August 2020 · Number 9H The Impact of COVID-19 on Formal Firms: Evidence from Uganda Pierre Bachas, Anne Brockmeyer, Kyle McNabb, Camille Semelet1 S UMMARY equivalent to 0.8% of GDP, suggesting that firms will need to substantially increase borrowing to survive. Firms would This note uses administrative tax data on firms to mea- cut 3.2% of total yearly payroll - wage subsidies can save a sure the direct impact of the lockdown on firms’ profitability, substantial share of payroll in the medium-impact sector, but employment and exit rates. It separates the economy in three will not be able to save employment in the high-impact sector sectors, which face different size shocks and considers two (tourism, transport, personal services), where firms can’t pay lockdown scenarios: one lasting three months and one lasting their fixed costs. five months. It estimates losses to corporate income tax rev- enue, increases in firms’ debt levels, cuts in payroll and their This note faces important limitations: (i) it does not in- mitigation through wage subsidies, and aggregate output losses clude the indirect impacts of the shocks which operate through from firms’ exit. firms’ trade linkages, (ii) it only models a demand shock and as such firms have no issues obtaining inputs (materials, labor), Overall, the impact on the economy is severe, with large (iii) Firms do not adapt to the crisis (for example by chang- falls in tax revenue, increases in debt and loss of employment. ing products, selling online etc.). Given these limitations, the Under a three-month lockdown, we estimate that 51% of firms numbers in this report should be considered as plausible lower only remain profitable and that about 26% of the firms in bounds arising from direct effects, in partial equilibrium. Dy- the highly-impacted sectors register losses. The corporate namic general equilibrium models of the economy, with link- income tax revenue loss is severe and would equal 22% of ages across sectors and firms, are needed to gauge longer term its baseline in 2020. In addition, firms accumulate losses effects. 1 Pierre Bachas: World Bank Research, pbachas@worldbank.org; Anne Brockmeyer: Institute for Fiscal Studies, University College London and World Bank, abrockmeyer@worldbank.org; Kyle McNab: Overseas Development Institute and Institute for Fiscal Studies TaxDev Center, k.mcnabb@odi.org.uk; Camille Semelet: World Bank Research, csemelet@worldbank.org. The findings and conclusions are those of the authors; they do not represent the views of the World Bank, its member countries or the countries mentioned in this study. We are grateful to the Ministry of Finance and the Ugandan Revenue Authority for providing the data used in this study. We thankfully acknowledge funding by the World Bank through the Knowledge of Change Trust Fund and the Fiscal Policy and Sustainable Growth Unit, and and by UKAID through the Centre for Tax Analysis in Developing Countries (TaxDev). 1 L OCKDOWN S IMULATIONS AND C ATEGORIZATION tant to reduce their labor costs as re-contracting is costly and OF S ECTORS BY I MPACT cannot adjust their fixed costs. Finally, we assume that credit constraints prevent borrowing beyond existing loans used to The COVID19 (coronavirus) pandemic and associated cover predictable losses (i.e. losses unrelated to the shock). containment measures are expected to cause far-reaching damage to economies around the world. Firms are suffer- We classify sectors into three impact categories - high, ing from reduced demand due to movement restrictions, from medium and low – depending on their expected loss in reduced labor supply and from constraints to sourcing mate- revenue during the shutdown, displayed in Table 1. This rial inputs. The breakup of otherwise healthy businesses in categories are based on a World Bank classification of sectors. response to a temporary shock implies large social costs. Gov- In the high-impact category are sectors which can’t operate ernments are therefore intent on designing emergency policies at all during the lockdown and lose 100% of their revenue to keep businesses afloat. during that period. These include tourism, transportation, non- essential retail and entertainment. In the medium impact cate- We present simulations using firm-level tax records gories are sectors which operate at half capacity and lose 50% from Uganda, which vary the duration of the lockdown of their revenue. These include manufacturing and education. and the relative impact across sectors. In these simulated Finally, the low impact sector only loses 20% of its monthly scenarios, demand shocks induce a loss in revenue which trig- revenue, in sectors such as essential retail, health, construc- gers a cut in profitability and possibly cuts in employment or tion and agriculture. Naturally there is still a fair degree of even firm closure. We compare these simulations to a baseline heterogeneity of exposure within the categories, with some (pre-COVID) situation, which corresponds to the last year of sub-sectors experiencing increased revenue. Table 2 shows the available administrative data. Our analysis relies on a few sim- number of firms and economic weight of each of the three im- ple assumptions about the structure of firms’ revenue and costs: pact sectors: the high-impact sector contains 17% of the firms we assume that firms aim to weather the shock such that they and 11% of the wage bill, the medium impact sector contains can scale their production capacity back up swiftly at the end 60% of the firms and 57% of the wage bill, and the low-impact of the lockdown. In this stylized world, firms can reduce their sector the remaining 23% of the firms and 33% of the wage bill. material costs proportionally to the drop-in demand, are reluc- Table 1: Sector Categories and Shocks Sectors Expected Monthly Categories (e.g., detailed list of sectors in Appendix Table 4) Revenue Loss Accommodation and Food Service Activities, Transport, and other highly High Impact affected sectors 100% Medium Impact Non-essential Retail, Education and other moderately affected sectors 50% Agriculture, Human Health and Social Work activities and other mildly Low Impact affected sectors 20% Table 2: Statistics for High, Medium and Low Impact Sectors Aggregates Averages Wage Avg. size Avg. Labor Material Fixed Number Share Revenue Categories of firms of firms share bill (LCU, in Profit costs (% costs (% costs (% share millions) margin total costs) total costs) total costs) High impact 3403 17% 5% 11% 1,547 11% 15% 37% 47% Medium impact 12093 60% 78% 57% 6,276 11% 9% 61% 26% Low impact 4698 23% 17% 33% 3,420 9% 15% 40% 44% 2 E FFECT ON F IRMS ’ P ROFITABILITY tion for firms’ ability to stay afloat is a non-negative profit rate. We start by simulating scenarios where firms lose a share of In this section, we ask what share of firms becomes un- their revenue, while all costs remain constant. The results are profitable, and could benefit from government support to displayed in Figure 1, and show that in the high and medium “stay afloat” under a three-month and a five-month lock- impact sectors the vast majority of firms become unprofitable down scenario. Assuming credit constraints, a rough indica- even under the three-month lockdown scenario. Figure 1: Firm Profitability Under a Shock to Revenue, No Adjustment to Costs (a) 100% Revenue loss (b) 50% Revenue loss (c) 20% Revenue loss Note: These figures show the distribution of profitability, at baseline, and assuming that firms face a loss in revenue corresponding to either three or five months of loss in yearly revenue. They show the distributions holding all costs constant. In addition to a pure revenue shock, we simulate a more high impact sector. On aggregate, only 51% (43%) of all firms realistic scenario where firms adjust their material costs remain profitable under a three-month (five-month) lockdown. proportionally to their revenue loss. The results are dis- We also observe that the distribution becomes multi-modal for played in Figure 2: 72% of firms in the high-impact sector high impact firms: while firms using mainly material inputs are profitable at baseline, a number which drops to 26% for and little labor or capital inputs can adjust to some extent and the three-month lockdown scenario and to 16% under a five- limit their losses, firms with a small share of material inputs month lockdown. The impact is less severe in the medium and in total cost have little margin to adjust and suffer much larger low impact sectors, since the shock they face is less severe and losses. since these sectors rely more heavily on material inputs than the Figure 2: Firm Profitability Under a Shock to Revenue, Material Costs Adjust in Proportion (a) 100% Revenue loss (b) 50% Revenue loss (c) 20% Revenue loss 3 E FFECT ON E MPLOYMENT AND S IMULATIONS OF firms which have to cut their wage bill proportionally to the WAGE S UBSIDIES shock in an attempt to stay afloat. In the middle of the distri- In this section, we study by how much employers would bution, a share of firms reduces their wage bill somewhat (but need to slash their yearly wage bill in the absence of gov- less than proportionally to the shock) and achieves zero profit ernment support. We continue to assume that material inputs (or retains to pre-shock projected losses): providing even mod- adjust first, and that firms only cut their wage bill if they are est wage subsidies to these firms has the potential to save jobs. still unprofitable after the material inputs adjustment. Figure 3 On aggregate, weighting by firms’ yearly wage bill, this would shows the resulting distributions of the reduction in the yearly lead to a cut in payroll of 3.2% (resp. 6.9%) of the formal econ- wage bill for a three or five month lockdown scenario. The fig- omy’s total yearly wage bill in the three-month lockdown [resp. ure is bi-modal: the first spike corresponds to firms which are five-month]. The