Impact Quantifying the of COVID-19 on the Private Sector inPakistan This report has been prepared by a team led by Rafay Khan (Economist, ESAF1, World Bank ) and Amjad Bashir (Senior Operations Officer, CSAA1, IFC), and included Mehdi Benyagoub (Private Sector Specialist, EAWF1, World Bank), Arti Grover (Senior Economist, ETIFE, World Bank) and Stavros Poupakis (Consultant). The analysis presented in the report is based off of data collected by Gallup Pakistan during the months of June and July 2020. Table of Contents Table of Contents I List of Figures II List of Tables III Executive Summary 1 1. Introduction 6 2. Impact of COVID-19 on the Private Sector in Pakistan 10 2.1 Operational Status 10 2.2 Impact on Sales 14 2.3 Impact on Employment 16 2.4 Shock Transmission Channels 18 2.5 Firm Survival and Resilience 21 3. Expectations and Uncertainty 23 4. Adaptation and Digitization 25 5. Government Policies and Mitigating the Economic Impact of the Crisis 27 Appendix 1 – Sample Characteristics 31 Appendix 2 – Sample and Sampling Methodology 35 Sampling Frame 35 Representativity 36 Sample Selection and Stratification 36 I List of Figures Figure 1: Global Growth 6 Figure 2: Growth in EMDEs 6 Figure3: Daily New Cases, Cumulative Cases and Active Cases of COVID-19 in Pakistan 7 Figure 4: Projected Impact of COVID -19 on Real GDP Growth (%) 8 Figure 5:Operational Status of Businesses 11 Figure 6: Operational Status by Firm Size 12 Figure 7: Operational Status by Province 12 Figure 8: Operational Status by Sector 12 Figure 9: Operational Status by Exporting Status 12 Figure 10: Change in Sales 14 Figure 11: Distribution of Change in Sales 14 Figure 12: % of Firms Reporting Change in Sales by Sector 15 Figure 13: % of Firm Reporting Change in Sales by Exporting Status 15 Figure 14: Predictive Effect of Size on Change in Sales 16 Figure 15: Margin of Adjustment in Employment 17 Figure 16: Percentage of Firms Affected by Shocks (aggregate) 19 Figure 17: What is Driving the Supply Shock? 19 Figure 18: Percentage of Firms Affected by Shocks (sectoral decomposition) 20 Figure 19: Percentage of Firms Affected by Shocks (size decomposition) 20 Figure 20: Percentage of Firms Affected by Shocks (decomposition by status as exporter) 21 Figure 21: Number of Day of Cash Buffers 21 Figure 22: % of Firms Reporting a Decline in Sales, Employment and Capital Investment over next 3 Months 23 Figure 23: Expected % Decline in Sales, Employment and Capital Investment over the next 3 Months 23 Figure 24: Distribution of Expected Change in Sales 24 Figure 25: Distribution of Expected Change in Employment 24 Figure 26: Expected Change in Sales, Employment and Capital Investments by Firm Size 24 Figure 27: Expected Change in Sales, Employment and Capital Investments by Exporting Status 24 Figure 28: COVID-19 Related Digital Adjustment 25 Figure 29: COVID-19 Related Investment in Equipment 25 Figure 30: COVID-19 Related Adjustments by Size 26 Figure 31: COVID-19 Related Adjustments by Exporting Status 26 Figure 32: COVID-19 Adjustments and Micro Firms 26 Figure 33: Most Needed Policy Support by Percentage of Firms 27 Figure 34: Policy Support Priorities by Exporting Status 28 Figure 35: Policy Support Priorities by Firm Size 28 Figure 36: Support Received in Response to the Crisis by Firm Size 29 Figure 37: Support Received in Response to the Crisis by Sector 29 Figure 38: Support Required vs Support Needed 29 Figure 39: Reasons why firms have not received support 29 Figure 40: Distribution of Firms by Size 32 Figure 41: Distribution of Firms by Province 32 Figure 42: Distribution of Firms by Sector 33 Figure 43: Distribution of Firms by Organizational Structure 33 II List of tables Table 1: Estimated Number of Jobs in Businesses Affected by the Pandemic 13 Table 2: Change in Sales Conditioned on Key Characteristics of Firms 15 Table 3: Percentage of Workers Affected by Margin of Labor Adjustment to the Shock 18 Table 4: Sample Distribution 31 Table 5: Data Estimates 34 Table 6: Distribution of Firms 35 Table 7: Sample Selection 37 III Executive Summary Pakistan, much like other countries in South Asia and elsewhere, has also been hit hard by the pandemic. The total case count has surged to approximately 310,275 with 6,457 confirmed deaths since the identification of the first cases in late February 2020. The crisis, associated lockdown and other containment measures are expected to lead to an economic contraction between 2.6 and 3.3 percent in FY20, and between 0.2 and 4.0 percent in FY21, compared to pre-COVID19 projections for growth of 2.4 percent for FY20 and 3.0 percent for Fy21. The crisis has had a significant negative effect on the private sector, despite the support measures instituted by the government. The private sector was struggling even before the onset of the crisis. Growth has been constricted by a number of factors in recent years, most notably infrastructural bottlenecks, poor investment climate and limited access to finance. The COVID-19 crisis only exacerbated existing challenges while also introducing a slew of new ones. This report summarizes the findings of the Pakistan COVID-19 Business Pulse Survey which was undertaken recently (June-July 2020) to quantify the impact of the crisis on the private sector, in addition to furthering policy makers understanding of how the impact of the crisis has evolved since the start of the crisis and previous surveys. The survey was administered to a representative sample of 1,515 firms operating around the country, leveraging a customized survey instrument designed to measure the impact of the crisis through both quantitative and qualitative measures. The private sector has been impacted by the crisis through multiple channels, including a slump in demand, breakdown in supply chains, tightening of credit markets and mobility restrictions. Only 28 percent of the entities surveyed reported being fully operational, with the rest either operating under reduced hours (51 percent), temporarily closed (13 percent) or closed permanently (9 percent). The impact, however, has been differentiated. Smaller firms and those operating in the services sector were the most impacted by the crisis. 10 percent of micro sized (0-9 employees) firms were closed as a result of the crisis as opposed to 7 percent on medium sized firms (31-250 employees). Off the 54,433 employees covered under the survey, close to 41 percent were working in vulnerable firms i.e. firms which were either operating under reduced operations or temporarily closed. The impact on the operational status, along with a high degree of uncertainty and a slump in demand has translated into a steep decline in sales. Approximately 68 percent of the surveyed firms reported a decline in sales in the months of June and July in comparison to January 2020. The average decline in sales amounted to - 27 percent. Retail sector and micro enterprises were some of the hardest hit segments. 87 percent of the firms in retail and 70 percent of micro enterprises reported a decline in sales. Exporters fared comparatively better. 59 percent of the exporters reported a decline in sales compared to 72 percent of non-exporters. Despite the pronounced impact on the operational status and sales, the impact on employment has not been as stark. Firms reported employing more flexible labor adjustment mechanisms such as sending workers on paid and unpaid leave, reducing working hours and reducing wages over more draconian measures such as 1 laying off employees in response to the crisis. 13 percent of the surveyed firms reported laying off employees, while 3 percent of the total labor force of the surveyed firms was laid off in response to the crisis. The impact was not homogenous by key characteristics of firms. Large firms (250+ employees) reported laying off fewer employees (1 percent of labor force), whereas micro (0-9 employees) and small sized firms (10-30 employees) laid off 10 percent of their labor force. Firms reported having poor expectations on the impact of the crisis on business operations in the coming months, while also indicating limited resilience. On average, firms had 66 days of cash buffers to see through the crisis in the 'prevailing circumstances'. However, there was a lot of variation among firms. 60 percent of the firms reported only having cash buffers for 30 days while a significant 18 percent had no cash buffers at all. These statistics when juxtaposed with firms' expectations about sales, employment and investment are telling of the uncertainty and anxiety facing businesses. 50 percent of the surveyed firms expected a decline in sales in the coming months, expecting an average decline of 21 percent. Similarly, over 50 percent reported expecting a decline in employment. Expectations, however, remain fluid. In the wake of the removal of most government-imposed restrictions in early August, business confidence and expectations are reported to have rebounded. While the impact of COVID-19 has been large and sweeping, the crisis is also driving digitization and technology adoption. More and more firms have started undertaking business through e-commerce platforms or other digital means. 54 percent of the surveyed firms have reported increasing or starting the use of digital platforms for their daily business operations and sales, while 20 percent of the surveyed firms reported investing in digitizing either internal processes of client facing delivery. The uptake in technology adoption and digitization, however, hasn't been uniform. For instance, a larger percentage of exporters reported digital adaptation, with 61.5 percent reporting that they started or increased the use of digital platforms, compared to 50 percent of non-exporters. The government has played its part in attenuating both the social and economic impact of the crisis. The government moved quickly, announcing a PKR1.2 trillion fiscal stimulus package soon after the imposition of a nationwide lockdown. Despite a raft of measures and packages announced by both the federal and subnational governments, however, only 10.7 percent of the surveyed firms reported receiving direct or indirect support from the government in response to the crisis. Formal and larger firms (19.8 percent) were more likely to receive government support compared to micro enterprises (7.69 percent). 