Determinants of health insurance enrollment and healthexpenditure in Ghana: An empirical analysis

Kwame Adjei-Mantey
Department of Sustainable Energy and Resources, University of Environment andSustainable Development, Somanya, Ghana
Charles Yuji Horioka
Research Institute for Economics and Business Administration, Kobe University, Kobe,
Japan; Asian Growth Research Institute, Kitakyushu, Japan; Institute of Social andEconomic Research, Osaka University, Osaka, Japan; Institute of Economic Research,
Kyoto University, Kyoto, Japan; and National Bureau of Economic Research, Cambridge,
USA

Working Paper Series Vol. 2022-04
February 2022

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Asian Growth Research InstituteDeterminants of health insurance enrollment and health expenditure in
Ghana: An empirical analysis
Kwame Adjei-Mantey
Department of Sustainable Energy and Resources, University of Environment andSustainable Development, Somanya, Ghana

Charles Yuji Horioka*
Research Institute for Economics and Business Administration, Kobe University, Kobe,
Japan; Asian Growth Research Institute, Kitakyushu, Japan; Institute of Social and Economic
Research, Osaka University, Osaka, Japan; Institute of Economic Research, Kyoto
University, Kyoto, Japan; and National Bureau of Economic Research, Cambridge, USA

February 26, 2022

Abstract
This paper analyzes the determinants of health insurance enrollment and health expenditure in
Ghana using micro data from wave 7 of the Ghana Living Standards Survey (GLSS 7) withemphasis on the role of risk preferences and the availability of health facilities in one’s own
community, neither of which has been emphasized in the previous literature on this topic. It is
possible to analyze the determinants of health insurance enrollment in Ghana because its publichealth insurance system (the National Health Insurance Scheme or NHIS) is, in theory,
mandatory, but is, in actual practice, voluntary, with only about 40% of the population enrolled
in the scheme. Our empirical findings show that risk preferences have a significant impact on
health insurance enrollment, with risk averse individuals being significantly more likely thanother households to enroll in health insurance, as one would expect. Moreover, our findings
also show that very poor households are significantly more likely to enroll in health insurance
than other households, perhaps because they are exempt from paying premiums for healthinsurance. This finding suggests that NHIS is achieving its intended objective of increasing thepoor’s access to health care. Finally, our findings also show that the availability of healthfacilities in one’s own community significantly decreases expenditures on health care, which
underscores the importance of ensuring an equitable spatial distribution of health facilities
throughout the country.
Journal of Economic Literature classification codes: D11, D12, D81, I12, and I13
Keywords: Ghana; health expenditure; health facilities; health insurance; medical insurance;risk preferences*Corresponding author: Charles Yuji Horioka, Research Institute for Economics and Business
Administration, Kobe University, 2-1, Rokkodai-cho, Nada, Kobe, Hyogo 657-8501, JAPAN.
Email: horioka@rieb.kobe-u.ac.jp

1. Introduction

Health and wellbeing are crucial global indicators of human development. Good health of a
population implies better human capital, which is crucial for implementing the development
agenda of any country. All else equal, healthy people are more productive as they do not haveto stay away from work due to sickness and are less likely to underperform at work comparedto sick individuals. To maintain good health, the population needs access to good health careservices, which requires all countries, especially developing ones, to overcome several
challenges from both demand and supply side perspectives. One of the crucial challenges
related to improved health care services is health care financing. Like most normal goods,
people demand more and better health care if they have the ability to pay for it. By implication,
persons without adequate means would have difficulty accessing health care, leading to a
proportion of the population without access to health care. To this end, household healthexpenditure is an important part of household expenditures as the level and quality of healthcare households can receive is intimately linked to how much they are willing and able to spend
to access health care. Health insurance has emerged as a key method for relaxing health carefinancing constraints and for accelerating the attainment of universal health coverage (Duku,
2018).
While some developed countries including Japan, Ireland, and Australia operate public,
compulsory health insurance systems, other countries only have private health insurance
providers while others run a voluntary public health insurance program. In the case of avoluntary public health insurance system, households decide whether or not to join the healthinsurance program. As explained in detail in the next section, private health insurance is almost
non-existent in Ghana, and there was no public health insurance program in Ghana until 2004.
The National Health Insurance Scheme (hereafter NHIS) was introduced in 2004, and inprinciple, it is mandatory, but it is, in actual practice, voluntary. According to wave 7 of the
Ghana Living Standards Survey (GLSS 7), only 0.36% of respondents had ever been enrolled
in a private health insurance program while 20% had never enrolled in any health insurance
program (N=58,563). The active enrollment rate of the NHIS stood at only 34% in 2010, 38%
in 2013, and 41% in 2015 and dropped further to 35% in 2017.1 These rates represent activeenrollment rates rather than a rate based on cumulative membership and therefore excludes
persons who have dropped out of the scheme by failing to renew their membership.
Nonetheless, these enrollment rates are extremely low for a scheme designed to facilitate1

