rafmacalaba/data-use-mentions-extended huggingface dataset:
Morocco’s irrigated agriculture is organized in 9 water districts (ORMVAs), 2 of which are isolated inform the rest of the economy and we consider the remaining 7 ORMVAs. Among the 82 agricultural and agriculture-related production activities, 66 are in crop production, five in livestock, and 11 in processing agriculture, both up and down stream from the farm firm. To capture the spatial nature of irrigated agriculture, 66 crop production activities are further distinguished according to whether they are within or outside the seven ORMVAs. Among the 33 activities within the water authority perimeters, 21 are irrigated crop production and 11 are rain-fed. Because water is either costly or presently impossible to transport between perimeters, the seven ORMVAs are further sub-divided into 20 perimeters.

The data are organized into a social accounting matrix (SAM). The data include perimeter level information on water charge fees, cropping mix, water and land allocation by crop and area, employment of labor and capital and intermediate input use by crop. National level data on employment, trade, non-farm production and resource flows are also entered into the SAM. These data are used to calculate the parameters of the model.

Unlike a standard SAM that often includes only national level data, the Morocco SAM in this study is multi dimensional, taking into account crop production activities. A schematic presentation of the major features of the macro framework is presented in Figure 2.

Data for the farm model (‘micro’ model) are from the same data source as those for perimeters in the ‘macro’ model, i.e., each perimeter is aggregated from farm level data. For this reason, the production activities in the farm model are compatible with the ‘macro’ CGE model. For the analysis preformed here, the representative farm is chosen from the irrigated area, and hence, only irrigated crops are included in the farm model.

The farm model accounts for monthly water allocation by crop. Typically, the representative farm only grows some of the crops produced in the perimeter
|9|Agriculture; forestry and fishing|1032|720|69.8|
|---|---|---|---|---|
|10|Accommodation and food service activities|1320|924|70.0|
|11|Administrative and support service activities|1692|1212|71.6|
|12|Water collection, treatment and supply|2460|1908|77.6|
|13|Human health and social work activities|2808|2184|77.8|
|14|Other service activities|1104|876|79.3|
|15|Construction|1368|1224|89.5|
|16|Public administration and defense|2964|2736|92.3|
|17|Information and communication|2988|2760|92.4|
|18|Financial and insurance activities|4440|4140|93.2|
|19|Wholesale and retail trade; repair of motor vehicles and<br>motorcycles|1212|1224|101.0|
|20|Arts, entertainment and recreation|1500|1824|121.6|
|21|Transportation and storage|1668|2904|174.1|

Source: ILOSTAT database, International Labor Organization Table 6 shows the gender wage gap in Pakistan across different industry sectors in 2016. A very skewed gender pay gap exists in the manufacturing sector with women earning only 37.2 percent of wages as compared to men for the same work. Other major sectors where majority of women are employed show similar trends, for instance, in agriculture, forestry and fishery group females gender wage gap is 69.8 percent, i.e., women earn 30.2 percent less as compared to men. The only sector which provides higher wages to female workers compared to male counterpart is transport and storage industry (with gender wage gap of 17.4 percent), however, this sector has very low concentration
of the impact of refugee intensity on the measures of household welfare and the status of poverty considered in the main analyses. The main findings remain robust quantitatively and qualitatively to this test.

