Repercussions of COVID-19 Pandemic and its Impact on Economies of the Arab Countries

Introduction

 The study aimed at examines the economic repercussions of Corona pandemic on the economic performance and macroeconomic indicators in the Arab countries. In addition to determine the economic policies and mechanisms that can be taken to limit the repercussions of Corona on the Arab countries.

 The study measures impact of COVID-19 pandemic on the Arab economies in the short run. The pandemic effect is measured on macroeconomic variables like investment, employment, imports, exports, prices, and government expenditure using the Bayesian Vector Auto-regressions model PVAR. The study also relies on the SWOT Analysis method to analyze the repercussions of Coronavirus on economies of the Arab countries through analyzing the internal environment, by monitoring strengths and weaknesses, and analyzing the external environment by monitoring opportunities and threats.

1. The Literature Review about Repercussions of COVID 19

   According to the statistics of the United Nations Industrial Development Organization (UNIDO), there is a decline in global industrial production, as global manufacturing growth has already slowed in 2020, and is expected to continue to decline due to the economic disruptions caused by the pandemic Corona.

 The International Energy Agency report indicated the proportion of oil demand in China was 14% of global demand in 2020, and the growth rate of oil demand in China accounted for more than 75% of the growth in global demand, so any setbacks to the Chinese economy was expected to have negative effects, indirectly to the global economy. The global oil demand declined in 2020 by 365,000 barrels per day, which is the worst demand performance since 2011.

 The International Labor Organization ILO indicated that 81% of the global workforce was affected by the total or partial closure of the workplace in 2020. The global labor market loosed 200 million jobs, and 1.25 billion workers in the most sectors had affected by the pandemic, such as tourism, accommodation and food services, manufacturing industries, retail trade, business and administrative activities, which account about 38% of global employment. Also, ILO estimated the Corona pandemic impact on the total working hours during the second quarter of 2020 by approximately 10.5%, compared to a decrease in the first quarter of 2020 by 4.5%.

 The International Civil Aviation Organization (ICAO) showed the aviation sector as one of the economic sectors most affected by the Corona pandemic, and estimated a significant decrease in the number of passengers during March 2020 compared to March 2019 by 54%, so that Asia and the Pacific recorded the largest decrease by 85 million passengers, then Europe 50 million passengers, then North America about 35 million passengers.

 The International Monetary Fund estimated the cumulative loss of global GDP during 2020 and 2021 due to the COVID 19 pandemic about 9 trillion $, which exceeds the GDP of Japan and Germany together, which are the third and fourth largest economies in the world, respectively.

 The United Nations Conference on Trade and Development (UNCTAD) indicated a decline in the global economic growth to less than 2% may add economic losses with trillion dollars, during March to June 2020.

 The economic slowdown affected on developing countries and labor-exporting countries, including Egypt, have witnessed a slowdown in workers' remittances. In addition, slowing global economic growth and disruption of global trade are expected to have severe impacts on the supply side. Egypt, for example, depends on production inputs and imports raw materials, so negatively affected production rates and employment levels.

 A World Bank report estimated that remittances of Egyptian workers abroad declined by 21.5% during 2020 compared to a growth of 5% in 2019. The report indicated that remittances around the world decreased by 142 billion $ in 2020, and remittances in the Middle East declined by 20% during 2020 compared to a growth of 2.6% in 2019.

 The COVID-19 pandemic inflicted heavy and increased losses in various countries. The global economy witnessed a sharp contraction in 2020, which is much worse than the consequences of the global financial crisis of 2008-2009. The global economy is expected to grow with 5.8% in 2021 as economic activity returns to be normal, with support of economic policies.

2. Analysis of the Economic Performance of the Arab Countries

 The economic activity in a number of Arab countries was affected by the conditions of the global economic slowdown since the second quarter of 2019, which led to lower levels of external demand, in addition to the international oil prices was remaining at low levels. For example, the GDP growth rate in Saudi Arabia decreased from 1.7% in the first quarter of 2019 to 0.5% in the second quarter, the growth rate in Kuwait decreased from 0.9% to 0.4%.

Figure (1) GDP at constant prices for some Arab countries (2018/2019)

                                                                                            billions $

Source: The Arab Monetary Fund, https://www.amf.org.ae

 The GDP growth rate increased slightly in Tunisia in the second quarter of 2019 to 2.1%. The Egyptian economy grew by 5.3%, representing the third highest economic growth rate recorded in the world after China and India. Jordan recorded a growth rate of 4.1% and emirates 3.7% in the first quarter of 2019.

Figure (2) Forecasted growth rates of the Arab countries in 2021

Source: International Monetary Fund: Considerations for Designing Temporary Liquidity Support to Businesses, (Washington DC, IMF, 2020)

 The International Monetary Fund IMF expected Algeria to have a growth rate of 6.20% in 2021, followed by Morocco 4.80%, Tunisia 4.10%, Jordan 3.70%, Kuwait 3.40%, then Emirates 3.30%, Oman and Bahrain 3%, Saudi Arabia 2.90%, then Egypt 2.80% (Figure 2).

Figure (3) Annual inflation rate in some Arab countries (%)

Source: The Arab Monetary Fund, https://www.amf.org.ae

 The inflationary pressures declined in some of Arab countries, as Emirates, Saudi Arabia and Qatar, as a result of the slowdown in economic activity, implementing reforms to liberalize energy prices, and imposing new taxes to achieve fiscal discipline. On the other hand, the inflation rate recorded a significant increase in Sudan, by 45.8% with the continuing inflationary effects resulting from the inflationary financing of the budget deficit and the depreciation of the local currency against the dollar. In Jordan inflation rates raised slightly, reaching 0.8% in the second quarter, compared to 0.6% in the first quarter of 2019 (Figure 3).

Figure (4) Annual exchange rates of the flexible Arab currencies against dollar %

Source: The Arab Monetary Fund, https://www.amf.org.ae

 The Sudanese pound recorded a significant decline against the dollar by 153% during the second quarter of 2019, reflecting the economic challenges facing the country and affecting the levels of foreign exchange supply. The Tunisian dinar fell against the dollar by 18.6% during the second quarter of 2019, reflecting continued pressures on the external sector. The Moroccan dirham fell against the dollar by 2.8%. On the other hand, the value of the Egyptian pound rose against the dollar by 4.4% during the second quarter, as a result of improved economic conditions and foreign exchange receipts in light of the reforms applied during the past years (Figure 4).

Figure (5) Unemployment rate in some Arab countries (2018-2019) %

Source: The Arab Monetary Fund, https://www.amf.org.ae

 The unemployment rate in Jordan rose to 19.0% during the first and second quarters of 2019, reflecting the slowdown in economic activity, compared to 18.4% and 18.7% recorded in the same two quarters of 2018. The unemployment rate in Tunisia during the first and second quarters of 2019 was 15.3%, On the other hand, unemployment rates recorded a remarkable decline in Egypt, reaching 8.1% and 7.5% during Q1 & Q2 of 2019, which resulted in stimulating aggregate demand and creating more job opportunities. The unemployment rate in Saudi Arabia was 5.7% and 5.6% in 2019, which is attributed to the continued investment spending on projects included in the Kingdom of Saudi Arabia 2030 vision, in Morocco also decreased to 9.1 and 8.1% in 2019 (Figure 5).

