rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

Ukraine with a population of 52 million people is the second largest country of the Former Soviet Union. It is weil endowed with fertile land and rich mineral resources. Huge coal and iron deposits have led to considerable industrial development especially in heavy industry. Ukraine was once known as the bread baskeL of the Russian Empire and later of the Soviet Union.

With such wealth and a highly educated labour force, Ukraine has the potential to become a rich country. Unfortunatcly, it is now faced with economic crisis, the severity of which is on an unprecedented scale. Output has been filling continuously since 1990. According to a recent World Bank (1993) report, Ukraine's real net material product (NMP) registered declines of 3 percent and 11 percent, respectively in 1990 and 1991 and is esdmated to have contracted by another 15 percent in 1992. The country is faced with hyperinflation. The Lkrainian currency is depreciating at a phenomenal rate. The financial conditions of its enterprises have deteriorated badly.

Faced with such crisis, it is hard for any government to secure a smooth tansition from a centrally planned to a market-oriented economy. To provide a stable macroeconomic enviromenti the government of Ukaine will need to follow rigorous demand mement policies. This entails lowering the standard of living of the population. Under these circumstances, it is important that the governent design policies which protect the poorest and most vulnerable groups of society.

Ukraine has very extensrve socia protetion programs inheried from Soviet times. The current levels of government benefits seem unsustinable in the long run and the overaU system will undoubtedly have to undergo an overhaul. Spending on social programs, including subsidies and pensions was estimated to be 44 percent of GDP in 1992 (World Bank 1993). The shrining tax base due to resion and a much needed contracion in the fiscal deficit would necessitae a substantial reduction in welfare expenditures together with a change in their composition. It is, therefore, important to adopt the most efficient ways of directing expenditures towards various populadon subgroups. Obviously, the most efficient policy will be to target expenditures to the poorest secton of the population. To design such policies, it is important to understand the nature of poverty and income distribution. This study attempts to do exactly this for Ukaine for the first time.

The study is cocerned with such questions as: What is the extent of the inequalty of income and in which way direction is it moving? How can the observed changes in inequality be explained? Is the burden of tax evenly distributed across the population? How do the vanous income and expenditure components affect total welfare? What are the effects of currnt government programs on total welfare? This study also attempts to measume the aggregate level of poverty in Ukraine and the extent to which it is affected by econonic growth and income redistribution.

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# **Appendix: Variable Description**

|Formality<br>Avgforma_cls_j|Percentage of sales reported for tax purposes.<br>Average level of formality at the country-location-size excluding the<br>reporting firm. Only averages for cells with more than 5 firms are<br>retained.|
|---|---|
|Credit|Dummy taking value of 1 if a firm has a credit line or an overdraft|
|EXT|Dummy taking value of 1 if a firm relies on external sources (local and<br>foreign banks, leasing and credit cards) to finance either working capital<br>or new investment.|
|INF|Dummy taking value of 1 if a firm relies on informal sources (family and<br>friends, money lenders, and trade credit) to finance either working capital<br>or new investment.|
|Obstacles to finance|Categorical variable taking values: 0 if the firm reports no obstacles to<br>access to financing, 1 (minor obstacles) 2 (moderate obstacles) 3 (major<br>|
||or very severe obstacles).|
|Size|<br>Categorical variable taking values: 1 (micro: 1-9 employees); 2 (small:<br>|
||10-49); 3 (medium: 50-99); 4 (large:100-249); 5 (very large: 250+).|
|Log total employment|Logarithm of the sum of permanent and temporary workers.|
|Fowned|Dummy taking value of 1 if more than 10% of the firm’s capital is<br>foreign owned.|
|Age|Business age calculated as the difference between the year of the survey<br>minus the year when the firm started operations.|
|Exporter|Dummy taking value of 1 if the firm exports more than 10% of total<br>sales.|
|Manager’s education|Dummy taking value of 1 if the manager has some university degree or<br>more.|
|Location|Categorical variable taking values: 1 ( if the headquarter is located in the<br>capital city); 2 (other large city with over 1 million population); 3 (city of<br>250,000-1 million); 4 (city of 50,000-250,000); 5 (town with less than<br>50,000 inhabitants).|
|Log sales|Logarithm of total sales.|
|Log fixed assets|Logarithm of net book value of machinery and equipment.|
|Sales growth|Sales growth (from retrospective information).|
|Corporation|Dummy taking value of 1 if a firm is publicly listed company or private<br>held, limited company.|
|Concentration|Percentage of shares owned by largest shareholder.|
|Reinvested earnings|Share of net profits reinvested (not distributed to owners).|
|Establishments|Logarithm of number of establishments in the country.|
|Workforce education|Percentage of firm’s work force with secondary school degree or higher.|
|Bribes|<br>Percentage of sales in bribes.|
|Capacity|Categorical variable taking values: 1 if the firm’s capacity utilization is<br>less than 50%; 2 if it is between 50 and 80%, 3 if it is above 80%.|
|Log GDP per capita|Log of GDP per capita is in US$ at constant 2000 prices, WDI.|
|Financial Development|Private Credit By Deposit Money Banks over GDP, Beck et al. 2001.|
|Time with tax inspectors|Total days spent in inspections, required meetings with officials.|

