rafmacalaba/data-use-mentions-extended huggingface dataset:
and we discuss several sensitive issues regarding, for example, what data should be made public and when, and the relationship between PCRs and private credit bureaus. The main focus of the paper is, however, on how PCRs can be used to improve the quality of credit analysis by financial institutions and to strengthen bank regulation and supervision. We present multicountry evidence of the importance of credit information sharing and on the importance of including positive and not just negative information. We also discuss the potential role of PCRs in banking regulation in the context of the proposed new capital accord known as Basel II.

The plan of the paper is as follows. In the next section we briefly discuss the development of PCRs across the globe, in Latin America and in more detail in the three countries where we conduct our empirical research. In section III we present the empirical analysis on the gains to information sharing and in section IV we present a policy discussion. This is organized around the various roles of credit bureaus. In section V we then discuss the main design parameters of PCRs relevant to using a PCR as a complementary tool to aid in the implementation of the Basel II proposals. Section VI concludes.

# **II. On the Development of Public Credit Bureaus**

Public credit registries (PCRs) contain information on the performance of borrowers in a financial system and are administered and maintained by either the central bank or bank supervisor. According to survey data collected by the World Bank, almost 60 countries have PCRs.<sup>5</sup> The region with the highest coverage of public credit registries is Latin America, where 17 countries have established PCRs, including all the largest economies (Argentina, Brazil, Chile, Colombia, Mexico).

The first countries to establish public credit registries were in Western Europe – Germany in 1934 followed by France in 1946. By the mid-1960s, three other European countries – Italy, Spain and Belgium – had also established PCRs. Early adopters included the former French

> 5 This
1.1. million in 2011 to about 1.8 million in 2016 providing many families an informal way of overcoming the limitation of housing supply in urban areas (e.g., Brueckner et al. 2018).<sup>17</sup> Densification of houses has many beneficial effects, including a reduction of land use costs as well as cost of connecting to utility infrastructure and services, particularly in areas of accelerated urbanization (e.g., Kurvinen & Saari 2020). While we deliberately apply a narrow definition of the _BHP_ , the presented methodology in this paper allows for easy transferability of other comparative units similar to _BHP_ that might be more suitable for other researchers’ focus.

# 4.2 Estimating the Basic House Price: Random Forest Estimation

To estimate the _BHP_ for each country, we run the following predictive regression specification using houselevel data that we obtain by web scraping online listings: Price,j,= f(Sizei, Typei, Chari, Locationi) where Pricei is the listed price of the property i; Sizei is the size in square meters; Chari is a vector of characteristics of the property i to include the number of bedrooms and bathrooms; Location is a vector to denote the location on property i, and includes, where available, the municipality, county, and/or city.

The predictive framework above is estimated in its linear form using OLS and through non-parametric estimation of the RF model. When it comes to ML approaches to predicting house prices, there is an

> 16 Recent research has applied deep learning models, especially convolutional neural networks (CNN), which allow the incorporation of the surroundings and other preferences into price estimation models (e.g., Law et al. 2019; Poursaeed et al. 2018).

> 17 Backyarding is not just a phenomenon in emerging economies. In Los Angeles or Sydney, for instance, so-called _granny flats_ or _casitas_ , which are essentially small backyard houses, are encouraged by the city administration to
# **6. Descriptive Statistics of Time Trends for Savings in Egypt**

# **6.1 ICA Data**

The ICA sample contains three waves of ICA data – one collected in 2003, 2005 and 2007. Each wave allows for calculation of two consecutive years of proxies for operating income to sales, “any investment” dummy and investment to capital ratio. However the retained earnings data are only available for the year of the survey. Thus we have 6 years of data for the first three measures (specifically data for 2002-2007) and three years of data for the last two measures (specifically 2003, 2005 and 2007). Also not all data are available for service firms.<sup>14</sup> Table 1 presents the number of observations for each of the five variables used in the study for industries and services. Some data appear unreliable and thus we exclude the outliers to prevent influential observation from driving our results.<sup>15</sup> Figure 10 shows the trends of three savings-related measures over time. The operating income to sales is slowly increasing over time, starting with about 18% in 2003 and reaching about 30% in 2007. This might indicate that the companies are becoming more profitable and have more cash flows available for investment or savings. Indeed over time a larger proportion of firms are making productive investment: “Any investment” measure is increasing from about 20% to 45% over this period. This pattern suggests that as more cash flow is available to firms they can make more investment.

The size of investment, measured as amount spent on investment relative to sales, has also increased in the past 4 years, rising from about 5% of total sales in 2004 to about 10% of total sales in 2007. However, as companies accumulate more capital, the incremental amount of new investment relative to existing fixed capital has declined (i.e., the investment to capital ratio (IK) has gone down from about
associated with higher income. This finding suggests that some aspects of mobility may be more relevant for development in some contexts than others.

