Effects of COVID-19 on catastrophic health expenditures and inequality in Benin: A microsimulation approach

Honlonkou, Albert N.; Bassongui, Nassibou; Daraté, Corinne B. Article Effects of COVID-19 on catastrophic health expenditures and inequality in Benin: A microsimulation approach Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Honlonkou, Albert N.; Bassongui, Nassibou; Daraté, Corinne B. (2025) : Effects of COVID-19 on catastrophic health expenditures and inequality in Benin: A microsimulation approach, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 8, pp. 1-27, https://doi.org/10.3390/economies13080222 This Version is available at: https://hdl.handle.net/10419/329502 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Fabio Clementi Received: 22 February 2025 Revised: 6 July 2025 Accepted: 9 July 2025 Published: 29 July 2025 Citation: Honlonkou, A. N., Bassongui, N., & Daraté, C. B. (2025). Effects of COVID-19 on Catastrophic Health Expenditures and Inequality in Benin: A Microsimulation Approach. Economies,13(8), 222. https://doi.org/ 10.3390/economies13080222 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Effects of COVID-19 on Catastrophic Health Expenditures and Inequality in Benin: A Microsimulation Approach Albert N. Honlonkou *, Nassibou Bassongui and Corinne B. Daraté National School of Applied Economics and Management (ENEAM), University of Abomey-Calavi, Cotonou BP 358, Benin; [email protected] (N.B.); [email protected] (C.B.D.) *Correspondence: [email protected] Abstract This study assesses the effects of the COVID-19 pandemic on catastrophic health expenditures and income inequality in Benin. A microsimulation was calibrated to estimate the impact of the pandemic under three different shock scenarios: low, moderate, and severe. The analysis relies on secondary data from household living condition surveys. The results indicate that the COVID-19 crisis would lead to a significant average income loss of up to 20% and income inequality, while the number of households with catastrophic health expenditures would increase by 4%. More importantly, the findings reveal heterogeneous impacts across households, with urban residents, younger individuals, more educated households, and male-headed households experiencing the greatest income decline. These findings underscore the need for targeted health coverage and employment policies to better protect vulnerable populations in Benin in the face of future shocks. Keywords: health expenditures; inequalities; microsimulation; COVID-19 JEL Classification: C53; D31; I18; I31 1. Introduction In 2019, the world experienced an unprecedented health shock, that of the COVID-19 pandemic. This deadly infectious disease caused by severe acute respiratory disorders of coronavirus syndrome was declared a global pandemic by the World Health Organisation (WHO) on 11 March 2020 (WHO,2020). The magnitude of the pandemic was estimated to be nearly half a billion people infected by 11 March 2022, and more than 6.5 million deaths worldwide, and 12 million people infected and 254 thousand deaths in Africa (WHO, 2022a). Thus, the number of people infected by the COVID-19 pandemic alone represents more than 71 times the number of people infected by the five largest epidemics the world has experienced in the last two decades, namely Zika in 2016, Ebola virus in 2014, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012, H1N1 virus in 2009, and Severe Acute Respiratory Syndrome (SARS) in 2003 (Loungani et al.,2021). Benin recorded the first confirmed case of COVID-19 on 16 March 2020, and the second case on 17 March 2020. The first death was recorded on 6 April 2020. These first events marked the beginning of the COVID-19 crisis in Benin. Figures 1and 2highlight that the crisis was characterised by two waves. The first wave indicates that the pandemic grew rapidly during the period from May to September 2020 before slowing down. The second wave spanned the period from June to August 2021, where both the number of new cases and the number of deaths increased more than in the first wave. Economies 2025,13, 222 https://doi.org/10.3390/economies13080222 Economies 2025,13, 222 2 of 27  0 10 20 30 40 50 60 70 80 90 2020/3/16 2020/4/16 2020/5/16 2020/6/16 2020/7/16 2020/8/16 2020/9/16 2020/10/16 2020/11/16 2020/12/16 2021/1/16 2021/2/16 2021/3/16 2021/4/16 2021/5/16 2021/6/16 2021/7/16 2021/8/16 2021/9/16 2021/10/16 2021/11/16 2021/12/16 2022/1/16 2022/2/16 2022/3/16 Figure 1. New confirmed COVID-19 cases per million in Benin. Source: Authors, using Johns Hopkins University CSSE COVID-19 data, 2022.  0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 2020/3/16 2020/4/16 2020/5/16 2020/6/16 2020/7/16 2020/8/16 2020/9/16 2020/10/16 2020/11/16 2020/12/16 2021/1/16 2021/2/16 2021/3/16 2021/4/16 2021/5/16 2021/6/16 2021/7/16 2021/8/16 2021/9/16 2021/10/16 2021/11/16 2021/12/16 2022/1/16 2022/2/16 2022/3/16 Figure 2. Deaths per million of the population in Benin. Source: Authors, using Johns Hopkins University CSSE COVID-19 data, 2022. Following confirmation of the first cases of COVID-19, the Beninese authorities took measures including the closure of land borders, systematic quarantine of all travelers entering by air, school closures, prohibition of public gatherings, restrictions on public transportation, the closure of certain commercial centers such as restaurants and bars, and of course the systematic wearing of masks, hand disinfection and the strict respect of social distancing. On 13 September 2021, the Benin government made COVID-19 testing convenient for USD 45 and later decided that COVID-19 testing or vaccination would be mandatory for access to all public services, including decentralised services, in the country. On 1 April 2020, the government’s first social measures for the population were adopted through the sale of masks at a government-subsidised price of USD 0.36, implying a subsidy rate of 60% in twelve high-risk districts in Benin (Gouvernement Benin,2022). On 20 April 2020, social measures continued with the government-subsidised sale of 250 mg chloroquine tablets for the treatment and prevention of COVID-19 at a subsidised price of USD 0.9 and a subsidy rate of 64%. Economies 2025,13, 