Minority household size and the life insurance purchase decision Michael A. Guillemette, Ph.D., CFP�a,*, M. Monica Hussein, Ph.D.,b G. Michael Phillips, Ph.D.b, Terrance K. Martin, Jr., Ph.D.c aDepartment of Personal Financial Planning, Stanley Hall 239, University of Missouri, Columbia, MO 65211, USA bCenter for Financial Planning and Investment and Department of Finance, Financial Planning, and Insurance at CSU Northridge, 18111 Nordhoff Street, Northridge, CA 91330-8245, USA cDepartment of Economics and Finance at the University of Texas-Pan American, 1201 W. University Drive, Edinburg, TX 78539, USA Abstract This study uses the 1992–2010 Survey of Consumer Finances to analyze whether the likelihood of life insurance ownership and the face value amount of life insurance changes for minorities as household size changes. We find that the likelihood of life insurance ownership declines for Black and larger Hispanic families as household size increases when controlling for a variety of socioeconomic and demographic variables. There is also a significant decline in the face value amount of term life insurance purchased by Black families as household size rises. We provide possible explanations for these effects and also discuss implications for financial planners. © 2015 Academy of Financial Services. All rights reserved. Keywords: Life insurance; Household size; Term life insurance; Cash value life insurance; Race, minority 1. Introduction A properly structured life insurance plan can be a powerful part of a family’s financial plan. It is especially useful in managing tax liabilities, unexpected expenses, lost income, and household services after the death of a family member. However, this basic observation is not a truism for all parts of American society. In particular, the existing literature suggests a very * Corresponding author. Tel.: �1-573-884-9188; fax: �1-573-884-8389. E-mail address: guillemettem@missouri.edu (M.A. Guillemette) Financial Services Review 24 (2015) 37–50 1057-0810/15/$ – see front matter © 2015 Academy of Financial Services. All rights reserved. diverse usage of life insurance ownership among households in different ethnic and racial groups. If these suggestions are correct, financial planners may face special challenges when constructing plans for clients who are in a group that tends to underutilize life insurance products. Alternatively, it may be that the decision to use life insurance may be a function of family size, rather than ethnicity. Studies on family support networks suggest that the presence of strong family support networks are important in determining how families manage household resources. Could family support networks be perceived by clients as a substitute for a properly designed life insurance program? This is an important distinction for financial planners because it could suggest a focus on family size rather than cultural background when deciding how to educate clients about insurance products. This study investigates how family support systems affect life insurance purchasing decisions across various ethnic groups. By performing this analysis, we are able to help distinguish whether possible underinsurance tends to result from strong family support networks accompanying larger family size or whether it tends to be a function of ethnic background, or whether it is an interaction between the two. Specifically, we focus on the effects of household size and ethnicity on the face value of insurance policies purchased according to data from the 1992–2010 Survey of Consumer Finances. The results of this study have practical value for financial planners by providing insight into minority households’ predilections regarding life insurance products and the perceived need for such tools in their financial plans. By recognizing minority households’ inclinations towards life insurance when crafting a financial plan, advisors can reinforce their relation- ships and long-term impact on their clients’ financial wellbeing. 