COVID‐19 economic vulnerability and resilience indexes: Global evidence

Abstract

This study complements the extant literature by constructing COVID‐19 economic vulnerability and resilience indexes using a global sample of 150 countries categorised into four principal regions: Africa, Asia‐Pacific and the Middle East, America, and Europe. Seven variables are used for the vulnerability index and nine for the resilience index. Both regions and sampled countries are classified in terms of the two proposed and computed indexes. The classification of countries is also provided in terms of four scenarios pertaining to vulnerability and resilience characteristics: low vulnerability‐low resilience, high vulnerability‐low resilience, high vulnerability‐high resilience, and low vulnerability‐high resilience to illustrate sensitive, severe, asymptomatic, and best cases, respectively. The findings are relevant to policy makers, especially as they pertain to decision‐making in resource allocation in the fight against the global pandemic.

Introduction

Two main factors motivate the focus of this paper on the development of COVID‐19 economic vulnerability and resilience indexes, notably: (i) disparities of countries in terms of vulnerabilities and resilience to the COVID‐19 crisis; and (ii) gaps in the extant COVID‐19 literature. Economic vulnerability can be defined as the risk a country faces when encountering a shock while resilience is defined as the capacity of a country to recover quickly from the effect of the shock (Noy and Yonson 2018). In this paper, we consider the COVID‐19 pandemic as the exogenous shock. The two main underlying factors are critically engaged in what follows.

First, consistent with the attendant literature (Asongu, Diop and Nnanna 2020), there are various geographical (i.e., country and regional) disparities on the effectiveness and consequences of COVID‐19 measures. These reveal varying levels of economic resilience and vulnerability to the pandemic. To put this emphasis in perspective, the findings of the study are based on thirty‐four COVID‐19 mitigating and preventing measures classified into five principal categories (i.e., publichealth, social distancing, economic and governance, movement restrictions, and lockdown measures), in 186 countries consisting of four main regions (i.e., America, Asia‐Pacific and the Middle East, Europe, and Africa). The results show that, inter alia: (i) the underlying measures designed to fight the COVID‐19 pandemic have had a favourable impact on European economies; (ii) at the global level, measures of lockdown have not engendered significant effects in decreasing the pandemic; (iii) movement restrictions have been instrumental in the fight in the American continent; (iv) measures of social distancing have been favourable in mitigating the crisis in Europe, while in Africa, similar measures have not been effective but have instead been counter‐productive; (v) economic and governance‐related policies have, for the most part, been beneficial to European countries; and (vi) the expected effect from public health measures have not been apparent, probably owing to the fact that the attendant measures may fundamentally be awareness policies that are largely aimed at the fraction of the population which is already infected. The present study improves the understanding on why some countries and regions have responded relatively better than others by providing COVID‐19 economic vulnerability and resilience indexes. The focus of the study is worthwhile because, to the best of our knowledge, the extant literature is sparse on such indexes pertaining to the COVID‐19 pandemic.

Second, while the extant literature on the COVID‐19 pandemic has focused on a plethora of nexuses between the COVID‐19 pandemic and macroeconomic outcomes, we know very little about existing measures of economic resilience and economic vulnerability to the crisis. The European Investment Bank has developed an index called the “EIB COVID‐19 Economic Vulnerability Index” (Davradakis, Santos, Zwart and Marchitto 2020). It is apparent from the index that low‐income countries are very vulnerable to the pandemic. Approximately 50 per cent of low‐income countries and about a quarter of their middle‐income counterparts are confronted with the highest COVID‐19 risk. As expected, the coping capacity of high‐income countries is better. However, approximately 56 per cent of these higher‐income countries, 63 per cent of middle‐income countries, and half of the poorest countries are confronted with frisk risk. Based on publicly available data, Acharya and Parwal (2020) report a vulnerability index to identify vulnerable regions in India on the basis of infrastructural and population features. The authors find a number of districts that are vulnerable in India, which although could be severely affected by the pandemic, still are not host to a significant number of COVID‐19 cases. Some studies have focused on the nexus between the scale of government measures and the corresponding economic consequences (Agbe 2020; Ozili 2020; Farayabi and Asongu 2020; Bisong, Ahairwe and Njoroge 2020; Price and van Holm 2020; Alvarez, Argente and Lippi 2020; Arnon, Ricco and Smetters 2020; Aum, Lee and Shin 2020). To put these in proper perspective, the literature has been concerned with the socio‐economic impacts of the crisis (Nicola et al. 2020); insights from scholarly and policy circles on the ramification of the corresponding crisis (Ataguba 2020); policy measures, socio‐economic effects and opportunities linked to the new coronavirus (Ozili 2020); how the remittance flows have been affected by the pandemic (Bisong et al. 2020); the impact of the pandemic on poverty experiences in childhood in the Middle East and North Africa (Agbe 2020); linkages between inequality, social stratification, and the COVID‐19 pandemic (Obeng‐Odoom 2020; Alon, Doepke, Olmstead‐Rumsey and Tertilt 2020); the nexus between the COVID‐19 crisis and the environment (Amankwah‐Amoah 2020); and assessing laboratory responses to the coronavirus (Odeyemi et al. 2020). There is no doubt that the COVID‐19 pandemic is the source of economic disruption at a scale and speed that is unprecedented (Baldwin and di Mauro 2020; Gopinath 2020). Employing an indicator of high frequency to assess the economic implications of the COVID‐19 pandemic in the United States and Europe at the initial stage of the novel coronavirus, it is established by Chen, Igan, Pierri, and Presbitero (2020) that the sampled countries with a higher outbreak have also experienced substantial economic losses. The authors find that the heterogeneous effect of the novel coronavirus is most apparent in observed variations in the mobility of people.

