Evidence map›Paper›PMID 36718313›Full record

ArticlePopulation, space and place2022

Trends in internal migration in Japan, 2012-2020: The impact of the COVID-19 pandemic.

Masaki Kotsubo, Tomoki Nakaya

Abstract read
In one paragraph

Article in Population, space and place, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Masaki KotsuboGraduate School of Environmental Studies Tohoku University Sendai Japan.ORCID 0000-0001-6126-2299
Tomoki NakayaGraduate School of Environmental Studies Tohoku University Sendai Japan.ORCID 0000-0002-3827-1012

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past 10 years or so, there have been growing concerns in Japan that migration trends, such as large in-migration to the Tokyo metropolitan area (TMA) at the national scale and large movements from the suburbs to the centres at the metropolitan scale, have widened the core-periphery disparities at each scale under national population decline. However, the COVID-19 pandemic led to unexpected changes in these migration patterns, such as a weakened population concentration in the TMA. This study aims to examine internal migration trends from 2012, on axes of core-periphery in Japan and centre-suburbs in metropolitan areas, and the changes in the migration patterns caused by the pandemic in 2020. First, we prepared intermunicipal origin-destination tables by estimating the suppressed flows in 2012-2020 using the iterative proportional fitting technique, and then we calculated the net migration and migration efficiency indices based on seven area types: centre and suburbs of the TMA, centres and suburbs of major metropolitan areas, centres and suburbs of other metropolitan areas and nonmetropolitan areas. The results indicate that the 2020 migration efficiency in the centre of the TMA drastically dropped to the lowest level during the study period, contrasting with an upward trend in 2012-2019. The pandemic strongly affected the migration patterns from/to and within the TMA, with much net gain in the suburbs from the centre, but the impact of migration efficiencies among the other areas was minimal. These findings can help in providing direction for the postpandemic policy challenge of spatial planning in Japan, for example, the weakened but still widening disparities between the TMA and the other regions and the renewed threat of urban sprawl caused by the increased migration from the centre to the suburbs within the TMA.

Indexed as

COVID‐19 pandemicinternal migrationJapanmigration efficiency

Identifiers

PMID36718313
PMCPMC9878244

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.