rafmacalaba/datause-encoder-data huggingface dataset:
# TABLE OF CONTENTS

||Page NQ.|
|---|---|
||Executive Summary ...............................<br>,<br>i|
|.|'The Context:|
||Egypt's Development Strategy for the 1990s ...........................<br>1|
|IL|History of Higher Education ............<br>..................<br>6|
|HI.|Performance and Its Deteiminants|
||-Quantities .11|
||<br>-Quality and Internal Efficiency .....................<br>12|
||-External Efficiency .15|
||-Equity......................................................<br>18|
||-Summary: Determinants of Performance .20|
|IV.|Concluion: Responses and Reforms|
||-Thie Government..............................................<br>22|
||-Employers..**.**. ...............................................<br>23|
||-Students ............................................<br>23|
||-Universities ............................................<br>24|
||References ............................................<br>25|
||Statistical Annex|
||Table 1 : Higher Educational Enrollments ......................<br>27|
||Table 2: Higher Education Graduates ........................<br>28|
||Table 3: Number of Students Passing the General|
||Secondary Exam ...........<br>......................<br>29|
||<br>Table 4: Technical School Enrollments ........................<br>30|
||Table_5_: Graduates from Technical Institutes ...................<br>31|
||Table 6: Spending on Higher Education .......................<br>32|
||Table 7: Government Employment ..........................<br>33|
||<br>Table 8: Educational Characteristics of the Unemployed .....<br>..... 34|
||<br>Table 9: Share of Higher Educational Spending in|
||Total Educational Spending ........................<br>35|
||<br>Table 10: Female Enrollment in Universities ....................<br>36|
||Box 1: University Exams: The Cases of Economics|
||<br>and Geology ............................................<br>37|
||Box 2: Some Innovative Responses to the Quality|
||<br>Problem ..............................................<br>39|
Global inequality and the global inequality extraction ratio: The story of the past two centuries Branko Milanovic<sup>1</sup> World Bank Research Department, Washington School of Public Policy, University of Maryland at College Park JEL classification: D31, N30 Key words: global inequality, history, inequality extraction ratio Number of words: around 6,900

> 1 Paper presented at the XV World Economic History Congress, Utrecht, May 3-8, 2009. I am grateful to Alberto Chilosi for the discussion of the issues raised in the paper, and to Daron Acemoglu, Peter Lambert and Peter Lanjouw for very useful comments. The  views expressed here are author’s own views and should be attributed to the World Bank or any affiliated organizations.
assessment, conducting a vulnerability assessment, developing resiliency plans, and providing training to stakeholders. The relevant stakeholders in Bangladesh involved in the Phase 1 study were the Local Government Engineering Department (LGED), the Roads and Highway Department (RHD), and the City Corporation of Chattogram and Sylhet.

Table 1 The weights used by IMC Worldwide and CEGIS for landslide susceptibility mapping of rainfall-triggered landslides.

|**Factor**|**Weight**|
|---|---|
|Distance to Road|0.14|
|Rainfall|0.14|
|Land Use|0.40|
|Lithology|0.24|
|Slope|0.08|

Building on the data collected during the above-described Phase 1 study, this paper proposes a statistical approach for determining the weights used for combining the input factors for landslide susceptibility mapping, alongside the uncertainty resulting from sampling landslide events used in weight quantification. We used the same data, factor classes, and scores of the Phase 1 study to be able to directly evaluate the outcomes of using our proposed statistical approach for determining weights compared to the qualitative approach used in the previous study. Our proposed approach combines a machine learning model (Random Forest Classification) and large-scale sensitivity analysis for sampling the testing and training data of the machine learning model. Data on factors contributing to landslide susceptibility are used to train and test machine learning models for predicting past landslides. Sensitivity analysis is conducted to sample the locations of past landslides used for training the models. The relative importance of factors contributing to landslide susceptibility is determined for each sensitivity iteration, with the relative importance of all sensitivity experiments representing the weight uncertainty range. We demonstrate the proposed approach for a case study of road susceptibility 4
# **Figures 7c and 7d: Government Revenue and growth: Saudi Arabia**

