Benin

Abstract

The chapter examined the ramifications of Covid-19 and its policy responses on different sectors of the Beninese economy and its diverse social groups. The research employed ordinal logistic regression to measure the effect of Covid-19 and its consequences to ascertain how the pandemic has affected the incomes, level of activity, and employment of different groups. The chapter also examined some control and mitigation measures introduced by the Beninese government and its technical and financial partners. This research targeted 1200 individuals and 327 enterprises in four departments in the country. The findings show that intervention programmes tailored for various groups were inadequate and only a few benefitted. Although there were universally applied support measures such as water and electricity subsidies, these were not the most desired by some social groups. The desirability of measures could have been better were the decision-making processes inclusive and the measures equitably applied.

Introduction

In Benin, the first case of the Covid-19 virus was detected on March 16, 2020. Benin’s statistical service, Institut National de la Statistique et de l’Analyse Economique (INSAE), estimates the population of Benin at 11,884,127 in 2019. Benin has the characteristic of a very young population. Almost 65% of its population is under 25 years. In 2019, the population under 25 reached 7,628,592 people. More than half (55.2%) of the population lives in rural areas, with Cotonou and Abomey-Calavi accommodating most of the urban population. Taking advantage of its political stability, Benin has continuously recorded an increase in its Human Development Index (HDI) over the past thirty years, which rose from 0.348 in 1990 to 0.531 in 2019 which is an increase of 0.182 points. This increase is the result of the progress made by the country on three dimensions namely, health, education, and livings standards. Indeed, between 1990 and 2019, Benin experienced successive gains in the life expectancy at birth of its citizens. An average of 0.3 years per year increase is recorded yearly but with a period of stagnation between 1996 and 2000. The country also generally recorded an improvement in its Gross National Income per capita (GNI/inhabitant) over the period, which rose from USD 1431 in 1990 to USD 2217.57 US PPP in 2019. Table 2.1 presents a selection of comparative macroeconomic and demographic statistics in 2018 (unless otherwise indicated) for Benin and some of its West African counterparts.

Table 2.1 Comparative macroeconomic and demographic statistics for 2018

The Covid-19 health crisis occurred when the country’s economy had seen a significant boost. Benin's economic growth remains robust and is estimated at 6.7% in 2019. The growth rate has been consistent over the past two decades (World Bank, 2020). The growth figures are partly due to the increase in public investments, which rose from 21% of GDP in 2016 to 29.6% in 2019. On the supply side, the growth is attributable to the performance of the agricultural sector, particularly in the cotton production sector, which is a major cash crop in the country. Cotton production rose from 269,222 tons in 2016 to 726,831 tons in 2019. Other sectors of the economy, including construction, public works, agro-industry, and the Port of Cotonou, have contributed significantly to the positive growth figures. Inflation in Benin has remained low at an estimated rate of −0.1% in 2019 and below the WAEMU limit of 3%. The CFA franc, pegged to the euro, appreciated against the dollar over 2017–2019 (AFDB, 2020).

Benin's economy is dominated by the services sector comprising banking, transport, telecommunications, public administration, and other trade sectors. The country’s economic boost is also driven by the dynamism of trade with Nigeria, which averaged 48.9% of nominal GDP between 2013 and 2019. The share of the agricultural sector, composed mainly of cotton, cashew, and pineapple cultivation, represented on average of 26.9% between 2013 and 2018. The manufacturing sector, comprised mainly of energy and construction, accounted for 15.9% of GDP during the same period, while taxes and duties contributed 8.2% of nominal GDP over the same period (INSAE, 2020). Table 2.2 shows the sectoral contribution to GDP trend from 2013 to 2019.

