rafmacalaba/datause-encoder-data huggingface dataset:
The method would be appropriate only in the unlikely event that the shares of migrants or migrant households were similar across area units, but finding a sufficient number of migrants in the select units might still be a challenge.

Other non-probability sampling techniques may also be used to capture rare events; for example, multiplicity methods such as snowballing have been used in the migration literature. One use of snowballing is to gather information on undocumented migrants, using as a starting point, or ‘seed’, a list of members of a diaspora organization or a list of migrants assisted by an NGO in destination countries. The ‘seed’ household is used to identify additional migrant households of the same country of origin, and so on until the necessary number of observations is reached. However, using data from Senegal, Beauchemin and González-Ferrer (2011) found snowballing to present a number of selection biases, including overrepresentation of migrants with close ties to the sending country.

Finally, techniques such as random walks and aggregation point intercept methods can be used to identify rare events, such as migration. Random walks using selected households in a community may act as a starting point to identify migration occurrences. A Brazilian survey of Nikkei population used the aggregation point intercept method, which, together with snowballing, was compared with more traditional censusbased random sampling (McKenzie and Mistiaen, 2009). In all cases, when using these non-probabilistic methods, it is crucial to collect ancillary information on the implementation of the sample to be able to identify the reference population in an attempt to make ‘educated inference’ to a larger population group. This is particularly important given McKenzie and Mistiaen (2009) findings that non-probability methods, such as the 24
OZIER - REPLICATION REDUX – p. 28 of 41 _some evidence of improvement in school attendance ... When both years were combined, there was strong evidence of an effect on attendance...”_ (Davey, et al., 2015) Thus, though the re‐analysis found that the study could be split into separate studies too small to provide definitive evidence, all the data combined to show the effect originally reported.

# 3.4. Aggregating deworming findings (“Systematic Review”)

Why spend too much energy focusing on a single study, such as Miguel and Kremer (2004), when the robustness of a finding across studies is probably more informative? The worm wars still might not have taken place, had it not been for the simultaneous release of an update to the Cochrane Collaboration’s systematic review on deworming (Taylor‐Robinson, et al., 2015).

As described earlier, systematic reviews are challenging to undertake, and the guidelines for conducting them are extensive. In the present case, the authors of the 160‐page 2015 Cochrane Collaboration systematic review of deworming clearly worked very hard to cover the subject at hand. Yet shortly after its publication, a range of critiques appeared in print. One critique pointed out that different levels of worm prevalence prior to treatment should change effect sizes, so the aggregation of low‐prevalence and high‐prevalence studies is both underpowered and pursues an average whose meaning may not be relevant to any particular context (de Silva, et al., 2015). Another critiqued the review for being restricted to short‐duration studies, thus missing any long‐term effects (Montresor, et al., 2015). Whatever the critiques, the authors of the systematic review are constrained both by the definitions they choose, and by the literature they are trying to summarize. For the effects of deworming on formal tests of cognition, they are able to find only five studies. For effects on school attendance, only two.

How do they treat Miguel and Kremer (2004)? Recall that the _text_ of Aiken, et al. (2015) stated that, upon re‐examination, “effects may be present at distances of up to 3 km.” However, in their
with lowest weight given to the richest. This implies that a Gini-discounted mean is called for in setting our upper bound. We implement the new measures for the lower and upper bounds on a global basis, including countries at all levels of development. Thus, we provide globally-unified measures of poverty, in contrast to past work which has been bifurcated between “rich” and “poor” countries, with two distinct literatures. Our estimates draw on 1,500 household surveys for 150 countries over 1990-2013.

The following section discusses our data on national poverty lines and some of their properties, as relevant to the rest of the paper. Section 3 reviews the measurement practices found in the literature. The paper’s main contributions are found in Sections 4-6. Section 4 outlines our theoretical approach to measuring relative poverty. In accounting for how national poverty lines vary across countries, we then show in Section 5 that a weakly-relative poverty measure using a Gini-discounted mean dominates both strongly and weakly-relative measures using either the ordinary mean or the median. We find that higher inequality calls for a lower national comparison mean, but that a higher share of that mean should be passed onto the poverty line. The net effect is generally a higher national line than implied by standard (strongly) relative measures, most notably in poor countries. Finally, Section 6 provides our new estimates of global poverty measures. We find that the aforementioned stylized facts about global poverty—that it is falling and that poverty is a greater problem in the developing world—are robust to taking relative income seriously. Some new insights also emerge, including that the rich world is making far less progress against poverty. Section 7 concludes.

