rafmacalaba/data-use-mentions-extended huggingface dataset:
Both the incidence and the amount remitted appear to be even higher among the most highly-skilled migrants from countries with high levels of brain drain. Gibson and McKenzie (2010) report that between 68 and 93 percent of the developing country high-skilled migrants in their sample remit, with an average amount remitted of around $5,000. Clemens (2011) also finds an annual remittance level of about $5,000 in his survey of African physicians in the U.S. and Canada. Existing empirical evidence therefore does support the idea that high-skilled migrants remit, particularly back to lower-income countries, and that the level of these remittances can be sizeable relative to per capita income in their home countries.

Less empirical evidence is available about the extent to which high-skilled emigrants invest or engage in knowledge flow or return investment, especially for the types of countries for which brain drain rates are highest. At the cross-country level, Kugler and Rapoport (2007) and Javorcik et al. (2011) find a positive relationship between the number of skilled migrants a country has in the United States and the level of foreign direct investment from the U.S. economy to that country. However, their dataset contains only two countries with population rates below one million, making it difficult to see whether this relationship holds in the smallest countries for which brain drain rates are highest. At the micro-level, Gibson and McKenzie (2010) find very low incidence rates of high-skilled migrants being involved in trade facilitation or investment in business start-ups in their home countries: they estimate a small country like Tonga or Micronesia might gain at most $500-$2,000 per high-skilled migrant from trade and foreign direct investment. This contribution is a positive one, but unlikely to have large effects on development.

Reductions in the cost of air travel and improvements in information technology make it significantly easier for high-skilled migrants today to continue to stay in contact with people in their home countries. In principle this opens up many opportunities for knowledge flow. However, again
As of today, however, few public registries are using their data in these more sophisticated ways. Only six of the 60 public registries surveyed indicated that the main use of PCR data in supervision was for provisioning<sup>24</sup> and even then, it was in the context of ensuring that problem loans were identified so that adequate provisions could be assigned, not for forward looking risk mitigation.

An important observation is that the typical structure of a PCR does not match Basel II’s minimum requirements for data for the IRB approach. Basel II suggests that there should be at least two dimensions for such data (i) on the risk of the borrower and (ii) on the instrument. Regarding the risk of the borrower, there should be at least 7 rating buckets or grades for loans that are not in default and at least one grade for loans in default. The proposals state that, “a borrower grade should be defined as an assessment of borrower risk on the basis of a specified and distinct set of rating criteria, from which PD (probability of default) estimates are derived.” Banks with loans concentrated in one a particular market segment should define rating grades such that loans are not unduly concentrated in a particular grade. The description of the ratings grades should be sufficiently detailed and clear to ensure loans of similar risks are classified in the same grades and that a third party can understand the assignment of borrower ratings. Rather than countries’ rushing to adapt their PCRs to this standardized description, we suggest here that PCRs may play two useful distinct and roles in terms of Basel II implementation.

First, and as suggested in a companion paper, for those countries applying the simpler Basel II approaches, the databases may be used to enhance forward looking provisioning such that capital plus provisions reflect an overall estimated Value at Risk. This implies setting capital requirements equal to, say, those given by the Standardized Approach (SA) and then setting provisions to equal the Value at Risk minus
506)|(0.604)|(0.563)|(0.623)|(0.652)|(0.719)|
|Land share of cofee*Log price post birth||0.365||0.755||-0.336|
|||(0.711)||(0.657)||(0.727)|
|_N_|3994|3994|3994|3994|3994|3994|
|_R_<sup>2</sup>|0.230|0.230|0.205|0.205|0.204|0.204|

Notes: Standard errors are clustered at zone level. This table is based on children in three rounds (2011, 2013, and 2015) of ESS survey and anthropometric measures of children under 5 years of age, after removing biologically implausible extreme values (WAZ, WHZ and HAZ values of _>_ 5 or _< −_ 5). All regressions include, whether mother is educated, gender and age of the child, month of birth and year of birth fixed effects, and log village rainfall. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _Log price in utero_ is log of the average coffee price when the child was in utero. _Log price post birth_ is log of the average coffee price over the period since the child birth to the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 29
s underestimation of their skills.

