rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_General Considerations about Provisioning_**

The provisioning approaches discussed above all represent some improvement over the incurred loss model, and the underlying concept is all expected losses. The exact formula and calibration may differ across countries, but there are basic requirements on loan loss data for any system. Although some approaches discussed may rely on or allow for the use of internal models, for most of developing countries, a model designed and calibrated by the regulator is probably needed due to widespread data constraints. A supervisory model also provides the added benefits in terms of competitive equality and discipline of the management. Countries with centralized credit registry may have an advantage in implementing such a system.

Another important consideration in designing a provisioning regime is the transparency of design and disclosure. There are concerns about income smoothing and capital management if too much management discretion is allowed in provisioning.<sup>31</sup> A prescriptive rule-based system certainly reduces the scope of management discretion, but depending on its design, it may not be able to distinguish different levels of credit risk in the portfolios of individual banks. This means that the design of a countercyclical provisioning regime should be transparent and the disclosure should at minimum distinguish between the countercyclical component and the provision for losses that have been individually identified.

A related issue is whether expected losses should be covered through countercyclical provisions or reserves. Provisions are charged as an expense on the income statement of a bank and affect earnings. On the balance sheet, the accumulated provisions act as an offsetting account to gross loans. With expected loss provisioning, this can still be the way of presenting financial statements. However, as a countercyclical objective is included in provisioning, a question arises from a nonregulatory perspective whether it is appropriate to present provisions (and thus earnings) that are smoothed out over a certain period as opposed to presenting the current state of affairs. A potential alternative is to cover the countercyclical component of expected losses using reserves:<sup>32</sup> that is,
**The discrepancy between reported income and true income, a common issue in tax data, indicates the extent to which people underreport their income on their tax returns or tax evasion; in Romania, this is higher among low-income individuals.** The reported income, also known as imputed tax income, is the income that individuals or companies report on their tax returns. In contrast, the survey income, also known as true income, is the actual income earned by individuals or companies **.** The results show that underreporting is exceptionally high for individuals in the bottom half of the income distribution, where income is likely easier to hide. At the median, the gap between reported and true income is 10%, which means that people report only 90% of their "true" income. At the 25th percentile, the gap is 17%, indicating that people in the lower quartile report only 83% of their true income. However, at the 10th percentile, the gap is still 4%, which means that even those with the lowest income still report less than their true income. Here, we should notice that at the bottom of the income distribution, the possibility of paying employees via envelope payments is limited because enough income must remain to pay the minimum wage. Otherwise, formal employment relations would be lost.  There could be several reasons for underreporting, including tax evasion, an informal economy, and criminal activities. Individuals may underreport incomes to the tax authorities to reduce their tax liability. Alternatively, there may be a high incidence of informal economy activities, including "envelope payments," where individuals are paid in cash, and the payment is not recorded. Criminal activities, such as drug trafficking or money laundering, may also result in underreporting of income.

**Our data and method used in this note are also not good at capturing the underreporting of income** . **at the top of the distribution because top earners tend to be underrepresented in the survey
. Financial regulatory environment

<!-- Start of picture text --> Number of reforms<br>590 Improving Deteriorating Difference<br>490<br>390<br>290<br>190<br>90<br>-10<br>DB2008-14 DB2014-20<br>F. Capital account openness<br>Number of countries<br>100 Less restrictive capital account<br>More restrictive capital account<br>80 Difference<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

# F. Capital account openness

Source: Chinn and Ito (2006), World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A.C. The score is measured on a scale from 0 (weakest) to 100 (best/the frontier). “DB” before the year indicates the related Doing Business publication. Average performance of four indicators (A: Starting a Business, Paying Taxes, Registering Property, and Dealing with Construction Permits; C: Access to Credit, Protecting Minority Investors, Enforcing Contracts, and Resolving Insolvencies). Indicators are spliced backwards where methodological changes affected the level.

B.D. The number of reforms is calculated using the business reforms by year and by country as listed in the World Bank’s Doing Business reports. These are codified from the text list of business reforms as reported by the Doing Business survey. B. Business regulatory reforms include those business reforms categorized under Starting a Business, Paying Taxes, Registering Property and Dealing with Construction Permits. Unweighted averages for Internet access.
The share of the poor that work outside agriculture, that are children, and have no formal education are all noticeably lower in the fixed samples **.** For example, the share of the poor working outside agriculture drops 3.3 percentage points, from 35.4 to 32.1, when limiting to observations where urban/rural and education are non-missing. The share of the poor under the age of 15 drops by 3.3 percentage points in the first fixed sample, because the sample differentially excludes poor children that did not report educational attainment. The proportion of poor adults with no schooling decreases 12 percentage points from 43.7 percent to 31.5 percent in the second fixed sample, which excludes all children and non-working adults. This sensitivity indicates that the pattern of missing variables is not independent across variables. Conducting the analysis on a fixed subsample for which all data are present would therefore distort the findings.<sup>34</sup>

