rafmacalaba/data-use-mentions-extended huggingface dataset:
Treatments are described below:

1. _Heterogeneous (HT):_ Registered and randomly selected students are assigned to take part in a club with an heterogeneous composition of clubmates according to their IVV.

2. _Homogeneous-Low (HM-Low):_ Registered and randomly selected students are assigned to participate in a club with low-violence peers if their IVV is lower than the median of the HM group within their respective stratum.

3. _Homogeneous-High (HM-High):_ Registered and randomly selected students are assigned to participate in a club with highly violent peers if their IVV is greater than the median of the HM group within their respective stratum.

4. _Control:_ This group of students was not selected to participate in the clubs during the 2016 academic year. They left school facilities after school hours. We were able to collect their information at follow-up because we gave them a “participation coupon” they could redeem to take part in the ASP the next year.

As opposed to Duflo et al. (2011) and similar to Lafortune et al. (2016), neither instructors nor participants knew details of the assignment because I wanted to capture the effects of interactions among participants instead of other channels such as teaching or curriculum adaptation. To test for changes in teaching methodologies, I collected information from a trainers’ survey and present the results in upcoming sections.

# **2.3 Data**

As previously mentioned, after the NGO advertised the ASP in school facilities, a research team returned to schools to enroll and register participants. In this stage, students were asked to bring 14
# **2.3 Linear Empirical Best Predictor Models**

We utilize the _povmap_ package in R to generate the Empirical Best Predictor (EBP) estimates.<sup>10</sup> This is an updated version of the _emdi_ package (Kreutzmann et al., 2019), which implements the models described in Molina and Rao (2010) with additional features. We implement a “sub-area level model,” in which we estimate the model at the sub-area level before aggregating to the area level for the final predictions. While we include descriptions of the other estimators in Appendix C, we discuss some details of the sub-area model, since it is one of the most common methods for small area estimation of welfare.

The subarea-level model is a model of the form: where _G_ ( _ysar_ ) is a transformation of outcome _y_ for sub-area _s_ in area _a_ in region _r_ ,<sup>11</sup> _Xsar_ is a vector of sub-area-specific geospatial features, _Xar_ is a vector of area-specific geospatial features – which may include region dummies – _ηar_ is an area-level random effect, assumed to be normally distributed and conditioned on the sample data, and _εsar_ is a classical error term assumed to be normally distributed. When predicting assets, no transformation is used, implying that _G_ ( _ysar_ ) = _ysar_ . When predicting poverty rates, the arcsin transformation is used, implying that _G_ ( _ysar_ ) = arcsin � _ysar_<sup>0</sup><sup>_._5�</sup> .

Because the random effects are conditioned on the survey data, they are shrunk towards zero, with the shrinkage factor depending on the relative estimated variances of the random effect _η_ and the ideosyncratic error term _ε_ . After estimating the model’s coefficients, estimates for areas are generated by calculating _E_ � _G_<sup>_−_1</sup> ( _ysar_ )�, conditional on estimated parameters _β,_<sup>ˆ</sup> ˆ _ση_<
_DRD_ . At a death rate for the poor that is 20% above the mean, the impact of differential mortality on the headcount index is about 0.04 percentage points. It rises to about 0.2 percentage points when the death rate for the poor is twice the mean. (The upper bound, when all deaths are among the poor, is 0.7 points.) These impacts may look small, but recall that they are the impacts in one year. So they should be compared with annual rates of poverty reduction. The Table 1 also gives the impacts expressed as a percentage of the annual rates of poverty reduction from 1990 to 2001. The impact at _DRD_ =1.2 represents 7% of the annual average rate of reduction in the headcount index over this period. Naturally the impacts are even higher for higher values of _DRD_ , rising to 15% for DRD=1.5 and 30% for _DRD_ =2.

