rafmacalaba/data-use-mentions-extended huggingface dataset:
We are also cognizant that there are trade-offs between reducing survey errors or increasing survey quality, and survey costs (Dillon et al., 2020). The most accurate measurement methods can be expensive and can come into direct competition with other survey objectives for example with covering a larger sample or collecting more information. Cost considerations are an important aspect in assessing how phone surveys can best complement or replace parts of in-person agricultural data collection. We discuss cost considerations in Section 5, highlighting the interactions and trade-offs between survey errors and survey costs.

## 2.2. Recent experience of agricultural and rural phone surveys in low-income settings

We draw from the experiences of several phone survey efforts implemented in rural areas, some of which were focused explicitly on agriculture, including: the World Bank-supported High Frequency Phone Surveys (HFPS), with an emphasis on the seven HFPS survey programs supported by the LSMS team in Burkina Faso, Ethiopia, Malawi, Mali, Nigeria, Tanzania, and Uganda (Living Standards Measurement Study, 2022); the Innovation for Poverty Action (IPA) RECOVR survey<sup>4</sup> ; the World Food Programme’s mVAM Project (World Food Programme, 2020); the Young Lives at Work program (Young Lives, 2022); the International Food Policy Research Institute (IFPRI) phone surveys (Alvi et al., 2021; Hirvonen et al., 2021a; Hirvonen et al., 2021b; Minten et al., 2020); World Bank’s Listening to Africa and Listening to Latin America and the Caribbean initiatives (Ballivian et al., 2015; Dabalen et al., 2016); a World Bank Gender Innovation Lab survey of women in agricultural households in Western Uganda (Sharma et al., 2021); and the recent Georgia Survey of Agricultural Holdings (50x2030 Initiative, 2020; FAO, 2018). Appendix I includes a brief description of these surveys.

We also draw on experiences from phone surveys used in regions affected by conflicts or
16 set that was used to calibrate the consumption model. Only specifications that are able to predict actual observed consumption levels well should be used to impute consumption into later rounds of survey data.

The simulation based SAE technique deployed here has some attractive features that are woth noting.<sup>10</sup> First, the approach of estimating consumption rather than estimating final welfare indicators directly allows the estimation of any number of poverty or inequality indicators with the same simulation.<sup>11</sup> Second, it provides consistent estimates of both the mean _and_ the variance of consumption, and thus also a consistent estimate of the welfare measure in the future.<sup>12</sup> Third, while the SAE method does impose some structure on the distribution of the idiosyncratic error term in the consumption model, the heteroskedasticity model applied within this approach permits a partial update of the distribution of the error term over time, reducing prediction error due to assumed stationarity of the error term.<sup>13</sup> Fourth, the technique is convenient to implement given the freely and readily downloadable PovMap2 software.<sup>14</sup>

> 10 To be sure, the small area estimation approach is just one of a variety of valid approaches that can be employed (see for example, Azzarri et al. (2006), Tarozzi and Deaton (2009), and Matthiasen (2009)). 11 This is in contrast to approaches, such as Deaton and Tarozzi (2009) or Tarozzi (2007), which require the estimation of different first stage models for different welfare indices.

> 12 Azzarri et al. (2006) use only<sup>_x_</sup> _t_<sup></sup>  _k_ <sup>ˆ</sup> _t_ to predict ln<sup>_c_</sup> _t_  _k_<sup>. Yet consistent estimation ofˆ</sup> _t_<sup>
central government does not affect the results.

# **III. Data and Methodology**

Our outcome variables are a set of district-level living standards measurements for 2007: average per capita expenditure, the poverty and extreme poverty headcount indexes, a measure of uncovered basic necessities, the illiteracy rate, and the Gini coefficient of consumption inequality.

We collect living standard measurements that are representative at the district level by drawing directly from the 1993 and 2007 Censuses and from a poverty map developed by the Peruvian Statistical Institute that combines data from the 2007 Census with 2007 household survey data from the _Encuesta Nacional de Hogares sobre Condiciones de Vida_ (INEI, 2009). The advantage of using districts as our unit

> 4 Districts are the smallest administrative entity in Peru. A group of districts forms a province and a group of provinces forms a Department. Peru is divided in 25 Departments, 195 Provinces, and 1841 districts.

