metadata: authors: 0: author: Hoy,Christopher Alexander
1: author: Simbeye,Laban
2: author: Malik,Muhammad Abdullah Ali
3: author: Koivisto,Aliisa
4: author: Maboshe,Mashekwa
count: Zambia
docna: 0: docna: How Have Formal Firms Recovered from the
            Pandemic ? Insights from Survey and Tax Administrative Data
            in Zambia
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines how formal firms
            have been impacted by and recovered from the pandemic, by
            drawing on two distinct but complementary data sources. This
            is the first attempt to use both survey and tax
            administrative data to measure the initial decline and
            subsequent recovery of firm sales and employment in a low-
            or lower-middle-income country. The findings of three rounds
            of follow-up surveys to a standard World Bank Enterprise
            Survey completed immediately prior to the pandemic are
            compared to information contained in the universe of
            value-added tax and personal income tax returns filled by
            firms during 2020 and the first half of 2021 in Zambia.
            Despite substantial differences in terms of the breadth and
            depth of these data sources, they show a very similar
            pattern. The sales of formal firms recovered from the
            pandemic far more strongly than their employment levels. By
            July 2021, both the survey and tax administrative data show
            that most firms experienced a complete recovery in sales,
            while levels of employment worsened over the course of the
            pandemic for many firms. Two key insights emerge from this
            analysis. First, formal firms appear to have adjusted their
            operations in a way that reduced their need for as much
            labor to achieve the same (or higher) level of sales.
            Second, if formal firms&#x2019; reduced reliance on
            labor persists, lower levels of formal employment in low-
            and middle-income countries may be a concerning consequence
            of the COVID-19 pandemic that lingers for years to come.
display_title: How Have Formal Firms Recovered from
            the Pandemic ? Insights from Survey and Tax Administrative
            Data in Zambia
listing_relative_url: /research/2022/08/33882649/formal-firms-recovered-pandemic-insights-survey-tax-administrative-data-zambia
topicv3: Firm Behavior and Jobs m1504204 2873,Labor Market Analysis m1582757 4428,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-08-16T00:00:00Z
dois: 10.1596/1813-9450-10139
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1595364
1: author_key: NA
2: author_key: 1433250
3: author_key: NA
4: author_key: 1287736
model_extractions: Policy Research Working Paper 10139

# How Have Formal Firms Recovered from the Pandemic?

Insights from Survey and Tax Administrative Data in Zambia _Christopher Hoy Laban Simbeye Muhammad Abdullah Ali Malik Aliisa Koivisto Mashekwa Maboshe_ Poverty and Equity Global Practice August 2022
Policy Research Working Paper 10139

# **Abstract**

This paper examines how formal firms have been impacted by and recovered from the pandemic, by drawing on two distinct but complementary data sources. This is the first attempt to use both survey and tax administrative data to measure the initial decline and subsequent recovery of firm sales and employment in a low- or lower-middle-income country. The findings of three rounds of follow-up surveys to a standard World Bank Enterprise Survey completed immediately prior to the pandemic are compared to information contained in the universe of value-added tax and personal income tax returns filled by firms during 2020 and the first half of 2021 in Zambia. Despite substantial differences in terms of the breadth and depth of these data sources, they show a very similar pattern. The sales of formal firms recovered from the pandemic far more strongly than their employment levels. By July 2021, both the survey and tax administrative data show that most firms experienced a complete recovery in sales, while levels of employment worsened over the course of the pandemic for many firms. Two key insights emerge from this analysis. First, formal firms appear to have adjusted their operations in a way that reduced their need for as much labor to achieve the same (or higher) level of sales. Second, if formal firms’ reduced reliance on labor persists, lower levels of formal employment in low- and middle-income countries may be a concerning consequence of the COVID-19 pandemic that lingers for years to come.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at choy@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Christopher Hoy<sup>1</sup> , Laban Simbeye<sup>2</sup> , Muhammad Abdullah Ali Malik<sup>3</sup> , Aliisa Koivisto<sup>4</sup> and Mashekwa Maboshe<sup>5</sup>

# **JEL-Classification:** D22, J21, J23, O12, O55.

# **Keywords:** Firms, COVID-19, Tax administrative data, Zambia, Economy recovery.

> * This study is reproduced here with full acknowledgement of UNU-WIDER, Helsinki. The study was originally published as a WIDER Working Paper under the UNU-WIDER project Building up efficient and fair tax systems – lessons based on administrative tax data, which is which is part of the Domestic Revenue Mobilization programme. The programme is financed through specific contributions by the Norwegian Agency for Development Cooperation (Norad). The authors wish to thank the Zambia Revenue Authority through the Research and Corporate Strategy department for facilitating the study of the tax administrative data and to acknowledge that the enterprise survey data was collected through a partnership between the Poverty and Equity Global Practise and Enterprise Analysis Unit of the World Bank. Particularly they would like to thank Arden Finn and Gemechu Aga for the pivotal role they played in facilitating the study of the enterprise survey data. The authors are grateful for comments made on an earlier draft by Pierella Paci, Arden Finn, Gemechu Aga, Filip Jolevski, Pierre Bachas and Rob Swinkels. The views expressed in this paper are those of the authors and do not necessarily reflect those of the organizations that they work for.<sup>1</sup> World Bank and corresponding author (choy@worldbank.org),<sup>2</sup> Zambia Revenue Authority,<sup>3</sup> World Bank,<sup>4</sup> UNU-WIDER,<sup>5</sup> University of Zambia and ZIPAR.
# **1 Introduction**

Policies introduced to control the spread of COVID-19 have caused unprecedented economic upheaval throughout the world, particularly for firms in low- and lower-middle-income countries that typically received little to no government support. Mobility and economic activity restrictions, alongside higher transaction costs and disruptions in cross-border trade, have hindered the allocation of resources within countries and across industries, lowering aggregate productivity growth (Apedo-Amah et al. 2020). Additionally, the pandemic has substantially shifted consumer demand, which has forced fundamental changes to business practices that are likely to persist in the medium term (Barrero et al. 2020). Therefore, it is imperative to develop a greater understanding of how firms have coped in this extremely challenging environment, especially in lower-income settings.