universal health coverage and reduce health care inequalities. Furthermore, Kwarteng et al.
(2019) noted that disparities exist in health insurance coverage with significantly lower
proportions of poor and vulnerable groups enrolling in the scheme, a view supported by Dake(2018).
The low enrollment rates of Ghana’s public health insurance program and the disparity inenrollment rates by income class suggest that a comprehensive examination of the determinantsof health insurance enrollment is needed in order to inform policymakers of the appropriate
policy response to the low enrollment rates. In line with this, our first objective is to examine
the determinants of health insurance enrollment with special focus on the role of behavioral
factors in the case of a voluntary-type scheme such as Ghana’s NHIS. In this study, we focus
in particular on the role of risk aversion in health insurance enrollment. Condliffe andFiorentino (2014) affirm that risk preferences are an important behavioral factor in households’
health care choices. Despite this, risk preferences have been largely unaccounted for in studies
on health insurance decision making in Ghana and in other countries. Since risk averse
individuals do not like to take risks, we hypothesize that they should be more eager than nonrisk averse individuals to enroll in health insurance to insure against the risk of falling ill andbeing denied health care due to not having the means to make out-of-pocket payments.
Examining this issue is useful to the extent that many studies on the determinants of healthinsurance enrollment in Ghana have failed to consider the effect of risk preferences onhouseholds’ decision to enroll in the NHIS. There have been a number of studies that havesought to investigate the determinants of health insurance enrollment in Ghana, but none ofthese studies considered the effect of risk preferences on health insurance, focusing mainly onother socioeconomic variables, mostly income and education. In addition, most of these studies
restricted their samples to specific sub-groups or sub-administrative levels, thus limiting thegeneralizability of their findings (see Nsiah-Boateng et al., 2019; Duku, 2018; Agyepong et al.,2016, Amu and Dickson, 2016; Fenny et al., 2016; Owusu-Sekyere and Chiraah, 2014, among
others) although a few studies such as Salari et al. (2019) and Amu et al. (2018) usednationwide data. Hence, our study makes an important contribution to the health insurance
enrollment literature by investigating the role of risk preferences using nationwide data.
The second objective of this study is to investigate the factors that influence household healthexpenditure with particular focus on health insurance enrollment and the presence of healthfacilities in one’s community. Previous studies have looked at the association between health2insurance enrollment and health care utilization in Ghana, but Blanchet et al. (2012) restrict
their analysis to women in Accra, Ghana’s capital city, while Rajkotia and Frick (2011) restrict
their analysis to the Nkoranza district, a sub-administrative jurisdiction in the Bono East
Region of Ghana. Moreover, both studies examine the impact of health insurance on hospital
visits and health care utilization. Our study differs from these studies by employing a nationally
representative sample and attempting to shed light on the causal impact of health insurance andthe presence of health facilities in one’s community on household health expenditure. We are
not aware of any studies that measure the effect of the presence of health facilities on household
health expenditure. By accounting for it, our study fills a serious gap in the literature andprovides implications for policy regarding the spatial distribution of health infrastructure.
Thus, this paper makes significant original contributions to the literature on health insurance
enrollment and the related literature on health care expenditures in at least the following fiveways. First, it is one of the first studies (other than Nghiem and Graves, 2019) to take accountof the simultaneity of the decision of whether or not to enroll in health insurance and thedecision of how much health care services to consume, which it does by using risk preferences
as an instrument for health insurance enrollment. Second, it is one of the first studies to analyze
the impact of risk preferences on the demand for health insurance using direct information onrisk preferences. Information on risk preferences is rarely available in household datasets fromdeveloping countries, thus making this contribution a bit more pronounced. There are alreadysome studies such as Buchmueller et al. (2013) that have examined the impact of risk
preferences on the demand for health insurance but they do not use direct information on risk
preferences. Third, it is one of the first studies to analyze the impact of the availability of healthfacilities in one’s own community on the demand for health care services. Fourth, it issomewhat unusual in that it analyzes the determinants of the demand for public healthinsurance. This is not possible in most countries because public health insurance programs areusually mandatory, but it is possible in the case of Ghana because Ghana’s national healthinsurance program is, in principle, mandatory, but is, in actual practice, voluntary (previous
studies for Ghana include Owusu-Sekyere and Chiraah, 2014; Duku, 2018; and Salari et al.,2019). Fifth, it improves upon many earlier studies for Ghana and other countries by using anationally representative sample (although there are already a limited number of studies for
Ghana such as Salari et al. (2019) that use a nationally representative sample).

3

The remainder of this paper is structured as follows: Section 2 provides a brief description of
Ghana’s public health insurance program, section 3 surveys the relevant literature, section 4
explains the data and the methodologies adopted to analyze the data, section 5 presents anddiscusses the findings of our empirical analysis, and section 6 concludes the study and provides
policy implications for Ghana and other developing countries.

2. Ghana’s public health insurance program
In this section, we briefly describe the history of Ghana’s public health insurance system. Prior
to the introduction of this public health insurance program, households in Ghana financed theirhealth care mostly through user fees or out-of-pocket payments, and this system is popularlyreferred to locally as the “cash and carry” system. Under this health care financing approach,
only relatively affluent households had access to health care, and poor households could notmeet some or all of their health care needs even in times of serious illness.
In 2004, a public health insurance system called the National Health Insurance Scheme (NHIS)
was rolled out in Ghana to phase out the “cash and carry” system and to provide a sustainable
and inclusive means of financing health care in Ghana.2 While the intention of the revised
parliamentary act of the NHIS (Act 852, 2012) was to make enrollment in the scheme
mandatory, in actual practice, the NHIS operates as a voluntary public health insurance system.
Those who enroll in the NHIS are required to pay a one-time registration fee of 20 Ghana cedis(about 3 US dollars at the current exchange rate) at the time of first enrollment as well as anannual premium of 30 Ghana cedis (about 5 US dollars). However, workers who are enrolled
in the public pension fund (this comprises all formal sector workers and a very limited numberof informal sector workers) do not have to pay the full premium or may not have to pay any
premium at all because the health insurance program is funded partly by 2.5% of pension
contributions.3
Hence, only enrollees who are not enrolled in the public pension fund are required to pay thestandard annual premium. Moreover, there are exemptions for households who are consideredpoor and vulnerable as well as for children under the age of 5 and the elderly over the age of
60. They pay either lower premiums or no premiums at all, depending on their circumstances.

4

The health insurance authority states that the NHIS covers 95% of common disease conditions.
Expensive procedures such as cancer treatment or dialysis and the like are not covered.
However, some people choose to make out-of-pocket payments even though they are enrolledin NHIS and even though their treatment is, in theory, covered under the scheme because theywant to procure better and/or faster treatment or because they are required to pay for drugs thatshould, in theory, be covered by the scheme because they are told that “insurance drugs” arenot available. Thus, in actual practice, NHIS enrollees often pay even for health services anddrugs that are, in theory, covered by the scheme.
Moreover, unlike in advanced countries such as the United States, private health insurance
provision is very limited in Ghana, as discussed in section 1.
3. Literature review
3.1. Studies of the determinants of health insurance enrollment
Literature on the determinants of health insurance enrollment abounds. In Ghana, Salari et al.
(2019) investigated the socio-economic determinants of health insurance enrollment and foundthat education, marital status, wealth and to a limited extent, age had positive correlations withenrollment in Ghana’s National Health Insurance Scheme (NHIS). They also found that thetype of employment had equally important associations with the probability of enrollment, with
agricultural workers being less likely to enroll compared with workers in other sectors, perhaps
due to the instability of the incomes of agricultural workers. Interestingly, unemployed persons
were more likely to enroll in the NHIS than agricultural workers. Some of the factors identified
in this study confirmed earlier findings by Duku (2018), who investigated the determinants ofhealth insurance enrollment in the Greater Accra and Western regions of Ghana. In addition toeducational level, wealth, and marital status, Duku (2018) found that travel time to the healthfacility, age, and sex significantly influenced health insurance enrollment. The study concluded
that differences in health insurance enrollment between the two regions are due to differencesin region-specific factors, and based on this finding, called for equitable premiums to promote
enrollment in socio-economically disadvantaged regions. Owusu-Sekyere and Chiaraah (2014)
conducted a study in the Kumasi metropolis of Ghana on the factors influencing healthinsurance enrollment. Contrary to the earlier studies discussed, this study did not find age, sex,
and marital status to be important determinants of enrollment. However, it found that years of5

