Modelling socioeconomic trajectories
An optimal matching approach
Bibliographic Data
| ID | 11196030 |
|---|---|
| Authors | Juliet Stone (0000-0003-4140-3036, Imperial College London, corresponding author), Gopalakrishnan Netuveli (0000-0002-5520-4778, Imperial College London), D Blane (0000-0002-3872-3621, Imperial College London) |
| Editors | Robert M Blackburn |
| Year | 2008 |
| Volume | 28 |
| Issue | 5/6 |
| Pages | 217-230 |
| Publication date | 2008-06-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Sociology and Social Policy (JOURNAL) |
| Journal identifiers | ISSN: 0144-333X • E-ISSN: 1758-6720 |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/01443330810881268 |
| OpenAlex | W1963804583 |
| Language | EN |
| Citations received | 2 |
| References cited | 28 |
Purpose The aim of this paper is to describe the use of sequence analysis to model trajectories of life‐course economic activity status, within a broader research agenda aimed at improving understanding of the relationship between socioeconomic position and health. Design/methodology/approach The analysis used data on 288 participants of the Boyd Orr Stratified Sub‐Sample, comprising a combination of prospective and retrospective information on economic activity status, as well as health in early old age. Economic activity was coded as a time‐based sequence of states for each participant based on six‐month periods throughout their lives. Economic activity was classified as: pre‐labour market; full‐time employment; part‐time employment; housewife; made redundant; stopped work due to illness; retired; other unemployed; or not applicable. Optimal matching analysis was carried out to produce a matrix of distances between each sequence, which was then used as the basis for cluster analysis. Findings The optimal matching analysis resulted in the classification of individuals into five economic activity status trajectories: full‐time workers (transitional exit), part‐time housewives, career breakers, full‐time workers (late entry, early exit), and full‐time housewives. Originality/value The paper presents the case for using sequence analysis as a methodological tool to facilitate a more interdisciplinary approach to the measurement of the life‐course socioeconomic position, in particular attempting to integrate the empirical emphasis of epidemiological research with the more theoretical contributions of sociology. This may in turn help generate a framework within which to examine the relationships between life‐course socioeconomic position and outcomes such as health in later life
Demographic economics · Economics · Life course approach · Matching (statistics · Optimal matching · Originality · Population · Qualitative research · Social science · Socioeconomic status · Sociology · Statistics · Birth, Development, and Health · Demography · Global Health Care Issues · Health disparities and outcomes · Mathematics · Psychology · Social Psychology
Association between children's experience of socioeconomic disadvantage and adult health
Socioeconomic determinants of health
Life Course Research
Inequalities in self rated health in the 1958 birth cohort
Fetal and placental size and risk of hypertension in adult life.
Family History and the Life Course
Lifetime socioeconomic position and mortality
A life course approach to chronic disease epidemiology
Sequence Analysis and Optimal Matching Techniques for Social Science Data
Some Comments on “Sequence Analysis and Optimal Matching Methods in Sociology
The duration and timing of exposure
Reply to Levine and Wu
Family History and the Life Course
The Life Course as Developmental Theory
Gender and inequalities in health in later life
Life-course influences on mortality at older ages
Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program
Optimal Matching Methods for Historical Sequences
But What Have You Done for Us Lately
Sequence Analysis and Optimal Matching Methods in Sociology
Class Careers as Sequences
| Citations per year | 0.17 |
|---|---|
| Citation span | 2014 - 2015 (2) |
| Highly cited | No |