Patterns and Factors Associated With Medical Expenses 

and Health Insurance Premium Payments

Shinae Choia and Justin Blackburnb

This study sought to investigate household sociodemographic characteristics as predictors of patterns of health 

insurance premiums and medical expenses of consumers using the 2014 Consumer Expenditures Survey. This 

study found that age, being married, educational attainment, and log of family salary income were associated 

with higher family spending on both health insurance premiums and medical expenses. Government employment 

status was associated with lower spending on health insurance premiums and medical expenses. Findings 

from this research are informative for both households in determining health insurance premiums and medical 

expenses throughout the life course as well as financial advisors in personal financial planning and counseling 

focused on health care.

Keywords: health care, health insurance premiums, household sociodemographic characteristics, payments for 

medical expenses

The Patient Protection and Affordable Care Act 

(ACA) enacted in 2010 has set forth sweeping reform of the health-care system including quality improvements and increasing access to health care. The largest 

impact of the ACA is on the health insurance industry, which 

includes the individual mandate to obtain health insurance, 

ensuring access for pre-existing conditions (guaranteed issue), extending coverage for young adults, and lowering 

costs through regulations on premiums, preventive services, 

and prescription drugs (Patient Protection and Affordable 

Care Act, 42, 2010; U.S. Department of Health and Human 

Services, 2015). Given these changes, the price of insurance premiums is currently volatile as insurance companies 

attempt to price products in light of the new regulations 

and the flood of new enrollees for those companies offering plans on the health insurance marketplaces and new 

employer-sponsored plans.

Health insurance companies set premium prices based 

on the risk pool, projected medical costs, administrative costs, and laws and regulations (American Academy of Actuaries, 2015). The national average monthly 

premium per person on the individual market in 2010 

was $215 (Kaiser Family Foundation, 2011). Prior to the 

ACA, premiums were determined by several factors including individual health status. Under the ACA, health 

insurance products provided on the marketplace are now 

able to consider five factors when setting premium prices for individuals: age, location, tobacco use, individual 

versus family enrollment, and plan category (HealthCare.gov, 2016). For example, average adjusted monthly 

premiums are about $35 higher in areas with the lowest 

population density compared to the highest (Barker, McBride, Kemper, & Mueller, 2014). Employer-sponsored 

health insurance coverage has also been affected by the 

ACA, which required employers with at least 100 employees to offer coverage (in 2016 this was reduced to 

50 full-time equivalent employees). There is wide variability in the cost of premiums for employer-sponsored 

health insurance nationwide. In 2010, the U.S. average 

total premium cost for single coverage among privatesector employees was $4,940, with an average out-ofpocket contribution by employees of $1,021 per year; 

for family coverage, average annual premium costs were 

a

Assistant Professor, Department of Consumer Sciences, College of Human Environmental Sciences, University of Alabama, 304 Adams Hall, Box 870158, 

Tuscaloosa, AL 35487. E-mail: schoi@ches.ua.edu b

Assistant Professor, Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana UniversityIndianapolis, Health Sciences Building RG 5194, 1050 Wishard Blvd, IN 46202. E-mail: jblackb@iu.edu

Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018, 6-18

© 2018 Association for Financial Counseling and Planning Education®

http://dx.doi.org/10.1891/1052-3073.29.1.6

6

JFCP-16-00047.indd 6 12-06-2018 
$13,871, with employee contributions of $3,721 (National Conference of State Legislatures, 2015). Notably, 

Medicare premiums were not specifically targeted by the 

ACA.

Out-of-pocket costs for medical expenses vary for families based primarily on health insurance coverage and 

health status. The national average cost for health services was $795 per person in 2009; for the non-elderly 

without insurance the cost was $862 and for those with 

insurance it was $706, while those with poor health it 

was $1,663 (Kaiser Family Foundation, 2012). Variation in spending is evident among demographic groups 

as a function of health status. For example, older adults 

and females have higher spending per year on average (Kaiser Family Foundation, 2012) and older adult 

households have higher financial burden (Hong & Kim, 

2000). There is also a negative relationship between 

health and financial stress, particularly when health care 

costs are increasingly high (Kim, Garman, & Sorhaindo, 2003; O’Neill, Sorhaindo, Xiao, & Garman, 2005). 

