rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 17: Difference in mean outcome between historical and RCP 4.5 drought shock simulations**

<!-- Start of picture text --> % Real GDP % % GVA - Agriculture %<br>0.1 0.1 1.0 1.0<br>0.0 0.0 0.0 0.0<br>-0.1 -0.1 -1.0 -1.0<br>-0.2 -0.2 -2.0 -2.0<br>-0.3 -0.3 -3.0 -3.0<br>-0.4 -0.4 -4.0 -4.0<br>-0.5 -0.5 -5.0 -5.0<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Lower 95% C.I. Mean Lower 95% C.I.<br>Note: Change in the mean outcome (light green) and the lower bound of the 95 percent confidence interval (dark green) are<br>shown.<br>6.2 Floods<br>If flooding continues at historical rates, the cumulative impact on GDP would reach over -0.2 percent<br>on average by 2100 (Figure 18). Capital stock would be depleted by 0.5 percent on average, with<br>investment levels reduced in the economy due repeated flooding diverting funds to repairs. These<br>impacts, while having roughly the same average impact as droughts on GDP by 2100, have smaller<br>confidence intervals, with the lower bound outcome resulting in -0.6 percent lower GDP. Under climate<br>change, flooding shocks become more frequent, with their frequency doubling for every degree Celsius<br>of temperature increase. Under this scenario, the mean impact on GDP increases to over -0.4 percent by<br>2100, and capital stock is reduced over 1.2 percent with the same frequency by 2100. (Figure 19).<br>Figure 18: Historical / baseline impacts of flooding<br>Real GDP Capital Stock<br>% % % %<br>0.2 0.2 0.5 0.5<br>0.0 0.0 0.0 0.0<br>-0.2 -0.2 -0.5 -0.5<br>-0.4 -0.4 -1.0 -1.0<br>-0.6 -0.6 -1.5 -1.5<br>-0.8 -0.8 -2.0 -2.0<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Median Mean Median<br><!-- End of picture text --> Note: Results are presented as percent deviations from baseline. Confidence intervals of 68 percent (dark blue) and 95 percent (light blue) are shown.

25
_Table_ 2 _. Descriptive statistics of the sample by income shock status_

||<br>|Argentina<br>|||Brazil<br>|||Mexico<br>||
|---|---|---|---|---|---|---|---|---|---|
||No Shock|Shock||No Shock|Shock||No Shock|Shock||
|Sample size|28,081|12,647||66,782|20,843||42,992|21,743||
|Weighted population|10,792,286|5,193,232||11,206,430|3,161,991||10,263,658|5,283,793||
|_Individual characteristics_||||||||||
|Age|17.9|18.1||17.8|18.0||17.5|17.7||
|Male (%)|0.49|0.48|(n.s.)|0.51|0.51|(n.s.)|0.51|0.51|(n.s.)|
|Years of education|10.9|11.0||9.4|9.6||10.6|10.7||
|_Activity (%)_||||||||||
|School only|0.87|0.84||0.72|0.69||0.85|0.82||
|School and employed|0.13|0.16||0.28|0.31||0.15|0.18||
|Economically active|0.17|0.19||0.34|0.38||0.17|0.20||
|_Household's Main earner_||||||||||
|Age|45.1|44.3||42.8|42.7|(n.s.)|42.5|41.8||
|Female (%)|0.29|0.31||0.31|0.36||0.26|0.31||
|Years of education|11.4|11.4|(n.s.)|9.9|10.2||10.3|10.4|(n.s.)|
|_Household_||||||||||
|Household size|4.9|5.0||4.1|4.1|(n.s.)|4.7|4.8|(n.s.)|
|Members employed|1.9|2.0||2.1|2.1||1.9|1.9||
|Labor income p.c.|294|391||387|520||218|303||
|Numberofsiblings|1.0|1.0||0.7|0.7||0.8|0.9||

_Source:_ Authors’ tabulations using LABLAC (CEDLAS and the World Bank). _Note:_ Labor income per capita is expressed in 2011 PPP-adjusted USD. Differences between mean values of characteristics of students with household shocks and those without household shocks are all statistically significant at the 1% level with the exception of the values followed by _(n.s.)_ .

As described above, we limit the sample of analysis to individuals who are enrolled in school in quarter= _q-4_ . As shown in Figure 3(a), this group becomes an increasingly smaller subsample of the age group as age increases from 15 to 25. Figure 3(b) shows the school exit rates per year of age for the sample. The likelihood of leaving school is higher as individuals get older, although the relationship is not monotonic. Since upper secondary education ends at age 17 or 18, there is a spike in exits around that age.<sup>12</sup> For instance, in Brazil the likelihood of exiting school doubles from 15% to 34% when moving from age 16 to 17, and then drops to 27% for individuals aged 19.

