rafmacalaba/data-use-mentions-extended huggingface dataset:
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Our results are the following. In terms of physical risks, we find that exposures to floods represent the most important source of credit risk for the LAC banking sector. This is compounded by high loan concentration in and around LAC capital cities, which, ceteris paribus, entails higher physical risks to province-specific natural disasters. Using a difference-in-difference method, we present empirical evidence of a significant increase in banks’ NPLs in the aftermath of large-scale natural disasters, with heterogeneous effects across portfolio types. In particular, we find that large-scale natural disasters could trigger an increase in total NPLs of up to 1.4 percentage points in affected provinces. Evidence points to a higher impact on the non-financial corporate loan portfolio. Our results are in line with previous findings and are robust to different estimation strategies.

In terms of transition risks, our results show that banks located in the largest emitter LAC countries (adjusted for the size of their economy) have credit portfolios tilted to transitionsensitive sectors. This is particularly the case for Argentina, Uruguay, Paraguay, and Bolivia, where the agriculture sector takes a lion’s share of greenhouse emissions and as well of banks’ assets. Additionally, pre-pandemic firm-level data shows that firms operating in transitionsensitive sectors already present signs of stress, especially those in the fossil fuel and agriculture industry.

The rest of the paper is organized as follows. Section 2 briefly describes the key characteristics of the LAC banking sector, the economic damage and frequency of natural disasters, and the climate-related and environmental risk regulations for the LAC region. Section 3 presents the data and the methodology employed to estimate banking sector credit risk exposures to physical 4
June 1996. Of this number, 934 are daily workers and relate to 1995 levels. Data on civil service employees include 25,000 police force. Data on Health employment include 673 daily workers and are from 1996 Civil Service Review Mission, Aide Memoire. Data on Education include 323 daily workers. All statistical data exclude the population of East Jerusalem.

Estimates of GDP at market prices are taken from Country Office and are for 1995. Average monthly net pay (excluding non-wage payments) for permanent employees is estimated at NIS 1360 as of October 1995.

Yemen Unemployment rates estimated at 25-30% for the years 1991-93 (Country Assistance Strategy, September 1995). Central Government employment figures are taken from S. Schiavo-Campo's Note on Civil Service and Administrative Reform in Yemen and are for 1996. Education Employment data are taken from Education Sector Investment Proiect of February 01, 1994. Health employment data is from Gail Richardson (MN2HR) after consultation of Project Implementation Unit data in Yemen. Military employment data include conscripts (25,000 est.), but do not include personnel in paramilitary units, i.e., Ministry of National Security forces (10,000), and tribal levies (20,000 + est.) GDP at market price is taken from World Tables 1995 and refers to 1993 (estimate). Data on wages and salaries is taken from EMTPM files (SSC BTOR of 3 February 1995) and relates to 1994.
is also in charge of _State Matura_ high school final year examinations. Therefore, since 2008 the performance of primary school students on leaving examinations has been comparable across schools.

59. We focus on five education outcomes at the school level. Three of them are generated by the leaving examinations corresponding to the academic year immediately preceding our data collection (the 2009 final year examinations). In each school, we look at: (1) the proportion of students who failed the final year examination; (2) the proportion of students who excelled (i.e. received a 9 or a 10) in the Albanian language test; and (3) the proportion of students who excelled (i.e. received a 9 or a 10) in the math test. In addition, we consider: (4) the drop-out rate (i.e. the proportion of students who were enrolled in the school in the 2008-2009 academic year and did not return to school the following year<sup>21</sup> ); and (5) the repetition rate (i.e. the proportion of students repeating a year at the time of the survey).

# _2.b.2. Measures of school physical and human resources_

60. The questionnaire directed at the schools‟ administrative representative provided extensive information about the status of the buildings and physical resources available in each school. Guided by the existing literature, we focus on the following school characteristics: the urban vs. rural location of the school; the distance (in kilometers) from the nearest town; whether the school can be reached by public transportation; the number of days with no electricity in a typical month; whether the school has a telephone; the proportion of classrooms in poor condition that cannot be

> 20 Gunnerson et al. (2009) use retrospective data to study the impact of parental participation and school outcomes on education outcomes in eight Latin American countries. They find that correcting for endogeneity substantially increase the estimated impact
|
|El Salvador|0.03|0.07|0.09|0.22|0.29|0.44|0.16|0.25|0.20|
|Uruguay|0.09|0.11|0.19|0.13|0.35|0.38|0.65|0.31|0.52|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Relative supply of individuals in the working age population (16-65). All education categories (college, high school, and primary education) follow country-specific classifications for university degrees, secondary education, and primary education, defined in each household survey. The college educated labor force comprises workers who completed a university degree or higher. “High school” includes complete secondary education and incomplete college education. “Primary or less” includes no formal education, incomplete primary, complete primary, and incomplete secondary education.