Another component of the ACA is the emphasis on 

lowering costs of medical care by emphasizing preventive care, more efficient delivery of care, and for older 

adults expanding prescription drug coverage in under 

Medicare Part D, and through the state-option to expand 

Medicaid.

This research was motivated by the often reported but yet 

incompletely understood relationship of household characteristics and health care and a lack of understanding of 

factors associated with health care. The overall goal of this 

research was to improve our understanding of the impacts 

of household characteristics on medical expenses and 

health insurance premiums in the U.S. While it is known 

that older adults spend more on health (Fahle, McGarry, 

& Skinner, 2016), including regional variations in Medicare claims (Fisher et al., 2003) and out-of-pocket costs 

(Chen, Norton, Langa, Le, & Epstein, 2014), less is known 

regarding household spending throughout the lifespan. As 

policymakers evaluate the ACA or discuss alternatives, 

understanding gaps in spending for health-related expenditures is paramount. We sought to investigate household 

sociodemographic characteristics as predictors of patterns of health insurance premiums and medical expenses 

of consumers using the 2014 Consumer Expenditures 

Survey.

Literature Review

What Types of Health Insurance Do Americans Have?

Health insurance in the U.S. has been intertwined with employment since the early accident and health plans were developed in the late 1800s (Scofea, 1994). To prevent inflation 

during World War II, Congress enacted the Stabilization Act 

which limited wage increases (Stabilization Act of 1942, 

1942). As a result, employers began offering additional compensation through health insurance plans and other benefits. 

Employer-based insurance peaked in the 1980s, then coverage 

began declining—by as much as 6.4% points between 1987 

and 2004 (Enthoven & Fuchs, 2006). Due to the rising costs of 

health insurance in the last two decades, the cost of employersponsored insurance has increased by 59% since 2000 (Cutler, 

2003; Baicker & Chandra, 2006). Employers shifted some of 

the increasing costs of health insurance coverage to employees, which resulted in lower take-up during the 1990s and 

2000s (Cutler, 2003; Gruber & McKnight, 2003). As of 2014, 

health insurance coverage is obtained by approximately 49% 

of the U.S. population through an employer, or approximately 

147 million non-elderly people (Kaiser Family Foundation, 

2016a). Other primary sources of health insurance coverage 

are Medicaid (19% of the U.S.), Medicare (13%), and other 

public insurance (2%) (Kaiser Commission on Medicaid and 

the Uninsured, 2015; Kaiser Family Foundation, 2016b).

What Drives the Cost of Health Insurance Premiums?

Market competition and firm consolidation seem to contribute to the cost of non-public insurance premiums. Dafny 

(2010) first demonstrated the lack of market competition 

lead to higher premiums, especially in areas with 6–8 or 

fewer firms. Further work by Dafny, Duggan, and Ramanarayanan (2012) observed that between 1998 and 2006 the 

merger of Aetna and Prudential resulted in increased premiums by 7%, approximately $34 billion in additional 

spending. A recent brief by Holahan (2014) observed that 

after the first open enrollment in the ACA Marketplace, premiums were lowest in areas of high concentration. Among 

Medicare beneficiaries, out-of-pocket costs for premiums, 

copayments, and coinsurance grew from $3,293 on average annually to $4,734 from 2000 to 2010, a 44% increase 

(Kaiser Family Foundation, 2014).

Who Pays More in Medical Expenditures?

Health care spending in 2014 accounted for 17.5% of the U.S. 

gross domestic product, a percentage that has risen steadily for 

over 30 years (Martin, Hartman, Washington, & Catlin, 2017). 

Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018 7

JFCP-16-00047.indd 7 12-06-2018 
Aside from specific health conditions, health expenditure differs by age and gender, and is closely tied to age. In 2010, the 

average elderly person spent $18,424 per year, or three times 

more than the average working-age adult (Lassman, Hartman, 

Washington, Andrews, & Catlin, 2014). Females accounted for 

56% of spending in 2010, $7,860 per person, which was 25% 

more per capita than males (Lassman et al., 2014). However, 

growth in spending is higher among males, which may be in 

part due to increases in prescription drug use (Lassman et al., 

2014). States also vary in per capita health care expenditures. In 

2009, the highest was $9,278 per capita in Massachusetts and 

the least $5,031 in Utah (Kaiser Family Foundation, 2016a). 

