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Health and Financial Strain: Evidence from the Survey of Consumer Finances. Angela Lyons University of Illinois at Urbana-Champaign Tansel Yilmazer Purdue University National Taiwan University November 2006. The Motivation (Recent Financial Trends in the U.S.).
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Health and Financial Strain: Evidence from the Survey of Consumer Finances Angela Lyons University of Illinois at Urbana-Champaign Tansel Yilmazer Purdue University National Taiwan University November 2006
The Motivation(Recent Financial Trends in the U.S.) • Uncertain economy and higher unemployment • Rise in bankruptcies and delinquencies • Large debt burdens from the 1990s • Rising health care costs The Research Question: What is the impact of financial strain on health?
Previous Research • Strong positive relationship between health and socioeconomic status (SES). • However, little consensus on the direction of causality. • Is poor health both a cause and a consequence of socioeconomic status (SES)?
On the one hand…. Some studies find that poor health affects SES. • Individuals who are in poor health work fewer hours or are unemployed, limiting abiltity to accumulate income and wealth. • Serious health conditions have a larger effect on SES than less serious conditions. • Smith and Kington 1997; Zagorsky, 1999; Wu 2003
On the other hand…. Studies find that lower SES affects health. • Individuals health can be affected in 2 ways: • Financial problems creates physical or psychosocial stress which affects health • Limited access to quality health care services and preventative care • Caplovitz 1974; Smith 1998, 1999; Roberts et al. 1999; Drentea and Lavrakas 2000; Meer, Miller, and Rosen 2003
Also, note…. Focus on income and wealth • Smith and Kington (1997) • Adams et al. (2003) • Zagorsky (1999) Focus on liability holdings and financial stress • Drentea and Lavrakas (2000) • Roberts et al. (1999)
Specific Studies • Smith and Kington (1997) • Health and Retirement Survey (HRS) and the Asset and Health Dynamics among the Oldest Old (AHEAD). • Find direction of causality primarily from health to SES. • Adams et al. (2003) • Panel data from AHEAD; distinguish between acute, chronic, and mental health conditions; control for existing health conditions. • Find some evidence that wealth increases incidence of some mental and chronic conditions. • But in general reject hypothesis that SES results in health problems. • Meer, Miller, and Rosen (2003) • Use PSID to examine changes in wealth and health. • Control for endogeneity of SES using IV that controls for changes in wealth (receipt of an inheritance). • The effect of wealth on health becomes insignificant when endogeneity of wealth is taken into account.
Contributions of this study to the literature: • Moves beyond income and wealth and focuses on the relative financial position of the household. • Controls for the possible endogeneity between health and financial burden. • Uses a representative sample of the U.S. population.
Description of the Data Data from the 1995, 1998, and 2001 Survey of Consumer Finances Features of the SCF: • Cross-sectional survey that collects data every three years. • Detailed info on financial holdings, income and demographics. • Includes a self-reported measure of health status. Households are identified as “financially strained” if • Delinquent on any type of loan payment by two months or more • Total assets/total debts < 1.0 • Liquid assets/disposable income < 0.25
Table 1 Demographic Statistics by Financial Strain and Health Status _________________________________________________________________________________ Financial Strain____________ _Health Delinq Assets/debts<1. Liq/inc<0.25 H PH FS=1 FS=0 FS=1 FS=0 FS=1 FS=0 H=0 H=1 No. of obs. (552) (12,250) (739) (12,063) (4,065) (8,737) (10,281) (2,521) _________________________________________________________________________________ Poor health 32.4 23.8 27.0 24.0 31.4 19.4 -.- -.- Measures of Financial Strain % delinquent 100.0 0.0 20.1 4.4 9.6 2.7 4.9 7.4 % (assets/debts) < 1.0 26.4 6.1 100.0 0.0 15.7 1.5 7.0 8.1 % (liq assets/inc) < 0.25 70.7 38.7 87.8 36.7 100.0 0.0 36.6 52.3 _________________________________________________________________________________ For each measure of financial strain, FS=1 indicates the household is financially strained and FS=0 indicates the household is not financially strained. H represents household heads who are not in poor health and PH represents household heads who are in poor health.
