rafmacalaba/datause-encoder-data huggingface dataset:
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# THE WORLD BANK

There has been a marked increase in proposed lending for population, health, and nutrition in 1989. The estimated SM content (broadly defined) is about 50 percent of the total, about the same as in the preceding three years. The volume of estimated SM activities for 1989, however, has tripled over 1988.

These estimates are tentative. Most SM programs fall under family planning and maternal and child health services. The data are insufficient to estimate financing for direct medical services to highrisk mothers.

As the table shows, the trends in World Bank financing are favorable. Whether financing will increase for specific measures to combat the prevailing causes of high maternal mortality may be an issue beyond Bank lending policy. The decision may be largely dependent on the borrowing countries' ability to identify risks to maternal death and to incorporate adequate counter-measures within project design. Thus measures to support borrowing covntries in identifying and articulating their demands are becoming far more critical than the supply of financing.

||Estimated Financia|l Flows to DeveloD<br>**S**millions|int Countries|||
|---|---|---|---|---|---|
||_1985_|1986|1987|1988|1989/a|
|Total**population,**<br>**health**||||||
|**and austriiom**<br>leding||||||
|*** Asi**|85.0|24Z0||74.5||
|E<br>Europe, Middle East||||||
|<br>North Africa<br>|41.9||13.1|||
|Latin America/||||||
|Caribbean||96.0|10.0|109.0||
|Africa|64.1|81 1|30.8|121.0||
|**Total**PHN sctor /b|191 0|4195|54.1|304.0||
|Total, PHN sector based<br>on Staff**Appraisal**Report (SAR)||414.0|539|**315.8**|729.0|
|Estimated SM content based<br>on SAR!c|-|177.9|36.5|1039|3660|
|SMasestimated percentage<br>of SAR||**420**|670|**320**|**50.0**|

I! Data excluded prior to iouance of 1989 Annual Report /b **Data** from 19S8 **Annual** Report

**/c** 19S8 **estimates are** tentative and reflect **an** increase in loan approva **l** evels.

Note: The table is based on Annual Reports. Because _r,_ the limited detail on projects in these reports, the Staff Appraisal Reports for 1986-89 have been used to provide more information.

The World Bank 1818 H Street N.W. Washington, D.C. 20433
6 details on bilateral tariffs (incorporating all significant reciprocal and non-reciprocal preferences) including ad valorem tariff equivalents of specific and compound tariffs and key non-tariff barriers such as tariff rate quotas (TRQs).<sup>2</sup> The new GTAP database has lower tariffs than the previous Version 5 database because of the inclusion for the first time of non-reciprocal trade preferences and because of major reforms between 1997 and 2001 such as China’s progress towards WTO accession (which alone contributed to the ratio of global exports plus imports to GDP rising from 44 to 46 percent over those four years) and the continued implementation of the Uruguay Round agreements (van der Mensbrugghe 2006).

According to this dataset, the average import tariff in high-income countries in 2001 is 20.2 percent for primary agricultural products, and 13.0 percent for processed food, compared with just 0.5 percent for other primary products and 2.1 percent for other manufactures.<sup>3</sup> In developing countries, by contrast, the average tariff for primary agriculture is lower at 17.0 percent, while it is higher for the other three sectors at 19.3 percent for processed food, 2.5 percent for other primary products, and 10.0 for other manufactures. Needless to say, these averages vary considerably within those two country groups, as shown in Table 1(a).

These import tariff averages can be poor indicators of overall assistance to farming, even if there were no farm production or trade subsidies or any exceptional tax

> 2 More information on the MAcMaps database is available in Bouët et al. (2004) and at <u>http://www.cepii.fr/anglaisgraph/bdd/macmap.htm. For details of its incorporation into the GTAP</u> Version 6 dataset, see Dimaranan and McDougall (2005) and <u>www.gtap.org.</u>

> 3 High-income countries include all members of the EU25, NAFTA and the OECD (except Turkey) plus Hong Kong, Singapore and Taiwan. All others (including the wealthy Middle East countries) are considered developing countries and are further split into middle- and low-income categories as defined at the bottom of Appendix Table A1.
defining as "pivot" a household of five members, two adults and three children (one under-five years child, one child between five and nine, and one over nine child). This second approach has been retained for poverty estimation.

