rafmacalaba/datause-encoder-data huggingface dataset:
Sustainable Development Goal Diagnostics: The Case of the Arab Republic of Egypt H. Amin-Salem, M.H. El-Maghrabi, I. Osorio Rodarte, and J. Verbeek Keywords: Sustainable Development Goals, diagnosis, cross country analysis, Egypt, measurement and analysis of SDGs JEL Classification: H11, H60, I00, O19, O21, O23, O53 We would like to thank Mahmoud Mohieldin for his continued intellectual guidance, Susanna Gable for her constructive _______________________________________________________ engagement, the Central Agency for Public Mobilization and Statistics (CAPMAS) for their efforts in assisting data collection, Assad Alam, Robert Beschel, and Tracey Lane for their unwavering support on the ground, and the participants of two workshops in Cairo held in collaboration with the Ministry of Investment and International Cooperation and the World Bank Office in Egypt. This note is part of a series of analytical initiatives by the Office of the World Bank’s Senior Vice-President of the 2030 Agenda, UN Relations and Partnerships. In addition, the Office of the Senior Vice President produced the “Trajectories for Sustainable Development Goals” (Gable et al, 2015), which included ten country case studies encompassing the SDG Country Development Diagnostic framework, as well as the report “Financing for Development Post-2015” (World Bank, 2013). * 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.
# **Table 12c. Base-Year Pollution Intensities by Sector for non-CO2 Pollutants (in grams per ruble)**

|||||||**Volatile**<br>|
|---|---|---|---|---|---|---|
||**Sulphur**|**Hydro-**|**Carbon**|**Nitrogen **|**Particulate**|**Organic**|
|**sector**|**Dioxide**|**carbons **|**Monoxide**|**Oxide**|**Matter**|**Components**|
|**NFM**|4.3|0.0|1.4|0.2|0.5|0.0|
|**ELE**|2.8|0.0|0.4|1.8|2.3|0.0|
|**FME**|1.9|0.0|1.1|0.1|0.3|0.0|
|**industry**<br>**average**|0.3|0.2|0.3|0.1|0.2|0.1|
|**OIL**|0.3|0.1|0.2|0.1|0.1|0.3|
|**CRU**|0.2|1.4|3.8|0.2|0.7|1.2|
|**CHM**|0.2|0.1|0.5|0.2|0.3|0.2|
|**CNM**|0.1|0.0|0.6|0.3|1.0|0.0|
|**CLI**|0.1|0.0|0.2|0.1|0.2|0.0|
|**HOU**|0.1|0.0|0.1|0.0|0.0|0.0|
|**COL**|0.1|3.7|0.1|0.1|0.3|0.0|
|**MAR**|0.1|1.5|0.3|0.2|0.2|0.1|
|**RLW**|0.1|1.3|0.3|0.1|0.2|0.1|
|**TRK**|0.1|1.2|0.3|0.1|0.2|0.1|
|**AIR**|0.1|1.1|0.2|0.1|0.2|0.1|
|**TRO**|0.1|1.2|0.3|0.1|0.2|0.1|
|**PIP**|0.1|1.5|0.3|0.2|0.2|0.1|
|**GAS**|0.0|0.2|0.3|0.0|0.0|0.1|
|**FOO**|0.0|0.0|0.1|0.0|0.1|0.0|
|**MWO**|0.0|0.0|0.2|0.1|0.2|0.0|
|**TPP**|0.0|0.0|0.1|0.0|0.1|0.0|
|**AGR**|0.0|0.0|0.1|0.0|0.1|0.0|
|**OTH**|0.0|0.0|0.0|0.0|0.0|0.0|
|**OTI**|0.0|0.0|0.0|0.0|0.0|0.0|

**Source: Authors’ calculations based on the SUST_RUS database. See the appendix for details.**

50
( _Superintendencia de Valores y Seguros, SVS_ ). The data on Chilean pension funds, the most comprehensive data, come from the Superintendency of Pensions ( _Superintendencia de Pensiones, SP_ ). The data on US mutual funds come from Morningstar. Additional data come from the Central Bank of Chile ( _Banco Central de Chile_ ) and other sources described in the paper. See Appendix Table 1 for a summary of the main data used.