Summary of Descriptive Statistics • Financially-strained households are significantly more likely to be in poor health. • Those who are financially strained by one measure are more likely to be financially strained by other measures. • With respect to reverse causality, those in poor health are more likely to be financially strained. • HOWEVER, it is likely that health status plays a more important role in explaining why some households are under financial strain than vice versa.
Empirical Framework Simultaneous two-equation probit models: where FSi* = the degree to which the household is under financial strain Hi* = the degree to which the head of the household is in poor health
Probability of Financial Strain X1i includes: • Financial factors: income of head, liquid assets, other assets • Demographics: head’s age, education, marital status, gender, ethnicity, employment status, number of children, whether household receives welfare, whether household has private health insurance coverage • Identification: whether household experienced negative income shock in past year that was unrelated to health; household’s attitudes, preferences, or values for borrowing specific consumption goods
Probability of Poor Health X2i includes: • Samefinancial and demographic factors as X1i • Identification: whether head currently smokes (health behaviors), whether household expects major medical expenses in the next 5-10 years (expectations), whether head’s father is still living (biological)
Testing the Overidentifying Restrictions(Hausman 1983, p. 444; Johnson and Skinner 1986, p. 465) • Each structural equation was estimated with and without the excluded variables from the other equation. • Null hypothesis: Addition of excluded variables should have little effect on explanatory power of the equation. • Use likelihood-ratio tests. • Tests reveal that overidentifying restrictions have not been seriously violated.
Table 2 Two-Stage Probit Models: Effect of Poor Health on Probability of Financial Strain (N=12,802) ________________________________________________________________________________________________________ DelinquentAssets/Debts < 1.0Liq Assets/Income < 0.25 Variable Coeff SE Coeff SE Coeff. SE Predicted value: Poor health 0.742 (0.146)*** 0.324 (0.117)*** 0.293 (0.088)*** log (Income) -0.009 (0.030) -0.156 (0.031)*** -.---- (-.----) log (Liquid assets) -0.044 (0.012)*** -.---- (-.----) -.---- (-.----) log (Other assets) 0.031 (0.009)*** -.---- (-.----)-0.082 (0.006)*** Age -0.020 (0.004)*** -0.033 (0.003)*** -0.029 (0.002)*** Education (years) 0.041 (0.013)*** 0.037 (0.013)*** -0.080 (0.009)*** Female -0.002 (0.085) 0.110 (0.073) -0.005 (0.058) Black 0.092 (0.078) 0.013 (0.063) 0.014 (0.048) Number of children 0.084 (0.022)*** -0.051 (0.023)** 0.036 (0.016)*** Divorced/Separated 0.155 (0.084)* 0.170 (0.084)** 0.119 (0.056)** Single 0.052 (0.091) 0.042 (0.079) -0.130 (0.051)** Widowed 0.029 (0.133) 0.094 (0.129) 0.178 (0.077)*** Retired -0.756 (0.112)*** -0.107 (0.112) -0.170 (0.048)*** Self-employed -0.050 (0.069) -0.319 (0.080)*** 0.033 (0.039) Receives welfare -0.426 (0.124)*** -0.038 (0.101) 0.118 (0.089) Private health insurance 0.066 (0.071) -0.250 (0.058)*** -0.593 (0.043)*** Negative income shock 0.249 (0.068)*** 0.051 (0.058) -0.002 (0.039) All right to borrow for vacation 0.083 (0.074) 0.077 (0.057) 0.027 (0.039) All right to borrow when income cut 0.130 (0.051)*** 0.099 (0.049)** 0.058 (0.032)** All right to borrow for fur/jewelry 0.048 (0.093) 0.186 (0.084)** 0.106 (0.057)** All right to borrow for car 0.129 (0.065)** -0.026 (0.060) 0.096 (0.042)** All right to borrow for education -0.019 (0.074) 0.094 (0.066) -0.179 (0.036)*** Year 1998 0.052 (0.056) 0.119 (0.054)** -0.136 (0.031)*** Year 2001 0.016 (0.053) 0.058 (0.060) -0.098 (0.029)*** Constant -0.835 (0.286)*** 1.301 (0.311)*** 3.744 (0.115)*** _________________________________________________________________________________________________________________