We examine this issue by drawing on a combination of survey and tax administrative data to analyze how formal firms in Zambia have recovered from COVID-19.<sup>1</sup> In general, the composition of the formal sector in Zambia is similar to many other medium-sized SubSaharan African countries, except there is a greater prevalence of firms in the mining sector (World Bank 2022). As is the case in most countries around the world, at the outset of the pandemic the Government of Zambia introduced restrictions to control COVID-19, which considerably curtailed normal economic activity. We investigate how firms have fared since this initial shock by using three follow-up surveys to a standard World Bank Enterprise Survey (ES) covering a representative sample of 601 formal firms that was conducted immediately prior to the pandemic and by drawing on the universe of monthly value-added tax (VAT) and pay-as-you-earn (PAYE) returns filed by around 20,000 firms throughout 2020 and the first half of 2021.<sup>2</sup> Both data sources have strengths and weaknesses. A key advantage of the survey data is that firms provide extensive details about their operations when completing the question-

> 1For the purposes of this study, formal firms are defined as firms that are registered with the Zambia Revenue Authority.

> 2In Zambia, personal income tax is recorded in PAYE returns.

1
naire, but a limitation is that respondents only make up a relatively small share of all formal firms and some sectors are excluded (e.g. the mining sector). In contrast, tax administrative data captures all firms in all sectors that file VAT and PAYE returns. However, not all firms file returns each month, and they are only required to provide limited information about their activities when they do (e.g. total monthly sales and number of employees paid each month). By comparing the findings across these different data sources, it is possible to have far greater confidence in what actually took place regarding the recovery (or lack thereof) of formal firms.

We show, in both the survey and tax administrative data, that the sales of formal firms in Zambia recovered from the pandemic far more strongly than their employment levels. The order of magnitude of the initial decline and subsequent recovery of firms varied between the survey and tax administrative data, with the latter presenting a more positive picture. However, the overall trend was the same. In June 2020, on average, formal firms reported a large decline in sales and a moderate fall in employment. In contrast, by July 2021, most firms had experienced a complete recovery in their sales, while the share of firms reporting that they had decreased their number of employees doubled. Econometric analysis examining the factors associated with an initial decline and subsequent recovery illustrates structural factors (e.g., the type of business activity a firm was involved in) were more closely correlated with changes in sales than changes in employment, whereas the latter was more closely correlated with firm-specific factors (e.g. the level of experience of the top manager).

These results provide several insights about how formal firms have recovered from the COVID-19 crisis. First, there has been a ‘decoupling’ between the levels of sales and employment among some formal firms in Zambia. Formal firms appear to have adjusted their operations in a way that reduced their need for labor to achieve the same (or higher) level of sales. As such, one way firms were able to be resilient and rebound from the crisis was to reduce their labor inputs, which can often be challenging to lower in more stable settings. Second, if formal firms’ reduced reliance on labor persists, lower levels of formal employment in low- and middle-income countries may be a concerning consequence of the COVID-19 2
pandemic that lingers for years to come. Consequently, there may be a need for government support to expand opportunities for workers to join formal firms. Third, firm-specific factors, more than structural factors, appear to be associated with whether firms experienced a recovery in employment. This implies that there may be further scope for some firms to fully return their number of employees to pre-pandemic levels by taking actions within their control.

This paper makes two contributions to the existing literature on the impact of COVID-19 on formal firms. First, we draw on two distinct but complementary data sources to track how firms were impacted by and have recovered from the pandemic. Previous studies have either relied on surveys of firms (e.g., Davies et al. 2021; Apedo-Amah et al. 2020; Cirera et al. 2021; Karalashvili and Viganola 2021) or tax administrative data (Angelov and Waldenström 2021; Bachas et al. 2020, 2021; Mascagni and Lees 2022). To the best of our knowledge, this is the first effort to combine these data sources, which allows us to present a far richer picture of how COVID-19 has impacted firms. Extensive efforts are made to identify whether differences (and/or similarities) in the findings across survey and tax administrative data can partly be explained by differences between the samples of firms.

Second, this is one of the first in-depth studies to specifically focus on the recovery of formal firms from the pandemic in a Sub-Saharan African country. The recovery in the region is likely to be distinct from what has occurred elsewhere in the world for several reasons (Aga and Maemir 2021). First, in general, firms in Sub-Saharan Africa received very low levels of government support and it has been argued that this led firms to be more likely to lower wages, lay off workers, and face bankruptcy (Davies et al. 2021). Second, at the outset of the pandemic, the operations of firms in the region relied heavily on face-to-face interactions, which were greatly disrupted by COVID-19 lock downs (Bachas et al. 2021; Davies et al. 2021). Lastly, small firms make up the bulk of economic activity in Sub-Saharan African countries, and due to having more limited financial, technological, and human resources, alongside higher dependence on supply chains, were more likely to fare worse than larger firms (Bachas et al. 2021; Davies et al. 2021; Muzi et al 2021).

3
This paper is structured as follows. Section 2 provides background about the related literature, the private sector prior to the pandemic in Zambia, and the government policies implemented to address COVID-19 in Zambia. Section 3 outlines the data sources that we draw on and the analysis that we conducted to produce the paper’s findings. Section 4 presents the findings and Section 5 discusses the implications, as well as areas for future research.

