rafmacalaba/data-use-mentions-extended huggingface dataset:
- We provide novel stylized facts on the share of HtM agents and the share of social transfers reaching them using survey-based dataset from Global Findex. We identify HtM agents in‡uenced by the work of Lusardi et al. (2011) as those who are not able to cope with a ...nancial shock.<sup>5</sup> The recipients of social transfers are identi...ed directly from Global Findex. Two ...ndings stand out from several interesting ones.<sup>6</sup> First, the share of HtM agents is, on average, 23 percent in the sample of developed countries and 60 percent in the Latin American sample.<sup>7</sup> Second, the share of social transfers reaching HtM agents is, on average, 25 percent in the developed sample and 65 percent in the sample of Latin American countries. That is to say, the share of social transfers reaching HtM agents is mostly a re‡ection of the share of HtM agents, rather than the result of a particular social transfers targeting ability. This result suggests that, at least when including all types of social transfers, the usual assumption that social transfers only reach HtM agents (known as perfect targeting) does not seem to hold. We also show that these empirical regularities equally hold for a sample of 99 developed and emerging countries.

- A calibrated standard two-agent New Keynesian (TANK) model is able to match key STM regularities identi...ed empirically. Moreover, we show that about 80-90 percent of the large di¤erence in the size of STMs between Latin American and developed countries is explained by the higher share of ...nancially constrained individuals who live HtM in the Latin American sample relative to the developed countries. This evidence on the quantitative e¤ect of HtM arguments, coupled with the strong evidence supporting a larger share of HtM agents in emerging markets, suggests that a bigger STM may be expected for emerging market
(573 firms in Kenya, 289 firms in Tanzania, and 366 in Uganda) with 5 or more employees. The Enterprise Surveys were conducted in all three countries using the same sampling methodology – stratified random sampling – along with a common questionnaire. Three levels of stratification were employed for each country: firm-size, sector of activity, and location within the country. Weights are used to ensure that the sample is representative of the non-agricultural, non-mining private sector of the economy.<sup>5</sup> Before exploring the relationship between mobile money use and investment, we provide some basic statistics to explore why firms have or have not adopted mobile money, and what types of firms tend to adopt mobile money for transactions. The latter has already been explored in depth (Gosavi, 2015) and thus we do not delve into details in the analysis. So why has mobile money been adopted by firms? Through the Enterprise Surveys, managers and owners of firms in Kenya, Tanzania, and Uganda provided responses as to why they have or have not adopted mobile money. Despite some heterogeneity in the ranking, the most cited reasons for mobile money adoption across the three countries are reducing costs and risks of transactions and satisfying costumers’ requests (Table 1). Concerning the reasons for non-adoption, the majority of non-adopter firms in Kenya cited large payments that were difficult to undertake using mobile money. In Tanzania and Uganda the most frequently cited reason is non-adoption by customers or suppliers.

What types of firms tend to adopt mobile money? In table 2 we present comparisons of averages between firms that use mobile money for transactions and those that do not. As expected, firms that use cell phones for operations are more likely to adopt mobile money; while larger and older firms are less likely to adopt mobile money for transactions. Manufacturing firms and firms that are part of a large company are also less likely to use mobile money, while a higher percentage of firms tend to adopt mobile money if they are in the capital
# **4. Poverty trends: Official and Revised**

## **4.1 Revised poverty-line construction**

In a recent assessment of possible alternatives to the official poverty figures, van den Boom (2010) compares the official figures with figures obtained by averaging the region-specific poverty lines and creating a unique aggregate national poverty line. The simulation leads to striking changes in poverty patterns in Mozambique. Under the aggregate PL, poverty appears significantly higher among the rural population (with a 65 percent poverty rate for 2008/09 compared to the official 57 percent), while the urban population appears considerably less poor (39 percent for 2008/09 compared to the official rate of just under 50 percent). These poverty dynamics would seem to be more consistent with other data sources, including regional economic growth rates and anthropometric indicators, both at the regional and national levels. In addition, van den Boom’s methodology produces dramatic improvements in the stability of poverty trends over time, as well as the near-complete elimination of the puzzling povertyrate swings described above.

