metadata: authors: 0: author: Raju,Dhushyanth
1: author: Younger,Stephen D.
count: Ghana
docna: 0: docna: The Financial Risk Reduction Provided by
            Ghana’s National Health Insurance Scheme
docty: Policy Research Working Paper
abstracts: cdata!: This paper estimates the monetary value
            of financial risk reduction associated with membership in
            Ghana’s National Health Insurance Scheme, based on recent
            national household survey data. The paper compares the risk
            premiums for distributions of out-of-pocket healthcare
            expenditures with and without insurance and find that the
            difference is small. This does not mean that the National
            Health Insurance Scheme has no value to members. Indeed, the
            findings show that the insured pay significantly less for
            healthcare than the uninsured on average. But that average
            reduction does not translate into a reduced spread of
            consumption net of out-of-pocket healthcare expenditures.
            Thus, the benefit of the National Health Insurance Scheme is
            entirely a transfer benefit, not a reduction in financial risk.
display_title: The Financial Risk Reduction
            Provided by Ghana’s National Health Insurance Scheme
listing_relative_url: /research/2022/06/33834969/financial-risk-reduction-provided-ghana’s-national-health-insurance-scheme
topicv3: Social Protection and Labor m1327194 697,Health Insurance m1327759 1287,Government Health Care Subsidy m1583083 4632
publishtoextweb_dt: 2022-06-02T00:00:00Z
dois: 10.1596/1813-9450-10073
author_keys: 0: author_key: 237905
1: author_key: 637678
model_extractions: Policy Research Working Paper 10073 The Financial Risk Reduction Provided by Ghana’s National Health Insurance Scheme _Dhushyanth Raju Stephen D. Younger_ Social Protection and Jobs Global Practice June 2022
Policy Research Working Paper 10073

# **Abstract**

This paper estimates the monetary value of financial risk reduction associated with membership in Ghana’s National Health Insurance Scheme, based on recent national household survey data. The paper compares the risk premiums for distributions of out-of-pocket healthcare expenditures with and without insurance and find that the difference is small. This does not mean that the National Health Insurance Scheme has no value to members. Indeed, the findings show that the insured pay significantly less for healthcare than the uninsured on average. But that average reduction does not translate into a reduced spread of consumption net of out-of-pocket healthcare expenditures. Thus, the benefit of the National Health Insurance Scheme is entirely a transfer benefit, not a reduction in financial risk.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at draju2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Financial Risk Reduction Provided by Ghana’s National Health Insurance Scheme**

Dhushyanth Raju Stephen D. Younger _Key words_ : out-of-pocket expenditures, public healthcare, health insurance, financial risk protection, Ghana, Africa _JEL codes_ : I13; I15; H51 Raju, World Bank, <u>draju2@worldbank.org; Younger, Commitment to Equity Institute, sdy1@cornell.edu. The study</u> was prepared under the World Bank’s advisory services and analytics task _Ghana Human Development Public Expenditure Review_ (P175117). We thank Jeremy Barofsky, Jishnu Das, Jeffrey Hammer, Magnus Lindelow, Patrick Mullen, and Paul Andres Corral Rodas for comments. We also thank Enoch Oti Agyekum and Alex Nartey for comments and assistance in obtaining relevant data and documentation.
# **1 Introduction**

Most governments spend a significant share of their budgets to provide healthcare services to their populations. Studies that attempt to value these services in monetary terms for benefit-cost or benefit incidence analysis typically assess their transfer value. That is, they calculate the difference between the value of the service received and the amount beneficiaries must pay for it (which may be nothing). Barofsky and Younger (2022) discuss a variety of methods to do this, and many examples exist for Ghana.<sup>1</sup> But publicly financed health insurance provides an additional benefit in the form of financial insurance. Even if the premium that insurance beneficiaries must pay is actuarially fair so that, on average, the premium equals the expenditure the insurer makes on the beneficiaries’ behalf, the insurance still provides a benefit to the insured because it lowers the financial risk associated with adverse health outcomes. The insured will have lower variation in income net of healthcare expenditures less any premiums paid. It is less common to estimate the monetary value of this reduction in financial risk, a gap this study aims to fill for Ghana.<sup>2</sup> The out-of-pocket (OOP) expenditures necessitated by an adverse health shock pose a financial risk because they add variance to income net of those expenditures. As with any risk, it is possible to calculate a risk premium, that is, the quantity of money a risk-averse person is willing to pay to completely insure against it. Publicly provided health insurance reduces financial risk and so provides a benefit to those who are insured. This benefit is over and above the value of the healthcare services they receive less what they pay in premiums. We call the latter difference a transfer value of publicly provided health insurance. To simplify, the transfer is a difference in the mean; financial risk protection is a difference in variance and skewness. In contrast to most studies which calculate the transfer values, here we want to estimate the increase in utility of lower dispersion of income net of healthcare expenditures, which we call the financial insurance value of public health insurance.

