Evidence map›Paper›PMID 39564129›Full record

ArticleThe history of the family : an international quarterly2024

Trends in assortative mating in the United States, 1700-1910. Evidence from FamiLinx data.

Giulia Corti, Saverio Minardi, Nicola Barban

Abstract read
In one paragraph

Article in The history of the family : an international quarterly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Analysing Biases in Genealogies Using Demographic Microsimulation.European journal of population = Revue europeenne de demographie · 2025
    Article
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

3 authors.

Giulia CortiCenter for Demographic Studies (CED-CERCA), Barcelona, Spain.
Saverio MinardiDepartment of Statistics "P. Fortunati", University of Bologna, Bologna, Italy.
Nicola BarbanDepartment of Statistics "P. Fortunati", University of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Couple formation and assortative mating significantly influence societal structures, as marriages between individuals from diverse geographical or social backgrounds promote intra-family diversity. Understanding these patterns is crucial for grasping the demographic processes that shape contemporary societies. However, the scarcity of comprehensive data has impeded progress in this area. This paper aims to fill this gap by investigating assortative mating trends in the United States among birth cohorts from 1700 to 1910, utilizing data from FamiLinx, an online crowdsourced genealogical database. We focus on two primary dimensions: migration background (including natives, first and second-generation migrants) and age at marriage. Our analysis yields three major findings. First, we document significant changes in assortative mating trends over time, reflecting the dynamic nature of mate selection and its responsiveness to societal shifts. Second, we uncover substantial heterogeneity in assortative mating patterns across different social groups, indicating varying social dynamics and preferences. Third, we illustrate how these trends can be differently interpreted depending on whether the perspective is individual or familial. Additionally, we explore the advantages and limitations of using online genealogical data for historical studies of assortative mating, highlighting its potential for offering new insights while acknowledging the challenges posed by data quality and representativeness.

Indexed as

Assortative matinghistorical demographyonline genealogical dataUnited States

Identifiers

PMID39564129
PMCPMC11573314

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LicenceCC BY-NC-ND
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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.