rafmacalaba/data-use-mentions-extended huggingface dataset:
Recently released summary results from the application of the TERCE (Third Regional Comparative and Explanatory Study) exam in 2013 across Latin America show that Central American students underperform the Latin America average, with the clear exception of Costa Rica (UNESCO 2015). A first snapshot of summary results is presented in Figure 19, for all the Central America countries participating (all except El Salvador). At both 3<sup>rd</sup> and 6<sup>th</sup> grades, students in Nicaragua, Honduras, Guatemala, and Panama underperform the Latin America average, and 30-40 percent are projected to fall into the lowest two performance levels (of five) in each country (compared to 26 percent across Latin America). Costa Rica stands out as a strong performer, with only 11 percent of students projected to fall into the lowest two levels (UNESCO 2015). Compared to SERCE (Second Regional Comparative and Explanatory Study) results from 2006, learning outcomes have improved in Guatemala, Panama, and to a lesser extent in Nicaragua. Performance in Costa Rica has declined (Figure 20).

In a global context, education quality and learning outcomes in Central America are also low. In Honduras, 6<sup>th</sup> grade students taking the 4<sup>th</sup> grade TIMSS 2011 (Trends in International Mathematics and Science Study) mathematics assessment scored well below the international average. In the 2012 PISA (Program for International Student Assessment), Costa Rica’s 15 yearold students performed near the bottom of the international distribution. In math, Costa Rica ranked 55 of 64 countries, and 51 of 64 in sciences. These results suggest that, while the quality of primary education in Costa Rica (as measured in SERCE and TERCE) is relatively strong, secondary education is weaker. They may also suggest that other Central American countries would have even weaker outcomes at the secondary level on the global scale, given Costa Rica’s outperformance in the
decreased from 55 percent in 2013 to 19 percent in 2020. This reduction is the result of both new arrivals preferring to reside outside the camps, as well as the departure of refugees after some years in the camps.

However, several reports have shown that out-of-camp refugees often live in bad living conditions, work in informal jobs, and pay high rent (World Bank, 2020). Considering that they maintain lower contact with officials, they may lack access to information and may not be fully informed about their rights. These issues can put out-of-camp refugees at a disadvantage compared to camp residents, affecting their QOL (Verme et al., 2016).

Indeed, the debate on refugee QOL in and out of camps has received some attention. It is even more critical in Jordan, where many refugees can choose to live either in or out of camps. In the survey of refugees, Abdo et al. (2019) found that Syrian refugees scored significantly lower than Jordanians in the psychological health and social relationships domains. Krafft et al. (2019) explained that both camp and out-of-camp Syrian refugees living in Jordan face challenges in participating in the labor market, but refugees residing in camps suffer higher food insecurity. Elsayed (2019) showed that camp refugees suffer worse living conditions; they mainly live in smaller living areas, have worse access to public facilities, and have less ownership of durable assets. Ginn (2020) found that out-of-camp refugees are more likely to work and receive higher household earnings. Gender researchers have also shown that the Syrian refugee households headed by women in Jordan cope with increased risk of poverty, insecurity, and vulnerability compared to male-headed refugee households (Care, 2016; Hanmer et al., 2020; Mohammad, Abu Awad, Creedy, & Gamble, 2018; UNHCR, 2014c). While most of these papers were conducted using simple descriptive statistics, which does not allow for presumptive conclusions, this paper takes a broader perspective by looking at differences between in-camp and out-of-camp refugees using multidimensional
problematic. Instrumental variable regressions also show Finance to be the most robust result.

We also find that the relative importance of different factors varies according to firm characteristics. Larger firms are affected by the Finance obstacle to a significantly lesser extent but being larger does not relax the obstacles related to Crime or Policy Instability to the same extent.

Examining the Finance obstacle in more detail, we find that although firms perceive many specific financing obstacles, such as lack of access to long-term capital and collateral requirements, only the cost of borrowing is directly associated with firm growth. However, we find that the cost of borrowing itself is affected by imperfections in the financial markets. Thus we find that the firms that face high interest rates are the ones that perceive banks they have access to, as being corrupt, under-funded, and requiring excessive paperwork. We also find that difficulties with posting collateral and limited access to long-term financing are also correlated with high interest rates. It is likely that these latter obstacles are also aggravated by underdeveloped institutions.<sup>1</sup> Several papers have specifically pointed to the importance of financing obstacles. Using firm level data, Demirguc-Kunt and Maksimovic (1998) and others provide evidence on the importance of the financial system and legal enforcement in relaxing firm’s external financing constraints and facilitating their growth. Rajan and Zingales (1998) show that industries that are dependent on external finance grow faster in countries with better developed financial systems.<sup>2</sup> Although these papers investigate different obstacles to firm growth and their impact, they generally focus on a small subset of broadly characterized obstacles faced by firms.