# **7.2. Representativeness of the Sample**

The national representativeness of the sample is affected by attrition of observations (e.g., households) across rounds. The household-level attrition rate is 5% and 6% between the first and the last two rounds of surveys, respectively. In the main analyses, I run weighted regressions whereby each sample observation is weighted by its sample weight, the inverse of its probability of inclusion, in the ESS2. This implies that the main results account for attrition across the first two rounds, as the sample weights in ESS2 are adjusted weights of ESS1 taking into account relisting, nonresponse and attrition of households across the two rounds (CSA and World Bank, 2015). However, these estimates may not be fully nationally representative, since attrition across the last two rounds is not taken into consideration. To assess if the national representativeness of the main results are significantly affected by attrition across the last two rounds, I replaced the sample weights used in the main analyses by the weights from the ESS3, which are adjusted weights of the ESS2 taking into account relisting, nonresponse and attrition of households across these rounds of surveys (CSA and World Bank, 2017). Table A2 presents estimates of the impact of refugee intensity on the measures of household welfare and the status of poverty used in the main analyses. The main findings remain valid. Additionally, I excluded a small number of households (67) from the ESS3. These are households that moved from their location in the ESS2 to outside their woreda after the ESS2. I assume that these households are small to significantly change the national representativeness of the main results.

# **7.3. Specification of the Main Variables**

In the main analyses, household consumption expenditure and its components are specified in per capita
|
|South Asia (3)|36.0|15.5|18%|47.8|133%|..|..|
|Latin America (9)|16.6|6.3|10%|48.2|290%|..|..|
|Other Europe & C. Asia (5)|10.6|5.4|19%|9.8|93%|..|..|
|Middle East & N. Africa (5)|36.9|3.8|7%|21.2|57%|..|..|
|Sub-Saharan Africa(6)|16.9|0.7|1%|53.6|318%|..|..|

Sources: World Bank Services Trade Restrictions database and Gootize & Mattoo (2009), Services in Doha: What's on the Table? World Bank PRD Working Paper, no. 4903.

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does not suffer the same defect; more populous settlements tend to be in villages without a low-caste dominant school. Thus, Policy 2

> 14From the village census, we know that only 0.1% of the high-caste population resides in settlements that are more than 90% low-caste and that 3.7% of the low-caste population resides in villages that are more than 90% high-caste.

> 15 Throughout our simulations we ignore the fact that building new schools will also reduce distance to school. In this sense, the enrollment gains from the policies that we consider are somewhat understated.

> 16The population proportion of girls among 9-12 year old children is calculated from our sample and, along with household size, is assumed to be constant across settlements and caste-types.

17
<!-- Start of picture text --> Plot crop dataset<br>Key: wave, season, hh_id_obs,<br>plot_id_obs, crop_name<br>\ Many-to-one<br>Many-to-one Key: wave,Household season,datasethh_id_obs < Key: wave,Individual season,dataset hh_id_obs,<br>One-to-many indiv_id_obs<br>y, Many-to-one<br>Plot dataset<br>Key: wave, season, hh_id_obs,<br>plot_id_obs<br><!-- End of picture text -->
Dalton, P. S., S. Ghosal, and A. Mani. 2016. “Poverty and aspirations failure.” _The Economic Journal_ 126(596): 165–188.

- Dalton, P. S., J. Rüschenpöhler, and B. Zia. 2018. “Determinants and dynamics of business aspirations: evidence from small-scale entrepreneurs in an emerging market.” _World Bank Policy Research Working Paper Series_ 8400. Washington, DC: The World Bank.

- Del Carpio, L., and M. Guadalupe. 2018. “More women in tech? Evidence from a field experiment addressing social identity.” _IZA Discussion Paper_ 11876. Bonn: Institute for the Study of Labor (IZA).

- Demeritt, A. D., and K. Hoff. 2018. “The making of behavioral development economics.” _World Bank Policy Research Working Paper Series_ 8317. Washington, DC: The World Bank.

- Denzau, A. T., and D. C. North. 1994. “Shared Mental Models: Ideologies and Institutions.” _Kyklos_ 47(1): 3–31.

- Dhar, D., T. Jain, and S. Jayachandran. 2020. “Reshaping Adolescents' Gender Attitudes: Evidence from a School-Based Experiment in India.” R _evise and resubmit at American Economic Review_ . August 8 version available at: https://bit.ly/32zc2bz

- Duckworth, A., C. Peterson, M. Matthews, and D. Kelly. 2007. “Grit: Perseverance and Passion for Long-Term Goals.” Journal of Personality and Social Psychology 92 (6): 1087–101.

