.12 (0.045) **-0.14 (0.060) *0.97 (0.31) **0.66 (0.48)

0.78 (0.20) ***1.038 (0.27) ***0.031 (0.018) *0.016 (0.028)

0.023 (0.012) *0.032 (0.016) .

0.39 (0.54)

0.60 (0.85)

0.27 (0.36)

0.20 (0.48)

0.03 (0.013) .

0.0023 (0.020)

0.014 (0.0083) .

0.019 (0.011) .

-0.00052 (0.38)

0.10 (0.59)-0.091 (0.25)-0.039 (0.33)

0.29 (0.076) ***0.63 (0.12) ***0.39 (0.050) ***0.55 (0.067) ***-0.23 (0.067) ***-0.28 (0.11) **-0.14 (0.045) **-0.19 (0.059) **0.68 (0.29) *1.93 (0.46) ***0.27 (0.20)-0.69 (0.26) **0.11 (0.032) .

0.030 (0.050)

0.000030 (0.021)-0.023 (0.028)

0.25 (0.53)

1.14 (0.83)

0.15 (0.35)-0.34 (0.47)

0.011 (0.011)

0.019 (0.018)

0.0041 (0.0077)

0.0026 (0.010)

0.11 (0.40)-0.022 (0.62)

0.054 (0.26)

0.31 (0.51)

0.15 (0.31) ***0.14 (0.026) ***0.24 (0.027) ***0.35 (0.026) *0.13 (0.031) **0.44 (0.028) ***0.028 (0.029)-0.069 (0.026) **-0.18 (0.036) ***-0.12 (0.056) *-0.10 (0.024) ***-0.15 (0.032) ***34

Gravity characteristics:
Even though the origin prefectures’ population matters significantly to the youngest generation(0.54 (0.12) ***) as much as it does to the others, the area (0.11 (0.11)) doesn’t seem to be of importance,
which makes the origin prefecture’s population density inconsequential in their decision. However, thesame observation cannot be made when it comes to the destination’s population density. Indeed, as it is
the case for all age groups, the higher the destination’s population density (pop_d = 0.63 (0.12) *** andarea_d = -0.28 (0.11) ***), the more likely people are to take residence there. With regard to the retired
generation, we however fail to establish the expected negative link between their migration flow and thedestination’s population density. Indeed, it seems that, like other age groups, retired people also tend to
hold population density as significant in their migration decision.
Economic variables:
When contemplating all age groups indiscriminately, we find out that the origin per capita GPP
has a positive and significant coefficient (0.97 (0.31) **), which is also somewhat the case for thedestination’s coefficient (0.68 (0.29) *). Moreover, our initial hypothesis saying that job opportunities area relevant (yet only slightly significant) factor in determining migration decisions is somewhat verified. Asa matter of fact, the origin’s unemployment rate coefficients are all positive, while its destination’s
counterparts are on the contrary only significant at the 10% level for the population in general and evennegative in the case of the elderly population. One of the most substantial differences between the
regression using the contiguity matrix as a base rather than the inverse distance matrix concerns the signof the destination’s unemployment rate’s coefficient. Indeed, in the alternative case, the sign for all is
negative and non-significant, which indicates that the unemployment rates of the origin prefecture (0.031(0.018) *) increases the benefits the workers experience through out-migration, making them move toprefectures with more opportunities. Adding this to the fact that a rise in the destination prefectures’35

