rafmacalaba/datause-encoder-data huggingface dataset:
# Talble **3** Poverty, Inicomie Levels anid Social Indicators in Different Regions

|Region|Pover<br>Rural|ty Rate<br>Total|Per Capita<br>GDP1993<br>(thi. dongs)<br>(1989<br>Prices)|Density of<br>Population<br>Pcr sq. kmi.|111i1<br> <br>racy<br> <br>Ratec|c-infant<br>Mortality<br>Rawe (Per<br>1000<br>births)|Maternal<br> <br>Mortality<br>Rate (Per<br>1000<br>births)|Agricultural<br>Productivity<br>(Paddy<br>equivalent)<br>Tons/Hcctare|Per Capita<br> <br>Produiction<br>Agriculture<br>+ Industiry<br>(Th. dong.,|
|---|---|---|---|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|1. Northern Mountains|63|59|332.5|115|18.9|52.3|2.1|2.07|321.5|
|2. Red River Delta|55|49|448.4|1085|7.2|44.2|1.7|2.74|431.4|
|3. North Centra Coast|74|71|277.6|181|10.2|54.2|2.1|2.25|257.2|
|4. South Central Coast|54|50|331.2|157|12.3|50.6|2.2|3.13|385.2|
|5. Central Highlands|50|50|380.8|50|25.6|69.1|2.8|2.36|328.5|
|6. Southeast|_45_|33|1117.5|358|10.1|37.4|_1.5_|2.67|1281.9|
|7. Mekong Delta|52|48|472|325|15.3|51.7|2|3.95|608.1|
|All Regions|57|51|478.2|209|13.1|49.5|2|3.06|523.7|

Percentage of population above 10 years who never attended the school has been taken as a proxy for illiteracy rate in the absence of regionwise data on literacy. The Census Atlas gives percentage of population never attended school for diffcrent agc groups above 10 in each of thc provinces. By applying the age of composition in the country (information on provincewise age composition is not available), the estimate for cach of the provinces and regions has been obtained.

Source: 1.Columns 2,3 and 4 - Staff Estimates 2.Columns _5,_ 9, 10, 12 and 13 - General Statistical Office 3.Column 6 - Viet Nam Population Census; Central Census Stcering Committee 4.Colurnns 7 and 8 - Ministry of Health, Government of Viet Nam.

7
only modestly less accurate when using predictions from the geospatial model for all villages. We therefore conclude that, although predictions from the census model predict the benchmark exactly in partial registry villages, this is not a major factor in explaining the improved performance of the partial registry predictions. Instead, the partial registry provides richer data with which to train a more accurate geospatial model.

# 4. Main Results

This section shows the results of the four alternative targeting methods. We compare welfare predictions generated using each method to the benchmark predictions using rank correlations, AUC coefficients, and R-squared coefficients.

Table 6 presents the metrics for each method. The partial registry is clearly the most accurate method for targeting the poor villages in the ten UBR districts in Malawi. The second best is using the RWI to rank villages. However, this method performs only moderately well, with an AUC coefficient close to 0.60 and a 0.2 rank correlation. On the other hand, using the PMT scores as a proxy for welfare does not show promising results; the PMT scores show zero correlation with our benchmark welfare and has an AUC coefficient equivalent to guessing poor villages at random. The IHS plus geospatial variable model shows the second-lowest coefficients, only slightly higher than the PMT scores in terms of AUCs. This may be due to the limited sample size. Because the sample size is restricted to the bottom half of households within UBR districts, there are only an average of about 8 households per village available to train the model. One indication of this is that performance improves noticeably when predicting average welfare across all households in the village, as noted below.

**<u>Table 6.</u>** <u>Rank correlations, AUC, and R</u><sup>2</sup> <u>of the targeting methods</u>

|**Ran**|**k correlations**|**AUC**|**R-squared**|
|---|---|---|---|
|Partial registry (10% of the census sample)|0.75<br>|0.89|0.57|
|PMT scores|(0.02)<br>|0.50|0.00|
|IHS training sample|0.13<br>|0.53|0.01|
|RWI|0.20<br>|0.60|0.04|

**Figure 2.** Rank correlations, AUC, and R<sup>2</sup> of the targeting methods

15
|1|2|5|
|---|---|---|
|2|3|2|
|3|2|4|
|4|2|5|
|5|3|2|

The respondent answered 30 questions and was placed at Level 2 (the respondent completed three batches of questions at Level 2, and the first time after that they got to Level 3, they only got two questions right).