schooling, income, and employment status were important predictors of enrollment. In addition,
it found that the cost of curative care positively predicted the decision to enroll in a healthinsurance scheme. Contrary to expectation, health status was found to be an insignificant
predictor of enrolling in health insurance.
In a further study on the NHIS in Ghana, Nsiah-Boateng et al. (2019) investigated thedeterminants of enrollment in, and dropout from, the health insurance scheme in the Ashiedu
Keteke sub-metropolis of Ghana. The study found that indigents are more likely to enroll but
also more likely to drop out of the scheme. The reasons for this are not altogether clear as thestudy pointed out that renewal premiums are waived for persons in this group, thus ruling out
the inability to afford premiums as the potential reason. On the contrary, the study found that
informal sector workers as well as formal sector workers who are contributors to the national
pension scheme are less likely to enroll but more likely to retain their membership in the healthinsurance scheme once enrolled. Fenny et al. (2016) conducted a study that examined thefactors responsible for the low enrollment and low retention of status in Ghana’s NHIS. The
study found that, beyond socio-cultural factors that impeded the uptake of health insurance,
there were systemic factors that were equally responsible for the low enrollment rates in NHIS
among Ghanaians. It found that vulnerability among social groups such as being poor, aged,
disabled, mentally challenged, or migrant served as a social barrier to enrollment. It further
argued that, despite the existence of premium payment exemptions for some categories ofvulnerable groups, the absence of family support and social support in general still constrained
them from enrolling in the NHIS. Additionally, cultural and religious norms were also foundto hinder health insurance enrollment to some extent. Among the systemic factors that impeded
enrollment were poor infrastructure, especially in rural communities, such as poortransportation infrastructure, the poor quality of care at health facilities, particularly for careseekers covered by the NHIS, and the NHIS’s own weak internal administrative systems. While
this study is too limited in its scope and methodological approach to allow for generalizations,its findings constitute important, contextually situated evidence on the reasons for low
enrollment among a subsection of Ghanaians.
Other studies on the determinants of health insurance enrollment in Ghana confirm the factors
identified in the review above as having correlations with health insurance (Amu et al., 2018;
Amu and Dickson, 2016; Agyepong et al., 2016). However, no study has yet been found thatinvestigated the impact of household risk preferences on enrollment in health insurance. Given
6the voluntary nature of Ghana’s national health insurance program, an examination of thisbehavioral factor is crucial to understanding enrollment behavior and enhancing policy
planning.
We now turn to a review of studies in jurisdictions other than Ghana where public healthinsurance might be compulsory, non-existent, or voluntary. Many of these studies explored thedeterminants of private health insurance purchase. Buchmueller et al. (2013) examined
preference heterogeneity in private health insurance enrollment in Australia. This study notedthat about half of the adult population has enrolled in private health insurance despite theexistence of a universal public health insurance system in Australia whose coverage for both
inpatient and outpatient care is described as comprehensive. Among other things, this study
found a strong association of risk aversion with the decision to purchase private healthinsurance. The study classified individuals who have purchased other insurance products suchas life insurance, sickness and accident insurance, and home content insurance as well as those
who engage less in risky behaviors such as smoking as being risk averse. The study foundevidence that those who have purchased other insurance products, i.e., risk averse people, aremore likely to purchase private health insurance, and conversely, that those who smoke, i.e.,less risk averse people, are less likely to purchase private health insurance. This study
underscores the relevance of risk preferences in determining health insurance enrollment,
especially in a voluntary health insurance program. These findings confirm those from anearlier study by Liu et al. (2011) on the impact of expanding public health insurance on private
health insurance demand in rural China. This study found that risk averse households weremore likely to purchase private health insurance for both adults and children. This was inaddition to the main finding of the study—namely, that there were large positive effects ofpublic health insurance expansion on private health insurance demand among higher income
groups and communities with a history of health care financing. Similarly, Hopkins and Kidd
(1996) found that less risk averse people were less likely to purchase private health insurancein Australia. Another study by Condliffe and Fiorentino (2014) confirm the findings from theabove studies. They examined the impact of risk preferences, measured by engagement in risky
behavior, on health insurance and health expenditures in the United States. They found that,
relative to low risk preference individuals, high risk preference and moderate risk preference
individuals were more likely to be uninsured. The study also found gender and educational
levels to be significant predictors of health insurance enrollment.
7However, the findings of these studies on the role of risk preferences contrast with the findingsof Costa and Garcia (2003), who found no significant effects of attitudes towards risk on the
demand for private health insurance in Catalonia, Spain. Their study found the demand for
private health insurance to be driven mainly by the gaps in quality between public and private
health care in addition to other socio-economic factors. This finding corroborates that of JofreBonet (2000), also for Spain, about the effect of the quality of health care, proxied by waitingtimes in the Spanish public health system, on the demand for private health insurance. It foundthat a reduction in waiting time reduced the probability of purchasing private health insurance.
On the contrary, Propper et al. (2001) found a negative effect of waiting lists on private healthinsurance purchase in the UK. Finn and Harmon (2006) examined the determinants of private
health insurance enrollment in Ireland where, similar to the Australian case, there is a nearrobust public healthcare financing system in place. About 50% of the population was found topossess private health insurance but the study did not examine the effect of risk preferences.
Factors including income, education, and health status were found be significant determinantsof whether or not to purchase private health insurance in Ireland.

