rafmacalaba/data-use-mentions-extended huggingface dataset:
transformation towards modern sectors such as manufacturing and services, the country continues to depend largely on the agricultural sector for national production and above all employment. Agriculture accounts for about a quarter of the GDP and provides work for over 70% of the labor force. Around 8 in 10 rural households are smallholders primarily engaged in subsistence agriculture.

Mozambique is also a risk-prone country. It ranks third among African countries exposed to risks from multiple weather-related hazards such as flooding, epidemics, cyclones and droughts (IFRC 2014). Nearly a quarter of the population lives in areas with high probability of experiencing a natural disaster (World Bank 2018a). Intense droughts are experienced in 7 out of 10 years in the Southern Region, and in 4 out of 10 years in the Central Region. The Mozambican coastline borders one of the most active basins of tropical cyclones. Floods generally occur every two or three years, mostly during the rainy season and along the nine major international river systems that cross the country or across the low-lying, densely-populated coastal areas (World Bank, 2012). Climate change is expected to intensify these trends. The average likelihood that a tropical storm will impact an enumeration area included in the Mozambican Integrated Agricultural Survey during the country’s nine-month storm season increased by a factor of four between 1968-1990 and 1991–2015 (World Bank 2018a).

We employ a triple-difference strategy that exploits variation in the shocks across space, time and cropping cycles using detailed information from household surveys, remote sensing data on weather outcomes and the geographic distribution of the main cropping cycles across the country. The shocks analyzed include 1) the floods that occurred between late 2006 and early 2007 along the Zambezi River Basin, 2) a category 3 cyclone (Jokwe) that struck northeastern Mozambique between March 8 and 18, 2008, and 3) a drought that affected parts of central and southern Mozambique between May 2005 to January 2006.

The findings demonstrate high levels of vulnerability. Irrespective of the type of weather anomaly, food security is systematically undermined
1–12.

- Haushofer, J. and Shapiro, J. (2016). The Short-term Impact of Unconditional Cash Transfers to the Poor: Experimental Evidence from Kenya. _The Quarterly Journal of Economics_ , 131:1973–2042.

- ILO (2018). _Global Wage Report 2018/19: What lies behind gender pay gaps_ . Geneva: ILO.

- ILO (2021). ILOSTAT database. _retrieved from https://data.worldbank.org/ on 1st December 2021_ .

- Imbens, G. W. and Rubin, D. B. (2015). _Causal inference in statistics, social, and biomedical sciences_ . Cambridge University Press.

- Imbert, C. and Papp, J. (2019). Short-term migration, rural public works and urban labor markets: Evidence from India. _Journal of the European Economic Association_ . Forthcoming.

25
<mark>groundwater are determined by the relative difference between groundwater level and river stage (Mukherjee et al., 2018). In regions that are exposed to over‐extraction of groundwater, the resulting lower discharge can reduce groundwater’s contribution to the baseflow and can substantially decrease the streamflow of rivers (Mukherjee, 2018). This in turn can enhance salt concentrations in rivers because of lower dilution capacity. Excessive irrigation can also raise water tables from saline aquifers and cause seepage of saline groundwater into freshwater (Mateo‐Sagasta et al., 2010)</mark> .

## 2.4 Weather Data

Our weather data come from Matsuura and Willmott (2001). This gridded data set contains monthly observations of precipitation and average temperature at the 0.5 degree gridcell level. We transform these data into average monthly temperature, and total precipitation (mm), per year, for each gridcell.

# 3. Empirical Strategy

To determine the impact of saline water on crop productivity, we estimate a crop production function. Specifically, we estimate the following equation: Δlog�NPP����α�λ∗g�WQ����δ∗f�climate����σ� �ρ� � θ � ∗Y�ε�� (1) where NPP�� is net primary productivity in gridcell i of year t; g�WQ��� is a measure of water quality; f�climate��� are measures of temperature and rainfall; σ� are gridcell fixed effects, which account for time invariant factors which impact agricultural productivity; ρ� are year fixed effects; and θ � ∗Y are region specific time trends, to account for local changes in economic or political structures which may impact agricultural production.

As discussed in the prior section, several restrictions are placed on the data set to ensure accuracy. First, only gridcells with at least 30 percent cropland according to the ESA data set are included. In robustness checks
that simply determine the extent of transport cost savings for a road by the likely road users. While this way of estimating the project’s rate of return (the so-called internal rate of return) is a standard practice, roads are important public investments that have both short- and long-term effects that go beyond transport cost savings. Transport cost savings reflect the short-run and immediate impact of road investment; roads, however, also have short- and long-term effects on employment, income, and productivity as well as investment in the human capital of children. Furthermore, these household effects can be both direct and indirect results of road investment, and often cannot be captured through the traditional transport surveys. Household surveys are necessary to capture the full treatment effects of road development, and in particular, panel data for project and comparable

> 30 These estimates are likely to be an upper estimate, because of the assumption that each household on average gained the same way.  To the extent that gains accrued to households vary by household income distribution, the predicted impact of poverty could be lower than the reported estimates.

