metadata: authors: 0: author: Simbeye,Laban
1: author: Lungu,Eliya
2: author: Kumwenda,Andreya
3: author: Banda,Edna
4: author: Msoni,Jonathan
5: author: Kuo,Ryan Chia
count: Zambia
docna: 0: docna: The Role of Firm Dynamics in Aggregate
            Productivity and Formal Job Flows in Zambia
docty: Policy Research Working Paper
theme: Inclusive Growth,Job Creation,Gender,Human Development and Gender,Economic Policy,Economic Growth and Planning,Environment and Natural Resource Management,Private Sector Development,Climate change,Urban and Rural Development,Jobs,Adaptation,Flood and Drought Risk Management,Macroeconomic & Structural Policy Modelling,Disaster Risk Management,Structural Transformation and Economic Diversification
abstracts: cdata!: Zambia&#x2019;s private sector must
            deliver quality jobs at scale to keep up with its expanding
            working age population, contribute to economic
            transformation, and reduce poverty. This entails both the
            creation of high-quality jobs and productivity improvement
            among existing jobs and firms. This paper analyzes the
            dynamics of formal firms to identify the drivers and
            barriers to productivity, formal employment, and formal wage
            growth in Zambia. Leveraging firm and worker administrative
            tax data from Zambia, the paper decomposes labor
            productivity and wage growth among formal firms and workers
            in Zambia into within-firm, between-firm, inter-sectoral,
            and dynamic components. The findings show that the aggregate
            labor productivity of formal firms declined over
            2014&#x2013;21, driven by secular within-firm declines
            in the non-mining industry and service sectors. By contrast,
            labor productivity grew in agriculture and remained flat in
            mining over the same period. Real wage trends for formal
            workers have mostly mirrored labor productivity dynamics,
            declining 40-50 percent across non-agriculture sectors but
            growing slightly in agriculture, largely driven by
            within-firm shifts rather than between-firm or
            between-sector dynamics. The declines in labor productivity
            and wages reflect business environment challenges related to
            access to finance and electricity, as well as burdensome
            formal compliance requirements and competition with the
            informal sector. Within-firm labor productivity challenges
            also reflect low skills and capacity&#x2014;including
            low technology adoption&#x2014;among both firms and workers.
display_title: The Role of Firm Dynamics in
            Aggregate Productivity and Formal Job Flows in Zambia
listing_relative_url: /research/2024/04/34303649/role-firm-dynamics-aggregate-productivity-formal-job-flows-zambia
topicv3: Investment Climate(IC) m1327235 401,Innovation, Technology and
            Entrepreneurship m1327691 376,Labor Productivity m1504060 2756,Firm Dynamics and Productivity Growth
            m1504195 2864,SME Development m1504603 2651
publishtoextweb_dt: 2024-04-18T00:00:00Z
dois: 10.1596/1813-9450-10756
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 34
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: NA
3: author_key: NA
4: author_key: NA
5: author_key: 1792443
model_extractions: Policy Research Working Paper 10756 The Role of Firm Dynamics in Aggregate Productivity and Formal Job Flows in Zambia _Laban Simbeye Eliya Lungu Andreya Kumwenda Edna Banda Jonathan Msoni Ryan Kuo_ Finance, Competitiveness and Innovation Global Practice April 2024
Policy Research Working Paper 10756

# **Abstract**

Zambia’s private sector must deliver quality jobs at scale to keep up with its expanding working age population, contribute to economic transformation, and reduce poverty. This entails both the creation of high-quality jobs and productivity improvement among existing jobs and firms. This paper analyzes the dynamics of formal firms to identify the drivers and barriers to productivity, formal employment, and formal wage growth in Zambia. Leveraging firm and worker administrative tax data from Zambia, the paper decomposes labor productivity and wage growth among formal firms and workers in Zambia into within-firm, between-firm, inter-sectoral, and dynamic components. The findings show that the aggregate labor productivity of formal firms declined over 2014–21, driven by secular within-firm declines in the non-mining industry and service sectors. By contrast, labor productivity grew in agriculture and remained flat in mining over the same period. Real wage trends for formal workers have mostly mirrored labor productivity dynamics, declining 40-50 percent across non-agriculture sectors but growing slightly in agriculture, largely driven by within-firm shifts rather than between-firm or between-sector dynamics. The declines in labor productivity and wages reflect business environment challenges related to access to finance and electricity, as well as burdensome formal compliance requirements and competition with the informal sector. Within-firm labor productivity challenges also reflect low skills and capacity— including low technology adoption—among both firms and workers.

This paper is a product of the Finance, Competitiveness and Innovation 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 rkuo@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
# The Role of Firm Dynamics in Aggregate Productivity and Formal Job Flows in Zambia

_Laban Simbeye,_<sup>◊</sup> _Eliya Lungu,_<sup>◊</sup> _Andreya Kumwenda,_<sup>◊</sup> _Edna Banda,_<sup>◊</sup> _Jonathan Msoni,_<sup>◊</sup> _Ryan Kuo_<sup>�</sup><sup>_,1_</sup>

**Keywords** : Productivity, Formal Jobs, Formal Wages, Structural Transformation **JEL Codes** : D24, E24, J24, J31, L16

- Zambia Revenue Authority

> � World Bank; corresponding author (rkuo@worldbank.org)

> 1 The team would also like to thank the following peer reviewers and colleagues for their contributions: Dino Merotto, Ekaterina Vostroknutova, Hanane Ahmed, Nicolo Dalvit, Fausto Patiño Pena, Sophia Muradyan, Jorge Tudela Pye, Albert Pijuan Sala, Joseph Simumba, Philip Schuler, Isaku Endo, Ahmed Rostom, Shaun Mann, and Mohammad Farid Al Azim Bin Noruzi, Soujanya Chodavarapu, and Vanina Daphne Forget.
# 1. Introduction

Zambia’s private sector must deliver quality jobs at scale to keep up with the expanding working age population, contribute to economic transformation, and reduce poverty. Based on population growth projections, Zambia needs to create over 10 million new jobs by 2050 to keep its labor force participation and employment rates from declining. The country’s working age population is projected to more than double between 2022 and 2050 (Merotto 2017). This growth will provide a demographic dividend if Zambia can drive productivity growth and economic transformation. However, if Zambia’s private sector fails to generate enough good jobs to absorb this expansion, economic growth will fail to permeate to the rest of the economy as new working-age Zambians will be unable to access productive opportunities. This will limit its impact on poverty reduction and increase the risk of upheaval.

