key: cord-0839451-wan4rrtr
authors: Diop, Samba; Asongu, Simplice A.; Nnanna, Joseph
title: COVID‐19 economic vulnerability and resilience indexes: Global evidence
date: 2021-06-15
journal: Int Soc Sci J
DOI: 10.1111/issj.12276
sha: fe19ad4574394326601d16df23eb8086161adbce
doc_id: 839451
cord_uid: wan4rrtr

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.

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., public health, 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 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 highincome 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 discus-sion are covered in Section 4. Section 5 concludes with implications and future research directions.

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, 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.

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.

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.

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:

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 val-ues imply lower resilience, we use the following transformation:

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 subindices in order to compile economic resilience and economic vulnerability indexes.

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 (Tchamyou , 2020 Diop and Asongu 2021) . As apparent in 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)

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. 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.

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.

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 (Continued) 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)

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

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 Table 2 ).

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 vul-nerable 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 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 

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 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 Soft-Max 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.

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.

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 decisionmaking, 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 ) and mental health ; 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.

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The authors are indebted to the editor and reviewers for their constructive comments.

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