rafmacalaba/data-use-mentions-extended huggingface dataset:
that are linked with georeferenced plot locations.

Intensive data quality controls and supervision measures were put in place to ensure that each crop cut sub-plot was placed in accordance with the desired protocol; was not managed by the farmer differently vis-`a-vis the rest of the plot; was not harvested by the farmer without the presence and supervision of the enumerator; and was harvested to ensure that production could be weighed and recorded separately for each quadrant, without including plants outside each quadrant.<sup>6</sup> These supervision measures contribute to the substantial additional costs associated with crop cutting and provide further impetus for our efforts to get more out of crop cutting, self-reported survey data and cutting-edge imputation techniques to improve agricultural survey data even when crop cutting may not be implemented at full scale. On the other hand, intensive supervision measures are unlikely to eliminate biases in SR yields<sup>7</sup> and these biases may in turn be correlated with self-reported information on the very agricultural inputs,

> 4One field team covered each stratum and resided in the most amenable village of that stratum throughout the

> 5Of the 600 plots selected, we drop 23 from this analysis for the following reasons: 18 crop cut subplots were harvested by farmers in the absence of the enumerators and as such had missing crop cut data, 3 plots had missing self-reported data due to a programming mistake, 1 plot had 100% self-reported loss but 0% enumerator assessed damage, and 1 plot was in the opposite situation: 100% enumerator assessed damage but 0% farmer reported loss.

> 6Appendix C.1 provides details of the fieldwork protocol followed for the crop-cut and the questionnaires fielded are available upon request.

> 7 When we compare household production reported by farmers on their selected plots with the non-selected pure sorghum plots, there is no statistically significant difference, indicating that they do not try to be more accurate when reporting the production on the selected plots.

5
# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of the infrastructure situation in Africa, including in the countries of the Economic Community of Central African States (ECCAS) region. The results have been presented in a variety of continental reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

The purpose of this regional economic community is to present the key AICD findings for ECCAS. The main value in doing so is that it allows ECCAS’s infrastructure situation to be benchmarked against that of other African peers, the main gaps in the regional infrastructure backbones to be identified, and the costs and benefits of regional integration (as well as their distribution across member states) to be quantified.

A number of methodological issues need to be borne in mind.

First, owing to the cross-country nature of the data collection, there is inevitably a time lag involved in the data. The period covered by AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today’s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis was based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build up a picture of the
measured using clinical vignettes focused on seven common conditions associated with the main causes of child and maternal mortality in Sub-Saharan Africa: malaria, tuberculosis, diarrhea, pneumonia, diabetes, neonatal asphyxia and post-partum hemorrhage. All seven of these are major global health challenges, with three of them – diarrhea, neonatal asphyxia, and malaria – together causing more than 180,000,000 disability-adjusted life years (Appendix Table S3). Whether providers are available to provide care at regular opening hours was assessed by verifying their presence through unannounced visits at the health facilities. Availability of drugs and functionality of equipment was assessed by visual inspections of storage facilities and consulting rooms at the health facilities.

Clinical vignettes have been used extensively in the medical literature to assess clinical knowledge of health workers. Data from vignette studies are directly policy relevant as they are closely linked to the quality of medical training and quality improvement programs. Validation studies show that clinical vignettes, for scenarios such as those assessed as part of the SDI program, are an effective way to capture provider knowledge.<sup>23,24</sup> Although a health worker’s clinical vignette performance is not a measure of quality of care per se, it represents the highest level of quality care which s/he can deliver given his/her current knowledge, assuming that there are no constraints in drugs, equipment and motivations. (More details on the clinical vignettes are provided in Section S4 in the appendix.) For a measure of providers’ actual performance, studies often use standardized patients, in which actors are trained to portray a given medical condition, appear incognito at facilities for recommended treatment, and report on provider activity.