Figure (6) The growth rate of bank deposits and loans (%) (Second quarter of 2019)

Source: The Arab Monetary Fund, https://www.amf.org.ae

 Bank deposit levels improved in some Arab countries during the second quarter of 2019, as Libya recorded the highest growth rate of bank deposits, amounting to about 63%. Also, bank deposits in Egypt increased by 12.3% in light of the growth recorded in the levels of output. Deposits in Iraq grew by 16.1%. In contrast, bank deposits in Qatar decreased by 6.1% during the second quarter of 2019. With regard to loans and credit facilities, has improved in a number of Arab countries. The highest growth rates of loans and credit facilities were in Egypt with a growth rate of 10.6% during the second quarter of 2019, followed by Jordan by 11.1% and Bahrain by 7% (Figure 6).

 The public revenues of three Arab countries recorded an increase, represented by Jordan, Qatar and Morocco, whose revenues grew by 4.3, 4.1 and 6.8%, respectively, compared to the second quarter of 2018, while public revenues recorded a decline in Saudi Arabia, Oman and Mauritania, with a decrease in public revenues in these countries by 4.7, 3.1 and 11.6%, respectively, during the second quarter of 2019. The public expenditures increased in four Arab countries during the second quarter of 2019, as Mauritania, Jordan, Qatar and Morocco increased by 10.3, 7.7, 7 and 3%, respectively. While public expenditures recorded a decline in Oman by 11.1% during the second quarter of 2019.

Table (1): The total Arab foreign trade during the period (2015-2019)

                                                                                              billion $

2019 2018 2017 2016 2015

1095.4 956.4 798 862 1250.2 Arab exports

824.6 808.8 811.1 864.3 930.1 Arab imports

19180.6 17730 16843.3 16482 18935 Global exports

19409.7 18024 17169.9 16766 19024 Global imports

5.7 5.4 4.7 5.2 6.6 The ratio of Arab exports to global exports

4.2 4.5 4.7 5.2 4.9 The ratio of Arab imports to global imports

Source: The Arab Monetary Fund, the Economic Outlook report 2020, www.atfp.org.ae

 The value of total Arab merchandise exports increased to 1095.4 billion $ in 2019, compared to 956.4 billion $ in 2018, the total Arab merchandise imports increased in 2019 to 824.6 billion $, compared to 808.8 billion $ in 2018 (Table 1).

Figure (7) Arab exports and imports from the important trading partners in 2019

Source: The Arab Monetary Fund, the Economic Outlook report 2020, www.atfp.org.ae

 Arab exports increased for important trading partners. Intra-Arab exports occupied 9.9% of total Arab exports, the United States 5.5%, the European Union 15.5%, and Asia 53.2%, while the rest of the world is about 15.5%. The Arab imports increased from most trading partners by 1.0%, Intra-Arab imports amounted 13.5% of total Arab imports, the United States recorded 7.5%, and the European Union 26.4%, as well as Asia 35.9%, while the rest of the world is about 16.8%, (Figure 7).

Figure (8) the commodity structure for Arab exports and imports in 2019

Source: The Arab Monetary Fund, the Economic Outlook report 2020, www.atfp.org.ae

 The fuel and minerals occupied the largest share of total Arab exports, about 63.4% in 2019, and manufactures recorded 28.8% of total exports. With regard to imports, manufactures amounted 65.1%, while agricultural commodities achieved 18.8% of total Arab imports during 2019 (Figure 8).

 The value of Intra-Arab trade reached 6.2% of total Arab trade, about 10.6 billion $ in 2019.

Figure (9) the commodity structure for intra-Arab exports and imports in 2019

Source: The Arab Monetary Fund, the Economic Outlook report 2020, www.atfp.org.ae

 Fuel and minerals presented 17.2% of intra-Arab exports, the manufactures commodities acquired 58.3%, and the agricultural commodities 19.7%. With regard to intra-Arab imports, fuel and minerals presented 25.9% of intra-Arab imports; the manufactures 46.2% and the agricultural commodities 20.8% (Figure 9).

3. Analyses of COVID 19’s Impact on the Arab Countries

 The COVID 19 pandemic has caused economic impacts on the Arab economy, through short and long-term repercussions, on the health, agriculture, food and trade sectors, according to a report issued by the League of Arab States. The International Monetary Fund indicated the economies of Arab countries to shrink by 5.7% in 2020, due to the Corona pandemic, the drop in oil prices, and the closure of economic activities for about 3 months.

a- The Impact on Economic Growth

 ESCWA estimated the Arab countries losses of the pandemic as follows: Loss of 420 billion $ from market capital (8% of the wealth of Arab countries); Loss of 63 billion $ of GDP’s Arab countries; additional debts of 220 billion $ (equivalent to 8% of GDP’s Arab countries); loss of 550 million $ per day in oil revenues if oil prices remain between 25-30 $ per barrel; 28 billion $ in exports; more than 2 billion $ in revenue from tariffs and lost about 1.7 million jobs in 2020.

 According to the Arab Monetary Fund, the Arab economies witnessed a recession during 2020 and a more shortage of public budget as a percent of GDP with high levels of public spending and a decline in oil and tax revenues.

 The productive services sectors in the Arab countries were affected by corona virus, especially tourism, transportation, internal and external trade and the manufacturing industry. These sectors are responsible for generating about 40% of the GDP in the Arab countries.

b- The Impact on Oil Sector

 The Corona pandemic led to sharp decline in oil prices, loosed oil revenues in the Arab region, with a net value of 11 billion $, during the period from January to March 2020. The profits of oil-importing Arab countries derived from these prices was negligible compared to the losses of the oil exporting Arab countries.

c- The Impact on Commodity Market

Table (2) The rise in commodity prices as a result of COVID 19 in a number of Arab countries

 Personal care items medical prevention basic goods and services food and beverages

Iraq 20% 50% 10%

Jordan 100% 20%

Sudan 100% 200% 25% 30%

Tunisia 8% 7% 10% 5%

Emirates 20% 25% 5% 10%

Qatar 20-25%

Palestine There are no regular price changes

Bahrain The government forbade changing prices

Saudi The government forbade changing prices

Source: International Monetary Fund: Consumer Price Index in Response to COVID-19, (Washington DC, IMF, 2020)

 The commodity prices rose as a result of COVID 19 in a number of countries, including Iraq, Jordan, Sudan, Tunisia, Emirates and Qatar, where the rate of increase in Sudan reached 30% for food commodities and 200% for medical protection goods. Saudi Arabia and Bahrain have taken decisions to prevent price hikes (Table 2).

d- The Impact on Labor Market

 The International Labor Organization indicated the impact of COVID-19 pandemic on labor markets around the world with high unemployment, and the pressure increased on the private sector that were forced to close their business and lay off Employees and workers. The sectors of health care and food security, oil, tourism, and air transport affected by the crisis.

 The International Monetary Fund estimated the decrease in working hours around the world as follows:

- Africa: The decrease in working hours during the second quarter of 2020 was 9.6% compared to a decrease of 1.6% in the first quarter of 2020.

- United States of America: The decrease in working hours during the second quarter of 2020 was 12.4% compared to a decrease of 1.3% in the first quarter of 2020.

- Arab Countries: The decrease in working hours during the second quarter of 2020 was 10.3%, compared to a decrease of 1.8% in the first quarter of 2020.

- Asia-Pacific: The decrease in working hours during the second quarter of 2020 was 10.0% compared to a decrease of 6.5% in the first quarter of 2020.

- Europe and Central Asia: The decrease in working hours during the second quarter of 2020 was 11.8% compared to a decrease of 1.9% in the first quarter of 2020.

 The High unemployment rates, as a result of the damage in a number of economic sectors that provide employment opportunities because of corona virus, especially the tourism sector, which every direct job opportunity contributes to create five other indirect jobs. The Arab countries lost 1.7 million jobs by the end of 2020.

