rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I.   INTRODUCTION**

Bank regulation and supervision has been the subject of much recent debate and attention, due in large part to the global financial crisis that started in the late 2000s. A number of studies have pointed to weaknesses in regulation and supervision as one of the factors leading to the crisis (Dan, 2010; Lau, 2010; Levine, 2010; Merrouche and Neir, 2010; and Barth et al., 2012). Not only did the crisis raise important questions on the appropriateness of the regulatory and supervisory approaches pursued in the run-up to the crisis, but also it prompted regulators to consider important changes in regulation and supervision. Despite the high level of interest in the topic and extensive work on the global regulatory framework (exemplified for instance by the various initiatives of the Basel Committee on Banking Supervision), there is a surprising lack of consistent and up-to-date information on the regulatory and supervisory approaches pursued in countries around the world and the changes brought about by the crisis.

This paper presents and examines the World Bank’s 2011–12 Bank Regulation and Supervision Survey (BRSS). The survey is an updated and substantially expanded version of earlier surveys of the same name, released by the World Bank in 2001, 2003, and 2007.<sup>2</sup> The current, fourth iteration of the survey provides information on bank regulation and supervision for 143 jurisdictions (Table 1) and includes close to 300 questions. It covers the period 2008–10, and therefore, allows us to examine the recent state of bank regulation and supervision in a wide range of countries and to compare it to the pre-crisis situation.

Using the BRSS, this paper addresses two main questions: What are the features of the regulatory and supervisory frameworks of countries that were directly hit by the global financial crisis, and did they differ from the rest of the world? How have national regulatory and

> 2 Note that the three previous surveys captured information as of 1999, 2001,
and aggregated to the county level.<sup>1</sup> To do so, we start with the daily measures of minimum and maximum temperature and total precipitation at each 2.5 _×_ 2.5 mile grid point. We then weight each grid point by the number of crop pixels of each type of crop (corn or soybeans) that are in each grid point’s unique catchment area. We then calculate the weighted average of each weather measure at the county level for each day.

We calculated the total growing degree days and extreme degree days for each crop in each county during the growing season (May 1 to September 30). We used bins from 10<sup>_◦_</sup> C to 29<sup>_◦_</sup> C and above 29<sup>_◦_</sup> C for corn and 10<sup>_◦_</sup> C to 30<sup>_◦_</sup> C and above 30<sup>_◦_</sup> C for soybeans. We calculated the total precipitation prior to the growing season and during the growing season for each county-year.

We measured cloud cover using data from the ERA5 re-analysis product that records the fraction of cloud cover each hour.<sup>2</sup> We calculated the daily average fraction of cloud cover from 6am to 8pm for each day during the growing season and the number of days the average total fraction of cloud cover falls into bins from 0–25, 25–50, 50–75, and 75–100 percent during the growing season.

## **4.1.4 Yield data**

For both corn and soybean, we downloaded annual data on yield in bushels per acre at a county level from the USDA’s National Agricultural Statistics Service (NASS) database. We also collected total bushels produced for each crop in each county and year
sector, therefore is an empirical question whether a TFP increase in agriculture entails positive or negative effects on the rest of the economy. Is our understanding that this paper is the first one that tries to answer the previous question.

The following pages describe the empirical analysis used to explore the relationship between agricultural development and the performance of the rest of the economy.

# _III.A. Data_

The data on agricultural and non-agricultural GDP, total labor force, total population were obtained from the World Development Indicators 2003 from the World Bank. We use two measures of agricultural output. The first measure corresponds to the agricultural sector’s output, while our second measure adds to the first the output in the food, beverages, and tobacco sector. The size of the rural labor force, reported every ten years, was obtained from FAOSTAT.<sup>12</sup> The data gaps were filled by assuming an exponential growth rate of the rural labor force. Appendix A contains the resulting rural labor force shares (divided by the national labor force).

# _III. B. Empirical analysis I: Revisiting Timmer (2002)_

As a first step we try to replicate the results reported by Timmer (2002) who report positive correlations between agricultural growth and growth of national GDP per capita. For that purpose we constructed a panel data set with five-year averages, covering 1960-2000. We computed the rates of growth of non-agricultural and agricultural output. For agricultural output we consider both of the aforementioned agricultural sectors. We estimated pooled and fixedeffects regressions. The results are reported in Tables 2 (a) through 2 (d). Tables 2 (a) and 2 (b) study the relationship between agricultural and non-agricultural growth using non-agricultural

> 12 See Appendix C for data definitions and primary sources.

