ai4data/datause-dataset-rechunked huggingface dataset:
28 In section three of this study, we could not find taxes and regulations among the potential barriers to sales and employment growth that clearly separated the ups from the downs in the formal sec- tor. We also showed that the large sector has become a smaller share of the economy rather than a bigger share due to self-employment growth and due to the process of de-industrialisation. In substance, the CIS-7 may fit the developing countries scenario better than the transitional countries scenario in the Schneider and Klinglmair (2004) regressions. If this is the case, we should expect that the growth of the informal sector negatively contributes to growth. The data we have do not contradict this hypothesis given that the shadow economy has been on the rise during the recession period and has stabilised during the growth period. This digression on informality suggests that self-employment may partially act as an host to in- formal and illegal activities especially during recessions where self-employment may constitute a refuge for small informal and illegal businesses. Self-employment is also evidently a sector of ne- cessity for those who wish to keep health and pension records alive and do not want to formally register anywhere else. In times of growth this sector may instead function as a first step to for- mality, an entry gate to the formal sector given its lower entry barriers and taxes.
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 these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http: / / econ. worldbank. org. * Contact author: Håvard Hegre; CSCW, PRIO, Hausmanns gate 7, N-0187 Oslo, Norway. Email: hhegre @ prio. no. Thanks to Joachim Carlsen for writing a program to create the dataset used in the analysis, to Siri Aas Rustad for research assistance, and to Kristian Gleditsch, Anke Hoeffler, Pat Regan, Mike Ward, Nils Weidmann and Jen Ziemke for valuable comments. The research has been funded by the Research Council of Norway, grant no. 163115 / V10. WPS4243 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
Refugees Hosts Refugees Age group <15 39% 46% 54% 50% 47% 56% 15 to 24 19% 19% 17% 22% 21% 21% 25 to 44 26% 25% 21% 17% 21% 16% 45 to 64 13% 8% 7% 9% 8% 5% >=65 4% 2% 2% 2% 3% 1% Gender Male 48% 51% 50% 49% 48% 46% Female 52% 49% 50% 51% 52% 54% Marital Status Never Married 22% 27% 21% 30% 22% 25% Married 62% 56% 69% 52% 66% 57% Other 16% 17% 10% 18% 12% 18% Household characteristics Household size 4.3 4.7 5.7 6.0 5.4 6.5 Dependency ratio 0.8 1.1 1.5 1.4 1.1 1.7 Female-headed 38% 43% 43% 61% 51% 84% Head’s age 45.4 39.3 41.0 43.6 40.7 37.1 Source: World Bank Staff based on SESRE 2023. Annexes 98 Table D.2: Education outcomes by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Education level No education 38% 49% 52% 56% 31% 43% Incomplete primary 20% 30% 13% 20% 24% 27% Complete primary 20% 17% 21% 18% 27% 25% Complete secondary 12% 4% 6% 4% 10% 3% Complete post-secondary 10% 0% 9% 2% 8% 2% Education level (youth -15 to 24 years) No education 4% 17% 20% 18%
Quality Metrics A standard Measure in the assessment of a sampling designs is the Root Mean Squared Error (RMSE) and calculated as: 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = ∑ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠 1000 𝑠𝑠𝑠𝑠𝑠𝑠 = 1 1000 = ൥ 1 1000 × ඥ (𝑌𝑌 ෠ − 𝑌𝑌) 2 𝑌𝑌 ൩ × 100 Expressed here as percentage deviation from the population mean Y and calculated for each parameter of interest. Equation.. is only the empirical representation though and a result of rearranging the definition of the Mean squared Error, 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌൯ 2 = 𝐸𝐸 ൣ ൫𝑌𝑌 ෠ − 𝑌𝑌෨൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯൧ 2 = 𝐸𝐸 (𝑌𝑌 ෠ − 𝑌𝑌෨) 2 + 2𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌෨൯൫𝑌𝑌෨ − 𝑌𝑌൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯ 2 And decomposing it into 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝑉𝑉𝑉𝑉𝑉𝑉൫𝑌𝑌 ෠ ൯ + 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 (𝑌𝑌 ෠) with 𝑌𝑌 ෠, 𝑌𝑌෨ and 𝑌𝑌 being the estimate from the sample, the mean of this estimate and the true value in the population respectively. Var is the corresponding variance, and Bias the resulting bias component, which is defined as: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 ൫𝑌𝑌෨൯ = 𝑌𝑌෨ − 𝑌𝑌
Having seen that mandatory e-auctions significantly increased land prices, we estimate (1) using data from   the entire 2015-22 period as an additional robustness check and using data from 2018/19 when e-auctions

the nearest main road, grain elevator, and city. A land use map, constructed based on remotely sensed data