The data on Chilean mutual funds contain detailed portfolios of all existing medium- and long-term funds at a monthly frequency during the period September 2002 to December 2005.<sup>5</sup> The database comprises 447,664 observations; 436,393 for domestic funds and 11,271 for foreign funds (those investing outside of Chile).<sup>6</sup> It includes information on the type of security, currency denomination, price, units held, and maturity date.

For pension funds, we use a panel of their portfolio investments in fixed-term assets for each of the existing funds during the period 1996-2005 at monthly and daily frequencies. We use panel data with the amount of deposits (including cash as deposits with a one-day maturity), corporate bonds, and government bonds held by fund per unit of time.<sup>7</sup> At a monthly frequency, there are a total of 7,501,210 observations, representing the portfolio holdings of the funds. The dataset contains information on the holdings of 104,789 different securities for 57 funds between July 1996 and December 2005. In addition to this monthly dataset, we use in other exercises a different dataset with daily portfolios of the universe of funds and pension fund administrators (henceforth PFAs) in

> 5 Chilean mutual funds are classified according to the type and investment horizon of their assets. Fixedincome funds include money management funds (with horizons of 90 or 365 days) and medium- and longterm funds. We only use the latter, since the former would be tilted toward the short term by construction. 6 We consider a fund to be foreign if its median investment abroad as a percentage of the total portfolio is 50% or higher. Otherwise, it is considered to be domestic. We obtained similar results when relaxing the criteria to 40% and 30%.

> 7 By law, since September 2002, each pension fund administrator (PFA) offers five funds with different risk profiles, subject to different portfolio regulations.

9
This models and results of this paper were the core contribution in the effort of the World Bank to assist the government of Russia in its WTO accession effort, especially in its public awareness campaign. It was, however, only part of multifaceted research and dissemination effort that was sustained over several years.<sup>57</sup> Although the Russian government was convinced of the benefits of WTO accession, prior to this work there was widespread public skepticism and Russian businessmen were calling for an explanation of the impacts on their sectors. Russian leaders and independent internal World Bank evaluators of the work have concluded that the work was instrumental in shifting public opinion in favor of WTO accession, and in explaining to businessmen and politicians in convincing terms the consequences of WTO at the sector level. Surveys taken after this work was done showed that, regarding the impacts of WTO accession on the sectors of Russia, the views of informed laymen (such as political leaders in the regions of Russia), were coincident with the assessments of the models discussed above. The latest internal review by a World Bank evaluation team (as part of a broader review of economic work on Russia by World Bank teams) concluded the following: The WTO analytical work for Russia consists of a large number of very high quality papers and publications assessing the economic and social effects of Russia‘s planned accession to WTO....The body of analytical work (about 15 papers) was disseminated through training events (a number of them requested by the authorities) attended by (prospective) civil servants, representatives of labor and industry and the academic community. It has clearly made a contribution to the policy dialogue and is seen as instrumental for establishing a sense that WTO accession would be beneficial for Russia among the government‘s trade reform team and parts of the research community. Prior to this project, editorials were rampant in the Russian press expressing fears of WTO accession and focusing on the fact that the government has not explained the consequences of WTO accession or the source of the benefits. Now polls of regional politicians reveal that they anticipate exactly the impacts suggested by the analytic studies. Moreover, polls of the public have shown progressively increasing endorsement, and now significant majority supporting WTO accession. Finally, as the analytic studies strongly emphasized the crucial role of liberalization against barriers to foreign direct investment in business services, Russian has agreed to major openings in the business services markets as part of its bilateral market access agreements in the WTO negotiations (see Tarr, 2007).

