ai4data/datause-dataset-rechunked huggingface dataset:
# **PROTECTION** **BRIEF** **CZECHIA** ## Operational Context Based on the number of refugees who applied for an extension of Temporary Protection in 2024 (320,000), the figures of Temporary Protection holders in Czechia, including new arrivals, were updated to **338,736** people as of 31 March 2024, compared to 373,745 at the end of 2023 (source: Ministry of the Interior). In January 2024, _Lex Ukraine_ was amended to extend Temporary Protection until March 2025. Changes introduced include an adjustment of the initial level of humanitarian benefits, maintaining support for the most vulnerable individuals, and the reduction of cost-free accommodation for all new arrivals from 150 to 90 days. This report presents an overview of refugees’ demographic profiles and displacement patterns as well as main findings regarding protection risks, priority needs and intentions of refugees from Ukraine in Czechia. The analysis is based on 480 Protection Monitoring interviews and findings from focus group discussions with refugees from Ukraine across the country in the first quarter of 2024. ## Key Figures 338,736 320,000 ## 338,736 320,000 Temporary Protection holders in Czechia among whom have registered for an extension ## 92% of Protection Monitoring respondents are Temporary Protection holders ## 32% of respondents have at
are found in the Mekong Delta, the Red River Delta, and the Southeast Coast (Figs. 5, 6, and 7). But the relative exposure (that is, the percent of the district population which is exposed to floods) shows a larger spread (Figs. 8, 9, 10). Most areas in the country – including the North Central Coast and the Northeast – have high percentages of their populations residing in flood-prone areas (Fig. 8). Flood Exposure and Poverty To examine the question of how many poor people in Vietnam are exposed to flooding, we multiply the population exposure estimates by the district’s poverty headcount rate (the percentage of people living below USD 1.25 per day), as calculated in (Lanjouw et al. 2013). For a 25-year historical flood, 30% of today’s poor population is exposed. This number increases by between 16 and 28% given climate change impacts. For a 50-year return period under a high climate scenario, 40% of today’s poor people in Vietnam are exposed to flooding. For a 200-year return period under a high climate scenario, more than half of today’s poor are exposed. Similar to the population analysis, the impact of climate change on the number of poor people exposed is
will be biased (Heckman 1979; Lee 1983). In Section 2 we develop a theoretical model that improves on past efforts to model irrigation with the Ricardian approach by explicitly addressing farmer choice and selection bias. We tested this model empirically using a sample of over 8400 farmers from across 11 African countries. The results reveal that the choice of irrigation is endogenous. Farmers select irrigation rather than dryland to maximize profits. As temperatures warm or precipitation declines, farmers turn to irrigation to keep their farms viable. As long as there is a sufficient flow of water, irrigation is an important adaptation strategy. We then used this empirical model to examine the welfare impacts of climate change on African agriculture. Using a mild and a severe climate scenario, we examined how irrigation and net revenues will be affected. We compared the results of our model with endogenous irrigation with a model that assumes irrigation is exogenous. We found evidence of selection bias but, more importantly, we found that treating irrigation as though it is exogenous leads to biased welfare estimates. The paper concludes by summarizing the results and discussing some policy implications. 4
**REFERENCES** Basist A et al., 2001. Using the Special Sensor Microwave Imager to monitor surface wetness. _Journal of Hydrometeorology_ 2: 297–308. Cline WR, 1996. The impact of global warming on agriculture: Comment. _American_ _Economic Review_ 86: 1309–1312. Darwin R, 1999. The impacts of global warming on agriculture: A Ricardian analysis: Comment. _American Economic Review_ 89: 1049–1052. Dubin JA & McFadden DL, 1984. An econometric analysis of residential electric appliance holdings and consumption. _Econometrica_ 52(2): 345–362. FAO (Food and Agriculture Organization), 1997. Irrigation potential in Africa: A basin approach. _FAO Land and Water Bulletin,_ 4, FAO Land and Water Development Division, Rome. FAO (Food and Agriculture Organization), 2003. The digital soil map of the world: Version 3.6 (January), Rome, Italy. Heckman JJ, 1979. Sample selection