wages (0.11 (0.032) .) improve in-migrants’ chances at receiving more, it is implied that job opportunities
instead of the prospect of earning more seems to be the main motivation for an eventual relocation.
Although the unemployment rate’s coefficients are as described above for the population in
general, the evaluation of these results may be different for each age group. Firstly, as expected, theyoungest generation attaches high significance to the destination’s per capita GPP (1.93 (0.46) ***), whichacts as a proxy for prefectural economic stability and their potential future private income.
Furthermore, the finding that the origin’s per capita GPP is highly significant and positive (0.78(0.20) ***), while the destination’s corresponding coefficient is also positive yet unsignificant (0.27 (0.20))
is intriguing. In fact, it would indicate that the working generation may be more heavily influenced by theprospect of job openings (0.000030 (0.021)) than greater salaries. It may explain the fact that lower
employment rates can be found in high density areas, alongside lower salaries for employees doing
manual labor.
Finally, as we had commonly hypothesized, there exists a strong relationship between per capita
GPP and the retired population’s migration. In truth, the origin’s highly significant and positive coefficient(1.038 (0.27 ***) signifies that elderly people tend to move out of strongly productive areas to move tomuch less productive prefectures after retirement, as proven by the statistically negative coefficient of
the destination (-0.69 (0.26) *).
Healthcare:
In all cases, none of the destination’s healthcare variables chosen in this regression were neither
high in magnitude nor statistically significant. However, we may notice that while the length of hospital
stay is negative at the origin (-0.00052 (0.38)), it is positive at the destination (0.11 (0.40)), meaning thatthe population seems to move from prefectures where they are projected to stay longer in the hospitalin the direction of prefectures where they may spend less time there.
36Moreover, lifestyle diseases induced deaths’ coefficient at the origin is positive and slightly
significant for the two older generations but small and non-significant at the destination. This fits in thesense that the higher the percentage of deaths attributed to lifestyle diseases, the more people may
decide to move to prefectures with lower rate of deaths due to diseases which could be prevented
depending on the prefectural healthcare system. However, whether this variable is easily observable on
the resident side is debatable as these are most likely only results which they could see or read in specificmedia medium.
Taking all into account, none of the healthcare variables are vital when it comes to migrating
among the oldest population either. This is a relationship that goes against our hypothesis as well.
Surprisingly, economic variables as well as distance were among the variables which retired people holdas most significant in their migration decision, even more so than heath related services.
Weight matrices:As hypothesized, introducing the spatial weight matrices results in positive and highly significant
correlations. It is to say that both migration from the origin prefecture to the destination’s neighboring
prefectures and migration from the origin’s neighboring prefectures to the destination both show
significant spatial dependence, except in the case of the working population’s surprising destinationbased spatial dependence (0.028 (0.029)). While the aforementioned is the case for the overall populationand youngest generation, a substantially negative coefficient can also be observed on the destination
weight matrix of the retired generation (-0.069 (0.026) **) while its respective origin-based weight matrix
is only significant at the 10% level (0.35 (0.26) *). This dictates a negative spatial dependence between
the origin and the destination’s neighbors as most retired people tend to either come back to their native
prefecture or stay in the prefecture in which they had been working prior to retiring.

37

Distance:
Like it is often the case, people tend to decide to move to prefectures that are closer to theirstarting point, which makes the distance matrix coefficient negative and more or less statisticallysignificant, depending on the age group. As it can be seen in Table 5, this is especially true for people over
25 years of age. Indeed, common sense would generally dictate that as people grow older, having to travel
longer distances may negatively impact their decisions. Keeping this in mind, the youngest generation’s
distance’s coefficient being negative and only slightly significant (-0.12 (0.056)*) may however only meanthat they can be willing to move farther way from their origin prefecture in order to get higher salaries
and better job opportunities.