Annual rates of poverty reduction naturally depend on the start date. Beginning instead with the Chen-Ravallion estimates for 1981, the annualized rate of poverty reduction for $1 a day is about one percentage point per year — about ten times the estimated impact of differential mortality in 2001 for _DRD_ =1.5. However (as Chen and Ravallion argue), the early 1980s were an unusual period, given the rapid poverty reduction achieved by China in that period (associated with its successful agrarian transition); see Figure 1. Starting the clock at 1984 instead, the annualized rate of poverty reduction in the developing world is similar to the trend since 1990. However, there has been a slowdown in the overall rate of poverty reduction (in percentage points per year, though not proportionately) since the late 1990s. If instead one focuses on the five year period from 1996 to 2001 then the trend drops to one third of a percentage point per year. Differential mortality at _DRD_ =1.2 accounts for about one eight of the trend, rising to over
Long An; V ̃ınh Long), Region 7 (H`a Ni), and Region 8 (H Ch ́ı Minh). In Senegal, we have 7 regional strata including Dakar, Diourbel, Kaolack, Kolda, St. Louis, Thies, and Ziguinchor. For Senegal, we additionally stratify by formality. To calculate the optimal distribution of the sample, we followed a similar methodology as described by the World Bank (2009).

For the state of Cear ́a, our universe includes 24,288 establishments. We collected data for 711 establishments randomly selected from RAIS. Table A.2 and Table A.3 provide the information on the distribution of firms in the population and the sample for Cear ́a, by size group and sectors.

Table A.2: Population Distribution, Brazil

|||Agri-|Food|Wearing|Other|Wholesale|Other|Total|
|---|---|---|---|---|---|---|---|---|
|Region|Size|culture|Processing|Apparel|Manuf.|& Retail|Services|Region|
|Cear ́a|Small|240|523|788|2487|9255|5362|24488|
||Medium|111|220|295|937|1764|1643||
||Large|47|51|54|202|243|266||
|Total||398|794|1137|3626|11262|7271|24488|

> 54An additional dimension was included for Senegal, splitting firms by formality status based on the ANSD definition.

> 55These specific stratifications were taken into consideration when determining sampling weights.

50
. The government is expanding access to health with the main goal of achieving universal health care in the near future. Figure 12 shows the growing percentage of informal-sector workers who are affiliated to the subsidized regime. While in the second quarter of 2001 approximately 19.42 percent of informal-sector workers were affiliated to the SR, in the second quarter of 2013 this was 47.65 percent.<sup>18</sup> Higher payroll taxes, changes in relative prices affecting the relative profitability of the two sectors, and expanding social assistance programs to informal workers<sup>19</sup> may explain some of the stylized facts observed in the labor markets in recent years.

> 15These payroll taxes are high when compared to the US, where contributions range between 15 and 20 percent of the gross wage, and similar to the levels observed in some EU countries (i.e. Sweden, Belgium, France), where contributions are close to 50 percent.

> 16Interest rate ceilings, exchange rate controls and restrictions on foreign direct investment were eliminated, as well as requirements to invest in government securities. International trade was largely liberalized, due to significant reductions in import tariffs.

> 17According to a recent Informality survey by Batini, Kim, Levine and Lotti (2010), 87 percent of self-employed and informal workers in Colombia are concentrated in non-tradables (i.e commerce and services).

> 18Author’s calculations based on Colombian Household Survey. No data are available before 2001.

> 19An inflexible wage structure in the formal sector may also be a factor since previous literature (Maloney, Nunez, 2001) suggests that minimum wages in Colombia are high and binding. Here I don’t consider any source of wage rigidity.

7
We use institutional records to identify the preparation TTLs for 3,355 projects, PMs for 3,154 projects and CDs at time of approval for 4,342 projects. The identity of supervision TTLs is already captured in routine reporting systems. The identity of preparation TTLs, PMs, and CDs are scraped from internal records – this process is described in more detail in Annex 2. From HR records we also observe the TTL’s age and education, previous Bank experience, and location (headquarters or a field office). Our measure of staff contribution is a ‘value-added’ measure defined as the difference between the predicted performance of the individual’s previous projects, based on national and fixed project factors, and the realized performance.<sup>8</sup> While we have collected data on projects evaluated by the World Bank’s IEG since the late 1990s up through the end of Fiscal Year 2017 (i.e. June 2017), we focus the analysis on two sub-samples. The main data set concentrates on 2,845 projects approved between Fiscal Years 1995 and 2009.<sup>9</sup> This date range is strategically chosen for two reasons: (a) by starting with FY1995 projects, the missingness of novel project input measures is minimized as missing values are more prevalent in projects that were approved before 1995, and (b) by limiting the start date to before 2009 minimizes data censoring, as many projects approved after 2009 have yet to close and be rated.

