rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. Mean Characteristics of Households and Tests for Differences by Round and INDH**

||**Control**|**2008**<br>**Treatment**|**Total**|**Control**|**2011**<br>**Treatment**|**Total**|**Control**|**2013**<br>**Treatment**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|**Poverty rate**|28.37***|30.91***|29.64|28.37***|30.91***|29.64|28.37***|30.91***|29.64|
|**Household size**|5.93|6.15|6.04|5.81***|6.29***|6.05|5.53***|6.03***|5.78|
|**Number of children**|2.17|2.32|2.24|1.93***|2.29***|2.11|1.84***|2.18***|2.01|
|**Number of adults**|3.77|3.83|3.80|3.88|4.00|3.94|3.70*|3.86*|3.78|
|**Age of head**|54.04|53.17|53.61|56.17**|54.65**|55.41|57.51*|56.12*|56.82|
|**Sex of head**||||||||||
|Male|0.94|0.94|0.94|0.92|0.93|0.93|0.90|0.92|0.91|
|Female|0.06|0.06|0.06|0.08|0.07|0.07|0.10|0.08|0.09|
|**Marital status of head**||||||||||
|Single|0.02|0.03|0.02|0.01|0.01|0.01|0.01|0.01|0.01|
|Married|0.92|0.89|0.90|0.90|0.90|0.90|0.89|0.90|0.89|
|Widowed|0.07|0.08|0.07|0.08|0.08|0.08|0.10|0.09|0.09|
|**Education of head**||||||||||
|None, illiterate|0.67|0.69|0.68|0.66|0.69|0.67|0.69|0.69|0.69|
|None, literate|0.06|0.06|0.06|0.11*|0.08*|0.10|0.11|0.12|0.11|
|Less than complete primary|0.20**|0.16**|0.18|0.18*|0.15*|0.16|0.14**|0.11**|0.12|
|Complete primary|0.03|0.04|0.03|0.03|0.04|0.03|0.02|0.04|0.03|
|Complete lower secondary|0.03|0.03|0.03|0.02|0.03|0.02|0.02|0.02|0.02|
|Complete upper secondary|0.01|0.02|0.02|0.01|0.02|0.01|0.01|0.01|0.01|
|Highereducation|0.00|0.01|0.00|0.00|0.00|0.00|0.00**|0.01**|0.00|
|**N (observations)**|744|743|1,487|744|744|1,488|744|744|1,488|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ Tests for equality of means across treatment and control estimated in each round with *p < 0.1; **p < 0.05; ***p < 0.01.

10
- 20 -

# Table 2.3 Summary of Typical Interchange Agreement

Basic Concept Railways A and B wish to interchange cars<sup>at a specific location. In this example, Railway A provides the</sup> specific track on which such interchange will occur.<sup>Railway A also permits Railway B to operate over</sup> so much of Railway A as to reach the interchange<sup>track.</sup> <!-- Start of picture text --> Contents of the Agrement<br>Identification of Interchange Track<br>Predse Definition of Interchange:<br>* Occurs when locomotive is uncoupled from cars left on track.<br>* [Precision is important because of car hire charges.]<br>Railway A to Maintain Track<br>Cost Arrangement:<br>.! * Railways A and B to split 50/50 the "annual normalized maintenance costs."<br>* Current cost level is specified.<br>e [There is no guarantee that Railway A will actually spend that money on maintenance of the<br>covered trackage.]<br>Applicable lRules:<br>* Interchange rules of Association of American Railroads (AAR).<br>* Federal/State safety rules: each party is responsible for its own adherence to the rules; each may<br>reject rolling stock not in compliance with the rles.<br>Wrecks and Liabilities:<br>* Complex provisions.<br>* In general, each railway is responsible for its own rolling stock, and "no fault" applies.<br>Arbitration Clause<br>Source: Norfolk & Western Railway Company, sample interchange agreement.<br><!-- End of picture text -->
**21**

# **Apgendix**

This appendix summarizes the various<sup>formulae used in the paper's</sup> calculations. The FGT class of<sup>poverty measures Pa used in sections 2 and 3</sup> can be written as: where f(y) denotes the probability<sup>density function of income y, and z is</sup> the poverty line. Following Kanbur<sup>(1987) and Kakwani (1989) it is readily</sup> verified that the elasticity of<sup>Pa with respect to the mean of the</sup> distribution of y, holding the<sup>Lorenz curve constant, is given by</sup> Under Kakwani's (1989) assumption<sup>about the shift in the Lorenz curve, the</sup> elasticity with respect to the<sup>Gini index, holding the mean#constant, is</sup> given by:
III.4. Which approach is better, NISR (2015) or NISR (2016)?