41 percent of the firms reported deferral of utility payments as the most needed support policy, this was followed by tax deferral (35 percent) and deferral of rent of mortgage (27 percent). Firms needs and requirements are not consistent and are differentiated by their size, formality status and sector of operation. Economic recovery going forward will be built on the recovery of the private sector, and most importantly, that of the smaller sized enterprises. Support to the private sector to facilitate a robust economic recovery in the coming months needs to be informed by two things. First, recognition of the truly global nature of the crisis and its manifestation through different channels. Simple demand side measures on their own would not suffice. The response, much like the crisis itself, would need to be multifaceted. Second, recognition of the differentiated impact of the crisis depending on key firm characteristics such as firm size, sector of operation, region of operation etc. 2 OPERATIONAL STATUS Impact of the crisis on Firms Temporarily Permanently Fully Operational Reduced Operations Closed Closed 28% 51% 13% 9% Firms in the Sindh showed greater resilience at the time of the survey V u l n e r a b l e 34% Sindh 10% Micro Firms 28% Punjab 6% Small Sized Firms 22% KPK 7% Medium Sized Firms 15% Balochistan 2% Large Firms Impact on Sales -28% -27% Micro Firms (0-9 employees) Exporters Non-Exporters -39% Average Small Sized Firms (10-30 employees) 68% Decline -25% Surveyed Firms Medium Sized Firms (31-250 employees) 72% Reported Decline in -13% Large Firms (250+ employees) 59% Decline in Sales Decline in Sales (Jan 2020) Sales Impact on Employment 13% Firms reported Only 3% Labor force of the surveyed laying off employees firms was laid off 43% Firms adjusted by reducing work hours, Large firms Micro and small cutting wages and laid off sized firm laid off granting leave 1% 10% of their labor of their labor force force 3 Shock Transmission Channels 60% 47% 38% Demand Supply of Inputs Availability of Finance Resilience and Survival On average firms had 66 days of cash buffers 60% Firms had cash 18% Firms had no cash buffers for 30 days buffers Expectations and Uncertainty 50% Surveyed firms expected decline in sales Average decline expected -21% 55% Firms expecting decline in employment Average decline expected -42% 27% Firms expecting decline in capital investment plans in the coming 3 months Average decline expected -10% Adaption and DIgitization COVID is driving a digital revolution in the country 20% Surveyed firms investing in digitizing internal processes or client facing delivery 54% 33% Surveyed firms have reported increased use Surveyed firms adjusted their of digital platforms for product mix to seize new operations and sales market opportunities 4 61.5% 50% Exporters reporting Compared to of non-exporters increased use of digital platforms 30% 16% Exporters reported Compared to of non-exporters investing in digital solutions as a response to the crisis Government support policies to mitigate the impact of the crisis Firms in the Firms operating in the manufacturing sector services sector identified have identified tax deferral utility bills deferral as the as the single most important need of the hour policy support measure 52% 41% Large sized firms identified tax deferrals Firms reported deferral of utility as a policy priority in comparison to payments as the most needed support policy, 30% of micro sized firms. this was followed by tax deferral (35%) and deferral of rent of mortgage (27%) Only 10.7% of the surveyed firms reported receiving direct or indirect support from the government 5 1. Introduction COVID-19 has triggered a global crisis like no other, turning a health crisis to an economic crisis, resulting in the deepest global recession since the second world war. Authorities in 214 countries and territories have reported about 32.9 million Covid-19 cases and 994,000 deaths as of the third week of September 2020, since China reported its first cases to the World Health Organization (WHO) in December 2019. 1 In a base case scenario, global GDP is expected to contract by 5.2 percent in 2020, which if realized would be the deepest global recession in eight decades (see Figure 1). 2 The World Bank projects negative growth for over 150 countries in 2020. This is despite policy measures and stimulus packages announced by governments around the world, which already dwarf support extended by authorities during the 2008-09 financial crisis. The global economic contraction may even be larger if measures to curb the spread are not effective. Under the adverse scenario, output may contract by almost 8 percent in 2020 (roughly equivalent to the combined GDP of France, Italy, and Spain). Figure 1: Global Growth Figure 2: Growth in EMDEs Percent Percent 6 8 4 6 2 4 0 2 Baseline -2 0 -4 -2 Downside/Upside -6 -4 range -8 -6 2020 2021 2020 2021 Note: Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. Baseline scenario: Three months of mitigation measures would be enough to stem the pandemic. A recovery would get underway once mitigation measures are lifted but would be hesitant. Downside scenario: Three months of stringent lockdowns would prove insufficient and another three months of mitigation would be required before the pandemic can be brought under control. Upside scenario: Mitigation measures would be lifted after three months, and all major economies would sputter back to life in the third quarter of 2020. Monetary and fiscal stimulus would remain in place and would be highly effective in supporting growth over the next 18 months. The crisis is exacting a substantial toll on the poor and the economically vulnerable, in addition to exacerbating inequalities. Lockdowns imposed by authorities around the world have led to a sharp contraction in the bottom lines of economic entities, but they've also left millions without jobs. It is estimated that nearly 80 percent of the world's 1.6 billion informal economy workers employed in wholesale and retail, food and hospitality, tourism etc. were some of the hardest hit by the lockdowns.3 Drop in per capita incomes in over 90 percent of EMDEs, in addition to the developed world, will markedly impact living standards causing millions to fall back into poverty, and those already in poverty to descend further into destitution. 1 Johns Hopkins University Center for Systems Science and Engineering 2 World Bank. 2020. Global Economic Prospects, June 2020. Washington, DC: World Bank. DOI: 10.1596/978-1-4648-1553-9. License: Creative Commons Attribution CC BY 3.0 IGO 3 World Bank Group COVID-19 Crisis Response Approach Paper: Saving Lives, Scaling-up Impact and Getting Back on Track (English). Washington, D.C.: World Bank Group. 4 http://documents.worldbank.org/curated/en/136631594937150795/World-Bank-Group-COVID-19-Crisis-Response-Approach-Paper-Saving-Lives-Scaling-up-Impact-and-Getting-Back-on-Track International Labor Organization. 2020a. ILO Monitor: COVID-19 and the World of Work. 8ird edition. Geneva: International Labour Office. 6 Estimates from the World Bank's Global Economic Prospects Report show that COVID-19 could push 71 million people into extreme poverty in 2020 under the baseline scenario and 100 million under the downside scenario. This will be the first time in over twenty years that global poverty has increased. The crisis is also likely to worsen inequality as various factors render the poor more vulnerable to the effects of the pandemic.5 Pakistan, much like other countries around the world and in South Asia, has also been hit hard by the crisis. The total case count has surged to approximately 310,275 with 6,457 confirmed deaths (as of the third week of September) since the identification of the first cases in late February 2020. An exponential increase in the number of cases in the early days of the crisis led the government to impose a lockdown in March. Borders were sealed, flights suspended, national highways blocked for transport and businesses were ordered to shutter operations. The extreme measures seem to have worked and Pakistan has effectively flattened the curve. Daily infection rates over the past six weeks have fallen precipitously. From highs of 7,000+ daily cases towards the end of May – early June, the daily case count has dropped down to an average of less than 500 in the first week of September. The decline witnessed in recent weeks is almost a mirror image of the spike in infections witnessed at the start of the crisis (see Figure 3). Case positivity rate has also fallen dramatically in recent weeks; from highs of 25 percent in late May to less than 2 percent in the first week of September. 6 Figure 3: Daily New Cases, Cumulative Cases and Active Cases of COVID-19 in Pakistan 350000 8000 300000 7000 6000 250000 5000 Total & Active 200000 Daily new cases Cases 4000 150000 3000 100000 2000 Daily new cases 50000 1000 Total cases 0 0 Active cases 20-Mar 20-Apr 20-May 20-Jun 20-Jul 20-Aug Source: National Institute of Health and World Bank staff estimates Despite progress made in containing the spread of the virus, Pakistan remains vulnerable to the impacts of COVID-19. The country was making steady progress in stabilizing its economy and implementing much needed structural reforms prior to crisis. The drive towards stabilizing the economy, however, lost considerable steam with the onset of the crisis. Closure of businesses and disruption to supply chains significantly affected the services and manufacturing sectors, which account for nearly 80 percent of total gross domestic product (GDP). According to budget documents, as many as 4-6 million people lost jobs as a result of the crisis in the Punjab province alone. The crisis, associated lockdown and other containment measures are expected to lead to an economic contraction between 2.6 and 3.3 percent in FY20, and between 0.2 and 4.0 percent in FY21, compared to pre-COVID19 projections for growth of 2.4 percent for FY20 and 3.0 percent for FY21 (see Figure 4). It is pertinent to note, however, that the situation remains fluid and that the forecasts may change in the coming weeks and months in response to fast changing ground realities and uncertainty. 5 Furceri, D., P. Loungani, J. D. Ostry, and P. Pizzuto. 2020. “Will Covid-19 Affect Inequality? Evidence from Past Pandemics.” CEPR Press, no. 12: 138–57. 6 Case positivity = New Cases ÷ New Tests 7 Figure 4: Projected Impact of COVID-19 on Real GDP Growth (%) 4.2 3.0 2.4 3.4 1.9 3.0 - 0.2 - 2.6 -3.3 -4.0 FY19 FY20 FY21 FY22 Pre-COVID Post-COVID (baseline scenario) Source: World Bank Staff Calculations Post-COVID (downside scenario) Macroeconomic dynamics are a manifestation of the significant impact of the crisis at the micro level, and more specifically at the firm level in Pakistan. A survey of 920 SMEs conducted by the Small and Medium Enterprise Development Authority (SMEDA) at the start of the crisis (April 2020) showed that 48 percent these firms had laid off employees and 95 percent had experienced a reduction in operations. A similar online survey of 123 firms carried out by Karandaaz, also in April 2020, showed that 47 percent of the respondents had laid off employees while 31 percent reported a high likelihood of becoming insolvent within a month of the lockdown. A pulse survey rolled out to 1,000 microenterprises in Pakistan in early April revealed that on average, these firms have faced 90 percent reductions in average week-on-week sales, resulting in a similar reduction in household income. 