rafmacalaba/datause-encoder-data huggingface dataset:
of the national population) in 1981 to 42 million (28%) in 2011. Over the decade from 2001 to 2011, Bangladesh’s urban population expanded by 35%, at an annualized growth rate of 3%. By 2050, its urban population is projected to account for 56% of the country’s total population (UN 2015a).

According to UN-Habitat, a settlement is defined to be slum if it lacks access to clean water, access to improved sanitation, sufficient living space, durable housing, or secure tenure (UN-Habitat 2016). Defined as such, slum settlements are a prevalent, persistent feature of the urban landscape in developing countries, arising from poor public planning, regulation, investment, and basic services. UN-Habitat (2016) estimates that 0.9 billion individuals resided in urban slum settlements in developing countries in 2014 (a 28% increase from 1990), representing an estimated 30% of the total urban population in these countries. In Bangladesh, a government census in 2014 counted about 14,000 slum settlements across all urban areas (GOB 2015).

The health and nutrition of slum residents are of interest for several reasons. First, theory and evidence suggest that the study of slum health and nutrition should be treated as distinct from the study of urban health, or the study of poverty and health (Ezeh et al. 2017). One argument for the separate treatment is that the physical and social environments of slum settlements may act to amplify health risks for residents, and produce negative health externalities that extend across a slum settlement, or even more widely. The exposure to concentrated health risks in slum settlements may be particularly harmful to young children, given that they are more immunologically susceptible than older children and adults. Marx et al. (2013) argue that the potential adverse health effects of slum settlements may create a low human-capital equilibrium which, in turn, contributes to making slum settlements into poverty traps. Second, the literature on slum health and nutrition is scant (see Ezeh et al. 2017 for a recent review). The little available evidence points to patterns that would benefit from deeper investigation. For example, the evidence indicates that mean health and nutrition outcomes are worse for slum residents than for nonslum residents, and are often worse than for rural residents (Ezeh et al. 2017; Mberu et al. 2016). Third, research is needed on the health risks and effects of residing in a slum settlement to guide the design and implementation of policies and interventions related to slum development in general and slum health in particular (Lilford et al. 2017).

3
# **Table C.8. Probability of being poor based on different specifications** **_Colombia 2008, 2010, 2012, 2014 and 2014_**

|**Dependent variable:**||**Specific**|**ation 1**|||**Specific**|**ation 2**||
|---|---|---|---|---|---|---|---|---|
|**Poor status**|**2008**|**2010**|**2012**|**2014**|**2008**|**2010**|**2012**|**2014**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Male|-0.2382***|-0.0558|-0.1246**|-0.1948***|-0.2595***|-0.1102*|-0.1737***|-0.2256***|
||(0.0562)|(0.0565)|(0.0551)|(0.0558)|(0.0585)|(0.0582)|(0.0585)|(0.0599)|
|Primary Education|-0.6477***|-0.4969***|-0.5389***|-0.5408***|-0.5999***|-0.4582***|-0.5020***|-0.5069***|
||(0.0487)|(0.0507)|(0.0468)|(0.0483)|(0.0497)|(0.0518)|(0.0478)|(0.0502)|
|Middle School|-0.9493***|-0.8604***|-0.9113***|-0.8381***|-0.8677***|-0.7918***|-0.8261***|-0.7680***|
||(0.0520)|(0.0538)|(0.0507)|(0.0532)|(0.0530)|(0.0549)|(0.0519)|(0.0552)|
|Secondary Education|-1.3215***|-1.0930***|-1.1295***|-1.0561***|-1.1622***|-0.9416***|-0.9873***|-0.9234***|
||(0.0519)|(0.0527)|(0.0499)|(0.0519)|(0.0530)|(0.0545)|(0.0507)|(0.0537)|
|Tertiary Education|-2.1906***|-1.9381***|-1.8836***|-1.7338***|-1.8611***|-1.6394***|-1.6006***|-1.4407***|
||(0.0562)|(0.0609)|(0.0574)|(0.0565)|(0.0591)|(0.0643)|(0.0593)|(0.0592)|
|Male x Primary Education|0.1142*|-0.1055*|-0.0028|0.0703|0.1620***|-0.0267|0.0567|0.1154*|
||(0.0605)|(0.0610)|(0.0601)|(0.0605)|(0.0618)|(0.0622)|(0.0619)|(0.0628)|
|Male x Middle School|0.0592|-0.0987|0.0058|0.0258|0.1762***|0.0673|0.1278*|0.1176*|
||(0.0629)|(0.0657)|(0.0649)|(0.0684)|(0.0648)|(0.0671)|(0.0676)|(0.0709)|
|Male x Secondary Education|0.0735|-0.2345***|-0.1158*|-0.0770|0.2298***|-0.0357|0.0657|0.0668|
||(0.0624)|(0.0630)|(0.0621)|(0.0642)|(0.0646)|(0.0644)|(0.0651)|(0.0678)|
|Male x Tertiary Education|0.0599|-0.2161***|-0.0755|-0.0137|0.1470**|-0.0449|0.0864|0.0663|
||(0.0676)|(0.0723)|(0.0727)|(0.0703)|(0.0705)|(0.0753)|(0.0762)|(0.0739)|
|Year of birth fixed effect|||||||||
|Labor market controls<br>Household Characteristics<br>Exposure to shocks|||||||||