Table 2.2 Sectoral contribution to GDP growth (2013–2019) in percentage

The country’s budget deficit, financed by loans and grants, narrowed to 2.5% of GDP in 2019. The current account deficit, which improved due to improvement in cotton production, is mainly financed by official loans (33%), private loans (27%), and foreign direct investment (19%) (AFDB, 2020). Benin’s foreign exchange reserve fell to 20.93 million USD in 2018 (or 0.07 months of import). Public debt was estimated at 54% of the GDP in 2019. The country issued an Eurobond of 500 million Euros (5.2% of GDP) in March 2019; however, its risk of debt distress is considered moderate. Benin is rated B + by the Standard and Poor’s Agency regarding its debt and financial position. However, 40% of the Beninese population is rated poor, reflecting the inequality and non-inclusiveness inherent in the country’s impressive growth figures (AFDB, 2020). The structural inequalities also show in the differential ways social groups access information, basic services, health care, and means of livelihood. This study seeks to analyse how different socioeconomic groups are differently affected by the Covid-19 pandemic and how various mitigation measures designed to target these multiple groups affected them differently.

Methodology

The study used multiple approaches for data and information gathering. These include key informant interviews, phone surveys, and media reports. Individuals and companies were the units of analysis for the study. In addition, state and non-state actors who coordinated and worked in the management of Covid-19 were also interviewed. Besides that, the research team also interviewed the leaders of various workers organisations and trade unions to have a broader view of the implication of the pandemic on their sectors of work and members.

In the context of the pandemic, the study's primary concern was not to reach all actors but to investigate those most impacted by the pandemic. Thus, teachers, students, hospitality sector workers, transporters, agricultural producers, merchants, artists, and marginalised people in the Social Promotion Centres (CPCs) were involved. The representatives of the organisations gave the list of their members which was used for the study.

The study also includes formal and informal enterprises. Formal companies were sampled from a list of formal enterprises (INSAE, 2017). Regarding informal enterprises, this group comprises only artisans whose lists and contacts were obtained from the artisans' association of each commune. Data were mainly collected using the phone survey method.

The study covered Benin's four territorial departments namely Atlantic, Littoral, Borgou, and Atacora. The study systematically considered the Atlantic and Littoral departments, which were the epicentres of Covid-19 in Benin. These locations are crucial because most cross-border transactions happen in these two departments. Furthermore, to see the differential effects of the pandemic on the various regions, two departments in the north namely Borgou and Atacora were also sampled. A rural and an urban commune in each of these departments were selected to account for locational differences. The seven communes chosen for the study were Cotonou, Abomey-Calavi, Toffo, Parakou, Kalalé, Natitingou, and Cobly.

Sample Size and Sampling Strategies

The target of this study include nine categories of individuals who are transporters (zem,¹ city cab, and minibus), agricultural producers, the marginalised people in CPCs,² traders, students, secondary school teachers, pharmacists, artists, and restaurant and eatery place operators. Since a sampling database was not at our disposal, we obtained the lists of participants from the diverse associations to which these individuals belong. The list for vulnerable people such as people with disabilities, older people, poor people, and orphans were obtained from Social Promotion Centers (CPS) in each of the municipalities covered by the study. All these lists together helped us define a database with different essential characteristics of these targets which have been used as a sampling base. Once the sampling frame was available, the strategy adopted was to determine the total sample size and distribute it among the different communes.

The sample size calculation was performed using the formulas below.

$$n_1 = \left( {z^2 \frac{{P\left( {1 - P} \right)}}{e^2 }} \right)$$

With “z” the confidence level of the estimates, “p” the proportion of individuals who have undergone, in one way or another, directly or indirectly, the influence of the “Cordon Sanitaire” on their activities, “e” the marginal error term and “$n_1$” the initial sample size. For the calculation of the initial size of the sample, we chose a confidence level of 95%, with a margin error of 3% and a proportion of p = 0.5.

Furthermore, since the population size was not infinite, an adjustment was made to account for this size using the formula:

$$n_2 = \frac{n_1 N}{{N + n_1 }}$$

With “N”, the size of the population of actors identified based on the exploratory phases and “$n_2$”, the modified sample size.

Finally, an adjustment is made for the expected response rate through the relationship, where the expected response rate provides the final sample size for the study. As a result of these various calculation procedures, the sample size selected for the study was 1200.

After collecting the data, we got a sample size of 1067 individuals corresponding to a non-response rate of 11–08%.