A second contribution of this paper is to test the influence of various aspects of project design and implementation on project outcomes. We do so in part with a stratified random sample of 120 projects from the full data set. This smaller data set, which we term the qualitative data set for convenience, supplements the existing measures with qualitative assessments of project characteristics, extracted from primary documents such as Project Appraisal Documents (PADs), Implementation Completion and Results Reports (ICRs) and IEG ICR Reviews
# **1 Introduction**

Policies introduced to control the spread of COVID-19 have caused unprecedented economic upheaval throughout the world, particularly for firms in low- and lower-middle-income countries that typically received little to no government support. Mobility and economic activity restrictions, alongside higher transaction costs and disruptions in cross-border trade, have hindered the allocation of resources within countries and across industries, lowering aggregate productivity growth (Apedo-Amah et al. 2020). Additionally, the pandemic has substantially shifted consumer demand, which has forced fundamental changes to business practices that are likely to persist in the medium term (Barrero et al. 2020). Therefore, it is imperative to develop a greater understanding of how firms have coped in this extremely challenging environment, especially in lower-income settings.

We examine this issue by drawing on a combination of survey and tax administrative data to analyze how formal firms in Zambia have recovered from COVID-19.<sup>1</sup> In general, the composition of the formal sector in Zambia is similar to many other medium-sized SubSaharan African countries, except there is a greater prevalence of firms in the mining sector (World Bank 2022). As is the case in most countries around the world, at the outset of the pandemic the Government of Zambia introduced restrictions to control COVID-19, which considerably curtailed normal economic activity. We investigate how firms have fared since this initial shock by using three follow-up surveys to a standard World Bank Enterprise Survey (ES) covering a representative sample of 601 formal firms that was conducted immediately prior to the pandemic and by drawing on the universe of monthly value-added tax (VAT) and pay-as-you-earn (PAYE) returns filed by around 20,000 firms throughout 2020 and the first half of 2021.<sup>2</sup> Both data sources have strengths and weaknesses. A key advantage of the survey data is that firms provide extensive details about their operations when completing the question-

> 1For the purposes of this study, formal firms are defined as firms that are registered with the Zambia
# **Figure 15. Required skills by preferred genders**

<!-- Start of picture text --> Male 50 20 4 4 6 5 3 3 31<br>Female 13 22 7 17 7 15 5 7 3 3<br>No Preference 69 9 6 4 3 22211<br>0 20 40 60 80 100<br>Programming Sales<br>Designing Communication<br>Finance Microsoft<br>Analytical/ Research Other soft skills<br>Leadership/ Managemen t Others<br><!-- End of picture text --> _Source_ : Authors using the data from Rozee.pk.

By applying a machine learning technique to rich text information, such as job titles and descriptions, we categorize each job post and jobseeker by the International Standard Classification Occupations 08 (ISCO08). In our text processing that applies a continuous bag of words (CBOW) model, the job title of a job post is mapped into the most similar occupation title in the ISCO08. Jobseekers’ occupations are identified by mapping their previous job titles into the most similar occupation titles. We can identify the occupations of jobseekers only if they report their previous jobs. Such jobseekers are about half of all jobseekers. The distribution of job posts and jobseekers at the ISCO08 one-digit level is shown in Table 2. The three most frequently demanded and supplied occupations are managers, professionals, and technicians and associate professionals. At the ISCO08 two-digit level (Table A1.1), highly demanded occupations are administrative and commercial managers (ISCO08 = 12), business and administration associate professionals (33), information and communications technology professionals (25), and sales workers (52).

**Table 2. Number of job posts and jobseekers by occupations (ISCO08 1 digit)**

|ISCO-08|Occupationtitles|Demand|Supply|
|---|---|---|---|
|1|Managers|312,768|230,938
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Singapore and Malaysia have centrally managed provident funds, which are not geared at investing in shares. In Malaysia, contractual savings institutions invested in shares from 4 to 7 percent of their financial assets during 1987-93. Singapore only recently has allowed some members to pick private managers and to determine how a portion of their Central Provident Fund balance will be invested.<sup>30</sup> Therefore, there should be no surprise that there is no causality in any direction between contractual savings and stock markets in these countries.