The following section reviews relevant studies of intergenerational educational mobility. Section 3 examines measures of mobility. Section 4 describes the study’s empirical methodology. Section 5 describes the data. Section 6 presents the main patterns of intergenerational educational mobility. Section 7 presents the main results of the empirical analysis and associated robustness tests. Section 8 concludes.

# **2. Literature review**

Multiple studies carried out a descriptive exploration of the patterns of intergenerational educational mobility. Hertz et al. (2007) use data from 42 countries between 1992 and 2005 to measure the persistence of educational attainment across generations by birth cohort. They find significant variation in the cross-country levels of intergenerational educational mobility, with high persistence of educational attainment in Latin America and much higher educational mobility in countries of Northern Europe. Chevalier, Denny, and McMahon (2009) study the patterns of intergenerational educational mobility in 20 developed countries, including the United States, Canada, New Zealand, and 17 European countries, and find that expanding access to higher education is not always associated with increased mobility. <u>Causa and Johanson (2010)</u> find a positive and significant correlation between parental human capital and their children’s educational and wage outcomes in countries of the Organisation for Economic Co-operation and Development (OECD).

Financial constraints may hold back high-ability individuals from disadvantaged backgrounds. <u>Razzu and Wambile (2022)</u> study intergenerational educational mobility in 34 countries of SubSaharan Africa for cohorts born in the 1960s–1990s. They find that children’s educational outcomes depend strongly on their parents’ educational status but that the strength of this association is weaker for younger generations. The authors point to high heterogeneity in educational mobility across African countries. Van der Weide and others (2023) compile a global database of intergenerational educational mobility in 153 countries and demonstrate that such mobility is estimated
The demand for enforcement also might be higher for large and medium firms due to higher labor union representation. While the trade union density or coverage of workers has reduced from close to 100% in the Soviet era, it still remains high for large and medium firms. For instance, the largest trade union in Russia, FNPR, claims to represent 70 percent of the workers in the large and medium firms. Small firms, on the other hand, have few workers who are represented by the union (Gimpelson 2010; Ashwin and Clarke, 2002).

The Labor Code itself has specific restrictions that apply to larger firms. Article 217 of the code stipulates that all organizations with over 100 employees need to either setup a labor protection service or employ a labor protection expert. In addition, these organizations need to provide an elective trade union body and a “room and appliances” for its functioning, all free of charge (Labor Code, 2002).

# **3. Data**

## **3.1 Labor regulation enforcement**

The measures of labor law enforcement used in this paper were originally published in Vishnevskaya and Kapelyushnikov (2007), and subsequently discussed and analyzed in more detail in Gimpelson et al. (2010). The measures reflect the capacity of labor inspectorates and courts to enforce the regulation or judge disputes, and the demand/voice for enforcement as expressed by the number of cases filed. The authors collected these data for the years 2000‐2005 from the records of the Federal Labor Inspectorate and the Judicial Department of the Supreme Court. The measures are averaged over 2000‐2005, and as suggested by the authors, expressed per hundred thousand employees in large and medium firms, the population effectively covered by the regulation. Table 1 summarizes these measures, which cover 79 regions.

Our preferred measure of enforcement capacity is the number of labor inspectors per hundred thousand employees in large and medium firms. This captures the capacity of the labor inspectorate. Another such measure, the
contrasts with the use of tariffs which have been on a downward trend over a similar time frame (see Figure 4).

When it comes to the use of NTMs to regulate trade, there is a clear divide between developed and developing countries. Often, the higher the income level of a country, the lower the level of tariffs and the higher the likelihood of making extensive use of standardlike NTMs (World Bank, 2020; Garcés and Vogt, 2024). We observe a similar pattern in our dataset: developed countries—measured in terms of their gross domestic products—are more likely to use standard-like NTMs as market access tools (see Figure 2b).

> 10Using NTM data from multiple sources, Garcés and Vogt (2024) review the intensity, prevalence and structure of NTMs globally and confirm that the majority of technical measures are formally applied in a non-discriminatory fashion across trading partners.

> 11This dataset contains information on antidumping, countervailing, and safeguard measures for about 30 countries. The data is available at `https://www.worldbank.org/en/data/interactive/2021/03/02/ temporary-trade-barriers-database`

> 12We limit our focus to standards and technical regulations, that is, SPS and TBT measures. At this stage, we only lose 1% of the observations when we do this. TBT measures make up 66% of the sample observations and SPS measures make up 32.74%. With the 1% we exclude made up of non-technical measures such as pre-shipment controls, quantity controls, and price controls.

