Demand for Spousal Health *Elena Capatina, Hyunjae Kang
January 2024

Abstract
This paper explores the importance of health for marital transitions and as adimension for assortative mating. We present new empirical patterns on marriage,
divorce, and assortative mating in relation to health, constructed over time and by
race using US data from 1996-2023. We document that high fractions of unhealthy
individuals are single, lack health insurance, and relatively small fractions have
healthy spouses. Unhealthy men have become significantly more likely to be single
over time. Unhealthy individuals are also more likely to be divorced and remain
single. When married, unhealthy individuals have high rates of unhealthy spouses(40-45%), higher probabilities of “marrying down” in terms of education, and women
have husbands with lower incomes (conditional on everything else) compared totheir healthy counterparts. These findings have important implications for the designof health and social insurance programs which interact closely with intra-household
insurance. Preliminary analysis shows the ACA is associated with a lower penalty ofbad health in the marriage market. Finally, the gain to marriage for females with low
education but good health surpasses that of females with high education but poor
health. Conversely, the gain to marriage for males exhibit the opposite pattern. Thisasymmetry was noticeable in the mid-’90s but has since diminished in recent years.
We develop a multidimensional matching model of education and health status toreveal the driving forces behind changes in the demand for spousal health.

Keywords: Health, Matching, Marriage, Intra-household risk-sharing, Health
Insurance
JEL classification: I10, I14, J12* Capatina: Australian National University; Kang: Kyoto University. We would like to thank Edoardo

Ciscato, Chung Tran, Fedor Iskhakov, and Esben Andersen for excellent comments and feedback.

1

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1.

E. CAPATINA, H. KANG

Introduction

Health and health shocks are important determinants of earnings, wealth, life-expectancy,
human capital, medical expenditures, and ability to have and raise children (e.g., French(2005), De Nardi et al. (2010), Capatina (2015), De Nardi et al. (2022), Capatina and Keane(2023)). In the context of marriage markets, these aspects matter greatly for couples’
marital surpluses.
This paper explores the importance of health for marital transitions and as a dimensionof assortative mating. We present new empirical patterns on marriage, divorce, fertility
and assortative mating in relation to health, constructed over time and by race using
US data from 1996-2023. We use individuals aged 20-64 and study Whites, Blacks andHispanics separately. We highlight the importance of these patterns for the degreeof intra-household risk sharing, the value of formal social and health insurance, andin explaining household income inequality. We aim to understand the underlying
mechanisms behind the change in demand for spousal health over time. We build
a multi-dimensional matching model of health status and education to empiricallyquantify the impact of health characteristics on marital matching, taking into accountage-related equilibrium effects in the marriage market.
First, we document that high fractions of unhealthy individuals are single, andrelatively small fractions have healthy spouses in 2022-23. Unhealthy Whites are themost likely to have a healthy spouse (approximately 25%) while Blacks are least likely(12-13%). However, Whites have become significantly less likely to have a healthy spouseover time, losing approximately 10pp since 1996-97. These patterns suggest that intrahousehold insurance against bad health is generally low, varies significantly by race, andhas declined for Whites.
Another interesting aspect is how marriage has been replaced by cohabitation withindifferent racial/sex/health groups. Marriage rates have declined by approximately 9pp
from 1996/97 to 2022/23 for White healthy individuals, but this has been made up
by an equal increase in cohabitation. Marriage rates have decreased by more among
the unhealthy sub-sample (16pp for men and 13pp for women). Women saw an equalincrease in cohabitation, but unhealthy men only experienced a 9pp rise. Thus, unhealthy
White men are increasingly left out of formal marriages as well as cohabitation arrangements

DEMAND FOR SPOUSAL HEALTH

3

that might provide some limited risk-sharing. The patterns also indicate that unhealthy
individuals are increasingly foregoing the benefits of forming a family with a married
spouse, such as raising children in a married household.
We also document that White single individuals have the highest rates of private
health insurance (PHI) relative to other races, but they are also the only group for whom
the PHI rates have declined over time despite the ACA, while Blacks and Hispanics
unhealthy singles have experienced significant increases in coverage bringing themalmost in line with Whites. Approximately only one third of unhealthy singles have PHI
in 2022/23. Hence, for White unhealthy men who increasingly lack a spouse, formal
health insurance has not offset the lost benefits of intra-household risk sharing. Thisis especially important because the rates of bad health among Whites have steadily
increased over time, rising from 12 to 15%.
Second, we explore the correlation between own health and spousal characteristics formarried individuals. When married, unhealthy individuals have high rates of unhealthy
spouses (40-45%), higher probabilities of ”marrying down” in terms of education, andunhealthy women have husbands with lower incomes (conditional on everything else)compared to their healthy counterparts. The latter is partly explained by the fact thatthe husbands of unhealthy women are also often unhealthy, but the relationship is still
evident even among couples where the husband is healthy. This spouse income penalty
to bad health has increased over time.
Third, preliminary analysis suggests that the Affordable Care Act (ACA) is associatedwith a lower penalty of bad health in the marriage market.
Fourth, utilizing the marriage matching function proposed by Choo and Siow (2006),
we demonstrate that the relative gain from marrying a spouse with low education andgood health, in contrast to marrying a spouse with high education and bad health,is significantly higher for males, a trend not observed among females. This gender
asymmetry is pronounced in 1996, diminishes over time, and eventually disappears inrecent years.
Lastly, we construct and estimate a multidimensional matching model to address
the gender asymmetries in the health dimension in marital matching. The theoreticalframework can capture the gender-specific demand for spousal health, accommodating

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E. CAPATINA, H. KANG

changes in returns to education and good health, and considering age-related equilibrium
effects in the marriage market.
We conclude with a discussion of the implications for the design of health and social
insurance programs which interact closely with intra-household insurance. A healthy
spouse can compensate for the partner’s poor health by working, taking care of theunhealthy spouse, or increasing time allocated to home production and childcare. On
average, the absence of a healthy spouse implies a higher burden of bad health and ahigher value of formal insurance.

2.