**Table** 7: **Shares of Stocks in Investment Portfolios: Selected Countries**

|**Country**|**Year**|**Contractual**|**Life**|**Pension**|
|---|---|---|---|---|
|||**Savigs**||**Funds**|
|**Malaysia**<br>|1993|7.01|17.86|5.17|
|**Singapore**|1996|5.67|33.50|0. 00|

**Source:** WB institutional investors database.

Another particular case is Chile, where causality for pension funds runs in both directions. This could be explained, in great part, by their investment regulations. When the system was introduced, they were quite draconian, at that time; the Government was mainly interested in preserving assets, hence, pension funds were allowed to invest almost exclusively in government securities. In addition, real interest rates on bonds and bills were very high. As the system and the market developed, the regulations allowed increasing participation of shares in pension fund portfolios. At the same time, real interest rates were declining thus demand for shares increased fueled by both effects. Obviously, regulation of investment policies of these institutions and after tax rates of return on
are Sao Tome and Principe, the Comoros, Namibia, Djibouti, Senegal, and Ghana.

Combined, these estimates illustrate the magnitude of the challenges facing PNG. The fact that PNG faces significant monetary and non-monetary poverty challenges is not surprising (e.g., World Bank 2013; Pandey and Howes 2021; World Bank 2023; etc.), and PNG’s status as the poorest ranking country in the East Asia and Pacific region is also not surprising (e.g., World Bank 2023). However, the depth of the challenges across all the indicators and the fact that PNG has some of the highest share of deprivations anywhere in the world is surprising given the relatively large level of resource-related economic activity occurring in the country (e.g., World Bank 2023).

# **Section 4. The Change in Well-Being Following the End of the Resource Boom**

The new estimates of monetary poverty and access to essential services in 2016-2018 described above illustrate that the significant resource-related growth, the associated increase in resource revenue for the government, and substantial foreign assistance have resulted in little change in well-being since 2009. The lack of a change in any measure of well-being is consistent with there being little improvement in either the financial ability of households to afford goods and services and with there being no substantial improvement in the availability of essential services to be utilized.

Figure 6 reports the share of the population that was monetarily poor and deprived in each of the World Bank’s MPM indicators in 2009 and in 2016-2018, and further reports the 95 percent confidence intervals.<sup>24</sup> The similarity in the indicators across the two surveys is striking. The magnitude of the differences are very small for all indicators, with the average change in deprivation across the indicators equal to 0.05 percentage

> 24The indicators were made to be comparable across surveys, where the same categories that were defined as deprived in 2009 were used to define deprivation in 2016-2018. The 2009 estimates
24 Fig. 2.5. Household plans to improve housing conditions in the near future (by quintiles based on per capita provision with floor space; % of each quintile) <!-- Start of picture text --> SO SO so<br>45 45 45-<br>40 40 40<br>35 35 35<br>30A 1a 10<br>30 -30 30-<br>25 l 25 25 11.7<br>6.7<br>20 20 620-<br>b. Household15 i.m15<br>10 ~ 710 310<br>0 ~ .4*~00<br>1 2 3 45 12 3 45 12 34 5<br>Moscow, 1996 Vladimir, 1996 Gorodets, 1996<br><!-- End of picture text -->

**Note: I** - **lowest quindile, 5** - highest **quintile**

**13o acareb0 ss[on**

<!-- Start of picture text --> freo-hag ai<br><!-- End of picture text --> Sources: **_1996._** vaitin The (Fg 2.6). **Moscow Longitudinal** Household **Survey, January** 1996; Vladiyir and Gorodets Longitudinal Household Survey, **Decenber** It is noteworthy that wheasin yMoscow the lowest quntile, which in all cities experiences the greatest urgency in its housing needs, is clearly dominated by those who count on free housing (30.6% of persons on the waiting list as compared to 17.6% of persons favoring market methods), in the oMter cities and towns such hopes are less common: 20-25% of the households in this **queintile** are prepared to buy it on the market.

# **_b. Household incomes_**

The impact **of incomes** on the
):<sup>17</sup> (i) the logarithm of the benefit (in PPP international dollars), (ii) the benefit as a percentage of GDP per capita, and (iii) the benefit as a percentage of the $1.25 a day poverty line. In addition, we incorporate variables

> 14 Out of the total 146 countries in the Global Findex 2014 data, the following countries are excluded from these specifications due to missing data on saving for old age: Liberia, Paraguay and South Sudan. 15 Across the developing world, only about 4% of adults − 160 million people − are unbanked but save by using a savings club or a person outside the family. But in Sub-Saharan Africa the share is three times that size. On average in the region’s economies, 13% of adults are unbanked and save semi-formally (DemirgüçKunt, et al., 2015).