**1.** Using the information contained in the 1996/97 survey we construct a single-basket poverty line:

### **Equation 4**

where<sup>is a bundle typically consumed by the poorest segment of the population in 1996/97 (see</sup> Appendix for a list of items and food shares). This bundle is developed by taking the population weighted average of bundle consumed by household below a certain consumption threshold (in our case the 60<sup>th</sup> percentile) and scaled up to the standard threshold of 2150 calories per capita per day. The bundle<sup>is then multiplied by a vector of prices</sup>

### **Equation 5**

Combining Equation 4 and Equation 5 and adding to account for the non-food share and , a spatial Paasche index for the 13 regions, we obtain

### **Equation 6*
by all countries to these major markets – that represented half of global GDP and 28% of global imports pre-COVID-19 – as the pandemic unfolded.

All the data sets include the country’s import flows from all partner countries for each product at the HS 6-digit or 8-digit level providing information on import value (in US dollars) and quantity or weight. We aggregate the data to the importing country-HS4 product-partner country-monthyear level concording product codes to the HS 2007 revision for comparability across countries, computational feasibility and to mirror the product resilience categories we rely on.

Using mirror import data to capture export flows has two advantages. First, import flows are better recorded than export flows, especially by high-income low-corruption countries such as our major markets (e.g., Javorcik and Narciso, 2017). Second, import flows are reported and made public quickly by those high-income countries while high-frequency export flows from all countries are reported with long delays to UN COMTRADE.

# **COVID-19 incidence data**

For COVID-19 incidence, we use the total number of reported COVID-19 deaths per capita per month in each country from the World Health Organization (WHO) Global Health Observatory Repository from December 2019 onward.<sup>1</sup> In robustness tests, we consider alternative measures of COVID-19 incidence. The number of COVID-19 cases per capita, also from the WHO, and a measure of stay-at-home requirements imposed by each country each month from the Oxford COVID-19 Government Response Tracker are used in other research on COVID-19 impacts (e.g., Bonadio et al., 2021; Chen et al., 2020).<sup>2</sup>

> 1 See https://github.com/owid/COVID-19-data/tree/master/public/data.

> 2 See https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker#data.

9
, in the Northeast this percentage was only 5.2 percent. Wide variation is also seen in program coverage over time. In 2008, 1,380 schools were enrolled in _Mais Educação_ , with 924,584 students participating in the program. A great expansion of the program’s coverage occurred, reaching 12,294 schools or 6.2 percent of the total number of Brazilian public schools in 2011 and 11.1 million students. The expansion in program's coverage between 2008 and 2011 was accompanied by a large increase in the resources transferred to schools. A significant increase is also seen in the transfer per student over the period, increasing from R$31.6 per student to R$74.8 per student.

# **4. Methodology**

> 31 Fields of knowledge changed slightly over the years. In some editions of the program, not all fields were available, but notable differences were not seen from one year to another.

> 32 Criteria for selection of students suggested by MEC were the following: students who are at risk and/or social vulnerable, students with high levels of age-grade distortions; students from the final grades of the first and second cycles of basic education (fifth and ninth grades) where the dropout rates are higher and beneficiaries of _Bolsa Família_ (Brazilian conditional cash transfer program).

> 33 The share of schools enrolled in _Mais Educação_ is the total number of schools participating in the program divided by the total number of public schools registered in the school census in the previous year. We exclude from the sample private schools.

11
Policy Research Working Paper 10792

# **Abstract**

Accurate trade data remain central for empirical investigations of international trade and informed formulation of trade policies. However, discrepancies in trade reporting, stemming from reasons such as logistics to deliberate misclassification, pose challenges to obtaining an accurate representation of trade activities. This paper provides a systematic examination of these discrepancies by using the Discrepancy Index, a measure of bilateral asymmetry in trade reporting. First, the paper proposes a rich set of country- and product-level indicators that capture both the frequency of misreporting and its impact on the overall recorded trade value. Second, it demonstrates how the Discrepancy Index database can aid analysis and resolve data reliability issues in international trade. This comprehensive data set is used to analyze the general trends in trade data reporting and its reliability, providing empirical insights into the nature and extent of reporting discrepancies. Finally, the paper demonstrates the practical application of the developed discrepancy database and aggregate indicators through case studies of Senegal and the trade relationship between Madagascar and France, shedding light on reporter-specific instances. The paper seeks to equip trade analysts and researchers with tools and resources to make informed decisions on the use of reported trade data and their mirror. In doing so, the study contributes to the broader endeavor of enhancing the reliability of international trade data, thereby contributing to a more accurate empirical investigation of global trade patterns and their policy ramifications.