222 3 of 27 All of these measures to contain the pandemic had many economic and social implications. At the macro-level, COVID-19 led to a decline in economic growth of 1.9% in 2020 in the Sub-Saharan Africa region (AfDB,2022). In terms of fiscal policy, public spending on health care, private sector subsidy programmes, and tax cuts led to an increase in the budget deficit of 8.4 percent of GDP in 2020, double its 2019 level of 4.6 percent of GDP. Beyond these macroeconomic implications, existing studies have relied on multiple regression and simulation approaches to show that the pandemic increased the poverty rate by 22.7% to 38.5% in a sample of 13 Latin American countries (Bracco et al.,2024). In comparison, it was around 60% in Ghana (Abu & Issahaku,2020), 22% in Ethiopia (Yimer & Alemayehu, 2021), 9% in Nigeria (Andam et al.,2020), and 4% in Burkina Faso (Ouoba & Sawadogo, 2022). Other studies have focused on health expenditure; for example, Rajalakshmi et al. (2023) showed that the pandemic led to a 26% increase in health expenditures in South India. Similarly, Ayanore et al. (2024) demonstrated that 52% of households in Ghana spent more than 5% of their total expenditures on COVID-19-related health costs. These findings demonstrate the drastic impact of COVID-19 across the globe. However, the magnitude of the impact also varies drastically, raising the issue of country-specific characteristics. Moreover, the underlying mechanisms through which the pandemic increased poverty, as well as the gender aspects of the impacts, are of crucial importance for formulating policyrelevant recommendations. Though the COVID-19 pandemic has passed, our findings remain relevant for better management of future pandemics. The main question underlying this study is whether and to what extent the COVID-19 pandemic affected households’ health expenditures and poverty in Benin. The objectives of the study are threefold: (i) to estimate the effect of COVID-19 on household income; (ii) to estimate the effect of COVID-19 on household health expenditures; and (iii) to evaluate the distributional effects of COVID-19. For the sake of having an in-depth understanding of the social implications of COVID-19 on household welfare, we analysed the effects of COVID19 on the share of health expenditures in relation to households’ income to determine whether the pandemic led to catastrophic health expenditures. Health expenditures are qualified as catastrophic when the out-of-pocket payments spent by households for health care negatively affect the households’ capacity to satisfy their basic needs (WHO,2001). Though there is no consensus on the threshold share of health expenditures for catastrophic characterisation, most of the definitions are based on threshold values ranging from 10% to 40% of a household’s income or non-food expenditures (Kockaya et al.,2021;WHO,2001; Xu,2005). The rest of the paper develops as follows. The literature review is presented in Section 2. The conceptual framework is presented in Section 3. In Section 4, we present the empirical strategy and data used. The main findings are presented and discussed in Section 5. Lastly, the concluding remarks are presented in Section 6. 2. Literature Review Empirical studies widely agree on the detrimental effects of the COVID-19 pandemic on welfare and poverty, but they differ significantly in methodology, focus, and explanatory power. One dominant strand of the literature adopts quantitative approaches (regressions and microsimulations), revealing that the pandemic has worsened income inequality, increased household vulnerability, and intensified multidimensional poverty. Bracco et al. (2024), using harmonised microdata from 13 Latin American countries, employed microsimulations to show that the COVID-19 crisis caused a poverty increase ranging from 22.7% to 38.5%, and they advocated for strong compensatory measures to mitigate long-term impacts. Similarly, Kang et al. (2023) analysed the impact of COVID-19 on household income using nationally representative surveys in Chad from 2020 and 2021. Economies 2025,13, 222 4 of 27 Based on multivariate regression, they found that two-thirds of households, both rural and urban, reported income reductions, with urban areas hit harder in 2020 and rural areas in 2021. In the health domain, multiple studies report rising catastrophic health expenditures during the pandemic. Haakenstad et al. (2023), using OLS regressions on microdata from Mexico and Peru, estimated 5.6% and 13.5% increases in such expenditures, respectively. Similarly, Rajalakshmi et al. (2023) showed that, in South India, 26% of households experienced catastrophic health spending. Ayanore et al. (2024) reported that, in Ghana, 52.2% of households spent more than 5% of their total expenditures on COVID-19-related health costs, and 4.2% exceeded the 40% threshold, highlighting inequality in the financial burden of the pandemic. Beyond developing countries, many studies have also confirmed the detrimental impact of COVID-19 on households. Militaru et al. (2024) used the EUROMOD tax– benefit microsimulation model to study income changes during the COVID-19 period (2019–2021) and the subsequent inflation crisis (2021–2023) in Romania. They found that although disposable income rose initially, the poorest benefited the least, and later inflation disproportionately impacted lower-income groups. However, their findings revealed that overall income inequality declined, emphasising the need for targeted policy support to protect vulnerable households. Kalar et al. (2023) relied on the same methodology to investigate the impact of COVID-19 on income in the European Union. This study finds that COVID-19 containment and mitigation measures had a regressive impact on income distribution, benefiting lower-income groups more, while their effectiveness varied by country context, with old democracies generally achieving better outcomes than new democracies, highlighting the need for tailored policy mixes. Alfani et al. (2024) relied on Recentered Influence Function regression to show that COVID-19 increased income inequality in the US and Brazil. Moreover, they highlighted that the disparities were persistent over