Table 3 Two-Stage Probit Models: Effect of Financial Strain on Probability of Poor Health (N=12,802) __________________________________________________________________________________________________________ Probability of Poor Health Variable Coeff SE Coeff SE Coeff. SE Pred value: Delinquent 0.114 (0.115) -.---- (-.----) -.---- (-.----) Pred value: Assets/Debts < 1.0 -.---- (-.----) 0.020 (0.195) -.---- (-.----) Pred value: Liq Assets/Inc < 0.25 -.---- (-.----) -.---- (-.----) 0.142 (0.185) log (Income) -0.061 (0.020)*** -0.131 (0.042)*** -.---- (-.----) log (Liquid assets) -0.033 (0.011)*** -.---- (-.----) -.---- (-.----) log (Other assets) -0.016 (0.006)*** -.---- (-.----) -0.023 (0.018) Age 0.018 (0.002)*** 0.016 (0.005)*** 0.018 (0.005)*** Education (years) -0.060 (0.005)*** -0.069 (0.006)*** -0.066 (0.019)*** Female -0.109 (0.052)** -0.114 (0.055)** -0.085 (0.053)* Black 0.101 (0.049)** 0.173 (0.050)*** 0.138 (0.050)*** Number of children -0.035 (0.013)** -0.024 (0.017) -0.033 (0.015)** Divorced/Separated -0.031 (0.054) -0.003 (0.061) 0.012 (0.056) Single -0.012 (0.055) 0.013 (0.070) 0.033 (0.064) Widowed -0.029 (0.058) -0.026 (0.070) -0.005 (0.083) Retired 0.188 (0.093)** 0.086 (0.057) 0.144 (0.055)*** Self-employed -0.091 (0.046)** -0.123 (0.079)* -0.150 (0.034)*** Receives welfare 0.447 (0.065)*** 0.528 (0.060)*** 0.469 (0.071)*** Private health insurance -0.096 (0.038)** -0.173 (0.073)*** -0.100 (0.136) Currently smokes 0.147 (0.042)*** 0.192 (0.042)*** 0.166 (0.051)*** Expects medical expenses 0.375 (0.051)*** 0.400 (0.055)*** 0.411 (0.039)*** Father still living -0.136 (0.037)*** -0.141 (0.047)*** -0.137 (0.039)*** Year 1998 0.003 (0.035) 0.007 (0.046) 0.012 (0.041) Year 2001 0.078 (0.033)** 0.086 (0.038)** 0.076 (0.036)** Constant 0.409 (0.169)** 0.779 (0.346)*** -0.555 (0.667) __________________________________________________________________________________________________________
Table 4 The Effect of a Change in Poor Health Status on the Probability of Financial Strain and a Change in Financial Strain on the Probability of Poor Health ______________________________________________________________________________________ Pred Prob Pred Prob ME of a change ME of a change of being under of being in in Health Status in Financial Strain Models Financial Strain Poor Health on Financial Strain on Poor Health ______________________________________________________________________________________ All Households Delinquent 2 months or more 0.033 0.207 0.054*** 0.033 (Total Assets/Total Debts) < 1.0 0.040 0.207 0.028*** 0.006 (Liquid Assets/Income) < 0.25 0.366 0.201 0.110*** 0.040 ______________________________________________________________________________________ Marginal effects were calculated using the weighted sample means.
Effects by Education Level The effect of poor health on financial strain may vary for different income groups. • Difficult to calculate permanent income using SCF. • We use education groups (high school education or less, some college, college degree) as proxies for permanent income. • The impact that poor health has on delinquency and assets/debts < 1.0 decreases and becomes less significant as education level of the head increases. • The impact that poor health has on liquid assets/income < 0.25 increases and becomes more significant as education level of the head increases.
