rafmacalaba/datause-encoder-data huggingface dataset:
# Table 3: Union and Non-union Hourly Earnings Functions

||Sector||Marginal<br>Contribution|
|---|---|---|---|
|Variable|Union|Nonunion||
|Education leveP||||
|Primary|0.320 t<br>(3.8)|0.249t<br>(6.8)|0.025|
|Secondary Incomplete|0.532t<br>(6.1)|0.621t<br>_(15.5)_|40.027|
|Secondary Complete|0.882t<br>(9.8)|0.961t<br>(21.6)|-0.011|
|Tertiary|1.226t<br>(13.6)|1.425t<br>(32.3)|-0.031|
|Experience|0.038 *<br>(12.1)|0.055t<br>(24.5)|-0.302|
|Experience squared|-0.001t<br>(7.8)|-0.001*****<br>(19.5)|0.176|
|Northern State|0.123t<br>(4.7)|0.258t<br>(13.9)|-0.031|
|Male|0.063<br>(0.1)|0.161t<br>(9.1)|-0.066|
|_Municipio_Indigenous Percent|0.001<br>(0.6)|40.008t<br>(7.8)|0.026|
|Industrial Sectorsb||||
|Industrial Manufacturing|-0.285<br>_(5.6)_|-0.275 $ (3.4)|-0.003|
|Utilities (electricity & water)|0.019<br>(0.2)|40.239<br>(1.8)|0.003|
|Construction|-0.197t<br>(2.8)|-0.362t<br>(4.4)|0.016|
|Commerce|-0.283t<br>(4.4)|40.318t<br>(3.9)|_0.005_|
|Transportadon|-0.045<br>(0.7)|-0.212 t<br>(2.4)|0.008|
|Financial Services|-0.034<br>(0.4)|0.075<br>(0.8)|-0.003|
|Services|-0.059|40.322 t|0.107|
||(1.2)|(4.0)||
|Constant|6.967|6.757|0.210|
|Adjusted R<sup>2</sup>|0.324|0.303||
|N|2,793|6,907||
|Adjusted Wage Gap|||0.104t|

Source: INEGI (1989) Notes: t significant at the 95% level. t signifcant at the 99% level. a Residual is no education. I Residual sector is extraction. Numbers is parenthesis are t values. , is the estimated coefficient. u and n subscripts represent union and nonunion subsamples, respectively. it is the full sample weighted variable mean.

13
**Policy Research**

<!-- Start of picture text --> Trade Policy E<br>I-<br>WPS 850<br><!-- End of picture text --> T'his paper -a product of thc Tradc Policy Division, Country Economics Departmentis part of a larger research project on the sustainability of trade rcform in structural adjustment loans (RPO 675-32). Thc paper was presentcd at Bank seminars held in August and October 1991. Copies of the paper are available rree from the World Bark, 1818 H Strect NW, Washington, DC 20433. Please contact Dawn Ballantyne, room N I0-007, extension 38004 (42 pages). February 1992.

Malin examines the effect of fiscal adjustment on the real exchange rate. He argucs that thle direction and extent of that effect depend on the way fiscal adjustment is carried out. If a fiscal delicit is reduced mainly by reducing total govemment spending, the effect on the real exchangc rate depends on whether the adjustment is achieved through proportionate cuts in both capital and current spending or through disproportionately greater cuts in capital spending. A disproportionately high cut in catpital spending affects the composition of government spending between tradables and nontradables.

nontradables is greater for government spending than for private spcnding and greater lor the governmcnt's current spending than for its capital spending. This result is highly robust across different measures of the real exchange rate and across different methods of estimation. Thus as Bangladesh's fiscal adjustment shifted government spending toward nontradables, the real cxchangc rate tended to appreciate.

Matin emphasizes two important i7molications of such fiscal adjustment for developing countries like Bangladesh: Matin extends the dependent-economy model of the rcal exchange rate, incorporating both the level and composition of government spending. He then estimates the model for Bangladesh, a country that reduced total govcrnmcnt spending in the face of growing current expenditures. Bangladesh's fiscal adjustment involved an unsustainably large decline in capital spending as a share ol total spending.

Econometric estimates of the model for Bangladesh sihow that the propensity to spend on

* When fiscal adjustment involves unsustainably heavy cuts in capital spending (a trend that is now being reversed in Bangladesh), appreciation of the real exchange rate misaligns that rate, causing a misallocation of resources.

***** When disproportionate cuts in capital spending occur at the same timc as trade liberalization, appreciation of the real exchange rate undermines the effectiveness of trade liberalization.