# **2. National poverty lines**

National poverty lines have long provided the data used in setting global lines. In assessing poverty globally, the World Bank has argued that one should use a line with constant purchasing power, as best can be determined, and that it should be set at a level that is reasonably representative of low-income countries (World Bank, 1990; Ravallion et al., 1991). Two people with the same real consumption are treated the same way no matter where they live. Ravallion et al. (2009) compiled a sample of national lines, including 75 observations for developing countries. On this basis, they set a line of $1.25 at 2005 PPP, which became the new international line for the World Bank. This was the mean poverty line of the poorest 15 countries in terms of consumption per capita. On allowing for the rates of price inflation in the set of 7
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1082 What Do Govemments<br>Buy? The<br>Composition<br>ofPublic Spending<br>and<br>Economic Performance|Shantayanan<br>DevaraJa<br>Vinaya Swaroop<br>Heng-fu<br>Zou|n February<br>1993|0. Jones<br>37699|
|WPS1083 Finance ard Growth: Schumpeter<br>Might Be Right|Robert G. King<br>Ross Levine|February<br>1993|D. Evans<br>38526|
|WPS1084 Stock Market Development<br>and<br>Financial<br>Deepening<br>inDevebping<br>Countries:<br>Some Correlation<br>Pattem|Dong He<br>Robert Pardy<br>s|February<br>1993|P. Infante<br>37665|
|WPS1085 Economic<br>Approaches<br>to Modeling<br>Fer'1ilty Determinants:<br>A Selective Re|Cristino R. Arroyo III<br>view|February<br>1993|0. Nadora<br>31091|
|WPS1086 Teachers' Salaries in Latin America:<br>AComparative<br>Analysis|George Psacharopoulo<br>Jorge Valenzuela<br>Mary Arends|s February 1993|G. Psacharopoulos<br>39243|
|WPS1087 Exchange-Rate-Based<br>Stabilization<br>Tales from Europe and Latin America|: Alberto F. Ades<br>Miguel A. Kiguel<br>Nissan Liviatan|February<br>1993|R. Luz<br>34303|
|WPS1088 A Primer on the MFA Maze|Riccardo Faini<br>Jaime de Melo<br>Wendy Takacs|February<br>1993|D. Ballantyne<br>37947|
|WPS1089 Equity Portfolio Investment<br>In<br>Developing<br>Countries:<br>A Literature Su|StiJn Claessens<br>rvey|February 1993|R. Vo<br>31047|
|WPS1090 Govemment<br>Expenditures<br>as a<br>Citizen's Evaluation<br>of Public Output<br>Public Choice and the Benefit Princip<br>of Taxation|Thanos Catsambas<br>:<br>le|February 1993|A. Correa<br>38549|
|WPS1091 Capital Market Imperfections<br>Before<br>and After Financial Liberalization:<br>A Euler-Equation<br>Approach<br>to Panel<br>Data for Ecuadorian<br>Firms|Fidel Jaramilo<br>Fablo Schiantarelli<br>Andrew Weiss|February 1993|W. Pitayatonakarn<br>37658|
|WPS1092 The Effect of Financial Liberalizatio<br>on the Allocation<br>of Credit: Panel<br>Data Evidence<br>for Ecuador|n Fidel Jaramillo<br>Fablo Schiantarelli<br>Andrew Weiss|February 1993|W. Pitayatonakarn<br>37658|
|WPS1093 Swiss Chilanpore:<br>The Way Forward<br>for Pension Reform?|Dimitri Vlltas|February 1993|W. Pitayatonakarn<br>37658|
|WPS1094 The New Regionalism:<br>A Country<br>Perspective|Jaime de Mebo<br>Arvind Panagarlya<br>Dani Rodrk|February<br>1993|D. Ballantyne<br>37947|
<!-- Start of picture text --> CAJAMARCA CHIACHAPOYAS<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1<br>-1<br>-2<br>-2<br>-3<br>-3<br>-4<br>-4<br>-5<br>-5 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>CUZCO GRANJA KCAYRA<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>-4 -4<br>-5<br>-5<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>JULIACA LA PAMPILLA<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>-4<br>-4<br>-5<br>-5<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Celsius)<br>average for 1960-1990 (degrees<br>Temperature Anomaly compared to<br>Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius)<br>Temperature Anomaly compared to Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius) average for 1960-1990 (degrees Celsius)<br>Celsius)<br>average for 1960-1990 (degrees<br>Temperature Anomaly compared to<br>Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> 29
Ukrainian government in the Budget Declaration 2011 for ordering the optimization of complete schools (levels I-III) may be very conservative, especially for urban schools, almost none of which are below this threshold and for which the estimated optimal point is often over 1000. This suggests there is a lot of room for consolidation.