> 57 Research papers included the following: Jensen, Rutherford and Tarr (2004; 2006, 2007); Rutherford and Tarr (2008; 2008a; 2010); Rutherford, Tarr and Shepotylo (2005); Shepotylo and Tarr (2008); Tarr (2006; 2007; 2010; 2010a); Tarr and Thomson (2004); Tarr and Volchkova (2010; forthcoming).

78
Academic high schools, by contrast, are focused on academic subjects tested on the college entrance examination, with roughly only 10% of time spent on subjects like music, computers, or physical exercise.

How do students choose to attend vocational high school? After graduating from junior high, students decide between entering the labor market, vocational high school, or academic high school. In China, the level of a student’s high school entrance examination (HSEE) score is the primary determinant for entry into academic high school. Every county ostensibly has a cutoff for whether a student’s score makes him/her eligible to enter academic high school (based on the number of positions in academic high school available that year). Those who test below the cutoff are unable to attend academic high school and must choose whether to enter the labor market or vocational high school. Those students that test just above the cutoff sometimes waver between whether to attend vocational or academic high school. Those who test far above the cutoff almost always attend academic high school.

# II. RESEARCH DESIGN

# _2.1 Sampling_

This paper draws on longitudinal survey data collected by the authors in October 2011 and May 2012. The sample for the longitudinal survey was chosen in several steps and covers vocational and academic high schools in different regions of China. First, we sampled two provinces in China: Shaanxi and Zhejiang. Shaanxi province is an inland province in Northwest China and ranks fifteenth out of thirty-one provinces in terms of GDP per capita (NBS 2012).
provided by donors. A group of processors have formed a holding company, the Association of Agribusiness Industries or AIA, which is based in the Nacala Corridor. Through this holding company the founding members and most of the other industry participants in Nampula Province market all of their processed kernels. This group monitors quality and health standards, manages transportation, and exports collectively under multiyear contracts with international buyers. They have also created Zambique brand cashew again with donor support.

Fig. 3 shows the ratio of raw cashew export prices to processed cashew export prices. It shows a steady decline in the ratio of raw to processed cashew prices which means higher processing margins for the processors. It is not clear why this is taking place. One possibility is the increasing power of the new processors or their easier and cheaper access to raw cashews compared to the exporters of raw cashew. On the other hand, a background paper for the Country Economic Memorandum (World Bank 2009) argues the opposite; that despite cost reductions associated with new processing systems, lower quality of raw cashews and other cost increases have increased costs therefore the processing margins.

<!-- Start of picture text --> (%)<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

**Figure 3: Mozambique Raw Cashew Export Prices (as Percentage of Mozambique Processed Cashew Export Prices)**

Thus the restructuring, which has eliminated the capital intensive processors, have opened the way for labor intensive factories, but this has taken a long time, probably due to the uncertainties created by the acrimonious debate. It is now clear that the future lies with smaller, labor intensive production facilities that are located close to the cashew producing areas<sup>18</sup> Thus,

> 18 . These factories also contribute to the rural economies by creating wage and business income in addition to the farming activities. Paul (2008) details the social and economic transformation as a result of the starting of such a factory in Mogincual in Nampula province.

13
areas where more than half of households defecate in the open and those who live in areas where less than half of households do. For both groups, reading outcomes are plotted conditional on consumption per capita. This ensures that the association between sanitation and achievement does not merely spuriously reflect household wealth: panel (a) shows a mean difference at all levels of household consumption per capita.

Panel (b) accounts for further dimensions of household heterogeneity that may be associated with children’s cognitive outcomes. The graph plots residuals after regressing reading and consumption on age-times-sex indicators and a vector of further wealth and education controls: 15 indicators for highest female education in the household; 15 indicators for highest overall adult education in the household; 13 indicators for the language in which the test was taken; a set of indicators that the child is in school, has been in school, or has never gone to school; and the asset count reported by the IHDS. The gap remains, indicating that the gradient between cognitive achievement and sanitation externalities additionally may not merely reflect parents’ education, child schooling, or these other dimensions of observed heterogeneity. The remainder of this paper uses heterogeneity in the timing of a government program to assess whether early-life exposure to fecal germs can impact later cognitive achievement.