4
One approach to considering these issues is by analyzing death on all-cause mortality during the COVID-19 period and making comparisons to previous years. Such an analysis of “excess mortality” by age group could contribute to an understanding of the extent to which the flatter agemortality profile in middle-income countries is a real phenomenon rather than one generated by differences across countries in attribution. There are challenges in using excess mortality data, however. For developing countries, such data are typically available only with a long time lag, if at all, and currently such data are not available with an age breakdown for the COVID-19 period for any of the countries considered here. Excess mortality data are also challenging to interpret because the pandemic has both direct and indirect effects.

The analysis in this paper is also a reminder of the value of demographic data as a tool amidst the COVID-19 crisis. Governments have been widely varying in the extent to which they have been transparent with COVID-19 data. Some countries release only very limited data, while others make regularly updated microdata of cases and deaths available to the public. Countries that have been the most forthcoming with data have benefitted because wide scrutiny of the data has allowed deficiencies to be identified and corrected, and analysis by the broad research community has helped inform government responses. Greater data transparency should be encouraged as a public good to help the global community understand and better address the pandemic.

20
, which can be used subsequently to finance counter-cyclical measures in bad times. By contrast, also using U.S. state data, Alesina and Bayoumi (1996) argue that fiscal rules have indeed reduced flexibility in state-level fiscal policymaking without, however, having a bearing on the cyclicality of state fiscal policy.

- 20 . The source of data on municipal finances is the National Treasury. The data refer to the balance sheets of the municipalities, excluding their decentralised agencies ( _autarquias_ and _fundações_ ) and off-budget funds (including pension funds). The number of municipalities in the sample, which spans the period 1998-2003 varies between 3 807 in 1998 and 4 881 in 2001. Due to methodological constraints, intertemporal comparability is ensured within the periods 1998-2001 and 2002-03. The National Statistics Bureau (IBGE) also publishes municipal data based on the national accounts but the coverage is less comprehensive and data are typically available with a two-year lag.

- 21 . This assignment of expenditure functions across the different layers of government is consistent with the principle of subsidiarity, whereby services should be delivered by the lowest level of government capable of internalizing the externalities associated with public provision. In principle, the assignment of expenditure functions to sub-national governments is constrained by the ability of smaller jurisdictions to make the most of economies of scale in service delivery. The other important constraints are spillover effects, by which the inhabitants of adjacent jurisdictions can benefit from spending in another, which may result in the underprovision of services or stretch local budgets beyond their means.

13
World Bank, 2006, 2020). However, other aspects of formality, such as paying taxes, enforcing contracts, and registering property remain extremely burdensome, with Egypt ranking 156<sup>th</sup> out of 190 economies in paying taxes, 166<sup>th</sup> in enforcing contracts, and 130<sup>th</sup> in registering property in 2020 (World Bank, 2020).

The assumption underlying the calls to formalize informal firms is that small informal firms, or at least subsets of them, can be formalized. This implies that informal and formal firms are similar to each other in terms of productivity, employment, types of activities, location, etc. This assumption implies that formalization would be feasible and requiring formality would not result in the firm instead disappearing altogether. This paper sets out to test whether currently informal firms in Egypt are “formalizable” and, if so, which firms, in particular, might be susceptible to formalization.

We use multiple data sources from Egypt to assess which firms are more likely to be formal. We then quantify the amount of overlap between formal and informal firms in terms of their predicted probability of formality, based on observable characteristics. While we find some overlap in the probability to be formal, particularly among firms in fixed establishments, we also find that many informal firms have a low probability of formality and may not be formalizable. We create profiles (clusters, groups) of firms with similar characteristics to explore which informal firms are more, or less, likely to formalize. We also explore the relationship between firm dynamics and formality. We conclude that, in the current landscape in Egypt, while enforcement and encouragement may yield some progress in expanding the formal sector, more substantive reforms to the process and practice of formality are likely needed to substantially expand the formal sector.