- Duflo, E. 2012. "Human values and the design of the fight against poverty." _The Tanner Lectures on Human Values._ Harvard University. Available at: https://bit.ly/2HWUbCt

Dweck, C. 2006. _Mindset: The New Psychology of Success_ . Random House.

- Eble, A
# **Why Do So Many Water Points Fail in Tanzania? An Empirical Analysis of Contributing Factors**<sup>**1**</sup>

**George Joseph, Luis Alberto Andres, Gnanaraj Chellaraj Jonathan Grabinsky Zabludovsky, Sophie Charlotte Emi Ayling, and Yi Rong Hoo**

## **JEL Classification: D02, O18, Q25**

**Key Words: Water Points, Tanzania, Functionality**

> 1 George Joseph: World Bank Water Global Practice, <u>gjoseph@worldbank.org</u> [Corresponding author] .Luis Alberto Andres: Global Water Practice, World Bank, <u>landres@worldbank.org. Gnanaraj Chellaraj: World Bank Water Global Practice, gchellaraj@worldbank.org. Jonathan Grabinsky Zabludovsky: World Bank Water Global Practice, jgrabinsky@worldbank.org.</u> Sophie Charlotte Emi Ayling: Global Water Practice, World Bank, <u>sayling@worldbank.org. Yi Rong Ho: Water Global Practice,</u> World Bank, yhoo@worldbank.org.

This work was made possible by the Swedish International Development Cooperation Agency and was a background paper to the WASH Poverty Diagnostics in Tanzania 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.
school. We assess the average impact of the program on student learning (measured by _Prova Brasil_ ’s test scores) and on school dropout rates. We exploit administrative data sets on the program implementation and merge these with the Brazilian school census, comparing schools that voluntarily joined the program with schools that are reasonably similar in several observable characteristics but that did not join the program. To match treatment and control schools we exploit the propensity score matching methodology. We contrast the results before and after the program’s implementation across the two groups of schools, treatment and control schools, all of which are in urban areas of Brazil.

The results show that participation in _Mais Educação_ has, on average, no statistically significant impacts on school dropout rates and Portuguese test scores and produces average negative impacts on mathematics test scores in these schools. The negative effect on the mathematics learning assessment is stronger in the short term than in the medium term. In addition, the negative impacts are greater for schools that joined _Mais Educação_ early on, in 2008, than for schools that joined the program later (in 2010, after it had been implemented in 396 municipalities). Taken together this evidence suggests different implementation challenges of applying a new school model to students and teachers, likely influencing student studying and teaching habits, and eventually replacing home studies. All these factors probably have influenced the impacts of the program in the initial phase of the program. Thus, as the program consolidates over the years, impacts may improve and the negative effect on student learning achievement on mathematics may be reversed. The establishment of more assertive and structured guidelines to schools along with close program monitoring would likely improve the program’s implementation and effectiveness (Fundação Itaú Social and World Bank 2015).

We also find that the impact of _Mais Educação_ differs depending on some school and city characteristics related to program implementation. For the sample of schools with fifth grades, the impacts are different depending on the type of courses used to extend the school day, the average
of the Engel curve method has been extended to developing and transitional economies in Asia and Latin America. De Carvalho Filho and Chamon (2012) argue that post-reform growth in real household incomes in Brazil and Mexico was underestimated because the CPI overstated the increase in the cost of living. Gibson, Stillman, and Le (2008) obtain similar results using data from Russia during the transition period between 1992 and 2001. In Indonesia, <mark>Olivia and Gibson (2013) find that t</mark> he CPI bias was negative during the 1997–98 financial crisis, but consistently positive since 2000. In China, Nakamura, Steinsson and Liu (2016) find that official inflation rates show less variability over time than actual inflation rates.