Literature

The literature studying matching patterns is dominated by models focusing on a single
characteristic driving the matching process. Income, wages, and education are the mostcommon examples of such a characteristic (e.g., Becker (1993); Pencavel (1998); Choo andSiow (2006)). Driven by empirical evidence, a growing literature builds multidimensionalmodels studying how non-income traits are traded off with income. Chiappori et al.
(2012) study the trade-offs between the economic dimension (i.e., wages or education)and physical attractiveness (i.e., BMI). Low (2023) use a bi-dimensional matching framework
where the tradeoff is between human capital and reproductive capital (fertility).
In our paper we consider health for the first time as an important dimension formatching in the marriage market. Physical attributes and BMI measures that havebeen previously studied are rough indicators of health, however, health is important forreasons beyond physical attractiveness due to its impact on economic aspects such as
productivity (both outside and inside the home), life-expectancy, medical expenditures,and fertility. As far as we are aware, only one paper has studied health and matching.
Guner et al. (2018) analyze the health gap between married and unmarried individuals ofworking-age, distinguishing between selection effects and the protective role of marriage.
They find that better innate health is associated with a higher probability of marriage anda lower probability of divorce, and there is strong assortative mating among couples byinnate health. They also find that the marriage health gap is similar for men and women.
A new aspect of our paper is to study differences across genders and races, and overtime. The focus on gender is partly motivated by the fact that different parental roles andDEMAND FOR SPOUSAL HEALTH

5

the gender-wage gap may imply an asymmetry in the value of health. Women’s health is
key for fertility and for the health and success of children (e.g. Eshaghnia and Heckman(2023)). Previous studies support the idea that mothers’ health is more important tochildren’s welfare than the health of the father. Mothers’ health is fundamental to thehealth of newborn babies and children (Lawn et al. (2006)). Bratti and Mendola (2014)provide empirical evidence that co-living children of ill mothers, but not of ill fathers,
are significantly less likely to be enrolled in education at ages 15–24. The focus on race
is motivated by very different marital patterns by race in the U.S. and very low rates ofinterracial marriages (e.g., Ciscato (2023)).
In Low (2023), the gender asymmetry arises entirely due to fertility. We add to thisresearch by providing more specific empirical support for how health in general is traded
off with income. While Low (2023) uses age-specific effects to explain the asymmetry, we
do this more generally by highlighting marriage market equilibrium effects. We do notmodel the human capital investment process itself as in Low (2023), and instead take it
as given.
Our paper is also closely related to the literature on intra-household risk sharing. The
income of a worker’s spouse plays a major role in consumption insurance (e.g., Blundell
et al. (2016)). Ortigueira and Siassi (2013) and Choi and Valladares-Esteban (2020) study
the role of a spouse’s labor supply as an insurance mechanism in a Bewley framework withexogenous income risks. We add to this literature by explicitly studying intra-household
risk sharing in the context of health risk, keeping in mind that household formationis endogenous and may depend on the social and health insurance environment (e.g.,
Persson (2020)).

3.

Data

3.1

CPS

We use the ASEC (March) sample of the CPS from 1996 to 2023. We keep adult civilians
aged 20-64 with positive sampling weights. We construct a binary variable for healthstatus equal to one (bad) if self-reported health status is Fair or Poor, or the person has awork disability. Self-reported health is not available before 1996.

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3.2

E. CAPATINA, H. KANG

The Medical Expenditures Panel Survey (MEPS)The MEPS is a set of large-scale surveys of families and individuals, their medical
providers, and employers. The survey uses an overlapping panel design collecting data ina series of five rounds of interviews over a 2 and a half year period. A new panel enters thesurvey every year. We use the MEPS Household Component which collects data from asample of civilian non-institutionalized families and individuals drawn from a nationally
representative sub-sample of households. We use years 2001-2021.1

4.

Health and Marriage: Descriptive Patterns

4.1

Health and Marital Status over Time

To begin, we study health and marital patterns across different demographic groups andover time. Table 1 shows the fraction of individuals aged 20-64 in bad health by race,
marital status and sex, pooling all years. We see that married individuals are significantly
healthier than singles in all sub-groups. Also, Black groups are significantly more likely
to be unhealthy.
Table 1: Fraction in Bad Health, ages 20-64, CPS

malefemaleWhite
Black
Hispanic
Marital Status
Marital Status
Marital Status
Single Married Single Married Single Married
0.167
0.111 0.231
0.169 0.137
0.122
0.180
0.116 0.230
0.169 0.173
0.132

Figure 1 shows the fraction of individuals aged 20-64 in bad health by race and sex
over time. Whites are becoming less healthy. An interesting question is whether thistrend has affected marital patterns over time.

1

Key variables on health, health behaviors and employment are missing before 2001 (e.g., BMI andchronic conditions).

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DEMAND FOR SPOUSAL HEALTH

.1

.12

.14

Mean Health.16.18

Mean Health
.15
.2

.2

.22

.25

Figure 1: Fraction in Bad Health, ages 20-64, CPS, men (left) and women (right)

1990

2000

2010
survey year
White
Hispanic

2020
Black

2030

1990

2000

2010
survey year
White
Hispanic

2020

2030

Black

Table 2 shows the fraction of married and cohabiting individuals by health statusover time, separately by race. We focus on the first two years in our sample (1996-97)
and the last two years (2022-23). We note that marriage rates decline in all groups, but
the largest drops are observed among unhealthy males, especially White and Hispanic.
Among unhealthy Whites, the marriage rate declines by 13pp for women and 16pp formen. The marriage rate declined by 18pp for Hispanic unhealthy men. To what extent
is declining marriage offset by higher cohabitation? For Whites, we see that for healthygroups and unhealthy women, cohabitation fully offsets the marriage decline. However,unhealthy White males had only a 9pp increase in cohabitation compared to a 16pp drop
in marriage. If formal marriage is associated with a higher degree of intra-household risk
sharing than cohabitation, the shifts indicate that all groups may have lower insurance
from a spouse today than in the late 1990’s, but White unhealthy men in particular
are affected the most as they become less likely to have a partner at all, even if only
cohabiting.
Blacks stand out as having the lowest marriage rates but highest cohabitation rates.
The declines in marriage among those in bad health has been less severe than for Whites.
Overall, the patterns in Table 2 suggest that health is a very important correlate of
marriage and cohabitation. However, because cohabitation does not imply the same
degree of intra-household risk sharing as marriage, we focus on married couples in theremaining sections of the paper.