 The remittances of workers abroad decreased in the Arab countries, which generate pressure on local currencies, and increase the cost of paying interest on foreign debt.

e- The Impact on Tourism Sector

 The Arab Tourism Organization and the Arab Civil Aviation Organization indicated the tourism and aviation sectors lost about 46 billion $ as a result of corona crisis, in addition to the loss of one million jobs and thousands of seasonal jobs in the Arab countries.

Table (3) Tourism revenues in the Arab countries (2019-2020) billions $

2020 2019 Countries

11.2 22.5 Emirates

7 14 Saudi

6.2 12.5 Egypt

4.2 8.4 Morocco

4.6 9.3 Lebanon

2.9 5.8 Jordan

1.8 3.7 Bahrain

1.2 2.4 Tunisia

1 2 Oman

Source: World Travel and Tourism Council (WTTC), www.wttc.org

 The tourism revenues decreased in emirates from 22.5 billion $ in 2019 to 11.2 billion $ in 2020, in Saudi Arabia decreased from 14 billion $ to 7 billion $, in Egypt decreased from 12.5 billion $ to 6.2 billion $, in Morocco decreased from 8.4 billion $ to 4.2 billion $, in Lebanon decreased from 9.3 billion $ to 4.6 billion $, in Jordan decreased from 5.8 billion $ to 2.9 billion $, in Bahrain decreased from 3.7 billion $ to 1.8 billion $, in Tunisia decreased from 2.4 billion $ to 1.2 billion $, and in Oman decreased from 2 billion $ to 1 billion $ in 2020 (Table 3).

Figure (10): The impact of Corona on the tourism sector

 Source: World Travel and Tourism Council (WTTC), www.wttc.org

 The Arab countries had a decrease in contribution of tourism and travel sector to the GDP and the labor market in 2020 compared to 2019 (Figure 10).

Figure (11): Corona's impact on the tourism sector compared to other crises

 Source: the Arab Organization for Tourism, https://www.arab-tourismorg.org

 Figure 11 indicates impact of the Corona pandemic on the tourism sector, compared to other crises, such as the global economic crisis, where the recovery period ranges from 3 to 6 years, and this is considered as the longest crises.

f- The Impact on Aviation Sector

 The International Air Transport Association (IATA) revealed that the sector suffered losses in all countries as a result of the suspension of air traffic, for example Saudi lost about 7.2 billion $, and 287,500 jobs at risk, the estimated damage in the economy about 17.9 billion $. Whereas the losses in Emirates amounted to 6.8 billion $, and 287,700 jobs at risk, the estimated damage in the economy amounted to 23.2 billion $. The losses in Egypt reached 2.2 billion $, and 279,800 jobs at risk, the estimated damage in the Egyptian economy amounted to 3.3 billion $.

Figure (12) Corona’s impact on the aviation sector in the Arab countries

Source: The International Air Transport Association (IATA), www.iata.org

 The Corona pandemic reduced the GDP in Emirates by 23.2 billion $, and the revenues of the aviation sector decreased by 6.8 billion $, followed by Saudi Arabia, where GDP decreased by 17.9 billion $, and the aviation sector revenues decreased by 7.2 billion $, then Morocco, where GDP decreased by 4.9 billion $, and the revenues of the aviation sector decreased by 1.7 billion $, followed by Egypt, where GDP decreased by 3.3 billion $, and the revenues of the aviation sector decreased by 2.2 billion $. They followed by the rest of the Arab countries, Qatar, Oman, Kuwait, Algeria, Tunisia, and Jordan (Figure 12).

Figure (13): The impact of Corona on passengers and jobs in the aviation sector

Source: The International Air Transport Association (IATA), www.iata.org

  The number of travelers decreased in the Arab countries due to the Corona pandemic, as Algeria was the most affected Arab country, the number of travelers decreased by 58%, followed by Emirates decreased by 53%, then Saudi Arabia and Morocco by 51%, Qatar 49%, then Algeria, Egypt and Oman 48%, Tunisia 44%, and Kuwait 41%. The number of jobs in the aviation sector was affected as Morocco, where 499,000 jobs reduced, followed by Emirates about 378,700 jobs, Saudi Arabia 287,500 jobs, then Egypt 279,800 jobs, Algeria 169,800 jobs, followed by Tunisia, Qatar, Oman, Jordan, and Kuwait (Figure 13).

4- The Impact on Foreign Trade

 The exports of Arab countries were affected by declining in global demand 50%, which was responsible for 48% of GDP, in addition to decline in oil and non-oil exports. The most important trading partners of Arab countries absorb 65% of Arab exports. The International Monetary Fund indicated the foreign trade of Arab countries declined significantly in 2020 due to the collapse in oil prices, as oil represents about 64% of total Arab exports, which affected the performance of intra-Arab trade. So, the surplus recorded in the balance of payments of Arab countries during the year 2019 turned into a deficit of 0.6% of GDP during 2020.

 As a result of the global economic slowdown, exports of the Arab region decreased by 28 billion $, which threatened the viability of export-dependent companies and industries. Companies in the Arab countries recorded losses in market capital, amounting to 420 billion $ during the period from January to March 2020, and losses in the wealth of these companies were 8% of the total wealth of Arab countries.

5- The Impact on Poverty

 There are 101.4 million people under the poverty line in the Arab countries, because the food production, supply, transport and distribution chains were negatively affected by corona pandemic. This led to a decrease in food exports from food-producing countries, which affected food security in many Arab countries, due to their heavy dependence on food imports, especially basic foodstuffs, as they import 65% of the wheat they need, and spend about 110 billion $ on food imports. The Corona pandemic threatened 55 million people in need of humanitarian aid in the Arab region, whether it was related to food, water, sanitation, medical supplies or health services.

4- Measure the Impact of COVID 19 on the Arab Countries

The study measured impact of COVID 19 pandemic on the Arab economies in the short run. The pandemic effect is measured on macroeconomic variables like investment, employment, imports, exports, prices, and government expenditure using the Bayesian Vector Auto-regressions model (BVAR), which is linear multivariate time-series model to capture the joint dynamics of multiple time series.

PVAR Model:

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COV19 = C(1,1)*COV19(-1) + C(1,2)*COV19(-2) + C(1,3)*CPI(-1) + C(1,4)*CPI(-2) + C(1,5)*EMP(-1) + C(1,6)*EMP(-2) + C(1,7)*EX(-1) + C(1,8)*EX(-2) + C(1,9)*GOVEXP(-1) + C(1,10)*GOVEXP(-2) + C(1,11)*IM(-1) + C(1,12)*IM(-2) + C(1,13)*INV(-1) + C(1,14)*INV(-2) + C(1,15)

CPI = C(2,1)*COV19(-1) + C(2,2)*COV19(-2) + C(2,3)*CPI(-1) + C(2,4)*CPI(-2) + C(2,5)*EMP(-1) + C(2,6)*EMP(-2) + C(2,7)*EX(-1) + C(2,8)*EX(-2) + C(2,9)*GOVEXP(-1) + C(2,10)*GOVEXP(-2) + C(2,11)*IM(-1) + C(2,12)*IM(-2) + C(2,13)*INV(-1) + C(2,14)*INV(-2) + C(2,15)

EMP = C(3,1)*COV19(-1) + C(3,2)*COV19(-2) + C(3,3)*CPI(-1) + C(3,4)*CPI(-2) + C(3,5)*EMP(-1) + C(3,6)*EMP(-2) + C(3,7)*EX(-1) + C(3,8)*EX(-2) + C(3,9)*GOVEXP(-1) + C(3,10)*GOVEXP(-2) + C(3,11)*IM(-1) + C(3,12)*IM(-2) + C(3,13)*INV(-1) + C(3,14)*INV(-2) + C(3,15)

