rafmacalaba/datause-encoder-data huggingface dataset:
relative to the median; in particular, Wolfson (1994, 1997) points out that shifts in density away from the median, and from the lower and upper extremes, can be generated by transfers that entail lower inequality (by the usual transfer axiom used in inequality measurement).

A better measure from this point of view is the Foster-Wolfson (FW) “polarization index,” based on the spread relative to the median of incomes normalized by the median.<sup>10</sup> More precisely the polarization index is the area under the “polarization curve” given by the vertical distance between the Lorenz curve and the tangent to that curve at the median. This gives how far each percentile is from the median. The emergence of a middle-income bulge due to a change in distribution can be interpreted as a downward shift in the polarization curve. To avoid confusion about “which way is up” I will use 100 minus the FW index (in %), and I shall refer to this as the FW index of concentration around the median rather than “polarization.” This provides a convenient summary statistic, although (as noted above), it is questionable whether the median is the relevant anchor in poor countries.

However, there is a more natural, and less restrictive, way of assessing the contribution of distributional changes to the “bulging middle,” namely to construct a counterfactual for the second date in which the Lorenz curve does not change relative to the base date, but the overall growth rate is the same as that observed in the data.<sup>11</sup> I will also examine whether there is a bulging middle relative to this distribution-neutral counterfactual.

# **2. Evidence on the developing world’s expanding middle class**

Using almost 700 household surveys for 115 developing countries, Chen and Ravallion (2008) estimate poverty measures for a range of international poverty lines anchored to the definitions of “poverty” found in practice in poor countries. The salient features of the ChenRavallion estimation methods are as follows. Each international poverty line was converted to local currencies in the benchmark year (2005) using the consumption PPP for 2005 from the ICP. It was then converted to the prices prevailing at the time of the relevant household survey using the best available consumer price index for that country. Then the poverty rate was calculated from that survey by standard methods. Interpolation and extrapolation methods were used to line

> 10  Wolfson (1994) refers to an earlier, un-published, paper with James Foster as the source of the concepts of a polarization curve and polarization index.

> 11  This approach is in the spirit of the Datt and Ravallion (1992) decomposition of changes in poverty measures into “growth” and “redistribution” components.

6
indicate that there is a significant difference between tertiary educated and other migrants. The first group seems to overcome initial gaps in placement while the latter do not.<sup>23</sup> The issue of convergence has also been explored by the literature (measuring migrants’ performance by earnings), sometimes with conflicting results.<sup>24</sup> To further test the strength of convergence detected by examining our charts, we regress the change in hypothetical individuals’ occupational placement during a decade on their occupational placement at the beginning of the decade. In accordance with the predictions of classical β-convergence models (Barro,1991, Barro,1997) the _θ_ coefficients conditional on human capital endowments are expected to be negative and significant. For each pair of Census years t=1980, 1990 and 2000, where _c_ represents the country of origin.<sup>25,26</sup> Table 5 presents the estimation results, which provide evidence of strong convergence as suggested by the negative and significant coefficients for the predicted log of OP in 1980 and 1990 respectively. This result holds for all the alternative specifications of the dependent variable.

# **_3.3. EXAMINATION OF MAJOR SOURCES OF BIAS_**

The findings of the previous section may reflect a distorted picture of the real trends in immigrants’ occupational placement if there are factors that systematically impact the composition of cohorts across censuses. This section aims at quantifying the effect of two important sources of bias: out-migration and usage of repeated cross-section datasets.

> 23 The details of this exercise are also available upon request.

> 24 Duleep and Regets (1997) detect significant negative correlation between the initial earnings and earnings’ growth when controlling for the education level of migrants. Conditional on similar initial human capital endowments, Borjas (2000) finds similar results, but they are not robust to simple attempts to control for the measurement error bias.  In addition, when human capital variables are not accounted for, Borjas obtains a positive, but weak correlation between wages and their growth. Like Duleep and Regets (1997), Hu (2000) provides evidence of significant conditional convergence.  He also finds that convergence is present even when not controlling for education and other human capital indicators, but that, as Borjas (2000) showed, it is weaker than the conditional convergence.

> 25 To account for the heteroscedasticity due to the sampling error, all regressions are weighted by a factor

 1  1  1 ( _nt_  _nt_  10) as described in Borjas (2000), where _nt_ is the number of skilled people by country and half-decade of arrival, in Census year _t_ .

26 10 stands for the number of years within a decade. When comparing the performance of the 1975 arrival in 1980 and 2000, the dependent variable is divided by 20.

14
VSL. We use this computation to better understand the value of lives lost as a result of COVID19 over time. The VSL for Côte d’Ivoire is given by the following formula: With VSLCote d′Ivoire and VSLUSA, the VSL for Côte d’Ivoire and the United States, respectively. The GNI is the gross national income, and elasticity<sup>3</sup> is the degree of change in the VSL associated with a change in income.

