Evidence map›Paper›PMID 41790306›Full record

ArticleEuropean journal of population = Revue europeenne de demographie2026

A Simulation Approach for the Spatial Testing of Migration Theories.

Micol Matilde Morellini

Abstract read
In one paragraph

Article in European journal of population = Revue europeenne de demographie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Micol Matilde MorelliniNuffield College, University of Oxford, New Road, OX1 1NF, Oxford, UK. micol.morellini@nuffield.ox.ac.uk.ORCID http://orcid.org/0000-0002-7461-5265

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10004034
6 · The paper itself

Abstract

Migration research has long been divided between studies of drivers, which focus on the factors shaping migration flows, and studies of patterns, which describe how these flows are organised across space. Theories of migration typically identify and operationalise drivers, but are often less explicit about patterns. As a result, migration theories are usually evaluated using goodness-of-fit measures that assess explanatory power but pay limited attention to spatial accuracy. This article addresses this limitation by introducing a simulation-based procedure to evaluate the spatial accuracy of migration theories. Starting from an observed system of origin-destination migration flows, the procedure generates synthetic systems that reflect the spatial outcomes implied by a given theory. These synthetic migration systems are then compared to the observed case to assess spatial accuracy. The procedure is applied to intra-European migration flows from 2002 to 2021 and illustrated using two long-standing migration theories: the gravity model and migration systems theory. Both theories achieve high explanatory power under conventional goodness-of-fit metrics, and migration systems theory performs better overall. However, the empirical analysis shows that both theories fail to reproduce important spatial features of the European context, including the high level of reciprocity of flows and the observed migration profiles of Eastern and Northern European countries. These findings highlight how strong statistical fit does not imply accurate spatial representation. Evaluating migration theories through their implied spatial outcomes provides new insights into their limitations and offers a complementary and integrative tool for migration research.

Indexed as

Migration systemsMigration theoryModel evaluationSimulation methodsSpatial analysis

Identifiers

PMID41790306
PMCPMC13035948

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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.