As well as creating new jobs, Zambia’s private sector needs to make existing jobs more productive. Low productivity is limiting Zambia’s potential to diversify its sources of growth and to create quality jobs and firms. While shifts in labor to higher-productivity sectors and within-sector productivity improvements can contribute to labor productivity growth and higher wages, Zambia’s private sector has had limited success in both areas. Lower-productivity sectors such as agriculture (58.7 percent of employment) and wholesale and retail trade (14.1 percent) still account for most employment. Existing high-productivity sectors tend to generate few jobs. These include financial services (0.5 percent of jobs) and mining, which accounts for only 1.1 percent of jobs despite its role as Zambia’s primary export driver. Meanwhile, lower-productivity informal employment accounts for three-quarters of jobs, and within-sector productivity has generally been flat (Zambia MLSS 2022, World Bank 2024).

It is thus critical to identify the drivers of and barriers to productivity, formal employment, and formal wage growth in Zambia. Leveraging firm and worker administrative tax data from Zambia, this paper decomposes labor productivity and wage growth among formal firms and workers in Zambia into withinfirm, between-firm, inter-sectoral, and dynamic components. We find that aggregate labor productivity of formal firms declined over the 2014-2021 period, driven by secular within-firm declines in the nonmining industry and service sectors. By contrast, labor productivity grew in agriculture and remained flat in mining over the same period. Real wage trends for formal workers have mostly mirrored labor productivity dynamics, declining 40-50 percent across non-agriculture sectors but growing slightly in agriculture, largely driven by within-firm shifts rather than between-firm or between-sector dynamics. The declines in labor productivity and wages reflect business environment challenges related to access to finance and electricity, as well as burdensome formal compliance requirements and competition with the informal sector. Within-firm labor productivity challenges also reflect low skills and capacity— including low technology adoption—among both firms and workers.

The rest of this paper is structured as follows. Section 2 provides background about the related literature and the private sector in Zambia. Section 3 outlines the data sources we leverage in the paper. Section 4 first outlines the methodology used for productivity analyses and then discusses the results of these analyses. Section 5 outlines the methodology and results for jobs-related analyses. Section 6 discusses the productivity and wage findings in the context of salient business environment and firmlevel constraints in Zambia. Section 7 concludes with the implications the paper’s findings and areas for further research.

2
# 2. Related literature

Prior research has documented numerous challenges with respect to aggregate productivity and the private sector in Zambia. World Bank (2019b) and the first chapter of World Bank (2024)<sup>2</sup> detail Zambia’s recent economic performance. They document how Zambia’s real GDP growth of 7.7 percent per year between 2001 and 2010 was mostly driven by mining. However, growth slowed significantly in the 2010s, with GDP growth falling despite increased public investment. On an economy-wide basis, labor productivity growth averaged 3.7 per year in the 2000s but reversed in the 2010s, remaining negative for most years over the 2011-2020 period. Since 2000, within-sector productivity gains were negligible in most sectors, with any productivity gains that did occur mostly coming from inter-sectoral reallocation of resources from less to more productive sectors (World Bank 2024). At the firm level, Hoy, et al. (2022) leverage firm-level tax data to analyze the impact of the COVID-19 pandemic on formal firms in Zambia, documenting large declines in sales and employment in formal firms at the outset of the pandemic, followed by a rapid recovery in sales but less robust recovery in employment.

Merotto (2017) analyzes trends in labor supply in Zambia through Labour Force Survey data, finding evidence of growing underemployment in Zambia. He shows how, despite the share of working-age workers employed in waged jobs increasing from 17 to 29 percent between 2008 and 2014, the bulk of new job creation continues to be informal, with wide wage gaps between the formal and informal sectors, between rural and urban workers, between men and women, and between unskilled and skilled workers. He further leverages data from Zambia’s 2012 Economic Census to analyze labor demand, finding low rates of micro firm survival and low productivity in manufacturing sectors.

This paper seeks to fill gaps in the literature with respect to recent firm-level productivity dynamics in Zambia. Research on productivity in Zambia tends to focus on macroeconomic data (World Bank 2024, World Bank 2019b) or rely on Economic Census data, which were last collected in 2012 and do not constitute a panel (Merotto 2017). Although Hoy, et al. (2022) do analyze more recent tax data—indeed, the same Corporate Income Tax database that this paper leverages—their research focus is more narrowly on firm performance during COVID-19. Enriching the understanding of such firm-level productivity dynamics would allow for a deeper understanding of the drivers and constraints of productivity, job creation, and wages, differentiating between the contributions of economy-wide issues, structural change between sectors, within-sector market dynamics, and within-firm technical efficiency (Cusolito and Maloney 2018). This could in turn yield more specific policy insights and uncover priorities for further research.