The present study contributes to the extant literature by proposing the indexes of economic vulnerability and economic resilience. The rest of the study is structured as follows. Section 2 provides definitions and highlights on some selected issues on vulnerability and resilience across different contexts. Section 3 discusses the construction of the indexes while the results and corresponding discussion are covered in Section 4. Section 5 concludes with implications and future research directions.

Definitions and framework of the vulnerability and resilience indexes

The vulnerability and resilience measures are not really new because they have been used before. The definitions of economic vulnerability and economic resilience have been provided in the previous section. The concepts of vulnerability and resilience were largely used both conceptually and empirically in inter alia: research linked to natural hazards or environmental degradation, population exposure, and physical assets (Kaly et al. 1999; Noy and Yonson 2018; Peduzzi, Dao, Herold and Mouton 2009). Originally, in order to elicit and comprehend social burdens related to risks, a Social Vulnerability Index (SoVI) was developed by Cutter, Boruff, and Shirley (2003) to assess the spatial tendencies of social vulnerability in relation to natural hazards at the level of the country in the United States.

Other indexes are created in contexts such as the economic context. For example, the United Nations Committee for Development Policy created the Economy Vulnerability Index (EVI). The EVI is a composition of the following indicators: remoteness, population size, share of agriculture, merchandise export concentration, forestry and fisheries in GDP, instability of agricultural production, homelessness owing to natural disasters, instability of export of goods and services, and the proportion of the population that live in the coastal areas that are not very elevated. The first purpose for the EVI is to identify the least developed countries (LDCs) that are recipients of preferential treatment when it comes to foreign aid and trade facilities.

While the majority of studies cover social, environmental, and economic spheres, researchers have only been recently interested in pandemics. The concepts of vulnerability and resilience are implemented in various contexts including health (particularly epidemics and pandemics recently). Noy, Doan, Ferrarini, and Park (2019) measure the economic risk of pandemics using a geo‐spatially detailed resolution. Data for the period 2014 to 2019 was used by the authors to compute measures, resilience, exposure, and vulnerability of the local economy in relation to the shock of a pandemic. They find that the economic risk of pandemics is particularly high in Southeast Asia, China, the Indian sub‐continent, and most of Africa. With the advent of the COVID‐19 pandemic, many studies are oriented towards measuring the impact through vulnerability and resilience indexes. In the same dynamic, Noy, Doan, Ferrarini, and Park (2020) have measured the economic risk of COVID‐19. They have used data from 2014–2018 and a conceptual disaster risk model to compute measures for exposure, vulnerability, and resilience of the local economy to the shock of the pandemic. Consistent with their previous work, they have established that economic risk is particularly high in most of Africa, South Asia, and Southeast Asia.

Construction of the indexes

The methodological framework for the construction of composite indicators imposes an iterative process with different steps. In this section, we respect this process by starting with the theoretical framework and data selection. Secondly, we present the normalisation of the data. Finally, the weighting and aggregation of the data is conducted.

Theoretical framework and discussion on the choice of variables

The theoretical framework is the starting point in the construction of the composite indicator. The objective of this step is to clearly define the phenomenon to be measured and the corresponding different indicators. For our index, the data selection is guided by the theoretical framework based on the direct and indirect economic impacts of the COVID‐19 pandemic. Global health pandemics usually impact economies and indicate their degree of vulnerability resilience. The economic channels through which shocks affect economies can be direct or indirect. The COVID‐19 pandemic is severely impacting economies in the world, but some countries are more exposed than others to the impact of the pandemic. In this paper, we attempt to quantify the vulnerability and resilience of countries to COVID‐19 based on the impact channels. At the moment, the direct impacts on many sectors are apparent. The vulnerability of a country to a health shock can be measured through characteristics such as trade openness as well as dependence on investment and tourism. On the one hand, the more a country (i) trades, (ii) receives foreign direct investment (FDI) and personal remittances, and (iii) depends on oil rents, natural resources, and tourism, the more the country would be affected by the attendant shock. On the other hand, while economic resilience is the ability of the country to face a shock, there are some variables that are particularly relevant. Among these characteristics, we can cite dependence on agriculture, governance, social development, employment, and stable/low inflation. In Table 1, the justification of every chosen variable is provided. Seven variables are used for the vulnerability index and nine for the resilience index. Moreover, a selection of the attendant variables are conditioned by data availability. For the vulnerability index, some variables are chosen as second best because of data availability constraints. For example, FDI is used as a proxy for capital outflows. It might also have been worthwhile to introduce some indicators related to global value chains and banking industry risk. However, these variables are available only for a limited number of countries. It is the same remark for governance introduced in the resilience index. Governance effectiveness, regulatory quality, and control of corruption are selected as factors to take into account when assessing the credibility of a government and its ability to formulate and implement policies and regulations. Moreover, variables such as accountability, integrity, and transparency in times of crisis are also worth considering.