We expand existing literature in three ways. First, this paper contributes to a wider discussion on gender gaps in SES. There is mixed evidence on gender differences in self-reported SES levels. A recent cross-country study using data from more than 40,000 individuals across 17 African countries, including the self-reported data in this study, documents a significant male advantage in all self-reported skills except self-control (Ajayi et al., 2022). However, 2
/cloud-shadow-free observations. Accordingly, if this constraint is not satisfied for a given pixel in the target year, we enlarge the time frame backwards (at 1-year steps) until the condition is met.

The 2015 edition of WSF-Evo, generated by jointly exploiting both optical (Landsat-8) and radar (Sentinel1) multitemporal satellite imagery, outlines the global settlement extent at 10x10-meter resolution.<sup>50</sup> Starting backwards from 2015 (using the WSF2015 as a reference), we iteratively extract settlement and 6
# _<u>Simulation</u>_

The combination of age-specific saving rates, population projections and inequality levels is used for projections of the weighted average of the saving rate. 2006 is used as the initial year for the projections based on IBGE estimates for Families’ Saving. The levels of the saving rates are used in the simulations but for the final analysis, the percentage changes in the saving rate are used to project the aggregate family saving rate as it was reported by IBGE at 4.84 percent in 2006. This is because the levels are based on a sample of households in Brazil and may not necessarily be representative to the whole population of non-poor and poor households.

In the simulations, the new rich — the share of the population that would still have been poor if the poverty headcount rate did not fall — are not assumed to save with the full amount as the rich population segment. Instead, we assume that the simple average of the age-specific saving rates for the rich and poor population segments applies to this “intermediate” group. Therefore, it is assumed that the transition from high to low saving rates is phased in line with improvements in the poverty headcount.

By combining the three types of projections: (i) projections for age-specific saving rate; (ii) projections for age-specific population changes; and (iii) projections for the share that is non-poor and poor, it is possible to project the weighted aggregate saving rate in Brazil. The main finding is that the saving rate may increase slightly from 4.84 to 7.49 percent in 2050 if the poverty headcount follows it trend (as illustrated in Figure 4a). If the poverty headcount remains unchanged, the trajectory of the saving rate will take a parallel downward shift and reach 6.92 percent in 2050.

**Figure 4a. Poverty has Fallen from 1995 to 2007;** **<u>Two Scenarios for Future Poverty Headcount Ratio</u>**

**Figure 4b.
banks’websites.|
|Log GDP per capita|Logarithm of GDP per capita.|<br>World Bank’s World Development Indicators|
|Urban<br>Population<br>%<br>of<br>Total|Share ofpopulation in the urban areas to totalpopulation in a country.|World Bank’s World Development Indicators|
|Branches per 100,000 Adults|Number of branches of commercial banks per capita.|IMF|
|Deposit Accounts per 1,000 Adults|Number of deposit accounts with commercial banks per capita.|IMF|

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this oversubscribed list. Selection into the training group was based on a “vulnerability score” that gave priority to younger, female and unemployed individuals. Despite this approach, intake was “fuzzy” - participants were ordered by their vulnerability score, with the most vulnerable entering up until capacity. In some intakes, individuals with comparatively high scores were not taken into the program. In others, individuals with comparatively low scores were included. We construct our treatment and control groups from these intake decisions. Data were collected from members of the host and refugee communities in each country.<sup>4</sup> In both Jordan and Lebanon, the intervention was implemented on a rolling basis. As soon as one training cycle was completed, another would begin. Data were collected in three waves during each training cycle. First, during an “outreach” phase, where data were collected in order to assign treatment status. Second, at “baseline”, which occurred before the training had begun but after treatment assignment was known. Third, data were collected at “endline”, immediately following the end of the training. Data collection for those assigned to the treatment and control followed the same pattern.<sup>5</sup> Outreach and baseline data collection took place less than a week apart and were collected between July 2018 and September 2019. Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019
natives of each age ~~-s~~ kill type due to the changes in education levels; and (4) changes in the number of natives of each age ~~-s~~ kill type due to ageing. We should note that it is not simple to separate the ageing effect (4) from the changes in the education levels (3) in the data ~~—~~ we nevertheless undertake this exercise later in Appendix C. Younger cohorts tend to have higher education levels, leading to increased education levels as time passes. Ageing occurs as the relative share of the older group in the labor force increases. This is due to each successive cohort of natives having fewer number of people, mainly due to demographic reasons such as declining fertility rates.

