Simulations of Policy Responses and Interventions to Promote Inclusive Adaptation to and Recovery from the COVID-19 Crisis in Ghana

It has been over two years since Ghana experienced its first lockdown due to the COVID-19 pandemic. Even as countries recover from the pandemic, its effects are still felt around the world. The COVID-19 pandemic affected almost every sector of the economy, from GDP to poverty and unemployment (Arezki et al., 2020; Maliszewska et al., 2020; OECD, 2020; World Bank, 2020). It is estimated that the number of poor people could increase by 420 to 580 million globally, thereby derailing countries’ ability to achieve the sustainable development goal of no poverty (Sumner et al., 2020).

The pandemic’s effect at the household level has not been uniform. World Bank (2020) reports that the extent of the damage a household suffers is dependent on some household-specific demographic and socio-economic characteristics. While this is the case, Ataguba (2020) reports that, given demographic and socio-economic characteristics, poor and vulnerable members of society are likely to be more affected by the pandemic than rich households.

The Ghana Statistical Service (GSS) reported that households suffered welfare losses due to lost income because of layoffs and job losses. GSS estimates that 77.4% of households in Ghana suffered income losses due to COVID-19 lockdown restrictions. A similar percentage of households experienced difficulty purchasing food due to increased food prices.¹ Households’ income losses will make it difficult for them to afford their usual basket of goods. This implies it will also be difficult to pay for food, utilities, education, health care, and household members’ other needs.

Job losses and business closures are contributing factors to the losses suffered by households in Ghana. GSS estimates that approximately 115,000 businesses closed either temporarily or permanently and that businesses’ sales decreased by 26% to 36% year-on-year in March and April 2020. The reduction in sales and input shortages affected most businesses’ operations and especially those of credit-constrained micro, small and medium-sized enterprises (MSMEs) in the formal and informal sectors. They limited businesses’ ability to meet their obligations to their workers and increase production and sales. Hotels and restaurants (and more generally, the tourism sector) were severely affected by closures and restrictions.

This paper aims to analyse (a) the impact of COVID-19 shocks on household welfare, and (b) the effectiveness of governmental programs implemented to reduce the pandemic’s impact on welfare in Ghana. It helps us to answer the following questions: First, to what extent did the pandemic increase poverty in Ghana, and did the regions that did not experience a lockdown suffer the same impact on poverty and inequality outcomes? Second, were the effects on welfare the same for men, women and children? And third, were the policies implemented gender-equitable and sufficient to reduce the pandemic’s impact on welfare? We focus on interventions implemented in the health and utilities sectors.

We simulate the following policies that aimed to reduce the pandemic’s impact on households:

1. Free electricity for lifeline consumers, and free water for all households

2. COVID-19 loans for the creative arts industry

3. Tax relief and allowances (50% of basic salary) for frontline health workers

We also consider one policy that was proposed but rejected during a parliamentary debate in 2020: for the government to absorb students’ fees to attend public universities. We therefore also assess the potential impact of the government absorbing 50% of the fees of public university students.

We contribute to the literature by assessing the COVID-19 pandemic in Ghana and some of the policies used to mitigate its impact. Additionally, we quantify the pandemic’s impact on poverty and inequality. Furthermore, we contribute by (a) simulating the impact of three policies implemented by the government (and one policy that was proposed but not pursued) to determine the pandemic’s impact on the population of Ghana, (b) comparing policy options and determining which were efficient and cost effective at mitigating the pandemic’s effects, and (c) providing gendered results to show the effects for men, women and children.