Table 1.

Variable selection

Variables	Sources	Year	Justifications
Economic vulnerability
Foreign direct investment, net inflows (per cent of GDP)	WDI	2018	The impacts of the pandemic on FDI flows to these economies may be particularly severe (especially in developing countries where the primary and manufacturing sectors depend a lot on FDI).
Personal remittances, received (per cent of GDP)	WDI	2019	COVID‐19 has considerably affected remittances in the world (especially for developing countries). This impact leads to a significant effect on poverty reduction, consumption expenditure, and, therefore, on demand.
Net ODA received (per cent of GNI)	WDI	2018	The more a country relies on ODA, the more it is exposed to economic vulnerability. Most of the donor providers are facing an unprecedented economic crisis.
Oil rents (per cent of GDP)	WDI	2017	The sharp decline in oil prices is set to compound the impact of COVID‐19, by exacerbating challenges in some of the regions’ largest resource‐intensive economies. For example, the economic growth in oil exporters is projected to decline from 1.8 per cent in 2019 to –2.8 per cent in 2020 corresponding to a downward revision of 5.3 per cent points from the October 2019 Regional Economic Outlook for Sub‐Saharan Africa. This impact could be explained by the reduction of the global demand in oil, especially in the transport sector.
Total natural resources rents (per cent of GDP)	WDI	2017	Economic growth in natural resource‐intensive countries is expected to decline drastically. In effect, global natural resources market demand (oil, gas, coal, etc.) is declining as COVID‐19 spreads around the world.
International tourism, receipts (per cent of total exports)	WDI	2018	Countries depending on tourism are expected to witness a severe economic contraction because of extensive travel restrictions (especially in air travel) and lockdowns. The latest report of the United Nations World Tourism Organisation (UNWTO) World Tourism Barometer shows that the near‐complete lockdown imposed in response to the COVID‐19 pandemic led to a 98 per cent fall in international tourist numbers in May 2020 comparatively to 2019. The report shows also a 56 per cent year‐on‐year drop in tourist arrivals between January and May 2020, inducing a fall of 300 million tourists and US$320 billion lost in international tourism receipts – more than three times the loss during the Global Economic Crisis of 2009.
Imports of goods and services (per cent of GDP)	WDI	2018	The more the country depends on the importation of goods and services, the more it would be exposed to the COVID‐19 shock with regard to the availability and cost of the imports. Indeed, food security represents a source of vulnerability in countries that strongly rely on food imports.
Economic resilience
Agriculture, forestry, and fishing, value added (per cent of GDP)	WGI	2018	A country with a higher value added (per cent of GDP) would be more resilient to the COVID‐19 economic impact. Substantial dependence on agriculture would protect the countries to a food import dependency. Agriculture can play a key role in supporting countries in response to the pandemic by reducing imports of food, oil rents dependency.
Government effectiveness	WGI	2018	This variable reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Government effectiveness ensures a successful response to COVID‐19 and strengthens the economy's resilience to the pandemic
Regulatory quality	WGI	2018	This variable reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. During the COVID‐19 pandemic, governments make numerous decisions with the aim of boosting economic activity. Thus, a good regulatory quality is essential for the implementation of these policies.
Control of corruption	WGI	2018	This indicator reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. Governments around the world are implementing rapid responses to the COVID‐19 pandemic. According to the World Bank (2020), corruption risks, present in government responses to all these challenges and heightened by the scale and speed of the emergency, undermine the effectiveness of responses.
External debt stocks (per cent of GNI)	WDI	2018	It is highly probable to assist to an implosion of the external debt to the increase in fiscal deficits. So, a country with a high level of external debt may find it more difficult to mobilise resources in order to offset the effects of external shocks. Thus, a low level of external debt could be a good indicator of resilience to the COVID‐19 pandemic.
Consumer price index (2010 = 100)	WDI	2018	The COVID‐19 pandemic has caused a large shock to both demand and supply via the implementation of social distancing, lockdown, and travel restrictions. A decrease of the supply could bring back inflation while the decrease of demand reduces consumption and therefore deflation. The pandemic settles a situation of uncertainty. A low and stable level of inflation would be a definite asset for resilience in a country.
Unemployment, total (per cent of total labour force) (modelled ILO estimate)	WDI	2019	Employment could be associated with resilience of a shock‐absorbing nature. A low level of unemployment can withstand the impact of the pandemic without excessive welfare costs. In addition, the COVID‐19 employment effects would be severe, especially in the secondary sector.
Fiscal deficit (per cent of GDP)	WEO	2018	The government budget could be an important tool during the COVID‐19 pandemic. A healthy fiscal position would allow adjustments to taxation and expenditure policies during the COVID‐19 pandemic. During this period, the budget deficit is expected to increase because of the loss of fiscal revenues and the increase of the government expenditures, especially on health and social assistances.
Human development Index	UNDP	2018	In the context of the COVID‐19 pandemic, the Human Development Index (HDI) can be considered as an indicator of social development, which is an essential component of economic resilience. In effect, a higher level of social development in a country could promote social inclusion, reducing inequalities (i.e., by mitigating inequality both from the pandemic and its aftermath)