bias as a specification error. _Econometrica_ 47: 153–161. Heltberg R & Tarp F, 2002. Agricultural supply response and poverty in Mozambique. _Food_ _Policy_ 27: 103–124. Kurukulasuriya P & Mendelsohn R, 2005. A regional analysis of the impact of climate change on African agriculture, Mimeo, Yale University. Lee LF, 1983. Generalized econometric models with selectivity. _Econometrica_ 51: 507–512. Mendelsohn R & Dinar A, 2003. Climate, water, and agriculture. _Land Economics_ 79(3): 328–341. Mendelsohn R & Nordhaus W,
period hazard maps used for each of the future scenarios are outlined in Table 2. Although simplistic, this method allows areas that may be impacted by increasing riverine and extreme rainfall driven flooding to be identified. Clearly there are some significant assumptions and uncertainties arising from this method. However, given the impracticalities of modeling future flood hazard in Vietnam, this approach provides a plausible and practical attempt to estimate changing flood hazard at the national scale. For each of the four return periods, four scenarios are modeled (historical, future with low sea level rise, future with medium sea level rise, and future with high sea level rise), combining the coastal and fluvial/pluvial hazard layers (Table 2). For full details on the methodology used to produce these hazard maps, see the online supplementary materials. Importantly, the flood hazard models do not include flood protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well-protected areas. In these wellprotected areas, our flood maps may overestimate the flood hazard. This is a data limitation that affects many studies of flood exposure: even in high-income countries like the US, flood protection databases are incomplete and
prevalence of ED incidents related also leads to restrictions on the mobility of civilian populations on the roads to weekly markets, and the main cities of communities/departments. About 36% of those interviewed [6] say they encounter mobility constraints due to the activities of the NSAGs and military operations. This indicator varies from one region to another. In the regions of Diffa and Tillabery, 56% of respondents say they encounter mobility constraints, one of the main reasons being the presence of ED. This considerably limits the periodic supply of households with means of subsistence and necessities in addition to the fear and psychological distress that the unpredictable presence of ED inflicts on communities. It is also important to highlight that of the 11 affected departments in the Diffa and Tillabéry regions, 9 (82%) are also affected by the food insecurity [7] . This could exacerbate 4 CNCCAI: The disaggregation by sex and age of the 2023 data for victims is not yet available. However, according to the Child Protection Sub-cluster, the Child Protection Working Group of the Diffa region recorded 18 child victims of ED in 2022, the majority of whom (74%) are girls who went to look for firewood. 5
the last quarter of 2022 to the 2nd reported mainly in the regions of Tillabery **2023, which indicates a worrying increase** **in the threat of ED in Niger.** This threat 1 UNHCR Niger montlhy PoC statistics, Juin 2023 2 Population displacement statistics, Ministry of Humanitarian Action and Disaster Management, july 2023 3 Source : National Commission for the Collection and Control of Illicit Weapons (CNCCAI in french)
already causes major problems in Vietnam, with some segments of the population more vulnerable than others (Adger 1999; World Bank 2010; World Bank and Australian AID 2014). In particular, evidence suggests poor people are more vulnerable than the rest of the population to natural disasters such as floods, as their incomes are more dependent on weather, their housing and assets are less protected, and they are more prone to health impacts (Hallegatte et al. 2016, Chapter 3). Poor people also have a lower capacity to cope with and adapt to shocks due to lower access to savings, borrowing, or social protection; and climate change is likely to worsen these trends (Hallegatte et al. 2016, Chapter 5). Therefore, it is important to quantify how many people are exposed to floods, how this distribution of exposure falls upon regions and socioeconomic groups, and how climate change may influence these trends. In the spirit of a Bstress-test^ to examine the seriousness of the issue, this paper employs flood hazard maps and spatial socioeconomic data to examine the following questions in context of Vietnam: 1. How many people are exposed currently? How might this change under climate change? 2. Where is exposure highest currently?