7 The Pakistan COVID-19 Business Pulse Survey was undertaken to bridge data and knowledge gaps around the impact of the crisis on the private sector, in addition to furthering policy makers understanding of how the impact of the crisis has evolved since the start of the crisis and previous surveys. The survey was administered to a representative sample of 1,515 firms operating around the country, leveraging a customized survey instrument designed to measure the impact of the crisis through both quantitative and qualitative measures. The private sector despite being heart of the economy and its engine of growth was struggling even before the crisis. Growth has been constricted by a number of factors in recent years, most notably infrastructural bottlenecks, poor investment climate and limited access to finance. Most Pakistani businesses operate in the informal sector because of the transaction costs of dealing with the public administration. Access to finance for investment, growth and expansion is another major constraint to the growth and development of the private sector. At 19 percent, Pakistan's private sector credit to GDP remains one of the lowest in the region.8 The confluence of the abovementioned factors has undermined the productivity of firms. Research carried out recently shows that productivity of publicly listed firms has remained stagnant in recent years and that growth in aggregate productivity has been driven by more productive firms gaining market shares.9 Poor productivity performance of the largest firms in the country is indicative of flagging productivity dynamics at the middle and lower end of the size distribution of firms. In response to the outbreak of COVID-19 in Pakistan, the government announced several support 10 programs, including a fiscal stimulus package of approximately PKR 1.2 trillion. The package was announced to: (a) support the medical health sector in combatting the spread of the virus and providing 7 Kashif Malik, Muhammad Meki, Jonathan Morduch, Timothy Ogden, Simon Quinn, Farah Said, COVID-19 and the Future of Microfinance: Evidence and Insights from Pakistan, Oxford Review of Economic Policy, , graa014, https://doi.org/10.1093/oxrep/graa014 8 World Bank staff calculations based on data from the State Bank of Pakistan 9 Lovo, Stefania; Varela, Gonzalo. 2020. Internationally Linked Firms, Integration Reforms and Productivity: Evidence from Pakistan. Policy Research Working Paper; No. 9349. World Bank, Washington, DC. © World Bank.” 10 Approximately USD7.5 billion 8 relief to those affected; (b) implement social welfare measures to support the poor and vulnerable whose livelihoods have been affected by the economic slowdown and partial lockdowns across the country; and (c) provide stimulus to businesses and industries to protect productive assets during the economic downturn. The fiscal stimulus was supported at the subnational level through provincial relief packages which entailed both direct cash support to the masses and more indirect support to businesses. The government of KP, for instance, gave tax exemptions worth PKR5 billion to businesses operating in the province, while the government of Punjab announced tax relief worth PKR18 billion. The State Bank of Pakistan (SBP) has also played its role, both through a substantial reduction in the key policy rate and through the institution of a number of refinance facilities. 11 One such facility, Temporary Economic Refinance Facility (TERF), was established to fuel new investment and support economic activity. Under this scheme, SBP provides refinance to Bank's at 1 percent which they can on-lend at a maximum of 5 percent.12 The SBP also introduced a temporary refinance scheme for payment of wages and salaries to the workers and employees of business concerns.13 The impact of the COVID-19 pandemic on the private sector has been large despite the support provided by the government through different packages and instruments. The private sector has been impacted by the crisis through different channels, including a slump in demand, breakdown in supply chains, tightening of credit markets and mobility restrictions. Only 28 percent of the entities surveyed reported being fully operational, with the rest either operating under reduced hours, temporarily closed or closed permanently, at the time of survey administration (June and July 2020). Over two thirds reported a drop in sales and reported sales dropping by 27 percent on average. Some 13 percent of the surveyed firms reported laying off employees and 41 percent of the employees covered as part of the survey were employed in vulnerable firms i.e. they are employed in firms which are either temporarily closed or operating under reduced hours. It is pertinent to note, however, that the impact has been differentiated. Smaller firms and those operating in the services sector, in particular retail services, were the most impacted by the crisis. These firms reported the largest drops in sales and the most pronounced employment adjustments. In the chapters which follow we have presented the results of the survey to inform the design of policies based on the ground realities. The report has been divided into the following sections: Section 2: Impact of COVID-19 on the Private Sector in Pakistan; Section 3: Expectations and Uncertainty; Section 4: Adaptation and Digitization; and Section 5: Government Policies and Mitigating the Economic Impact of the Crisis. 11 The key policy rate has been reduced by a total of 625 basis points since the start of the crisis and currently stands at 7 percent. 12 Details can be accessed at: http://www.sbp.org.pk/smefd/circulars/2020/C1.htm 13 Details can be accessed at: http://www.sbp.org.pk/corona/pdf/KeyFeaturesRozgarScheme.pdf 9 2. Impact of COVID-19 on the Private Sector in Pakistan This chapter covers the impact of the pandemic on the operations of the whole of the private sector in Pakistan – both formal and informal. The impact has been studied at different levels and using different metrics, such as the impact on the operational status, impact on sales, impact on employment etc. The chapter has been structured into five sections. In section 2.1, the operational status of the firms has been discussed in the wake of the pandemic induced restrictions. The survey was carried out over the months of June and July 2020, by which point in time parts of the economy had started to reopen. As a result, the impact captured in the analysis is not fully reflective of the pressures faced by businesses at the peak of the crisis between March and April, but instead is reflective of the state of the economy and the private sector in the months of May, June and July when the economic engine had started revving up once again.14 Sections 2.2 and 2.3 present analysis on the adverse effects of the pandemic on sales and employment. Impact on sales has been analyzed by comparing sales in the month of May 2020 with January 2020.15 The impact on employment has been studied both at the intensive (reduction of hours, wages etc.) and extensive margins (layoffs, hiring). Section 2.4 delineates the channels through which economic entities have been impacted by the crisis. Lastly, section 2.5 presents data on the ability of firms to survive the crisis by specifically looking at the cash buffers of firms and reconciling this data with firms' expectations about when they will be able to resume operations. 2.1 Operational Status The impact of the crisis has been large and far reaching, with only 28 percent of firms reporting being open. In contrast, 51 percent of the surveyed firms reported operating under reduced operations and 13 percent reported being temporarily closed (see Figure 5). The fact that less than a third of the surveyed firms had returned to full operations and approximately two thirds remained vulnerable (i.e. temporarily closed or not fully operational) in the months of June and July when the sweeping lockdowns imposed earlier in the crisis had already been partially or completely lifted are a testament to the intensity of the crisis and its associated impact on the private sector. The impact can also be gauged by the fact that 9 percent of the surveyed firms reported being permanently closed. 16 On average, firms which reported being closed, either temporarily or on a permanent basis, had been closed for 13.2 weeks. 14 Some of the questions asked relate specifically to the month of May, whereas other questions asked about the 'current' state of affairs. For instance, questions related to the firms operations asked the firm about its operational status at the time of the phone interview (last interviews were carried out in the 3rd week of July), whereas questions related to sales and employment relate to sales and employment in the month of May. 15 January 2020 was used as a benchmark as opposed to comparing data with the same month in the previous year i.e. 2019 to account for the economic slowdown the country was charting its course out from under the aegis of the IMF EFF even before the crisis struck. This comparison, however, introduces the problem of seasonality which may bias the results. It may be that historically sales in January tend to be higher than sales in May, and as such, the comparative decline may not just be attributed to the impact of COVID-19. 16 The actual number of closed firms may even be higher since some firms had already closed down by the time the survey of administered and as such were not reachable. 10 Figure 5: Operational Status of Businesses 60% 50% 40% 51% 30% 28% Open 20% Temporary closed 10% Reduced operations 13% 9% Permanently closed 0% There is considerable heterogeneity in the firm's operational status conditioned on firm size and location, with micro firms reporting a steeper impact and firms in Sindh province relatively less impacted. A significantly larger percentage (10 percent) of micro firms reported being permanently closed in comparison to larger sized firms; only 6 percent of small firms and 7 percent of medium sized firms reported being permanently closed as a result of the crisis. Similarly, only 24 percent of micro firms reported being fully operational in the months of June and July, in comparison to 32 percent of small, 28 percent of medium and 66 percent of large sized firms (see Figure 6). The numbers are reflective of the greater resilience of larger sized firms.17 Larger firms, ones which are more established, more formal and therefore have greater access to finance, were able to jumpstart operations as soon as the government started withdrawing its restrictions. These firms also had greater financial buffers and access to finance to survive the crisis (see section 2.5). Micro firms, on the other hand, are more informally organized, have lower financial buffers and limited access to finance stemming from a high degree of informality. There is a possibility that these firms, albeit nimbler as compared to large firms, have had difficulty restarting operations at scale owing to the depletion of working capital and/or productive assets during the lockdown. Firms in the province of Sindh, on average, showed greater resilience. Only 7 percent of the firms surveyed in the province reported being permanently closed in comparison to 9 percent in Punjab and KP. A greater percentage of firms (34 percent) in Sindh were also fully operational compared to all the other regions. Provincial heterogeneity is a function of the nature of the lock down, its intensity, but also the core characteristics of the firms in the region. The result for Sindh is counterintuitive in that the province had imposed the most stringent lockdown in the country. As opposed to other parts of the country where provincial governments had imposed 'smart lockdowns', Sindh had opted for sweeping lockdowns. However, the result may be explained by the characteristics of the private sector in the province. Sindh, and in particular Karachi, is the manufacturing and exporting hub of the country..18 The lower impact reported by the private sector in Sindh, as such, may be an outcome of the greater resilience of the manufacturing and export oriented firms located in Sindh. 