Our work differs from that of Aduhene and Osei-Assibey (2021), Bukari et al. (2021), Nkrumah et al. (2021) and Schotte et al. (2021) in terms of the methods used and the additional gender dimension of our analysis. Bukari et al. (2021) apply econometric tools to estimate COVID-19s impact on poverty and welfare in Ghana. They observe a decline in welfare and an increase in poverty that was relatively more severe for female-headed households and rural households. However, households in the middle and upper quantiles were the least affected by the pandemic. Schotte et al. (2021) use the difference-in-differences framework to estimate the lockdown’s impact on employment, working hours and earnings in Ghana. They find that informal workers suffered relatively more from the lockdown, and women and self-employed individuals saw a decline in their earnings due to the lockdown. Aduhene and Osei-Assibey (2021) approach their analysis using discourse analysis to provide the pandemic’s impact on households in Ghana. Finally, Nkrumah et al. (2021) draw from consumer demand theory to simulate the effect of the water and electricity subsidies on households. Their results show that the subsidies were regressive and therefore benefitted the poor relatively less than the richer segments of society.

We consider our work to be an extension thereof addressing the pandemic’s impact on women and children beyond the sex of the household head. Our paper’s weakness lies in the assumptions made regarding changes in remittances and income across sectors and updates made to the Ghana Living Standards Survey (GLSS) based on information about population growth and price increases from 2017 to 2020. Despite our assumptions, our results can be considered the lower bound for the pandemic’s potential impact on household welfare, and the results do inform us of the direction of the change in welfare outcomes, which is qualitatively consistent with the results of the previously mentioned papers.

In the next sections, we briefly review the literature, describe the conceptual framework and methodological approach used for our microsimulation, present our results and conclude the paper.

Ghana, like most nations, instituted various containment measures and interventions to stop the spread of COVID-19, save lives, protect livelihoods, and ensure economic recovery amid the COVID-19 pandemic. Since Ghana recorded its first two confirmed cases on March 12, 2020, the various containment measures put in place by the Government of Ghana, as announced by the President in his first national address include: social distancing measures; travel restrictions; the compulsory wearing of “nose masks” (also known as face masks); enhanced testing; contact tracing and further testing; the suspension of all public gatherings, which resulted in the closure of all schools and universities; and mandatory quarantine (for at least 14 days) for all people arriving by air on the night all borders closed. After the four-week period of partial lockdown in the Greater Accra and Ashanti regions and Kasoa Township, the government started gradually easing restrictions, with public gatherings again being allowed, initially for a maximum of 25 people, and later, 100 people.

While these measures were essential to reduce the spread of the virus, their effects on people’s livelihood and the economy cannot be underestimated. Given that most of the Ghanaian working population is in the informal sector, the economic implications of the lockdown measures on this subset of the population remained a major challenge and forced the government to lift lockdown restrictions even when the number of infected cases was increasing. The easing of lockdown measures despite a surge in infected cases at the time was a common phenomenon, as the poor and vulnerable people were more likely to die out of hunger than the virus itself. Thus, the large informal sector, in Ghana, and the many daily wage earners, who have little or no savings, made it very difficult for the government to enforce a total lockdown of the country. This is because, for the many daily wage earners, a total lockdown implies no work and hence no income and no access to food or other household necessities. More importantly, the informal sector often consists of small and informal businesses, which are a source of sustenance and livelihoods for many, particularly the poor (Gondwe, 2020). In lieu of the loss of work and income, the Government of Ghana instituted the provision of free water for household and commercial use, cooked and uncooked food, and tax holidays, among other economic recovery measures, to lessen the impact of the lockdown on citizens (Ozili, 2022).

Gentilini et al. (2020) summarise the social assistance interventions implemented in Ghana: (a) food vouchers for about 400,000 people in locked-down areas of the Greater Accra and Ashanti regions and Kasoa township; (b) access to food at school for students allowed to return to high school and junior high school for their final exams; (c) the removal of mobile money transfer fees; (d) the payment of consumers’ water and electricity bills; (e) the waiving of taxes for health workers; and (f) the provision of GHS500,000 by the Social Security and National Insurance Trust (SSNIT) for COVID-19 relief efforts.