EX = C(4,1)*COV19(-1) + C(4,2)*COV19(-2) + C(4,3)*CPI(-1) + C(4,4)*CPI(-2) + C(4,5)*EMP(-1) + C(4,6)*EMP(-2) + C(4,7)*EX(-1) + C(4,8)*EX(-2) + C(4,9)*GOVEXP(-1) + C(4,10)*GOVEXP(-2) + C(4,11)*IM(-1) + C(4,12)*IM(-2) + C(4,13)*INV(-1) + C(4,14)*INV(-2) + C(4,15)

GOVEXP = C(5,1)*COV19(-1) + C(5,2)*COV19(-2) + C(5,3)*CPI(-1) + C(5,4)*CPI(-2) + C(5,5)*EMP(-1) + C(5,6)*EMP(-2) + C(5,7)*EX(-1) + C(5,8)*EX(-2) + C(5,9)*GOVEXP(-1) + C(5,10)*GOVEXP(-2) + C(5,11)*IM(-1) + C(5,12)*IM(-2) + C(5,13)*INV(-1) + C(5,14)*INV(-2) + C(5,15)

IM = C(6,1)*COV19(-1) + C(6,2)*COV19(-2) + C(6,3)*CPI(-1) + C(6,4)*CPI(-2) + C(6,5)*EMP(-1) + C(6,6)*EMP(-2) + C(6,7)*EX(-1) + C(6,8)*EX(-2) + C(6,9)*GOVEXP(-1) + C(6,10)*GOVEXP(-2) + C(6,11)*IM(-1) + C(6,12)*IM(-2) + C(6,13)*INV(-1) + C(6,14)*INV(-2) + C(6,15)

INV = C(7,1)*COV19(-1) + C(7,2)*COV19(-2) + C(7,3)*CPI(-1) + C(7,4)*CPI(-2) + C(7,5)*EMP(-1) + C(7,6)*EMP(-2) + C(7,7)*EX(-1) + C(7,8)*EX(-2) + C(7,9)*GOVEXP(-1) + C(7,10)*GOVEXP(-2) + C(7,11)*IM(-1) + C(7,12)*IM(-2) + C(7,13)*INV(-1) + C(7,14)*INV(-2) + C(7,15)

Where,

COV19: World Pandemic Uncertainty Index (WPUI)

CPI: Consumer Price Index

EMP: Number of Employment

EX: Value of Exports, by Major Commodity Groups

GOVEXP: Government Expenditure

IM: Import Value of Imports, by Major Commodity Groups

INV: Investment Capital of Domestic and Foreign

The weekly sample data covered the Arab countries during 2020 from 1/1/2020 to 31/1/2020. The Value of Exports by Major Commodity Groups and the Value of Imports by Major Commodity Groups were collected from the World Trade Organization (WTO), Statistics on Merchandise Trade Database 2020, https://data.wto.org/. The rest variables were collected from the World Bank database 2021, http://www.worldbankdatabase.org.

The Unit Root Test

A unit root test was performed using the augmented Dickey–Fuller (ADF) method to examine the stability of the time series, as the first step to analyze the data and examine the properties of the time series to avoid the false regression problem. The null hypothesis was: the time series has a unit root problem (the time series is not static), and the alternative hypothesis was: the time series does not have a unit root problem (the time series is static).

Table (4): Results of the Augmented Dickey–Fuller Test (ADF)

At Level COV19 CPI EMP EX GOVEXP IM INV

With Constant t-Statistic -1.6874 -1.0175 -1.6257 -1.8998 -1.2149 -0.6688 -0.8616

 Prob. 0.4316 0.7405 0.4625 0.3299 0.6614 0.8453 0.7926

With Constant & Trend t-Statistic -1.6194 -1.9619 -1.4387 -1.7693 -1.3575 -1.6304 -1.5280

 Prob. 0.7717 0.6079 0.8375 0.7052 0.8618 0.7672 0.8070

Without Constant & Trend t-Statistic 0.7726 0.9572 -0.2203 -0.3610 -0.2287 0.6628 0.4811

 Prob. 0.8774 0.9080 0.6021 0.5499 0.5991 0.8561 0.8156

At First Difference d(COV19) d(CPI) d(EMP) d(EX) d(GOVEXP) d(IM) d(INV)

With Constant t-Statistic -7.1806 -7.1311 -7.0052 -7.0163 -7.0072 -7.0624 -7.0330

 Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

With Constant & Trend t-Statistic -7.2382 -7.0583 -7.1589 -7.0342 -7.1413 -7.1882 -7.1763

 Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Without Constant & Trend t-Statistic -7.0711 -7.0711 -7.0711 -7.0711 -7.0711 -7.0711 -7.0711

 Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Source: Author using Eviews 10.

Table 4 showed the augmented Dickey–Fuller (ADF) test results that time series for all variables aren’t static. And after taking the first differences, the time series of all variables for the first differences are static with a confidence degree of 99%, so all variables are being stationary at the first differences.

Table (5): Results of the PVAR Model

 COV19 CPI EMP EX GOVEXP IM INV

COV19(-1) 0.404973 0.002300 -0.001507 -0.016210 0.000238 0.002532 0.001598

  (0.07496) (0.00087) (0.00068) (0.00578) (0.00212) (0.00219) (0.00319)

COV19(-2) 0.082472 0.000667 -0.000232 -0.002463 -7.96E-05 0.000855 0.000755

  (0.04567) (0.00053) (0.00041) (0.00351) (0.00129) (0.00133) (0.00194)

CPI(-1) 14.86609 0.523094 0.014037 1.140531 0.192362 0.344822 0.300272

  (0.06710) (0.06644) (0.05161) (0.04393) (0.01615) (0.01667) (0.02429)

CPI(-2) 3.507709 0.107179 0.005543 0.207551 0.014415 0.097382 0.097942

  (0.08169) (0.04490) (0.03476) (0.02957) (0.01087) (0.01123) (0.01636)

EMP(-1) -19.47333 -0.016602 0.177213 1.245583 -0.388428 0.369175 0.598343

  (0.07528) (0.01395) (0.08973) (0.05732) (0.07857) (0.02760) (0.041897)

EMP(-2) -3.558638 -0.019233 0.026950 0.159995 -0.064406 0.043853 0.080153

  (0.16877) (0.06027) (0.04751) (0.00048) (0.04733) (0.05211) (0.02158)

EX(-1) -2.891310 0.022426 0.018232 0.284792 0.011244 0.008570 0.016209

  (0.05866) (0.01234) (0.00964) (0.08253) (0.03015) (0.03113) (0.04534)

EX(-2) -0.541932 0.002449 0.003106 0.046129 0.001508 0.000432 0.001841

  (0.09727) (0.00696) (0.00544) (0.04668) (0.01702) (0.01757) (0.02560)

GOVEXP(-1) 0.580070 0.043204 -0.038378 0.110982 0.235465 -0.195851 -0.292747

  (0.09280) (0.03491) (0.02727) (0.03190) (0.08596) (0.08814) (0.02840)

GOVEXP(-2) 0.010606 0.010774 -0.005042 0.038921 0.039133 -0.028895 -0.044148

  (0.03700) (0.01909) (0.01491) (0.02684) (0.04709) (0.04818) (0.07019)

IM(-1) 2.292231 0.043077 0.038162 0.056491 -0.195037 0.255823 0.337502

  (0.08719) (0.03368) (0.02630) (0.02369) (0.08235) (0.08560) (0.12388)