This paper is a product of the Development Data Group, Development Economics. 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 skaushik@worldbank.org and smitikj@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

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# **Table 6: Regressions Including the Interaction of Competition and Concentration with Government Bank Share**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration _. Log firm size_ is the logarithm of the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). _Share of government banks_ is the fraction of banking system's assets in banks that are 50% or more government-owned. Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|Variables||Acc|ess to finance||
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-0.018<br>[0.294]|0.045<br>[0.261]|||
|Concentration 3|||0.180<br>[0.198]||
|
bank e-money issuers to protect against liquidity risk and loss of customer funds. The electronic money indicator measures the entry and operational requirements for non-bank institutions issuing e-money. It covers the licensing and operational standards, as well as requirements on safeguarding funds collected by non-bank e-money issuers.

Lacking a legal system governing the use of movable collateral has been a constraint for small businesses and households at the low-income level to obtain a loan (World Bank, 2013). The movable collateral indicator has two dimensions: warehouse receipt and getting credit. Warehouse receipt is an effective financing tool for creating liquidity and easing access to credit. An appropriate legal framework is a prerequisite for a functioning warehouse receipts system. Legislation protects the rights of depositors and lenders and facilitates easy enforceability of the security (i.e. a few days after the default, without court intervention) and, thereby, makes warehouse receipt good collateral (EBRD, 2004). The warehouse receipt sub-indicator covers the existence and scope of rules regulating warehouse receipts systems, including insurance and other performance guarantee requirements for warehouse operators, and the form and content required for legally valid receipts. The getting credit sub-indicator uses data from the Doing Business data set and measures the legal rights of borrowers and lenders in secured transactions and bankruptcy laws and the strength of credit registries and bureaus.

Data collected have shown that regulations for deposit-taking MFIs have been established in 33 countries of the sample. Financial cooperatives are regulated in 56 of the sampled countries either through a general cooperative law or a specific financial cooperative law. Most countries establish capital adequacy requirements for MFIs as a stabilization and protection mechanism. Though less than 50% of the sampled countries have established a capital adequacy ratio requirement for financial cooperatives, other risk management options, such as reserve ratio and insolvency ratio, are common. It is worth noting that less than half of the countries with legislation on MFIs and financial cooperatives require those institutions to disclose the full cost of credits to customers. Furthermore, although a majority of countries require traditional banks to
> 3. World Bank Population Data on Population and Refugees: https://data.worldbank.org/. [Data accessed on April 17, 2021].

> 4. World Bank Population Data: https://data.worldbank.org/. [Population data accessed on April 17, 2021].

> 5. OECD (2021), Foreign-born population (indicator). doi: https://doi.org/10.1787/5a368e1b-en (Website accessed on May 8, 2021). The top three countries include: Luxembourg (47.3%), Australia (29.9%) and Switzerland (29.7%).

> 6. Sources: The Federal Statistical Office, Swiss Labour Force Survey (SLFS). [Information retrieved from the Federal Statistics Office Website (https://bit.ly/2RM8xv5) on April 17, 2021].

3
is a lot more variation in the international sample compared to the sample of counties in the US.

Table 3: International Regressions of Global Tropical Cyclone Damages and Fatalities

|Impact|Constant|Log(Income)|Log(Population<br>Density)|Adj Rsq/<br>F Stat|N|
|---|---|---|---|---|---|
|Damages|15.17|0.415|-0.21|0.066|807|
||(22.77)|(6.44)|(3.04)|29.6||
|Fatalities|6.25|-0.477|0.07|0.158|1089|
||(18.20)|(14.01)|(1.86)|103.2||

Source: EMDAT 2009. The t statistics are in parenthesis. The functional form of the regression is log log.

The results in Table 3 also reveal that fatalities fall with income. The elasticity implies that a doubling of income reduces fatalities by 50%. Fatalities increase with population density but only slightly. A doubling of population density increases fatalities by only 7%. However, both the income and population density coefficient are significantly different from 1. That is, the 95 percent confidence interval for the income fatality coefficient lies between -.48 and -.47 and the confidence interval for the population density coefficient lies between -.01 and 0.22. The data suggest wealthier people protect themselves from tropical cyclones and that the risk per person falls as populations become more urbanized. Note that there are more storms that cause fatalities in the international data set than cause damages. This is because the international data set has a minimum damage requirement before storm damages are reported. It is not likely that this will bias the results because the bulk of damages done by tropical storms
predictions based on the sum of a sequence of regression forests, which are estimated by iteratively predicting the residual from the sum of the previous regression forests.

To our knowledge, Krennmair et al. (2022) is the first paper to specify a model that combines a conditional random effect with tree-based machine learning, specifically random forests. When applied to Austrian income data, the mixed effect random forest model tends to generate more accurate predictions of mean income than traditional EBP. The authors conclude that random forest models offer substantial advantages over linear models in the presence of complex and non-linear interactions between covariates.