The pandemic’s economic and social effects are well documented and, as suggested by the World Bank, not short-term. They continue to be felt around the world. The economic disruptions include a decline in export revenues, remittances, foreign direct investment and tourism (Gentilini et al., 2020; World Bank, 2020)

A significant number of studies have been published to help us understand the pandemic and its effects. One strand of the literature investigates the impact of lockdowns. For instance, Abdelkhalek et al. (2020) provide evidence that a two-month lockdown could directly lead to an increase in poverty (of 4.5 percentage points (p. p) in Tunisia and 6 p. p in Morocco) and in inequality in the short-run. Lustig et al. (2020) provide evidence for Argentina, Brazil, Colombia and Mexico. Their microsimulation shows that the lockdown had a severe impact on poverty.

Some studies have also suggested that mitigation measures can reduce the impact of lockdowns and the pandemic on the poor (Lustig et al., 2020; World Bank, 2020).

Another strand of the literature suggests that COVID-19’s impact was heterogeneous, affecting people differently depending on their sex and age (International Monetary Fund, 2020; United Nations Economic Commission for Africa, 2020; United Nations Organization, 2020; World Trade Organization, 2020). International Monetary Fund (2020) reports the lockdown’s impact was not proportionate for men and women in Spain, Italy and Portugal. As the International Monetary Fund (2020) alluded to, the lockdown coincided with the closure of schools and had a greater impact on women than men since more women were employed in the education sector and provided childcare services. Similarly, United Nations Organization (2020) found that close to 300 million people globally lost their jobs and livelihoods, with women and children being the most affected. Similar findings have also been reported by United Nations Economic Commission for Africa (2020)—with the explanation that women and youth normally occupy lower ranks such as temporary and part-time jobs, which made them vulnerable to job loss during the pandemic. Abdelkhalek et al. (2020) also arrived at qualitatively similar results for Tunisia and Morocco.

Dang and Viet Nguyen (2021) show that gender-based differences in terms of permanent job and income losses exist not only in Ghana, but also in other countries. However, they did not find significant gender differences for temporary job losses. Meanwhile permanent job losses were more pronounced in China, Italy and the United States than Japan, South Korea and the UK (Dang and Viet Nguyen, 2021). Similarly, Hossain (2021) found significant gender differences in Ethiopia and India, and comparatively less significant ones in Peru and Viet Nam due to women having access to formal employment. Extensions beyond gender differences, for example, by Ranchhod and Daniels (2021) to include education level, age, race and geography, and Witteveen (2020) to include race and income class, indicate that minorities within the various demographic groups considered are relatively more affected by job losses.

In terms of age, the International Monetary Fund (2020) found that the lockdown in Italy, Spain and Portugal had stronger adverse effects on those aged 25 to 44 than those aged 45 to 65. The effects were relatively weaker for those over 65 years old. The explanation provided is that the younger generation relies strongly on current income for consumption, and thus, not working due to the lockdown reduced their access to current income. The older and retired individuals, on the other hand, could rely on their retirement income when they stayed at home during the lockdown. For Abdelkhalek et al. (2020), the lockdown had a larger and more significant negative effect on the welfare of children than adults.

The pandemic’s effects across various sectors of the economy must also be emphasised. United Nations Organization (2020) and Abdelkhalek et al. (2020) note that SMEs and the informal, tourism and hospitality sectors were the most affected. Furthermore, World Trade Organization (2020) notes that, apart from informal jobs, jobs that require face-to-face interaction, such as retail, education and care service jobs, were strongly affected by the lockdown measures. United Nations Economic Commission for Africa (2020) also found Southern African countries saw similar sectoral impacts as well as an impacts in the transport sector.