Standard errors in parenthesis. *p<0.1 **p<0.05 ***p<0.01

**Note:** Results are constrained to the sample of households whose heads were born between 1948 and 1973. Labor market controls included the employment status, sector of the economy and type of employment. Household characteristics includes household size and access to basic services as water, electricity and sewage. Exposure to shocks includes losing the job and the cause. **Source:** Own estimations based on GEIH from 2008 to 2016.
m. **FsU Exports** by Republics<sup>7</sup> The PSU republics' foreign trade data give valuations of trade flows in both domestic and world prices. The latter are reportedly based on weighted averages of trade with different partners and subsequently aggregated to 110 items. Since domestic pricing policies were a major source of trade distortions', we employ wotid prices for valuations despite several reservations. For example, about 20 percent of FSU trade was with the CMEA and probably an equivalent amount was on the basis of "soft" .ttlements (e.g., India) and it is not clear how this exchange was accounted for in terms of world prices. In spite of such shortcomings, world prices provide a better 'measuring stick" than domestic prices for assessing past export performance and potential vulnerability to OECD trade barriers.

Given their large size, it is not surprising that foreign trade of the FSU was the preserve of t,e European republics, although factors other than size accounted for Russia's share. Excluding the Russian Federation, Ukraine and Belarus, the combined contribution of all other republics amounted to less than 7% of total FSU 1989-91 exports. Russ.a accounted for around 77% of this total, Ukraine for around 13%, and Belarus for about4%. In 1Q91, the joint contribution of the two largest Asian republics - Kazakhstan and Uzbekistan - was only 3%, and the Baltic and Transcaucasian republics' share in total exports was 1.2% and 1.3%, respectively (see Table 3).

The earlier observations concerning developments in FSU trade apply especially to Russia because of its prior dominant position in Soviet exports. The other republics were more inward-oriented, i.e., their shares in FSU exports were significantly lower than in inter-republic trade, reflecting state monopoly of trade at the Union level and the centrally controlled internal division of labor. The greater '-'Me analysis in this section is based mainly on data collected by Goskamstat on the flows of goods between former Soviet republics and the vast of the world in 1990. Trade data are derived from input-output tables, and the maximum item breakdown concurs with the Soviet I 10-sector input-output tables. International agencies did not tabulate 1990 FSU trade data for the republics because they were not independent states.

'The prices were not market-clearing and did not reflect relative scarcities of products. They were also highly distorted by implicit and explicit subsidies. Intermediate products and raw materials tended to be undervalued in relation to the final output, since the bulk of value added was collected through turnover taxes levied on final products. As a result, exports of intermediate products and raw materials were understated and those of final products overstated.
collected data across a large cross-section of countries on both types of firm behaviors – their role as victims proxied by the percentage of revenue that they pay as gifts or informal payments to public officials to “get things done” as well as their role as perpetrators proxied by the percentage of income that they hide from tax authorities.<sup>5</sup> Similar data has been used by several papers, including Svensson (2005) and Fisman and Svensson (2007) in a single country context. Note that since our sample is dominated by small and medium enterprises, the corruption being measured is small scale bribe payments to government officials of different agencies to obtain business licenses and access to essential services.