3
**Non-Pharmaceutical Interventions (NPIs)** :  We use mandatory closures of nonessential businesses as both most relevant to the issue of economic mobility and figuring most prominently in the policy debate.  State-level data for the United States are collected from Raifman et al (2020) and NPIs enter as indicator variables taking a value of 1 if a given NPI is implemented and 0 otherwise.  Globally, we employ information on national NPIs available from the Blavatnik School of Government at Oxford University. For select countries for which we employ subnational mobility data to explore the impact of local case incidence, we use national data on the nationally implemented NPIs as controls. The exception is Brazil for which NPIs are established by states, and we collect data at that level.

# **III. Results: United States**

Figure 1 plots the level of mobility against the log of the number of cases per capita by U.S. state for the United States. It further divides the sample by whether the states are covered by restrictions on nonessential businesses (red) or not (blue). Two drivers appear as potentially important. First, the data are consistent with restrictions leading to lower levels of mobility. However, more strikingly, there is a clear downward sloping relationship between reported cases and mobility independent of such restrictions.

**Figure 1: Mobility, COVID Cases and Official Restrictions, United States**

<!-- Start of picture text --> US<br>-5 0 5 10<br>Log(case per 1 million people)<br>No shutdown Shutdown<br>20<br>0<br>-20<br>-40<br>-60<br>Workplace mobility (% deviation from baseline)<br><!-- End of picture text --> Notes: Workplace mobility is Google measure of work-related mobility index. See text for sources.

Table 1 more formally tests this relationship by estimating Table 1 more formally tests this
find that individuals with higher risk propensities are more likely to migrate within Germany.<sup>6</sup> Gibson and McKenzie (2011) use the same risk measure and find that among high‐skilled people from three Pacific countries the more risk‐seeking individuals are more likely to have emigrated. They also find that individuals who are more patient in a discounting question are more likely to have emigrated. Three recent papers have also examined the association between risk‐seeking and internal migration within China. Akgüҫ et al. (2015) and Dustmann

> 6 In addition to their main analysis, which focuses on how risk preferences predict migration, they use panel data over two years to show that changes in migration status are not correlated with changes in self‐reported risk attitudes over this short period.

8
# **Annex 3. Detailed Transport Questions in LSMS Surveys (12 Countries in LSMS Collection)**

|**Survey Question**|**Albania**|**Burkina Faso**|**Cambodia**|**Ethiopia**|**Iraq**|**Malawi**|**Mali**|**Nepal**|**Niger**|**Nigeria**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_I. Education_**<br><br>d f d f|||||||||||||
|1.<br>Main moe o transport to an rom<br>education facility|X|||X|X|X||X||X|X|X|
|2.<br>Time taken to get to school by this<br>means of transport|X|||X|X|X||X||X|X|X|
|3.<br>Education cost on transportation|X||X|||X||X|X|X||X|
|4.<br>Transportation difficulty as main<br>reason for non-attendance in|||||||||||||
|education/missingthe classes|||||||||||||
|**_II. Health_**|||||||||||||
|1.<br>Main mode of transport to and from<br>health facility|
Figure 23: Probability of accessing public support by country type (severity of initial lockdowns) <!-- Start of picture text --> 60<br>49<br>44<br>40 37<br>34<br>20<br>0<br>Below median Above median<br>First round Latest round<br>P-value of test that exporters in above median countries are different than exporters in below median:<br>First round: 0.071; Latest round: 0.005<br>Probability of accessing support<br><!-- End of picture text --> _Notes:_ The sample includes 38,908 firms in 42 countries.

# **6. Conclusions and Policy Recommendations**

This paper provides novel evidence on the impact of the COVID-19 pandemic and the subsequent recovery for globally engaged firms, as well as on their adjustment mechanisms. This evidence has been grouped into a set of six stylized facts on trade outcomes; changes in sales and financial fragility for these firms; their labor market adjustments; the heterogeneity across attributes pertaining to firm, sector, and country; their response in terms of digitalization, product innovation, and changes in trading partners; and finally their access to public support. In all cases these patterns are compared not only to non-engaged firms but also across different types of global engagement using a rich survey spanning three waves of data collection covering nearly 65,000 firm-year observations across 45 countries. Our analysis from the BPS data shows an immediate negative impact of the COVID-19 pandemic on trade outcomes at the firm-level, which is also confirmed in the customs data. The adverse impact is more pronounced for GVC firms, that is, those that participate in both exports and imports perhaps because shocks in the multiple nodes of global production networks magnify for these firms. The adverse impact of the pandemic on total sales (and other outcomes such as financial fragility) of firms are recovering faster for globally engaged firms,
mark>_ <mark>.</mark>