Thc Policy Research Working PapcrSeriesdisseminates the findingsofwork under way in the Bank. Anobjectivcof hc scries i is to get thesc findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and I conclusions in these papers do not necessarily represent official bank policy.

Produced by the Policy Research Dissemination Center
<!-- Start of picture text --> AGO<br>ARG<br>BGD<br>BOL<br>ECU<br>EGY<br>GHA<br>IDN<br>KAZ<br>NGA<br>PAK<br>VNM<br>0 20 40 60 80 100<br>Percentage of respondents<br>Both support Cond. support (spend) Cond. support (other) No support<br><!-- End of picture text -->
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- MoWRI (Ministry of Water Resources and Irrigation, Government of Southern Sudan). 2007. ―Draft Water Policy.‖ Juba.

- ———. 2010. ―GoSS Water Strategic Framework.‖ Advanced draft, Juba.

- Nathan Associates. 2010. ―Definition and Investment Strategy for a Core Strategic Transport Network for Eastern and Southern Africa: Corridor Review and Performance Report.‖ Draft Document submitted to the World Bank, Washington, DC.

Platts. 2006. WEPP database.

- ———. 2009. WEPP database.

- Southern Sudan Center for Census, Statistics and Evaluation. 2010. _Statistical Yearbook for Southern Sudan 2010_ . Juba.

- WHO (World Health Organization) Joint Monitoring Programme (JMP). 2010a. ―Sudan Estimates for the Use of Improved Drinking-Water Sources.‖ www.wssinfo.org/resources/documents.html.

- ———. 2010b. ―Sudan Estimates for the Use of Improved Sanitation Facilities.‖ <u>www.wssinfo.org/resources/documents.html.</u>

- World Bank. 2007. ―Ethiopia Power Export Project Stage 2: Ethiopia-Sudan Interconnector Project.‖ World Bank Project Appraisal Document, Washington, DC.

- ———. 2008. ―Revitalizing Sudan’s Non-Oil Exports: A Diagnostic Trade Integration Study (DTIS).‖ Prepared for the Integrated Framework Program, World Bank, Washington, DC.

- ———. 2009a. ―Sudan: The Road toward Sustainable and Broad-based Growth.‖ Poverty Reduction and Economic Management Unit, Africa Region. Washington, DC.

- ———. 2009b. ―Sudan Investment Climate.‖ Finance and Private Sector Development Unit, Africa Region, Washington, DC.

- ———. 2010a. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Northern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, Washington, DC.

- ———. 2010b. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Southern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, World Bank, Washington, DC.

- ———. 2010c. ―Sudan: Issues in Urban Development: Phase 1—Overview of the Urban Landscape.‖ Urban and Water Unit / Sustainable Development Department, Africa Region, World Bank, Washington, DC.

- ———. 2011. ―Energy Sector Strategy Note for South Sudan.‖ Draft note, World Bank, Washington, DC.

- Yoshino, Yuataka. 2010. ―Presentation of Key Findings from the Trade Facilitation Assessment Field Work: Case of Southern Sudan.‖ Draft presentation, Juba, Sudan.

53
Looking to the future, according to United Nations projections, by 2040, the world will have at least 9 billion inhabitants, one-third of whom will be living in India and China. The U.S. Grain Council (USGC 2011) estimates that real per capita income of the citizens of the world will increase considerably over the next 30 years, from US$9,727 to US$25,000. Tilman et al. (2011) estimate increases in the 2005 levels of per capita GDP for Asian nations—including the most populous countries in East and South Asia—between 2.5 and 4 times by 2050. The Asian Development Bank (ADB 2011) estimates that the middle class in China, which currently represents 12 percent of its population, could become as high as 75 percent, and that it could reach 70 percent in India and 80 percent in Indonesia by the year 2050. Parallel to this expansion, the upper classes are also expected to increase their global presence, reaching 190 million in China, 210 million in India, 40 million in Indonesia, and 35 million in the Republic of Korea. The population of Asia is expected to age considerably, reaching an estimated “over 65” cohort of 580 million, which in the case of China will represent 27 percent of all its inhabitants, a considerable change from its current level of 10 percent. The USGC study also indicates that the role of women is likely to change in important ways, with an increase in trends such as marrying at a later age or deciding not to marry at all; these trends are beginning to emerge in the wealthier Asian countries and in the Chinese middle class. Increasingly, Asian women are joining the work force, getting more education, and consequently enjoying higher incomes. Other processes, such as urbanization of the developing world (with urban concentration in China estimated to increase from 34 percent of the population in 2010 to 70 percent in 2040) and epidemiological transitions toward a greater prevalence of diseases that used to be the exclusive domain of the wealthy countries, could also have important nutritional consequences (Fan, Menon, and Brzeska forthcoming).