IM(-2) 0.649831 0.007168 0.005571 -0.007522 -0.035387 0.045976 0.059997

  (0.05618) (0.01850) (0.01445) (0.02290) (0.04522) (0.04710) (0.06801)

INV(-1) 0.491588 0.014343 0.028167 0.045820 -0.135101 0.156533 0.221800

  (0.02399) (0.02361) (0.01844) (0.05683) (0.05773) (0.05962) (0.08745)

INV(-2) 0.213315 0.001456 0.004065 -0.004523 -0.023873 0.026671 0.037616

  (0.09362) (0.01275) (0.00996) (0.08474) (0.03118) (0.03219) (0.04731)

C -80.11937 -0.705041 0.451842 -5.062416 22.57040 7.136359 14.58100

  (103.040) (1.20172) (0.93860) (7.98369) (2.95628) (3.03484) (4.42424)

R-squared 0.901328 0.914629 0.853377 0.878386 0.852502 0.888927 0.873456

Adj. R-squared 0.862956 0.881430 0.782468 0.692203 0.781253 0.845732 0.824245

Sum sq. resids 23.57936 0.003207 0.001809 0.143616 0.017982 0.018538 0.039244

S.E. equation 0.809310 0.009439 0.007088 0.063161 0.022350 0.022692 0.033017

F-statistic 23.48894 27.54938 13.84654 9.031773 13.75535 20.57938 17.74906

Mean dependent 11.43693 4.619304 0.862071 10.66549 24.94396 10.40778 17.49439

S.D. dependent 2.186171 0.027411 0.015197 0.113846 0.047786 0.057775 0.078756

Source: Author using Eviews 10.

Results indicated that the explanatory level of the model, R-squared, for all equations greater than 0.85 which means that the independent variables can explain more than 85% of variation in the dependent variable in the seven equations, and the rest were due to other factors, including random errors. Also, the overall significance of the model, F-statistic, was significant for all equations, which means that the estimated model is significant (accepting alternative hypothesis). This indicated that the independent variables have a significant effect on the dependent variable in the seven equations. Moreover, results indicated partial significance of the model where parameters were statistically significant. These parameters differed substantially from zero and as a result the importance of the independent variables was reflected (table 5).

   The Substituted Coefficients are as follows:

COV19 = 0.404972731888*COV19(-1) + 0.0824722614938*COV19(-2) + 14.8660865148*CPI(-1) + 3.50770909398*CPI(-2) - 19.4733303562*EMP(-1) - 3.5586381283*EMP(-2) - 2.89130992275*EX(-1) - 0.541932251716*EX(-2) + 0.58006975468*GOVEXP(-1) + 0.0106059889222*GOVEXP(-2) + 2.29223130463*IM(-1) + 0.649831413524*IM(-2) + 0.491588244593*INV(-1) + 0.213315153247*INV(-2) - 80.1193729714

CPI = 0.00229975280799*COV19(-1) + 0.000667325451275*COV19(-2) + 0.523093977972*CPI(-1) + 0.107179000575*CPI(-2) - 0.0166021631521*EMP(-1) - 0.0192325617572*EMP(-2) + 0.0224259121633*EX(-1) + 0.00244944094129*EX(-2) + 0.0432035742124*GOVEXP(-1) + 0.0107744476991*GOVEXP(-2) + 0.0430772032414*IM(-1) + 0.00716832335456*IM(-2) + 0.0143432792829*INV(-1) + 0.00145616846021*INV(-2) - 0.705040906556

EMP = - 0.00150736674682*COV19(-1) - 0.000231919436434*COV19(-2) + 0.0140373974227*CPI(-1) + 0.00554278090635*CPI(-2) + 0.177212937493*EMP(-1) + 0.0269497904601*EMP(-2) + 0.0182323972979*EX(-1) + 0.00310597450621*EX(-2) - 0.0383777414681*GOVEXP(-1) - 0.00504156826719*GOVEXP(-2) + 0.0381623073226*IM(-1) + 0.00557117858943*IM(-2) + 0.0281668056523*INV(-1) + 0.00406484830501*INV(-2) + 0.451841641683

EX = - 0.0162101331622*COV19(-1) - 0.00246271227684*COV19(-2) + 1.14053116855*CPI(-1) + 0.207551402746*CPI(-2) + 1.24558253729*EMP(-1) + 0.159994678834*EMP(-2) + 0.284791895034*EX(-1) + 0.0461293597432*EX(-2) + 0.110981707694*GOVEXP(-1) + 0.038921385305*GOVEXP(-2) + 0.0564913520648*IM(-1) - 0.0075221116349*IM(-2) + 0.0458201943547*INV(-1) - 0.00452342979406*INV(-2) - 5.0624164138

GOVEXP = 0.000237584975082*COV19(-1) - 7.95895156216e-05*COV19(-2) + 0.192361634923*CPI(-1) + 0.0144151653003*CPI(-2) - 0.388427889382*EMP(-1) - 0.0644062077481*EMP(-2) + 0.0112439456316*EX(-1) + 0.00150796373282*EX(-2) + 0.235465166043*GOVEXP(-1) + 0.0391333095613*GOVEXP(-2) - 0.195036505524*IM(-1) - 0.0353871011175*IM(-2) - 0.135101279389*INV(-1) - 0.0238728901531*INV(-2) + 22.5704034269

IM = 0.00253183982672*COV19(-1) + 0.000855173123171*COV19(-2) + 0.344821960374*CPI(-1) + 0.0973824244247*CPI(-2) + 0.369174581705*EMP(-1) + 0.0438533176493*EMP(-2) + 0.00856973726513*EX(-1) + 0.000431924402619*EX(-2) - 0.195850742777*GOVEXP(-1) - 0.0288954247371*GOVEXP(-2) + 0.255822742067*IM(-1) + 0.0459764239677*IM(-2) + 0.156533146801*INV(-1) + 0.0266710700125*INV(-2) + 7.13635876775

INV = 0.00159835764357*COV19(-1) + 0.000754880441889*COV19(-2) + 0.300271718123*CPI(-1) + 0.0979418438619*CPI(-2) + 0.598343175661*EMP(-1) + 0.0801527442163*EMP(-2) + 0.0162089857513*EX(-1) + 0.00184077061795*EX(-2) - 0.292747388303*GOVEXP(-1) - 0.0441480070149*GOVEXP(-2) + 0.337501609669*IM(-1) + 0.0599968567622*IM(-2) + 0.221799614577*INV(-1) + 0.0376160926389*INV(-2) + 14.5810033418

Table (6): Variance Decomposition

  Period S.E. CPI COV19 EMP EX GOVEXP IM INV

Variance Decomposition of

 CPI 1 0.008174 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

  2 0.009844 99.58210 0.256337 0.161559 3.93E-06 5.35E-07 6.72E-08 1.13E-09

  3 0.010989 98.45631 1.252375 0.291305 4.15E-06 9.81E-07 1.09E-07 1.87E-09

  4 0.011774 96.99745 2.576159 0.426387 3.73E-06 1.39E-06 1.39E-07 2.43E-09

  5 0.012362 95.35199 4.106415 0.541592 3.39E-06 1.74E-06 1.61E-07 2.87E-09

  6 0.012819 93.67985 5.687162 0.632987 3.23E-06 2.03E-06 1.79E-07 3.21E-09

  7 0.013185 92.07303 7.226617 0.700344 3.21E-06 2.26E-06 1.91E-07 3.47E-09

  8 0.013483 90.58543 8.667949 0.746612 3.28E-06 2.45E-06 2.01E-07 3.65E-09

  9 0.013728 89.24206 9.982103 0.775828 3.40E-06 2.58E-06 2.08E-07 3.78E-09

  10 0.013930 88.04985 11.15803 0.792111 3.54E-06 2.68E-06 2.13E-07 3.87E-09

  11 0.014098 87.00488 12.19597 0.799140 3.67E-06 2.75E-06 2.16E-07 3.93E-09

  12 0.014238 86.09730 13.10275 0.799943 3.79E-06 2.80E-06 2.18E-07 3.97E-09

Variance Decomposition of COV19 1 0.700883 0.699305 99.30069 0.000000 0.000000 0.000000 0.000000 0.000000