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E. CAPATINA, H. KANG

Table 2: Fractions Married and Cohabiting, ages 20-64, CPS
Good HealthTime Period
1996-97 2022-23
White
male
MarriedCohabiting
femaleMarriedCohabiting
Black
male
MarriedCohabiting
femaleMarriedCohabiting
Hispanic
male
MarriedCohabiting
femaleMarriedCohabiting

Bad HealthTime Period
1996-97 2022-23

0.645
0.053

0.568
0.143

0.566
0.062

0.404
0.156

0.667
0.050

0.604
0.135

0.571
0.061

0.441
0.186

0.421
0.115

0.358
0.213

0.332
0.124

0.252
0.236

0.363
0.098

0.335
0.185

0.278
0.090

0.240
0.195

0.553
0.066

0.474
0.159

0.555
0.071

0.388
0.176

0.600
0.063

0.515
0.159

0.515
0.053

0.450
0.137

Next, we study the spouse’s status of unhealthy individuals by race and sex over time.
Table 3 shows 4 categories: no married spouse, married spouse who is in bad health,married spouse in good health, and married spouse whose health information is missing.
A key statistic is the fraction of unhealthy individuals with healthy spouses as this is likely
the group that benefits the most from risk-sharing when faced with bad health. We seethat unhealthy Whites are most likely to have a healthy spouse, followed by Hispanics.
Unhealthy Blacks have a healthy spouse only 11-14% of the time. We note that in 1996-97,
White unhealthy males had the highest fractions of healthy spouses (34%). However,this declined to only 24% in 2022-23. We also see a significant decline for White women(-8pp) and Hispanic men (-9pp). These numbers indicate that intra-family risk sharing

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DEMAND FOR SPOUSAL HEALTH

when faced with health shocks may be declining over time for Whites, but remains thelowest for Blacks.
Table 3: Spouse Status of Individuals in Bad Health, ages 20-64, CPS
male
Time Period
1996-97 2022-23
White
Spouse Status=No Spouse
Spouse Status=Bad Health Spouse
Spouse Status=Good Health Spouse
Spouse Status=Spouse Health N/ABlack
Spouse Status=No Spouse
Spouse Status=Bad Health Spouse
Spouse Status=Good Health Spouse
Spouse Status=Spouse Health N/A
Hispanic
Spouse Status=No Spouse
Spouse Status=Bad Health Spouse
Spouse Status=Good Health Spouse
Spouse Status=Spouse Health N/AfemaleTime Period
1996-97 2022-23

43.4
21.2
33.7
1.8

59.6
14.3
23.6
2.5

42.9
23.2
32.1
1.8

55.9
15.5
25.4
3.2

66.8
17.3
14.1
1.8

74.8
9.4
13.6
2.2

72.2
14.8
11.1
1.9

76.0
9.3
12.1
2.7

44.5
25.2
24.8
5.5

61.2
15.9
16.6
6.3

48.5
24.0
22.4
5.1

55.0
17.9
21.1
6.0

Notes: The table considers only married spouses.

4.2

Health Insurance, Health and Marriage

In this section, we study the correlation between health, marriage and health insurance.
Table 4 reports the fractions of individuals married, separating the sample into thosewith and without private health insurance (PHI). As expected, insured individuals are
more likely to be married, likely due to higher socioeconomic status.
We see that healthy individuals are more likely to be married than the unhealthy, andthe gap is roughly 3-5pp for most groups. However, White and Hispanic women in badhealth are 10pp less likely to be married than their healthy counterparts. Women often
get PHI from their husbands, so their PHI status is co-determined with their marital
status, explaining some of the difference. However, formal health insurance mightenable/protect marriage for those with bad health, and might be especially important in

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E. CAPATINA, H. KANG

this aspect for White and Hispanic women. We explore this further when we study theeffects of the ACA on marital patterns.
Table 5 presents this information differently, showing health status by marital status,
sex, race and insurance. We see that singles and married individuals have similar health
if they are covered by insurance. But among the uninsured, married individuals arehealthier, especially women.2
The above findings are opposite to Guner et al. (2018) who find that there is no
significant marriage health gap among the uninsured, but there is a significant gap forthe insured.3 While their results suggest that insurance is a facilitator for the positiveeffects of marriage on health (potentially through healthy behaviors), our results suggest
other possible effects which we aim to explore further in future versions.
In Table 6, we study PHI rates for single individuals over time, since this group is likely
most vulnerable to consumption changes in the face of health shocks. While Whites
have the highest rates of PHI relative to other races, they are also the only group forwhom the PHI rates have declined over time among those in bad health, despite the ACA,
while Blacks and Hispanics unhealthy singles have experienced significant increases incoverage bringing them almost in line with Whites (approximately one third). White
unhealthy men increasingly lack a spouse and also formal health insurance. This is aconcerning finding especially in light of Figure 1 which shows White men are getting lesshealthy over time.

2

Regression analysis controlling for age, education and year dummies indicate that the correlations
hold after controlling for such demographics.
3
Guner et al. (2018) use PSID data from 1984-2013 and construct their health measure based on
self-rated health.

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DEMAND FOR SPOUSAL HEALTH

Table 4: Fraction Married by Private Health Insurance, ages 20-64, CPS
Private Health Insurance
Not Covered by PHI
Covered by PHIGood Health Bad Health Good Health Bad Health
male
White
Black
Hispanic
femaleWhite
Black
Hispanic

0.380
0.242
0.454

0.346
0.214
0.415

0.655
0.479
0.565

0.636
0.511
0.600

0.458
0.218
0.504

0.354
0.179
0.406

0.669
0.408
0.599

0.645
0.413
0.602

Notes: The insurance variable used (HINSEMP) indicates whether respondents had any employersponsored health insurance coverage during the previous year.

Table 5: Fraction Unhealthy by Private Health Insurance, ages 20-64, CPS
Private Health Insurance
Not Covered by PHI Covered by PHISingleMarried Single Married
male
White
Black
Hispanic
femaleWhite
Black
Hispanic

0.319
0.354
0.183

0.287
0.319
0.160

0.089
0.106
0.081

0.083
0.119
0.093

0.350
0.348
0.232

0.259
0.294
0.169

0.100
0.124
0.102

0.091
0.126
0.103

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E. CAPATINA, H. KANG

Table 6: Fraction of Singles with PHI, ages 20-64, CPS
Good HealthTime Period
1996-97 2022-23
White
male
femaleBlackmalefemaleHispanic
male
female4.3

Bad HealthTime Period
1996-97 2022-23

0.703
0.731

0.756
0.764

0.380
0.411

0.359
0.384

0.516
0.544

0.648
0.665

0.191
0.280

0.256
0.313

0.405
0.416

0.536
0.529

0.235
0.180

0.308
0.321

Divorce and Health

Health shocks are often associated with medical expenditures, lower ability to work(outside and inside the home), and possibly lower fertility and life expectancy. For thesereasons, the spouse may find it optimal to exit a marriage with someone who enters badhealth. We expect health and social insurance that cushion the impact of the shock onhousehold resources to dampen the effect on marriage dissolution. In addition, Chen
(2023) find health insurance causes a “marriage lock” in which couples in the U.S. staymarried for the sake of maintaining health insurance coverage.4
We begin with descriptive statistics on divorce, health, and health insurance. To best
see the correlations, we run a logit regression of an indicator equal to one if the person
is currently divorced and single on health interacted with health insurance, age, race,
education, and year dummies. Health insurance is measured at the time of interview
rather than at the time of divorce, and we do not know how long ago the divorce was.
Table 7 shows the predicted probabilities for different groups. We see that bad health isassociated with a 11-12pp higher probability of being divorced and single for uninsured
individuals compared to those in good health, but only 4pp for those insured.
Clearly these correlations could be generated through multiple channels which we
aim to investigate in future research with alternative data sets. Nevertheless, the simple
4

Chen (2023) use data on individuals aged 60-70 in the HRS.

