rafmacalaba/datause-encoder-data huggingface dataset:
# **Geo-Spatial Modeling of Access to Water and Sanitation in Nigeria**<sup>**1**</sup>

Luis A. Andres, Samir Bhatt, Basab Dasgupta, Juan A. Echenique, Peter W. Gething, Jonathan Grabinsky Zabludovsky, and George Joseph JEL Classification: C51, C55, )18, Q01, Q25 Key Words: Geo-Spatial Modeling, Water, Sanitation, Sustainable Development Goals, Nigeria.

> 1 Luis A. Andres: World Bank Water Global Practice, landres@worldbank.org. Basab Dasgupta: Social Impact, Impact Evaluation Division, bdasgupta@socialimpact.com. Juan A. Echenique: School of Public Policy, University of Maryland, jae@umd.edu. Samir Bhatt: Imperial College London, bhattsamir@gmail.com. Peter W. Gething: Big Data Institute, Nuffield Department of Medicine, University of Oxford, <u>peter.gething@bdi.ox.ac.uk. Jonathan</u> Grabinsky Zabludovsky: World Bank Water Global Practice, gjoseph@worldbank.org. George Joseph: World Bank Water Global Practice, gjoseph@worldbank.org. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its executive directors, or the countries they represent. The findings, interpretations, and any remaining errors in this paper are entirely those of the authors.
- 9 -

In contrast to CEW, sector work is credited with closer links to project identification and design. In the sector studies undertaken in FY85, FY87 and FY88, well over half discussed institutional issues. Annual reports by sector departments, however, indicate that major inadequacies in sector work exist across almost all sectors. For example, a recent report on transport policy reform noted: the Bank was generally more successful when it dealt with technical matters and was least successful when Bank staff attempted to use a technical, economic and operational rdtionale to deal with issues that were inherently political and bureaucratic in nature... CESW was good at providing broad policy advice, but was weak in identifying the rationale for existing policies and developing a practical program for reforming them. In many cases, the issues identified in such work furthermore appeared to be ill-advised; they were not relevant in the country context and often appeared doctrinaire.15 In addition, the Population Lending and Sector Review (1988) stated that it was uncommon for sector studies to go beyond a description of service delivery and a preliminary assessment of the management structure of the relevant central government agencies.16 A review of sector work in all education projects approved during FY84 and FY85 showed that there was no analytical treatment of matters dealing with the relevant institutional and socio-political settings." While there are signs of recent improvements, the unevenness and gaps in the Bank's sector work is especially worrisome given the potential for good sector work to exert a positive influence on pro<sup>4</sup> ect level ID component design. (See Box A ).

# Design Features

The salient features of ID components in Bank projects in terms of their content, focus, and design process are the following: (1) The interventions or elements included in an ID component reflect the substance of the des4gn. There is a discernible pattern in the

**15** Ian C. Heggie, TransRort Policy Reform: Review of Experienlce and DeveloRment of a Framework for Designing Policy Reform, Discussion Parer INU 15, World Bank, p. iii.

16 George Simmons and Rushikesh Maru, The World Bank's Population Lending and Sector Review, PPR Working Paper, WPS 94, Population and Human Resources Department, September 1988, p. 18.

17 Annual ORerational Review: Fiscal 1988 Education and Training, World Bank, February 1989, p. iii.
WPS4178

# **School drop-out and push-out factors in Brazil: The role of early parenthood, child labor, and poverty**

## **Ana Rute Cardoso**<sup>*****</sup>

IZA Bonn and University of Minho and

## **Dorte Verner**

World Bank, Latin American and the Caribbean Region

## **Abstract**

This paper aims at identifying the major drop-out and push-out factors that lead to school abandonment in an urban surrounding, the shantytowns of Fortaleza, Northeast Brazil. We use an extensive survey addressing risk factors faced by the population in these neighborhoods, which covered both in-school and out-of-school youth, of both genders. The role of early parenthood, child labor, and poverty in pushing teenagers out of school is subject to particular attention. The potential endogeneity of some of determinants is dealt with in the empirical analysis. We take advantage of the rich set of variables available and apply an instrumental variables approach. Early parenthood is instrumented with the age declared by the youngsters as the ideal age to start having sexual relationships; work is instrumented using the declared reservation wage (minimum salary acceptable to work). Results indicate that early parenthood has a strong impact driving teenagers out of school. Extreme poverty is another factor lowering school attendance, as children who have suffered hunger at some point in their lives are less likely to attend school. In this particular urban context, working does not necessarily have a detrimental effect on school attendance, which could be linked to the fact that dropping out of school leads most often to inactivity, and not to work.

