rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 6: Change in the absolute and relative valuations of equity and debt and the PEPP The dependent variable in regression 1 is the change in the market value of equity on March 19, 2020 divided by the book value of assets at the end of 2019. The dependent variable in regression 2 is the change in the market value of debt on March 19, 2020 divided by the book value of assets at the end of 2019. The dependent variable in regression 3 is the change in the market value of equity on March 19, 2020 divided by the sums of the changes in the market values of equity and debt on March 19, 2020, conditional on both of these being positive. The dependent variable in regression 4 is the change in the market value of equity on March 19, 2020 divided by the sums of the changes in the market values of equity and debt on March 19, 2020, conditional on the sum of these being positive. Investment grade is a dummy variable indicating a long term, domestic or foreign, issuer credit rating of at least (lower than) BBB-/Baa3 issued by either Moody’s, S&P or Fitch. Leverage is liabilities divided by assets at the end of 2019. Log of assets is the natural logarithm of assets at the end of 2019. ROA is net income before extraordinary items divided by assets at the end of 2019. Book-to-market is the book value of equity divided by the market value of equity at the end of 2019. Beta is the coefficient of a regression of the daily log stock return on the daily log return of the MSCI World index. Regressions include country fixed effects. Robust standard errors clustered at the country level are in parentheses. *, ** and *** denote significance at 10%, 5%, and 1%, respectively.

||(1)<br>dMVE/Book<br>assets|(2)<br>dMVD/Book<br>assets|(3
0292|0.023|
||[0.00208]***|[0.00425]***|[0.00555]***|[0.00208]***|[0.00579]***|
|Household head|0.104|0.105|0.105|0.104|0.105|
||[0.00398]***|[0.00646]***|[0.00645]***|[0.00397]***|[0.00641]***|
|Spouse works|-0.00869|-0.00577|-0.00609|-0.00903|-0.0057|
||[0.00366]**|[0.00953]|[0.00968]|[0.00366]**|[0.00950]|
|Income stability|0.156|0.156|0.156|0.156|0.156|
||[0.00445]***|[0.0111]***|[0.0111]***|[0.00444]***|[0.0111]***|
|At least one person receives remittances regularly|-0.0655|-0.066|-0.0664|-0.0659|-0.0657|
||[000252]***|[000374]***|[000382]***|[000250]***|[000377]***|
|Household Home & WASH index|.<br>0.00839|.<br>0.0079|.<br>0.00764|.<br>0.00821|.<br>0.00782|
||[0.00162
# Where on Earth is Everybody? The Evolution of Global Bilateral Migration 1960–2000

_Çağlar Özden, Christopher R. Parsons, Maurice Schiff, and Terrie L. Walmsley_ Keywords: emigration, immigration, labor migration, international migration, gender JEL Codes: F22, O15, J11, J16 Çağlar Özden (corresponding author; cozden@worldbank.org) is a senior economist in the Development Research Group of the World Bank. Christopher Parsons (lexcrp1@nottingham.ac.uk P) is a consultant at the World Bank and a doctoral candidate at the University of Nottingham. Maurice Schiff (mschiff@worldbank.org ) is a lead economist in the Office of the Chief Economist for Latin America at the World Bank. Terrie Walmsley [twalmsle@purdue.edu] is an associate professor and executive director of the Global Trade Analysis Project, Purdue University, and an associate professor at the University of Melbourne. First and foremost the authors thank the United Nations Population Division for spearheading the creation of the Global Migration Database. In particular, they thank Bela Hovy and Hania Zlotnik for their close support and shared vision that ensured the completion of this project. They are grateful to Richard Black, Ronald Skeldon, and especially L. Alan Winters for having the foresight to initiate this project and for their unwavering support. They also extend thanks to the librarians at the British Library, the Library of Congress, and the London School of Economics and to Lorraine Wright at the United States Census Bureau for assistance beyond the call of duty. The authors thank Steven Vertovec and Norbert Winnige of the Max Planck Institute for the Study of Religious and Ethnic Diversity for helping surmount the data issues concerning Germany and the former Soviet Union. They thank Michel Beine, Frédéric Docquier, and the journal editor, as well as three anonymous referees, for advice and comments. They gratefully acknowledge financial support from the World Bank Knowledge for Change Program and Ivar Cederholm’s help with administration of the funding. The findings, interpretations, and conclusions expressed
and the adjusted headcount ratio. This is natural since these indicators have additive separability properties that make them easy to decompose, as opposed to the multidimensional headcount, which is nonlinear in terms of deprivation scores. Decompositions of the multidimensional headcount ratio, in particular dynamic ones, appear to be lacking in attention.