Additionally, climate change exacerbates existing pressures, with rising temperatures and erratic rainfall further straining local resources and food security. 2. **These economic and climate constraints contribute to persistently high levels of inequality and social** **vulnerability** . Using the World Bank poverty line for Low Middle-Income Countries (LMIC) of US$3.65 a day (2017 purchasing power parity), the extreme poverty rate for 2022 is estimated at 39 percent (World Bank 2023a). Inequality in Djibouti is among the highest in the Middle East and North Africa, with an estimated Gini coefficient of 0.42 in 2017. The highest income decile of the population consumes approximately 16 times as much as the lowest decile and twice that of the ninth decile (World Bank 2019a). Extreme poverty is particularly severe in rural areas, where it reaches 62.6 percent, much higher than in urban Djibouti. Water management and planning performance, integrating cooling systems, diversifying livelihoods, and responsive safety nets were key recommendations from the Climate Change Development Report (2024) for Djibouti. 3. **Low human capital further constrains economic and climate resilience and limits opportunities for inclusive** **growth.** Djibouti’s LMIC status contrasts sharply with its poor human capital outcomes (World Bank, HCR, 2024). Deficiencies are evident throughout the
stem from the same occupational variable in the DHS data, and are mutually exclusive. The surveyed individual is told to report their main occupation. The coefficients can therefore be interpreted as relative increases of each specific sector. Women are more likely to earn cash for work, and the likelihood increases by 5.4 percentage points, which is equal to a 6 percent increase. While the directionality of the occupational outcomes is broadly in line with previous results (Kotsadam and Tolonen, 2016, for 29 African countries, and Benshaul-Tolonen, 2018, for 8 African gold-producing countries), the estimates are largely insignificant, potentially due to a limited sample size. Two categories have positive, albeit insignificant, coefficients: services and manual labor. The (insignificant) estimate for service jobs [9] is equivalent to 6.7% increased employment, and manual labor 10.2%, alongside which the likelihood that a woman earns cash for her work increases with 6%. For men (panel B of table 4), the estimates point toward an increase in agriculture, services, and professional (all statistically insignificant estimates), but a decreased likelihood of working in manual labor. Results for men in panel B are largely insignificant, and it is worthwhile noting that the sample size is only slightly above
these mines. **Figure 2 Gold mines and DHS clusters in Ghana** **Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones** 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative. 8
regions alike. Figure 10 especially highlights high-risk regions in coastal regions and large river basins – such as the Mississippi, Nile, Ganges, Brahmaputra, or Mekong basins. _Figure 10. Share of total subnational population that is exposed to significant flood risk (percent)_ Pool (Congo Republic) Pathum Thani (Thailand) N'djamna (Chad) Sing Buri (Thailand) Red River Delta (Vietnam) Unity (South Sudan) Mekong Delta (Vietnam) Phra Nakhon Si Ayu (Thailand) Vientiane (Laos) Nonthaburi (Thailand) _Figure 11. Top 10 countries: Share of total subnational population that is exposed to significant flood risk_ **5.4.** **Income levels, poverty, and flood exposure** **The flood exposure of economic production value.** By taking into account per capita income levels, it is possible to obtain estimates of the value of economic activity that is exposed to flood risks around the world. Specifically, we multiply flood exposure headcounts with subnational estimates of daily income per capita (in $PPP terms, as obtained from the World Bank household survey database, Section 3.4). This yields the estimate that $5.3 trillion of 16
43.7 60.5 24.5 Number of trees per acre 71.1 99.8 15.1 79.7 138.3 12.8 Planted coffee trees during the past 5 years 8.7 11.7 2.8 7.3 12.8 1.1 Planted fruit trees during the past 5 years 16.2 21.2 6.5 12.3 20.9 2.6 Planted soil fertility trees past 5 years 3.8 4.8 2.0 2.8 4.8 0.5 Planted (any) trees during the past 5 years 18.6 24.4 7.3 14.5 24.6 3.0 Number of trees planted past 5 years 16.6 22.4 5.3 17.8 28.1 6.1 Soil cons. (bunds, terracing, mulching) 20.5 24.7 12.3 19.9 27.6 11.0 Use of manure during the past year 7.8 9.6 4.2 7.2 11.2 2.7 Number of observations (parcels) 13130 8652 4478 5448 2904 2544 _Source:_ Own computation from 2005/06 UNHS III 18