> 16 The housing affordability index is from Numbeo (2015). The data and description are available at: http://www.numbeo.com/property-investment/rankings_by_country.jsp?title=2014. 17 The data and detailed description are available at: http://www.pension-watch.net/pensions/about-socialpensions/about-social-pensions/social-pensions-database/.

12
Figure 6: The real value of declared foreign or domestic assets by asset type

# (a) Foreign assets

<!-- Start of picture text --> Billions of<br>2015 pesos 2016<br>Amnesty<br>1500<br>1000<br>RealEstate<br>Deposits &<br>Currency<br>500<br>Stocks<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>(b) Domestic assets<br>Billions of<br>2015 pesos 2016<br>Amnesty<br>1500<br>Accounts<br>Receivable<br>Business<br>1000<br>Vehicles<br>Deposits & Currency<br>500<br>Real Estate<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> _Notes:_ this figure plots the absolute value of the different types of foreign or domestic assets reported by taxpayers in panels (a) and (b), respectively, in constant 2015 pesos (billions). More than Arg$400 billion (US$30 billion) of foreign equities were disclosed to the authorities, equivalent to 9.9% of GDP in 2016. Likewise, more than Arg$360 billion or US$25 billion deposited in foreign bank accounts were reported in tax returns after the amnesty. Panel (b) shows a drop in domestic real estate after the 2019 reform created a separate wealth tax exemption threshold for primary residences, nine times larger than the threshold for all other assets.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks.

29
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**TABLE 2.6 Reconciliation of US Census of Manufactures and National Accounts Rstimates for 1975**

||**ASM**|**BEA**|**BEA**|**Taxes**|**Col.(2)**|**Ratio of**|
|---|---|---|---|---|---|---|
|<br>V<br>a<br>(m|Census<br>Concept<br>of Gross<br>alue Adde<br>t Factor<br>Cost<br>ill. USS)|National<br>Accounts<br>Concept<br>d of Gross<br>Value Adde<br>at Market<br>Prices<br>i|Inventory<br>Valuation<br>Adjustmen<br>(mill.USS<br>d<br>|<br>Minus<br>Subsidies<br>t (mill.US<br>)|Minus<br>Col.(3)&(4)<br>S)<br>at Factor<br>Cost<br>(mill.USS|Adjusted<br> National<br>Accounts to<br>Census Values<br>) (per cent)|
|||(mll. USS|||||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Food & Kindred|||||||
|Products|39,985<br>|}38,886<br>|}1,193<br>|}6,219<br>|}31,474<br>|165.4<br>|
|Beverages|8,110
money when, officially, there should have been no charge. A nationally representative survey in Côte d’Ivoire similarly found that women are less likely than men to understand the financial products offered through their phones and more likely to lose money to scams (CGAP 2022).

Further Global Findex questions about fees likewise show that in some economies, a high percentage of adults receiving digital wage payments directly into a mobile money account paid unexpected fees to do so. While it is not clear whether these fees were illicit or that the recipient did not understand the fee schedule of their account, they point to ways in which financially 15
poorest groups of Americans with the richest Indians: the second poorest ventile of American population is approximately at the same level of income (just 1 global percentile lower) than the richest 5% of Indians.<sup>10</sup> Figure 1. Importance of location vs. class: United States compared to BRIC countries, 2005 <!-- Start of picture text --> USA<br>Russ i a<br>China<br>Brazil India<br>1 5 10 15 20<br>country ventile<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>1<br><!-- End of picture text --> Note: Based on national household surveys; people ranked by per capita income or per capita consumption adjusted for price differences between the countries using the most recent PPPs. BRIC is an acronym denoting the emerging market economies of Brazil, Russia, India and China.

The figure could be made both more dramatic or less dramatic without losing anything of its essential message: namely, that most of global income differences today depend on location. We can make the figure more dramatic by contrasting incomes of people living in a

> 10 Note that if class alone determined one’s global income position, and all country means were the same, the line for every country would be a straight 45 degree line.

8