rafmacalaba/datause-encoder-data huggingface dataset:
## **Box 2.9 World Bank-supported projects for treated wastewater use in agriculture (FY99-09)**

Projects strengthening institutions and building capacity on wastewater use in agriculture

- Iran: Tehran Sewerage Project, 2000

- Iran: Ahwaz and Shiraz Water Supply and Sanitation Project, 2004

- Lebanon: Ba'Albeck Water and Wastewater Project, 2002

Projects supporting treated wastewater being made available for use in agriculture:

- Tunisia: Tunis West Sewerage Project, 2006

- China: Second Tianjun Urban Development and Environment Project, 2003

- Thailand: Sapthip Wastewater Biogas and Renewable Energy Project, 2009

- Cape Verde: Energy and Water Sector Reform and Development Project, 1999

Project supporting the establishment of irrigation schemes using existing treated wastewater:

- Tunisia: Waters Sector Investment Project, 2000

Project supporting improvements in wastewater treatment and the establishment and rehabilitation of irrigation schemes:

- Tunisia: Second Water Sector Investment Project, 2009

_Source_ : Authors.

# **2.3 Powerful drivers toward growing wastewater use in irrigation**

Four key drivers can be identified that are leading to growing use of wastewater in agricultural activities in and around urban centers. They are expected to become even more powerful in the near future, making improved wastewater use in agriculture in many regions an emerging priority.

_Growing water stress_ . Many parts of the world are experiencing growing water stress and water scarcity.<sup>8</sup> Projections indicate that the population living in water-stressed and water-scarce countries will grow from about 1.2 billion (or 18 percent of the world population) in 2007 to 4.0 billion (or 44 percent of the world population) by 2050 (Comprehensive Assessment of Water Management in Agriculture, 2007).<sup>9</sup> Water stress and scarcity may even become a concern in regions that are generally thought to have abundant water supplies, because of the unequal spatial distribution of water resources. For example, Latin America is generally considered a humid tropical region. Yet the 20 percent of the land that is arid or semiarid, with only 5 percent of the region’s water resources, supports 60 percent of the region’s population (Bartone, 1990).

The trend toward growing water stress is likely to accelerate due to climate change. The Intergovernmental Panel on Climate Change (2007) predicts that global warming will alter 8 A country is considered water-stressed when its annual supply of renewable freshwater is less than 1,667 m3 per capita, and water scarce when it is less than 1,000 m<sup>3</sup> per capita (Falkenmark and Lindh, 1993).

9 It should be noted that such figures only provide an impression of the annual average on a country level, disregarding the spatial and temporal distribution of precipitation, groundwater layers and surface flows. Due to a number of reasons, such as the availability of infrastructure, not all of these resources can actually be used. Furthermore, the figures do not consider that many water uses are nonconsumptive, and that return flows can be reused.

19
|Variables|Povertyrate|
|---|---|
|Share of population <1 hour to a town|0.059*<br>(0.103)|
|Share of population 1-2 hours to a large city|0.080**<br>(0.026)|
|Share of population 1-2 hours to an intermediate city|0.090**<br>(0.012)|
|Share of population 1-2 hours to a small city|0.168***<br>(0.000)|
|Share of population 1-2 hours to a town|0.070**<br>(0.050)|
|Share of population 2-3 hours to a large city|-0.025<br>(0.494)|
|Share of population 2-3 hours to an intermediate city|0.009<br>(0.792)|
|Share of population 2-3 hours to a small city|0.083**<br>(0.022)|
|Share of population 2-3 hours to a town|0.045<br>(0.214)|
|Legend: * p<.05; ** p<.01; *** p<.001||

## Source: authors’ elaboration

These results shed a much finer lens on poverty along the rural–urban continuum (going well beyond the traditional rural–urban characterization) and help draw attention to the role of local governments in smaller towns and cities in fighting poverty, in their urban areas as well as their hinterlands. By exploiting recent advances in artificial intelligence and access to satellite imagery, the prediction accuracy of the disaggregated poverty measures could further be enhanced and the geographies in which the disaggregated poverty measures are predicted better aligned with the geography of the urban–rural catchment configurations (Engstrom, Newhouse, & Soundararajan, 2019; Jean et al., 2016). This would enable more precise estimation of the role of the urban hierarchy and their related hinterlands in poverty outcomes, using consistent city-location categories and similar poverty predictors across countries.