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of countries (e.g. Chad and Papua New Guinea). Even in countries where we successfully classify the gender structure of almost everyone, our independent variable of interest is an interval rather than a point in 85 of 193 countries — because the proportion of native speakers whose languages we classify is less than one.

This is a case described by Horowitz and Manski (1998) as “censoring of regressors.” Our analysis so far assumes that this missingness is ignorable. Without this assumption, however, we can still estimate worst-case bounds for the maximum and minimum possible values of the parameter of interest; following Imbens and Manski (2004), we can construct a confidence interval around these bounds.

We use numerical optimization to search the space of possible dependent variable values to establish worst-case upper and lower bounds, _β_<sup>ˆ</sup><sup>_u_</sup> and _β_<sup>ˆ</sup><sup>_l_</sup> , that would result from estimation of Equation 1.<sup>19</sup> We then use the associated standard errors on these extrema to compute a confidence interval, employing a formula analogous to that of Equations 6 and 7 in Imbens and Manski (2004). A confidence interval with coverage probability _α_ is equal to: where _C_<sup> ̄</sup> satisfies for the CDF of Student’s t-distribution with the appropriate number of degrees of freedom.<sup>20</sup> Intuitively, the Manski and Imbens approach formalizes a method for shortening each end of the confidence interval relative to the union of the OLS confidence intervals around the worst-case point estimates, since from the perspective of each end, the other end is further away than a 95-percent confidence interval would require.

> 19We use MATLAB’s `fmincon` interior point algorithm, and confirm results using a simple hill-climbing algorithm in Stata.

> 20Imbens and Manski do this using the normal distribution, but using the Student t-distribution yields a wider, more conservative confidence interval.

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Policy Research Working Paper 10088

# **Abstract**

Oportunidades was an innovative anti-poverty program that put additional resources in the hands of women and their families and encouraged parents to invest in the human capital of their children. This program was the first in its kind, and early evaluations demonstrating its success informed its large expansion within Mexico and the implementation of similar conditional cash transfer programs across the world. However, the existing evidence, which arguably captures causal positive impacts, relies on a sample of very poor rural children. This paper conducts the first evaluation of the program using representative data. It focuses on child height as a marker of long-term nutritional status. The causal impact of the program on child height is isolated by exploiting insights from the biology of child growth in combination with the timing of the rollout of Oportunidades and the panel dimension of the survey. Height for age among children exposed during the first four years of life is contrasted with similar children who were not exposed. Consistent with previous evidence, this analysis finds positive and sizable effects on children who lived in rural poor communities incorporated at the beginning of the intervention. In contrast, the impacts of the program in rural localities incorporated later and in suburban and urban communities are, at best, very modest.

This paper is a product of the Poverty and Equity Global Practice and Duke University. 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 gfarfan@worldbank,org and mgenoni@worldbank.org.

_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
During **these** two administrations economic prospects were also bright,4/ and public resources were made increasingly available for education. The share of public expenditure allocated to education leaped from<sup>just 14Z in</sup> 1950 to 232 in 1968, representing about 1.6C and 5Z of GDP (Drysdale, 1975; World Bank, 1973). As a result of these investments, the proportion of Peruvian children enrolled in school relative to the size of the school-age population far exceeded participation rates in the average Latin American country.51 During the subsequent decade, the school system grew at a more moderate, but steady, pace under the leadership of Generals Velasco Alvarado (1968-1975) and Morales Bermudez (1975-1980). Between 1970 and 1980 only 1,500 new primary schools were opened. Enrollment increases levelled off at 3 and 8 percent a year in primary and secondary schools, respectively, and at 8 percent in post-secondary institutions (Appendix Table A.4). Public resources also became more constrained and less available for education as the economy 4I Growth in GDP was sustained at 5.52 per annum, reaching 7.2? between 1960-1965. (World Bank, 1985) Much of this growth resulted from policies promoting foreign and public investment in the manufacturing, mining and fisheries industries, particularly under Belaunde. As a result of these investment priorities, manufacturing replaced agriculture as the leading sector.

