rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Variable names, definitions, means and standard deviations,** Philippine Grade **5 science, 1983**

|Name<br>Definition|
|---|
|Eamily Background (classroom average)<br>MAGE<br>A f tdt i h|
|<br>ge o suens n monts<br> <br> <br> <br>|
|MFAMSIZE<br>1-<br>Families with<5 children; 0<br>Other<br> <br>|
|WEDUCAO<br>1- Mothers with no formal schooling; 0- Other<br> <br>|
|WEDUCAl<br>1- Mothers with schooling<br><grade 10; 0- Other<br>|
|WEDUCA2<br>1-<br>Mothers with schooling>-grade 10; 0-<br>Other<br> <br> <br>|
|FOCCR1<br>1-<br>Fathers with unskilled occupation; 0- Other<br> <br>|
|FOCCR2<br>1- Fathers with service or semi-skilled<br>occupations;<br>0-<br>Other<br> <br>|
|FOCCR3<br>1- Fathers with white collar occupations;<br>0- Other<br> <br> <br>|
|FOCCR4<br>1- Fathers with professional<br>occupations;<br>0- Other<br> <br>|
|MHOMEBOO<br>Number of books in the home (1-<br>1-10;<br>2-<br>11-25; 3-<br>26-100; 4-<br>101-250;<br> <br> <br>|
|5-<br>251-500; 6-<br>more than 500)<br> <br> <br>|
|MHOME1<br>1-<br>Speak local dialect at home;0-<br>Other<br> <br>|
|MHOMEP<br>1- Speak only or mostly Pilipino at home; 0-<br>Other<br> <br>|
|MHOMEE<br>1- Speak only or mostly English at home; 0-<br>Other|
|Schoo-l<br> <br> <br> <br>|
|URSUBl<br>1-<br>School in Manila;0-<br>Other<br> <br>|
|STUTOT10<br>Total number of students in school<br> <br>|
|RATIOST<br>Student teacher ratio<br> <br>|
|CLSSTP<br>School type<br>(1-<br>secondary;0-<br>primary)|
|Teacher and classroom<br>TCHEXP1<br>hi i|
|<br>Teacng experence in years<br> <br> <br>|
|TPOSTS34<br>Postsecondary<br>science education (1-<br>some; 0-<br>none)<br> <br>|
|NTOTIM<br>Number of students in class|
|Teacher practices<br> <br>|
|PRACWRK2<br>Proportion of student time on practical work (1-<br>50% or more;<br>0-<br>less than 50%)|
|TCHLAB2<br>Proportion of time teaching in lab (1- 50% or more; 0- less<br>than 50%)|
|DTXT<br>Use of textbooks for teaching (1-<br>frequent; 0- not frequent)<br> <br> <br>|
|TCHTST<br>Use of tests (1-<br>frequent; 0- not frequent)<br> <br>|
|DGRPS<br>Use of groups (1- frequent; 0- not frequent)|
|Student achievement<br> <br>|
|TOTLMH<br>Total score on science test 1MM (range: 0- 24)<br>|
|TOT1QM<br>Total score on math test 1QM (range: 0- 20)|

24
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31
# **A. Survey characteristics**

Table 2 presents some general characteristics of both surveys including the date and duration of the fieldwork, sample size, and total (weighted) estimated population. The HBS has a large sample size and is restricted to the mainland, while NPS has a smaller sample size but covers the Zanzibar archipelago. However, eliminating Zanzibar from the comparative analysis does not induce significant differences in the results. The sample sizes in both the HBS and NPS are considered large enough to give reasonably precise estimates of poverty at the national level and by geographic domain (rural, other urban, and Dar es Salaam).

All surveys (the two rounds of the HBS and the three rounds of the NPS) relied on the 2002 Population and Housing Census to draw a sample of households (using the National Master Sampling Frame). However, in the HBS 2011/12 and the NPS 2012/13 _ex‐post_ weight adjustments were conducted based on the 2012 Population and Housing Census.

There appear to be important differences in the total weighted population size between the two surveys. HBS data show an increase of the total population size to from 38.3 million in 2007 to 42.3 million on the mainland in 2011/12 while NPS shows an increase from 35.4 in 2008/09 to 43.3 million in 2010/11 and then to 43.5 million in 2012/13. The 2012 Population and Housing Census showed that Tanzania had a population of 44.9 million, of which 43.6 million were on mainland Tanzania. This puts the HBS figures a little low relative to the estimated population figures from the census.

While the increase of the population of approximately 25 percent shown by the NPS data appears to be an overestimate, adjusting the NPS weights using a linear interpolation of population between the 2002 and 2012 census and re‐scaling weights accordingly revealed only slight changes in the NPS poverty and inequality levels and no changes in the trends. This points to the need to further investigate this issue.