We should note that the existing literature often confounds these two effects. A recent extensive review of the literature finds that, at best, “financial protection” is used to refer to the combined transfer and financial risk reduction benefits, though it often refers only to the transfer

> 1 For recent benefit-cost analyses, see Asuming, Kanmiki, and Wong (2020) and Nketiah-Amponsah et al. (2020). For benefit incidence analyses, see Coulombe and Wodon (2012) and Younger, Osei-Assibey, and Oppong (2017). 2 Barofsky and Younger (2022) do make this calculation for Ghana. We have updated and modified their approach considerably. Appendix A discusses the differences.

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(Das and Do 2022). This despite the fact that the early literature in the field of financial risk reduction is clear about the distinction.<sup>3</sup> The specific insurance we evaluate is Ghana’s National Health Insurance Scheme (NHIS). Since 2005, NHIS has been open to all Ghanaians, and the government’s stated goal is to enroll everyone, though actual membership falls far short of that. In 2017, the year of our data, 42 percent of the population was enrolled in NHIS. To enroll, individuals pay a small registration or an annual renewal fee, and an annual premium, though some may qualify for exemptions from one or both fees. These fees fund only a small part of the NHIS budget; most is funded through an earmarked value-added tax of 2.5 percent. Thus, the NHIS includes a larger transfer to its members on average. While this is sometimes called “financial protection,” it is not the focus of our study, which is financial risk reduction as defined above.

Our approach is to calculate the risk premium associated with a distribution of net income with NHIS membership and without it. The difference in these two is the financial insurance value of NHIS membership, in monetary terms. To do this, we must estimate the distribution of net income with and without NHIS membership. We then calculate and compare the risk premiums for each distribution to obtain an estimate of the financial insurance value of NHIS.<sup>4</sup> Of course, we do not observe people’s expenditures with and without NHIS membership, so we must compare those with membership to those without, who serve respectively as “treatment” and “control” groups. Ideally, these would be randomized to avoid selection bias, but that is not possible with our data,<sup>5</sup> nor is it usually possible with a large insurance program such as NHIS. Instead, we create a control group with coarsened exact matching (Blackwell 2009).<sup>6</sup>

> 3 Englehart and Gruber (2011) (in p. 97) state “Of course, the introduction of Part D will shift the mean level of outof-pocket spending as well as its risks. The shift in the mean is simply a transfer from the government to the insured, and so should not enter these risk calculations.” Finkelstein and McKnight (2008) (in p. 1663), whose approach we follow, note that “Since our interest is in the social welfare benefits from Medicare rather than the private benefits from transfer payments, we assume for the purposes of our calculations that the beneficiaries “pay for” the actuarial expected cost of Medicare, so that the welfare benefits come only from reductions in the variance, and not the mean, of medical expenditures.”

> 4 This is a standard approach in the literature on health insurance. For example, see Feldstein (1973), Feldstein and Gruber (1995), and Finkelstein and McKnight (2008). For an application to Ghana, see Barofsky and Younger (2022).

> 5 Powell-Jackson et al. (2014) do randomize health insurance coverage in one district of Ghana.

> 6 Finkelstein and McKnight (2008) use a difference-in-differences strategy to estimate the insurance value of Medicare in the United States, comparing those just under the qualifying age of 65 to those just over, before and after the introduction of Medicare in the 1960s. This approach is not available to us either, as everyone qualifies for NHIS and our data were collected well after NHIS was established.

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The control group is not completely without publicly provided health insurance. While those not in NHIS must pay fees at public healthcare facilities, those fees only have to cover supplies, maintenance, medicines, and diagnostic costs. Capital expenditures and, importantly, staff salaries are covered by the general government budget regardless of a user’s NHIS affiliation. While not called health insurance, this support to public healthcare facilities does function like insurance: General revenue is collected from all taxpayers to provide benefits to those who draw unfortunate health outcomes. Our comparison estimates the financial insurance value of NHIS relative to this “reduced insurance” state, not to no insurance at all.

The data we need from treatment and control groups are more detailed than those used in a typical evaluation in that we need to compare full distributions of OOP healthcare expenditures, not just their means. We create these distributions from the observed distribution of OOP healthcare expenditures for treatment and control groups, assuming that the cross-person distribution reflects each individual’s _ex ante_ distribution of possible expenditures.

Given these OOP healthcare-expenditure distributions for those with and without NHIS membership, we then calculate the risk premium associated with each distribution for each person in the sample using a stylized constant relative risk aversion (CRRA) utility function. The difference in those risk premiums is our estimate of the financial insurance value of NHIS membership.