2. Literature review Insurance provides an effective way for individuals to protect against a severe downturn in consumption. By pooling and sharing risks, beneficiaries will receive their insurance payouts when a covered death occurs (Bajtelsmit, 2005). This is an example of smoothing consumption over time, a primary goal of financial planning (Ando & Modigliani, 1963). On the other hand, households with extended familial ties may plan to rely on the safety net provided by family members to offset the financial loss after the death of a household earner, illustrated in part by the increase since 1990 in the number of older individuals who live in multigenerational households (Fry & Passel, 2014). There is evidence of interdependence and strong family support networks among minority households (Harrison, Wilson, Pine, Chan, & Buriel, 1990). Minority households also receive greater financial assistance from family members compared with White households (Mutran, 1985). White households have relatively higher rates of insurance coverage compared to Black households (Gutter & Hatcher, 2008). Large family support systems could be used as a substitute for life insurance ownership as earnings produced by the lost family member may be replaced by other household members. Findings indicate that Black, Hispanic, and Asian households utilize adaptive strategies of strong extended family networks and collectivism, and also exhibit group loyalty (Harrison et al., 1990). These adaptive strategies foster child-rearing goals of socialization for interdependence. When comparing Black and His- 38 M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 panic households, Black families have larger support systems (Mui, 1993; Wasserman, Brunelli, Rauh, & Garcia-Castro, 1990). Studies on family support networks suggest they are important in determining how families manage household resources. Tienda and Angel (1982) analyze Hispanic, Black and White families to determine whether extended household structure moderates the impact of labor market disadvantages. They report the following: (1) Hispanic and Black households are similar in their dependence on extended household support whereas White households are less likely to rely on extended family support; (2) non-immediate members in Black and Hispanic households contribute significantly to total household income; (3) Non-immediate members in White households do not appear to participate contribute significantly to total household income. Mutran (1985) finds that when controlling for socioeconomic status, elderly Black parents are more likely than elderly White parents to provide financial assistance to adult children. Padgett (1997) uses the 1988 National Survey on Families and Households to assess the extent of network involvement and its relationship with household labor. When just examining married couples, approximately half of Black couples receive tangible assistance related to household production (Padgett, 1997). Most commercial transactions include some element of trust (Arrow, 1972). If we define trust as the likelihood an individual attributes to the possibility of being cheated (as defined in Guiso, Sapienza, & Zingales, 2008), we may better understand the greater dependence of minorities on their families in contrast to a product sold by an insurance salesman. Within a principal-agent theoretical framework, an individual’s level of trust may affect financial decisions (Akerlof, 1970), including the decision to purchase life insurance. Exchange systems involve a series of individual actions based on assymetric information. Trust helps to manage responses to the innate uncertainty of exchange relationships (Tyler & Stanley, 2007). Brehm and Rahn (1997) argue that experiences with discrimination may explain the pervasiveness of low trust among Black households. Discriminatory practices from agents within the financial and capital markets, combined with a history of restricted access to these markets, may lead to a negative perception by minority families of all agents and the products they sell. For example, in the early part of the 20th century major life insurance companies excluded Black customers or set discriminatory rates (Weems, 1996). As a result, Black- owned insurance companies began selling cash value burial policies to provide affordable life insurance coverage for working class Black families (Weems, 1996). 