To perform sensitivity analyses, Equation (1) could be written in accordance with the formulation of Cutler and Katz (1992) as follows: In this case, the effect of the household size is captured by  while the effect of the household composition is captured by a single discount factor  .

The study is based on data from the World Bank’s African Database. Only surveys over the period 2007-2012 are considered, which represents 35 African countries and accounts for around 75 percent of the total population of Sub-Saharan Africa. The main variables include household consumption and other socioeconomic and demographic characteristics of households.

# **4 Results**

# **4.1 Poverty estimates and age group comparisons**

Table 1 shows initial and adjusted poverty rates in Africa by various age groups. Poverty is based on the international poverty line ($1.90 a day, 2011 PPP). With the initial poverty based on the per capita approach, there are some statistically significant differences between various age groups of children, as well as between children and adults. In fact, the poverty incidence appears to be higher for under-five children, followed by the poverty incidence for children aged 5 to 9 years. All together, all under 18 children have a poverty incidence of 50.4 percent, which is higher than that of adults by 11.4 percentage points. When poverty is adjusted using the FAO/WHO equivalence scale, gaps between groups of children disappear, while the difference between children and adults is reduce to 6.7 percentage points. A similar pattern is observed for the squareroot equivalence scale with somewhat lower poverty rates.

10
Figure 5.15: Monitoring of Credit Risk Exposure Figure 5.16: Stress Testing on SME Portfolio <!-- Start of picture text --> Which of the following characterize some of the ways the bank monitors credit risk Do you run any of the below mentioned stress test exercises on the SME<br>outlook over time for each particular SME? portfolio?<br>M o nitoring of client's health relies on No 75%<br>the diligence of the relationship 75%<br>manager/credit analyst Slow-down in a particular sector 13%<br>Interest rate shocks 13%<br>Indicators/triggers are automatically<br>75%<br>generated<br>GDP shock (when relevant) 0%<br>Commodity price shock (when 0%<br>relevant)<br>Preventive indicators/triggers are<br>38%<br>monitored Currency shocks 0%<br><!-- End of picture text --> _Source: Authors’ analysis of bank questionnaires (simple averages across all banks). Note: Figure 5.15 does not add up to 100 percent because some credit institutions provided more than one response._

# **_5.2.3  Management of NPLs_**

Banks carry out similar actions once a loan is past due, while recovery efforts usually take place in-house. Once a loan is past due, similar actions are undertaken by most banks. In the initial stage, the relevant commercial manager or RM attempts to contact the borrower in order to receive the late payment; if unsuccessful, this is followed by the initiation of a pre-judicial recovery process (administrative collection) and the account is transferred to a dedicated SME recovery unit. Most interviewed credit institutions have such a unit that manages past-due loans (Figure 5.17), while others delegate this task to the relevant branches or regional centers (but they maintain a central roster of lawyers). Finally, once the loan officially moves to non-accrual status, a judicial procedure against the borrower is initiated. Few banks outsource SME loan recovery operations (in the sense of selling these loans to private collectors), although several of them use external lawyers in the foreclosure process. Interviewed credit institutions also state that they do not have standardized loan restructuring policies, and most of them prefer to tailor solutions to the client’s repayment capacity.

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# Table 5a. Association of ECED sequence and test scores in primary school