COVID-19 is expected to generate a variety of shocks. For instance, a reduction in labour supply, an increase in prices and a decrease in remittances may occur, as discussed in the literature review. Shocks can affect households in different ways depending on their onset, duration and impact. According to World Bank (2020) the pandemic’s impact on households includes:

1. The direct effect of lost earnings

2. An indirect effect on earnings and employment due to decreased demand, and production and supply disruptions

3. A decrease in both international and domestic remittances due to the global nature of the pandemic

4. Price gouging due to an inadequate supply of household necessities and basic goods

5. A potential increase in health care costs

6. A disruption to schooling and school-related programmes, including school meal programmes

7. The potential effect of the focus on COVID-19 reducing the availability of care for other medical emergencies

Figure 1 shows the different channels through which the pandemic can impact welfare. In this paper, we focus on the ones that are most important in the Ghanaian context. Specifically, we focus on how the loss of earning, remittances and price changes due to the COVID-19 pandemic affected household welfare—and assess the policies implemented by government to mitigate adverse impacts on poverty and welfare.

Figure 1

Download asset Open asset

Formally, we assume that the impact of COVID-19 shocks on welfare is approximated by aggregating the effects on the main impact channels. If we denote the welfare of household $h$ at time $t_0$ by $w_{h,t_0}$ , the change in welfare due to COVID-19 shocks is:

where $∆w_{h,t_{\mathrm{0,1}}}$ denotes the change in the welfare of household $h$ between period $t_0$ (pre-COVID-19) and $t_1$ (after COVID-19). The variable $∆F_{j,t_{\mathrm{0,1}}}$ is the change in factor$j$ between $t_0$ and $t_1$ , and $f_j\left(.\right)$ is the function linking factor $j$ to welfare. We use per adult equivalent expenditures as the proxy for welfare. The factors that are expected to affect household welfare in Ghana are remittances, labour income (distinguished by economic sector, source, etc.), public transfers, and consumer food prices.

To estimate the impact of (consumer) price variation on welfare, we use the first-order Taylor series approximation, namely,

where $e_{good,h}$ represents the expenditures of household $h$ on the food items in the consumers basket.

The decrease in labour income due to COVID-19 shocks depends on:

• How vulnerable the sector or job in question is (for example, the tourism sector, or the trade and retail sector, which includes many informal jobs)

• The amount of income earned by active household members

• The duration and intensity of income shocks

Total household income equals the sum of the income or revenues of all economically active members. Total household income for household $h$ at time $t$ is:

where $r_{h,s,t}$ is the average income of the household $h$ in sector $s$ at time $t,$ and $n_{h,s,t}$ is the number of household members that are active in sector $s$ at time t. The change in income compared to the initial period $\left(t_o\right)$ is given by:

where ${∆\gamma }_{,s,t}$ is the proportional change in average personal income in sector $s$ between $t_0$ and $t_1$ . We use information from COVID-19 rapid surveys conducted by GSS to estimate the proportional variation in income by sector.

We assume that household disposable income depends on the following factors: (i) the net income earned by each active household member, by economic sector, (ii) households’ internal and external remittances, (iii) public transfers (including those in place before COVID-19 broke out and those introduced after), and (iv) other factors depending on the availability of data. We assess the pandemic’s impact on welfare by estimating the amount of income that is earned in each sector and by each active household member. We draw on information from the GLSS about household members’ economic activity status and the economic sector in which they are active. Our results are reported by economic sector.

We estimate the food and beverage component of the CPI by forecasting CPI values based on past CPI values—an autoregressive approach that involves using a one-month lag in the CPI to obtain the optimistic scenario for prices and then adding 0.2 p. p to obtain the pessimistic scenario. Since the COVID-19 rapid surveys do not report actual wage changes, we instead used the observed change in sales, the probable and expected change in sales, and the share of labour (in terms of value added) from the 2015 Social Accounting Matrix. We used the survey self-reported pessimistic and optimistic expectations of business sales to estimate the predicted change in wages in our pessimistic and optimistic scenarios, which gives us:

where $a_i$ is the share of labour (value added),² Sales represents reported sales, j indexes whether the scenario in question is pessimistic or optimistic, ${\mu }_{ij}$ is the scenario, ESales is the business in question’s predicted change in sales.

We developed two scenarios for our simulations. The first scenario uses conservative estimates of the food price increases and income losses by sector from March to May 2020. The second scenario uses slightly higher prices, implying a more pessimistic scenario. The two scenarios are used in generating the results presented in the next section.