Furthermore, the costs of medical procedures vary across geography. For example, Newman, Parente, Barrette, and Kennedy (2016) observed commonly performed procedures such 

as pregnancy ultrasounds can be as much as three times higher 

between cities just 60 miles apart.

What Drives Spending on Health Care?

Circulatory system conditions accounted for 17% of personal health spending in 2005 and was the health condition 

associated with the largest source of growth from 1996 to 

2005 (Roehrig, Miller, Lake, & Bryant, 2009). Direct costs 

of health care for cardiovascular conditions is estimated to 

be over $324.1 billion (American Heart Association, 2010). 

Chronic conditions contributed to an overall rise in out-ofpocket spending from 1996 through 2005 regardless of sex, 

race/ethnicity, and income and from middle-age onward 

(Paez, Zhao, & Hwang, 2009). Chronic diseases not only 

have high direct costs, but can also increase the costs of other complications the disease creates. For example, in 2005 

spending on diabetes treatment was $27.9 billion; however, 

spending by people with diabetes was $190.4 billion (Roehrig et al., 2009). The rise of new treatments and expanded 

definitions of who should be treated have also increased 

spending on chronic conditions, most notably cholesterol 

(Roehrig et al., 2009).

Research Questions

Specifically, we sought to address the following research 

questions:

RQ 1: What are the patterns of the payments for medical 

expenses and health insurance premiums of consumers?

RQ 2: Who pays more for medical expenses and health insurance premiums?

RQ 3: What are the roles of sociodemographic factors in 

predicting the payments for medical expenses and health 

insurance premiums?

Methods

Data and Sample

This study used the publicly available dataset drawn from 

the 2014 Consumer Expenditure Survey (CE) for nationally representative of the U.S. civilian population. The CE 

consists of two surveys, the Quarterly Interview Survey and 

the Diary Survey, which provide information on the buying 

habits of American consumers, including data on their expenditures, income, and consumer unit (CU, families, and 

single consumers) characteristics. The U.S. Census Bureau 

collected the survey data for the Bureau of Labor Statistics. 

We mainly used the Interview Survey, which is a rotating 

panel basis. After each consumer unit has been interviewed 

in the sample for five consecutive quarters, it is dropped 

from the survey, and a new address is selected to replace it.

Reference person is the first member mentioned by the respondent when asked to “Start with the name of the person 

or one of the persons who owns or rents the home.” It is 

with respect to this person that the relationship of the other 

consumer unit members is determined. A consumer unit 

comprises either (a) all members of a particular household 

who are related by blood, marriage, adoption, or other legal 

arrangements, (b) a person living alone or sharing a household with others or living as a roomer in a private home 

or lodging house or in permanent living quarters in a hotel 

or motel, but who is financially independent, or (c) two or 

more persons living together who use their income to make 

joint expenditure decisions. Financial independence is determined by the three major expense categories: Housing, 

food, and other living expenses. To be considered financially independent, at least two of the three major expense 

categories have to be provided entirely, or in part, by the 

respondent.

For this study, we used the CE dataset collected between the 

first quarter of 2014 and the first quarter of 2015. The total 

number of consumer units during this period is 32,321. We 

excluded 8,425 cases with missing data on main independent variables, sociodemographic variables (i.e., age, family size, family salary income, gender, number of owned 

vehicles, marital status, homeownership, educational attainment, race, region, Metropolitan Statistical Area (MSA), 

8 Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018

JFCP-16-00047.indd 8 12-06-2018 
having children under 18 years old, and having adults over 

64 years old). With these exclusions, 23,896 consumer units 

are included in this analysis.

A detailed description of the sample is presented in Table 1. 

Most of the respondents in this study were White (82.8%), 

married (54.8%), currently working (68.6%), and privately 

employed individuals (73.7%). The age of the reference 

person ranged between 16 and 87, with a mean age of 

51.5 years old. The sample included slightly more females 

(51.7%) than males (48.3%). With respect to employment, 

a mean of weekly work hours was 40.7. The mean of family size was 2.5 persons, 16.2% had children less than 18 

years old, and 20.3% had adults over 64 years old in the 

household. Regarding homeownership, 69.9% owned a 

home, whereas 30.1% indicated rented or other arrangement. 87.9% of respondents reside inside a Metropolitan 

Statistical Area (MSA) and the average of the number of 

owned vehicles was two. The average of annual family salary income was $51,832 and the median was $33,000. Family salary income is the amount of wage and salary income, 

before deductions, received by all CU members in past 12 

months.