At the enterprise level, the sampling base was the list of formal enterprises (INSAE, 2017). The enterprises used for the sampling are those operating in the sectors probably most affected by Covid-19, such as accommodation and food services, arts and recreation services, wholesale and retail trade, pharmaceutical services, beverage manufacturing, food manufacturing, real estate activities, office services activities, human health activities, and social action, specialised scientific and technical support activities. The total number of enterprises working in these sectors for the seven communes was 1624. The same sampling method was used to determine the size of 101 enterprises to be surveyed.

The sampling of informal enterprises follows the same process described for individual actors. Applying the same methodology, we obtained a sample size of 200 artisans distributed among the different communes. After the phone data collection, 113 individuals and 217 enterprises were surveyed in the formal and informal sectors.

Covid-19 Response Measures

Following the record of the 1st case of Covid-19 infection in Benin on March 16, 2020, the number of contaminations exceeded 1000 infections by June 24, 2020. The first case of death linked to Covid-19 was recorded on April 6, 2020. On January 4, 2021, the country recorded 75 active cases for 3,304 confirmed cases, including 3,185 cured and 44 deaths. It should be noted that the relative spread of Covid-19 was triggered by its trivialisation and the vulgarisation because of false information spread. Thus, some believe that the Covid-19 virus is a divine punishment in response to the multiple injustices, atrocities, and wickedness that people on earth generally commit. Others believed that this health crisis was God's will for mankind. Others still link this pandemic to fate, believing that those who die from it can do nothing to prevent it. Another category of people confused the coronavirus disease with ordinary flu that can be treated with usual herbal remedies.

However, the state took pragmatic measures to curb the infections with a US $ 320,338,983 national prevention plan. Following the confirmation of this first case, the government and its partners set a plan to fight against the pandemic. It also worked on proposing mitigation strategies to limit the effects of this pandemic on the socioeconomic activities of the populations. The public health measures included cordon sanitaire imposed on April 14, 2020, which restricted the movement of populations in Cotonou, Abomey-Calavi, Allada, Ouidah, Tori, Zè, Sèmè-Podji, Porto-Novo, Akpro-Missérété, and Adjarra. In addition, the public gathering was restricted to fifty people while there was a total ban on funerals, parties, and concerts. Furthermore, rotational and remote work arrangements were enforced across the country. In addition, social distancing measures were instituted together with promoting hygienic practices such as hand washing, sanitiser use, and nose mask-wearing.

The state used many media channels to disseminate Covid-19 information. Access to Covid-19 information was determined by channels through which information was disseminated. Among the surveyed population, more than 74% of people received Covid-19 information through radio, 72% through television, and 53% through social networks (Fig. 2.1).

Fig. 2.1

(Source ASE data collection, December 2020)

Information channel of the surveyed population

We also assessed people’s perception of the risk of contamination since this determines their risk-taking behaviour. Since the advent of Covid-19, the perception of some Beninese on the risk of contamination has seemed the same during the lockdown,³ and the post-lockdown. More specifically, about 32% of people said that the risk of contamination was “high” during the lockdown compared to 33% of respondents during the post-lockdown (Fig. 2.2).

Fig. 2.2

(Source ASE data collection, December 2020)

Contamination risks of the surveyed population

The research team also assessed the risk exposure of the surveyed population generally in the social life compared to the risk during working periods. Among the working population, more than 36% felt that they were exposed to the risk of contamination of Covid-19. However, only 28% of individuals in social life asserted that the risk of exposure was high (Fig. 2.3). This means that people felt they could be exposed to the virus through their work rather than through their social activities.

Fig. 2.3

(Source ASE data collection, December 2020)

Risk exposure in social life and employment

Among socioeconomic groups surveyed, 91.41% of teachers and 89% of traders think their economic activities expose them to the virus. However, more traders (84%) and interurban taxis (80.36%) operators thought that they were more exposed to the virus through their social life (Table 2.3).