# **3 Context: India’s Total Sanitation Campaign**

Open defecation without using a toilet or latrine is particularly widespread in India. In 2001 – – the beginning of the time period this paper studies 63.6 percent of households defecated in the open, according to the Indian census. This declined to 53.1 percent in 2011, in part due to the government’s Total Sanitation Campaign. Clearly much open defecation remains; indeed more than half of all people anywhere who defecate in the open live in India.

The TSC, a “flagship” program of the central Indian government, was a large effort to improve rural sanitation. Over the approximately ten years of the program, it reported 7
<u>Storms.</u> The estimated losses in income caused by storm events are given in Table 14. In general, the impacts of storms result in a general decrease in income between US$0.04 and US$32 per capita in 1995, US$0.03 and US$47 per capita in 2000, and US$0.02 and US$15 per capita in 2005. The highest amount of losses in income due to storm events is estimated for Australia in 1995, for the Philippines in 2000 and for China in 2005.

_Table 14. Differences in the value of real GDP (RGDP) per capita with and_ _<u>without the impacts of storm, in 2005 constant US$, selected countries, 1995-2005</u>_

|Selected Countries|1995|2000|2005|
|---|---|---|---|
|Average country*|2.69|3.72|2.17|
|High Income Non-OECD**|24.27|29.54|0.02|
|High Income OECD**|1.44|1.69|0.28|
|Low Income**|0.91|1.13|0.38|
|Low Middle Income**|0.73|0.90|4.05|
|Upper Middle Income**|0.47|0.57|3.34|
|Good Governance***|0.83|0.67|0.05|
|Average Governance***|0.74|10.57|1.02|
|Poor Governance***|0.95|0.03|0.43|
|Japan|0.01|3.96|14.31|
|China|2.99|0.67|14.56|
|Philippines|24.13|46.65|14.66|
|Australia|175.90|0.70|0.38|
|Thailand|0.04|0.36|0.10|

Notes for Table 13 and Table 14: *Calculated by using the average values of independent variables. **The income groups of countries were based on the World Bank grouping. ***Countries were classified into three levels of governance based on their scores and rankings with respect to the following indicators: Rule of Law, Control of Corruption, Voice and Accountability, Political Stability, Regulatory Quality and Government Effectiveness. “Good Governance” group includes countries whose ranking in the aforementioned indicators falls in the Top 60; “Average Governance” group consists of countries ranked between 61 and 120, and “Poor Governance” group is comprised of countries with ranking of 121 and above. Note that if a country’s income loss in a given year is equal to zero, it implies that there is no reported disaster in that country during that year, e.g., Thailand in 1995 and 2000.

# **VI. Conclusions**

This paper presents a set of estimates of the impacts of natural disasters on different forms of capital (physical, human, natural and energy), and thereby on real GDP per capita. The capital database is compiled by the World Bank for three periods – 1995, 2000 and 2005 and for 210 countries. This was combined with data on four types of natural disasters – droughts earthquakes, floods, and hurricanes/storms – for 196 countries, taken from the Emergency Events Database (EM-DAT). The disasters database lists a total of 55 events of drought, 82 events of earthquake, 447 events of flood and 303 events of storm that started during the three years of 1995, 2000 and 2005.