7
Figure C15: Heterogeneity in poverty gradient across countries, DB <!-- Start of picture text --> MWI<br>LSO<br>AGO<br>UGA<br>GHA<br>GNB<br>BFA<br>NER<br>SEN<br>GIN<br>TCD<br>CIV<br>BGD<br>ETH<br>TZA<br>MRT<br>Core<br>COL<br>Suburb<br>GAB<br>Towns<br>VNM<br>−.5 −.4 −.3 −.2 −.1 0 .1 .2<br>Regression coefficients and confidence intervals<br><!-- End of picture text --> Source: International Urban Poverty Database. Each dot represents the regression coefficients _DBj_ ( _i_ ) of regression 6 for each country _c_ , excluding control variables. Confidence intervals are represented with a line. The dependent variable _POVi_ is a dummy variable taking the value 1 if a household’s per capita expenditure expressed in PPP and spatially deflated falls below the poverty line. _DBj_ ( _i_ ) is a vector of dummy variables: "Core", "Suburb", "Town" respectively take the value 1 if household _i_ lives in an "Core", "Suburb" or "Town" as defined by the DB method. The baseline category is "Other rural". WorldPop 250m is used.

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benefit. We present weighted averages from the 2013 round of the ENCV. As can be seen, coverage levels are fairly low in each of the target groups. Even among individuals who are older than 65 years of age, only slightly over 50 percent of the indigent and of persons living in households that are both poor and in Sisben groups 1 or 2 receive the benefit. Among all other groups, coverage is significantly lower.

**Table 3: Share of Beneficiaries according to ENCV 2013, percent**

||_Age eligible_|_60 or older_|_65 or older_|
|---|---|---|---|
|Sisben eligible|32.44|37.20|40.69|
|Poor|31.85|39.91|47.88|
|Indigent|36.72|44.83|54.65|
|Sisben eligible or poor|28.69|34.80|39.92|
|Sisben eligible andpoor|40.84|46.79|52.04|

A different way to address this question is shown in table 4. Here, we look at the share of program beneficiaries within each group in 2013. As becomes clear, almost 30 percent of the beneficiaries did not fulfill the Sisben requirement, and almost 40 percent are not poor in monetary terms. While only 10
Figure 9: Average Sorghum and Millet Prices across Sudan (a) Average Sorghum and Millet Prices in Markets across Sudan 2003–2016 (2010 = 100) <!-- Start of picture text --> 2009 NBHS 2014–15 NHBPS<br>500<br>Millet Sorghum<br>400<br>300<br>200<br>100<br>0<br>(b) Average Sorghum and Millet Prices in Markets across Sudan, 2015–2019 (2010 = 100)<br>1500<br>Millet Sorghum<br>1000<br>500<br>0<br><!-- End of picture text --> _Source:_ Global Food Prices Database World Food Programme.

Rural poverty has reduced across regions of Sudan, although states experiencing civil conflict have not seen reductions (Figure 10). South Kordofan, West, and Central Darfur are the only states where rural poverty has not reduced. These states witnessed conflict due to separatist movements, which have led to greater disparities in poverty rates between these regions and the rest of Sudan. In 2014/15, these states had the highest rates of rural poverty. More recently, due to armed conflicts subsiding in Darfur, there has been a steady trend of internally displaced persons (IDPs) returning over the past few years (FAO 2019). As these households return to engaging in agriculture, the Darfur region’s agricultural production has also seen significant gains.

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Interviews were conducted by phone and the primary respondent was the respondent from the initial MRALS study, typically the household head. The labor module asked this respondent about their own labor participation and additionally to report the labor activities of up to two other adult household members ages 18 to 65. These adults were pre-selected randomly in households with more than three adults. While survey protocols allowed interviews to be done with other household members if the respondent was unavailable, in practice this happened very infrequently.<sup>7</sup> Column 1 of Table 1 shows descriptive statistics of study participants who were successfully reached for the full set of quarterly interviews and who constitute the sample used in the empirical analysis. Household level characteristics, those of the designated respondent, and those of proxied respondents are shown in panels A, B, and C, respectively. Panel A shows that our final sample contains 701 households, with an average of just over five members split evenly between children and adults. In Panel B we see that 75 percent of primary respondents are men and their average age is 44. Sixty-nine percent are in a monogamous marriage while 10% are in polygamous marriages with 6.6 years of education on average. Panel C shows the characteristics of the other household members whose data was collected in the survey. These household members are younger, 30years-old on average, and 76% are female with similar levels of education.<sup>8</sup> Appendix Table 1 shows these summary statics from the analysis sample with those targeted from the sampling of the initial MRALS sample. They are nearly indistinguishable.

