Beyond Income

Expanding our Empirical Toolkit to Better Predict Caregiver Well-Being

The BTS Consortium PI’s

Research output: Contribution to journalArticle

Abstract

Objectives: Despite growing concern that income alone does not capture how low-income families are managing financially, it continues to be one of the most commonly used indicators of socioeconomic status and is routinely used as a qualifying factor for government assistance programs. Income can be difficult to measure accurately and alone may not be the best predictor of caregiver well-being, in particular among ethnically diverse families. A more nuanced understanding may be critical for identifying families in need of services and supporting success after enrollment in need-based programming. Thus, the current study investigated the relationship between traditional (low income, low education, unemployment), and less traditional (economic pressure, economic hardship, perceived social status, crowding) socioeconomic indicators and caregiver well-being (caregiver depressive symptoms, anxiety, dysfunction in the parent-child relationship) using data from a multisite study. Methods: Participants were 978 racially/ethnically diverse caregivers (97% female) of young children enrolled in Early Head Start programming from six sites across the United States. Results: Exploratory factor analyses resulted in a three-factor model, capturing demographic risk, resource strain, and perceived social status. The Resource Strain factor was most strongly associated with greater caregiver depressive and anxiety symptoms, and dysfunction in the parent-child relationship. Further, hierarchical regression models revealed up to a four-fold increase in variance explained when adding economic strain along with traditional variables to models predicting caregiver well-being. Conclusions: Results support the need to supplement traditional economic measurement when supporting families experiencing low income and for measuring poverty among ethnically diverse families.

Original languageEnglish (US)
JournalJournal of Child and Family Studies
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Caregivers
caregiver
well-being
income
Economics
Parent-Child Relations
social status
low income
parent-child relationship
Social Class
economics
Anxiety
programming
Government Programs
Depression
anxiety
Crowding
Unemployment
family income
Poverty

Keywords

  • Caregiver mental-health
  • Children in poverty
  • Economic strain
  • Income
  • Low-income families

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Life-span and Life-course Studies

Cite this

Beyond Income : Expanding our Empirical Toolkit to Better Predict Caregiver Well-Being. / The BTS Consortium PI’s.

In: Journal of Child and Family Studies, 01.01.2019.

Research output: Contribution to journalArticle

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abstract = "Objectives: Despite growing concern that income alone does not capture how low-income families are managing financially, it continues to be one of the most commonly used indicators of socioeconomic status and is routinely used as a qualifying factor for government assistance programs. Income can be difficult to measure accurately and alone may not be the best predictor of caregiver well-being, in particular among ethnically diverse families. A more nuanced understanding may be critical for identifying families in need of services and supporting success after enrollment in need-based programming. Thus, the current study investigated the relationship between traditional (low income, low education, unemployment), and less traditional (economic pressure, economic hardship, perceived social status, crowding) socioeconomic indicators and caregiver well-being (caregiver depressive symptoms, anxiety, dysfunction in the parent-child relationship) using data from a multisite study. Methods: Participants were 978 racially/ethnically diverse caregivers (97{\%} female) of young children enrolled in Early Head Start programming from six sites across the United States. Results: Exploratory factor analyses resulted in a three-factor model, capturing demographic risk, resource strain, and perceived social status. The Resource Strain factor was most strongly associated with greater caregiver depressive and anxiety symptoms, and dysfunction in the parent-child relationship. Further, hierarchical regression models revealed up to a four-fold increase in variance explained when adding economic strain along with traditional variables to models predicting caregiver well-being. Conclusions: Results support the need to supplement traditional economic measurement when supporting families experiencing low income and for measuring poverty among ethnically diverse families.",
keywords = "Caregiver mental-health, Children in poverty, Economic strain, Income, Low-income families",
author = "{The BTS Consortium PI’s} and Eliana Hurwich-Reiss and Watamura, {Sarah Enos} and Raver, {C. Cybele} and Lisa Berlin and Clancy Blair and Constantino, {John N.} and Hallam, {Rena A.} and Myae Han and Hustedt, {Jason T.} and Harden, {Brenda Jones} and Michelle Sarche and Vu, {Jennifer A.}",
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AU - Watamura, Sarah Enos

AU - Raver, C. Cybele

AU - Berlin, Lisa

AU - Blair, Clancy

AU - Constantino, John N.

AU - Hallam, Rena A.

AU - Han, Myae

AU - Hustedt, Jason T.

AU - Harden, Brenda Jones

AU - Sarche, Michelle

AU - Vu, Jennifer A.

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N2 - Objectives: Despite growing concern that income alone does not capture how low-income families are managing financially, it continues to be one of the most commonly used indicators of socioeconomic status and is routinely used as a qualifying factor for government assistance programs. Income can be difficult to measure accurately and alone may not be the best predictor of caregiver well-being, in particular among ethnically diverse families. A more nuanced understanding may be critical for identifying families in need of services and supporting success after enrollment in need-based programming. Thus, the current study investigated the relationship between traditional (low income, low education, unemployment), and less traditional (economic pressure, economic hardship, perceived social status, crowding) socioeconomic indicators and caregiver well-being (caregiver depressive symptoms, anxiety, dysfunction in the parent-child relationship) using data from a multisite study. Methods: Participants were 978 racially/ethnically diverse caregivers (97% female) of young children enrolled in Early Head Start programming from six sites across the United States. Results: Exploratory factor analyses resulted in a three-factor model, capturing demographic risk, resource strain, and perceived social status. The Resource Strain factor was most strongly associated with greater caregiver depressive and anxiety symptoms, and dysfunction in the parent-child relationship. Further, hierarchical regression models revealed up to a four-fold increase in variance explained when adding economic strain along with traditional variables to models predicting caregiver well-being. Conclusions: Results support the need to supplement traditional economic measurement when supporting families experiencing low income and for measuring poverty among ethnically diverse families.

AB - Objectives: Despite growing concern that income alone does not capture how low-income families are managing financially, it continues to be one of the most commonly used indicators of socioeconomic status and is routinely used as a qualifying factor for government assistance programs. Income can be difficult to measure accurately and alone may not be the best predictor of caregiver well-being, in particular among ethnically diverse families. A more nuanced understanding may be critical for identifying families in need of services and supporting success after enrollment in need-based programming. Thus, the current study investigated the relationship between traditional (low income, low education, unemployment), and less traditional (economic pressure, economic hardship, perceived social status, crowding) socioeconomic indicators and caregiver well-being (caregiver depressive symptoms, anxiety, dysfunction in the parent-child relationship) using data from a multisite study. Methods: Participants were 978 racially/ethnically diverse caregivers (97% female) of young children enrolled in Early Head Start programming from six sites across the United States. Results: Exploratory factor analyses resulted in a three-factor model, capturing demographic risk, resource strain, and perceived social status. The Resource Strain factor was most strongly associated with greater caregiver depressive and anxiety symptoms, and dysfunction in the parent-child relationship. Further, hierarchical regression models revealed up to a four-fold increase in variance explained when adding economic strain along with traditional variables to models predicting caregiver well-being. Conclusions: Results support the need to supplement traditional economic measurement when supporting families experiencing low income and for measuring poverty among ethnically diverse families.

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