# Efficiency of health systems in developing countries: the case of the member countries of the Economic Community of West African States

Kossivi Akoetey, Anne Viallefont

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Kossivi Akoetey, Anne Viallefont. Efficiency of health systems in developing countries: the case of the member countries of the Economic Community of West African States. 2025. hal-05056440

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Centre d'Études
et de Recherches sur le
Développement International

WORKING PAPER

No. 2025/2

Efficiency of health systems
in developing countries:
the case of the member countries
of the Economic Community
of West African States

Kossivi Akoetey

Anne Viallefont
## Cite this working paper

Akoetey, K., Viallefont, A. "Efficiency of health systems in developing countries: the case of the member countries of the Economic Community of West African States", CERDI Working Papers, 2025/2, CERDI.

## The authors

### Kossivi Akoetey

PhD in Health economics,
Université Clermont Auvergne,
CNRS, IRD, CERDI, F-63000,
Clermont-Ferrand, France.

Email: [kossivi.akoetey@doctorant.uca.fr](mailto:kossivi.akoetey@doctorant.uca.fr)

(Corresponding author)

### Anne Viallefont

Associate Professor in Statistics,
School of Economics,
Université Clermont Auvergne,
CNRS, IRD, CERDI, F-63000,
Clermont-Ferrand, France.

Email: [anne.viallefont@uca.fr](mailto:anne.viallefont@uca.fr)

(Co-author)

## Acknowledgments

We thank Mbaye DIENE, University Cheikh-Anta-Diop and Consortium pour la Recherche Economique et Sociale (CRES), Dakar, Sénégal for his review of the initial version of the manuscript.

<figure><img src="image_2.png" /></figure>

Ce travail a bénéficié d'une aide de l'Etat français gérée par l'Agence Nationale de la Recherche au titre du programme "France 2030" portant la référence "ANR-16-IDEX-0001".

This work was supported by the Agence Nationale de la Recherche of the French government through the program "France 2030" "ANR-16-IDEX-0001".

CERDI Working Papers are available online at: [https://tinyurl.com/2xwfzw8s](https://tinyurl.com/2xwfzw8s)

Director of Publication: Simone Bertoli

Editor: Catherine Araujo Bonjean

Publisher: Marie Dussol

ISSN: 2114 - 7957

### Disclaimer

Working papers are not subject to peer review; they constitute research in progress. Responsibility for the contents and opinions expressed in the working papers rests solely with the authors. Comments and suggestions are welcome and should be addressed to the authors.
**Abstract**

Developing countries are faced with numerous health challenges such as lack of funding, increasing frequency and magnitude of epidemic risks, organizational and socio-cultural difficulties. In this context, we developed this study to assess for the first time the efficiency of health systems in the countries of the West African sub-region, firstly, to identify the systems that best adapt to these challenges and secondly, to highlight the factors that influence the health production process. To achieve this, we used the World Bank's worldwide governance indicators database, supplemented by data from the World Health Organization from 2000 to 2018. We used the stochastic fixed-effect frontier method of Kumbhakar et al. (2014) to account for unobservable heterogeneity in the estimates. We used a novel multiple imputation approach to deal with missing data, while determining the fractions of missing information in the estimates.

The results show that the average relative efficiency for all countries in the sub-region is 74%. Countries in the West African sub-region could theoretically increase life expectancy at birth by an average of 26 percentage points or 19.7 life years, with the same level of resources used. The results also show that health systems in these countries have higher permanent inefficiency than temporal inefficiency, suggesting that they mainly face structural challenges. Per capita health expenditure, gross domestic product per capita, literacy rate, and quality of governance are positively associated with the efficiency of their health systems.

**Keywords**

Efficiency of health systems; developing countries; multiple imputation, stochastic frontier analysis; ECOWAS.

**JEL codes**

I11, I18, D61.
## Introduction

Health systems contribute to improving the health of populations and saving the lives of many people. However, they face major challenges, including epidemic risks, issues of quality and equity in patient care, and limited resources, exacerbated by increasing health expenditure. The management and means used to respond to these challenges differ from one country to another. However, countries devoting the same levels of resources to their health systems have been shown to achieve divergent results. Thus, in some of the poorer performing countries, huge amounts of health spending, between 20 to 40% worldwide are wasted each year (1). As a result, they struggle to provide the necessary healthcare to their populations. The results are death, disability, and hopelessness among populations (2).