> 12 See Appendix 3 for an explanation of the educational system in the three countries.

11
# **Figure 3 Global production, yield, and area**

Index, 1961 = 100 <!-- Start of picture text --> 400<br>Production Yield Area<br>350<br>300<br>250<br>200<br>150<br>100<br>1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016 2021<br><!-- End of picture text -->

# **Figure 4 Yield by commodity grouping**

<!-- Start of picture text --> KCal/hectare<br>15,000<br> Big-4 All Others<br>10,000<br>5,000<br>0<br>1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016 2021<br><!-- End of picture text --> _— 26 —_
corruption must not be too severe if growth is strong. The Bangladeshi case suggests that such inferences are not paramount in making assessments; it routinely ranks at the bottom of the TI index, despite experiencing fairly rapid growth in recent years. For small countries, however, on which experts tend to have less information, corruption assessments may rely more heavily on proxies such as economic conditions, or type of political regime. More research is needed concerning the impact of optimism, recent economic performance, and recent corruption scandals on country-level corruption indicators, of the expert-assessment type as well as firm and household surveys.

More research is also needed inquiring into the actual content of commonly-used indicators, as distinct from their purported content. The criteria for several sources (including ICRG, CPIA, and NIT) place great weight on state capture, but appear to be measuring primarily administrative corruption. Evidence in this paper is based solely on the ECA region, but this issue could be examined further using the full WEF sample.

The conceptual and methodological discussions, as well as empirical evidence reported here form BEEPS and other sources, strongly support the message that no single corruption measure, nor single data source on corruption, is most appropriate for all purposes. Expert ratings are defined too vaguely and broadly-and constructed too nontransparently - to be suitable for some purposes. For example, it is difficult to hold governments responsible for improving their scores on such measures, as a condition for receiving aid, if there is little indication of how scores can be improved.

Composite indexes of corruption should be used with more caution by development agencies and by researchers, recognizing that their conceptual breadth makes them appropriate for some purposes but not for others. There should be more 46
tween 2007 and 2017. Table 7 shows the number of active regulations in 2017 by country and NTM-type and its share implemented after 2007, the first year of NTM data. The majority of NTMs reported in 2017 are implemented after 2007. For the whole sample, this is the case for 53% of SPS measures, 48% of TBTs and 59% of quantity control measures. Costa Rica and Panama stand out in particular as they have implemented more than 86% of their SPS measures and TBTs since 2007. Given the prominence of technical measures, the empirical section will focus on the effect of SPS, TBTs, quantity control and PSIs measures.

Table 7: Regulations implemented since 2007 and effective in 2017

||SPS|measures||TBTs|Pre-shi|pment Inspections|Quantit|y control measures||Other|
|---|---|---|---|---|---|---|---|---|---|---|
||2017|Since 2007|2017|Since 2007|2017|Since 2007|2017|Since 2007|2017|Since 2007|
|ARG|143|41%|265|26%|9|22%|10|40%|18|28%|
|BOL|31|23%|37|19%|1|0%|5|60%|8|50%|
|BRA|199|54%|491|63%|3|67%|36|50%|7|29%|
|CHL|260|45%|194|49%|5|40%|4|50%|13|31%|
|CRI|163|86%|115|56%|0|0%|8|75%|6|33%|
|CUB|24|50%|40|25%|1|0%|6|33%|1|0%|
|ECU|150|73%|802|92%|6|67%|24|83%|6|67%|
|GTM|75|20%|71|56%|0|0%|8|50%|5|0%|
|HND|84|50%|52|50%|0|0%|0|0%|4|75%|
|MEX|134|45%|236|64%|3|33%|18|78%|3|0%|
|NIC|146|61%|258|57%|0|0%|22|91%|5|80%|
|PAN|539|95%|128|47%|11|100%|8|75%|8|0%|
|PER|231|72%|92|39%|3|33%|1|0%|10|30%|
|PRY|27|22%|74|49%|3|33%|15|73%|7|14%|
|SLV|18|78%|221|33%|0|0%|5|100%|6|50%|
|URY|114|60%|174|55%|6|33%|12|58%|8|62%|
|VEN|58|33%|91|40%|3|33%|10|80%|12|42%|
|Total|2,396|53%|3,341|48%|54|27%|192|59%|127|35%|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: This table reports the number of active regulations in 2017 by country and NTM type.

In addition to the number of active regulations and the relative importance of each NTM-type, another way to measure the incidence of NTMs is through their product coverage. Table 8 reports the number and share of products affected by at least one active NTM in 2017. Cuba and Argentina exhibit the highest product coverage: 98% of products that could be produced and imported at the HS 6-digit level are affected by at least one technical measure (SPS, TBT or PSI) while non-technical measures, such as price and quantity restrictions, affect at least 96% of imported products. At the other extreme, Paraguay has the lowest product coverage with 9% of products affected by at least one technical measure and less than 14
of BDT 400 per child per month to allow them to improve the feeding practices of their children based on the knowledge they gained through these sessions.