Sl Gross enrollment ratios for Peru reache £07, 34, and 10 percent in primary, secondary, and higher education respectively compared to 78, 19, and 3 percent for the region as a whole. (UNESCO, 1^97) As explained in footnote 2, we do not know the proportion of over-age students attributable to repetition, delayed entry, or re-entry. The proportion of repeaters in primary school in Peru (about 15?) is comparable to the average Latin American country.

7
|_E_(_Y |T_ = 1_, X_)_−E_(_Y |T_ = 0_, X_)<br>(3)<br>(4)<br>(5)<br>OLS<br>OLS<br>IV<br>0.037***<br>0.025***<br>0.031***<br>(0.012)<br>(0.009)<br>(0.011)|0.015<br>-0.015<br>-0.002<br>(0.042)<br>(0.034)<br>(0.044)<br>0.037<br>0.014<br>0.022<br>(0.042)<br>(0.032)<br>(0.043)<br>0.064<br>0.018<br>0.040<br>(0.043)<br>(0.035)<br>(0.049)<br>No<br>Yes<br>Yes<br>0.4462<br>0.6232<br>-<br>pondents only. Columns (1) and (2) report<br>ns (3) and (4) report the difference between<br>nce between households who report to have<br>gram participation with the treatment group<br>dummy variables representing information<br> the malaria vector and are informed of the<br>e respondent is concerned and fully aware<br>rs clustered at village level are reported in<br>usehold size, tribe and religion, information<br>ze and share of women living in the village).<br> selected groups of variables and we report|
|---|---|
|(1)<br>(2)<br>Control<br>Treatment<br>1. Concern and knowledge of malaria<br>0.804<br>0.841<br>[0.197]<br>[0.148]|2. In the previous 6 months, heard or saw messages about:<br>2a. ITNs<br>0.469<br>0.484<br>[0.499]<br>[0.500]<br>2b. Early seeking behavior<br>0.501<br>0.537<br>[0.500]<br>[0.499]<br>2c. Environmental management<br>0.387<br>0.450<br>[0.487]<br>[0.498]<br>Controls<br>Joint tests on variables (with comparable sample size):<br>2a-2c<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per household, data available for res<br>sample means restricted to control and treatment group, standard deviations in brackets. Colum<br>treatment and control groups using OLS regression (model2). Column (5) estimates the differe<br>received the spraying campaign in the last 5 months and those who didn’t by instrumenting pro<br>indicator (model3). Concern and knowledge of malaria is an index computed by averaging 16<br>on whether respondents believe malaria is a problem in the community, are acknowledged of <br>categories of individuals that are most affected by the infection. The index is equal to 1 if th<br>of malaria. We discuss the construction of the index in detail in Appendix B.2. Standard erro<br>parentheses. Controls include gender, age, education and marriage status of the respondent, ho<br>about access to water, dwelling characteristics, sub-zone dummies and village characteristics (si<br>To control for joint signifcance, we run a probit regression of the treatment indicator on the <br>p-values of an F-test for the joint signifcance of the coeffcients on the selected variables.|

30
and may have difficulties to adapt to entry and exit to and from poverty because registers of beneficiaries cannot be updated frequently.