Specifically for developing countries, in addition to these challenges, there are organizational and socio-cultural difficulties, a frequent lack of qualified human resources, and problems of access to data. In this context, our work focuses on these countries, specifically those of the West African sub-region that are members of the Economic Community of West African States (ECOWAS). ECOWAS is an institution created in 1975, composed of 15 West African countries namely, Benin, Burkina Faso, Cape Verde, Ivory Coast, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Sierra Leone, Senegal and Togo. In 2020, the West African region was the area with the highest maternal mortality in the world. The average maternal mortality ratio was 754 deaths/100000 births, against only 4 deaths/100000 births for Australia and New Zealand, for example. Also, the risk of maternal death is 400 times higher in the West African sub-region than in the latter countries (3). Health spending accounts for less than 6% of gross domestic product (GDP), compared to about 9% for countries of the Organization for Economic Co-operation and Development (OECD) (4). The 0.5 average human development index (HDI) of the West African sub-region countries in 2022 (5) is one of the lowest and their health systems are among the most inefficient in the world (6–10). Insurance systems are poorly developed, 70% of health expenditure is borne by households through out-of-pocket payments, infrastructure is often inadequate and there are huge inequalities in access to care. Moreover, the Ebola epidemic in 2013 and the Coronavirus Disease (COVID-19) pandemic have weakened their health systems (11). In addition, political instability and terrorism are increasingly common in the sub-region (12,13). However, although these countries are all members of the same economic institution and eight of them are in the same monetary institution, the efficiency of their health systems is disparate. In fact, in 2020, the maternal mortality ratio ranged from 42 per 100,000 live births in Cape Verde to 1,047 per 100,000 live births in Nigeria. The health systems of Sierra Leone, Nigeria, Mali, and Ivory Coast were reportedly wasting more resources than those of Cape Verde, Gambia and Guinea (10,14,15). To address these challenges, countries should assess the efficiency of their health system, comparing it to others through a process of benchmarking in order to reduce resource waste while learning from those with best practices.

Efficiency in economics is a relative measure between observed production and the maximum achievable production (isoquante) for a given number of inputs. The technical efficiency of health systems is defined as the maximum possible level of health production (output) obtained from a given set of health resources (input). There are studies in the literature that have investigated the technical efficiency of health systems in developing countries in order to rank their health systems and to highlight
best practices "peers" from which they can learn. Indeed, in developing countries, high levels of public spending, human immunodeficiency virus / acquired immunodeficiency syndrome (HIV/AIDS) caseloads, income inequalities and dependence on external aid are associated with low efficiency of public spending on health (16). The efficiency of education and health spending is lower in African countries than in Asian and South American countries (14). The latter authors argue that improving education and health in Africa requires more than increased budgets. However, another study estimates that health spending in sub-Saharan African countries is too low to ensure efficiency in the production of health systems (17). The authors argue that low public spending on education and inadequate health insurance coverage will have a negative impact on the long-term efficiency of health systems in these countries. Increasing health-related spending would be a critical condition, and efficiency in its use, a necessary condition for improving their health systems (10). Of these countries, Zimbabwe, Zambia, Namibia, Botswana, Malawi, and Lesotho had health systems that were reportedly more inefficient than others (7). More recently in 2016, Mauritania, Cape Verde, Malawi, Botswana, and Tanzania were reported to be the most efficient countries, while Equatorial Guinea, Sierra Leone, Mali, and Angola were reported to be less efficient (10). In West Africa specifically, the efficiency of the health systems is even lower, estimated at 59% on average, with public spending on health, corruption, political instability and the adoption of new technologies as the main determinants (18). However, this study was more focused on public spending in general, in the subregion, and not on health systems. Unobservable heterogeneity across countries was not separated from technical efficiency, which could lead to biased estimates (19). Second, as in most studies in developing countries, missing data limited the number of observations in the study to between 107 and 150 observations. This induces biases related to the loss of information about the variables under study (20). In our framework, we used the more recent data from 2000 to 2018 and the stochastic frontier method (SFA) model of Kumbhakar et al. (2015) which allows for unobservable heterogeneity in the estimates. Besides, we overcame the problem of missing data by a multiple imputation approach (21) and (22), which removes the bias due to missing observations.

## Methodology and Data

### Methodology

Two types of methods are used in the literature to estimate the efficiency of health systems, that is, parametric and non-parametric methods. Parametric methods impose a predefined functional form on the efficiency frontier, whereas nonparametric methods determine the efficiency frontier empirically from the raw observations of the sample, without imposing a functional form on it a priori (23). In developing countries in general, there is a great diversity among the countries that deserves to be considered in determining efficiency (8). In our framework, given the importance of exogenous factors (climatic hazards, insecurity and external dependence) and heterogeneity (differences in size and economic areas) between countries, we have opted for the parametric SFA method with a country fixed effect.

### Parametric SFA method

In the literature, the adjusted ordinary least squares (AOLS) method and the stochastic frontier method are the two parametric approaches mainly used to determine the
efficiency of health systems. In both approaches, an outcome variable (output) indicating the efficiency of a health system is regressed on explanatory variables including variables of interest (inputs) and environmental variables. The part not explained by the model (the residuals) is used to estimate the inefficiency. In contrast to the AOLS, the SFA method distinguishes the residual error due to inefficiency, which can be improved, from the random error (24). The SFA analysis is generally done in two steps: The first step consists in determining the parameters of the estimated model by maximizing the likelihood function. In the second step, the inefficiency is estimated through the conditional distribution of the random error. Observations may lie above or below the frontier, given the error decomposition. An observation may lie above the frontier if its random error is very large, otherwise it lies below the frontier. For our analysis, we opted for the SFA method, more specifically, the panel model of Kumbhakar et al., 2014.