time. Furthermore, Shen and Zhong (2023) focused on both human and animals and showed that the COVID-19 pandemic negatively affected both household income as well as animal food consumption in China. Despite the robustness of these quantitative findings, a key limitation is their limited attention to causal mechanisms. These studies document income loss, increased food insecurity, or reduced health access, but often do not explain their mechanisms. Moreover, the effects vary widely across and within countries, pointing to the importance of contextual and institutional factors that are not sufficiently unpacked in these analyses. Complementing this research is a second strand of studies that rely primarily on qualitative methods, offering a more nuanced view of the pandemic’s impact on lived experiences, though often at the expense of generalisability. Musoke et al. (2024) use focus group discussions, key informant interviews, and household narratives to analyse the social and economic effects of the COVID-19 lockdown in Uganda. Their findings reveal family breakdowns, increased gender-based violence, rising child labour, and food insecurity, along with deteriorated educational outcomes due to school closures. Similarly, Kerschbaumer et al. (2024) employed qualitative methods with 151 participants in Austria to document heightened poverty, social exclusion, and psychological distress during the pandemic. In Indonesia, Mafruhat et al. (2025) used a non-random sample of 100 households across 30 districts to explore both material and spiritual poverty, finding that the pandemic has deepened both. In Spain, Ortega-Martin and Alvarez-Galvez (2025) conducted semi-structured interviews with 23 participants to highlight how COVID-19’s effects extended beyond physical health to include economic stress, legal uncertainty, and weakened social cohesion. Finally, Karunarathne et al. (2025), using 22 years of panel data from 20 low-income countries, estimated the long-running impact of the pandemic on life Economies 2025,13, 222 5 of 27 expectancy, concluding that it has significantly reduced life expectancy, particularly in countries with fragile health systems. Together, these qualitative and mixed-method studies offer crucial insight into the social, psychological, and gendered dimensions of COVID-19’s impact on welfare. However, their methodological limitations, including their small, non-representative samples and the lack of causal inference, limit the external validity and policy applicability of their findings. In sum, while the existing literature convincingly demonstrates the negative impact of COVID-19 on poverty and welfare, two major gaps persist. First, the importance of the impacts from quantitative studies widely varies, and these studies fail to identify the mechanisms through which these effects unfold, especially across different social groups or policy environments. Second, qualitative studies, while rich in context, suffer from limited quantification and generalisability, preventing broader policy conclusions. 3. Conceptual Framework COVID-19 affected household income and health expenditures through labour markets (demand and supply), moderated by government measures (transfers and barrier measures), and preventive and curative health expenditures by households. Figure 3 describes the channels. Figure 3. Effect of COVID-19 on household health expenditures. Source: authors. The population affected by the COVID-19 outbreak can be classified into two groups, namely the infected and the susceptible, with the possibility of having both groups of people within the same household. On the one hand, those infected are drawn out of the labour market, increasing unemployment by reducing the labour supply and leading to a decrease in household income. The government intervenes to temper the decrease in income by cash transfers and tax redistribution. The treatment expenses increase directly with health expenditure (curative and preventive). However, since household income decreases, health expenditures may decrease as a consequence. On the other hand, the Economies 2025,13, 222 6 of 27 susceptible population takes prevention measures that increase health expenses while simultaneously experiencing income losses. Moreover, to prevent contamination, the government takes barrier measures (e.g., lockdowns, internal and external border closures, and bans on public gatherings) that slow down economic activities and cause a recession that affects the labour markets through unemployment due to the decrease in labour demand. These governmental measures lead to a decrease in income and ultimately to a decrease in health expenditures at the household level. The channels through which COVID-19 affects income and health expenditures are instrumental to the evaluation of the effects of the pandemic. 4. Empirical Strategy The best strategy for evaluating the effect of COVID-19 on household health expenditures is to use experimental—or by default quasi-experimental—impact assessment methods. These methods assume the existence of both control and treatment groups. The use of these methodologies is not feasible in the case of COVID-19 because no one could be excluded from exposure to the epidemic, resulting in the absence of an appropriate control group. Also, the lack of household and individual data before and after COVID-19 precludes the use of quasi-experimental impact assessment methods. In the face of these empirical constraints, we use a microsimulation approach to assess the effect of COVID-19. This approach is based on the behavioral function of individuals through which their reactions to a shock or policy are evaluated in terms of averages and distributional effects, considering the entire distribution (especially the tails) of the shock or policy effects. Unlike Computable General Equilibrium (CGE) models, which are criticised for aggregating economic phenomena (Hansen & Heckman,1996), microsimulation models have the advantage of analysing in detail the effect of a shock or policy at the individual level. Microsimulation models have been widely used in recent decades for the ex ante evaluation of poverty reduction policies and exogenous shocks, including the effects of COVID-19 (Andam et al.,2020;O’Donoghue et al.,2021;Ouoba & Sawadogo,2022;Yimer & Alemayehu,2021) and mostly for social programs and fiscal policies (Benczúr et al.,2018; Bover et al.,2017;Maskaeva et al.,2019,2021). Our estimation strategy of the short-term effect of COVID-19 follows that of Aran et al. (2021). Our microsimulation strategy entails four steps as stated below. 