3.2. Studies of the determinants of health expenditure and health care utilization
Our study also relates to the strand of literature that has examined the determinants of healthexpenditure and health care utilization. Jeon and Kwon (2013) examined the effect of private
insurance on health care utilization and health expenditure in Korea, which has a universal
public insurance system. Using matching techniques and Heckman-type treatment effectmodeling to control for endogeneity in health insurance purchase, the study found a higher
probability for health care utilization in both inpatient and outpatient care for people withprivate health insurance. Private health insurance was found to have a positive impact on
outpatient expenditure (but not necessarily on the number of visits) but an insignificant effecton inpatient days and inpatient expenditure. These findings could potentially be linked to thefact that individuals have greater discretion when it comes to outpatient care compared to
inpatient care because decisions about inpatient service utilization and expenditure are often
made by physicians based on patients’ disease condition, as a result of which their private
health insurance status is irrelevant. Liu and Zhao (2014) examined the impact of a voluntary
health insurance program on health care utilization in urban China. Employing an instrumental
variable approach, the study finds that enrollment in the health insurance program significantly8increases formal health care utilization for both inpatient and outpatient care in the case ofchildren, low-income families, and residents in the poorer western region of China. However,no evidence was found that the program had reduced out-of-pocket health expenditure.
Condliffe and Fiorentino (2014) found risk preferences to have a negative relationship withhealth expenditure in the United States--i.e., that high and moderate risk preference individuals
spent significantly less on health care than low risk preference individuals--and that insured
persons actually spend more on health care than uninsured individuals, contrary to expectation.
In Ghana, only a few studies exist, especially at the micro level, on the determinants of healthexpenditure or health care utilization. Blanchet et al. (2012) investigated the effect of Ghana’s
NHIS on health care utilization using data on a sample of women in Accra. Using matching
techniques, the study found that individuals who are enrolled in the NHIS were more likely tovisit clinics, to seek formal health care, and to obtain prescriptions, indicating an increase inhealth care utilization. The results may not be easily generalizable due to the limited study area
(Accra), which is more economically advantaged and better off in terms of health infrastructure
and personnel. That notwithstanding, the findings confirm those of Rajkotia and Frick (2011)
in the Nkoranza district of Ghana. This study attempted to test for the existence of adverse
selection into the NHIS by examining how the observed payoffs to enrollment relate tohousehold enrollment costs (where enrollment costs for a household were measured as the sumof premium payments by non-exempt members). They found that household enrollment costs
are positively correlated with the odds and intensity of outpatient health care use by children.
At the macro level, Angko (2013) examined the determinants of public healthcare expenditurein Ghana using annual time series data. Per capita Gross Domestic Product (GDP), health statusof the population, and the age structure of the population were found to be the major
determinants of health expenditure in Ghana. A similar macro level study in 26 Africancountries by Okunade (2005) shows that, in addition to per capita GDP and the dependency
ratio, overseas development assistance, the level of income inequality, internal conflicts, andthe percentage of births attended by trained personnel were identified as determinants of publichealth expenditure in the countries included in their sample.
This review of the literature shows that, in the case of determinants of public health insurance
enrollment, the role of risk aversion has been left largely unexamined, especially in Ghana
where the public health insurance system is a voluntary one. Furthermore, studies on the effectof public health insurance enrollment on health expenditure are limited in Ghana and other9countries that are unique in operating voluntary public insurance programs. Our study fills
these gaps in the literature and provides the basis for further research in other jurisdictions aswell as a basis for effective health policy planning.

4. Data and methodology
4.1. Data
The data for this study were taken from wave 7 of the Ghana Living Standards Survey (GLSS
7). The GLSS is a nationwide household survey conducted by the Ghana Statistical Service(GSS), the official statistics bureau of Ghana. This survey collects economic, social, anddemographic data from a large sample of households nationwide, and it is the mostcomprehensive source of data at the household level in Ghana due to its scope and coverage.
Households are selected from enumeration areas stratified so that there is proportionate
representation from all administrative regions in the country. Data collection for GLSS 7 wascompleted in October 2017 and it is the most recent round of the GLSS survey. It has data on
more than 14,000 households and 55,000 individuals. This study uses information from thissurvey on risk preferences and health insurance enrollment of household heads and theirspouses and on household health expenditure. Thus, respondents are household heads andspouses of household heads. The data on household health expenditure includes all out-ofpocket expenditures on health care but does not include the portion of health expenditures
covered by health insurance or expenditures on preventive health care, for which data are notavailable.
The GLSS also collects community-level data from a subsample of communities that are
selected to take part in the community survey. Community leaders serve as respondents for thecommunity survey and are asked to respond to questions about the features of the infrastructurethat is available in their communities. This study makes use of data from this survey on whether
there are any health facilities (hospitals, clinics, pharmacies, maternity homes, traditional
herbal clinics, etc.) in the community in which the respondent lives.
Although the GLSS surveyed about 14,000 household heads and about 8,000 spouses ofhousehold heads, for a combined total of about 22,000 respondents, the size of the sample weused for our analysis was only 13,131 because we had to drop respondents for which there were
missing values for one or more of the variables we used in our analysis. The sample size was10reduced further to 9,914 for our analysis of the determinants of health expenditures because
one variable (the availability of a health facility in one’s own community) had many missing
values because the sample for the community survey was smaller.
Table 1 shows the summary statistics for the variables used in this study. Looking first at thedependent variables used in our analysis, 63.5% of respondents were actively enrolled in healthinsurance, and the natural logarithm of household health expenditure was 1.8 (household healthexpenditure averaged 96.7 cedis (about 15-16 US dollars at the current exchange rate)) during
the two weeks preceding the survey.4 Health insurance enrollment is measured by whether the
respondent was actually covered under a health insurance program in the year of the survey. In
this study, an affirmative response classifies the respondent as being enrolled in healthinsurance.
Turning to the explanatory variables of most interest, Table 1 shows that 81.9% of respondents
are risk averse, where risk aversion is measured as a dichotomous variable wherein individuals
are considered to be risk averse if they choose (1) over (2) where (1) is to invest in a business
in which there is no chance of losing any money but would make low profits and (2) is to invest
in a business that has a small chance of losing money but potentially makes high profits.5 Of
respondents whose communities participated in the community survey, only 50.5% of themhad a health facility in their communities. This implies that about half of them necessarily haveto go outside their communities in order to access health care. With an average household sizeof nearly 5 in our sample, this could prove crucial in its effect on household health expenditure.
Turning to the other explanatory variables we used, health status is proxied by reporting illness
or injury during the two weeks preceding the survey. About a sixth of respondents (16.5%)
reported an illness/injury during the preceding two weeks and can therefore be regarded as
being in poor health.
Educational attainment is relatively low, with 62.7% of respondents having only basic school
education and only 18.0%, 4.7%, and 5.2% of respondents having a senior high school, postsecondary, or university education, respectively. 9.4% of respondents are poor and 3.9% arevery poor, where “poor” is defined as living below the upper poverty line and “very poor” isdefined as living below the lower poverty line, where the upper and lower poverty lines are1760 and 982.2 Ghana cedis (about 157 and 110 US dollars) per adult equivalent per year,
respectively (see Ghana Statistical Service, 2018). 83% of respondents were found to haveaccess to information, which is proxied by ownership of a functioning television set or radio.
11