17 “The Pandemic Shock Will Make Big, Powerful Firms Even Mightier.” The Economist, Mar. 2020, p. 22. 18 80 percent of the firms surveyed in Sindh were in Karachi. 11 Figure 6: Operational Status by Firm Size Figure 7: Operational Status by Province 70.0% 66% 70.0% 59% 60.0% 60.0% 54% 50% 50% 51% 51% 52% 50.0% 48% 50.0% 40.0% 40.0% 34% 32% 29% 28% 30.0% 28% 28% 30.0% 24% 22% 20.0% 18% 18% 20.0% 15% 14% 15% 13% 13% 12% 10% 9% 10.0% 8% 9% 6% 7% 10.0% 7% 8% 4% 5% 2% 0.0% 0.0% Micro (0-9) Small (10-30) Medium (31-250) Large (250+) Punjab Sindh KPK Baluchistan Others Open Temporary closed Reduced operations Permanently closed The agriculture sector was the least affected of all the major sectors of the economy, while exporters also fared better compared to non-exporters. 42 percent of firms operating in the agriculture sector reported being open at the time of the survey, in comparison to 32 percent of firms in the manufacturing sector and 27 percent of firms in retail services (see Figure 8). The numbers are in agreement with the nature of the mobility and operational restrictions and their phasal removal, with the government first facilitating critical agricultural and food supply chains, followed by easing restrictions on industries (e.g. construction), manufacturing and the operations of industrial estates and lastly withdrawing curbs on the services sector. The road to recovery, however, remains long. Close to 50 percent of all the firms operating in the manufacturing sector reported operating under reduced operations in the months of June and July, while 11 percent reported being temporarily closed. Integration in global value chains and global trading networks proved once again to be critical enabler of firm resilience, despite the truly global nature of this crisis. 19 36 percent of the surveyed exporters reported being open, in comparison to 26 percent of non-exporters (see Figure 9). The lasting toll of the crisis on the exporting sector was also lower compared to the non-exporting sector with only 5 percent of firms reporting being closed down permanently, compared to 10 percent of non- exporters. The results hold even when controlling for firm size and other characteristics. 33 percent of micro sized exporters reported being open compared 23 percent of micro sized non exporters. A more diversified market along with more diversified suppliers' networks may just be some of the drivers behind the relatively better performance of exporters, in addition to higher productivity and larger cash buffers. 20 Figure 8: Operational Status by Sector Figure 9: Operational Status by Exporting Status 60 60.0% 55 52 51% 49 50.0% 49% 42 40 40.0% 36% 35 32 27 26 30.0% 26% 20 20.0% 14 11 13 13% 10 11 10% 9 9 10% 6 10.0% 5% 0 0.0% Agriculture Manufacturing Retail Other Services Open Temporary Reduced Permanently closed operations closed Open Reduced operations Temporary closed Permanently closed Non Exporter Exporter 19 Risk, Resilience and Rebalancing in Global Value Chains. McKinsey & Company, www.mckinsey.com/business-functions/operations/our-insights/risk-resilience-and-rebalancing-in-global- value-chains. Accessed 6 Aug. 2020. 20 See Lovo and Varela (2020) for a more detailed discussion on the productivity premium of exporting firms in Pakistan. 12 41 percent of total workers covered in the survey are employed in vulnerable firms, classified as firms that are partially open or are temporarily closed. These vulnerable firms account for a total of 64 percent of all firms. Jobs in these firms are vulnerable since these firms are more likely to run into liquidity (or solvency) problems and end up in a state where they are unable to pay salaries and start laying-off employees. There is significant heterogeneity in the quantum of vulnerable jobs, when vulnerability is studied from the prism of size, age, sector and exporting status. Labor employed in micro enterprises has a much higher probability of being in a vulnerable state, with 69 percent of all jobs in microenterprises being vulnerable (see Table 1). This is a natural outcome of the operational status of microenterprises relative to larger enterprises; more microenterprises are either temporarily closed or operating under reduced operations compared to larger firms. Similarly, labor in more mature and established firms is less likely to be vulnerable compared to labor in younger firms. Only 32 percent of the labor working in established firms (i.e. firms which have been in operations for 15+ years) is in a precarious employment position, as opposed to 75 percent of the labor working in young firms (i.e. firms which have been in operations for less than 4 years). Table 1: Estimated Number of Jobs in Businesses Affected by the Pandemic Open Temporary Reduced Vulnerable Permanently Total closed operations (temporary closed + reduced operations) closed Total 57% 6% 34% 41% 3% 54,433 Micro (0-9) 25% 14% 54% 69% 7% 2,565 Small (10-30) 32% 13% 49% 62% 6% 2,440 Medium (31-250) 28% 12% 55% 67% 4% 8,405 Large (250+) 66% 4% 28% 32% 2% 41,022 Agriculture 50% 6% 44% 50% 0% 1,732 Manufacturing 67% 8% 25% 33% 1% 15,177 Retail 19% 12% 69% 81% 0% 1,746 Other Services 54% 5% 36% 42% 4% 35,778 Punjab 56% 8% 33% 42% 3% 36,062 Other regions 58% 3% 37% 40% 2% 18,370 Young (0 - 4) 18% 16% 59% 75% 7% 4,648 Maturing (5 - 14) 39% 11% 49% 60% 1% 9,787 Established (15+) 65% 4% 28% 32% 2% 39,998 Exporter 61% 10% 26% 36% 3% 18,941 Non Exporter 54% 4% 39% 43% 2% 35,492 13 2.2 Impact on Sales 68 percent of the surveyed firms reported a decline in sales in the month of May 2020 compared to January 2020, while 19 percent reported that sales were holding steady. In total, 87 percent of the surveyed firms reported either a decline or no change in sales since the start of the crisis (see Figure 10). Sales in May 2020 were compared to January 2020, as opposed to May 2019, given the turmoil witnessed by the economy in 2019. A comparison with the same time period last year, as such, would not only have captured the impact of the crisis but also that of the economic slowdown. The average decrease in sales amounted to - 27 percent while the median decrease in sales amounted to -30 percent (see Table 2). While both the average and median change in sales at the aggregate level indicate that sales have dropped by a third since the onset of the crisis, there was considerable variation between firms (see Figure 11). 63 firms, for instance, reported a 50 percent drop in sales, while 15 firms reported an 80 percent drop in sales. At the extreme end, 2 firms reported a 95 percent decline in sales compared to January 2020. Figure 10: Change in Sales Figure 11: Distribution of Change in Sales 80 68 .01 60 .008 Probability .006 40 .004 20 19 12 .002 1 0 0 Don’t know Increase Remain the same Decrease -100 -50 0 50 100 Change in sales (%) The retail sector and micro enterprises were the worst hit segments with 87 percent of firms in retail and 70 percent of micro firms reporting a decline in sales. The survey data shows significant variation in the percentage of firms reporting a decline in sales when conditioned on key dimensions such as size, sector of operation, location etc. This differential also holds for the quantum of the decline. Only 51 percent of the firms operating in the agriculture sector reported a decline in sales in May 2020 compared to January 2020, in contrast to more than 70 percent of the firms operating in the manufacturing and services sectors (see Figure 12). The average decline in sales in the agriculture sector, similarly, was also lower and amounted to 18 percent, in contrast to the manufacturing and services sectors, where sales on average witnessed a larger decline of approximately 27 percent. Exporters also fared better. 59 percent of the exporters reported a decline in sales compared to 72 percent of non-exporters (see Figure 13). The difference holds when controlling for firm size. 51 percent of micro sized exporters reported a drop in sales in comparison to 71 percent of micro sized non-exporters. The quantum of decline in sales between exporters and non-exporters, however, was found not be statistically significantly different at a 95 percent confidence level. For most dimensions observed in Table 2, the median change is close to the mean, indicating that the numbers are not driven by outliers. The differential impact of the crisis on sales along key dimensions is partly an outcome of factors internal to the firms and partly factors external to the firms. Internally, firms with more structured 14 production processes and financial buffers were able to restart operations faster than more traditional and less innovative firms. This jumpstart in operations translated into a faster turnaround in sales. Externally, sales performance was driven by when the government's phasal approach to easing restrictions. Firms operating in sectors on which restrictions were eased earlier in the crisis were likely able to recover sales faster. Figure 12: % of Firms Reporting Change in Figure 13: % of Firms Reporting Change in Sales by Sector Sales by Exporting Status 80% 75% 73% 68% 80% 70% 72% 70% 60% 59% 51% 60% 50% 41% 50% 40% 40% 30% 30% 23% 16% 19% 18% 18% 18% 20% 14% 20% 11% 10% 10% 7% 6% 10% 0% 0% Agriculture Manufacturing Retail Other Services Increase Remain the same Decrease Increase Remain the same Decrease Non Exporter Exporter Table 2: Change in Sales Conditioned on Key Characteristics of Firms Mean 10 th 25 th Median 75 th 90 th change percentile percentile percentile percentile Total -27 -70 -50 -30 0 5 Micro (0 - 9) -28 -70 -50 -30 0 10 Small (10 -30) -39 -80 -60 -50 -20 0 Medium (31-250) -25 -60 -50 -30 0 0 Large (250+) -13 -50 -30 0 0 20 Agriculture -18 -50 -50 0 0 0 Manufacturing -27 -60 -50 -30 0 0 Retail -28 -60 -50 -30 0 0 Other Services -28 -72.5 -50 -30 0 20 Punjab -26 -70 -50 -30 0 5 Other regions -32 -80 -60 -37.5 0 9.5 Young (0 - 4) -29 -80 -56.5 -30 0 30 Maturing (5 -14) -30 -70 -50 -35 0 0 Established (15+) -24 -65 -50 -30 0 0 Exporter -22 -70 -50 -17 0 20 Non-Exporter -29 -70 -50 -30 0 0 15 Small firms registered the largest adverse impact of the crisis on sales, with sales decreasing by 39 percent on average. Larger firms, in comparison, fared better with medium sized firms reporting an average decline of 25 percent and large firms reporting a decline in sales of only 13 percent. Overall, in line with the analysis presented above, the impact of the crisis on sales seems to dilute as we move along the size distribution i.e. larger firms reported a smaller impact on sales and faster recovery, with a notable exception being micro firms. The effect of size on change in sales was also found to be statistically significant when controlling for the different characteristics of businesses like age, sector, regional location etc. This predictive effect of size on change in sales is presented in Figure 14. Figure 14: Predictive Effect of Size on Change in Sales 0 -10 Average change in sales (%) -20 -30 -40 -50 Micro (0-9) Small (10-30) Medium (31-250) Large (250+) 2.3 Impact on Employment Most firms which made employment adjustments in the month of May 2020 did so not by laying off employees (13 percent of firms), but by reducing their working hours, cutting wages and granting leave (43 percent of firms). This rather flexible employment response is an inevitable consequence of the impact of the COVID-19 crisis on the operational status and sales of firms. 