|||**Sequence**<br>|<br>|
|---|---|---|---|
||Language<br>(1)|Maths<br>(2)|Cognitive skills<br>(3)|
|**Sequence**(Base: No ECED [I])||||
|Playgroup then primary [II]|0.0558**|0.0861***|0.0284|
||(0.0263)|(0.0261)|(0.0298)|
|Kindergarten then primary [III]|0.237***|0.220***|0.132***|
||(0.0228)|(0.023)|(0.0269)|
|Playgroup then kindergarten then primary [IV]|0.408***|0.416***|0.198***|
||(0.0283)|(0.0286)|(0.0347)|
|Other combination [V]|0.441**|0.305|0.118|
||(0.175)|(0.196)|(0.251)|
|Constant|-0.559***|-0.511***|-0.353***|
||(0.0379)|(0.0381)|(0.0445)|
|Observations|12,710|12,710|12,710|
|R-squared|0.281|0.233|0.108|
|Pairwise contrasts||||
|Sequence:||||
|[II] - [I]|0.0558**|0.0861***|0.0284|
|[III] - [II]|0.182***|0.134***|0.0660**|
|[IV] - [III]|0.171***|0.196***|0.09|

Note: Robust standard errors clustered at the village level in parentheses. All regressions include child characteristics (age, grade, gender, and stunting), family characteristics (household wealth, mother’s education, parenting practices), ECED characteristics (average quality, average fees), and district fixed effects.

*** p<0.01, ** p<0.05, * p<0.1

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from the United States.<sup>1</sup> One exception is Dillon (2010), who compares two different child labor modules within the same survey, one a standard set of labor questions that collects information on participation and hours across various activities posed to parents about their children, and another subjective game played by children that reveals the distribution of their time. Comparisons between the two modules suggest that adults report lower hours of child labor when using a standard labor module relative to children who play the subjective game, but the paper cannot disentangle the proxy effect versus the effect of question type/design.

The objective of this paper is to explore further what aspects of survey design affect child labor indicators to assess these tradeoffs. We focus on two main areas: the effect of including screening questions to structure the questionnaire regarding labor market activities and the respondent type. The sequencing of employment questions is posited to have a large influence on labor statistics. This may be particularly relevant in a setting where a significant proportion of individuals are employed in household enterprises or home production and are not directly remunerated in the form of a salary or wage. Classification of activities between those that are considered “work” and those that are not may induce confusion in survey respondents who are not familiar with internationally recognized definitions of labor market activities and may have a very personal concept of “employment.” For example, the stand-alone question “Did you work in the last 7 days?” is hypothesized to systematically undercount persons who work in household enterprise activities without direct wage payments, e.g., unpaid family workers or women (Anker, 1983), who may not recognize themselves (or be recognized by other household members) as “employed individuals.” This type of employment question may also be flawed for measuring child labor, especially when children participate in economic activities related to the household enterprise or home production, and even more so when such activities are seasonal, occasional, or occupy only a few hours a week. This is especially the case in developing countries.

Respondent type may also influence the labor statistics generated. Borgers et al. (2000) illustrate that, given the appropriate question structuring and interview

> 1 Bardasi et al. (2010) review some of the literature with a focus on evidence from low-income settings.

3
# Inclusive Statistics: Human Development and Disability Indicators in Low- and Middle-Income Countries

Sophie Mitra, Jaclyn Yap, Justine Hervé and Wei Chen

## 1. **Introduction**

The Sustainable Development Goals (SDGs) endorsed by the United Nations member states have the mission to “leave no one behind”. Monitoring the SDGs is critical. The Inclusive Data Charter signed by the World Bank and other global data partners in 2018 aims to mobilize political support to improve the quality, quantity, availability and financing of disaggregated data to support the ambition of the 2030 Agenda for Sustainable Development of leaving no one behind. More data are required to highlight different situations of various socioeconomic and geographic groups including persons with disabilities.

The lack of data and statistics on disability is in part responsible for the lack of attention to disability in development, but there has been growing awareness on the need for data on disability and improved tools to collect data on disability in an internationally comparable manner. The report by the High-Level Panel on the Post-2015 Development Agenda repeatedly refers to disability, including in the call to end extreme poverty and “leave no one behind” and in the call for a “data revolution”: “data gathered will need to be disaggregated by gender, geography, disability, and other categories, to make sure that no group is left behind.” Disability has been included in several SDG targets and as a cross-cutting issue in the 2030 Agenda for Sustainable Development. As argued in the United Nations report (2019), “efforts need to be stepped up to ensure that the goals and targets will be achieved for persons with disabilities too, in line with the Convention on the Rights of Persons with Disabilities (CRPD)”.