In terms of its methodological approach and key topics of interest, this paper is related to other studies that decompose productivity and job trends within countries utilizing firm-level data. Olley and Pakes (1996), Melitz and Polanec (2015), and Patiño Peña and Ferro (Forthcoming), among others, develop methods for decomposing productivity growth into between-, within-firm, entry, exit, and inter-sectoral components—and applied these methods to the United States and Slovenia, respectively.<sup>3</sup> Similar productivity analyses and decompositions have since been conducted for Côte d’Ivoire (World Bank 2021a), Croatia (World Bank 2022a), Ecuador (Patiño Peña and Ferro Forthcoming), Kosovo (World Bank 2021b), and Türkiye (World Bank 2019a), among other countries. Findings vary widely across countries,

> 2 This paper is a background technical note on which the third chapter of World Bank (2024) is based, whereas the first two chapters were authored in parallel with this paper.

3 For a more detailed overview of the productivity estimation literature, refer to Cusolito and Maloney (2018).

3
reflecting their different economic structures, stages of development, and business environments. For example, in Côte d’Ivoire, Croatia, Ecuador, and Türkiye, studies find rising misallocation of resources— that is, less productive firms not being outcompeted by more productive ones—to be the main drag on productivity growth (World Bank 2021a, World Bank 2022a, Patiño Peña and Ferro Forthcoming, World Bank 2019a). By contrast, in Kosovo, the data suggest that falling productivity of incumbent firms is the main driver of overall productivity declines (World Bank 2021b).

# 3. Data

The empirical analyses in this paper leverage two databases from the Zambia Revenue Authority. One draws information from Corporate Income Tax (CIT) returns, while the other draws information from Pay As You Earn (PAYE) Tax returns. The CIT database covers over 5,000 firms that paid corporate income tax in Zambia for the 2014-2021 period. In Zambia, firms that have annual turnover (sales) exceeding ZMK 800,000 or engage in specified business activities are liable for CIT.<sup>4</sup> The standard income tax rate is 30 percent. However, certain industries have special rates that apply to them. For firms with annual profits below K250,000, the tax rate is 30 percent. For industries with annual profits exceeding K250,000, the rate is 40 percent. For those in farming and agro-processing, the rate is 10 percent, while certain manufacturing industries have a rate of 15 percent. If a firm’s turnover is below the CIT filing threshold, the firm is liable for the Turnover Tax at 4 percent monthly across all sectors.<sup>5</sup> With variables drawn from firms’ returns, the CIT database contains panel data on firms’ registration location, sales, cost of sales, and wage bills. It is thus the primary source of firm-level variables for output, capital, labor, and inputs used for productivity estimation (see below discussion on specific variables used for estimation). Available and comparable CIT data cover the 2014-2021 period.

The PAYE database covers over 800,000 workers that paid into PAYE tax in Zambia over the 2014-2022 period. In Zambia, every firm that offers employment is required to operate PAYE, which is a method of collecting income tax from their employees' earnings, although in practice compliance varies across firms and across workers within firms and is limited overall. The employer is obligated by law to deduct income tax from its employees' taxable salary or wages—regardless of whether they are full-time workers or casual employees—on a weekly, fortnightly, or monthly basis. The tax is deducted from all types of payments made to an employee, including salaries and wages, overtime payments, bonuses, profit sharing, fees, leave pay, commissions, allowances, cash benefits, and any benefit, advantage, or allowance (excluding non-money fringe benefits) and payments on taking up or leaving employment.<sup>6</sup> PAYE income tax calculation is based on a progressive slab system that splits the total taxable income of the employee into brackets, with each tax bracket having a corresponding tax rate. Drawing information from fields in PAYE returns, the PAYE database provides information on formal workers’ monthly gross pay and sector of operations. Thus, the PAYE database is the main source for jobs-related analyses in this paper. Available and comparable PAYE data cover the 2014-2022 period.

> 4 Mining and consultancy firms are required to register for Corporate Income Tax regardless of their turnover. 5 Turnover Tax filings are not part of the CIT database and not accounted for in this paper’s analyses because Turnover Tax filings do not contain the necessary variables on labor and costs necessary for productivity estimation in line with this paper’s methodology.

> 6 Employee emoluments that are not subject to PAYE include Labour day Awards, Ex-gratia payments, Medical expenses and Funeral expenses.

4
The principal caveat of the data sources used in this paper is that the universe of firms and workers corresponds to only formal economic activity and specifically the subset of formal firms and workers that pay into CIT and PAYE, respectively. Thus, informal firms and workers are necessarily excluded, as are formal firms and workers not meeting or complying with the filing thresholds for CIT. Given statutory thresholds for tax filing and firm- and worker-specific drivers of tax compliance, firms and workers in the CIT and PAYE databases are likely to be quite different from excluded firms and workers. However, the firms and workers in the datasets constitute an economically significant portion of the Zambian economy. For 2021, the CIT dataset covers over 5,000 firms, which collectively account for ZMK 329 billion in sales, ZMK 118 billion in value added (equivalent to 27 percent of GDP), and 527,000 estimated workers. For the same year, the PAYE dataset covers over 800,000 unique workers, filing over 6.3 million monthly returns. Beyond their collective size, formal firms, and workers—as proxied through these datasets—are especially critical to economic transformation as they are likely to constitute the most productive segment of the economy and most likely to export (Amin, Ohnsorge and Okou 2019).