  2 0.861177 0.903663 98.97456 0.121765 8.45E-06 3.52E-07 1.92E-08 1.74E-10

  3 0.973906 1.164141 98.58399 0.251859 1.10E-05 4.22E-07 2.48E-08 1.98E-10

  4 1.051827 1.385760 98.20026 0.413965 1.15E-05 4.03E-07 2.53E-08 1.80E-10

  5 1.108970 1.569599 97.84392 0.586471 1.14E-05 3.71E-07 2.44E-08 1.62E-10

  6 1.151811 1.713037 97.52544 0.761509 1.11E-05 3.44E-07 2.32E-08 1.53E-10

  7 1.184482 1.820273 97.24721 0.932502 1.08E-05 3.27E-07 2.21E-08 1.54E-10

  8 1.209674 1.896919 97.00768 1.095387 1.05E-05 3.20E-07 2.12E-08 1.64E-10

  9 1.229248 1.949017 96.80332 1.247656 1.02E-05 3.20E-07 2.06E-08 1.81E-10

  10 1.244537 1.982206 96.62983 1.387951 9.99E-06 3.27E-07 2.01E-08 2.03E-10

  11 1.256521 2.001384 96.48289 1.515716 9.82E-06 3.39E-07 1.99E-08 2.27E-10

  12 1.265938 2.010592 96.35845 1.630950 9.68E-06 3.54E-07 1.98E-08 2.54E-10

 Variance Decomposition of EMP 1 0.006138 1.615605 66.50687 31.87753 0.000000 0.000000 0.000000 0.000000

  2 0.007686 1.903519 65.07642 33.02005 3.58E-06 5.88E-06 1.74E-07 7.16E-09

  3 0.008757 2.285302 63.81983 33.89485 5.32E-06 8.63E-06 2.58E-07 1.05E-08

  4 0.009506 2.684801 62.63453 34.68065 6.49E-06 1.03E-05 3.11E-07 1.25E-08

  5 0.010055 3.095888 61.52578 35.37832 7.39E-06 1.15E-05 3.50E-07 1.39E-08

  6 0.010464 3.508347 60.49565 35.99598 8.14E-06 1.24E-05 3.81E-07 1.51E-08

  7 0.010774 3.914988 59.54727 36.53772 8.78E-06 1.32E-05 4.07E-07 1.59E-08

  8 0.011012 4.309775 58.68229 37.00791 9.33E-06 1.38E-05 4.29E-07 1.67E-08

  9 0.011196 4.687880 57.90072 37.41137 9.82E-06 1.43E-05 4.47E-07 1.73E-08

  10 0.011339 5.045590 57.20095 37.75344 1.03E-05 1.48E-05 4.63E-07 1.78E-08

  11 0.011451 5.380232 56.57994 38.03980 1.06E-05 1.51E-05 4.77E-07 1.82E-08

  12 0.011539 5.690088 56.03351 38.27637 1.10E-05 1.54E-05 4.89E-07 1.86E-08

Variance Decomposition

of EX

  1 0.054699 28.78003 71.04114 0.178637 0.000185 0.000000 0.000000 0.000000

  2 0.065681 27.93223 71.30314 0.764490 0.000140 2.22E-08 5.78E-09 2.59E-10

  3 0.072192 27.56967 71.14567 1.284532 0.000121 1.06E-07 1.26E-08 5.35E-10

  4 0.076150 27.32910 70.81844 1.852354 0.000110 2.74E-07 2.14E-08 8.73E-10

  5 0.078714 27.18554 70.40230 2.412051 0.000104 4.89E-07 3.11E-08 1.23E-09

  6 0.080441 27.10734 69.94955 2.943006 9.96E-05 7.30E-07 4.10E-08 1.59E-09

  7 0.081640 27.07567 69.49511 3.429120 9.67E-05 9.76E-07 5.07E-08 1.93E-09

  8 0.082495 27.07656 69.06082 3.862521 9.47E-05 1.22E-06 5.98E-08 2.25E-09

  9 0.083118 27.09972 68.65934 4.240849 9.33E-05 1.44E-06 6.82E-08 2.53E-09

  10 0.083580 27.13746 68.29691 4.565545 9.23E-05 1.64E-06 7.58E-08 2.79E-09

  11 0.083929 27.18406 67.97549 4.840360 9.17E-05 1.82E-06 8.25E-08 3.01E-09

  12 0.084195 27.23530 67.69434 5.070267 9.12E-05 1.98E-06 8.84E-08 3.20E-09

 Variance Decomposition of GOVEXP 1 0.019355 6.145042 13.80632 80.04830 0.000307 3.34E-05 0.000000 0.000000

