rafmacalaba/data-use-mentions-extended huggingface dataset:
Nigeria. World Development, 152(105818). doi:10.1016/j.worlddev.2022.105818 Subbaraman, B., & Goodwin, E. (2017). FAO AMIS Nigeria. Final Report. Contract Report, Knoema Corporation, Agricultural Data Exchange, Kaduna.

Taylor, R. (1990). Interpretation of the correlation coefficient: A Basic Review. JDMS, 1, 3539. doi:10.1177/875647939000600106 Thomas, A. (2023). Food Insecurity in Nigeria: Food Supply Matters. IMF Country Report No 2023/094 and Special Issue SIP/2023/018. 27p. IMF Country Report, International Monetary - Fund (IMF). Retrieved 01 02, 2024, from https://www.imf.org/ <u>/media/Files/Publications/Selected-Issues-Papers/2023/English/SIPEA20230</u> Tukey, J. (1977). Exploratory Data Analysis; Addison-wesley series in behavioral sciencequantitative methods. Reading, MA, USA: Addison-Wesley.

UNFPA. (2023). World Population Dashboard Nigeria. United Nations. Retrieved 01 03, 2024, - from United Nations Population Fund Dashboard: https://www.unfpa.org/data/world <u>population/NG</u> Wang, D., Andrée, B.P.J., Chamorro, A., & Girouard, S. (2022). Transitions into and out of food insecurity: A probabilistic approach with panel data evidence from 15 countries. World Development, 159. doi:10.1016/j.worlddev.2022.106035 Waterlander, W. E., Jiang, Y., Nghiem, N., Eyles, H., Wilson, N., Cleghorn, C., et al. (2019). The effect of food price changes on consumer purchases: a randomised experiment. The Lancet Public Health, 4(8), e394-e405.

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respondents on the ground actually believe that is the case and are more willing to provide sensitive information on average. We ask a number of questions about sensitive behaviors and viewpoints in the internet survey that we would expect to be underreported in less anonymous modalities. And then we estimate whether a larger share of internet respondents were willing to choose the sensitive answer than in a concurrent mobile phone survey asking identical questions, and further compare these differences in sensitive questions to differences in non-sensitive questions. Importantly, this empirical approach does not assess whether _all_ respondents in the internet survey are willing to reveal sensitive information, as some share of individuals might never openly respond to sensitive questions regardless of the modality.

We find that there are in fact large differences in all sensitive questions between the two modalities, with respondents in the internet-based survey being much more likely to choose sensitive responses than in the mobile phone survey. The results illustrate that the modalities were significantly different in instances where responses might potentially threaten the safety of respondents, such as naming parties responsible for violence witnessed by respondents; and the modalities were different in instances where potential responses were not widely socially acceptable, such as whether violence might ever be moral or preferences over the targeting of humanitarian assistance that might be viewed by some as selfish.

On average, the share choosing the sensitive option in the internet survey was approximately 16.2 percentage points greater than in the mobile phone survey, with a maximum difference of 29.4 percentage points and minimum difference of 9.9 percentage points. Importantly, given the relatively large percentage point differences in all sensitive questions, some of the percent differences between the modalities were especially large for sensitive viewpoints to which almost nobody admitted in the mobile phone survey.

The large differences in response to sensitive questions stands in stark contrast to the much smaller differences between the modalities in less sensitive questions. When asked whether respondents witnessed violence over the course of the conflict or about respondents’ subjective feelings about safety in the past month, the responses were very 3
# **(i) India**

The estimates for India underpinning the 2020 global inequality and poverty estimates are based on Roy and van der Weide (2022).<sup>1</sup> Depending on the data sources used, there exists a wide range of estimates of growth shocks in 2020 for India. This, combined with the large population, means that the global estimates of inequality and poverty are sensitive to the choice of estimate used for India. Below, we outline the derivation of the 2020 COVID-19 welfare distribution for India used in the current study and report alternate estimates based on other growth rates reported for India. The last official household survey released for India dates back to 2011/12. Roy and van der Weide (2022) use various methods to create a comparable time trend using the Consumer Pyramid Household Survey (CPHS) which is a private survey collected in more recent years. Their _approach 1_ uses survey-to-survey imputation techniques to estimate consumption in the CPHS using a set of (non-monetary) household characteristics that are common to both the CPHS and the 2011/12 National Sample Survey (NSS). This approach is similar to poverty mapping methods (Elbers et al., 2003) and does not exploit the consumption data that the CPHS collects. In contrast, _approach 2_ converts the observed CPHS consumption into NSS-type consumption in an attempt to account for the differences between the two aggregates (e.g. questionnaire design, sampling design).