# 4. Firm dynamics and productivity

## 4.1. Methodology for productivity analyses

Leveraging these data, productivity-related analyses in this paper focus on labor productivity (LP) rather than total factor productivity (TFP). LP and TFP are two ways of computing firms’ efficiency in converting inputs (capital, labor, etc.) to produce goods or services (output). Conceptually, LP measures output per worker and therefore looks only at how labor is converted into output, while TFP incorporates adjustments in other production factors (e.g., capital stock and intermediate inputs) as well as technology, innovation, management practices, and other ways of augmenting firm output with the same input endowment (Syverson 2011). Although TFP presents a more complete view of productivity, this analysis relies on LP given the lack of reliable data measuring capital or investment, which is a key prerequisite for TFP estimation. As no data on firm-level prices or quantities are available for Zambia, this paper calculates LP in revenue terms rather than in terms of physical quantity. LP calculated in revenue terms reflects not only firm physical efficiency in production but also prices that reflect product quality and marketing in addition to input costs (Cusolito and Maloney 2018). This analysis is unfortunately unable to capture these price-related dimensions and thus cannot definitively conclude that phenomena such as productivity dispersion are the result of allocative efficiency (or lack thereof), quality, or markups. The specific analytical implications of this limitation are further discussed in the subsequent discussion of the results.

Labor productivity is defined as value added per worker for the purposes of this paper. Value added is defined as sales less non-labor cost of sales. The number of workers is estimated by dividing firms’ total wages bills by average annual wages at the ISIC A*38 level (calculated from the PAYE database). Value added is deflated in line with sectoral deflators obtained from ZamStat, which are roughly in line with the ISIC A*38 level of aggregation.

The methodology for decomposing labor productivity growth is drawn from Olley and Pakes (1996), Melitz and Polanec (2015) and Patiño Peña and Ferro (Forthcoming). Labor productivity is estimated and decomposed at the ISIC 1-digit level given the low number of observations per sector at the ISIC 2-digit 5
=@ + (w —-@ )(s ).

(o -@& j(s )

-)a

-)a AD +2 +2 +2 +) aa

AD
={1,....,N } {fiel } ={i¢l } = Ao ° <u>eT</u> Ao -@ ° <u>eT</u> (0 -@ )(s ) @ -a Ys ) <u>le |</u>

<u>le |</u>
( je 4 Ye Ye =D =) aa +a +2 +2 +) aa
20 percent, and non-industrial services’ share 50-55 percent, with little discernible overall trend.<sup>8</sup> These findings are reflected in Figure 1-Figure 9.

<!-- Start of picture text --> Figure 1. Share of formal firms by Figure 2. Share of real value Figure 3. Share of labor of formal<br>province of registration added of formal firms by province firms by province of registration<br>of registration (constant 2010<br>prices)<br>100% 100% 100%<br>90% 90% 90%<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>Lusaka Copperbelt Other Lusaka Copperbelt Other Lusaka Copperbelt Other<br>Figure 4. Share of formal firms by Figure 5. Share of real value Figure 6. Share of labor of formal<br>size added of formal firms by size firms by size<br>(constant 2010 prices)<br>100% 100% 100%<br>90% 90% 90%<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>Micro (1-10 employees) Small (11-50 employees) Micro (1-10 employees) Small (11-50 employees) Micro (1-10 employees) Small (11-50 employees)<br>Medium (51-100 employees) Large (>100 employees) Medium (51-100 employees) Large (>100 employees) Medium (51-100 employees) Large (>100 employees)<br>Figure 7. Share of formal firms by Figure 8. Share of real value Figure 9. Share of labor of formal<br>overall sector added of formal firms by overall firms by overall sector<br>sector (constant 2010 prices)<br>100% 100% 100%<br>90% 90% 90%<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>Agriculture, forestry, and fishing Mining and quarrying Agriculture, forestry, and fishing Mining and quarrying Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services Rest of industry Non-industrial services Rest of industry Non-industrial services<br>Source: World Bank and ZRA staff calculations based on CIT database<br><!-- End of picture text --> Across the same period, formal firms in mining and quarrying generally had the highest labor productivity, followed by formal firms in the rest of industry and services, then formal firms in agriculture. However, in more recent years, the labor productivity of formal firms in agriculture has exceeded that of formal firms in rest of industry, as will be discussed further (see Figure 10).

8 At the ISIC 1-digit level, the largest constituent sectors within rest of industry are manufacturing (12 percent of formal firms’ labor in 2021) and construction (5 percent). The largest constituent sectors within services are retail trade (20 percent), wholesale trade (7 percent), administrative and support service activities (5 percent), financial and insurance activities (4 percent), and transportation (4 percent). For additional details, refer to Table 3.

9
<!-- Start of picture text --> i 2014 2015 2016 2017 2018 2019 2020 2021 i<br>=== Agriculture, forestry, and fishing=== Mining and quarrying<br>= Rest of industry == Non-industrial services<br><!-- End of picture text -->
<!-- Start of picture text --> Distribution of labor productivity<br>4 } \\<br>i 2—— 2014 :i<br>5\ — 2019 i<br>: \ 2021 :<br>0 ee: _———_—_—<br>5 10 15<br>: Log labor productivity :<br>i kernel = epanechnikov, bandwidth = 0.1245 H<br><!-- End of picture text -->
Figure 14. Cumulative change in weighted average log labor productivity, 2014-2021 (non-mining industry, constant 2010 prices) <!-- Start of picture text --> 0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 16. Cumulative change in weighted average log labor productivity, 2014-2021 (manufacturing, constant 2010 prices) <!-- Start of picture text --> 0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 18. Cumulative change in weighted average log labor productivity, 2014-2021 (retail trade, constant 2010 prices) <!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database

- Figure 15. Cumulative change in weighted average log labor productivity, 2014-2021

- (non-industrial services, constant 2010 prices)

<!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database

- Figure 17. Cumulative change in weighted average log labor productivity, 2014-2021

- (construction, constant 2010 prices)