The clinical vignettes were adjusted to country-specific diagnostic and treatment protocols. The enumerators presented the provider with the patient’s symptoms and prompted the provider to take the patient’s history, suggest physical examination and diagnostic tests 6
observe for at least four years before and three years after the event, allowing us to evaluate events happening in event periods _p_ = _{_ 2010 _,_ 2011 _,_ 2012 _}_ . For the sales tax, we consider a balanced panel of rms that we can observe for at least ve months before and after the event, allowing us to evaluate events happening in event periods between February 2009 and August 2014.<sup>40</sup> Each panel in Figure VI displays the trend in (real) reported taxable income for the event group (orange dots) and the control group (blue crosses), scaled by the pre-event average, along with the dierence-in-dierence coecient obtained from estimating The unit of observation in this estimation is a rm _i_ in event period _p_ at event time _k_ . For instance, a rm may be in the control group group for events happening in 2010 and 2011, but in the treatment group for event happening in 2012. Each rm-year observation for this rm will thus appear in the event dataset three times, for event years 2010, 2011 and 2012. We estimate the rm's reported taxable income as a function of event-time dummies _γk_ , rm-event-period xed eects _αip_ , and the post-event and treatment group dummy _I{k ≥_ 0 _, g_ = _E}_ .<sup>41</sup> Outcomes are in levels rather than logs, to include the large number of rms with zero values, and winsorized at the 95th percentile to limit the inuence of outliers. Panels on the left side of gure VI correspond to the self-employed, and panels on the right side correspond to corporations.

For most rms, the rst transacting partner reporting to the tax authorities is a supplier, leading to a D151 cost report about the rm's purchase. As panels A1 and B1 show, receipt of this rst information report is associated with a 26% increase in reported taxable income for the
of the regulatory framework for PPPs, through its Private Participation in Infrastructure Database. For the past 10 years, the WBG’s green bonds have helped the private sector invest in projects related to climate change (SDG 13), mostly for renewable energy and clean transportation (World Bank 2018i). More recently, the WBG has begun issuing sustainable development bonds focused on clean water and sanitation (SDG 6), life below water (SDG 14), and gender issues (SDG 5), and has been active in promoting green _sukuk_ . The IFC’s Managed Co-Lending Portfolio Program has mobilized financing from the private sector for SDG-related projects. The Pandemic Emergency Financing Facility is an example of the WBG’s effort to partner with the private sector for global public good.

The WBG has also made efforts to systemize the SDG financing dialogue with investors at the highest level and formulate concrete action to promote sustainable investments. In early 2019, the IFC launched its “Operating Principles for Impact Management,” a market standard for managing investment funds developed in consultation with major asset managers and development finance

> 23 The Maximizing Finance for Development approach has been used in a range of countries and sectors—for instance, in agricultural value chains (World Bank 2018j).

> 24 The WBG’s Independent Evaluation Group found that the Creating Markets strategy has been successful in making markets more inclusive, competitive, and sustainable in an evaluation of 16 case studies (World Bank 2019d).

17
.0|94.0|99.9|
|Distribution losses|% production|22.1|26.0|15.4|15.7|
|Cost recovery|% total cost|90.0|41.9|57.1|125.7|
|Total hidden costs as % of<br>revenue|%|121.2|175.3|86.6|3.5|
|US cents|Tanzania<br>2005|Tanzania<br>2010|Predominantly<br>generatio|hydro<br>n<br>O|ther developing<br>regions|
|Power tariff (residential at 75 kWh)|5.5|7.3|10.27||5.0 – 10.0|
|Power tariff (commercial at 900 kWh)|10.1|9.4|11.73|||
|Power tariff (industrial at 50,000 kWh)|9.1|6.2|11.39|||

_Source:_ Eberhard and others 2009. Derived from AICD electricity database, http://www.infrastructureafrica.org/aicd/tools/data and more recent data provided by World Bank staff. For Tanzania, data for 2005 and 2010 are reported to show progress over time. Benchmark data, however, relates to 2006. — = Data not available.

10
Policy Research Working Paper 11031

# **Abstract**

This paper examines the dynamics of poverty in Bhutan between 2017 and 2022, utilizing cross-sectional data from the Bhutan Living Standards Surveys. The paper constructs synthetic panels and estimates poverty transition probabilities. Three main findings emerge. First, poverty turnover in Bhutan is low overall (not many people, as a share of the population, moved in and out of poverty during the period considered). Second, chronic poverty, defined as the probability of remaining poor in both years (2017 and 2022), was also low, both in absolute terms and compared to other countries for which similar estimates are available. The probability of being poor in both years is 6 percent of households in Bhutan, compared to over 15 percent in India and 17.5 percent in Pakistan. Third, upward poverty mobility (the probability of escaping poverty between 2017 and 2022) is 20 times higher than downward poverty mobility.

