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Housing Wealth, Health and Deaths of Despair

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Abstract

We use household-level data to study the causal effects of exogenous changes in housing wealth on health and the drug crisis in the US attributed to “deaths of despair”. We find that a one standard deviation positive shock in housing wealth increases the probability of an improvement in self-reported health (mental health) by 1.0 (1.10) percentage points, decreases the change in drug-related mortality rate by 4.3%, and has no effect on alcohol- or suicide-related deaths. The opposite effect also holds, such that a negative shock on wealth increases the probability of a decline in health. We also find that the impact of housing wealth on health varies across socioeconomic groups and is more pronounced in MSAs in which housing supply is more inelastic, which explains the differential effect of economic cycles across geographical areas. Our results suggest that housing-related policies could have important implications for general health outcomes as well as for the opioid crisis.

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Notes

  1. Hedegaard et al. (2017) document that the age-adjusted rate of drug-overdose has increased from 6.1 per 100,000 in 1999 to 19.8 per 100,000 in 2016.

  2. Case and Deaton (2015, 2017) named this crisis “deaths of despair”. They suggest that this increase has been due to difficult social and economic environments that have led to cumulative disadvantage over time.

  3. See Kish and Lansing (1954); Follain and Malpezzi (1981); Goodman Jr and Ittner (1992); Agarwal (2007); Benítez-Silva et al. (2015); Kuzmenko and Timmins (2011); Corradin et al. (2017).

  4. Housing wealth misestimation is large, even with the proliferation of online real estate appraisals such as Zillow, as well as the existence of real estate municipal tax assessments and appraisals for extracting home equity value. Zillow documents that 45.6% (25.5%) of Zillows estimates are off by 5% (10%) or more (see https://www.zillow.com/zestimate). Moreover, the geographical variation is sizable. For example, 32.7% (14.7%) of Zillows estimates are off by 5% (10%) or more in Phoenix, while 62.1% (44.9%) of Zillows estimates are off by 5% (10%) or more in New York.

  5. We define change in health outcome as the difference in health from two years after the unexpected wealth shock to the year of the wealth shock (i.e., when the household moves). This definition addresses a potential concern related to the fact that health shocks might trigger moving houses.

  6. Adler et al. (1994); Backlund et al. (1999); Chandola (2000); Contoyannis et al. (2004); Cutler et al. (2010); Cutler et al. (2016); Feinstein (1993); Golberstein et al. (2016); Humphries and Van Doorslaer (2000); Lewis et al. (1998); Lleras-Muney (2005); Meara (2001); Meer et al. (2003); World Health Organization (2003)

  7. Panel Study of Income Dynamics, restricted use dataset. Produced and distributed by the Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI (2017). The collection of data used in this study was partly supported by the National Institutes of Health under grant number R01 HD069609 and R01 AG040213, and the National Science Foundation under award numbers SES 1157698 and 1,623,684.

  8. As we focus on the SRH of the head of the household, we drop observations that indicate a change in age of more than five years from one period to the next. We also remove observations with a negative change in age.

  9. If a household sells its house and buys a new one between years t − 1 and t, we can only obtain its declared value of the previous house at time t − 1 (before selling it) and the transaction price of the new house at time t. This

  10. In the PSID data there are many variables related to health outcomes. For instance, there is information about specific health conditions such as strokes, cancer, high blood pressure, and diabetes in PSID. Instead, we use the most common composite measures of health status in the health economics literature: (1) SRH, (2) total ADLs, and (3) mental ADLs. Moreover, we have long time series of the variables that we need to calculate SRH and ADLs in the PSID data.

  11. The list of activities asked at the PSID are: bathing or showering, dressing, eating, getting in or out of bed or a chair, walking, getting outside, using the toilet, preparing own meals, shopping for personal toilet items or medicines, managing own money, using the telephone, doing heavywork, doing lightwork.

  12. “Has a doctor ever told you that you have... Any emotional, nervous, or psychiatric problems?”; “...loss of memory or loss of mental ability?”; “...a learning disorder?”

  13. See https://wonder.cdc.gov/mcd.html.

  14. See https://seer.cancer.gov/popdata.

  15. We also show that our results are robust to the control for portfolio choice characteristics at the household level such as the ratio of housing to net wealth and stock holdings over total net wealth. Table 7 in the Appendix reports these results.

  16. See http://www.arf.hrsa.gov.

  17. See http://www.ddorn.net/data.htm.

  18. See https://www.ers.usda.gov/data-products/county-level-data-sets/county-level-data-sets-download-data. 21 See http://www.pdaps.org.

  19. An alternative approach could be to use interval regressions. Both methodologies produce coefficients of the same significance and order of magnitude, and have a similar fit in terms of log-likelihood. Although our empirical analysis is based on an ordered probit approach, we present results for both methodologies in the next section.

  20. Our results are robust to the use of interval regressions.

  21. The p-values for the t-tests on employment status, number of family members, and marital status are 0.28, 0.78, and 0.61.

  22. Even if they do not sell, they would report a lower value of their house if they found that it was worth less because the question in PSID states “Could you tell me what the present value of this house (farm) is? I mean about what would it bring if you sold it today?”

  23. Changes in the elasticity of supply at the MSA level are large in the cross-section but small in the time-series since we consider time lags of 2 years for changes in health outcomes in our panel. Recent studies that consider changes in the house price elasticity do not find relevant changes over short periods of time (e.g., Kirchhain et al. (2019)). Furthermore, there are no available time-varying measures of land elasticity at the MSA or city level that cover our period of study (1986–2015). For instance, Kirchhain et al. (2019) cover the time period 2014–2016.

