rafmacalaba/data-use-mentions-extended huggingface dataset:
radiative forcing significantly within a decade (Shindell et al. 2012). To assist the stakeholder community, we have worked with the World Bank’s Development Data Hub to publish an online database that makes georeferenced CH4 atmospheric concentration data more accessible to non-specialists.

# **2.2 Satellite Measurement Components**

We have constructed the atmospheric CH4 concentration database from observations by the ESA’s Sentinel-5P platform that typically number over 200,000 per day. We locate each observation in the 25-km global grid developed for CO2 analysis by Dasgupta, Lall and Wheeler (2022a,b). The dominant component in each observation is the global stock of atmospheric CH4 molecules that have accumulated during the past decade. The second component is seasonal, determined by natural processes over the annual cycle. The third component is geographic: Emitted CH4 molecules tend to remain within their latitude bands of origin for significant periods before full global mixing.

The fourth observation component is determined by local anthropogenic CH4 emissions, which remain near their point of origin long enough to be distinguished from the three “background” components. We term this the “concentration anomaly” since it measures the local deviation from the global background CH4 concentration. As previously noted, its primary sources are emissions from agriculture, fossil fuel production and use, and waste disposal.

# **2.3 Data Collection and Processing**

The ESA’s Sentinel-5P (S5P) operates in a Sun-synchronous near-polar orbit with an equatorial crossing at 13:30 local solar time. It completes 14 orbits of the Earth per day, with a site revisit time of one day and a current spatial resolution of 5.5 x 3.5 km. We use the S5P L2 Offline georeferenced measures of XCH4 (the column average dry air mixing ratio of methane<sup>4</sup> ), corrected for bias associated with XCH4 dependence on surface albedo.<sup>5</sup> Recent global research by Sha et al. (2021) has shown that
are not always conducted randomly** . Analyzing tax audit data can provide detailed information on noncompliance data. This information can be enriched by linking the audit data to population census data. However, this method is not always feasible, given the restrictions on access to audit data.

**Another standard method in the literature to estimate tax evasion is to use alternative sources such as income surveys, household budget surveys with consumption data, or discrepancies in economic statistics** . For instance, it is possible to compare the incomes reported in administrative tax records

5
– **Figure 1** Four types of slumps <!-- Start of picture text --> (a) finished & unchanged trend (b) finished & decelerated trend<br><!-- End of picture text --> <!-- Start of picture text --> Nicaragua (NIC) Switzerland (CHE)<br>1950 1955 1960 1965 1970 1975 1940 1960 1980 2000 2020<br>2<br>4<br>1.8<br>1.6<br>3.5<br>1.4<br>1.2 3<br><!-- End of picture text -->

- **(c)** finished & accelerated trend **(d)** unfinished & negative trend

<!-- Start of picture text --> Philippines (PHL) Madagascar (MDG)<br>1940 1960 1980 2000 2020 1960 1980 2000 2020<br>2 1<br>.8<br>1.5<br>.6<br>1<br>.4<br>.5 .2<br><!-- End of picture text --> _Notes:_ Models refitted using the estimated breaks<sup>�</sup> _tb_ 1 and<sup>�</sup> _tb_ 2 but without the optimal AR( _p_ ) terms to emphasize the trend breaks. The bold vertical lines are at<sup>�</sup> _tb_ 1 and<sup>�</sup> _tb_ 2, respectively. The dashed vertical line indicates<sup>�</sup> _tmin_ . _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

The slump begins in 1958 and the trough is found in 1959. This is a relatively short-lived crisis before the ousting of the Somoza dictatorship ushers in a
households from the DHS into homogeneous cohorts: households whose heads have the same date of birth (we define five-year cohorts), the same level of education (no education, primary and secondary and more) and the same place of residence (ten departments and urban/rural distinction) are regrouped into cells. After regrouping some low-sized cells, 261 cells were constituted for each year of the DHS dataset, with an average size of around 150 households and 950 individuals in each cell.

# **_Aggregate Estimates_**

Our estimates of the vulnerability index follow the different steps recalled previously. First, log per capita asset index is regressed on various household’s characteristics such as log of household size, age of the head and its square, education and gender of the head, location and the presence of children under 5 years old. Residuals are

> 25 As exposed by Verbeek and Nijman (1992), the bias in the standard within estimator based on pseudo panel data is decreasing with the number of individuals in each cell, more than with the number of cells. However, Verbeek (2008) notes that there is no general rule to judge whether cell size is large enough. Deaton (1985) also suggests that measurement error decreases as a function of the size of the cells.

