ai4data/datause-dataset-rechunked huggingface dataset:
However, the carbon savings and reduced deforestation will only be realized if the stoves


continue to be consistently used (Simon et al. 2012). The results from the choice experiment

In this paper we use a choice experiment survey to elicit preferences for improved stoves

that used a stove satisfaction survey and controlled cooking tests to evaluate actual Mirt Stoves.

8 In a stove satisfaction survey 100% of the users rated the MIRT stoves as good or very good, 90% said that they would buy the stove at full market price.
This paper contributes to the existing literature, by focusing on the impact of drought on poverty in
Somalia. Four consecutive seasons of poor rains between April 2016 and December 2017 resulted in a
severe drought across Somalia (FEWSNET, 2018). The drought exacerbated preexisting food insecurity, as
half of the population faced acute food insecurity in mid-2017 (FEWSNET, 2016; FSNAU, 2017). The
drought threatened the livelihoods of many Somalis.

This analysis uses a regression framework similar to Hill and Porter (2016) to estimate the effect of the
drought on poverty and consumption. To isolate the drought effect, the analysis exploits two
characteristics of the SHFS data set. First, fieldwork timing was such that data were collected before the
drought shock (wave 1) and during the drought (wave 2), allowing for a before-and-after comparison.

Journal of
Development Economics 106, 132-143.
Dercon, S., Krishnan, P., 2000. Vulnerability, seasonality and poverty in Ethiopia. The Journal of
Development Studies 36, 25-53.
FEWSNET, F., 2018.

Food Security and Nutrition Analysis Unit - Somalia and Famine Early Warning Systems Network. FSNAU, 2017. Special Brief: Focus on Post Gu 2017 Early Warning.
Rural households are more likely to openly defecate compared to  urban households (DHS, 2015).

Rural households are about twice as likely to have access to unimproved sanitation compared to their urban counterparts (DHS, 2014).

[4]  Sixty-five percent of the households in the country have access to a private sanitation facility in  2014 (DHS, 2016).

In 2014, only 45 percent of the households had a private improved sanitation facility -  an increase from 39 percent in 2011 (DHS, 2016).

pdf 2   <!-- PAGE 5 --> an unshared toilet facility compared to urban households (DHS, 2016).
They then developed a series of questions that they could ask this person to understand their per- ception of social cohesion. This participatory process based on “ personas ” developed a better understanding of the concepts and outlined key questions that participants felt were relevant to social cohesion in eastern DRC. The ﬁndings from the seven focus groups were combined to draw a single concept map (Figure 3). Together, the participants outlined three broad domains that can be subsequently divided into dimensions, sub-dimensions, and ﬁnally indicators. Some overlap between dimensions is unavoid- able because of the conceptual proximity of many of the topics discussed in the focus groups. As such, the three domains of social cohesion, as well as their respective dimensions and sub dimen- sions, are interrelated. These represent a subjective and synthesized conceptualization of social cohesion in eastern DRC according to Congolese participants. Three main domains emerged as particularly salient for focus group participants: solidarity, relationships and governance.
Fortunately, there is a very large national longitudinal dataset from Taiwan (China), which permits a robust estimation of the impact of mother's HBV status on sex ratios at birth.

However, a large medical dataset from Taiwan (China) shows that hepatitis B infection raises women's probability of having a son by only 0.25 percent.

However, the findings from the detailed medical dataset from Taiwan (China), indicate that she has massively overestimated the impact of maternal hepatitis B infection on the sex ratio at birth.

She therefore compiles data from the Demographic and Health Surveys (DHS) carried out in 18 Sub-Saharan African countries, cautioning that these survey data suffer from defects such as recall bias.