While Engel curve estimates have become a widely used methodology to cross-triangulate official inflation trends, the method is not without limitations. Its major weakness is that any drift in the Engel curve that cannot be explained by the covariates in the regression model is attributed entirely 7
Figure 2. Value of Indonesian Exports by Aggregate Industrial Groups. 2005-2010 and 2011-2015 Panel A: 2005-2010 <!-- Start of picture text --> 1,800<br>1,600<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>2005 2006 2007 2008 2009 2010<br>Agriculture, forestry and fishing Manufactures of food products<br>Other manufactures Mining<br>Textiles<br>Panel B: 2011-2015<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>2011 2012 2013 2014 2015<br>Agriculture, forestry and fishing Manufactures of food products<br>Other manufactures Mining<br>Textiles<br>Value of IDN exports<br>100 millions of 2010 USD<br>Value of IDN exports<br>100 millions of 2010 USD<br><!-- End of picture text --> Source: Authors’ calculations based on UN Comtrade database 2005-2010 and 2011-2015.

50
less than 40 hours a week ( **<u>Table A.1, Annex A</u>** ). While in the labor market, women are more likely to be unpaid family workers and less likely to be wage-employed, both formally and informally ( **<u>Table A.2, Annex A</u>** <u>). Moreover, women may face perceived societal biases that could contribute to their occupational</u>

> 7 A Survey of the Nepali People, 2020. A series of this public opinion survey is led by School of Arts, Kathmandu University (KUSOA), in collaboration with Interdisciplinary Analysts (IDA) and The Asia Foundation. 8 DHS, 2022.

> 9 A Survey of the Nepali People, 2018.

> 10 Nepal Labor Force Survey, 2017/18. More than 90 more percent of females (but less than half among males) are involved in at least one activity of producing services for own final use (activities include preparing meals, doing the dishes, cleaning the house, caring for elderly and children).

4
this is rarely the case; none of the indirect sources provides a log of the amendments made to the original data received, or engages in a systematic feedback process with DMOs to ensure original records are also corrected.

# “ _4.5 Hidden debt”_

Finally, not all debt instruments may be disclosed (“hidden debt”). Compared to the dimensions listed above, the contribution of “hidden debt” is hard to quantify. Non-disclosed loans can be identified in a timely fashion only when granular information is provided by the creditor, which is rarely the case (see Section 4). Otherwise, given the existing voluntary-based data disclosure framework, progress can only be triggered by parallel engagements of external stakeholders (e.g., analysis of project funding) that may allow some form of cross-validation.

# 5. Implications of the current data disclosure framework and policy recommendations

The debt data recording and disclosure framework described above results in the multiplication of debt statistics that differ across sources. In the absence of enforceable international definitions, these idiosyncratic differences are explained by agents’ needs to present debt data according to their own standards.

The international community has been trying to address these data challenges by (i) independently targeting broader<sup>37,38</sup> coverage of debt statistics in each country, and (ii) providing technical assistance (TA) to countries to help increase transparency. This has greatly contributed to an overall expansion of debt data disclosure, but uncoordinated interactions between direct and (multiple) indirect reporting may lead to the following major shortcomings: _1- Erroneous sense of statistical homogeneity and cross-country comparability in debt statistics_ , which can lead to major misunderstandings in the fiscal and debt policy debate. This paper shows that debt statistics are not comparable across sources, (see Section 3). Even when external agents try to centralize and standardize records, they need to rely on the availability of each country’s public debt data. For instance, the WB’s IDS are supposed
**Figure 10. The share of adults who saved formally was low in most developing economies in Europe and Central Asia**

<!-- Start of picture text --> Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herzegovina<br>Bulgaria<br>Croatia<br>Georgia<br>Kazakhstan<br>Kosovo<br>Kyrgyz Republic<br>Moldova<br>Montenegro<br>Republic of North Macedonia<br>Romania<br>Russian Federation<br>Serbia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>0% 10% 20% 30% 40% 50% 60%<br>Percent of adults who saved formally in 2016<br>Formal savings Semi‐formal savings Other<br><!-- End of picture text -->

# _Source_ : Global Findex database.

_Note:_ The length of the bars represents the percentage of adults (aged 15 and above) who reported saving in any way in the previous 12 months.