> 26 As in Deaton and Paxson (1994), we can normalize so that the fitted age effect at, for instance, age 35-40 equals the average residual variance of the logarithm of the asset index for 35-40 year-olds over all cohorts.

16
What explains these observed differences in early outcomes between girls and boys? A number of recent studies focus on the role of schooling and family environments in explaining observed gender gaps in cognitive and non-cognitive skills (Autor et al. 2016).<sup>7</sup> In the case of gender gaps observed in the first few years of primary school, early schooling experiences are likely to play a role. Research on the effect of preschool duration suggests that children with longer exposure to preschool demonstrate advantages over children with shorter exposure (Arteaga et al. 2014, Domitrovich et al. 2013, Loeb et al. 2007). A review of 30 preschool interventions in 23 countries shows that early childhood education programs lasting one to three years had an average effect size of 0.312 SD, compared to only 0.196 SD for programs lasting less than one year (Nores and Barnett 2010). In addition to duration, the literature points to the importance of quality of early childhood education programs in sustaining impacts on children’s cognitive and non-cognitive skills (Engle et al. 2011). Thus, if girls and boys are exposed to different quantity and quality of preschools, we would expect to see these early schooling factors explain part of the gender gaps observed in the early years of primary school.

In addition to early schooling factors, children’s family environment is likely to play a role in the emerging gender gap in cognitive and non-cognitive skills. Boys may react differently than girls to the family environment, and/or parents may adjust their parenting practices depending on the gender of the child. Using panel data from the United States, Owens (2013) shows that the higher levels of early childhood behavior problems exhibited by boys—which account for up to 25 percent of the gender gap in educational attainment at age 22—are largely explained by gender differences in parent-child interactions. Specifically, boys respond more negatively than girls when they are exposed to parental conflict and parental harshness. Baker and Milligan (
of variables at the steady state. The respective restrictions are reported in the last column of Tables B2 and B3.

- The consumption utility scaling parameter is set to ensure that GDP is equal to one at the steady state. This is done for convenience only as most other steady-state values then have a clear interpretation as ratios to GDP.

- The share parameter in RoW wholesale production function is restricted to give real exchange rate equal to 1 at the steady state.

- The ratio of high-skilled and low-skilled labor hours is roughly 1, taken from main labor force indicators by education level data of TurkStat. The corresponding consumption ratio is around 2, calculated using distribution of consumption over income quantiles data of TurkStat under the assumption that low-skilled households constitute the lowest 50% of the income distribution. Hence, H-households consume twice as much as L-households while they provide the same number of labor hours. This is possible because the formers’ real wage is higher, and they receive capital income.

- The share of private non-energy, renewable energy and fossil energy investment to GDP ratios are around 23.3%, 0.3% and 0.4%, respectively. Total investment share of GDP comes from GLORIA database. We use World Input Output Database (WIOD) to split investment to core and energy.

- The annualized public net debt-GDP, corporate net debt-GDP, and RoW net debtGDP ratios are around 33%, 57%, and -40%, respectively, from the Financial Accounts Reports of the Central Bank of the Republic of T ̈urkiye (CBRT).

- The ratio of renewable and fossil energy is around 0.2, averaged over 2019-2021 from the energy balance of IEA<sup>27</sup> . We assume the ratio of final use (residential, public, and half of transportation) and intermediate use (industry, and half of transportation) of the TFC is 0.94, average value over 2000-2022. Finally, about 87% of the Turkish TPES is imported,
two bedrooms. Tracking study participants was not any easier given that dwellings in these areas typically have no official addresses.

We start by documenting that pre-treatment variables are balanced for the sample of children analyzed in this paper. The first column of Table 1 shows means for “control children”, i.e., the children with higher lottery numbers who were put on the waiting list. The second column shows differences in means between the control and the “treatment children” (i.e., the children with the

> 8 We only use information from the original waiting list, before applicants had the opportunity to accept or reject their placements (as opposed to using lottery status after some original winners already refused the offered slot, which was then offered to the first available person in the waiting list), thereby avoiding concerns about randomized list designs raised in Chaisemartin and Behagel (2020).

> 9 Because of budget constraints, the 2012 survey targeted only 60% of the original sample.