<!-- Start of picture text --> 0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 19. Cumulative change in weighted average log labor productivity, 2014-2021 (wholesale trade, constant 2010 prices) <!-- Start of picture text --> 0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>-1.6<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database 12
<!-- Start of picture text --> Figure 20. Cumulative change in weighted average<br>log labor productivity, 2014-2021<br>(transportation and storage, constant 2010 prices)<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database <!-- Start of picture text --> Figure 21. Cumulative change in weighted average<br>log labor productivity, 2014-2021<br>(financial services, constant 2010 prices)<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT<br>database<br>Log-changes<br><!-- End of picture text --> The most notable exception to this overall trend is the accommodation and food service subsector, which has experienced overall productivity growth over the 2014-2021. Further study is needed to assess the drivers of the sector’s strong labor productivity performance, but the broad trend may be related to recent high-profile foreign direct investment (FDI) in the sector, including the acquisition of the Protea Hotels brand by Marriott and subsequent expansion of the Protea Hotel in Lusaka, the acquisition of the InterContinental Hotel Lusaka by Mauritius-based QG Africa Hotel, and acquisition of a portion of Sun International’s portfolio (including the Royal Livingstone Hotel) by the Thailand-based Minor Hotels. The sector has also experienced steady growth in its share of formal firms’ labor (from 1.8 percent in 2014 to 3.1 percent in 2021), although its aggregate share of formal firm employment remains modest (see Figure 22).

Figure 22. Cumulative change in weighted average log labor productivity, 2014-2021 (accommodation and food service, constant 2010 prices) <!-- Start of picture text --> Cumulative contribution to labor productivity growth (log)<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT database<br>Log-changes<br><!-- End of picture text -->

# 4.3.2. Productivity trends in agriculture, forestry, and fishing

In contrast to trends in other segments of the economy, formal firms in agriculture, forestry, and fishing have actually experienced steady increases in labor productivity over the 2014-2021 period, driven by within-firm growth (see Figure 23). The divergence between the formal firms in the CIT dataset and broader agricultural trends in Zambia reflects how the firms in the CIT dataset are often large-scale commercialized farms. Such farms have a relatively greater ability to leverage Zambia’s strong resource 13
endowments thanks to higher investments in productivity-enhancing technological and process improvements, diversification away from maize, and ability to tap into global value chains and export markets. Formal agricultural firms’ strong performance also underscores the potential for increased commercialization and adoption of improved technology to drive jobs and economic transformation in Zambia despite the headwinds to overall agricultural productivity growth highlighted in World Bank (2024). Nevertheless, formal agricultural firms account for an as yet marginal portion of the total agricultural labor force (only an estimated 119,000 workers). In general, large-scale commercialized farms—of which the firms in the CIT database are a subset—only farm about 0.7 percent of Zambia total cropped area and represent less than 0.08 percent of all farming entities, highlighting the scale of both the potential opportunity and need to drive increased commercialization (World Bank 2024).

At the same time, the analyses also highlight key climate change-related vulnerabilities faced by formal agricultural firms—as well as farmers in Zambia write large—that drive significant year-on-year volatility. A notable exception to the general upward trend in labor productivity was a sharp drop in 2016 (see Figure 24). This sudden deterioration was driven by an intense drought in 2015-2016, attributable to El Niño and climate change, which translated into steep within-firm productivity declines (Alfani, et al. 2019). Looking forward, climate change is likely to exacerbate the likelihood and intensity of such extreme climactic shocks, highlighting the need to increase resilience of the sector (Thorton and Lipper 2014). Interestingly, although the impact of the 2015-2016 drought is evident in the data, there is no similarly steep drop in productivity in 2019, when Zambia experienced another drought. Formal agricultural firms’ relatively strong performance it attributable to the 2019 drought being relatively less intense and the relative concentration of large commercial farms in the Copperbelt and Easter provinces, which were less affected by drought.

Figure 23. Cumulative change in weighted average log labor productivity, 2014-2021 (agriculture, forestry, and fishing; constant 2010 prices) <!-- Start of picture text --> 1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database <!-- Start of picture text --> Figure 24. Annual change in weighted average log<br>labor productivity, 2014-2021 (agriculture, forestry,<br>and fishing; constant 2010 prices)<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>-0.50<br>2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation Entry* Exit*<br>Note: Averages are weighted by firms’ share of labor.<br>Source: World Bank and ZRA staff calculations based on CIT<br>database<br>Log-changes<br><!-- End of picture text -->

# 4.3.3. Productivity trends in mining

As with agriculture, forestry, and fishing, the mining and quarrying sector has also avoided the secular labor productivity declines seen in other sectors, but it has been subject to even more extreme year-toyear volatility. Overall average labor productivity levels among formal mining and quarrying firms for 2021 14
are roughly in line with 2014 levels in log terms and somewhat higher in level terms (see Figure 25).<sup>10</sup> However, unlike with agriculture, there is not a clearly discernible secular trend, with growth and even overall productivity levels fluctuating significantly on a year-to-year basis (see Figure 26).