# **2 Background**

## **2.1 Related Literature**

This paper is related to the growing literature on the impact of COVID-19 on the private sector. Dai et al. (2021), Bartik et al. (2020), Humphries et al. (2020), Apedo-Amah et al. (2020), Adams-Prassl et al. (2020), and Fairlie (2020a, 2020b) have all documented COVID19’s impact on businesses across countries in terms of revenue loss, business closures, mass layoffs, and liquidity. In most cases, such as Karalashvili and Viganola (2021), Cirera et al. (2021), Apedo-Amah et al. (2020), and Davies et al. (2021), research has been conducted using surveys, primarily World Bank Business Pulse Surveys (BPS), as well as the COVID-19 follow-up rounds of World Bank ES. This research paints a sobering picture of the impact of COVID-19. Firms have reported a significant drop in sales and reduced access to finance, and they envision additional losses in the future due to exceptionally high uncertainties going forward. This has prompted firms to make many difficult decisions involving significant reductions to their labor force. For example, following sales declines and firm closures in North Macedonia, firms interviewed in COVID-19 follow-up surveys to the ES exhibited a net reduction in employment of 7 percent since October 2020, highlighting the persistent struggle of businesses as they continue to navigate the COVID-19 era.

Another strand of literature has used tax administrative data to estimate the impact of COVID-19 on the private sector. For example, Bachas et al. (2020, 2021), Mascagni and 4
Lees (2022), and Angelov and Waldenström (2021) highlight the role tax administrative data can play in measuring economic activity in the private sector. Data is collected continuously, often at high frequencies, and typically covers a high share of formal firms. Similar to the aforementioned research based on surveys, analysis of tax administrative data has highlighted the pandemic’s negative impacts on firms’ sales and employment levels. Many firms have struggled to stay profitable, and at times have exited the market completely. For example, Mascagni and Lees (2022) use VAT returns to outline the severe repercussions of COVID-19 lock downs in Rwanda. Despite reporting low numbers of COVID-19 cases, VAT-registered firms in Rwanda experienced a 30 percent drop in aggregate sales over the lockdown period. Over this period, losses in sales amounted to 5.2 percent of GDP, with smaller firms suffering the most in proportionate terms. Mascagni and Lees (2022) also show that firms belonging to the accommodation, food, and transport sectors have struggled in particular since the onset of the pandemic.

There is also a relevant strand of the literature that goes beyond measuring the crippling impacts of COVID-19 by examining what characteristics of firms are associated with their recovery from crises. Jin et al. (2018) studied the recovery of firms’ performance in the aftermath of the 2007–08 global financial crisis. They found that firms with stronger financial constraints experienced a more sluggish recovery from the crisis relative to firms with weaker constraints. Additionally, well-developed financial institutions and structures were able to contribute significantly to firms’ recovery by easing their financial constraints. Amin and Viganola (2021) corroborate these findings: firms with better access to finance experience a less significant drop in sales, albeit results are highly heterogeneous. The reduction in likelihood of a decline in sales is much higher for firms that have a stronger and longstanding relationship with prominent stakeholders such as skilled workers and input suppliers. Cowling et al. (2018) also analyzed the 2008 global financial crisis and found that firms that had been established for longer periods of time were in a better position to stave off negative impacts from the crisis compared to young firms. Furthermore, the severity of the crisis meant that previous entrepreneur experiences did not have any substantive effects on small 5
business performances. Jolevski et al. (2021) suggest that firms that survived the COVID-19 crisis were older and more productive; they also tend to be innovators, use digital technology, and operate in less burdensome business environments. For example, more than half of (reporting) surviving firms in Mongolia adjusted their production or services; 31 percent of these businesses started online business activity, and 40 percent started or increased contact less delivery.

# **2.2 Setting**

## **2.2.1 Formal firms in Zambia before the pandemic**

Zambia’s annual real GDP grew at an average annual rate of 4.3 percent from 2010 to 2019, before registering a real contraction of 2.8 percent in 2020 (World Bank 2022). The largest economic sec- tors by percentage share of GDP in the pre-pandemic period (2010–19) were wholesale and retail, mining, construction, manufacturing, and agriculture (which contributed approximately 21, 14.5, 10, 7, and 6.5 percent of GDP, respectively). The five largest sector contributors to GDP growth over the pre-pandemic period were wholesale and retail, information and communication technology (ICT), construction, manufacturing, and finance, which contributed 25, 15, 10, 10, and 6 percent to GDP growth, respectively.

While real growth has been positive, it has been declining in most major sectors due to various factors (Figure 1). Agricultural firms experienced volatile output in the period before the pandemic. The negative growth observed in agriculture largely coincided with the drought and electricity shortages the country experienced around 2013, 2015, and then later in 2018. The mining sector largely experienced relatively steady real annual growth of around 2.2 percent over the pre-pandemic period, although the sector suffered output loss arising from the debilitating 2015 electricity crisis and drop in the international copper prices in 2019. Figure 1 also highlights that the manufacturing and wholesale and retail sectors largely maintained positive real growth, although real growth declined around 2017–19, presumably due to declining consumer demand as inflation and budget deficits increased.

6
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average Sub-Saharan African growth rate was 3 percent. The below-average employment growth may have been due to the negative impact of the 2015–18 electricity outages that affected economic output and thus employment demand in the manufacturing, hotel and restaurant, and construction sectors. The negative growth in real sales is likely due to both reductions in actual economic output due to the electricity crisis and relatively high inflation faced in Zambia just before the COVID- 19 pandemic. Other factors, such as poor access to adequate finance, poor access to reliable electricity, and competition from informal sector players, are also thought to have impacted Zambian formal firms’ ability to increase sales and employment (World Bank 2020).

# **2.2.2 COVID-19 in Zambia and government responses**

Since the confirmation of the first case on March 8, 2020, Zambia has seen a significant rise in COVID- 19 cases, manifesting in four waves. The first wave surged around June/July 2020, and the second between December 2020 and May 2021; the third wave commenced in June 2021 and the fourth peaked in January 2022 (Figure 2). The number of daily COVID-19 cases was much higher in each of the subsequent waves. In total, as of April 1, 2022, there have been 316,850 confirmed cases of COVID-19, with 3,966 deaths, reported to WHO.