3. Data We use the 1992, 1995, 1998, 2001, 2004, 2007, and 2010 SCF for our analysis. The SCF is a triennial cross-sectional survey that provides detailed financial information on U.S. households. It contains the most detailed balance sheet information of any publicly available nationally representative dataset (Campbell, 2006). It is sponsored by the United States Federal Reserve Board in conjunction with the Department of the Treasury and other governmental agencies. Since the SCF oversamples wealthy households, the descriptive statistics in our analysis are weighted to generalize to a nationally representative population (Kennickell & Woodburn, 1997). The total sample size over the time period was 32,371. 39M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 There are several factors that have been found to influence the decision to purchase life insurance. The presence of a spouse may increase the demand for life insurance as the purpose of life insurance is human capital replacement for an individual with insurable interest. Truett and Truett (1990) find age, income, and education level affect the demand for life insurance. Because human capital declines with age, age should be negatively associated with the likelihood of owning life insurance. A bachelor’s degree is a human capital signal that proxies for a steeper earnings path that should increase the likelihood of owning life insurance. There is evidence that the demand for life insurance is positively related to the number of dependents in a household (Burnett & Palmer, 1984; Hammond, Houston, & Melander, 1967). A large amount of liquid assets may decrease demand because of the ability to self-insure. Campbell (1980) finds that accumulated household wealth acts as a substitute for life insurance. Self-employed individuals do not have access to employer-provided group life insurance which may reduce the likelihood of ownership. Households that are currently unemployed may lose access to employer-provided life insurance if the coverage is not portable, which may reduce the likelihood of ownership. Campbell (1980) finds bequests to be positively associated with the demand for life insurance. Planning to leave a sizable estate may also increase the demand for life insurance, particularly for cash value polices. In terms of premium pricing, a respondent who indicates that they are in fair or poor health should have a relatively higher premium payment than someone who indicates good or excellent health. Females have a longer life expectancy compared to males, decreasing the cost of a policy covering females. Finally, individual risk preferences should influence the demand for life insurance ownership. 4. Descriptive statistics Table 1 displays the ownership percentages of life insurance based on race and household size. Life insurance ownership declines for Black households as they move from a household size of one, three, five, or more members. That trend is not evident for any other racial groups. Table 2 shows the median face value amounts of term life insurance held by those who own term life products, sorted by race and household size. Table 3 displays the median face value amounts of cash value life insurance held by those who own cash value life products, also sorted by race and household size. In the larger household size categories, only Table 1 Ownership of life insurance by race and household size (1992–2010 SCF) Household size Black Hispanic Other White 1 11.73%** 2.75%** 27.55%** 57.97%** 2 6.70%** 2.65%** 28.52%** 62.13%** 3 8.17%** 5.17%** 28.80%** 57.86%** 4 5.11%** 5.96%** 28.40%** 60.53%** 5� 5.84%** 7.34%** 27.13%** 59.69%** *p � 0.05; **p � 0.01. 40 M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 Black and “other” races show a decrease in the face value amount of term life insurance when comparing a household size of four to a household size of five or more. For the “other” race category the same trend holds for cash value life insurance. However, for Black households, the face value amount of cash value life insurance increases when moving from a household size of four to a household with five or more members. For Hispanic households, when comparing a household of four to a household of five, the face value amount of cash value life insurance actually declines. 