Also, there may be concerns about the potential effect of attrition in the panel surveys since non‐ random attrition can cause the survey samples to become unrepresentative of the general population over time. However, the attrition rates of 3 percent in NPS 2010/11 and 4 percent in NPS 2012/13 are too low to significantly bias poverty and inequality estimates and to affect their trend. It should be noted that the NPS used propensity score matching to address attrition by compensating for the lost households.

10
We find generally better training outcomes for men compared to women. While improvements in self-reported skills were similar across genders, male trainees exhibit greater improvement in subjective measures of well-being and confidence compared to women. Any negative outcomes of treatment we observe tend to be associated with women – reduced savings and decreased earnings-related activities following treatment.

These differences are explained by both the conditions under which women participate in training, as well as gender differences in the training _experience_ . First, women drop out due to adverse shocks (severe illness or injury), and are more likely to participate when alternative opportunities disappear (e.g. they get fired). Men’s participation decisions are not affected by adverse external conditions. Girls are on average less educated than boys at baseline, have more dependents, and spend more time on domestic chores as opposed to paid labor or business activities. Women (but not men) self-report constraints such as ‘family obligations’ and ‘getting married’ as the main reasons they drop out. Second, participating in training is expensive and trainees – especially girls – have to draw down their savings to do so. This is partly due to differences in the training experience: men are more likely to have received financial support from trainers or master-craftspersons (MCs) during the training. Distance to the training facility is more of a constraint for girls, boys are also more likely to attend regularly, and three times as likely to receive a paid job offer from the MC following the training period. Overall, the results suggest that women are significantly more constrained in their decision-making, which lead to a worse training experience, less support, and ultimately results in worse training outcomes. These results shed light on the more stringent constraints under which poor girls have to make decisions in developing countries, and how these inhibit skill acquisition. They can help to explain why other development programs, such as capital infusion for micro-enterprises have had much lower returns for women in Sri Lanka and Ghana (de Mel, McKenzie, and Woodruff 2008; Fafchamps et al. 2011). The results echo Field et al (2010) who show that Muslim women in India do not benefit from business training, likely due to social restrictions.

# **2. Context and Experimental Design**

# **2.1. Background**

Malawi is one of the world’s poorest countries. Over 50% of the population falls below the national poverty line, while GNI per capita is just $360 (The World Bank 2012a).

5
61

# capital payments.

||**Tabl**|**e 20 Size**|**of Ann**|**uity Mar**|**ket**|||
|---|---|---|---|---|---|---|---|
||1987|1992|1994|1996|1998|2000|2002|
|in m. CHF||||||||
|Annuity<br>Payments<br>(AP)|5,503|9,000|10,751|12,533|14,492|16,292|18,127|
|Capital<br>Payments<br>(CP)|948|1,830|2,320|2,842|2,993|3,910|3,525|
|Total Payments|6,451|10,830|13,071|15,375|17,485|20,202|21,652|
|GDP(currentprices)|263,743|350,807|367,729|373,993|390,191|415,529|431,064|
|# Recipients (AP)|||609,875|647,111|694,912|748,124|803,064|
|Average<br>Annuity|||17,628|19,368|20,854|21,777|22,572|
|(AA)||||||||
|# Recipients(CP)|||29,684|30,342|29,145|31,164|28,308|
|CP (% of AP)|17%|20%|22%|23%|21%|24%|19%|
|AP (% of GDP)|2.09%|2.57%|2.92%|3.35%|3.71%|3.92%|4.21%|
|CP(% of GPD)|0.36%|0.52%|0.63%|0.76%|0.77%|0.94%|0.82%|
|Note: Form year 1994|on, annuit<br>Sou|y paymen<br>rce: Bun|ts are cur<br>desamt f|rent paym<br>ür Statist|ents as at<br>ik|31.12||

Available data do not report the proportion of accumulated capital of newly retired workers (both under the mandatory and super-mandatory parts of the system) that is withdrawn as a lump sum and the part that is converted into an annuity. They also do not report the number of new retirees who convert all their accumulated capital into an annuity, those who withdraw the total capital, and those who withdraw a fraction of the available capital and convert the rest.

**Figure 21  Growth of Annuity and Capital Payments since 1994**

<!-- Start of picture text -->
1.80<br>1.70<br>1.60<br>1.50<br>1.40<br>1.30<br>1.20<br>1.10<br>1.00<br>0.90<br>0.80<br>1994 1996 1998 2000 2002<br>Annuity Payments (AP) Capital Payments (CP)<br># Recipients (AP) # Recipients (CP)<br><!-- End of picture text -->

Source: Authors’ Calculations
VG group but were not beneficiaries. These three strata, i.e., GL, RF, and ENB, within which the group investment, training, and household cash transfer experiments were conducted, are the focus of this paper.