Her third approach uses United States census data to look for patterns in the sex ratios of children born to Chinese immigrants, who are assumed to have levels of HBV prevalence similar to their place of origin.
received/ reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts (Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except Biscuits. Refugee households still report lower quantities, even correcting for shares indicated as sold (Table E.2). Table E.3: Aggregate food expenditures Value/year (per capita) Value/year (per adult) Food expenditure (all) [A] 11,412 13,898 Food expenditure (UNHCR items only) [B] 3,528 4,335 Food expenditure (UNHCR in-kind) [C] 11,313 13,933 Food expenditure (UNHCR in-kind + cash) [D] 16,179 19,965 Source: UNHCR and World Bank Staff based on SESRE 2023. Note: Food expenditure (all): aggregate food expenditure. Food expenditure (UNHCR items only): aggregate food expenditure from UNHCR items only Food expenditure (UNHCR in-kind): aggregate food expenditure from UNHCR items valued using SESRE prices Food expenditure (UNHCR in-kind + cash): aggregate food expenditure from UNHCR items valued using SESRE prices plus the cash equivalent transfers Annexes 126 Food aid data received from WFP Food aid information received information from the WFP includes five food items and their
`o` Respondents were selected randomly from the ProGres database out of the total pool of beneficiaries
who received the assistance from August to November 2021.

`o` With a 95% confidence level, and 7% for confidence interval, the sample size needed for the total of
households (HH) assisted was 182. Nevertheless, a total of 400 HH were selected to address the high
mobility and lack of phone numbers on record.

`o` A total of 174 households answered, out of which 141 were headed by women.

- Limitations and challenges faced:

`o` PoCs are usually registered at the border state of Roraima; however, many individuals continue their
journey to the country resulting, on most occasions, in a change of phone number. As data is collected
using phone calls, this represents a real challenge. An alternative was to reach respondents through
WhatsApp for those that were not initially answering.

`o` This monitoring exercise was carried out during the COVID-19 pandemic, under the impact of restrictive
measures to combat the spread of the disease.

### 3.KEY FINDINGS

**Household demographics**

- Average household size 3.8

- Nationalities: 97% Venezuelans and 3% other

nationalities

- Number of people by age group who live in the
**Several key messages emerge.** **First, SOEs in Romania were larger-employed more people and had**
**larger assets per worker-and paid better wages, on average, than their POE peers from 2011 to**
**2019.** On average, they had lower revenue per worker than POEs over the same period. These results are
robust for the various SOE ownership degrees (i.e., minority and majority owned SOEs) and align with
other studies. In addition, the average SOE experienced higher job growth, investment, and labor
productivity growth but slower wage growth than the average POE over the same period. Nevertheless,
these growth effects are not uniform across the various ownership degrees.

employment Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sector size in economy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations **[†]** 704 704 704 704 704 704 704 697 704 704 Within R-squared **[†]** 0.095 0.009 0.039 0.267 0.147 0.193 0.046 0.072 0.045 0.040 Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.

<!-- PAGE 43 -->
Source: World Bank staff analysis using Romania MoF firm-level data from 2018 to 2020. The sample includes all firms. Growth is calculated as the difference between the values
in year t and t-1 divided by the average of the values in year t and t-1.

5 According to the World Bank Businesses of the State database, SOEs with at least 10% state ownership accounted for 3.6% of the formal employment as of 2019 in Romania.