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**Decoupling and recoupling**

answer is no –not surprisingly, since equity inflows tend to be closely correlated with foreign equity holdings (Levy Yeyati and Williams, 2011).

**Figure 13** presents a graphical illustration of these results by plotting country-by-country equity betas against US holdings, and showing how, unlike in the case of frontier markets, the expected positive link between betas and FG fails to materialize.

As noted, there are important difference between the average international investor and the professional asset manager, not least of them the fact that the latter are usually benchmarked, which limits their decisions to small deviations from the market weights, forcing them to purchase or liquidate in all markets proportionally in the event of massive allocations or withdrawals from the asset class. In other word, global flows may explain the growing correlation of EM assets with US markets better than the holdings of the average U.S. resident investor.

With that in mind, in **Table 10** we replicate the exercise in column 9 of **Table 9** using EPFR flow data. Now, we find a significant amplifying effect: larger fund flows (both in and out of individual countries) are associated with higher equity betas. Interestingly, the effect is asymmetric: as columns 2 and 3 show, the volume of fund flows plays a significant role only during equity sell-offs. A similar asymmetric pattern is found for credit spreads (relative to U.S. high yield corporate spreads) and currency returns (relative to the inverse of the Broad Dollar Index).<sup>19</sup> The directional causation of the previous findings is not trivial. They could reflect the behavior of benchmarked professional investors facing contributions and redemptions while keeping a balanced portfolio. However, they could alternatively reflect money-chasing returns (or rushing to the exit) whereby asset managers increase their positions in countries with excess returns and cut their exposure in falling markets. To control for this possibility, we re-estimate our models in
.fth of the population, and skilled emigration rates are extremely high).<sup>8</sup> In this context, Batista and Vicente (2011) set up a "voting experiment" along the following lines: following a survey on perceived corruption in public services, respondents were asked to mail a pre-stamped postcard if they wanted the results of the survey to be made publicly available in the national media. Controlling for individual, household and locality characteristics, they regressed participation in the voting experiment, which they interpret as demand for accountability, on migration prevalence at the locality level. They show that current

> 7In the penultimate paragraph of their article on "arti...cial states", Alesina, Easterly and Matuszeski (2008) write: "probably the single most important issue that we have not addressed is that of migrations. One consequence of arti...cial borders is that people may want to move, if they can. ... In some cases, migrations that respond to arti...cial borders may be partly responsible for economic costs, wars, dislocation of people, refugee crises and a host of undesirable circumstances. ... But sometimes the movement of people may correct for the arti...cial nature of borders."

> 8Brain drain ...gures for Cape Verde are 67 percent in Docquier and Marfouk (2006) and remain very high (60 percent) even after excluding people who emigrated before age 18 and acquired their tertiary education abroad (Beine et al., 2007).

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that are central to the network of migration flows and that “broker” the movement of migrants along different human corridors.

Table 4 presents the model specification that produced the best results in terms of goodness of fit (Hunter et al., 2008).<sup>11</sup> The results of STERGM are to be interpreted in a way similar to logit estimates: they assess the likelihood of observing the formation or dissolution of a link between two provinces with respect to a situation in which migrants move at random.<sup>12</sup> Results indicate that the only significant driver in the formation of a migration link between two provinces is conflict; the probability of observing a migration flow toward a province with fewer conflicts is 18% higher with respect to a situation in which migrants move at random. By contrast, the statistically significant (counter) drivers of dissolution of migration flows are the variables intransitivity and differences in economic activity. The former indicates a lower probability to observe the dissolution of hub structures in the network than by chance: i.e. over time, migrants depart from several provinces and reach different destinations, but their routes tend to overlap in some provinces that are used as a bridge in the network.<sup>13</sup> The latter shows that it is less likely to observe a flow to dissolve when this is directed to a wealthier province. Based on these results, we argue that migration in Libya should be characterized as forced migration because conflict intensity is the main determinant of relocation decision across provinces.

# _5.2.1 Hotspot provinces and clusters of provinces in Libya_

Next, we use spatial statistics to compare the numbers of migrants in neighboring provinces. In practice, we test for the presence of spatial autocorrelation in migrant distribution across the Libyan provinces (whether the number of migrants was similar in provinces that are near each other) by identifying hotspot provinces and clusters of provinces (Anselin 1995; Barthel et al. 2015).<
5 individually optimized as well as fully shared meals (the assumptions underpinning the calculations of the lower and upper bound respectively) are unlikely to fully capture household eating habits. Practice most likely lies somewhere in between. A fair degree of proportional sharing of all meal ingredients across all household members according to caloric needs has been observed in many places (Berti 2012). But some targeting of individual foods also undoubtedly occurs for reasons of their nutrient content considering (perceived) differential nutrient requirements across members as well as reasons of individual preferences and/or biases.