The Government of Zambia introduced measures to contain the spread of COVID-19, including nation- wide lock downs. Regulatory protocols on movement, congregation, and contact were put in place to address the health and medical aspects of the pandemic. Disease surveillance measures included (1) increased screenings at airports, border entry points, and hot spots; and (2) institution of 14-day monitoring for individuals at high risk of COVID19. Measures to minimize contact included introduction of social distancing, the use of face masks, and increased hygiene measures, such as the use of sanitizers. Movement restrictions involved closing some airports, lock downs at home, travel bans, and transport limitations. Congregation restrictions targeted: (1) closure of schools, universities, and religious institutions; (2) bans on public, social, and religious gatherings; (3) restrictions on permits for conferences, weddings, funerals, and festivals; (4) restrictions on restaurants, entertainment, 8
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Additional financial sector support included: (1) upwards revision of transactions and balance limits for individuals, small-scale farmers, and enterprises, and also of limits by agents to give them more float to process transactions; (2) removal of transaction limits on agents and corporate wallets; and (3) reduction of processing fees for real-time gross settlements (RTGS). In addition, the central bank extended liquidity support to financial service providers in response to the Bank of Zambia’s announcement of ‘Measures in Response to the Deteriorating Macroeconomic Environment and the Coronavirus’. These measures included a targeted medium-term refinancing facility with an initial amount of ZAR10 billion (US$569 million) that aimed to relieve cash-strapped enterprises and enable financial institutions to meet local business capital needs (BOZ 2021).

# **3 Methodology**

## **3.1 Data sources**

### **3.1.1 World Bank Enterprise Survey data**

As part of the efforts of the World Bank Group to understand the impact of COVID-19 on the private sector, the Enterprise Analysis Unit conducted follow-up surveys on recently completed ES in several countries. Wherever possible, these short surveys follow the baseline ES, re-contacting all establishments sampled in the standard ES using stratified random sampling. The universe of inference is all formal establishments with five or more employees that are engaged in one of the following activities defined using ISIC Rev. 3.1: manufacturing (group D); construction sector (group F); services sector (groups G and H); transport, storage, and communications sector (group I); and information technology (division 72 of group K).

In Zambia, three rounds of follow-up surveys to the firms that participated in a World Bank ES completed immediately prior to the pandemic provide the most comprehensive source of survey data measuring the impact of COVID-19 on formal firms. The baseline ES 10
was conducted from September 2019 to March 2020 and included 601 firms. Three follow-up phone surveys focusing on a subset of questions (including about sales and employment) were conducted in June and July 2020 (June 2020 round), December 2020 to February 2021 (December 2020 round), and July to September 2021 (July 2021 round). The response rate was 96.1 percent in the June 2020 round and 95.1 percent in the other rounds. Questions about changes in firm sales used a ‘pre-pandemic’ level of the same month in 2019 (to address concerns about seasonality across the calendar year) and changes in employment (both permanent and temporary) were based on a ‘pre-pandemic’ level of February 2020.

The survey samples of formal firms were largely based in urban areas, had 20–100 workers, and were mainly involved in retail, manufacturing (other than food), and other services (excluding agricultural inputs and equipment). The distribution of firms by location is Kitwe (100, 16.6 percent), Livingstone (105, 17.5 percent), Lusaka (294, 48.9 percent), and Ndola (102, 17.0 percent). By firm size, it is small (231, 38.4 percent), medium (250, 41.6 percent), and large (120, 20.0 percent)<sup>3</sup> . By primary activity, it is manufacturing (121, 33.9 percent), retail (123, 20.5 percent), and other services (194, 32.3 percent).

# **3.1.2 Tax administrative data**

There are three main types of tax in Zambia; corporate income tax (CIT), personal income tax (PIT), and VAT. CIT in Zambia is taxed in accordance with the principal activity of the business. The CIT rates range from 10 percent for incomes earned from agriculture to 40 percent for incomes in the banking and telecommunications sectors. All businesses with annual turnover exceeding ZAR800,000 (approximately US$45,000) are expected to register, file returns, and pay income tax. It is compulsory for some sectors, such as activities involving scientific and professional services, to register for CIT irrespective of the size of their annual sales/turnover.

There are few self-reported PIT taxpayers in Zambia, which means the responsibility to pay PIT largely falls on employers. In this form, PIT is a PAYE tax and is withheld at

> 3Small, medium, and large firms comprise 5–19, 20–99, and 100+ workers, respectively

11
source. PAYE directly taxes personal emoluments including wages, salaries, overtime pay, leave pay, commissions, fees, bonuses, and any other payments from employment. However, there are some exempt emoluments, such as pension benefits and gratuities. The design of PAYE tax in Zambia is largely progressive, with a certain proportion of earnings below the defined tax threshold of ZAR4,500 (approximately US$250) taxed at a rate of zero. The top rate for PAYE in 2022 was 37.5 percent, compared to a Southern African Development Community (SADC) average of 30.1 percent and an OECD average of 56 percent.

Zambia introduced VAT as a principal consumption tax on goods and services in 1995. VAT in Zambia is imposed on the final consumers of both imported and locally manufactured standard rated goods. Goods and services are classified into three broad categories for VAT purposes. These include zero- rated supplies, where input VAT incurred is claimable while output VAT is charged at 0 percent. The other class of supplies is exempt supplies, where input VAT incurred is non-claimable and output VAT is not chargeable. The remainder of inputs are taxed at a standard rate of 16 percent of sale value. This standard tax rate is similar to the SADC average.