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--> 1<br>0.8<br>0.6 2006<br>2010<br>0.4 2011<br>2012<br>0.2<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Share of bottom 40 percent<br>sales<br>Share of crop income from crop<br><!-- End of picture text --> Source: Staff calculations using RIGA income aggregates calculated from UNPS 2006-2012 Figure 2 presents data on weather, price and conflict by region across the years considered in the study. Weather conditions were in general good, with rainfall deficits less than 20 percent in most cases. However, 2010 was a more challenging year for households and higher losses were observed (although no higher than 30 percent). The four years of data presented suggest that rainfall is more volatile in the north and east than in the center and west. The data also suggest larger losses on average in the west across the four years, but this may be on account of the fact that a maize model has been used to calculate the losses while this is not a crop grown in the west. The inclusion of regional dummies or household fixed effects controls for this persistent difference in the analysis.

> 7 Including prices of matooke, cassava, potatoes, rice, sorghum and coffee.

7
Policy Research Working Paper 10139

# **Abstract**

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

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series
are largely driven by between-country earnings inequality, and current labor market structures.

_As a share of spending on education, the poorest countries will lose more_ Low-income countries would be losing almost twice as much as Upper Middle Income countries and more than three times as much as high-income countries, when the losses from the intermediate scenario are expressed as a percentage of public spending on education. IDA/Blend countries could sustain learning losses that represent almost a quarter of their public spending on education. This finding underscores the urgent need to protect investments in education especially in the poorest countries, which are likely to suffer the highest relative losses, when it comes to investments they have already made in educating their students.

# _This crisis is still ongoing_

This crisis is not over, and our understanding of the ramifications to the economy and household welfare are being updated daily. Since March 2020 global growth projections have been frequently revised, and the recently released Global Economic Prospects (World Bank, 2020b) indicates that growth projections are likely to continue to go down. In each of these revisions, our expected number of student’s dropping out due to the household income shock is revised upwards. Our initial estimate, based on the March MPO suggested that approximately 2 million students would drop out of the education system; by May, this number had already been revised to 7 million, and

> 75 We estimate that this is the $ amount of public spending needed to deliver the current global average of 11.2 years of schooling as recorded in the HCI database. See Annex Table A.3.7.

> 76 Assuming a discount rate of 3% per year, a work life of 45 years, and average time to enter the labor market of 10 years for currently enrolled students.

26
institutions and government policies. They also use the Amadeus dataset and employ principal component analysis to determine the business environment. However, their analysis covers only eight European countries, and their sample is somehow biased by the data availability in Amadeus.<sup>15</sup> We improve on this by using resampling techniques and a larger universe of countries and firms.

This paper offers several novel additions to the literature. The Amadeus database is used in conjunction with a resampling technique to represent the underlying population and generate a representative, cross-country sample. By combining firm-, industry-, and country-level variables

> Scarpetta (2003) also measure MFP but use industry-level information, while Conway et al. (2006) use industrylevel data but measure labor productivity growth. All of these studies rely on OECD country samples. 11 For more on FDI and growth, see Barba Navaretti and Venables (2004), Bernard and Jensen (1995), and Vogel and Wagner (2009).

> 12 Specifically, Burda and Hunt (2001) suggest five mechanisms for productivity transfers: (1) capital accumulation, (2) migration, (3) FDI, (4) Hecksher-Ohlin factor price equalization, and (5) knowledge/technology spillovers. See also Ackerlof et al. (1991).

> 13 There has been considerable disagreement among studies as to the causes of productivity growth. On one hand, using a panel of fourteen OECD countries for 1970–1987, Bernard and Jones (1993) find growth in total factor productivity (TFP) due to within-firm technological improvement and capital accumulation. Olley and Pakes (1996) and Restuccia and Rogerson (2007), on the other hand, find that productivity gains are primarily the result of reallocation of resources to high-productivity firms from low. Still other studies have found net entry to be the most influential motor.