> 10 The original sample drawn based on the information from the applications consisted of 4,350 children, which would imply an attrition rate of 51.8%. The first survey, which was conducted in 2008, managed to survey just 3,776 of these children. That is, almost 15% of the original sample was lost in 6 months (the time between when applications were submitted and when the 2008 survey was conducted). Such attrition occurred mostly because the application data did not have accurate contact information on the applicant-families. Relative to the 2008 survey, the attrition rate was of 44%.

7
underwent Know-Your-Customer checks to obtain accounts at centralized intermediaries such as cryptoexchanges.

Several studies have taken different approaches to measure crypto-assets activity. To estimate the number of bitcoin and ether owners, Wang (2020, 2021) utilizes on-chain data to count the total number of on-chain deposit addresses<sup>10</sup> which are required to deposit funds into crypto exchanges. One of the heuristics used by Athey, et al (2016) to analyze Bitcoin activity is to associate multiple Bitcoin addresses to user wallets. Estimating country-level activity is also challenging, as cryptoassets networks are global with no on-chain information regarding the geographic origin or

> 10 Deposit addresses are temporary on-chain addresses of users transferring funds to crypto exchanges.

12
Development Report with the intent of measuring poverty across countries in a consistent manner. This international poverty line used data on 33 national poverty lines for the 1970s and 1980s and represented the predicted poverty line for the poorest country in the sample, equal to about $0.76 USD PPP (1985). The international poverty line was subsequently adjusted for inflation as new sets of PPP were made available through the International Comparison Program. The computation of the current international poverty line of $1.90 USD PPP per day was obtained as the unweighted average of the poverty line for the 15 poorest countries, as such: i) by adjusting the national poverty lines of the 15 poorest countries for inflation up to 2011; ii) then converting the national poverty lines to real USD using the 2011 PPPs; and iii) then computing the simple average of the 15 national poverty lines. The resulting average poverty line is equal to $1.88 USD PPP (2011) per person per day, which was rounded up to $1.90 USD PPP (2011).

13
Table 5– Net Migration Flows by Governorate

|Governorate|Net Migration Flows|Governorate|Net Migration Flows|
|---|---|---|---|
|Port Said|36.5%|Beni Suief|-1.7%|
|Suez|35.7%|Cairo|-2.4%|
|Red Sea|19.4%|Luxor|-2.4%|
|6 October|18.2%|Fayoum|-3.5%|
|Ismailia|17.4%|Beheira|-4.0%|
|North Sinai|9.5%|Gharbeyya|-4.8%|
|Qalubia|8.8%|Aswan|-4.8%|
|Giza|8.6%|Dakahlia|-4.9%|
|Alexandria|7.2%|Minya|-4.9%|
|Helwan|6.2%|Qena|-5.0%|
|Matrouh|4.6%|Menoufia|-5.1%|
|New Valley|1.6%|Sharqia|-5.4%|
|Kafr el-Sheikh|0.3%|Assyut|-6.0%|
|South Sinai|0.0%|Sohag|-6.0%|
|||Damietta|-7.2%|

Source: Authors’ calculations using Labor Force Survey Urban governorates, new cities, Lower Egypt and Frontier governorates are the destinations for most migrants, the majority of whom originated in Cairo and Upper Egypt governorates.

# **Reasons for Migration**

The LFS asks respondents the reasons for migrating, and allows several possible responses: for work, for education, for marriage, to accompany others, or other reasons. Table 6 presents the distribution of migrants, by gender, according to the reason for migrating. The majority of internal migrants (
migration, and reason for movement including asylum/refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category),<sup>71</sup> nor is it common practice for national censuses to include questions related to forced displacement.<sup>72</sup> Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement.<sup>73,74</sup> In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes.

There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility.

# **_Sample surveys_**

Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals’ and households’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data
, section 6 concludes.

# **2. Data and Methodology**

This report uses data from 2015-2016 household- and individual-level surveys. The principal data source is the Syrian Refugee and Host Community Surveys (SRHCS), implemented in Jordan, Lebanon, and the Kurdistan region, Iraq. Estimates from nationally representative surveys, where available, are also used to establish baseline or benchmark conditions. The surveys were designed to include detailed information on both displaced and host community households, to allow for within- and across-context comparisons.

There were several challenges associated with conducting a nationally representative survey of the host community population and the forcibly displaced. The first difficulty is the lack of a recent or reliable sample frame, especially for Lebanon, where the last official population census was conducted in 1932. In Jordan and KRI, available sampling frames were also considerably dated.<sup>5</sup> In Lebanon, an additional difficulty was that there was no available cartographic division of the country into geographic areas small enough

> 5 Typically, such a sample frame consists of the universe of enumeration areas in a country, with associated estimates of population. We constructed our own sample frame by selecting a number of Small Area Units (SAUs) and then conducting a full listing operation by visiting every household within the selected SAUs and collecting basic demographic and contact information.