The data come from the Ghana Living Standards Survey (GLSS) 2016/17, fielded between October 2016 and September 2017. GLSS 2016/17 is the latest available round of the national household sample survey administered by Ghana’s statistical agency (GSS 2019). The survey collected information on household expenditures, including expenditures for healthcare for each person in the household. Our data, then, are individual level for each person in the survey, with 58,844 people considered in all.

The remainder of the paper is organized as follows. Section 2 provides background information on government healthcare financing and spending in Ghana and for NHIS. Sections 3 and 4 describe how we calculate the distribution of OOP healthcare expenditures for those with and without NHIS membership. Section 5 calculates and discusses the risk premium associated with each distribution and their difference, which is the financial insurance value of NHIS membership. Section 6 concludes.

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<!-- Start of picture text --> 120<br>a<br>” a<br>s =<br>8 80<br>ae<br>oO<br>a NS<br>40<br>2011 2013 2015 2017 2019<br>—Ghana —SSA, average —LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br>:<br>8<br>(5)<br>5 ——_<br>ao ee<br>0<br>2011 2013 2015 2017 2019<br>—Ghana —LMICs, average<br><!-- End of picture text -->
<!-- Start of picture text --> Grant, lending (concessionaland commercial)<br>Budget<br>transfer<br>SSNIT<br>National Health Insurance<br>Authority<br>Premium reimbursement-<br>payment Claims Budget transfer<br>= Health facilities<br>Bf Household contributions —— External support<br>i Public funds B Intergovernmental transfers<br><!-- End of picture text -->
NHIS membership is voluntary. Although the government encourages all to join, less than half of the population is affiliated in any given year (Nsiah-Boateng and Aikens 2018). To obtain membership, individuals must pay a registration or annual renewal fee which, over our analysis period, varied between zero and 8 cedis (zero to 4.6 PPP dollars),<sup>8</sup> and an annual premium of 15 cedis (8.6 PPP dollars) in rural areas and 22 cedis (12.6 PPP dollars) in urban areas (Nsiah-Boateng and Aikens 2018). The premium is waived for several selected groups, including those under 18 years old and those aged 70 years old or older, as well as the “indigent,”<sup>9</sup> Social Security and National Insurance Trust (SSNIT) contributors and pensioners,<sup>10</sup> pregnant women, and women with infants under 3 months (and the infants). The indigent, pregnant women, and women with young infants also do not have to pay the registration or renewal fee.

The fees charged, even with increases in recent years, are far short of what would be actuarially fair. Instead, over our analysis period, NHIS received the bulk of its funding— roughly three-fourths—from a special 2.5 percent addition to the VAT called the National Health Insurance Levy (NHIL). A further one-fifth of its funding came from SSNIT, which pays NHIS fees for its members (Wang, Otoo, and Dsane-Selby 2017).

NHIS covers most illnesses found in Ghana. In principle, members can receive healthcare free of charge from all public providers and some participating private providers.<sup>11</sup> In practice, NHIS has a long history of being slow to reimburse healthcare providers (Wang, Otoo, and Dsane-Selby 2017), leading some providers to refuse to treat NHIS members and others to charge them despite that they are insured—something to keep in mind as we examine the difference between reported OOP healthcare expenditures for NHIS members and nonmembers.

> 8 In 2017, the purchasing power parity conversion factor for cedis to international dollars was 1.75 cedis per dollar. We use this conversion factor wherever we report values in PPP dollars. The conversion factor was obtained from the World Bank’s World Development Indicators databank.

> 9 NHIS defines an “indigent” as an individual who (a) “does not have any visible source of income,” (b) “does not have a fixed place of residence,” (c) “does not live with a person who is employed and has [a] fixed place of - residence,” or (d) “does not have a consistent source of support from another person” (https://nhis.gov.gh/Faqs/the <u>benefits-of-the-national-health-insurance-scheme-2?msclkid=60d5acdcab8811ec95eecede25b33dfb; accessed on</u> March 24, 2022).

> 10 SSNIT is a statutory organization that administers a national social security scheme. The scheme offers old-age, disability, and survivor benefits to its members.

> 11 Thirty-six percent of all participating healthcare providers are private. Because staff salaries at private healthcare providers are not paid by the government, NHIS reimburses these providers more generously than it does public healthcare facilities, but not by enough to cover the entirety of provider salaries.

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Anecdotal reports suggest that this problem is more severe at private providers because they must worry about covering staff costs as well as medicines, supplies, and diagnostic tests.

Ghanaians who are not covered by NHIS must pay for healthcare on a fee-for-service basis. However, as we noted above, at public healthcare facilities, salaries and capital expenditures are covered by the central government budget, so the fees cover only nonsalary, noncapital costs.