payroll loss is of course concentrated in the sufficiently profitable at baseline: they absorb the shock and high-impact sectors which would cut 16.6% (resp. 34.6%) of keep paying their workers. The second spike corresponds to payroll under the three-month lockdown (resp. five-month). Figure 3: Wage Bill Reduction from a Revenue Shock, Material Costs Adjust Proportionally (a) 100% Revenue loss (b) 50% Revenue loss (c) 20% Revenue loss To counteract these payroll losses and destruction of To understand this, note that we assume that these firms still jobs, the government might consider offering wage subsi- have to pay their fixed costs (e.g. rent) and a reduction in la- dies to firms, in order to protect formal employment. Figure bor costs is not sufficient to counteract the revenue loss. On the 4 shows each sector’s aggregate payroll losses when varying other hand, wage subsidies can save payroll for the low, and es- the size of the wage subsidy, measured as the share of firms’ pecially the medium-impact sector: in the latter sector, a 60% payroll paid by the government. In the case of a zero-wage wage subsidy over the lockdown period would roughly halve subsidy the loss in payroll corresponds to the numbers men- the sector’s payroll loss. On aggregate, applying a 50% wage tioned above. As the wage subsidy increases the loss in payroll subsidy across all sectors would reduce the yearly payroll loss decreases, as some firms now return to zero profits (or to their from 3.2% to 2.5% (three-month lockdown) or from 6.9% to baseline losses). The impact on payroll loss is however very 5.4% (five-month lockdown). It would take a substantial sub- different across the three impact sectors: On the one hand, for sidy to save more payrolls: even with a 90% wage subsidy the the high impact sectors (Figure 3a), the loss in revenue is too loss in yearly payroll would be reduced to 2.2% (three-month severe to be compensated by wage subsidies and these firms lockdown) or to 4.7% (five-month lockdown). are forced to cut employment, even for large wage subsidies. 4 Figure 4: Aggregate Sector Loss in Payroll as a function of the Size of the Wage Subsidy (a) 100% Revenue loss (b) 50% Revenue loss (c) 20% Revenue loss Note: These figures show to what extent a government wage subsidy for the retained labor force can absorb the aggregate loss in payroll, if the lockdown lasts three or five months. Firms readjust their decision after receiving a wage subsidy: they first adjust their material costs, and then their wage bill. It is still assumed that the drop-in wage bill can’t be more than proportional to the revenue fall and that due to re-contracting costs, firms keep paying wages as long as they remain profitable. S HARE OF FIRMS CLOSING I NDUCED BY THE R EV- ous year have an exit rate which is almost 10 percentage points ENUE S HOCK higher than firms which had positive profits. In our previous analysis, we estimated the share of firms which have negative Here we predict the increase in firms closing due to the profits due to the crisis, for each impact sector. We thus com- different lockdown scenarios. We use the panel dimension bine these results to measure the percentage increase in closing of the data to measure the excess share of closing firms in firms induced by the crisis, by multiplying the share of newly pre-crisis years separately for negative and positive profit firms loss-making firms with their excess exit rate. We show the re- (and in each of the three impact sectors). Figure 5 (a) shows sults for the three and five month lockdown scenario in 5 (b): these shares in regular times: on average 21% of firms close in under a three (five) month lockdown scenario, the share of clos- any given year; however firms which had losses in the previ- ing firms from the formal economy increase by 90% (120%). Figure 5: Share of Closing Firms (a) Pre-Crisis Average Closing Rate (b) Closing Due to Crisis Note: Panel (a) shows the average exit probability for all firms, and then for loss-making and profit-making firms, using panel data before the crisis. Panel (b) shows the percentage increase of firms’ exit induced by a three or five month output loss, compared to baseline levels. 5 AGGREGATE N UMBERS AND I MPACTS ON THE gesting that firms will need to substantially increase borrowing. E CONOMY Payroll losses are also substantial, ranging between 3.2% and The impact on the overall economy is severe, with large 6.9% of the annual wage bill - wage subsidies can safeguard falls in tax revenue, increases in debt and loss of employ- some employment, especially in the medium-impact sectors: a ment. Table 3 summarizes the key numbers for the three and 50% wage subsidy would reduce the payroll losses from 1.6 five months lockdown scenarios and the aggregate impact on to 1.0% [3.8% to 2.4%] in the three [five] months lockdown the economy. 