rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Policy, Ruoaoh, and Extenl Ahakl<br>Pouton, HIth, *lid Nutrition<br><!-- End of picture text --> WPS 591

- This paper a product of the Population, Health, and Nutrition Division, Population<sup>and Human</sup> - Resources Department is part of a larger effort in PRE to provide information on malnutrition<sup>to Bank</sup> staff working in operations. Copies are available free from the World Bank,<sup>1818 H Street NW,</sup> Washington DC 20433. Please contact Otilia Nadora, room S6-065, extension 31091<sup>(17 pages).</sup>

The purpose of this paper is to provide a quick reference on the prevalence of protein-energy malnutrition by presenting available weight-forage data for children in developing countries.

The data are also shown geographically on a map of the world.

These country data are arranged in tables by area of the world and by World Bank-designated income groups.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy,<sup>Research, and External</sup> Affairs Complex. Anobjective oftheseries is to getthese findings outquickly, even ifpresentations<sup>are less than fully polished.</sup> The findings, interpretations, snd conclusions in these papers do not necessarily represent<sup>official Bank policy.</sup> Produced by PRE Editorial Services
urban spaces reflects rule of law’s breakdown as well as variant emerging micro- and locallevel political and security orders and dispute resolution processes. Secondly, localized orders are embedded in, stem from, and affect wider political and economic institutions and practices central to development. Thirdly, variance in local armed dominance enables and constrains reforms and stakeholder mobilization behind them (Lee and McCrary, 2005). Prioritizing, sequentially, the economic security and peace building incentives of vulnerable armed actors, dilutes the risk of their remobilization and enhances prospects for consolidating sustainable peace and the accompanying benefits (rather than costs).

Considering the knowledge and gaps outlined above, we: (i) outline a method for capturing heterogeneity based on earlier empirical studies undertaken on this issue; and (ii) demonstrate the method and enhanced APP using primary data from a study conducted in 2006 on the costs and outcomes associated with a developmental crime prevention intervention undertaken in Australia; (iii) apply our method to develop CBA outcomes that incorporate the heterogenous distribution across intervention subgroups; (iv) discuss how the method we developed in this paper is incorporated into the enhanced APP; (v) discuss future developments of the enhanced APP that will incorporate modern data science techniques. Overall, the method we describe, test, and apply to the enhanced APP can capture the complexity of prevention and will assist policy makers in producing evidence sensitive to contextual social group-specific variation.

# **2. A Systematic method for weighing costs and benefits**

# **_Accounting for heterogeneity_**

The main purpose of CBA is to inform policy makers on the economic viability of publicly funded programs. As briefly highlighted above, the benefits identified and measured are based on parameter estimates of the average effect of a given program or intervention, which are then compared to the associated average costs (Boardman et al., 2017). However, substantial heterogeneity often exists in the benefits and/or costs across individuals and groups. As such, the traditional CBA approach may hide valuable information about which social groups benefit the most net of costs.

The Kaldor–Hicks efficiency criterion is commonly applied in CBA and states that a publicly funded program can be justified when the overall benefits outweigh the costs. This criterion is in contrary to the earlier Pareto criterion. It requires that no one will suffer from any change under the proposed intervention conditions. Theoretically, a program under the 13
ones, but even the 50 African MFIs that reported profitability figures to the MIX in all years from 2005 to 2009 displayed a leveling off in terms of OSS (Figure 6, panel A). At the same time, the average loan size (relative to GDP per capita) increased more for African MFIs than others, for both the balanced panel that reported consistently to the MIX and the full sample (Figure 7). Similarly, but not shown, the share of women borrowers declined steadily among the consistent African reporters to the MIX. Within the full sample of African MFIs (panel B), the average share of lending to women was relatively stable, suggesting that new African entrants focused more on lending to women while better established MFIs were moving away from that market segment.

