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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=0H=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

    % delinquent100.0 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

    VariableCoeffSECoeffSECoeff.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

    VariableCoeffSE CoeffSE 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 more0.033 0.207 0.054*** 0.033

    (Total Assets/Total Debts) < 1.00.040 0.207 0.028*** 0.006

    (Liquid Assets/Income) < 0.250.366 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.


    Other directions for future research….


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