# **5. Conclusions**

Using harmonized household data from 89 countries, this paper provides an overarching demographic profile of the global extreme poor and moderate poor in 2013, shedding light on where they live and who they are, and the extent to which they work in the agricultural sector. Not only is this the most updated and most comprehensive profile of the poor in terms of global coverage, but the analysis breaks new ground by examining the characteristics of the moderate poor, and presenting conditional correlations between demographic variables and household welfare. Five main conclusions emerge.

First, both the extreme and moderate poor are rural and young, and mostly live in larger households with more children. More than four in five of the extreme poor live in rural areas, which is slightly more than previously thought. Moreover, 44 percent of the extreme poor are children under 15, and households with three or more children comprise nearly 60 percent of the extreme poor. The prevalence of child poverty raises the prospect of long-term consequences on the physical and
SME, both qualitative and quantitative. It is noticeable that very few banks use automated application processing. This result reflects to some extent the weak state of development of credit reporting systems in MENA, and the limited reliance on the quality of internal credit scores. However, the other two surveys also reported that internal scoring is only one element in the lending decision, suggesting that many banks in other developing countries are struggling with similar challenges.

At the same time, the average results reported by non-GCC banks disguise significant differences between state and private banks. As shown in Figure 15b, a significantly lower share of state banks has developed internal credit scores or internal ratings systems, and a very low share has adopted automated application processing, again revealing that these banks have not developed their SME lending technologies and risk management systems as much as the private banks.

A very low share of GCC banks informs their SME clients about the factors driving their internal score or ratings, as shown in Figure 16a. The same is true for state banks, as shown in Figure 16b. In the case of state banks, this result is probably associated with the low development and use of credit scores. In any case, these are not welcome findings, as they reveal missed opportunities to increase awareness among SMEs of their weaknesses and encourage improvements in performance.<sup>9</sup> A much larger share of non-GCC and private banks informs 9 Ayadi (2005).
012|-0.006|
||(0.007)|(0.009)|(0.007)|(0.009)|(0.008)|(0.009)|
|Obs|490371|490371|486594|486594|490371|490371|

_Notes: The dependent variable is the value of bilateral imports. Annual data from 2000 to 2018. All columns include importer-year and exporter-year fixed effects. Column (2) include country-pair (directed) fixed effects. Columns (5) and (6) include interactions between the internal trade dummy and year dummies. Standard errors three-way clustered by undirected country-pair, importer and exporter in parenthesis. * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level._ In Figure 4, we display the coefficient on the interaction between the internal trade dummy and the protectionism indicator obtained by estimating our baseline gravity regression by aggregate sector. The specification includes all the bilateral as well as the importer-specific controls (column (4) in Table 2 and odd-numbered columns in Table 3). We confirm the finding from 3: the border effect is significantly higher for countries whose law on government procurement has a preferential treatment provision, and this holds in the primary, manufacturing and service sectors. This effect is strong and consistent in the manufacturing sector, whereas it is less precisely estimated in the other two sectors. In services, the estimates suggest that importers without a national treatment clause, with preferential treatment and a domestic sourcing provision (what we classified as “protectionist”) have a lower border effect. This counterintuitive result is however reversed for importers with preferential treatment provisions, and is not significant for importers that impose domestic sourcing.

The results by industry summarized in Table A-6 in the Appendix are less conclusive than the results by sector, because of the incompleteness of the data at this level of disaggregation.<sup>8</sup
# **Jobs’ Amenability to Working from Home:**

**Evidence from Skills Surveys for 53 Countries**<sup>∗</sup>

Maho Hatayama<sup>§</sup> Mariana Viollaz<sup>ɬ</sup> Hernan Winkler<sup>ʄ</sup> _Updated August 2020_ JEL codes: J22, J61, O30 Keywords: Home-based-work, telework, internet, ICT, tasks.

> ∗ The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent. The empirical results for the Middle East and North Africa region are part of a background paper for the report “Economic Transformation and Jobs: Making markets work for people in the MENA region” of the World Bank MNA Chief Economist Office.

> § Junior Professional Officer. The World Bank, Jobs Group, Washington DC, e-mail: mhatayama@worldbank.org ɬ Senior Researcher. CEDLAS-FCE-UNLP. E-mail: mviollaz@cedlas.org

> ʄ Senior Economist. The World Bank, Jobs Group, Washington DC, e-mail: hwinkler@worldbank.org
>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 5-17 Children 5-17 Children 5-17<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17. Note: Individuals with missing age were dropped from the analysis.