# **Figure 6: Operational Self-Sufficiency of MFIs over Time**

<!-- Start of picture text --> Panel A Panel B<br><!-- End of picture text --> <!-- Start of picture text --> Avg. OSS balanced sample Avg. OSS full sample<br>1.25 1.25<br>1.20 1.20<br>1.15 1.15<br>1.10 1.10<br>1.05 1.05<br>1.00 1.00<br>2005 2006 2007 2008 2009 2003 2004 2005 2006 2007 2008 2009 2010<br>Africa (mean) Other regions (mean) Africa (mean) Other regions (mean)<br><!-- End of picture text --> It seems plausible that the reach of MFIs in Africa will continue to expand, extending to much larger numbers of customers. At the same time, the figures presented here suggest that continued expansion could coincide with greater competitive pressure on MFI profitability and, perhaps, somewhat less focus on reaching the poorest. At the least, the explosive growth of microfinance in Africa since 2000 makes it a sub-sector that bears monitoring over the coming years.

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-2-

The purpose of this report is to provide ar. evaluation of the impact cf program participation on the employment and incomes of trainees. We seek to address four questions: (i) what is the impact of training on the subsequent employment experiences of trainees?; (ii) does training increase the speed with which trainees move from unemplovmen **t** o employment?; (iii) conditional upon employment, what effect does training have on monthly earnings, weekly hours worked, and hourly wages of trainees?; and (iv) do the monetary benefits from program participation outweigh the costs of providing retraining?

In answering these four questions, we pose the counterfactual question: how do post-training outcomes differ from what would have happened had participants not received training? The approach we adopt is to compare the post-training labor market experiences of trainees with those of a control group--a matched sample of unemployed individuals who were eligible for, but did not participate in, the PROBECAT program. Since selection of individuals into the program is not random, the evaluation methodology must take into account any systematic differences between participants and non-participants; otherwise, these differences between groups could lead to biased estimates of the program effects.

Our evaluation focuses not only on overall program effects, but also on the effects of the program on specific groups of trainees. We seek to determine whether, and how, training effects vary across groups of trainees with different demographic attributes, such as age, sex, level of schooling completed, prior work experience, or degree of attachment to the labor force. These results will be important for providing Mexican policymakers with useful guidelines on the specific groups of unemployed individuals who should be targeted for retraining.

Previous PROBECAT evaluations have been constrained by several data and methodological limitations.11 Specifically, they lacked (a) a comparison group: outcomes (employment and earnings) for training completers six months out were compared to outcomes three months out for the group that failed to complete the training program ("deserters"); and (b) detailed wage data: earnings information was bracketed, aid was only reported in reference to the minimum wage. Both of these limitations have been largely overcome in our evaluation. First, we have access to detailed data on the post-training experiences of the 1990 trainee cohort from a retrospective survey conducted by the Secretariat of Labor and Social Welfare (STPS) in February 1992.

/ Sam Carlson, Mexico Labor Retraining Program: Poverty Alleviation and Contribution to Growth, LATHR Report #6, August 1991.
**Figure 12: Family and mother’s education are key factors of access to healthcare**

<!-- Start of picture text --> 100%<br> 5<br> 12 Region<br>90% 21 22<br> 24<br>80% 21 42 Mother's<br>Educational<br>70% 21 20 Attainment<br>Father's<br>60% 12 19 Educational<br>50% 12 10 7 5 AttainmentWealth<br> 23<br>40%<br> 31 Consumption<br>30% 1 32 34 27<br>20% 3 Number of<br>10% 31 3 3 11 4 6 children at home<br> 10 11 7 9 Gender of child<br>0% 0 1 1<br>During Birth assisted Birth given at Baby postnatal Child received<br>pregnancy, by trained health facility check within 2 all<br>blood sample health staff months of birth immunization<br>was taken from<br>mother<br>Shapley decomposition<br><!-- End of picture text --> _Source:_ DHS 2000 and 2008; HIECS 2000 and 2008 used for the imputation of household consumption.

# **Malnutrition and Micronutrient Intake**

While malnutrition indicators such as stunting, wasting and underweight prevalence have deteriorated in Egypt, inequality of opportunity for these indicators remains low, suggesting no significant disparity among circumstance groups. Similarly, the HOI for nutrition indicators does not vary widely across regions. The decomposition of the variability in anthropometric measures explained by circumstances further shows that circumstances explain only a small percentage of their variance. Therefore, malnutrition is a problem for Egyptian children regardless of their circumstances. On the other hand, equality of opportunity concerning micronutrient intake has largely improved between 2000 and 2008 (see Figure 13). The increase in the HOI has been mainly due to the scale effect with more households overall having access to adequately iodized salt. The Shapley decomposition of inequality of opportunity in malnutrition indicators shows that females are more likely to be stunted than males, while household wealth explains the largest portion of the variation in access to micronutrient intake in 2008.