17
<!-- Start of picture text --> 100 100<br>90 90<br>80 80<br>70 Urban 70 IDPs in settlements<br>60 60<br>Nomads<br>50 50<br>Rural<br>40 40<br>30 30<br>Poverty line (US$ 1.9 PPP)<br>20 Poverty line (US$ 1.9 PPP) 20<br>10 10<br>0 0<br>0 2 4 6 8 0 2 4 6 8<br>Daily consumption expenditure per capita (US$) Daily consumption expenditure per capita (US$)<br>Source: Authors' calculations. Source: Authors' calculations.<br>% of population % of population<br><!-- End of picture text -->

# 7. Conclusions

The lack of data in Somalia poses a risk to evidence‐based interventions aimed at alleviating poverty and inequality. To mitigate this risk, the World Bank implemented Wave 2 of the Somali High Frequency Survey to better understand the welfare conditions of the population and to estimate the incidence of poverty. An analysis of the data set has been published as the Somali Poverty and Vulnerability Assessment (World Bank, 2018b).

This paper contributes to several themes in the literature on poverty measurement and data collection in the context of conflict and fragility, involving hard‐to‐survey populations. It outlines how challenges associated to the context of insecurity and lack of statistical infrastructure in Somalia were overcome through four methodological and technological adaptations: i) building a probability‐based population sampling frame; ii) minimizing the time spent in the field using the Rapid Consumption Methodology; iii) estimating poverty in completely inaccessible areas with correlates derived from satellite imagery and other geo‐spatial data; and iv) employing a special sampling strategy for the nomadic population.

Further improvements in terms of human resource capacity should be considered to minimize disruptions to
# **_Table A2.2 Data sources and availability of_** **_<u>project level staffing variables</u>_**

||**Full Data Set**|**Analytical Data Set**|
|---|---|---|
||(N = 4,348 Projects)|(N = 2,845 Projects)|
|**Panel A - Data Sources**|Frequency<br>Percent|Frequency<br>Percent|
|**For Preparation Task Team Leader (At Approval)**|||
|Data Scraping from PADs|1,740<br>52|1,696<br>60|
|Manually Collected|929<br>28|538<br>19|
|TTL as Listed on 1st ISR|610<br>18|541<br>19|
|Staff time charge code data|76<br>2|65<br>2|
|**For Mid-Point Supervision Task Team Leader (Mid-**<br>**point ISR)**|||
|ISR Reports (stored on internal database)|3,649<br>100|2,833<br>100|
|**For Closing Supervision Task Team Leader (Last ISR)**<br>ISR Reports (stored on internal database)|3,819<br>100|2,838<br>100|
|**For Practice Manager (At Approval)**|||
|Manual Collection cross referenced with HR Reports|3,123<br>99|2,842<br>99|
|Data Scrapingfrom PADs|31<br>1|17<br>1|
|**For Country Director (At Approval)**|||
|"Ask an Archivist" Service|4,342<br>100|2
# _Refugees from Sudan_

The number of Sudanese refugees in Ethiopia was around 42,000 in 2020, residing in five camps in the ethnically mixed region of Benishangul-Gumuz (UNHCR, 2020b). There are similar shares of adult women and men, while children are 60 percent of the total.

Sudanese refugees arrived in successive waves since the 1980s, with the first group escaping from the Blue Nile areas of Sudan and arriving in 1987-89 (Vemuru et al., 2020). Most of the current refugees arrived in 2011. This includes double refugees, individuals who arrived as refugees in the late 1980s and returned to Sudan in the 2000s only to return as refugees to Ethiopia in 2011 (Vemuru et al., 2020).

Sudanese refugees, like South Sudanese, have limited livelihood opportunities, and primarily engage in activities like artisanal gold mining, wood cutting, and daily farm labor (Vemuru et al., 2020). Access to the local market helped refugees to generate income by running businesses including small shops, restaurants, and cafes. Cross-border trade is also important in the region, which benefits both refugees and locals by providing access to more dynamic markets in Sudan.

# 3 Data

The Skills Profile Survey 2017 (SPS 2017) is an excellent source of data to examine and compare refugee and host community profiles and economic activities – although the data predates the most recent reforms which liberalized economic opportunities for refugees and strengthened their economic and social rights on various fronts, as outlined above. While the situation of refugees may well have improved since the survey, the results are still important and instructive, especially in casting light on constraints associated with camp-based locations in poor developing country settings.