## Kumbhakar, Lien and Hardarker (2014)

The Kumbhakar, Lien, and Hardaker model allows to distinguish between heterogeneity in countries, permanent factors, and temporal factors, in the presence of random shocks. Indeed, when temporal inefficiency is relatively high, it indicates that inefficiency is related to random factors that could disappear over time. On the other hand, if permanent inefficiency is more important, it implies the existence of permanent factors (culture, geography, colonial history ...) that impact it over multiple periods, unless there is a specific change in governance or policy. This distinction is particularly important for the implementation of long-term policies. The model is specified by the following equation:

$$Y_{it} = \alpha + \beta X_{it} + \mu_{i} + V_{it} - \eta_{i} - u_{it} \quad (1)$$

where $Y_{it}$ represents the dependent variable (output) of country i at time t, $\alpha$ is a constant, $X_{it}$ represents the vector of explanatory variables (inputs and environmental factors) expressed in logarithm, $\mu_i$ represents the fixed effect of country i, $V_{it}$ represents the random shock for country i at time t, and $\eta_i$ and $u_{it}$ represent, respectively, the permanent technical inefficiency and the time-varying technical inefficiency. Equation (1) can be rewritten as follows:

$$Y_{it} = \alpha^* + \beta X_{it} + \alpha_i + \varepsilon_{it} \quad (2)$$

where:

$$\alpha^* = \alpha - E(\eta_i) - E(u_{it}) \quad (3)$$

$$\varepsilon_{it} = V_{it} - u_{it} + E(u_{it}) \quad (4)$$

$$\alpha_i = \mu_i - \eta_i + E(\eta_i) \quad (5)$$

The model is estimated in three steps. First, a fixed-effects panel regression is fitted to estimate $\alpha^*$, $\beta$, the $\alpha_i$'s, and the $\varepsilon_{it}$'s from equation 2. In the second step, $u_{it}$, the time-varying technical inefficiency, is estimated in equation 4 (see Kumbhakar, Lien, and Hardaker, 2015, §10.6). Finally, in the third step, $\eta_i$, the permanent technical inefficiency, is estimated in equation 5. The overall annual efficiency of each country is obtained by multiplying the time-varying technical efficiency by the permanent technical efficiency.
# Data and variables

## Data

The data used come from the World Bank (WB) (in particular the Worldwide Governance Indicators (WGI)), United Nations Educational, Scientific and Cultural Organization (UNESCO), and the World Health Organization (WHO). The study covers a panel of 19 years from 2000 to 2018 and 15 countries in the West African sub-region. The estimation of panel efficiency coefficients allows for more observations, which improves the results of the estimations compared to cross-sectional analyzes (23).

## Variables

### Result variable

In the literature, health status is identified as the main outcome of health systems. However, it is not easy to determine and it depends on other factors that are independent of health systems (2). For these reasons, life expectancy at birth or disability-adjusted life expectancy, the universal health coverage index, avoidable mortalities or vaccination coverage are the most common used in literature as better representing the production of health systems (25). In our framework, the disability-adjusted life expectancy variable was not available over the periods considered. We therefore opted for life expectancy at birth as the outcome variable. It captures the effort of health systems in the production of health care while taking into account all age groups of the population.

However, this variable has a natural minimum, thus its use as a mere output of the health system is controversial. Life expectancy in a theoretical area without any modern health system would not be zero (26). As a robustness check, we used various values of the number of years of life expectancy as an output that can be imputable to the health system, we removed 20, 25, 30 or 35 years from the life expectancy variable itself.

## Input and environmental variables

Health being multidimensional, several factors contribute to its production. However, health expenditure measured as a percentage of GDP or per capita is commonly used in the literature as the main input in health systems production (25). Here, we used health expenditure per capita as the main input variable. It is expressed in purchasing power parity (PPP) and deflated based on the 2017 reference. Additionally, the number of healthcare professionals as a whole is also considered in the literature as a separate input in health production. Due to data availability reasons, we used the variable of the number of general practitioners and specialists per 1000 inhabitants. Finally, we included environmental variables (also considered as explanatory variables in our model) such as the proportion of direct health expenditure in total health expenditure, which refers to healthcare expenses directly borne by patients in the absence or partial coverage of health insurance. Other environmental variables include GDP per capita based on PPP (deflated, 2017 reference), the poverty gap at $1.9/day as defined by the World Bank, the literacy rate as a percentage of the population aged 15 and above, perception of political instability and/or politically related violence, including terrorism, and perception of the government's capacity to define and implement sound policies and regulations for private sector development. The latter two variables are used in an index ranging from -2.5 (poor perception) to 2.5 (good perception), as
constructed by the World Bank. However, we have adjusted them by adding the minimum value (var + min) in order to enable logging. All variables used, as well as their sources, are presented in table 1 below, along with their links to the dependent variable in appendix 1.