8
.247|
||(0.076)|(0.270)|(0.172)|(0.116)|(0.156)|
|MDist|0.047|–0.425***|–0.001|0.185***|0.114|
||(0.053)|(0.146)|(0.109)|(0.064)|(0.110)|
|Year|–0.564***|–0.530**|–0.388***|–0.686***|–0.465***|
||(0.055)|(0.212)|(0.109)|(0.093)|(0.106)|
|South|–0.078|–0.041|–0.023|–0.124|–0.145|
||(0.048)|(0.205)|(0.113)|(0.082)|(0.101)|
|Constant|4.258***|5.236***|4.474***|4.199***|3.385***|
||(0.283)|(0.446)|(0.452)|(0.373)|(0.258)|
|_N_|2,206|378|400|1,050|378|
|_R_<sup>_2_</sup>-statistic|0.454|0.546|0.301|0.419|0.433|

_Source:_ Authors’ estimates based on data from the 2003 National Industrial Census and the 2014 Integrated Business Establishment Survey.

_Note:_ This table reports estimates for equation
129 among survey respondents as compared to $3,050 among non-respondents. The average credit card expenditures are $1,135 among survey respondents and $1,080 among non-respondents. The one possible exception is gender, where we find that men are more represented among survey respondents (67%) than among non-respondents (57%).

Table 3 provides additional descriptive statistics, based on survey and administrative data, and presents a test of randomization balance. Column (1) is based on the sample of all 2,872 survey respondents. The summary statistics show that the respondents in this sample are highly educated (87% have a college degree), around half (54%) are married, and around 10% are selfemployed. In columns (2) through (4) of Table 3, we compare the baseline characteristics and expenditures of the three treatment groups. Column (5) reports p-values for the null hypothesis that these characteristics are equal across all three treatment groups. The results indicate that, consistent with successful random assignment, pre-treatment observables are balanced across treatment groups. As expected, all differences across treatment groups are economically small. The difference is statistically significant (p-value=0.09) for only one of the 11 characteristics reported in the table: the number of dependents. This result is within expectations, given that 1 out of every 10 differences are expected to be statistically significant at the 10% level simply by chance. We nonetheless follow standard practice and include the number of dependents as a control variable in all regressions.

> 10. Source: Malaysian Department of Statistics, 2017.

15
with teachers, and the linkage with school principals and teachers. The training intervention showed the least significant effects, a finding consistent with the lack of impact found on learning outcomes; while the largest totals were with linkage and linkage+election interventions, suggesting that the significant effects indeed indicate a pathway towards learning. Surprisingly we find the smallest share of significant effects for the school committee, which was the focus of the pilot.

# _School committee and community support for education (Tables 10-13)_

Here we examine whether any of the interventions has an effect on school committee activities, engagement with the community and perceptions of effectiveness, by the principal, teachers and school committee representatives.<sup>33</sup> First we look at whether the school committee improves interaction and outreach, specifically meetings (e.g., with members only, principals, parents, district or sub-district education offices, village council, teachers), shown in Table 10. Naturally, the number of meetings does not indicate the _quality_ of interactions among stakeholders, but the quantity is at least the first step in improving interaction. On average, school committee meetings with most stakeholders increase, corroborating analysis from midterm data that the grant funds were primarily used for meetings.<sup>34</sup> However, only the grant intervention has a significant effect on the summary index, with individual effects demonstrated for internal meetings (increase by 0.95 meetings) and those with the local education office (increase by 0.32 meetings). Interestingly, with the linkage interventions, meetings initiated by the local education office actually declined significantly, suggesting that the local education office may have pulled back as a result of greater school engagement with a more decentralized administrative body.