Source: authors

Normalisation

Since we have different measurement units in our dataset, the normalisation is required prior to data aggregation. There are numerous normalisation methods. For our index, we apply the well‐known min‐max method (Diop and Asongu 2020). The transformation is:

$$I_{qc}=\frac{x_{qc}-mi{n}_c\left(x_q\right)}{ma{x}_c\left(x_q\right)-mi{n}_c\left(x_q\right)}$$

where $x_{qc}$ is the value of indicator q for country c. The minimum and the maximum values for each indicator are calculated across countries. For indicators such as external debt, consumer price index, unemployment, and fiscal deficit, where higher values imply lower resilience, we use the following transformation:

$$I_{qc}=1-\frac{x_{qc}-mi{n}_c\left(x_q\right)}{ma{x}_c\left(x_q\right)-mi{n}_c\left(x_q\right)}$$

Weighting and aggregation

The aggregation and corresponding weighting are of notable relevance in the computation of the overall index and, hence, the rankings of countries most exposed to the COVID‐19 pandemic. While a plethora of methods have been employed in weighting, in the present study, a technique for the analysis of multivariate data is used. The fundamental principal component analysis (PCA) is employed. The choice is motivated by the perspective that with the PCA, the variables can be summarised without loss in substantial data variability in the main data. Moreover, it is worthwhile to note that the purpose of the PCA is to elucidate the variability of data that is observed via some linear combinations pertaining to the original data. Loadings obtained from the PCA are used to compute the different weights instead of giving the same weight to all variables. The first step consists of applying the PCA to the variables for each dimension in view of deriving different weights. With the weights derived, the PCA is then again employed on the weighted sub‐indices in order to compile economic resilience and economic vulnerability indexes.

Results and discussion

The first step is the application of PCA to the selection of the number of components. We apply the general rule (Kaiser Criterion) from which all factors with eigen values below 1 are dropped (Tchamyou 2017, 2020; Diop and Asongu 2021). As apparent in Table 2, the first three factors elucidate most of the variance. Hence, it is worthwhile establishing that the first three principal components elicit the variability of the vulnerability and resilience dimensions. We also run the Kaiser‐Meyer‐Olkin (KMO) test. This test measures how suited the data is for factor analysis. Regarding the resilience index, the KMO value of 0.75 indicates that the sampling is adequate. For the vulnerability index, the value is 0.57 and corresponds to average adequacy. Nevertheless, it is interesting to note that the KMO test is more an index of the compressibility of information than an indicator of fundamental interest in PCA. With the results obtained, we can now deal with the development of the different weights (see Table 2).

Table 2.

Number of principal components and weighting

	Vulnerability index							Resilience index
	1	2	3	4	5	6	7	1	2	3	4	5	6	7	8	9
Eig. val.	2.05	1.40	1.04	0.93	0.77	0.58	0.22	3.58	1.93	1.11	0.90	0.78	0.25	0.21	0.15	0.09
Prop.	0.29	0.20	0.15	0.13	0.11	0.08	0.03	0.40	0.21	0.12	0.10	0.09	0.03	0.02	0.02	0.01
Cum	0.29	0.49	0.64	0.77	0.88	0.96	1.00	0.40	0.61	0.73	0.83	0.92	0.95	0.97	0.99	1.00

KMO measure of sampling adequacy (overall)
	0.57							0.75
Squared loadings
Variables	Fdi	Remi	Oda	Oil	Nat	Tour	Imp	Agri	Gov	Reg	Corr	Debt	Cpi	Unem	Def	Hdi
F1	0.07	0.15	0.01	0.23	0.18	0.20	0.14	0.16	0.22	0.18	0.13	0.03	0.03	0.04	0.01	0.18
F2	0.29	0.00	0.19	0.01	0.34	0.01	0.16	0.00	0.00	0.02	0.04	0.00	0.40	0.09	0.43	0.00
F3	0.27	0.18	0.50	0.01	0.00	0.02	0.02	0.18	0.04	0.14	0.26	0.14	0.00	0.13	0.00	0.08
Weights
Weights	0.19	0.11	0.18	0.11	0.19	0.10	0.12	0.12	0.13	0.12	0.13	0.04	0.13	0.07	0.13	0.11

Sources: Authors. Fdi: Foreign direct investments, Remi: Remittances, Oda: Official Development Assistance, Oil: oil rents, Nat: natural resource rents, Tour: tourism receipt, Imp: importation of goods and services, Agri: Agriculture, forestry, and fishing, value added, Gov: Government Effectiveness, Reg: Regulatory Quality, Corr: Control of corruption, Debt: External debt stocks, Cpi: Consumer price index, Unem: Unemployment, Def: Fiscal deficit, Hdi: Human Development Index.

