Affinity models for career sequences

Research output: Contribution to journalArticle

Abstract

We develop an affinity model for longitudinal categorical data in which the number of categories is large and we apply the technique to 20 years of labour market data for a contemporary cohort of young adult workers in the USA. The method provides a representation of the underlying complexity of the labour market that can then be augmented to include covariate effects. These can be understood as effects net of transition patterns associated with labour market sorting. We include in our model pairwise affinities to relate nominal categories representing types of job. The affinities capture complex relationships between these types of job and how they change over time. We evaluate the role of gender and education to illustrate the different types of questions and answers that are addressed by this methodology.

Original languageEnglish (US)
Pages (from-to)417-436
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume60
Issue number3
DOIs
StatePublished - May 2011

Fingerprint

Affine transformation
Nominal or categorical data
Longitudinal Data
Sorting
Categorical or nominal
Covariates
Pairwise
Model
Methodology
Evaluate
Market
Labour market
Gender
Education
Relationships
Cohort
Young adults
Workers
Categorical data
Market data

Keywords

  • Categorical sequences
  • Longitudinal data
  • Markov model
  • National longitudinal survey

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Affinity models for career sequences. / Scott, Marc A.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 60, No. 3, 05.2011, p. 417-436.

Research output: Contribution to journalArticle

@article{b0f7773477694594a0408266f8c1a1a6,
title = "Affinity models for career sequences",
abstract = "We develop an affinity model for longitudinal categorical data in which the number of categories is large and we apply the technique to 20 years of labour market data for a contemporary cohort of young adult workers in the USA. The method provides a representation of the underlying complexity of the labour market that can then be augmented to include covariate effects. These can be understood as effects net of transition patterns associated with labour market sorting. We include in our model pairwise affinities to relate nominal categories representing types of job. The affinities capture complex relationships between these types of job and how they change over time. We evaluate the role of gender and education to illustrate the different types of questions and answers that are addressed by this methodology.",
keywords = "Categorical sequences, Longitudinal data, Markov model, National longitudinal survey",
author = "Scott, {Marc A.}",
year = "2011",
month = "5",
doi = "10.1111/j.1467-9876.2010.00752.x",
language = "English (US)",
volume = "60",
pages = "417--436",
journal = "Journal of the Royal Statistical Society. Series C: Applied Statistics",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "3",

}

TY - JOUR

T1 - Affinity models for career sequences

AU - Scott, Marc A.

PY - 2011/5

Y1 - 2011/5

N2 - We develop an affinity model for longitudinal categorical data in which the number of categories is large and we apply the technique to 20 years of labour market data for a contemporary cohort of young adult workers in the USA. The method provides a representation of the underlying complexity of the labour market that can then be augmented to include covariate effects. These can be understood as effects net of transition patterns associated with labour market sorting. We include in our model pairwise affinities to relate nominal categories representing types of job. The affinities capture complex relationships between these types of job and how they change over time. We evaluate the role of gender and education to illustrate the different types of questions and answers that are addressed by this methodology.

AB - We develop an affinity model for longitudinal categorical data in which the number of categories is large and we apply the technique to 20 years of labour market data for a contemporary cohort of young adult workers in the USA. The method provides a representation of the underlying complexity of the labour market that can then be augmented to include covariate effects. These can be understood as effects net of transition patterns associated with labour market sorting. We include in our model pairwise affinities to relate nominal categories representing types of job. The affinities capture complex relationships between these types of job and how they change over time. We evaluate the role of gender and education to illustrate the different types of questions and answers that are addressed by this methodology.

KW - Categorical sequences

KW - Longitudinal data

KW - Markov model

KW - National longitudinal survey

UR - http://www.scopus.com/inward/record.url?scp=79954629516&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79954629516&partnerID=8YFLogxK

U2 - 10.1111/j.1467-9876.2010.00752.x

DO - 10.1111/j.1467-9876.2010.00752.x

M3 - Article

VL - 60

SP - 417

EP - 436

JO - Journal of the Royal Statistical Society. Series C: Applied Statistics

JF - Journal of the Royal Statistical Society. Series C: Applied Statistics

SN - 0035-9254

IS - 3

ER -