# **3 Calculating Out-of-Pocket Healthcare Expenditures**

GLSS 2016/17 asks about healthcare expenditures for each individual if in the past two weeks she or he consulted a healthcare provider, purchased medicines, or was admitted to a hospital. For outpatient services, survey respondents can report what they paid for registration at the healthcare facility, consultation, diagnosis (laboratory fees, etc.), and medicines, or they can report the total amount they paid.<sup>12</sup> In our calculations, we use the larger of the sum of the individual items or the reported total. For inpatient services, survey respondents only report what they paid for “staying in a hospital or health facility.”<sup>13</sup> Survey respondents can also report “medicines and medical supplies” purchased outside the context of a healthcare visit.<sup>14</sup> Finally, they can report “total medical expenses.”<sup>15</sup> To calculate total OOP healthcare expenditures, we add the total of outpatient expenses, inpatient expenses, and medicine expenses and take the larger of this sum or the “total medical expenses” reported. In a few cases, respondents report exactly the same amount for medicines acquired in the context of a consultation and medicines acquired without a consultation.<sup>16</sup> For these, we set the value for OOP healthcare expenditures outside the context of a consultation to zero to avoid double counting these expenditures.

All the raw variables in GLSS 2016/17 have some very large positive outliers. For each of the raw variables and again for our calculated total OOP healthcare expenditures, we replace any value that is larger than the respondent’s household consumption (not per capita) or five

> 12 These are questions 9 through 12 and question 13 in Section 3A of the GLSS 2016/17 questionnaire. We have excluded question 14 (“any other services”) and question 15 (“travel”) from our total out-of-pocket healthcare expenditures as these are not healthcare expenditures per se.

> 13 Question 20 in Section 3A of the GLSS 2016/17 questionnaire.

> 14 Question 22 in Section 3A of the GLSS 2016/17 questionnaire.

> 15 Question 23 in Section 3A of the GLSS 2016/17 questionnaire.

> 16 Questions 12 and 22 in Section 3A of the GLSS 2016/17 questionnaire, respectively.

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standard deviations larger than the mean share of that type of OOP healthcare expenditures in total household consumption with the mean share of those expenditures in household consumption times the respondent’s household’s consumption. That is, for respondent _i_ in household _h_ and OOP type _j_ , we replace _OOPihj_ with the sample mean value of ( _OOPihj_ / _Ch_ ) times _Ch_ , where _Ch_ is household consumption. In all, we change 112 healthcare expenditure records (0.2 percent of the sample) with these procedures.

An important problem with this estimate of OOP healthcare expenditures is that it applies only to expenditures incurred in the past two weeks. But healthcare expenditures in Ghana are not regular or frequent, so if we want to know how much is spent in a year, simply multiplying the reported amounts by 26 will be inaccurate. Survey respondents who happened to have incurred healthcare expenditures in the past two weeks are not the only people who will incur healthcare expenditures through the year, nor will their annual healthcare expenditures be anywhere near 26 times what they spent in the past two weeks.

To address this, we divide the sample into deciles based on consumption per adult equivalent and into NHIS members and nonmembers; 20 subgroups in all.<sup>17</sup> For each respondent, we then draw and sum 26 random values from the distribution of the share of OOP healthcare expenditures in total household consumption for her/his subgroup whether or not she or he reports healthcare expenditures in the past two weeks. We then multiply the sum of the 26 random draws by total household consumption to obtain our estimate of the individual’s annual OOP healthcare expenditure. This assumes that every member of a subgroup has an equal probability of incurring a healthcare expenditure, though the probability can vary across subgroups. In doing so, we ignore the possibility of serial correlation of OOP healthcare expenditures, that is, the possibility that those who report OOP healthcare expenditures in the past two weeks are more likely to have OOP healthcare expenditures in the rest of the year than those who do not. However, simply multiplying observed OOP healthcare expenditures in the past two weeks by 26 assumes perfect serial correlation throughout the year, something that seems much farther from the truth than our assumption of zero correlation. This is particularly so

> 17 We also tried dividing the sample by groups that might be expected to have greater demand for healthcare: (income quartile)-by-(infants/children/adults/the elderly (60+)/women who have been pregnant in the past year)-by(urban/rural); 40 subgroups in all. This produces results of a similar magnitude to our main results but with the risk premium being _higher_ for NHIS members than nonmembers.

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in Ghana where most healthcare is curative for non-chronic conditions, usually for fever, upper respiratory infections, or birthing assistance. Finally, in our calculations, 415 people, 0.8 percent of the sample, had estimated annual OOP healthcare expenditures greater than their household’s consumption per adult equivalent. For these, we reduced estimated OOP healthcare expenditures to household consumption per adult equivalent.<sup>18</sup> One further detail of our calculations is important to the results. In the raw data, NHIS members actually have slightly _higher_ OOP healthcare expenditures in the past two weeks than do nonmembers because they are much more likely to seek healthcare. To account for this when we draw from the OOP healthcare expenditure distributions for a subgroup, we adjust the probability associated with each nonzero OOP healthcare expenditure by nonmembers upward by the ratio of the share of NHIS members who sought healthcare in the past two weeks over the share of nonmembers who sought care.<sup>19</sup> Table 1 reports the probability of healthcare seeking by decile of the welfare distribution and NHIS member status. Note that we apply this adjustment