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Table 10: Principal factors analysis of teacher characteristics

|Factor|Eigenvalue|
|---|---|
|1|1.26|
|2|0.3|
|3|-0.17|
|4|-0.26|

Rotated Factor Loadings <u>(varimax)</u>

|Variable|1|2|
|---|---|---|
|Formal education|-0.17|0.44|
|Short seminars|0.70|-0.12|
|Experience|0.67|-0.04|
|2-year on the job training|0.54|0.30|

Scoring coe¢ cients for the factors

||"Experience"|"Education"|
|---|---|---|
|Formal education|-0.05|0.36|
|Short seminars|0.40|-0.15|
|Experience|0.34|-0.04|
|2-year on the job training|0.25|0.33|

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parameters to predicting behavior at extremely low income levels, which is what we need to predict the consumption impacts on the poor. Estimation of this demand system is undertaken using the 80-country, per capita consumption data set offered by GTAP, version 6.1 and the resulting parameters are reported in Annex IV.

The best way to understand the implications of this approach to estimation of demands across the income spectrum is to view the results for a particular country. Figure 3 plots the predicted household budget shares for Peru, across the income spectrum. These show how the pattern of consumer expenditures are predicted to vary from the subsistence level (origin of horizontal axis), where expenditures on food and clothing are dominant (budget share of nearly 60%), to the national per capita expenditure level where the household budget is more diversified (the horizontal axis reports the natural logarithm of consumption expenditure, per capita). Vertical lines denote the $1/day, $2/day and national per capita expenditure levels. Note that at $1/day poverty line, 49% of the budget is devoted to food, with the bulk of this spent on crops. The initial levels of utility at the two poverty lines are each fixed, and the estimated demand system is used to determine the change in the cost of attaining this exogenous poverty level of utility when prices and demands change due to trade liberalization.

# **Policy Scenarios**

Our attention in this paper is on the distributional impacts of WTO reforms in agriculture. Since such reforms are most contentious in the rich countries, we focus initially on impacts from liberalizing agricultural policies in only the rich countries. The OECD produces annual estimates of the producer support estimate for its member countries. Rice is far and away the most protected commodity by this measure, with on 21
|Table 5. Nonresi|dential blocks:|Number, size, a<br>Com<br>First bl|nd price le<br>mercial<br>ck|vel||I<br>First|ndustrial<br>block|Price|
|---|---|---|---|---|---|---|---|---|
|||||Price differential<br>||||differential<br>|
||Number<br>of<br>blocks|Size|Price<br>(cents)|between first<br>and last block<br>(%)|Number<br>of<br>blocks|Size|Price<br>(cents)|between first<br>and last block<br>(%)|
|Benin|1|n.a.|15.1|—|1|n.a.|10.7|—|
|Botswana|1|n.a.|6.7|—|1|n.a.|3.1|—|
|Burkina Faso|2|TOU|31.6|–47|2|TOU|22.6|–54|
|Cameroon|2|180kWh/kVA|11.3|–12|2|TOU|8.7|–2|
|Cape Verde|1|n.a.|21.8|—|1|n.a.|17.7|—|
|Chad|3|30 kWh|15.9|152|3|TOU|20.5|85|
|Congo, Dem.<br>Rep. of|5|200 kWh|11.1|–4|5|200|15.2|-4|
|Congo,Rep.|1|n.a.|9.7|—|1|n.a.|11.2|—|
|Côte d’Ivoire|2|18/kVA<br>bimonthly|18.6|–15|3|TOU|10.7|–18|
|Ethiopia|3|TOU|6.7|–6|3|TOU|4.7|26|
|Ghana|3|300|11.1|44|1|n.a.|5.4|—|
|Kenya|1|n.a.|21.4|—|3|20<br>kWA|16.4|–15|
|Lesotho|1|n.a.|1.2|—|1|n.a.|1.1|—|
|Madagascar|1|n.a.|16.9|—|1|n.a.|9.9|—|
|Malawi|1|n.a.|3|—|1|n.a.|2.4|—|
|Mali|1|n.a.|23.2|—|0|n.a.||—|
|Mozambique|1|n.a.|5.4|—|1|n.a.|4.5|—|
|Namibia|1|n.a.|8.4|—|1|n.a.|7.7|—|
|Niger|1|n.a.|12.2|—|1|n.a.|8.8|—|
|Nigeria|4|15 kVA|5|30|5|15<br>kVA|5|30|
|Rwanda|1|n.a.|17.2|—|1|n.a.|17.2|—|
|Senegal|2|TOU|14.4|44|2|TOU|11.8|45|
|South Africa|3/1|25 kVA|4|138|2|TOU|2.6|–31|
|Tanzania|1|n.a.|5.3|—|1|n.a.|4.9|—|
|Uganda|1|n.a.|21.8|—|1|n.a.|16.7|—|
|Zambia|1|n.a.|3.7|—|4|1200|2.2|–45|
|Zimbabwe|1|n.a.||—|1|n.a.||—|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Notes_ : For details see Annexes 3 and 4. n.a. Not applicable; — Not available; kVa = kilovolt-ampere.