based on data from BASIX. In panel B, I restrict the analysis to villages that had at least one buyer the year after insurance outcomes, creating a sample analogous to the “Marketing Restricted Sample” in table 3. The logic behind this is, if a village had zero buyers, it is likely that insurance was not marketed in the village that year, and therefore customers did not have an opportunity to purchase insurance. Restricting the data set in this way gives a much clearer pattern. Column 5 now shows much stronger effects of payouts on repeat buying, though the pattern is the same as in column 2. Small payouts have a negative effect, whereas increasing the payout ratio increases repeat buying. The squared term on the payout ratio is now negative and significant, indicating that high payout ratios have diminishing effects. The coefficients for new buyers in column 6 are now all significantly different from the coefficients for repeat buyers. In fact, the coefficients in column 5 flip signs, suggesting that payouts have the opposite effect on people who did not receive payouts. These results suggest that low payouts actually induce more new buyers, but that these effects decrease and then
Based on requests, the World Bank will transfer advances to the DAs. Subsequent replenishment of the DA will be based on the submission of application of withdrawal, accompanied by quarterly IFRs, which are reviewed and cleared by the World Bank. 57. **For activities implemented at the district level, MINEMA will disburse funds directly to each district into a** **separate operational project account denominated in RWF opened at the Central Bank.** Funds will be disbursed as per modalities stipulated in the TDIAs, which will include at a minimum an approved annual work plan and cash flow plan. Subsequent requests will be based on modalities defined in the PIM and the TDIAs. **IV.** **PROJECT APPRAISAL SUMMARY** **A. Technical, Economic and Financial Analysis** **Economic Analysis** 58. Based on the economic and financial analysis, the net present value (NPV) is estimated at US$25.2 million at a 12 percent discount rate, and the economic rate of return (ERR) at 26 percent. [25] For the purpose of this analysis, we consider anticipated economic benefits as the income of the project investments—therein applying the discounted cash flow model for financial analysis of private investments within the context of a World Bankfinanced project. Key variables used as assumptions
incorporate our long-run panel of cyclone data may contribute important insights about climate change adaptation on the coastal “front line”. 23
**Figure A1. Access to infrastructure: Varying the cutoff and spatial lag model** **Panel A Household has electricity** **Panel B Household has radio** **a. Varying cutoff** **a. Varying cutoff** **b. Spatial lag model** **b. Spatial lag model** **c. Spatial lag model by migration status** **c. Spatial lag model by migration status** _Note:_ The figure shows the main treatment coefficients ( _active*mine_ ) using the baseline estimation strategy (with DHS individual-level data; see table 4 for more information) in panel A, but with different distance cutoffs (10 km, 20 km, 30 km, 40 km, or 50 km). *** p<0.01, **p<0.05, *p<0.1. Panels Ab, Ac, Bb, and Bc show the result using spatial lag models, which divided the plane into different treatment bins (0–10, 10–20, 20–30, 40–50) and compares them with farther away distances. Panel B shows the result for all individuals, and panel C shows the main treatment result (active mine) when the sample has been split into migrants and nonmigrants. 44
The World Bank’s Environment Strategy focuses on both the threat to biodiversity from human encroachment, and the value of biodiversity resources for human populations. Our indicator for this two-way relationship in each country is its total human population in critical biodiversity areas. For marine biodiversity, we draw on estimates of reef ecosystems at risk by Bryant, et al. (1998). Summing across all endangered reefs, we use each country's share of the total as our index of marine biodiversity threat. While terrestrial and marine threats are quite distinct geographically, we create a composite indicator to match the Bank's thematic category (biodiversity conservation). Since the two indices are weakly correlated (ρ = .27), assignment of relative weights has a significant impact on the result. We assign equal weights, because we have no scientific basis for a differentiated weighting scheme. 5