In equilibrium, the wages and employment levels of workers of each type adjust as they respond to these demographic changes. For example, the ratios of the wages between natives and immigrants, between young and old workers, or between high ~~-s~~ killed and low ~~-s~~ killed workers are given by:

# 2.5 Alternative labor market structures

Our benchmark model assumes that unemployment is voluntary and linked to the en ~~-~~ dogenous labor force participation decision of workers. We account for the difference in employment rates between groups by introducing heterogeneity in the preference param ~~-~~ eters in the utility functions in equation (9). Labor force data reveal that differences in employment rates are mainly governed by differences in participation rates (Burzynski et al., 2018). This motivates our choice to endogenize participation rates and disregard invol ~~-~~ untary unemployment. However, as a robustness check, we relax certain assumptions and use an alternative specification, which involves smaller responses to soci ~~o-~~ demographic changes.

We first assume that unemployment can be partly involuntary. At each moment in time, active workers face exogenous job separation and matching rates,
Section B.2 we provide additional descriptive statistics on our trade policy measures. Table A.1 shows the (unweighted) average levels of bilateral applied tariffs and NTMs AVEs for the 45 countries covered by both tariff and NTM data. The lowest levels of tariffs are observed in developed countries (e.g. 0.8 percent in Japan). NTM AVEs also differ across countries, China having the highest (25 percent), which seems in accordance with anecdotal evidence of traderestrictive application of regulatory measures. Countries with similar regulations (e.g. members of the European Union) may nevertheless have different AVEs if they enforce them differently, which is the case for some of the Eastern European members (e.g. Hungary vs. the Czech Republic). Note that while some countries are characterized by high levels of both NTMs and tariffs, the overall correlation between the two is not statistically significant at common confidence levels.

The online appendix discusses other features of the NTMs and tariffs data. We show for instance that tariffs and NTMs data are observed in all 2-digit HS sections, with a larger coverage for tariffs (Table A.2). Different types of NTMs affect different sectors: SPS measures mostly impact animal, vegetable and processed food products, while TBT measures mostly affect

> 18These include for e.g. trade-related investment measures or intellectual property, although data on those is very scant. For more information on the MAST nomenclature, see: http://unctad.org/en/PublicationsLibrary/ditctab20122 ~~e~~ n.pdf.

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This decomposition approach can be applied to a large number of individual countries and sectors to capture country-sector-specific trends.

Similarly, within-sample estimates are produced using data for years 1992-2016, and out-of-sample estimates are produced with data for years 1992-2011 (and later compared to actual employment growth for years 2012-2016).

# **3.4. Average Annual Elasticities Calculated from Country-Sector Data**

Two versions of these estimates are produced to test if more up-to-date representation of the economic structure and technology embedded in the data increases predictive power. One taking the full time series available (“Simple Full”, using annual data since year 1992) and another taking only recent history into account (“Simple Short”, using annual data since year 2000). This estimation approach can also be easily applied to a large number of individual countries and sectors to capture country-sector-specific trends.

# **4. Data and Descriptive Statistics**

World Bank World Development Indicators (WDI) and International Labor Organization’s (ILO) datasets are used for GDP-employment elasticities analysis using different methods. The data on gross value added for agriculture, manufacturing, and services were obtained from the WDI in 2010 US dollars. The ILO provides data on sectoral employment for the same three sectors. According to the ILO, employment includes all persons of working age who are in the following categories: paid employment (whether at work or with a job but not at work); or self-employment (whether at work or with an enterprise but not at work). The availability of time series data on employment only for agriculture, manufacturing, and services limits the analysis to these three aggregate sectors. Given the mix of data sources, the combined employment data across all sectors from the ILO was compared with the total employment from the WDI.

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**Figure 2.5: Informality Rates**

<!-- Start of picture text --> Informality rates<br>68.8%<br>70% 45%<br>67.5%<br>65.1%<br>66% 41%<br>63.2%<br>62% 37%<br>58% 33%<br>30.2%<br>27.8%<br>54% 29%<br>28.3%<br>50% 27.0% 25%<br>without access to health non_IMSS contributors<br>self employed and others informaility as defined by ENOE<br>contributors<br>firms (INEGI definition)<br>2007/01 2007/02 2007/03 2007/04 2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03<br>Share of workers without access to health/non-IMSS Share of self employed workers / workers in informal<br><!-- End of picture text --> _Source:_ Own calculations using data from INEGI, BIE, and ENOE, 4th quarter 2010.