Furthermore, age is considered because COVID-19 has had a disproportionately negative effect on elderly people. As a result, we estimate a constant value per statistical life year (VSLY) (Aldy & Viscusi, 2007). Following Robinson et al. (2017a), we divide the VSL by the discounted expected life years remaining for an individual at the average age of the population being studied. The VSLY, which is often called the value of a life year, measures how much people are willing to spend for a change in their life expectancy. The VSLY is given by the following formula: With _r_ and _L_ the discount rate and the remaining life expectancy, respectively.

We apply the standard 3% discount rate and the 5% discount rate that Haacker et al. (2020) advised for low- and lower-middle-income countries. Based on information from the WHO's Global Health Observatory Data Repository, we estimate that the remaining life expectancy is half of Côte d’Ivoire’s life expectancy at birth. The COVID-19 mortality costs are calculated by multiplying the VSLY values by the total number of deaths associated with COVID-19.

Lastly, we carried out a desk analysis (World Bank., 2020a, b, 2021, 2022) to comprehend the health policies put in place by policy makers to fight COVID-19, and we provide effective health policy recommendations to assist the policy makers in Côte d'Ivoire in better preventing, preparing for, and responding to future pandemics.

> 3 Using different income elasticity of the VSL values, we examine the uncertainties around the VSL estimates.

4
deviations is presented in Table A2. Table A2 clearly shows that the averages of selected characteristics are virtually indistinguishable in the two samples, and are very similar in magnitude to the national averages (when all 2,443 municipalities in Mexico are used).

# **3.  Empirical strategy**

# **3.1. Using miscarriages to identify the impact of adolescent childbearing**

Identification of the causal effect of early childbearing on mother‟s welfare, during adolescence as well as later in life, presents considerable problems. Unquestionably, women who become pregnant as teenagers differ along a number of important dimensions from women who delay childbearing. Table 1 illustrates this point. In Mexico, women who become pregnant as teenagers (before turning 19 years old) are significantly more likely to be indigenous, more likely to be members of less educated and less wealthy families. They are less likely to live with their fathers. They begin sexual activity and form unions at significantly earlier ages. Indisputably, there is a selection on observable (and most likely unobservable) characteristics in the group of women who experience pregnancy before age 19.

To address this selection bias, the present paper uses miscarriages as a natural experiment. It estimates the difference in outcomes among women whose teen pregnancy resulted in a live birth, and women who became pregnant as teenagers but whose pregnancy did not result in a live birth due to a miscarriage. Let _Y_ be an outcome of interest, _B_ a dummy equal to one if a pregnancy resulted in a live birth, and **X** a vector of individual characteristics. To capture the effect of early childbearing on _Y_ , we run the following regression on a sample of women who became pregnant as teenagers and whose pregnancy resulted in a live birth or a miscarriage: This identification strategy poses several concerns. We assume that pregnancy results in three possible outcomes: live birth, abortion, and miscarriage.<sup>7</sup> Following Hotz et al. (2005), we can also

> 7 ENADID survey distinguishes between stillbirth and miscarriage: if a pregnancy loss occurs in the first 5 months of pregnancy, it is recorded as miscarriage ( _aborto_ ); if the loss of pregnancy occurs after 5 months of

6
Policy Research Working Paper 11074

# **Abstract**

This study employs a cluster randomized controlled trial and administrative health center data to investigate the effects of authorizing community health workers to deliver a new generation of contraceptive injections directly to women during routine home visits following comprehensive training. The paper observes a significant increase of approximately 70 percent in the administered quantity of these injections, which provide average protection for three months. However, the results suggest that the intervention does not produce a statistically significant change in contraceptive coverage because of significant substitution effects away from long-acting contraceptive implants and intrauterine devices that women might otherwise have adopted.

This paper is a product of the Development Impact 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/prwp. The authors may be contacted at mandreottola@worldbank.org, obasenya@worldbank.org, vorozco@worldbank.org, arndt.reichert@ihe.uni-hannover.de, or paulade@iadb.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Adhikari, N.P. and M. Wegstein, 2011. “Financial Analysis of Jatropha Plantations in the context of Nepal”, _Journal of the Institute of Engineering_ 8(1): 143-148.

- AEPC, 2009. “Renewable Energy Data Book”, Alternative Energy Promotion Center (AEPC), Ministry of Environment, Government of Nepal, Lalitpur.

- AEPC, 2010. “ _Jaibik Indhan Tatha Sajiwon Sambandhi Jankarimulak Pustika (in Nepali)”,_ Alternative Energy Promotion Center (AEPC), Ministry of Environment, Government of Nepal, Lalitpur.

- Becker, K. and H.P.S. Makkar, 2008. “ _Jatropha curcas_ : A potential source for tomorrow’s oil and biodiesel”, _Lipid Technology_ 20(5): 104-107.

- Boccanfuso, D., M. Coulibaly, G.R. Timilsina and L. Savard, 2013. “Macroeconomic and Distributional Impacts of Jatropha-based Biodiesel in Mali” The World Bank, Development Research Group, Environment and Energy Team, Policy Research Working Paper 6500.

- DOA, 2010. “Annual Progress Report FY 2066/067 (2009/10)”, Monitoring and Evaluation Division, Department of Agriculture (DOA), Ministry of Agriculture and Cooperatives (MoAC), Government of Nepal, Lalitpur.

