rafmacalaba/data-use-mentions-extended huggingface dataset:
An extensive applied literature exists, dating back to the 1980s (see e.g. Berki 1986; Wyszewianski 1986), measuring ‘catastrophic’ out-of-pocket medical expenses — expenses that exceed a specific that it has become one ofthe UN Sustainable Development Goals (indicator 3.8.2).

paper explores three of them: whether to relate out-of-pocket expenses to income (or consumption) or to a transformation of income (or consumption) that might better proxy a and how to capture the lifetime consequences of the various ways offinancing (or<sup>‘coping’</sup> with) expenditures in studies of poverty and income distribution (Blinder 1985; Citro and Michael 1995; Deaton and Zaidi 2002). medical expenditures (or at least some elements thereof) that may be discretionary (plastic surgery for aesthetic reasons is the commonest example), and while this view contrasts with the (cf. e.g. Grossman 1972), the consensus seems to be that, on balance, it makes sense when <u>As Blinder</u> (1985) put it: “...dollars [spent on medical care] do not buy happiness; they just maintain human capital”. Or as the US National Academy of Sciences (NAS) Panel on Poverty and Family Assistance put it: “A state” (Citro and Michael 1995, p236). Or as Deaton and Zaidi (2002) put it when discussing whether a (p32).

nondiscretionary underpins the approach of the U.S. Census Bureau which subtracts them (and health insurance premiums) from income in its supplemental poverty measure (SPM)(Fox 2017). Interestingly, despite the recommendations of Deaton and Zaidi (2002), in practice 95% of World Bank poverty ( ) yielded 178 documents of which 76 were actual aggregate. Of the rest, 5 used income (and did not subtract medical expenses), 24 explicitly included medical
effects for children of second- and higher-order birth order using the REDS sample. Each column is from a separate OLS regression. We control for _Female_ , _Firstborn girl_ , and fixed effects for birth year, birth order, and state in all columns. The vector _Xijt_ comprises mother’s age at birth, caste, religion, educational attainment of child’s parents, and mother’s birth cohort. Breastfeeding results are based on the last two surviving births of a mother and restrict the sample to children above age 2. Vaccination and health expenditure results are based on all surviving children of a mother. Standard errors in parentheses are clustered by state. Baseline mean refers to the average of the relevant outcome variable in the pre-ultrasound period. *** 1%, ** 5%, * 10%.
capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well‐being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations.

**Risks and vulnerabilities** . The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs. How to take these factors into account with risks and vulnerability studies is a question that has not really been addressed yet by the economics profession.

# 5. An illustration: The welfare of Syrian Refugees

In this section, we review a study on the welfare of Syrian refugees in Jordan and Lebanon jointly conducted by the World Bank and the UNHCR in 2014. The study relied entirely on existing data collected by the UNHCR and provides an example of what welfare economics can do for the forcibly displaced from an analytical and policy perspective.

At the height of the crisis in 2014, Syria had an estimated 6.5 m IDPs, 4.4 m. refugees, and 1.5 m. additional migrants, which amounted to about half of the Syrian
factors are so frequent that energy users experience daily outages. In other words, a fragile system is vulnerable not only to natural shocks, but also to a host of other stressors and shocks that include unmet demand, equipment failure, and accidents.

The 28 countries considered in this study cover about 1 billion people, and a wide range of geographies and climatic zone.<sup>1</sup> The US electricity network for instance consists of 275,000 km of high voltage transmission lines (>200 kV), almost 10 million km of low-voltage transmission lines, covering climatic zones from the Subarctic to the Subtropics, and serving over 325 million people (IEA, 2014). This means that its network elements – from generation to transmission and distribution – are exposed to a wide range of natural hazards, including tropical cyclones, tornados, wildfires, floods, winter storms, and droughts. The network is particularly dense in the Western mainland USA, a region that is frequently affected by storms – and outages.

In international comparison the USA have a high-quality electricity supply system. The electricity reliability index by the World Economic Forum (WEF, 2018) assess the risk of electricity supply interruptions and voltage fluctuations. According to this index, the United States ranked 26<sup>th</sup> in 2018 with a score of 6.2 on a scale from 1 (not reliable at all) to 7 (extremely reliable), out of 137 countries included in the study. While the US – expectedly – offers more reliable power supply than most developing countries, it ranks behind many of the developed economies in Europe considered in this study – such as France, the Netherlands, or Norway, which all score 6.8 or higher. This suggests that power supply interruptions are more common in the US than in other developed economies. The rest of this study will show that natural shocks (such as storms) are a key driver of these power supply interruptions.