4.1. Calculating the Effects of COVID-19 4.1.1. Calculating COVID-19 Household Income and Health Expenditure We compute the monthly household health expenditures and monthly household income before the COVID-19 pandemic using the Harmonized Survey on Living Conditions of Households (EHCVM) dataset. Concerning household income, our income generation process follows Sologon et al.’s (2021) approach. The total income is given by Equation (1): y=yL+yK+yO+yB(1) where yis the household monthly income, y L refers to the labour income, y K measures the capital income, y o is other household income, and y B is public benefits (transfers). Contrary to Sologon et al. (2021), who used household disposable income, we used gross income. This choice is motivated by the fact that the tax information and public benefits are not available in our dataset. However, the consequences of this oversight of taxes are likely to be negligible, because, in developing countries like Benin, labour income is lightly taxed and the tax cost is probably balanced by public transfers. Economies 2025,13, 222 7 of 27 The labour income y L in a household equals the sum of individuals employed and self-employed within the household, aggregated as follows: yL= n ∑ i=1Iemployed i∗yemployed i+Isel f −employed i∗ysel f −employed i(2) where irefers to the individual member of the household and n the size of the household. Individual iis employed ( Iemployed i = 1) and ( Iemployed i = 0), with an income equal to yemployed i . Isel f.employed i and ysel f.employed are similarly defined. This aggregate formula also addresses the situation of individuals who combine salaried work and self-employment. Household capital income comes from investment income and property income, most notably rental income. Capital income is then modelled as follows: yK=∑n i=1Iinvestment iyinvestment i+Iproperty iyproperty i(3) Apart from labour and capital income, households may benefit from other incomes, such as private pensions and transfers received from relatives and nonrelatives. Such sources of income are computed as follows: yO=∑n i=1Iprivate.pension iyprivate.pension i+Itran f ers.relatives iytran f ers.relatives i+Itran f ers.non−relatives iytran f ers.non−relatives i(4) Finally, household income comes from public benefits (if available), both at the individual level (pensions, sickness or disability, unemployment insurance) and at the household level such as social security programs (food distribution, cash transfers, free healthcare for children and pregnant women). The aggregated income from public benefits at the household level is computed as follows: yB=ysocial. sec urity +∑n iIpensions iypensions i(5) The World Health Organisation defines health expenditures based on healthcare functions as out-of-pocket money spent by households in satisfying their core healthcare needs. Those healthcare functions included core items such as curative care, rehabilitative care, inpatient care, outpatient care, day care, long-term care, home-based care, ancillary services, pharmaceutical goods, therapeutic appliances, and preventive care (WHO,2022b). Following this definition, we modelled the household health expenditure equation into three components, namely curative expenditures eC , preventive expenditures eP , and nonmedical expenditures eO, as follows: e=∑n i=1∑jej i, withi =1, 2, . . . n and j =C,P,O (6) where eis the aggregated household monthly health expenditures for all the household members. All three components of health expenditures take into consideration both modern and traditional medicine. Curative health expenditures are composed of doctor consultation fees econs , medicines emed , diagnostic testing ediagn , and bed charges for hospitalisation ebed as follows: eC=econs +emed +ediagn1+ebed (7) Preventive health expenditures equal the sum of preventative diagnostic testing ediagn2 , vaccines evac, and other nonmedical goods eother such as face masks. eP=ediagn2+evac +eother (8) Economies 2025,13, 222 8 of 27 Finally, household health expenditures encompass other nonmedical expenditures eo , notably transport etrans and communication fees ecom as follows: eO=etrans +ecom (9) It is worth noting that the measure of health expenditures used in this study refers exclusively to out-of-pocket payments reported by households. It does not include health spending covered by government subsidies, public insurance schemes, or donor-funded programmes. Data on these variables were not available. 4.1.2. Calculating the COVID-19 Job Loss Index After the Outbreak The COVID-19 job loss index is calculated by multiplying the risk of job loss conditional to the sector of activity, gender, and residence as follows1: Job loss indext=1=Risk(job loss|sectork)∗Risk(job loss|genderi)∗Risk(job loss|residencej)(10) where t= 1 refers to the COVID-19 period. The risk of job loss conditional to sector of activities and residence is calculated using the results of the rapid evaluation conducted during the COVID-19 pandemic (March–July 2020) by the National Institute of Statistics and Economic Analysis (INSAE BENIN,2020). This study reported that 80%, 77% and 74% of Cotonou city residents, other urban residents, and rural residents, respectively, did not work during the period considered. Equally, the risk of job loss in the wholesale and retail trade was the highest (37.29%), while it was 0% in the agriculture sector. The full job loss probabilities according to the sector of activities, are reported in Table A1 in Appendix A. Furthermore, this report indicated that 50% of households lost their job during the period, which accounts for 87.5% of men and 13% of women. Hence, we calculated the probabilities of job loss based on gender as follows2: Pr(job loss|genderi) = Pr(job loss ∩genderi) Pr(genderi)(11) The results from Equation (11) indicated probabilities