# _Example 3_ .

|**Batch**|**Level**|**# of items correct**|
|---|---|---|
|1|2|2|
|2|1|6|
|3|2|0|
|4|1|4|
|5|1|4|

The respondent answered 30 questions and was placed at Level 1 (the respondent completed three batches of questions at Level 1, and the last score was 3-4 questions right).

# **C.2. Item Response Theory**

We aggregate items from the tablet-based adaptive test and the paper-based test for each subject into Urdu, Mathematics, and English scores using Item Response Theory (IRT). Item Response Theory is a set of mathematical models that describe the relationship between an individual’s latent “trait” , and their manifestations (performance on a test). They establish a link between that latent trait, the properties of items on a test, and how individuals respond to these items. θθ In the two-parameter IRT model, the likelihood of answering a question correctly is determined by the ability of the respondent, , and two items parameters – difficulty (labeled a) and discrimination (labeled b). In this model, the probability that a respondent answers a given question _j_ correctly is modeled as: θθ 1 )) jj PP (θθ) = jj jj jj 1 + exp (−aa (θθ −bb 55
**Table S9. Analysis stratified by categories of** **_P.falciparum_ prevalence at baseline.** Effect of the IST intervention at 12 and 24 months follow-up on the prevalence of anemia, by baseline prevalence category of _P.falciparum_ (control school prevalence estimated using 12 month follow-up data) with adjustment for age, sex and stratification effects.

|**Prevalence of anemia**|**C**|**ontrol**|**Inte**|**rvention**|**Risk ratio**<sup>**c**</sup>|**p-value**|
|---|---|---|---|---|---|---|
||**(50**|**schools)**|**(51**|**schools)**|**(95% CI)**||
|||**n (%)**<sup>**~~b~~**</sup>||**n (%)**<sup>**~~b~~**</sup>|||
|**Follow-up 12 months**|N=2478||N=2631||||
|**Baseline %****_P.falciparum_**<sup>**a**</sup>|||||||
|<5%|787|265 (33.7%)|751|270 (36.0%)|1.01 (0.84,1.23)||
|5-19.9%|606|220 (36.3%)|858|358 (41.7%)|1.09 (0.95,1.26)|0.578|
|≥20%|655|303 (46.3%)|533|230 (43.2%)|0.99 (0.87,1.13)||
|**Follow-up 24 months**|N=2468||N=2619||||
|**Baseline %****_P.falciparum_**<sup>**a**</sup>|||||||
|<5%|740|264 (35.7%)|710|243 (34.2%)|0.95 (0.78,1.16)||
|5-19.9%|572|226 (39.5%)|803|364 (45.3%)|0.99 (0.86,1.14)|0.840|
|≥20%|623|275 (44.1%)|514|235 (45.7%)|1.03 (0.86,1.24)||

N=numbers not withdrawn or died by the time of follow-up. a Control school _P.falciparum_ prevalence was estimated using 12 month follow-up data. **b** Number and percentage with outcome c Risk ratios presented are obtained from GEE analysis accounting for school-level clustering and baseline outcome (anemia).

48
Table I.8. Robustness Analysis for the Financial Products Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.329***|0.454***|0.347***|0.341***|0.340***|0.350***|0.329***|0.454***|0.347***|0.341***|0.340***|0.350***|
||(0.071)|(0.109)|(0.088)|(0.066)|(0.075)|(0.071)|(0.071)|(0.109)|(0.088)|(0.066)|(0.075)|(0.071)|
|Obs. left|487|317|415|469|339|374|487|317|415|469|339|374|
|Obs. right|1,101|563|829|1,447|1,025|1,405|1,101|563|829|1,447|1,025|1,405|
|Mean values (Non-RAMV)|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.444***|0.463***|0.462***|0.369***|0.326***|0.379***|0.410***|0.521***|0.434***|0.371***|0.408***|0.379***|
||(0.110)|(0.109)|(0.110)|(0.085)|(0.089)|(0.084)|(0.129)|(0.154)|(0.140)|(0.095)|(0.104)|(0.093)|
|Obs. left|466|415|466|480|413|469|466|415|466|480|413|469|
|Obs. right|992|829|992|1,866|1,594|1,940|992|829|992|1,866|1,594|1,940|
|Mean values (Non-RAMV)|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.451***|0.277|0.387***|0.253**|0.274**|0.204*|0.354**|0.283**|0.434***|0.280***|0.174|0.261***|
||(0.129)|(0.177)|(0.146)|(0.113)|(0.119)|(0.118)|(0.150)|(0.134)|(0.140)|(0.099)|(0.108)|(0.101)|
|Obs. left|593|377|499|377|365|385|593|377|466|377|365|385|
|Obs. right|1,274|755|1,135|1,822|1,779|1,868|1,274|755|992|1,822|1,779|1,868|
|Mean values (Non-RAMV)|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|0.030|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
Figure 18: Ownership of Non-transport- and Transport-related Assets, 2014/15