Three standard indices of physical growth used to describe the nutritional status of children<sup>17</sup> include (i) height-for-age or _stunting_ ; (ii) weight-for-height or _wasting_ ; and (iii) weight-for-age or _underweight_ . Each of these indices provides different information about the growth and body composition that reflects a child’s nutritional status. For instance, • <u>Stunting</u> - or height-for-age - measures linear growth. A child who is more than two standard deviations below the median (-2 SD) of the WHO reference population in terms of height-for-age is considered short for his or her age, or stunted. This condition reflects the cumulative effect of chronic malnutrition. Stunting reflects a failure to receive adequate nutrition over a long period of time and is worsened by recurrent and chronic illness. Height-for-age, therefore, reflects the long-term effects of malnutrition in a population and does not vary appreciably according to recent dietary intake.

• <u>Wasting - weight-for-height - describes current nutritional status. A child who is more</u> than two standard deviations below (-2 SD) the reference median for weight-for-height is considered to be too thin for his or her height, or wasted. This condition reflects acute or recent nutritional deficit.

• <u>Underweight - or weight-for-age - is a composite index of weight-for-height and height-</u> for-age. Thus, it does not distinguish between acute malnutrition (wasting) and chronic malnutrition (stunting). A child can be underweight for his age because he or she is stunted, because he or she is wasted, or both. Children whose weight-for-age is below two standard

> 17 For the purposes of this evaluation, the nutritional status of children in the survey population is compared with the World Health Organization (WHO) Child Growth Standards, which are based on an international sample of ethnically, culturally, and genetically diverse healthy children living under optimum conditions that are conducive to achieving a child’s full genetic growth potential. The WHO Child Growth Standards identify breastfed children as the normative model for growth and development and document how children should grow under optimum conditions and with optimum infant feeding and child health practices. These standards can be used therefore to assess the nutritional status of the beneficiary children in our sample.

27
INOOUC1nON UCxI Country, Most recent *concm,- or Infant mrtality, 1965-90 **Age-sex** structure, 1985 contraceptive prevalence territory U.N. 1990 rovisIon Bank est. IPPF Chad UNPYSA 10/89 (official est.) UNDY 1981 - Chanoi Islands U.N. 1990 revision U.N. 1988 revision Ross et al. 1988 Chile U.N. 19O revision Bcsed on Bank report 1988 Ross *t al. 1988 China (exeL. Tai wan) U.N. 1990 revision U.N. 1988 revision DHS 1986 Colombio U.N. 1990 revision U.N. 1988 revision Comoros Sased on 1984 census Bank st. -- Congo, Peoples Rep. of the Cook Island U.N. 199 revilson U.N. 1988 revision Ross *t at. 1988 Costs Rica U.N. 1990 revision U.N. 1988 revision Ross et at. 1988 Cote d'Ivoire UNPVYS 10/U (officiol est.) U.N. 1988 revision Ross et **et.** 1988 Cuba **BSed** on ULLPVU 10/89 U.N. 1988 revision Cyprus (officat est.) _UNPVYS_ 10/t (official est.) U.N. 1988 revision Survey 1977 Czechoslovskia Euroetat 1969 Eurostat 1989 U.N. 1988 Donmark U.N. 1990 revision U.N. 1988 revision -- Djibouti Based on official INK Based on UNDY 1970 -- Dominica U.N. 1990 revision U.N. 1988 revision DHS 1986 Dominican Rep. U.N. 1990 revision U.N. 1988 revision DHS 1987 Ecuador U.N. 198 revision U.N. 1988 revision DHS 1988 Egypt U.N. 1990 revision U.N. 1988 revision CPS 1988 El Salvador U.N. 1990 revisicn Bank st. -- Equatorial Guinea U.N. 1990 revision U.N. 1988 revision Ross et a*. 1988 Ethiopia -FVroe Islnds Falkland Islands **Based on** US=OC 1968 for Pacific Bank est. Federated States Isarnds of Micronesia USOCC 1968 U.N. 1988 revision Ross et **Al.** 1988 Fiji Nonnitr 1966 U.N. 1988 revision U.N. 1968 Finland Eurostat 1989 Eurostat 1989 U.N. 1988 France French Guian USSC 1966 Bank est. French Polynesia U.N. 1990 revision Bank **est.** Gabon U.N. 1990 revision Bank est. IPPF Gambia, The USSOC 1986 Bank est -. **Gaza** Strip U.N. 1990 revision U.N. 1990 revision USSOC 1986
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA

