rafmacalaba/datause-encoder-data huggingface dataset:
Figure 23: Top Three Cultivated Crops in Sudan by Region (Proportion of Households), 2014/15

# (a) Nile Region (Northern, River Nile)

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br> wheat egypt beans dates<br><!-- End of picture text -->

- (c) Khartoum

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br> sorghum other fruit other vegetable<br><!-- End of picture text -->

- (b) Eastern Region (Red Sea, Kassala, Al-Gadarif)

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br> sorghum seasam groundnut<br><!-- End of picture text -->

- (d) Blue and White Nile Regions (Al-Gezira, White Nile, Sinnar, Blue Nile)

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br> sorghum seasam groundnut<br><!-- End of picture text -->

- (e) Kordofan Region (North, South, and West Kordofan)

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br> sorghum millet seasam<br><!-- End of picture text -->

- (f) Darfur Region (North, West, Central, and East Darfur)

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br> sorghum millet groundnut<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

26
Figure 15: Change in sales and available liquidity: number of days to cover costs and average predicted probability of falling in arrears.

<!-- Start of picture text --> 150 .7<br>.6<br>100<br>.5<br>50<br>.4<br>.3<br>0<br>-100 -50 0 50 -100 -50 0 50<br>Percentage change in sales Percentage change in sales<br>can cover costs with cash available<br>Average number of days that the business or expected to fall in the coming 6 months<br>Average predicted probability of falling into arrears<br><!-- End of picture text --> Note: Predicted probability from Probit models that control for change in sales, and dummies for country, size, sector, and weeks before and after the peak of the mobility shock. The analysis first conditions on the variable on the x-axis and computes the average over businesses in that category in the y-axis.

Figure 16: Fraction of businesses across responses to the shock.

<!-- Start of picture text --> 35 34<br>30<br>25<br>21<br>20<br>17<br>15<br>10<br>5<br>5<br>0<br>Increased use Investment in Other products Innovation<br>of digital digital innovation into health<br>platforms solutions products<br><!-- End of picture text --> Note: Unconditional mean.

28
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.1 Trends in infrastructure endowments, by world region, 1960–2000**

||Region|1960|1970|1980|1990|2000<br>a|Average<br>annual<br>growth<br>b<br>(percent)|
|---|---|---|---|---|---|---|---|
||SSA|6|8|21|26|39|5.0|
||SAS|60|76|115|113|146|2.3|
|Density of paved road network<br>|EAP|12|21|37|39|54|4.0|
|(km/1,000 km<br>2)|ECA|54|239|312|362|351|4.9|
||LAC|132|185|59|274|361|2.6|
||MNA|14|19|26|39|45|3.1|
||SSA|71|120|544|652|1,037|7.1|
||SAS|477|434|585|711|670|0.9|
|Density of paved road network<br> <br>2|EAP|239|392|563|453|556|2.2|
|(km/1,000 arable km<br>)|ECA|110|498|687|800|1,197|6.3|
||LAC|472|808|527|1,973|3,958|5.6|
||MNA|196|320|3,283|4,594|5,729|9.0|
||SSA|73|80|106|119|143|1.7|
||SAS|104|145|204|204|303|2.8|
|Density of total road network|EAP|55|75|144|157|175|3.0|
|<br>(km/1,000 km<br>2)|ECA|298|576|567|630|620|1.9|
||LAC|267|198|241|516|583|2.0|
||MNA|45|50|77|92|104|2.2|
||SSA|1,525|1,577|2,106|2,343|2,556|1.3|
||SAS|757|699|888|1,084|1,408|1.6|
|Density of total road network|EAP|3,064|2,671|3,244|3,063|3,252|0.2|
|<br>(km/1,000 arable km<br>2)|ECA|651|1,188|1,261|1,543|2,045|3.0|
||LAC|1,962|2,138|2,743|4,929|7,021|3.3|
||MNA|558|760|33,263|28,850|30,704|10.8|
||SSA|—|4|7|11|22|5.6|
||SAS|—|2|2|5|21|8.9|
|Density of fixed-line telephones|EAP<br>|—|5|10|21|58|8.9|
|(subscribers per 1,000 people)|ECA|—|32|76|134|229|6.8|
||LAC|—|24|44|75|155|6.4|
||MNA|—|9|18|41|84|7.7|
||SSA|—|—|71|82|73|0.1|
||SAS|—|—|26|127|137|8.6|
|Electrical generating capacity|EAP|—|—|123|178|229|3.2|
|(MW per 1 million people)|ECA|—|—|587|786|1,022|2.8|
||LAC|—|—|309|409|495|2.4|
||MNA|—|—|207|421|413|3.5|
||SSA|—|—|—|51|63|1.0|
||SAS|—|—|—|72|70|–0.2|
|Water|EAP|—|—|—|73|73|–0.03|
|(% of households with access)|ECA|—|—|—|93|89|–0.4|
||LAC|—|—|—|81|89|0.7|
||MNA|—|—|—|83|83|–0.02|