Table 2.3 Risk exposure by socioeconomic groups

Due to the high level of access to Covid-19 information, we found that there was high compliance with the protocols and measures. Compliance to facial mask-wearing (99%) was the highest while the weres are hand washing (92%), reduction in participation in public events (92%), social distancing (92%), and restriction on movement (90%) among others. As the next section will show, compliance also came with consequences and this will have implications for economic activities and income losses.

Impacts of Response Measures on Sectors and Social Groups

It is abundantly clear that the Covid-19 measures had impacts on the population. Figure 2.4 shows the type of measure that had the most impact on the survey population. Over 70% of respondents mentioned cordon sanitaire as having adverse effect on them. The rest include measures that ban large gatherings and those that enforced closures of places such as closure of schools, places of worship, and ban on large gatherings (Fig. 2.4).

Fig. 2.4

(Source ASE data collection, December 2020

Impacts of control measures on population

Differential Impacts of Covid-19 on Social Groups and Sectors

The measures instituted by the Beninese State affected social groups differently. In Benin, the government did not prohibit access to markets for goods and services to fight Covid-19. That is shown by the proportion of individuals (64.48%) who declared they had no difficulty accessing markets. However, some Covid-19 related measures prevented a significant proportion of people from accessing markets. A total of 21.65% of the survey participants believed they did not have access to some local markets in their municipalities, 4.69% could not access the stores; 2.25% could not access supermarkets, and 1.31% believed they could not access international markets.

The leading cause of non-accessibility to markets for goods and services was the closure of specific shops. More than half (51.48%) of respondents attributed the inaccessibility of markets to the physical closures of those spaces. Also, 40.66% of respondents blamed the implementation of the safety measures in fighting against Covid-19 for constraining their access to markets. The rest of the reasons included travel restrictions (26.89%) and quarantining (3.93%).

The Differential Impact of Covid-19 on the Employment of Socioeconomic Groups

Since the cordon sanitaire was mentioned as the measure with the most adverse effects on the population, we examined how its impacts were felt by different sectors. The ordinal logistic regression shows the effect of lockdown on the activity level of some socioeconomic groups. Table 2.4 reveals the impact of the lockdown on the level of activity. Almost all the socioeconomic groups, except agricultural producers, have completely stopped their activity during the lockdown.

Table 2.4 Activity level of various socioeconomic groups

Table 2.5 presents the Average Adjusted Predictions (AAP) for diverse activity groups. Looking at the results of this table, teachers (59.2% of probability) were more likely to have their activity completely stopped during the lockdown. There are followed by students (47%), bartenders(42.3%), and artists (41.9%). The result reflects the nature of the pandemic response which included closure of schools and places of entertainment.

Table 2.5 Average adjusted predictions for the level of activity of socioeconomic groups

We also found that agricultural producers had a higher probability (5.6%) of seeing their level of activity increase during the lockdown. In contrast, a lower chance (0.4%) is observed at the teachers' level. Agricultural production was considered an essential activity during the period and therefore had little restrictions compared to other non-agricultural sectors.

The activities of the various socioeconomic groups evolved with the intensity of the measures and the pandemic. Overall, 36.72% of respondents reported that their economic activity levels dropped during the lockdown against 19.04% during the post-lockdown period. The effect was highest among city taxi drivers (60%). In general, 35.77% stopped their economic activities altogether during and after confinement. High school teachers (63.64%) had the highest economic activity decline rate due to school closures.

Effects of Changes in the Level of Economic Activities

Changes in activity level during or after confinement have essential consequences for workers' employability. While 33.47% temporarily stopped their activities during confinement, only 6.07% of the same was reported post-lockdown. Also, most of those who have kept their activities have experienced significant reductions in working hours or wages.

Thus, 29.08% had a moderate decrease in working hours or wages during and after confinement. Tables 2.6, 2.7, 2.8, 2.9, and 2.10 show the results on implications of Covid-19 on employment conditions.

Table 2.6 Consequences in Activities Level Changes During the Lockdown

Table 2.7 Consequences in activities level change during post-lockdown

Table 2.8 Covid-19 and employability difficulties on sectors

Table 2.9 Effect of Covid-19 on the income of socioeconomic groups

Table 2.10 Average adjusted predictions (AAP) on revenue decreasing

The Covid-19 pandemic has negatively impacted the conditions of employability at the level of socioeconomic groups. About 43% of the individuals interviewed saw their wages reduced. For the few of these workers who continue to receive their regular wages without reduction, nearly 5% maintain that their salaries came late.