VI. Conclusion
Although the post Second World War era brought economic rejuvenation thanks to high growthrate of total factor of productivity, Japan experienced a short but inevitable “baby boom”. Later on,
because of the serious impact of the 1973 oil crisis on the “bubble economy”, the start of economic
uncertainty resulted in the decrease in the frequency of inter-prefectural migration rates. Coincidentally,
the simultaneous decline in the overall Japanese population also reaped changes in the demographics’
dynamics. As is now a well-know fact, Japan is home to the oldest population in the world with the current
65+ year-old individuals taking up 29% of the total population (by more than tripling in the last 40 years).
The early 1950’s metropolization slowly faded out two decades later and economic inequalities
between rural and urban areas began shrinking. On the other hand, as a result of the ageing-society
phenomenon impact on the population movements, prefectural governments’ responsibilities regarding
healthcare and financial situations also increased. In the late 2000s - early 2010s, the population growth38rate officially becoming negative meant that the weight internal migration bears on prefectural growth
rates increased.
The use of a Spatial Autoregressive Model with weight matrices allows for an efficient study ofspatial dependence and spatial correlation. The regression performed on four different demographics’
data revealed some interesting results. As expected, the two socio-geographical variables are highly
significant, except in the case of the younger generation which only considers the destination prefecture's
area to be significant. Moreover, the 18-25 year-olds regard only the destination’s per capita GPP as
relevant, trend that is reversed for the other generations. It is to say that without previous work
experience, the young new workers would only consider the location of their first work to be the most
pertinent in their decision to relocate. Similarly, we could attribute the fact that only the working
population is significantly influenced by the unemployment rates of the origin prefecture to their
willingness to relocate in order to get better job opportunities. However, the older retired generation
exhibited other curious results. While the economic variables remain comparably vital in their decision,none of the health care variables never had any real significant impact. Distance, however, is always
significantly negative, because performing long distance moves is even more so a detrimental obstacle to
all and especially to the elderly.
In an attempt to curb the declining population growth rate crisis’ impact on the economy, theJapanese government has already attempted to implement a series of policies in the 2000s, as well assome more in the 2010s. While the last couple of years’ focus was centered on reacting to the COVID-19
pandemic, which also directly impacted the population’s death toll, the government is expected to direct
its attention once again to the issue. As one of the countries with higher-than-average proportion of 65+
individuals, Japan should rather stray away from implementing financial policies or unpopular political
reforms. As it is, economic theory states that the impact of excessive financial policies may be damped by39

the discrepancies between different demographics’ activity levels which would both diminish the role ofmonetary policies in response to shocks.
One of the reforms the government had in mind involved, among other things, raising the ages
for retirement and pension eligibility. As of now, elderly citizens can receive the Employee’s Pension
Insurance payments from age 60 and the National Pension Program any time between age 60 and 70.
However, in 2018, Prime Minister Abe’s government envisioned a scheme which would “increase the ageof eligibility for the earnings-related pension to 65 years for both men (by 2025) and women (by 2030)”
as well as strictly 70 years old for the National Pension Program [2].
Rather than considering the ageing of the population as an issue and systematically trying to
remove the elderly population’s negative impact on the population, exploring the presence of elderly
individuals and their respective inter-prefectural migration may help advance the economy in a new
direction.
Just like environmental preservation is mainly considered as a financial constraint by some,sagacious entrepreneurs take it as a leading opportunity to pursue innovation and create whole new
sectors in the economy. Ignoring the potential benefits of elderly population’s contribution may mean
missing on improvements in, among others, personalized medicine (deemed “comfort” medicine) as wellas the development of specific home appliances, some requiring the use of automatization and robotics.
More importantly, sectors which had recently been close to abandoned by fear of unprofitability could
benefit from the recording and transmission of knowledge and skills harbored by the more experienced
individuals in sectors. Also, the right policies eventually permitting to combine retirement benefits andincome in a proportion to be determined could revive lower return sectors that are less attractive to theworking age population (25-64). These would benefit the whole economy in further diversifying it aselderly people would now profit from a (reduced) part-time salary without impacting the company which40

decided on it based on their productivity goals and nature of the work. The fields more advantaged arethose from the artisanal and artistic sectors as well as the education field and eventually urban agriculture.
In a country that is known for its craftmanship, it is imperative to find new approaches to tutor and mentor
new generations through a modern type of schools and teaching methods.
Moving on from the paradigm of the “Slow Industry”, responding to the needs and demand froma population that is progressively seeking goods with more meaning, niche fields could beneficiate fromharvesting the rare and important skills of the elderly. Requiring less immediate productivity, some niche
sectors are thus more suitable for those who choose to voluntary keep working longer. As recently
reported by the Japanese Cabinet Office, around 70% of people aged 60 to 64 were still working in 2020
[1]. Moreover, an online survey conducted in 2020 by Nippon life Insurance Co. stated that 64% of its
7,543 surveyed policyholders desired to keep working beyond the retirement age (38.7% want to keep
their current jobs while the other 25.3% would like to try something different). Despite thebeforementioned governmental plans to raise the minimum age of those who can receive Employee
Insurance benefits (page 39), the recently retired individuals may still have fewer financial constraints andcould most likely be more willing to revitalize, or even create new sectors which may not provide theimmediate financial rentability larger companies or younger workers are looking for. The same goes for
entire prefectures where the cost of living is lower and possesses a certain environment more profitable
to the development of all age groups, not only that of retired individuals. As found as part of the regression
results, the elderly population’s desire to reside in prefectures with somewhat high population density
but low productivity (relationship difficult to achieve due to the natural, and almost inevitable,
development of job opportunities in high density areas) may birth the perfect environment for thedevelopment of such methods (page 34).
That said, although taking advantage of the “Silver Economy” as a whole is an important step, it
can become detrimental if not first implemented carefully. Multiple measures should first be put forward41