The World Bank’s Poverty and Inequality Platform (PIP) uses the CPHS data adjusted using approach 2 for years 2015/16, 2016/17, 2017/18, 2018/19, and 2019/20, which we follow in the present study.<sup>2</sup> However, estimates based on approach 2 are not yet available for 2020/2021. Estimates here are based on approach 1 using the surveys conducted face to face (see Annex 1G of World Bank 2022 for a discussion on the construction
telephone networks are a good example, since they generally comprise relatively heavy infrastructure that is not easily destroyed through the type of warfare occurring in South Sudan. This is also a good indicator of sample selection favoring wealthier areas, especially in the context of South Sudan where only one in four households is covered. Access to electricity is a similar indicator given that it is exclusive to a few selected areas of South Sudan. Again, the latter two indicators do not seem different from 2015 and 2016. Finally, the share of households living far from schools, health centers, and markets, did not change significantly – this generally holds for various thresholds.

More importantly, the path of enumerators and geographic coverage of Wave 3 data was closely inspected to ensure that it remained broadly comparable to that of previous HFS waves and other sources of population data. This helped to control that entire areas were not systematically excluded. As an exception, the city of Yei was not surveyed at all in Wave 3 because it was the site of several large battles during fieldwork and subsequently experienced a massive wave of displacement. This was likely the most severe case, and in many other instances where fighting affected specific areas enumerators simply delayed fieldwork until it was safe to continue. This explains to some extent the prolonged duration of fieldwork relative to the low number of interviews conducted in total.

# 3. Measuring Poverty in a Fragile Context

## Calculating Consumption Aggregates

Poverty in the HFS was measured according to a standardized methodology best described in the seminal contribution by Deaton and Zaidi (2002). Poverty analysis consists of comparing a welfare measure to a predetermined poverty line. Therefore, the first step is to calculate a measure of welfare. The measure chosen for the HFS is the households’ consumption expenditure per capita.<sup>17</sup> The nominal household consumption aggregate consists of the sum of consumption expenditure per person on three primary components, i) total expenditures on food items, ii) total expenditures on non‐food items,
4 details). The fixed year effects account,for influences (such as world fuel prices) not fully captured by GDP and affecting all countries equally in a given year. The fixed country effects are time-invariant parameters representing the unique characteristics of countries (such as climatic factors) that affect emissions independently of GDP. F-tests report that a1 are jointly significantly different from zero, as are the d,. The parameter estimates imply a diminishing MPE relationship between per capita emissions and per capita GDP. The turning point and subsequent downturn in per capita emissions occurs at currently out-of-sample levels of per capita income (around $36,000), but these levels are reached by developed countries during the planning horizon. As will be seen later, the diminishing MPE has profound distributional implications for the future.

Future emissions trajectories by country are predicted by incorporating GDP forecasts and population projections into (1). For per capita GDP projections, also following Holtz-Eakin and Selden (1995), a univariate time-series was fitted on the historic data. The GDP model implies convergence in growth rates in the very long run.' Population projections are provided by the World Bank (1994).

# 2.2. _Global emissions_

Figure I shows the predicted path of global carbon emissions, population (left scale), and gross world product (right scale) to 2100 obtained by summing the country-level projections.8 Global emissions grow to more than double their 1992 levels by 2050, and to slightly less than four-fold by 2100, in line with the Intergovermnental Panel on Climate Change IS92a reference scenario projections (IPCC 1992). In 1993, emissions and population are roughly equal, with a worldwide emissions average of one metric ton per person per year. By 2100, global per capita emissions more than double. These global business-as-usual emissions exceed the cumulative limits for stabilization of CO2 concentrations at

> 7Convergence, the notion that economic growth rates and/or real GDP levels per capita will tend to equalize across countries, was bome of the Solow growth model
and rule of law, even after controlling for potential reverse causality (Abdih, Chami, Dagher, and Montiel 2012).

A related literature explores whether temporary migration contributes to the transfer of cultural and social norms to the home country. Migrants can be a vector of cultural transmission, and empirical findings using the World Values Survey data indicate that this transmission occurs from host to home countries rather than the other way around (Rapoport, Sardoschau, and Silve 2020). The few country-specific empirical studies are consistent with this finding. Return migrants in Egypt have been found to adjust their fertility choices to the norms that prevail in their previous country of destination (Bertoli and Marchetta 2015). Return migration can also contribute to transfer of gender norms back home. In Jordan, controlling for both emigration and return migration selections, women with a family member who returned from more conservative Arab countries are more likely to bear traditional gender norms than women in households with no migration experience (Tuccio and Wahba 2018). In Mali, girls who live in villages with a higher share of returnees from other African countries, where female circumcision is uncommon, are significantly less likely to suffer from genital mutilation (Diabate and Mespl ́e-Somps 2019).