Within this sample, we conducted a number of surveys.<sup>11</sup> First, we conducted a comprehensive three-wave household survey. The survey included data on consumption, income and revenue, savings and borrowing, revenue, food security, productive assets, livestock, housing quality, food security, health, and education. We also tracked the progress of the groups, using a detailed enterprise survey. This not only collected a standard battery of business practices, as well as asset and profit/loss data for the groups, but it attempted to conduct the mapping of group activities back to the individual level – tracking contributions of time, inputs, and money to the group from each member as well as the rate of profit-taking by members from the group enterprise. For the enterprise survey we have an additional ‘rapid resurvey,’ conducted roughly six months after disbursement of TASAF funds to the Early treatment group, intended to capture outcomes immediately after disbursement.<sup>12</sup> As this study began in 2008 with data collection ending in 2012, it pre-dated the standard use of trial registration and pre-analysis plans. Thus, we use two seminal papers in the literature – Banerjee et al. (2015) and Haushofer and Shapiro (2016) – to guide our grouping and selection of primary and secondary outcomes. Our primary outcomes include consumption (food, non-food, and total, measured as real USD monthly per capita adult equivalents – adjusted for differences in price levels across districts and over time), housing quality, livestock, and productive assets. For

> 11 All survey instruments and enumerator manuals for the survey can be found at https://sites.google.com/site/decrgberkozler/datasets.

> 12 An important issue in survey design, given the capture of assets at both the group and household levels, is avoiding double-counting the same assets. In an attempt to avoid this, we carefully distinguished group assets as those considered to be owned collectively by the group, and in the household survey we asked separate questions about individually-owned assets versus group-owned assets (typically livestock) that might be on household land or supported with household labor and expenditure, but were not owned by the household.

15
12 transformation, approved in June 1989, that enables state enterprises to convert themselves into joint stock companies; and (vi) a new Securities Market Law that is under preparation and is expected to be enacted later in 1989. The law will cover the establishment of a securities supervisory body, a stock exchange, rules for trading, information disclosure requirements, and specific aspects in the area of investors protection such as those related to insider trading.

By early 1989 the number of financial intermediaries (excluding the savings cooperatives) had increased from 8 in 1983 to 2717: four banks had foreign participation,<sup>1 8</sup> and three brokerage houses had begun operations. The types of financial services and products that banks could offer were almost unlimited.<sup>19</sup> The securities market capitalization had grown essentially from zero in the early 1980s, to approximately Ft 120 billion (equivalent to S2.3 billion), an impressive growth even though the figure is still small compared with the size of the total domestic credit granted by the financial sector, of approximately Ft 1200 billion.<sup>2 0</sup> An International Training Center for Bankers, formed as a joint venture between the Hungarian banks and the Centre Internationale de

> 17 One development financial institution, 18 commercial banks, 4 specialized financial intermediaries, and 4 insurance companies.

18 Citibank and Unicbank deal in both foreign and local currencies. The Central-European International Bank, Ltd. (CEIB) is an off-shore bank, and the Central European Creditbank is its subsidiary that has been set up to deal in domestic currency.

19 Significant restrictions were found only in the area of foreign exchange transactions carried by domestic banks, which currently can operate only in that market with the central bank and exclusively on behalf of their clients. Note further that banks are still not allowed to borrow directly from abroad.

20 Data on the ratio of long-term debt securities and equities to total assets of deposit banks for selected countries are as follows: the Federal Republic of Germany 0.75, Japan 0.70, the United States 1.40, Chile 0.ec, Korea 0.20, Thailand 0.30, Hungary 0.14.
# _2.4. Institutional Investors Data_

To analyze what types of investors are behind the purchases of East Asian corporate bonds, we use data on foreign and domestic investors for 2008-16. The data come from different sources and with different breakdowns. Regarding foreign investors, we have data on East Asian corporate bond holdings by all types of investors from the United States (U.S. investors) as well as mutual funds domiciled outside East Asia (including mutual funds in the United States). The former come from the U.S. Treasury International Capital (TIC) System, whereas the latter come from information collected by Calomiris et al. (2019). East Asian corporate bonds are those issued by a firm located in East Asia either in an East Asian market or an international market.

Regarding domestic investors, we collect data on security holdings for pension and insurance as well as mutual funds. Data on investments by pension and insurance funds cover Hong Kong SAR, Korea, Malaysia, and Taiwan and come from the financial supervisory authorities, insurance associations, and pension fund websites. For these investors, we typically have data on their total security holdings, which include public bonds, corporate bonds, and equity from issuers located in East Asia and abroad. These data distinguish whether a pension/insurance fund holds securities issued in their own local capital market or abroad. For Hong Kong SAR, we have data on pension funds only. For pension funds in Korea, we have additional data on corporate bond holdings. For East Asian mutual funds, we have data on their East Asian corporate bond holdings (issued in East Asian and international markets).