**Table 1. Probability of Seeking Healthcare in the Past Two Weeks** **<u>by NHIS Membership Status</u>**

|Welfare decile|Nonmembers<br>(1)|Members<br>(2)|
|---|---|---|
|1<sup>st</sup>(poorest)<br>|0.040|0.082|
|2<sup>nd</sup>|0.054|0.124|
|3<sup>rd</sup>|0.065|0.103|
|4<sup>th</sup>|0.060|0.108|
|5<sup>th</sup>|0.061|0.108|
|6<sup>th</sup>|0.052|0.124|
|7<sup>th</sup>|0.067|0.115|
|8<sup>th</sup>|0.058|0.123|
|9<sup>th</sup>|0.077|0.115|
|10<sup>th</sup> (richest)|0.084|0.127|

_Note:_ Welfare = household consumption per adult equivalent.

only to the OOP healthcare expenditures associated with a health consultation or hospitalization, not the separately reported OOP expenditure for “medicines and medical supplies” purchased

> 18 Barofsky and Younger (2022) do simply multiply by 26, an important difference with our calculations here. This makes their distribution of OOP healthcare expenditures much more skewed, increasing the risk.

> 19 And, of course, we reduce the probability of zero OOP healthcare expenditures to keep the sum of the probabilities equal to one.

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outside the context of a healthcare visit. Most of this expenditure is likely to be direct purchases from a pharmacy. Because NHIS does not cover the costs of medicines and supplies acquired outside the context of a consultation, we think it unlikely that NHIS covers the OOP expenditure reported as such.<sup>20</sup>

# **4 Estimating the Distribution of Income Net of OOP Healthcare Expenditures**

There are several options for identifying NHIS members.<sup>21</sup> We use only those who were able to show a valid NHIS card to the GLSS 2016/17 interviewer. This produces an estimated 9.3 million beneficiaries, compared to 10.8 million registered beneficiaries in 2016 and 12.2 million in 2017 based on program administrative data obtained from the government.

To generate treatment and control groups, we use coarsened exact matching (Blackwell 2009). We match NHIS members and nonmembers on region and area of residence (urban versus rural), the log of household consumption per adult equivalent, and several individual-level characteristics that increase the likelihood of NHIS membership because they reduce or eliminate the annual registration and membership fees: those aged 70 years old or older; a child under 18 years; a woman who is currently pregnant, has been pregnant in the past year, or currently has an infant under 3 months old; a contributor to SSNIT; or a recipient of a SSNIT pension.

Table 2 reports the results from our matching exercise. L1 is a measure of distance between NHIS members and nonmembers, bounded by 0 (perfect match) and 1. The ex-ante data show that the two groups are well-matched except with respect to region of residence and the log of household consumption per adult equivalent. After matching, some imbalance with respect to consumption remains, but it is considerably less than in the ex-ante comparison. Overall, the criteria used for the match generate 141 strata, 95 of which are successfully matched. Only 313 individual records out of 58,844 respondents could not be matched and are excluded from the analysis.

> 20 Indeed, the distribution of the values reported for “medicines and medical supplies” purchased outside the context of a healthcare visit are almost identical for members and nonmembers.

> 21 Questions 1, 3, and 6 in Section 3B of the GLSS 2016/17 questionnaire.

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# **<u>Table 2. Ex-Ante and Ex-Post Match of NHIS Members and Nonmembers</u>**

# **a. Ex-ante results**

|Multivariate L1:||0.326||||||
|---|---|---|---|---|---|---|---|
|Univariate Imbalanc|e|||||||
||L1|Mean|Min.|25%|50%|75%|Max.|
|Urban|0.0472|0.0472|0|0|0|0|0|
|Infant <3 months)|0.0040|–0.0040|0|0|0|0|0|
|Child (<18 years)|0.0679|0.0679|0|0|0|0|0|
|Pregnant woman|0.0293|0.0293|0|0|0|0|0|
|Elderly (≥70 years)|0.0203|0.0203|0|0|0|0|0|
|SSNIT pensioner|0.0029|0.0029|0|0|0|0|0|
|SSNIT contributor|0.0187|0.0187|0|0|0|0|0|
|Region|0.1266|0.1266|0|1|1|1|0|
|Ln(hh cons. pae)|0.0616|0.0599|0|0.0428|0.0839|0.0940|–1.081|