Other welfare outcomes such as dietary diversity and nutritional status (stunting as well as obesity) could be similarly mapped and examined across the urban hierarchy and their hinterlands. As urbanization proceeds, and dietary habits change, often towards more sugary, fat and convenient foods (Cockx, Colen, & De Weerdt, 2018), understanding how the dietary patterns and related health outcomes evolve across the urban–rural spectrum will be important to guide and target food and nutrition interventions. Important research questions include understanding if obesity is limited to large cities, or whether it also appears in their immediate hinterland or smaller towns. Or understanding if stunting improves at low-levels of urbanization and is sustained at high-levels of urbanization, where changing child feeding practices may offset the effects of improved sanitation, as observed in some African countries (Ameye & De Weerdt, 2020). Such questions can now be readily explored across multiple countries by overlaying the many existing georeferenced living standard measurement or demographic health surveys with the urban categorizations and their hinterlands.

# 4.4 Access to services

Two other policy areas that could be served by public access to the FEA and URCA databases are access to health services and education. These services have a disproportionate impact on the well21
70 accumulative indicators of nutrition.

Much less economic analysis has focused on actual individual consumption patterns by gender within the family, because (1) they appear to be accurate only over a short recall period, of say 24 hours, and (2) they should be repeated at least several **times over a year** to reflect the variation associated with seasonal changes in food availability and activities, and (3) they appear to embody more measurement errors than do anthropometric indicators. In the available studies of individual nutrient intakes ib low income countries, small working samples also **weaken the** evidence. **One** suggestive study of Central India finds that calorie intakes **are** not significantly biased in favor of boys compared to girls, *xcept during **the lean** season, when households have lower overall consumption levels (Behrman, 1988). Further studies of actual nutrient intakes within the family should be encouraged along with the demand for health care and time allocation within the family (e.g. Pitt and Rosenzweig, 1985, 1988).

# Determinants of Child Achievements In School

There are surprisingly few studies of the household determinants of school enrollment and attendance rates by gender in low income countries, though this may be changing. In an analysis of district-level data from the Indian Census of 1961, Rosenzweig and Evenson (1977) found that where child wages were higher, children supplied more labor to the market and enrolled less in school. Male wage rates were negatively associated with child schooling levels, while female wages were positively associated with the enrollment rates. These partial effects of **wage** rates on schooling were implied by their model, but only after a number of cross-comoodity substitution patterns were imposed and price (wage) effects were assumed to outweigh income effects.
**Table 7: World Bank Data Transparency and Sovereign Bond Spreads (IV), Interactions with Trade and Financial Openness**