This paper is a product of the Poverty and Equity Global Department. 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/prwp. The authors may be contacted at aetangndip@worldbank.org; nicola.amendola@uniroma2.it; federico.belotti@uniroma2.it; giulia.mancini@uniroma2.it; and giovanni.vecchi@uniroma2.it.

_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
- Training Workshops for Staff and Country Representatives, sustained through continuing guidance and technical assistance;<sup>14</sup>

- Cost platforms or templates, together with training on those platforms as needed, to assist users in the appropriate application of VFM methods;

- Greater dissemination and promotion of studies that use CEA and CBA methods so that evaluators and authorities become accustomed to their methods, findings, and implications;

- Codification (or even more pointed guidance) of a common set of methods that is sufficiently flexible to allow for reasonable alternatives that can be specified with a discussion of a basis for deviations from the “standard” and the probable consequences. A standard set of defaults could also be used where the user is not sure of the best assumptions;

- Bolstered vigilance on the quality of efficiency calculations in Project Appraisal Documents and Implementation Completion Reports;

- Expectations of sensitivity analysis on deviations from standard methods and assumptions; and

- Consensus on standard outcome measures and specifications for their construction. These may come from measures derived from the Demographic and Health Surveys (DHS), and the Living Standards Measurement Survey (LSMS), for example. Relatedly, the field needs policy-relevant standard numerators for CEA in different sectors, as has been done with the disability adjusted life year (DALY) in health, for example, or months of additional schooling in the Education sector.

If some evaluators opine that demand for VFM from policymakers may not appear to be terribly strong, it is useful to recall that initially, impact evaluations were not in high demand by policymakers either. Instead, the demand for impact evaluation largely came from development funding agencies and actors interested in basic research. The former have become increasingly interested in VFM in more recent times, in part as a result of domestic resource constraints leading to increased scrutiny of the value achieved from the aid budget. As for the academic world, the demand for VFM as exemplified through its coverage in academic journals is still decidedly less strong than for impact evaluations. There is
The CEQ assessment aims to incorporate the largest number of policies for which there are data of a certain quality and level of disaggregation. Any fiscal components for which the expenditure categories are too broad, or for which the benefits (or payments) cannot be clearly mapped into the household survey data, are not deemed as informative and are not included in the exercise. Table 1 provides a snapshot of the fiscal expenditures covered by this assessment. Defense spending and infrastructure are not covered, nor are the subsidies destined to farmers or water subsidies. Subsidies on food items and energy products are captured by the exercise. Crucially, a large share of the social protection portfolio is also incorporated, with the exception of the budget line on housing and public utilities. Government expenditures on education are integrated into the analysis, although the level of disaggregation to assign benefits is restricted by the availability of two budget lines only: primary, preparatory, and secondary, on one hand, and tertiary education, on the other. The public service pension fund is included, but, as will be explained, the study treats these expenditures as part of the public sector or civil service wage bill rather than as a tax and transfer program.

It is important to highlight that the CEQ exercise should not be understood as an estimate of all expenditure categories that may have an effect on households’ welfare. Some public expenditure elements have private analogues. In certain countries, the existence of private pension funds may allow individuals who do not belong to the public contributory pension system to contribute and receive income from these. Such goods and services are included in measures of income but are not part of the fiscal system and we do not attempt to determine their impact on welfare or inequality. Households may also spend on private education and tutoring for their children. As long as these are not payments or benefits from the public system, they are not part of the exercise.

In what follows, we provide an overview of each of the main fiscal tools and illustrate the
>4|19<br>18633<br>4|19<br>18633<br>2|19<br>67397<br>4|19<br>18633<br>4|19<br>18633<br>4|19<br>18633<br>2|19<br>67397|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1 . Robust standard errors are in parentheses. WorldPop 250m is used for the DB methods. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Core” or “Suburb”) are included in specifications (4) and (8). The baseline category is “Other rural” in all specifications, except (4) and (8), where it is “Suburb”. Baseline categories are the following: “Married” for the marital status of the household head, “No education” for the education of the household head, and “Employed in Agriculture” for the employment of the household head. Observations are weighted using population weights.