Among other difficulties, among the Zem operators surveyed, some said they had not experienced problems in developing their activity while others evoked the lack of customers since people were afraid to go out. This fear has also caused a considerable decrease in the number of passengers among city taxi drivers due to restrictions on movement.

Artists raised difficulties related to the pandemic, such as reduction in assets and especially the prevention of honouring their social contributions as members of their associations. Teachers said that although they did not have so much financial difficulties, they were nevertheless exposed to psychosocial risks at work attributed to the fear of being contaminated in school.

The Covid-19 health crisis has relatively impacted employment. In general, 11.40% of respondents were dismissed from work during the period. The rate of dismissals was high people in the hospitality industry (32.32%), city taxi/minibus drivers(24%), agricultural producers(15.97%), and traders (13%). Zem operators (6.67%) and teachers(6.57%) had the least dismissals because of the high rate of layoffs among workers in bars or restaurants (32.32%).

In terms of location, the communes of the cordon sanitaire such as Cotonou and Abomey-Calavi have experienced more layoffs in companies due to the coronavirus pandemic. More than half of the individuals surveyed who declared having seen the companies in which they work to carry out redundancies were in the municipalities of Abomey-Calavi (28.69%) and Cotonou (22.95%). Moreover, this phenomenon has also been more frequent during the period of the cordon sanitaire in the commune of Natitingou, where 20.49% reported layoffs. The tourist status of the city could explain this last observation. In reality, tourism drives economic activities in Natitingou. Since travel was almost restricted, especially for foreign visitors, the economic actors who previously lived off this have almost closed or at least have had a consideration reduction in workforced. As a result, even though, Natitingou was not part of the cordon sanitaire area, it was significantly affected by the effects of the pandemic response measures. The lowest employee layoff was reported at Cobly, which recorded 2.46%.

Differential Impact of Covid-19 on the Income of Socioeconomic Groups

In this section, an ordinal logistic regression has been run to show how Covid-19 has impacted diverse socioeconomic groups regarding their income. In Table 2.9, only the categories of bartenders and artists have seen their revenue significantly decrease during the cordon sanitaire period.

To get a tangible feel of how significant and essential these differences are, we computed the Average Adjusted Predictions (AAP) for these categories of economic groups. Looking at the results, among the bartender/restaurant workers and the artists, more than three quarters (37.0% versus 38.7%) were likely to have their income decreased with a proportion of more than 75%( Table 2.10).

The model shows that bartender/restaurant workers and artists have a higher probability (76.2% versus 74.9%) of having their income decrease with the proportion decreasing more than 50%.

Consistent with the earlier results, the marginal effects (Table 2.11) show that, on average, bartenders/restaurant workers were 24.4 percentage points more likely than Zem operators to say their income decreased in proportion more than 75%, and about 20.2 percentage points less likely to say their income decreases in a proportion from 25 to 50%.

Table 2.11 Marginal effect of income decrease by socioeconomic groups

Artists are 22.8% points more likely than Zem to say their income decreases in proportion more than 75%, and about 19.2% points less likely to say their income decreases in a proportion from 25 to 50%. Table 2.12 shows the income-level variation in the context of the pandemic among the surveyed population with interurban taxi operators, artists, and restaurant workers having major declines in income which is linked to restrictions on movement and ban on large gatherings.

Table 2.12 Income level variation in Covid-19 context

Table 2.13 shows the percentage of surveyed populations that experienced an increase, decrease, or stability of revenue during and after Cordon sanitaire containment measures. In all, 81.63% have seen their income decline during the main phase of the cordon sanitaire and the figure fell to 69% and 56% after applying the mitigation measures to the population. That shows that the mitigation measures had positive impacts on the population.