21 percent of the surveyed firms reported that they reduced the working hours of at least one employee to deal with the crisis and its impact on the finances of the firm (see Figure 15). This response was most popular amongst firms operating in the retail sector with 31 percent of their employees facing a reduction in working hours (see Table 3). Small sized firms also made ample use of the flexibility granted by this option, with 26 percent of their workers having their hours reduced. 7 percent of the surveyed firms also reported to having reduced the wages of their employees. This response too was most widely used by firms operating in the retail services sector with 19 percent of the employees in the retail services sector having their salaries reduced. While not as economically detrimental as layoffs, reduction in wages and working hours still compromise livelihoods, and also broader demand, through their impact on the purchasing power of employees. 16 Figure 15: Margin of Adjustment in Employment 21 20 15 15 13 10 6 6 7 5 0 Hired Workers Granted Granted Reduced Reduced workers laid off leave leave wages hours with pay Leave with and without pay was the second most utilized option for employment adjustment. A total of 21 percent of the surveyed firms reported sending employees on leave with or without pay (see Figure 15). This response was likely a function of both the firm's internal decision making (i.e. labor requirements were low on account of sagging demand and sales) and the governments lockdown which prevented labor from commuting to work. This employment adjustment response was most widely employed by firms operating in the retail sector (26 percent of employees) and by medium sized (21 percent of employees) firms (see Table 3). The revealed preference of firms for labor adjustment mechanisms which do not entail firing employees may be an outcome of the intangible firm specific human capital these employees embody, capital which has been cultivated through direct and indirect investments by the firm over time. Only 13 percent of the surveyed firms reported laying off employees as a response to the crisis, while only 3 percent of the total labor force of the surveyed firms were impacted by this measure. Given that the government didn't roll out a wage subsidy scheme akin to the much touted Kurzarbeit scheme of Germany, economists had predicted a more pronounced adverse response along the extensive margin i.e. layoffs. 21 The survey, however, shows that this was not the case and that firms have instead employed more flexible mechanisms, with a lower economic and labor market impact, to retain working relationships with employees while also lowering the wage bill. There was, however, heterogeneity when the response is conditioned on key characteristics of firms. Large sized firms only laid off 1 percent of their labor force, as opposed to micro and small firms which laid off approximately 10 percent of their labor force (see Table 3). Labor in the retail sector also suffered higher job losses than labor employed in other sector as firms in the retail sector laid off approximately 6 percent of their labor force in comparison to two percent in manufacturing. 21 Kurzarbeit is a social insurance program whereby employers reduce their employees' working hours instead of laying them off. Under Kurzarbeit, the government normally provides an income “replacement rate” of 60 percent (more for workers with children). 17 Table 3: Percentage of Workers Affected by Margin of Labor Adjustment to the Shock Workers Workers Workers Workers Workers with Workers with Workers in hired laid-off granted granted leave wages hours businesses leave or or absence reduced reduced permanently absence with pay closed Total 1% 3% 4% 9% 3% 12% 3% Micro (0 -9) 2% 10% 4% 12% 5% 21% 7% Small (10 -30) 3% 10% 4% 12% 5% 26% 5% Medium (31-250) 1% 9% 7% 14% 8% 21% 4% Large (250+) 1% 1% 4% 8% 2% 10% 2% Agriculture 3% 4% 2% 2% 2% 6% 0% Manufacturing 1% 2% 0% 15% 4% 18% 0% Retail 0% 6% 3% 23% 19% 32% 0% Other Services 1% 3% 6% 7% 2% 10% 4% Punjab 1% 3% 2% 11% 4% 16% 3% Other regions 1% 3% 9% 7% 1% 8% 2% Young (0 -4) 3% 10% 6% 9% 7% 14% 8% Maturing (5 - 14) 1% 5% 5% 18% 6% 27% 1% Established (15+) 1% 2% 4% 7% 2% 9% 2% Exporter 1% 2% 0% 14% 4% 12% 3% Non Exporter 1% 3% 7% 7% 3% 13% 2% 2.4 Shock Transmission Channels Firms were impacted by a multiplicity of shocks with 60 percent reporting a drop in demand and 47 percent a drop in inputs. The impact of the COVID-19 crisis on the economy has been unique in that the impact has manifested itself through different channels, most notably supply and demand, with multiple feedback loops.22, 23 The impact has also been further amplified by uncertainty. Much like countries and firms around the world, firms in Pakistan also reported being impacted by the crisis through different channels. Not only were firms hit by demand and supply shocks which impacted production, sales and cashflows, firms also reported a decline in the availability of the finance. The percentage of firms reporting a decline in the availability of finance at 38 percent was lower than the percentage of firms reporting a demand or supply shock (see Figure 16). This result, however, needs to be viewed in the context of low financial inclusion and low financial sector intermediation in Pakistan, where only 188,000 SMEs are currently availing loans from the financial sector. 24 22 Guerrieri, V., Lorenzoni, G., & Werning, L. (April 2020). Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? (NBER Working Paper No. 26918). Retrieved from https://www.nber.org/papers/w26918 23 Baldwin, Richard, and Beatrice Weder di Mauro. Economics in the Time of COVID-19. London, EC1V 0DX, CEPR Press, 2020, cepr.org/sites/default/files/news/COVID-19.pdf. 24 State Bank of Pakistan 18 Figure 16: Percentage of Firms Affected by Shocks (aggregate) 60 60 62 57 40 47 38 20 0 Decrease in Decrease Decrease in Decrease in Decrease in hours worked in demand cash flow availability availability of of finance inputs The supply shock manifested in different forms with some firms reporting a lack of availability of inputs and others reporting an increase in prices. Of the firms which reported a decrease in the availability market, while close of inputs, 27 percent reported that key production inputs were simply not available in the7 to 33 percent reported that while inputs were available, they were only available at higher prices (see Figure 17). The supply shock, while smaller in comparison to the demand shock, may have also exacerbated the demand shock given the strong interlinkages between the two.25 Figure 17: What is Driving the Supply Shock? 40 36.83 35 32.86 30 27.34 Percentage 25 20 15 10 5 2.97 0 Not available Cost increased Lower quality Others While there was heterogeneity in the percentage of firms reporting being affected by a shock by key characteristics, this heterogeneity was limited. Limited heterogeneity by key characteristics is symptomatic of the truly global nature of the crisis. The COVID-19 crisis and associated lockdowns affected firms across the board, operating in all sectors and of all sizes. 65 percent of the firms operating in the manufacturing sector and 73 percent operating in the retail services sector reported a decline in demand (see Figure 18). Supply of inputs, while being disrupted, was less affected in comparison to demand. 56 percent of 25 Guerrieri, V., Lorenzoni, G., & Werning, L. (April 2020). Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? (NBER Working Paper No. 26918). Retrieved from https://www.nber.org/papers/w26918 19 manufacturing firms and 60 percent of firms operating in the retail sector reported a decrease in the availability of inputs. The uniformity of the impact extends to impact disaggregated by firm size. 53 percent of micro firms and 54 percent of large firms reported a decline in the supply of inputs (see Figure 19). Unlike the other transmission channels, however, noteworthy heterogeneity was reported by firms in the availability of finance. A significantly larger number of micro enterprises (46 percent) reported a decline in the availability of finance as compared to large enterprises (34 percent). This result may well have been an outcome of firm characteristics, including the higher prevalence of informality among micro and small enterprises. Larger firms, which are more integrated with the country's financial sector and have greater access to formal finance, were less likely to report a decrease in access to finance as a result of the crisis. 26 Figure 18: Percentage of Firms Affected by Figure 19: Percentage of Firms Affected by Shocks (sectoral decomposition) Shocks (size decomposition) 80% 73% 75% 80% 69% 70% 69% 67% 67% 66% 65% 67% 68% 70% 65% 65% 64% 70% 59% 66% 64% 65% 61% 60% 56% 60% 56% 60% 51% 53% 48% 54% 51% 46% 50% 48% 50% 46% 43% 40% 40% 35% 40% 40% 34% 30% 30% 20% 20% 10% 10% 0% 0% Decrease in Decrease in Decrease in Decrease in Decrease in Decrease in Decrease in Decrease in Decrease in Decrease in hours demand cash flow availability availability hours demand cash flow availability of availability worked of finance inputs worked finance inputs Manufacturing Retail Other Services Micro (0-9) Medium (31-250) Small (10-30) Large (250+) The confluence of shocks has been largely similar for exporters and non-exporters, with both reporting a similar drop in demand and availability of inputs. 