rafmacalaba/data-use-mentions-extended huggingface dataset:
identical across all jobseekers assigned to the treatment group, and the categorization of jobseekers into specific and general matching was one result of this flexibility.

Beneficiaries in the control group received job counseling services but did not have access to job matching. These services are similar in content to those provided by the public employment services. The sample for the analysis included a total of 1,541 jobseekers including 765 jobseekers in the treatment group receiving job matching services and 776 in the control group receiving only basic counseling services. A downside to this design, it is important to note, is that this study does not have a “pure” control group – i.e., a group receiving services only from the PES. As a result of this, the paper cannot compare the efficacy of a private employment service provider relative to the PES.

# **3. Data Sources**

The analysis made use of three sources of data which are described below.

## **3.1 Administrative Data from Federal Employment Institute (FEI)**

Data from FEI provided two strands of information. The first was information on the individual characteristics of program participants including gender, age, education level and length of unemployment. An individual is classified as unemployed if they are registered as such with the FEI, an approach followed by Balavac-Orlic and Posadas (2023). In some sense, this is a less strict definition relative to ILOs criteria, which additionally requires active job-search efforts and readiness to start a job to be classified as unemployed. As benefits for the unemployed are only available upon registration with the FEI, we view it as unlikely that large shares of those out of work are not registering. The FEI also provided information on the formal employment status of jobseekers after their engagement with the private provider. To do this, the FEI drew on a database maintained by the Tax Administration. As described above, payment to the private employment service provider was based on independent verification of a jobseekers’ formal employment status. Whenever
# **4.2 Accuracy of population density predictions based on census population counts**

Poisson regression results using the census data for all villages, based on the variables selected from LASSO regularization indicate that satellite indicators have exceptionally strong predictive power in predicting village level population density (Table 5). At the national level, publicly available indicators explain a large amount of variation in village population density. In this case, the value added in using high-resolution indicators (out-of-sample _R_<sup>2</sup> is 0.75) as opposed to low resolution indicators (out-of-sample _R_<sup>2</sup> is 0.702) is minimal. In the 55 sub-districts, adding proprietary contextual features to public imagery-based models does not improve the prediction accuracy (out-of-sample _R_<sup>2</sup> remains at around 0.7). However, adding object classifiers to the model improves the out-of-sample _R_<sup>2</sup> by 10 points (0.83).<sup>20</sup> The results differ across sectors. Using the full national sample, the publicly available satellite indicators explain more variation in population density in the rural and estate sectors (0.808) than the urban sector (0.569) (panel A in Table 6). In the sample with 55 sub-districts, the predictive power of urban population density increases tremendously when adding the proprietary contextual and object features, relative to only including publicly available indicators (0.804 compared to 0.525). Adding proprietary indicators leads only to a limited improvement in rural areas (0.869 compared to 0.838) (panel B in Table 6). This result is consistent with greater uniformity in the relationship between built-up area and population density in rural areas, where simple measures of built-up area are sufficient to predict population density. In urban areas, due to the complex nature of the relationship between population density and buildings where the latter can be both commercial
- 12* Marketing _Você faz alguma pesquisa para saber a satisfação dos clientes?_ Do you conduct customer satisfaction surveys?

- 13* Marketing _Você planeja ações para promover e divulgar seus produtos e serviços?_ Do you engage in marketing campaigns?

- 14 Marketing _Sua empresa oferta novos produtos/ serviços para seus clientes? (Observa critérios para escolha dos produtos e teste dos produtos novos)_ Do you offer new products or services to your clients (purposefully developing and testing new products)?

- 15* Marketing _Você avalia seus concorrentes para saber o que deve melhorar?_ Do you assess your competitors to find out how you can do better?

- 16 Strategic _Você planeja os objetivos que deseja alcançar, tendo alguma forma de registro de_ Planning _onde quer chegar?_ Do you strategically set and plan your goals?

- 17* Strategic _Você tem seus objetivos e metas escritos e bem definidos?_ Planning Are your objectives and goals written down and clearly defined?

- 18* Strategic _Você toma nota das decisões e resultados de seu planejamento, observando_ Planning _situações em que houve resultados diferentes dos que você queria, para aprimorar o que deu certo e não repetir erros do passado?_

- Do you keep track of decisions and results of your planning, paying attention to situations where the results were different than desired, to gauge what went well and which errors not to repeat?

- 19 Strategic _Existem ações com prazos definidos para que os objetivos planejados pela_ Planning _empresa para o futuro possam ser alcançados?_ Have you defined concrete actions with deadlines to help reach the firm’s goals?

- 20* Organization _Você possui um controle de quem são os fornecedores de seus produtos e prestadores de serviço, facilitando sua pesquisa de preços?_ Do you keep a register of suppliers and service providers for your products to help research prices?