Figure 25. Cumulative change in weighted average log labor productivity, 2014-2021 (mining and quarrying; constant 2010 prices) <!-- Start of picture text --> 1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 26. Annual change in weighted average log labor productivity, 2014-2021 (mining and quarrying; constant 2010 prices) <!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Structural transformation Entry* Exit*<br>Log-changes<br><!-- End of picture text -->

Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Fluctuations in labor productivity in the mining sector are largely due to idiosyncratic factors within individual firms operating in the sector. The between-firm component—corresponding to changes in the correlation between firms’ market share and productivity—was the primary driver of changes in productivity over the 2014-2021 period (see Figure 26). This trend reflects how the mining sector has a high level of industry concentration and individual mines are subject to significant operational variability from year to year (for instance, variation in ore grade or unexpected disruptions). For example, the sharp decrease in productivity seen in 2016 was due to safety incidents that occurred in some of the largest copper mines in Zambia, which led to temporary closures. On the other hand, the sharp increase in productivity in 2017 was due to the rapid expansion of a newly opened mine that employed the latest technology (i.e., a high-productivity firm), resulting in improved efficiency in resource allocation.<sup>11</sup>

# 5. Firm dynamics and formal job flows

## 5.1. Methodology for jobs analyses

This paper analyzes trends in the stock, flow, and wages of formal jobs in Zambia by leveraging the PAYE database. Paying into PAYE taxes serves as a proxy for formal employment. For the purposes of this paper, formal employment (i.e., jobs covered by PAYE taxes) can occur at both formal firms (i.e., firms paying into CIT) and informal firms. Similarly, formal firms may have both formal and informal employees. We define one formal full-time equivalent worker (FTE) as 12 monthly returns to account for seasonal labor. Wages are annualized and deflated by the Consumer Price Index (CPI) to align real wages to spending power.

> 10 The slight difference in trend between the two measures stems from the convexity of natural log function. 11 Analyses of mining sector performance are based on interviews with Zambia Revenue Authority staff and the annual reports of relevant mining companies in Zambia. Due to the sensitive nature of the firm-level tax data, further details on the specific firms cannot be disclosed.

15
<!-- Start of picture text -->
-ya +ya +a +2 +) aa<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 27. Total formal employment by sector, 2014- Figure 28. Total formal employment by province of<br>2022 (FTE) employer registration, 2014-2022 (FTE)<br> 600 600<br> 500 500<br> 400 400<br> 300<br> 300<br> 200<br> 200<br> 100<br> 100<br> -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services Lusaka Copperbelt Other<br>Note: FTE defined as 12 monthly returns to account for Note: FTE defined as 12 monthly returns to account for<br>seasonal employment. seasonal employment. Data on province of employment (as<br>Source: World Bank and ZRA staff calculations based on PAYE opposed to registration location of employer) is not sufficiently<br>database complete for analysis. In some cases (most commonly in<br>agriculture), the employer’s province of registration may differ<br>from actual province of operations. Slight differences in totals<br>with other charts are attributable to completeness of provincial<br>data.<br>Source: World Bank and ZRA staff calculations based on PAYE<br>database<br>Figure 29. Total formal employment by size of firm, Figure 30. Year-on-year change in formal<br>2014-2022 (FTE) employment by driver, 2014-2022 (FTE)<br> 700 120<br> 600 100<br>80<br> 500<br>60<br> 400<br>40<br> 300 20<br> 200 0<br>-20<br> 100<br>-40<br> - -60<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>-80<br>Large (>100 employees) Medium (51-100 employees) 2015 2016 2017 2018 2019 2020 2021 2022<br>Micro/small (1-50 employees) Within-firm expansion Within-firm contraction New firm entry Firm exit<br>Note: FTE defined as 12 monthly returns to account for seasonal employment.<br>Source: World Bank and ZRA staff calculations based on PAYE database<br><!-- End of picture text -->

# 5.3. Results of jobs analyses

In line with labor productivity trends, real wages have declined in Zambia across several subsectors. Real wage declines have been driven by within-firm real wage deterioration, pointing to the broad-based nature of wage declines as productivity and pay have struggled to keep pace with inflation (Figure 31). The average Zambian formal worker in the PAYE dataset earned over ZMK 73,000 annually in 2014, which decreased by almost half to about ZMK 39,000 in 2022 (in constant 2010 ZMK terms). This pattern is consistent across a wide array of sectors and subsectors (Figure 32)―real wages for formal workers have declined in mining and quarrying, manufacturing, construction, wholesale and retail trade, financial services, and transport. Real wage declines in the formal sector are also in line with real wage declines in Zambia (i.e., for both formal and informal workers). These are observed in Zambia’s Labour Force Survey data: between 2017 and 2021, real average monthly earnings declined by 24 percent (Zambia MLSS 2022). An exception to this trend is formal workers in agriculture, forestry, and fishing, which is in line with the sector’s relatively stronger labor productivity performance. Although formal workers in this sector 17
continue to have lower wages than in other sectors, real wages remained stable over this period and actually increased in the last year of the study period.

<!-- Start of picture text --> Figure 31. Cumulative change in log real wage, Figure 32. Real wage by overall sector,<br>2014-2022 (base year: 2010) 2014-2022 (thousand 2010 ZMK)<br>0.2 160.0<br>0.1 140.0<br>0 120.0<br>-0.1 100.0<br>-0.2 80.0<br>-0.3 60.0<br>-0.4 40.0<br>-0.5 20.0<br>-0.6 2014 2015 2016 2017 2018 2019 2020 2021 2022 -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services<br>Note: Wages are deflated by Consumer Price Index (CPI).<br>Source: World Bank and ZRA staff calculations based on PAYE database<br>Log-changes<br><!-- End of picture text -->

**Net formal job creation has generally been in lower paying subsectors.** At the ISIC 1-digit level, the largest net formal job creators have been agriculture, forestry and fishing (about 28,000 net new jobs between 2014 and 2022), retail trade (28,000), manufacturing (21,000), and other service activities (23,000),<sup>12</sup> all relatively low-wage sectors. By contrast, there has been limited new formal job creation in financial services (5,000 new jobs), the highest-wage subsector, and there has been net formal job destruction in mining (5,000 fewer jobs in 2022 versus 2014), the third highest paying subsector (see Figure 33). As a result, although not quite as stark as the impact of within-firm real wage declines, across-sector shifts in the formal workforce have also contributed to average real wage declines for formal workers in Zambia. In general, formal job creation in lower-paying subsectors may still translate into better livelihoods on a net basis for Zambians as a whole insofar as workers are transitioning from lower-paying informal jobs. However, this trend highlights the limited remaining job-creating potential in mining—which is constrained by natural resource endowments—as well as the still untapped potential for job creation at scale in higher value-added service subsectors such as financial services and information technology.