# **b. Ex-post results**

|Multiva<br>Univariate Imbalanc|riate L1:<br>e<br>|0.286<br>||||||
|---|---|---|---|---|---|---|---|
||L1|Mean|Min.|25%|50%|75%|Max.|
|Urban|0.0451|0.0451|0|0|0|0|0|
|Infant <3 months)|0.0042|–0.0042|0|0|0|0|0|
|Child (<18 years)|0.0871|0.0871|0|0|0|0|0|
|Pregnant woman|0.0300|0.0300|0|0|0|0|0|
|Elderly (≥70 years)|0.0176|0.0176|0|0|0|0|0|
|SSNIT pensioner|0.0028|0.0028|0|0|0|0|0|
|SSNIT contributor|0.0163|0.0163|0|0|0|0|0|
|Region|0.0247|0.0247|0|0|0|0|0|
|ln(hhcons. pae)|0.0447|0.0055|0|0.0200|0.0035|0.0139|0|

_Note_ : SSNIT = Social Security and National Insurance Trust. ln(hh con. pae) = log of household consumption per adult equivalent. Min. = minimum. Max. = maximum. Pregnant woman = a woman who is currently pregnant, has been pregnant in the past year, or currently has an infant under 3 months old.

To achieve this match, some reweighting of the sample is required. In all calculations that follow, we weight each observation by its sampling weight times the weights generated by the matching procedure.

To estimate a distribution of (possible) healthcare expenditures for each individual, we first divide the sample into NHIS members and nonmembers. For each group, we regress the log of OOP healthcare expenditures on the log of household consumption per adult equivalent, draw 100 random observations of the residuals, and add those 100 draws to the predicted value from 12
the regression to generate 100 possible healthcare expenditures.<sup>22</sup> This allows healthcare expenditures to increase, on average, with consumption, which is to be expected. The regression for nonmembers has an elasticity of OOP healthcare expenditures with respect to household consumption per adult equivalent of 0.53 and an _R_ -squared statistic of 0.14. That for NHIS members has an elasticity of 0.66 and an _R_ -squared statistic of 0.18. In both the NHIS member and nonmember samples, considerable variance remains in the error term, so there is significant variation in the 100 estimated healthcare expenditures.

Figure 3 shows the inverse of the cumulative distribution function for estimated annual OOP healthcare expenditures, by NHIS membership status. The distribution for both NHIS members and nonmembers is highly skewed with a roughly constant increase in expenditures up to about the 90<sup>th</sup> percentile and a sharp increase above that. This is typical for healthcare expenditures by the uninsured. One might expect, however, for healthcare expenditures of the insured to be much less skewed since the insurance should limit the need for catastrophic healthcare expenditures. While that is true to some extent in Ghana, the effect is rather limited. OOP healthcare expenditures for NHIS members still skew strongly after about the 90<sup>th</sup> percentile. (See appendix B for a comparison to results for Medicare in the United States.) On the other hand, OOP healthcare expenditures by the insured are consistently lower than those for the uninsured throughout the distribution, with the difference increasing across the distribution.

> 22 We repeated the analysis with 1,000 draws, with similar results.

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**Figure 3. Distribution of Annual Out-of-Pocket Healthcare Expenditures by NHIS Membership Status, 2016/17**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Percentile of Healthcare Expenditure Distribution<br>Nonmember NHIS member<br>4000<br>3000<br>2000<br>Cedis per Year<br>1000<br>0<br><!-- End of picture text --> Table 3 reports mean annual OOP healthcare expenditures, by NHIS membership status. Overall, such expenditures for nonmembers are 344 cedis (196 PPP dollars) with a standard deviation of 525 cedis (299 PPP dollars). For NHIS members, the mean is 289 cedis (165 PPP dollars) with a standard deviation of 523 cedis (298 PPP dollars). NHIS membership reduces mean OOP healthcare expenditures but not the standard deviation, suggesting that having NHIS coverage is not so much reducing catastrophic healthcare expenditures as it is everyday healthcare expenditures.<sup>23</sup> That is, NHIS is providing an effective transfer, but not financial insurance.

> 23 The literature calculates “catastrophic” OOP healthcare expenditures in a variety of ways. We present a set of standard results in appendix C where we find very little difference in catastrophic payments by NHIS members and nonmembers.

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**Table 3. Estimated Mean Annual Out-of-Pocket Healthcare Expenditures** **<u>by NHIS Membership Status, Cedis</u>**

|Welfare decile|Nonm|embers|Me|mbers|
|---|---|---|---|---|
||Mean<br>(1)|Std. dev.<br>(2)|Mean<br>(3)|Std. dev.<br>(4)|
|Overall|344|525|289|523|
|1<sup>st</sup>(poorest)<br>|162|169|91|108|
|2<sup>nd</sup><br>|209|199|150|153|
|3<sup>rd</sup>|250|273|177|189|
|4<sup>th</sup>|214|241|175|217|
|5<sup>th</sup>|216|232|226|281|
|6<sup>th</sup>|278|294|242|269|
|7<sup>th</sup>|283|320|308|335|
|8<sup>th</sup>|393|467|295|329|
|9<sup>th</sup>|396|495|406|488|
|10<sup>th</sup> (richest)|877|1,084|678|1,202|

_Note_ : Welfare = household consumption per adult equivalent. Std. dev. = standard deviation.