- 21 Organization _Os documentos importantes de sua empresa e contas estão bem organizados?_ Are your important documents and accounts well organized?

- 22 Organization _As atividades realizadas regularmente possuem um procedimento
Figure B.3: **Land Distributions by Bank Type and Audit Result** (Audited Accounts, N=2,064) <!-- Start of picture text --> 1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>−.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5 −.5 −.4 −.3 −.2 −.1 0 .1 .2 .3 .4 .5<br>Hectares from acre or hectare cut−off Hectares from acre or hectare cut−off<br>Non−matching land records Matching land records Non−matching land records Matching land records<br>Cumulative density Cumulative density<br><!-- End of picture text --> Notes: Panel (a) plots the cumulative density of land records by audit result for the subsample commercial bank accounts. Panel (b) plots the cumulative density of land records by audit result for commercial bank accounts.

which manipulation cannot be ruled out. The reported results are also robust to a more restrictive definition of the robustness sample that excludes non-matching land on both sides of the discontinuity. As an additional test, Figure B.3 plots the empirical cumulative distribution functions for commercial and cooperative landholdings, separately for matching and non-matching accounts. If the observed spikes in the land distribution were indeed due to manipulation of land records, rather than e.g. rounding, we would expect to see no evidence of bunching for the distribution of audit matches. For bank accounts, shown in Panel (a), matching and non-matching land appear to follow very similar distributions. To a slight extent, matching land appears more heavily concentrated at the low end of the distribution. This pattern is similar but more pronounced for the cooperative landholding distributions
many other economic statistics (e.g., Howes et al. 2022). Based on this information, the key contributors to overall economic activity include mining for gold, copper, cobalt, and zinc; drilling for natural gas and oil; and the production of timber from logging (e.g.,

> 4As mentioned in the Introduction, the last Household Income and Expenditure Survey was conducted in 2009. Economic statistics depend in a large part on the information from these surveys, and much of the basis for GDP estimates in the country are based on likely outdated information (e.g., World Bank 2023).

5
enrollment while female migration appears to have the opposite effect. At the same time, female migration tends to reduce child labor, in contrast to male migration from El Salvador. The results are also consistent with the findings of Guzman et al. (2007) and Pfeiffer and Taylor (2007) that female migration, as opposed to male migration, is associated with lower household expenditure on education

# **7. Conclusion**

Using the large IFLS data, this paper is the first attempt to quantitatively assess the impacts of migration, differentiated by gender, from Indonesia on labor supply and child 22
classified by several characteristics of individuals, such as age, gender and education; then, the lifetime income of the representative individual within each cross-classified group is simulated; finally, aggregating across these groups yields an estimate of the monetary value of the human capital stock for each country.

In addition to strengthening the theoretical consistency within the wealth accounting framework, the lifetime income approach, by bringing together the influence of a broad range of factors (demography, mortality, education and labor market aspects), allows comparison of the relative importance of these factors, which can lead to useful policy implications. For instance, information drawn from the estimates could be used to answer questions such as to what extent investment in higher education could offset the negative impact on human capital accumulation due to population aging.

The OECD human capital project has required the creation of a database for the participating countries over a range of observed years, including all the elements needed for human capital estimation based on the lifetime income approach. The estimated results and analyses based on these results are presented in Liu (2011).

To the purpose of this paper, the relevant data for thirteen countries (Canada (CAN), France (FRA), Israel (ISR), Italy (ITA), Korea (KOR), the Netherlands (NLD), New Zealand (NZL), Norway (NOR), Poland (POL), Romania (ROU), Spain (ESP), the United Kingdom (GBR) and the United States (USA))<sup>6</sup> for the year 2005 or closest available year are drawn from the OECD database; these data are combined with the World Bank (2011) estimates of the total wealth for these same countries in 2005.

# **4. The wealth decomposition with human capital included**

Since the wealth decomposition with human capital included is based on two different sources, Liu (2011) and World Bank (2011), the assumptions used by each should be compared
<!-- Start of picture text --> Wildfire<br>Drought<br>Landslide<br>Extreme heat<br>Coastal flood<br>River flood<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>HIG MED LOW VLO Non coastal No data<br><!-- End of picture text --> Source: Authors based on AECM survey; Think Hazard.

**Within CGSs’ portfolios we estimate that 24-31 percent of guarantees are towards sectors that have elevated exposure to climate change and weather variability.** Since our classifications are both at a NACE 1 level, we use our largest sample covering 84 percent of total guarantees. For the classification by Lazo et al. (2011) based on US weather variability impact, we find that the combined exposures of CGSs in our sample to these sectors is EUR 65 billion (24 percent of total guarantees in the sample). This is driven primarily by manufacturing (EUR 29 billion) and agriculture (EUR 28 billion). For the classification obtained from Skougaard et al. (2017) on relevant sectors from a climate change perspective in Europe, we find that the combined exposure of CGSs in our sample to these sectors is EUR 83 billion (31 percent of total guarantees in the sample). This is driven primarily by agriculture (EUR 28 billion) and accommodation and food services (EUR 27 billion).