||<br>Freedom of information, lag of ICRG, lag of<br>interaction between transparency and ICRG, lag<br>of public debts and lag of interaction between<br>|||dependent va<br>|riable: spread<br>|||
|---|---|---|---|---|---|---|---|
|IV variables|<br>transparency and public debts OR lag of external<br>debts and lag of interaction between<br>transparency and external debts, lag of trade<br>openness (in logs) and lag of financial openness|||1995-|2018|||
|Variables||[1]|[2]|[3]|[4]|[5]|[6]|
||GDP per capita (constant 2010 US$)|-0.545**|-0.625**|-0.458*|-0.527*|-0.630**|-0.762**|
|||(0.235)|(0.267)|(0.245)|(0.274)|(0.283)|(0.320)|
||Growth (annual %)|-0.0298***|-0.0407***|-0.0353***|-0.0457***|-0.0309***|-0.0405***|
|||(0.00673)|(0.00740)|(0.00708)|(0.00791)|(0.00731)|(0.00804)|
||CPI Inflation (average consumer prices)|0.0898***|0.0806**|0.0709**|0.0657*|0.0739**|0.0649*|
|||(0.0294)|(0.0336)|(0.0328)|(0.0374)|(0.0332)|(0.0374)|
||Primary Balance to GDP (%)|0.0166**|0.0169*|0.0198**|0.0174*|0.0182**|0.0172*|
|||(0.00800)|(0.00879)|(0.00883)|(0.00982)|(0.00892)|(0.00985)|
||Current Account Balance to GDP (%)|0.00189|0.000288|0.000251|-0.00441|0.000449|-0.00106|
|||(0.00482)|(0.00523)|(0.00503)|(0.00573)|(0.00553)|(0.00602)|
||VIX Index|0.561***|0.481***|0.541***|0.516***|0.559***|0.525***|
|||(0.0568)|(0.0646)|(0.0650)|(0.0748)|(0.0662)|(0.0755)|
||US 10-year Treasury Bond Yield|-0.406***|-0.461***|-0.333***|-0.382***|-0.431***|-0.471***|
|||(0.0892)|(0.0983)|(0.0903)|(0.0982)|(0.100)|(0.110)|
||Transparency (WB) x ICRG|-0.577***|-0.664***|-0.583***|-0.612***|-0.490***|-0.548***|
|||(0.121)|(0.132)|(0.123)|(0.134)|(0.133)|(0.143)|
||Transparency (WB) x trade openness|-0.140***|-0.160***|||-0.109**|-0.138***|
|||(0.0395)|(0.0426)|||(0.0486)|(0.0509)|
||Transparency (WB) x financial openness|||-0.00603|-0.0261***|-0.00388|-0.0201**|
|||||(0.00893)|(0.00989)|(0.00887)|(0.00986)|
||Transparency Dummy (WB) x public debt|0.00243***||0.00344***||0.00279***||
|||(0.000434)||(0.000479)||(0.000511)||
||Transparency Dummy (WB) x external debt||0.00112||0.00324***||0.00164*|
||||(0.000798)||(0.000879)||(0.000970)|
||Constant|8.830***|9.163***|6.799***|7.185***|9.679***|10.52***|
|||(2.286)|(2.510)|(2.223)|(2.440)|(2.739)|(2.987)|
||Observations|482|407|454|380|422|354|
||No. Countries|51|44|54|46|50|43|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 29
<!-- Start of picture text --> Change of Gini coefficient<br><!-- End of picture text --> <!-- Start of picture text --> 00004 ---- ---- - ------- ----- ----- Exp. 2.1 Corporate tax<br>Exp. 2.2 VAT<br>0.0002 X - - ---------- - ---- --- 1- Exp. 2.3 Personal Tax - - --<br>1 X X I _ Exp. 2.4 Final Demand Tax<br>0 < O i, .. e.Y'(e- . _ . _ , _ . _ , _ _ _ . _ _ _ ,~~, _ _ _ _ _ _ _ _ _ _ _ _ d,lp _ _ _ _ _ _ _ l _<br>-0.006 .--- X X X<br>-0~~~~~~~~~ ~~~~~~~~~~~ ~ ~ 000 _III ~~~~ < ~ ~ ~~~~ - ----- - ---- -- -:/ ------<br>0,0004 t --- - --- -- ~ -------- ~~~- ------------- - - N<br>-0 .0 0 0 6 - - -- - -- - - - - - - -- - - - -<br>-0 .0 0 1 - -- -- - - - -*-- - - - - - - - - - - - - - - - - - - - - - - . - - - - - - - - -<br>-0.0018 .<br>-0.0012 --- - -. ------<br><!-- End of picture text --> Figure 8. Change in GINI coefficient in experiment 2.1-2.4 <!-- Start of picture text --> Percentage change of GDP with baseline<br>1 .6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -<br>1.2<br>Figur . Chan n Exp e VAT 22 m 2<br>XX Exp. 2.6 MDK2<br>1 -- - - - - - - - -- - - - -- - - - - - - - - - -<br>--- -- -- - -- -- -- - -- - -----<br>0 4 + - - - - - - -- -- - - - - - -- - - - - -- - -- - - - - - - - - - - - - -- -- - - --- - - - --- - ---------- - ----- ----- -- ----- -<br><!-- End of picture text --> Figure 9. Change in GDP in experiment 2.2-2.6 39
<!-- Start of picture text --> Changes in Wage - Philippines 2003-2007<br>wo<br>oO<br>e<br>z<br>(a)<br>“<br>=)<br>0 20 40 Percentiles. 60 80 100<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in NCR<br>- Philippines 2003-2007<br>oO<br>oO 20 40 _ 60 80 100<br>Percentiles<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in Luzon<br>- Philippines 2003-2007<br>oO<br>0 20 40 Percentiles_ 60 80 100<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in Visayas<br>- Philippines 2003-2007<br>6<br>,<br>0 20 40 60 80 100<br>Percentiles<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in Mindanao<br>- Philippines 2003-2007<br>Sw<br>0 20 40 60 80 100<br>Percentiles<br><!-- End of picture text -->
are often cited as being important (Kremer _et al_ , 2005). In fact, in the Indian case between 10 and 15 percent of teachers are absent at any given time during the school day without even reported authorization – despite the fact that the level of ostensibly authorized absences runs considerably higher than what should be allowable.