In the euro area, 67 percent of all adults reported having saved in the past year. Seventy‐three percent of savers (about half of all adults) saved formally. These shares are slightly lower than in an average high‐income economy.

Globally, saving patterns vary by gender and income level. In developing economies, men are more likely than women to save formally. The average gender gap in the share of adults who saved formally was 6 percentage points in 2017. In the developing economies of ECA, the gender gap widened slightly, from 2 percentage points in 2014 to 3 percentage points in 2017. In the euro area, it remained stable, at about 6–9 percentage points, between 2011 and 2017.

Wealthier adults are more likely than poorer adults to save formally. Globally, the income gap between the top 60 percent and the bottom 40
ENADID data. Consequently, if women who would have chosen to abort are included in the sample of women who miscarried, this sample is more favorably selected than the sample of women who gave birth. The results from estimation carried out on these samples would be biased towards finding adverse effects of teenage childbearing.

To address the issue of the potential bias, we can take advantage of the fact that ENADID reports the amount of time elapsed between the start of the pregnancy and the date when miscarriage occurred. It is reasonable to assume that those women, who would choose to abort, would prefer to do so during the early months of pregnancy. Medical literature confirms that the likelihood of complications due to abortion increases with every month of pregnancy (Bartlett et al., 2004). Many pregnancies are associated with physical discomfort. Additionally, it is easier to conceal a pregnancy during earlier months. Overall, it appears that a pregnant woman who does not want to give birth does not have any incentive to extend the length of her pregnancy. Therefore excluding women who miscarried during the first two months of pregnancy from the control group seems to be a reasonable safeguard against contaminating the sample with women who are not comparable to the “treated” in their latent status. Intuitively, we include in the control group only women whose pregnancies lasted long enough to conclude that they were not planning to have an abortion. However, it is important to keep in mind that such modification reduces the sample size, which may increase the standard errors and result in lower significance of the estimated coefficients.

Table 11 shows that our results are highly robust to such modification in the sample. Raising a child has a significant and positive effect on the level of social assistance income, the likelihood of employment and on participation in _Oportunidades_ . Indeed, nearly all of the outcomes that appear to be significantly impacted by bearing a child in the full sample of potential teenage mothers remain significant in the reduced sample. Their magnitude is nearly identical. The only exception is
**Figure 2. Schematic of urban informality, by firm**

**More Informal**

|||**Less informal**|
|---|---|---|
|**Low**<br>**educated**<br>**Self-**<br>**employed**|**High educated**<br>**Self-employed**|**Employers**|

Source: authors’ rendering The IES and GMD samples are not strictly comparable since they differ in several respects. First, the country samples are different. We use data from 26 countries in the worker analysis and from three countries in the firm analysis. Second, all the firms surveyed in the IES are not registered, while in the worker analysis we do not have (nor wish to use) a registration variable, so we use the job category and education level of the respondent to proxy informal firm owners. Third, the IES sample frame is restricted to a few cities that represent the main urban centers while GMD is based on the urban sample from nationally representative household surveys. The IES only has information on manufacturing and service firms, whereas the GMD covers urban agriculture and other sectors as well. Finally, the IES includes firm level variables and aims at analyzing firm level characteristics. In contrast, the GMD is derived from household surveys and therefore focuses on householdrelated variables, with very limited information on firms.

# **3. Methodology**

We use three main methodologies to describe informal sector workers and enterprises in urban SSA. First, we use simple, unconditional summary statistics to profile the distribution of worker characteristics, firm, and urban variables within employment or enterprise type.<sup>31</sup> Second, we use Linear Probability Models to estimate conditional correlates to better understand which worker, firm-characteristics, and urban variables are disproportionately represented in each employment or firm type, while controlling for