In each scenario, we calculate the level of welfare based on the change in the factors affecting welfare. We then estimate the level of poverty and inequality, and compare the welfare baseline with the simulated measure of welfare following the COVID-19 shocks and the simulated policy response.

In our first simulation, we adjust households’ spending on electricity and water to account for the payment of those utilities by the government.

In the second simulation, we randomly select self-employed individuals from the GLSS data who work in the creative arts industry, randomly assign them loans, and then calculate the new level of welfare. The National Board for Small Scale Industries (NBSSI) has indicated that over 15,228 beneficiaries received support, with food vendors, barbers, beauticians and dressmakers receiving up to a maximum of GHS2,000.³ We add the loan amount to the household welfare variable to determine the new level of household welfare.

In our third simulation, we simulate the waiving of taxes for health workers and the additional 50% income allowance they were granted. We compute each health worker’s gross income using the PAYE tax schedule. After obtaining their gross income, we calculate the difference between gross and reported income to get our estimate of the amount of taxes paid. The taxes plus 50% of their reported income are added to their income to determine the new level of welfare.

In the fourth simulation, we adjust the fees of all public university students considered absorbed by the government. We increase public university students’ spending on fees in the household’s spending basket by 50% to account for the subsidy.

After obtaining our new welfare measurements, we closely follow the approach by Araar (2006) to check the dominance in poverty and inequality. To estimate the difference in Lorenz curves,⁴ we compare the distributions of the old and new welfare measurements, that is, welfare prior to any policy simulation (Distribution 1) and welfare after a given policy has been simulated (Distribution 2). We would consider Distribution 2 to dominate Distribution 1 if Distribution 2 has a lower level of inequality than Distribution 1 everywhere on the welfare distribution (see, Araar, 2006):

where p is the percentile and L_i(p) is the Lorenz curve for Distributions 1 and 2.

Foster, Greer and Thorbecke (FGT) poverty index curves require a monotonic transformation (P^S(z)) of the following form to show dominance (Araar, 2006):

where c is a constant term. Distribution 2 dominates Distribution 1 in poverty for the order $s=\alpha +1$ if

where $P_i\left(\alpha ,z\right)$ is the FGT index for Distributions 1 and 2, and ${∆}^{\alpha }$ is the difference in the distribution of poverty between the two distributions.⁵

Female-headed households form 31.2% of the sample, which makes it possible to compare differences across households. We also analyse the sex of household members to complement the differences studied at the household level. As Boudet et al. (2018) note, one limitation of individual-level data is the limited differences in welfare that are observed at the individual level. Thus, we use data on household members for outcomes that involve more variation from person to person—when individual members make the choices. For example, electricity and water expenditures, unlike spending on mobile phone calls and other personal expenses, is made at the household level rather than the individual level, so a comparison by sex of the household head can provide insightful results. On the other hand, using information on individual household members allows us to show disaggregate effects on men, women and children.

We obtained the data required for our analysis from Round 7 of the GLSS (GLSS7), which was administered in 2017. The GLSS is a nationally representative sample of respondents living in Ghana at the time the survey is conducted.

We updated the expenditure values using January 2017 and March 2020 CPI values obtained from GSS.⁶ We updated sampling weights based on GSS population projections (total population, from 27.9 million in 2017 to 30.9 million in 2020).

We used averages computed from GSS COVID-19 rapid surveys: (a) COVID-19 Business Tracker Rapid Surveys and (b) COVID-19 Household and Job Tracker Rapid Surveys– to adjust our assumptions/values for job losses and changes in sales, profits, income and remittances. The information from the GSS COVID-19 rapid surveys is useful for choosing appropriate price increases for products and to project job losses suffered by households. The business tracker surveys were based on a sample of 4,311 (for Wave 1 – interviews conducted between May 26 and June 17, 2020) and 3,658 (for Wave 2 – interviews conducted between August 15 and September 10, 2020) household and non-household businesses. The household and job tracker surveys had two modules: (a) one on aggregate household effects and (b) one on changes within the household focusing on children. The sample size varied from one module to another, with Module A having the larger sample (3,265 respondents) than Module B (2,063 respondents). The samples are nationally representative and were collected in June 2020.