  2 0.023890 4.557698 15.91526 79.52676 0.000248 3.86E-05 4.39E-07 1.70E-08

  3 0.027090 3.553019 17.55195 78.89477 0.000218 4.06E-05 6.33E-07 2.44E-08

  4 0.029362 3.110544 18.71265 78.17656 0.000200 4.14E-05 7.36E-07 2.81E-08

  5 0.031067 3.051855 19.46850 77.47942 0.000187 4.17E-05 8.00E-07 3.04E-08

  6 0.032375 3.245211 19.90778 76.84679 0.000178 4.19E-05 8.44E-07 3.18E-08

  7 0.033395 3.593726 20.11115 76.29491 0.000171 4.20E-05 8.77E-07 3.29E-08

  8 0.034197 4.029188 20.14752 75.82308 0.000166 4.21E-05 9.02E-07 3.36E-08

  9 0.034833 4.504773 20.07220 75.42282 0.000162 4.22E-05 9.22E-07 3.42E-08

  10 0.035340 4.989012 19.92795 75.08284 0.000159 4.22E-05 9.38E-07 3.46E-08

  11 0.035747 5.461328 19.74661 74.79186 0.000157 4.22E-05 9.51E-07 3.50E-08

  12 0.036074 5.908798 19.55111 74.53990 0.000155 4.23E-05 9.62E-07 3.53E-08

Variance Decomposition

Of IM 1 0.019652 3.089840 12.03831 84.87166 8.00E-05 0.000104 3.92E-06 0.000000

  2 0.024696 4.379078 12.63056 82.99018 8.83E-05 8.56E-05 3.11E-06 2.14E-08

  3 0.028316 6.049396 12.52305 81.42738 8.91E-05 7.78E-05 2.77E-06 3.02E-08

  4 0.030938 7.700403 12.24242 80.05701 8.79E-05 7.35E-05 2.60E-06 3.46E-08

  5 0.032932 9.296092 11.84293 78.86082 8.63E-05 7.08E-05 2.49E-06 3.72E-08

  6 0.034482 10.78832 11.39781 77.81372 8.48E-05 6.89E-05 2.42E-06 3.88E-08

  7 0.035706 12.15775 10.95028 76.89182 8.35E-05 6.75E-05 2.38E-06 3.99E-08

  8 0.036684 13.39596 10.52958 76.07430 8.25E-05 6.64E-05 2.34E-06 4.06E-08

  9 0.037474 14.50248 10.15344 75.34392 8.15E-05 6.56E-05 2.31E-06 4.12E-08

  10 0.038116 15.48163 9.831219 74.68700 8.08E-05 6.49E-05 2.29E-06 4.15E-08

  11 0.038642 16.34073 9.566166 74.09296 8.01E-05 6.44E-05 2.28E-06 4.18E-08

  12 0.039077 17.08879 9.357294 73.55377 7.96E-05 6.39E-05 2.26E-06 4.20E-08

Variance Decomposition

of INV 1 0.028593 0.772151 17.99897 81.22870 0.000108 6.94E-05 4.54E-06 6.52E-07

  2 0.035887 1.616820 18.76716 79.61585 0.000104 6.23E-05 3.43E-06 4.34E-07

  3 0.041090 2.800860 18.83995 78.35902 9.94E-05 5.91E-05 2.98E-06 3.44E-07

  4 0.044840 4.058548 18.66786 77.27343 9.56E-05 5.73E-05 2.75E-06 2.97E-07

  5 0.047674 5.331873 18.31989 76.34808 9.26E-05 5.61E-05 2.61E-06 2.69E-07

  6 0.049861 6.565149 17.87916 75.55554 9.01E-05 5.53E-05 2.52E-06 2.51E-07

  7 0.051575 7.728723 17.39930 74.87184 8.83E-05 5.47E-05 2.46E-06 2.38E-07

  8 0.052935 8.805363 16.91878 74.27571 8.68E-05 5.43E-05 2.42E-06 2.28E-07

  9 0.054023 9.786900 16.46332 73.74964 8.56E-05 5.40E-05 2.39E-06 2.21E-07

  10 0.054901 10.67107 16.04898 73.27981 8.47E-05 5.37E-05 2.37E-06 2.16E-07

  11 0.055614 11.45957 15.68452 72.85577 8.39E-05 5.35E-05 2.35E-06 2.12E-07

  12 0.056197 12.15664 15.37335 72.46987 8.33E-05 5.33E-05 2.33E-06 2.08E-07

Source: Author using Eviews 10.

    The Variance Decomposition is an alternative method to the impulse response functions to examine effects of shocks to the dependent variables by determining how much of variance for any variable, is explained by innovations to every explanatory variable. Usually, own series shocks explain most of the error variance, although the shock will also affect other variables. The result of Variance Decomposition on each endogenous variables of the model in response to one standard deviations of COV19 is presented in table 6.

The Variance Decomposition of CPI shows fluctuations in the first month were due to the innovation of the same variable with 100%, and the effect of the rest of the variables on CPI began in the second month. The contribution of COV19 in fluctuations of CPI increased from February to December to be 13% of variance of CPI.

    The Variance Decomposition of COV19 shows fluctuations in the variable were due to the innovation of the same variable COV19 with 99% in the first month and continued to the last month to be 96%.

    The Variance Decomposition of EMP shows the COV19’s contribution in fluctuations of EMP in the first month with 66% and continued during the period to reach 56% in December, and EMP contributed with 38%.

    The Variance Decomposition of EX indicates the COV19’s contribution in fluctuations of EX in the first month with 71% and continued during the period to reach 67% in December 2020, and CPI contributed with 27% of EX’s variance.

    The Variance Decomposition of GOVEXP shows the COV19’s contribution in fluctuations of GOVEXP starting from January with 13% and continued during the period to reach 19% in December 2020, and EMP contributed with 74% of GOVEXP’s variance.

    The Variance Decomposition of IM shows the COV19’s contribution in fluctuations of IM with 12% the first month and decreased to 9% in the last month, CPI contributed with 17% and EMP contributed with 73% of IM’s variance.

    The Variance Decomposition of INV indicates the COV19’s contribution in fluctuations of INV with 18% the first month and decreased to 15% in the last month, CPI contributed with 12% and EMP contributed with 72% of INV’s variance

Figure (14): Impulse Response Function

          Source: Author using Eviews 10.

The Impulse response functions used to show the time path of the dependent variables in the PVAR, to shocks from all the independent variables. If equations are stable any shock should decline to zero, the shock produces an explosive time path. The result on Impulse Response Function of each endogenous variable in response to one standard deviations of COV19 is presented in figure 14.

        The result of impulse response function reveals that the COV19 impact lasts at least 30 months (2 years and half) to shake the Arab economies. The VAR estimate indicates that COV19 uncertainty shock results a massive rise in CPI in the six months following the outbreak of the pandemic, then decrease CPI next 24 months.

        The findings represent COV19 uncertainty shock results a decrease in EMP during 2 years following the outbreak of the pandemic then its effect will be zero. And the pandemic has the same effect on EX.

        Regarding the effect on GOVEXP, COV19 uncertainty shock results a decrease in GOVEXP during 2 years following the outbreak of the pandemic. And make a decrease in IM during 10 months after the outbreak then beginning to rise during the next 20 months.

        The impulse response function shows the COV19 results a decrease in INV in 12 months following the outbreak of the pandemic then induces a rise in INV until end of the period.

        The findings revealed the breakups of the Arab economies; COVID 19 was a supply shock in its first-time impact, but quickly trans-passes to demand shock. the pandemic effect decreases employment, exports and government expenditure, but investment and imports decline up then show a slight increase, and results a massive rise in consumer price index then a slight decrease.

5-SWOT Analysis of COVID 19 Impact on the Arab Countries

A- Strengths

 The strengths achieved by the Arab countries from the repercussions of the Corona pandemic are as follows:

1- High profits in vital sectors

 The sectors that become the most profitable as a result of the repercussions of the Coronavirus are; Pharmacy, sterilizers, electronic commerce, telecommuting, and logistics.

2- Saving and rationalizing expenditures

 The Arab countries seek to save and rationalize expenditures, which saves public budgets to support many economic activities and face the repercussions of the Corona pandemic.

3- Reducing polluting emissions

 The Arab countries are reducing the polluting emissions resulted from many industrial activities and the movement of transportation. In addition to give sufficient time to conduct regular maintenance of factory machinery and transportation networks.

B- Weaknesses

 the Arab countries achieved a set of weaknesses because of the spread of "COVID-19" virus, as follows: -

1- Poor health systems

 Arab countries beard great costs in dealing with the Coronavirus, by providing necessary funding for the medical infrastructure to confront the pandemic, including a lot of medical, safety equipment and sterilization materials.

2- High loss rates in vital sectors

 The sectors that cost huge loss as a result of the virus are: aviation, travel and tourism, hotel and hospitality, conferences, exhibitions and festivals, trade services, and oil.

3- Expatriate workers are harmed

 The labor-exporting countries, including Egypt, have witnessed a slowdown in workers' remittances due to layoffs and delays in paying salaries in the countries in which they work.

4- Poor living conditions

 The chronic symptoms of structural imbalance in some Arab countries have resulted in economic diseases, including over-consumption, poor saving and investment, and corruption, in addition to the imbalance of market structures, represented by poor distribution, class inequality, erosion of the middle class, and spread of poverty and unemployment.

3- Low oil prices

 Oil prices have decreased in the Arab Gulf states, as falling fuel prices is the main driver of sharp decline in the free-market commodity price index, as it decreased by 33.2% in March 2020.

4- Poor information infrastructure

 Data in some Arab countries are still being collected on paper rather than digital, so it is difficult for citizens to deal directly with service providers as a result of outbreak of the disease.