Filing of tax returns is the principal means by which a taxpayer’s tax liability is determined by the Zambian Revenue Authority. VAT and PAYE returns contain information about formal firms’ revenue and employment levels each month. Further background information about firms is also available, such as their locations, sectors, and registration dates, which can be used to estimate the age of the firm (see examples of VAT and PAYE returns in Appendix A).

Firms that filed VAT and PAYE returns in 2020 were largely based in urban areas and were mainly involved in wholesale/retail trade and manufacturing. Almost two-thirds of firms were in Lusaka (the capital and by far the largest city). Almost half of firms were involved in wholesale and retail trade, and another 14 percent were involved in manufacturing (these were by far the two most common types of business activities). This is a somewhat similar composition of firms to the survey sample. A noteworthy difference is that the bulk of firms that file VAT and PAYE returns have fewer than five employees (and these firms were 12
disproportionately involved in retail trade).

## **3.1.3 Differences between the two data sources**

Table 1 summarizes the key differences between the tax administrative and survey data. It is important to keep these differences in mind when considering what analysis will be completed and comparing the findings across the data sources.

Table 1: Key differences between the survey and tax administrative data

||**Tax administrative data**|**Survey data**|
|---|---|---|
|**Coverage**|All frms|Subset of frms|
|**Frequency**|Monthly|Every 6 months|
|**Number**|Around 20,000 frms|601 frms|
|**Location**|All locations|Only 4 largest cities|
|**Size**|All sizes above thresholds|Only frms with 5 or more employees|
|**Sector**|All sectors|Manufacturing, retail and wholesale|
|||trade, other services|
|**Information**|Sales, employment,|Many frm characteristics|
||location, sector and age||

# **3.2 Analysis**

There are two main parts to the analysis. The first focuses on measuring descriptive trends in sales and employment in the tax administrative and survey data over time, and the second examines what characteristics are associated with changes in firm sales and employment throughout the pandemic.

## **3.2.1 Descriptive trends in the tax administrative and survey data**

The first and most straightforward part of the descriptive analysis is to calculate the overall trends in terms of sales and employment from pre-pandemic levels until mid-2021 according 13
to the tax administrative and survey data. The general patterns across the different data sources are compared to see whether they suggest a similar trajectory in terms of the impact of COVID-19 on formal firms in Zambia. The tax administrative data is adjusted for inflation, drawing on monthly inflation rates released by the central bank of Zambia, so that comparisons over time can be made in real terms. The survey data is weighted to ensure it is representative of formal firms in Zambia that share the key characteristics that the original ES sampling frame is based on (e.g., having more than five employees, operating in specific sectors, located in urban areas). The survey weights also factor in the small amount of attrition between the baseline and follow-up surveys; however, this has negligible impact because there was a very high response rate across all three rounds. In addition, both data sources are winsorized at 2.5 and 97.5 percent to ensure that the average trends are not entirely driven by outliers.

The next part of the descriptive analysis is to solely use the tax administrative data for the subset of firms that share similar characteristics to the firms that participate in the survey. The first step is to only focus on firms that file each month (i.e. they are ‘perfectly compliant’). This is necessary as the overall trends in the tax administrative data are partly influenced by which firms choose to file in a given month and there is seasonality in when firms choose to file. A drawback from restricting the focus to these firms is that they are likely to be quite distinct from other formal firms. For example, they may be quite profitable and/or large so each month they always exceed the threshold for filing a tax return, whereas other firms may have sales that fluctuate around the threshold for filing. Another difference between the ‘perfectly compliant’ firms and other formal firms is that they may have a lower risk tolerance and/or greater exposure to the tax authority, which means they are less willing to avoid paying taxes. These characteristics could also be associated with other aspects of business operations (e.g. investment in R&D).

The second step in aligning the two data sources involves solely focusing on firms for which there are five or more employees and dis-aggregating the analysis by firm size. Specifically, we look at the trends over time across both data sources for ‘perfectly compliant’ firms with 14
5–19 employees (small), 20–99 employees (medium), and 100+ employees (large). These types of firms are also quite distinct from those with fewer than five employees (micro); in particular, it is expected they do not experience the same extent of fluctuations in sales and employment levels. A shortcoming of this approach is that the VAT returns do not include information about the number of employees of each firm. As such we merge the VAT and PAYE return data and can only report on the subset of firms that provided both types of returns in a given month. Once again this is likely to mean the results may not be generalizable beyond firms that share these characteristics.

The final part of the descriptive analysis we conduct is to examine the trends from both data sources based on characteristics other than firm size. The three most notable characteristics are in terms of sector, location, and firm age, as this information is available for all firms in both data sources. The sector analysis is decomposed into three categories that are identical across the survey and tax data (manufacturing, retail and wholesale services, and other). The location analysis in both data sources focuses on a simple binary split between firms in Lusaka and firms everywhere else.

# **3.2.2 Determining the covariates of recovery**

Both data sources, but particularly the survey data, provide the opportunity to explore what firm characteristics are associated with experiencing an initial reduction in sales and employment, as well as a recovery by 2021. This analysis complements exploring the descriptive trends because through conducting econometric analysis we can identify what firm characteristics appear to be most closely correlated with firm performance. It is important to emphasize that this regression analysis is not causal as there is no exogenous variation. We cannot rule out, among other things, reverse causality and omitted variable bias. For example, on the former, if we find a positive relationship between receiving government support and recovering from the pandemic, we cannot be sure whether this is because firms that received support were better placed to recover anyway or whether government support actually aided their recovery. An example of the latter is that both data sources (partic15
ularly the tax administrative data) only have a limited number of variables and there are likely to be other factors, such as the determination of the firm manager/owner, that may be impacting firm performance. These caveats need to be kept in mind when examining the findings of the econometric analysis. For both data sources we conduct an ordinary least squares (OLS) regression in the form of a linear probability model.<sup>4</sup> Specifically, we have four main outcomes (Y) that are in the form of a dummy variable. These are: (1) whether a firm experienced a decline in sales compared to pre-pandemic levels in June 2020; (2) whether a firm experienced a decline in employment compared to pre-pandemic levels in June 2020; (3) whether a firm experienced a complete recovery in sales compared to pre-pandemic levels in July 2021; and (4) whether a firm experienced a complete recovery in employment compared to pre-pandemic levels in July 2021. The econometric model can be expressed formally as follows: whereby, _β_ 1 captures the extent to which the size of a firm (small, medium or large) is associated with an outcome, _β_ 2 captures the extent to which the age of a firm (in years) is associated with an outcome, _β_ 3 captures the extent to which the activity of a firm (manufacturing, retail and wholesale services or other) is associated with an outcome, _β_ 4 captures the extent to which the location of a firm (Lusaka or elsewhere) is associated with an outcome, _β_ 0 is the intercept and _ε_ is the model error term.