Both NISR (2015) and NISR (2016) produce a similar poverty trend (and more or less similar rates). But NISR’s (2016) approach is the one that produces comparable poverty estimates, using a common poverty line and a consistent COLI, one which uses the same budget weights, source data on prices, and base month.

# **IV. Poverty trend based on International Poverty Lines**

Rwanda’s poverty rates measured at the $1.90 poverty line are 60.3 percent in EICV3 and 60.4 percent in EICV4 (Povcalnet as of March 2018). In contrast, Rwanda’s official poverty rate declined from 46 percent to 39 percent between the two surveys. This section explores the reasons behind these differences.

As mentioned earlier, poverty measurement typically hinges on the estimation of a nominal welfare aggregate, adjustment for spatial and temporal price differences, further adjusted for household composition differences. When comparing national and international poverty measurement, we find that both estimates are based on the same nominal consumption expenditure. Thus, any differences in national and international poverty trend are driven by differences in the price index used for spatial and temporal deflation, and the adjustments made for household age and size composition. In the case of Rwanda, we find three key differences in the latter two adjustments.

First, the international measure uses household expenditure per capita while the national measure uses household expenditure per adult equivalent. Second, the international poverty measure uses an average inflation rate for each of the EICV surveys and deflates household expenditure with this average inflation rate derived from survey-weighted monthly CPIs. In contrast, the national poverty measure first deflates household expenditure by a price index that adjusts for spatial and monthly price variations _within_ a survey year, and then adjusts for inflation _between_ survey years. Third, the two aggregates are deflated to different base periods. The international measure uses 2011 as the base period while NISR (2016) uses January 2014 as the base period.

IV.1. Estimating international poverty rates for Rwanda using the World Bank’s methodology To estimate the international poverty rates, we first convert the $1.90 per day per capita line to an annual per capita poverty line expressed in local currency units (LCUs), and then compare it to a welfare aggregate also expressed in annual per capita terms.

We do this by first expressing the international poverty line (IPL) in annual terms (multiplying it by 365). We then convert the annual line to local currency units (LCUs) using the 2011 Purchasing Power Parities (PPPs), and finally adjust the resulting poverty line to reflect average 2011 prices. This is done by multiplying the annual PPP adjusted line with the inflation rate between the 2011 average and the EICV survey month average. Thus, the IPL for EICV3 can be written as: 15
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel D: Regulatory framework for deposit taking (0 4)</u>

|||<br>Version 1(Ful sampl|<br>es)<br>Depvar: Financial S|Version 2<br>elf-Sufficiency|<br>(DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory framework for deposit-taking (0-4)|0.0156|0.0397*|0.0277|0.0234|0.0470**|0.0334|
||[0.736]|[0.052]|[0.517]|[0.599]|[0.028]|[0.418]|
|NBFI dummy * Regulatory framework for deposit-taking (0-4)|-0.0216|-0.0530*|-0.028|-0.0258|-0.0569**|-0.0307|
||[0.579]|[0.052]|[0.448]|[0.518]|[0.045]|[0.406]|
|Combined group dummy *Regulatory framework for deposit-taking (0-4)|-0.0493|-0.0581*|-0.0437|-0.0876|-0.0980**|-0.0677|
||[0.459]|[0.062]|[0.405]|[0.291]|[0.019]|[0.285]|
|NBFI dummy|0.0437|0.1202**|0.0667|0.0425|0.1171**|0.0582|
||[0.652]|[0.021]|[0.458]|[0.668]|[0.031]|[0.513]|
|Combined group dummy|0.0654|0.1008|0.0729|0.0911|0.1305|0.0694|
||[0.670]|[0.128]|[0.551]|[0.614]|[0.108]|[0.616]|
|Observations|636|636|611|579|579|556|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.212|0.227|0.251|0.223|0.239|0.256|
|Adjusted R-squared|0.188|0.204|0.227|0.197|0.213|0.23|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.00599|-0.0133|-0.0003|-0.00239|-0.00995|0.00267|
||[0.854]|[0.591]|[0.993]|[0.941]|[0.701]|[0.937]|
|Combined group dummy|-0.0336|-0.0185|-0.0161|-0.0643|-0.051|-0.0344|
||[0.458]|[0.540]|[0.732]|[0.308]|[0.206]|[0.586]|

44
- **Step 2.** We assign each settlement pixel to one of the five considered flood hazard categories defined in line with risk to lives and livelihoods (table 1). We repeat this step for all the world’s settlements and each year.