Analysis of the economic vulnerability and resilience indexes

The results of the economic vulnerability index and economic resilience index by regions are provided in Table 3. These show that the Asia‐Pacific and Middle East region are the regions that are most vulnerable economically to the COVID‐19 pandemic with a value of 0.29. It is followed by Africa (0.26). Europe earns the lowest score, corresponding to the best region regarding the vulnerability index. When we consider the results at the worldwide level, as apparent in Table 4, highest scores are traceable to the Congo Republic (0.56), Liberia (0.48), Kuwait (0.42), Iraq (0.40), and the Central African Republic (0.40), while Hungary (0.09), the Netherlands (0.11), China (0.12), and Argentina (0.13) are the top performing countries because they present the lowest scores on economic vulnerability to the COVID‐19 pandemic.

Table 3.

Vulnerability and Resilience indexes by regions

Regions	Observations	Mean	Standard Deviation	Minimum	Maximum
Vulnerability index
Europe	40	0.19	0.04	0.09	0.30
Africa	50	0.26	0.08	0.14	0.56
Americas	25	0.20	0.06	0.13	0.36
Asia‐Pacific and Middle East	35	0.29	0.08	0.16	0.40
World	150	0.23	0.08	0.09	0.56
Resilience index
Europe	40	0.57	0.10	0.39	0.70
Africa	50	0.39	0.06	0.29	0.58
Americas	25	0.47	0.08	0.30	0.69
Asia‐Pacific and Middle East	35	0.47	0.09	0.30	0.71
World	150	0.47	0.11	0.29	0.71

Sources: authors’ computations

Table 4.