Households were contacted approximately once per quarter over the course of a year. While the surveys were intended to be spaced evenly, three months between surveys, in practice logistical issues affected this timing and led to some variation in the actual interval between interviews. Dates and timing of each survey wave are illustrated in Figure 1. These quarterly surveys were designed to be brief, averaging under 20 minutes, focused on labor
RCS converted these national poverty lines to a common currency using the household consumption PPPs derived from the 2005 round of the _International Comparison Program_ (ICP) (World Bank, 2008a,b). We use the same PPPs to convert the international line back to local currency for measuring poverty. The 2005 ICP is the most complete and thorough assessment to date of how the cost of living varies across countries. The ICP collected primary data on the prices for 600-1000 (depending on the region) goods and services grouped under 155 ―basic headings‖ deemed to be comparable across 146 countries. The prices were obtained from a large sample of outlets in each country. The price surveys were done by the government statistics offices in each country, under supervision from regional authorities.

While these are clear improvements, the 2005 PPPs still have some limitations.<sup>8</sup> There is a problem of ―urban bias‖ in the ICP price surveys for some counties in that it appears that the surveys were largely confined to urban areas.<sup>9</sup> Based on ICP sampling information we treat the 2005 consumption PPPs as urban PPPs for Argentina, Brazil, Bolivia, Cambodia, Chile, China, Colombia, Ecuador, Pakistan, Peru, Thailand and Uruguay. We then use existing differentials in urban-rural poverty lines at country level for these countries (from Ravallion et al., 2007) to correct the national PPP.

As was argued in Ravallion et al. (1991), a further concern is that the weights attached to different commodities in the conventional PPP rate may not be appropriate for the poor. Deaton and Dupriez (2008) have estimated ―PPPs for the poor‖ for a subset of countries with the required data; the results do not suggest that the implied re-weighting has much impact on the consumption PPP, as we show in Chen and Ravallion (2010).

Figure 1 plots the poverty lines for developing countries compiled by RCS against log household consumption per capita from national accounts
IHS(Ebola Cases)|||||||
|Total|0.206|0.096|0.082|0.11|-0.029|5,440|
||(0.47)|(0.043)**|(0.053)|(0.051)**|(0.059)||
|Confrmed|0.009|0.033|0.029|0.037|-0.007|5,440|
||(0.1)|(0.011)***|(0.013)**|(0.013)***|(0.013)||
|Negative|0.179|0.066|0.047|0.086|-0.038|5,440|
||(0.433)|(0.036)*|(0.045)|(0.043)**|(0.051)||

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Covariates included (all measured as proportions of households): (1) beyond primary education, (2) Mende, (3) ill in last month, and (4) own mobile phone. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

A62
We use reported Colombian GDP in constant 2005 pesos as a proxy for licit aggregate income that includes both private as well as public income (RGDP ) in the following way We also use an estimate of aggregate real consumption in the Colombian economy, an observable quantity, obtained from National accounts We use a measure of the real stock of capital (KS) for the Colombian economy, for which more than one estimate is available. We use the measure of real aggregate capital stock in each period from the Departamento Administrativo Nacional de Estadísticas (DANE), Colombia’s National Bureau of Statistics Finally, for our calibration measure of laundered assets we use the real interest rate which can be obtained by using the Fisher identity which says that the real interest rate (RIR) plus one is equal to the nominal interest rate plus one divided by the in‡ation rate plus one, yielding the following measure All of the data used were collected by the Unidad de Información y Análisis Financiero (UIAF) from September 2013 to March 2014.<sup>18</sup> All macroeconomic time series come from o¢ cial sources, mainly from the Banco de la República of Colombia, National Bureau of Statistics DANE, National O¢ ce of Taxes and Customs DIAN, National Planning Department DNP and Treasury Department. Data on crimes and drugs come from the Ministry of Defense, National Police Department, National Institute of Prisons INPEC, United Nations O¢ ce on Drugs and Crime UNODC, Ministry of Justice and the District Attorney’s O¢ ce. Although the data were initially to be collected quarterly for the period 1999 to 2013, data limitations made this unfeasible. Hence, data were collected at an annual frequency for 1985 to 2013, which a¤ected the size of the time series. Not all of the data collected were used since the theoretical model guided the ...nal data necessary for the application of the estimation methods. Descriptive statistics for the variables used in the estimation are reported in Table 2.
5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0<br>Male-owned Female-owned<br><!-- End of picture text --> Source: Author’s calculations using ESS 2018/2019 (period 1) and ESS2021/2022 (period 2).

Most are microenterprises (defined as having five employees or fewer) and 94% of female businesses are sole proprietors (with no employees) compared to 83% of male businesses (Table 3).<sup>12</sup> Female enterprises earn less revenue and are less likely than male enterprises to be registered. Manufacturing or sale of local beverages – a female-dominated activity – generates the lowest median annual sales of all the sectors (Table 12 The government defines microenterprises to be those with no more than five employees and assets valued at or less than 1 million birr (World Bank 2021).

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