Measures

The dependent variables of this study are the amount paid 

for health insurance premiums and the amount of payments 

for medical expenses. The payments for medical expenses 

include eye care service (i.e., eye examinations, treatment, 

or surgery, purchase of eye glasses or contact lenses), dental 

care, hospital care and physicians’ services, medical care 

services (i.e., lab tests or x-rays, care in convalescent, or 

nursing home), medicine and medical supplies (i.e., hearing aids, prescription drugs, rental/purchase of supportive 

or convalescent equipment, rental/purchase of medical or 

surgical equipment for general use), care for invalids, convalescents, handicapped, or elderly persons at home, and 

adult day care centers.

Our review of literature identified that health insurance 

spending is generally a function of demographic and socioeconomic characteristics, which we included as independent variables. Demographic characteristics included 

in the study are age, gender, race, educational attainment, 

Metropolitan Statistical Area (MSA), region, employment 

status, weekly work hours, and marital status of reference 

person. In addition, family size, having children under 18 

TABLE 1. Sample Descriptive Statistics 

(N = 23,896)

Category Percent or Mean (Median) 

Age 51.5 (52) years old 

Family size 2.48 persons 

Gender Male 48.3% 

Female 51.7% 

Marital status Married 54.8% 

Othera 45.2% 

Race White 82.8% 

Black 9.7% 

Otherb 7.5% 

Educational attainment Less than high school 8.3% 

High school 22.9% 

Some college 30.7% 

Bachelor’s degree 23.3% 

Grad/Prof degree 14.7% 

Metropolitan Statistical 

Area 

Yes 87.9% 

No 12.1% 

Region Northeast 19.2% 

Midwest 23.2% 

South 33.4% 

West 24.2% 

Employment Privately employed 73.7% 

Government employed 17.1% 

Self employed 9.2% 

Weekly work hours 40.7 hours 

Homeownership and 

mortgage debt status 

Owned with mortgage 42.0% 

Owned without mortgage 

27.9% 

Non-homeownerc 30.1% 

Family salary income $51,831.61 ($33,000) 

Number of owned 

vehicles 1.97 vehicles 

Having children under 

18 years old 

Yes 16.2% 

No 83.8% 

Having adults over 64 

years old 

Yes 20.3% 

No 79.7% 

a

Included widowed, divorced, separated, and never married.

b

Included Asian, Native American, Pacific Islander, and 

Multirace.

c

Included rented, occupied without payment of cash rent, and 

student housing.

Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018 9

JFCP-16-00047.indd 9 12-06-2018 
years old, and having adults over 64 years old are included. 

Financial factors included in this study are log of family salary income, homeownership and mortgage debt status, and 

the number of owned vehicles.

Data Analysis

One-way analysis of variance (ANOVA) using Duncan’s 

post-hoc test was employed to investigate what household 

characteristics are associated with the amount of health insurance premiums and the payments for medical expenses. 

Also, t-tests were used to evaluate differences in health 

insurance premiums and medical expenses. Multivariate 

analysis using Ordinary Least Square (OLS) regression was 

utilized to test potential effects of household characteristics 

variables on the amount of the health insurance premiums 

and the payments for medical expenses.

Results

Patterns of the Payments for Medical Expenses and 

Health Insurance Premiums

We were able to analyze total of 18,080 CUs for health insurance premiums and 15,320 CUs for medical expenses 

due to nonresponse CUs for health care spending (i.e., 

24.3% did not report the amount paid for health insurance 

premiums and 35.9% did not report the payment for medical expenses among 23,896 CUs). The respondents reported 

that the average quarterly amount paid for health insurance 

premiums was $1,002.68 and the median amount was $709. 

Also, the respondents reported that the average quarterly 

amount of the payments for medical expenses was $523.16 

and the median amount was $200.