(2022b) to supplement the World Bank BOS database.
7 Figure 4 ‐ Research on Migration, Refugees and IDPs (% of total hits) Source: Authors ’ estimations based on Econpapers, SSRN and Google Scholars searches. This phenomenon can be explained by essentially two factors. The first relates to the humanitarian ‐ development nexus. For the longest time, refugees and IDPs remained the quasi monopoly of humanitarian organizations whose mandate is essentially the humanitarian protection of refugees and IDPs. These organizations are not typically staffed by economists and analysists but by field workers and lawyers. There was, therefore, little demand for hard economics on forced displacement for a very long time. This is changing as development organizations typically staffed by economists have started to work on forced displacement situations. The second factor relates to lack of good data. As we will see in the data section, data collection of mobile populations is complex and the main organizations in charge of data collection of refugee and IDPs data are humanitarian organizations that do not necessarily have the complex skills required for issues like sampling, questionnaire design and data analysis and have a duty to protect data by mandate. This, in turn, has resulted in very few micro data that would be both of good quality and accessible to researchers.
Data Description for the HEIS We use the most recent two rounds of Jordan ’ s Household Expenditure and Income Survey (HEIS) in 2008 and 2010, which are the nationally representative surveys used to produce official poverty statistics. The HEIS has been implemented nine times since 1966, and every other year between 2006 and 2010. In addition to household expenditures, it collects data on 18 According to the UNHCR (http: / / data. unhcr. org / syrianrefugees / country. php? id = 107), in July 2012 there were about 29, 000 registered Syrian refugees in Jordan; a year later the number of refugees rose to about 115, 000, and by August 2014 the number further increased to slightly more than 600, 000, which is roughly a tenth of Jordan ’ s population. 20
From 1997-98, 274 households, randomly selected from 10 purposively sampled villages, [2] were surveyed in each of four periods, including the 1997 post-harvest and the subsequent 1998 hunger season.

availability. Women in these households were asked to recollect the quantities of


different foods consumed in the previous seven days. These data were converted into



<!-- PAGE 12 -->
8

daily per capita caloric availability using household size and locally adapted tables that


convert physical units of food into calories. Were data available, however, the measure

The incidence and depth of current caloric shortfall can be directly calculated


from the survey data collected in November 1997. We take 2,345 kcal/person/day as


caloric threshold which corresponds to the needs of a 60 kg male, aged 30-59,

Government of Mali and the USAID sponsored [Famine Early Warning System for Mali]


use it as a leading indicator of food insecurity. Since [the availability of food is necessary]
refugees in Addis Ababa residing in rented houses. Most Eritrean refugee households (61 percent) live in temporary30 shelters provided by the UN or NGOs, whereas 50 percent of Somali refugee households and 71 percent of South Sudanese refugee households live in UN or NGO-provided permanent31 shelters. In Addis Ababa, 97 percent of refugee households live in rented houses. SESRE data also show that OCP refugees pay higher rents than hosts; on average, refugees pay roughly ETB 31,600 per year per adult equivalent, while hosts pay slightly less than half of that (ETB 18,700) (Annex D, Figure D.14). Refugees do not qualify for government 29 At least having difficulty with seeing, hearing, walking, remembering, selfcare or communicating. 30 Temporary shelters have walls mainly made of tent, plastic cover, and irons sheet. 31 Permanent shelters have walls mainly made of wood, mud, non-plastered blocks. 0 5 10 15 20 25 30 35 40 In camp Addis Ababa Total In camp Addis Ababa Total In camp Addis Ababa Total Stunted Underweight Wasted Hosts Refugees Percent Figure 2.18: Child nutritional indicators Source: World Bank Staff based on SESRE 2023. 0 1 2 3 4 5 6 Hosts Refugees Hosts Refugees Hosts Refugees In camp
Survey (UNHS) and the 2009/10 Uganda National Panel Survey (UNPS), both implemented

The 2005/06 UNHS covered all the districts in Uganda surveying 7,421 households

from 783 Enumeration Areas. The 2009/10 UNPS collected information on 2,975 households
(2022b) to supplement the World Bank BOS database.

The database was built using data from ORBIS and complemented with data from government sources, such as business registries, central depositories, central oversight bodies, and the Ministry of Finance.

EC (European Commission). 2015. "Macroeconomic imbalances Country Report - Romania 2015,"
Occasional Papers 223. Directorate-General for Economic and Financial Affairs,

