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.

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