<table><caption>Table 1: Selected variables</caption><thead><tr><th>Variables</th><th>Abbreviations</th><th>Sources</th></tr></thead><tbody><tr><td><strong>Outputs</strong></td><td></td><td></td></tr><tr><td>Life expectancy at birth</td><td>LIFEXP</td><td>United Nations Population Divisions</td></tr><tr><td><strong>Inputs</strong></td><td></td><td></td></tr><tr><td>Per capita health expenditure in deflated international dollars (base 2017)</td><td>CEXPPCD</td><td>WHO Global Health Expenditure database</td></tr><tr><td>General practitioners and specialists per 1000 inhabitants</td><td>PH</td><td>WHO Global Health Expenditure database</td></tr><tr><td><strong>Environmental variables</strong></td><td></td><td></td></tr><tr><td>Direct health expenditure as a percentage of total health expenditure</td><td>OPEXP</td><td>WHO Global Health Expenditure database</td></tr><tr><td>GDP per capita in international dollars, PPP deflated (base 2017)</td><td>GDPD</td><td>World Bank, Development Research Group</td></tr><tr><td>Poverty gap at $1.9/day, expressed as a percentage of the poverty line, by PPP</td><td>POV</td><td>World Bank, Development Research Group</td></tr><tr><td>Literacy rate as a percentage of the population aged 15 years and over</td><td>LIT</td><td>UNESCO Institute for Statistics</td></tr><tr><td>Perception of political instability and/or politically related violence, including terrorism</td><td>POLISTAB</td><td>WGI</td></tr><tr><td>Perception of the government's ability to define and implement sound policies and regulations for private sector development</td><td>REGUQUAL</td><td>WGI</td></tr></tbody></table>

## Managing missing data

Some variables introduced in the model have a high number of missing values. Including these in the estimation without completing them was not possible. However, for Stead & Wheat, while imputing the dependent variable in the SFA analysis would affect the error term, imputing the explanatory variables improves the global information taken into account in the model, without introducing any bias (22). In our case, the missing process is not completely at random. For example, the variables on the political instability or governments policies and regulations, have more missing values in 2001 or another example, maternal mortality is only missing for some countries. For these reasons, we used a multiple imputation method (21). This involves generating at least 5 sets of data (M=5) on which parameter values will be estimated (27). Here we imputed M=9 datasets on which the model parameters' values are estimated. The process of generating imputed values is primarily based on the temporal quasi-stability of most variables within each country.

The imputation process follows the following steps: firstly, we normalized the variables with non-normal distributions. Then, we treated the data as a panel and generated lagged variables, which we completed simply based on previous or
subsequent observations. We then successively imputed the variables using the nearest neighbor method with linear regression, starting with the variables that have the fewest missing observations. Finally, in the last step, we checked the imputation status based on graphs. Graph 1 below illustrates the example of imputing the variable 'number of general practitioners and specialists per 1000 inhabitants' for Benin using the real observations, and appendix 2 illustrates, for the same variable, a comparison of the generated bases with the initial base for all countries over the entire studied period.

After fitting the stochastic frontier model, we computed the mean values of the estimates across the M datasets and empirical variances, as in equation (6):

$$ \hat{\beta} = \frac{1}{M} \sum_{m=1}^{M} \hat{\beta}^{(m)}; \quad \mathrm{Var}(\hat{\beta}) = V_W + \left(1 + \frac{1}{M}\right) V_B \quad (6) $$

Where

$$ V_W = \frac{1}{M} \sum_{m=1}^{M} \widehat{Var}(\hat{\beta})^{(m)} \quad \text{and} \quad V_B = \frac{1}{M-1} \sum_{m=1}^{M} (\hat{\beta}^{(m)} - \hat{\beta})^2 $$

$\beta$ is the estimated parameter, m =1,...,M is the number of the imputed data set, and $\hat{\beta}^{(m)}$ is the estimate of the parameter $\beta$ from dataset m.

The fraction of missing information (FMI), which is the part of the variance $(\widehat{Var}(\hat{\beta}))$ that is due to missing data, for each model parameter, was computed as in equation (7):

$$ \text{FMI}(\hat{\beta}) = \frac{V_B + \frac{V_B}{M}}{\widehat{Var}(\hat{\beta})} \quad (7) $$

<figure><img src="image_3.png" /><figcaption>Graph 1: Multiple imputations of the number of physicians per 1000 inhabitants: the case of Benin.</figcaption></figure>
# Results

## Descriptive analyses

The first part of table 2 presents the number of observations, the mean, the minimum and the maximum of the initial variables, and the second part presents the characteristics of the values generated in the phase of multiple imputation. There is a wide variation in variables indicating the presence of heterogeneity between countries. The average life expectancy at birth is 57 years and ranged from 47.84 years in Sierra Leone to 70.94 years in Cape Verde. Annual health expenditure per capita averages $686.35 and ranges from about $82.36 in Benin to $8,420.65 in Liberia. GDP per capita averaged $2,590.7 and ranged from $1,036.5 in Niger to $5,684.61 in Cape Verde. After imputation, the variables are estimated to be identical on average to the initial data. However, there is a slight variation between the minima and the maxima of the data imputed to the initial data.