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# **Corporate governance**

As discussed in the second section of this chapter, supervision of corporate governance is one of the primary means that securities regulators worldwide use to ensure that SROs meet their public interest and regulatory responsibilities and manage conflicts appropriately. In the EU, the Markets in Financial Instruments Directive (MiFID) requires all regulated markets to have governance arrangements to identify and manage the potential adverse consequences of any conflict of interest between the regulated market, its owners or its operators, and the sound functioning of the market.

# **Transferring regulation responsibilities**

Chapters 2 and 3 discussed the trend of transferring responsibilities for regulation. For instance, the UK government transferred the “Listing Authority” function from the LSE to the FSA in 2000 because it did not believe that an exchange that competes with other exchanges for listings should have authority to establish general standards for public listing of issuers.

In Hong Kong SAR, China, the SFC assumed responsibility for supervision of broker-dealers (member regulation) from the exchanges in 2000 as part of an overall government policy to consolidate and demutualize Hong Kong SAR, China’s exchanges. The HKEx’s remaining selfregulatory role is to supervise compliance with its trading rules, which are mostly business or operational rules. The SFC has been primarily responsible for market conduct and trading abuses since 1990.

Before Nasdaq was spun off from the NASD, the two organizations concluded, with the SEC’s encouragement, that conflicts were much better managed if the business was separated from the regulator. They decided that Nasdaq would be more successful as a business and the NASD more successful as an SRO if their roles were separated.

# **Organizational separation of business and regulation**

The organizational separation of the regulatory and the business operations of an exchange is now established as a best practice for exchanges that retain significant SRO functions. Some exchanges have established separate subsidiary companies with independent boards of directors and management to house SRO operations, including the NYSE and ASX.

In 2007, France’s Association française des marchés financiers (AMAFI) surveyed 41 organizations for IOSCO’s SRO Consultative Committee.<sup>37</sup> The survey covered 25 exchange and 16 member associations or CSDs. It showed that of 31 organizations that operated or regulated markets, 21 formally separated their commercial and regulatory activities, and 5 did not. (The others did not respond, perhaps because they had no commercial activities.)

> 37 AFEI/VD – 29/04/08 – SRO Consultative Committee Working Group on Self Regulation (unpublished). The Association française des marchés financiers (AMAFI) is the representative body for professionals working in the securities industry and financial markets in France. AMAFI has more than 120 members. Most are investment firms, but some are credit institutions authorized to provide investment services. AMAFI’s primary mandate is to represent the interests of its members.

44
_activities_ changes as a result of time savings in domestic production.

# **RCT Implementation**

The key facts of the implementation are presented in Table 1. A detailed description of the implementation including a map of the survey area and a figure illustrating the participant flow can be found in the supplemental appendix. A discussion of the external validity of our results is also presented in the supplemental appendix.