Many other studies estimate the effect of NHIS membership on mean OOP healthcare expenditures, usually based on cross-sectional data such as our sample. Garcia-Mandicó et al. (2021) is unique among these studies as the authors use data from the 2005/06 round of the GLSS, whose fielding straddled the rollout of NHIS at that time. Thus, they use a difference-indifferences strategy comparing survey respondents in the same districts, some of whom gained access to NHIS during the survey-fielding period and some of whom did not. The survey data they use, GLSS 2005/06, are very similar to the GLSS 2016/17 data we use, though it is likely that all OOP expenditures for medicines are reported in one variable in GLSS 2005/06, while they are separated between OOP expenditures in the context of a consultation and other OOP expenditures on medicines in GLSS 2016/17. They find a mean decrease in OOP healthcare expenditures of 18 percent.

Powell-Jackson et al. (2014) investigate a randomized allocation of free healthcare to children ages 6–59 months in one district of Ghana, Dangme West, and find that this comprehensive insurance reduced OOP healthcare expenditures by 27 percent on average. By comparison, we estimate a mean reduction of 16 percent for the overall Ghanaian population, 20 percent for children ages 6–59 months across Ghana, and 46 percent for children ages 6–59 15
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# **5 Valuing the Risk Inherent in the Distribution of Net Income**

We assume that each person must satisfy a per period budget constraint of _c_ = _y_ − _m_ where _y_ is income, _m_ is OOP healthcare spending, _c_ is other expenditure, and utility is determined under a constant relative risk aversion utility function, as follows: We assume that each person’s income, _y_ , is her or his household’s consumption per adult equivalent. Essentially, we assume that each person in a household gets an equal share of real income before healthcare expenditures, and that income is best measured by household consumption. In what follows, we place a lower bound on _c_ , ensuring that OOP healthcare expenditures do not drive it below some percent of household consumption per adult equivalent. We assume that the burden of healthcare expenditures for an individual is not shared through the household; only her or his utility falls with the health shock.

Call the distributions of healthcare expenditure calculated in section 4<sup>_P_</sup> _k_ (<sup>_m_</sup> )<sup>, where</sup> _k_ = [ 0,1 ] indicates those without and with NHIS membership, respectively. The difference between income minus _P_ 0<sup>(</sup> _m_<sup>)</sup> or _P_ 1<sup>(</sup> _m_<sup>)</sup> determines the change in risk exposure from NHIS membership. Expected utility is given by where _M_ is the maximum healthcare expenditure and γ is an assumed minimum consumption value under which a person’s consumption does not fall regardless of the cost of healthcare. Previous studies, mostly in high-income countries, set this limit between 20 percent and 40 percent of household expenditure.<sup>25</sup> The risk premium represents the quantity of money a risk-averse household would be willing to pay to completely insure against a given financial risk distribution. The risk premium for a household is

> 25 See Finkelstein and McKnight (2008), Engelhardt and Gruber (2011), Shigeoka (2014), Barofsky (2015), and Limwattananon et al. (2015).

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where _Ek_ ( _y_ − _m_ ) is the expected value of nonhealthcare expenditure for distribution _k_ , and _CEk_ is the household’s certainty equivalent for the same distribution of healthcare expenditure. The difference in risk premiums between those with and without NHIS coverage, (π 1 −π 0 ) , is the monetary value of financial risk protection provided by NHIS.

To ensure that we are capturing only the difference in risk premiums—not any difference due to the fact that net consumption is higher for the insured because of the transfer implicit in NHIS membership—we adjust each observation by the difference in the overall mean of the nonmember and member samples, thus making the overall means equal in the adjusted data. Finally, because of the randomness of the healthcare expenditure draws, we bootstrap the calculation 200 times and average the results.

Table 4 reports the mean values for (a) estimated expected values of consumption net of healthcare expenditures (which are independent of the risk aversion parameter), (b) estimated certainty equivalence values, and (c) estimated risk premiums by NHIS membership status. The striking result here is that the change in risk premium from being uninsured to being insured is tiny—less than one percent of mean household consumption per adult equivalent except at a very high coefficient of relative risk aversion. Thus, the financial insurance value of NHIS membership is very small.<sup>26</sup> This does not mean that NHIS membership has no value to its members, only that its value is not coming from reduced financial risk from potentially catastrophic healthcare expenditures made necessary by health shocks. Instead, the value is from lower mean OOP healthcare expenditures for the insured.

> 26 The results in table 4 assume a minimum consumption of 20 percent of household consumption per adult equivalent. Results assuming a minimum of 1 or 40 percent are smaller still.