We find that about 40% of the firms in our sample neither pay bribes nor underreport revenue for tax purposes, 23% do both, 14% only pay bribes, and another 23% only underreport revenue. Univariate statistics show that there is a wide variation in the distribution of firms paying and not-paying bribes across countries and firm characteristics such as size, legal status, industry composition, domestic or foreign ownership and exporting status. In particular, summary statistics show that firms in more regulated economies pay more bribes as well as evade more taxes.

When we examine firm characteristics associated with bribe payments in a multivariate setting, we find that smaller and younger firms report paying a larger percentage of their sales as bribe payments. Individual or family owned firms pay higher bribes than if the firm was owned by another corporation, bank, investment fund, manager / employees of the firm or the state. Controlling for country and industry fixed effects and several firm characteristics, we find that the log odds of having to pay bribes increases by 0.310 for innovators compared to noninnovators. Thus, in our sample of countries, corruption acts as a tax on innovation. However, we find no association between innovation and private protection payments to organized crime to prevent violence. This is consistent with Murphy, Shleifer, and Vishny (1993) who differentiate between private and public rent-seeking and argue that private rent-seeking attacks the productive rather than the innovative sector of the economy where as public rent-seeking particularly targets the innovators.

> 5 In robustness tests, we also examine the under-reporting of total workforce and the wage bill for tax purposes, which may be other important measures of tax evasion in developing countries. The survey and the steps takes to induce reliable and accurate survey responses are provided in the data section of the paper.

4
**Figure 8: Regulatory capital over RWA**

**Panel A: Top 20% banks**

**Panel B: Bottom 80% banks**

<!-- Start of picture text --> 28.7<br>27.7<br>25.1 25.4 25.4<br>24.4<br>21.3 21.8 21.8 22.4 22.8 23.0 23.1<br>19.6 19.1 19.1<br>17.116.9 16.417.9 15.9 15.6 16.5 17.2 18.4 18.4 15.617.6 16.8<br>14.9 14.6<br>13.9 13.9 13.9<br>11.4 11.6<br>EU CE & BC WB SC CA Russia Turkey EE RoW EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017 2007 2017<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>RWA over Total Assets (%) RWA over Total Assets (%)<br>5 5<br>0 0<br><!-- End of picture text --> Source: Archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe. We report means weighted by bank size.

While regulatory capital has been increasing, the increases in simple leverage ratios have been more limited. Figure 9 shows how average capital over total assets ratio (simple leverage ratio) has changed over the past ten years. EU countries started from a lower base in 2007 and have increased their leverage ratios more than other ECA sub-regions over time. Nonetheless, on average large banks in EU still have lower capital holdings than large banks in other ECA sub-regions. In general, by the end of 2017 smaller banks hold more capital (as a % of total assets) than their larger counterparts except for Central Europe and Baltic countries, Turkey and Western Balkans. In the same vein, by the end of 2017 there appears to be on average a wide gap between leverage ratios of small banks and large banks, especially in Russia, Eastern Europe and South Caucasus. Overall, simple leverage ratios increased 1.3 percentage points between 2007 and 2017 excluding ECA EU countries.

10
and Niehaus et al. [2013] in South Asia find evidence of manipulation on the survey administration side. Tohari et al. [2019] made a case for considering the full set of programmes with socio-economic criteria when conducting targeting simulations via their extensive linking of Indonesian survey data.

A growing literature considers PMT from a predictive modelling perspective. McBride and Nichols (2018) highlighted the importance of using out-of-sample validation data in model selection, which has been adopted in Indonesia. A simplified scorecard approach supported by machine learning prediction was suggested by Kshirsagar et al. [2017], who showed impressive results with very limited data collection needs for Zambian data. A systematic evaluation of machine learning methods for construction of the predictive model in PMT was carried out by Areias and Wai-Poi [2022] with data from 12 African countries, but it found that accuracy gains tend to be limited and context specific; no clear machine learning works best across the board. A similar conclusion emerged from a study of Indonesia’s PMT model [Ohlenburg, 2020].

The model presented here has a household-specific stopping criterion as its adaptive component. Adaptive methods link to extensive literatures on the design of experiments, adaptive algorithms and active learning. As an example of the latter, Saar-Tsechansky et al. [2009] studied active feature acquisition when data collection is costly and variables differ in their information value. They proposed a framework that aims to maximize the expected utility of each data item and tailors the sequence and length of data acquisition to each instance, achieving a cost reduction for a given level of accuracy.