5- High unemployment rates

 The unemployment rates have increased, especially for some sectors as the tourism, which contributes at rates ranging between 12 to 19% of the GDP in some Arab countries that are global tourist destinations.

6- Difficulty in coordinating economic policies

 The Arab countries vary in degrees of economic growth, in addition to the different economic systems, which leads to the difficulty of coordinating economic policies.

7- Economic and financial dependency

 Many Arab countries are economically dependent on developed countries, and this dependence makes private interests more urgent.

8- The weakness of the competitive structure of the Arab economies

 There is a similar competing industry with low productivity and high production expenditures that only produce under protection with weak production base in addition to external orientation of the development strategy in the Arab countries.

C- Opportunities

 The Arab countries can exploit many opportunities resulted from the Corona virus at the external level, as follows:

1- The prosperity of the medical industries

 Arab countries can benefit from prosperity of the medical industries, because the total imports of medical products during 2019 amounted to 2 trillion $, including the European Union trade, which represents 5% of the total world trade. Arab countries can invest this to engage in medical industries and benefit from the increasing global demand on them.

2- Digital finance to stop the spread of COVID-19

 The digital finance is considered as a tool to stop the spread of COVID-19; along with their partners, are implementing procedures to transfer a larger volume of payments using the mobile.

3- Enhancing intra-Arab trade

 The Arab countries can turn inward to enhance intra-trade, especially in pharmaceuticals and basic foods, in addition to start manufacturing medicines, which leads to lowering their prices, and boosts trade between Arab countries. Also, the Arab countries can support local investments in the pharmaceutical industry, it helps ensure the quality and safety of imports, and contributes to financial sustainability with the increase in import bills across the Arab countries.

4- Increasing the most attractive investment opportunities

 The most attractive investment opportunities for Arab countries are investment in the energy sector and the transport sector, in addition to investment in education, building and managing economic zones, and specific service activities, such as information and communication technology services, financial and health services, and professional services.

5- Creating a balanced and integrated development strategy among the Arab countries

 The priority of Arab countries is for strategic dimensions such as food security, infrastructure, and human capital, in addition to partnership with the private sector in planning and implementing these investments.

D- Threats

 There are a number of threats as a result of the repercussions Corona virus, as follows: -

1- Slowdown in the global economic growth

 The slowdown in global economic growth and the disruption of global trade led to lower rates of growth. The global economic slowdown reached 2% for 2020, and its costs amounted to 1 trillion $, resulted in the decrease in oil prices.

2- Poor financial market performance

 The losses resulted in a global economic depression, as investors’ withdrawal their capital from investment in the financial markets to protect their investments from economic fluctuations resulted from corona pandemic.

4- Lack of competencies in the labor market

 There is a temporary shortage of competencies in the labor market, lead to shortage of production in the public and private sectors.

Table (7): Results of the SWOT Analysis

Strengths Weaknesses

Saving and rationalizing expenditures in budgets Poor health systems

Reducing polluting emissions High loss rates in some sectors

Implementing and conducting periodic maintenance Poor living conditions

High profits in vital sectors Weak information infrastructure

Reducing fuel consumption and energy resources Low oil prices

Expanding the umbrella of social insurance Difficulty coordinating economic policies

Increasing support for the medical sector and equipping hospitals Economic and financial dependency

Expanding the provision of services electronically Weakness of the competitive structure

New technologies and business models Restriction of travel and movement for individuals and goods

Restructure supply chains and move towards decentralization in manufacturing Sanitary isolation measures and the closure of markets and service centers.

  Opportunities Threats

Booming medical industries Slowing global economic growth

Digital Finance to Stop the Spread of COVID-19 Reduced inflow of foreign direct investment

Promote intra-Arab trade and Strengthening Arab relations poor performance of the financial markets

Increasing the most attractive investment opportunities Lack of competencies in the labor market

Creating a balanced and integrated development strategy New waves of Corona virus

Diversify export partners and sources of financing

     Source: By Author

6- Mechanisms to confront COVID19 in the Arab Countries

 The corona pandemic can be dealt with in two phases: a phase of containment and stabilization, followed by a phase of recovery, there is a crucial role for both public health and economic policies. These measures can help avoid a more severe and longer-term recession and pave the way for the economy to recover. Increased spending on health care is necessary to ensure that health care systems have the initial capacity and resources. This requires fundamental measures directed through public finances, monetary policy and the financial sector to maintain economic links between employment, companies, lenders and borrowers, thus preserving the integrity of the economic and financial infrastructure. In addition to digital technology can be used to provide targeted support to those who need it.

 The corona crisis can be classified as a double crisis, it is an external and internal crisis, it is a crisis of demand and supply, and it is a market crisis and a government crisis. it is difficult for countries to rely on the market because of the market collapses and it is difficult to rely on demand because the crisis extended both sides of demand and supply, so these mechanisms are divided into three groups, as follows:

A- At the national level

  The efforts and measures taken by the Arab countries in order to reduce the economic repercussions of the crisis, and call on Arab countries to continue:

1- Supporting national banks to implement the procedures for postponing the payment of customer debt installments, restructuring the credit facilities granted, providing concessional financing for small and medium enterprises to maintain support for effective aggregate demand, and helping companies bear the consequences of the pandemic.

2- Increase the coverage of social protection programs and facilitate access to them to ensure the population most exposed to risks is not neglected. The implementation of these measures will require a combination of domestic and foreign resources.

3- Follow expansionary monetary policies, by reducing interest rates and the cash reserve ratio for bank grants, as well as reducing fees, profit rates, and commissions that financial institutions charge from their clients, especially small borrowers, and giving banks greater flexibility to provide direct credit to the health services sector.

4- Increasing allocations for education, scientific research and health in the public budgets of Arab countries. The percentage of spending on scientific research in Arab countries does not exceed 1.5%, so it is important to increase allocations for these sectors to achieve the desired results in the Arab countries.

B- The Arab Financing Institutions

1- Establishing an Arab Crisis Fund similar to the Small and Medium Enterprises Support Fund. To manage the funds in accordance with the purpose to lend procedures in the fund are more flexible and expeditious, avoid routine, and provide loans to the private sector affected by the pandemic with easy terms and low interest rates.

2- Forming crisis task forces from the relevant joint Arab action institutions and Arab financing institutions whose task is monitoring the impact of corona pandemic on Arab countries, in order to prepare sectoral studies and suggest the necessary policies.

3- Maximize benefit from the trade financing mechanisms available with the Arab Trade Finance Program to mobilize funds and support credit lines directed to financing intra-Arab trade.

4- Coordinating between the existing regional funds by directing part of their investments towards the health sector and small and medium enterprises; Providing dependency allowances for those who have lost their jobs; building stocks of food and medicine; Designing and financing appropriate programs to support Arab exporters and importers.

Conclusion

 The study determined the COVID 19 pandemic impacts on the Arab countries, through macroeconomic variables like investment, employment, imports, exports, prices, and government expenditure, using the Bayesian Vector Auto-regressions model PVAR. As well as the SWOT Analysis method to analyze the repercussions of Coronavirus on the Arab economies. The Arab countries need a help of bilateral creditors from advanced economies and the international financial institutions, so multilateral cooperation is necessary. In addition to exchanging equipment and specialized expertise to strengthen health care systems, a global effort must be made to ensure that rich and poor countries have access to the required drugs and vaccines for the COVID-19 virus as soon as they are reached. The international community will also need to intensify financial aid to many emerging market and developing economies.

 Finally, building an Arab stock of personal protective equipment, to protect individuals and institutions, preventing more severe economic suffering and creating favorable conditions for recovery.

Endnotes