Our key variables are household’s estimate of food expenditures, non-food expenditures, total household expenditures, adult equivalent expenditures (welfare) and total household income. These variables allow us to estimate the effects of prices on household welfare and income using the methodology described above.

Finally, the GLSS7 provides limited information on household members’ income (only a small proportion of respondents—less than 21% of workers—reported their income). Given that national income conventions suggest an equivalence between income and expenditures, we predict income by sector based on a regression of total expenditures on a set of demographic and household variables (including age, region dummies, religion, sex, father’s education, father’s occupation, marital status, own education, an urban dummy, and own occupation) for single-member households. We use a Poisson pseudo-maximum likelihood estimator to ensure that our estimated expenditures are positive. The coefficients from the regression are then used to predict household income—which is used in calculating the relief granted to health workers and incorporating the change in income of economically active household members. Average total household expenditures were GHS 18,779.88, while the average estimated household income was GHS 19,422.05.

The COVID-19 pandemic led to job losses, more people falling into poverty, students missing key periods in their educational development journey and exposed the limited health infrastructure available in most African countries.

Our simulation of the first three and nine months after the first case of COVID-19 was confirmed in Ghana shows the pandemic resulted in an increase in poverty, a decrease in household welfare and an increase in inequality. Its effect varies by sector of work, sex and area, and depending on whether the region was locked down. The hardest-hit sector in terms of welfare loss and poverty increase was the education sector. The remaining sectors, apart from the mining and quarrying sector, and to a lesser extent the “other manufacturing” sector, also saw higher poverty and lower welfare for men, women and children. We find that in the short term after the pandemic, those in regions that were not locked down and in rural areas experienced a relatively higher poverty incidence. If we extend our simulation to the end of the year (December 2020), the results are similar in terms of sectoral impacts and across groups (including men, women and children), with a few exceptions.

We simulated the following policies: (a) the provision of free electricity to lifeline consumers and free water to all households; (b) the provision of loans to workers in the creative arts industry; and (c) the provision of tax and other reliefs to health workers. We also added an additional policy—the absorption of 50% of public university fees—that was considered but ultimately not implemented by the government. All three implemented policies had some effect in marginally reducing poverty, with the highest reduction being 0.55 p. p. attributable to the provision of three months of free water and a 50% tariff reduction on electricity. The other policies reduced poverty much less due to their limited household coverage. Additional policies that directly target the poor, such as free electricity for lifeline consumers, are important to mitigate the increase in poverty due to COVID-19. Our estimates of costs indicate that the loans to creatives was the cheapest policy (GHS 33.38 million) and the free water and electricity policy was the most expensive (GHS 2.69 billion). Free water alone cost GHS 521.9 million. The relief to health workers cost GHS 918.2 million. However, when we account for the reduction in poverty, the free water and electricity policy generates the lowest costs per person lifted out of poverty of GHS 13,362 to GHS 20,596 while it is highest for the reliefs for health workers (GHS 329,688). Our costs are lower bound estimates and exclude the administrative costs to administer the policies.

Our welfare results are qualitatively similar to those of Aduhene and Osei-Assibey (2021), Bukari et al. (2021) and Schotte et al. (2021). Like these studies, we find poverty and inequality rise and welfare falls. While we report a 34.2% to 36.3% decline in welfare, Bukari et al. (2021) arrived at an 18% decline in welfare.

Our assumptions about prices, income, remittances, public transfers and other factors can affect our simulation of the pandemic’s impact on our welfare outcomes. If the factors are underestimated, then the changes in the factors would lead to underestimating the pandemic’s impact.