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**Table 4. Mean Values for Expected Value of Net Consumption, Certainty Equivalents,** **<u>and Risk Premiums by NHIS Membership Status, Cedis per Year</u>**

|ρ|Certainty eq|uivalent|Riskpre|mium<br>|Difference in risk|
|---|---|---|---|---|---|
||Nonmember|Member|Nonmember|Member|premiums|
|(1)|(2)|(3)|(4)|(5)|(6)|
|0.5|4,171|4,174|10.8|7.8|3.0|
|1.0|4,158|4,165|23.4|16.8|6.6|
|1.5|4,143|4,154|38.3|27.3|11.0|
|2.0|4,125|4,142|56.5|40.0|16.6|
|3.0|4,074|4,107|107.1|74.9|32.2|
|4.0|3,998|4,054|183.3|127.4|55.9|
|5.0|3,901|3,987|281.1|194.3|86.8|

Expected <u>value of net consumption = 4,182</u><sup>*</sup> _Note_ : Assumed minimum consumption is 20 percent of household consumption per adult equivalent. * The expected value of net consumption is the same for NHIS members and nonmembers by construction.

# **6 Conclusion**

Publicly funded health insurance provides both a transfer value—the difference between the value of health services received and what a beneficiary must pay for them—and a financial insurance value, that is, the reduced financial risk that comes from having to pay potentially large sums for healthcare. In this paper, we have estimated the latter in monetary terms which, we imagined, would be an input to benefit-cost and benefit incidence analyses. As it happens, the estimated benefits are so small that their use for either purpose does not seem worthwhile.

This is not to say that NHIS membership is not valuable for beneficiaries. Several studies suggest that there is a significant transfer value associated with NHIS membership either as a whole or for specific treatments it covers (for example, see Okorah et al. 2018; Asuming et al. 2020; Nketiah-Amponsah et al. 2020; García-Mendicó et al. 2021), and we find comparable transfer benefits in our calculations.<sup>27</sup> But the financial insurance value is quite limited largely because the distribution of OOP healthcare expenditures for the insured differs very little from that for the uninsured, except that it has a lower mean. The variance and skewness—the aspects that insurance usually protects against—are similar. This is not an unusual result. In their review of the literature, Das and Do (2022) state: “Should [public health insurance] programs still be regarded as health insurance schemes? We believe that the answer is “probably not,”...”

> 27 Dadzie, Raju, and Younger (2022) analyze the distributional consequences of the transfer implicit in NHIS.

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Why might NHIS provide little insurance value even as it provides a significant transfer? One explanation, based on anecdotal evidence, is that healthcare facilities are reluctant to provide high-cost medical care to NHIS members because NHIS is notoriously slow to reimburse them. In a wide-ranging review of the literature on NHIS, Christmals and Aidem (2020) found eight papers reporting slow reimbursement as a complaint of healthcare facilities. Agyepong et al. (2014) note that slow or nonexistent reimbursement for medicines is a particular problem sometimes involving catastrophic payments for NHIS members. Thus, when NHIS members have a large adverse health shock, they may end up paying for services even though they have insurance coverage. On the other hand, because NHIS is intended to pay for almost all healthcare without copay, those with less expensive healthcare needs may in fact receive free care. This would explain the fairly consistent difference between member and nonmember OOP healthcare expenditure distributions in figure 3, but also the long tail in the NHIS member distribution that insurance should, in theory, eliminate.

A second explanation, again based on anecdotal evidence, is that nonmembers suffering a serious illness or injury may be able to join NHIS ex post (Gajate-Garrido 2013). This would reduce the top end of the nonmember distribution in OOP healthcare expenditures, biasing downward our estimate of the gap between NHIS member and nonmember expenditures at the highest percentiles of the OOP healthcare expenditure distribution. While this is a concern, our estimated distributions of OOP healthcare expenditures for NHIS members and nonmembers are similar to those in studies with randomized assignment to free health insurance (Powell-Jackson et al. 2014) or difference-in-differences estimates spanning the introduction of NHIS in 2005 (García-Mandicó et al. 2021). Those same studies give us some confidence that our matching estimation based on a single cross-section provides a reasonable control group for NHIS membership.

A further limitation of our study is that we rely on a stylized utility function to estimate the risk premiums associated with the distribution of healthcare expenditures with and without NHIS membership. While this is standard in the literature, we are not aware of any empirical evidence to support this assumption. Further, our simple individual-level utility function ignores the possibly complex interdependence of utilities within households, including risk sharing among household members. We try to accommodate this by checking the sensitivity of our 20
results to the assumed lower limit of nonhealthcare consumption for an individual but, again, we are not aware of any empirical evidence that suggests this is adequate.

Finally, we use reported healthcare expenditure information, but we know nothing about the severity of the illness or injury that drove people to seek healthcare in the first place, the impact that has on respondents’ welfare, or the effect that has on respondents’ decisions to seek care and willingness to pay for it.

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