before exploiting such an opportunity. Indeed, the same set of criteria cannot be applied to the elderly
the same way as it is to the young or working population. Criteria directed to the older generation shouldreflect each age group’s values conditional on the current trends and cultural globalization. Working hours
as well as working environment should be adapted to facilitate their integration in a new field or theircontinuation at the same company. The same way we would analyse a first-hand situation with the latest
data, new specifications should be created in order to optimize the current circumstances rather thandoing “more of the same”. Because retaining intergenerational equity will, without doubt, be challenging
if a “business-as-usual” mentality is continued, understanding what the elderly population really needs
and aspires to, rather than force-feeding them the same system which has been part of the problem inthe past is key. The government’s decision to transmit more responsibility to the prefectures may bebeneficial as each region has its own characteristics, needs and unique sets of opportunities. What otherregions of the world may consider a hindrance to this type of development strategy may create“compensating” rather than “complimentary” action plans which would not be relevant to Japan’s current
and future situation. In addition to the prospect of strong positive fiscal impact on both the governmentand companies, this evolutionary system will allow the elderly population to enjoy short term benefit thatsatisfy their needs as well as long-term advantages for their future final retirement.
Yet, the hereby recommended course of action regarding the ageing population is heavily basedon three assumptions that are particular to Japan:
1. More than in any other countries, Japanese elderly individuals have the motivation to keep
working even after the pre-set “retirement age” of 60 or 65.
2. The unemployment rate among the younger generation is low enough so that the “retired”
population does not get fully crowded out by the new workers entering the market upon
graduation. This assumption is strongly backed up by the World Bank Database information which42

places Japan’s youth (15-24 y.o) unemployment rate at 4.9% while, in comparison, the UnitedStates is up to 14.9% and France points all the way to 20.2% [26].
3. The belief that individuals above 60 (or even 65) years old still remain somewhat productive inthe field they plan on working in
Ensuring labor market flexibility towards a continuous high productivity growth throughout is thus
essential. It is imperative that Japan creates a setting in which working older people can still prove
themselves and their skills for a longer period of time. One of the policies which so far have come theclosest to presenting the most adequate first set of goals is the 2018 “Guidelines of Measures for Ageing
Society”, its mission put forward in the subsequent Annual Reports on the Ageing Society (2019, 2020 and2021) [1]. According to the Cabinet Office website, these Guidelines aim to implement status of measuresin six fields, each with more-or-less detailed medium-term goals. Among those are: stably operating apublic pension system as well as sustainably managing long-term medical care system for elderly people.
One of the more well-accepted programs involves increasing the mandatory retirement age for civil
servants from 60 to 65 in addition to helping companies keep elderly workers in order to somewhat lessen
the burden that the dependence ratio imposes on the working generation. Aiming to do so by 2025, theratio of working individuals to the total population would allegedly be restored to its 2008 level, whichcoincides with the beginning of the population decline. The government, in addition to providing full
pension benefits to only those over 65 or 70 (premium now increasing to 20% rather than 10% as per pastdecree), it would also distribute reduced pension benefits to those still working over 65 years of age. Other
areas of focus of these Guidelines include promoting the elderly’s learning activities in social
environments, comprehensively stimulate small town development which were affected by consecutive
urbanization waves, encourage R&D initiatives which would potentially attract knowledge and challenges
from/to foreign countries as well as widen the use of adult guardianship.

43

In conclusion, although most of the results obtained from the regression models were as expected,
the non-dependency of the Japanese population on the chosen health-care variables indicates that aJapanese elderly may have the same intensions as a healthy, working person. Hence, in addition toprogressive fiscal policies, combining diminished governmental pensions (distributed to 65+ only
individuals) and appropriate income/pension to motivated elderly people, the awareness of both the restof the population and companies concerning the promotion of elderly individuals’ social integration
activities should be optimized. This is why establishing an Age-Free society where creating mutual support
where “people of all ages can make use of their motivation and abilities depending on their hope” isencouraged [1].