Overlooking the multidimensional headcount ratio is conspicuous, considering that this indicator is particularly important on its own. From an academic perspective, focusing on the evolution of the adjusted headcount ratio as opposed to the multidimensional headcount ratio variations in that come disregards poverty only from changes in the number of poor and arise only in the identification step. Using datasets form several countries, Apablaza et al. (2010) and Apablaza and Yalonetzky (2013) show that declines in the adjusted headcount ratio are mostly due to changes in the multidimensional headcount and not to changes in intensity. From a policy perspective, the multidimensional headcount is frequently used by governments as ”the rate of multidimensional poverty”—instead of the adjusted headcount ratio— as it is easily communicable and also comparable to the monetary rate of poverty.

In this we an to variations in the multidi- paper, propose approach decompose mensional headcount ratio into changes attributed to different categories of dimensions. Our approach builds on counterfactual simulation approaches, which have traditionally been used to decompose poverty and inequality indicators. These approaches were first proposed by Barros et al. (2006), and then extended in a series of papers by Azevedo et al. (2012b, 2013a,b). The approach relies, first, on expressing the indicator of interest as a function of the distribution of its determinants over a finite population. Then it replaces these distributions by counterfactual ones, where one of the determinants has been altered.

Our approach allows us to estimate the extent to which an observed change in a dimension—or category of dimensions—can explain the observed change in the multidimensional headcount ratio. In the presence of panel data, it allows doing this without separately tracking the transitions in
# 4.1. Data

The main databases used in this section are the household surveys. Given constant improvements and changes to the surveys (e.g., changes in questions, sample, and sampling methods, etc.), and lack of comparability across countries, we use the harmonized household surveys developed by the World Bank, named SEDLAC (the Socio‐Economic Database for Latin America and the Caribbean). SEDLAC database has harmonized socioeconomic household survey information for 24 countries in Latin America, from 1990 onwards.

In the case of Central America, SEDLAC provides information from 2000 particularly on employment, income, and expenditure. However, surveys are not available or comparable in all years. Since 2000, the available and comparable surveys are as follows: Honduras (2001 – 2013), El Salvador (2004 – 2012), Guatemala (2000, 2006, and 2011), Costa Rica (2001 – 2013), Nicaragua (2005 and 2009), and Panama (2008 – 2013).<sup>23</sup> This data limitation shapes the empirical approach adopted in section 4.2.

Our unit of analysis is a sub‐national region. SEDLAC makes available information that is representative at a sub‐national level based on the sampling design of each country. Based on this criteria, and prioritizing the comparability over time (i.e., the lowest sub‐national level that is covered in all available years), we selected 27 sub‐national regions for the six countries in Central America. The average number of (sub‐ national) regions is 5 per country.

There are four main dependent variables at the household level: per capita labor income, per capita total income, poverty (less than US$ 4 per day) and extreme poverty (less than US$ 2.5 per day). Descriptive statistics are shown in Table 4. Guatemala, Honduras and Nicaragua are the poorest countries in the region with more than 50 percent population living under
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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

This paper was written in preparation for the Tanzania Country Economic Memorandum. Robert Utz was instrumental in starting the paper and his support and feedback are gratefully acknowledged. The authors thank Emmanuel Mungunasi for collecting the data on regional growth rates and Trudy Owens and Patrick Ward for assistance with the consumption aggregates for 1991/92 and 2000/01. Helpful comments and suggestions were also provided by Louise Fox, Tim Harris, David Sahn, Erik Torbecke, Stephen Younger, participants at the PADI workshop in Dar es Salaam in February 2004, and various members of the Research and Analysis Working Group.
. Studying the period from 2004 to 2016, Jbaily et al (2022) show that in addition to ethnic minorities, low-income populations also face systematically higher PM2.5 pollution levels – and these exposure disparities have been increasing over time. Air quality is also documented to influence real estate prices, so that lower-income households tend to locate into cheaper, more polluted neighborhoods (Chay & Greenstone, 2005).

Outside the US, evidence on the inequality in air pollution exposure is more limited – often due to a lack of socio-economic data with high spatial disaggregation. Hajat et al. (2015) offer a systematic review of the literature on socio-economic disparities and air pollution exposure, compiling country- and city-level evidence from all regions. They confirm the strong evidence of unequal exposure in the US, but find mixed 4
pandemic period and therefore is unknown.