1. 1
   Strengthening Multi-Country Evidence of COVID-19 on Poverty and Inequality Impact and the Role of Social Protection

   1. T Abdelkhalek
   2. G Agbe
   3. S Bibi
   4. D Boccanfuso
   5. J Cockburn
   6. MSL Marzi
   7. L Savard
   8. S Silva-Leander
   9. L Tiberti

   (2020)

   Working Paper.

   • Google Scholar
2. 2
   Socio-Economic Impact of COVID-19 on Ghana’s Economy: Challenges and Prospects

   1. DT Aduhene
   2. E Osei-Assibey

   (2021)

   International Journal of Social Economics 48:543–556.

   https://doi.org/10.1108/IJSE-08-2020-0582

   • Google Scholar
3. 3
   Poverty, Inequality and Stochastic Dominance, Theory and Practice: Illustration with Burkina Faso Surveys

   1. A Araar

   (2006)

   Canada: CIRPÉE.

   • Google Scholar
4. 4
   Prices and Welfare: An Introduction to the Measurement of Well-Being When Prices Change

   1. A Araar
   2. P Verme

   (2019)

   Cham: Palgrave Pivot.

   https://doi.org/10.1007/978-3-030-17423-1

   • Google Scholar
5. 5
   Middle East and North Africa Economic Update, April 2020: How Transparency Can Help the Middle East and North Africa [Serial]

   1. R Arezki
   2. D Lederman
   3. A Abou Harb
   4. N El-Mallakh
   5. RY Fan
   6. A Islam
   7. H Nguyen
   8. M Zouaidi

   (2020)

   World Bank.

   https://doi.org/10.1016/978-1-4648-1561-4

   • Google Scholar
6. 6
   COVID-19 Pandemic, a War to be Won: Understanding its Economic Implications for Africa

   1. JE Ataguba

   (2020)

   Applied Health Economics and Health Policy 18:325–328.

   https://doi.org/10.1007/s40258-020-00580-x

   • PubMed
   • Google Scholar
7. 7
   Gender Differences in Poverty and Household Consumption through the Life-Cycle: A Global Perspective

   1. AMM Boudet
   2. P Buitrago
   3. BL de la Briere
   4. D Newhouse
   5. ER Matulevich
   6. K Scott
   7. P Suarez-Becerra

   (2018)

   World Bank.

   • Google Scholar
8. 8
   Impact of COVID-19 on Poverty and Living Standards in Ghana: A Micro-Perspective

   1. C Bukari
   2. G Essilfie
   3. MA Aning-Agyei
   4. IC Otoo
   5. C Kyeremeh
   6. AA Owusu
   7. KF Amuquandoh
   8. KI Bukari
   9. C Nsiah

   (2021)

   Cogent Economics & Finance 9:1879716.

   https://doi.org/10.1080/23322039.2021.1879716

   • Google Scholar
9. 9
   Gender Inequality During the COVID-19 Pandemic: Income, Expenditure, Savings, and Job Loss

   1. H-A Dang
   2. C Viet Nguyen

   (2021)

   World Development 140:105296.

   https://doi.org/10.1016/j.worlddev.2020.105296

   • PubMed
   • Google Scholar
10. 10
    Social Protection and Jobs Responses to COVID-19: A Real-Time Review of Country Measures

    1. U Gentilini
    2. M Almenfi
    3. I Orton
    4. P Dale

    (2020)
11. 11
    Assessing the Impact of COVID-19 on Africa’s Economic Development (UNCTAD/ALDC/MISC/2020/3)

    1. G Gondwe

    (2020)

    UNCTAD.
12. 12
    Gender Differences in Experiencing Coronavirus-Triggered Economic Hardship: Evidence from Four Developing Countries

    1. M Hossain

    (2021)

    Research in Social Stratification and Mobility 71:100555.

    https://doi.org/10.1016/j.rssm.2020.100555

    • PubMed
    • Google Scholar
13. 13
    A Long and Difficult Ascent: Vol. World Economic Outlook

    1. International Monetary Fund

    (2020)

    65–68, The Great Lockdown: Dissecting the Economic Effects, A Long and Difficult Ascent: Vol. World Economic Outlook, International Monetary Fund, p.