While grant and to a certain extent linkage help explain school committee interactions, election interventions improve perceptions of school committee effectiveness, especially by teachers, indicating that the democratic election improves legitimacy (Table 11). The school committee effectiveness summary index is significantly positive for election
of indicators assume some form of data homogeneity, which might be problematic given the diversity of counties in the sample considered. Even for a more homogeneous group of countries, such as the OECD, there is call for caution when comparing expenditure levels in member countries (Jounard,

> 6 The data sources are: the World Bank World Development Indicators (WDI), Barro-Lee database, and Crouch and Fasih (2004)  the World Health Organization (Mathers _et al,_ 2000).A complete list of variables and data sources can be found in Table B.6 of Appendix B.

> 7 Crouch and Fasih (2004) consider several international tests of learning achievement in math, science and literacy applied at different levels of the school system.  The tests are the following: TIMSS (Third International Mathematics and Science Survey),  PIRLS (Progress in International Literacy Study), PISA (program for International Student assessment), Reading Literacy Study,  LLECE (Laboratorio  Since the tests have different samples, they converted all test scores through iterative comparisons to a single numeraire.Latinoamericano de Evaluaciond ela Calidad de la Educacion, SACMEQ (Southern Africa Consortium for Monitoring of Education Quality), MLA (Monitoring Learning Achievement)

> 8 The correlation coefficients and Figure 6 exclude developed nations for the Crouch and Fasih (2004) sample.

> 9 DPT is Diphtheria-Pertussis and Tetanus

10
regimes typically only focus on the legal basis for data collection and some rights of data subjects but fail to offer a comprehensive framework that differentiates personal from nonpersonal data, provides rules on transfer of data, or sets out conditions for conservation or destruction of personal data. UAE does not have a framework for privacy protection, but Dubai has adopted a full-fletched framework under the context of the Dubai International Financial Centre (DIFC) Authority that applies to business conducted in the special economic zone -thus having a geographical limitation to the scope of the regulation. Despite not having a comprehensive framework on data governance, provisions on personal privacy can be found in other countries in the region dispersed in sectoral or other specific regulations. For instance, in Egypt, the criminal code imposes a punishment for the unlawful collection of images or recordings of individuals in private places, and the Banking Law requires confidentiality for information regarding clients and accounts (CipherCloud 2017). These discrete provisions can be essential in their own context but should ideally be integrated in a broader framework that balances the different priorities of data privacy protection.

|**Table 12: Reg**|**ulation on da**<br>**Sensitive**<br>**data**|**tagoverna**<br>**Legal bas**|**nce in MENA**<br>**es for data col**|**lection and**|**processing**|**Data**<br>**subject’s**<br>**rights**<br>|**Cross-b**<br>**tra**|**order data**<br>**nsfers**|
|---|---|---|---|---|---|---|---|---|
||Special<
. It is of interest to ask which measures of the distribution of income are the most important (to individuals) in this context: Is it (as is commonly assumed) the Gini coefficient, or rather something else?” In here we argue and show empirical evidence that inequality perceptions depend not only on the monetary metrics (individuals might be more worried about the inequalities generated through the labor market than others), but might be influenced by individuals’ attitudes (e.g., self-interest) or ideology.

In contrast with our paper, a common explanation for these discrepancies is that they originate from mistakes of the individuals. Studies on perceptions of inequality have focused on individuals’ (in)ability to correctly perceive inequality (Niehues 2014; Norton & Ariely 2011; Chambers et al., 2014; Kuhn, 2015; Page and Goldstein 2016) or, correspondingly, their own position within the income distribution (Cruces et al 2013; Fernandez-Albertos & Kuo 2015; Karadja et al.2017). There is, however, an exception to the dismissal of subjective data: expectations. These are clearly subjective and play an important role in

> 7 As noted above, these preferences tend to be more complex than earlier literature assumed. In this paper, we focus on these preferences and their link with perceptions of inequality, and we are not concerned about the link between these preferences and the final policy outcomes. In modern democracies, as a large literature has established (see Page and Shapiro 1983, Burstein 2003), majority preferences do influence policy outcomes. Similarly, Luebker (2014) shows that redistributive preferences translate into redistributive outcomes.