of job loss of 0.55 and 0.30, respectively, for men and women. Then, we calculated the risk of job loss conditional to gender and residence using a multiplier as follows: Risk(job loss|genderi) = 2Pr(job loss|genderi) ∑2 i=12Pr(job loss|genderj), (12) and Risk(job loss|residencek) = 3Pr(job loss|residencek) ∑3 i=12Pr(job loss|residencel) The risk of job loss coefficients by gender and residence are reported in Table A2 in the Appendix A. This risk factors are not probabilities, but multipliers addressing the fact that certain individuals are more susceptible to job loss than some others depending on their characteristics (gender or place of residence). We considered the job loss indices calculated in Equation (12) as the mean shocks, so they are set as the baseline assumptions (moderate shock). The low and severe assumptions are set by considering that low shock is 0.5 times the baseline and severe is twice the baseline assumption. The job loss indices in the low, moderate, and severe assumptions are reported in Table 1. Economies 2025,13, 222 15 of 27 5.3. Effect of COVID-19 on Income Inequality To assess whether the effects of COVID-19 are significantly heterogeneous, we calculated the Gini index. The results reported in Table 7indicate that COVID-19 would increase income inequality in Benin by 0.1 percentage point. Moreover, the increase in inequality is more pronounced among men, younger individuals, and those with higher levels of education, who appear to be the most affected groups, as outlined in Table 8. These findings are consistent with the magnitude of the income decline observed within these subgroups, as discussed in the previous section. Table 7. Gini index by age and severity of shock. GINI Index BEFORE LOW MODERATE SEVERE Overall 0.622 0.623 0.624 0.628 Young 0.577 0.579 0.583 0.598 Adult 0.550 0.548 0.546 0.544 Elderly 0.640 0.640 0.641 0.647 Table 8. Gini index by gender and severity of shock. (a) Gini index by gender and severity of shock. GINI index BEFORE LOW MODERATE SEVERE Overall 0.622 0.623 0.624 0.628 Women 0.672 0.672 0.672 0.673 Men 0.562 0.562 0.563 0.571 (b) Gini index by education level and severity of shock. GINI index BEFORE LOW MODERATE SEVERE Overall 0.622 0.623 0.624 0.628 Primary and below 0.490 0.488 0.487 0.485 Secondary 0.573 0.574 0.577 0.586 Higher 0.377 0.383 0.393 0.405 (c) Gini index by place of residence and severity of shock. GINI index BEFORE LOW MODERATE SEVERE Overall 0.622 0.623 0.624 0.628 Cotonou 0.695 0.698 0.703 0.715 Urban 0.559 0.561 0.563 0.575 Rural 0.599 0.599 0.600 0.606 Source: authors. 5.4. Effect of COVID-19 on Household Health Expenditures As depicted in our conceptual framework, it is argued that the COVID-19 crisis has affected (increased) household health expenditures through direct and indirect channels. Indeed, the health expenditures of households increase with exposure to COVID-19 contamination risk. Thus, the expenditures for averting measures (hand sanitiser, face masks, vaccine, COVID-19 tests, etc.) and money spent out-of-pocket on medical visits and medicine directly increase household health expenditures. The effects of COVID-19 on health expenditure were examined by estimating the health expenditure equation in (14). The drivers of health expenditure were estimated using multilevel mixed-effects linear regression (Table 9). The validation parameters of the model show that the random intercept at the household level significantly varies, as indicated by a variance coefficient equal to Economies 2025,13, 222 16 of 27 1.38 with a standard error coefficient equal to 0.40. This result indicates that ignoring the random effect by estimating only the fixed effect would lead to biased estimates. Moreover, the likelihood ratio (LR) test strongly supports that the mixed-effects model better fits our data than the ordinary least squares (OLS) model, as confirmed by the p-value of 0.000. Thus, the results of the fixed-effects components revealed in Table 9indicate that income, age, and risk of diseases such as cough, high blood pressure, malaria, and road accidents have positive and significant effects on health expenditure. Notably, a 10% income improvement increases health expenditure by 3%, confirming previous findings in Africa. Indeed, Olasehinde and Olaniyan (2017) relied on the ordinary least squares technique to estimate the determinants of household health expenditure in Nigeria. They found that a 1% increase in household income increases health expenditures by 0.57%. Ampaw et al. (2019) used a nationally representative sample of 16,772 households to analyse the effect of income on health expenditure in Ghana. Their findings from ordinary least squares regression confirmed a positive and significant effect of household total income on health expenditure. Equally, Houeninvo and Assouto (2023) recently used a sample of 20 low-income African countries over 1995–2018 to calculate income elasticities. Based on panel-pooled mean group estimates, they concluded that a 1% improvement in per capita income increases private health expenditure by 0.54% in the long run. Furthermore, our findings revealed that households residing in rural and urban areas are less likely to have higher health expenditures than those in the capital city of Cotonou. Our results further confirmed the nonlinear relationship between age and health expenditure, as found in previous studies such as those of Ampaw et al. (2019) in Ghana and Olasehinde and Olaniyan (2017) in Nigeria. Table 9. Multilevel mixed-effects linear regression model of household health expenditure. Log (Health Expenditure) Coef. St. Err. [95% Conf Interval] Log (income) 0.292 *** 0.089 0.117 0.466 Gender head household (ref = female) Male −0.191 0.181 −0.546 0.165 Log (Age) 0.035 ** 0.017 0.002 0.067 Log (Age square) −0.001 ** 0.000 −0.001 0.000 Education of head household (ref = primary) Secondary −0.124 0.231 −0.576 0.328 Higher −0.221 0.309 −0.825 0.384 Health coverage (ref = no) −0.362 0.435 −1.215 0.490 Residence (ref = Cotonou) Urban −0.523 ** 0.252 −1.017 −0.029 Rural −0.731 *** 0.274 −1.268 −0.194 Cough last 3 months (ref = no) 1.817 *** 0.414 1.004 2.629 High blood pressure (ref = no) 2.729 *** 0.679 1.397 4.060 Malaria last 3 