rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9434

# **Abstract**

This paper provides a comprehensive assessment of the short-term impact of the COVID-19 pandemic on businesses worldwide with a focus on developing countries. The results are based on a novel data set collected by the World Bank Group and several partner institutions in 51 countries covering more than 100,000 businesses. The paper provides several stylized facts. First, the COVID-19 shock has been severe and widespread across firms, with persistent negative impact on sales. Second, the employment adjustment has operated mostly along the intensive margin (that is leave of absence and reduction in hours), with a small share of firms laying off workers. Third, smaller firms are disproportionately facing greater financial constraints. Fourth, firms are increasingly relying on digital solutions as a response to the shock. Fifth, there is great uncertainty about the future, especially among firms that have experienced a larger drop in sales, which is associated with job losses. These findings provide a better understanding of the magnitude and distribution of the shock, the main channels affecting businesses, and how firms are adjusting. The paper concludes by discussing some avenues for future research.

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 Liacovone@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
in the combined infrastructure plus border improvements experiment than in the experiment with transport infrastructure alone. This dependence of results on the experiment is of course as it should be, but does alert us to the fact that the direct effects – which parts of the transport network have the largest improvement – are not known with any precision; a fortiori, the results from modeling their full economic impacts are indicative, not predictive.

This paper supplements other recent work written on the BRI. Lall and Lebrand (2019) also provide analysis of the spatially differentiated effects of the initiative, using similar data. They build an economic geography model, a development of work by Fajgelbaum and Redding (2014) in Argentina, which allows them to find the potential winners in terms of population and wages. They find that gains are concentrated in locations near to border points, and in urban centers. Higher levels of labor mobility help lower any potential impacts on spatial inequalities. Our paper complements this work, looking at the cells and countries as part of one wider trading system rather than as individual countries each facing an independent shock, and also providing a richer modeling of the supply side response of the economy to shocks. We draw heavily on de Soyres et al. (2018), who estimate the transport costs within and across the region, and how these may be affected by the Belt Road Initiative, including through improving rail infrastructure and reducing border times. From this they construct a detailed database of transport times pre‐ and post‐BRI for cities across the region, and beyond. They use the data in a quantitative trade model assessing how the BRI may increase international trade flows. We use their analysis to construct our transport network and the experiments that we simulate.

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# **I.   INTRODUCTION**

Emerging markets (EMs) represent a relatively large and rising share of world GDP. Taken together, EMs now constitute the largest block in the world economy and contribute the most to global growth (Figures 1 and 2). From a trade standpoint, advanced economies (AEs)’ imports are sourced in large part from EMs (about 50 percent) and EMs have also become important destinations for AE’s exports (Figure 3 and 4).<sup>2</sup> EMs are as a block the world’s largest consumer of commodity including metals and energy (Figures 5 and 6). The EM block is thus expected to be a large source of (cyclical) spillovers to the rest the world. To date, however, little is known about these spillovers. Indeed, the spillover literature has focused mostly on spillovers from AEs.<sup>3</sup> While AEs were arguably the most important sources of spillover for most of the second half of the 20<sup>th</sup> century, it is high time to consider the EM block as not just the destination of these spillover effects but also the origin—considering the growing importance of EMs. Some 2 It should be noted however that trade data used in the analysis are on a gross basis and thus double or multiplecount vertical trade or flows in intermediates. Trade flows in gross basis may overestimate the extent of trade links between AEs and EMs. That said, to the extent that supply chains are at least in part regional in nature the issue of double counting when considering trade links between AEs and EMs is less prevalent. Trade in value added allows incorporating the specificity of the new business model on which global manufacturing is based. Due to limited country coverage of value-added trade data we are however unable to use such data in our analysis.

3 See for example Österholm et al. (2007) and Utlaut et al. (2010) for
the area level. This is consistent with previous research showing that these texture algorithms or contextual features reflect spatial variability in building and road patterns, as well as poverty.<sup>26</sup> A Shapley decomposition indicates that the higher R<sup>2</sup> in Tanzania, compared with Sri Lanka, can be attributed to more predictive sub-area variables, which predict 13 percent of the variation in Tanzania as opposed to 6 percent in Sri Lanka. This is likely due to two main factors. The first is that the Tanzanian indicators include measures of building footprints at the subarea level. The second is that Tanzania is a less developed setting in which building density and climactic variables, which most of the satellite variables are capturing, are more strongly correlated with non-monetary welfare.<sup>27</sup>