5. Method A logistic regression model (A) is constructed to better understand how interactions between different races and different levels of household size affect the likelihood of owning life insurance. If the respondent owns a life insurance policy (including individual and group policies, but not accidental life insurance), the variable is coded as one, with no ownership of life insurance as the reference group and coded as zero. Race is broken into categories that include White, Black, Hispanic, and other races. Respondents who identified themselves as White are used as the reference group in the multivariate analysis. Household size does not include people who do not usually live in the household or who are financially independent. A household size of five includes households with five or more members. Our control variables include inflation-adjusted income, inflation-adjusted net worth, and inflation-adjusted liquid assets. Income, net worth, and liquid assets were indexed to 2010 dollars and sorted from lowest (Q1) to highest (Q4) quartile. Other control variables include the age of the respondent and whether the respondent has a bachelor’s degree, owns a home, is married, is a male, is self-employed, is employed, plans to leave a sizeable estate, has a child present, self-identifies as healthy,1 and is willing to take substantial financial risk with Table 2 Median face value of term life insurance by race and household size (1992–2010 SCF) Household size Black Hispanic Other White 1 $ 30,000 $ 66,000 $ 42,000 $ 27,300 2 $ 40,200 $ 86,250 $100,000 $ 71,550 3 $ 61,500 $ 92,250 $172,500 $135,300 4 $132,090 $115,000 $246,000 $230,000 5� $115,000 $128,790 $136,752 $230,000 Table 3 Median face value of cash value life insurance by race and household size (1992–2010 SCF) Household size Black Hispanic Other White 1 $20,000 $ 79,950 $ 82,362 $ 15,750 2 $35,775 $ 75,040 $ 59,052 $ 43,050 3 $40,404 $ 69,930 $105,000 $ 77,700 4 $67,000 $105,000 $200,000 $115,000 5� $73,800 $ 93,800 $155,400 $141,669 41M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 personal investments. In addition, a dummy variable was included to control for the year in which the survey was conducted. The year 1992 was used as the reference group. (A) Own Any Life Insurance � b0 � bj Race Dummy � bk Household Size Dummy � � bi Control Variables � � Where, i � control variables: income, net worth, liquid assets, bachelor’s degree, ownership of a home, age, married, male, healthy, self-employed, unemployed, plan to leave a sizeable estate, the presence of a child, willingness to take substantial financial risk and the year in which the survey was conducted. Two separate Tobit models were constructed to understand how interactions between different races and different levels of household size affect the face value of term life insurance (B) and cash value life insurance (C). Common examples of cash value policies include whole, straight, or universal life insurance. The dependent variables contain a large percentage of zero values because of non-ownership. When a dependent variable contains a large number of zero values, the use of an ordinary least squares model may result in biased coefficient estimates (Madalla, 1987). A Tobit model is not subject to this same bias. The insurance face value amounts were inflation adjusted to 2010 dollars and square- rooted to reduce skewness. The face amount of cash value life insurance was broken into quartiles and included as a control variable when the dependent variable was the square- rooted face amount of term life insurance. The face amount of term life insurance was broken into quartiles and included as a control variable when the dependent variable was the square-rooted face amount of cash value life insurance. (B) Face amount of term life insurance � b0 � bj Race Dummy � bk Household Size Dummy � � bi Control Variables � � Where, i � control variables: income, net worth, liquid assets, bachelor’s degree, ownership of a home, age, married, male, healthy, self-employed, unemployed, plan to leave a sizeable estate, the presence of a child, willingness to take substantial financial risk, the year in which the survey was conducted, and the face amount of cash value life insurance (C) Face amount of cash value life insurance � b0 � bj Race Dummy � bk Household Size Dummy � � bi Control Variables � � Where, i � our control variables: income, net worth, liquid assets, bachelor’s degree, ownership of a home, age, married, male, healthy, self-employed, unemployed, plan to leave a sizeable estate, the presence of a child, willingness to take substantial 42 M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 financial risk, the year in which the