<!-- Start of picture text --> Fig. 7c Gov. budget gains as % GDP (Carbon tax =<br>$25)<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>2020 2022 2024 2026 2028 2030 2032 2034 2036<br>$25 carbon tax phase out fuel subsidies<br><!-- End of picture text --> <!-- Start of picture text --> Fig. 7d. Growth contribution of carbon tax vs<br>removing fuel subsidies<br>0.08<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>2020 2022 2024 2026 2028 2030 2032 2034 2036<br>Baseline GDP growth GDP growth with $25 carbon tax<br>GDP growth: remove subsidies<br><!-- End of picture text --> Note: Fig 7a: For a modest carbon tax of $5 per ton of CO2, removing fossil fuel subsidies is more effective in reducing CO2 emission whether or not carbon tax is imposed (yellow and blue lines coincide and produce less CO2 emission). In fact, imposing carbon tax does little to reduce CO2 (orange and gray lines coincide and produce more CO2). Fig 7b: Only at much higher carbon tax of $50 per ton of CO2 does carbon tax-based CO2 reductions exceed subsidy elimination (compare gray-orange gap with orange-blue gap). Further, removing fossil fuel subsidy and imposing no carbon tax (yellow line) still ties, until 2029, with a policy of imposing a carbon tax and keeping subsidies (orange line) and only after 2030 is carbon tax more effective in reducing CO2. As for government revenues, while both carbon tax of $25 and removal of fossil fuel subsidies improves the revenue, removal of fossil fuel subsidies dominates by a wide margin (Fig. 7c). Both policy instruments cause growth to decline very slightly in the short run, with no long run effect as growth rates converge to the baseline in the long run (Fig. 7d).

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Figure A12: Share of Services in Top 5 Investing Firms (Developing and Highincome economies) a. High-income economies <!-- Start of picture text --> .6<br>.5<br>.4<br>.3<br>FDI Concentration Ratio<br>Share of Services in Top 5<br>.2<br>2005 2010 2015 2020 2025<br>Project year<br>b.<br>Developing economies<br>.55<br>.5<br>.45<br>.4<br>.35<br>FDI Concentration Ratio<br>.3<br>Share of Services in Top 5<br>2005 2010 2015 2020 2025<br>Project year<br>Ratio and Share (%)<br>Ratio and Share (%)<br><!-- End of picture text --> Note: The analysis relies on greenfield FDI capital expenditure, excluding from the sample destination economies with, on average, less than 10 investment projects per year.

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The proportion of observed second child arrivals is larger in comparison to first child arrival, as for second child arrival, the group of observed women is much more homogenous: Women in this group (test and control group) are in most cases in a partnership and as they already have one child, they are unlikely to be infertile. The fact that the proportion of observed third child arrivals is smaller in comparison to second child arrival in all countries is in line with the finding presented in the previous section of smaller transition probabilities from second to third child arrival in comparison to those from first to second child arrival.

Tables A to C in the appendix give a descriptive overview of the exogenous variables including partner characteristics<sup>11</sup> used in our regression models. The proportion of women in stable full-time employment is low for all women and decreasing with child rank. The proportion of women in stable full-time employment is significantly lower for those women who are going to have a second and third child in comparison to those who stay with one or two children. For example, the first line in table B shows that 16% of those women who have one child and who will not have a second child in the following year are in stable full-time employment, whereas among those women who will have a second child, only 8% are in stable full-time employment. Most women are observed inactive, and the proportion is larger for those women who will have a child in the next year. The proportion is largest for women having a third child: 84% of these women are reported inactive during the three months before conception of the third child (line 8 table C). Part-time work as an employee is not common for Turkish women, but selfemployment is quite frequent (either full time or part time). Women are actually reported as selfemployed in SILC when working as contributing family workers, in subsistence activities in agriculture and in informal and non-registered work. The majority of women’s partners are in stable employment.

_Estimation results_

> 11 Note that we control for the presence of a partner only for 1st child arrival as women having already one or two children are all reported with an observed partner. We do not drop women without an observed partner for first child arrival as almost half the Turkish women having a first child in year _t_ are observed without a partner in year _t‐1_ , suggesting that children of rank 1 are likely to arrive in less than 12 months after partners move together. The SILC actually reports zero out of wedlock births in Turkey (proportion of women with child arrival who have an observed partner but are not married). The fact that in Turkey, household formation and the arrival of a first child are quite contemporary events raises the issue of attrition: We might lose a certain number of women in the longitudinal database, who live with their parents in the beginning of the observed period, and are not followed up as they found their own household due to imminent family formation.