49
of refugees, Venezuelan refugees and migrants and IDPs (in 2021) is taken from World Bank _World Development Indicators_ (WDI) <u>(https://databank.worldbank.org/source/world-development-indicators), UNHCR</u> Refugee Finder, and Global Internal Displacement Database by the International Displacement Monitoring Center (IDMC) <u>(https://www.internal-displacement.org/database/displacement-data), respectively. See Annex A: List of</u> countries included in this study.

13 The extraction of metadata from the UNHCR and WB MDL was performed in December 2022. This involved scraping the JSON files of all existing datasets from both sources and compiling them into a master list.
than urban areas, it matters whether countries account for such price differences when calculating the real income or real consumption expenditure of households. Currently, it differs whether countries use such spatial deflation (Mancini & Vecchi 2022). At times, there are also comparability issues within-countries over time, as countries frequently change the survey design and consumption aggregation methodology.

# **5.** **<mark>Choice of threshold</mark>**

Regardless of the inequality measure and data source used, there are no widely accepted standards for what constitutes a high level of inequality. The World Bank has decided to classify countries with Gini coefficients greater than 40 as high inequality countries. Here we show how this threshold relates to previous definitions of high inequality, the distribution of Gini coefficients in the World Bank’s Poverty and Inequality Platform (PIP), and a poll of World Bank experts.

## _5.1 Existing definitions of high inequality_

We reviewed existing reports that have proposed cut-offs designating high inequality. The United Nations Statistics Division occasionally conducts a ‘Progress Chart’ of the Sustainable Development Goals (SDGs) in which they classify the current level of selected SDG indicators into various groups. In the 2022 edition (United Nations 2022), they classified countries as having _low inequality_ if their Gini coefficient was less than 25, _moderately low_ if between 25-30, _moderately high_ if between 30-40, _highly unequal_ if between 40 and 45, and _very high_ if above 45. They rely on PIP data, and do not distinguish between consumption and income surveys when classifying. Hence, their threshold is identical to the one proposed by the World Bank.

A UNICEF report stated, “ _it’s commonly recognized that Gini index<0.2 corresponds with perfect income equality, 0.2–0.3 corresponds with relative equality, 0.3–0.4 corresponds with a relatively reasonable income gap, 0.4–0.5 corresponds with high income disparity, above 0.5 corresponds
Lastly, in addition to the performance indicators assessed here, survey data have also shown that corporate taxpayers in Latin America associate higher levels of autonomy with higher levels of performance.<sup>62</sup> That is, perceptions of higher levels of autonomy are associated with perceptions of better performance along key dimensions of administrative capacity in tax administration. These results, along with the objective performance indicators discussed in this section, provide support for the contention that semiautonomous revenue authorities are mechanisms to promote credible political commitments to reform in developing countries.

# **IV.  ORGANIZATIONAL DESIGN: HOW DOES AUTONOMY MATTER?**

The ARA reform model is a set of organizational features designed to enhance managerial and bureaucratic autonomy. Some features focus on general management, while others address financial or personnel autonomy. As the elements of bureaucratic autonomy are intertwined, the design features are inherently multifunctional, which means that a particular feature will have an impact on multiple dimensions of the organization’s autonomy. For example, greater financial autonomy, in addition to providing greater control over the use of resources, has the effect of increasing autonomy over personnel management.

The fact that the ARA model is actually a bundled set of reforms has three implications. First, there are many ways that an agency can be made more autonomous— there are a number of variations on the basic model. One agency might have more financial autonomy while another might have greater autonomy with respect to corporate governance. There is no set accepted best practice. Second, the impact of each particular design element cannot be easily isolated from the whole and evaluated rigorously on its own. This implies that an evaluation of each individual element must be analyzed _ceteris paribus_ . Third, given the plethora of design options and the difficulty in evaluating both each feature individually and the set of features taken as a whole, controversies have arisen over specific organizational characteristics that these agencies should have. The purpose, then, of this section is to set out the main issues in the organizational design of
# **5 References**

Armington, Paul. 1969. “A Theory of Demand for Products Distinguished by Place of Production.” IMF Staff Papers, 16, 159–178.

Becker, Elizabeth. 2004. “Amid a Trade Deal, a Debate Over Labor.” _The New York Times_ , April 6.

Brown, D., K. Kyota and R. M. Stern (2005) “Computational Analysis of the U.S. FTAs with Central America, Australia, and Morocco”, RSIE Discussion Paper, No. 527, University of Michigan.