Using 24 population censuses and general household surveys for 21 low- and middle-income countries (LMICs) that have internationally comparable disability questions, this paper disaggregates human development indicators across disability status to assess the situation of persons with disabilities and disability related inequalities and facilitate future monitoring. We aim to contribute to SDG target 17.18 “to increase the availability of data disaggregated by disability”.

Disability status is measured through self-reports of functional difficulties (e.g. difficulty seeing, hearing) as per the Washington Group Short Set (WGSS) or questions deemed similar. The WGSS is a widely accepted and internationally tested tool (Groce and Mont 2017) that can be used for disaggregation based on disability (Loeb 2015, Washington Group on Disability Statistics 2016). Statistics are comparable across disability status within a country and to a large 2
between landlord control and industry employment, we compare average (firm size adjusted) industry employment in our three industry categories in landlord and nonlandlord districts (Table 3). We find significantly lower levels of industry concentration in landlord districts for all industry types. This suggests that the differences between these two categories is not simply an industry composition effect, but truly reflects lower levels of industrial activity in landlord districts. We also ran OLS estimations and find that having a historic landlord based land revenue system is negatively associated with industry concentration.

# **4 Results**

# A. <u>Data</u>

To estimate the models set out in the preceding section, we have combined industry survey data from the World Bank’s Investment Climate Survey (ICS) of India along with establishment level data from the Annual Survey of Industries (ASI).<sup>15</sup> The ICS survey was conducted from March to July 2003 on a random selection of 1,860 manufacturing establishments sampled from 40 cities in 12 of India’s 14 major states.<sup>16</sup> A list of cities covered in the ICS is provided in Appendix 1. These cities are representative of the main industrial centers in the country as each state is represented by its top 3 or 4 industrial cities accounting for the bulk of its manufacturing Gross State Domestic Product (GSDP). These 12 states themselves account for 94 % of India's

> 15 The World Bank and the Confederation of Indian Industry (CII) have jointly carried out the ICS, and the Indian Central Statistical Office annually carries out the ASI. The ASI covers factories registered under sections 2m(i) and 2m(ii) of the Factories Act 1948, employing 10 or more workers and using power, and those employing 20 or more workers but not using power on any day of the preceding 12 months. 16 These are Andhra Pradesh, Delhi, Gujarat, Haryana, Kerala, Karnataka, Madhya Pradesh, Mahrashtra, Punjab, Tamil Nadu, Uttar Pradesh, and West Bengal.

24
However, there are categories in Table 1 where the mapping is less straightforward. Specifically, items related to rights and guarantees established in various kinds of regulations (in category 2) require country-specific institutional knowledge for a detailed mapping. Similarly, clauses for wage or asset adjustment or countercyclical fiscal policy (in category 4) are difficult to determine within the BOOST without deeper country background. Items related to automatic stabilizers or special stimulus programs are not easy to find in the BOOST, and input from a country economist is required. Finally, after reclassifying each country’s expenditures, remaining categories of spending are classified as discretionary spending.

**Figure 12: Moldova’s budget by discretionary and Rigidity Figure 13: Bulgaria’s budget by discretionary and Rigidity shares shares** <u>(percent)rcent)cent)nt)t) (percent)rcent)cent)nt)t)</u>

<!-- Start of picture text --> (percent)rcent)cent)nt)t) (percent)rcent)cent)nt)t)<br>Bulgaria (shares of the budget)<br>Moldova (shares of the budget)<br>Discretionary Expenditure Rigid Expenditures Discretionary Expenditure Rigid Expenditures<br>100% 100%<br>32% 30% 26% 21% 18% 14% 18% 23% 23% 20% 20% 18%<br>80% 80%<br>60% 60%<br>40% 68% 70% 74% 79% 82% 86% 40% 82% 77% 77% 80% 80% 82%<br>20% 20%<br>0% 0%<br>2006 2007 2008 2009 2010 2011 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: BOOST dataset, Force Toolkit and authors’ calculations Source: BOOST dataset, Force Toolkit and authors’ calculations