# (a) Transport-related Assets

<!-- Start of picture text --> Urban - Non-Poor Urban - Poor Rural - Non-Poor Rural - Poor<br>60%<br>40%<br>20%<br>0%<br>Motor Vehicle Rickshaw Motorcycle Bicycle Canoe/boat Animal (for transport)<br><!-- End of picture text -->

- (b) Non-transport-related Assets

<!-- Start of picture text --> Urban - Non-Poor Urban - Poor Rural - Non-Poor Rural - Poor<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Telivision Radio Cell phone Phone (landline) Refrigerator Fan Mosquito Net<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

20
the level of economic development. When there is nothing to redistribute, public goods provision will be disturbed.

- c) Education has a positive effect on the performance of democracy. Therefore the positive effect of democracy on human development will be higher the higher the level of education in a society.

- d) Social fragmentation lowers the positive impact of democracies on human development. The more socially diverse a country is the more difficult it is to provide broad-based services even in democracies.

- e) The redistribution effect of democracy compensates the negative effect of income inequality on human development. Furthermore, the higher the level of inequality and the more right skewed the distribution of income is, the bigger is the positive effect of democracy on human development.

# **3 Empirical Links between Democracy and Human Development**

Does our theoretical argumentation withstand empirical evidence? In the next paragraphs, we present the data and variables we use as well as our empirical analysis. We rely on descriptive statistics like kernel density estimates and a more sophisticated panel analysis.

## **3.1 Empirical Implementation**

To quantify human development we focus on the non-income components of UNDP’s Human Development Index and consequently use UNDP’s data on life expectancy and 15
on aggregate values from 2005-2016) Airway and port infrastructure also play a significant role in attracting Chinese foreign investment. Interestingly, a positive relationship is observed between the host-country number of days required to start a business and the volume of FDI flows from China. PTA depth, on the other hand, does not have a significant relation with China’s investment flow.

# 4.3 Missing FDI

In this sub-section, we examine countries’ potential as FDI destination countries and how their potential compares to the realized value of FDI inflow. We refer to the difference between FDI potential and realized FDI as missing FDI. Specifically, we obtain predicted values of FDI based on the baseline estimation results reported in Table 13 and compute the difference between the predicted value of FDI based on each country’s characteristics and the estimated effects of these characteristics on FDI and the country’s actual value of FDI.

Figure 19 plots the ratio of missing FDI relative to actual FDI by country groups. We find the ratio to be the highest (around 4.6) for low-income countries and substantially lower for the rest of the country groups and lowest for the upper-middle income group. Within BRI, we notice that the ratio of missing FDI relative to actual FDI is around 0.7 on average, suggesting a significant potential for attracting additional foreign investment.

<!-- Start of picture text --> Missing FDI/Actual FDI (by income)<br>5.00 4.61<br>4.00<br>3.00<br>2.00<br>0.88 0.95<br>1.00 0.33<br>0.00<br>High income Low income Lower middle Upper middle<br>income income<br><!-- End of picture text --> Figure 19: Missing FDI across Countries

# 4.4 Quantifying the FDI Impact of BRI

Based on the above empirical results, we now estimate the impact of BRI on countries’ ability to attract FDI by reducing transport costs and improving host-country infrastructure. The results in Table 15, in particular, the elasticity of FDI with respect to driving time estimated for BRI host countries, suggest that a 10-percent decrease in driving time between host and source countries could increase FDI flows by 12 percent. Specifically, a 10-percent decrease in driving time could 31
The post‐conflict outlook for the growth drivers in turn, depend on the political settlement outcome which directly affects the availability of reconstruction funds and the voluntary mobility of refugees. Voluntary mobility would not only be preferable on humanitarian grounds but also on economic terms. The political settlement scenario will also affect significantly human capital growth and productivity growth.

Focusing on the voluntary mobility case, under our moderate scenario of partial political settlement with strong guarantees for micro‐security and property rights, the average GDP growth and GDP per capita growth are 8.4 percent and 6.1 percent respectively over 2019‐2038, assuming that a final and stable resolution of the conflict is achieved. With the inflow of reconstruction funds amounting to USD 140 billion spread over 20 years, the main growth driver over the 20‐year period is physical capital accumulation amid investment‐to‐output of about 41 percent on average. As investment‐to‐output reverts to a lower level especially after the assumed 20‐year annual inflow of reconstruction funds, the contributions from human capital and TFP growth are just as relevant as physical capital growth. In this scenario, Syria reaches its pre‐conflict/2010 GDP level by 2031, and its pre‐conflict GDP per capita level by 2033, implying two “lost” decades from conflict.