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DEMAND FOR SPOUSAL HEALTH

correlation tells us that bad health and lack of private insurance are strongly associatedwith having been through a divorce and remained single. Divorce costs are an additional
penalty on the unhealthy that have not been accounted for in life-cycle models withhealth.
Table 7: Predicted probabilities of being divorced/separated and single, ages 20-64, CPS

Not Covered by PHI × Good Health
Not Covered by PHI × Bad Health
Covered by PHI × Good Health
Covered by PHI × Bad Health
Observations

Women
0.276
(0.001)
0.395(0.002)0.160(0.001)
0.196(0.002)1072506

Men
0.253(0.002)0.367(0.003)0.130(0.001)0.171(0.002)935966

Standard errors in parentheses
Notes: Predictions calculated at the means of all other variables (age, education, race and year).
We find the coefficients on health do not vary with race, so we do not run separate regressions.
We exclude individuals “never married” from this regression. Individuals previously divorced andre-married are classified as married if they are married at the time of the interview.

4.4

Assortative Mating and Health

We now study the correlations between own health and spousal health. Table 8 shows thefractions of spouses in bad health, by own health, race, and sex. Among healthy Whites,
the frequency of bad health among spouses is only 7-8%. However, for unhealthy Whites,
40-42% of spouses are in bad health. (Approximately 12% of married Whites are in badhealth in total.) We see similar patterns for the other race groups. Unhealthy marriedBlack women have unhealhty spouses 52% of the time.
These patterns suggest that not only are unhealthy individuals less likely to be married,
but when they do marry, they are very likely to have unhealthy spouses. Of course, thisrelationship could be explained by age, education and other characteristics that are
correlated with both spouse’s healths.
To take these into account, Table 9 shows results from a logit regression of spousal
health (0=good, 1=bad) on own health, conditional on gender, age, spousal age, education,
spousal education, and year. The predicted probabilities from these regression are

14

E. CAPATINA, H. KANG

presented in Table 10. Comparing this with table 8, we see that the main patterns arepreserved.5 We do not see a gender asymmetry. Blacks and Hispanics are more likely tohave unhealthy spouses than Whites generally, but the racial differences are especiallylarge among those in bad health.
We also run the same regression controlling for health insurance and allowing it
to interact with own health. Table 11 presents the estimated probabilities by healthinsurance. As we would expect, uninsured individuals are more likely to have unhealthy
spouses conditional on everything than the insured. Even among the insured sample,
there is a high probability the spouse is unhealthy if own health is bad. However, theuninsured have particularly high rates of unhealthy spouses.
Table 8: Fraction of spouses in bad health, married individuals aged 20-64, CPS
sexmale female
White
HealthGood Health
Bad Health
Total
Black
HealthGood Health
Bad Health
Total
Hispanic
HealthGood Health
Bad Health
Total

5

0.074
0.400
0.111

0.083
0.421
0.122

0.097
0.471
0.161

0.117
0.515
0.185

0.080
0.484
0.131

0.081
0.482
0.134

The probability the spouse is unhealthy given own health is bad is lower in Table 10 than in table 8
since age and education account for some of the correlation.

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DEMAND FOR SPOUSAL HEALTH

Table 9: Logit regression of spousal health (1=bad), married individuals aged 20-64, CPS
White

Black

Hispanic

1.760∗∗∗(0.009)female-0.085∗∗∗(0.009)Age (own and spouse)
Yes
Education (own and spouse)
Yes
Years
Yes
Observations
1159228

1.774∗∗∗(0.022)-0.016(0.022)Yes
Yes
Yes
112336

2.123∗∗∗(0.017)-0.173∗∗∗(0.017)
Yes
Yes
Yes
246396

Bad HealthStandard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 10: Predicted probabilities from logit regression of spousal health (1=bad), married
individuals aged 20-64, CPS

male × Good Healthmale × Bad Healthfemale × Good Healthfemale × Bad Health
Observations

White
0.072∗∗∗(0.000)0.310∗∗∗(0.002)
0.066∗∗∗(0.000)0.292∗∗∗(0.002)1159229Black
0.099∗∗∗(0.002)
0.394∗∗∗(0.005)0.098∗∗∗(0.002)0.390∗∗∗(0.005)112336

Hispanic
0.080∗∗∗(0.001)
0.422∗∗∗(0.004)0.068∗∗∗(0.001)0.380∗∗∗(0.004)246397

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Predictions calculated at the means of all other variables (i.e., age, age of spouse, education,
education of spouse, sex, and year).

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E. CAPATINA, H. KANG

Table 11: Predicted probabilities from logit regression of spousal health, married
individuals aged 20-64, CPS

Not Covered by PHI × Good Health
Not Covered by PHI × Bad Health
Covered by PHI × Good Health
Covered by PHI × Bad Health
Observations

White
0.113∗∗∗(0.001)0.404∗∗∗(0.003)0.063∗∗∗(0.000)0.259∗∗∗(0.002)1159229Black
0.142∗∗∗(0.003)0.448∗∗∗(0.007)0.088∗∗∗(0.001)0.355∗∗∗(0.006)112336

Hispanic
0.085∗∗∗(0.001)0.444∗∗∗(0.005)0.067∗∗∗(0.001)0.354∗∗∗(0.005)246397

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Predictions calculated at the means of all other variables (i.e., age, age of spouse, education,
education of spouse, sex, and year).

4.5

The Affordable Care Act (ACA) and Marriage/Divorce

Health insurance protects household resources in the face of health shocks, so an
expansion of health insurance could potentially affect the relationship between healthand family formation/dissolution as well as assortative mating patterns. To begin
exploring the issue, we present some preliminary results on marriage patterns before
and after the Affordable Care Act (ACA) which came into effect mainly in 2014. Researchin this area could shed light on costs and benefits of insurance reform that have not beenpreviously considered. For example, if the ACA led to more marriages between healthy
and unhealthy individuals, its cost might be lower because the extra intra-household risk
sharing that arises would partly substitute the need for other health and social insurance
programs: a healthy spouse might help prevent the unhealthy individual from reachinga point of reliance on means-tested transfers like Medicaid and welfare. On the other
hand, the ACA might have reduced ”marriage lock” leading to more divorce.
We construct a dummy equal to 1 if the year is 2014 or after, and zero otherwise. Table
12 shows that the time period after the ACA is associated with lower marital rates, forboth young (ages 20-39) and old (40-64) individuals. For the young, the marriage gap
between healthy-unhealthy individuals decreased, while for the old it increased. The
probability of being divorced increased with the ACA only for those in bad health.