19
education in camps, where more than 80% of IDPs state that better access to education is what makes them want to stay in camps.

_Table 11: Proportion of deprived individuals across displacement status by gender, Sudan_

|||IDPs<br>|<br>||Hosts<br>||
|---|---|---|---|---|---|---|
||Male|Female|Difference|Male|Female|Difference|
|_All households_|||||||
|Years of schooling|0.28|0.29|-0.01|0.26|0.19|0.07<sup>**</sup>|
|School attendance|0.22|0.21|0.00|0.15|0.12|0.02|
|Unemployment|0.08|0.10|-0.02<sup>**</sup>|0.07|0.18|-0.11<sup>***</sup>|
|Legal identification|0.10|0.10|-0.00|0.07|0.10|-0.02<sup>***</sup>|
|Early marriage|0.06|0.50|-0.43<sup>***</sup>|0.07|0.42|-0.35<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|0.38|0.43|-0.05|0.42|0.26|0.15|
|School attendance|0.36|0.36|-0.01|0.29|0.39|-0.10|
|Unemployment|0.14|0.17|-0.03|0.17|0.29|-0.12<sup>*</sup>|
|Legal identification|0.13|0.14|-0.01|0.18|0.21|-0.03|
|Early marriage|0.09|0.64|-0.56<sup>***</sup>|0.14|0.63|-0.50<sup>***</sup>|

Source: Authors’ calculations using the Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

Early marriage remains an important issue for both displaced and non-displaced females, as it displays the highest deprivation rate among women and exhibits the lowest deprivation rate among men. The results show a substantial gender gap in early marriage among the displaced population (43-percentage points) and hosts (35-percentage points). Among MPI poor households, the highest gender gap is also observed in early marriage, for both groups.

# **_5.3 Intrahousehold inequalities_**

Our final hypothesis investigates whether forcibly displaced households have higher rates of intrahousehold inequality than their non-displaced peers, and whether girls are more likely to be disadvantaged. We focus on four individual-level indicators – school attendance, primary school completion, unemployment, and legal identification. Displaced people experience multidimensional deprivations that result both from their current circumstances and the disadvantages they may have accumulated in their home country. For example, refugees fleeing long-term conflicts may not have completed 22
global scope, staff travel regularly. From July 2015 until June 2016, 56,090 World Bank flights were booked originating from Headquarters (Washington, D.C.), with top destinations all over the map, from Johannesburg to Kathmandu. Adding another 11,086 trips originating from country offices, the average number of World Bank flights per week amounts to 1,291. Third, web-based surveys are frequently used at the institution to collect data on a wide range of issues – from the quality of commuter services to workforce perspectives on managerial effectiveness. For instance, since 2009 IEG alone sent out 208 web-based surveys, corresponding to an average of 23 surveys per year.<sup>5</sup> As Porter et al (2004) note, overexposure to surveys may lead to survey fatigue and declining response rates.

Indeed, World Bank survey response rates are generally low and tend to be declining over time. For example, since 1999 IEG has surveyed its clients – WBG staff, the Board of Directors and external stakeholders – annually to gauge their perceptions of the quality and impact of IEG evaluations. In 2006, 22 percent of WBG staff responded to the client survey. In 2012 the response rate dropped to 11.3 percent while the 2015 client survey response rate plunged to a dramatic 4.7 percent.

