rafmacalaba/data-use-mentions-extended huggingface dataset:
the countries in the sample using our calibrated model. The postlockdown _R_ 0 is similar across countries and is close to those reported for the European countries in Flaxman et al. (2020). The average _R_ 0 among the 85 countries one week after the reference lockdown is 0.57. In comparison, the same statistic among the 11 European countries in Flaxman et al. (2020) is 0.66. Given that the _R_ 0 estimates in Flaxman et al. (2020) is our calibration target, the consistency among 85 countries provides some reassurance, but not external validation. Our estimates are also broadly consistent with the _R_ 0’s reported in the economics literature, such as Atkeson et al. (2020) and Fern ́andez-Villaverde and Jones (2020). While our estimated _R_ 0 is not directly comparable to the _R_ 0 reported in the papers above - the main difference is that the _R_ 0’s in our paper are computed based on simulated data assuming a 7-week reference lockdown while the _R_ 0’s reported in these two papers are based on the actual data where the strength and length of the lockdown policies vary - the general consistency is reassuring. For example, the average _R_ 0 reported in Fern ́andez-Villaverde and Jones (2020) in May 2020, a period immediately after the first wave of lockdown policies in the world, is 0.86. Atkeson et al. (2020) shows that after the initial outbreak, the estimated _R_ 0 usually hovers around 1.0. Figure 1 in Atkeson et al. (2020) shows that _R_ 0 usually first drops below 1.0 at around 50 days after the initial outbreak, before increasing again to hover around 1.0. The dip in _R_ 0 is probably due to the lockdown policies implemented immediately after the outbreak.

> 18A corner solution in this step occurs for the four countries that we have dropped (Denmark, Sweden Switzerland, and Norway). If the country imposes a mild lockdown,
labor supply (Odozi and Oyelere, 2021). Our paper differs from these existing studies in that, although we use a similar research design with similar data, we take advantage of the highly-granular nature of the geo-location data to define exposure to conflict within a very narrow window (as close as 10 km.), where earlier studies using a similar estimation approach have relied on exposure to violence at the level of the local governance area (LGA). Our approach allows us to narrowly define a set of comparison individuals and to test for sensitivity to the definition of treatment. Additionally, we provide more specific results that take into account the seasonality of herder-related violence and characterize how individuals respond to exposure throughout the agricultural season. Lastly, our results complement these studies by focusing on an additional dimension, the informal sector, which has previously received little attention.

This study makes three core contributions. First, we add to the literature on coping mechanisms in low- and middle-income countries. In the absence of adequately functioning markets for savings, credit, and insurance (Burgess and Pande, 2005; Cole _et al._ , 2013; Dupas and Robinson, 2013; Karlan _et al._ , 2014), informal coping mechanisms in the form of, for example, intra-household transfers (Townsend, 1994), temporary migration (Bryan _et al._ , 2014; Morten, 2019), and selling assets such as livestock (Lange and Reimers, 2020) persist. Specifically, we add to a subset of this literature on coping mechanisms for households exposed violence and conflict (Verpoorten, 2009), and show that work in the informal sector can act as a safety net for individuals exposed to herder-related violence, specifically for those under a so-called “shadow of violence” that may heighten reactions to violence exposure.

Second, we add to the literature on the role of the informal sector in the process of economic development. This literature finds that the informal sector plays an important counter-cyclical role amid macroeconomic dynamics (Fiess
restructuring. 98 episodes were identified based on the new methodology shown in in Figure AIII.1 (versus 76 under the old methodology).

**Step 2:** Running a probit model for each of 4 debt burden indicators, all pertaining to PPG XD and including: PV of XD to GDP or exports; and debt service (interest plus maturing principal on PPG XD) to revenue or exports. The probit model, which estimates the probability of XDD for a particular country in a particular year, used the following control or explanatory variables:

1. Debt burden indicator

2. World Bank Country Policy and Institutional Assessment (CPIA) 3. Country growth rate 4. Reserves (scaled by imports) 5. Reserves squared 6. Remittances (scaled by nominal GDP) 7. World growth rate (proxy for external shocks)

**Step 3:** Classifying countries as weak, strong, or medium based not just on the CPIA (as earlier) but on a new, composite indicator CI based on variables 2 to 7 in the list in Step 2 and using the coefficient for each variable averaged over the four probit regressions (one for each debt burden indicator). The classification is based on evenly spaced percentiles for the CI.