From a policy point of view, our findings suggest that consolidating the network of schools through a downsizing in the number of small schools and a transfer process of students in those schools to neighboring ones is unlikely to harm school performance on the EIT. If anything it may even increase educational outcomes. Of course, an important caveat here is that this “no harm conclusion” is only likely to hold if the organization of the transfer and of the commuting to the receiving schools is organized efficiently.

While our results are suggestive, they further need to be interpreted with caution as we have not been able to investigate in detail to what extent our findings reflect causal relationships rather than correlation. Nor can we claim that these empirical findings can be extended to schools other than complete schools (level I-III), since the analysis was undertaken with measures of school performance for 12<sup>th</sup> -graders. Both these areas remain avenues for further research as well as the analysis of the student-level EIT data as opposed to the school-level analysis presented here<sup>34</sup> .

The consolidation of the network of schools in Ukraine is an important area of reform for improving the efficiency of educational spending and for achieving fiscal sustainability of the sector. If managed well, a sound optimization strategy can bring tangible results on the access, equity and quality fronts. But rationalizing the network of schools is usually one of the most difficult education reforms since it usually faces with strong resistance in communities, and among parents and teachers. Therefore, a necessary pre-requisite to embark in such reforms is a strong political will and coordinated efforts of Government agencies and local authorities to materialize. A detailed roadmap with a thorough nationwide analysis and realistic investment plans should be clearly defined. And public campaigns need to be incorporated as an essential

> 34 In a first attempt, Thoryk (2011) uses 2007 TIMSS data to estimate the effect of school size on school performance in transition countries. Overall his country specific estimates suggest there is little relation between school size and performance. His results for Ukraine suggest an insignificant effect of size on 4<sup>th</sup> grade science and 8<sup>th</sup> grade mathematics, an inverted U shape for 4th year math and a U-shaped relationship for 8<sup>th</sup> grade science. While the latter two results are significant, the size of the effect is found to be negligible.

55
to test using household, district, and provincial data.<sup>2</sup> Following Schultz (1981), human capital can be assumed to consist of three components: bealth, schooling and on-the-job training. There are complementarities between these components. The most well documented, for the U.S. labor market, is that between schooling and on-the-job training. Complementarities between health and the other two components have not been empirically explored in detail. At the high levels of health observed in developed countries, investments in health do not add substantially to overall human capital. Since neoclassical labor economics until recently has been primarily directed towards developed economies, the relative neglect of health as a component of human capital reflects these low returns to incremental health. In poorer countries, given the high incidence of ill-health, increments in health substantially increase in the effectiveness of the other components of human capital. The lack of emphasis on health results in serious gaps in our understanding of the process of human capital accumulation, and hence of economic development.

Just as investigating the complementarity between schooling and 2See Gill (1990) for household and province level evidence on these issues for Peru in the mid-1980s.

3This assertion relies on the following argument: Given the relatively high "levels" of health (as reflected, for example, in life expectancy, mortality and morbidity rates) in developed countries, marginal investments in health will not substantially effect the productivity of other forms of human capital. Hence the returns to investment in schooling and on-the-job training are not strongly correlated with changes in health levels. In less developed countries, given the low health levels, marginal investments in health will substantially increase the returns to schooling and on-the-job training.

2
# Unfortunate Moms and Unfortunate Children: Impact of the Nepali Civil War on Women's Stature and Intergenerational Health<sup>_∗_</sup>

Lokendra Phadera<sup>_†_</sup> Poverty and EquityGroup, World Bank Keywords: Civil Conict; Adult Height; Intergenerational Health; Nepal JEL Classication: I12; J13; O12

> _∗_ I am grateful for guidance from Benjamin Crost, Richard Akresh, Rebecca Thornton, Hope Michelson, Mary Arends-Kuenning, and Alex Winter-Nelson. Mateus Nogueira Meirelles De Souza and Bhim B. Phadera provided excellent research assistance. I am thankful for comments from Aine McCarthy, Derek Headey, Jennifer Alix-Garcia, the participants of the 16th MWIEDC, and the IFPRI's PHND Brown Bag Seminar.