12
'anoe o Fixeo erfect estimacion using annuaaL data ano tevets or trade poticy

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|TRI|0.10*<br>(.05)|||||-|
|TRIt||0.02"<br>(.005)|-|||-|
|BLACK|--||0.01**<br>(.00)||||
|DOLLAR||||*O.05*<br>(.02)|||
|TRADESHARES|-||-||0.01<br>(.02)||
|INDIRECT|||-|||2.82*<br>(1.65)|
|LAND|0.00<br>(.03)|0.20<br>(.21)|0.13<br>(.05)|0.01<br>(.03)|0.04<br>(.03)|0.04<br>(.07)|
|LABOR FORCE|0.37<br>(.30)|-0.14<br>(.25)|0.68<br>(.18)|0.22<br>(.18)|0.64<br>(.17)|0.41<br>(.29)|
|EDUCATION<br>(0.3Years)|0.11<br>(.12)|0.33<br>(.14)|0.11<br>(.05)|0.04<br>(.07)|0.09<br>(.04)|0.06<br>(.09)|
|EDUCATION<br>(3-9 Years)|0.01<br>(.07)|0.01<br>(.06)|0.02<br>(.04)|0.02<br>(.05)|0.01<br>(.03)|0.03<br>(.07)|
|CAPITAL<br>STOCK|0.58<br>(.06)|0.67<br>(.10)|0.51<br>(.03)|0.56<br>(.04)|0.52<br>(.03)|0.50<br>(.06)|
|F-Value<br>1/|1.3|1.7|1.6|2.1|1.7|1.4|
|N|380|258|1498|838|1737|395|
|R-Square|.35|.37|.28|.33|.25|.38|

Table 7 Fixed effect estimation using anmual data and changes in trade policy

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|TR I0.13|(.09)|||||||
|TR 11|-|0.01<br>(.01)||||||
|BLACK||.|O<br>.**<br>(.004)|-||||
|OOLLAR||||0.01<br>(0.04)||||
|TRADE<br>SHARES|.|_-_|-|-|0.06*<br>(.03)|||
|INDIRECT|-|-||-|-|0.16<br>(2.29)||
|Movement toiwards<br>International<br>Prices|-|-|-|-|||0.04**<br>t.01)|
|F-Value 1/|1.3|1.1|1.9|1.9|1.9|1.2|1.1|
|R-Square|.36|.31|.28|.33|.26|.39|.34|

Note: Stmnoard errors in 0). '" indicate **s** ignificant at S percent level; * indicates significant at 10 percent level. 1/ **F-Value** for test of Null hypothesis that country effects are _0._ 29
variables and private credit has been found significant in previous studies (La Porta, et al, 1997).

To summarize, our results find that ordinary factoring is an important source of financing in countries with better availability of credit information and weaker contract enforcement. Access to credit information is a challenge, however, in many developing countries with privacy laws prohibiting the sharing of credit information or where banks choose not to share their customer information. In the next section we discuss an alternative form of factoring that may overcome this barrier to factoring and succeed in weak business environments.

# **5. The Advantage of “Reverse Factoring”**

In ordinary factoring, a small firm sells its complete portfolio of receivables, from multiple buyers, to a single factor. Many factors will only purchase complete portfolios of receivables in order to diversify their risk to any one seller. In fact, many factors require sellers to have a minimum number of customers in order to reduce the exposure of the factor to one buyer – and to the seller’s ability to repay from receipts from other buyers – in the case that a buyer defaults. However, this diversified portfolio approach requires factors to collect credit information and calculate the credit risk for many buyers.