<table><caption>Table 2: Descriptive analysis</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">N</th><th colspan="3">Raw data</th><th colspan="3">Data completed by multiple imputation</th></tr><tr><th>Average</th><th>min</th><th>max</th><th>NxM</th><th>Average</th><th>min</th><th>max</th></tr></thead><tbody><tr><td colspan="9"><strong>Output variables</strong></td></tr><tr><td>LIFEXP</td><td>285</td><td>57</td><td>39.44</td><td>72.78</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="9"><strong>Input variables</strong></td></tr><tr><td>CEXPPCD</td><td>285</td><td>686.35</td><td>39.98</td><td>14485.01</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PH</td><td>121</td><td>0.13</td><td>0.013</td><td>0.78</td><td>285×9</td><td>0.14</td><td>0.01</td><td>1.15</td></tr><tr><td colspan="9"><strong>Environmental variables</strong></td></tr><tr><td>OPEXP</td><td>285</td><td>49.50</td><td>9.88</td><td>77.23</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>GDPD</td><td>285</td><td>2590.69</td><td>929.22</td><td>6864.19</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>POV</td><td>45</td><td>16.52</td><td>0.7</td><td>35.8</td><td>285×9</td><td>16.90</td><td>0.03</td><td>40.17</td></tr><tr><td>LIT</td><td>58</td><td>43.93</td><td>14.38</td><td>86.79</td><td>285×9</td><td>45.01</td><td>10.77</td><td>90.65</td></tr><tr><td>REGUQUAL</td><td>270</td><td>-0.62</td><td>-1.86</td><td>0.13</td><td>285×9</td><td>-0.62</td><td>-1.90</td><td>0.27</td></tr><tr><td>POLISTAB</td><td>270</td><td>-0.52</td><td>-2.40</td><td>1.22</td><td>285×9</td><td>-0.51</td><td>-2.54</td><td>1.54</td></tr></tbody></table>

## Technical efficiency estimation

### Estimation of regression coefficients and FMI

Table 3 below presents the results of the regression estimation and the fractions of missing information due to missing data. In the first estimation, we used only complete data. Then, we added the imputed variables necessary for the analysis of the healthcare systems' efficiency and determined the proportion of missing information for each parameter in the model. The variables related to political instability and government capacity are strongly correlated, so we included them separately in estimations 2 and 3.

In estimation 1, healthcare expenditure per capita and GDP per capita are significant. A 1% increase in healthcare expenditure per capita significantly increases life expectancy at birth by 0.05%. GDP per capita is the component that explains life expectancy at birth.
expectancy at birth the most. A 1% increase in GDP per capita significantly increases life expectancy at birth by 0.26%. The share of direct healthcare expenditures tends to decrease life expectancy at birth, but this effect is not significant. Adding the imputed variables in regressions 2 and 3 did not change the significance or sign of these three explanatory variables.

Among the imputed variables, the perception of government capacity is significant at 5% in estimation 2, while the perception of political instability has no effect on life expectancy at birth in estimation 3. Literacy rate is highly significant in both estimations, while the number of doctors and the poverty gap are nearly significant, respectively in estimation 2 and estimation 3. A 1% increase in literacy rate and perception of government capacity statistically increases life expectancy at birth by 0.1% and 0.05% respectively. The poverty gap at $1.9/day tends to decrease life expectancy at birth.

The second column of estimations 2 and 3 presents the fractions of missing information for the estimate $\hat{\beta}$ of each variable. They are higher for variables with a large number of missing observations. For instance, the fraction of missing information is 44% to 75% for the poverty gap variable, which has more missing observations (240 out of 285 total observations), and approximately 30% to 40% for the literacy rate, with 225 out of 285 total observations. On the contrary, it is low for complete variables, around 7% to 19%.

<table><caption>Table 3: Regression Results and Fraction of Missing Information (FMI)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">ECOWAS zone</th></tr><tr><th>Estimate 1</th><th>Estimate 2</th><th>Estimate 3</th></tr></thead><tbody><tr><td>LIIFEXP</td><td>Coefficients &amp; (SD)</td><td>Coefficients &amp; (SD)</td><td>FMI</td></tr><tr><td>CEXPPCD</td><td>0.0496*** (0.0096)</td><td>0.0359*** (0.0095)</td><td>0.0765</td></tr><tr><td>GDPD</td><td>0.264*** (0.0198)</td><td>0.1999*** (0.0204)</td><td>0.0781</td></tr><tr><td>OPEXP</td><td>-0.0072 (0.0135)</td><td>-0.0049 (0.0128)</td><td>0.073</td></tr><tr><td>PH</td><td></td><td>0.0095* (0.0055)</td><td>0.302</td></tr><tr><td>LIT</td><td></td><td>0.1028*** (0.0184)</td><td>0.2977</td></tr><tr><td>POV</td><td></td><td>-0.0056 (0.0044)</td><td>0.7485</td></tr><tr><td>REGUQUAL</td><td></td><td>0.046** (0.0227)</td><td>0.1752</td></tr><tr><td>POLISTAB</td><td></td><td></td><td>0.0076 (0.0058)</td></tr><tr><td>Constant</td><td>1.768*** (0.166)</td><td>1.952*** (0.1721)</td><td>0.131</td></tr><tr><td>Observations</td><td>285</td><td>285</td><td>285</td></tr><tr><td>Country</td><td>15</td><td>15</td><td>15</td></tr><tr><td>R<sup>2</sup></td><td>0.556</td><td>0.6484</td><td>0.646</td></tr></tbody></table>