44VII. Bibliography[1] Cabinet Office, Government of Japan., 2021. Annual Report on the Ageing Society.
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https://www.fraserinstitute.org/sites/default/files/age-of-eligibility-for-public-retirementprograms-in-the-oecd.pdf[3] Dzienis, Anna Maria, 2011. Japanese Internal Migration. ᒸᒣ኱Ꮫ኱Ꮫ㝔♫఍ᩥ໬⛉Ꮫ◊✲⛉ჾ⏝➨ 32 ྕ. 2011-11[4] Frum, David, 2000. How We Got Here: The '70s. New York: Basic Books. ISBN 978-0-465-04195-4.
[5] Hayashi, Fumio; Prescott, Edward C, 2002. The 1990s in Japan: A Lost Decade. Review of Economic
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[6] Healthcare Structural Reform Package Act, 2006. Ministry of Health, Labour, and Welfare of Japan.
[7] Holecki, T., Rogalska, A., Sobczyk, K., Wozniak-Holecka, J., & Romaniuk, P., August 5, 2020. Global
Elderly Migrations and Their Impact on Health Care Systems. Frontiers - PERSPECTIVE Article.
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https://doi.org/10.3389/fpubh.2020.00386[8] Koji, Murayama; Jun, Nagayasu, 2020. International and Inter-prefectural Migration in Japan:
Implications for the Spatial Assimilation Theory. Graduate School of Economic and Management[9] Kureishi, W., McKenzie, C., Sakata, K., & Wakabayashi, M.,, 2016. Does a Mother’s Early Return to Workafter Childbirth Improve Her Future Employment Status? Evidence Using the Birth Month ofJapanese Babies. Panel Data Research Center at Keio University.
[10] Lee, Young-Jun; Sugiura, Hirosaki, 2018. Key Factors in Determining Internal Migration to Rural Areasand its Promoting Measures - A case study of Hirosaki City, Aomori Prefecture. Policy ResearchInstitute, Ministry of Finance, Japan, Public Policy Review; Vol. 14, No.1
[11] Lesage, J. P., 1997. Bayesian Estimation of Spatial Autoregressive Models. International Regional
Science Review, 20(1–2), 113–129. https://doi.org/10.1177/016001769702000107[12] LeSage, J. P., and R. K. PacOrde., 2008. Spatial Econometric Modeling of Origin-Destination Flows,
Journal of Regional Science, 48(5), 941-967. 2008[13] Life Expectancy at birth, total (years) – Japan, 2020.

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https://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=JP.
[14] Long-Term Care Insurance Act, 2008. Health, and Welfare Bureau for the Elderly. Ministry of Health,Labour, and Welfare.
[15] Marrocu, E.,Paci, R., 2013. Different tourists to different destinations. Evidence from spatial
interaction models” Tourism Management, Elsevier, vol. 39(C), pages 71-83[16] Matsuda S., 2019. Health Policy in Japan – Current Situation and Future Challenges. Japan Medical
Association Journal (JMA) 2(1):1-10. https://doi.org/10.31662/jmaj.2018-0016[17] National Institute of Population and Social Security Research, 2019. Population and Social Security inJapan. ISSN 2186–0297. https://www.ipss.go.jp/s-info/e/pssj/pssj2019.pdf[18] Ord, K., 1975. Estimation Methods for Models of Spatial Interaction. Journal of the American
Statistical Association, 70(349), 120–126. https://doi.org/10.2307/2285387[19] Population Census: Tabulation on Internal Migration for Population. Portal Site of Official Statistics ofJapan. https://www.e-stat.go.jp/en/stat-search/files?page=1&toukei=00200521.
[20] Prefectural data: System of social and demographic statistics (SSDS). Portal Site of Official Statistics
of Japan. https://www.e-stat.go.jp/en/regional-statistics/ssdsview/prefectures[21] Suesse, T., 2018. Marginal Maximum Likelihood Estimation of SAR models with missing data.
Computational Statistics & Data Analysis, 120, 98–110.
https://doi.org/10.1016/j.csda.2017.11.004[22] Tinbergen J, 1962, Shaping the World Economy – Suggestion for an International Economy Policy, The
Twentieth Century Fund, New York.
[23] Xi, Qu; Zhao, Chuanmin 2020. Social Networks and Internal Migration in China: A Spatial
autoregressive model. John Wiley & Sons Ltd 2021
[24] Yamaguchi, Takashi, 1982. Population Redistribution of Japan 0 within the context of the national
settlement system. Sixth Meeting of the Commission on National Settlement Systems of theInternational Geographical Union – Toronto, Canada[25] Yorimitsu, Masatoshi, 1987. A review of recent population changes in Japan. Hitotsubashi Journal ofSocial Studies 19 pg. 15-30. The Hitotsubashi Academy[26] Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate) – France,
Japan, United States. https://data.worldbank.org/indicator/SL.UEM.1524.ZS?locations=FR-JP-US[27] Zaiceva, A., 2014. The impact of aging on the scale of migration. IZA World of Labor, 99.
https://doi.org/10.15185/izawol.99