51% or less of firms remain profitable after the scenario. Increases in firm exit are relatively small, meaning shock, and almost all firms in the highly impacted sectors reg- that associated output and payroll losses are also small, but ister losses. The Corporate income tax revenue loss is severe, this is likely an under-estimate: Our panel data features only reaching 22% overall in the three-month shock scenario and a smaller number of firms that experience large revenue losses 35% in the five-month shock scenario. In the high-impact sec- and hence allow us to estimate the effect, presumably because tors, almost all CIT revenue is lost. This is because, despite most such firms exit the panel. Our estimates mean that the size the temporary nature of the shock, the shock generates large of government rescue packages for firms and workers needs to losses which are counted against the profits made during the be large, and the budget support from donors to lower-income remainder of the year. The absolute increase in losses is 0.8% countries even larger, to compensate for the massive loss in tax [1.7%] with the three-month shock [five-month shock], sug- revenue. Table 3: Aggregate Impacts by Lockdown Duration and by Impact sectors High Impact Medium Impact Low Impact All Sectors 3 5 3 5 3 5 3 5 months months months months months months months months Share of firms 1 profitable at baseline 71.7 77.9 71.9 75.5 Share of firms still 2 profitable (materials adj.) 26.4 15.8 55.8 48.1 54.4 47.8 50.5 42.6 CIT revenue loss 3 relative to baseline (%) 55.4 73.5 23.5 38.0 11.8 19.0 22.2 35.0 Absolute losses 4 increase (% GDP) 0.4 0.8 0.2 0.5 0.2 0.3 0.8 1.7 No wage subsidy 16.6 34.6 1.6 3.8 1.5 3.0 3.2 6.9 50% wage 5 Payroll Loss subsidy 16.1 33.7 1.0 2.4 0.5 1.1 2.5 5.4 90% wage subsidy 15.5 32.7 0.7 1.6 0.3 0.5 2.2 4.7 Percentage increase in firms’ 6 exit relative to baseline 140.5 175.7 92.6 125.6 47.6 68.6 89.7 119.8 Permanent output loss 7 from firm exit (% GDP) 0.1 0.1 0.3 0.4 0.1 0.1 0.5 0.6 Permanent payroll loss 8 from firm exit (% GDP) 0.8 1.0 5.9 8.0 0.8 1.2 7.4 10.1 6 A PPENDIX Table 4: Sectors and Impact Categories SECTORS (ISIC Rev 4 code) High - Medium - Low Impact A AGRICULTURE, FORESTRY AND FISHING Low Impact B MINING AND QUARRYING Low Impact C MANUFACTURING Medium Impact D ELECTRICITY, GAS, STEAM AND AIR CONDITION- Medium Impact ING SUPPLY E WATER SUPPLY, SEWERAGE, WASTE MANAGE- Medium Impact MENT F CONSTRUCTION Medium Impact G WHOLESALE AND RETAIL TRADE Medium Impact H TRANSPORTATION AND STORAGE High Impact I ACCOMMODATION AND FOOD SERVICE ACTIVI- High Impact TIES J INFORMATION AND COMMUNICATION Low Impact K FINANCIAL AND INSURANCE ACTIVITIES Medium Impact L REAL ESTATE ACTIVITIES Medium Impact M PROFESSIONAL, SCIENTIFIC AND TECHNICAL AC- Low Impact TIVITIES N ADMINISTRATIVE AND SUPPORT SERVICE ACTIVI- Low Impact TIES O PUBLIC ADMINISTRATION AND DEFENCE; COM- Low Impact PULSORY SOCIAL SECURITY P EDUCATION Medium Impact Q HUMAN HEALTH AND SOCIAL WORK ACTIVITIES Low Impact R ARTS, ENTERTAINMENT AND RECREATION High Impact S OTHER SERVICE ACTIVITIES High Impact 7 Calculation details for Table 3 divided by (3) GDP (current LCU of the same year), ex- pressed as percentage. Each figure is calculated for a specific Impact category (High, Medium, Low impact and All sectors) and for a specific 5. Payroll Loss, at different wage subsidy rate: (1) sum of lockdown scenario (three and five months): all firms’ new labor costs under lockdown, divided by (2) the sum of all firms’ labor costs at baseline, expressed as 1. Share of firms profitable at baseline: (1) number of firms percentage. with positive profit margin before output shock, divided by (2) total number of firms, expressed as percentage. 6. Percentage increase in firms’ exit relative to baseline: (1) exit rate of firms after lockdown minus (2) exit rate of 2. Share of firms still profitable (materials adj.): (1) number firms at baseline, divided by (2) and expressed as per- of firms with positive profit margin, after material costs centage. adjustment proportional to the shock, divided by (2) total number of firms, expressed as percentage. 7. Permanent output loss from firm exit (% GDP): (1) ad- ditional exit rate relative to baseline multiplied by (2) 3. CIT revenue loss relative to baseline: (1) sum of all the sum of all firms’ turnover at baseline, divided by (3) firms’ profits at baseline multiplied by the corporate in- GDP (current LCU of the same year), expressed as per- come tax rate minus (2) sum of all firms’ profits after centage. lockdown multiplied by the corporate income tax rate, divided by (1) and expressed as percentage. 8. Permanent payroll loss from firm exit (% GDP): (1) ad- ditional exit rate relative to baseline multiplied by (2) the 4. Absolute losses increase (% GDP): (1) absolute value of sum of all firms’ labor costs at baseline, divided by (3) the sum of all firms’ losses after lockdown minus (2) ab- GDP (current LCU of the same year), expressed as per- solute value of the sum of all firms’ losses at baseline, centage. 8