SD = Standard deviations in () *** p<0.01. ** p<0.05. * p<0.1 FMI = Fraction of missing information

## Efficiency scores and ranking of the efficiency of health systems in West African countries

Table 4 presents the results of average efficiency score (ES) and ranks of the healthcare systems of the studied countries from 2000 to 2018. Estimations 2 and 3 show the same rankings. Estimation 1 presents a slightly different ranking compared to the other two (correlation = 94%). The average efficiency scores of estimation 3 is higher (0.823), followed by estimation 1 (0.821) and estimation 2 (0.743). This is due
to the inclusion of the perception of government capacity, which is significant in estimation 2. We ranked the countries primarily based on the results of this estimation. However, 6 out of the 15 countries, namely Niger, Senegal, Sierra Leone, Ghana, Ivory Coast, and Nigeria, maintain the same rank in all three estimations. According to our results, Niger is the most efficient country, followed by Burkina Faso in second place (third in the 1st regression). Ghana, Ivory Coast, and Nigeria are ranked last. There is a significant difference in efficiency scores among countries. Our model predicts that, on average, Niger could increase life expectancy at birth by 7.4%, equivalent to 4.5 years, while Nigeria could increase life expectancy at birth by an average of 44.2%, equivalent to 39.8 years, using the same level of resources.

<table><caption>Table 4: Efficiency score (ES) and rank of health systems in West African countries</caption><thead><tr><th rowspan="2">Country</th><th colspan="3">Estimate 1</th><th colspan="3">Estimate 2</th><th colspan="3">Estimate 3</th></tr><tr><th>Observations</th><th>Rank</th><th>ES</th><th>Observations</th><th>Rank</th><th>ES</th><th>Observations</th><th>Rank</th><th>ES</th></tr></thead><tbody><tr><td>Niger</td><td>19</td><td>1</td><td>0.896</td><td>19</td><td>1</td><td>0.926</td><td>19</td><td>1</td><td>0.898</td></tr><tr><td>Burkina Faso</td><td>19</td><td>3</td><td>0.868</td><td>19</td><td>2</td><td>0.822</td><td>19</td><td>2</td><td>0.875</td></tr><tr><td>Guinea</td><td>19</td><td>6</td><td>0.855</td><td>19</td><td>3</td><td>0.801</td><td>19</td><td>3</td><td>0.865</td></tr><tr><td>Senegal</td><td>19</td><td>4</td><td>0.858</td><td>19</td><td>4</td><td>0.783</td><td>19</td><td>4</td><td>0.859</td></tr><tr><td>Togo</td><td>19</td><td>2</td><td>0.87</td><td>19</td><td>5</td><td>0.782</td><td>19</td><td>5</td><td>0.856</td></tr><tr><td>Gambia</td><td>19</td><td>5</td><td>0.857</td><td>19</td><td>6</td><td>0.774</td><td>19</td><td>6</td><td>0.852</td></tr><tr><td>Benin</td><td>19</td><td>8</td><td>0.838</td><td>19</td><td>7</td><td>0.772</td><td>19</td><td>7</td><td>0.851</td></tr><tr><td>Mali</td><td>19</td><td>9</td><td>0.837</td><td>19</td><td>8</td><td>0.762</td><td>19</td><td>8</td><td>0.849</td></tr><tr><td>Guinea-Bissau</td><td>19</td><td>7</td><td>0.846</td><td>19</td><td>9</td><td>0.76</td><td>19</td><td>9</td><td>0.84</td></tr><tr><td>Liberia</td><td>19</td><td>12</td><td>0.804</td><td>19</td><td>10</td><td>0.741</td><td>19</td><td>10</td><td>0.826</td></tr><tr><td>Sierra Leone</td><td>19</td><td>11</td><td>0.806</td><td>19</td><td>11</td><td>0.716</td><td>19</td><td>11</td><td>0.814</td></tr><tr><td>Cape Verde</td><td>19</td><td>10</td><td>0.808</td><td>19</td><td>12</td><td>0.677</td><td>19</td><td>12</td><td>0.782</td></tr><tr><td>Ghana</td><td>19</td><td>13</td><td>0.787</td><td>19</td><td>13</td><td>0.659</td><td>19</td><td>13</td><td>0.771</td></tr><tr><td>Ivory Coast</td><td>19</td><td>14</td><td>0.713</td><td>19</td><td>14</td><td>0.614</td><td>19</td><td>14</td><td>0.727</td></tr><tr><td>Nigeria</td><td>19</td><td>15</td><td>0.668</td><td>19</td><td>15</td><td>0.558</td><td>19</td><td>15</td><td>0.673</td></tr><tr><td>ECOWAS</td><td>285</td><td></td><td>0.821</td><td>285</td><td>Total</td><td>0.743</td><td>285</td><td></td><td>0.823</td></tr></tbody></table>

Correlation between rank1 and rank2 = 0.94; rank1 and rank3 = 0.94; rank2 and rank3 = 1; ES1 and ES2 = 0.94; ES1 and ES3= 0.98; ES2 and ES3 = 0.96

## Robustness of the results

### Exclusion of Liberia

Over the studied period, the health expenditures per capita in Liberia appear to have been much higher than in any other country of the sub-region (appendix 1). This might be partly explained by the high level of inflation over the studied period in the country (28).