Ordinary factoring has in general not been profitable in emerging markets. First, if good historical credit information in unavailable, then the factor takes on a large credit risk. For instance, in many emerging markets, the credit information bureau is incomplete (i.e. may not include small firms) or non-bank lenders, such as factors, are prohibited from joining. Second, fraud is a big problem in this industry – bogus receivables, non-existing customers, etc. – and a weak legal environment and non15
Policy Research Working Paper 0

# **Abstract**

The extent to which teachers and school directors are held to account may play a central role in determining education outcomes, particularly in developing and transition countries where institutional deficiencies can distort incentives. This paper investigates the relationship between an expanded set of school inputs, including proxies for the functionality of “top-down” and “bottomup” accountability systems, and education outputs in Albanian primary schools. The authors use data generated by an original survey of 180 nationally representative schools. The analysis shows a strong negative correlation between measures of _top-down_ accountability and students’ rates of grade repetition and failure in final examinations, and a strong positive correlation between measures of top-down accountability and students’ excellence in math. _Bottom-up_ accountability measures are correlated to various education outputs, although they tend lose statistical significance once parent characteristics, school resources and top-down accountability indicators are considered. An in-depth analysis of participatory accountability within the schools focuses on parents’ willingness to hold teachers to account. Here, the survey data are combined with data from lab-type experiments conducted with parents and teachers in the schools. In general, the survey data highlight problems of limited parental involvement and lack of information about participatory accountability structures. The experiments indicate that the lack of parental participation in the school accountability system is owing to information constraints and weak institutions that allow parent class representatives to be appointed by teachers rather than elected by parents.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. 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 tpackard@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
# **IV. SECIYR FINANCING STRATEGIES**

**1.** Adjustment to public expenditure priorities Maintain level of funding to education sector?_ Increase public spending on education?9 Reduce public spending on education?_ Unclear/Not stated

2. How is this adjustment to be effected' Redistribute budget allocations among sectors? How?

# Reduce educational unit costs? How?

Increase recourse to other sources of financing? _foreign aid? ljocal/regional government? .NGOs/religious organizations? -user fees/cost recovery? _other private sector contribution to education financing?

3. Are the financing conclusions realistic in light of the overall macroeconomic situation? Yes/No If not, how and why not?

4. Do the financing conclusions agree with the educational sector objectives? Yes/No If not, how and why not?

# **V. METhODOLOGY**

- A. Sector coverage

1. Did an education sector specialist participate in the mission/report preparation? Yes/No

2. What is known about prior public expenditures? There have been several/many years of Bank experience and reliable data___ There are underlying courtry/expenditure/sector studies that are: included in the review____ referred to in the review implied but not mentioned_ The Bank has little experience in the countryNot clear/not stated61
Table A.3: International comparison of top average incomes (2019 PPP USD 000’s)

||Top 0.1%|Top 1%|Top 10%|
|---|---|---|---|
|_Pivotal benchmark countries_||||
|China|1,687|358|103|
|France|2,429|637|190|
|India|1,047|273|72|
|South Africa|1,365|457|155|
|United States|7,050|1,581|379|
|_High income share_||||
|Angola|2,614|605|135|
|Bahrain|5,919|1,567|359|
|Central African Rep|314|74|15|
|Mozambique|396|92|19|
|Yemen|324|100|24|
|_LAC (good data quality)_||||
|Argentina|3,697|1,313|427|
|Brazil|1,308|509|143|
|Chile|2,927|946|232|
|Colombia|1,033|447|138|
|Costa Rica|1,247|580|157|
|Dominican Rep|2,086|700|176|
|Ecuador|483|208|70|
|El Salvador|421|207|66|
|_Honduras (WID data)_|514|199|56|
|Mexico|1,862|828|192|
|Peru|1,205|448|115|
|Uruguay|1,282|519|173|

_Note:_ This table presents the average income of the top 0.1%, top 1% and top 10% highest earners in selected countries in 2019 (PPP USD 1,000s). Data is from the World Inequality Database (WID). The figures for Honduras in this table are from the WID dataset, not the estimates in this paper.

46
|||Table 3:Effe|ctiveness ofCC<br>|Tprograms<br>|||
|---|---|---|---|---|---|---|
|Program<br>(country)|Type of<br>evaluation|Age|School<br>enrollment<br>increase<br>(percentage<br>points)|Child work<br>reduction<br>(percentage<br>points)<br>Finding|Stipend<br>(annual)|Public<br>education<br>expenditure<br>(% GDP<br>pc)|
|_Red de_||7-13|18.0*|4.9*|||
|_Protección_|||||||
|_Social_|||||||
|(Nicaragua)|DID|10-13||9.0*|$21|9.1|
|||8-11 boys|1.1-1.8*|1.3|||
|||12-17 boys|3.2-5.8*|3.2-4.7*|||
|_Oportunidades_||8-11 girls|--|--|$109-||
|(Mexico)|Random|12-17 girls|7.5-9.5*|1.1-2.3*|$218|14.4|
|||10-14|--|8.3*||11.4|
|PETI(Brazil)|DID|7-14|--|N.S.|$173||
|_Programa de_<br>_Asignación_|||||||
|_Escolar_|||||||
|(Honduras)||6-12|--|N.S.|$60||
|Bangladesh’s|||||||
|Food for|||||||
|Education||5-16|16.0-17.0*|4.2*|$35|7.2|
||DID|10-13 rural|1.5*|10.3-14.1*<sup>a</sup>|$96|16.7|
|_Familias en_||14-17 rural|5.0*|N.S.|$192||
|_Acción_||10-13 urban|7.0*|Decrease|||
|(Colombia)||14-17 urban|2.5*|Decrease|||