An analysis of the panel data for four forms of capital was carried out using fixed and random effects panel data estimation methods. For each capital, different explanatory variables were 19
8 Table 1

||GDP Growth Rates for Surv6yed Coun<br>(Constant 1980 US$)|tries|
|---|---|---|
|Country|1980-86<br>1987<br>1988|1980-88|
|Egypt<br>|6.6<br>2.5<br>3.2|5.7|
|Malaysia<br>|4.8<br>5.3<br>8.9|4.6|
|Nigeria|-2.1<br>-4.2<br>4.7|-1.1|
|South Korea|9.2<br>1,.9<br>11.3|9.9|
|Thailand<br>|5.5<br>8.1<br>10.0|6.0|
|Tunisia|3.5<br>5.8<br>1.5|3.4|

Source: World Bank The picture was different, however, for Egypt, Nigeria, and Tunisia. Egypt, after undergoing strong oil-led growth in the late 1970s and early 1980s, accompanied by rising revenues from the opening of the Suez Canal and remittances from workers abroad, faced a drop In oil demand and a rapidly deteriorating economy as the decade progressed. Also an oil producer, Nigeria registered negative growth for most of the decade before engineering a recovery in 1988. Tunisia also experienced a drop In oil demand, but an adjustment program and a devaluation in 1986 led to a recovery in 1987. This recovery was cut short, however, by a severe drought in 1988 with GDP growth falling to 1.5 percent.

# Budaet Deficits and Balance of Trade

Budget deficits were the rule in the six countries from 1980 to 1986 (Table 2). This expansionary fiscal policy appeared to work in Malaysia, South Korea, and Thailand where the subsequent Increase in real output led to a reduction of deficit spending as a percent of GDP. Tunisia also showed signs of success in 1987, befo,e the sharp downtum in 1988. The policy was less succassful in Egypt, however, as measured by the slowing of the economy and the rising budget deficit as a percent of GDP. A similar pattern in Nigeria was reversed with the uptum of the economy In 1988 and a reduction of the budget deficit in relation to GDP.
The Monetary Value of Externalities: Experimental Evidence from Ugandan Farmers<sup>_∗_</sup>

**Benedetta Lerva**<sup>_†_</sup>

**JEL Codes: O12, Q12, Q16.**

**Keywords: externalities, technology adoption, agriculture, Becker-DeGrootMarschak, field experiments, social networks.**

> _∗_ I thank the Handelsbankens Research Foundation and Vetenskapsr ̊adet for generous financial support; Charles Angebault and Apollo Tumusiime for their collaborative efforts throughout the project; seminar participants at Bocconi University, Oxford University, Paris School of Economics, Stockholm University, University of California Berkeley, and World Bank for providing useful comments; Ingvild Almas, Tessa Bold, Konrad Burchardi, Jonathan de Quidt, Dahyeon Jeong, Florence Kondylis, John Loeser, Anna Tompsett, Jakob Svensson, Hannah Uckat for their helpful feedback. All errors and omissions are the responsibility of the author alone. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

- World Bank; E-mail: blerva@worldbank.org
Table 6: Counterfactuals: Eligibility and Take-up

|Policy:<br>Acronym:|Cash transfers<br>CT|Universal vouc<br>UV|her<br>Employment-<br>voucher<br>EV|based<br>Income-targeted<br><br>voucher<br>IV|Quality-based<br>voucher<br>QV|Public provision<br>PP|
|---|---|---|---|---|---|---|
||||Aggregate||||
|Eligibility (baseline)|100.00|100.00|45.85|25.00|100.00|100.00|
|Eligibility (equilibrium)|100.00|100.00|54.26|26.93|100.00|100.00|
|Take-up rate|100.00|54.90|41.83|7.54|47.87|26.84|
||||Locations:||||
|||Location 1:|Low Population –|Low Income|||
|Eligibility (baseline)|100.00|100.00|48.77|46.21|100.00|100.00|
|Eligibility (equilibrium)|100.00|100.00|53.44|46.93|100.00|100.00|
|Take-up rate|100.00|46.24|39.44|14.16|41.60|25.41|
|||Location 2:|Low Population –|High Income|||
|Eligibility (baseline)|100.00|100.00|44.24|21.01|100.00|100.00|
|Eligibility (equilibrium)|100.00|100.00|52.44|21.33|100.00|100.00|
|Take-up rate|100.00|46.29|40.42|7.58|50.75|27.87|
|||Location 3:|High Population –|Low Income|||
|Eligibility (baseline)|100.00|100.00|44.46|26.76|100.00|100.00|
|Eligibility (equilibrium)|100.00|100.00|52.97|28.81|100.00|100.00|
|Take-up rate|100.00|36.94|36.19|9.20|54.47|19.45|
|||Location 4:|High Population –|High Income|||
|Eligibility (baseline)|100.00|100.00|47.64|10.43|100.00|100.00|
|Eligibility (equilibrium)|100.00|100.00|56.63|10.65|100.00|100.00|
|Take-up rate|100.00|51.03|41.01|4.11|51.03|16.82|