We tried to exclude Liberia from the set of studied countries, and the results are the same as those with Liberia. The efficiency scores are highly correlated at 99.6%.

### Looking for different efficiency results over different subperiods

Next, we divided the study period into three sub-periods, 2000-2005, 2006-2011 and 2012-2018, in order to see if the results observed over the entire period studied, are also observed over the 6-year sub-periods. During the period from 2000 to 2005, the
rankings of countries are slightly modified, with Cape Verde taking first place and
Niger, the fifth place. However, the efficiency scores are still strongly correlated
(97%) with those obtained over the entire study period. Nevertheless, during the period
from 2006 to 2018, the rankings do not change significantly compared to the initial
ranking, and the efficiency scores are also highly correlated with the initial efficiency
scores (98% and 99%, respectively for 2006-2011 and 2012-2018).

***Modifying the output measurement***

In order to take into account the fact that the minimum life expectancy in a theoretical area without any health system would not be zero, we conducted the analyses by subtracting a number of years that could be considered as the minimum life expectancy at birth without healthcare. We subtracted 20, 25, 30, and 35 years from life expectancy at birth, and the efficiency scores evolve similarly to those obtained with the raw life expectancy at birth (appendix 3).

**Discussion**

The average relative efficiency estimated for countries in the West African sub-region was 74%. This implies that, on average, these countries could theoretically increase life expectancy at birth by 26 percentage points, equivalent to 19.7 years of life, using the same level of resources. These results indicate that the level of efficiency in the sub-region is low, suggesting that resource wastage remains high for all countries, especially those with very low efficiency. These findings are consistent with those of Novignon and Lawanson, who estimated the relative efficiency level of the healthcare systems of Sub-Saharan African countries at 80% (10). Extraction of the data on countries of the West African sub-region specifically, showed on average a 73% relative efficiency level. Aloka (2015), on the other hand, found an average relative efficiency of 59% for the same group of countries, albeit with slightly different variables, mainly focused on public expenditure. Taking into account more recent data in our study could explain this slight improvement in the average relative efficiency in our results, which nevertheless remains below the average for Sub-Saharan African countries, 0.80 (10) and 0.86 for all African countries (25). However, there is a large gap of 36.8 percentage points between the most efficient and the least efficient countries. Therefore, it is necessary to analyze the most and least efficient countries more closely to understand the factors that these countries could work on to increase their level of efficiency.

In the overall efficiency ranking, Niger ranks first with an average efficiency of 92.6% over the 19 years, followed by Burkina Faso with 82.2%. On the other hand, Ghana, Ivory Coast, and Nigeria occupy the lowest positions in the ranking, with efficiency scores of 65.9%, 61.4%, and 55.8%, respectively. The efficiency in Nigeria is very low. Indeed, if Nigeria were to increase its efficiency by 44.2%, it could potentially achieve an average life expectancy at birth of 90 years, exceeding the maximum life expectancy observed in the world. This can be attributed to the significant gap in all factors between Nigeria and other countries.

Beyond the factors included in the model, other factors must be taken into account. When decomposing the overall efficiency of countries, it is observed that temporal efficiency is higher than permanent efficiency for all the years studied (appendix 4). This implies that the challenges faced by the healthcare systems of these countries are structural in nature. These sources of inefficiency are more difficult to resolve
compared to those stemming from temporal factors (29). Only Niger does not exhibit
permanent inefficiency (appendix 5). This implies that the effects of long-term factors
seem to have a negligible impact on its healthcare system. However, its healthcare
system also faces short-term factors such as the definition, regulation of health policies
and increase healthcare expenditures. Contrary, to the case of Nigeria, which has the
largest gap between temporal efficiency and permanent efficiency, with lower
permanent efficiency, its healthcare system faces more structural difficulties. This can
be explained by first, the total healthcare expenditures and the share of direct
healthcare expenses in the total are likely to be underestimated. Indeed, the use of
traditional medicine remains widespread in these countries and is often the only
healthcare option available to the majority of the population in the most disadvantaged
countries (30), as is the case in Niger. A significant reliance on traditional medicine
and its underestimation in healthcare expenses could explain Niger's ranking with the
lowest healthcare expenditures in the sub-region ($57.2 per capita per year compared
to the average of $686.35 per capita per year for the region). Furthermore, unlike
Niger, Nigeria has a healthcare system dominated by the private sector, with high
healthcare costs and a large prevalence of counterfeit products (31,32). This is
attributed to behavioral factors and habits that would require more time to rectify. This
would explain the higher level of ongoing inefficiency in Nigeria. So, Nigeria should
focus more on good regulation rather than increased financing. Ivory Coast, Ghana,
Cape Verde, and to a lesser extent Sierra Leone, all of which have a permanent
efficiency below 80% (appendix 5), face difficulties similar to those of Nigeria and
should also focus on better regulation and policies.