# C. Evaluation Results

The previous section demonstrated that remote sensing indicators are predictive of both area-level poverty and household non-monetary welfare. This section turns to evaluating the small area estimates generated by different methods. Before considering the evaluation results, Table 7 reports model diagnostics for the unit-level model. The geospatial indicators in the model predict about 37 and 30 percent of the variation in household non-monetary welfare in Sri Lanka and Tanzania respectively.<sup>28</sup> The conditional R<sup>2</sup> , which reflects the gain in model fit from conditioning the mixed effect on the sample, is about 3 percentage points higher than the marginal R<sup>2</sup> in both countries. The distribution of both the area effect and the household error term is roughly normal, reflecting the success of the normalizing transformation. The Wilks-Shapiro test for normality of the area effect fails to reject the null hypothesis of a normally distributed area random effect in both countries. Figure 1, which plot the quantiles of both the area effects and the household residuals against the quantiles of the normal distribution for each country, confirms that the distribution of both components of the
<mark>heterogeneity in response to slow-onset changes, ranging from resource-constrained</mark> immobility to permanent long-distance migration. If you have rich covariate data, consider the use of machine learning algorithms suited for a data-driven search for heterogeneity, such as causal trees and causal forests (Athey & Imbens, 2016; Wager & Athey, 2018), properly adapted for their use in an observational panel data setting, in order to estimate conditional average treatment effects.

If you are interested in fast-onset localized shocks or other specific events/disasters, remember that nationally representative household surveys are usually ill-suited for the purpose due to both timing and sample size issues, and that you should seek access to _ad hoc_ post-disaster surveys that oversample the most affected areas. Regarding identification, stick with difference-in-difference types of estimators based on pre- _vs_ . post-shock comparisons. If there is ‘staggered adoption’, here meaning non-simultaneous exposure to the shock, consider the adoption of modern difference-in-difference estimators for settings with differential treatment timings (Roth et al., 2023).

# **5. Conclusions**

The focus of this paper was to propose a conceptual framework and a methodological agenda and provide empirical guidelines for the investigation of climate-induced migration leveraging the use of multi-topic household surveys such as those implemented under the Living Standards Measurement Study (LSMS) program of the World Bank. The agenda we propose is ambitious but is meant to be the first step in this direction. If pursued, we deem it would deliver large benefits in terms of closing existing data gaps and provide a more accurate and granular measurement of the complex and highly heterogeneous phenomenon that is climate migration. In the meantime, however, we showed that there is a lot that researchers can do – and do in better ways – even with currently existing instruments. Undertaking microlevel climate migration research in a more systematic way and based on improved, more homogeneous methods and tools, will contribute to shedding decisive light on the nexus itself, its underlying mechanisms, and the socioeconomic implications for the main
# **4.3 Robustness check and incidence of measurement error**

To explore if limited representativeness and comparability of the areas chosen as treatment and control might explain differences in results between the DID approach and the block panel based on administrative data, we re-estimate (2) and (3) only for the 455 blocks in wards MMC 1, 2, 3 and 27 chosen for LARP impact evaluation. Results from these regressions are in panel B of tables 5, 6, and 7. While differences in the size of some coefficient estimates between restricted and full samples indeed imply that the wards chosen to evaluate LARP impact are not a good representation of Maseru overall, virtually all coefficient estimates for the restricted sample are still highly significant statistically, though in some cases slightly smaller intervention effects. It suggests that sample selection alone cannot explain the lack of significant results in the DID specification.

Several differences emerge. First, as to gender of registered owners (table 5 panel B) we find that (sporadic) registration of land plots in distant wards was more gender-biased than in the full sample; the point estimate for 1 is -0.50 in the restricted and -0.26 in the overall sample. Gender effects are broadly comparable to those in the unrestricted sample in terms of the likelihood of joint vs. male (0.34 vs. 0.37), female vs. male ββ (0.23 vs. 0.18), and joint vs. female (0.10 vs. 0.18) registration. Registration modalities display marked gender differences with sporadic registration favoring female sole owners (0.47) over joint ones (-0.35) and the opposite for systematic registration (-0.25 for female vs. male and 0.48 for joint vs. female). While the elimination of barriers to first-time registration 2) was estimated to have been more important than reduction of the transaction costs for registering mortgages 1) in the total sample,
of Australia’s climate policies. These include: what were the combined and individual effects of the policies during the time of implementation? If emissions were reduced, were the reductions transitory, or did the policies shift emissions onto a long-run path below what would have occurred in the absence of the policy? Did firms sink costly investments that yielded future emissions reductions? Did they wait and see if uncertainty over the tax would be resolved? And finally, did the pricing policy lead to production leakage--in other words, was more Australian coal shipped abroad during and after the tax was in place?