But there are reasons to doubt that interviewing teachers about reasons for absence will yield accurate responses. Like other workers, teachers themselves may sometimes claim socially acceptable excuses such as illness when they are actually absent for other reasons. Head teachers, too, may prefer to make excuses for their teachers, rather than to acknowledge unexcused absences or informal arrangements that allow frequent absence. As noted above, the head teachers’ reports of attendance – which can be verified by enumerators – are not fully accurate; their reports on harder‐to‐verify reasons for absence are likely to be even less accurate.

# **_Factors that do not explain absence:  Low base salaries_**

An alternative approach is to put less weight on what teachers say, and more on what they do. Which types of teachers are more likely to be absent, and in what geographical and institutional contexts? Careful analysis of these questions allows us to test out different hypotheses about what is driving teacher absence.

Chaudhury _et al_ (2006) carry out this type of analysis for the six countries in which they had fielded comparable surveys. By analyzing each country individually and also aggregating them for a multi‐country analysis, they assess the likely role of various factors in explaining teacher absence.

One candidate explanation is low levels of teacher salaries. This explanation sometimes appears in media accounts of absenteeism, as when teachers note that to support themselves or their families, they have to moonlight in outside jobs that sometimes pull them away from their teaching duties. If this explanation were correct, the lowest‐paid civil‐service teachers would be the most absent. This is typically not the case, however, and in fact the opposite may be true. The highest‐ ranked teachers – the head teachers and their deputies – in fact tend to be more absent than other teachers. And in some cases teachers who are more highly educated, and hence who are paid more according to civil‐service pay scales, are also more likely to be absent.

# **_Poor incentives and accountability_**

A lack of accountability and incentives for performance likely explains more of the problem of teacher absence. The data provide several clues pointing us in this direction. First, as noted above, higher‐ranking teachers tend to be more absent; but
In this section we present our empirical results.<sup>7</sup> We start by analyzing stay-on rates of individuals aged 7-15. Following model (1), in Table 5 we regress a dummy equal one if the individual is currently enrolled in school on a dummy for preschool attendance whose coefficient we allow to vary by age. In this and all the other regressions we include age dummies interacted with cohort dummies, and locality-year-month of interview dummies. In column (2) we present the same specification with household fixed effects. In column (3) we present a specification like the one in column (1) where we additionally control for child's birth order, gender dummies, mother's age at birth and dummies for mother's completed years of education. In column (4), we present within household estimates of the specification in column (3). Standard errors in these and all other regressions are clustered by locality.