Country‐specific rankings

Countries	Vulnerability index	Ranking	Countries	Resilience index	Ranking
Congo Republic	0.563	1	New Zealand	0.714	1
Liberia	0.478	2	Netherlands	0.699	2
Kuwait	0.422	3	Switzerland	0.699	3
Iraq	0.402	4	Norway	0.697	4
Central Africa Republic	0.401	5	Finland	0.695	5
Mongolia	0.397	6	Hong Kong, China	0.693	6
Mozambique	0.377	7	Canada	0.691	7
Chad	0.362	8	Sweden	0.691	8
Guyana	0.361	9	Denmark	0.690	9
Haiti	0.359	10	Australia	0.680	10
Gambia. The	0.347	11	Luxembourg	0.679	11
Kyrgyz Republic	0.346	12	United States	0.671	12
Oman	0.344	13	Iceland	0.669	13
Equatorial Guinea	0.331	14	United Kingdom	0.663	14
Sierra Leone	0.330	15	Japan	0.654	15
Congo Democratic Republic	0.323	16	Ireland	0.653	16
Sao Tomé and Principe	0.321	17	Austria	0.650	17
Hong Kong, China	0.320	18	Germany	0.638	18
Saudi Arabia	0.318	19	Estonia	0.634	19
Lesotho	0.314	20	Belgium	0.625	20
Malawi	0.302	21	France	0.617	21
Dominica	0.297	22	Israel	0.607	22
Grenada	0.297	23	Chile	0.595	23
Guinea‐Bissau	0.296	24	Korea Republic	0.595	24
Azerbaijan	0.295	25	Czech Republic	0.590	25
Nepal	0.292	26	Slovenia	0.587	26
Mauritania	0.290	27	Senegal	0.581	27
Cabo Verde	0.289	28	Portugal	0.581	28
Burundi	0.289	29	Cyprus	0.571	29
Maldives	0.285	30	Latvia	0.569	30
Burkina Faso	0.283	31	Poland	0.568	31
Montenegro	0.283	32	Malaysia	0.562	32
Seychelles	0.281	33	Qatar	0.557	33
Comoros	0.280	34	Spain	0.556	34
Qatar	0.279	35	Uruguay	0.546	35
Guinea	0.277	36	Mauritius	0.540	36
Niger	0.274	37	Georgia	0.531	37
Mali	0.273	38	Hungary	0.531	38
Gabon	0.272	39	Italy	0.524	39
Togo	0.271	40	Oman	0.518	40
Afghanistan	0.269	41	Costa Rica	0.517	41
Jamaica	0.267	42	Croatia	0.513	42
Georgia	0.266	43	Rwanda	0.509	43
Rwanda	0.266	44	Seychelles	0.507	44
Jordan	0.263	45	Bulgaria	0.505	45
Ethiopia	0.261	46	Fiji	0.503	46
Uzbekistan	0.260	47	Thailand	0.500	47
Albania	0.260	48	Dominica	0.495	48
Honduras	0.258	49	Argentina	0.492	49
Uganda	0.253	50	China	0.491	50
Lebanon	0.251	51	Romania	0.491	51
Djibouti	0.250	52	Botswana	0.487	52
Cambodia	0.249	53	Albania	0.484	53
Cyprus	0.248	54	Greece	0.478	54
Fiji	0.246	55	Indonesia	0.477	55
Algeria	0.243	56	Peru	0.476	56
Madagascar	0.241	57	Panama	0.473	57
Armenia	0.240	58	Philippines	0.472	58
Belize	0.238	59	India	0.472	59
St. Lucia	0.237	60	Montenegro	0.468	60
Luxembourg	0.234	61	Grenada	0.467	61
El Salvador	0.233	62	Kuwait	0.466	62
Ghana	0.231	63	Jamaica	0.466	63
Croatia	0.230	64	Colombia	0.465	64
Lao PDR	0.229	65	St. Lucia	0.464	65
Senegal	0.228	66	Cabo Verde	0.463	66
Zambia	0.227	67	Sri Lanka	0.460	67
Egypt.	0.227	68	Vietnam	0.458	68
Vietnam	0.227	69	Kazakhstan	0.455	69
Tanzania	0.225	70	Kenya	0.453	70
Ireland	0.225	71	Jordan	0.452	71
Moldova	0.224	72	Armenia	0.451	72
Zimbabwe	0.220	73	Mexico	0.449	73
Kazakhstan	0.220	74	Macedonia	0.447	74
Nicaragua	0.219	75	Benin	0.446	75
Angola	0.217	76	Morocco	0.446	76
Russia	0.207	77	Belarus	0.446	77
Bosnia	0.206	78	Turkey	0.445	78
Dominican Republic	0.204	79	Ghana	0.445	79
Ukraine	0.202	80	Moldova	0.437	80
Tunisia	0.201	81	Sierra Leone	0.435	81
Estonia	0.200	82	Ecuador	0.433	82
Chile	0.200	83	Bolivia	0.432	83
Bulgaria	0.199	84	Namibia	0.432	84
Benin	0.196	85	Russia	0.432	85
Australia	0.194	86	Dominican Republic	0.430	86
Nigeria	0.193	87	Paraguay	0.430	87
Morocco	0.192	88	Tunisia	0.429	88
Sudan	0.192	89	Guyana	0.427	89
Mauritius	0.192	90	El Salvador	0.425	90
Myanmar	0.191	91	Azerbaijan	0.425	91
Slovenia	0.191	92	Brazil	0.424	92
Macedonia	0.189	93	Niger	0.423	93
Malaysia	0.189	94	Cote d'Ivoire	0.422	94
Czech Republic	0.188	95	Belize	0.421	95
Guatemala	0.187	96	Uzbekistan	0.420	96
Bolivia	0.186	97	Honduras	0.419	97
Latvia	0.186	98	Nepal	0.418	98
Portugal	0.185	99	Ethiopia	0.418	99
Cameroon	0.185	100	Guatemala	0.416	100
Philippines	0.181	101	Burkina Faso	0.415	101
Greece	0.181	102	South Africa	0.415	102
Norway	0.180	103	Mongolia	0.413	103
Sri Lanka	0.177	104	Maldives	0.411	104
Iceland	0.176	105	Pakistan	0.409	105
Thailand	0.175	106	Uganda	0.408	106
Panama	0.175	107	Mali	0.406	107
Poland	0.175	108	Myanmar	0.406	108
Romania	0.175	109	Cambodia	0.406	109
Peru	0.174	110	Algeria	0.402	110
Kenya	0.174	111	Togo	0.398	111
Ecuador	0.173	112	Saudi Arabia	0.398	112
Austria	0.171	113	Ukraine	0.397	113
Spain	0.170	114	Bosnia Her	0.396	114
Namibia	0.169	115	Kyrgyz Re	0.394	115
New Zealand	0.169	116	Lao PDR	0.392	116
Colombia	0.168	117	Sao Tomé and Principe	0.388	117
Belarus	0.167	118	Tanzania	0.386	118
Eswatini	0.166	119	Bangladesh	0.384	119
Canada	0.164	120	Lebanon	0.381	120
Mexico	0.163	121	Nicaragua	0.381	121
Denmark	0.163	122	Gambia. The	0.380	122
Germany	0.163	123	Madagascar	0.378	123
Cote d'Ivoire	0.162	124	Comoros	0.377	124
Israel	0.162	125	Egypt	0.377	125
Uruguay	0.162	126	Cameroon	0.376	126
Belgium	0.161	127	Guinea‐Bissau	0.375	127
Costa Rica	0.160	128	Liberia	0.374	128
France	0.159	129	Chad	0.367	129
Sweden	0.159	130	Mozambique	0.366	130
United Kingdom	0.157	131	Sudan	0.365	131
Italy	0.154	132	Eswatini	0.364	132
South Africa	0.154	133	Mauritania	0.363	133
Korea Republic	0.150	134	Malawi	0.360	134
United States	0.147	135	Zambia	0.356	135
Finland	0.146	136	Nigeria	0.349	136
Indonesia	0.144	137	Guinea	0.346	137
Pakistan	0.143	138	Gabon	0.345	138
Bangladesh	0.142	139	Burundi	0.345	139
Botswana	0.141	140	Lesotho	0.333	140
India	0.141	141	Central African Republic	0.318	141
Turkey	0.141	142	Zimbabwe	0.314	142
Japan	0.138	143	Djibouti	0.313	143
Paraguay	0.138	144	Angola	0.311	144
Brazil	0.136	145	Afghanistan	0.307	145
Switzerland	0.136	146	Iraq	0.303	146
Argentina	0.128	147	Congo Republic	0.303	147
China	0.120	148	Congo Democratic Republic	0.302	148
Netherlands	0.112	149	Haiti	0.302	149
Hungary	0.092	150	Equatorial Guinea	0.288	150