A line of research that focuses on the aspect of fairness in targeting emerged in the work of Noriega-Campero et al. [2020]. It puts an emphasis on achieving equitable accuracy rates across subgroups of the population, thereby avoiding systematic disadvantages in programme eligibility for specific groups. In contrast to the evidence of limited accuracy improvements mentioned in the machine learning for targeting literature mentioned above, this work also suggests scope for meaningful improvements in targeting accuracy from changes in data preparation, such as the use of a deep feature embedding, and predictive modelling methods including neural networks.

Bakker et al. [2019] pursued the fairness theme further in a paper that leverages reinforcement learning for selection of fair targeting questions that tailor the questionnaire sequence to each household according to group membership and other characteristics. Closely related to this paper is Bakker et al. [2021], an adaptive design that computes a household-specific sequence of questions and stops when a predictive certainty threshold is met in classifying households as poor or non-poor. Our approach differs in the use of a uniform question sequence and the prediction of a continuous consumption level. Estimation of the household consumption level enables us to rank households, which is important for adjusting eligibility to meet coverage tar5
1.

1. The major differences between domestic and international debt is that the former are legal obligations, enforceable in courts and that domestic debtors who cannot meet their obligations have the option of filling for bankruptcy. In contrast, repayment of international debt depends largely on the borrower's beliefs about the lender's resolve to penalize a disruption of repayment. Along these lines, three simples rules can be distinguished: First, the debtor country will reimburse its debt if the payment required to - - offset the penalties applied in case of default is higher than the debt itself. Second, the debtor country will only pay a fraction of its total debt-service if the payment required to offset the penalties is positive but lower than the face value of the debt. The third possibility is that the debtor country will call a total default on its external debt because the economic and political benefits in case of full default would be higher than the sanctions applied by the lenders.

2. The major problem related to this approach is that the willingness to pay and potential penalties are unobservable variables. The idea in this study is to use the information on the secondary market of the price of debt to obtain a measurement of these variables. Indeed, the market value of the debt can be viewed as the result of the simple rules described above. The country examines the alternative to pay according to the values of its potential payments and its willingness to pay, thereby determining the market value of the debt. Using the option-pricing theory, this study identifies explicitly the factors driving the price of debt on the secondary market : the willingness to pay, the interest rate, the maturity and the volume of debt.

3. The approach is applied to six highly indebted countries - Argentina, Brazil, Chile, Mexico Poland and Venezuela - in order to measure their willingness to pay over the last four years. The results indicate that the willingness to pay were quite volatile in all countries. Interestingly, the model provides ue the probability of full and partial default of the debtor countries. Moreover, the approach can be used to forecast the price of debt. The comparison between the observed and the estimated price of debt for
Appendix C – Cost-of-Living Indexes

**Table 9: Estimated Food-based, Food + Rent-based, and Food + Standard Rent-based Indexes by Region and by Rural and Urban areas (National Average = 100%)**

|||Total||Urban||Rural|
|---|---|---|---|---|---|---|
||Rent|Price|Rent|Price|Rent|Price|
||(1000s)|(1000s)|(1000s)|(1000s)|(1000s)|(1000s)|
|Andijon|4800|110000|7800|130000|4800|100000|
|Buxoro|3600|180000|7200|170000|2400|180000|
|Farg`ona v.|2400|120000|4800|140000|1800|120000|
|Jizzax|1200|100000|4800|120000|1200|85000|
|Namangan|1800|110000|3600|110000|1200|100000|
|Navoiy|1800|110000|3000|100000|1800|120000|
|Qaraqalpaqstan|1800|100000|3000|150000|1440|100000|
|Qashqadaryo|2400|150000|6000|200000|1800|150000|
|Samarqand|6000|150000|9600|200000|6000|120000|
|Sirdaryo|1200|100000|2400|300000|960|100000|
|Surxondaryo|1560|80000|3600|100000|1440|80000|
|Toshkent sh (city)|18000|420000|18000|420000|||
|Toshkent v. (region)|2400|120000|3000|70000|2400|150000|
|Xorazm|1500|200000|3600|200000|1440|200000|

_Source: L2CU Baseline, Author’s Calculations._ _Note: provides a comparison of average imputed rent (left) with hedonic rent estimate (right). Appendix (B) includes the OLS regression model and coefficients used for the imputation procedure._ 54
- 26 -

must of necessity be related to the factor share equation (3). By Shephard’s lemma, the predicted share equation can be used to assess how the total cost would respond to a change in factor prices. This is of particular interest from the country’s economic competitiveness perspective. Any price change will have a cost implication for firms. Given an exogenous shock on energy prices, the economy may lose its competitiveness if increased energy prices increase firms’ operating costs significantly. Conversely, if the cost elasticity with respect to energy prices is small, the economy would be less vulnerable to exogenous energy shocks.