67 percent of non-exporters and 60 percent of exporters reported a decrease in demand as a result of the crisis, while 51 percent and 50 percent respectively reported a decrease in the availability of inputs. The responses were largely homogenous even when controlling for firm size. 80 percent of micro sized non exporters and 78 percent of micro sized exporters respectively reported a decline in demand, while 66 percent and 64 percent of micro exporters and non-exporters respectively reported a decrease in the supply of inputs. Exporters reported a significant drop in demand, one similar to the drop reported by non-exporters, because much like domestic markets, international markets have also been reeling from the impact of the crisis. Broad-based lockdowns imposed by Pakistan's major trading partners to contain the outbreak have not only crippled local output, the ripples created by these measures have also had a distinct impact on exporters in Pakistan. The supply shock has also been largely comparable. Approximately the same percentage of exporters and non-exporters reported a decrease in the supply of inputs, alluding to the limited integration of Pakistani exporters in Global Value Chains (GVCs). Greater integration in GVCs would have led to a more pronounced impact of the crisis on exporters as global trading links were the first channels through which the economic impact of the crisis manifested itself. 20 Figure 20: Percentage of Firms Affected by Shocks (decomposition by status as exporter) 80% 69% 65% 67% 70% 60% 62% 60% 54% 51% 50% 50% 44% 36% 40% 30% 20% 10% 0% Decrease in hours Decrease in Decrease Decrease in Decrease in worked demand in cash flow availability of availability finance inputs Non Exporter Exporter 2.5 Firm Survival and Resilience Firms reported having the means to remain open for an average duration of 66 days under the circumstances prevalent at the time of survey administration. However, there was significant variation in the financial and operational position of the surveyed firms, as indicated by the sharp contrast between the average and the median (see Figure 21). 60 percent of the firms surveyed reported only having the ability to bear operational costs (such as payroll, suppliers bills, taxes or loan repayment) for 30 days or less with the cash available, if the situation remains unchanged. A substantial 18 percent of the surveyed firms reported having no financial buffers whatsoever. These firms, at the time of survey administration, were on the cusp of closure and could have been pushed into hibernation or permanent closure from any additional shocks subsequent to the administration of the survey. Figure 21: Number of Days of Cash Buffers 80 60 66 40 20 20 0 Average Median 21 Firms expectation about resumption of normal operations, at the time of survey administration, suggested that survival would be difficult and economic ramifications large. While close to 60 percent of the surveyed firms reported only having financial buffers for one month to cover key operational costs, over 74 percent didn't expect normal business operation to resume for at least 2 months, with 45 percent reporting that they didn't expect normal business operations to resume for at least another 6 months. It is pertinent to note, however, that the situation on the ground has improved significantly since the administration of the survey and the situation remains fluid. Most of the restrictions imposed by the government have been lifted and the economic engine which had come to a grinding halt with the onset of the crisis is starting to churn again. As such, there is a strong possibility of an uptick in business confidence and expectations since the administration of the survey. This uptick, however, has not yet been captured in data, with the SBP's Consumer Confidence Index and Expected Economic Conditions indices being reported at their lowest levels in July 2020 since March 2013. 26 Large sized firms reported having the ability to continue operations in the 'current circumstances' for 45 days, in comparison to 12 days for micro firms at the median. Differences in the cash buffer positions of firms by size and exporting status were found to be statistically significant at a 95 percent confidence level... 27 The survival position of firms is very similar to the impact of the crisis on the firm's operational status and sales. Firms which have reported being most impacted by the crisis, when conditioned on key firm characteristics, are also the ones which have reported the most compromised ability to survive going forward. Larger sized firms and those operating in the manufacturing sector indicated having greater buffers to last the crisis indicating greater resilience. These firms, at the same time, are also the ones which have reported lowest relative impact from the crisis. On average, firms in the manufacturing sector reported having the ability to pay all operational costs with the cash available 'if the situation remains unchanged' for 80 days, in contrast to the 63 days of cash buffers firms have in the agriculture sector. 26 Consumer Confidence Survey (CCS) is a stratified random telephone survey of more than 1600 households across Pakistan. The survey is being conducted by Institute of Business Administration (IBA), Karachi and State Bank of Pakistan (SBP) since January 2012 with the frequency of every two months 27 The two-sample t-tests were carried out to test whether the unknown population means of two groups are equal or not. 22 3. Expectations and uncertainty 50 percent of the surveyed firms expected a decline in sales in the coming months, expecting an average decline of 21 percent. Firm confidence and expectations have taken a significant hit as a result of the crisis, as already mentioned in section 2.5 and captured in the SBPs Consumer Confidence Index. The expected continuing impact of the crisis on sales, a function of the lockdown and depressed demand among other factors, was expected to translate into further employment adjustment. 54.7 percent of the firms reported expecting a decrease in employment i.e. layoffs in the coming 3 months. Steep losses realized by the private sector as a result of the crisis coupled with elevated uncertainty have also dampened firms' appetite for investment, with close to 30 percent of the firms expecting a decline in capital investment plans. Expectations, however, remain fluid. In the wake of the removal of the majority of government-imposed restrictions in early August, business confidence is reported to have rebounded. Figure 22: % of Firms Reporting a Decline in Sales, Figure 23: Expected % of Decline in Sales, Employment and Capital Investment Employment and Capital Investment over next 3 Months over next 3 Months 100% 0 90% 80% -10 -10 70% Percentage 60% % change 50% -20 -21 40% 30% 20% -30 10% 0% Sales Employment Capital Investment -40 -42 Decrease Increase No Change Sales Employment Capital Firms expectations about sales, employment and investment remain marred by high uncertainty with significant variation between firms. Uncertainty facing businesses and percolating in the economy is typified by the distribution of firm's expectations about change in sales and employment in the coming months (see Figure 24 and 25). At the lower end, over 8 percent of firms expect a 90 percent or higher drop in sales in the coming months. A number of firms, however, have also reported expecting an increase in sales, with some expecting sales to increase by over 100 percent. The skewness of the distribution to the left is indicative of the generally negative sentiment prevalent in the economy at the time of survey administration, while its dispersion (i.e. spread) is indicative of uncertainty. The distribution of expected change in employment is largely consistent with the distribution of the expected change in sales; the distribution is significantly skewed to the left indicating that firms expect employment to fall drastically. 23 Figure 24: Distribution of Expected Change Figure 25: Distribution of Expected Change in Sales in Employment .015 .025 .02 .01 Probability .015 Probability .01 .005 .005 0 0 -100 0 100 200 -100 0 100 200 Expected Change in Sales Expected Change in Employment -20 Larger firms and exporters reported a higher expected decline in planned capital investments in comparison to other firms. Larger firms expect a 14 percent decline in investments as a result of the crisis in the coming months. In comparison, small sized firms on average expect only a 9 percent decline in capital investment stemming from the fallout of the crisis. The difference in expectation, however, was not found to be statistically significant at the 95 percent confidence level. Exporters expectations, similar to larger firms, were also subdued in comparison to non-exporters. On average, exporters expect capital investments to drop by 18 percent in the coming months, an outcome of both the realized impact of the crisis and the uncertainty prevalent in the global economy. Results showed a statistically significant difference between the change in the investment plans of exporters and non-exporters with exporters expecting a steeper decline at the 95 percent confidence level. Poorer expectation of exporters and large firms may be the outcome of the fact that these firms had sizable projects and investment plans in the pipeline, ones which have been put on hold given the impact and expected impact of the crisis. In contrast, smaller firms might not have had any substantial investment plans, hence the relatively positive outlook. Figure 26: Expected Change in Sales, Figure 27: Expected Change in Sales, Employment and Capital Investment Employment and Capital Investment by Firm Size by Exporting Status Sales Employment Capital NonExporter Exporter 0 0 -5 -5 -10 -6 -10 -8 -15 -9 -15 -12 -14 -20 -17 -20 -18 -25 -20 -25 -20 -22 -23 -30 -25 -30 -35 -35 -40 -40 -37 -45 -40 -45 -41 -43 -50 -45 -50 -46 Micro (0-9) Medium (31-250) Sales Employment Capital Small (10-30) Large (250+) 24 4. Adaptation and digitization 54 percent of the surveyed firms have reported increasing or starting the use of digital platforms for their daily business operations and sales. While the crisis has had far reaching consequences on the lives and livelihoods of millions, it is also fundamentally altering the operations and operational delivery of economic entities. The pandemic is driving digitization, one which may pay dividends for years to come, as a result of consumers relying ever greatly on digital platforms owing to mobility restrictions caused by the lockdown. Crises, including epidemics, can spur the adoption of new technologies and business models. The SARS outbreak is just one example. The outbreak in 2003 is often credited with the adoption of online shopping among Chinese consumers, accelerating Alibaba's rise. 28 The same trends are at play in Pakistan. Firms have not only increasingly taken up digital platforms to execute sales, they have also invested in their digital capabilities through the acquisition of required software, equipment and other digital solutions. 