months (ref =no) 0.637 *** 0.269 0.110 1.164 Road accident last 3 months (ref = no) 3.292 *** 0.958 1.414 5.170 Constant 3.579 *** 0.994 1.631 5.527 Random-effects parameters Intercept at household level 1.383 0.405 0.779 2.456 Residual variance 2.281 0.387 1.635 3.181 Mean dependent var 2.078 Number of obs 78.188 Prob > chi2 1761.402 Note: Any statistically significant estimates are denoted with asterisks: * p< 0.10, ** p< 0.05, *** p< 0.01. Economies 2025,13, 222 17 of 27 We now simulate the effects of COVID-19 on health expenditure. The results presented in Figure 5indicate that households experienced an increase in health expenditures after the COVID-19 shock, with heterogeneous effects. Indeed, a decrease in household income, ceteris paribus, will lead to a decrease in household health expenditures. Before COVID19, the pre-COVID-19 shock curve, as shown in Figure 5, was above the post-COVID-19 one. In addition, the results show that the effect of COVID-19 on health expenditures is heterogeneous, meaning that households with low health expenditures were the most affected by the COVID-19 pandemic. The gap between the before-COVID-19 curve and the after-COVID-19 curves decreases from low health expenditures to high health expenditures.  Figure 5. Household health expenditures6. Source: authors. These results can be explained by the fact that households with low health expenditures are generally characterised by low income. This result confirms our early findings, indicating that the low-income groups of households were the most affected by COVID-19. More importantly, our summary statistics reported that less than 1% of Benin’s households do not have health coverage. Indeed, though COVID-19 decreased both household income and health expenditure, the increase in health expenditures indicates that the decline in income was greater than that of health expenditures. Figure 6depicts the effects of the COVID-19 crisis on households’ catastrophic health expenditures. Two main conclusions can be drawn from these results. First, the COVID-19 crisis pushed a significant share of households into catastrophic health expenditures, with a greater effect when the magnitude of the shock increased. Notably, the share of households with catastrophic health expenditures at the 10% threshold increased from 16% before the COVID-19 period to 20% after a severe shock. Second, these findings are robust when moving from a 10% to a 40% threshold of catastrophic health expenditures. A gender analysis of the catastrophic health expenditures of the COVID-19 crisis revealed that men were the most affected (Figure 7). Although the share of women with catastrophic health expenditures was greater than that of men before the pandemic, it is worth noting that the share of men with catastrophic health expenditures after the COVID-19 pandemic increased more than that observed among women. Furthermore, the regional analysis also supports the evidence of heterogeneous effects of the COVID-19 pandemic on household health expenditures. More specifically, the results from simulations indicated that urban residents experienced the greatest burden in terms of an increase in Economies 2025,13, 222 18 of 27 health expenditures, though Cotonou residents were the most vulnerable before COVID-19 (Figure 8). Figure 6. Share of households with CHE7. Source: authors. Figure 7. Change in the share of households with CHE by gender (percentage points) 8 . Source: authors. 5.5. Discussion Our findings revealed that, regardless of the considered scenario (low, moderate, and severe COVID-19 shock), households in urban areas saw their income decline faster than those in rural areas. Equally, low-income households and men were the most vulnerable to COVID-19. This can be explained by the fact that, in urban areas, the commercial sector, representative of the most employed section of urban residents, was strongly affected by COVID-19. Indeed, the public health measures undertaken by the government to contain the spread of the pandemic, such as limitations on public gatherings, border closures, and the restriction imposed on public transportation, drastically limited households’ day-to-day activities. Equally, low-income households are generally those with low skills, meaning that they cannot work remotely as skilled workers. These findings align with those of Laborde et al. (2021) and Andam et al. (2020), who found that the incomes of urban households were the most affected by the COVID-19 pandemic compared to rural households, who lost an average of 18% of their income. Economies 2025,13, 222 19 of 27  Figure 8. Change in CHE by place of residence (percentage points)9. Source: authors. We found that the share of household health expenditures between the before COVID19 and after COVID-19 periods is increasing with regard to the magnitude of the shock. This result can be explained by the decline in household income on the one hand and the increase in preventive and curative health expenditures on the other. Thus, based on 10% and 20% thresholds, the simulations showed that the share of households with catastrophic health expenditures increased with the magnitude of COVID-19 shock. More importantly, the heterogeneous effects on health expenditures revealed that households with a higher share of health expenditures before the pandemic experienced a greater increase in health expenditures. Before the COVID-19 pandemic, the proportion of households in rural areas with catastrophic health expenditures was higher than that of urban households, including Cotonou. Similarly, the proportion of households with catastrophic health expenditures considering different shock scenarios (low, moderate, and severe) was high in urban areas, especially in Cotonou, compared to households in rural areas. These results are in line with those of Rajalakshmi et al. (2023), who showed that during the COVID-19 pandemic, catastrophic household health expenditure increased. We explain this result by the fact that the established cordon sanitaire limited the movement of humans and goods between rural and urban areas as much as possible, which made it possible to reduce the risks of infection in rural areas and therefore limit medical