Financial Strains and Depression among Elderly Women

By Brown, Susan; Lee, Yoon G. | Consumer Interests Annual, Annual 2007 | Go to article overview

Financial Strains and Depression among Elderly Women


Brown, Susan, Lee, Yoon G., Consumer Interests Annual


This study examined the effects of financial strains on depression, how unmarried status affects depression and what sociodemographic factors are associated with depression among women age 65 and above. HRS data from 3,523 unmarried and 2,674 married women was used for the study. Results of the t-test showed that mean levels of net worth, household income, and credit card debt were statistically significant between unmarried and married elderly women. OLS regression analysis showed that holding other factors constant, unmarried elderly women reported significantly higher depression levels than did married elderly women. Also, variables representing financial strains were all statistically significant in predicting depression levels among older women aged 65 and above

Introduction

Depression is the most common psychiatric disorder among the elderly (Sidik, Zulkefli & Shah, 2003) and it is increasingly viewed as a potential risk factor among the elderly population (Black, Markedies and Ray, 2003). During 1992-2001, the portion of income coming from assets dropped from 21% to 16% and the portion coming from pensions decreased from 20% to 16% for most seniors (Loomin and Reneuart, 2006).. While 18.6% of older Americans aged 65 and above had outstanding balances on credit cards in 1992, this percentage had increased to 46% in 2000 (Dugas, 2002). When older individuals have lower income, fewer assets, and higher debts, they can be vulnerable to financial strains in later life and these financial strains can lead to depression among older individuals. A few researchers have studied the effects of financial strains on depression among the elderly and little is known how financial strains are related to depression among elderly women in particular. The purpose of this study was to examine the effects of financial strains on depression, to examine how unmarried marital status affects the level of depression among elderly women, and to investigate what socio-demographic factors are associated with depression among older women aged 65 and above.

Data and Sample

Data for the study were drawn from the 2000 Health and Retirement Study (HRS). The HRS is a nationally representative, longitudinal survey of individuals over 50 years of age. The HRS is designed to investigate the dynamic experience of older individuals as they advance from work to retirement, with particular emphasis on health insurance, saving, and trajectories of economic and physical well-being. Using the 2000 HRS data file, the total sample (N=6,202) included households headed by women ages 65 and older and the sub-sample of this study consisted of 3,523 unmarried and 2,674 married women.

Analyses and Empirical Models

Frequencies, percentages, means, and medians were performed to obtain the descriptive information. The ttests were conducted to compare means in the value of net worth, household income, credit card balance, financial assets, and non-financial assets. Ordinary Least Squares (OLS) regression analysis was performed to identify the effect of financial strains and socio-demographic factors on the level of depression among elderly women aged 65 and above. In the OLS regression analysis, the CES-D score which is the sum of 8-item depression battery was used to measure the level of depression. In this study, debt, net worth, and household income were included in the OLS regression model to measure financial strains. The debt, net worth, income, and family size were coded as continuous variables. To examine how unmarried marital status affects the level of depression among elderly women, a dummy categorical variable for marital status (unmarried and married women) was included in the OLS regression model. In addition, independent variables reflecting socio-demographic characteristics of the elderly women consisted of age, family size, race, education, and self-reported health. In the analyses, age was categorized by a dummy categorical variable: ages 65-74, ages 75-84, and ages 85+; race was coded as a dummy categorical variable (White, non-White), and self-reported health status was also coded as a dummy categorical variable (poor, good, very good, and excellent). …

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