For the survey data we conduct further analysis to fully utilize the richer information available on respondents. First, we rerun the regression above and include additional baseline variables captured in the baseline ES (top manager’s years of experience, >5 competitors entering the market in the last two years, new products introduced over the last three years, access to finance is a very severe/major obstacle, training is offered to permanent

> 4As a robustness check we rerun the regressions as a logit model and the results are qualitatively similar.

16
employees). Second, we rerun the regression above with the variables captured in the baseline ES and include further variables captured in the follow-up survey rounds reflecting how firm circumstances changed during the pandemic (changed business practices, received any cash support from government). A noteworthy limitation of this analysis is that there is a nontrivial number of missing values for several of these survey questions. As such, as a robustness check we present in Appendix B the analysis excluding all respondents with missing values.

# **4 Findings**

## **4.1 Sales**

### **4.1.1 Survey findings**

According to the survey data, on average, formal firms in Zambia experienced a very large decline in sales in 2020 and a substantial recovery in the first half of 2021. Figure 3 shows the average decline in sales (compared to the same month in 2019) was 42.7 percent in June 2020, 33.9 percent in December 2020, and 16.4 percent in July 2021. The results dis-aggregated by firm size are striking. By July 2021, the average sales of large firms had fully recovered to 2019 levels; in contrast, small and medium-sized firms reported a larger initial decline and a slower recovery.

These average changes in sales somewhat mask variation between firms. Figure 4 shows that most firms reported a decline in sales (compared to the same month in 2019) in June and December 2020, but by July 2021 less than half of the firms reported a decline in sales. As was the case for the average change, large firms fared much better than small and mediumsized firms. In July 2021 only around one-third of large firms reported a decline in sales (compared to the same month in 2019), and almost half reported an increase in sales.

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## **4.2.3 Summarizing the findings across the survey and tax administrative data**

Both data sources paint a sobering picture about how the pandemic has impacted the employment levels of formal firms. There was an initial moderate decline in employment and there is evidence to suggest that over time the situation has only worsened. Regardless, there are clearly no meaningful signs of recovery, with a non-trivial share of people who were employed in formal firms in Zambia prior to the COVID-19 lockdowns no longer having a job even almost 18 months later. As discussed at length in Section 3, these data sources are quite different, but complementary. In the case of the survey data, em- ployment refers solely to full-time employment, which may be less volatile than all types of employment captured in the tax administrative data. The fact that we see the same pattern in both the survey and tax administrative data illustrates how robust these findings are likely to be.

# **4.3 Covariates of recovery**

We conduct econometric analysis to understand what factors are associated with firms experiencing an initial decline in sales and employment as well as a recovery. Table 2 reports on the factors associated with a decline in sales and employment levels in June 2020 from pre-pandemic levels, while Table 3 reports on the factors associated with a recovery in sales and employment to pre-pandemic levels by July 2021. As discussed at length in Section 3.2, we are not claiming that there is a causal relationship between the factors that we examine and firm sales and employment. Rather, the findings that follow show which factors are most closely correlated with firms experiencing a decline and/or recovery. In the discussion that follows we make an important distinction between structural (e.g. location of firms) and firm-specific (e.g. management experience) factors as it is likely that policy makers can more easily influence the latter.

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Table 2: Factors associated with a decline in sales and employment

||**Sales **|**(decrease) **|**June 2020**||**Employ**|**ment (decr**|**ease) June **|**2020**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Variables in tax admini**<br>Activity<sup>1</sup>|**strative and sur**|**vey data**|||||||
|Retail|0.0449|-0.0151|-0.00125|-0.0136|-0.0149|-0.0155|-0.00484|-0.0327|
||(0.0282)|(0.102)|(0.0990)|(0.0812)|(0.0269)|(0.0925)|(0.0887)|(0.0861)|
|Other|0.0421|0.121**|0.101*|0.0350|0.0101|0.0357|0.0154|-0.0270|
||(0.0276)|(0.0559)|(0.0595)|(0.0540)|(0.0265)|(0.0823)|(0.0674)|(0.0758)|
|Size<sup>2</sup>|||||||||
|Medium|-0.0008|0.0771|0.0576|0.0368|-0.0299|0.111|0.0947|0.0720|
||(0.0207)|(0.0584)|(0.0599)|(0.0519)|(0.0197)|(0.0764)|(0.0673)|(0.0713)|
|Large|-0.0351|-0.0841|-0.128*|-0.0670|-0.0530**|0.0216|0.0541|0.0707|
||(0.0273)|(0.0723)|(0.0755)|(0.0778)|(0.0258)|(0.0689)|(0.0769)|(0.0831)|
|Lusaka|-0.0022|-0.0185|-0.00128|-0.0114|-0.0108|0.00471|-0.0549|-0.0918|
||(0.0191)|(0.0489)|(0.0498)|(0.0483)|(0.0184)|(0.0719)|(0.0570)|(0.0615)|
|Firm age|0.0000008|0.000863|0.00102|0.00002|-0.000008***|0.00385|0.00514|0.00429|
||(0.000002)|(0.00211)|(0.00284)|(0.00268)|(0.000002)|(0.00335)|(0.00315)|(0.00320)|
|**Other baseline survey c**|**haracteristics**||||||||
|Manager experience|||-0.00132|-0.00175|||-0.00241|-0.00283|
||||(0.00302)|(0.00271)|||(0.00325)|(0.00331)|
|Competitive market|||-0.0393|0.00144|||0.0842|0.114|
||||(0.0508)|(0.0474)|||(0.0738)|(0.0747)|
|Introduced new products|||0.0597|0.0362|||-0.172***|-0.208***|
||||(0.0500)|(0.0504)|||(0.0544)|(0.0566)|
|Limited fnance|||-0.0522|0.0157|||0.0942|0.141*|
||||(0.0574)|(0.0496)|||(0.0769)|(0.0791)|
|Trained employees|||0.103*|0.0828|||0.0523|0.0408|
||||(0.0545)|(0.0504)|||(0.0820)|(0.0833)|
|**Information from follo**|**w-up surveys**||||||||
|Changed practices||||-0.0975*||||0.00701|
|||||(0.0499)||||(0.0772)|
|Receive gov support||||-0.270||||0.318|
|||||(0.249)||||(0.256)|
|Observations|2918|578|578|514|2918|578|578|514|