> 1 This includes about
# 1 Introduction

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

The purpose of this regional report is to pull together in one place the key AICD findings relevant for East Africa. The main value in doing so is that it makes it possible to benchmark the infrastructure situation in the region against that of other African peers, to identify the main gaps in the regional infrastructure backbones, and to quantify the costs and benefits of regional integration, as well as their distribution across member states.

A number of methodological issues should be borne in mind.

First, due to the cross-country nature of the data collection, there is inevitably a time lag 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 is 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 regional situation. Nevertheless, some specifically regional issues—
Croke et al. (2016) than the cost.<sup>29</sup> This implies that analyses which divide up the set of available studies will likely be underpowered, limiting the scope for further subsample tests. While the results here suggest that overall, multiple-dose MDA increases child weight, they also suggest that if MDA had similar impacts on weight across drug type or worm species, a meta-analysis focusing on any one species of worm or drug may well be underpowered. We therefore think it would be appropriate for any future studies designed to explore heterogeneity, across worm species or drug type, for example, to report a test of the hypothesis that the average effect of MDA for each worm species or each drug is the same, rather than to simply test the hypothesis that the effect of any one individual drug or MDA against any one species has zero impact on weight. Beyond its relevance for health research, greater awareness of the limitations of under-powered meta-analyses will become increasingly important as more social scientists conduct meta-analyses (Vivalt, 2015).

A further methodological lesson is that it is appropriate to explicitly test the null hypothesis of a common zero effect. Finally, in evaluating policy, it is appropriate to focus on all mass-drug administration studies conducted in environments where mass deworming is recommended under WHO guidelines rather than to mix in studies from environments in which worm prevalence is sufficiently low that the WHO does not recommend mass treatment, or to selectively exclude studies of mass drug administration conducted in highprevalence environments.

> 29 The estimated effect in the full sample is about 50% greater than the estimate in the TMSDG sample, though well within the confidence interval of (-0.11 kg, 0.27 kg) found by TMSDG in their meta-analysis of the impacts of multiple-dose MDA on weight. However, the confidence interval shrinks by 47% to (0.03 kg, 0.24 kg) with the full data set. Thus, incorporation of additional information into the analysis helps address the problem of insufficient
., Mejía Morales, M., & Lara Haro, D. (2023). ¿Es Ecuador una economía informal? Aproximación al tamaño del sector informal ecuatoriano desde la perspectiva del análisis multivariado.</mark>

-

- <mark>Melitz, M. J., & Polanec, S. (2015). Dynamic Olley Pakes productivity decomposition with entry and exit.</mark> _<mark>The Rand Journal of Economics</mark>_ <mark>, 46(2), 362-375.</mark>

- <mark>Midrigan, V., & Xu, D. Y. (2014). Finance and misallocation: Evidence from plant-level data. American economic review, 104(2), 422-458.</mark>

- <mark>Olley, G., & Pakes, A. (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry.</mark> _<mark>Econometrica</mark>_ <mark>, 64(6), 1263-1297.</mark>

- <mark>Partow, Z, & Vijil, M. Country Private Sector Diagnostic: Creating Markets in Ecuador - Fostering a Dynamic and Resilient Private Sector. Country Private Sector Diagnostic (CPSD) Washington, D.C.: World Bank Group.</mark>

- <mark>Restuccia, D., & Rogerson, R. (2008). Policy distortions and aggregate productivity with heterogeneous establishments.</mark> _<mark>Review of Economic dynamics,</mark>_ <mark>11(4), 707-720.</mark>

- <mark>Restuccia, D., & Rogerson, R. (2013). Misallocation and productivity. Review of Economic Dynamics, 16(1), 1-10.</mark>

- <mark>Restuccia, D., & Rogerson, R. (2017). The causes and costs of misallocation. Journal
# **Changing Perceptions of Institutions and Standard of Living in Iraq**