To be sure, in actual food choice other factors beyond cost are likely at play as well. Time or fuel use, difficulty of meal preparation, tastes and preferences for each individual, as well as intra-household allocation all can influence food choices, household resource allocation, and dietary intakes. These are abstracted from in this analysis, and as such the normative least-cost diets are themselves a lower bound on actual costs, computed as a metric of food system performance. Affordability thereof is thus a necessary but not sufficient condition for individuals and households to achieve nutrient adequacy.

Second, by drawing on monthly price data, the paper provides a practical way to assess the extent to which seasonality in individual food availability and prices affects the affordability of nutrient adequate diets. Seasonality in food availability and prices in rural agricultural settings is well known and much evidence has shown it to be particularly pronounced in Malawi (Gilbert, Kaminski and Christiaensen 2017; Devereux, Sabates-Wheeler and Longhurst 2011; Chirwa and Chinsinga 2015; Sassi 2012; Ellis and Manda 2012). Two related studies have examined the seasonality in total diet cost for adult women in Malawi and India (Bai, Naumova and Masters 2020; Raghunathan et al. 2021), while another used seasonal data to develop recommended diets for low-income consumers in several places (Chastre et al. 2009). We add to this growing
# Figures

Figure 1: Secondary school density and teenage birthrate over time, Brazil <!-- Start of picture text --> 0.4 9.0<br>8.0<br>0.3<br>7.0<br>0.2<br>6.0<br>0.1<br>5.0<br>0.0 4.0<br>1997 1999 2001 2003 2005 2007 2009<br>Year<br>School density Teenage birthrate<br>Number of secondary schools (per 100)<br>Children born to mothers aged 15-19 (per 100)<br><!-- End of picture text --> Notes: This figure shows the annual number of secondary schools and births for the period 1997 to 2009 per 100 females age 15–19.

Source: School data come from the 1997–2009 waves of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age of conception from Brazilian Vital Statistics.
Jabal Al Akhdar (red). This might indicate that, from 2017 to 2018, there was a shift in migrants' routes, and eastern cities became passage points to reach the other side of the country.

# **6. Conclusion**

This paper has presented the first analysis of migration in Libya. To analyze this complex phenomenon, we have used DTM data of IOM and a rich toolkit that combines spatial statistical analysis and network analysis. Methods from spatial statistics were used to analyze location choices and to identify common patterns in migration movements and the presence of hotspots and clusters. Social network analysis was used to map the network of migration movements, determine the level of migratory pressure in different provinces, and identify the formation of network hubs.

> 24 Community search to study networks of migration flows has been used in Davis et al. 2013; Peres et al. 2016.

> 25 Several community detection algorithms have been proposed (see Fortunato 2010). The choice of one algorithm over another is usually data driven and determined by the algorithm’s ability to produce a meaningful community structure, with significant within-community cohesion and between-community separation (Clauset, Newman, and Moore 2004). A standard measure used to determine algorithm fit to the data is modularity, a metric ranging between -1 and 1, with lower values reflecting poor community structure (many between-community edges and few within-community edges) and values closer to 1 indicating good community structure. In the case of the Libyan migration network, the algorithm producing the best partition is the spinglass algorithm (Reichardt and Bornholdt 2006), which returns a modularity value greater than 0.2 for 2017 and 2018.

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ages. Figure 6 plots the proportion of individuals ages 7-18 who were enrolled in the schooling system circa 2013. The main feature of the figure is that between ages 7 and 11, relatively small differences of around 10 percentage points are observed across the six Central American countries, with Nicaragua at the lower end of the spectrum, and Costa Rica and Panama at the upper end.<sup>10</sup> However, at age 12, when secondary entry is generally expected to occur, considerable and expanding disparities arise, to the extent that by age 17 the difference between the highest enrollment rates observed in Costa Rica and Panama (75 percent) are practically 30 percentage points higher than that observed in Honduras (46 percent). In between is El Salvador with an enrollment rate of 65 percent of 17 year olds, followed by Nicaragua with 60 percent, and Guatemala with 51 percent.