- **Step 3.** On a yearly basis, we compute the total settlement area per flood hazard category for each subnational administrative unit (such as state or province) and calculate its share relative to the corresponding overall settlement area. To address possible limitations of settlement over/under-estimation due to poor Landsat-5 data availability before 2000 in some parts of the world, we apply SPM (see above). To obtain wider trends, we further aggregate exposure estimates to national and regional levels and compute the long-term trends of settlement exposure to flood hazards.

**Table 1.** Flood hazard categorization applied in this study

|**Category**|**Hazard**<br>**class**|**Flood depth**<br>**(meters)**|**Description**|
|---|---|---|---|
|0|None|0|Unaffected during a 1-in-100 year flood|
|1|Low|0 – 0.15|No significant risk to life or economic activity|
|2<br>3<br>4|Moderate<br>High<br>Very high|0.15 – 0.5<br>0.5 – 1.5<br>Over 1.5|Disruptions to livelihoods and economic activity; some risk to life for<br>select populations, especially vulnerable groups such as children and<br>disabled people (including through water-borne diseases)<br>A significant share of the affected population is expected to face risk<br>to life, especially if flood waters have a current; major disruptions to<br>livelihoods<br>Most affected people could face substantial risk to life and severe and<br>prolonged disruptions to livelihoods|

# 4. Results

## 4.1. Substantial flood hazard exposure in all regions

Estimates from this study show that in 2015, at least 11.3 percent of all built-up areas globally face high or very high flood hazards; i.e. inundation depths of at least 50 cm during 1-in-100 year flood events (figure 3). Exposure is lowest in Sub-Saharan Africa (4.6 percent) and North America (4.5 percent), and highest in the East Asia and Pacific region (18.4 percent). These figures confirm that flood exposure levels are substantial across all global regions and income groups. While the normalized shares of exposed settlements (Figure 3) illustrate the relative proportions of safe and at-risk settlements over time, they hide that total settlement extents have expanded significantly in the considered period. This is assessed further in the following.

8
(2006) apply a variant of the Bourguignon et al. (2007) approach to a set of African countries.

Nevertheless, the empirical study of inequality of opportunity remains a nascent – though increasingly vibrant – field. This paper aims to make three contributions, the first two of which are methodological. First, we provide a simple conceptual framework which derives a class of indices of inequality of opportunity directly from Roemer’s theory. The parametric measure proposed by Bourguignon et al. (2007) and the nonparametric indices in Checchi and Peragine (2005) are shown to be members of this class, which can therefore be seen as a unifying concept in the measurement of inequality of opportunity. Indices within the class differ along two dimensions: _decomposition path_ and _estimation procedure_ . Drawing on the earlier literature on path dependence in inequality decomposition (Foster and Shneyerov, 2000), we show that there exists a unique inequality index (the mean log deviation) for which our measure of inequality of opportunity is path-independent.<sup>6</sup> For that index, this class of measures collapses to a parametric and a non-parametric alternative along the estimation procedure dimension. We show that the two methods provide a narrow range of lower-bound estimates for inequality of opportunity in a set of six Latin American countries.

Second, we introduce the concept of an _opportunity-deprivation profile_ : a vector of characteristics of the groups with the most limited opportunity sets in a given society (a precise definition follows). Following Roemer’s (2006) suggestion that “the rate of economic development should be taken to be the rate at which the mean advantage level of the worst-off types grows over time.” (p.243), we compare these profiles across our sample of six Latin American countries. We also compare these profiles to the analogous poverty profiles, and suggest an interpretation of the differences.