Merfeld and Newhouse (2023) compare estimates produced using gradient boosting applied to geospatial data to those from a linear mixed effect EBP model. This differs from Krennmair et al. (2022) in two ways: It uses extreme gradient boosting instead of a single random forest model, and it assumes an unconditional random area effect instead of a conditional random area effect. The predictions are evaluated against census aggregates (for the asset index) or whether predicted welfare in the census falls below a threshold (for poverty in Malawi). Uncertainty is estimated using a block random effects booststrap approach, as proposed by Chambers and Chandra (2013) and applied in Krennmair et al. (2022). The results in Table 5 show that XGboost, even without the conditional random effect, is more accurate than EBP in each case. For out-of-sample areas, the greater flexibility of the XGboost algorithm eliminates much of the selection bias associated with out-of-sample prediction using linear models under informative sampling. In results not shown here, coverage rates vary from 94 to 97 percent, reflecting the success of the bootstrap procedure in estimating uncertainty accurately.

_Table 5: R_<sup>_2_</sup> _by method and in or out of sample_

|Country|Indicator|In-sampl|e|Out-of-S|ample|
|---|---|---|---|---|---|
||
Peer Effects on Violence: Experimental Evidence from El Salvador<sup>_∗_</sup> Lelys Dinarte<sup>_†_</sup>

**Keywords:** Peer effects, Segregation, Integration, Violence, After-School Programs.

**JEL Classification:** I29, K42, Z13

> _∗_ I am grateful to my advisors – Claudia Martinez, Jeanne Lafortune, Rodrigo Soares, and Nicol ́as Figueroa – for their encouragement and invaluable support during the course of this project. This paper greatly benefited from helpful feedback from Francisco Gallego, Jos ́e Tessada, Micaela Sviatschi, Tom ́as Rau, Pablo Egana, Juan Vargas, Owen Ozier, and Eeshani Kandpal. I appreciate the comments and suggestions of participants at the ASSA 2020, WADES 2019, LACEA-LAMES 2018, 2017 NEUDC Conference, 2017 ESA World Meeting, 7th Annual Meeting of AL-CAPONE, 2017 SECHI Annual Meeting, and the 5th Antigua Experimental Economics Conference. Additionally, I appreciate comments from participants during seminars at IADB, World Bank, Universidad El Rosario, Universidad de Los Andes, Universidad Javeriana, Pontificia Universidad Cat ́olica de Chile, Universidad de Chile (FEN), and Universidad Diego Portales. I am truly grateful for the incredible support of Glasswing International as an implementer partner, especially to Stephanie Martinez and the education sector team. I am also grateful to principals, teachers, students, and instructors of the five public schools in El Salvador. This study was funded by CEDLAS-IDRC (Canada) in the context of “Fostering Capacities in Impact Evaluation in Latin America” Project, CONICYT Chile “Soporte Operacional para Becas Nacionales,” and USAID’s El Salvador Office. The contents of this study are the responsibility of the author and do not necessarily reflect the views of her employer, funding agencies, or governments. This study was registered in the AEA RCT Registry with unique identifying number AEARCTR-000160

> _†_ The World Bank. Development Research Group. Email: ldinartediaz@worldbank.org
3.6|1.8|0.7|0.01|
|Swaziland|2|3.2|38|1.9|0.0|0.0|0.00|
|Mozambique|1.4|4.6|15|2.3|0.1|0.1|0.00|
|Côte d’Ivoire|0.9|2.5|57|1.8|0.0|0.0|0.00|
|Malawi|0.6|2.9|9|2.2|0.1|0.1|0.00|
|Tanzania|0.6|2.5|16|1.9|0.2|0.0|0.00|
|Ghana|0.3|1.1|66|1.4|0.1|0.0|0.00|
|South Africa|0.2|0.9|87|1.2|0.0|0.0|0.00|
|Ethiopia|0.2|0.8|19|1.3|0.0|0.0|0.00|
|São Tomé and Príncipe|0.2|0.9|56|1.7|0.0|0.0|0.00|
|Mali|0.1|0.4|22|1.4|0.0|0.0|0.00|
|Nigeria|0.0|0.0|56|0.3|0.0|0.0|0.00|
|Angola|0.0|0.0|41|0.1|—|—|—|

_Source_ : World Bank staff calculations based on utility information and household survey data. _Note_ : For South Africa,