Adding data on (offline and online) auctions conducted in the 2015 to 2020 period serves not only as a
in primary compared to their hosts, refugees struggle to attend secondary education. Secondary GER22 and NER23 for refugees are almost half those of hosts. For example, only 23 percent of secondary school-aged refugee children and youth attend secondary school. This share is higher (41 percent) for hosts. Refugee Secondary NER also is very high compared to the national 5 percent in 2021/22 (MoE, 2022). By country 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis refugees South Sudanese Addis refugees 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali Incomplete primary Complete primary Complete secondary Complete post-secondary Percent Percent Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE
fact that they are time consuming and therefore expensive to collect. At least three other less resource
intensive alternatives to the FAO approach have been suggested to derive hunger numbers:

anthropometric data, self-assessments, and direct use of HCES.

quicker and cheaper to collect than full HCES efforts. However, how well they correlate with other

The third approach, and the one that we concentrate on here, is to use HCES to derive hunger statistics

HCES are positioned between the single subjective hunger question and the intensive 24-hour recall.
Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et
(1) GDP growth, (2) agricultural GDP value added, (3) industrial GDP value added, and (4) poverty

growth, (2) agricultural GDP value added (%), (3) industrial GDP value added (%), and (4) poverty

<!-- PAGE 28 -->
###### **_References_**

Alley, William: The Palmer Drought Severity Index: Limitations and Assumptions, Journal of Climate and Applied
Meteorology, 1984, 23,1100 - 1109.

16 For this research, the model was updated by the Institute of Water Modeling (IWM) with river alignments of GBM basins using available physical maps of India, Nepal, Tibet; and sub catchments of the GBM basins were redelineated using the Digital Elevation Model (DEM) based on SRTM-version 3. The model has been first calibrated using round the year hydrological feature of 2005 and validated for two subsequent years 2006 and 2007. Then the model was further updated using most recent data of last hydrological year 2009. 17 Bathymetries of the rivers have been updated incorporating the available latest cross-sections and bathymetries of floodplain routing channels are taken from national land terrain model developed in FAP (Flood Action Plan) 19.

channels. It is based on the existing national DEM for Bangladesh and model simulations
This means that night lights data can be used to estimate economic activity at levels that are not usually captured in national accounts, such as subnational administrative units (provinces, districts, counties, cities, etc.) or regions not coinciding with national borders (coastal vs inland regions, connected vs unconnected regions, etc.). For instance, Henderson, Storeygard and Weil (henceforth HSW) estimate, counter to intuition, that coastal areas in Sub-Saharan Africa are growing slower than the hinterland.

Data on night lights are provided by the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The DMSP satellites circle the earth 14 times a day and record the intensity of Earth-based lights.

We used stable light imagery of SSA derived from scores of orbits of the DMSP OLS in from 1992-2013 since this product inter-calibrated where fires and other ephemeral lights have been removed, although there are noteworthy blunders associated with over-glow effects where lighting spreads to neighboring pixels (and hence economic activity is wrongfully attributed to certain places). The stable lights imagery has annual quantized pixels with values (Digital Numbers) with integers ranging from 0 to 63.

Data on GDP and other indicators used in the analysis (surface of country territory, electricity consumption, etc.) are provided by the World Development Indicators (2014). Following HSW, we use constant GDP in local currency units.
refugees, as defined under the 1951 UN Convention. It is consequently not surprising
that surveys of refugee studies only give very limited attention to environmental
aspects, except for the degradation that refugees might cause themselves (Black and
Robinson 1993; Richmond 1988; Zolberg, Suhrke, and Arguayo 1986).