The third contribution is substantive. We apply these two methodological innovations to a rich set of household data for six countries in Latin America: Brazil, Colombia, Ecuador, Guatemala, Panama, and Peru. In each case, we observe information on six “circumstance” variables, namely gender, race or ethnicity, place of birth, mother’s education, father’s education and father’s occupation. We are not aware of a comparable

> 6 Strictly, this uniqueness is within the set of inequality measures that satisfy the transfer principle, and use the arithmetic mean as the representative income (see Foster and Shneyerov, 2000).

4
# **Lassoing welfare dynamics with cross-sectional data***

Leonardo Lucchetti Paul Corral The World Bank The World Bank Andrés Ham Santiago Garriga Universidad de los Andes Paris School of Economics _Updated December 16, 2020_ _Keywords_ : Poverty; Poverty transitions; LASSO; Machine learning; Multiple Imputation; Synthetic panels; Welfare dynamics.

_JEL classification_ : C53, D31, O15, I32. _Sector Board_ : POV Leonardo Lucchetti (llucchetti@worldbank.org) is a Senior Economist with the Poverty and Equity Global Practice, World Bank. Paul Corral (pcorralrodas@worldbank.org) is a Senior Economist with the HD Chief Economist Office, World Bank. Andres Ham (a.ham@uniandes.edu.co) is an Assistant Professor in the School of Government at Universidad de los Andes. Santiago Garriga (garrigasantiago@gmail.com) is a Ph.D candidate at the Paris School of Economics. We thank Aziz Atamanov, Eduard Bukin, Oscar Calvo, Andres Castañeda, Francis Addeah Darko, Carolina Diaz-Bonilla, Reno Dewina, Leonardo Gasparini, Gabriela Inchauste, Jose Montes, Laura Liliana Moreno, David Newhouse, Minh Cong Nguyen, Obert Pimhidzai, Carolina Sanchez-Paramo, Carlos Silva-Jauregui, Liliana D. Sousa, Shinya Takamatsu, Mariana Viollaz, Judy Yang, Nobuo Yoshida, Matthew Wai-Poi, Bagus Arya Wirapati, and Salman Zaidi. We also thank participants of the LACEA-LAMES 2018 meeting. Carlos Santiago GuzmánGutiérrez provided outstanding research assistance. Pablo Gluzmann provided outstanding support in the construction of all panel datasets. All remaining errors are ours.
# **7. IV Regression Results**

In this section, we discuss the results from the instrumental variables (IV) estimation. According to Ahlerup and Ollson (2012) and Ahlerup (2009), the antiquity of uninterrupted human settlement in each area should be positively correlated with current levels of ethnic fractionalization in that area. This hypothesis is based on the idea that among prehistoric hunter-gatherer populations, random genetic and cultural drift accumulated over time and caused new groups to form to secure an efficient provision of collective goods. This process of group formation is slow and occurs over long periods of time. Thus, longer periods of human settlement correspond to more time for ethnic group formation. Following this body of work, our instrument for ethnic fractionalization is the duration of uninterrupted human settlement in the country, scaled to 100,000 years ( _Origtime_ ). The data source for the variable is Ahlerup and Ollson (2012). Instrument relevance is confirmed by a large, positive, and statistically significant relationship between ethnic fractionalization and the duration of human settlement. Regarding the exclusion restriction, there is no known reason to expect any direct effects of the duration of human settlement on labor productivity level and growth rate between 2007 and 2019.

IV regression results are presented in Table 9. For brevity, only the results for the final specification are shown. Columns 1-4 contain the results for the level of labor productivity and columns 5-8 contain the results for the growth rate of labor productivity. For the interaction term estimation, we first obtain the instrumented values of ethnic fractionalization from a linear regression (without any interaction terms). The instrumented values are multiplied by the SME dummy and used directly in the regression. The split sample results are obtained by separately 34
is 1.7 percent, and for those countries with statistics for children under age 14 the prevalence rate is 0.7 (see Annex Tables for a summary of the data from DISTAT).

Perhaps surprisingly, of the 11 surveys analyzed here, those that list more types of impairments do not systematically identify a higher percentage of the population as disabled. For example in the six countries that include visual, hearing, speech, and physical disabilities the percentages are 1.51 (Cambodia 1999), 0.64 (Indonesia); 2.77 (Jamaica 2000); 1.19 (Mozambique); 1.32 (Romania); and 0.55 (Sierra Leone) which span close to the entire range of prevalence across all the surveys. In Mongolia which inquires only about visual/hearing impairments the prevalence is the highest observed in this collection of datasets (3.2 percent), while in Burundi and the 2000 DHS in Cambodia which cover only physical disabilities the prevalence rates are 1.24 and 0.86 percent respectively.