> 8 Gruendler and Koellner (2017) confirm that actual inequality increases redistribution (measured as the difference between market and net Gini coefficient), however, they find an even stronger link between perceived inequality and redistribution.

5
# **1. INTRODUCTION**

Mobile phones have spread dramatically: there are over 4.5 billion mobile phones in developing countries (ITU, 2011). In addition to improving communication, these devices make it possible to provide new forms of credit. Telecoms often extend credit to help smooth phone consumption itself: as incomes rise, consumers are expected to transition from prepaid to postpaid plans, which are essentially rolling credit accounts. Phone networks can also be used to mediate loans for consumer durables, such as pay-as-you-go solar energy systems.<sup>1</sup> And through mobile money, phone networks can provide loans directly to consumers at close to zero transaction costs. This can enable forms of credit different from microcredit or traditional bank lending (consider M-Shwari in Kenya, or independent smartphone lending apps).

However, all of these applications face a fundamental problem: how can a lender assess whether a potential borrower will repay? Few developing country residents have traditional credit scores. Few interact with formal institutions at all: 2 billion people lack bank accounts (Demirguc-Kunt, Klapper, Singer, & Van Oudheusden, 2014). And in person methods, like interviews or peer groups in microfinance, are costly for small loans and remote households.<sup>2</sup> This paper develops a method to predict repayment of the poor at scale. Its key insight is that most poor consumers do have at least one rich record of formal interaction—their interaction with a mobile phone itself. The way that a person uses a mobile phone can predict whether they will repay credit.

This project develops and assesses a low cost method to predict repayment of credit using mobile phone metadata, which are already being collected by mobile phones. From raw phone transaction records it extracts signals plausibly related to repayment, and uses a machine learning approach to combine these signals into a prediction of repayment. Using only minor tweaks to off-the-shelf machine learning methods,

> 1 Typically, if approved for a household solar energy loan
tween segregation and test score gaps than with test score mean. A one standard deviation change in the level of segregation raises the locality mean test scores by .78 points while it will increase the gap between the wealthiest and the poorest groups by 2.6 points, i.e. the change in the gap is three times as large as the change in average test scores. Similar effects are found using provincial data. The model of peer effects estimated in this paper is able to account for the observed pattern.

Figure 2: Segregation and school achievement <!-- Start of picture text --> by province<br>CFE<br>BUE TFG BUE CHU SFEGBA MENTUC<br>LPA CRBSGO RNG<br>LRI SJN ERI<br>SCR TFGLPA SJN CRBERICHUSLS SFEGBACFE MEN RNG SAL SCR CAT CHASLSJUJMISCOR SAL<br>MIS TUC<br>FOR JUJ<br>LRI CAT CHASGOCOR FOR<br>.15 .2 .25 .3 .35 .15 .2 .25 .3 .35<br>Segregation index Segregation index<br>slope= 24.4 (p-value = .16) slope= 30.15 (p-value = .19)<br>by localities<br>0 .2 .4 .6 .8 0 .2 .4 .6<br>Segregation index Segregation index<br>slope= 6.91 (p-value = .013) slope= 22.8 (p-value = .001)<br>75<br>15<br>70<br>65 10<br>Mean test score 60<br>Gap in test scores<br>55 5<br>100 40<br>80 20<br>60 0<br>Mean test score 40 -20<br>Gap in test scores<br>20 -40<br><!-- End of picture text --> Note: OLS regressions, with no controls. Standard
.0<br><!-- End of picture text --> _Source_ : World Bank staff calculation using BLSS 2017.

Source: World Bank staff calculation using BLSS 2017 for Bhutan. See references section for estimates for other countries. Pensions are treated as deferred income.