**A problematic concept**

One last element that may have curbed the study of links between environment and
migration is that several researchers have rejected the very concept of environmental
refugees (Black 2001). Rightly highlighting the shaky empirical character and sloppy
nature of most work on the subject, they have brought to the fore problems arising
from a unidirectional link between environmental changes and migrations in the face
of well-established results from research on population flows. For Castles, "the term
environmental refugee is simplistic, one-sided and misleading. It implies a
monocausality which very rarely exists in practice (…) [Environmental and natural
factors] are part of a complex pattern of multiple causality, in which [they] are closely
linked to economic, social and political ones." (Castles 2002, 5).
154 P. B. Spiegel & P. V. Le

Discussion

Overall, the majority of surveys that met the inclusion criteria were of insufficient
methodological rigor to be reproducible. Other important methodological issues,
such as households or persons who were absent or refused to participate, as well
as whether replacement of such households or persons occurred, were not stated
in most of the reports. Sample sizes had a wide range and some may have been of
insufficient size to be precise enough to interpret results or make meaningful
comparisons with future surveys. The majority of questionnaires were not field
tested or back-translated nor were qualitative methods used to complement the
quantitative methodology used in many of these surveys. Only a minority of
surveys obtained informed consent from participants. Few provided definitions
of essential terms, such as high risk sex or non-regular partners. Although most
of the surveys included training of surveyors, few reported gender balance of
interviewers. All of the above methodological flaws inject sufficient biases into
these surveys to make the most of them unacceptable.
The majority of the written reports lacked sufficient detail and structure to be
analysed and interpreted by the reader in a meaningful way. Most reports
provided objectives for the survey as well as conclusions and recommendations.
However, few stated limitations and biases and only half appended the
questionnaire to the report. Important methodological details were missing in
many surveys, including information on how the sample size was chosen, the
specifics of the sampling methodology and whether replacement was used. Most
of the analyses were descriptive in nature with few reports using comparative
statistics. The majority of reports used some variation of the internationallyaccepted HIV indicators for knowledge (e.g. prevention and misconceptions) but
only a minority used some variation of these indicators for practice and attitudes.
The disaggregation by age or gender varied considerably among reports which led
to difficulties in comparing results; furthermore, few studies reported indicators
according to both gender and age.
There are limitations to this article. Despite an attempt to search as widely as
possible in the published and grey literature
age or gender varied considerably among reports which led
to difficulties in comparing results; furthermore, few studies reported indicators
according to both gender and age.
There are limitations to this article. Despite an attempt to search as widely as
possible in the published and grey literature, as well as to contact organizations
known to undertake BSSs in conflict and post conflict settings, some surveys will
have been missed. In addition, those included are not just BSSs but reproductive
health KAP surveys with an HIV component. The latter may not contain as much
detail on HIV as BSSs, however, this would neither affect the basic methodological weaknesses nor the absence of key internationally-accepted standardized
HIV indicators. Some of the BSSs examined in this report were conducted before
2002 when the internationally accepted UNGASS indicators were developed; this
together with the changing of indicators over time makes it difficult to interpret
the usage of internationally-accepted indicators. Misclassification may have
occurred because results were based on findings written in the reports reviewed.
For those reports that omitted key methodological issues or results, the data were
recorded in a negative fashion (e.g. if type of sampling was not mentioned, the
survey was recorded as not employing random sampling).
[8] Daewoo Securities (1988 and 1999) _Handbook of Listed Companies,_ Daewoo Securities: Korea.

scope data were missing or incomplete, we collected additional company information from Wisenet

Korea. [7] This internet server provides detailed financial information, including income statements

Second, using data on foreign ownership from 1998 and 1999 and firm characteristics from the