17

DEMAND FOR SPOUSAL HEALTH

These correlations are consistent with the hypothesis that the ACA reduced “marriagelock” and therefore increased divorce, and lowered marital rates especially for old
unhealthy people. (Since unhealthy people are often married with other unhealthypeople, this might be why we do not see an increase in divorce for healthy people, ormaybe because healthy people are quick to re-marry.)Table 13 also shows that after the ACA, there is less assortative mating based on health.
The coefficient on bad health interacted with the post-ACA time period is negative.
Health insurance could lower the penalty associated with bad health in the marriage
market. However, we aim to explore other possible explanations in the future, taking into
account equilibrium effects.
Table 12: Logit of being married and ”divorced and single,” CPS

Bad Health
ACA=1
Bad Health × ACA=1
age
Education
Sex
Race
Observations

Mar (young)

Mar (old)

Divorced (all)-0.724∗∗∗(0.015)-0.290∗∗∗(0.006)0.082∗∗∗(0.021)0.159∗∗∗(0.001)
Yes
Yes
Yes
923785-0.714∗∗∗(0.008)-0.089∗∗∗(0.006)-0.099∗∗∗(0.012)0.008∗∗∗(0.000)Yes
Yes
Yes
1111452

0.590∗∗∗(0.009)-0.030∗∗∗(0.006)
0.091∗∗∗(0.013)0.012∗∗∗(0.000)Yes
Yes
Yes
1562236

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: ACA is a dummy equal to one if year is 2014 or greater.

18

E. CAPATINA, H. KANG

Table 13: Logit of wife health on husband health, CPS
Wife H (young)

Wife H (old)2.478∗∗∗(0.037)0.306∗∗∗(0.027)-0.335∗∗∗(0.056)
Yes
Yes
Yes
189292

1.873∗∗∗(0.018)0.030∗∗(0.015)-0.095∗∗∗(0.027)
Yes
Yes
Yes
376916

Bad Health
ACA=1
Bad Health × ACA=1
Education
Race
Ages (own and spouse)
Observations

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: ACA is a dummy equal to one if year is 2014 or greater.

4.6

The Income Penalty of Bad Health over Time

Previous literature has studied how changes in the college wage premium are related toassortative mating and inequality (e.g., Greenwood et al. (2016)). In the same spirit, weinvestigate the returns to good health in the labor market over time, and whether there is
any relationship with assortative mating.
Figure 2 presents the marginal effect of being in bad health vs good health on log
total income, controlling for education, quadratic in age, race and sex, and year, andallowing for an interaction between year and health. Bad health was associated with 46%
lower income than when in good health in 1996 vs. 55% lower income in 2023. This is a
substantial increase in the income penalty of bad health.
In addition to the income penalty, medical prices have increased faster over timethan regular consumption prices, further worsening the negative impact of bad healthon households’ budgets. (The ACA offset this to some extent by reducing overall out-ofpocket costs.)

19

DEMAND FOR SPOUSAL HEALTH

Figure 2: Average Marginal Effects of Bad Health from log(income) Regression, CPS−.9Effects on linear prediction
−.8
−.7
−.6

−.5

Average marginal effects of 1.health_status with 95% CIs

1990

2000

2010
survey year

2020

2030

Notes: From OLS regression of log income on health, year, health*year, race, age, age squared,
education and sex, ages 20-64.

4.7

The Correlation Between Own Health and Spousal Income

We now explore the relationship between own health and the spouse’s income. Is good
health associated with richer spouses conditional on everything else? Is there evidenceof an added worker effect from the spouse to compensate for the other’s bad health?
We run OLS regressions of log spousal income on own health, year, year interactedwith health, race, education and a quadratic in age, separately by gender. The left panel of
Figure 3 shows the average marginal effect of women’s bad health on husband’s income,and the right hand side panel shows the average marginal effect of men’s bad health on
the wive’s income. First we note that the AME in the left figure is large and negative andincreases in absolute value over time. Women in bad health have husbands with lower
incomes relative to those in good health, and the gap increases over time.
On the other hand, the right panel shows that for most years, men’s health status is
not significantly correlated with the wive’s incomes. Only in years 2019-2023 we see anegative correlation emerging, but this is much smaller than in the left panel.
The pattern in Figure 3 is to some extent explained by the fact that unhealthy women
marry unhealthy men, and since the income penalty of bad health has increased over20

E. CAPATINA, H. KANG

time, these unhealthy husbands are earning less over time relative to the mostly healthyhusbands of healthy women.
We dig deeper by repeating the same regressions, but keeping only spouses who arein good health. Figure 4 presents the results. We see that women in bad health still havehusbands with lower incomes than good health women, even among the group where all
husbands are healthy. And we see this still worsens over time. On the other hand, we see
the AME of men’s bad health is now mostly positive (right hand side of figure 4), whichmeans that unhealthy men have wives with higher incomes relative to healthy men, inthe group where the wives are in good health. There are two likely driving forces here: (1)a selection effect where unhealthy people get matched with lower income spouses, and(2) an added worker effect where the spouse might be working more to compensate forthe individual’s bad health. For women, the former seems to dominate, whereas for men,the latter seems to dominate.
Finally, Figure 5 shows the fraction of spouses in bad health by own total individual
income group (income in thousands). We see that low income individuals are most likely
to have spouses in bad health, especially males. The fraction of spouses in bad healthdeclines very quickly as own income rises from 0 to 100K, and remains fairly constant at
incomes greater than 100K.
Figure 3: Average Marginal Effects of Bad Health, Spouse’s log(income) Regression, CPS(Husband income - left, wife income - right)
Average marginal effects of 1.health_status with 95% CIs

Effects on linear prediction
−.2
0
−.4

−.7

Effects on linear prediction
−.6
−.5
−.4

−.3

.2

Average marginal effects of 1.health_status with 95% CIs

1990

2000

2010
survey year

2020

2030

1990

2000

2010
survey year

2020

2030

Notes: From OLS regression of log income of husband on health, year, health*year, race, age, age
squared, and education, ages 20-64.

21

DEMAND FOR SPOUSAL HEALTH

Figure 4: Average Marginal Effects of Bad Health, Spouse’s log(income) Regression, CPS(Husband income - top, wife income - bottom), keeping only spouses in good health
Average marginal effects of 1.health_status with 95% CIs

−.2

−.5

Effects on linear prediction
−.4
−.3
−.2

Effects on linear prediction
0
.2

−.1

.4

Average marginal effects of 1.health_status with 95% CIs

1990

2000

2010
survey year

2020

2030

1990

2000

2010
survey year

2020

2030

Notes: From OLS regression of log income of husband on health, year, health*year, race, age, age
squared, and education, ages 20-64.

