rafmacalaba/datause-encoder-data huggingface dataset:
# **Active Conflict and Access to Education: Evidence from a Series of Conflict-Related Shocks in the Republic of Yemen**<sup>∗</sup>

Safa Almoayad† , Eliana Favari‡ , Samira Halabi¶ , Siddharth Krishnaswamy§, Almedina Music<sup>I</sup> , and Sharad Tandon<sup>∗∗</sup>

# Keywords: Conflict, Education, Yemen

JEL classification: D12, E24, J16, O12, O53 ∗The authors would like to sincerely thank Mr. Arif Husain, the VAM team of the World Food Programme, the WFP country Office, and the Regional Office teams for their work and support which made this paper possible. The authors would also like to thank Johannes Hoogeveen for comments on earlier drafts. The views expressed here are those of the authors and may not be attributed to either the World Food Programme or the World Bank. All remaining errors are attributable to the authors.

> † Poverty and Equity Global Practice of the World Bank, 1818 H St. NW, Washington DC, 20420, USA, salmoayad@worldbank.org.

> ‡ World Food Programme, Regional Bureau- Middle East, North Africa, and Eastern Europe, 72 El-Nahda, Maadi as Sarayat Al Gharbeyah, Maadi, Cairo, Egypt, eliana.favari@wfp.org.

¶Education Global Practice of the World Bank, 1818 H St. NW, Washington DC, 20420, USA, shalabi@worldbank.org.

§World Food Programme, Regional Bureau- Middle East, North Africa, and Eastern Europe, 72 El-Nahda, Maadi as Sarayat Al Gharbeyah, Maadi, Cairo, Egypt, siddharth.krishnaswamy@wfp.org.

> I I Education Global Practice of the World Bank, 1818 H St. NW, Washington DC, 20420, USA, amusic@worldbank.org.

∗∗Poverty and Equity Global Practice of the World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, standon3@worldbank.org
Jovanovic, Boyan, and Peter L Rousseau. 2005. “General purpose technologies,” 1:1181–1224.

- Lee, Katherine J, and John B Carlin. 2010. “Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.” _American Journal of Epidemiology_ 171 (5): 624–632.

- Levinsohn, James, and Amil Petrin. 2003. “Estimating production functions using inputs to control for unobservables.” _The Review of Economic Studies_ 70 (2): 317–341.

- Mokyr, Joel. 2014. “Secular stagnation? Not in your life.” _Geneva Reports on the World Economy:_ 83–89.

- Olley, G. Steven, and Ariel Pakes. 1996. “The dynamics of productivity in the telecommunications equipment industry.” _Econometrica_ 64 (6): 1263–1297.

- Syverson, Chad. 2011. “What determines productivity?” _Journal of Economic Literature_ 49 (2): 326–65.

- ~~.~~ 2014. “The importance of measuring dispersion in firm-level outcomes.” _IZA World_

- _of Labor:_ 1–53.

- ~~.~~ 2017. “Challenges to mismeasurement explanations for the US productivity slow-

- down.” _Journal of Economic Perspectives_ 31 (2): 165–86.

- Van Buuren, Stef, Hendriek C Boshuizen, and Dick L Knook. 1999. “Multiple imputation of missing blood pressure covariates in survival analysis.” _Statistics in Medicine_ 18 (6): 681–694.

- World Development Report. 2019. _The changing nature of work._ Edited by License: Creative Commons Attribution CC BY 3.0 IGO. World Bank Group.