Using 1995, 2004, and 2008 data from the Chinese Industrial Census, Brandt, Kambourov, and Storesletten (2020) indicate that a key factor underlying the dispersion and dynamics of aggregate total factor productivity and wages across Chinese prefectures were entry barriers, which in turn were linked to significant state presence in economic activities.
a pre-sampling assessment by visiting the camps to verify on-the-ground conditions. 56 Refugees from Sudan were not included in SESRE as, at the time of sampling, there were less than 50,000 Sudanese refugees in Ethiopia and inclusion was not deemed cost effective. Annexes 93 households in a row from the list; that is, 50-200 HHs grouped as EA1 and the next 150-200 households grouped as EA2, and so on. EAs and households from each sampled EAs were selected. (b) Refugees in Addis Ababa We used a slightly different approach for refugees in Addis Ababa because of the difficulty of obtaining a reliable, complete list of locations for refugees living there. The refugee sampling frame in Addis was based on UNHCR’s proGRES registration data, sorted by location. The UNHCR list has information about how many refugee households live in each Woreda in Addis Ababa, their contact details, location, and other information. We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they
likelihood of return. Moreover, the sensitivity of this return decision to conditions in Syria (i. e., conﬂict intensity) is also markedly diﬀerent along such characteristics. 4. 3 Conditions in Host Countries A refugee ’ s livelihood opportunities and housing conditions in the host country may also be important determinants of her return decision. In fact, this point often appears in the popular media in diﬀerent forms like"good conditions make refugees stay"and, by extension,"bad conditions make refugees return". 19 In this section, we show that our results do not necessarily support this view. First, we explore how individual return decisions are aﬀected by living conditions in the host countries by estimating equation 4. These results are reported in Table 8, where individual controls from ProGres are suppressed for ease of exposition. In column (1), we ﬁnd that more food secure households have a higher hazard rate of return, a result that is highly signiﬁcant. However, the magnitude of the eﬀect is small: a one standard deviation increase in the food security PCA index increasing the hazard rate by 0. 27 %. Moreover, Column (2) shows that better housing conditions- proxied for by the housing PCA index- also increase the hazard rate by a similar magnitude.
(0.04) (0.24) HH operates a nonfarm enterprise 0.07** -0.00 -0.05 0.26*** (0.03) (0.03) (0.10) (0.07) Share of employed members 0.35*** 0.57*** 0.14** 0.06 (0.08) (0.06) (0.06) (0.08) Health shock 0.07 -0.02 -0.04 -0.20 (0.06) (0.06) (0.10) (0.18) Market shock -0.01 -0.09*** -0.04 -0.19*** (0.02) (0.03) (0.04) (0.05) Employment shock 0.01 -0.11 -0.06 -0.07 (0.07) (0.11) (0.31) (0.18) Drought shock -0.03 0.08 0.24 (0.05) (0.09) (0.32) Political shock -0.05 -0.23* -0.23** -0.06 (0.05) (0.12) (0.11) (0.38) Constant 10.05*** 10.22*** 10.58*** 10.49*** (0.07) (0.09) (0.32) (0.36) Survey domain Yes Yes Yes Yes Survey time Yes Yes Yes Yes Observations 1286 1287 432 430 Source: World Bank Staff based on SESRE 2023. Note: Dependent variable is the log of total per adult equivalent consumption expenditure. All regressions include fixed effects for the survey domain and time (year
Yes Survey time Yes Yes Yes Yes Observations 1286 1287 432 430 Source: World Bank Staff based on SESRE 2023. Note: Dependent variable is the log of total per adult equivalent consumption expenditure. All regressions include fixed effects for the survey domain and time (year and month) to account for locational and temporal differences. Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 114 The regression specification used is: where is the vector of control variables that include demographic characteristics (sex of household head, age of household head, family size, years of schooling completed by the head), assets and wealth (mobile phone ownership,
24% 64% 30% 37% 10% 8% Source of lighting Electricity (meter) 74% 13% 25% 5% 29% 1% Electricity (meter, generator, solar) 89% 91% 38% 17% 34% 4% Source: World Bank Staff based on SESRE 2023. Annexes 100 0 10 20 30 40 50 60 70 80 90 100 < 15 15 to 24 25 to 44 45 to 64 >= 65 < 15 15 to 24 25 to 44 45 to 64 >= 65 < 15 15 to 24 25 to 44 45 to 64 >= 65 < 15 15 to 24 25 to 44 45 to 64 >= 65 Eritrean Somali South Sudanese Addis Ababa Male Female Percent Figure D.1: Age group by gender Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Average share of primary school age children in primary education Average share of secondary school age children in secondary education Percent Figure D.3: Share of school-age children in education per household Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South
the optimal travel time between Woredas using the transport network of 2020. The trade elasticity, has a value of 8.28 (Eaton and Kortum, 2002). Box 6.1: Measurement of proximity and market access index in Ethiopia 37 43 19 To Woreda City 1-10km 10-20km >20km 1-20km 20-100km >100km 1-30km 30-50km >50km 45 26 29 To Zone capital 18 59 23 To nearest border 44 28 11 17 Proximity to economic hub Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 24 40 36 Market accessibility High accessibility Medium accessibility Low accessibility Figure 6.1: Refugee incidence Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. a. Against distance to cities and borders b. By market accessibility, proximity to resource hub Markets and Opportunities 59 Labor market outcomes47 differ by proximity to resource hubs and connectivity. The labor force participation rate for refugees is highest in remote locations and areas with poor connectivity (Figure 6.2). Yet, most refugees in the labor force in remote and low-connected locations are unemployed, highlighting
The 10-item parenting stress scale used in this sample was found to have a high internal-consistency reliability, with a Cronbach ’ s alpha of. 85 in this sample. Independent (Stressor and Other Covariate) Variables A variety of known stressors and correlates of maternal mental health and child development were also assessed. Stressors, including food insecurity, poverty, flooding impact, experiences of domestic violence, felling unsafe in one ’ s community, discrimination, living in a rural area, lack of enrollment in ECE, and low maternal educational attainment have consistently been associated with lower levels of maternal mental health conditions and child development across contexts (e. g., Hentschel et al., 2023; Iqbal & Ali, 2021; Reed et al., 2012: Sitwat et al., 2015; Yousuf et al., 2023) Other covariates, such as refugee status and child age, were also assessed. Refugee Status. At the time of the survey, sensitivities around “ refugee status ” were particularly heightened. As such enumerators were asked to keep notes on interviews to help identify whether
returns to human capital. Then, we find that relative _pcGDP_ is 53 _._ 5 percent, accounting