The most common types of insurance plan were health 

maintenance organization (39.2%) and fee for service plan 

(39.7%) such as traditional fee for service plan or preferred 

provider option plan. In addition, 13.7% had other special 

purpose plan such as dental insurance, mental health insurance, vision insurance, and prescription drug insurance. 

64.9% of the respondents reported that they obtained the 

policy on group through place of employment, 24.6% did 

individually, and 10.5% did group through other organization. Most health insurance premiums were paid through 

payroll deductions.

One-way ANOVA indicated significant differences between 

groups in regards to type of insurance plan and whether the 

policy was obtained on an individual or group basis (see 

Table 2). That is, the groups who had health maintenance organization ($1,005.56) and fee for service plan ($1,056.30) 

spent more on health insurance premiums than the groups 

who had commercial Medicare supplement type ($817.31). 

Interestingly, compared with the households who had fee 

for service plan ($555.72) or other special purpose plan 

($556.21), the households who had health maintenance organization type ($469.19) spent less for medical expenses. 

Additionally, compared with the households who obtained 

the policy through group or other organizations, households 

with individual policies spent more on both health insurance premiums and the payments for medical expenses.

Moreover, this study examined the enrollment status of 

Medicare and Medicaid. 28.2% of the respondents reported 

that they or any members of their CU were presently enrolled in Medicare or had been enrolled in Medicare. While, 

only 8.9% reported that they or some people in their CU 

were enrolled in Medicaid.

Who Pays More for Medical Expenses and Health Insurance Premiums?

One-way ANOVAs and t-tests were used to examine the 

relationship between the amount paid for health insurance 

premiums and the amount of the payments for medical expenses and household characteristics, revealing significant 

differences between the groups (see Table 3). The t-tests revealed differences in the mean health insurance premiums 

in terms of gender, Metropolitan Statistical Area (MSA), 

and marital status. While, t-tests revealed differences in the 

mean of medical expenses in regards to marital status only. 

Specifically, married respondents spent more on health 

insurance premiums and medical expenses. Also, the residents inside of MSA spent more on health insurance premiums but there was no significant difference in the payments 

for medical expenses.

In addition, one-way ANOVAs indicated significant differences between groups in regards to region, race, employers, homeownership and mortgage debt status, and 

educational attainment with a post-hoc analysis using 

Duncan’s procedure. In particular, Midwestern residents 

spent less on health insurance premiums payments while 

Northeastern residents spent less on payments for medical expenses. With regards to race, Blacks spent less and 

Whites spent more on health insurance premiums and 

medical expenses. In terms of employment, government 

10 Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018

JFCP-16-00047.indd 10 12-06-2018 
employees spent less and the self-employed spent more on 

both health insurance premiums and payments for medical 

expenses. In addition to employment, homeownership and 

educational attainment were associated with both health 

insurance premiums and payments for medical expenses. 

Compared with other groups, homeowners and higher educational attainment respondents spent more on both health 

insurance premiums and payments for medical expenses.

Factors Associated With Medical Expenses and Health 

Insurance Premiums

Using OLS regression analyses, we examined demographic and financial factors associated with health insurance premiums and the payments for medical expenses. 

The analyses revealed that the proposed model provided 

a satisfactory fit to the data (see Table 4). The F-test indicated that the combined effects of the independent variables were related significantly to the amount of health 

insurance premiums and the payments for medical expenses at the 0.1% level or better. The results indicated 

that age, being married, educational attainment, and log 

of family salary income were positively associated with 

the amount paid for both health insurance premiums and 

medical expenses. Family size was only positively associated with the amount paid for health insurance premiums, 

while homeownership without mortgage and the number 

of owned vehicles were only positively associated with 

the amount paid for medical expenses.