**Table 1. Key Facts on RCT Implementation**

|Baseline survey|November 2011|
|---|---|
|Delivery of Pico‐PV kits|December 2011|
|Follow‐up survey|June 2012|
|Study population|15 nonadjoined communities in four rural districts of Rwanda<br>located in the Northern, Western and Southern Province.|
||No Pico‐PV kits available on the market|
||~5.5 hours of sunlight per day (which is similar to country average)|
|Sample|300 randomly sampled households|
||Stratified randomization and additional re‐randomization using|
|Randomization|minmax t‐stat method at the household level; random assignment of<br>150 Pico‐PV kits|
|Stratification criteria|Consumed lighting hours per day, usage of mobile phones (binary),<br>radio usage (binary), and district|
||Balancing criteria are marked in The surveyedhouseholds are|
|Re‐randomization|mainly subsistence farmers that live in very<br>modest conditions. The educational level of the|
||head of household is low and households own|
Policy Research Working Paper 5545

# **Abstract**

Scaling-up adoption of renewable energy technology, such as solar home systems, to expand electricity access in developing countries can accelerate the transition to low-carbon economic development. Using a purposely collected national household survey, this study quantifies the carbon and distributional benefits of solar home system programs in Bangladesh. Three key findings are generated from the study. First, dissemination of solar home systems brings about significant carbon benefits: the total carbon emissions avoided from replacing kerosene use for lighting by solar home systems in nonelectrified rural households was equivalent to about 4 percent of total annual carbon emissions in Bangladesh in 2007. This figure increases to about 15 percent if the grid-electricity generation is used as the energy baseline to estimate the carbon avoided from the installation of solar home systems. Second, solar home system subsidies in rural Bangladesh are progressive when the program is geographically targeted. Third, there exists a market potential for solar home systems in many rural areas if micro-credit schemes are made available and the propensity to install solar home systems is very responsive to income, with a 1-percent increase in per capita income increasing the probability of installing solar home systems by 12 percent, controlling for other factors.

This paper is a product of the Environment Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lwang1@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
Table 4 - Description and summary statistics of socio-economic variables included in the QUAIDS

|**Variable**|**Description**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|Age of the household head|Years|1,461|48.236|12.468|24|94|
|Female head|Dummy equal to 1 if the household head is<br>a female and 0 otherwise|1,461|0.190|0.392|0|1|
|Primary|Dummy equal to 1 if the household head<br>has a primary education and 0 otherwise|1,461|0.253|0.435|0|1|
|Tertiary|Dummy equal to 1 if the household head<br>has a higher education in college or<br>university and 0 otherwise|1,461|0.093|0.291|0|1|
|Wage|Dummy equal to 1 if the household head<br>earns a salary from a hired job and 0<br>otherwise|1,461|0.458|0.498|0|1|
|Farmer|Dummy equal to 1 if the household head is<br>self-employed in agriculture, aquaculture or<br>forestry and 0 otherwise if HH head|1,461|0.531|0.499|0|1|
|Self employed|Dummy equal to 1 if the household head is<br>self-employed but not in agriculture,<br>aquaculture or forestry and 0 otherwise|1,461|0.243|0.429|0|1|
|Household size|Logarithm of number of HH members|1,461|1.477|0.269|0.693|2.398|
|Share of children|N. children in the household/N. of<br>household members|1,461|0.350|0.167|0|0.75|
|Share of young adults|N. young adults aged 18-35 in the<br>household/N. of household members|1,461|0.243|0.190|0|1|
|Share of elders|N. of elders aged 65 or more in the<br>household/N. of household members|1,461|0.047|0.105|0|0.75|
|Food|Household's share expenditure on food|1,461|0.057|0.041|0.001|0.325|
|Foodstuff|Household's share expenditure on foodstuff|1,461|0.055|0.034|0.001|0.271|
|Beverage and tobacco|Household's share expenditure on beverage<br>and tobacco|1,461|0.025|0.019|0.000|0.165|
|Household equipment|Household's share expenditure on<br>household equipment|1,461|0.056|0.045|0.001|0.375|
|Clothing and accessories|Household's share expenditure on clothing<br>and accessories|1,461|0.153|0.085|0.000|0.565|
|Transport|Household's share expenditure on transport|1,461|0.031|0.040|0.000|0.354|
|Post and communication|Household's share expenditure on post and<br>communication|1,461|0.098|0.061|0.000|0.380|
|Education|Household's share expenditure on education|1,461|0.240|0.177|0.005|0.832|
|Medical and health|Household's share expenditure on medical<br>and health|1,461|0.142|0.167|0.000|0.940|
|Electricity|Household's share expenditure on<br>electricity|1,461|0.142|0.077|0.002|0.520|

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