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-5-

**TABLE 1.2**

|International<br>**Usin**|**Comparisons of Real Output Levels**<br>**in Nanufacturing**<br>**g**"Equilibrium"<br>**Exchange**Rates<br>or PPP Proxies<br>"EQUILIBRIUM" EXCHANGE RATE COMPARISONS|
|---|---|
|Sadler<br>and Grossman<br>(1982)|Output<br>per man hour<br>and joint<br>factor<br>productivity<br>for main<br>economic<br>sectors<br>and 10 branches<br>of manufacturing<br>in the USA<br>and Japan in 1975 prices converted to U.S. dollars at 1975<br>exchange rates.|
|Sadler (1986)|Updates former to 1983.|
|Asian Productivity<br>Organisation<br>(periodically)|Output per employee in main economic sectors (including<br>manufacturing as a whole) for 12 Asian countries, 1971-83 in<br>1975 prices converted to U.S. dollars at 1975 exchange rates.|
|PROXY CO|MPARISONS USING ANALOGOUS ICP EXPENDITURE COMPONENTS|
|Kuznets(1972)|Used reweighted<br>OEEC and ECLA expenditure<br>PPPs to estimate<br>sector PPPs for large groups of countries.|
|Jones (1976)|Used reweighted Kravis et al. (1975) expenditure PPPs to<br>derive sector PPPs.|
|Prais (1981)|Used reweighted Kravis et al. (1975) expenditure PPPs to<br>derive PPPs for 10 manufacturing industries in Germany, U.K.<br>and U.S.A.|
|Roy. A.D. (1982)|Used reweighted Kravis, Heston and Summers (1978) expenditure<br>PPPs to derive sector PPPs.|
|Klodt (1984)|Applied Kravis, Heston and Summers (1978) PPPs to 16 branches<br>of manufacturing for Germany, Japan and U.S.A., 1960, 1970 and<br>1978.|
|Guinchard (1984)|Uses Kravis, Heston and Summers (1982) expenditure PPPs (with<br>adjustment for taxes and trade margins) to derive PPPs for<br>some branches of manufacturing. For intermediate products he<br>used the exchange rate.|
|Jorgenson, Kuroda<br>and Nishimizu (1J36)|Applied "remapped" Kravis et al. (1975, 1978) PPPs to estimate<br>productivity differentials in Japan and USA (1960-79).|
|Roy,D.J. (1987)|Used reweighted expenditure PPPs from ICP IV, derived from a<br>tape provided by UNSO, for 60 countries for 1980.|
|SHORTC|UTS USING LIMITED INDUSTRY OF ORIGIN INFORMATION|
|Shinohara (1966)|Used 53 "representative" commodities for 89 countries in 1968<br>from UN Statistics of the kind now published in the Yearbook<br>of Industrial Statistics with value added weights from the<br>Japanese, UK, and US census of manufactures.|
|Maddison (1970)|Used a trade adjusted version of Shinohara's estimates at US<br>prices for 29 countries for 1965.|
|Blades (1982)|Used 54 commodities to compare USA and USSR in 1970, 1975 and<br>1978.|
**Figure 1- Country Averages: Tax Corruption and Tax Simplification (2002-2012)**

<!-- Start of picture text --> Country averages: Tax Corruption and Tax Payments<br>(2002-2012)<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 20 40 60 80 100<br>Tax corruption (%)<br>Country averages: Tax Corruption and Tax Time<br>(2002-2012)<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>0 20 40 60 80 100<br>Tax corruption (%)<br>Tax Payments (number per year)<br>Tax Time (hours per year)<br><!-- End of picture text --> Source: Authors’ calculations based on series from the World Bank’s Enterprise Survey and Doing Business Databases.

15
Figure 4. Private <u>Credit to GDP Ratio (1995-2009), MENA vs Non-MENA</u> <!-- Start of picture text --> MLT<br>ISR<br>JOR LBN<br>TUN KWT<br>BHR<br>MAR EGY<br>ARE<br>DJI OMNSAU QAT<br>IRN<br>LBY<br>YEM IRQ SYR DZA<br>7 8 9 10 11<br>Log GDP per capita (2005,USD)<br>Source: WDI<br>200<br>150<br>100<br>privatecredit<br>50<br>0<br><!-- End of picture text -->

# **4.4. SECTOR-LEVEL DEPENDENCIES**

Two measures of sector-level dependencies are used: external finance dependence, and asset tangibility.

Measures of an industry’s need for external finance are taken from Rajan and Zingales (1998). External finance dependence is calculated as the fraction of capital expenditures not financed with cash flow from the firm’s own operations, using data from U.S. firms in the 1980s. The United States is a good example to isolate industrial financing needs since markets are relatively frictionless especially among large companies. For example, industries requiring a lot of R&D such as drugs and pharmaceuticals require a lot more financing than say pottery. By treating external finance dependence as similar across all countries, one also avoids the problem of entangling country financial sector effects into industry-specific measure.

An industry’s level of asset tangibility is derived in Braun (2003) using U.S. Compustat data from 1986-1995. The most tangible industries include metals and chemicals, less tangible industries include apparel and chinaware.