Source: authors

Regarding economic resilience, Europe is at the top with a score of 0.57. It is followed by the Asia‐Pacific and the Middle East (0.47) and the Americas (0.47) regions. Africa ranks last with a score of 0.39. On the one hand, as apparent in Table 4, New Zealand (0.71), Switzerland (0.70), Norway (0.70), Canada (0.69), Denmark (0.69), China (0.69), Luxembourg (0.69), Sweden (0.69), Australia (0.68) are the top performing countries for the economic resilience index. On the other hand, Equatorial Guinea (0.29), Haiti (0.30), Iraq (0.30), Zimbabwe (0.31), Afghanistan (0.31), Congo Republic (0.30), Republic Democratic of Congo (0.30), Angola (0.31), Djibouti (0.31), and Lesotho (0.33) have the lowest score and so are the least resilient countries to the COVID‐19 pandemic in the world.

Cross analysis between economic vulnerability and economic resilience

For the cross analysis between economic vulnerability and economic resilience indexes, we follow the approach of Briguglio (2003) and Briguglio et al. (2009). We make a classification of the countries in four scenarios corresponding to quadrants. The position of each country depends on their vulnerability and resilience characteristics. Then, we combine the two indexes to indicate the level exposition of all countries to the COVID‐19 pandemic. The scenarios are: low vulnerability‐low resilience, high vulnerability‐low resilience, high vulnerability‐high resilience, and low vulnerability‐high resilience. To adapt these quadrants within the context of the COVID‐19 pandemic, we use “sensitive cases”, “severe case”, “asymptomatic cases”, and “best cases”, respectively, to characterise these different scenarios. The results of the cross analysis between the two indexes are shown in Figure 1. We use the averages of the indexes for all countries (dashed lines in the figure) to separate the different quadrants. Overall, these tendencies derived from the Figure 1 are:

• ‐Approximately 90 per cent of African countries are either in the low vulnerability‐low resilience quadrant (the “sensitive cases”) or in the high vulnerability‐low resilience quadrant (the “severe cases”) and include a few European countries (Turkey, Russia Federation, Ukraine, Belarus, North Macedonia, Moldova, Bosnia, and Herzegovina) and Asia‐Pacific and Middle East countries. We also recognise that some Latin America countries such as Brazil, Ecuador, Mexico, and Bolivia fall into these quadrants.
• ‐Only 13 out of 150 countries are apparent in the high vulnerability‐high resilience quadrant (the “asymptomatic cases”). We note two African countries (Seychelles and Rwanda), six European countries (Cyprus, Georgia, Albania, Montenegro, and Grenada), three Asia‐Pacific and Middle East countries (Fiji, Oman, and Qatar), two American countries (Jamaica and Dominica), and one Asian country (Hong Kong).
• ‐More than half of European countries fall in the low vulnerability‐high resilience quadrant, corresponding to the “best cases”. For African countries, only Mauritius and Botswana are in this scenario. Senegal is on the borderline with the high vulnerability‐high resilience quadrant. The United States and Canada are the most well‐positioned American countries in this quadrant.

Figure 1.

Economic vulnerability and economic resilience indexes [Colour figure can be viewed at wileyonlinelibrary.com]

Source: authors’ computations

Robustness and sensitivity analysis

Figure 2 presents the results of a robustness check for the indexes. To this end, we consider alternative methods for the normalisation procedure. The min‐max scaling used so far has been criticised because extreme values can distort the distribution of normalised values. To avoid this issue, the SoftMax method is employed. One of the advantages of this technique is its ability to reduce the influence of extreme values or outliers. Using the SoftMax method for normalisation, results do not change much because most of the countries considered in our sample remain in the same quadrant. Thus, we conclude that results are robust to the use of alternative normalisation procedures.

Figure 2.

Economic vulnerability and economic resilience indexes (robustness and sensitivity analysis) [Colour figure can be viewed at wileyonlinelibrary.com]

Source: authors’ computations

Macroeconomic impact, vulnerability, and resilience

It would be interesting to complement the analysis above by investigating the relationship between the COVID‐19 pandemic and our indexes as well as the extent to which the resilience and vulnerability indexes can explain the impact of the pandemic. The economic impact of the pandemic is evaluated by assessing the economic impact, which is proxied as the difference between macroeconomic projections made before the COVID‐19 pandemic and the revised 2020 macroeconomic projections provided by the IMF. Using ordinary least squares, we regress the macroeconomic impact of the COVID‐19 pandemic on the vulnerability and the resilience indexes of 150 countries. The results as apparent in Table 5 confirm the hypothesis that countries with high vulnerability suffer more from the COVID‐19 pandemic. The findings also confirm the perspective that a higher resilience is synonymous to low economic impact. These results are not surprising because they validate a plausible assumption while at the same time enabling us to verify/confirm our framework.