> 12 In Zambia, this mostly accounts for personal care and service jobs.

18
<!-- Start of picture text --> Figure 33. Change in formal employment, mean real wage, and total formal employment by ISIC 1-digit<br>subsector, 2014-2022<br>120<br>100 Mining and quarrying Financial and insurance activities<br>Electricity, gas, steam, air<br>80 conditioning supply<br>Information and communication Public administration and defence<br>Education<br>60 Human health and social work<br>Water supply, sewerage, waste Transportation and storage<br>management Administrative and support services Other service activities<br>40<br>Real estate activities Professional, scientific and Retail trade<br>technical services<br>Trade/repair of motor vehicles<br>20 Wholesale trade<br>Construction<br>Accommodation and food service<br>Arts, entertainment and recreation Manufacturing Agriculture, forestry and fishing<br>0<br>-10 -5 0 5 10 15 20 25 30 35<br>Change in employment (2014-2022; thousand workers)<br>Agriculture, forestry and fishing Mining and quarrying Rest of industry Non-industrial services<br>Note: Bubble size corresponds with 2022 formal employment.<br>Source : World Bank and ZRA staff analysis based on PAYE database<br>Mean real wage (2022; thousand 2010 ZMK)<br><!-- End of picture text -->

# 6. Discussion

That the within-firm component is the main drag on labor productivity and wage growth across various sectors suggests that economy-wide productivity constraints are a critical factor in Zambia. Cross-cutting factors such as poor infrastructure, burdensome regulations, and low technology adoption are more likely to affect firms and sectors similarly, thus impacting the within-firm component of productivity growth and leading to broad-based declines across sectors. By contrast, distortions to competition from entry barriers, policies that create an uneven playing field, and poor enforcement of anticompetitive behavior are more likely to impact allocative efficiency (the between-firm, entry, exit, and structural transformation components of productivity growth). The salience of the within-firm component in explaining productivity and wage declines in Zambia underscores the severity of business environment and firm capability constraints in the country.

Moreover, Zambia stands out from other developing countries for which similar analyses have been conducted. Analyses in other countries such as Croatia, Côte d’Ivoire, Ecuador, and Türkiye have often found the between-firm component to be the main drag on productivity growth, indicating allocative efficiency problems (Patiño Peña and Ferro Forthcoming, World Bank 2022a, World Bank 2021a, World Bank 2019a). It is less common to see economy-wide productivity declines driven by the within-sector component, although a study in Kosovo came to similar findings (World Bank 2021b). We now analyze specific business environment constraints that are especially salient and relevant to firm productivity— and, by extension, wage—trends in Zambia.

Recent evidence suggests that labor productivity declines within formal firms in non-mining industry and services partly reflect business environment constraints. Over 600 Zambian firms across manufacturing, construction, wholesale and retail trade, hotels, restaurants, transportation, and information technology 19
<!-- Start of picture text --> 35 mmm Zambia2019 :<br>: —@— Sub-Saharan Africa i<br>A uv H<br>E20 18<br>10 8<br>0<br>i Access to Electricity Practices of | Corruption Taxrates Customs and Political Tax Access to land Labor H<br>: finance the informal trade instability administration regulations i<br>i sector regulations H<br><!-- End of picture text -->
<!-- Start of picture text --> ii ¥~> SF@ FFaw© SFLko ¥<Ny SF@ FRS)> SFSS& SFRs\ ECs@ SF@ i<br>x es WY? Ww a Ss Ba Vv Ke .& i<br>i < ws < g ~<br>: Ss \ x :<br>| RoRS ce& >oe” :i<br><!-- End of picture text -->

<!-- Start of picture text --> I<br>150<br>| 100<br>647 628<br>39 «57 68<br>| 0<br>2016 2022<br>| @Banks Pensions Olnsurance M# Other NBFls<br><!-- End of picture text -->
# 6.2. Firm-level capabilities and skills

Poor labor productivity trends among formal Zambian firms in the non-mining industry and service sectors also reflect firm-level capability and technological constraints. Firms often lack critical managerial, technological, and vocational skills. The Zambian training system provides inadequate training with respect to business and entrepreneurial skills, which are necessary for the establishment and growth of businesses (Government of the Republic of Zambia 2020). A relatively low percentage of firms in Zambia have licenses for the use of international technologies (Figure 37) or have internationally recognized quality certifications (Figure 38), proxies for the level of technology adoption, and managerial and operational sophistication. As a result, Zambia ranks 100th out of 132 countries with respect to business sophistication in the World Intellectual Property Organization’s Global Innovation Index (2022), below countries such as Peru (49th), Thailand (43rd), and even Zimbabwe (90th) (World Intellectual Property Organization 2022).

<!-- Start of picture text --> Figure 37. Firms using technology licensed from Figure 38. Firms with international recognized quality<br>foreign companies certification<br>(Percent of latest Enterprise Survey respondents) (Percent of latest Enterprise Survey respondents)<br>40% 25%<br>35%<br>20%<br>30%<br>25% 15%<br>20%<br>15% 10%<br>10%<br>5%<br>5%<br>0% 0%<br>Cross-country averages Structural peers Aspirational peers Zambia Cross-country averages Structural peers Aspirational peers Zambia<br>Note: Select aspirational peers such as Thailand also have lower scores, although in such cases the low score reflects the ready<br>availability of domestically developed technology rather than low business sophistication.<br>Source: World Bank staff calculations based on Enterprise Survey data and the Investment Climate Assessment 2.0 toolkit<br><!-- End of picture text --> Zambian firms also face skills and capability constraints at the worker level. Zambia’s Human Capital Index (HCI)—a measure of health and education’s contribution to worker productivity—is well below most aspirational and structural peer countries (Figure 39).<sup>14</sup> Zambian firms are unable to overcome these challenges after onboarding workers. Although a high portion of formal Zambian firms report offering formal training, they offer it to a low share of workers (Figure 40).