92
11 Carlin, Schaffer, and Seabright (2005) argue that only in the case of the financing constraint, reverse causality makes the within coefficient more negative than the true value, thus making this method inapplicable 12 This is further confirmed by the Weak Identification test statistic (Kleibergen-Paap Wald statistic) which is much larger than the critical value of 16.38 13 In unreported results we also computed the DFITS statistic of Welsch and Kuh (1977) which identifies the influence of each observation on the fitted model. Besley, Kuh, and Welsch (1980) suggest that a cutoff of |DFITSj| > 2sqrt(k/N) indicates influential observations where k is the number of estimated coefficients and N is the number of observations. We have 145 observations in our sample with |DFITS| greater than the cutoff value. When we drop these influential observations we find that Financing, Policy Instability, and Crime are all negative and significant

14 The residual remains significant if in addition to the residual and the significant individual financing obstacles (collateral, paperwork, high interest rates, special connections, lack money to lend and lease finance), we were to include all the general obstacles (Policy Instability, Crime, Inflation, Exchange rates, Taxes & Regulation, Anti-Competitive Behavior, Judicial Efficiency, Corruption and Infrastructure).

15 An implicit assumption with the use of any survey data is that firm managers are knowledgeable about the different obstacles and understand the true workings of the financial and legal systems.

34
This article presents evidence that the introduction of the National Rural Employment Guarantee Scheme (NREGS) reduces households’ uncertainty about future income streams and enables them to produce a higher share of high-risk, high-profit crops. The National Rural Employment Guarantee Act (NREGA) was passed in India in September 2005; the implementation thereof began in 2006. The NREGA entitles every rural household to up to a 100 days of work per year at the state minimum wage. In the financial year 2010/11 the NREGS provided work to close to 55 million rural households, generating a total of 2.5 billion person-days of employment (Ministry of Rural Development, Government of India, 2012).

The empirical analysis builds on the Young Lives data; a household panel that is representative of the state of Andhra Pradesh in southern India. The quality of implementation of the NREGS has been shown to vary immensely across India (Dutta et al., 2012). In most states the provision of work under NREGS is far too unpredictable to completely offset the effects of a shock. Under such circumstances, the NREGS would not affect households’ risk expectations. Andhra Pradesh, however, is one of the states with the highest number of days of employment generated per rural household. I find that the provision of work in Andhra Pradesh does effectively respond to weather shocks and thus supports households in managing agricultural production risks.

The estimation strategy builds on the sequenced introduction of the NREGS at the district level, and explores the fact that the scheme was introduced in four out of the six survey districts in 2006 and in the remaining two districts in 2008 and 2009. Because this approach relies heavily on the parallel trends assumption, a number of robustness checks are warranted. The use of alternative treatment variables (e.g. block-level spending and employment days generated under the NREGS, as well as households’ registration with NREGS) does not change the results. Several additional robustness checks rule out the possibility that the observed effect is due to alternative mechanisms.

The results
Finally, we collected socio-economic data for all the household members including education attainment, age, labor and non-labor incomes, family composition, employment status, household wealth, access to health and community services, and health shocks.

# 3 Data

## 3.1 Baseline descriptive and sample balance

Table 1 describes key characteristics for our sample of 2,916 principal caregivers and their households. There are three sets of columns, one for each treatment arm. We show the mean of each variable and the number of observations in each group. The last two columns of the table display p-values of tests of whether the values of each variable are equal for the control group and for each of the treatment groups.

Caregivers are mostly mothers (94.8%), followed by grandmothers (3.6%). The father is the main caregiver for the child only in 1.2% of all households. This is consistent with what we see in the administrative records from the program. The average age of caregivers is 29 years old, with most caregivers being between 21 and 30 years of age. Moreover, 37.5% of caregivers are high school dropouts, and 16.3% have some level of tertiary education.

The intervention targets the most disadvantaged section of the population in Chile. Of the households in the sample, 52.1% belong to the bottom quintile and 80.2% belong to the bottom two quintiles of the household income distribution in the country (the definition of poverty in the table). Among the participants, 41.3% are single mother households, while the remaining are biparental non-extended families (consisting of father, mother, and children, but no other adults at home).

We find no significant differences between families across treatment arms at baseline whenever we test for equality of these characteristics individually or jointly (last line).

11