**Figure 14: Armenia’s budget by discretionary and Rigidity shares** <u>(percent)</u>

<!-- Start of picture text --> Armenia (shares of the budget)<br>Discretionary Expenditure Rigid Expenditures<br>100%<br>80%<br>54% 58% 50% 54% 51% 50% 45%<br>60%<br>40%<br>20% 46% 42% 50% 46% 49% 50% 55%<br>0%<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Source: BOOST dataset, Force Toolkit and authors’ calculations

**Figure 15: Paraguay's budget by discretionary and Rigidity shares** <u>(percent)</u>

<!-- Start of picture text --> Paraguay (shares of the budget)<br>Discretionary Expenditure Rigid Expenditures<br>100%<br>80%<br>48% 45% 47% 46% 46% 46% 44% 46% 48%<br>56%<br>60%<br>40%<br>52% 55% 53% 54% 54% 54% 56% 54% 52%<br>20% 44%<br>0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Source: BOOST dataset, Force Toolkit and authors’ calculations
row is the total attributable to measured characteristics (E+C). We also provide, for information, two additional rows giving the raw means for the two groups of firms (the difference is the amount to be explained, in the “Total” row); and two additional columns giving the mean characteristics (endowments) for the two groups of firms. The explanatory variables are size (log L), location (a “big city” dummy), export activity (dummy variable), majority foreign ownership (dummy variable), and sector dummies. The treatment of sector dummies is different – for reporting purposes, manufacturing is chosen as a benchmark category and the “Sector” row reports the total decomposition for non-manufacturing vs. manufacturing. The results are based on fixed-effects regressions with country dummies.

We begin with Table V.5, which reports the decomposition applied to our basic benchmarking question: how do firms in transition economies (TEs) differ from firms in developed market economies (DMEs) in 2005? The base category is TEs, so the differences are what make DMEs different from TEs. The “DME mean E” and “TE mean E” columns show that the two sample of firms do differ significantly in basic characteristics: the TE firms are substantially larger (employment is about 35% higher); are less concentrated in manufacturing than the DME sample; and are more likely to be found in big cities. The big difference between the two groups of firms is the lower reliance of DMEs on retained earnings (14.2 percentage points) and the higher use of bank credit (7.6 percentage points). It turns out that these raw differences reported in the “Total” row are only modestly different from those in the “Region (DME dummy)” row, which is the difference between the groups after account for the impact of different endowments E and coefficients C. The biggest single identifiable impact is size: although the smaller size of the DME firms make them slightly more likely to rely on retained earnings (1.8 percentage points), this is reversed by the smaller (in absolute value) negative size effect for DMEs. But this is dwarfed by the large shift coefficients that remain after accounting for measurable characteristics: firms in TEs rely more on retained earnings (9.1 percentage points), more on family and informal sources (5.6), and less on bank financing (-6.6) and other sources (-6.7). The fact that the differences

> 11 We used Ian Watson’s (2005) “decomp” addin for Stata for the decompositions in this section.

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Policy Research Working Paper 5369

# **Abstract**

There has been substantial research in recent years examining the regional evolution of economic growth across states in Mexico—with a particular focus on the post North American Free Trade Agreement period. There is also a vast literature using cross-country regressions to examine institutional determinants of economic growth, including government transparency, or “corruption,” as a key institutional variable. This paper uses more recently available data for Mexican states to both update the general state convergence/divergence literature, and incorporate into the analysis more recently developed state level indicators of institutional factors related to government transparency. The authors do not find a systematic relationship between measures of government transparency and gross domestic product per capita growth in Mexico during 2001–2005; however, they do find that corruption is negatively associated with the level of state gross domestic product per capita. The contrasting results may imply that more years of data are necessary to be able to establish statistically significant relationships between state growth rates and measures of corruption.

This paper—a product of the Agriculture and Rural Development Unit, Latin America and Caribbean Region —is part of a larger effort in the department to understand regional dimensions of economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at eweiss@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team