Supply Chain Sustainability in the Context of COVID-19 Pandemic in Pakistan’s Economy: Using Computable General Equilibrium (CGE) Model

The COVID 19 pandemic has had tremendous economic impacts and continues to wreak havoc around the world. This research work has been conducted to analyze the macroeconomic effects of the COVID 19 in the context of Pakistan. The impact ECON Supply Chain (IESC) Computable general equilibrium (CGE) Model which was formulated by Walmsley and Minor (2016) has been employed so that the supply chain effects of many of Pakistan’s government policies in response to the coronavirus pandemic can be assessed. An 8% shock was given to 11 sectors of the economy and a 5% shock was given to electricity. Lastly, the impact of these shocks on all 31 sectors of the economy that are included in the model was assessed. Results discovered that there was a decline in real GDP, real exports, real imports, and per capita utility from private expenditure, meanwhile, terms of trade and regional household income increased. This study also illuminated that during pandemic goods market prices increased for 16 sectors while supply price of commodities decreased for 15 sectors. Based on the empirical findings, some relevant policy implications are suggested to overcome the pandemic.


INTRODUCTION
The coronavirus pandemic has had tremendous economic impacts and continues to wreak havoc around the world. This research work has been conducted to analyze the macroeconomic effects of the coronavirus in the context of Pakistan. There exists great uncertainty about how long this infection will last, and how severe it will be; hence three scenarios have been analyzed, starting from moderate events to disastrous circumstances. To be specific, this research work takes into account an 8% shock to 12 different sectors of the economy including oil, extraction, textile, wearing apparel, light manufacturing, heavy manufacturing, electricity, natural gas, tourism and accommodation, utility consumption, transport and communication, as well as services. After this, we seek to understand the effect of this 8% shock in terms of the 31 sectors of the economy that have been included in the model. The value of 8% was obtained through division of one by a total of twelve (months in a year) and further multiplication by hundred to get percentage of loss if a lockdown is put in place for one month. A table was formulated whereby we checked whether growth rate have increased or decreased for each sector. According to the positive or negative value of each of the sectors, a positive or negative shock of 8% was given. In case of electricity, a negative 5% shock was given, as much less electricity was used in the country because of closure of offices as well as educational institutes. This is demonstrated in the table below: Analysis has been carried out using computable general equilibrium (CGE model), which is a novel economy wide technique used for modeling. This model may also be called a multi-market model that considers behavioral responses with regard to consumers and producers alike, considering the fluctuating prices, regulations as well as different conditions relevant to markets that are interconnected, also taking into account constraints with regard to resources. CGE models characterize an economy based on their interconnectedness in terms of supply chains. CGE models have been used in previous literature to analyze the economic impacts of threats to health for example influenza (Dixon et al. 2010(Dixon et al. , 2020Prager et al. 2017;Walmsley et al. 2020). We have employed the Impact ECON Supply Chain Model which has been adapted from the GTAP model which is one of the most commonly used CGE model, and has the capacity to further analyze supply chain effects that are connected to economic activities as well as policies around the globe.
We have used the assumptions, the variables as well as the parameters for analysis that have been explained in detail later. These assumptions have been used so that the analysis can be carried out without difficulty. Sensitivity tests were carried out regarding some major assumptions as well as parameters to ensure the robustness of the results. However, the mix of assumptions allows our results to be taken as upper bound estimates. The main factor that has an impact on the results about each of the three scenarios is a mix of closures and business reopening. The 2 nd most important factor is pent up demand which arose because of restrictions on spending owing to closures as well as reopening that was partial. Asian Development Bank predicted in initial March that the economy of Pakistan would face a loss of nearly 16 million US$ in case of best scenario, while standing at 61 million US$ in the case of worst scenario. In case there was a significant outbreak, this would lead to an approximate loss of 5 billion US dollars, a 1.57% contraction in GDP as well as loss of jobs for about a million people. Near the end of March, the Asian Development Bank provided revised estimates standing at 415 million US dollars in the case of best scenario and a loss of 6.6-17 billion US dollars if the outbreak was significant. Employment loss was expected to range between 1.2-3.2 million jobs while GDP growth was expected to contract from 2-5%.

Figure 1: Channels through which COVID-19 affects the Economy
Other studies have found that there is a presence of stress points and goes on to identify points that can be helped the most through interventions of policy. Individuals and businesses adapt rapidly, as shown by a rise in telework, and so the adverse economic effects of the pandemic are being reduced through individual motivations. Together with this, considering that uncertainty still exists with regard to the main drivers, studies have taken into account this uncertainty by considering 3 different scenarios, emphasizing on the time duration, the level of severity, as well as the course of the outbreak, that allows us to bound the extent of potential effects. The scenarios that have been considered, as well as the decomposition analysis about specific factors allow the discovery of further differences in driving forces through combination of components in terms of analysis.
We intend to carry out an analysis explaining the macroeconomic effects of the coronavirus pandemic with regard to a given set of factors that cause it. This makes our research work comprehensive. Through this study, we aim to add to available literature, and provide information to policymakers through decomposition with respect to the relative impacts of different causal factors.