_4.3 Potential losses in adverse scenarios_

**We find that for transition and physical risk scenarios the annual expected loss on the guarantee portfolio could increase by EUR 181 million and EUR 128 million, respectively.** For our transition scenario the outcome is calculated for 2025, which represents the time horizon for which the ECB (2022) obtained the highest increases in default rates. After 2025 the annual expected loss declines. For our physical risk scenario (“hot house world”), the highest
Figure 7: Distribution of share of local suppliers, by type of purchase <!-- Start of picture text --> 0.20<br>200<br>150<br>100<br>50<br>0<br>0.0 0.2 0.4 0.6 0.8 1.0<br>Share of same-municipality suppliers<br> (Competitive tender)<br>0.22<br>200<br>150<br>100<br>50<br>0<br>0.0 0.2 0.4 0.6 0.8 1.0<br>Share of same-municipality suppliers<br> (Non-competitive tender)<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the distribution of the percentage of firms located within the same municipality where the tender process occurs, regardless of whether it is a headquarters or a branch. The distribution is disaggregated by type of purchase - competitive and non-competitive. Data is drawn from the tenderparticipant table and includes only suppliers who won the tender process and whose identifier has 14 digits. We match this data with the _Cadastro Nacional de Pessoas Jurídicas_ (CNPJ), a dataset provided by the Ministry of the Economy that contains information on every firm registered in Brazil. The temporal coverage of the data ranges from 2014 to 2021. See Table 1 for more details.

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We proxy for the size of the firm by the natural log of the number of permanent employees. In the ES, we are also able to construct a number of variables on firm characteristics that are described in Table 1. We defer to size classifications provided by the International Finance Corporation and the European Union to identify MSMEs as those firms that have less than 250 permanent employees. MSMEs are further divided into micro firms as those with less than 10 employees and the rest as SMEs.

In Table 2, we present summary statistics. In the ES sample, 24% of firms report having access to a loan. The average number of permanent employees in the sample of firms is 114, and the average annual growth rate is 6.5%. 53% of firms in the ES sample are in the manufacturing sector, 6% are government-owned, 10% have foreign ownership, and 23% are exporters. Furthermore, firms on average have been in business for about 17 years. Of the surveyed firms, 21% report being part of a larger ownership structure. In the Orbis sample of over 4.3 million observations comprising over 1 million firms in 29 countries, a relatively larger percentage of firms (37%) report receiving short or long term financing. Firms on average employ just under 100 permanent employees. Firms in the sample grow employment at an average annual rate of close to 7%.

# **_B. Introduction of credit bureaus_**

Institutions facilitating the sharing of credit information (such as private credit bureaus and public credit registries) have been shown to be critical for the development of credit markets across countries (La Porta et. al. (1998), Jappelli and Pagano (2002), Detragiache, Gupta, and Tressel (2005), and Djankov, McLiesh, and Shleifer (2007)). Martinez-Peria and Singh (2014) in particular show that the introduction of private credit bureaus has a stronger effect on firm 10
three parameters, the user can specify a variety of models. For instance, the random walk process for the logs of exchange rate can be easily specified by putting a unit coefficient for the lagged value and zeros for all other. If the user does not know the values for some parameters, he can check the _estimated_ option, then _INFRISK_ will use the data specified at the _data for estimation_ textbox to obtain its own estimate.

The _Random Component_ section allows the user to specify the distribution function, and other parameters associated with it. If the data are expected to possess a stochastic term, the user can provide the parameters for the error term, namely its distribution and the standard deviation. Several options can be used to specify the standard deviations for the random process:

- User-defined standard deviation

- Estimated from the historical data (the location to historical data is specified in the _data for estimation_ text box) using their residuals from the trend or deviations from a random walk model (first differences), when the projected values are used as the mean.

- Estimated from the ranges provided by the user in the input sheet. The ranges must be labeled following the format <VarLabel>Range (for instance, "ORRange").

In this section, the user can also specify the following:

- _Distribution._ Allows the user to select the probability distribution function for the error term. Presently, _INFRISK_ can handle four different error distributions: Normal, Lognormal, Beta, and Uniform. The "Beta distribution" option allows the user to model the errors around the trend using a right-skewed distribution based upon a member from the _beta_ family (as an example, see Box 4 for a formal description of _Beta_ distribution, its probability density function, and some important parameters).