# **3. Corporate Bond Financing: Domestic vs. International Markets**

We document the role of domestic and international bonds markets in East Asia in terms of issuance activity and types of firms using each market, focusing on three periods, 1990-98, 1999-2007, and 2008-16. This allows us to compare trends before and after two important milestones: (i) the AFC 12
# Data source: HIES 2009-2010

_Caloric and Protein Intake by Social and Economic Status_ Caloric and protein intake increases with social and economic status. Social and economic status is measured by log per capita expenditure. Both log per capita calories intake and protein intake increase with increase in log per capita expenditure, implying that both food quality and quantity improves with the increase of wealth status.

Figure 6. Log per Capita Expenditure and Log Daily Calorie and Daily Protein Intakes.

<!-- Start of picture text --> 4 6 8 10 4 6 8 10<br>Log per capita expenditure Log per capita expenditure<br>7.8 60<br>7.6<br>40<br>7.4<br>Log daily Calorie intakes Daily protein intakes (g) 20<br>7.2<br>7<br>0<br><!-- End of picture text --> Data source: HIES 2009-2010 _Wealth, Calories and Protein Intake and Malnutrition_ In general, child malnutrition rates decline with an increase in wealth, daily calorie and protein intakes. Figure 7 confirmed such trends in PNG. However, child malnutrition rates started to decline with increased wealth only when the log per capita expenditure reached a critical point. The critical point, as shown in Figure 7, occurred when log per capita per month expenditure was around 5, which was equivalent to per capita per month expenditure 148 kina, or USD $1.67 per capita per day. This was still below the poverty line USD $2 per capita per day. Only when wealth passed through this critical point did the malnutrition rate started to drop. The incidence of malnutrition decreased with the increase of calorie and protein intake. The slight inverse U shape relationship between malnutrition and protein intake was mainly due to the noise at the lower end of little protein intake.

Figure 7. Malnutrition and per Capita per Day Calories Intake 9
# **1. Introduction and Executive Summary**

Despite recently announced delays, the members of the Basel Committee on Banking Supervision remain set to implement a new Accord on bank capital, replacing the 1988 Basel I agreement, by 2006<sup>2</sup> . A natural question, and the focus of this paper is, what should the remainder of the 184 members of the World Bank do<sup>3</sup> ? Should non-G10+ countries also implement Basel II, as many of them have implemented Basel I? If so, which of the many alternatives under Basel II should be adopted? The alternatives under Basel I and Basel II now represent such a wide “sea of standards” there appears to be need for navigational aids. This policy paper represents a first attempt to develop a navigational chart.

The World Bank’s “client countries” range from a relatively small number of more sophisticated emerging economies to a relatively large number of countries that, to date, are still struggling to bring banking regulatory and supervisory standards to international “best practices”<sup>4</sup> . The experience from the World Bank’s and IMF’s surveillance of financial standards indicates that 50% of countries are only compliant with a maximum of 10 of the 30 Basel Core Principles (BCPs) of Effective Banking Supervision and one-third are only compliant with a maximum of 5 BCPs<sup>5</sup> . The statistics for developing countries lag this mixed group. The average developing country is compliant with just 7 BCPs. The corresponding figure for developed countries is 19, implying there is still work to be done across the board – see Figure 1.

> 2 The members of the Basel Committee for Banking Supervision (BCBS), responsible for the development of Basel II, come from the Central Banks of 13 countries: Belgium, Canada, France, Germany, Italy, Japan, Luxembourg, the Netherlands, Spain, Sweden, Switzerland, United Kingdom and the United States.  There is an implicit understanding within these countries that the new Accord will be implemented in each jurisdiction although there is no legally binding international treaty to this effect. In the words of the BIS website the BCBS, “formulates broad supervisory standards and guidelines and recommends statements of best practice in the expectation that individual authorities will take steps to implement them through detailed arrangements - statutory or otherwise - which are best suited to their own national systems”.

> 3 The World Bank website lists 184 countries with voting rights in the IBRD. Not all countries listed have individual bank regulators (e.g.: the countries of the East Caribbean Currency Union) and in a few cases a country may have more than one e.g.:  Hong Kong is not included in the 184.

4 I will refer to the non-G10+, “non-Committee” or World Bank client countries that are the focus of this paper henceforth as developing countries noting that this label includes a very diverse group of economies. 5 In fact there are 25 BCP’s. Here I count the sub principles of Core Principle 1 as Principles in their own right. The figure is calculated on the basis of a survey of 60 (developed and developing) countries.

4