'° Of course, a drawback of this approach is that the estimates of elements of E will not be very precise, as the crosssectional dimension in these regressions is large relative to the time dimension. For this reason, we use only 20 states
because annual data for gross state product is only available for 23 years, from 1963-1986. To give some idea of the
magnitude of the spatial correlations, note that the average cross-sectional correlation for the U.S. state data is .193, with
a maximal value of .629, while for the O.E.C.D., the corresponding figures are .312 and .761.
While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned / managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA. 2 UNHCR defines a protracted refugee situation as “ one in which 25, 000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country ” (2012: 23). 3 The figures are based on refugees (including those in refugee ‐ like situation). Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). The number of refugees and people in refugee ‐ like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19 % in the world and 8 % for SSA).
14 Data Analysis All analyses were conducted with Stata / SE 14. 0 (StataCorp LP, College Station, TX). A bivariate model was also used to examine the relationship between the main predictor (fatalities) and each outcome. For the final model, a multilevel approach was used to account for the nested structure of the data, with clustering of women within districts. Model Specification Multilevel logistic regression models were used to quantify the effect of district level conflict on the odds of IPV after sequentially adding blocks of independent variables as described in Table 2. The models included a random intercept for district, to account for the geographic clustering of the sample and systematic differences between districts that would not otherwise be captured in a simple logistic regression. This approach has been used in similar analysis in past research (Kelly et al, 2018; Kelly, 2019). Multilevel Model- Dichotomous Exposure: In the regression equation above, i indexes the district and j indexes the individual. 𝑌𝑌𝑖𝑖𝑖𝑖 is the indicator for whether a woman (j) in district (i) has reported experiencing violence in the last 12 months. β_0 + b0i defines the district level odds of a woman experiencing violence in district i given no conflict holding the individual-level covariates fixed.
• The government, UNHCR, and GIZ are working together to develop a national roadmap for including refugees in the national TVET system. Protection/Capacity “Strengthening Asylum System and Social Protection: (i) Refugee Status Determination (RSD), refugee registration, civil documentation, and permits; (ii) National social protection system in refuge hosting areas-particularly for vulnerable individuals.” • The 2019 Refugee proclamation improved provisions related to registration, documentation, and protection of refugees and asylum seekers as well as refugee status determination, and three implementation directives adopted. • RSD procedures are simplified for asylum seekers from Syria and Sudan. • Refugees are included in a Civil Registration and Vital Statistics Systems (CRVS) and the National Social and Behavior Strategy (awareness raising about the need for vital events registration). • A backlog of birth registration of 120,000 refugee children is cleared, and 72,286 vital events (62,816 birth, 8,177 marriage, and 757 divorce ) have been registered since 2017. • 890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS). Refugee ID cards and proof of registration are issued to 55 and 98 percent of refugees, respectively. Out-of-camp Permit is issued to 48,346 refugees. • One-stop shops have been established in 13 refugee camps
Page 32 of 51 Figure 25: Education profile of Temporary employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point. It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper. When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases. 10 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time).
Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months. 50 % 59 % 30 % 27 % 34 % 13 % 31 % 50 % 62 % 34 % 33 % 47 % 23 % 41 % 37 % 20 % 10 % 14 % 6 % 17 % 23 % 47 % 23 % 29 % 31 % 6 % 23 % 40 % 42 % 49 % 3 % 18 % 19 % 16 % 8 % 19 % 16 % 8 % 13 % 29 % 3 % 17 % 25 % 13 % 15 % Bangladesh (Urban) Bangladesh (Province) Brazil (Urban) Brazil (Province) Ethiopia (Province) Japan (Urban) Namibia (Urban) Peru (Urban) Peru (Province) Thailand (Urban) Thailand (Province) Tanzania (Urban) Tanzania (Province) Serbia Samoa sexual violence ever physical violence ever physical violence past 12 months Source: Unpublished data from the WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women. The final published comparative report is forthcoming. Cited with permission. Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “ forced ” sexual debut among sexually experienced young people and found rates from 7 % (New Zealand), to 46 % (in the Caribbean) (Heise and Garcia Moreno, 2002).
Finally, Gunther and Fink (2010) analyze data from 172 DHS  surveys and find that households having flush toilets have 13 percent lower odds of diarrhea

We use a subsample of 209,762 children between 0 to 48 months of age that live in ru ral areas from the Third Round of the District Level Household Survey (DLHS-3). The

The sample  design of the DLHS-3 survey makes this measure possible since in rural areas the DLHS-3  uses census villages as PSU (International Institute for Population Sciences (2010)).

DLHS is a nationwide survey with district level representation of India's households, which

collects information on family planning, maternal and child health, reproductive health of

These ratios are reasonably comparable with the estimations of JMP for rural  areas in India. Table 2 presents the summary statistics of these variables for the 209,762