Racial disparities in life insurance coverage

ABSTRACT

We evaluate the extent to which there are racial disparities in life insurance coverage using multiple years of the Survey of Income and Program Participation between 2001 and 2010. We find that African Americans hold significantly more life insurance – especially whole life insurance – after controlling for other factors. We demonstrate that our findings diverge from prior work because we examine all households instead of focusing exclusively on married and cohabitating households. Although earning shocks due to mortality likely contribute to racial disparities in wealth, the influence is mitigated by the racial composition of life insurance holdings.

KEYWORDS:

• Life insurance
• racial disparities

JEL CLASSIFICATION:

• D31
• G22
• J15

Acknowledgement

We thank Maria Apostolova, Glenn Blomquist, Tony Creane and Frank Scott for helpful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Net worth for the median white household was approximately $190 000 and $19 000 for the median black household in 2007 as reported in the Survey of Consumer Finances (Kochhar and Fry 2014).

² Racial differences in mortality are still present after controlling for education (Waldron, 2002).

³ Data come from the Centers for Disease Control/National Center for Health Statistics Vital Statistics System in 2009.

⁴ Ten-year mortality rates are 5.7% and 9.5%, respectively, for white and black individuals at age 50.

⁵ Douglas et al. (2003) use data from the Health and Retirement Survey (HRS), which is limited to individuals aged 51–61 in 1992. Harris and Yelowitz (2014) demonstrate that life insurance demand varies by age and the need for life insurance coverage diminishes as an individual approaches retirement. Consequently, findings from the HRS are likely not representative of the relevant population of life insurance purchasers.

⁶ In 2010, 20% and 38% of whites and blacks, respectively, were single parents.

⁷ Following Gruber and Yelowitz (1999), we exclude imputed values for life insurance due to criticism of the SIPP wealth imputation methodology by researchers (Curtin, Juster, and Morgan 1989; Hoynes, Hurd, and Chand 1998). Dropped imputed observations constitute 21% of the sample.

⁸ Unless noted, we use sample weights for both the SIPP and SCF specifications.

⁹ To aggregate the SIPP to the household level, we use relevant individual information from the head of the household and compile household-level responses from individuals within a household.

¹⁰ The individual life insurance variable is subject to some measurement error due to top-coding.

¹¹ All figures on the composition of ESLI come from Tables 17, 18, 20 and 21 of the March 2010 National Compensation Survey.

¹² The IRS provides an exclusion for the first $50 000 of group-term life insurance coverage provider under a policy carried directly or indirectly by an employer.

¹³ Using payroll data, Harris and Yelowitz (2017) find that the median employee at a large university had 1× salary in ESLI; the modal worker did not elect any supplemental ESLI.

¹⁴ Other life insurance policies that also incorporate investment motives include universal life insurance and variable life insurance.

¹⁵ Additional earners in the household can be an imperfect form of self-insurance. The same might be true for individuals with larger families or extended networks. See Ehrlich and Becker (1972) and Dehejia, DeLeire, and Luttmer (2007).

¹⁶ Results from a Probit regression yield similar results.

¹⁷ When a household applies for a mortgage, they may be offered credit insurance, which protects the loan on the chance that the applicant cannot make payments. Such insurance is usually optional. Credit life insurance pays off all or some of the loan if the applicant dies. See https://www.consumer.ftc.gov/articles/0110-credit-insurance.

¹⁸ Portability and convertibility clauses in ESLI policies allow some individuals to keep ESLI policies after they leave their employment; however, they either change the type of coverage and/or significantly increase premiums paid.

¹⁹ The results come from an implementation of the ‘oaxaca’ command in Stata (Jann 2008).