48See Web Appx. Fig. 8(a) for the mortality distributions.
49See Web Appx. Fig. 8(b) for the distribution of schooling we use, based on the I2D2 data (1990-2016).
50We compare GDP per capita in levels. An alternative approach would be to compare GDP per capita
in log levels. In that case, the gap is of 51%. In any case, the main point of our inquiry in this section is to
compare the relative contribution of education and experience, so the absolute metric is not important.

According to I2D2 data, workers in developed economies work   43 hours per week on average compared to 50 in developing economies. Assuming three   weeks of time off, we get that workers in developed economies on average train for 3 _._ 4%   of their working time, compared to 2 _._ 0% in developing economies.

To see whether the differences in the experience premium might be accounted for   by differences in hours spent training, we draw on recent data from _OECD.Stat_ (OECD,   2021). The data contain information on 33 developed economies and 4 developing   economies.

[2 Global SDG Indicators Database, accessed April 26, 2022: https://unstats.un.org/sdgs/indicators/database/](https://unstats.un.org/sdgs/indicators/database/)

Using data from the Armenia Land Tenure and Area (ALTA) study and in the context of land tenure rights, this paper addresses questions on: (i) the data quality implications associated with respondent strategy; (ii) the data quality implications associated with level of data collection; (iii) the interaction of biases stemming from the use of proxy respondents and aggregated level data collection, as relevant; and ultimately, (iv) the implications of these design decisions on the computation and monitoring of SDGs
implications associated with level of data collection; (iii) the interaction of biases stemming from the use of proxy respondents and aggregated level data collection, as relevant; and ultimately, (iv) the implications of these design decisions on the computation and monitoring of SDGs 1.4.2 and 5.a.1.