SPS 2017 is a household survey carried out by the World Bank that covered refugees in camps and members of surrounding host communities. The sample consisted of four strata based on the main refugee hosting regions: Tigray-Afar (Eritrean), Gambella (South Sudanese), Benishangul-Gumuz (Sudanese and South Sudanese), and Somali (Somali). Using
in column 2 but following Conley (1999) to account for spatial correlation of the error term with a 50-km buffer area. The results are very similar. Given the scope for spatial correlation in internet access, we henceforth adopt Conley (1999) standard error correction procedures.

This association between internet access and jihadist recruitment is robust to including addi-

> 6Note that the terrorist acts documented therein were committed between 2004 and 2018; the timespan the data cover is thus different from that covered by our data on Daesh recruitment

10
and inform next steps.

Of particular note is the fact that `outdetect` allows for the use of complex survey settings, so that the aforementioned comparisons can be performed using population statistics.

In the last part of the paper we use `outdetect` to investigate the influence of extreme values on key inequality and poverty statistics, computed on the basis of household budget survey data. We use a collection of data sets from the Rural Livelihoods Information System (RuLIS), a joint initiative of the Food and Agriculture Organization (FAO), the World Bank, and the International Fund for Agricultural Development (IFAD). We find that the share of observations flagged as outliers of per capita consumption is 0.8% on average, never exceeding 2.5%; that the presence or inclusion or exclusion of these observations from calculations causes differences of as many as 6 percentage points for the

> 3 Commands `bacon` (Billor, Hadi and Velleman, 2000; Weber, 2010), and `gboxplot` (Bruffaerts, Verardi and Vermandele, 2014; Verardi and Vermandele, 2018) are also available to detect outliers with Stata, though their functionalities and range of application are adjacent, rather than overlapping, with `outdetect` .

3
2 "downsizing" of the food subsidy program. At present, the Egyptian subsidy system only includes four foods - coarse baladi bread,<sup>2</sup> coarse baladi wheat flour, edible oil and sugar - and accounts for less than 6 percent of total government expenditures. The coarse baladi bread is sold to consumers without quantity restrictions by private sector bakeries, the coarse wheat flour is sold by government warehouses, and edible oil and sugar are sold at fixed monthly quotas by private groceries.

In theory, subsidizing cheap, "calorie-dense" foods like coarse bread and coarse wheat flour should help protect the income and nutritional status of the poor in Egypt. In practice, however, since the Egyptian food subsidy system is a general, untargeted one that is virtually open to all Egyptians, the benefits of this system are in much dispute. For instance, while critics charge that the system is plagued by large amounts of leakage to non-needy groups, government policymakers are often reluctant to reform the systern for fear of political unrest.

This paper extends an earlier study by the author in order to shed new light on the political and distributional impact of Egyptian food subsidies.<sup>3</sup> It is divided as follows. To provide a broader context, Section 2 presents an overview of the role of food subsidies in developing countries. Section 3 then reviews the history and operation of the food subsidy system in Egypt, paying particular attention to the 1977 food riots. Section 4 presents the details from a new, 1997 household budget survey in Egypt. This sunrey, which collected heretofore unavailable expenditure data on each type of subsidized food in Egypt, makes it possible to pinpoint the current distributional impact of food subsidies. Sections 5 and 6 use these new survey data to analyze the impact of each subsidized food
# 1. Introduction

Labor force surveys are a useful data source to understand employment dynamics in both developing and developed countries. These surveys provide vast information on the labor market status at higher frequency levels than living conditions surveys. In some cases, they are the only source of information to describe and examine the structure of the labor markets. In the Latin American and the Caribbean region, countries like Bolivia, Costa Rica, Ecuador, Jamaica, Mexico, Peru, and Uruguay collect their labor force surveys quarterly as opposed to a yearly basis, which is the case of most household and living standard surveys.

One of the key features of these labor surveys is that they provide information on the wages and salaries of workers. This allows to estimate job market trends and obtain inequality measures of labor income among workers. However, the full income distribution in many countries cannot be retrieved because labor income is reported in brackets. Because of this, the estimation of inequality or poverty measures, as well as regression type analysis, is difficult. This is the case of the labor force survey for all countries in the Organization of Eastern Caribbean States (OECS).

This is not unique to the Caribbean region. Countries like Colombia, Germany, Australia, New Zealand, Bosnia and Herzegovina, North Macedonia and Serbia, among others, have similar data collection protocols for their micro census (Walter and Weimer 2018). In the U.S, the current population survey (CPS) collects detailed family income only once a year, in the March supplement, but collects family income in brackets on a monthly basis.

