rafmacalaba/data-use-mentions-extended huggingface dataset:
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
As part of the village leaders survey, respondents were collectively asked to recall information from July 14 to September 15 on whether the PHU was closed, disrupted, and an overall PHU functioning score. They were also asked about whether the nearest secondary school opened on time (many openings were delayed from April 2015 but all schools would have re-opened when our endline survey began in February 2016). We combine components to construct a village specific disruption index, following Anderson [2008] in using the data covariance matrix to construct a weighted sum of components, giving less weight to components that have less variation across villages and that are more correlated with each other.<sup>32</sup> Panels A and B in Table 4 shows descriptive evidence on the components the index is constructed from. Each varies across villages, with there being substantial variation within and between districts (Column 2). Panel C shows the constructed disruption index, that by definition is standardized with mean zero and standard deviation one. To operationalize (1), we define a disruption dummy, _Evd_ , so that _Evd_ = 1 if the index is above its 75th percentile, and _Evd_ = 0 otherwise. We refer to these as high and low disruption villages, where 17% of villages are then defined to be high disruption locations.

To get a sense of what this classification means, low disruption villages never have their PHU ever closed, 18% have PHU disruptions, and their average PHU functioning score is 94. In contrast, 82% of high disruption villages have their PHU ever closed, all of them have the PHU ever disrupted and the PHU functioning score is 67. In low disruption locations, 88% of secondary schools opened on time, while this falls to 62% in high disruption locations.

Our _Evd_ construct is a proxy for all Ebola-related disruption experienced by village _v_ . Hence we do not interpret moving from a low to high disruption village as literally _only_ capturing the persistent impacts of having the nearest PHU more disrupted during
# 2. **Data**

Senegal represents a valuable case study. The rise in the number of cellphone users and Internet subscribers in the country over the past decade has been significant compared with peer countries. Figure 1 illustrates the mobile cellular subscriptions and fixed broadband subscriptions in Senegal relative to peers—Côte d’Ivoire, Kenya, and Rwanda—as well as among the 5th and 95th percentiles overall in Sub-Saharan Africa.

The data on consumption and poverty are derived from official household budget surveys: the 2011 ESPS-II and the 2017–18 ELEPS. The 2011 ESPS was a nationally representative survey that was fielded between August and November 2011. It was conducted effectively with 17,891 of the 18,180 households sampled given that the response rate was 98.4 percent. About a third of these households were interviewed using the entire household questionnaire on expenditure. The 2017–18 ELEPS is comparable with the 2011 survey. The ESPS was administered among 1,065 of the 1,200 households sampled given that the response rate was 89 percent. The sample was spread across the country according to three geographic areas: Dakar, other urban centers, and rural areas.

To measure access to fixed broadband Internet services across Senegal, the analysis relied on information on the locations of fiber-optic transmission nodes that was obtained from the Africa Bandwidth Maps database.<sup>5</sup> The analysis considers how distance to operational fiber-optic nodes may influence welfare and poverty across the country. Map 1, panel a, shows the locations of fiber-optic transmission nodes in Senegal. These nodes correspond to add or drop points (entrance or exit) in the long-haul fiber networks. It is useful to think of long-haul fiber networks as motorways that have junctions (on and off ramps that is, add and drop points) that feed smaller class roads (access fiber, wireline, and wireless networks). In the motorway scenario, even if a household is located close to the motorway, it may be a long drive to
reliable poverty estimates (section 4).

Third, the SHFS relies on correlates derived from satellite imagery and other geo‐spatial data to estimate poverty in areas that remained completely inaccessible as a result mainly of insecurity. A growing field of research is dedicated to predicting a range of outcomes based on a diverse set of such data sources. Early applications use night‐time lights data to predict economic activity. These data are particularly successful at predicting GDP at the country‐level (Henderson et al., 2012; Pinkovskiy and Sala‐i‐Martin, 2016), but appear less well‐suited for measuring income and when variation in welfare is desired at a highly disaggregated level (Engstrom et al., 2017; Mellander et al., 2015). More recently, deep learning techniques applied to daytime imagery in order to classify such objects as roof types, roads, tree coverage, and crops has led to advances in measuring welfare at more disaggregated levels (Krizhevsky et al., 2012). Jean et al. (2016) use a convolutional neural network based on daytime satellite features to predict per capita consumption at the level of the enumeration area from living standards measurement surveys. Their model is successful in predicting consumption and explains 46 percent of variation on average across four countries and out‐of‐sample. Engstrom et al. (2017) provide a recent overview of the state of the literature and use high‐resolution satellite features to estimate poverty at the village‐level. In the SHFS, estimating poverty in inaccessible areas relied on a linear model with the objective of creating reliable and transparent poverty measures (section 5).