> _†_ E-mail: lphadera@worldbank.org
Policy Research Working Paper 8578

# **Abstract**

An extensive literature on poverty traps suggests that high levels of poverty deter growth. However, a seemingly basic implication of the underlying theoretical models, namely that countries suffering from higher levels of poverty should grow less rapidly, has remained untested. A parallel literature has suggested a variety of mechanisms through which inequality may affect growth in opposing directions. Because inequality and poverty are different aspects of the income distribution, inequality can also affect growth through poverty, an indirect channel that has not been explicitly analyzed. This paper contributes to fill both gaps. Using a large cross-country panel data set, it estimates a reduced-form growth equation adding both inequality and poverty to an otherwise standard set of growth determinants. Given inequality, the correlation of growth with poverty is consistently negative. In contrast, given poverty, the correlation of growth with inequality can be positive or negative, depending on the empirical specification and econometric approach used. Yet, the indirect effect of inequality on growth through its correlation with poverty is robustly negative. Closer inspection shows that these results are driven by the sample observations featuring high (but not extremely high) poverty rates. These empirical findings are consistent with the predictions from an analytical framework with learning-by-doing and knowledge spillovers, in which consumers cannot save and invest if their initial endowment is below a minimum consumption level.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at serven@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
We obtain data on government investment and total investment from the IMF’s World Economic Outlook (WEO) database, measured in current local currency units.<sup>10</sup> We convert both to constant local currency units using the overall GDP deflator. Data on GDP in constant local currency units comes from the World Bank’s World Development Indicators. Data on predicted disbursements on loans from official creditors are taken directly from Kraay [2013].

We focus on a set of 39 low-income countries eligible for concessional lending from the International Development Association (IDA, the soft-loan window of the World Bank). We restrict attention to those countries for which at least 15 annual observations on investment are available. These countries are shown in Figure 2, which plots the time-average of government investment and private investment as a fraction of GDP for each country in the sample. Government investment rates vary widely across countries, ranging from as low as 2.5 percent of GDP in Cameroon to nearly 15 percent of GDP in Guyana. There is also a weak negative correlation across countries, with countries with higher government investment rates also having lower private investment rates.

We focus on these low-income countries because of the importance of official creditor financing of government spending in this sample. To see this, consider the over-time average of total disbursements on loans from official creditors as a fraction of total government spending, and as a fraction of total government investment. Among these 39 low-income countries, the median values of these ratios are 18 and 63 percent, respectively, suggesting important because disbursements on loans from official creditors finance both government consumption and government investment expenditures, and so our predicted disbursements instrument is correlated with fluctuations in both types of spending. If government consumption spending raised investment or output, this would violate the exclusion restriction. See Kraay [2012], Section 6 for a more complete discussion.

10 We are grateful to Toh Kuan of the WEO team for kindly making this data available to us. The government investment series is not available in the public WEO database, but is maintained by the WEO team as part of their larger internal database. This is to our knowledge the largest existing dataset for public investment.

16
The averages in Figures 5-7 suggest important differences in the performance of services within the ECA region. In particular, there is a clear divide among the most efficient services sectors of EU-8 countries and the least efficient services sectors of South Eastern European countries and Ukraine. At the same time, the figures show that labor productivity in services is enjoying healthy growth in the ECA region, particularly in the countries with lower average productivity, suggesting that catch-up and convergence is happening. However, these average patterns mask differences across very heterogeneous services sub-sectors, which we examine in the rest of the section.

First, we discuss the findings from comparisons of labor productivity across services sub-sectors within each country. Such comparison is subject to the ‘apples to oranges’ criticism. While this criticism would apply equally to the comparison of productivity across manufacturing sub-sectors, it is magnified for the services sector, given the difficulties in defining and measuring output. Nevertheless, we pursue these sub-sector comparisons within countries, focusing on the rankings rather than the magnitudes of labor productivity.<sup>14</sup> Based on WIIW labor productivity, some clear patterns emerge. Real estate, renting, and business activities exhibits the highest productivity across sub-sectors in 5 of the 10 countries and the second highest productivity in another 3 countries of the group of EU-5 countries, South Eastern European countries, and Ukraine. This finding is not surprising given the aforementioned ‘inflation’ of labor productivity in real estate activities. Indeed, we confirm based on the KLEMS database that real estate activities displays the highest average KLEMS labor productivity in all EU-5 countries also covered in the WIIW database. Financial intermediation displays the highest average productivity across sub-sectors in Croatia, Macedonia, and Ukraine, and the second highest productivity in another 5 of the 10 countries.<sup>15</sup> Hotels and restaurants exhibit the lowest productivity across sub-sectors in 7 of the 9 countries and the second lowest productivity in another country of the group of EU-5 countries and South Eastern European countries.<sup>16</sup>

> 14 For brevity, we do not show the corresponding figures which are simply a different aggregation (by country) of the same statistics shown in Figures 8 and 9.

> 15 Poland is an exception to the relatively high productivity of financial intermediation as this sub-sector exhibits the second lowest productivity in the country, only higher than that of hotels and restaurants. 16 Ukraine is excluded for lack of data on hotels and restaurants.

17
Figure 7: CDF – village-level open defecation (households practice regular open defecation) <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Proportion of household members in village mainly defecating in the open<br>control TSSM<br>HWWS Combination<br>1<br>.9<br>.8<br>.7<br>Cumulative probability<br>.6<br>.5<br><!-- End of picture text --> 57