  24. See http://www.federalreserve.gov/.

  25. When limiting the specification to only those who move, results are consistent since RHWM will only be different from zero for those households that move when they move.

  26. Davidoff (2016) criticizes the use of housing-supply constraints as IVs for house prices in studies in which the dependent variable has an economic component, such as consumption growth, leverage, or investments, because some demand factors that could affect both house prices and the dependent variable of interest might have been omitted. This is not the case in our study, as the dependent variable is change in health status.

  27. We estimate this model using maximum likelihood. The estimation is performed using the CMP user-provided package in STATA. See https://ideas.repec.org/c/boc/bocode/s456882.html and Roodman (2009). This approach has been used extensively in the literature (e.g., Einav et al. (2012); Cullinan and Gillespie (2016)).

  28. This choice of 33% divides our sample in about half, that is, 50% of the households in our sample live in the top 33% inelastic MSAs. Our results are robust to the choice of 33% as the threshold between elastic and inelastic cities. In the Appendix, we also report these results using a continuous measure of elasticity. These results are also robust, but less significant.

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Acknowledgements

We thank David Cutler, Adriana Lleras-Muney, Ellen Meara, Seow Eng Ong, Albert Saiz, Judit Valls, Nancy Wallace, Xin Zou, and participants at the AREUEA-ASSA Annual Meetings 2019, Los Angeles Conference in Applied Economics (LACAE), Catalan Economic Society Conference 2019, AREUEA International Meetings 2019, IESE Business School and CaixaBank for their helpful comments.

Nuria Mas acknowledges financial support from the Spanish Ministry of Science and Innovation (Ref. ECO2015-71173-P) and AGAUR (Ref. 2017-SGR-1244). She is the holder of the Jaime Grego Chair of Healthcare Management.

Vergara-Alert acknowledges financial support from the State Research Agency of the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (Ref. PGC2018-097335-A-I00, MCIU/AEI/FEDER, UE) and AGAUR (Ref: 2017-SGR-1244).

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  1. Department of Economics, UCLA, 8283 Bunche Hall, Mail Stop 147703, Los Angeles, CA, 90095, USA

    Ariadna Jou

  2. IESE Business School, University of Navarra, Pearson Avenue 21, 08034, Barcelona, Spain

    Nuria Mas & Carles Vergara-Alert

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  1. Ariadna Jou
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Appendices

Appendix 1

Table 7 Robustness control for asset allocation. This table reports estimates of the effect of Realization of Housing Wealth Misestimation (RHWM) on the change in health outcomes when controlling for the ratio of housing wealth over total net wealth and stock holdings over total new wealth. All specifications include year fixed effects and division fixed effects. Specifications [1]-[3] show the estimates for self-reported health, (SRH). Specification [1] is equivalent to the baseline ordered probit specification. Specifications [2] and [3] show the second and first stage IV regressions. Specification [4] shows the estimates for mental ADLs, (Mental ADLs). Specifications [5] and [6] report the estimates for change in drug death rates and change in alcohol or suicide death rates, respectively. t-statistics are reported in parentheses. All the specifications include year and division fixed effects and all errors are clustered at the family level
Full size table
Table 8 Robustness check using population SEER data. This table reports estimates of the effect of Realization of Housing Wealth Misestimation (RHWM) on the change in health outcomes when controlling for SEER-adjusted population data. Specifications [1]-[2] report the estimates for change in drug death rates and [3]-[4] report the change in alcohol or suicide death rates. Robust standard errors are reported in parentheses. All the specifications include year and division fixed effects and all errors are clustered at the family level
Full size table
Table 9 Full Table 3 including all the covariates coeffcients. This table presents the exact same specifications as in table 3, but including the coeffcients for the whole set of covariates
Full size table
Table 10 The effects of RHWM on four-year health outcomes. This table presents estimates of the effect of Realization of Housing Wealth Misestimation (RHWM) on the change in health outcomes after four years from the housing wealth shock. In a longer run the effects of RHWM on the different health outcomes is smaller, and it become not statistically significant for some specifications
Full size table
Table 11 Effects of housing supply constraints and the housing market cycles using the continuous measure of housing supply elasticity in Saiz (2010). This table reports the effects of housing supply constraints during periods of sharp increasing house prices (booms) and periods of sharp decreasing house prices (busts). Errors are clustered at the family level in all the specifications. This table presents the equivalent results than Table 6 when using the continuous measure of housing supply elasticity in Saiz (2010). The estimates are robust to our main specifications, but less statistically significant
Full size table

Appendix 2

Table 12 Analysis of the variation in house value misestimation. Summary statistic average of misestimation (by household ID and year). This table reports the summary statistics of the variable house wealth misestimation (HWM) in aggregate terms, by household ID, and by year
Full size table
Table 13 Analysis of the variation in house value misestimation. Analysis of within and between R-squared using our simple OLS approach (no panel). This table shows the results of the analysis of the variation in House Wealth Misestimation (HWM) using an OLS approach. The dependent variable in all the specifications is HWM. Specifications [1]–[3] do not include covariates, while [4]–[6] do include them. Specifications [2]–[3] and [5]–[6] include household individual fixed effects (FE). Specifications [1], [3], [4], and [6] include year FE
Full size table
Table 14 Analysis of the variation in house value misestimation. ANOVA. This table shows the results of the one-way ANOVA test using individual household ID. It compares the variance between and within individual households. Let F denote the variation between the sample mean squares, MS, of the model divided by the variation within the sample MS.
Full size table

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Jou, A., Mas, N. & Vergara-Alert, C. Housing Wealth, Health and Deaths of Despair. J Real Estate Finan Econ 66, 569–602 (2023). https://doi.org/10.1007/s11146-020-09801-5

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