Bussolo and Medvedev (2005) “What is the Role of Labor Markets in Making Trade Reforms ProPoor in Latin America” background paper for LAC FY06 Flagship “Virtuous Circles of Poverty Reduction and Growth”.

Bussolo, Lay, and van der Mensbrugghe (2005) “Trade options for Latin America. A Poverty Assessment Using Macro-Micro Linkages” mimeo World Bank, Washington DC.

Chen, Shaohua and Martin Ravallion. 2003. “Hidden Impact? Ex-Post Evaluation of an Anti-Poverty Program.” World Bank Policy Research Working Paper 3049.

Deininger, Klaus and Pedro Olinto. 2000. “Why Liberalization Alone Has Not Improved Agricultural Productivity in Zambia: The Role of Asset Ownership and Working Capital Constraints.” World Bank Policy Research Working Paper 2302.

Francois, J. F., L. C. Rivera and H. Rojas-Romagosa (2005) “Economic Perspectives for Central America after CAFTA: a GTAP-based Analysis”, presented at the GTAP Eighth Annual Conference on Global Economic Analysis, Lübeck, Germany, 9-11 June 2005.

- Hertel, Thomas W., Maros Ivanic, Paul V. Preckel, and John A. L. Cranfield 2004, “The Earnings Effects of Multilateral Trade Liberalization: Implications for Poverty” World Bank Economic Review. Vol. 18: pp. 205 - 236

Hilaire, A. and Y. Yang (2004) “The United States and the New Regionalism/Bilateralism”, Journal of World Trade,
the

> 5 In Peru, official statistics show a challenging scenario in terms of mental health in general. Data indicate that Mental, Neurological and Substance Abuse Disorders (MNS) are the leading cause of disease burden in the country, accounting for 17.5 percent of the total disease burden and for an average of 33.5 Disability-Adjusted Life Years for every 1,000 inhabitants (MINSA, 2018). Moreover, the severity of these effects tends to be worse among economically impoverished groups. Specifically, the annual prevalence of mental disorders among poor people is estimated to be up to 41.8 percent, twice as high as that of non-poor individuals (15.8 percent). The cost of inaction due to MNS is estimated at 1.45 percent of GDP in Peru (Bloom et al, 2011). Costs are higher when mid- to longterm effects of MNS are considered.

> 6 Using the National Longitudinal Survey of Youth in the United States, Frank and Meara (2009) do not find evidence that maternal symptoms of depression affect contemporaneous cognitive scores in school-aged children, as measured by the Peabody Individual Achievement Test in Math and Reading Comprehension, either. Along the lines of Surkan et al.’s (2012) argument for nutritional studies, it is possible that the heterogeneity of results across studies is the result of differences in measurements of both cognitive development and maternal depression, length of follow-up, sample characteristics, and sample sizes across studies, as well as varying level of social resources, caregiving norms, and both level of cognition, as well as prevalence of maternal depression in the populations under study.

5
# **2. Motivation and Theoretical Overview**

The empirical work here is motivated by a macroeconomic model based on micro behavior of workers describe in detail in Krebs and Maloney 1 998. It is built as a growth model so that secular movements in labor productivity can be incorporated and makes predictions about movements in formal and informal sector employment, the degree market segmentation, and labor turnover rates across the course of the development process. It also attempts to incorporate two emerging stylized facts about LDC labor markets.

# _1. The informal sector is extremely heterogeneous containing both voluntary and_

_involuntary members._ The informal sector is frequently consiidered the disadvantaged segment of a labor marketed segmented by government or union intervention in the wage setting process in the formal sector.<sup>4</sup> During downturns, the sector is thought to expand as it absorbs displaced workers, then contracting again with recovery.

While some fraction of the sector corresponds to this view, recent studies find that many of the informal employed are voluntarily so and should probably be viewed as unregulated entrepreneurs. Comparisons of formal/informal wage differentials traditionally used to show segmentation have been shown to be meaningless, and there appears to be high degrees of mobility among sectors.<sup>5</sup> The Mexican micro-enterprise survey suggests that 70% of workers enter the sector voluntarily for reasons of independence or higher income and recent time series _4_ See the classic statement of this view in Harris and Todaro(1974).

_5_ In the absence of any distortions, we should find a wedge between formal and informal incomes that incorporates the value of benefits forgone, the value of taxes evaded, the value of lifestyle differences between wage and self-employment, capital costs, implicit training costs and payments in kind. Without this information, wage comparisons-tell us nothing about segmentation or relative welfare between sectors. See MacIsaac and Rama (1997) and Maloney (1995, 1997a.) 3