**Table 1: Conditional Cash Transfers in Latin America and the Caribbean**

|**Country**|**Program**|**Target Population**|**Amount**<br>**(monthly US$)**|**Number of Beneficiaries**<br>**(latest available)**|**Cost**<br>**(% of GDP)**|
|---|---|---|---|---|---|
|**Argentina**|Programa Familias|Household Heads, Pregant<br>Females,Children <19|40-80 per child|500,000 households||
|**Bolivia**|Juancito Pinto|Public school children up to<br>grade 6|2 per child|500,000 households||
|**Brazil**|Bolsa Familia|Poor and extreme poor<br>households|30 per household +<br>7per child|11,100,000 households|0.36|
|**Chile**|Chile Solidario|Extreme poor households<br>(means-tested)|14 per child|256,000 households|0.08|
|**Colombia**|Familias en Accion|Extreme poor households<br>with children <7(health),and|Education: 8-33 per<br>child;Health: 28per|1,700,000 households|0.20|
|**Dominican**<br>**Republic**|Solidaridad|Poor and extreme poor<br>households with children <17|29 per household|461,000 households||
|**Ecuador**|Bono de Desarrollo<br>Humano|Households in first 2 income<br>quintiles with children <17|15 per household|1,060,000 households|0.60|
|**El Salvador**|Comunidades<br>Solidarias Rurales|Extreme poor households<br>with children <16 in 100 rural|Education: 15 per<br>household;Health:|100,000 households||
|**Guatemala**|Mi Familia Progresa|Extreme poor households<br>with children <16 in 130|Education: 20 per<br>household;Health:|250,000 households|0.20|
|**Honduras**|Programa de<br>Asignacion Familiar|Poor households with<br>children 6-12years old uptp|Education: 5 per<br>household;Health:|240,000 households||
|**Jamaica**|Program of<br>Advancement throug|h<br>Poor households (means-<br>tested)until they graduate|100 per child|100,000 households||
|**Mexico**|Oportunidades|Extreme poor households<br>(means-tested)|Education: 12-23<br>per household;|5,000,000 households|0.40|
|**Panama**|Red de<br>Oportunidades|Extreme poor households<br>(means-tested)|50 per household|70,000 households||
|**Paraguay**|PROPAIS II|Extreme poor households<br>with children <15 in rural|120 per household|5,800 households|0.08|
|**Peru**|Juntos|Poor households with<br>children <15|33 per household|454.000 households|0.11|

Source: Based on Fiszbein and Schady (2010).

It is not therefore always obvious that poverty-targeted social assistance programs are the best approach to alleviate poverty. At the heart, the optimal design relates to societal preferences for redistribution and taxation (Mkandawire, 2005), as well as tolerance to exclusion errors or deviating from a “rights” approach. But the choice of design can also be informed by technical considerations, such as looking at the accuracy and cost effectiveness of different targeting mechanisms, which is the focus of this paper.

3
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|---|---|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Integrated Coastal Zone Management|Milen F. Dyoulgerov Vollen|
|Kampala Solid Waste Project|Martin Onyach-Olaa|
|Kampala Solid Waste Project|Martin Onyach-Olaa|
|Kampala Solid Waste Project|Martin Onyach-Olaa|
|Kampala Solid Waste Project|Martin Onyach-Olaa|
|Kampala Solid Waste Project|Martin Onyach-Olaa|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Second Multi-sectoral HIV/AIDS Project|Mohamed Ali Kamil|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|Market-Led Smallholder Development in the Zambezi Valley|Pedro Arlindo|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
|IntegratingEnvironment and Forest Protection into the Recovery|anTimothyH. Brown|
<!-- Start of picture text --> Beijing Chaigqlng<br>3,000,000 . --..-....................................................-----...............................................................<br>23.O000 - -<br>2,000,0&00 _,-;-Z_<br>1.0o~~~~~~~~~~~~~~~~o~~6D.a<br>1 Wo0,0&00,,_ ........ ,_ .. _ _ _. _. 2so,00o .. - __ -,.,, '<br>5W6DD0, - I _.i-O I h_.<br>1993 20D0 2010 2020 19932 2010 2020<br>Shanghas Guangzhou<br>3, D 00 ..................................... :...................... 1,mX mi0 *-------.-.--..................................................<br>2,000,00a -_-- .. __.60Z _-,--.,<br>1,500,00Q i. '---.. T-h sa .<br>1,0SS,QOQ -- 4> 40Da)o_, ~ &<br>Soo,Oo| 20D0,00 . W4<br>illustration Beijing,fm2lO 2010 202s 1993 2000 2010 2C20<br><!-- End of picture text -->

inreSed-lev scnro are caculte byj The full results of strengnaened regulation are portrayed in Figure 26. To produce these FiaTh satet9s, we have calculated the cumulative projected annual deaths from 1997 to 2020 in 7ma),0c . three scenariost Net livethe **s** av ed in the two sand-s increased-levy scenarios are calculated by 3 subtracting cumulative deaths from cumulafive sQaxo deaths in the pure reform case. The lifesaving 2azo Q* Q* vZalue of the levy emerges clearly in thtis o illustration. Beijing, for examnple, saves about 14,000 lives with a 5% annual increase and 24,000 lives with a IO% increase. Chongqing saves 9,000 and 16,000 lives, respectively, and **Scenarios** lifesaving in the other cities is also counted in the thousands.

<!-- Start of picture text --> FiaTh<br>7ma),0c . .. _. .<br>sand-s<br>..<br>sQaxo >s<br>2azo Q* Q*<br>o fi0<br>1ssmm 2,o 2<br><!-- End of picture text -->

**Figure 25c: Industrial TSP Emissions in Three Scenarios**

30