Our synthetic control estimates examine the path of Australia’s aggregate emissions per capita from 1980-2018 in the spirit of Andersson (2019). There are several challenges unique to this context, most importantly the selection of a donor pool of economies to replicate the trajectory of Australia’s emissions during the 2000s. A m o n g o t h e r a d j u s t m e n t s t o e s t i m a t i o n , we limit the donor pool to economies with no national emissions policies during the sample period and focus on other high-emitting economies following the recommendations of Abadie (2021). A battery of robustness and sensitivity checks including the analysis of placebo policies at different times and leave-one-out analyses are carried out to test the validity of the research design and ensure the robustness of the results to changes in the donor pool. Both traditional permutation tests and uncertainty quantification based on prediction intervals are explored. To explain the mechanisms behind the causal effects, we explore patterns in data on emissions by sector and changes in generation capacity by fuel. Finally, a time series analysis of Australian coal production and exports is performed to explore the question of emission leakage.

We estimate that Australia’s increase in renewal subsidies in tandem with its short-lived carbon tax caused a 7% reduction in average emissions per capita from 2009 to 2018. This is in sharp contrast with the general
LATE = _E_ [ _Y_ 1 _i_ – _Y_ 0 _i_ | _D_ 1 _i_ ≠ _D_ 0 _i_ ]. (1)

Imbens and Angrist (1994) show that if _Y_ 1 _i_ , _Y_ 0 _i_ , _D_ 1 _i_ , and _D_ 0 _i_ , are independent of _Zi_ and if _D_ 1 _i_ ≥ _D_ 0 _i_ , for all _i_ (monotonicity), Since all the participants in our program were invited, _D_ 0 _i_ is 0 and _D_ 1 _i_ is either 0 or 1. Those invited entrepreneurs with _D_ 1 _i_ = 0 are refusers and those with _D_ 1 _i_ = 1 are participants. Thus, _D_ 1 _i_ ≠ _D_ 0 _i_ in equation (1) means that entrepreneur _i_ will participate in the training if invited, and LATE in our case is equivalent to _E_ [ _Y_ 1 _i_ – _Y_ 0 _i_ | _Di_ = 1], i.e., the average training effect on the participants. It is easy to show that the monotonicity condition is satisfied in our case. Equation (2) implies that LATE can be estimated as coefficient  in a regression model, _Yi_ =  +  _Di_ +  _i_ , by using _Zi_ as an instrumental variable (IV). In our case, the sample size is small and, hence, it is advisable to use the pre-training data as well as entrepreneurs’ attributes as follows:

where subscript _t_ denotes year 2000, 2002, 2004, or 2008, _Tt_ is a dummy variable which is equal to 1 if year _t_ is after the training (i.e., _t_ = 2008), _Xit_ is a vector of the entrepreneur’s background attributes (which are mostly time invariant),  _t_ is a year effect, _ui_ is an unobserved individual effect, and  _it_ is an error term. The average training effect on the participants,  , is estimated by using _Zi_ and _ZiTi_ as instruments.

The magnitude of the training effect may vary from participant to participant. Heterogeneous training effects may
# Firm Recovery during COVID-19: Six Stylized Facts<sup>∗</sup>

Xavier Cirera (World Bank) Marcio Cruz (World Bank) Arti Grover (World Bank)<sup>†</sup> Leonardo Iacovone (World Bank) Denis Medvedev (World Bank) Mariana Pereira-Lopez (World Bank) Santiago Reyes (World Bank)

**JEL classification** : D22, L20, L25, O10

**Keywords** : COVID-19, Crisis, Firms, Recovery, Employment, Sales, Uncertainty

∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that have supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible (a complete list of all colleagues, collaborators, and institutions is available in the Appendix). Likewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. We are indebted to Edgar Avalos, Tanay Balantrapu, and Joao Belivaqua Basto for research support, and to Besart Avdiu, Elwyn Davies, Umut Kilinc, Kyungmin Lee, Franklin Okechukwu Maduko, Stavros Poupakis, Jesica Torres, and Trang Thu Tran for their efforts in coordinating BPS data collection and their leadership of complementary BPS research initiatives which have informed this paper. We gratefully acknowledge feedback and advice on survey questionnaires from Nicholas Bloom, Francisco Campos, Ana Paula Cusolito, Mark Dutz, Caroline Freund, Jorge Rodriguez Meza, David McKenzie, Silvia Muzi, and Bilal Zia. We thank Mona Haddad and Caroline Freund in their consecutive roles as Global Director for Trade, Investment, and Competitiveness at the World Bank for their guidance and strategic support of the BPS initiative, and USAID, the World Bank, and the IFC for financial support. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

> †Corresponding author, email: agrover1
Lee (2013) and an assumed rate of return to education is based on Mincer equation estimates from Psacharopoulos (1994).<sup>20</sup> Data on dependency ratio, population, and real interest rate are taken from the WDI.

> 19 An important caveat worth mentioning is that many self-employed workers in high-income countries are not necessarily informal workers although their labor productivity is low compared to the rest of the workers. Similar patterns emerge in the service sectors.

> 20 Human Capital in PWT 9.0: https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf.