**Saniya Ansar, Bledi Celiku, Leora Klapper, and Wael Mansour**

1All authors are at the World Bank. Ansar: sansar1@worldbank.org; Celiku, bceliku@worldbank.org; Klapper: <u>lklapper@worldbank.org; Mansour: wmansour@worldbank.org. We thank Sakshi Hallan for excellent research assistance,</u> Jake Hess, Renad Mansour, and Vijayendra Rao for helpful comments, and the World Bank for financial support. We are grateful to Gallup, Inc. for their support using the Gallup World Poll survey data. 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.
# **A5. Information Available on Cost and Revenue**

**Table A.2.** Information availability for crop costs and revenues in LSMS-ISA surveys

|**Country**|**Item**|**Level**|
|---|---|---|
||(+) Total Production|Crop level|
||(-) Seeds|Crop level|
|**Ethiopia**|(-) Fertilizer<br>(-) Labor (post-planting)|Household level<br>Plot level|
||(-) Labor (post-harvest)|Crop level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|
||(+) Total Production|Crop level|
||(-) Seeds|Plot level|
|**Malawi**|(-) Fertilizer|Plot level|
||(-) Labor|Plot level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|
||(+) Total Production|Crop level|
||(-) Labor|Plot level|
|**Tanzania**|(-) Seeds|Crop level|
||(-) Fertilizer|Crop level|
||(-) Land rent|Plot level|
||(-) Machinery rent|Plot level|
||(+) Total Production|Crop level|
||(-) Transport cost, seeds and fertilizer|Plot level|
|**Uganda**|(-) Labor|Plot level|
||(-) Other cost, Machinery, extension services|Household level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|
||(+) Total Production|Crop level|
||(-)
# **<mark>3. Assessing differences in sampling and survey design</mark>**

In this paper, we examine eight aspects of sampling and survey design that are directly related to poverty measurement. These include the following: (i) sampling design; (ii) monetary welfare measure; (iii) food consumption questionnaire and data collection methods; (iv) self-production and meals outside home; (v) non-food durables; (vi) durables; (vii) housing expenditures; and (viii) health and education expenditures.

# _Sampling design_

Table 4 presents a summary of the sampling design for each of the eight countries in South Asia. With few exceptions, household-level surveys used to measure poverty in the region are nationally representative. Afghanistan, Bangladesh and India do not survey all the regions within the borders of their respective countries. In 2011/12, Afghanistan excluded the provinces of Helmand and Khost from the survey for poverty measurement.<sup>10</sup> These two provinces had an estimated population of 864,600 (Helmand) and 537,800 (Khost) in 2012.<sup>11</sup> The total population of Afghanistan in 2012 is about 24.8 million, so these two provinces combined represent around 5.65 percent of the population. Bangladesh did not traditionally include the slum population as part of the sampling frame for the HIES until 2016/17. According to the Bangladesh Bureau of Statistics Census of Slum and Floating Population collected in 2012, the slum population is about 2.22 million, which corresponds to 5.5 percent of the total population in urban areas.<sup>12</sup> The NSS 68<sup>th</sup> round from India excluded from its sampling frame the remote areas of Nagaland, and Andaman and Nicobar Islands. The population of Andaman and Nicobar Islands is 380,000, while that of Nagaland is 1.98 million. Given that India had a population
This subsection assesses the difference in the variance estimations and the associated implications for the predicted vulnerability rates between the Günther and Harttgen (2009) approach and the small area estimation method. To render the results comparable between survey estimates and imputations into the census, the vulnerability predictions across communes are aggregated by the department and nationwide rates.

Despite the use of a slightly different econometric technique and the limitation in the covariates available in the census and household survey, the results of the analysis closely matches the estimates based directly on the household survey. In the analysis, the vulnerability predictions by department from the small area estimations in the census were aggregated and compared with the results of the Günther and 15
the short-run, which attenuates to zero after five years, even though their index includes women’s work itself, which increased due to the public works offer.

Another dimension of women’s agency is their physical mobility. As noted earlier, Djibouti’s restrictive gender norms limit women’s mobility. For this reason, our baseline survey asked respondents about several dimensions of movement, which we subsequently used to construct an index on mobility that was then taken into consideration in our heterogeneity analysis.<sup>14</sup>

# _Attrition_

Table 2 presents the attrition results for men and women and includes regression results (i.e., not being interviewed at midline or endline) as a function of treatment status, controlling for group and strata effects. On average, 7.5 percent of the women in the control group were not interviewed at midline. This fraction increases to 11.4 percent at endline. Given their daily work schedules and temporary absence from the household, it was difficult to interview husbands: about 28 and 22 percent of husbands were not re-interviewed at midline and endline, respectively.