survey was conducted, and the face amount of term life insurance 6. Results The results for Model (A) are displayed in Table 4. As household size rises for Black and Hispanic families, the likelihood of life insurance ownership declines. A Black family with a household size of three is 28.85%2 less likely to own life insurance compared with a non-Black family of the same household size. A Black household with five or more members is 53.55% less likely to own life insurance compared with a non-Black family of a comparable household size. A Hispanic family with a household size of two is 24.82% less likely to own life insurance compared with a non-Hispanic family of two. A Hispanic family with five or more members is 59.61% less likely to own life insurance compared with a non-Hispanic family of a comparable household size. Fig. 1 graphically displays the likeli- hood of life insurance ownership for Black and Hispanic families as household size rises. The odds ratios listed in Figure 1 are statistically significant. Tables 5 and 7 display the results for Model (B), or the relation between the face value amount of term life insurance and household size. For Black families, as household size rises the conditional mean face amount of term life insurance declines. A Black household with two members has $20,4753 less term life insurance compared with a non-Black family of the same household size. A Black family of three has $74,038 less term life insurance compared with a non-Black family of three. The face value of term life insurance is $98,910 less for a Black household with five or more members compared with a non-Black family of a comparable household size. The average face amount of term life insurance is $28,560 less for a Hispanic family of four compared with a non-Hispanic family of the same household size. A Hispanic family with five or more members has $68,583 less term life insurance compared with a non-Hispanic family that is the same size. Fig. 2 graphically represents the results in Table 7. The odds ratios that are listed are statistically significant. The results for Model (C), or the interaction between the face amount of cash value life insurance and household size, are shown in Tables 6 and 8. As household size rises the conditional mean face amount of cash value life insurance has a statistically significant decline only for Hispanic families with a household size of four or more members. A Hispanic family with a household size of four has a face amount of cash value life insurance that is $24,092 less than a non-Hispanic family of the same household size. A Hispanic family with more than four members has a face amount of cash value life insurance that is $27,680 less than a non-Hispanic family of a comparable household size. 7. Conclusions The multivariate results indicate a negative relation between household size and the ownership of life insurance for Black and Hispanic families when controlling for a variety 43M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 of demographic and socioeconomic variables. The conditional mean face value amount of term life insurance declines for Black families as household size rises compared with non-Black families. This relation is statistically significant for all Black household sizes. The Table 4 Ownership of life insurance Parameter Estimate SE Odds ratio Intercept �1.092** 0.0889 Not employed �0.833** 0.0406 0.435 Male �0.0021 0.0446 0.998 Age 0.0124** 0.0012 1.012 Income (Q2) 0.5637** 0.0388 1.757 Income (Q3) 0.8704** 0.0496 2.388 Income (Q4) 1.0568** 0.0687 2.877 Net worth (Q2) 0.5274** 0.0457 1.695 Net worth (Q3) 0.4417** 0.0596 1.555 Net worth (Q4) 0.0985 0.0824 1.103 Degree 0.1306** 0.0332 1.140 Married 0.4226** 0.0525 1.526 Child 0.2816** 0.0542 1.325 Homeowner 0.4364** 0.0402 1.547 Self-employed �0.4688** 0.0418 0.626 Healthy 0.00267 0.0359 1.003 Sizable estate 0.0261 0.0314 1.026 Liquid assets (Q2) 0.5815** 0.0404 1.789 Liquid assets (Q3) 0.5562** 0.0492 1.744 Liquid assets (Q4) 0.404** 0.0617 1.498 Substantial risk �0.126* 0.0619 0.882 1995 �0.0583 0.0575 0.943 1998 �0.2641** 0.0566 0.768 2001 �0.3721** 0.0561 0.689 2004 �0.5121** 0.0554 0.599 2007 �0.5783** 0.0556 0.561 2010 �0.5856** 0.0513 0.557 Black (B) 1.0399** 0.0838 2.829 Hispanic (H) �0.3993** 0.1400 0.671 Other (O) �0.3267* 0.1542 0.721 Household size 2 (HHS2) 0.1528** 0.0536 1.165 Household size 3 (HHS3) 0.1066 0.0825 1.112 Household size 4 (HHS4) 0.251** 0.0919 1.285 Household size 5� (HHS5P) 0.1794 0.0975 1.197 BHHS2 �0.2786* 0.1174 0.8818 BHHS3 �0.447** 0.1352 0.7115 BHHS4 �0.7646** 0.1535 0.5983 BHHS5P �0.9463** 0.1569 0.4645 HHHS2 �0.4381* 0.1751 0.7518 HHHS3 �0.5052** 0.183 0.6713 HHHS4 �0.6937** 0.1804 0.6423 HHHS5P �1.0859** 0.1791 0.4039 OHHS2 0.1891 0.1971 1.4076 OHHS3 0.0341 0.2177 1.1511 OHHS4 �0.1363 0.2356 1.1215 OHHS5P �0.2618 0.2383 0.9209 *p � 0.05; **p � 0.01. 