46

VIII. Appendix
Table Ap. 1 – Moran’s I Test (Contiguity Matrix)
Spatial Weight Matrix

Moran’s I statistics

ExpectationSD

p-valueܹ௢

0.297-0.000453

0.0153

0.000

ܹௗ

0.303

-0.000453

0.01530.000

ܹ௢ (18-25)

0.271-0.000453

0.01590.000

ܹௗ (18-25)

0.533-0.000453

0.01590.000

ܹ௢ (26-64)

0.286-0.000453

0.01500.000

ܹௗ (26-64)

0.229-0.000453

0.01500.000

ܹ௢ (65+)0.367-0.000453

0.01460.000

65+ y.o.

Table Ap. 2 – Spatial Autoregressive Model Results (Contiguity Matrix)

Intercept
Pop_o
Area_opcGPP_o
Unemployment rate_oMedical expenses_o
Lifestyle disease deaths_oLength of hospital stays_o
Pop_d
Area_dpcGPP_d
Unemployment rate_d
Medical expenses_d
Lifestyle disease deaths_dLength of hospital stays_d
WoYod
WdYod
Distance_od

All years

18-25 y.o.

26-64 y.o.

-18.031 (5.37) ***-29.18 (8.19) ***-12.53 (3.54) ***-8.37 (4.71) .

0.54 (0.077) ***0.65 (0.12) ***0.29 (0.051) ***0.24 (0.068) ***-0.21 (0.068) **0.038 (0.10)-0.13 (0.045) **-0.14 (0.060) **0.76 (0.31) *0.29 (0.47)

0.81 (0.20) ***1.17 (0.016) ***0.025 (0.018)

0.015 (0.028)

0.024 (0.012) .

0.034 (0.016) *0.42 (0.55)

0.58 (0.83)

0.42 (0.36)

0.36 (0.48)

0.024 (0.013) .

0.0026 (0.020)

0.015 (0.0083) .

0.020 (0.011) .

-0.045 (0.38)-0.086 (0.58)-0.17 (0.25)-0.13 (0.33)

0.61(0.076) ***0.816 (0.12) ***0.38 (0.050) ***0.52 (0.067) ***-0.25 (0.068) ***-0.38 (0.10) ***-0.15 (0.045) **-0.18 (0.059) **0.44 (0.30)

1.49 (0.45) **0.12 (0.20)-0.69 (0.26) *-0.0019 (0.032)-0.0060 (0.049)-0.0069 (0.021)-0.027 (0.028)

0.20 (0.53)

0.94 (0.81)

0.072 (0.35)-0.20 (0.47)

0.013 (0.012)

0.027 (0.018)

0.0055 (0.0077)

0.0058 (0.010)

0.11 (0.40)

0.037 (0.61)

0.064 (0.26)

0.24 (0.35)

0.17 (0.036) ***0.14 (0.025) **0.19 (0.026) ***0.33 (0.023) ***0.19 (0.027) **0.42 (0.022) ***0.16 (0.029) ***-0.0023 (0.030) **-0.14 (0.036) ***-0.064 (0.055)-0.082 (0.024) ***-0.12 (0.032) ***47