# **8 Conclusions and areas for further research**

This paper offers a summary and discussion of the multiple and complex linkages between temporary migration and economic development in origin countries. Despite a rapidly expanding literature, significant knowledge gaps remain. First, much of the existing literature focuses on high-skilled temporary migration. Evidence on the impacts of low-skilled migration is much sparser, despite the fact that temporary emigration from many developing countries is largely low-skilled. For instance, while high-skilled migration increases educational investment in origin countries in various settings, it remains unclear whether low-skilled emigration reduces incentives to invest in education. Similarly, while high-skilled migrants have been shown to accumulate human capital while they are overseas which ultimately benefits home labor markets, there is still limited evidence on whether low-skilled migrants experience human capital gains. Second
variables. If DAG identifies a particular obstacle as having a direct effect on firm growth, that obstacle would also have a significant coefficient in all OLS regressions regardless of which subset of other obstacles are entered as control variables in the regression equation. Ayyagari, Demirguc-Kunt, and Maksimovic (2005) further illustrate the use of this methodology.<sup>5</sup> We also use regression analysis to do further robustness tests, such as testing for possible endogeneity bias via instrumental variable methods. We also perform other robustness tests, controlling for additional variables at the firm and country level, growth opportunities, influential observations and potential perception biases in survey responses using regression analysis.

The obstacles a firm faces depend on the institutions in each country, but are not likely to be the same for each firm in each country. Thus, our unit of analysis is the firm. As described below, the regressions have country-level fixed effects.

# **II. Data and Summary Statistics**

The main purpose of the WBES survey is to identify obstacles to firm performance and growth around the world. Thus, the survey contains a large number of questions on the nature and severity of different obstacles. Specifically, firms are asked to rate the extent to which Finance, Corruption, Infrastructure, Taxes and Regulations, Judicial Efficiency, Crime, Anti-Competitive Practices, Policy Instability/Uncertainty, and macro issues such as Inflation and Exchange Rate constitute obstacles to their growth.<sup>6</sup> In addition to the detail on the obstacles, one of the greatest values of this survey is its wide coverage of smaller firms. The survey is size-stratified, with 40 percent of 8
|P|||
|Tanzania|X|P|X||P|X|X|
|Uganda|X|P|P|P||X|X|
|Ethiopia|X|X|X|X|X||X|
|Sudan||||||||

_Note_ : P = preferential (cheaper tariffs and free incoming calls when using same group network—such as Zain in Kenya, Tanzania, and Uganda, and MTN in Rwanda and Uganda—and typically available for prepaid subscribers). X = available but with regulator roaming charges and typically limited to postpaid subscribers. _Source_ : Derived from Ampah 2009 and others.

The East African Regulatory Post and Telecommunications Organization (EARPTO),<sup>2</sup> an EAC regulatory body, appears to be less active than similar subregional organizations, but it has made some progress. Although the agency led the drafting of an EAC interconnection policy, agreements on border frequencies and common standards still tend to be bilateral, and there do not appear to be any model laws or regulations. Even for approvals of equipment types there does not seem to be any regional agreement.

> 2 http://www.cck.go.ke/earpto_issues/

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constructed using information from the Oxford Government Policy Response Tracker (see tables A.2 and A.3 for details).

The first group of dummy variables captures the variability in lockdown policies, defined as those policies which restrict work and business activities and internal mobility. As described in the background section, most countries moved from a full lockdown to a partial lockdown and then later lifted lockdown measures, with almost all work activities resuming regular schedules and free internal mobility. Some countries however, moved swiftly from a full lockdown to removing all restrictions at once. Therefore, we classify each country on a given day to be in one of the following four categories: full lockdown (which is the reference category), partial lockdown implemented after a full lockdown (captured by the dummy variable _FPc,t_ ), no restrictions in place following a partial lockdown ( _PNc,t_ ), and no restriction in place following a full lockdown ( _FNc,t_ ). Countries then progress from the reference category to either no restrictions at all or from the reference category to a partial lockdown and then later to full reopening with no restrictions in place. Importantly, we distinguish between a situation when no restrictions are in place after a partial lockdown which represents a more gradual reopening and a situation where restrictions are completely lifted directly after a full lockdown.

The second group of dummy variables focuses on school reopening policies. All countries in our sample, except Sweden, started their reopening process with schools closed (the reference category). Countries then either partially reopened schools, allowing in-person classes for some grades or groups of students (captured by the dummy variable _SPc,t_ ) or moved to almost normal, pre-COVID-19 mode of school operation ( _SNc,t_ ).