Figure 5 illustrates the average external finance dependency and asset tangibility by country averaged over the period 1995-2009. The two sector dependency indicators are trade weighted by sector using BACI export values from this same time period. Figure 5a shows that richer, more developed countries tend to have lower average dependence on external finance. Figure 5b shows that richer, more developed countries tend to be skewed toward sectors comprised of more intangible assets, and MENA countries are no exception. Interestingly, MENA countries are split in terms of which export more in sectors with higher asset tangibility or external finance dependence, yielding a high-level of heterogeneity for analysis.

14
**Table 10: Factor Decomposition** All Variables

||||Factor||C|ommunality|
|---|---|---|---|---|---|---|
||1|2|3<br>4|_5_|6||
|Tax|0.73|--|--<br>0.26|--|-0.11|0.62|
|Site|0.71|--|--<br>0.27|--|-0.12|0.60|
|Hours|0.62|--|--<br>--|--|0.10|0.40|
|K/L|0.58|-0.10|--<br>-0.14|0.24|--|0.43|
|Earnings|0.41|--|0.11<br>0.37|--|0.34|0.45|
|Experience|--|0.88|--<br>--|--|-0.16|0.80|
|Education|0.20|-0.77|--<br>--|--|_0.15_|0.67|
|Firm life|--|0.68|--<br>--|--|0.36|0.60|
|Move/stay|--|0.13|0.92<br>--|--|--|0.87|
|Expand|--|--|0.91<br>--|0.12|--|0.86|
|Workers|0.24|--|--<br>0.72|--|--|0.58|
|Benefits|--|--|--<br>0.72|--|--|0.53|
|No problems|--|--|--<br>0.15|_-0.65_|-0.12|0.47|
|Credit problems|--|--|-0.10<br>--|-0.61|0.12|0.41|
|Financing (new)|0.15|--|--<br>0.30|0.55|--|0.41|
|Financing (initial)|0.17|--|--<br>0.13|0.45|--|0.26|
|Voluntary|0.13|-0.13|0.14<br>0.18|--|0.70|0.57|
|Clients|0.22|--|0.11<br>0.27|0.11|0.48|0.38|
|Eigenvalue|3.07|1.93|1.58<br>1.23|1.05|1.02||
|% of variation|17.15|10.73|8.76<br>6.88|5.85|5.71||
|_Moments and mixing_|_parameter (p_|_) offactor di_|_stributions'_||||
|,1|-0.71|-0.85|--<br>-0.33|--|-0.64||
|PL2|0.48|0.48|--<br>1.16|--|0.62||
|a,|0.36|0.58|--<br>0.47|--|0.94||
|**CF2**|**1.01**|0.86|--<br>1.41|--|0.59||
|p|0.40|0.36|--<br>0.78|--|0.49||
|<br>LR|-848.6**|-157.8**|--<br>-2225.0**|--|-308.5**||

> **Statistically significant at the 5% level. Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.67 I Factors 3 and 6 are clearly tri-modal so moments could not be estimated.

32
between the access to opportunities in early childhood to the corresponding income level at adulthood.

# Figure 6. Primary completion and Median income

<!-- Start of picture text --> Argentina: census(1980) - survey(2014) Bolivia: census(1976) - survey(2015) Brazil: census(1980) - survey(2015)<br>40 15 25<br>30 20<br>10<br>20 15<br>10 5 10<br>0 0 5<br>.5 .6 .7 .8 .9 0 .2 .4 .6 .8 0 .2 .4 .6<br>Chile: census(1982) - survey(2015) Colombia: census(1985) - survey(2015) Costa Rica: census(1984) - survey(2015)<br>40 15 20<br>30<br>20 10 15<br>10 5<br>10<br>0 0<br>.5 .6 .7 .8 .9 .2 .4 .6 .8 .5 .6 .7 .8 .9<br>Dominican Republic: census(1970) - survey(2015) Ecuador: census(1990) - survey(2015) Mexico: census(1970) - survey(2014)<br>20 15 12<br>15 10 10<br>8<br>10 5 6<br>4<br>5 0<br>.1 .2 .3 .4 .5 .5 .6 .7 .8 .9 0 .2 .4 .6<br>Panama: census(1980) - survey(2015) Peru: census(1993) - survey(2015) Uruguay: census(1985) - survey(2015)<br>25<br>15 25<br>20<br>10 20<br>15<br>10 5 15<br>5 0 10<br>.2 .4 .6 .8 .2 .4 .6 .8 .6 .7 .8 .9<br>Share of population<br>Rural Urban 95% CI<br>Medican income 2011 PPP US$ a day<br><!-- End of picture text -->

**Source:** Own elaboration based on IPUMS data and national household survey.

22
energy efficient techniques for desalination - both at a large, centralized scale and in smaller, decentralized systems have greatly increased availability and access.