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Table 3: Equivalent Years of Schooling of selected learning interventions

|**Eff**<br>**e**|**ect size**<br>**rcentile**|**Intervention**|**Effect size**|**Effect size i**|**n Equivalent Years**|**of Schooling**|
|---|---|---|---|---|---|---|
|**p**||||**Method 1**|**Method 2a**|**Method 2b**|
||25<sup>th</sup>|India PicTalk|0.06|0.41|0.39|0.28|
|**Structured pedagogy**|50<sup>th</sup>|Kenya HALI|0.13|0.88|0.85|0.61|
||75<sup>th</sup>|Brazil new curriculum|0.29|1.97|1.89|1.36|
||25<sup>th</sup>|Peru OLPC|‐0.02|‐0.14|‐0.13|‐0.09|
|**Computer assisted learning**|50<sup>th</sup>|China remedial class|0.01|0.07|0.07|0.05|
||75<sup>th</sup>|Ecuador learning lab|0.06|0.41|0.39|0.28|
||25<sup>th</sup>|Senegal school grant,<br>grade 5 female|‐0.06|‐0.41|‐0.39|‐0.28|
|**School based management**|50<sup>th</sup>|Gambia school grant|0.01|0.07|0.07|0.05|
||75<sup>th</sup>|Senegal school grant,<br>grade 6 male|0.04|0.27|0.26|0.19|

Notes: EYOS of each method is based on Table 2 Column 2 (pooled) and multiplied by the effect size of each intervention. References of included interventions: India Pic Talk: He, Linden and Macleod 2009; Kenya HALI: Jukes and Dubeck 2015; Brazil new curriculum: Leme 2010; Peru OLPC: Quispe et al. 2013; China remedial class: Lai et al. 2011b; Ecuador learning lab: Carillo et al. 2010; Senegal school grant, grade 5 female: Carneiro et al. 2015; Gambia school grant: Blimpo et al. 2015; Senegal school grant, grade 6 male: Carneiro et al. 2015. Sources: Authors’ calculations and Snilstveit et al. (2015) 23
nature of the international economic order. Second, even locally creditworthy firms may be constrained for several reasons. Corporate ratings are often subject to sovereign ceilings. Corporate assets are not easily amenable to collateralization in international debt markets. Swap markets for credit derivatives are better developed and more liquid for emerging sovereign names than for corporate names. And, private corporate borrowers’ relations and interactions, regarding default or debt restructuring, with foreign creditors are largely shaped by economic considerations, whereas sovereigns’ relations are driven by a mix of politics and economics in mind.

# **_Estimation results_**

We begin with estimating equations (5) and (6) separately to establish empirically the structural differences between private and sovereign bond markets in emerging market economies. The dependent variable in both set of equations is at –issue spread, quoted in basis points and measured as the bond’s offering spread over the yield of a maturity –matched U.S. Treasury security or, in the case of a euro issue, a comparable German Bunds obligation. The primary data sources are Dealogic DCM Analytics and Bloomberg, with filling in missing data by the author. To capture common local and global systematic risk factors, we include data on the macroeconomic, institutional, financial market development of each issuer’s home country, along with data on international interest rates that we match by month, quarter, or year with the issue from a variety of sources. We also control for the state of global investor sentiment to account for common shocks affecting both private and public bond markets. We use the bond issuers’ general industry group defined by dialogic DCM analytics to control the sector of issuers. Tables 2 and 3 summarize estimation results for public and private bond spread determinants, respectively, with country fixed effects and under alternative model specifications.

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Teacher Incentives in the Kyrgyz Republic (a) What are teachers’ perceptions of the motivational aspects of the teacher incentive program, particularly with respect to changed teaching practices and participation in professional development?

(b) Do teachers who receive lower performance ratings and therefore lower extrinsic rewards hold different perceptions from those who receive higher ratings and higher extrinsic rewards?

This paper adds to the literature in two ways, by examining an incentive program for individual teachers that rewards their mastery of knowledge and skills (“certification pay”), and by providing new evidence about teachers’ attitudes towards extrinsic incentive programs.