- _Positioning._ Specifies whether the selected distribution is positioned around, above, or below the trend. Also the user can _truncate_ all generated values above or below the mean value of the given distribution.

- _Correlated With_ option allows
# **3 Data and Methodology**

## **3.1 Data sources and description**

The dataset used for this paper was compiled from a variety of sources, the full details of which are described in the technical annex. Here, we limit our discussion to the main variables of interest.

The main dependent variable that we consider is quarterly total foreign bank claims for BISreporting banks with lending to developing countries. The claims detail the foreign exposure, on a worldwide consolidated basis with inter-office positions netted out, of up to 5,615 BISreporting international banks with foreign exposure to as many as 138 developing economies per quarter. This coverage within the foreign claims universe is nearly 100 percent, and the foreign claims include cross-border claims in all currencies, along with local claims of foreign affiliates in both foreign currencies and local currency. In our robustness checks, we also consider the exposures of just the 150 U.S. banks with foreign exposure, along with those of the 4,488 BIS-reporting banks based in Europe.<sup>8</sup> Overall, claims appear to have responded with some lag from the start of the crisis in 2007Q3 (peaks were attained about year later), and troughed at the start of 2009 (Figure 2).

<!-- Start of picture text --> $ Trillions<br>4.0<br>3.5 Foreign claims, other Foreign claims, US banks<br>Foreign claims, EU banks<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2004Q1 2005Q1 2006Q1 2007Q1 2008Q1 2009Q1 2010Q1 2011Q1<br><!-- End of picture text --> Figure 2: Total cross-border foreign claims by BIS-reporting international banks to developing countries, disaggregated by exposure into EU, U.S., and other. Aggregate
depth of RTAs. In this case, the positive effect of migration-related provision shown so far can simply be the consequence of the overall depth of RTAs on bilateral migration and not that of a reduction in bilateral migration cost. To address this concern, in columns (1) and (2) of Table 10 we control for the overall depth of the RTA (i.e. the total number of provisions covered by the RTA from World Bank Deep Trade Agreement database), and show that our variable of interest keeps its sign and magnitude even after controlling for the overall depth of the RTA. It is not the overall depth of an RTA that affects bilateral migration, but the specific inclusion of migration related provisions reducing the bilateral migration costs.

– Table 10 about here –

> 30See Appendix section B for a detailed discussion on the construction of right wing nationalist party share of votes. The share of vote for the right wing party is comprised between 0 and 1. The median share of votes for the right wing party in our sample is 9% (i.e. 0.09 in our data), and the maximum level is 54%.

14
30 estimate an annualized return. We then take the mean of the annualized returns across the seven bins to obtain the _mean annualized return_ throughout the experience distribution (remember that we use any available survey data during the 1990-2017 period).<sup>43</sup> The results in Col. (4) of Panel A, Table 9 show that urban returns to education might be lower in NRXGDP and DEINDU countries, however not significantly so. If we use the product of the years of schooling and the returns, to obtain an imperfect measure of the _value_ of education, we see a substantially lower value of schooling in DEINDU countries, but not in NRXGDP countries (col. (5)).

In Panel B and the first column of Panel C of Table 9, we investigate the same correlations for the urban returns to education or their interactions with the urban number of years of education when also including country-year-sample-location fixed effects in eq. (13). Locations correspond to macro regional areas (“Reg 01”), first level administrative areas (“Reg 02”), or primary sampling units, hence survey clusters (“PSU”).<sup>44</sup> Doing so allows us to compare individuals residing in the same regional or local urban labor market. While the number of available country estimates decreases, we tend to find lower returns to education in NRXGDP and DEINDU countries, and no significant differences for the interactions with the number of years (not shown). Finally, we tend to find lower returns to experience in NRXGDP and DEINDU countries, but not all the estimated differences are significant (see the last 4 columns, Panel C).

We then use data from IPUMS to obtain the urban number of of education for years adults (ages 25 to 65) in 54 countries. We end up with 151 country-years. Since we select countries with at least two years of data, the sample size decreases to 104 observations and therefore we cannot include lags.
|0.3||||4|
|South Africa||17||||13||||||
|South Sudan|||||||||1|||
|Sudan|||||||||0.1|||
|Swaziland|||||33|||||20||
|Tanzania||11|||13|||3||||
|Togo|||||||37|||65||
|Uganda||24|||23|||34||||
|Zambia|51|||||12||24||||
|Zimbabwe||||||||9||8||

_Note:_ The statistics show the ratio of total exports across all firms sampled in a survey (calculated from the firmlevel World Bank Enterprise Surveys), relative to the aggregate national exports for the same year (calculated from the UN Comtrade data). Average exchange rates for the year from IMF are used to convert local currencies.

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