The average age of respondents is 42.9, average household size is 4.4, and 74.5% of
respondents are male.6
We calculated a correlation matrix of all of the explanatory variables used in our analysis, andalthough it is not shown due to space limitations, we found that multicollinearity was not a
problem, with pairwise correlation coefficients being 0.42 or less in all cases.

4.2. Theoretical framework and empirical methodology
This study employs random utility theory and expected utility theory for insurance under
uncertainty and risk (von Neumann and Morgenstein, 1994) to model health insurance demand.
Random utility theory assumes that the utility obtained from consuming a commodity, in thiscase health insurance (HI), depends on the observable and unobservable factors of thehousehold and of the commodity itself. Thus, an individual i will decide to enroll in a healthinsurance scheme if the utility derived from being enrolled exceeds the utility to be derived
from non-enrollment. Mathematically stated,
Pr (𝐸𝑛𝑟𝑜𝑙𝑙 𝑖𝑛 𝐻𝐼) = Pr 𝑈 ,

>𝑈,

.

(1)

Adapting expected utility theory under uncertainty to this study, this framework assumes that
individuals are risk averse and make decisions based on the implications of their decisions fortheir health (original theory: wealth).
This study employs random utility theory and expected utility theory for insurance under
uncertainty and risk (von Neumann and Morgenstein, 1994) to model health insurance demand.
Random utility theory assumes that the utility obtained from consuming a commodity, in thiscase health insurance (HI), depends on the observable and unobservable factors of thehousehold and of the commodity itself. Thus, an individual i will decide to enroll in a healthinsurance scheme if the utility derived from being enrolled exceeds the utility to be derived
from non-enrollment. Mathematically stated,
Pr (𝐸𝑛𝑟𝑜𝑙𝑙 𝑖𝑛 𝐻𝐼) = Pr 𝑈 ,

>𝑈,

.

(1)

Adapting expected utility theory under uncertainty to this study, this framework assumes that
individuals are risk averse and make decisions based on the implications of their decisions fortheir health (original theory: wealth).
12

Table 1: Summary Statistics of Data
Variable

Obs.

SurveyMean

Std. Dev.

0.635

0.481

Min

Max

0

1

0

1

0

1

0

1

0

1

Weight
Health Insurance

6.90(1= enrolled)

13,131

Risk averse

13,131

6.90
Health status (1=ill/injured)

6.90

0.819
0.165

0.385
0.371

13,131

Health facility in

5.075

one’s own
community (1=
yes)

9,914

0.505

0.500

0.627

0.484

Education (ref:
none)
6.90
Basic

13,131

Secondary

13,131

6.90

0.180

0.384

0

1

Post-secondary

13,131

6.90

0.047

0.212

0

1

University

13,131

6.90

0.052

0.223

0

1

Urban

13,131

6.90

0.588

0.492

0

1

Poor

13,131

6.90

0.094

0.292

0

1

Very poor

13,131

6.90

0.039

0.193

0

1

0.830

0.376

0

1

Poverty status(ref: non-poor)

Access to

6.90

information

13,131

Household size

13,131

6.90

4.412

2.758

1

28

Age

13,131

6.90

42.89414.35815

99

Age squared

13,1316.90

2046.018

1394.776

225

9801

Sex (1= male)

13,131

6.90

0.745

0.436

0

1

0

9.241

Ln (Healthexpenditure)

5.075
9,914

1.796
13

2.438

Source: Authors’ calculations based on data from wave 7 of the Ghana Living Standards
Survey.
Based on the foregoing theories, a model for estimating the probability of enrolling in healthinsurance is specified as follows:
Pr (𝐸𝑛𝑟𝑜𝑙𝑙 𝑖𝑛 𝐻𝐼) = 𝛽 + 𝛽 𝑅 + 𝛽 𝑋 + 𝜀 ,(2)where R denotes risk aversion and X is a vector of relevant control variables that might affectthe decision to enroll in a health insurance scheme. Household risk preferences are a behavioral
trait that is considered to a large extent to be exogenous. Thus, we employ a logit model toestimate equation (2) to ascertain the effect of risk aversion on health insurance enrollment.
The theory of the demand for health is used to explain household health expenditure. According
to Grossman (1972), health capital serves both as a consumption good and an investment good,and individuals can increase their stock of health capital by the amount they invest in theirhealth. This implies that health expenditure can be viewed as an investment in one’s healthcapital with the amount invested being dependent on the characteristics of the commodity andof the individual as well as unobserved components. Based on this, we model the amount ofhousehold health expenditure as follows:
𝐿𝑛 (𝐻𝑒𝑎𝑙𝑡ℎ 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒) = 𝛽 + 𝛽 𝐻𝐼 + 𝛽 𝐻𝐹 + 𝛽 𝑋 + 𝜀 ,(3)where HI denotes enrollment in a health insurance scheme, HF denotes the presence of a healthfacility in the respondent’s community, and X is a vector of other control variables.