**<u>Table 4. The prevalence of disability among youth ages 6 to 17</u>**<sup>**+**</sup> **<u>by household economic status quintile</u>**

||_All_|_Poorest_<br>_quintile_|_2_<sup>_nd_</sup><br>_quintile_|_3rd_<br>_quintile_|_4th_<br>_quintile_|_Richest_<br>_quintile_|_Concen_<br>_-tration_<br>_Index_|_Std._<br>_Error_<br>_of CI _|
|---|---|---|---|---|---|---|---|---|
|Burundi 2000|1.24|1.28|1.19|1.11|1.36|1.28|0.032|(0.064)|
|Cambodia 1999<sup>#</sup>|0.88|0.91|0.84|0.87|0.81|0.94|-0.007|(0.687)|
|Cambodia 2000|0.86|1.08|0.71|0.86|0.82|0.86|-0.044|(0.045)|
|Indonesia 2003<sup>#</sup>|0.51|0.70|0.55|0.41|0.50|0.38|-0.084|(0.038)*<br>*|
|Jamaica 1998|0.82|1.01|1.05|0.48|0.68|0.89|-0.064|(0.082)|
|Mongolia 2000|3.20|3.40|3.01|2.88|2.81|3.92|0.019|(0.037)|
|Mozambique 1996<sup>#</sup>|1.19|0.86|0.81|1.57|1.40|1.29|0.111|(0.045)*<br>*|
|Romania1995<sup>#</sup>|0.60|0.91|0.47|0.54|0.47|0.58|-0.110|(0.067)*|

_Note_ :<sup>+</sup> Maximum age is 14 in Burundi and Myanmar.<sup>#</sup> Survey includes vision, hearing, speech, and physical disabilities. ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels respectively. Standard errors in parentheses.

Of course this variability combines both actual prevalence and differences in survey techniques. However, in the countries with more than one survey (Cambodia and Jamaica) the survey with the more extensive definition of a person with a disability does not always result in the larger prevalence. The SES in Cambodia in 1999 characterizes 1.51 percent of the population as having a disability with an extensive definition, whereas the DHS in 2000 characterizes 1.57 percent of the population as having a disability with a definition restricted to physical disabilities. In Jamaica the more extensive definition in 2000 characterizes 2.77 percent as having a disability—more than the 2.09 percent identified in 1998 with a more limited definition. Clearly there is substantial variation 6
Table 6: Baseline - September 2013 survey.

|||PRO|BIT|||LO|GIT||
|---|---|---|---|---|---|---|---|---|
|**Information**|**0.202**|**0.131**|**0.166**|**0.101**|**0.346**|**0.225**|**0.289**|**0.178**|
||(0.141)|(0.151)|(0.143)|(0.153)|(0.239)|(0.256)|(0.243)|(0.260)|
|**Delivery**|**1.104*****|**1.260****|**1.096*****|**1.276****|**1.798*****|**2.050****|**1.788*****|**2.064****|
||(0.137)|(0.435)|(0.139)|(0.438)|(0.228)|(0.729)|(0.232)|(0.732)|
|**Partisanship**|**0.337****|**0.335***|**0.344****|**0.343***|**0.578****|**0.567***|**0.590****|**0.570***|
||(0.129)|(0.138)|(0.130)|(0.138)|(0.221)|(0.236)|(0.222)|(0.236)|
|Constant|YES|YES|YES|YES|YES|YES|YES|YES|
|Personal||YES||YES||YES||YES|
|Dummies|||YES|YES|||YES|YES|
|Observations|527|527|527|527|527|527|527|527|
|PCP|0.72|0.73|0.72|0.72|0.72|0.73|0.72|0.73|
|ROC|0.69|0.74|0.70|0.74|0.69|0.74|0.70|0.74|
|AIC|621.7|632.3|622.6|633.4|621.6|632.5|622.5|634.1|
|BIC|638.8|747.1|648.2|756.7|638.6|747.3|648.1|757.5|

> _⇤⇤⇤_ indicates at 0.1% level, _⇤⇤_ at 1% level and _⇤_ at 5% level.