We use data from a post-hoc evaluation of a teacher incentive program in the Kyrgyz Republic to examine teacher awardees’ perceptions of the program’s motivational aspects and their self-reports of the impact of the program on their teaching behaviors. We also ask whether differences in teachers’ standards-based performance ratings are associated with differences in these opinions and self-reports.

# 2. The Kyrgyz Republic

The Kyrgyz Republic is a low-income mountainous country in central Asia, with a GDP per capita of $2,400 in 2011 and a population of 5.6 million; over a third of the population lives on income below the official poverty line (World Bank 2013). The Kyrgyz Republic’s nine provinces are each divided into up to 20 districts. While literacy is widespread and primary and secondary net enrollment rates are relatively high, 88.4 and 80.4 respectively in 2011, learning achievement is poor. The Kyrgyz Republic participated in the Program for International Student Assessment (PISA) in both 2006 and 2009; its reading, mathematics and science scores for 15-year-olds were the lowest among participating countries in both years, although some improvement between 2006 and 2009 was observed (OECD 2007, 2010a, 2010b).

As in many countries, teachers in the Kyrgyz Republic traditionally were provided few incentives for good performance, once they entered the teaching profession. Uniform salary scales were largely 8
mostly due to the abolition of internediaries during the 1950s. Little real change in landlessness has taken place since then. It is generally agreed that subdivision of landholdings and the sale/purchase of land have been more important in bringing about structural redistribution than have redistributive land reforrns, although land reforms have brought substantial gains to many poor rural households, particularly in Kerala (with mixed effects) and West Bengal. Indeed, the land redistribution that has been brought about through the market is partly attributable to land reforms, where there has been a credible threat of ceilings enforcement. Table 3 summarizes some salient features of India's agrarian structure. Figures 1 and 2 illustrate the relative shifts in distribution of ownership holdings and share of owned area by size of holding since independence.

Table 3 Key **parameters in India's agrarian structure**

|_Parameters_|_1961-62_|_1971-72_|_1982_|_1992_|
|---|---|---|---|---|
|Estimated number of rural households (millions)|72.5|77.8|93.9|n/a|
|Share not owning any land (%)|11.7|9.6|11.3|n/a|
|Share not operating any land (%)|26.8|27.0|24.0|n/a|
|Gini coefficient for ownership holdings|0.73|0.71|0.71|n/a|
|Gini coefficient for operational holdings|0.58|0.59|0.63|n/a|
|Share of landowning households leasing out (%)|7.0|9.9|6.2|n/a|
|Share of owned area leased out (%)|4.4|5.8|4.3|_5.0_|
|Share of operating households leasing in (%)|23.8|25.2|14.7|15.0|
|Share of operational area leased in (%)|10.7|10.6|7.2|9.0|

Sources: Vaidyanathan (1994), based on data from _Sarvekshana_ 11(2), 1987, and 12(1), 1988; and GOI (1997b) <!-- Start of picture text --> Fig 1 Distribution of household ownership<br>holdings: all India, 1953-64 to 1982<br>100%<br>X 80%<br>M>6ha<br>60%. *2-6 ha<br>.~40% [31-2ha<br>20%. 0.4 -I ha'<br>* **.*** *~~< 0.4 ha<br>1953-54 1961-62 1971-72 1982 * Landless<br><!-- End of picture text --> 14
# 9.7 ADDITIONAL DESCRIPTIVE STATISTICS

108. This section includes tabulation of the mean and standard error of dependent and independent variables for tenders administered manually and electronically. The tabulations have also been done by procurement method and by financial year.