**Step 4:** Obtaining a threshold for each debt burden indicator by first choosing a cutoff probability of debt distress. This cutoff probability is chosen to strike a desired balance between Type 1 (missed crises) and Type 2 (false alarms) errors. The estimated probit regression equation is then inverted to obtain the debt burden threshold corresponding to the cutoff probability as a function of the CI (as shown on p 26 of Annex III, IMF 2017) and to value these thresholds at the same percentiles for the CI which define the country classification so as to obtain a numerical debt threshold for each country classification.

13
Policy Research Working Paper 10241

# **Abstract**

This study seeks to support the efforts of Sudan in the targeting of the Sudan Emergency Safety Nets Project (SESNP) and any other future safety net programs in Sudan to those most in need. Sudan has faced numerous challenges for several years. Since its separation from South Sudan in 2011, the economy has experienced macroeconomic imbalances that have lasted for over a decade. In addition, the country has faced political instability, internal conflicts, and challenging climatic conditions. All of these challenges pose direct consequences to the poor. More recently, the onset of the COVID-19 pandemic and the Russia-Ukraine crisis have only exacerbated the economic and social situation in the country. In this context, the number of poor and vulnerable is expected to have increased considerably since the last reported official poverty rate, which accounted for 61.1% of the population in 2015. In light of these events, there is a growing consensus on the need of social safety net programs in the country. The proposed program, the SESNP, is expected to provide unconditional cash and food transfers to nearly 2 million Sudanese people (i.e., about 5% of the population). To support this program in targeting beneficiaries to improve the poverty impact of the program, we develop a Proxy Mean Tests (PMT) for Sudan based on the National Household Budget and Poverty Survey (NHBPS) 2014/2015. The results indicate that the use of a PMT could considerably improve the program in reaching those most in need, while reducing expenditure towards those with adequate resources. This could improve both the poverty impact, as well as the sustainability of the program.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/
Policy Research Working Paper 9519

# **Abstract**

In recent years, researchers have demonstrated that digital footprints from mobile phones can be exploited to generate data that are useful for transport planning, disaster response, and other development activities—thanks mainly to the high penetration rate of mobile phones even in low-income regions. Most recently, in the effort to mitigate the spread of COVID-19, these data can be used and explored to track mobility patterns and monitor the results of lockdown measures. However, as rightly noted by other scholars, most of the work has been limited to proofs of concept or academic work: it is hard to point to any real-world use cases. In contrast, this paper uses mobile data to obtain insight on urban mobility patterns, such as number of trips, average trip length, and relation between poverty, mobility, and areas of Freetown, the capital of Sierra Leone. These data were used in preparation of an urban mobility lending operation. Additionally, the paper describes good practices in the following areas: accessing mobile data from telecom operators, frameworks for generating origin and destination matrices, and validation of results.

This paper is a product of the Global Indicators Group, Development Economics and the Transport and Digital Development Global Practice.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at dmatekenya@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do
**3.1 Predicting population density**

We use Poisson regressions to model population density at the village level. This takes the following form: where _Pv_ is the population density (persons/km<sup>2</sup> ) of village _v_ . _Xv_ consists of the set of satellite imagery-based indicators defined for village _v_ . We include the natural logarithm of village area ( _Ln areav_ ), and an indicator for urban villages ( _ρS_ ).<sup>15</sup> _ηd_ represents binary indicator

> 12 While GUF was made with TerraSAR-X/TanDEM-X, which are radar data sets, GUF+ adds Sentinel and Landsat which are optical remotely sensed data which substantially improves the estimation.

> 13 Supplementary Figure S1 shows examples of developed area building classification, with the raw image (left) and CNN based building classification (right) in Panel A, and a sample car classification in Panel B. In a test sample, the average precision of the building classification was 71 percent, indicating a moderate share of false positives.

> 14 The contextual and object features are identified using both deep learning-based Convolutional Neural Networks (CNN) and classification of spectral and textural characteristics (Engstrom et al., 2017).

> 15 While the official sectoral classifications in Sri Lanka include three categories: the urban, rural, and estate sectors, we combine the second and the third to represent two categories: (1) Urban (2) Rural and estate.