> 14 The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education.

22
<!-- Start of picture text --> i i & * & co Se ss & < KS H<br>it &” RN Q© SS& s& & & & Hi ¥ & «8 we is & s v xX HH<br>Pet FF sg * ¥ e<br>ee 8 s<br><!-- End of picture text --> <!-- Start of picture text --> : 120% :<br>: 80%<br>i 40% i<br>i: 20% i<br>ii . . H: FTFoO SFge CK2 KF2 FF& 2 CK FT© SCwe SCww SFg i:<br>H & & & oe we ¢& ee es we : <> s ee es g ee s AS & yw as Ce<br>i SO SS SK SF we re we Fv * s<br>v as s we : é &é Ss 7 i<br>aS SS RS °<br>: rT | 7 rt : TM Cross-country averages @TM Structural peers @Aspirational peers TM Zambia :<br><!-- End of picture text -->
percent of installed power generation capacity, making the country vulnerable to droughts. Droughts in 2015/2016 and 2019 led to blackouts and power rationing (UNCTAD 2022). Financial challenges at the state-owned electricity utility, a weak coordinating environment, and limited private sector participation further contribute to electricity issues in Zambia (UNECA 2021, UNCTAD 2022).

## 6.4. Regulatory environment and confluence with informality

Formal firms in Zambia compete heavily with informal firms, reflecting difficulties in accessing highervalue markets and amplifying distortions from excessive formal regulatory and tax burdens. Informal firms may hinder the scale-up of formal competitors if their informal status provides them with cost or operational advantages (e.g., lower regulatory or tax burdens), creating an uneven playing field (Amin, Ohnsorge and Okou 2019). Since informal firms are not covered in this analysis, their impact on productivity can be observed as productivity declines among formal firms that experience sales or margin declines, rather than as cross-firm reallocation of resources. According to the 2019 Enterprise Survey, nearly 65 percent of firms compete with informal firms—higher than many peer countries— (Figure 43) and informal sector practices are the third-most cited business environment obstacle. These competitive distortions result from informal competition coinciding with burdensome requirements for formal firms. Senior managers of formal firms in Zambia report spending 8 percent of their time dealing with regulatory requirements, higher than the averages for SSA and aspirational peers (Figure 44). Competition with the informal sector may also reflect weak market access, forcing formal firms to compete with informal firms for a limited pool of local customers.

<!-- Start of picture text --> Figure 43. Firms competing with informal firms Figure 44. Senior management time spent on<br>(Percent of latest Enterprise Survey respondents) regulatory requirements (Average of latest<br>Enterprise Survey respondents)<br>80% 14%<br>70% 12%<br>60% 10%<br>50% 8%<br>40% 6%<br>30% 4%<br>20% 2%<br>10% 0%<br>0%<br>Cross-country averages Structural peers Aspirational peers Zambia<br>Cross-country averages Structural peers Aspirational peers Zambia<br>Source: World Bank staff calculations based on Enterprise Source: World Bank staff calculations based on Enterprise<br>Survey data and the Investment Climate Assessment 2.0 toolkit Survey data and the Investment Climate Assessment 2.0 toolkit.<br><!-- End of picture text -->

# 7. Conclusion

Aggregate labor productivity of formal firms weakened between 2014 and 2021, driven by secular within-firm declines in the non-mining industry and service sectors. By contrast, labor productivity grew in agriculture and remained flat in mining over the same period. Real wage trends for formal workers have mostly mirrored labor productivity dynamics, declining 40-50 percent across non-agriculture sectors but growing slightly in agriculture, largely driven by within-firm shifts rather than between-firm or between-sector dynamics. The declines in labor productivity and wages reflect business environment challenges related to access to finance and electricity, as well as burdensome formal compliance 24
requirements and competition with the informal sector. Within-firm labor productivity challenges also reflect low skills and capacity—including low technology adoption—among both firms and workers.

These findings suggest that financial and business environment reforms are needed to unlock the private sector’s role in driving jobs and economic transformation. To improve access to finance, Zambia could consider dismantling arrears, scaling up and refining credit guarantee schemes for private sector lending, improving financial infrastructure (e.g., credit information systems), and working with financial intermediaries to diversify product offerings. Regulatory streamlining—including for trade and foreign direct investment—would help decrease competitive distortions from competition with the informal sector and increase exports and FDI, which are critical channels for productivity improvement. Energy sector reforms such as improved systems planning would help to increase the quality of and access to electricity for firms and citizens alike. At the firm level, programs to enhance technology transfer linkages between anchor firms and their suppliers and buyers could help Zambian firms upgrade their capacity and improve productivity, as could programs to directly promote technology use by firms. At the same time, investments in water management and other climate change mitigation and adaptation measures will be critical to build resilience and reduce volatility.

Further research priorities related to firm productivity and wages in Zambia could include more detailed analysis of how specific policy issues, access to finance, infrastructure, and capability gaps interact with firm productivity (e.g., the impact of FDI on firm productivity within Zambia) and pay as well as analysis of wage dynamics and inequality within firms across individual workers. The CIT and PAYE data utilized in this paper could also be used to conduct research related to revenue mobilization and tax compliance.

25