Table 2.13 Impact of control and mitigation measures on revenue

Covid-19 and Food Price Variability

Most individuals surveyed believed that there was an increase in the prices of certain than during the pandemic compared to pre-Covid-19 prices. For example, from the analysis of Table 2.14, more than 95% of respondents reported an increase in the prices of maize, sorghum or millet, rice, and gari.⁴ beans, pepper, peanut oil, and palm oil. In the post-confinement period, an average of 85% of participants still believed that the price of these products remained high compared to the pre-Covid-19 period.

Table 2.14 Covid-19 impacts on cost of food and essential goods

The increase in staple food items coupled with declines in economic activities, income, and job losses have implications for households' food security.

The Covid-19 pandemic has also impacted the cost of sanitary products, including soap, bleach, ointment, detergent, and hydroalcoholic gel, which increased compared to pre-pandemic times. This was confirmed by 96% of the people surveyed during the lockdown and 76% at the end of the lockdown period confirmed this information (Table 2.15).

Table 2.15 Hygienic products price variability during Covid-19

Similarly, more than 93% of the respondents confirmed an increase in the cost of essential pharmaceutical products like paracetamol, amoxicillin, and chloroquine during the confinement. This same proportion is about 75% during the post-lockdown period. The implication is that, access to this item would be difficult for poorer households.

Perhaps, one of the high cost elements associated with Covid-19 was transportation. More than 95% of survey participants reported an increase in transport fares during confinement against 77.51% during the post-confinement period.

Covid-19 Impact Mitigation Measures

The government and other non-state actors instituted measures to improve health care resources, communication, and information dissemination by the media. The plan to mitigate the effects caused by Covid-19 was based on three dimensions, namely, 63.380 billion FCFA financial support for businesses, 4.98 billion FCFA financial support for artisans and small traders, and 5.76 billion FCFA water and electricity supply subsidies for all citizens. Besides the actions taken by the Benin government, the partners have worked to set up some specific measures for specific groups. Table 2.16 contains those measures. They targeted pregnant women, people with HIV, older people, and rural populations. In addition, Social Promotion Centres, which usually help people in need, the partners have increased their contribution to vulnerable groups. Those groups typically depend on other people that support them, and as activities have ceased, it increases the dependence on vulnerable people. Social Promotion Centers have then switched their intervention plan and devoted more financial resources to their target groups such as orphans, people with disabilities, older people, and poor households. The state actors have promoted three main types of mitigation actions. First, there is financial support for businesses, artisans and small traders, and household electricity and water bill subsidies. Besides those measures to mitigate Social Promotion Centers have undertaken the impact of the pandemic, specific mitigation measures, and the reorganisation on public service delivery among others.

Table 2.16 Mitigation measures promoted government and its partnersFootnote

https://sgg.gouv.bj/cm/2020-06-10/.

Mitigation Actions Received by Socioeconomic Groups

Although mitigation actions were put in place, the analysis of the surveyed population shows that only a tiny proportion of the population actually accessed them (Table 2.17). Except for water and electricity subsidies which were universally applied, the access options for other social support schemes was not accessible to everyone. In addition, the total amount devoted could not cover the target population. As a result, decision-makers had proposed a second round of registration to obtain mitigation measures, which were implemented when the data collection was completed.

Table 2.17 Mitigation actions received by specific socioeconomic groups

Regarding support, 66% of the surveyed population did not receive tangible support, while 31% received facial masks, and 16% and 10% received washing hands kits and alcohol solutions respectively. The donations were from social support centres and some international partners. In addition, the survey population proposed several social actions and programmes to mitigate the impacts of Covid-19. In all, support for economic activities topped with 40.58%, followed by unemployment support (31.49%), ease of remote administrative procedures (24.09%), deadline waivers for statutory payments (21.37%), and others such as subsidies, food aid, and increased sensitisation among others which constituted 39.64% (ASE Data Collection, December 2020).