7
Policy Research Working Paper 5634

# **Abstract**

This paper presents empirical analysis of corporate savings in Egypt using two datasets: a survey of small and medium enterprises and data from accounting statements for the largest publicly traded firms. There are two main findings. First, larger firms invest more (they have more physical saving) and have greater access to finance than smaller firms. Second, despite the financial deepening, the use of credit products has been declining during the past decade. The study reaffirms the importance of improving access to financial services in Egypt and points out the need for more research. In addition, policies aimed at reducing macroeconomic volatility are likely to result in increased investment and growth in Egypt.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at ilove@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
further robustness checks to select a preferred model and argue that none of these models is completely satisfactory.

# _Validation Checks_

To validate the results of the survey-to-survey imputation, we use the CES 2011/12 as training data to project poverty rates backward and compare them against the poverty rates observed in Health SCS 2014/15, and CES 2009/10 and CES 2004/05.<sup>15</sup> Table 3 compares the poverty estimates observed in CES 2009/10 and CES 2004/05 to predicted poverty rates based on the consumption model estimated on the CES 2011/12 as training data and CES 2009/10 and CES 2004/05 as target data. In both cases, predicted poverty based on our model is considerably lower than observed poverty. In 2009/10, our estimates hardly vary across models with national poverty rates between 17.23 and 17.65 percent, although the differences are somewhat larger for urban areas (13.04 to 15.65 percent). In all cases, predicted poverty rates are substantially lower than the poverty rates observed in the 2009/2010 survey (31.7 percent nationally) (see Table 3 middle panel). In 2004/05, the predicted national poverty rates range from 28.40 percent to 30.38 percent, which are up to 10 percentage points lower than the observed poverty rate (38.9 percent). The difference is wider in rural areas, whereas some of our estimates for urban areas overlap with the 95 percent confidence interval of the observed poverty rates (see Table 3 top panel).

In 2014/15, we compare the poverty rates that we predict using our four models against the poverty rates predicted by Newhouse and Vyas (2019) (see Table 3 bottom panel). Since the CES 2014/15 does not include actual consumption data, we rely on the estimates by Newhouse and Vyas (2019). In our prediction, we use the Health SCS 2014/15 applied to a
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we should see higher teachers' wages in such villages. Conversely, if the GSS eect works through the supply channel, we should observe lower wages. Therefore, one should test for dierences in skilled women's wages in villages with and without a GSS.<sup>15</sup> However, the challenge in doing so is a data issue: The only available village-level data that captures skilled women's wages is the private school census, which records average teacher wages in all private schools.<sup>16</sup> Since we do not observe wages in villages without private schools, a simple correlation of wages and GSS may be biased, with the bias depending both on how GSS were placed and on the truncation of the wage distribution due to missing wages in villages without private schools. We follow two approaches to address the selection problem. We use a Heckman selection model, where the selection stage is the probability of observing a positive wage, which corresponds to having a private school in the village. Another alternative is to use the control-function approach, where we condition on the predicted probability of observing a non-missing value of the wage-bill in the wage equation (Angrist 1995). Details of both approaches are in Appendix I.

We should caution that we cannot structurally estimate the size of the supply-side eects. For instance, simultaneous changes in the demand for schooling due to GSS construction imply that the supply-side impact of GSS construction on (decreasing) the wage-bill represents a lower bound. Therefore, our strategy indicates the presence of a supply-side impact but has less to say about its size.

> 15If there is a preference to teach in private schools, increased demand could drop wages as teachers may be willing to accept lower wages in new private schools. However, instead there is a strong preference for public schools (better pay and easier job). In addition, the labor market for public and private schools is quite dierent, with
phones.<sup>3</sup> The data presents changes in visits to these different places relative to some baseline value for a given geographic location, and it is available at the subnational level in some countries. These data are only obtained from mobile phone users with location services turned on but nonetheless can reflect useful signals in the data. We extracted Google mobility and search trends at the subnational level. The raw GPS data on mobility were first grouped by categories of places with similar characteristics, namely: grocery and pharmacy, parks, transit stations, retail and recreation, residential areas, and workplaces.<sup>4</sup> The resulting data shows how visits to categorized places change relative to the baseline period, which is the region-specific median value picked from the 5-week period between January 3 and February 6, 2020, just before the onset of the global pandemic. The Google mobility information is available only from February 2020, which naturally limits its usage—when tracking changes in economic activity—to the COVID-19 period.