20 percent of the surveyed firms reported investing in digitizing either internal processes or client facing delivery (see Figure 29). Figure 28: COVID-19 Related Digital Figure 29: COVID-19 Related Investment Adjustment in Equipment 80% Started using No digital platforms 46% No 21% Increased use of digital platforms 33% 20% Yes 33 percent of the surveyed firms reported adjusting their product mix to seize new market opportunities. Firms, where possible, have also been adjusting their product mix to cater to emerging needs, this includes redeploying idle production lines towards the production of COVID-19 essentials. Firms in the textiles sector are case in point. Approximately 35 percent of firms operating in the textiles sector reported changing their product mix in response to the COVID-19 crisis. The speed with which firms have repurposed of production lines towards COVID-19 essentials, including face masks and other personal protective equipment, is evident in the fact that Pakistan has gone from being a net importer of face masks in February 2020 to being a net exporter in April 2020. Flexibility and agility in the face of adversity is the hallmark of resilience which has not only helped firms survive but has also attenuated the impact of the crisis on the private sector. Just one company, for instance, was reported to have saved over 500 stitching jobs by opening a new production line for masks.29 28 Carlsson-Szlezak, Philipp, et al. “What Coronavirus Could Mean for the Global Economy.” Harvard Business Review, 2020, hbr.org/2020/03/what-coronavirus-could-mean-for-the-global- economy. 29 Jamal, Nasir. “Textile Sector 'Masking' Its Way Forward.” Dawn News [Islamabad], 11 May 2020, www.dawn.com/news/1556258/textile-sector-masking-its-way-forward. 25 Larger firms, those which are operating in the manufacturing sector and those which are exporting were more agile in adapting and adjusting in response to the crisis. There was significant heterogeneity in the firm's adjustment response to the crisis. 55 percent of large firms reported starting the use of or increasing the use of digital platforms for sales and operational purposes, in comparison to 52 percent of small firms. Greater percentage of large firms also reported investing in digital solutions and repackaging product/service mix in comparison to small and medium enterprises. The probability of digital adaptation was also much higher for exporting firms in comparison to non-exporters. A significantly larger percentage of exporters reported digital adaptation, with 61.5 percent reporting that they started or increased the use of digital platforms and 30 percent reporting investing in digital solutions as a response to the crisis. Among the main sectors, firms operating in the manufacturing sector were the most inclined to adapt and adjust. Figure 30: COVID-19 Related Adjustments Figure 31: COVID-19 Related Adjustments by by Size Exporting Status 60 56.65 70 55.88 52.33 61.53 50 47.06 60 50.43 50 40 39.86 31.67 31.47 40 30 30.07 29.80 23.78 30 20.59 20 17.6 20 16.05 10 10 0 0 Use digital platforms Invest in digital solutions Repackage product mix Use digital platforms Invest in digital solutions Repackage product mix Small (10-30) Medium (31-250) Large (250+) Non Exporter Exporter Micro firms, despite having limited capabilities on average and limited resources have also made operational adjustments to weather the crisis. Approximately 30 percent of micro firms reported making business model adjustments in response to the crisis. 30 While this number is lower in comparison to larger sized firms, the percentage of micro firms reporting making an adjustment is notable when viewed in the context of where these firms stood before the crisis, existing vulnerabilities at the start of the crisis, high degree of informality and limited access to finance. Use of digital platforms and social media was reported as the most frequent adjustment made by these firms. Other notable adjustments included measures put in place to meet government SOPs. Figure 32: COVID-19 Adjustments and Micro Firms 70.34 29.66 No Yes 30 Business Model means different ways that a business handles things like pricing (for example reducing prices or increasing) , product (offering more or less products to change the environment), place (for example selling through online or digital methods), promotion (for example using new ways of selling like using Facebook etc. to advertise or using SMS to advertise) 26 5. Government policies and mitigating the economic impact of the crisis About 41 percent of the firms reported deferral of utility payments as the most needed support policy, this was followed by tax deferral (35 percent) and deferral of rent of mortgage (27 percent). Governments around the world have instituted a kaleidoscope of policy measures to prop up their economies and prevent the health crisis which turned into an economic crisis from morphing into a large-scale financial crisis. Measures have ranged from direct cash transfers to more indirect policies such as tax deferrals, deferral of principal payments on loans, and additional measures to bolster the cash flows of economic entities and directly or indirectly inject liquidity into the businesses needing support. Firms in Pakistan have reported the need for support to survive the crisis. This need for support permeates across firm size, sector, and other key characteristics of firms, again indicating the truly wide-ranging and deep impact of the crisis. Figure 33: Most Needed Policy Support by Percentage of Firms 45% 40% 35% 41% 30% 36% 25% 27% 20% 15% 10% 16% 11% 13% 5% 0% Cash Transfer Deferral of Rent/Mortgage Deferral of Utility Payments Deferral of Credit Payments Access to New Credit Tax Deferral A significantly larger percentage of exporters compared to non-exporters have indicated the need for more formalized policy support. While 44 percent of exporters have indicated the need for tax deferrals, only 34 percent of non-exporters indicated the same option as a priority. Similarly, 23 percent of exporters in comparison to 15 percent of non-exporters have indicated the need for credit deferrals as a policy support measure to survive the crisis (see Figure 33). Relatively greater percentage of exporters have selected more 'formal' mechanisms of support as compared to non-exporters, which have indicated a relatively greater preference for direct support measures such as cash transfers, owing to generally higher sophistication and capabilities of exporters, in addition to their more formalized organizational structure and greater access to finance. 27 Figure 34: Policy Support Priorities by Figure 35: Policy Support Priorities by Exporting Status Firm Size 50% 60% 44% 52% 45% 41% 38% 50% 40% 34% 41% 35% 39% 40% 30% 28% 26% 30% 25% 23% 30% 27% 19% 22% 22% 20% 15% 20% 17% 15% 12% 11% 12% 13% 10% 10% 11% 10% 10% 5% 0% 0% Cash Deferral of Deferral of Deferral of Access to Tax Cash Deferral of Deferral of Deferral of Access to Tax Transfer Rent/Mortgage Utility Credit New Credit Deferral Transfer Rent/Mortgage Utility Credit New Credit Deferral Payments Payments Payments Payments NonExporter Exporter Micro (0-9) Small (10-30) Medium (31-250) Large (250+) Firms in the manufacturing sector have identified tax deferral as the single most important policy support measure, whereas firms operating in the services sector identified utility bills deferral as the need of the hour. 52 percent of large sized firms identified tax deferrals as a policy priority in comparison to 30 percent of micro sized firms. This differential is potentially driven by the fact that larger firms are more likely to be formal and registered with the tax authorities in comparison to micro and small enterprises which predominantly operate out of the tax net. A greater percentage of micro and small enterprises indicated the need for deferral of utility bills and rental payments in comparison to larger firms. This divergence in policy preference, on a relative basis, is understood to be the outcome of the fact that rental payment and utility payments account for a larger percentage of total operating costs of micro and small firms in comparison to larger firms, for whom input and labor costs are typically the largest components of total costs. Firms operating in the manufacturing sector on average have indicated a greater need for credit payment deferrals. 20 percent of the firms in the manufacturing sector indicated the need for credit deferrals in comparison to 15 percent of firms operating in the services sector. As mentioned before, the divergence is the outcome of structural characteristics of the firms. In this instance, firms operating in the manufacturing sector are more likely to secure access to finance from a formal financial institution given that they tend to be larger and more formalized. It is most immediately indicated by the private sector credit portfolio of Bank's in Pakistan, more than 70 percent of which on average over the past decade has been directed to the manufacturing sector.... 31 Higher access to finance, leading to higher borrowing from the formal financial system therefore explains the greater propensity of manufacturing firms to select this policy support option in comparison to firms in other sectors. Only 10.7 percent of the surveyed firms reported receiving direct or indirect support from the government in response to the crisis. The number suggests that government policies and programs to support the private sector have had limited outreach, curbing the impact of these programs. Outreach and impact also hasn't been uniform. A greater percentage of large size firms (19.8 percent) reported benefitting from government support in comparison to micro enterprises (7.69 percent) (see Figure 35). Firms in the manufacturing sector, similarly, reported getting government support at higher rates (12.45 percent of manufacturing firms reported getting government support) in comparison to firms' other sectors (see Figure 36). The differential in support received by different sized firms and those operating in different sectors is not necessarily an outcome of the government favoring a particular subset of firms at the expense of other firms, but that of the organizational structure and formality status of firms. Larger firms, those which are documented and are in the government's radar, could be supported more easily as compared to smaller firms for which data and other pre-requisites of support are absent. 31 State Bank of Pakistan 28 Figure 36: Support Received in Response to Figure 37: Support Received in Response to the Crisis by Firm Size the Crisis by Sector 20 15 19.81 9.68 15 12.45 10 10 10.73 10.98 6.25 7.69 5 5 4.17 0 0 Micro (0-9) Small (10-30) Medium (31-250) Large (250+) Agriculture Manufacturing Retail Other Services Only 4.69 percent of firms reported having applied for government support. Firms cited different reasons for not applying for government, including not being aware (33 percent), difficult process of application (19 percent) and not being eligible (32 percent) (see Figure 37). The responses suggest that there is a case for mass media campaigns to raise awareness about government support schemes, in addition to the need for streamlining the application process. Limited uptake among business may also be an outcome of the inconsistency between the support required by firms and the support available. Approximately 64 percent of the businesses which reported having received support from the government received support in the form of deferral or suspension of utility payments. In contrast only 23 percent of all indicated the need for such support. 