Main Supports Desired by Socioeconomic Groups Underemployment

Following the measures to fight Covid-19, several measures to mitigate the adverse effects have also been initiated and implemented by the government. These include, among others, nationally applied water and electricity subsidies, financial assistance provided to specific individuals belonging to certain socioeconomic groups such as artisans and transporters, and tax exemption for formal businesses. However, the appropriateness of the measures targeting the groups must be examined. The data shows a disjuncture between what the groups wanted and what was provided. It emerged that, first and foremost, many survey participants (62%) wanted financial support (62%) from the government to mitigate the pandemic effects. Others include the provision of health kits (9.93%) or adaptive training (7.87%). Interestingly, many individuals did not desire support measures such as Covid-19 advisories, food supply, electricity and water subsidies, and exemption from taxes or levies.

However, location-based differences existed in the support measures desired by the survey population. From the results of Fig. 2.5, we essentially note that in the cordon sanitaire areas of Cotonou and Abomey-Calavi, socioeconomic groups mainly saw their activity cease during the lockdown. Since the most affected were teachers and students, they preferred water and electricity subsidies, training, supply of health kits, and Covid-19 advisories. However, in the area outside the cordon sanitaire areas such as Cobly, Toffo, Natitingou, Parakou, and Kalalé, financial aid and food supply were the most desired support measures. In this area, merchants, bartenders or restaurant owners, artists, and taximen constitute the target socioeconomic groups of the study. These groups had a significant drop in their level of their economic activities (Fig. 2.5).

Fig. 2.5

(Source ASE data collection, December 2020)

Differences economic activities in and outside cordon sanitaire areas

Covid-19, Citizenship and Governance

Most Benin citizens were not involved in the Covid-19 management decision-making processes. We found that 19 per cent of the surveyed population engaged in taking actions related to control measures. However, participation in the public protest was not substantial. Only a few respondents participated in Covid-19 management decisions. Nineteen per cent reported being part of control measure decisions, 8.15% in cordon sanitaire decision making, and 6.94% in mitigation measure decision making (ASE Data Collection, December 2020).

Conclusion and Recommendations

Like all countries, Benin has been affected by the health crisis, weakening its reformed economy. Benin has opted for a cordon sanitaire to curb the spread of the pandemic to counter the negative impacts on the economy. The cordon sanitaire measure seems to be preferable and, at the same time, the most appropriate to avoid spreading the virus in the Beninese context. Benin is a small country with a large informal economy where a general lockdown would create more than considerable damage at the socioeconomic level. Therefore, the country instituted measures to mitigate the economic impacts of the pandemic. Our findings show that most of the various socioeconomic actors would primarily like financial support from the government in terms of mitigation measures. In addition, others would have liked support measures such as the supply of health kits or adaptive training. These individuals less desired supportive measures were counselling, food supply, subsidies for electricity and water bills, and exemption from taxes or levies. In light of this, the response put in place by the government by providing financial assistance to certain key socioeconomic actors aligns to meet the most pressing needs of these various actors.

The financial assistance measure also targeted stakeholders affected by the pandemic in their daily activities, such as stylists, hairdressers, transporters, restaurant workers, and artists. However, the available amount to assist the population was insufficient to cover most targeted group. Only 1–14% of the targeted people received such support. Therefore, although most socioeconomic actors desired an accurate mitigation measure, it has also been a potential source of inequality since many did not receive them despite high compliance with Covid-19 response measures.

Regarding the stakeholder’s decision-making implication, only 19% of the actors’ lead representatives were informed about the control measures decisions taken for their groups’ categories. The proportion of group actors involved in the mitigation measures decisions is even fewer. The relevance of the measures taken by the government is well established for companies and socioeconomic groups. However, they did not seem to have experienced in practice the involvement of local authorities in managing the crisis. They were also observed disparities in the allocation of grants.

In summary, the governments’ methods and actions for fighting the Covid-19 crisis agree with the population’s expectations. However, there were some shortcomings in terms of participation and the distribution of resources.

In light of the study’s findings, here are three main recommendations:

• Continuous awareness creation on safety measures and the risk of contamination.

• Increase efforts to subsidise agricultural producers to reduce the inflation in the prices of food products induced by the pandemic.

• Strengthen financial aid programmes for different socioeconomic groups in the different locations to relaunch their activities.