Google search data—that is also available at the subnational level in some countries—provide information regarding the search intensity of a given combination of keywords for the target location and time period. Each data point can be interpreted as a share of a given request in the total searches in a given location and time period. However, as Abay et al. (2020) point out, the raw Google search series are not comparable across terms and geographic areas, which in our case are subnational regions. The primary reason is the following: when requesting Google search data for a given search term, it is possible to obtain the information only for a limited number of geographic locations per request. Thus, to obtain the information for the desired number of geographic locations, one has to file multiple requests in succession. The resulting data points are comparable across terms and geographic locations only within one request.

To achieve the desired comparability of Google search data points across different requests –
We obtained household listings by GN from the Department of Census and Statistics (DCS). The DCS maintains the Pre‐Listing forms (also called F1 forms) for the 2001 Census of Population and Housing. These selected households constituted our survey sample and were contacted by our enumerators by using their names and addresses. We devised a geographical replacement rule for situations in which it was impossible to interview the selected household after repeat attempts. In addition to this rule, each enumerator was given a guideline document providing additional suggestions for using judgment to maintain the random sampling process.

Each survey was conducted as an in‐person interview that lasted approximately 50 minutes. At the end of each field day, field coordinators checked the returned questionnaires for completeness and accuracy according to a quality checklist. During the field work, field directors periodically monitored interviews for quality purposes, ensured targeting of the appropriate population, discussed complications regarding the survey instrument with enumerators, recorded enumerator opinions regarding the quality of the interviews, maintained a list of sample household addresses for follow‐up surveys and maintained a log of sample returned surveys. The survey process was completed in approximately 1,800 households over the course of 2 months.

Data recorded on the paper questionnaires were transferred into a statistical data set by an MS Excel program, which employed range, intra‐record, and inter‐record checks to ensure quality control. The identification of anomalies and outliers through the computation of descriptive statistics provided an additional quality control check.

# **_Sample Description_**

Table 1 shows that the average family size is 4.8 members, with 1.3 children under 18 years of age. The typical (i.e., median) household head has 10 years of education. Over 90 percent of 13
https://doi.org/10.1021/acs.est.7b01014

- Bose, R. (2009). The impact of Water Supply and Sanitation interventions on child health: evidence from DHS surveys. _International Initiative for Impact Evaluation_ . Retrieved from

- http://math.agrocampus-ouest.fr/infoglueDeliverLive/digitalAssets/20054_Bose.pdf

23
The 2018/19 NLSS was conducted between September 2018 and October 2019 and was designed to provide estimates for a wide range of socioeconomic indicators – including consumption and poverty – for Nigeria’s 36 states and the Federal Capital Territory (FCT), Abuja. The sample of around 22,000 households is representative at the national, zone, and state levels, aside from Borno state (which accounts for around 2.5 percent of the Nigerian population).<sup>9</sup> Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. The household questionnaire provides information on demographics, education, employment, food and non-food consumption, food security, shocks, safety nets, housing conditions, assets, information and communication technology, agriculture and land tenure, and other sources of household income. Overall estimates of household consumption were constructed using the modules on food and non-food consumption and spatially and temporally deflated using a price index constructed from unit prices in the food consumption module: this provides a consistent measure of welfare for the whole of Nigeria. From this, poverty estimates at the international poverty line of US$1.90 2011 PPP per person per day can be constructed by deflating over time using CPI data and converting to dollars using Purchasing Power Parities (PPP) (Atamanov et al. 2018, Lakner et al. 2018).

The backcasting exercise uses two sources of national accounts data. First, we use yearly data on sectoral (agriculture, industry, and services) GDP growth rates (available from the MFMod tool of the World Bank, see Burns et al. 2019) to backcast the 2018/19 consumption vector to 2009. Real GDP _levels_ are expressed in constant local currency units – meaning that they have already been deflated by the GDP deflator – and are shown in Table _3_ . Second, we use GDP and GNI data from the WDIs to estimate different pass-through rates between