expenses. Our findings reveal that the negative impact of the COVID-19 pandemic on household income was more pronounced among male-headed households, more educated ones, young headed-households, and urban residents, with a simultaneous rise in catastrophic health expenditures. These results can be explained by the socioeconomic structure of Benin. Households headed by younger individuals appear more affected by COVID-19related income shocks because they are more likely to be engaged in informal, unstable, or entry-level jobs, such as petty trade or apprenticeships, that were severely disrupted during lockdowns. In contrast, older household heads may benefit from more stable occupations or accumulated resources. Male-headed households experienced greater income losses, which reflects their higher concentration in sectors heavily impacted by the crisis, such as transportation, construction, and large-scale trade. Female-headed households, although often more economically vulnerable overall, are more likely to be involved in small-scale, community-based commerce, which proved relatively resilient. Interestingly, households with more educated heads suffered larger income declines. This may be due to their higher dependence on formal sector employment, which was more exposed to wage cuts, layoffs, and contract suspensions during the pandemic. Finally, urban households, Economies 2025,13, 222 20 of 27 particularly those in Cotonou, were the most affected, due to their greater exposure to mobility restrictions, market disruptions, and the informal urban economy’s vulnerability to shocks. In contrast, rural households, while generally poorer, tend to rely more on subsistence agriculture and informal mutual support systems, which offered a relative buffer against income losses. These findings highlight the differentiated impact of COVID19 and suggest that recovery policies should be sensitive to age, gender, education level, and place of residence. Furthermore, the increased catastrophic health expenditure suggests a heightened vulnerability to out-of-pocket health costs during the pandemic, possibly due to limited access to subsidised care or increased reliance on private health providers in emergency contexts. Furthermore, the disproportionate effect on urban households may reflect the concentration of COVID-19 cases, the stricter enforcement of containment measures, and the higher cost of living in urban areas compared to rural ones. Urban residents also tend to depend more on cash-based, non-agricultural livelihoods that were acutely sensitive to lockdowns and economic disruptions. While 39.5% of households already live in poverty (INSAE BENIN,2019), these findings underscore the importance of tailoring social protection responses to account for gender dynamics and spatial inequalities, particularly in the design of post-crisis recovery and health financing policies. It is worth noting that this study is subject to several limitations, and as such, the findings should be interpreted with caution. A key limitation concerns the use of selfreported data. The survey datasets rely on household-reported information related to health and income, which may be subject to reporting biases, notably recall bias or social desirability bias. In addition, due to the absence of tax-related information in the datasets, gross income was used as a proxy for household welfare instead of disposable income. This empirical choice may lead to an underestimation of the impact of COVID-19 on the ratio of health expenditures to income, suggesting that post-COVID-19 health-related spending may have been even more catastrophic than indicated by the findings of this study. 6. Conclusions and Policy Implications In this study, we investigated the socioeconomic implications of the COVID-19 pandemic in Benin. To this end, a nationally representative dataset from the Benin household living conditions survey and a rapid survey after the COVID-19 outbreak were used for empirical estimations. We adopted static microsimulation modelling to estimate the shortterm effect of COVID-19 on household income and health expenditures under different scenarios of COVID-19 shock. The results showed that the COVID-19 pandemic had a huge negative effect on household income, on the one hand, due to the closure of borders, which limited trade between Benin and its neighbouring countries, and on the other hand, because of the sanitary cordon that limited trade between urban towns and rural areas. Similarly, COVID-19 increased household health expenditures with heterogeneous effects across income levels, place of residence and gender. Due to the absence of tax information in our dataset, we used gross income instead of disposable income as a measure of welfare. This empirical strategy may lead to the undervaluation of the effects of COVID-19 on the health expenditures–income ratio, meaning that the post-COVID-19 health expenditures may be more catastrophic than the results of this study have shown. These findings have implications for health financing in Benin. Targeted health coverage policies, along with social policies aimed at reducing employment vulnerability and targeted cash transfers, are necessary to address the vulnerability of households to health crises in Benin. The government needs to target the poorest households led by men and urban residents for health coverage programmes to address the vulnerability of households to health crises, as highlighted by the COVID-19 pandemic. Though Benin’s government, with the Economies 2025,13, 222 21 of 27 support of organisations like the WHO, has been working towards expanding health coverage and implementing measures to improve access to healthcare for the most vulnerable populations 10 , it is crucial to focus on both the supply and demand sides when formulating health policies. According to the 2018 demographic and health surveys report, only 1.2% of the population in Benin had access to health coverage, while 38.5% lived below the monetary poverty line. These data emphasise that even in the absence of health pandemics, a significant portion of the population faces difficulties in accessing health due to financial constraints. United Nations organisations such