Endogeneity issue and the instrumental variable
Enrollment in health insurance is potentially endogenous. It is possible that persons with poor
health will realize their need for frequent health care services and hence be more likely to enroll
in health insurance, and since they are likely to access health care more often than others, they
may have a higher health expenditure as well. If this is the case, it will hide the true impact ofhealth insurance enrollment on health expenditure. There is also the possibility that otherunobserved factors influence households’ decision to enroll in health insurance, thus
confounding the effect of health insurance enrollment on health expenditure. To address thisproblem, we employ an instrumental variables approach. We use risk aversion as our
14

instrument for health insurance enrollment. Given that risk aversion has no direct relationshipwith health expenditure but has a direct relationship with the endogenous variable – healthinsurance enrollment (see section 4), it presumably satisfies the exclusion restriction criteria
for a good instrument. The presence of a health facility, on the other hand, is a communitylevel variable and can be considered to be exogenous to the household. Thus, the coefficient of
this variable obtained from estimating equation (3) is sufficient to indicate its effect on healthexpenditure.
Due to the presence of an instrument to estimate the effect of health insurance on healthexpenditure, we estimate (3) using the approach proposed by Lewbel (2012) that identifies
structural parameters in models with endogenous regressors. According to Baum et al. (2013),
the approach is useful both in the absence of external instruments and in the presence of
external instruments. In the case of the latter, the approach is useful as a way of supplementing
external instruments to improve the efficiency of the IV estimator. Using Lewbel’s (2012)
method, we are able to compare standard two-stage least squares (2SLS) results with resultsfrom generated instruments only as well as results from both generated and external
instruments. Equations (4) and (5) show the two models fitted for a 2SLS approach where R,
HF and X are as previously explained. HÎ in (5) is the fitted value of health insurance from (4).
𝐻𝑒𝑎𝑙𝑡ℎ 𝐼𝑛𝑠𝑢𝑟𝑎𝑛𝑐𝑒 = 𝛽 + 𝛽 𝑅 + 𝛽 𝐻𝐹 + 𝛽 𝑋 + 𝜀 ,(4)

𝐿𝑛 (𝐻𝑒𝑎𝑙𝑡ℎ 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒) = 𝛽 + 𝛽 HÎ + 𝛽 𝐻𝐹 + 𝛽 𝑋 + 𝑒(5)

5. Estimation results and discussion
This section presents the results from estimations of the models introduced in the previous
section and discusses the findings.

5.1. Determinants of health insurance enrollment
Table 2 shows the marginal effects from a logit estimation of the effect of risk aversion andother variables on health insurance enrollment in Ghana.
From Table 2, it can be seen that the probability of enrolling in health insurance is 3.1 to 3.3
percentage points higher for those who are risk averse compared to those who are not risk
15

Table 2: Determinants of Health Insurance Enrollment

Variables(1)(2)
Health Insurance Enrollment

Risk averse

0.033***(0.011)0.019*(0.011)0.031***(0.011)
0.022**(0.011)

0.015(0.013)0.051***(0.015)0.010(0.013)0.045***(0.015)-0.002(0.014)0.035**(0.017)0.129***(0.022)0.148***(0.021)
0.054***(0.009)0.001(0.002)-0.008***(0.002)0.0001***(0.000)0.034***(0.011)0.004(0.014)0.054***(0.017)0.141***(0.022)0.162***(0.021)
0.056***(0.009)-0.005***(0.002)-0.012***(0.002)0.0001***(0.000)0.027**(0.011)-0.060***(0.012)

Health status (1= ill/injured)
Poverty status (ref: non-poor)
Poor
Very poor
Education (ref: none)
Basic
SecondaryPost-secondary
University
Urban
Household size
Age
Age squared
Access to information
Sex of head (1=male)
Marital status (ref: nevermarried)Married

0.199***(0.019)0.092***(0.022)0.129***(0.031)0.016(0.029)0.135***(0.026)Consensual union
Separated
Divorced
WidowedObservations

13,131
16

13,131

Source: Authors’ calculations based on data from wave 7 of the Ghana Living Standards
Survey.
Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
averse and that the difference is statistically significant. This finding is consistent withtheoretical expectation as well as with the findings of Hopkins and Kidd (1996) andBuchmueller et al. (2013) for Australia, those of Liu et al. (2011) for rural China, and those of
Condliffe and Fiorentino (2014) for the United States (discussed in subsection 3.1) that persons
with low risk tolerance are more likely to enroll in voluntary health insurance programs because
they are more eager to insure against the risk of falling ill and being denied health care due tonot having the means to make out-of-pocket payments.

We further found that persons who reported an illness/injury two weeks prior to their interview
were 2.2 percentage points more likely to enroll in a health insurance scheme, and thedifference was found to be statistically significant. Since this category of persons is likely to
be in poorer health, this variable can be regarded as a proxy for health status, and our result
suggests that those in poorer health are more likely to enroll in health insurance because theyrequire more health care services. This finding is consistent with the findings of Duku (2018)
and Buchmueller et al. (2013).
Turning next to the impact of access to information, we found that those with access to
information are, as one might expect, more likely to enroll in health insurance.
Turning next to the impact of age, we found a non-linear relationship between age and healthinsurance enrollment, with both younger people and older people being more likely to enroll,with the enrollment rate bottoming out around age 40. The latter result could be due to the factthat one’s health capital depreciates with age and hence that older people have a greater needfor health care services compared to younger people, making them more likely to enroll in
health insurance. The finding is largely consistent with the previous literature (see, for example,
Amu et al., 2018; Dake, 2018).
Turning next to the impact of poverty, very poor households were found to be 4.5 to 5.2
17percentage points more likely to enroll in health insurance compared to non-poor people, andthe differences were found to be statistically significant. To the extent that this finding is
attributable to exempting poor and vulnerable households from paying premiums, it suggeststhat the NHIS in Ghana is achieving its objective of being pro-poor (Amu et al., 2018).
Turning next to the impact of educational attainment, persons with higher levels of education
were found to be more likely to enroll in the health insurance program compared with persons
with no formal education, and the differences were found to be statistically significant.
Furthermore, it is observed that the magnitudes of the coefficients for secondary, postsecondary and university increase with the level of education, thus confirming that thepropensity for an individual to enroll in health insurance is higher, the higher is his/her
educational attainment. The opportunity cost of time and good health is presumably higher for
more highly educated people, and thus they would be expected to take every possible measure
to enhance their health and wellbeing, and enrolling in health insurance is one such measure.
Furthermore, we found that urban households are more likely to enroll in health insurance andthat males are less likely to enroll in health insurance (but the coefficient of the latter isstatistically significant only if variables relating to marital status are included).
Finally, household attributes such as household size and marital status had significant impacts
on the likelihood of enrolling in health insurance. Larger households were less likely to enroll
in health insurance, with an additional household member reducing the likelihood of healthinsurance enrollment by 0.5 percentage points. This could be due to the financial burden oflarger households reducing the amount of fiscal space the household has to spend on healthinsurance. With respect to marital status, it was found that, relative to the never married, themarried, those in consensual unions, the separated, and the widowed had a significantly higher
likelihood of enrolling in health insurance, with the married having the highest likelihood. This
could be due to the fact that married couples are better able to afford such things as healthinsurance because they are able to reap economies of scale in consumption.
5.2. Determinants of health expenditure
Table 3 shows the estimation results from a tobit regression of the effect of risk aversion on
health expenditure. It shows a statistically insignificant effect of risk aversion on health18