TABLE 2. Patterns of Health Insurance Premiums and Medical Expenses

Variables 

Percent or 

Mean (Median) 

Medical expenses (n = 15,320 ) 

Amount of the payments for medical expenses (per quarter) $523.16 ($200)

Health insurance premiums (n = 18,080) 

Amount paid for health insurance premiums (per quarter) $1,002.68 ($709)

Type of insurance plan Health maintenance organization 39.2% 

Fee for service plan 39.7% 

Commercial Medicare supplement 7.4% 

Other special purpose plan* 13.7% 

Special purpose insurance plan* Dental insurance 5.8% 

Vision insurance 2.8% 

Prescription drug insurance 1.1% 

Mental health insurance 4.0% 

N/A 86.3% 

Policy was obtained on an individual or group 

basis 

Individually obtained 24.6% 

Group through place of employment 64.9% 

Group through other organization 10.5% 

The premiums paid through payroll deduction Yes 49.1% 

No 28.3% 

N/A 22.6% 

Medicare and Medicaid enrollment (N = 23,896) 

Medicare enrollment Yes 28.2% 

No 7.1% 

N/A 64.7% 

Medicaid enrollment Yes 8.9% 

No 26.8% 

N/A 64.3% 

Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018 11

JFCP-16-00047.indd 11 12-06-2018 
TABLE 3. The t-test and One-Way ANOVA Results Regarding Health Insurance Premiums and Payments for Medical Expenses

Variables Health Insurance Premiums (n = 18,080) Payments for Medical Expenses (n = 15,320) 

t-test M SD t M SD t

Gender Male 1022.59 1165.86 2.243* 538.28 1181.39 1.485 

Female 983.96 1148.31 510.19 1150.11 

Marital status Married 1216.72 1294.30 30.454*** 622.94 1285.48 13.246*** 

Othera 697.50 836.83 369.21 927.96 

Metropolitan Statistical 

Area 

Yes 1021.52 1177.84 

5.870*** 

519.38 1091.97 

−0.819 No 867.53 984.26 550.93 1600.98 

One-way ANOVA M SD Duncan F M SD Duncan F

Region Northeast 1103.13 1375.07 c 15.305*** 480.50 1077.54 a 4.958** 

Midwest 943.09 1021.06 a 543.40 1131.34 b 

South 968.62 1069.20 a 490.71 1031.62 a 

West 1039.67 1224.72 b 566.82 1284.73 b 

Race White 1023.50 1173.68 b 29.829*** 547.52 1204.45 c 24.382*** 

Black 797.96 986.68 a 308.89 745.18 a 

Otherb 1032.40 1142.56 b 449.11 988.12 b 

Employment Privately employed 992.06 1165.23 b 27.632*** 494.12 1010.75 b 97.236*** 

Government employed 876.25 916.23 a 413.56 704.73 a 

Self-employed 1453.43 1595.67 c 700.59 1260.48 c 

Homeownership and mortgage debt status Owned with mortgage 1104.87 1208.51 c 110.390*** 549.38 1054.84 b 61.858*** Owned without 

mortgage 

1023.41 1168.13 b 619.33 1485.19 c 

Non-homeownerc 791.30 1010.57 a 339.78 806.70 a 

Educational attainment Less than high school 827.59 965.81 a 37.858*** 375.57 923.34 a 12.137*** 

High school 906.32 1046.16 b 453.34 934.53 b 

Some college 964.96 1079.85 b 524.46 1306.68 c 

Bachelor’s degree 1069.98 1240.26 c 577.11 1254.81 c d 

Grad/Prof degree 1187.42 1348.83 d 599.43 1111.69 d 

(Continued)

12 Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018

JFCP-16-00047.indd 12 12-06-2018 
Variables Health Insurance Premiums (n = 18,080) Payments for Medical Expenses (n = 15,320) 

One-way ANOVA M SD Duncan F M SD Duncan F

Type of insurance plan Health maintenance organization 1005.56 1233.47 b c 19.438*** 469.19 1191.05 a 6.078*** 

Fee for service plan 1056.30 1203.48 c 555.72 1146.68 b 

Commercial Medicare 

supplement 

817.31 839.39 a 526.34 1246.01 a b 

Other special purpose plan 948.70 951.91 b 556.21 1097.91 b 

Policy was obtained on an 

individual or group basis 

Individually obtained 1035.03 1191.28 b 4.629** 565.80 1314.30 b 4.390* 

Group through place of 

employment 

998.40 1120.32 b 514.58 1122.13 a b 

Group through other organization 926.79 1356.15 a 469.21 1019.64 a 

*p < .05. **p < .01. ***p < .001. 

aIncluded widowed, divorced, separated, and never married.bIncluded Asian, Native America, Pacific Islander, and Multi-race.c

TABLE 3. 

Included rented, occupied without payment of cash rent, and student housing.