# Estimated crop-surplus regions

Based on the observed patterns above, we develop an indicator that gives an approximate measure of the crop surplus or deficit in each pixel. The indicator measures the gap between crop production and demand. The supply side is simple: the total crop value produced in a pixel. On the crop-demand side, we measure effective (or normative) demand, rather than observed (or supply-constrained) demand. The basic assumption is that the total crop demand in a pixel is proportional to the total population size in the pixel. To make supply and demand comparable, we normalize crop production and demand in each pixel using sample averages (Sub-Saharan African total).

 total crop <u>productioni</u> normalized crop production i mean of total crop production  <u>population i</u> normalized crop demandi mean of population As crop production is the major source of rural household income, this crop balance indicator also indirectly measures rural income generation relative to population. But we do not include food imports in the analysis as in Miller **Figure 2.3 Crop surplus and shortage, by travel time** (2001).<sup>15</sup> Figure 2.3 and table Crop Surplus/Shortage 2.5 show how crop balance varies across different travel time deciles. As travel time (to the nearest city with 100,000 people or more) increases, crop production responds by decreasing almost linearly, but crop demand (proportional to 0 2 4 6 8 10 population) decreases Travel time decile exponentially. This crop surplus/shortage=(a)-(b) total crop production,normalized(a) pattern can be explained total population,normalized(b) by agglomeration economies. Agglomeration of _Source:_ Own calculations. populations near well- _Note:_ Travel time deciles are based on travel time to the nearest city with 100,000 people or more. connected road networks produces positive externalities, or external economies of scale, which in turn induce people to cluster more. This process creates urban centers and clustering of road networks where 15 Miller (2001) calculates food supply as the sum of total domestic food production and food imports. For food demand, in scenario (i) he uses the 1998 Food and Agriculture Organization Statistical Databases (FAOSTAT) national averages for daily caloric consumption and the 1998 Oak Ridge National Laboratory (ORNL) population density data, and in scenario (ii) he uses an average food consumption of 2,000 calories per day per person.

10
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43
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 51 Under homoscedasticity and with clusters equal to the areas, the predicted area effects _u_ ̃ _dcw,_ 2 are identical to those of the Pseudo EB in Guadarrama, Molina and Rao (2014), _u_ ̃ _dw_ , given in (6.19). Addition ̃ ally, if no survey weights are considered ( _wdi_ = 1 for all _i_ and _d_ ), _udcw,_ 2 reduces to the best predictor of the area effect _u_ ̃ _d_ of Molina and Rao (2010), given in (6.10).

The new bootstrap procedure proposed by Van der Weide (2014) draws a sample of clusters from the present in the survey. Using the data from this bootstrap sample of clusters, the model (6.1) is fitted, and predicted cluster effects _u_ ̃ _dcw,_ 2 are calculated for the clusters _c_ that appear in the bootstrap sample of clusters. For those that do not appear, predicted cluster effects are set to zero. These predicted cluster effects are then retained in the generation of the census data of the response variable. In all the simulation experiments conducted by Corral et al. (2021), this new procedure, referred to as clustered bootstrap-EB (CB-EB), appeared to be seriously biased, while EB and Census EB performed better than ELL and CB-EB even in simulation scenarios without normality.

In an attempt to combine the positive aspects of each procedure, Corral et al. (2021) extended the Census Pseudo EB estimators of Guadarrama, Molina and Rao (2018), to the heteroscedastic nested error model by using (6.21) and (6.22), for the case when the clusters are equal to the areas ( _Cd_ = 1 for all _d_ ). They also incorporated the survey weights in the estimates of the variance components, as proposed by Van der Weide (2014). Furthermore, they adapted the parametric bootstrap procedure for MSE estimation of the Census EB, as described above, to the extended Census Pseudo EB estimators. The Stata `sae` command was updated by incorporating these extensions; for more details on the implemented computational procedures, refer to see Section 6 of Corral et al. (2021). The Pseudo EB by Guadarrama, Molina and Rao (2014) accommodates the survey weights and is expected to mitigate the design bias of EB in cases of informative sampling (or sample selection bias). However, this
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 (2013) also provide evidence from the U.S., Canada and the U.K. that parents spend more time with girls than boys in parental teaching activities such as reading and the use of numbers and letters, and these higher parental inputs for girls account for 23 percent of the gender gap in reading abilities in preschool. In developing countries, differences in parental expectations toward girls and boys are widely documented (see Bhardwaj et al. 2014 for a comprehensive review) and as such, gender

> 7 In addition to school and family factors, a range of other explanatory factors has been explored in the gender gaps literature. For example, Goldin et al. (2006) show that macro-level social and economic changes are a key explanation for the gender gap in educational attainment, while Bertrand (2011) provides an overview of how psychological and socio-psychological factors explain gender differences in educational and labor market outcomes.

5