As already mentioned, one advantage of household surveys for the present analysis is that they allow identifying within-country differences. Figure 7 plots the average differences in enrollment rates for 12-17 year olds between females and males, indigenous and non-indigenous groups, rural and urban areas, and individuals belonging to households in the poorest and richest quintiles of the household income distribution.<sup>11</sup> These underlying factors, as presented in the conceptual framework, affect the marginal costs and benefits of staying in school, and therefore are expected to be correlated with dropout.

The largest differences in enrollment rates in Costa Rica, Panama, El Salvador, and Guatemala are observed between youth belonging to households in the richest and poorest quintiles. In Honduras and Nicaragua, more pronounced differences are observed when the population is classified by its urban/rural location, which in addition to relating to income, is likely related to the supply of secondary school services. Disparities across indigenous and non-indigenous groups are sizable in Panama, Guatemala, and Honduras. An important result is that gender differences are relatively small, with females generally showing slightly higher enrollment rates
Studies of the United States are able to model direct occupational flows between industries (e.g., Ellison et al. 2010, Glaeser and Kerr 2009). We unfortunately lack such data for India. We instead take a very simple approach. Greenstone et al. (2010) calculate from the Current Population Survey the rate at which workers move between industries in the United States. Using their measure of labor similarity for two industries, we define Labordi=∑k=1,...,I Edk/Ed∙Mobilityi←k. This metric is a weighted average of the labor similarity of industries to the focal industry i, with the weights being each industry‘s share of employment in the local district. The metric is again by construction mostly orthogonal to city size.

These metrics condense large and diverse industrial structures for cities into manageable statistics of local industrial conditions. The metrics do have limitations, though. First, we do not capture potential interactions that exist beyond the local district, but factor and product markets can be wider than a district (e.g., Rosenthal and Strange 2001, Kerr and Kominers 2010). Second, the metrics do not consider final consumers. In unconditional estimates, we separately model city populations. Third, the metrics do not measure differences across districts in worker or input quality except to the extent that they are reflected in industry compositions or observable features like education levels. Finally, these metrics can suffer from omitted variable biases should another district-industry factor jointly determine both incumbent structures and entry rates. We will use changes in industrial conditions from 1989 to 2005 to partially address this concern.<sup>12</sup>

# **Unconditional Estimations of Spatial Entrepreneurship**

We characterize entry through a series of linear regressions with the above determinants as explanatory variables: ln(Entrydi) = ηi + β∙Xd + γ∙ln(Incumbent Employmentdi) + γI∙Inputdi + γO∙Outputdi + γL∙Labordi + γC∙Chinitzdi +
In all, the municipality characteristics variables paint a vivid picture of the types of markets where agent services seem to be flourishing. These are low income, highly and densely populated areas with a relatively high level of commercial activity. These patterns suggest strongly that FINCA DRC agents are targeting and effectively reaching the urban poor. Note also that the r- squared of models 9 and 10 with municipality characteristics is 0.136, an indication that these variables can account for a large share of the substantial gain in the overall fit of the regressions when controlling for municipality-level fixed effects.

Finally, we include the variables describing financial development at the municipal level in models 11-16. Agents perform 49 fewer cash-in transactions in municipalities with a dense network of financial service providers, an indication that their services compete with, and thus are substitutes for, those of other providers. In models 13-16, we include the dummy variables describing the predominant type of financial services provider in each municipality. As hypothesized, agent cash-in transactions are more prevalent where microfinance institutions are identified as the dominant providers. This could be a reflection that clients have greater demand for and familiarity with the services from that type of provider since FINCA DRC is itself a microfinance institution. While the patterns for the financial development coefficients are suggestive of where FINCA DRC agents are best fitting into the local financial sector, we put less stock in those results since they are generally insignificant when we use standard errors clustered at the municipality level.

In Table 2, the number of cash-out transactions replaces cash-in transactions as the dependent variable and we rerun the same set of regressions. Results are very similar to those for cash-in transactions. Two minor differences are that significance levels for some variables, especially those describing agent and retail business characteristics, are lower and the magnitudes 19
of countries with harmonized survey data by income level, region and timing is shown in Table 1. More than half are LICs and LMICs, including 19 Fragile and Conflict-Affected Situations (FCS). Of the 80 countries with surveys, 53 are located in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (SSA), so the country averages reported are strongly influenced by these regions (Figure 1).<sup>6</sup> Previous papers using the HFPS describe the harmonization process and dataset in more detail (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021).

> 6Annex 1 provides more detail on the coverage and timing of surveys. Figure A1.1 shows countries with HFPS waves by the time periods used in the analysis, and Figure A1.2, by income level. Figure A1.3 lists all countries with HFPS data included in the analysis and the timing of HFPS waves between April 2020 and December 2021 at the country level.

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