Water utilities are better able to manage their own system by incorporating digital water strategies, such as in-pipe sensors for leaks and pressure changes, as well as new chemical sensors for contamination. Non-revenue water is reduced substantially, allowing utilities to reinvest money into capital upgrades and maintenance. Communities that in 2018 lack adequate, safe water are served by a mix of new connections to the main system and a new generation of distributed water purification and treatment systems. New neighborhoods in megacities and new water distribution systems in previously unserved areas are often built with separate plumbing for drinking water and separate disposal systems for gray and brown water. New connections to the mains are done with trenchers rather than the disruptive excavators.

Under the “ <mark>Lopsided”</mark> scenario in 2040, water resource management agencies cooperate only in closely politically aligned areas, and where neighboring managers have long-established relationships and best-practice sharing traditions. In watersheds where floods and drought affect industrial operations or where there is some concentration of wealth, water management agencies take advantage of the availability of data.

_In watersheds where floods and drought affect industrial operations or where there is some concentration of wealth, water management agencies take advantage of the availability of data._ Some wealthier regions in water scarce regions have adopted new desalination technologies, and some wealthy communities have adopted new, small-scale desalination; but lack of incentive for targeted water utility spending, and lack of long-term planning generally conspire to limit investment in new desalination technology.

Private utilities incorporate digital water strategies and reduce their non-revenue water, allowing them to re-invest money into capital upgrades and maintenance. In publicly-owned and operated systems, budgets and politics conspire to underinvest in data. Wealthy communities are built with plumbing systems necessary for local water reclamation and purification. Some communities that in 2018 lack adequate access to water are served by new connections to the main system or remain underserved.

Under the <mark>“Lock-in”</mark> scenario, water management agencies and utilities are often stove-piped with little coordination of data or efforts. Water resource agencies use satellites and drones to collect data, but data processing is often burdened by legacy data systems, and data remains in the hands of governments. Irrigation systems are not modernized. Lack of awareness of new technologies and lack of incentive for water resource spending means that investment in desalination is done only in emergency years. The availability of clean water is not as improved in Lock-in as in other scenarios, and water resource conflicts increase as population growth outpaces supply advances.

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# **Determinants of repayment performance in Indian micro-credit groups**

## **1. Introduction**

The past 30 years have witnessed a veritable boom of micro-finance. As of the end of 2006, 3,316 microcredit institutions reported reaching more than 133 million clients, about 92.9 million of whom were, at the point of taking their first loan, among the poorest (Daley-Harris 2007). The reason for such expansion is believed to lie in the ability of such groups to mitigate the challenges of moral hazard and adverse selection by applying peer monitoring and pressure that are less costly than those available to formal institutions (Stiglitz 1990, Banerjee _et al._ 1994, Besley and Coate 1995, Conning and Udry 2005). However, there is considerable debate about whether such groups will do so sustainably by ensuring that even poor borrowers will pay back their loans. While factors affecting repayment performance would thus be of great policy relevance, results in the literature vary widely (Zeller 1998, Ahlin and Townsend 2007, Cull _et al._ 2007), and few studies have explored cross-group variation in management practices as a factor that could affect this outcome variable.

We examine whether and how much repayment is affected by loan source, groups’ provision of public goods in the form of insurance substitutes, and exogenously imposed management practices. The data used are from more than 2,000 groups, federated in 299 village organizations (VOs) in a program to deliver micro-credit to poor rural women in the Indian State of Andhra Pradesh. The fact that each VO’s management practices vary considerably but are not a choice variable for groups who joined the program after the VO had come into existence provides a source of exogenous variation. Section two describes the program and data. Section three describes the model and estimation method. Section four reports results and section five concludes.

## **2. Program Description and Data**

The program under study builds on a tradition of women self-help groups (SHGs) which consist of 10-15 members who meet regularly to discuss social issues and activities, deposit a small thrift payment into a joint account, and make decisions on loans. The program aims to trigger SHG-formation by nonparticipant households and federation of all SHGs in a village into Village Organizations (VOs) to help obtain loans from commercial banks and provide public goods.