However, SDI data can provide information on counts of medical staff at facilities, including breakdowns by cadre of provider. In particular, the data from Kenya indicate that the average hospital has a total of 34 health workers on staff; approximately three of whom are doctors, four are clinical officers, 16 are nurses, and the remainder are other types of health care workers (such as community health workers or medical assistants) (Table 1). A typical lower-level facility in Kenya has an average of five total medical providers; a doctor is unlikely to be on staff and nurses are the most common type of provider. A typical hospital in Sierra Leone has more than 46 medical staff, the vast majority of whom are nurses (and only about two of whom are doctors). In lower-level facilities in Sierra Leone, health care providers ranking lower than nurses are the most common cadre of staff available. Perhaps as expected, hospitals have more staff than non-hospitals, and more higher-level providers (doctors and clinical officers) than nonhospitals. While it is not possible to comment on whether these provider counts are optimal, these data are crucial for understanding potential areas of human resource constraint or flexibility.

Beyond counts of providers, adequate health worker education and training is required to ensure that appropriate care is administered to patients. Encouragingly, across all countries and levels of facilities, the numbers of providers with post-secondary education or higher is far greater than the numbers of health workers with secondary or primary education (Table 1).

**Table 1** : Total medical staff (cadre and education) by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

|||Kenya<br>|Sierr<br>|a Leone<br>|Ta<br>|nzania*<br>|
|---|---|---|---|---|---|---
2.0 User Manual.”

https://library.pppknowledgelab.org/documents/5783/download.

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- ———. 2015. “The Simple Microeconomics of Public-Private Partnerships.” _Journal of Public Economic Theory_ 17 (1): 4–48.

- Irwin, Timothy C. 2007. _Government Guarantees: Allocating and Valuing Risk in Privately Financed Infrastructure Projects._ Directions in Development: Infrastructure, Washington, DC: World Bank.

- Laeven, Luc, and Fabian Valencia. 2020. “Systematic Banking Crises Database II.” _IMF Economic Review_ 68: 307-361.

- Lambert, Paul C., and Patrick Royston. 2009. “Further Development of Flexible Parametric Models for Survival Analysis.” _Stata Journal_ 9 (2): 265–290.

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17 only 64% of trainees were aware that biogas residues can also be used to soak the seeds before they are planted, suggesting some scope for further optimization of biogas utilization.

# **_Suspension_**

Biogas, like other similar energy technologies, can be susceptible to problems with technical reliability and consumer acceptance. To sustain its benefits, it is vital to overcome such problems. National statistics indicate a high level of suspension. In 2007, of the 26.5 million biogas digesters installed in China’s rural areas, only 60% were operating normally (Chen et al. 2010b), with some sort of problem affecting the remainder. The majority of the suspension nationally occurred during the first year after installation and appears to be rooted in poor follow-up services (Chen et al. 2010b).

Our data echo this to some extent, though with one important nuance. First, among those that have ever adopted biogas, 36% expressed that they had at some point stopped using it for some reason and most of them (78 percent) during the first year. However, the suspension was often temporary and not necessarily related to any flaw in the equipment, but more often than not, related to temporarily having too few animals, sometimes in combination with cold winter temperatures that slow down digestion (Table 6).

Thus, in this sample, suspension of biogas appears to be temporary and related to having too few animals at some point during the year. Two-thirds of the households reporting an incident of biogas suspension cite issues with insufficient quantity of gas and too few animals to supply the dung needed to keep the system operating. These two reasons (insufficient gas and too few animals) amount to the same, as the key reason for insufficient gas production is shortage of animals, in particular pigs, which supply the feedstock for the digester. Animal ownership fluctuates over the year, as it is customary to sell or slaughter pigs in winter in time for the Lunar New Year celebration and to restock a
hazards if not properly maintained. For example in Zambia, Ashraf et al.(2017 <mark>) found that service interruptions were associated with an increased incidence of diarrheal disease, upper respiratory infections and typhoid fever</mark> [22 <mark>]. Equally, sewer networks are considered to be the top of the sanitation service ladder according to the Joint Monitoring Program of the WHO (JMP-WHO, 2020) with risks from onsite facilities more well documented in the literature (</mark> Bancalari & Martinez, 2018 **)** [23 <mark>]. Nevertheless, when bursts or leakages are not properly addressed, liquid sewage can potentially be even more hazardous than waste from onsite sanitation facilities [</mark> 24].

This paper uses geo-spatial analysis to explore the role of water and sanitation infrastructure in cholera risk. The study focuses on Harare, Zimbabwe, using the cholera outbreak of September - December 2018 as a case study. We use spatial modeling, detailed data on infrastructure and mobility, and georeferenced case data to identify factors associated with cholera risk. Second, we model different scenarios of infrastructural improvements and compare which of them contributes the most to reducing such risk. Third, we look at how the implementation of infrastructural improvements can be selectively targeted by ward throughout the city in order to potentially reduce cost and maximize health impacts.