One argument in favor of using interval-censored questions to collect information on income is the higher response rate compared to questions asking to report exact amounts (Wang et al., 2013). This happens because income information is considered “sensitive”, and people are reluctant to report actual earnings, and may choose not to respond to those questions at all (Moore et al., 2000; Hagenaars and Vos, 1988
results shown in table 2, we find that an increase in the risk of polygamy leads to a higher probability for women to save, out of home only. However, this impact is significant only for women living in the poorest households. These results thus give credence to the precautionary motive for savings. Moreover, they allow us to be confident that our results are not driven by our risk variable capturing wealth, as could be argued based on our identification assumption relying on the exclusion of the polygamy status of the husband’s father from our second step equation (see discussion of identification issues in the Identification Assumptions and Exclusion Restrictions section).

## _Consumption, Labor, and Transfers_

Going one step further, we are interested in understanding the trade-offs behind saving decisions in response to a higher risk of polygamy and in particular the way consumption is affected.

Household Food Consumption and Cell Nonfood Consumption Levels As noted above, a unique feature of the PSF data is that information on consumption is available at the cell level, defined as household consumption subunits.<sup>22</sup> Note that the definition of cells has no impact on the measurement of savings, nor on labor or income, since only information on consumption is collected at the cell level.

We thus additionally analyze the impact of the risk of polygamy on the level of household food consumption per capita for all monogamous unions and cell nonfood expenditures, as well as the contributions of both husband and wife to cell expenditures.

# [Table 4 about here]

Table 4 presents the effect of the predicted risk of polygamy on the level of household per capita food expenditures (column (1)), on the level of per capita nonfood expenditures of the wife’s cell (column (2)), on the level (column (3)) and share (column (4)) of the wife’s contribution to the per capita nonfood expenditures of her own cell, and on the level (column (5)) and
frequency phone surveys were typically carried out by National Statistics Offices and are more plausibly representative than convenience web surveys.

Overall, the estimates of COVID-19 vaccine hesitancy from the HFPSs were more conservative than other published estimates (Figures 9 and 10). There are various possible explanations for the differences. The first relates to the timing of data collection. Most of the existing literature is based on surveys carried out in mid to late 2020 (e.g., de Figueiredo and Larson 2021, Gallup 2021, Lazarus et al. 2021, Solis Arce et al. 2021, and Wouters et. A. 2021), whereas the HFPS data included in our sample were collected from end2020 through August 2021. It is plausible to believe COVID-19 vaccine hesitancy generally declined during this period as multiple vaccines became available and widespread vaccination was safely rolled out in high-income countries.

Another possible reason for the differences is the variation in how the vaccine hesitancy question was framed and the response options available. For example, the HFPS question in countries without general access to the vaccine at the time of data collection was <mark>“When a vaccine to protect you from COVID-19 is available to you, are you planning to be vaccinated?” whereas other studies included the phase “at no cost to you” or descriptors about the quality of the vaccine such as “COVID-19 vaccine proven safe and effective”) (Gallup 2021, Lazarus et al. 2021). There was even more variation across the studies in the answer options available to the respondents. One study relied on a 5-point Likert scale of agreement with the statement “I</mark> f a COVID-19 vaccine is proven safe and effective and is available, I will take it”: <mark>‘completely disagree’, ‘somewhat disagree’, ‘neutral/no opinion’, ‘somewhat agree’ and ‘completely agree” (Lazarus 2021). Others relied on a 4-point Likert scale to the question such as “y</mark> es,
of a functional system. This is particularly true for transportation networks. A government, for example, may consider impleme <mark>nting a new road section designated as an emergency transport route instead of a similar project that serves no emergency relief function. Given the nature of road infrastructure assets as network components, the construction of a new road may also enhance resilience by increasing redundancies in the network that allow access to alternative routes and absorb increased traffic in the case of emergency.</mark> The methodology proposed in this paper is intended to estimate project-level resilience and not system-level resilience, though project functionality is inherently dependent on and related to the asset's relationship to other parts of a system. Further, applied to the transport sector, the goal is to use information that is likely to be available in project feasibility studies for proposed road infrastructure or public data sets to estimate the effect of a disruption on various aspects of functionality. Keeping in line with the nature of the Infrastructure Prioritization Framework (IPF), the analysis at this level is also designed to draw on less complex and less analytically-demanding indicators to make the approach more feasible to apply in information- and capacity-constrained environments.

# 1.4. Dimensions of Functionality

Infrastructure services attend to a wide array of interconnected purposes: they facilitate trade, allow mobility of people and goods, provide access to services, and support a host of other human activities. The relationships between assets in different infrastructure sectors are complex, and due to sectoral variations with respect to physical structures, locations, and degrees of interconnectivity, natural disasters affect infrastructure assets in different ways. The functionality (and potential loss of functionality) of an infrastructure system is difficult to measure for all sectors within a given region. As such, it is presently more meaningful to make judgments of relative resilience within a sector and for similar types of projects. This paper, therefore, focuses specifically on applying concepts of resilience to road infrastructure projects. While measurable aspects of functionality may be directly observable in