Elasticities • Use marginal effects and predicted probabilities to calculate elasticities: E= (% financial strain / % in poor health) = 0.054 * [20.7 / 3.3] = 0.339 • 10% increase in percentage of households in poor health increases percentage of delinquent households by 3.39%. • Increases percentage of households with assets/debts < 1.0 by 1.45% and liquid assets/income < 0.25 by 0.62%. • Poor health has the largest effect on the percentage of delinquent households.
Conclusions • Using a more robust conceptualization of SES, evidence shows that the direction of causality is primarily from health to SES than SES to health. • Findings are robust across all 3 measures of financial burden. • Poor health increases the probability of financial strain. • Little evidence that financial strain contributes to poor health.
Implications • Gaps in health inequality may be contributing to widening financial disparities. • Those most likely to be affected are low-to-middle income families, especially those already in poor health. • Those with lower incomes who are in poor health may find themselves in a vicious cycle. • Severe health conditions may result in larger financial burdens while large financial burdens are unlikely to accelerate a decline in health status.
Policy Implications • May result in greater dependency on government assistance. • Reduction in overall household welfare. • More affordable and quality health care services for the poor may result in improved health outcomes and overall economic well-being.
Limitations and Directions for Future Research • Longitudinal data to examine in more detail the relationship between household finances and health. • Further investigation of the definition of financial strain and the definition of health. • Issues of identification and instruments. • Additional research on the relationship between financial burden and health across households (i.e. income, age, gender, and race).
Where do we go from here? No Pain, No Strain: Impact of Health on the Financial Security of the Elderly (with Hyungsoo Kim, University of Kentucky) Motivation: • U.S. population is rapidly aging. • Rising costs of health care (insurance premiums and medical expenses). • Dramatic growth in household debt levels for families near or in retirement. • Elderly will be particularly vulnerable to financial strain from rising health care burdens.
Description of the Data Data from the 2004 Health and Retirement Study (HRS) Measures of health status: • Self-reported health status (SRH) • Objective measures of health: • Severe chronic health condition • Mild chronic health condition Households are identified as “financially strained” if • Solvency ratio: total assets/total debts < 1.0 • Liquidity ratio: liquid assets/monthly income < 2.5 • Wealth accumulation ratio: investment assets/net worth < 0.25
Direction of Causality • At retirement, shift from accumulating wealth to spending it down. • Also, there is a point where additional spending on health services results in little improvement in health status. • Research shows the pathway from health to financial strain is more likely to be dominant. • Smith (1997, 1999) As individuals grow older, changes in economic resources have little additional impact on health. • Smith & Kington (1997) and Lee & Kim (2003) Direction of causation for older populations is from health to wealth.
Empirical Framework • Focus on effect of health on financial strain. • Assume the effect of financial strain on health is negligible for elderly. Two-stage probit model: where FSi* = the degree to which the respondent is under financial strain Hi* = the degree to which the respondent is in poor health
Probability of Financial Strain Xi includes: • Financial characteristics of household: income, assets, monetary transfers • Demographics: elderly person’s age, education, gender, marital status, race/ethnicity, living arrangements, employment status, health insurance coverage (Medigap, Medicare HMO, employer-sponsored health insurance plan, Medicaid) Instruments for H*i : smoking and exercise (measures of health behaviors)
Key Findings • Health problems significantly increase likelihood of financial strain for the elderly, especially those with severe chronic conditions. • Findings were consistent for all measures of financial strain and health. • Impact of poor health was significantly larger for severe chronic conditions than for mild chronic conditions and SRH. • Supplementary health insurance coverage significantly mitigated financial strain for the elderly. • The oldest elderly (aged 80+) may be most vulnerable.
Implications • Using financial ratios provides a more comprehensive picture of how health affects overall financial security of the elderly. • Important to consider both subjective and objective health measures to determine who is likely to bear greatest financial burden. • For elderly persons who have not adequately saved for retirement, a severe chronic condition could result in rapid wealth depletion, resulting in serious financial strain. • The results could be devastating for low-income elderly, who do not qualify for Medicaid and who cannot afford health insurance.