Finally, the paper will pay attention to the degree of technical (in)efficiency, which is computed as the difference between the linear prediction of cost and the possible frontier. One advantage of pooling micro data from different countries is that the relative technical inefficiency of each country can be inferred from the estimated function. The estimated technical inefficiency in cost terms is defined by: This allows us to measure to what extent each firm’s production would involve technical inefficiency, which may include energy inefficiency. The following analysis calculates _u_ / _C_ as a primary technical inefficiency index.

The used data come from the 2005 BEEPS for 7 countries in the SEE region. The sample size amounts to about 1,000. This excludes a number of observations of which the relevant cost data are not available.<sup>27</sup> The number of observations per country in our sample varies from 79 in Bosnia and Herzegovina to 269 in Romania, but mostly around 150.

> 27 The original sample size of the BEEPS covering the seven countries is 2,040. Although the data selection is merely dependent on data availability and thus considered fairly automatic, one might be concerned that there might be the self-selection mechanism where the data availability would be correlated with certain cost characteristics of firms. This is hardly testable by nature, but one possible indication could be the Wilcoxon rank-sum test, which hypothesizes the sampled and non-sampled observations would be drawn from the same population distribution. There are a number of observations in our data, for which _C_ is available but other data items are missing so that they are not used for the analysis. The Wilcoxon test statistic varies across countries;

(continued)
18 Tahle 4. The Incidence of Discriminacory U.S. Trade Barriers on Japan, Federal Republic of Germany, United Kingdom and French Exports, (Trade values nre for 1986 and trade barriers are those in place in 1990)

|||Ja|pan|Fed.Re<br>Ger|public of<br>many|United|Kingdom|Fra|nce|Affected<br>Four C|Trade of all<br>ountries|
|---|---|---|---|---|---|---|---|---|---|---|---|
|NTB<br>CoJe|Description|Tariff<br>Lines|Value<br>($mill.)|Tariff<br>Lines|Value<br>(Smill.)|Tariff<br>Lines|Value<br>(Smill.)|Tariff<br>Lines|Value<br>($mill.)|Value<br>(Smill.)|Japan's<br>Share %|
|11 72|Retaliatory duties|3|1,429|7|30|6|4|7|6|1,496|97.3|
|2510|Anti-dumping duties|83|6,297|16|872|6|473|19|419|8,061|78.1|
|2520|Countervailing duties|2|139|1|8|4|10|3|11|168|82.7|
|3230|Quotaallocated by country|16|70|7|32|11|10|11|40|152|46.1|
|3410|Voluntaryexportrestraint|106|27,676|105|810|98|270|93|496|29,252|94.6|
|3500|MFA restraint agreement|489|1,163|0|0|0|0|0|0|489|100.0|
|4220|Impot monitoring|3|942|4|358|1|-|0|0|1,300|72.5|
|6310|Anti-dumping investigations|29|29,945|9|633|9|332|3|348|31,258|95.8|
|6400|Undertaking|2|942|1|3|1|_|I|1|946|99.6|
||All Above Measures?|733|68,603|150|2,746|136|1,099|137|1,321|73,122|93.9|

The United States has also employed two additional discriminatory trade measures, namely, voluntary price restraints (primarily against Republic of Korea) and countervailing duty investigations. Neither of these two measures has been used against Japan, Germany, France or the United Kingdom.

2 The totals recorded in this row may involve some double counting of discriminatory barriers if two or more of these measures are applied to the same tariff line product. For this reason one should not attempt to relate the value figures shown above to total trade in order to derive an **NTB** 'coverage ratio'.

Source: Authors' tabulations using the UNCTAD Data Base on Trade Control Measures and the World Bank SMART system.