    • Google Scholar
14. 14
    The Impact of COVID-19 Lockdowns and Expanded Social Assistance on Inequality, Poverty and Mobility in Argentina, Brazil, Colombia and Mexico

    1. N Lustig
    2. VM Pabon
    3. F Sanz
    4. SD Younger

    (2020)

    CEQ Institute, Tulane University.

    • Google Scholar
15. 15
    The Potential Impact of COVID-19 on GDP and Trade: A Preliminary Assessment

    1. M Maliszewska
    2. A Mattoo
    3. D van der Mensbrugghe

    (2020)

    World Bank.

    https://doi.org/10.1596/1813-9450-9211

    • Google Scholar
16. 16
    COVID-19 Water and Electricity Subsidies in Ghana: How do the Poor Benefit?

    1. RK Nkrumah
    2. FK Andoh
    3. J Sebu
    4. SK Annim
    5. PY Mwinlaaru

    (2021)

    Scientific African 14:e01038.

    https://doi.org/10.1016/j.sciaf.2021.e01038

    • Google Scholar
17. 17
    COVID-19 Crisis Response in MENA Countries

    1. OECD

    (2020)
18. 18
    COVID-19 in Africa: Socio-Economic Impact, Policy Response and Opportunities

    1. P Ozili

    (2022)

    International Journal of Sociology and Social Policy 42:177–200.

    https://doi.org/10.1108/IJSSP-05-2020-0171

    • Google Scholar
19. 19
    Labour Market Dynamics in South Africa at the Onset of the COVID-19 Pandemic

    1. V Ranchhod
    2. RC Daniels

    (2021)

    The South African Journal of Economics. Suid-Afrikaanse Tydskrif Vir Ekonomie 89:44–62.

    https://doi.org/10.1111/saje.12283

    • PubMed
    • Google Scholar
20. 20
    The Labour Market Impact of COVID-19 Lockdowns (27)

    1. S Schotte
    2. M Danquah
    3. RD Osei
    4. K Sen

    (2021)

    The Labour Market Impact of COVID-19 Lockdowns (27).

    • Google Scholar
21. 21
    Estimates of the Impact of COVID-19 on Global Poverty

    1. A Sumner
    2. C Hoy
    3. E Ortiz-Juarez

    (2020)

    UNU-WIDER.

    • Google Scholar
22. 22
    Socio-Economic Impact of Covid-19 in Southern Africa

    1. United Nations Economic Commission for Africa

    (2020)

    UNECA.
23. 23
    The Impact of COVID-19 on Children

    1. United Nations Organization

    (2020)

    Policy Brief.

    • Google Scholar
24. 24
    Sociodemographic Inequality in Exposure to COVID-19-Induced Economic Hardship in the United Kingdom

    1. D Witteveen

    (2020)

    Research in Social Stratification and Mobility 69:100551.

    https://doi.org/10.1016/j.rssm.2020.100551

    • PubMed
    • Google Scholar
25. 25
    Poverty and Distributional Impacts of COVID-19: Potential Channels of Impact and Mitigating Policies

    1. World Bank

    (2020)

    Text/HTML.
26. 26
    The Economic Impact of Covid-19 on Women in Vulnerable Sectors and Economies

    1. World Trade Organization

    (2020)

    The Economic Impact of Covid-19 on Women in Vulnerable Sectors and Economies, Information Note.

    • Google Scholar

Author details

1. Edgar F A Cooke

   Ashesi University, Berekuso, Ghana

   For correspondence

   ecooke@ashesi.edu.gh

2. AbdelKrim Araar

   Université Laval, Laval, Canada

3. Edward Abrokwah

   Ministry of Finance, Accra, Ghana

4. Vera Acheampong

5. Sarah Appiah

   University of Ghana, Legon, Ghana