Keywords: school drop-out; investment in human capital; education; development. JEL codes: I21, O15, D1.

## World Bank Policy Research Working Paper 4178, March 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Corresponding author: Ana Rute Cardoso, IZA, Schaumburg-Lippe-Str. 7/9, 53113 Bonn, Germany. Email: cardoso@iza.org
Annex 8: <u>Niger - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
|||||**Niger National**||||
|Rice Imported|4.4|54.7|**62.1**|62.8|63.6|62.7|63.5|
|Rice local|1.7|15.4||62.2|62.3|62.2|62.3|
|Total riz|6.1|67.7||63.0|63.8|62.9|63.8|
|Millet & Sorghum|30.6|94.2||64.5|67.2|64.4|67.1|
|Maize|4.3|30.4||62.5|63.0|62.5|63.0|
|All|41.0|98.5||66.1|70.0|65.9|69.6|
|||||**Niger Urban**||||
|Rice Imported|7.8|85.1|**44.1**|45.5|47.0|45.5|47.0|
|Rice local|1.0|11.2||44.4|44.5|44.3|44.4|
|Total riz|8.9|92.6||45.8|47.4|45.8|47.4|
|Millet & Sorghum|11.5|78.0||45.1|46.1|45.1|46.1|
|Maize|5.8|61.7||44.9|46.1|44.9|46.1|
|All|26.1|97.0||47.4|51.8|47.4|51.8|
|||||**Niger Rural**||||
|Rice Imported|3.1|48.5|**65.7**|66.3|66.9|66.2|66.8|
|Rice local|2.0|16.2||65.8|65.9|65.8|65.9|
|Total riz|5.0|62.6||66.5|67.1|66.4|67.1|
|Millet & Sorghum|38.4|97.5||68.4|71.5|68.3|71.3|
|Maize|3.7|24.0||66.1|66.4|66.1|66.4|
|All|47.1|98.9||69.9|73.6|69.7|73.2|

Source: Authors’ estimation using country household survey data 27
# **2.1  Underlying Data Sources**

We rely on a large number of individual data sources that provide us with information on the perceptions of governance of a wide range of stakeholders. These data sources consist of surveys of firms and individuals, as well as the assessments of commercial risk rating agencies, non-governmental organizations, and a number of multilateral aid agencies and other public sector organizations. A full list of these sources is presented in Table 1. For the 2008 round of the WGI, we rely on a total of 441 individual variables measuring different dimensions of governance. These are taken from 35 different sources, produced by 33 different organizations. Appendices A and B provide a detailed description of each data source, and document how we have assigned individual questions from these data sources to our six aggregate indicators. Almost all of our data sources are available annually, and we use the data only from the most recent year available from each source in our aggregate indicators. In a few cases, as noted in Appendix A, we use data lagged one or two years if current data are not available. In some cases we use several individual variables from a single data source in our aggregate indicators. When we do so, we first compute a simple average of these variables from a single source, and then treat the average of these individual questions as a single observation from that data source.

The WGI data sources reflect the perceptions of a very diverse group of respondents. Several are surveys of individuals or domestic firms with first-hand knowledge of the governance situation in the country. These include the World Economic Forum’s Global Competitiveness Report, the Institute for Management Development’s World Competitiveness Yearbook, the World Bank / EBRD’s business environment surveys, the Gallup World Poll, Latinobarometro, Afrobarometro, and the AmericasBarometer. We refer to these as "Surveys" in Table 1. We also capture the perceptions of country analysts at the major multilateral development agencies (the European Bank for Reconstruction and Development, the African Development Bank, the Asian Development Bank, and the World Bank), reflecting these individuals’ in-depth experience working on the countries they assess. Together with some expert assessments provided by the United States Department of State and the French Ministry of Finance, Industry and Employment, we classify these as "Public Sector Data Providers" in Table 1. We also have a number of data sources provided by various 7
their own societies (arguably more than they care about global inequality and poverty). So, a different issue is, what is happening to income inequality within countries. One of the common claims about globalization (see the quotes in the introduction) is that it is leading to greater inequality within countries and hence fostering social and political polarization.

To assess this claim Aart Kraay and I (2002a) collected income distribution data from over 100 countries, in some case going back decades. We found first of all that there is no general trend toward higher or lower inequality within countries. One way to show this is to look at the growth rate of income of the poorest 20% of the population, relative to the growth rate of the whole economy.