However, the net fiscal system leads to a small increase in poverty as of 2017. The $3.20 poverty rate (in 2011 purchasing power parity terms) is 8.2 percent using pre-fiscal (market) income. Once the burden of direct and indirect taxes and contributions is taken into account, the share of the population whose consumable income falls below the same poverty line increases to 9.3 percent. Most of this increase is due to the burden of indirect taxes, and to a lesser extent from contributions. The impact of direct taxes (i.e., personal income taxes) is very low, largely because of the high exemption threshold. This results in a fiscal system that is poverty-increasing in the absence of direct transfers or indirect subsidies. Increases in the poverty gap and poverty severity indices are also observed when moving from market income to consumable income (table 5). It is customary in the literature to not consider in-kind transfers when measuring the poverty effect because households do not observe the monetary benefits deriving from education and health spending. For this reason, Table 5 only reports poverty estimates for market income, disposable income and consumable income. The result that the fiscal system increases poverty in Bhutan is not unique—as shown in Figure 4, the fiscal system decreases poverty in some countries, while it increases it in others.

17
Policy Research Working Paper 8729

# **Abstract**

According to the 2015 Tanzania Water Point Mapping data, about 29 percent of all water points are non-functional, out of which 20 percent failed within the first year. This paper analyzes the various factors which impact water point failure and measures the relative contributions of these determinants. The results indicate that water points managed by village committees had a much higher likelihood of failure than those managed by private operators or water authority. Factors that cannot be modified such as hydrogeological factors play a major role in determining water points failure during the first year after installation. However, management type as well as the type of pump and technology matter considerably more in the short and medium term.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/research. The authors may be contacted at gjoseph@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
The second aspect concerns the failure of the debt ratio, i.e. government debt divided by annual GDP, to experience a significant decline despite the achievement of a primary surplus since 1994 and the fact that the rate of interest has been lower than the growth rate of GDP throughout the 1990s. Data compiled by the Bank of Greece show that the general government debt ratio fell from 110.3 to 104.9 percent of GDP between 1993 and 2002, a reduction of 5.2 percentage points (Table 3.12).<sup>25</sup> However, the cumulative primary surplus over this period amounted to 36 percent of GDP, while the net effect of income growth and interest cost contributed to a decline of the debt ratio by an additional 10 percentage points.

|**Table 3.11: Intere**<br><br>|**st Cost (% of**<br>|**GDP)**<br>|
|---|---|---|
|1981<br>4.8|1993|13.1|
|1982<br>4.6|1994|14.0|
|1983<br>5.9|1995|12.7|
|1984<br>6.9|1996|11.8|
|1985<br>8.0|1997|9.8|
|1986<br>8.2|1998|9.1|
|1987<br>9.6|1999|8.7|
|1988<br>8.7|2000|8.0|
|1989<br>8.4|2001|7.4|
|1990<br>12.0|2002|6.5|
|1991<br>9.3|2003|6.1|
|1992<br>11.6|||

Source:
of classes, and it is used to measure the separability between land use classes, such as mature forest, secondary growth, and farmlands. In satellite image classification, TDI is needed to assess the quality of the statistics prior to image classification. After careful analysis of the TDI, we ended up using eight land use classes: mature forests, secondary growth, degraded forest land, farmland, barren land, water, cloud, and no data. We classified cloud in the images if clouds were seen in all the images. However, if the 2000 and 2001 panchromatic images showed forest in a certain area and that area was under cloud cover in rest of the images, we used a mask to categorize such areas under forest for all the images.

Ideally, we would have liked to validate satellite data by using information collected on location. However, the scope at which such a ground-truthing exercise would have to be conducted would not only be prohibitively expensive but also time consuming. To a large extent, we have verified our land use and cover classes using a priori knowledge of a number of forest officers who have worked in various regions of Nepal for the past 25-30 years. Some areas that were not confirmed in the images prior to 2000 and 2001 from this a priori knowledge were cross-checked using the aerial photos and topographic maps. We also cross validated the ETM+ 2000 and 2001 classified images using the IKONOS (1m x 1m) images taken in 2003. These cross-validations were only conducted on selected 11 Road lengths were calculated using this procedure on the classified images.

18