and good governance. Additionally, the project supports key findings from the World Development Report 2023 on Migrants, Refugees and Societies, [18] by enhancing the freedoms, self-reliance and inclusion of both refugees and host community within national delivery systems. Furthermore, each activity will integrate climate-resilient design to reduce material risks to an acceptable level, ensuring long-term sustainability in the face of the country’s exposure to identified climate risks (extreme heat, droughts, floods). 18. **The challenges faced by Djibouti are further highlighted in the Country Climate and Development Report (CCDR)** **(2024), which identifies key areas for intervention, including strengthening water management, integrating cooling** **systems, diversifying livelihoods, and enhancing responsive safety nets** . The Integrated Community Resilience Project (ICRP) aligns with these recommendations, supporting Djibouti’s efforts to build climate resilience while addressing urgent needs in rural areas, particularly through enhancing the adaptive capacity of vulnerable populations. 19. **The project is aligned with the National Adaptation Plan (NAP) (2019)**, aiming to reduce vulnerability to climate change and to facilitate integration of adaptation into all levels of development planning including (i) access to water for all; ii) the promotion of best practices in the agricultural, forestry, fishery, and tourism sectors and reduced vulnerability to the
-0.094** -0.062 3.893 -0.064 -0.031 0.126*** (0.042) (0.040) (3.842) (0.066) (0.038) (0.042) _3. drop 2 years before_ active*mine -0.094** -0.062 3.708 -0.071 -0.026 0.125*** (0.041) (0.039) (3.459) (0.067) (0.036) (0.043) _4. mine FE_ active*mine -0.123** -0.094* 8.233 -0.068 -0.049 0.113** (0.057) (0.051) (5.425) (0.075) (0.044) (0.045) _5. mine clustering_ active*mine -0.086*** -0.055** 3.705 -0.058 -0.032 0.125** (0.025) (0.025) (2.898) (0.086) (0.032) (0.051) Mean dep var 0.715 0.705 45.71 0.491 0.259 0.028 _Note:_ The table uses GLSS data for Ghana for the survey years 1998, 2005, 2012. The sample is restricted to women and men aged 15–49. Robust standard errors clustered at the village or neighborhood level in parentheses (except if otherwise stated). All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. The treatment distance is defined to 20 km. Rows 2 drop sample between 20 to 40 km of a mine, and rows 3 drop sample
effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. The treatment distance is defined to 20 km. Rows 2 drop sample between 20 to 40 km of a mine, and rows 3 drop sample that was surveyed two years before mine opening. *** p<0.01, **p<0.05, *p<0.1. FE = fixed effects. 30
poor households’ productivity and resilience to lift their consumption permanently above the official poverty line. 4. **The government has embraced the long-term development approach to refugee management, aligning with** **international good practice.** [6] Important gains over the last five years include: (a) almost all refugee children now attend national schools rather than camp-based schools; (b) most urban refugees have access to the government’s national health insurance scheme; (c) plans are in place to transition camp-based health clinics to government management to lower costs and support integration of refugees into national service delivery systems; and (d) the government is implementing an economic inclusion strategy for refugees and host communities. Investments made over the last five years have addressed many of the impacts of the refugee presence that had created tensions with host community members. Survey data from the mid-term review (MTR) of Jya Mbere Phase I showed that this has strengthened social cohesion between refugees and host communities, creating a strong foundation for future economic progress. 5. **Building on the gains made, the government’s strategic aim for refugee management is now to promote** **self-reliance.** As one of the four objectives of the GCR, self-reliance is also central to both the National