The remainder of this paper proceeds as follows. Section 2 discusses the sampling strategy. Section 3 provides an overview of the data collection process. Section 4 describes the derivation of the consumption aggregate, including the Rapid Consumption Methodology. Section 5 describes the imputation of poverty in inaccessible areas, and section 6 gives an overview of poverty in Somalia.

4
(Core NTMs) for each HS six-digit tariff line item estimated by Kee, Nicita and Olarreaga.<sup>24</sup> AVEs of core NTMs<sup>25</sup> (price and quantity control measures, technical regulations, as well as monopolistic measures, such as single channel for imports) are computed by Kee et al. using Leamer’s comparative advantage approach.<sup>26</sup> The second is an all-encompassing measure of protection, Overall Protection, which is the sum of all three possible sources of protection: tariffs, NTMs and domestic subsidies in agriculture.<sup>27</sup> Since they are measured as ad valorem equivalents, they are summed to yield an overall measure of protection.

The third is a complete tariff measure, All Tariff, which combines ad valorem tariffs with (ad valorem equivalents of) specific tariffs.<sup>28</sup> Both measures have been historically prevalent in agriculture, and since agriculture was untouched by the multilateral rounds of negotiations 24 Kee, Nicita and Olarreaga (2004b).

25 Core NTMs include quantitative restrictions, price control measures, monopolistic measures and technical regulations corresponding to codes 6000, 8000, 7000 and 8100 in the UNCTAD TRAINS database, which is the source of the NTB indicators.

> 26 Leamer’s (1990) method is as follows. First, imports are predicted using factor endowments. Then the impact of NTBs on imports is measured as deviations of imports, in the presence of NTMs, from these predicted values. Finally, this quantity impact of NTMs on imports is converted into a price equivalent (or AVE) by using the import demand elasticities estimated in Kee, Nicita and Olarreaga (2004).

27 The source of the dollar value of agriculture domestic subsidies is Ng and Olarreaga (2004), obtained from WTO members’ notifications during 1999. The method of computing the AVE of domestic support is to treat them as NTMs and apply the same method as for
may not be a straightforward concept and lack an intuitive “benchmark” to correctly assess differences in magnitude along a percentage scale.<sup>29</sup> Secondly, the respondents in our sample are overwhelmingly low-educated with over two-thirds not having any school education and only a rough one-fifth having completed primary education in the case of Ethiopia. They also likely have little practice in performing this kind of task as the LSMS-ISA survey only has a twoto three-year frequency. Thirdly, the LSMS-ISA questionnaires are of substantial length, frequently requiring interviewing respondents over several hours and on multiple days. This may increase respondent fatigue and decrease the motivation to provide an accurate estimate.As a result, a percent difference of 10 percent or 20 percent, well within the conceivable margin of error for the amount of heaping we observe, may appear small to the farmer (and hence of satisfactory accuracy) but would lead to substantial measurement error (“weak satisficing”). Alternatively,

> 27 Our exposition will focus on Ethiopia where we can measure both planted area losses and plant productivity reductions. In Malawi, we only have loss reports from one round (IHPS19) and also only for planted area losses which equates to a scenario where di,j,t = 0, that is, where realized yields in harvested areas are equal to attainable yields in harvested areas and the only losses occur due to complete destruction of parts of the plot. When limiting our analysis to this proxy though, results between Ethiopia and Malawi are largely comparable with some notable differences in the incidence of area losses (60 percent of plots in Malawi vs. 20 percent of plots across rounds in Ethiopia). This may reflect the planted area loss question compensating for a lack of a separate question asking for productivity reductions.

