How are Income and Assets Associated with Food Insecurity? An Application of the Growth Mixture Modeling

Jun Hong Chen, Chi Fang Wu, Minchao Jin

Research output: Contribution to journalArticlepeer-review

Abstract

Food insecurity remains prevalent in the United States, affecting millions of households. Research has confirmed that low income and limited assets are risk factors for food insecurity, but how income and assets are associated with food insecurity has not been fully explored in light of the fact that food insecurity endures or worsens over time for some people but not others. Using 2015, 2017, and 2019 waves of the panel study of income dynamics, this study (1) investigated the heterogeneity of food insecurity trajectories using Growth Mixture Modeling; (2) performed a multinomial logistic regression to examine how income and assets are associated with the relative risk of facing a more severe food insecurity trajectory; and (3) compared the coefficient of income with the coefficient of assets. Results of this study showed that both higher income and more assets are associated with a lower probability of facing food insecurity that worsens rather than improves with time. This study also observed that the association strength was stronger for income than for assets. These results offer insights for policies aimed at creating efficient financial support strategies (e.g., income assistance, asset building) that reduce recipients’ risk of experiencing long-term food insecurity.

Original languageEnglish (US)
JournalSocial Indicators Research
DOIs
StateAccepted/In press - 2022

Keywords

  • Assets
  • Food insecurity
  • Food insecurity trajectory
  • Growth Mixture Modeling
  • Heterogeneity
  • Income

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Sociology and Political Science
  • Social Sciences(all)

Fingerprint

Dive into the research topics of 'How are Income and Assets Associated with Food Insecurity? An Application of the Growth Mixture Modeling'. Together they form a unique fingerprint.

Cite this