Note: This table is based on Equation (1) in Section 3 of the paper. Columns (1) and (5) present the results for the tax administrative data. Columns (2)-(4) and (6)-(8) present the results for the survey data.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. 1 The omitted category is manufacturing. 2 The omitted category is Small. _Manager experience:_ Defined as the number of years experience of the top manager in the firm. _Competitive market:_ Dummy variable for whether a firm has had more than five competitors entering their market in the previous two years. _Introduced new products:_ Dummy variable for whether a firm introduced new products in the previous three years. _Limited finance:_ Dummy variable for whether a firm stated that access to finance was a very severe or major obstacle for their business. _Trained employees:_ Dummy variable for whether a firm stated that they offered their permanent employees training. _Changed practices:_ Dummy variable for whether a firm changed their business operations in response to the COVID-19 pandemic. _Receive gov support:_ Dummy variable for whether a firm received some kind of cash support from the government to help deal with impact of the pandemic.

# **4.3.1 Factors associated with a decline in sales and employment levels in 2020**

Table 2 shows that there were key structural factors associated with firms experiencing a decline in sales, but more firm-specific factors emerge regarding declines in employment. A decline in sales was closely correlated with business activities being outside of manufacturing 27
and wholesale/retail trade. These firms were largely involved in services (other than retail), which were probably substantially impacted by social distancing restrictions introduced to reduce the spread of COVID-19. Larger firms were far less likely to report a decline in sales by June 2020, which may be because they have greater stability in operations. In addition, firms that reported changing their business practices in response to the pandemic were less likely to experience a decline in sales. In terms of employment, firms that faced large or very severe obstacles to access to finance immediately prior to the pandemic (according to the baseline ES) were more likely to reduce their number of employees. Firms that had reported introducing new products in the three years prior to the pandemic were far less likely to report reducing their number of employees.

# **4.3.2 Factors associated with recovery in sales and employment levels by 2021**

Table 3 shows that a combination of structural and firm-specific factors were associated with firms ex- periencing a recovery in sales and/or employment by July 2021. Recovery in sales was more common among larger firms and those in manufacturing (these factors were also associated with less likelihood of a decline in sales initially). In addition, the top manager’s level of experience was positively associated with a recovery in sales. A lack of recovery was more common among firms that were in a partic- ularly competitive market (immediately prior to the pandemic they stated more than five competitors had entered their market in the previous two years). In regard to a recovery in employment, this was positively associated with top managers having more experience and receiving government support. A lack of recovery in employment was more common among older firms according to the survey data (the opposite was the case according to the tax administrative data) and those outside of manufacturing and retail trade.

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Table 3: Factors associated with a recovery in sales and employment

||**Sales**|**(increase) **|**July 2021**||**Empl**|**oyment (in**|**crease) July **|**2021**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Variables in tax admini**<br>Activity<sup>1</sup>|**strative and sur**|**vey data**|||||||
|Retail|-0.0276|-0.171**|-0.174**|-0.227**|0.0045|0.00266|-0.0318|0.0113|
||(0.0284)|(0.0727)|(0.0780)|(0.0885)|(0.0281)|(0.105)|(0.102)|(0.118)|
|Other|-0.0957***|-0.109*|-0.137**|-0.167**|-0.0659**|-0.185***|-0.179**|-0.207***|
||(0.0277)|(0.0656)|(0.0621)|(0.0715)|(0.276)|(0.0708)|(0.0715)|(0.0746)|
|Size<sup>2</sup>|||||||||
|Medium|0.0453**|0.00234|-0.0270|-0.0518|0.0224|-0.0130|-0.0133|0.0763|
||(0.0206)|(0.0598)|(0.0551)|(0.0663)|(0.0207)|(0.0729)|(0.0722)|(0.0673)|
|Large|0.0710***|0.135*|0.0863|0.0879|0.0804***|-0.0228|-0.0264|-0.0157|
||(0.0274)|(0.0706)|(0.0749)|(0.0872)|(0.0271)|(0.0733)|(0.0781)|(0.0633)|
|Lusaka|0.0234|0.0722|0.0867|0.0817|0.0194|-0.136*|-0.140*|-0.0763|
||(0.0191)|(0.0556)|(0.0575)|(0.0709)|(0.0191)|(0.0737)|(0.0723)|(0.0805)|
|Firm age|0.000004|-0.000139|-0.00309|-0.00510|0.000007***|-0.00483*|-0.00828***|-0.00722**|
||(0.000002)|(0.00251)|(0.00266)|(0.00338)|(0.000002)|(0.00267)|(0.00313)|(0.00308)|
|**Other baseline survey c**|**haracteristics**||||||||
|Manager experience|||0.00518*|0.00709*|||0.00752**|0.00792**|
||||(0.00313)|(0.00416)|||(0.00337)|(0.00346)|
|Competitive market|||-0.117*|-0.145**|||-0.0229|-0.0289|
||||(0.0603)|(0.0726)|||(0.0659)|(0.0655)|
|Introduced new products|||-0.0199|-0.0533|||-0.0130|0.0615|
||||(0.0595)|(0.0679)|||(0.0740)|(0.0779)|
|Limited fnance|||0.0241|0.0256|||0.0415|0.0343|
||||(0.0597)|(0.0705)|||(0.0723)|(0.0717)|
|Trained employees|||0.0932|0.0828|||-0.0602|0.0362|
||||(0.0614)|(0.0712)|||(0.0763)|(0.0721)|
|**Information from follo**|**w-up surveys**||||||||
|Changed practices||||-0.0110<br>(0.0721)||||-0.0624<br>(0.0747)|
|Receive gov support||||0.0440||||0.562***|
|||||(0.198)||||(0.166)|
|Observations|2918|578|578|434|2918|578|578|434|