> 28 To avoid a hard-to-read histogram due to a dominant spike at 0 percent area-based loss and 0 percent damage, we constrain the histograms depicted to those plots who record any loss (i.e. positive values on the horizontal axis)
<mark>for households and their lack of granularity in assessing the ways in which they are poor.</mark><sup>9</sup> <mark>Unfortunately, we are again limited by the availability and informational content of the existing data with respect to the outcomes we would like to measure, as the best known multidimensional poverty indicators are available only since 2010.</mark><sup>10</sup> <mark>We thus perform this part of the analysis using indicators that proxy some of the components in multi-dimensional poverty indicators, for which longer time series exist.</mark> <mark>Indicators proxying access to nutrition and electricity, as well as health outcomes also suggest that defaults lead to marked declines in standards of living. All of our outcome variables are indexed to 100 the year before default. Calorie availability consistently lags in defaulting countries with the cumulative gap relative to synthetic controls growing to 4 percentage points ten years after default. Our findings on energy supply, though not statistically significant, also suggest that defaulters lag their controls by about 10 percentage points cumulatively after four years. Infant mortality and life expectancy show significant cumulative gaps for defaulters relative to their controls. On average, by year ten, defaulters have 13% more infant deaths than the counterfactual. This result is consistent with Baird et al. (2011) who find that infant mortality is sensitive to aggregate income shocks. And those infants that survive are expected to have shorter lives: with the life expectancy dropping to 1.5 percent below the synthetic counterfactual – the equivalent of 1.2 years – below that in the synthetic control. This result is consistent with Baird et al. (2011) who find that infant mortality is sensitive to aggregate income shocks.</mark> <mark>The last set of findings presented here take a more detailed view of how poverty and the incomes of the poor are affected by defaults – albeit with a reduced sample that should be interpreted
w r (5) pp pp CCjj (iibbdThird, we estimated changes in the energy cost of brick production [wweett ccCCwwbbEEee ttCCOO) = CC̅jj (tt) + (C rree(tt) ∙CCOOjj(tt)) ] by partially pp substituting coal with advanced biomass fuels, such as pellets, for ZZK brick kilns across different ii CC (tt) provinces ( _Equation 6_ ). The energy cost constitutes a major cost of brick production. The costs associated with a different combination of coal [ c ] and pellets [ ] is calculated using the fuel price information (fuel price index with the price of coal index as 1) collected from the rrcc ppeec eerr eeh eeh rapid survey.

Fourth, we estimated the social cost of brick production. The social cost includes both private costs, i.e., direct costs to producers, and indirect or external costs to society that are not reflected in the market price. These external costs associated with brick production include environmental costs, such as costs associated with air pollution, GHG emissions, and soil degradation, health costs, such as workers’ exposure to dust, and other societal impacts. In this study, the estimated social costs are limited to environmental costs [ 2 ], which is the product SS of the environmental damage cost of CO2 emissions from brick production using carbon tax as a jj,C CC (tt) proxy for the social cost of CO2 [ C 2 ] and the corresponding CO2 emissions (Equation 7). The CO2 emissions for brick production are estimated using the emission factor [ 2 ], SSCC (tt) specific energy consumption [ ] and the average weight of the brick [ ]. Likewise, jj,C OOEE (tt) we estimated health damage costs associated with PM2.5 [jj 2.5 ] using a similar approach ii,jj SSOOCC (tt) ww (tt) SS as that for CO2 emissions ( _Equation 8_ ). The social cost values for COii,jj,
|>5%|>10%|almost<br>nil|14.5|36.0|>5%|

_* Estimated_ _Sources_ : Apartment sector size: National Statistical Offices. Ownership status: LSMS, HBS, and Lithuania Household Survey. Condominium status: Armenia, UN-ECE; Morkunaite, R., et al. (2003); Poland, National Statistical Office; Russia, Ellis (2003). Buildings in other whole ownership structure and other buildings: Authors’ assessment. Buildings with scattered public-private apartment ownership: Romania, UN-ECE; Russia, Ellis (2003); the authors’ assessment.

*Buildings owned by companies, individual landlords, cooperatives, or government.