 The t-test and One-Way ANOVA Results Regarding Health Insurance Premiums and Payments for Medical Expenses (Continued)

Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018 13

JFCP-16-00047.indd 13 12-06-2018 
Conversely, government employment status was negatively 

associated with both health insurance premiums and medical 

expenses. In particular, the findings showed that, compared 

to private sector employees, the self-employed spent more 

and government employees spent less on health insurance 

premium and medical expenses. In addition to employment 

status, region was associated with medical expenses, which 

explains some of the relationship between health insurance 

premiums and medical expenses. Compared with the households living in the West, the households living in Northeast 

paid lower medical expenses. Further, having adults over 65 

years or older in the household was negatively associated 

TABLE 4. Multivariate Ordinary Least Squares (OLS) Regression Results on Health Insurance Premiums 

and Payments for Medical Expenses

Variables 

Health Insurance Premiums

(n = 18,080) 

Payments for Medical Expenses

(n = 15,320 ) 

Coefficient SE Coefficient SE

(Constant) −651.602*** 158.980 −328.439* 150.249

Age 6.821*** 1.117 4.359*** 1.061

Family size 119.782** 16.533 3.133 15.915

Married 266.566*** 29.210 152.685*** 28.155

Male −7.166 24.368 −27.138 23.216

Race (ref. = White) 

 Black −58.134 40.351 −63.116 42.932

 Othera −34.618 46.245 −104.317* 44.550

Educational attainment (ref. 

= < High school) 

 High school 79.207 59.837 94.954 61.107

 Some college 39.551 58.177 127.810* 59.219

 Bachelor’s degree 131.479* 59.222 162.275** 60.335

 Grad/Prof degree 216.451*** 63.072 195.293** 62.834

Region (ref. = West) 

 Northeast 35.539 37.624 −112.264*** 34.293

 Midwest 8.609 34.794 −63.167 32.669

 South 73.824* 32.456 −16.736 30.719

Homeownership (ref. 

= Owned with mortgage) 

 Owned without mortgage 26.297 32.446 74.371* 29.826

 Non-homeownerb −47.243 30.249 −19.745 29.938

Employment (ref. = Privately 

employed) 

 Government employed −140.804*** 31.214 −72.273* 28.277

 Self-employed 63.027*** 48.520 127.758** 44.614

Weekly work hours −1.651 1.094 1.265 1.014

Log of family salary income 75.105*** 13.568 29.162* 12.782

Number of owned vehicles 11.050 8.866 25.708*** 7.987

Having children under 18 −37.197 35.285 .611 33.531

Having adults over 64 −149.139*** 42.684 −62.466 38.264

R2 (Adjusted R2

) 0.092 (0.089) 0.035 (0.032)

F 37.994*** 10.887***

*p < .05. **p < .01. ***p < .001. a

Included Asian, Native American, Pacific Islander, and Multirace.

b

Included rented, occupied without payment of cash rent, and student housing. 

ref. = reference category.

14 Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018

JFCP-16-00047.indd 14 12-06-2018 
with health insurance premiums. However, the association 

between having adults 65 years or older in the household 

and medical expenses was not statistically significant.

Discussions

This study sought to investigate household sociodemographic 

characteristics as predictors of patterns of health insurance 

premiums and medical expenses of consumers using 2014 

the Consumer Expenditures Survey. The average quarterly 

amount paid for health insurance premiums was $1,003 and 

the median amount was $709. The average quarterly amount 

of the payments for medical expenses was $523 and the median amount was $200. The most common types of insurance 

plan were health maintenance organization and fee for service 

plans. The households who had health maintenance organization plans spent more on health insurance premiums but spent 

less on the payments for medical expenses. Nearly two-third 

of households obtained the policy on group through place of 

employment. The households which individually obtained 

policies spent more on both health insurance premiums and 

the payments for medical expenses.

This study has shown that sociodemographic characteristics 

are correlated with health care spending. It has provided empirical support to testimonial evidence that an older age, being married, educational attainment, and log of family salary 

income are associated with higher family spending on both 

health insurance premiums and medical expenses. Interestingly, family size was only positively associated with the 

amount paid for health insurance premiums. The findings of 

this study are consistent with past research on health care expenses (Hong & Kim, 2000; Kaiser Family Foundation, 2012; 

Lassman et al., 2014). Homeownership without mortgage and 

the number of owned vehicles were only positively associated 

with the amount paid for medical expenses.