# Figure 12.  Growth is good for the poor

<!-- Start of picture text --> Average annual change in log<br>(per capita income in poorest quintile)<br>0.2<br>0.1<br>Average annual change<br>in log (per capita income)<br>-0.1 0.1<br>-0.1<br>y = 1.185x - 0.0068<br>R 2 = 0.4935<br>-0.2<br><!-- End of picture text --> In general, growth rate of income of the poorest quintile is the same as the per capita growth rate (Figure 12). This is equivalent to showing that the bottom quintile share (another common measure of inequality) does not vary with per capita income. We found that this relationship has not changed over time (same for the 1990s as for earlier decades). In other 22
# **1.  Introduction**

This paper presents the latest update of the Worldwide Governance Indicators (WGI) research project.<sup>1</sup> The indicators measure six dimensions of governance: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. They cover 212 countries and territories for 1996, 1998, 2000, and annually for 2002-2008.<sup>2</sup> The indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 35 separate data sources constructed by 33 different organizations from around the world. We assign these individual measures to categories capturing the six dimensions of governance, and use an unobserved components model to construct six aggregate governance indicators in each period.

We complement our estimates of governance for each country with margins of error that indicate the unavoidable uncertainty associated with measuring governance across countries. These margins of error have declined over time with the addition of new data sources to our aggregate indicators, and are substantially smaller than for any of the individual sources. We urge users of the governance indicators to take these margins of error into account when making comparisons of governance across countries, and within countries over time. A useful rule of thumb is that when confidence intervals for governance based on our reported margins of error overlap when comparing two countries, or a single country over time, the data do not reveal statistically (or likely practically) significant differences in governance.

The margins of error we report are not unique to the WGI, nor are they unique to perceptions-based measures of governance on which we rely. Measurement error is pervasive among all indicators of governance and institutional quality, including individual indicators as well as ‘objective’ or fact-based ones -- if these are available at

> 1 This paper is the eighth in a series of estimates of governance across countries. Documentation of previous rounds can be found in Kaufmann, Kraay, and Zoido-Lobatón (1999a,b,2002), and Kaufmann, Kraay, and Mastruzzi (2004, 2005, 2006a, 2006b, 2007b, and 2008).  For broader survey review on governance indicators see Kaufmann and Kraay (2008).

> 2 A few of the entities covered by our indicators are not fully independent states (e.g. Puerto Rico, Hong Kong, West Bank/Gaza, Martinique, French Guyana and others). A handful of very small independent principalities (e.g. Monaco, San Marino, Lichtenstein and Andorra) are also included. For stylistic convenience all 212 entities are referred to in this paper as “countries”.

2
Filho, Nepstad et al. 2006), this program corresponds to preventing the loss of 12% of the remaining Amazon rainforest in 2014; equivalent to about 55.7 million hectares (138 million acres); and avoiding 10 percent of species facing a high risk of extinction. We estimated the current area of Amazon (in 2014) forests by deducting from the estimate of forest area in 2003 by Soares-Filho (Soares-Filho, Nepstad et al. 2006) deforestation documented in 2004-2014 (PRODES 2015). The area of avoided rainforest loss this scenario concerns, illustrated in yellow in Fig.2, is about 56 million hectares, or about 0.8 times the size of Texas. We find that households in the U.S. and Canada are willing to pay, on average, $92 (95-percent confidence interval $60$124), per household per year (Table 2), to help finance the program.

# _4.2 Aggregate willingness to pay in the U.S. and Canada_

To estimate the total WTP in the U.S. and Canada for the above large-scale program under consideration, we used information from the U.S. Census (Bureau 2016) and Statistics Canada (Canada 2013) to determine that there were about 123.2 million households in the U.S. and about 13.9 million households in Canada in the year of the survey (2014), for a total of 137.1 million households. For Canada, the latest estimate of the number of households is from 2011; we assumed a linear trend in 2006-2011 prevailed when determining the 2014 estimate. Given the total number of households (137.1 million) and using our preferred WTP estimate of $92 per household per year (95-percent confidence interval $60-$124), we estimate an aggregate North American WTP of about $12.0 billion per year for the program discussed above. This estimate corresponds to avoiding the loss of 12% of the remaining Amazon rainforest in 2014; about 55.7 million hectares (138 million acres). We estimated the current area of Amazon (in 2014) forests by deducting from the estimate of forest area in 2003 by Soares-Filho’s (Soares-Filho, Nepstad et al. 2006) deforestation documented in 2004-2014 (PRODES 2015).

23
fundamental assumption behind the DiD design; and (ii) to assess whether the impact of Internet connectivity on FDI is instantaneous or accumulate over time. Also, motivated by the recent advances in the literature in relation to the limitations of the regular TWFE estimator in producing consistent estimates in the presence of differential weighting and heterogeneous treatment effects, we use the Borusyak et al. (2021) estimator and conduct event study analysis.