Column (1) shows a significant positive effect of preschool on school enrollment that grows monotonically with age. While at age 7 the difference in enrollment between treated and untreated individuals is in the order of 3 percentage points, by age 15 this difference is in the order of 21 percentage points and statistically significant.

As said, it might be the case that years of preschool education completed are correlated with household traits that also determine drop out rates. The evidence in column (2) where household fixed effects are included, suggests that - if anything - the omission of household characteristics leads to estimates that are slightly downward biased. For example, we estimate the effect of treatment at age 15 to be 28%, around 30% higher than the OLS estimates.

One interpretation for this finding is that household unobserved characteristics affecting latent school attainment are negatively correlated with exposure to preschool. However, the evidence in Table 3 - based on household observable characteristics - suggests that this is unlikely to be the

> 7 Two studies before us analyze the effect of preschool attendance on subsequent school progression among Uruguayan children. ANEP (2001) analyzes a panel of 268 children who attended pre-primary education since the ages of 4 or 5 and follows them up to first grade. ANEP (2005) uses administrative data from the _Evaluación Nacional de Aprendizaje en el primer nivel de la escolaridad_ plus survey data from the education module of 2001 ECH. Both studies find a significant positive effect of preschool attendance on promotion rates and school progression. Differently from us these studies only analyze the short-term effects of preschool and ignore the potential endogeneity of treatment

15
- 1.68 -

# Scenario 4: Effects of a Policy of Food Self Sufficiency on the Cocoa Supply -- Response of Scenario 3

The thrust of this scenario Is to remove the possibillty of food Imports from the adjustments of the previous sconario. Solutions to experiments in the previous scenario, particularly at higher price levels, Included Imports of up to 2.5 million tons of maize and rice In later time periods of the 50-year horizon. Elkmination of these removes the gains from trade achieved by importing maize and rico at constant prices and shifting land and labor Into the more valuable cocoa crop. In consequence, cocoa production virtually collapses at the iower price levels once the horizon extends beyond 20 years. As Fgure I.C.21 shows, only a price of 160 1987 cedis or higher assures cocoa production throughout the 50-year horizon. Moreover, the quantities produced, when cocoa is produced, are much less than under previous scenarios.

Patterns of cocoa acreage under zero food Imports, shown in Figure I.C.22, confirm the collapse of cocoa Investment at the lower buying prices. Even a price of 160 cedis per kliogram results In a decilne In cocoa acreage of 50 percent. Only at the higher prices does cocoa production and acreage increase. Thus, as Table 1.C.6 indlcates, at the iower prices supply elasticities are essentially zero. In the range from 140 to 180 cedis per kligram, supply Is elastic; and only In the Interval from 180 to 200 cedis is supply Inelastic.

However, under this scenario, farm Incomes Increase consistently (ignoring ths dip In the last two periods due to horizon effects) reaching the highest levels In any of the simulatlon experiments, as Figure l.C.23 shows. By ellmInating the galns from trade and thus shifting more land Into producing food grains via higher food grain prices, the prices of other foods and peremial crops tend to Increase In the competitlon for the supply of land and labor. Thus, agricultural prices, land rents and agricultural wages tend to Increase. These changes Increase farm Incomes. As cocoa prices exogenously Increase In this context, resources are drawn away from food production causing food prices to
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project website: www.infrastructureafrica.org. For papers go to the document page (http://www.infrastructureafrica.org/aicd/documents), for databases to the data page (http://www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (http://www.infrastructureafrica.org/aicd/tools/models) and for maps to the map page (http://www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

Africa’s Infrastructure: A Time for Transformation (AICD Web site), http://www.infrastructureafrica.org Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for_

- _Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa,‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

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