GENERAL CGE METHODOLOGY
The impact ECON Supply Chain (IESC) CGE Model which was formulated by Walmsley and Minor (2016), Footnotel has been employed so that the supply chain effects of many of Pakistan's government policies in response to the coronavirus pandemic can be assessed. The model has its basis in the GTAP model which is widely used, and also possesses all of its characteristics (Hertel and Tsigas 1997;Corong et al. 2017). The model is taken to be a benchmark when it comes to analysis related to policy issues as well as global trade. The database which underlies it, consists of input output tables as well as trading relations with respect to one hundred and forty one countries and sixty five commodities taken from the database of GTAP (Aguiar et al. 2019), together with more detail related to the source of intermediate as well as final products. So that the model can be calibrated, many substitution and demand elasticities are taken in combination with this data. The GTAP model is adapted by the IESC model so that detail with respect to tariff and trade data regarding sources of imported goods (intermediate and final) can be included, thus adding to the analysis with respect to effects on global supply chains. For this case, we had information regarding the sectors that were affected most by the lockdown imposed due to the situation of the pandemic, which enables us to find out how disruptions or delays. In this case, information is available about how the hindrance or delay in the import of intermediate goods from foreign countries has negatively affected Pakistan's ability related to production or export of commodities. A comparative static CGE model is called the IESC model which gives a method that is consistent theoretically, to analyze effects of global shocks on Pakistan's economy. The model incorporates demand by households, firms, government as well as for purposes of investment. It also incorporates supply related to 8 factors of production by households including five categories of labor, land, natural resources and capital. To capture the effect of mandatory closures, the production of affected sectors is reduced by 8 percent and as a result, final demand falls. Some iterations have been run on the basis of this so the indirect effects regarding closure of these sectors may be considered in relation with rest of the sectors. An advantage of these models lies in the fact that indirect effects that business closures done within one sector have on the rest of the sectors, can be captured. An example of this is that as restaurants close down, demand related to vegetables and fruits also decreases as they are no longer used to produce meals in restaurants. Sometimes, indirect effects turn out to be even larger compared to the sector which has been closed down, and thus these indirect impacts are allowed to become dominant. Sectorial production may decrease more as compared to the sector's share which has been closed down. Resultantly, a decrease in production must be imposed in case of those sectors only where direct effects of mandatory closures are higher compared to indirect impacts that are the result of closure of other sectors particularly recreation related services as well as construction. Mandatory closures cause production to decrease, while pent up demand and avoidance are likely to increase and reduce private consumers' final demand respectively. When it comes to avoidance in terms of education, a decrease in government demand is also likely. Per capita utility from Private expend -3.565721

EMPIRICAL FINDINGS
Regional household income 2.634635

(Author Simulations)
As a result of lockdown, an 8% shock was given to 11 sectors of the economy including oil production, extraction, textile, wearing apparel, light manufacturing, heavy manufacturing, tourism, natural gas, utility consumption, transport, and communication, as well as services. In addition, a 5% shock was given to electricity. The results are as follows: An 8% shock to the 11 selected sectors and a 5% shock to electricity caused a 5.04336 million USD decline in Real GDP, 13.544453 million USD decline in real exports, 4.178948 million USD decline in real imports, a 2.793341 million UD rise in terms of trade, 3.565721 million USD decline in per capita utility from private expenditure, and a 2.634635 million USD rise in regional household income.    gas, while showing a declining trend for oil production, extraction, wearing apparel, light manufacturing, heavy manufacturing, tourism, utility and communication, services and CGDS.      shock to electricity.    Figure 6 shows Percentage sales in 21 sectors of the economy following an 8% shock to 11 sectors of the economy and a 5% shock to electricity.    Figure 7 shows Percentage change in 8 sectors of the economy following an 8% shock to 11 sectors of the economy and a 5% shock to electricity.  Figure 8 shows effects on real returns to factors before and after lockdown. The bar graph shows a rise in returns for technical and professionals, clerks, service shops, officers and managers and capital. In contrast, real returns declined for land, agriculture low skilled labor and natural resources.    Figure 9 shows market price of commodity for 31 sectors pre and post the lockdown period. The bar graph shows an increase in market prices for technical and professionals, clerks, service shops, officers and managers, agriculture low skilled workers, capital, extraction, textile, wearing apparel, chemicals, light manufacturing, pharma, heavy manufacturing, tourism, utility consumption, financial business, transport and communication, services and CGDS. However, market prices fell in the case of land, leather, natural resources, grain crops, vegetables and fruits, meat and livestock, processed food, metals, motor parts, electricity, natural gas and financial business.          Figure 12 shows private consumption price for commodity following an 8% shock to the 11 selected sectors and a 5% shock to electricity.

CONCLUSIONS
Pakistan's economy was in a fragile state and had just began to become stable when the pandemic hit. Experts have feared that the economic fallout of the corona virus pandemic is likely to derail Pakistan's process of recovery in a considerable way. In our research work, we seek to understand the effects of an 8% shock to 11 sectors of the economy and a 5% negative shock to electricity on the overall 31 sectors that are part of the model. The results are as follows: • There was a decline in Real GDP, Real Exports, Real imports, and Per Capita Utility from Private Expenditure. Terms of trade and Regional household income increased.
• Sectorial output remained unchanged for 18 sectors, declined for 11 sectors, and rose for 2 sectors.
• Exports decreased for 10 sectors and rose for 12 sectors, while imports rose for 10 sectors and declined for 12 sectors.
• Sectorial prices rose for 16 sectors and declined for 15 sectors.
• Domestic sales increased for 3 sectors and decreased for 19 sectors.
• Real returns to factors rose for 4 sectors and decreased for 4 sectors.
• Value addition declined for 19 sectors and increased for 4 sectors.
• Private consumption price of commodity increased for 10 sectors and declined for 12 sectors.
It is suggested Pakistan's government should treat the crisis due to COVID-19 as an opportunity to undertake economic, political, and foreign policy reforms so that prospects of increased downturn in the economy can be quashed. The most highly affected sectors due to Covid-19 within the services sector are tourism and transport industries. SMEs in millions are likely to close in the long run.