14 Wagner (2011) suggests several pathways by which firms may benefit from inward FDI, including knowledge transfers and spillover effects. See Smarzynska Javorcik (2004
The share in Variant 2 is likewise lower, at 60.7 percent. In period 2, these shares fall quite sharply, ranging between 41.4 and 44.4 percent.

By way of an independent check on these findings, one can compare the above estimates with those yielded by a completely different approach. The one that comes immediately to mind involves the derivation of the so-called Value of a Statistical Life (VSL) from variation in wage rates across occupations or industries and the associated variation, if any, in fatality rates, an approach vigorously promoted by Viscusi (for a survey, see Viscusi [1993]). Pursuing this line, Simon _et al._ (1999) employ data from Indian manufacturing firms and arrive at the rather startling estimate that the VSL is 20 to 48 times larger than the present value of lifetime foregone earnings. As the authors note, this greatly exceeds the estimate of 7 to 8 obtained by Liu _et al._ (1997) for Taiwan, China in the 1980s.

Be that as it may, what is the ratio implied by the approach adopted here? Consider Variant 1 in period 1. The equivalent sum _T_ 1<sup>2(Δ</sup><sup>_q_),whichisdefinedinrelationto</sup> changes in mortality alone, stems from a reduction in _q_ 1 of 0 _._ 0125 (= 0 _._ 1 _×_ 0 _._ 125). Since the said sum is paid twice, the implied VSL is 19 _._ 264 (= 2 _×_ 0 _._ 1204 _/_ 0 _._ 0125), which is 2.5 times the value of output in old age of a surviving young adult, namely, 7 _._ 65 (= 5 _×_ 1 _._ 7 _×_ 0 _._ 9). The implied VSL in period 2 is 82 _._ 566 (= 2 _×_
pricing scheme could help relieve peak-hour traffic congestion and lead to annual welfare gains of CNY 1.5 billion.

With a similar focus but a different method, Kreindler (2018) uses GPS data on more than 100,000 commuter trips in Bangalore, India, to conduct a randomized experiment for the morning commute. He compares two congestion charge policies that impose fees for driving through certain areas during peak hours. Based on the experimental price variation, he estimates commuters’ preference for scheduling flexibility relative to their value of time. He concludes that the costs of rescheduling travel to inconvenient times will almost entirely offset the benefits of the saved travel time, resulting in only a small consumer welfare gain.

# **_2.4 Ride-Sharing Services_**

Ridesharing services have become increasingly prevalent in many parts of the world. As travel demand and vehicle ownership increase in developing countries, encouraging ridesharing could potentially help combat severe air pollution and congestion. By increasing the flexibility of travel and providing a new travel mode, ridesharing could increase consumer surplus (Cohen et al. 2016). However, the impact of ridesharing on the environment hinges critically on the emissions of the substituted travel modes and on total travel demand.<sup>19</sup> Leard and Xing (2020) estimate a discrete choice model of household mode choice using nationally representative data on travel behavior in the United States and simulating household mode choices in a setting where ridesharing was not available. They find that the availability of ridesharing has led to modest increases in total vehicle miles traveled and greenhouse gas emissions. Using survey results on Uber use by residents of Santiago, Chile, and Monte Carlo simulation, Tirachini and GomezLobo (2017) find that unless ridesharing substantially increases the average occupancy rate of trips, ridesharing is like to increase vehicle kilometers traveled.

By exploiting the spatial and temporal variation of Uber entry and Uber penetration in the US market, Hall et al. (2018) estimate the impact of ridesharing on public transit ridership. They

> 19 If
.4||-2.2|7.5|3.3|276|
|Russia|41.3||5.8|17.4|8.0|706|
|Saudi Arabia|N/A||6.9|36.4|13.5|N/A|
|South Africa|24.4||-1.6|6.8|2.8|226|
|Turkey|42.9||-2.9|9.8|4.6|395|
|Average|34.1||0.7|14.5|7.4|365|

*Excluding the period 2002-2004 when Argentina was in default with foreign creditors. Sources:  WDI;  WEO Database; Authors’ Calculations