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Foster, J., Smallcombe
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- Bown, C. 2020a. “Trump's trade policy is hampering the US fight against COVID-19,” March 13. https://www.piie.com/blogs/trade-and-investment-policy-watch/trumps-trade-policy-hampering-us-fightagainst-covid-19

- Bown, C. 2020b. “EU limits on medical gear exports put poor countries and Europeans at risk,” March 24. https://www.piie.com/blogs/trade-and-investment-policy-watch/eu-limits-medical-gear-exports-put-poorcountries-and

- Bown, C. 2020c. “COVID-19: Trump’s curbs on exports of medical gear put Americans and others at risk,” April 9. https://www.piie.com/blogs/trade-and-investment-policy-watch/covid-19-trumps-curbsexports-medical-gear-put-americans-and

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(SDG 6.4: _ensure sustainable withdrawals and supply of freshwater to address water scarcity_ ). Strategies in meeting these integrated targets can benefit from geospatial analysis of layers of digital data on groundwater quality and quantity.

Here, we develop, for the first time, multi-hazard groundwater risk maps for Bangladesh incorporating three hydrological indicators: groundwater arsenic, salinity and water storage. Our national-scale maps identify critical areas across the entire Bangladesh that may have both groundwater quality (arsenic and salinity contamination) and quality (water storage decline) issues – affecting access to drinking and irrigation water supplies. We have estimated exposure of population groups, particularly poor and socially vulnerable population, number of households with access to tubewells and piped water supplies, and the access to irrigation water supply to various levels of groundwater risks. These groundwater risk maps, and associated hydrological information and data will be made publicly available so that additional waterrelated indicators (e.g., _E. coli_ , access to sanitation services) can be incorporated to the models in future to develop further these national-scale groundwater risk maps to estimate population exposure.

# **2. Materials and Methods**

Water-related multi-hazard groundwater risk mapping at the national scale of Bangladesh is conducted in this study that applies a number of hydrological indicators or variables such as (i) shallow groundwater arsenic (As) concentrations, (ii) groundwater salinity (i.e., Electrical Conductivity (EC) of shallow groundwater), and (iii) the mean depth (2004–2013) to dryseason (December–April) groundwater levels (GWD). A brief description of the hydrological data is provided here but a broader description can also be found in the supplementary text.

## **2.1 Arsenic concentrations in groundwater**

We use the groundwater arsenic (As) data set from the national-scale survey of groundwater in Bangladesh (National Hydrochemical Survey) that was conducted jointly by the British Geological Survey (BGS) and the Department of Public Health Engineering (DPHE) (
and Raw Distributions**

Table 1 documents the demographic balance for households assigned to recall modules. This randomly selected group of roughly 8,000 households constitutes our “treatment” sample, while all remaining households form the “baseline” sample. This table shows treatment-control differences from regressions that control for strata (EAs) used in the randomization design. All variables are well balanced between groups, as can be seen from the small and nonsignificant coefficient estimates in the two panels.

Randomization to the recall module did not affect the reporting of diary entries, as shown in Table 2. Respondents might adjust their diary to reconcile aggregates with those in the recall module, as in bounded interviews (see Neter and Waksberg, 1964), but the results in column 2 rule out this possibility. Specifically, Panel A reports treatment-control differences in cumulative distributions of diary acquisitions _Y_<sup>_d_</sup> at selected percentiles, as well as differences in averages. Treatment-control differences in the dispersion of _Y_<sup>_d_</sup> are reported in Panel B. Results in column 2 imply that diaries collected in treatment and baseline samples yielded statistically indistinguishable distributions of household acquisitions.

Recall data yielded higher consumption figures than diary acquisition. This can be seen from column 3 in Panel A of Table 2, which presents coefficients from the same regressions in column 2 but using

> of the 17 most important food items considered in the Beegle et al. (2012) Tanzania experiment.

> 9As noted in the sampling and fieldwork documentation, the recall module “ _should be administered in the first visit to the household, before the recording of food consumption by diaries. Asking these questions afterwards (when both the respondent and the interviewer will know the diary records) would defeat the purpose of this module, which is to compare the results obtained from the two instruments, to assess the possibility of applying in future surveys the recall method instead of diaries_ ”.

> 10The PDS includes 13 rationed products, of which four, whole wheat