0.05

Mean Health Spouse
.1
.15
.2

.25

Figure 5: Fraction of spouses in bad health by own income group, CPS

0

100

200

300

inctot_cat
malefemaleNotes: Ages 20-64 and married.

4.8

Health and Spouse’s Education

Another way to look at the penalty of bad health in the marriage market is looking at
the probability of marrying down with respect to education. We run a logit regressionof marrying down (1=married to someone of strictly lower education than own) on own

22

E. CAPATINA, H. KANG

health status, a quadratic in age and own education, separately by race and sex. We
exclude individuals with high school or less since they cannot marry any further down.
Table 14 presents the estimated predicted probabilities from these regressions for Whites
aged 25-44. We compare two periods: 1996-97 and 2022-23.
Individuals in bad health are significantly more likely to marry down than those ingood health. We see that the average marginal effect of bad health on marrying down isabout the same for men and women, and decreased over time. But while the probabilities
of marrying down have decreased a lot for men (16pp for unhealthy men), they stayed
constant for women. This is likely because the supply of educated women has increased
substantially over time. Overall, while unhealthy White men marry less over time, when
they do marry, they marry on average higher or equal educated women. Unhealthy
women on the other hand still marry down about a third of the time.
Table 14: Predicted probabilities from Logit of ”marring down in terms of education,”
Whites aged 25-44, CPS
Men (96-97)
Good Health0.23∗∗∗(0.00)Bad Health0.33∗∗∗(0.02)
Observations
16018

Men (22-23)
0.13∗∗∗(0.00)
0.17∗∗∗(0.02)
11555

Women (96-97)
0.23∗∗∗(0.00)
0.32∗∗∗(0.02)
17988

Women (22-23)
0.25∗∗∗(0.00)
0.31∗∗∗(0.02)
12917

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: Whites, married, ages 25-44, excludes ”less than HS.” ”Marry down” = 1 if the spouse’s
education is strictly lower than own education. Logit regression includes own health, age, age sq.,and own education.

4.9

Various Health Dimensions: Evidence from MEPSThe MEPS data set has the advantage of containing detailed information on respondents’
health and health conditions. However, the sample size is smaller than the CPS, which isa concern especially when studying minorities and restricting to certain characteristics
that reduce the sample size (e.g., Black married men in bad health).
We use the MEPS to investigate which dimensions of health are most strongly correlatedwith marital patterns. We consider BMI, mental health, self-reported health and thenumber of chronic conditions reported. We acknowledge that these variables are endogenous,
with mental health likely being strongly determined by marital status and when married,DEMAND FOR SPOUSAL HEALTH

23

the spouse’s health status. Our estimates here are meant to simply present descriptive
patterns.
We run a logit regression of being married on these health measures, education,age, race, and years. We split the sample into young (ages 20-39) and old (ages 40-64)
ages. In terms of the marriage market equilibrium, the gender-specific effects of healthconditions on marital status could have different implications depending on a person’sage. For older people, the effects might arise from the different supplies of potential
partners. For example, due to mortality issues, older men are likely to have a higher
number of potential spouses relative to older women. Such an imbalance in the marriage
market would not occur in the young people’s marriage market.
Table 15 reveals some important gender asymmetries. Higher BMI in men is associatedwith higher probabilities of being married. BMI is not statistically significant for young
women, but is negatively correlated with marriage for old women. A higher number ofchronic health conditions is associated with lower marriage for both genders, but thecoefficients are about twice as large in absolute value for women compared to men. For
both gender, worse mental health is associated with a lower probability of being married,
and the effect is stronger for men. Worse self-reported health is positive associated withmarriage for young men, but negatively associated for old women.
We also take advantage of the panel dimension in MEPS to explore marital transitions.
Table 16 shows results from a logit regression of transitioning from single to married
from round 1 of interview to round 5 (approximately 2 years apart), for young men andwomen. We see again that high BMI in men is associated with higher probabilities ofgetting married, mental health is associated with lower probabilities, and for women
only, the number of chronic health conditions has a negative association. These findings
point to the need for a careful construction and interpretation of the health variables infuture research looking at health and marriage.

24

E. CAPATINA, H. KANG

Table 15: Logit Regression of Being Married, MEPS

BMI
Mental HealthSelf-Reported Health
Number of Chronic Conditions
Age
Education
Race
Years
Observations(Young Men)

(Old Men)(Young Women)(Old Women)

0.204∗∗∗(0.016)-0.298∗∗∗(0.016)0.049∗∗∗(0.017)-0.084∗∗∗(0.030)
Yes
Yes
Yes
Yes
34921

0.201∗∗∗(0.015)-0.252∗∗∗(0.013)0.003(0.014)-0.072∗∗∗(0.014)
Yes
Yes
Yes
Yes
38864

-0.002(0.014)-0.210∗∗∗(0.014)0.018(0.014)-0.205∗∗∗(0.024)
Yes
Yes
Yes
Yes
39761

-0.057∗∗∗(0.013)-0.189∗∗∗(0.011)-0.053∗∗∗(0.013)-0.139∗∗∗(0.012)
Yes
Yes
Yes
Yes
44597

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Note: Young men (women) indicates people whose ages are between 20 and 39. Old men (women) indicates people whose ages
are between 40 and 64. Mental health and self-reported health are standardized and higher numbers indicate worse health. The
maximum number of chronic conditions is 7: coronary heart disease, angina, heart attack, emphysema, diabetes, asthma, andarthritis.

Table 16: Logit Regression of Getting Married from Round 1 to Round 5 of Interview,
MEPS

BMI
Mental HealthSelf-Reported Health
Number of Chronic Conditions
Age
Education
Race
Years
Observations(Young Men)

(Young Women)

0.158∗∗∗(0.033)-0.148∗∗∗(0.032)0.019(0.031)-0.078(0.063)
Yes
Yes
Yes
Yes
18368

0.024
(0.030)-0.114∗∗∗(0.029)0.015(0.029)-0.107∗∗(0.052)
Yes
Yes
Yes
Yes
20448

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Note: Young men (women) indicates people whose ages are between 20 and 39. Old men (women) indicates people whose ages
are between 40 and 64. Mental health and self-reported health are standardized and higher numbers indicate worse health. The
maximum number of chronic conditions is 7: coronary heart disease, angina, heart attack, emphysema, diabetes, asthma, andarthritis.

DEMAND FOR SPOUSAL HEALTH

5.