- Because of the incomplete privatizations, mixed public-private apartment ownership currently characterizes almost the entire Russian M-F housing stock. Condominiums and cooperatives have been created in only an estimated 7 percent of the stock<sup>32</sup> ; the ownership of common areas and land in the remainder is ambiguous. Because maintenance has rarely been privatized, local governments spend on average 70 percent of their budgets on housing and communal services, making the owner-occupied sector heavily subsidized and essentially a form of public rental. The situation is exacerbated by large number of entitlements ( _l’goti_ ) granted to numerous population categories in the form of exemptions from and abatements of housing and utility payments.<sup>33</sup> Because cost recovery is still largely insufficient at ratios of about 60 percent, persistent deferred maintenance and insufficient energy conservation measures continue to trouble the sector.<sup>34</sup>

- In Serbia, mixed public-private apartment unit ownership is still common because many units remained un-privatized owing to quality or tenant idiosyncrasies. There is no reliable information about the share of condominiums in the apartment stock, but it appears to be rather very low. It is not clear who owns the common areas and land in the

> 32  See UN-ECE (2004b) and Skyner (2004).

> 33    The
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45

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Keesing’s record ofworld events (various years). London : Longman, (weekly).

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_Reduction in effective labor supply due to reduced hours worked_ As discussed above labor force ( ) in Pakistan depends on the participation rate ( ) and the working age population ( 15+ ). tt tt WWFF PPRR 1564 PPCCpptt ∗P , tt tt tt WWFF = PPRR PP To account for reduced working days we introduce the notion of effective labor force and substitute this notion for the labor force in the standard MFMod system, where the effective labor force, � is equal to the product of the normal labor force and the ratio of days of work lost to pollution/ divided by total tt WWFF working days (assumed to be 240). ∗�1 −<sup>WWDDWWggCCE</sup> �.<sup>ssttiiC,tt</sup> tt tt The equation above will modify structural employment and hence potential GDP: WWFF� = WWFF tt WWDD _Reduction in effective labor supply due to premature death_ Calculating the macroeconomic impact of a death due to air pollution is more difficult to quantify. World Bank (2020c) provides an age distribution of deaths due to air pollution. With this, we could estimate the number of working years lost due to workers dying prematurely from air pollution. This could then be used to estimate the reduction in GDP.

We could consider the effect of death due to air pollution on GDP per capita. Note that a death reduces not only labor supply, but also the denominator, population size. The death of a retired person would increase GDP per capita. According to World Bank (2020), 57.7% of deaths due to air pollution occur in persons older than 60 years. This means that air pollution deaths can be expected to increase GDP per capita. Notice that this difficulty does not occur for working days lost as sickness caused by air pollution does affect labor supply, and hence GDP, but not the population size. We report deaths due to air pollution separately.

_Fiscal implications of pollution-related disease_ Data show that about 60% of health care expenditure
Policy Research Working Paper 6103

# **Abstract**

While innovation is a source of competitiveness, it may expose plants to survival risks. Using a rich set of plantproduct data for Chilean manufacturing plants during the period 1996–2006 and discrete-time hazard models controlling for unobserved plant heterogeneity, this paper shows that innovating plants have higher survival odds. However, risk plays an important role for the innovationsurvival link: only innovators that retain diversified sources of revenues survive longer. Single-product innovators are at greater risk of exiting. In addition, only innovators facing lower market risk, measured by fewer innovative competitors, low-pricing strategies, or lower sales volatility in the new products’ markets, see their odds of survival increase significantly. Technical risk, measured by the proximity of product innovations to the plants’ past expertise, the degree of sophistication of new products, or their novelty to the Chilean market, does not play a substantial role in the innovation-survival link. Engaging in risky innovation is not an irrational decision, since plants reap big payoffs—higher productivity, employment and sales growth—from such innovations. However, those payoffs are not always higher than those from cautious innovation, suggesting that constraining factors, such as credit constraints, force plants to take on more risk when innovating. An implication of the findings for industry dynamics is that among innovators, only the survival of cautious innovators is guaranteed. Since engaging in cautious innovation may not be feasible for all plants, there could be a role for policy in reducing innovators’ exposure to risks and providing assistance to deal with failed innovations, while setting the right incentives.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and