as the World Health Organisation (WHO), civil society, and local and foreign private sectors could play a core role in supporting governments to design and mobilise sustainable financing. The Benin government should design social policies aimed at reducing employment vulnerability among the population, notably for urban residents and the poorest households led by men. The private sector could be the core partner in improving the conditions of job vulnerability. The COVID-19 pandemic has led to income loss, disproportionately affecting lower-income quintiles. While the Benin National Institute of Statistics and Demography reported an unemployment rate of about 2% in 2018, with a high rate of vulnerable employment at 84.1%, it is worth noting that the government addresses the issue of under-employment, especially during periods of crises. In addition to targeted health coverage policies and reducing employment vulnerability, it is essential to design targeted cash transfer programs to reduce the vulnerability of the poorest households when pandemics occur. Author Contributions: Conceptualization, A.N.H. and N.B.; methodology, A.N.H. and N.B.; software, N.B.; validation, A.N.H.; formal analysis, A.N.H. and N.B.; investigation, A.N.H. and N.B., C.B.D.; resources, A.N.H. and N.B., C.B.D.; data curation, NB and C.B.D.; writing—original draft preparation, A.N.H. and N.B.; writing—review and editing, A.N.H. and N.B., C.B.D.; visualization, N.B.; supervision, A.N.H.; project administration, A.N.H.; funding acquisition, A.N.H. and N.B., C.B.D. All authors have read and agreed to the published version of the manuscript. Funding: African Economic Research Consortium (AERC): RC225/6. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest. Appendix A Table A1. Sectoral job vulnerability coefficients. Type of Activity Percent 1. Agriculture, fishing, and forestry 0.00 2. Manufacturing and mining 16.95 3. Construction 18.64 4. Wholesale and retail trade, repair of motor vehicles, and other motors. 37.29 5. Hotels and restaurants 3.39 6. Transport and communication 5.08 7. Education and public administration 0.00 8. Health and social work activities 6.78 9. Other services 11.86 Economies 2025,13, 222 22 of 27 Table A2. Risk of job loss coefficients by gender and residence. Gender Residence Male Female Cotonou Urban Rural Risk coefficients 1.29 0.71 1.04 1.00 0.96 Table A3. Health expenditures before and after COVID-19. Household Health Expenditures Obs Mean Std. Dev. Min Max Before COVID-19 899 2158.64 4655.426 0 50,250 Low shock 899 86,545.321 193,570.45 0 2,835,360 Moderate shock 899 81,208.87 182,267.61 0 2,670,720.3 Severe shock 899 73,968.176 169,792.54 0 2,506,080.3 Table A4. Summary statistics for catastrophic health expenditures. Threshold Obs Mean Std. Dev. Min Max Catastrophic health expenditures (Before COVID-19) 10% 899 0.165 0.371 0 1 20% 899 0.083 0.277 0 1 30% 899 0.051 0.22 0 1 40% 899 0.034 0.183 0 1 Catastrophic health expenditures (After COVID-19-low shock) 10% 899 0.175 0.38 0 1 20% 899 0.086 0.28 0 1 30% 899 0.055 0.227 0 1 40% 899 0.036 0.185 0 1 Catastrophic health expenditures (After COVID-19-oderate shock) 10% 899 0.181 0.385 0 1 20% 899 0.092 0.29 0 1 30% 899 0.058 0.234 0 1 40% 899 0.038 0.191 0 1 Catastrophic health expenditures (After COVID-19-severe shock) 10% 899 0.199 0.4 0 1 20% 899 0.102 0.303 0 1 30% 899 0.065 0.246 0 1 40% 899 0.042 0.201 0 1 Table A5. Summary statistics for health expenditure by gender. Threshold Scenario Man Woman 10% Before COVID-19 0.136 0.195 Low 0.149 0.202 Moderate 0.156 0.209 Severe 0.181 0.218 Economies 2025,13, 222 23 of 27 Table A5. Cont. Threshold Scenario Man Woman 20% Before COVID-19 0.073 0.094 Low 0.076 0.096 Moderate 0.084 0.101 Severe 0.099 0.106 30% Before COVID-19 0.043 0.06 Low 0.045 0.064 Moderate 0.052 0.064 Severe 0.056 0.073 40% Before COVID-19 0.03 0.039 Low 0.032 0.039 Moderate 0.037 0.039 Severe 0.043 0.041 Table A6. Summary statistics for health expenditures by residence. Threshold Cotonou Urbain Rural 10% Before COVID-19 0.188 0.155 0.166 Low 0.194 0.17 0.172 Moderate 0.208 0.177 0.175 Severe 0.215 0.198 0.194 20% Before COVID-19 0.076 0.085 0.085 Low 0.076 0.087 0.087 Moderate 0.097 0.095 0.087 Severe 0.104 0.102 0.101 30% Before COVID-19 0.056 0.052 0.048 Low 0.056 0.058 0.051 Moderate 0.056 0.06 0.056 Severe 0.063 0.065 0.065 40% Before COVID-19 0.035 0.037 0.031 Low 0.035 0.04 0.031 Moderate 0.042 0.04 0.034 Severe 0.049 0.048 0.034 Economies 2025,13, 222 24 of 27  Figure A1. Health expenditures after a low shock.  Figure A2. Health expenditures after a moderate shock. Notes 1 Note that in the absence of information on job loss by gender and residence within the sector of activities, we assume independence between the sector of activity, gender, and residence. With the absence of job status of individuals who have lost their job, we assume a random selection of job loss within each sector, each gender, and each residence. Hence, we assume that the percentage of job loss equals the percentage of income loss. 2 In the absence of information on the gender composition of the INSAE BENIN (2020) study, we assume that this study is nationally representative, so we used the gender composition of the Harmonised Survey on Living Conditions of Households in Benin (INSAE BENIN,2019). These data indicated that 78.55% and 21.45% of heads of households were men and women, respectively. 3 Recall that within the same household, members can have different branches of activity, places of residence, and genders. To take into consideration the composition of households, we calculated the income loss coefficient at the individual level and then aggregated it at the household level. 4The Chi-2 distance of the CDM test was 27.63, and the p-value equaled 0.016. 5 For a better visualisation of the plot, we limited the upper value of the distribution of income to 100,000 F CFA. Full descriptive statistics are reported in Table 4. 6 For a better visualisation of the plot, we limited the upper value of the distribution of health expenditures to 10000 F CFA. Plots for low and moderate shocks are reported in Figures A1 and A2 in Appendix A. Full descriptive statistics are reported in Table A3 in Appendix A.