44 M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 conditional mean face value amount of term and cash value life insurance declines for larger Hispanic families as household size rises from four to five or more members. 8. Implications and future research Our findings suggest that Black and Hispanic families used household members as a substitute for life insurance between 1992 and 2010. The use of household members as a life insurance substitute is not utility maximizing and a contingent claim should be purchased to replace the lost human capital in the event of a household member’s death. Financial planners or life insurance agents can use these findings to help ensure that Black households and larger Hispanic households are adequately insured. One possible explanation for why Black families act as though household members are a substitute for the face value amount of term life insurance, but not cash value life insurance, is that Black families purchase cash value policies specifically for burial purposes. The low median face value amounts of cash value policies among Black households, as compared with all other racial groups, provides some additional indication that these cash value policies may be used for that purpose.4 However, Black families have also been found to have lower risky asset ownership compared with White families as household size rises (Gutter, Fox, & Montalto, 1999) and so it is possible that Black families are using cash value life insurance as an alternative investment. Financial planners have many tools available to help their clients allocate resources over time. Life insurance is an important part of a financial plan as it protects families against a sharp decline in consumption. However, when families have alternative arrangements to meet the needs of the household, such as through a large family network, purchasing an adequate amount of life insurance might appear to be excessive. Clients who believe they have a strong family network may be underinsured. Heo, Grable, and Chatterjee (2013) find Fig. 1. Likelihood of life insurance ownership by household size. 45M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 Table 5 Face value of term life insurance Parameter Estimate SE t Value Intercept �564.9753** 36.214530 �15.60 Cash value policy face amount (Q2) �271.9539** 19.338508 �14.06 Cash value policy face amount (Q3) �372.1336** 19.815740 �18.78 Cash value policy face amount (Q4) �279.7793** 20.650291 �13.55 Not employed �253.0329** 16.341234 �15.48 Male �11.0729 19.311746 �0.57 Age �3.8124** 0.477248 �7.99 Income (Q2) 165.9838** 17.076209 9.72 Income (Q3) 281.7889** 19.989716 14.10 Income (Q4) 605.0263** 25.720547 23.52 Net worth (Q2) 132.6310** 18.898002 7.02 Net worth (Q3) 88.1648** 23.424179 3.76 Net worth (Q4) 188.2409** 30.886998 6.09 Degree 98.3133** 12.157400 8.09 Married 182.5603** 22.248095 8.21 Child 155.3139** 22.170767 7.01 Homeowner 123.0864** 16.529780 7.45 Self-employed �18.7360 14.569702 �1.29 Healthy 32.8656* 14.642588 2.24 Sizable estate �8.311813 11.949690 �0.70 Liquid assets (Q2) 174.3139** 16.859052 10.34 Liquid assets (Q3) 166.7142** 19.465660 8.56 Liquid assets (Q4) 223.5183** 23.296634 9.59 Substantial risk 73.9107** 22.788373 3.24 1995 �4.9478 20.317286 �0.24 1998 �37.6212 20.403211 �1.84 2001 �37.0724 20.249540 �1.83 2004 �6.9363 20.193088 �0.34 2007 4.7159 20.323864 0.23 2010 14.2123 18.903550 0.75 Black (B) 302.9927** 35.091726 8.63 Hispanic (H) �94.7716 64.669447 �1.47 Other (O) �106.9755 70.962724 �1.51 Household size 2 �15.9366 22.818493 �0.70 (HHS2) Household size 3 8.7066 34.430109 0.25 (HHS3) Household size 4 72.5314* 36.898153 1.97 (HHS4) Household size 5� 116.6348** 38.641196 3.02 (HHS5P) BHHS2 �127.1559** 47.217569 �2.69 BHHS3 �280.8052** 53.911229 �5.21 BHHS4 �357.7793** 58.620302 �6.10 BHHS5P �431.1337** 62.465342 �6.90 HHHS2 �66.4825 80.235433 �0.83 HHHS3 �145.0277 82.318090 �1.76 HHHS4 �241.5283** 80.492362 �3.00 HHHS5P �378.5186** 79.949937 �4.73 OHHS2 135.1139 85.355958 1.58 OHHS3 �22.0967 92.065171 �0.24 OHHS4 78.0881 93.930658 0.83 OHHS5P �132.6312 99.445551 �1.33 *p � 0.05; **p � 0.01. 