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Health Shocks and Economic Well-Being of the Aging Population: Evidence from Mexico

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Abstract

We exploit the longitudinal Mexican Health and Aging Study to estimate the effects of health shocks in the short-run on the subsequent economic well-being of the aging population in Mexico. While there is substantial evidence indicating negative economic effects of such changes in industrialized countries, little is known about health impacts on the future economic position of older adults in low- and middle-income countries. This paper takes an important step towards filling this gap in knowledge. Our results are widely relevant, with a large percentage of the world’s population residing in developing countries such as Mexico that are experiencing rapid aging. We find evidence of negative impacts of health shocks on subsequent economic well-being of older adults in Mexico, but the effect varies according to several dimensions. First, the impact is clearly on income, not wealth. Second, responses are heterogenous across sources of income, with evidence of an impact mainly on labor income. Third, we find clear differences by gender in the impact of a health shock, with a larger negative impact on men. Fourth, we conclude that the population groups most negatively affected are those with the greatest degree of vulnerability prior to the shock, as measured by education and access to health insurance. Even though Mexico has made important gains with anti-poverty programs such as the Programa 70+ pension and a move towards universal health insurance, additional interventions targeted at the most vulnerable subsets of the aging population might be warranted.

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Notes

  1. See Barrientos et al. (2003) for a summary of evidence on the incidence of poverty among older adults in developing countries.

  2. For more information and to access data, see the MHAS website at www.MHASweb.org.

  3. Other longitudinal datasets on aging for developing countries have emerged over the last decade. These include the Ageing, Wellbeing and Development Study in select areas of Brazil and South Africa (Barrientos & Mase, 2012), as well as national samples in countries such as China, India, and Brazil. Also see Kaiser (2013) and Suzman and Harper (2013) for a review of longitudinal datasets on aging in industrialized and developing countries.

  4. Parker et al. (2018) provide evidence that the MHAS sample that persists to 2012 does not display evidence of selection based on health insurance coverage.

  5. The individual income measure uses half of couple income only for income from a family business, family transfers, and financial assets. For other income sources (employment, pension), it uses individual values.

  6. To minimize non-response in income and asset values, respondents were first asked if they had any of each category. Those non-response rates were less than 2%. Those answering yes were asked for a value and, for assets, any debt was subtracted. Those unable to provide exact values were further queried using the unfolding brackets method. Imputation was used both for bracket responses and non-responses. For example, for the most commonly held asset, a home (owned by 75% of the sample), 63% of owners in 2001 provided an exact value, another 28% provided a bracket value, and 9% did not respond. Overall, exact values were more often reported by the less wealthy. Imputation utilized the SAS-based IVEware, distributed by the University of Michigan and used by the HRS, as described in Wong and Espinoza (2004) and Wong et al. (2016). Wong and Espinoza (2003) assess external consistency. A very small number of observations (less than 1%) were dropped due to extreme income outliers as in our earlier work (DeGraff, Wong, and Orozco-Rocha 2018).

  7. In our analysis sample, 39% and 13% were lacking health insurance in 2001 and 2012, respectively.

  8. For total income and asset value, we also estimate parallel models where the dependent variable is defined as the change from 2001 to 2003 to check for evidence of short-run effects of the health shock.

  9. Because these diseases are chronic, once diagnosed an individual is always characterized as diseased.

  10. It is for this reason that we do not estimate a conventional difference-in-differences model including a variable indicating the time period, as we do not have retrospective data to establish the parallel trends criterion. Instead, the difference across time periods for each observation is embedded in the definition of dependent variables (∆Y). This approach also accounts for any effects of the global financial crisis mentioned above, in that all observations were subject to this economic shock. Our goal is to assess whether an additional shock experienced by only some observations – a health shock early in the period – systematically worsens income and asset trends.

  11. We also compared several indicators of health conditions in early life (before age 10): whether their house had a toilet, whether the respondent had any serious health issues in general, and whether they had each of polio, rheumatic fever, tuberculosis, typhoid fever, or a head injury. The distributions for these variables are also highly similar across treatment and control groups.

  12. To further check for sensitivity of results to the definition of the control group, we use propensity score methods for the likelihood of experiencing treatment, applied to the model for changes in total income or assets. We use two propensity score approaches, one based on nearest neighbor matching from the pool of all potential control observations described above, and the other trimming from the pool of potential controls observations with a score outside the range for those in the treatment group. The results reported are robust to these alternative approaches.

  13. Because living spouses are also respondents in the MHAS, this specification is equivalent to estimating the effect of a health shock for the respondent on their spouse.

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Acknowledgements

This work was supported by National Institutes of Health/National Institute on Aging [grant R03 AG048809], a Bowdoin College Faculty Leave Supplement Award, and the University of Texas Medical Branch WHO/PAHO Collaborating Center on Aging and Health. The Mexican Health and Aging Study (MHAS) is partly supported by the National Institutes of Health/National Institute on Aging [grant R01 AG018016] in the United States and the Instituto Nacional de Estadística y Geografía (INEGI) in Mexico. The authors thank the audience and discussants for comments on earlier versions of this paper presented at meetings of the Population Association of America, the Western Economics Association International, and the Southern Demographic Association.

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Authors and Affiliations

  1. Department of Economics, Bowdoin College, Brunswick, ME, USA

    Deborah S. DeGraff

  2. Maryland Population Research Center and School of Public Policy, University of Maryland, College Park, MD, USA

    Susan W. Parker

  3. Centro Investigación y Docencia Económicas, Ciudad de Mexico, Mexico

    Susan W. Parker

  4. Faculty of Economics, Universidad de Colima, Colima, Mexico

    Karina Orozco-Rocha

  5. Sealy Center on Aging and School of Medicine, University of Texas Medical Branch, TX, Galveston, USA

    Rebeca Wong

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  1. Deborah S. DeGraff
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Appendices

Appendix 1

Table 7 Variable Definitions for Appendix Tables; Characteristics at Baseline (2001) unless Noted Otherwise
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Appendix 2

Table 8 Effect of Health Shock on Income Change, Main and Interactive Effects by Vulnerable Status, No-No-Yes Control Group
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Appendix 3

Table 9 Effect of Health Shock on Income Change, Main and Interactive Effects by Vulnerable Status, No-No-No Control Group
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Appendix 4

Table 10 Effect of Health Shock on Income Change, Main and Interactive Effects by Vulnerable Status, No-No Control Group
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Appendix 5

Table 11 Effect of Health Shock on Asset Value Change, Main and Interactive Effects by Vulnerable Status, No-No-Yes Control Group
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Appendix 6

Table 12 Effect of Health Shock on Asset Value Change, Main and Interactive Effects by Vulnerable Status, No-No-No Control Group
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Appendix 7

Table 13 Effect of Health Shock on Asset Value Change, Main and Interactive Effects by Vulnerable Status, No-No Control Group
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DeGraff, D.S., Parker, S.W., Orozco-Rocha, K. et al. Health Shocks and Economic Well-Being of the Aging Population: Evidence from Mexico. Population Ageing 15, 641–675 (2022). https://doi.org/10.1007/s12062-021-09349-z

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