Sources Raju 2005; Maluccio & Flores 2004; Pianto & Soares 2004; Attanasio & others 2006a; Glewwe & Olinto 2004 Note: Public expenditure data: Nicaragua 2004, Mexico 2004, Brazil 2000, Honduras, Bangladesh 2004, Colombia 2004 N.S. Not significant.<sup>a</sup> = all ages

# <u>Overview tables</u>

Our crude simulations of various options appear in Table 4. The lack of data in general, and more importantly the scarcity of rigorous, makes it difficult to go beyond the theoretical stage. Nevertheless, the evidence is sufficient to draw a few broad conclusions. Studies of the effects of pre-school education and other interventions on later performance have provided fairly conclusive evidence that quality early intervention programs can increase children’s educational potential, and likely have long-lasting effects. Other interventions produce, in comparison, small benefits relative to cost. We present different scenarios to highlight these differences. These include improved school indicators: 5-16 percent reduction in grade repetition; 18-21 percent 11
# Evening the Credit Score? Impact of Psychometric Loan Appraisal for Women Entrepreneurs<sup>∗</sup>

Salman Alibhai<sup>†</sup> Rachel Cassidy<sup>‡</sup> Markus Goldstein<sup>§</sup> Sreelakshmi Papineni<sup>¶</sup> Gender, Entrepreneurship, Firms, Finance, Credit [JEL] C93, D14, D25, J16, L25, L26, O12

> ∗This paper is a product of the World Bank Africa Gender Innovation Lab, within the office of the Africa Region Chief Economist. The project was conducted in partnership with the Women Entrepreneurship Development Project (WEDP) and Wasasa Microfinance. We thank Menaal Ebrahim for excellent research assistance, and Yemsrach Kinfey Edey for expert field and relationship management. We also thank Girum Abebe, Niklas Buehren, Rachel Coleman, Toni Weis, Mengistu Bessir Achew, Marlon Rawlins, Dr Amsalu Alemayehu and Zerihun Takele for invaluable support and comments. Funding for this study was provided by Global Affairs Canada under the Innovations in Financing Women Entrepreneurs (IFWE) project, as well as the World Bank Umbrella Fund for Gender Equality (UFGE) and other World Bank sources. 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. IRB Approval was obtained from HML IRB Research and Ethics, reference 969TWBG21. The analysis plan is registered on the AEA RCT registry at https://doi.org/10.1257/rct.8608

> †World Bank Finance, Competitiveness and Innovation (FCI) Global Practice `aalibhai@worldbank.org`

> ‡World Bank Africa Gender Innovation Lab `rcassidy@worldbank.org`

``` (Correspondingauthor)1818HStNW,Washington,D.C.,20433,USA ```

> §World Bank Africa Gender Innovation Lab `mgoldstein@worldbank.org`

> ¶World Bank Africa Gender Innovation Lab `spapineni@worldbank.org`
# **Polarization and Its Discontents: Morocco before and after the Arab Spring**

_Fabio Clementi Haider A. Khan Vasco Molini_<sup></sup> University of Macerata University of Denver, JKSIS The World Bank (Washington (Macerata, Italy) (Denver, USA) DC, USA) _Francesco Schettino_ University of Campania (Naples, Italy) _Khalid Soudi_ Haut Commissariat au Plan (Rabat, Maroc)

**Keywords:** polarization, perception, subjective measures, absolute measures, Morocco **JEL classification:** C14; D31; D63; I32.

>  Corresponding author. The authors acknowledge financial support from the World Bank. We thank colleagues from the Haut Commissariat au Plan of Morocco (HCP) for their excellent data preparation. We also thank Paolo Verme, José Antonio Cuesta and the participants to the DEMM seminar series (Università degli Studi di Milano) for their comments and suggestions on an earlier version of the manuscript. The authors are solely responsible for any errors this paper may contain.
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