46 M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 Table 6 Face amount of cash value life insurance Parameter Estimate SE t Value Intercept �1746.4416** 56.326721 �31.01 Cash value policy face amount (Q2) �388.6134** 25.007965 �15.54 Cash value policy face amount (Q3) �482.7647** 24.978475 �19.33 Cash value policy face amount (Q4) �454.0463** 24.623215 �18.44 Not employed �196.3087** 23.796685 �8.25 Male 10.8115 29.600057 0.37 Age 4.9954** 0.706360 7.07 Income (Q2) 79.3575** 26.340385 3.01 Income (Q3) 123.2469** 30.008617 4.11 Income (Q4) 379.9408** 36.794123 10.33 Net worth (Q2) 372.4267** 31.083055 11.98 Net worth (Q3) 507.1737** 36.780105 13.79 Net worth (Q4) 816.6910** 45.801190 17.83 Degree 62.4141** 17.690651 3.53 Married 164.8562** 34.034548 4.84 Child 67.8820* 33.701665 2.01 Homeowner 108.7534** 25.947366 4.19 Self-employed 110.9823** 20.409938 5.44 Healthy 11.5404 21.558259 0.54 Sizable estate 120.6049** 18.027918 6.69 Liquid assets (Q2) 222.8966** 27.076805 8.23 Liquid assets (Q3) 225.5325** 30.046516 7.51 Liquid assets (Q4) 284.6579** 34.369930 8.28 Substantial risk �36.0096 33.015220 �1.09 1995 �47.9328 28.214129 �1.70 1998 �123.0544** 28.516715 �4.32 2001 �207.8471** 28.581703 �7.27 2004 �229.4625** 28.698742 �8.00 2007 �309.7293** 29.104553 �10.64 2010 �375.2035** 27.518708 �13.63 Black (B) 336.7514** 56.580048 5.95 Hispanic (H) �145.7467 114.830500 �1.27 Other (O) �58.1589 109.976406 �0.53 Household size 2 152.4559** 34.650184 4.40 (HHS2) Household size 3 213.6898** 52.449452 4.07 (HHS3) Household size 4 237.3292** 56.109391 4.23 (HHS4) Household size 5� 233.6837** 58.688419 3.98 (HHS5P) BHHS2 �110.5899 74.170272 �1.49 BHHS3 �124.4946 83.166274 �1.50 BHHS4 �157.8035 89.189548 �1.77 BHHS5P �124.0041 93.680331 �1.32 HHHS2 �189.7301 140.064691 �1.35 HHHS3 �232.3589 145.041940 �1.60 HHHS4 �392.5461** 143.669057 �2.73 HHHS5P �400.0568** 141.389434 �2.83 OHHS2 �60.1170 130.539406 �0.46 OHHS3 �215.8021 143.603131 �1.50 OHHS4 �182.7971 144.790031 �1.26 OHHS5P �230.8507 153.042356 �1.51 *p � 0.05; **p � 0.01. 47M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 evidence to suggest that life insurance acts as a compliment, rather than a substitute, for wealth. Therefore, minority households may not be thinking about life insurance as a wealth replacement option upon the death of a household member. It can be detrimental to use rules of thumb (such as household size) when assessing the need for life insurance (Collins & Ham, 2011). Recognizing potential cultural biases and the possible role of household members are topics that financial planners should explore when meeting with their clients to discuss what type of life insurance, and how much coverage, is needed to meet the goals outlined within a financial plan. There are multiple areas for future researchers to explore as it pertains to minority household size and the decision to purchase life insurance. Future research should focus on why there is a divergence in the decline of the face amount of term and cash value life insurance as household size rises for Black families. The question of how much human Table 7 Decline in term face amount as household size rises Parameter Estimate BHHS2 �$20,475** BHHS3 �$74,038** BHHS4 �$81,366** BHHS5P �$98,910** HHHS2 �$ 6,793 HHHS3 �$18,583 HHHS4 �$28,560** HHHS5P �$68,583** OHHS2 �$14,203 OHHS3 �$ 179 OHHS4 �$22,686 OHHS5P �$ 256 *p � 0.05; **p � 0.01. Fig. 2. Face value amount of term life insurance by household size. 48 M.A. Guillemette et al. / Financial Services Review 24 (2015) 37–50 capital is left uninsured by minority households as family size rises also remains unanswered. These are questions we hope will be explored in future studies. Notes 1 As defined as “excellent” or “good” health. 2 The odds ratio was derived by taking the coefficient for a household size of three (0.1066) and adding it to the interaction variable coefficient (�0.4470). The interac- tion variable coefficient is multiplied by 1 to designate a Black household (0.1066) � (�0.4470)*(1) � �0.3404. 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