25

Structural Analysis of the Demand for Spousal HealthIn this section, we first show that there is a notable difference in the systematic gain tomarriage for an individual with good health but low education depending on their gender.
Specifically, the gains to marriage for females with low education but good health surpass
those of females with high education but poor health. The gain to marriage for males
show the opposite pattern. That is, the gains to marriage for males with low education
but good health are less than those of males with high education but poor health. We
show that this gender asymmetry decreases over time, and has recently disappeared. To
understand the driving force behind this change, we build a multi-dimensional matchingmodel where individuals sort based on education and health status. The model allows
returns to education to differ by gender and time.

5.1

Changes in the Systematic gain to Marriage

We first explore how the marriage gain to the individual with bad health changes overtime. Then, we examine the relative gain to marriage for a (low education - good health)individual compared to a (high education - bad health) individual. For this analysis,
we use a bi-dimensional marriage matching function of education and health status,
which extends the uni-dimensional marriage matching function in Choo and Siow(2006). The systematic marriage gain is measured by the log of the relative ratio of
the number of couples to the number of singles of the local distribution. For males,
we let h denote health related characteristics (h = good, bad) and e denote education(e = college, < college). For females, health and education are denoted as h′ and e′ . The
total systematic gain to marriage of a couple with health characteristics h and h′ andeducational characteristics e and e′ is defined as

πh,h′ ,e,e′ = ln Πh,h′ ,e,e′ ,
and

26

E. CAPATINA, H. KANG

µh,h′ ,e,e′,Πh,h′ ,e,e′ = p P P
P P( e h µh,0,e,0′ )( e h µ0,h′ ,0,e′ )where µh,h′ ,e,e′ is a number of couples which consist of husband and wife with (h, e) and(h′ , e′ ) health-education pair, respectively.
To investigate the impact of bad health on marital transition for an individual, we firstcompute the systematic gain to marriage for people with bad health across all education
PP
levels, for husbands ( h πh,bad′ ) and for wives ( h′ πbad,h′ ). Figures 6 and 7 show how thegains changes over time for both husbands and wives, respectively. For both males andfemales, the systematic gain declines over time. This suggests that the gains to marriage
relative to being unmarried have significantly decreased, which is consistent with ourearlier findings that unhealthy individuals are more likely to remain unmarried.
Figure 6: Systematic Gain to Marriage for Unhealthy male

−9

h

−10

Year

Source: CPS data surveyed from 1995 to 2023

2020

2015

2010

2005

2000

−11
1995

∑ πh, bad′

−8

27

DEMAND FOR SPOUSAL HEALTH

Figure 7: Systematic Gain to Marriage for Unhealthy female−10h′∑ πbad, h′

−9

−11

2020

2015

2010

2005

2000

1995

−12

Year

Source: CPS data surveyed from 1995 to 2023

Next, motivated by earlier empirical findings suggesting that the effects of healthdepend on an individual’s gender, we investigate how the gains from marriage varydepending on the spouse’s health and education. In particular, we consider men andwomen in good health and with high education, and we compute the relative marriage
gain of a spouse with low education and good health compared to the gain of a spousewith high education and bad health. Specifically, the relative gain for females and malesare:ρf emale =πgood,good′ ,high,low′ − πgood,bad′ ,high,high′ ;ρmale =πgood,good′ ,high,low′ − πgood,bad′ ,high,high′ ,respectively. If the sign of ρ is positive, it suggests the benefits of spousal good healthoutweigh those of the spouse being highly educated.
Based on our empirical findings in the earlier section, we expect there to be a notabledifference in the relative gain between males and females. Figure 8 presents the differencein the relative gains from good health to marriage, namely ρf emale − ρmale . This figure
shows two interesting patterns. First, there is a significant difference in the relative gain,
especially before 2000, between females and males. It suggests that female good health ishighly valued in the marriage market and outweighs the effects of obtaining a college
degree (i.e., being highly educated). For men, the sign of ρmale is the opposite. Second,such gender asymmetry declines over time and disappears after 2021. This suggests that28

E. CAPATINA, H. KANG

the gain to marrying individuals with good health and low education has significantly
dropped. There are a number of potential channels behind this trend.
First, the significant gender asymmetry observed in the mid-1990s and early 2000s
might be attributed to the dominance of health-related concerns over income-related
considerations when considering a potential wife. Conversely, income-related considerations
could play a crucial role in choosing a potential husband, possibly due to the gender pay
gap. Secondly, the shift in the trend could be the result of several factors: (i) a significant
increase in female income, which could influence the decision to stay single or choose ahusband, and (ii) potential changes in the wife’s income contribution to the householdin comparison to contributions potentially related to health, such as fertility.
Figure 8: Gender Asymmetry in Spousal Health (Good Health)
Gender Asymmetry in Demand for Spousal Healthasym_good

0.75

0.50

0.25

2020

2015

2010

2005

2000

1995

0.00

year
Source: CPS data surveyed from 1995 to 2023

5.2

Theoretical Framework

Our aim is to capture the mechanisms underlying the health-related gender asymmetry
and its evolution over time. To this end, we build a multidimensional matching model
where individuals match based on education, health status, and other observable as wellas unobservable characteristics. We specify a transferable utility matching model and lethouseholds contribute to the matching surplus in two different ways: household income
and health-related public good that is shared within the household. Additionally, weDEMAND FOR SPOUSAL HEALTH

29

allow for changes in returns to education and health over time. The following sections
provide the initial draft of our model, with plans to incorporate additional features infuture versions.
5.2.1

Timeline and Environment

Define i ∈ I and j ∈ J as sets of males and females in the marriage market. n thefirst stage, each person chooses the number of years of education, based on theirinitial conditions. An individual with a higher education attainment can make a bigger
contribution to the joint household income. A higher income leads to the higher marital
surplus through increased total consumption of the household. At the same time, it
increases the value of remaining single. Thus, increase in returns to education might
result in increasing in reservation match value.
In the second stage, people are matched based on the match surplus given the
distribution of potential spouses and the equilibrium restrictions.
5.2.2

Surplus function

We specify a transferable utility in marriage surplus function (Choo and Siow, 2006; Adda
et al., 2023; Low, 2023). We adhere to the assumptions of Choo and Siow (2006), which
include additive and separable shocks of the surplus function, distributed following TypeI extreme value.
The marriage surplus Φij is a function of household income Iij , health-related public
good Hij , a match specific unobserved shock εij , and a set of parameters, θ. The marriage
surplus for a couple with male i and female j is represented by:Φ(Xi , Yj , θ) = ϕ(Iij , Hij , θ) − ωi (Xi ) − ωi (Yj ) + εij ,where ϕ is an observable part of the marital surplus, Xi and Yj are sets of demographic
characteristics for male i and female j, respectively, and ω represents the value ofremaining single.
The function ϕ is specified asϕ(Iij , Hij ) = αy ln Iij + αh ln Hij .