4
# **Appendix Table 1**

Summary Statistics of Current Population Survey Merged Outgoing Rotation Group Workers, Means and (Standard Deviations), 1983-2002

|||**Overall**|**Sample**|||**19**|**83**|||**20**|**02**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Manufact-||||Manufact-||||Manufact-|||
||All|uring|Services|Agriculture|All|uring|Services|Agriculture|All|uring|Services|Agriculture|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Panel A: Demographic and Wag|e Information||||||||||||
|Number of Observations|3,481,692|647,105|2,770,662|63,925|176,454|38,071|134,239|4,144|185,798|26,229|156,319|3,250|
|Age|37.3|38.8|37.0|34.2|36.2|38.1|35.8|32.4|39.2|41.2|38.9|36.0|
||(12.1)|(11.4)|(12.2)|(12.8)|(12.6)|(
extent to which China does, or does not, conform to the crosscountry regularities that we describe. Our sample of countries consists of 62 countries where we have at least 15 annual observations on net foreign assets after 1980.<sup>5</sup> The data on net foreign assets are taken from Lane and Milesi-Ferretti (2001a). We

> 4 In part because of only recent data availability, empirical papers on the determinants of net foreign asset positions are scarce.  Exceptions are Lane and Milesi-Ferretti (2001a) and Calderon, Loayza, and Serven (2003).  Lane (2000) and Lane and Milesi-Ferretti (2003) document determinants of gross foreign asset positions among industrial countries, and Lane and Milesi-Ferretti (2001b) study relate decadal changes in net foreign asset positions in industrial and developing countries to per capita incomes, public debt, and demographic variables. 5 We drop from this sample Cote d'Ivoire which has a very large negative net foreign asset position and is quite influential for the magnitudes of the slope coefficients in the regressions that follow.

17
Table 10. Heterogeneity in the impact of the program on the probability of student dropout: LATE estimates.

|||Interaction:<br>||
|---|---|---|---|
||(i) Pre-treatment<br>dropout probability|(ii) School size<br>(=1 if large school)|(iii) Gender<br>(=1 if boy)|
|ENTRE_j_+ (ENTRE_j_) x (_Interaction j_)|-0.061*|-0.046***|-0.044***|
||(0.032)|(0.018)|(0.017)|
|_ENTRE j_|-0.007|-0.013|-0.018|
||(0.017)|(0.020)|(0.018)|
|Observations|124,626|124,626|124,626|
|R-squared|0.327|0.325|0.325|
|Controls at student level|YES|YES|YES|
|School fixed effects|YES|YES|YES|

Notes: (1) Local Average Treatment effects were estimated by instrumenting treatment (proxied as the effective reception of the ENTRE guide according to self-reported data in follow-up 3) with treatment assignment. (2) Standard errors clustered at schoolyear level in parenthesis. (3) *** p<0.01, ** p<0.05, * p<0.1. (4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year t and year t+1. (5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that was treated) and _Postt_ : (1 = year of the intervention; 0
; education and literacy status of the household head or respondent, and the presence of children under the age of three in any of the three waves.

# Pre-treatment trends

By focusing on mobile broadband coverage and not mobile broadband usage (which is a household choice), the estimation strategy follows an intention-to-treat (ITT) framework, similar to Beuermann et al (2012). However, mobile operators do not expand or upgrade their networks randomly. Instead, they are likely to first target areas that will generate higher expected returns, and one would expect households in these areas to have higher levels of consumption and lower incidences of poverty.

In order for the ITT framework to produce unbiased results, we therefore rely on the assumption that outcomes for the ‘treated’ group of households (those that receive coverage) would have evolved in the same way as the ‘control’ group of households (those that do not receive coverage) if they had not benefitted from 3G or 4G coverage. This is not possible to test, but it is more likely to hold if the two groups have similar pre-treatment trends. We therefore exploit the fact that we have three waves of survey data and apply a test based on the approach proposed in Angrist and Pischke (2008). Specifically, we estimate: (3) i 1D i 2 i i ii ii ii+1 ii ii ii YY = αα + ββ + ββ XX + γγ + εε In equation 3, we test whether changes in the outcome of interest between Wave 1 and Wave 2 are predicted by getting coverage between Wave 2 and Wave 3. If the coefficient is statistically significant for consumption, this would suggest that consumption ‘leads’ coverage and so one could not conclude that coverage has a causal impact on consumption. On the other hand, if the coefficient is not

> 23 For a detailed review of the literature on the economic impact of mobile money, see Aron (2018). 24 https://globalfindex.worldbank.org/

9
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<