In contrast to the literature that mostly looks at individual loan repayment, we focus on SHG loans. Funds for these can come from three sources, namely commercial bank loans via the VO, internal savings by participating SHGs, or grants provided to the VO under a “community investment fund” (CIF). Our 2
# The Dynamic Market for Short-Cycle Higher Education Programs<sup>∗</sup>

Juan Esteban Carranza<sup>†</sup> María Marta Ferreyra<sup>‡</sup> Ana Maria Gazmuri<sup>§</sup> Originally published in the Policy Research Working Paper Series on _June 2021._ This version is updated on _February 2022._ To obtain the originally published version, please email prwp@worldbank.org.

_JEL codes_ : E24, I21

> ∗We thank Andrea Franco, Manuela Granda, and Nicolas Torres for excellent research assistance. We thank Marina Bassi, Rajeev Darolia, Gabriel Demombynes, Jean-Francois Houde, Armando Mendoza, Ricardo Paredes, Kevin Stange, Daniel Toro, Jaime Torrado, Sergio Urzua, and Ulrich Zachau for helpful comments and conversations, as well as participants at multiple seminars and conferences. We thank Colombia’s Ministry of Education and SENA for facilitating our access to data. Ferreyra thanks The World Bank for financial support. Gazmuri acknowledges funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d’Avenir) program, grant ANR-17-EURE-0010. All errors are ours.

> †Banco de la Republica. Email: jcarraro@banrep.gov.co

> ‡The World Bank. Email: mferreyra@worldbank.org

> §Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France. Email: ana.gazmuri@tse-fr.eu
Financing Lifelong Learning Miguel Palacios reducing the amount of taxes that an individual has to pay if he or she invests in education. 23

# Advantages:

There are two main reasons why economists and others advocate vouchers: vouchers (1) are a subsidy that increase consumer sovereignty, thereby increasing efficiency in the education market, and (2) vouchers can be used to target particular social groups, thus fostering equity in society.

Vouchers give their holder the option to attend any educational institution as long as it is "approved" by authorities. By choosing, individuals reveal their preferences regarding what they want to study, and what particular provider they like. By choosing some institutions over others, and some courses over others, individuals spur competition among educational institutions and give them an incentive to offer the courses that individuals demand. The result is a more efficient educational market.

In addition to the increased efficiency in the educational market, governments can use vouchers to target certain social groups. In particular, targeting vouchers to social groups that are systematically misinformed regarding the value of education can help compensate for the particular circumstances that lead them to systematically under-invest in human capital.

# Weaknesses and Obstacles to Implementation:

As with any source of public aid, the administration of a voucher program requires controls in order to prevent their misuse. In turn, these controls increase the cost of the program and make the system more complex from the point of view of the user. The recent Individual Learning Accounts program in the United Kingdom testifies to the results of a poorly controlled voucher system.

An obstacle to implementation is the way public opinion and legal institutions view the transfer of funds to private entities. In the United States in particular, a major obstacle to the introduction of voucher-like systems is the perception that public funds are being used to sponsor religious activities. In spite of this being principally a problem for basic education, it is conceivable that the issue could be raised when vouchers are used to train in vocational institutions run by particular groups, such as religious organizations. This, however, is not an important issue in developing countries.

# Conclusion:

Vouchers enhance efficiency in the education market and can be used to promote equitable access to education, making them a preferable alternative to the direct transfer of public funds to educational institutions. However, their design can be quite complex 23 See Olsen, Darcy and Brouillette, Mathew. "Another Path to School Choice" _Washington Times,_ December 13, 2000.

25
Policy ReseaRch WoRking PaPeR 4865

# **Abstract**

Despite recent reforms, world agricultural markets remain highly distorted by government policies. Traditional indicators of those price distortions can be poor guides to the policies’ economic effects. Recent theoretical literature provides indicators of trade and welfare-reducing effects of price and trade policies which this paper builds on to develop more-satisfactory indexes.

The authors exploit a new Agricultural Distortion database to generate estimates of them for developing and high-income countries over the past half century. These better approximations of the trade and welfare effects of sector policies are generated without a formal model of global markets or even price elasticity estimates.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to better understand trends in policy distortions to agricultural incentives globally. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kym.anderson@adelaide.edu.au or via wmartin1@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