Figure 4 presents event study estimates from Borusyak et al. (2021) estimator showing trends in the number of FDI projects (all sectors) before and after the arrival of high-speed Internet at the district level. Before the arrival of fast Internet enabled by SMC connectivity, point estimates are close to zero and statistically insignificant, suggesting that connected and unconnected districts were not on differential trends in FDI receipts prior to the arrival of SMCs at the country level, conditional on our fixed effects. This provides support to our parallel trends assumption. However, once connected to the backbone network, and the subsequent availability of high-speed Internet to treated districts, FDI receipts into connected districts began to rise at a faster rate relative to unconnected districts. The effect persists throughout the five years after the SMC’s arrival. We conduct similar analyses focusing on the service sector (and subsectors) as shown in Figure 5. Again we find consistent evidence of an increase in FDI in the service (sub)sector(s) after the arrival of SMCs and the absence of pretrends. The results from these event study analyses provide further support to the robustness of our results in Tables 1 and 3.

# **5.2 Role of Complementary Enablers**

In this section, we explore the role of complementary infrastructure, such as access to road and electricity, in amplifying or reducing the effects of access to high-speed Internet in attracting FDI. In other words, is access to high-speed Internet enough to drive FDI, or the effect conditional on having complementary (hard) infrastructure (road and electricity)?

To address this question, we revisit the baseline analysis and leverage spatial data on access to road and electricity grid network: two critical infrastructure that supports the productive sectors of the economy by aiding transport and providing needed energy for the production of goods and services. Specifically, we compute the density of electricity grid and road networks at the district level and classify districts into high and low (grid/road) density based on the sample median. Thus "high electrification (high road density)" is a dummy equal to 1 if the electricity grid (road) density is above the sample median and 0 otherwise. The density of the electricity grid is used as a measure (proxy) of the availability of electricity and hence the cost of electricity connection (supply). Places with high grid intensity plausibly have low connection costs, all else equal. Similarly, high road density is used as a proxy for transport cost, i.e., high road density implies low transport cost and vice versa.

15
# **Alternative Cash Transfer Delivery Mechanisms: Impacts on Routine Preventative Health Clinic Visits in Burkina Faso**<sup></sup>

Richard Akresh University of Illinois at Urbana-Champaign Damien de Walque The World Bank Harounan Kazianga Oklahoma State University _Keywords_ : Cash transfers; Conditionality; Gender; Child Health; Africa _JEL classification_ : I15, I38; J13; O15

* These data were collected for a project evaluating social protection strategies in Burkina Faso, which greatly benefited from the support of Marie-Claire Damiba, Seydou Kabré and Victorine Yameogo from the Secrétariat Permanent du Comité National de Lutte contre le SIDA et les Infections Sexuellement Transmissibles (SP-CNLSIST) in Burkina Faso and Hans Binswanger, Nono Ayivi-Guedehoussou, Ousmane Haidara, Timothy Johnston, Mead Over and Tshiya Subayi-Cuppen at the World Bank. Data collection was supervised by Robert Ouedraogo, Jean-Pierre Sawadogo, Bambio Yiriyibin and Pam Zahonogo from the University of Ouagadougou, Department of Economics. The project is funded by the NBER Africa Project and the following World Bank trust fund grants: Strategic Impact Evaluation Fund (SIEF), Bank-Netherlands Partnership Program (BNPP), Gender Action Plan (GAP), Knowledge for Change Program (KCP), WB-DFID Evaluation of the Community Response to HIV and AIDS, and Luxembourg Poverty Reduction Partnership (LPRP). The authors would also like to thank Pascaline Dupas and Adam Wagstaff as well as participants at the NBER Africa workshop in Zanzibar for helpful comments on an earlier draft. Finally, the authors thank Emilie Bagby, German Caruso, Igor Cunha, Christine Jachetta, Moussa Kone, Marleen Marra, and Nga Thi Viet Nguyen for their research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Contact information:

Richard Akresh, University of Illinois at Urbana-Champaign, Department of Economics, 1407 West Gregory Drive, David Kinley Hall, Room 214, Urbana, IL 61801. Email: akresh@illinois.edu Damien de Walque, The World Bank, Development Research Group, 1818 H Street, N.W., Washington, D.C., 20433. Email: ddewalque@worldbank.org Harounan Kazianga, Oklahoma State University, Department of Economics, 324 Business Building, Stillwater, OK 74708. Email: harounan.kazianga@okstate.edu