Table 5.

Macroeconomic impact, vulnerability, and resilience

$\mathrm{Impac}{\mathrm{t}}_{\mathrm{i}}$= –9.56 + 15.20$\mathrm{Vulnerabilit}{\mathrm{y}}_{\mathrm{i}}$ t‐stat (–6.89) (2.64) ${\mathrm{R}}^2=0.05$, N = 150 observations

$\mathrm{Impac}{\mathrm{t}}_{\mathrm{i}}$= –0.99 – 10.83$\mathrm{Resilienc}{\mathrm{e}}_{\mathrm{i}}$ t‐stat (–0.50) (‐2.64) ${\mathrm{R}}^2=0.05$, N = 150 observations

Source: authors

Concluding implications, Caveats, and Future research directions

The study complements the extant literature by constructing COVID‐19 economic vulnerability and resilience indexes using a global sample of 150 countries, which are categorised into four principal regions, namely: Africa, Asia‐Pacific and the Middle East, America, and Europe. Seven variables are used for the vulnerability index and nine for the resilience index. Both regions and sampled countries are classified in terms of the two proposed and computed indexes. The classification of countries is also provided in terms of four scenarios pertaining to vulnerability and resilience characteristics, notably: low vulnerability‐low resilience, high vulnerability‐low resilience, high vulnerability‐high resilience, and low vulnerability‐high resilience.

The established findings have obvious scholarly and policy implications. On the scholarly front, the scientific community has been provided with indexes via which to understand how countries have been affected by and/or resisted the COVID‐19 pandemic. On the policy front, policy makers can leverage on the attendant indexes for decision‐making, especially as it pertains to the allocation of resources in the fight against the pandemic.

Concerning the caveats of the study, it is important to note that the study can underestimate the long‐run consequences given that three principal factors affected by COVID‐19 exposure and tough mitigation strategies are not addressed, notably: cognitive consequences (Bogliacino et al. 2020) and mental health (Codagnone et al. 2020); intra‐household violence (Perez‐Vincent et al. 2020; Asongu and Usman 2020); and social mobility through education (Oster 2009; Oster and Steinberg 2013). These caveats should be considered in future studies.

Future research can also improve this study by using the established indexes within the framework of understanding how they are related with other macroeconomic indicators; moreover, it is worthwhile for future studies to provide insights into why some countries are lagging behind in terms of resilience and why others are leading in terms of vulnerability. Based on these future assessments, more could be known on why some countries have failed and/or succeeded in the fight against the COVID‐19 pandemic and, by extension, what lessons can be drawn, respectively, from the attendant failures and successes of corresponding countries.

Biographies

Dr. Samba Diop holds PhD from Gaston Berger University (Senegal). Currently, he is an Associate Professor in Economics at Alioune Diop University (Senegal). His research focuses on Applied Econometrics, African Integration and Macroeconomics. He is currently working on themes such as Resource Rents and Economic growth, Governance and Infrastructure, the creation of an African Monetary Integration Index and Covid‐19 Economic Vulnerability and Resilience Indexes. Dr Diop has presented several research papers in international conferences. His scholarly works have been published in the Journal of African Economies, Journal of African Development, Journal of Public Affairs, among others. Email: diopapasamba@gmail.com.

Prof. Simplice Asongu (corresponding author) holds a PhD from Oxford Brookes University and is currently the Lead Economist and Director of the African Governance and Development Institute (Yaoundé, Cameroon). He is also a: Senior Research Fellow at the Africa Growth Institute (Cape Town, South Africa); PhD Supervisor at Covenant University (Ota, Nigeria), the University of Ghana (Accra, Ghana) and Midlands State University (Gweru, Zimbabwe); DBA Supervisor at Management College of Southern Africa (Durban, South Africa) and Research Associate at the University of South Africa (Pretoria, South Africa). He is also Associate Editor in some journals including the Journal of Economic Survey and the Journal of African Business. Email: asongus@afridev.org; asongusimplice@yahoo.com.

Prof. Joseph Nnanna currently serves as the chief economist of the Development Bank of Nigeria (DBN) PLC. A seasoned professional with numerous years of experience in the U.S mortgage, banking, manufacturing, and telecommunication industry before joining academia. Prior to joining DBN, Nnanna was a tenured professor of business and economics at Northwestern Oklahoma State University. His‐scholarly works have been published in the DBN Journal of Economics and Sustainable Growth, Foreign Trade Review, and International Journal of Business Economics and Management, among others. Nnanna's areas of research are development finance, macroeconomics, and trade, respectively. He is a member of the American Economic Association. Email: jnnanna@devbankng.com.

Data availability statement

The data that support the findings of this study are openly available in World Development Indicators (WDI), World Governance Indicator (WGI), and United Nations Development Programme (UNDP) at the following URLs, respectively:

https://data.worldbank.org/indicator

https://databank.worldbank.org/source/worldwide‐governance‐indicators

http://hdr.undp.org/en/data