Given these negative trends, it was found important to increase the response rate for this survey. Therefore, IEG’s Director General sent out the initial email invitation, including the expected duration of the survey, the survey deadline and a web link to the survey at the bottom of the invitation. Two reminder emails were sent out, one three weeks after launching the survey and another one the day before closing the survey.

> 5 This includes both internal surveys targeting IEG staff as well as external surveys targeting the World Bank Group and other stakeholders (e.g., client governments).

4
Table C13: Results Pooled: Job Benefits - Heterogeneity by University

||(1)<br>Area<br>Studies|(2)<br>Contract<br>Written|(3)<br>Contract<br>Permanent|(4)<br>Job<br>Benefts|(5)<br>Registered<br>Firm|
|---|---|---|---|---|---|
|Offered Training|0.05<sup>_∗_</sup>|0.03|0.03|-0.00|0.05<sup>_∗_</sup>|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.03)|
|Offered * University degree|-0.17<sup>_∗∗∗_</sup>|0.04|0.04|0.05|0.04|
||(0.06)|(0.06)|(0.04)|(0.04)|(0.06)|
|Control Mean: University degree|0.39|0.30|0.08|0.15|0.41|
|Control Mean: Diploma|0.60|0.28|0.10|0.13|0.39|
|Observations|2,597|2,597|2,597|2,597|2,597|
|p-value University|0.03|0.17|0.05|0.27|0.07|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

Table C14: Results Pooled: Channels - Heterogeneity by University

||(1)<br>Searched For Job<br>in Last Month|(2)<br>Confdence Approaching<br>Employers (0-3)|(3)<br>CV in<br>Digital Format|(4)<br>People Discusses<br>Jobs with|
|---|---|---|---|---|
|Offered Training|0.04|0.03|0.02|0.82<sup>_∗∗∗_</sup>|
||(0.03)|(0.04)|(0.01)|(0.20)|
|Offered * University degree|-0.10|0.12|0.03|0.35|
||(0.06)|(0.08)|(0.04)|(0.43)|
|Control Mean: University degree|0.63|2.53|0.91|5.53|
|Control Mean: Diploma|0.68|2.48|0.85|5.07|
|Observations|1,728|2,597|2,597|2,597|
|p-value University|0.26|0.03|0.12|0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

49
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# **Appendix Table 1: Descriptive statistics of initial year characteristics of women in migrant and non-migrant households**