Lastly, the third group of dummy variables focuses on restrictions on public events. Countries started the reopening process with all public events canceled (reference category) and then either removed some restrictions, allowing certain public events to resume (a situation captured by dummy variable _PPc,t_
Reserve Bank of New York; Penn World Tables; World Bank.<br>Note: EMDEs = emerging market and developing economies.<br><!-- End of picture text --> A. Chart shows the New York Federal Reserve’s Global Supply Chain Pressure Index on a monthly basis since 1998. The index is normalized such that a zero indicates that the index is at its average value, with positive/negative values representing how many standard deviations the index is above/below average. B. The effect of supply bottlenecks is derived from an ordinary least squares regression of global trade on the manufacturing PMI for new export orders, the manufacturing PMI for supplier delivery times, and two lags. Dotted lines show counterfactual scenarios derived by assuming that the PMI for supply delivery times (a proxy for supply bottlenecks) in January 2020-November 2021 had remained at its average 2019 level. Estimations are performed over the period from 2000-19.

C. Blue line shows global trade in percent of global GDP. Red line shows unweighted average tariffs for all products. Orange line shows oil production in the Russian Federation in percent of global oil production. Shaded area indicates cold war period of 1950-1990.

D. Chart shows the share of foreign value added content of gross exports in advanced economies and EMDEs, as defined in the OECD’s TiVA database.
# **Finding 3.2:  Domestic Exposures**

- <mark>Public debt is still comparatively low in most countries, although Hungary (and possibly</mark> Albania) are important exceptions.

- The rapid growth in private credit poses the risk of non-performing loans in some countries.

- <mark>The sectoral balance sheet data show that households, corporates and banks have increased the size of their balance sheets but these remain relatively small compared to advanced economies.</mark>

# **4. Debt Overhang at the Firm, Household and Bank Level**

# **_4.1 Debt Overhang in the Enterprise Sector_**

In this section we examine the level and currency structure of bank credit among firms in Emerging Europe. Relating firm debt to aggregate contraction in GDP, export volume as well as to currency depreciation we assess in which countries firms are more likely to face debt overhang due to excessive leverage and unhedged foreign currency borrowing.

# <u>The Use of Bank Credit</u>

The main data source of our analysis is the _Business Environment and Enterprise Performance Survey_ ( _BEEPS_ ). The European Bank for Reconstruction and Development (EBRD) and the World Bank jointly conducted this survey in 1999, 2002, 2005, and 2009.<sup>6</sup> Our analysis is based on the 2009 wave of the survey, which was carried out at the onset of the financial crisis (October 2008 – April 2009). BEEPS 2009 provides data on 9,098 firms from the 21 ECA countries covered in this background paper. The sample provides a representative sample of firms for each of these countries.<sup>7</sup> By contrast, databases that offer financial statement information, such as Worldscope or Amadeus, typically cover only the larger, formal enterprises in the region.

We use three indicators of the use of bank credit by firms. Our first indicator _Bank loan_ is a dummy variable measuring whether the firm had a bank
war narrative (Crilley (2017)). The presence of perspectives from all sides of the conflict leads Gohdes (2020) to note that “the Syrian conflict is one of the first conflicts where lines between offline and on-line conflict engagement have become blurred” (Gohdes (2020)). Outside of the Syrian context, social media data is increasingly used alongside, or even in place of, public opinion surveys, often with highly similar results (Schober et al. (2016)).

In addition to Twitter and Facebook, the role of which have been more widely studied (Khamis et al. (2012), Metzger and Siegel (2019)), we extend analysis to include data from Telegram “an encrypted platform that is harder for governments to monitor” (Mitts (2019)). Much of the research on Telegram has focused on its use by ISIS (Prucha (2016), McDowell-Smith et al. (2017), Yayla and Speckhard (2017)), though Telegram’s privacy policies also support civilians and protest movements worldwide (Urman et al. (2020)). Though much work has been dedicated to the role of social media in constructing violence narratives, it also plays a vital role in migration movement (Frouws et al. (2016), Miconi (2020), Sánchez-Querubín and Rogers (2018)). Refugees share information about routes and conditions in potential host countries to ease the journey for future displaced peoples (Frouws et al. (2016)). In interviews conducted with 44 young refugee or immigrant Syrian social media users, Miconi (2020) finds that these platforms are used for not only staying connected to war developments but also facilitating resettlement in the host country. A similar Facebook 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
# **b. 50% price increase, medium-bound elasticity**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Chile Ukraine Moldova<br>South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br>c. 100% price increase, medium-bound elasticity<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Chile Ukraine Moldova<br>South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br>Income Gains (%)<br>Income Gains (%)<br><!-- End of picture text -->

# **c. 100% price increase, medium-bound elasticity**

_Source:_ Based on national household budget surveys; GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool. _Note:_ Deciles are calculated based on household per capita consumption, excluding identifiable rent and lumpy expenditures. Years of life lost are calculated are based on death events related to the risk of smoking tobacco.

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