30 percent of surveyed firms indicated the need for support with rent or mortgage deferrals, whereas only 4.2 percent reported actually getting this support. Figure 38: Support Required vs Support Figure 39: Reasons why firms have not Needed received support 100% 40 80% 33 Percentage 60% 30 32 40% 20% 20 0% 19 Support Required Support Received Other 10 Tax deferral or exemption Access to new credit 0 Cash Transfer I was not Too difficult I am not aware to acquire elligible Deferral of credit payments, suspension Deferral or suspension of utility payme Deferral of rent or mortgage 29 The multiplicity of shocks and their confluence demands a commensurate and holistic policy response. The truly global nature of this crisis and the fact that it has enveloped the economy, impacted demand, supply, access to finance, in addition to mobility, necessitates that government response should also be multifaceted. Measures on the demand side to support economic activity through cash transfers and other stimulus needs to be complemented with measures on the supply side to ensure that firms remain afloat, that the intangible bond between the firms and their employees remains intact through the crisis and supply chains are not disintegrated. Because the recession was caused by a shock which came originated outside the economic system, it should be possible for recovery to be strong and swift as economic activity bounces back to its previous level. However, the vigor of the recovery will significantly be a function of how economic entities are supported through the crisis. Holistic measures which prevent temporary closures from becoming permanent and which prevent the dissolution of the bond between firms and their employees, especially those with firm specific human capital, will to a significant degree determine whether the recovery is V-shaped, U-shaped or the much dreaded L-shaped. Government policies need to be informed by the requirements of the private sector, disaggregated by size, sector of operation, formality status etc. As this report points out, the impact of the crisis on the private sector has been heterogenous, with some firms faring better than other and some sectors bouncing back quicker than others. Heterogeneity in terms impact extends to heterogeneity in terms of requirements and needs to be built into the government's response. Policies and support programs which may yield desired results for large firms and those operating in the manufacturing sector may not be as effective for smaller firms operating in the services sector. Impact, as such, at a scale envisioned by the government and required to ensure robust recovery, would require differentiated policy responses which have been informed by the underlying characteristics of firms. 30 Appendix 1 – Sample Characteristics Table 4: Sample Distribution Retail and Other Agriculture Industry 32 Wholesale Services Punjab Micro 46 124 97 443 710 Small 5 53 20 173 251 Medium 3 43 9 70 125 Large 1 10 12 23 Subtotal 55 230 126 698 1109 Sindh Micro 3 27 28 88 146 Small 1 10 1 36 48 Medium 1 8 1 8 18 Large 1 8 9 Subtotal 5 46 30 140 221 KPK Micro 5 12 6 44 67 Small 1 2 2 12 17 Medium 1 1 2 4 Large 1 1 Subtotal 7 16 8 58 89 Baluchistan Micro 2 5 1 21 29 Small 2 2 4 Medium 1 1 1 3 Large 2 2 Subtotal 3 8 1 26 38 Others Micro 1 9 10 16 36 Small 1 3 4 Medium 1 1 Large 0 Subtotal 2 10 10 19 41 Grand Total 72 310 175 941 1498 33 32 Industry includes manufacturing, construction and utilities. 33 A total of 1,515 firms were surveyed, however, 17 firms did not respond to the question on their total number of full-time employees. 31 10.08% 2.336% 21.63% Figure 40: Distribution of Firms by Size 65.95% Micro Small Medium Large Size Classification - Micro (0-9); Small (10-30); Medium (30-250); Large (250+) 2.574% 2.706% 5.875% 14.92% Figure 41: Distribution of Firms 73.93% by Province Punjab Sindh Others KPK Baluchistan 32 4.752% 20.79% Figure 42: 62.84% Distribution of Firms by Sector 11.62% Agriculture Industry Retail and Wholesale Other Services Industry includes manufacturing, construction and utilities .264% 1.914% 1.782% 11.35% 17.76% Figure 43: Distribution of Firms by Organizational Structure 66.93% Not Registered Sole Proprietorship Partnership Private Limited Company Public Limited Company Didn’t Answer 33 Table 5: Data Estimates Mean Observations Min Median Max Numberof Number offull - time workers full-time 34.93 1498 0 5 4043 Number of part-time workers 4.13 1497 0 0 1500 Share of female workers 2.79 1509 0 0 70 Age of the firm 15.03 1515 0 11 118 Share of exports in 2019 in 2019 11.59 1409 0 0 100 Rental Costs out of Total Costs 17.37 1053 0 20 80 Wage Costs Wage out of Costs out of Total Costs 28.03 988 0 25 90 Material Costs out of Total Costs 938 32.20 0 30 100 Debt Repayment Debt Repayment Costs out of Total Costs Costs out 944 4.32 0 0 50 Utility Costs out of Total Costs 1035 15.91 0 10 100 34 Appendix 2 – Sample and sampling methodology Sampling Frame Survey administration was preceded by an exercise to draw out a stratified random sample from a representative sampling frame. The sampling frame for the business pulse survey was the register of businesses housed at Gallup Pakistan. The Gallup Business Registry is composed of many different sources the survey firm has consulted and reviewed over the last few years. The sources/databases consulted in building up the registry include: 1. Online and hard databases of Chambers of Commerce. There are approximately 50 such Chambers across Pakistan; 2. Databases of Trade Associations. There are 150+ Ministry of Trade recognized Trade Associations; 3. Databases of Trade Development Authority; 4. Yellow Pages; 5. Databases of Provincial Board of Investment; 6. Databases of Market Associations; 7. Databases of various Provincial Facilitation Bodies Data e.g. Punjab Small Industries Corporation; 8. Other compilations. The Gallup Business Register has details of close to 45,000 businesses. In order to ensure that there is no duplication and no closed firms are included in the registry, Gallup initiated an exercise to update the registry prior to drawing out the survey sample from the frame. The composition of Gallup's Business Register and its comparison with the distribution of businesses in the economic census is given in the table below: Table 6: Distribution of Firms Gallup Business Register Economic Census Province Punjab + Islamabad 73% 65% Sindh 20% 18% KP 6% 15% Baluchistan 1% 2% Sector Manufacturing 19% 19% Services 73% 78% Others/Agri 8% 3% 35 Representativity One of the most fundamental assumptions made in this report is that the Gallup Business Register is a microcosm of the private sector at large in Pakistan. This assumption, coupled with the sampling methodology described in the next section, means that the results presented in the report are not just representative of the firms surveyed, but instead, the findings and results are applicable to the private sector at large. The assumption is not unfounded and is anchored in two facts. First, the register was pieced together using different databases, both public and private, spread out across Pakistan. The wide variety of sources used to stitch together the register means that it encompasses firms operating across the economic spectrum which embody diversified characteristics. Second, the distribution of firms in the register conditioned on sector and location of operation was found to be closely aligned with the distribution of firms in the economic census, which served as the stratification source for the survey. The Economic Census was conducted in Pakistan by Federal Bureau of Statistics and released in the year 2005. The census covered 26,144 Enumeration Blocks in four Provinces and Federal Capital, Islamabad excluding FATA and the restricted areas. The FATA was excluded due to prevailing unrest situation therein. It covered 3.249 million Establishments /Households where any economic activity was being carried out during the time of enumeration. While dated, the Economic Census is the most comprehensive source of the distribution of economic entities in Pakistan, by core characteristics such as region of operation, sector of operation, size of operation etc. An alignment between the distribution of firms in the register and economic census can be taken as an indication of the register being a representative subset of economic entities operating in Pakistan's private sector. Sample Selection and Stratification The sample was selected employing stratified sampling with proportional allocation. Stratified sampling is a technique which uses auxiliary information which is referred to as stratification variables to increase the efficiency of a sample design and accuracy of point estimates. Stratification also permits separate analyses on each stratum and allows different interests to be analyzed for different groups. Two levels of stratification were employed: 1. Sector and 2. Geographical Location/Provinces, in drawing out the sample from the sample frame for the business pulse survey. At the sectoral level, enterprises in the frame were allocated to three main sectors: a) Agriculture, b) Manufacturing and c) Services. At the regional level, enterprises were allocated to 5 regions: a) Punjab, b) Sindh, c) Baluchistan, d) KP and e) Other Regions/Territories. Once the frame had been stratified, a simple random sample was taken from each stratum to establish the total sample. Enterprises were selected from the individual stratums in proportion to the weight of these stratums (i.e. proportional distribution) in the Economic Census, which served as the stratification source. In proportional allocation, the sample allocated to each stratum is proportional to the number of units in the strata. Say we were sampling 10% of the population; we would then sample 10% of each stratum. This method takes into account the size of each stratum; larger strata will have larger samples taken from them. A replacement sample was also drawn out in anticipation of non-response which tends to be much higher for phone-based surveys. In totality, a sample of 15,000 firms was drawn to achieve a completed sample of 1,500 firms. 36 The following table was used to determine how many and what type of firms to draw from the frame: Businesses Needed Accounting for Table 7: Sample Selection % Share in Total Businesses Substitutes Required Businesses in Needed for BPS (Column 2 multiplied Pakistan (1) Sample (2) by 10) Punjab – Manufacturing Punjab - Services Punjab – Other/Agri Sindh – Manufacturing Sindh - Services Sindh – Other/Agri KP – Manufacturing KP - Services KP – Other/Agri Baluchistan – Manufacturing Baluchistan - Services Baluchistan – Other/Agri Other – Manufacturing Other - Services Other – Other/Agri 100% 1,500 1,5000 37