Analysis of job creation/destruction by economic activity also tells a story of how the crisis evolved. Figure 2.6 shows annual job creation/destruction for each sector of activity by quarter. Agriculture experienced three consecutive quarters of job destruction at the end of 2008 and beginning of 2009, and again shows destruction in the last two quarters of 2010. A similar pattern is observed for manufacturing: almost continuous destruction of jobs in 2008 and 2009, only recovering in 2010. The second quarter of 2009 shows the highest job decrease for manufacturing, a loss of about 672,000. By comparison, the largest job creation, in the third quarter of 2010, was only 382,
As has been the case in rnany studies of female labor market participation, particularly in developing countries (Behrman and Wolfe, 1984), the dependency ratio was hypothesized to be an important factor in determining non-farm participation. The dependency ratio here is the ratio of the total number of children and non-working adults to the number of working adults in the family. This was thought to be the best relative measure of the family responsibility of each eaming individual. The results are not entirely clear. In 1987, the dependency ratio has a significant impact only on agriculture in Model 1, where the effect is negative. In 1992, however, the effect on agriculture is the opposite (significant and positive). It is also significant and positive for participation in non-farmn activities. For 199:2, when the interaction dependency ratio "if female" is included, the separate female effect is insignificant for agriculture but positive and significant for non-farm. This interaction term is not significant when using the 1987 data, but at least for 1992, there is some indication that the effect of dependents was more important for females' participation in non-farm work than for males' and that this effect became more important over time.

The regional dummies reveal expected results. The coastal region which is closest to trade routes and urban areas hEas the most non-farm participation and the least agriculture. The forest region has more, but not significant, agriculture participation than the savanna region which is the excluded dummy. The forest and savanna regions appear to have about the same amount of non-farm activity.

In an attempt to link individual sectoral participation decisions to the differing fortunes of sectors within agriculture, we included household crop acreage. Most families have several crops and nIot one dominant crop, so we use acreage in each of the 25
for livelihood measurement. Given the technical capacity needed for reliable survey measurement and the substantial logistical difficulties in carrying out nationally or sub- nationally representative livelihood surveys, such surveys are often infrequently completed in much of the world, rendering com- prehensive and timely measures of poverty and related outcomes unavailable for many periods in many regions [6, 9]. Meanwhile, survey data are typically based on samples meant to be representative at larger spatial scales and are thus usually inadequate for generating reliable estimates at the village or neighborhood level – the level at which anti-poverty interventions often need to be targeted. Consequently, there is a pressing need for more cost-effective and scalable alternatives to local-level livelihood measurement that can complement and scale existing household survey–based efforts.

In recent years, the abundance of publicly available remote sensing data and recent advances in machine learning have transformed the livelihood measurement landscape, progressively shifting from national censuses and related household surveys to efforts to combine this information with information from satellites and other sensors. Early studies used coarse, publicly available satellite imagery and/or mobile phone data, combined with early machine learning and deep learning architectures, to show how these new sources of information could be used to support broad- scale measurement of wealth and poverty [4, 17, 29]. Subsequent studies introduced further refinements that used publicly available or proprietary geospatial data to improve satellite-based wealth measurement [8, 12, 2]. These advances confirmed that leveraging satellite images and machine learning can be an accurate, inexpensive, and scalable solution to estimate wealth [6, 21, 23].

Here we assemble a large-scale, multi-resolution, and multitemporal wealth dataset using national censuses or extracts obtained from National Statistics Offices and multi-spectral satellite imagery from multiple public and private sensors of varying resolutions. Our dataset comprises over 12 million households in four African countries (Malawi, Mozambique, Burkina Faso, and Madagascar) and, uniquely, contains precisely georeferenced measurements within two Malawian cities as well as repeated measurements of the same locations over time – two
37.1|0.0|0.0|5.3|1.2|53.2|17.7|0.6|15.1|0.0|14.9|0.0|0.3|

Source: Based on WITS database, World Bank **_SA_** _=Intra-Regional South Asia Trade;_ **_ROW_** _=Rest of the World_ . The product classifications are based on SITC 2-digit heading (Rev. 2). _Food and Live Animals_ includes meat and meat preparations, dairy, fish, vegetables sugar coffee; _Minerals and Fuel_ includes petroleum products, gas etc.; _Chemicals and Material_ includes fertilizer; _Manufactured Goods_ includes textile yarn, rubber products, iron and steel etc.; _Misc. Manufactures_ includes articles of apparel and clothing.

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