||to|tal|migrant|households|non-<br>hous|migrant<br>eholds|
|---|---|---|---|---|---|---|
|aged 16-20|0.075|(0.264)|0.062|(0.241)|0.08|(0.272)|
|aged 21-25|0.072|(0.258)|0.034|(0.181)|0.083|(0.276)|
|aged 26-30|0.103|(0.304)|0.04|(0.196)|0.123|(0.328)|
|aged 31-35|0.124|(0.330)|0.104|(0.306)|0.132|(0.339)|
|aged 36-40|0.113|(0.316)|0.128|(0.335)|0.108|(0.310)|
|aged 41-45|0.132|(0.338)|0.198|(0.399)|0.108|(0.310)|
|aged 46-50|0.138|(0.345)|0.196|(0.397)|0.118|(0.322)|
|aged 51-55|0.113|(0.316)|0.13|(0.337)|0.107|(0.310)|
|aged 56-60|0.084|(0.278)|0.07|(0.256)|0.091|(0.288)|
|aged 61-64|0.046|(0.209)|0.037|(0.190)|0.05|(0.217)|
|years of schooling|6.747|(3.556)|6.729|(3.331)|6.736|(3.675)|
|years of schooling of head|6.821|(3.486)|6.771|(3.184)|6.817|(3.613)|
|age of head|47.302|(10.659)|46.923|(8.865)|47.45|(11.291)|
|ethnic minority household head|0.136|(0.343)|0.162|(0.369)|0.127|(0.333)|
|household size|4.292|(2.145)|4.691|(2.293)|4.117|(2.062)|
|number of female children younger than 6|0.102|(0.318)|0.071|(0.283)|0.114|(0.329)|
|number of male children younger than 6|0.133|(0.371)|0.094|(0.309)|0.146|(0.390)|
|number of female children aged 7-15|0.308|(0.571)|0.47|(0.677)|0.248|(0.514)|
|number of male children aged 7-15|0.354|(0.579)|0.52|(0.664)|0.292|(0.530)|
|number of working age men (16-60)|1.556|(0.784)|1.682|(0.841)|1.515|(0.757)|
|number of working age women (16-60)|1.509|(0.865)|1.528|(0.798)|1.518|(0.885)|
|number of elderly women (60 or older)|0.136|(0.350)|0.133|(0.342)|0.139|(0.355)|
|number of elderly men (60 or older)|0.117|(0.324)|0.087|(0.282)|0.131|(0.340)|
|married|0.787|(0.410)|0.826|(0.379)|0.775|(0.418)|
|single|0.17|(0.376)|0.128|(0.334)|0.183|(0.387)|
|household real income per capita (log)|7.937|(1.084)|7.777|(1.114)|8.012|(1.062)|
|household member has bad health|0.088|(0.283)|0.111|(0.314)|0.08|(0.271)|
|Liaoning|0.052|(0.222)|0.041|(0.198)|0.057|(0.231)|
|Heilongjiang|0.117|(0.322)|0.031|(0.174)|0.153|(0.360)|
|Jiangsu|0.092|(0.289)|0.098|(0.298)|0.089|(0.285)|
|Shandong|0.138|(0.345)|0.087|(0.282)|0.158|(0.365)|
|<br>Henan|<br>0.113|<br>(0.316)|0.127|<br>(0.333)|<br>0.108|<br>(0.310)|
|Hubei|0.114|(0.318)|0.172|(0.377)|0.09|(0.286)|
|Hunan|0.107|(0.310)|0.116|(0.321)|0.105|(0.306)|
|Guangxi|0.143|(0.350)|0.189|(0.392)|0.122|(0.328)|
|Guizhou|0.124|(0.330)|0.139|(0.346)|0.118|(0.323)|
|obs.|34|01|1|252|2|149|

45
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140
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persistent decline in GDP per capita.<sup>7</sup> Noy and Nualsri (2007) argue that a negative shock to human capital (loss of human lives) due to natural disasters has a significant negative effect on the medium-run (five year average) growth rate of real per capita GDP, while a similar shock to physical capital has a positive but insignificant effect. Loayza et al. (2012) find that the intensity of floods (measured using the fraction of the population affected) has a significant positive impact on medium-run GDP growth. Fomby, Ikeda, and Loayza (2013) find that the cumulative effect of droughts on GDP per capita in developing countries is negative, while it is positive for floods. Both Loayza et al. (2012) and Fomby, Ikeda, and Loayza (2013) attribute the positive impact of floods to possible productivity-enhancing effects in terms of improved land fertility and irrigation.

In contrast to the mixed findings on the medium- to long-run impacts of natural disasters, there is a broad consensus that their short-run impacts are negative. For example, Raddatz (2007) finds that the incidence of climatic disasters (floods, droughts, extreme temperature, and windstorms) and humanitarian disasters (famines and epidemics) has large, negative, and statistically significant impacts on the real GDP of low-income countries in the short run (one year after the event). Similarly, Noy (2009) argues that a negative shock to the capital stock due to property damage caused by natural disasters negatively affects the growth of real GDP in the same year. Adverse impacts of natural disasters on output are also documented by Hochrainer (2009). Raddatz (2009) notes that most of the negative impact of climate disasters on economic growth is realized in the year of the disaster itself. Felbermayr et al. (2022) investigate how weather anomalies affect economic activity approximated by nighttime light emissions. They find significant effects on local growth and significant spatial spillovers, both driven by lower income regions. Studies differentiating the impact on growth in developing and advanced countries consistently show that developing countries are more sensitive and vulnerable to natural disasters (Noy 2009; Raddatz 2009; Loayza et al. 2012; Fomby, Ikeda, and Loayza 2013).

> 7They define large natural disasters using the distribution of number of people killed due to natural disaster from EM-DAT.

6