TABLE 31: DESCRIPTIVE STATISTICS OF OUTCOME INDICATORS (UNWEIGHTED)

|Ot<br>Vibl|M|ean/Observatio|ns|Coefficient/ p-value|
|---|---|---|---|---|
|ucomes<br>araes|Manual|E-proc.|Total|Treatment vs Control|
|Ln of Number of<br>Bidders|1.537<br>(0.013)|1.362<br>(0.004)|1.384<br>(0.004)|-0.175***<br>(0.012)|
||10009|69240|79249||
||0.169|0.194|0.191|0.025***|
|Access<br>Single Bidders|(0.004)|(0.002)|(0.001)|(0.004)|
||10009|69240|79249||
|Non-Local Winners|0.129<br>(0.003)|0.216<br>(0.002)|0.204<br>(0.002)|0.087***<br>(0.004)|
||9850|60472|70322||
|Winning Rebate|0.947<br>(0.092)|6.968<br>(0.035)|6.219<br>(0.033)|6.021***<br>(0.098)|
||9753|68705|78458||
|Economy<br>Time Overrun|0.305<br>(0.005)|0.191<br>(0.011)|0.291<br>(0.004)|-0.115***<br>(0.013)|
||9551|1349|10900||
||-0.015|-0.017|-0.016|-0.002|
|Cost Overrun|(0.001)|(0.004)|(0.001)|(0.003)|
||9256|1307|10563||
||25.7|19.48|20.65|-6.21***|
|Submission Period|(0.08)|(0.02)|(0.03)|(0.06)|
||10022|43137|53159||
||25.78|21.07|21.83|-4.71***|
|Decision Period|(0.25)|(0.07)|(0.07)|(0.2)|
|Effii|8203|43108|51311||
|cency<br>Signing Period|17.6<br>(0.14)|16.05<br>(0.04)|16.27<br>(0.04)|-1.54***<br>(0.11)|
||9763|60068|69831||
||83.24|65.91|68.35|-17.34***|
|Lead Time|(0.41)|(0.09)|(0.1)|(0.28)|
||9879|60114|69993||
||1.481|1.828|1.783|0.346***|
|Procurement Method|(0.005)|(0.001)|(0.001)|(0.004)|
|Independent|10163|69240|79403||
|Variables|14.82|14.902|14.892|0.082***|
|Ln of Contract Value|(0.014)|(0.005)|(0.004)|(0.013)|
||10163|69240|79403||

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 62
Second, we make use of the United Nations Population Division Migrant Stock data which provides information regarding the stock of international migrants by origin and destination for 232 countries between 1990 and 2020, on five-year based intervals (DESA, 2020). The main source of information collected is extracted from population censuses. When not available, the information has been retrieved through population registers and nationally representative surveys. Finally, we gather information on country-pair characteristics using the ’Gravity’ CEPII database (Conte et al., 2021). From this dataset we use information on the GDP of both origin and destination countries, common religion dummy, common language dummy and a colonial linkages dummy. Information on the share of right wing (i.e. nationalist party) votes is based on the Manifesto Project Database (MPD) and the variable used in regressions built as in Moriconi et al. (2022) – see appendix section B for a detailed discussion.

Some important points for the construction of the estimation data set deserve careful explanation. First, a country-pair in a specific year may have multiple trade agreements in place. To address this issue, for a given country pair in a given year, we keep the highest value regarding the ’Visa’ provisions. Second, we keep every country pair-year combination in the migrant stock database. Namely, if a given country pair-year combination does not have corresponding information in the DTA database, we assume that this specific country pair-year combination does not share a trade agreement with visa-provision (i.e. and ’visa’ dummy is therefore equal to zero). Conversely, every pair-year combination in the DTA database that does not match the migrant stock data is dropped. Indeed, assuming zero-migration for these specific cells would be inaccurate. Finally, every pair-year has been merged with the ’Gravity’ data. We are left with a total of 49,450 country-pair combinations observed in 7 points in time (1990, 1995, 2000, 2005, 2010, 2015 and, 2020). Table A1 presents summary statistics of selected variables in our database. Table A2, in the Appendix, presents the list of countries covered in the baseline estimation.

# **2.2 Descriptive**

Before moving to the formal econometric analysis, this section shows some stylized facts on the number of RTAs covering visa related provisions and their qualitative correlation with the bilateral settlement of migrants. Figure 1 shows the increasing number of RTAs signed in the last 30 years and, among them, those including visa-related provisions.<sup>7</sup> It clearly emerges an increase in the share of newly signed RTAs including visa provisions: from around 5% in the 1990s to 20% in the post-2007 period. Such a surge in the number (and share) of RTAs that

> 7Figure 1 bases exclusively on the 279 RTAs coded by World Bank and for which we have information on their contents.

4