Under the optimistic scenario of robust political settlement, with exceptionally high investment‐to‐output of over 60 percent in the first decade, it would still take Syria about one decade to surpass its 2010 GDP and GDP per capita levels. Under the pessimistic scenario of limited guarantees for micro‐security and property rights, reconstruction funds amounting to only USD 30 billion, and investment‐to‐output close to the pre‐conflict average, it would take Syria at least two decades to meet its pre‐conflict GDP level and close to three to surpass its pre‐conflict GDP per capita. Respectively for the optimistic and pessimistic scenarios, the average GDP per capita growth rate over the next two decades is projected to be 8.2 or 3.1 percent.

While the reconstruction and expansion of physical infrastructure is essential, the importance of strengthening human capital and the factors underlying TFP growth cannot be overstated. In this paper, we have only accounted for population and enrollment effects on human capital growth. However, the quality of education and health (as illustrated in the World Bank’s human capital index) will also likely be impeding factors that would have to be addressed in Syria’s quest for growth.

34
exports of goods that are traditionally labelled as complex, such as electronic devices, was particularly low as it relied heavily on imported components. Johnson and Noguera (2012, 2017) quantified the scope of production sharing in the global economy, finding amongst others an increasing share of service activities in manufacturing exports. We continue this line of work breaking down the value-added content of exports and revisiting the relation between export development and income growth from an activity perspective.

To do so, we perform two empirical exercises. First, we redo the analysis of Hausmann and Klinger (2007) and Hidalgo et al. (2007) who identified development paths using the concept of “proximity”. The degree of proximity between a pair of products is empirically inferred from cooccurrence of revealed comparative advantages (RCAs) for these products in a cross-country panel data set. Instead, we perform export analysis based on proximities of activity pairs rather than product pairs. The results show that countries first diversify predominantly through shifting export activities across industries and later in the development process through upgrading of activities within industries. The former diversification process is picked up in traditional industry analyses, see, e.g., Imbs and Wacziarg (2003). The latter process will not show up as export diversification when viewed from a product or industry-based perspective. Moreover, the activity perspective also provides a natural basis to probe further into the role of technology in export specialization as technological change is typically task specific rather than sector or product specific (Acemoglu and Autor, 2011). We find that proximity appears to be particularly predictive for new specializations in activities that are intensive in routine manual tasks, which differs across advanced and developing economies. For developing economies, a 1 standard deviation increase in average proximity relates to a 10.9 percentage point increase in the probability of specializing in a new routine manual activity. In advanced economies the effect is only 2.9 percentage points. In a next step, we also analyze trade specialization patterns of individual countries in the vein of Coniglio et al. (2021), adapting their product-based methodology to the activity perspective. We find that some countries acquire comparative advantages in activities that are not proximate, denoted as path-defying specialization in Coniglio et al. (2021). Parsimonious cross-country regressions suggest that path-defying activity specialization appears to be positively correlated with higher growth in GDP per capita. In contrast, Coniglio et al (2021) found that the effect turns negative for high-income countries when viewed from a product perspective.

4
Policy Research Working Paper 10807

# **Abstract**

Preparing for—and responding to—disasters requires a people-centered approach and a strong understanding of households’ ability to cope with shocks. Relying on novel household survey data, this paper examines the ability of households in the Caribbean to cope with disasters caused by natural hazards. The analysis sheds light on disaster preparedness in five “data deprived” countries: Belize, Dominica, Haiti, Saint Lucia, and Suriname. The analysis points to a clear income gradient in possession of emergency supplies needed to cope with disasters. This gradient can be observed at both the country and household levels. In contrast, no such income gradient is observed for other key elements of preparation for disasters: community disaster management systems and discussion of risk mitigation strategies within households (both of which are common in the Caribbean hurricane belt). There is substantial variation in preparedness to cope with disasters across sociodemographic groups, as households with less educated heads, with children, and residing in rural areas are generally less able to handle disasters. All in all, a large share of households in all five countries indicates that they are not prepared to cope with a natural disaster. The COVID-19 pandemic had a negative impact on disaster risk preparedness, primarily due to households’ deteriorating financial circumstances.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at banglade@worldbank.org, mcucagna@worldbank.org, jdehoop@worldbank.org, and apaffhausen@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. 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 this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team