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how children’s skills and classroom quality vary by student, teacher, and school characteristics and assess the correlations between these variables to understand the role that quality may play in supporting young children’s acquisition of school readiness skills. We use a large, representative dataset of public schools in South Punjab to answer the following core research questions:

- 1: What do pre-primary classrooms in South Punjab look like?

- 2: What is the structural and process quality of these classrooms?

- 2.1: How does quality differ by classroom and teacher factors?

- 3: What can children in pre-primary classrooms do?

- 4: How are children’s early learning and development related to individual, teacher, and class factors?

# **Data**

The data presented in this paper draw on the Baseline Survey on Early Childhood Care and Education (ECCE) carried out under the Education Component of the Punjab Human Capital Investment Project (PHCIP). Child-level data on the Anchor Items for the Measurement of Early Childhood Development: Direct Assessment (AIM-ECD DA) was collected from January to April 2022. AIM-ECD DA data was collected by 32 certified enumerators. Observations of classroom 3
<!-- Start of picture text --> Figure 8: Tertiary Gross Enrollment Rate by Income<br>Quintiles, circa 2001 and 2009<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2001 2009 2000 2009 2000 2006 2001 2009 2001 2009 2001 2009<br>Costa Rica Panama Guatemala El Salvador Honduras Nicaragua<br>Quintile 1 Quintile 5 Mean<br>Source: Authors’ calculations based on several household surveys circa 2001 and circa<br>2009<br>Gross enrollment rate<br><!-- End of picture text --> The relatively high enrollment rates of the richer section of the population are related partly to the growth of the private sector. As Table 4 shows, the private sector accounts for a high share of tertiary education enrollment<sup>15</sup> in many countries: the shares are close to 70 percent in Nicaragua, 60 percent in El Salvador, 55 percent in Costa Rica and 48 percent in Guatemala. The lowest share is in Honduras at about 35 percent of total enrolment<sup>16</sup> . In all countries, **Table 4: Percentage enrolled in private** the top quintile constitutes the overwhelming **tertiary education, circa 2009** share of private tertiary enrollment.

|**Country**|**All**|**Quintile 1**|**Quintile 5**|
|---|---|---|---|
|**Costa Rica**|54.7%|33.8%|58.9%|
|**Guatemala**|47.5%|0.0%|50.0%|
|**El Salvador**|63.2%|55
# I. Introduction

Individual income dynamics characterize society in important ways. The degree to which individuals move across different sections of the income distribution is often summarized by one parameter, income mobility. Indeed, income mobility is probably the single most important indicator of individual income dynamics used in public policy discussions.<sup>1</sup> Income mobility is important as it informs us about the opportunities afforded by society to escape one’s origins.<sup>2</sup> At the same time, mobility may also be driven by variability in incomes that reflect the risk to which individuals are exposed in the economy.<sup>3</sup> In this paper, we develop an analytical framework for the estimation and welfare-theoretic evaluation of individual income dynamics that takes into account these different drivers of income mobility. In addition, we provide an application of our framework using individual income data from Mexico that yields striking results: Much of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral. A smaller part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catchingup of low-income individuals with high-income individuals, both of which have economically significant effects on social welfare. Decomposing mobility into its fundamental components is thus crucial from the standpoint of welfare evaluation.

The literature on income mobility has often focused on two important questions: the quantitative/empirical measurement of the extent and nature of the change in individual incomes and, separately, the social-welfare-theoretic evaluations of such changes.<sup>4</sup> Two methodological issues have arisen in this area. First, the parametric formulations used in the

> 1In a developing country context, see, for example, a flagship publication of the World Bank in 2012, “Economic Mobility and the Rise of the Middle Class,” that focuses on the income mobility in Latin America. For the US, the New York Times article “Harder for Americans to Rise From Lower Rungs,” by Jason deParle
> This paper benchmarks management quality of firms in Croatia against other countries such as the United States and quantifies the link between management capabilities and firm performance (e.g. productivity, profits, adoption of technology).

How are structured management practices measured? The module on Business Capabilities and Entrepreneurship includes 15 core questions of the United States Census Management and Organizational <u>Practices Survey</u> (MOPS) main module. The survey was deliberately designed to replicate the United States Census MOPS to maximize comparability with the United States as well as other countries where this survey has been conducted (e.g. UK, Canada, Russia, Mexico and Punjab province in Pakistan). Following the scoring matrix of Bloom et al. (2019a), firm responses to questions on managerial practices is aggregated into a single management score that ranges from between 0 and 1 with 1.0 reflecting perfect

> 3 In 2008 and 2009, the European Bank for Reconstruction and Development (EBRD) in cooperation with the World Bank (WB) conducted a new survey, the EBRD-WB Management, Organization and Innovation (MOI) survey.

> 4 Financial Agency (Fina) is the leading Croatian provider of financial data services. The database comprises of all tax registered firms in Croatia.