3
is the “STEP” school gain in cognitive skill compared to that in other education systems? First, we can compare the absolute learning gains. Using PIACC data from Hanushek et al. (2015) and the same descriptive method (method 1), we estimate that students from Finland – one of the top performing education systems – improve reading proficiency by approximately 0.3 standard deviation with an additional year of schooling, or one standard deviation is an equivalent of 3.3 years of Finnish schooling. Similarly, the average improvement in reading from an additional year of schooling in grades 3‐8 in Texas is also about 0.3 standard deviation, or one standard deviation is an equivalent of 3.3 EYOS of Texas education. Hence, in absolute terms, students in these two countries learn about twice as much as students in the 5 STEP countries.

However, taking the quality of education systems into account, students in high‐performing systems learn even more. Although one additional year of schooling is associated with 0.3 standard deviation in both Texas and Finland, it does not mean that students in Texas and Finland make the same improvement within one school year, because the quality of the two education systems is not the same. This can be illustrated by the fact that within the PIAAC pooled sample, with a common test, an additional year of schooling in the US is associated with 0.2 standard deviation of reading proficiency improvement, while that number is 0.3 standard deviation in Finland.<sup>14</sup> In this paper, we propose the pooled STEP standard as a metric for low‐ and middle‐income countries for two reasons. First, we do not have data for all countries where the interventions were implemented; second, we try to evaluate the impacts of interventions using the same STEP EYOS to avoid biases from the variance in the quality of different education systems.

# 4.2 Impacts of Increasing Cognitive Skills

The data available to calculate the
# V. Timing of early warning and drought response

## V.1 Timeline of drought impacts and early warning information

In responding to drought events, the timeliness of the response is key in preventing harmful welfare impacts of the drought. The existing early warning tools in Ethiopia can be used to facilitate early action during the onset of a drought, thereby preventing the adverse welfare impacts of droughts.

Early action could be based on variables obtained from the existing early warning systems. For instance, as detailed in section IV.1.1, the LEAP tool produces the intermediate outputs of planting dates, WRSI and crop loss, as well as the final output of beneficiary numbers. Intermediary outputs are available for every dekad (10 day interval) as rainfall data is collected on a continual basis. While outputs can be produced by the LEAP tool at any point in time, the accuracy of estimates increases as the season progresses and the rainfall pattern during the season emerges. If intermediate outputs – such as crop loss – or the final output of beneficiary estimates are computed early during any given season, the LEAP tool assumes a normal rainfall pattern for the remainder of the season, leading to less accurate estimations. LEAP output variables are most accurate when computed in December for the _meher_ season, and in July for the _belg_ season. However, it would be possible to use the LEAP tool to predict drought as early as August / September in any given year, thereby enabling a timely drought response as early as December<sup>53</sup> . This could make a critical difference in protecting lives and livelihoods, as detailed in Section V.2 below.

Currently, the LEAP early warning information is produced jointly by GoE and the WFP, based on available rainfall data. LEAP outputs are developed and monitored within the NDRMC (former DRMFSS) as part of the GoE’s overall drought monitoring and assessment framework. While LEAP data constitutes a key component of Ethiopia’s early warning framework, at present,
The World Bank Economic Review 17

**Table 8.** Attitudes: Network Spillover and Reinforcing Interaction Effects

||**Condom use**<br>**attitudes**<br>**subindex**|**Sexually**<br>**conservative**<br>**attitudes subindex**|**Sexual abuse**<br>**reporting attitudes**<br>**subindex**|**General**<br>**attitudes index**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Treatment student|−0.020|0.072|0.024|0.043|
||(0.064)|(0.074)|(0.070)|(0.073)|
|Spillover student|−0.021|0.082|0.021|0.032|
||(0.049)|(0.058)|(0.051)|(0.051)|
|Treatment student * % of friends treated|0.213***|0.114|0.166*|0.236***|
||(0.075)|(0.071)|(0.093)|(0.078)|
|Spillover student * % of friends treated|−0.098|−0.059|−0.003|−0.072|
||(0.170)|(0.143)|(0.131)|(0.147)|
|Number of friends|0.068***|0.