Differential attrition by treatment status is a potential source of bias in program effectiveness, since the balance in observable and unobservable characteristics that ensues from the randomization of treatment status at baseline may be lost. In our study, there is some indication of differential response at midline, with participant women 3.6 percentage points more likely to complete the midline survey than control women, and 4 percentage points less likely to fully complete the weekly survey at endline. This attrition moves in two different directions, suggesting that differential attrition by treatment is not systematic. No differential attrition is observed among women at the endline household survey or in husband’s responding to any of the questionnaires. Lastly, the minor imbalances in baseline that we observe in Table 1 correspond to the imbalances in the post-attrition sample (not shown). As noted earlier, we control for baseline characteristics to partially address potential concerns about non-random attrition
<sup>23</sup> PISA runs tests in mathematics, reading, and science every three years with groups of fifteenyear-olds in countries around the world. Testing on such scale requires a level of simplicity and “standardization”, thus the emphasis is on written examinations and extensive use of multiplechoice tests so that students’ answers can be easily codified and processed (Robinson and Aronica 2015). Demonstrating competence on fundamental learning tasks certainly has its place, but critics have increasingly argued that such tests are based on an incorrect assumption that what drives successful career and life outcomes is the kind of learning that is capable of being codified via a standardized test (Khan 2021, Claxton and Lucas 2015).

<mark>In reality, the gap between the skills that children learn and are tested for, and the skills that they need to excel in the 21st century, is becoming more obvious. The World Economic Forum noted in 2016 that the traditional learning captured by standardized tests falls short of equipping students with the knowledge they need to thrive.</mark><sup>24</sup> <mark>Yong Zhao, the presidential chair and director of the Institute for Global and Online Education in the College of Education at the University of</mark>

> 23 These tables are based on student performance in standardized tests in mathematics, reading, and science, which are administered by the Paris-based Organisation for Economic Co-operation and Development (OECD).

> 24 <u><mark>https://www.weforum.org/agenda/2016/03/21st-century-skills-future-jobs-students/</mark></u>

> <u><mark>https://www.weforum.org/agenda/2016/01/the-10-skills-you-need-to-thrive-in-the-fourth-industrial-revolution</mark></u> <mark>“Whereas negotiation and flexibility are high on the list of skills for 2015, in 2020 they will begin to drop from the top 10 as machines, using masses of data, begin to
63 above, we only managed to obtain citation data on USPTO utility patents, from Jaffe and Trajtenberg (2002). While utility patents constitute only a potion of total patents, they are likely to be the largest portion. Therefore, even though the patent count variable includes all types of patents, we include the logarithm of the utility patent citations as a separate regressor in order to account for the differences in the quality of patents. The results of the regressions that account for patent quality are presented in Regs A2.3 and A2a.3. We see that the citations variable returns an estimated coefficient that is negative, which is theoretically inconsistent, and is not statistically significant. The estimated coefficient of the patent count variable remains positive and highly statistically significant. In addition, note that in both regressions, there is a substantial jump in the R- square of more than 13 percentage points when the citation variable is included as an additional regressor. This leads us to believe that the inclusion of the citations variable does account for the quality of innovation, but the variable may be an inaccurate measure of innovation quality. In Reg A2.4 and Reg A2a.4, we account for possible interaction effects between the total patent count and the number of utility patent citations received. The coefficient of this interaction variable is negative, again theoretically inconsistent and not statistically significant. Also, the magnitude of the coefficient is extremely small. Note that the inclusion of the interaction variable has no effect on the qualitative nature of the rest of the variables.

For the next set of regressions, Regs A2.5 through A2.7 and Regs A2a.5 through A2a.7, instead of using the total patent count as the basic patent variable, we used the number of USPTO utility patents granted. The results are largely similar as before. The utility patent variable coefficient consistently bears the positive sign and is statistically significant, while both the citations and interaction variables have coefficients that are of the incorrect negative sign and are not