expenditure, confirming that risk preferences do not directly impact health expenditure7. Risk
aversion is therefore used as an instrument for the determinants of health expenditure in themain analysis, and the results are shown in Table 4.
Table 3: Determinants of Household Health Expenditure: Tobit Results
Variables(1)

Risk averse

0.195(0.205)Control variables
Observations

Yes
9,850

Source: Authors’ calculations based on data from wave 7 of the Ghana Living Standards
Survey.
Note: Standard errors in parentheses.
Table 4: Determinants of Household Health Expenditure: IV Results
2SLS
Generated
Generated andinstruments
external
only
instruments
Variables
Ln (Health Expenditure)
Health ins. (1=enrolled)
Health status (1= ill/injured)
Health facility in one’s own
community (1=yes)
Poverty status
Years of education
Urban
Household size
Sex of head (1=male)Age
Age squared
Marital status (1=livingwith a partner)

6.061(8.391)
0.538***(0.173)-0.162*0.212(0.396)
0.638***(0.062)-0.147***0.242(0.395)0.638***(0.062)-0.147***(0.087)-0.571***(0.146)-0.061(0.060)-0.469(0.663)0.157***(0.048)0.324(0.605)0.014(0.063)-0.0003(0.001)0.548(0.054)-0.480***(0.043)-0.019***(0.007)-0.011(0.064)0.125***(0.009)-0.091(0.076)-0.028***(0.009)0.0003***(0.0001)
0.153*(0.054)-0.480***(0.043)-0.020***(0.007)-0.013(0.064)0.125***(0.010)-0.089(0.076)-0.028***(0.009)0.0002***(0.0001)0.150*(1.012)(0.088)(0.088)19

Constant-1.876(5.494)

1.948***(0.337)1.928***(0.336)

Observations
9,914
9,914
9,914
F15.7338.39
38.38
Critical value max IV size
8.96
20.88
22.06
Source: Authors’ calculations based on data from Wave 7 of the Ghana Living Standards
Survey.
Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
Instrumented: Health insurance enrollment.
Table 4 presents the results from the IV estimation of the determinants of household healthexpenditure. Column (1) show the results from a standard 2SLS IV estimation, column (2)shows the results from Lewbel’s (2012) method with generated instruments only, and column(3) shows the results with generated and external instruments. As can be seen from the table,
the F-statistics are greater than the Stock-Yogo critical values in all models. Therefore, theweak instrument identification test hypothesis is rejected in favor of a strong one. In otherwords, risk preference is valid as an instrument for identifying the effect of health insurance on
health expenditure. The results in columns (2) and (3) are similar and are preferred to those incolumn (1) since they improve the efficiency of the results relative to the standard 2SLS
regression. The results in Table 4 show that health status, the presence of a health facility in
one’s own community, poverty status, education, household size, age, and marital status are
significant predictors of household health expenditure. Persons in poor health were found to
spend about 89% more on health compared to persons in good health status, and the difference
was found to be statistically significant. Persons in poorer health require more frequent visits
to the hospital and thus may end up spending more on health care. This finding is consistent
with those of Azzani et al. (2019) and Su et al. (2006).
Households living in communities that have a health facility were found to have lower healthexpenditure, and the difference was found to be statistically significant. They spend about
13.7% less on health compared to households without health facilities in their communities.
Health facilities often provide public health education to their host communities which may inturn help community members protect themselves from common disease causes, thus
contributing to lower health expenditure. Our finding that the presence of health facilities is
helpful in reducing households’ health expenditure is a novel contribution to the literature andpolicy making as it underscores the importance of the equitable spatial distribution of healthinfrastructure throughout the country.
20

As households’ poverty status deteriorates, they spend an average of about 38% less on healthexpenditure. Given that health is a normal good, persons with higher incomes will demand
more of such goods compared to lower income households, and thus this finding is as expected.
Household size was found to increase health expenditure, as expected, and the difference wasfound to be statistically significant. An additional household member increases the healthspending of the household by 13% while an additional year of education reduces healthspending by about 2%. Persons with higher education may spend less on health because they
have a higher opportunity cost of time and hence are more likely to focus more on preventive
health practices that might reduce their likelihood of falling sick. As expected, older persons
spend more on health expenditure than younger persons due to the fact that one’s stock ofhealth deteriorates as one grows older. Finally, marital status has a weakly significant impact
on health expenditure. Those living with a partner spend 16% more on health than thosecurrently without a partner. Couples are more likely to have children and hence, a higher healthbill than persons without partners. Similar results on the effect of these socioeconomic factors
on health expenditure are found by Olasehinde and Olaniyan (2017) in Nigeria, Mehrabi et al.
(2018) in Iran, and Tur-Sinai et al. (2018) in Israel.
An important finding from our study is that health insurance was found not to have astatistically significant effect on households’ out-of-pocket health expenditure. In other words,
enrolling in health insurance does not reduce one’s out-of-pocket health expenditure relative
to those not enrolled. This finding is contrary to expectation because the out-of-pocket healthexpenditure of those enrolled in health insurance should, in theory, be close to zero, but there
are at least two possible explanations for our finding. The first possible explanation is thatenrolling in health insurance leads to moral hazard, with enrollees seeking more health carethan is absolutely necessary, but this cannot be the full explanation because out-of-pocket
health expenditure of enrollees should be close to zero regardless of how much health care theyreceive. However, our finding is not entirely unexpected within the Ghanian context. The NHIS
in Ghana has been bedeviled with several challenges in its implementation. Agyepong et al.
(2016) reported, among other things, that it was not an uncommon practice for provider
facilities to charge both formal payments and informal (covert) charges even to insured patients.
Since health facilities strongly prefer patients who pay in cash rather than patients who seek
treatment pursuant to their health insurance policies because they must complete tedious paper
21