On the other hand, government employment status was 

associated with lower spending on both health insurance 

premiums and medical expenses, while the self-employed 

spent more on health insurance premium and medical expenses since they have limited choices and are not able to 

take advantage of collective bargaining power in purchasing health insurance policies (Hong & Kim, 2000).

Finally, region is also associated with health care spending. 

Since Wennberg and Gittelsohn’s (1973) research, many 

researchers have discussed regional variation in health 

care. Particular attention has been paid to differences seen 

in health care spending, health insurance coverage, and 

health-care access for both children (Kogan et al., 2010; 

Fisher-Owens et al., 2016) and adults (Radley & Schoen, 

2012; Ozieh, Bishu, Walker, Campbell, & Egede, 2016) by 

geographic variation. We found that compared to the households living in the West, Southern residents paid higher 

health insurance premiums while Northeastern residents 

paid lower medical expenses.

Limitations and Future Directions

The findings must be considered within the context of its 

limitations. Some limitations of this study were primarily 

data restrictions. Out-of-pocket costs for medical expenses 

vary for families based primarily on health status and health 

insurance coverage (Kaiser Family Foundation, 2012). We 

were not able to include health factors such as health status 

or need for health care in this analysis since the CE survey data did not cover respondents’ health information. We 

recognize that one of the most important factors associated 

with health care utilization is health status or need for health 

care. Further investigation could look into the health related variables for predicting health care. Health insurance 

also enables individuals and families to access health care. 

Health insurance coverage status impacts the out-of-pocket costs for medical expenses. Therefore, future research 

should control the health insurance coverage status to predict medical expenses. In addition, as noted above, further 

studies are needed to strengthen the current findings and 

correct for nonresponse bias, in particular studies in health 

care spending since there are substantial nonresponses to 

the amount paid for health insurance premiums and medical 

expenses in a final sample. Although it is beyond the scope 

of this present study, nonresponse to the U.S. household 

surveys is increasing and nonresponse might lead to nonresponse bias in survey estimates (Groves, 2006).

On a methodological front, further research might consider the use of longitudinal data because our findings might 

reflect cohort differences. Given the skewed distribution 

of expenditures, further research could also be conducted to use alternative estimators such as OLS model with 

log transformed expenses and two-part models. Though 

the OLS regression analysis was widely utilized to analyze the expenditure data (e.g., Zan & Fan, 2010; Lou & 

Holden, 2014), future research might investigate whether 

there would be a mediating role of possible variables on the 

Journal of Financial Counseling and Planning, Volume 29, Number 1, 2018 15

JFCP-16-00047.indd 15 12-06-2018 
association between households’ sociodemographic characteristics and health care spending in a large sample over 

various time points to seek trends changes since the Patient 

Protection Affordable Care Act became law.

Furthermore, race/ethnicity and geography might associated 

with health care spending patterns (e.g., Baicker, Chandra, 

Skinner, & Wennberg, 2004; American Hospital Association, 2009). Future research might build on our model and 

expand it to provide a more comprehensive explanation for 

race/ethnicity differences in health care spending. In particular, more research on the role of race/ethnicity in health 

insurance premiums might be needed that was uncovered 

in this study.

Implications for Financial Counselors and Policymakers

This research gauged household characteristics to add 

something neglected in the previous literature. Findings 

from this research are informative for both households in 

determining health insurance premiums and medical expenses throughout the life course as well as financial advisors in personal financial planning and counseling focused 

on health care. Further, policymakers can also legislatively 

apply the findings in recognizing the importance of factors associated with household health care decisions and 

use this information as the basis for providing guidelines 

for developing recommended strategies to improve health 

care service use among U.S. populations. Both policy decisions and market dynamics drive regional differences in 

the amount paid for health care service (American Hospital Association, 2009). In light of the regional variance 

in health care spending, policy decisions and educational 

programs might need to be more culturally sensitive to regional differences.

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Acknowledgments. This research was supported by the UA 

System Collaborative Research Grant at the University of 

Alabama and the University of Alabama at Birmingham.

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