30

E. CAPATINA, H. KANG

The specification implies that we assume constant returns to scale and neither substitutability
nor complementarity between H and I. Income equation is specified asln Iij = γ0 +

K
X

f
m
{γk,i
I(degreei )I(worki ) + γk,jI(degreej )I(workj )},
|
{z
}
|
{z
}
k=1
Husband’s income

Wife’s incomeand Hij is a function of husband’s health hi and wife’s health hj . In particular,
Hij = g(hi , hj , θih , θjh ).
We let the associated parameters vary by gender in order to allow the husband and wife
contributions to Hij to be different.

5.3

Equilibrium

The marriage market equilibrium is equivalent to the outcome of the social planner
problem including the option of remaining singles:max{αij }M,F
i=0,j=0X

αij Φij

subject to

αij = 0, 1, ∀i, j,
M
X
i=0
F
X
j=0

T.B.C.

αij = 1, ∀j
αij = 1, ∀i

DEMAND FOR SPOUSAL HEALTH

6.

31

Conclusion

This paper presents new patterns in terms of health and marriage, by race, gender andover time. Overall, given the importance of health for economic outcomes, it is not
surprising that health is an important dimension to consider in the marriage market. A
key finding is that individuals in bad health are most often single or married to unhealthy
partners, thus not benefiting from intra-household insurance from a healthy spouse. Bad
health is also associated with lower spousal education (conditional on everything else)and lower spousal income (for women only). Trends over time suggest that the income
penalty of bad health has increased. Given these findings, it is crucial to study how
the benefits of health insurance are distributed among different groups. A concerning
finding is that White single unhealthy men who do increasingly poorly in the marriage
market have also seen a decrease in private health insurance over time despite the ACA.
We believe that our findings open an interesting line of research for the future. We areworking on building a model that can capture the observed patterns, and expanding ouranalysis of the importance of health insurance.
In addition, future research could study how inter-generational mobility and childoutcomes are related to marital decisions and assortative mating based on health. If
unhealthy people marry unhealthy people, or if unhealthy women marry lower incomemen, or end up single, their children will be more likely to face challenges. It would be
fruitful to determine how policy can be used to change such marital patterns.

32

E. CAPATINA, H. KANG

References
Adda, J., P. Pinotti, and G. Tura (2023). There’s More to Marriage than Love: The Effect ofLegal Status and Cultural Distance on Intermarriages and Separations. Unpublished
Manuscript.
Becker, G. S. (1993). A treatise on the family: Enlarged edition. Harvard university press.
Blundell, R., L. Pistaferri, and I. Saporta-Eksten (2016, February). Consumption inequality
and family labor supply. American Economic Review 106(2), 387–435.
Bratti, M. and M. Mendola (2014). Parental health and child schooling. Journal of healtheconomics 35, 94–108.
Capatina, E. (2015). Life-cycle effects of health risk. Journal of Monetary Economics 74,
67–88.
Capatina, E. and M. Keane (2023). Health shocks, health insurance, human capital, andthe dynamics of earnings and health.
Chen, T. (2023). Health insurance coverage and marriage behavior: Is there evidence ofmarriage lock? International Studies of Economics 18(2), 136–158.
Chiappori, P.-A., S. Oreffice, and C. Quintana-Domeque (2012).

Fatter attraction:

Anthropometric and socioeconomic matching on the marriage market. Journal of
Political Economy 120(4), 659–695.
Choi, S. and A. Valladares-Esteban (2020). On households and unemployment insurance.
Quantitative Economics 11(1), 437–469.
Choo, E. and A. Siow (2006).

Who marries whom and why.

Journal of political

Economy 114(1), 175–201.
Ciscato, E. (2023). Assessing racial and educational segmentation in large marriage
markets. Working paper.
De Nardi, M., E. French, and J. B. Jones (2010). Why do the elderly save? The role ofmedical expenses. Journal of Political Economy 118(1), pp. 39–75.

33

DEMAND FOR SPOUSAL HEALTHDe Nardi, M., S. Pashchenko, and P. Porapakkarm (2022, October). The lifetime costs ofbad health. Working Paper 23963, National Bureau of Economic Research.
Eshaghnia, S. and J. J. Heckman (2023, October). Intergenerational transmission of
inequality: Maternal endowments, investments, and birth outcomes. Working Paper
31761, National Bureau of Economic Research.
French, E. (2005). The effects of health, wealth, and wages on labour supply andretirement behaviour. Review of Economic Studies 72(2), 395–427.
Greenwood, J., N. Guner, G. Kocharkov, and C. Santos (2016). Technology and thechanging family: A unified model of marriage, divorce, educational attainment,
and married female labor-force participation.

American Economic Journal:

Macroeconomics 8(1), 1–41.
Guner, N., Y. Kulikova, and J. Llull (2018). Reprint of: Marriage and health: Selection,
protection, and assortative mating. European Economic Review 109, 162–190.
Lawn, J. E., A. Tinker, S. P. Munjanja, and S. Cousens (2006). Where is maternal and childhealth now? The Lancet 368(9546), 1474–1477.
Low, C. (2023, June). The Human Capital - Reproductive Capital Tradeoff in Marriage
Market Matching. Journal of Political Economy, 726238.
Ortigueira, S. and N. Siassi (2013). How important is intra-household risk sharing forsavings and labor supply? Journal of Monetary Economics 60(6), 650–666.
Pencavel, J. (1998). Assortative mating by schooling and the work behavior of wives andhusbands. The American Economic Review 88(2), 326–329.
Persson, P. (2020). Social insurance and the marriage market. Journal of Political
Economy 128(1), 252–300.

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A.

Appendix

A.1

Gain from marriage for different races

While the figures in the main text showed strong gender asymmetries for Whites, we
do not observe these for Blacks and Hispanics. This is consistent with out descriptive
patterns where we saw it was mainly Whites that experienced the most pronounced
changes over time in marriage patterns.
Figure A.1: Gender Asymmetry in Spousal Health (black)
Gender Asymmetry in Demand for Spousal Health (Black)asym_good

0.4

0.0

2020

2015

2010

2005

2000

1995

−0.4

year

Source: CPS data surveyed from 1995 to 2023

Figure A.2: Gender Asymmetry in Spousal Health (Hispanic)
Gender Asymmetry in Demand for Spousal Health (Hispanic)

1.5

asym_good

1.0
0.5
0.0

2020

2015

2010

2005

2000

1995

−0.5

year

Source: CPS data surveyed from 1995 to 2023