work and wait for a long time before they are reimbursed by the NHIS, patients desiring more
timely and higher quality health care choose to make formal or informal (covert) out-of-pocket
payments and sometimes even forego using their insurance policies altogether. These
considerations partly explain why enrollment in health insurance does not lead to significant
reductions in health expenditure. This finding is relevant to how the government ought to repackage the implementation of the NHIS to the benefit of subscribers and to motivate
enrollment among non-subscribers.8

6. Conclusions and policy implications
This study examined the determinants of health insurance enrollment and health expenditurein Ghana using micro data from wave 7 of the Ghana Living Standards Survey (GLSS 7) withemphasis on the role of risk preferences and the availability of health facilities in one’s own
community, neither of which has been emphasized in the previous literature on this topic. It is
possible to analyze the determinants of health insurance enrollment in Ghana because its publichealth insurance system (the National Health Insurance Scheme or NHIS) is, in theory,
mandatory, but is, in actual practice, voluntary, with only about 40% of the population enrolled
in the scheme. Our empirical findings show that risk preferences have a significant impact on
health insurance enrollment, with risk averse individuals being significantly more likely thanother households to enroll in health insurance, as one would expect. Moreover, our findings
also show that very poor households are significantly more likely to enroll in health insurance
than other households, perhaps because they are exempt from paying premiums for healthinsurance. This finding suggests that NHIS is achieving its intended objective of increasing thepoor’s access to health care. Somewhat surprisingly, our findings show that those who areenrolled in health insurance do not show significantly lower health expenditures than otherhouseholds even though enrolling in health insurance should greatly reduce out-of-pocket
health expenditure. This finding may be due to the fact that even enrollees need to pay
substantial out-of-pocket health expenditures in Ghana due to various defects in its health careand health insurance system. Finally, our findings also show that the availability of healthfacilities in one’s own community significantly decreases expenditures on health care, which
underscores the importance of ensuring an equitable spatial distribution of health facilities
throughout the country.

22Turning finally to the policy implications of our findings, our findings suggest that (assumingthat the populace is relatively risk averse, as they apparently are in Ghana), educating the
populace about the importance of insuring against health risks would be an effective way of
increasing health insurance enrollment rates from their current low levels. Second, our findings
suggest that exempting poor households from paying premiums for health insurance or
reducing their premiums has been an effective way of increasing the insurance enrollment ratesof the poor. Third, our findings underscore the importance of reforming Ghana's healthinsurance system so that those enrolled in this system are not required or feel the need to pay
various formal and informal (covert) payments when receiving care, which defeat the purposeof enrolling in health insurance. Fourth and finally, our findings suggest that ensuring anequitable spatial distribution of health infrastructure throughout the country would be aneffective way of reducing household health expenditures and ultimately increasing household
welfare. Implementing these recommendations would be an effective way of increasing thequality, affordability, and equity of health care delivery not only in Ghana but also in otherdeveloping countries.

Acknowledgements
We are grateful to the handling editor Enrica Croda and two anonymous reviewers for theirinvaluable comments and suggestions. This work was supported by JSPS (Japan Society for
the Promotion of Science) KAKENHI Grant Numbers 18H00870, 20H01513, and 20H05633
to Horioka. We are very grateful for this support.

Compliance with ethical standards
Conflicts of interest
The authors declare that they have no conflict of interest.

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1

Coverage rates were taken from the 2010 and 2013 annual reports of the NHIS and NsiahBoateng and Aikins (2018).
2

For a more comprehensive overview of Ghana’s NHIS, see Blanchet et al. (2012), Alhassan
et al. (2016) and Wang et al. (2017).
3

Note, however, that the informal sector employs a far greater proportion of workers than theformal sector in Ghana.
4

Since the natural logarithm of zero is not defined, 1 cedi (about 0.14 U.S. dollars) was added
to the health expenditure of all households to eliminate zero values.
5This study is constrained to measure risk aversion as a binary variable as opposed to an ordinalmeasure with different levels of risk aversion due to the nature of the question asked in thesurvey. Studies such as Adjei-Mantey and Adusah-Poku (2021) used a similar measure of risk
preference.
6Note, however, that because the Lewbel method that we used to estimate the healthexpenditure equation could not accommodate categorical variables, we had to re-define thevariables relating to poverty status, education, and marital status when estimating this equation.
The poverty status variable equals 0 for the non-poor, 1 for the poor, and 2 for the very poor,
the education variable measures years of education, and the marital status variable equals 0 for
the never married, separated, divorced, and widowed and 1 for the married and consensual
union. When calculating years of education, “none” was assigned a value of 0 years, “basic” avalue of 9 years, “secondary” a value of 12 years, “post-secondary” a value of 15 years, and“university” a value of 16 years.
7

The lack of statistical significance here could be partly attributable to the fact that themeasurement of risk aversion in this study is not directly related to investment in one’s own
health and its potential returns.
8Note, however, that the fact that the health expenditure of those enrolled in the national healthinsurance scheme and those not enrolled are not significantly different from one another27

implies that enrollees receive more health care than non-enrollees since at least some of theirhealth expenses are presumably covered by health insurance. Thus, although NHIS does notnecessarily lower the health expenses of insurees, it enables them to receive a greater quantityof health care.

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