Note: _Eligibility (baseline)_ and _Eligibility (equilibrium)_ denote the percent of households who fulfill the policy eligibility requirements at the baseline and at the policy equilibrium, respectively. _Take-up rate_ is the percent of households who use the policy. Both eligibility and take up rates are relative to all households in the economy.

Table 7: Counterfactuals: Childcare Mode Choices

|||||Change with respe|ct to Baseline|||
|---|---|---|---|---|---|---|---|
|Policy:|Baseline|Cash transfers|Universal voucher<br>voucher|Employment-based<br>voucher|Income-targeted<br>voucher|Quality-based|Public provision|
|Acronym:||CT|UV|EV|IV|QV|PP|
|HQ Center|13.85|16.48|33.25|26.71|1.01|34.02|-0.99|
|LQ Center|15.03|-10.10|-7.24|-9.45|2.46|-15.03|-1.05|
|Non-relative|3.02|-0.16|-1.63|-0.84|-0.33|-0.69|2.08|
|Relative|19.84|-3.02|-11.27|-7.73|-1.81|-8.50|-0.01|
|Home|51.74|-3.21|-13.11|-8.69|-1.34|-9.81|-0.03|

Note: The Baseline column shows the percent of households using each childcare mode in the baseline. The remaining columns show the difference, relative to the baseline, in the percent of households using a given childcare mode, expressed in pp. _HQ_ and _LQ_ denote high-quality and low-quality childcare centers, respectively. _Home_ indicates that the child receives exclusive parental care.

50
# **Mongolia**

Table 10a : Returns to levels of education - Employed for wages

||1998*|2002**|2007-8***|Change****<br>1998-2007|
|---|---|---|---|---|
|_Basic controls only_|||||
|Primary|-0.250|0.059|0.116|_0.366_|
|Junior Sec./primary|0.521|0.203|0.290|_-0.231_|
|Senior Sec./ primary|0.603|0.249|0.786|_0.183_|
|Higher Diploma/primary|0.667|0.500|1.306|_0.639_|
|University/primary<br>_Basic controls + Industry dummies_|0.979|0.705|1.387|_0.408_|
|Primary|-0.243|0.074|0.108|_0.351_|
|Junior Sec./primary|0.463|0.195|0.292|_-0.171_|
|Senior Sec./ primary|0.520|0.259|0.776|_0.252_|
|Higher Diploma/primary|0.592|0.524|1.294|_0.702_|
|University/primary|0.896|0.725|1.365|_0.469_|

* Estimates not statistically significant except for university. ** Estimates not statistically significant except for higher diploma and university. *** Estimates are all statistically significant except for primary. ******** Changes rather than percentage changes are given.

||1998|2002|2007-8|Change****<br>1998-2007|
|---|---|---|---|---|
|_Basic controls only_|||||
|High school general/ primary|0.650|0.215|0.743|_0.093_|
|High school vocational/ primary|0.525|0.325|0.874|_0.349_|
|Tertiary/primary|0.854|0.603|1.349|_0.495_|

19