# Methods

## Ethics Statement

<mark>This study was approved by the Medical Research Council of Zimbabwe. The approval number is MRCZ/E/302 and it was assessed as exempt. All data obtained and herein reported were anonymized prior to analysis.</mark>

## The Study Area

Zimbabwe is a landlocked Sub-Saharan African country with a population of just under 15 million people (World Bank, 2021). It is divided into 60 districts and bordered by Zambia, Mozambique, Botswana and South Africa. Its capital, on which this analysis is focused, is Harare, a city with a population of 1.
Instrumental variables estimates will vary depending on which instrument is used and estimates may vary widely when heterogeneity is important. What is important to recognize is that the nature of the effect of the instrument on the distribution of returns is critical in understanding the estimated coefficient, and may in times be useful in bounding the returns in the population. In addition, when the instrument used is the same as the policy being studied and the policy is exogenously imposed, the instrument can identify the effect of the exogenously imposed policy on the outcome being studied.

In this paper we followed a potentially promising approach (similar to that by Ichino and Winter-Ebmer 1999), where we looked for different instruments that are likely to affect different subgroups in the population, by assessing to which part of the returns distribution the complier groups belong.

In Venezuela, the returns to schooling based on OLS estimates are rather low. However, the IV estimates using the compulsory reform instrument seem to indicate that the impact of compulsory schooling may have been benefitial to those who are liquidityconstrained. Using natural policy experiments provides a useful assessment of the impact of education policies on the targeted groups.

Empirical evidence on developing countries, such as the evidence from this study, contributes to our understanding of the effect of education initiatives – which are part of a development policy agenda – on certain sections of the population, such as those who are liquidity-constrained. The evidence suggests that the returns to schooling for the subgroup of individuals affected by education reforms, such as compulsory schooling 19
: US Trends and International Comparisons. Science & Engineering Indicators 2018.” _National Science Foundation_ .

Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. 2019. “PyTorch: An Imperative Style, High-Performance Deep Learning . Library.” arXiv. https://doi.org/10.48550/ARXIV.1912.01703 Phillips, Brian J., and Kevin T. Greene. 2022. “Where is Conflict Research? Western Bias in the Literature on Armed Violence.” _International Studies Review_ 24 (3): viac038.

Porteous, Obie. 2020. “Research Deserts and Oases: Evidence from 27 Thousand Economics Journal Articles on Africa.” _Oxford Bulletin of Economics and Statistics_ .

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Sabet, Shayda Mae, and Annette N Brown. 2018. “Is Impact Evaluation Still on the Rise? The New Trends in 2010–2015.” _Journal of Development Effectiveness_ 10 (3): 291–304.

Sanh, Victor, Lysandre Debut, Julien Chaumond, and Thomas Wolf. “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.” _arXiv preprint arXiv:1910.01108_ (2019).

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specific study notes that members of the diaspora turn to the platform to maintain social ties (Ramadan (2017)). Given the ubiquity of social media in the conflict, it follows that Syrians use social media not just for the war effort or to learn of migration routes but also to assess local conditions for return.

In acknowledgement of the convenience and importance of social media, a recent line of inquiry involves combining social media data with traditional variables to predict displacement (Singh et al. (2019), Abrishamkar et al. (2018)). Singh et al. (2019) rely on Twitter data from Iraq to assess when violent events are taking place using sentiment analysis of tweets with the hashtag “ISIS”. In combining this with traditional movement variables, they improved the accuracy of displacement predictions. To show the flexibility of such techniques, they are adapting a Spanish language model for use on the Venezuelan crisis (Singh et al. (2020)). Similarly, Abrishamkar et al. (2018) adapted a variety of techniques to get a signal of violence from news articles, and used this signal as a factor in predicting displacement (Abrishamkar et al. (2018)). By assessing topic variations in return and non-return areas and areas with and without IDPs, and noting differences in discussion of violence, governance, and services, we show that social media reveals information about return decisions and builds a foundation for adapting such techniques to consider signals which favor return, as well as bringing in new aspects such as image analysis.

# **4 Research Design and Data Collection**

Our analysis relies on novel data collected from Telegram, Twitter, and Facebook in the latest stage of the war beginning after the recapture of Ar-Raqqa from ISIS on 17 October 2017 and going through 1 December 2020. Group selection processes focus on entities which are located in Syria or primarily discussing issues related to Syria. The success of work on predicting displacement using social media conversation (Abrishamkar et al. (2018