Note: This table is based on Equation (1) in Section 3 of the paper. Columns (1) and (5) present the results for the tax administrative data. Columns (2)-(4) and (6)-(8) present the results for the survey data.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. 1 The omitted category is manufacturing. 2 The omitted category is Small. _Manager experience:_ Defined as the number of years experience of the top manager in the firm. _Competitive market:_ Dummy variable for whether a firm has had more than five competitors entering their market in the previous two years. _Introduced new products:_ Dummy variable for whether a firm introduced new products in the previous three years. _Limited finance:_ Dummy variable for whether a firm stated that access to finance was a very severe or major obstacle for their business. _Trained employees:_ Dummy variable for whether a firm stated that they offered their permanent employees training. _Changed practices:_ Dummy variable for whether a firm changed their business operations in response to the COVID-19 pandemic. _Receive gov support:_ Dummy variable for whether a firm received some kind of cash support from the government to help deal with impact of the pandemic.

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# **5 Discussion and Conclusion**

## **5.1 Explanations for the findings of the study**

This study has illustrated that, on average, the sales of formal firms in Zambia have recovered from the pandemic far more strongly than their employment levels. These findings suggest that formal firms have adjusted their operations in a way that reduces their need for labor to achieve the same level of sales. In other words, there has been a shift in the capital–labor mix that formal firms use, away from labor. If this is a persistent change, lower levels of formal employment in low- and lower-middle-income countries may be a concerning consequence of the COVID-19 pandemic that lingers for years to come. This pattern of a weak recovery of formal employment has also been observed in several emerging-market economies (Andaloussi et al. 2022). Ultimately, this means more people are in vulnerable employment situations that lack a formal safety net and there is less income tax being paid to fund the provision of public goods and services. Detailed investigation of the survey and tax administrative data does not suggest that the overall findings are due to improvements in formal sector labor productivity. There is no evidence of existing firms reporting higher average wages for the employees they retained or large shifts between relying on full-time compared to casual employees. In addition, the tax administrative data does not suggest there have been a large number of new firms entering the formal sector.<sup>5</sup> Rather, a more compelling explanation for what we observe is that many firms have tried to rebound from the crisis by reducing (in some instances potentially unproductive) labor inputs, which can often be challenging in a more stable setting. An unavoidable limitation of this study is that the focus is on only formal sector employment, which means that it is entirely possible that people who were previously in formal employment have moved to the informal sector and maintained (or even increased) their productivity.

> 5If this was the case, it could be possible that employees are leaving existing firms and moving to more productive new firms.

30
# **5.2 Implications for policy makers**

The key implication for policy makers from this study is that there is a clear need to foster greater recovery of employment among formal firms in Zambia. Non-trivial numbers of workers who were previously employed by formal firms no longer had formal jobs even over a year after the pandemic began. This means that across the economy as a whole, it is likely that formal employment has not rebounded to anywhere near pre-pandemic levels. There may be a need for government support to expand opportunities for workers to join formal firms. A potentially promising sign that emerges from the econometric analysis is that firmspecific factors, more than structural factors, appear to be associated with whether firms experienced a decline or recovery in employment. This may imply that there is greater scope for specific actions to be taken by firms to increase their number of employees. If structural factors were more closely correlated with changes in employment (e.g. the sector that firms operate in), there would potentially be far less scope for policy makers to have an influence.

# **5.3 Contribution to knowledge**

We draw on two distinct but complementary data sources that illustrate this same pattern. To the best of our knowledge, this is the first attempt to use both survey and tax administrative data to track how firms have recovered from the pandemic. We have carefully examined the strengths and weaknesses of both data sets to try to ensure as much comparability as possible. We hope that this effort will be replicated and improved upon elsewhere as triangulating findings across different data sources can provide both researchers and policy makers far more confidence in the robustness of the results. Particularly, the use of tax administrative data as a low-cost way to monitor economic activity is rather novel, and this study shows how it can complement the largest existing effort to collect survey data from firms across countries, which is undertaken by the World Bank Enterprise Analysis Unit.

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# **5.4 Areas for future research**

At least three areas for future research emerge from this study. First, developing a better understanding of how formal firms have adjusted their capital–labor mix during this time of crisis is key as this will provide an indication as to whether the apparent ‘decoupling’ of sales and labor is transitory. Second, there is obvious value in generating causal evidence (our study does not do this, nor does it claim to) about what is driving the recovery of firms. Finally, more in-depth country studies in Sub-Saharan Africa, drawing on multiple data sources, could help to build a comprehensive picture of the recovery of formal firms in the region.

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