Burkina Faso has the smallest gap between temporal and permanent efficiency, which remain relatively stable across all periods studied. The temporal efficiency of Sierra Leone, Mali, Guinea, Gambia, and Ivory Coast increases over time, while that of other countries remains relatively stable (appendix 5). However, almost all countries in the sub-region have a similar healthcare system organization, following a "pyramidal" structure, and face similar challenges, including insufficient healthcare service coverage, inadequate financing, a shortage of qualified personnel (an average of 0.13 general practitioners and specialists per 1000 inhabitants), and a low level of health insurance coverage (50% of total healthcare expenditure consists of direct out-of-pocket expenses). This calls in general for short-term efforts to increase coverage of health services. However, our results indicate that the majority of countries face more difficulties that have structural impacts on the efficiency of their healthcare systems. Nassar et al. (2020) also identified such structural challenges in the efficiency of healthcare systems in middle-income countries. According to them, this requires a change in direction. This change in direction can be translated within our framework as good governance, quality regulation, and increased health financing in accordance with the Abuja recommendations to limit dependence on external resources (33).

Finally, our results reveal that, among these temporal and permanent factors, health
expenditure per capita, GDP per capita, literacy rate, and the perception of government
capacity have a significant impact on the efficiency of the health systems in these
countries. These findings are consistent with those of previous studies (1,6,10,18,25).
The positive effects of health expenditure per capita and GDP per capita on life
expectancy at birth demonstrate a wealth effect. High income levels generally lead to
increased investments in the social determinants of health, which, in turn, improve
overall health outcomes. Similar to the income effect, the impact of education on
health has been well-established in the literature. Educated individuals are typically better equipped to comprehend instructions given by health professionals, and have greater health-related knowledge, enabling them to apply this knowledge effectively. Furthermore, the perception of the government's capacity to define and implement policies and regulations has a significant effect on the efficiency of health systems. This result underscores that, in addition to financial resources and health knowledge, organizational capacity plays a crucial role in determining the efficiency of these countries' health systems.

## Limitations of the Study

First, it is important to note that these results are theoretical, relative, and dependent on the choice of method and on variables taken into account. Considering another country or changing an output or input variable would alter the efficiency scores. Nevertheless, the results regarding the rankings and the factors affecting the efficiency of health systems are robust.

Additionally, as shown by the calculated rates of missing information in our models, regardless of the imputation method used, a loss of information due to missing data cannot be avoided. However, since the imputation is based on random methods, the estimation of the model's parameter standard deviations takes this missing information into account.

## Conclusion

The aim of this study was to determine the efficiency of healthcare systems in countries in the West African sub-region and identify factors that explain this efficiency, in order to minimize resource wastage while learning from those with best practices. To our knowledge, this is the first study specifically determining the efficiency of healthcare systems in these countries. We used a fixed-effect stochastic frontier method to account for unobservable heterogeneity in the estimates. We also employed an approach of multiple imputation to address the issue of missing data. This approach would enable other researchers to more easily conduct studies on developing countries while eliminating biases associated with missing observations. The results have shown that countries in the West African sub-region could theoretically increase their average life expectancy at birth by 19.7 years, with the same level of resources utilized. The permanent inefficiency of all countries studied, except Niger, is higher than the temporal inefficiency. This suggests a need for a shift in new policies, with a focus on funding, control, and governance. Healthcare expenditure per capita, GDP per capita, literacy rate, and perception of government capacity are positively associated with life expectancy at birth.

Finally, in light of these results, it is interesting to ask about the importance and effectiveness of traditional medicine in healthcare systems and what example can be drawn from the case of Niger.
**Availability of data and materials**

The datasets generated and analyzed during the current study are available in the
[World Bank] repository, [https://datacatalog.worldbank.org/public-licenses#cc-by]

**License Specified Externally**

The authors declare that they have no competing interests

**Funding**

The authors declare that they have not received any funding for the research reported

**Authors' information**

KA is a PhD in health economics, specializing in the evaluation of health policies and programs in developing countries.

AV is an associate professor in Statistics at the School of Economics, Université Clermont Auvergne.

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# Appendix

## Appendix 1: Life expectancy at birth against input variables

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
## Appendix 2: Distribution of the multiple imputations of the number of physicians per 1000 inhabitants

<figure><img src="image_11.png" /></figure>

## Appendix 3: Sensitivity of results

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Appendix 5: Temporal and permanent efficiency by country from 2000 to 2018</figcaption></figure>

<figure><img src="image_14.png" /></figure>