- Eijck, J.v. and H. Romijn, 2008. “Prospects for Jatropha biofuels in Tanzania: An analysis with Strategic Niche Management”, _Energy Policy_ 36: 311-325.

- FACT Foundation, 2010. “The Jatropha Handbook: From Cultivation to Application”, FACT Foundation, The Netherlands.

- Garg, K.K., L. Karlberg, S.P. Wani and G. Berndes, 2011. “Jatropha production on wastelands in India: opportunities and trade-offs for soil and water management at the watershed scale”, _Biofuels, Bioproducts and Biorefining_ 5(4): 410-430.

- Gübitz, G.M., M. Mittelbach and M. Trabi, 1999. “Exploitation of the tropical oil seed plant _Jatropha curcas L._ ”, _Bioresource Technology_ 67(1): 73-82.

International Energy Agency (IEA), 2012. Energy Balances of No-OECD Countries, 2012 Edition. IEA, Paris.

- K.C., S., S.K. Khanal, P. Shrestha and B. Lamsal, 2011. “Current status of renewable energy in Nepal: Opportunities and challenges”, _Renewable and Sustainable Energy Reviews_ 15: 4107-4117.

- Kantipur Publication, 2012. “Petroleum is Nepal’s number one import”, http://www.ekantipur.com (August 24, 2012), http://www.ekantipur.com/2012/08/24/business/petroleum-is-nepals-number-oneimport/359217.html

- Kumar, A. and S. Sharma, 2008. “An evaluation of multipurpose oil seed crop for industrial uses ( _Jatropha curcas_ L.): A review”, _Industrial Crops and Products_ 28(1): 1-10.

- Kumar, N. and P.B. Sharma, 2005. “ _Jatropha curcus_ – A sustainable source for production of biodiesel”, _Journal of Scientific and Industrial Research_ 64: 883-889.

- Mitchell, D., 2011. “Biofuels in Africa: Opportunities, Prospects and Challenges”, The World Bank, Washington, D.C.

26
3 Bourguignon, Ferreira and Menéndez (2007), Checchi and Peragine (2005), Ferreira and Gignoux (2008), and Lefranc, Pistolesi and Trannoy (2008, 2009).

Although these papers differ in important respects in how they propose to measure inequality of opportunity, they share some common features. In particular, they typically rely on individual- or household-level data on at least two sets of variables: an _advantage_ (Roemer’s term for an outcome that everyone can reasonably be presumed to value, such as income, wealth, educational achievement, or good health), and a number of _circumstances_ (Roemer’s term for variables that may be correlated with advantages, but over which individuals cannot exercise any control – such as race, gender or family background).

In practice, most studies have typically used some measure of economic wellbeing (such as earnings, income or consumption) as an advantage variable, and data on race, parental education and/or parental occupation as circumstances. For many countries, however, even such a limited set of variables is seldom available in the same data set. Specifically, most cross-sectional household income or expenditure surveys do not contain information on the education, occupation or socioeconomic status of the parents of today’s adult earners. This limitation has prevented the application of existing techniques for measuring inequality of opportunity in a number of countries.<sup>3</sup> This paper proposes two alternative methods for measuring inequality of opportunity in settings where a standard household survey does not contain information on the family background of today’s adults, but where an alternative survey might. In particular, we explore the use of Demographic and Health Surveys (DHS), which are available for 83 countries around the world, and which contain relatively rich information on parental characteristics for a large subset of the adult population, namely all evermarried women. DHS surveys do not typically contain any estimate of household income or consumption expenditures, but they do include information on the ownership of an array of assets and durable goods, as well as on various indicators of housing quality and access to amenities. These variables have been used in the past to construct composite indicators of household wellbeing and we show how they can also be used to generate

> 3 At the very least, these data limitations have sometimes caused researchers to use much older data sets that do contain the information on parents. An example is the use of PNAD 1996 data for Brazil in Ferreira and Gignoux (2008).
# **Table 4: The sensitivity of the FAO FBS-CV method to module type**

# PANEL A: MEAN FIXED AT PERSONAL DIARY LEVEL

|Module|mean<br>(μ*)|CV|z-score<br>Eq (1)|proportion<br>hungry*|
|---|---|---|---|---|
|1. Long list of 58 food items; 14 day recall|2,677|0.35|-0.59|0.28|
|2. Long list of 58 food items; 7 day recall|2,677|0.32|-0.67|0.25|
|3. Short list of 17 food items; 7 day recall^|2,677|0.26|-0.87|0.19|
|4. Long list of 58 food items; usual 12 month recall|2,677|0.35|-0.59|0.28|
|5. 14 day household diaries with frequent visits|2,677|0.27|-0.85|0.20|
|6. 14 day household diaries with infrequent visits|2,677|0.25|-0.94|0.17|
|7. 14 day individual diaries with frequent visits|2,677|0.26|-0.89|0.19|

* The proportion hungry is derived as the _p_ -value from the _z_ -score, i.e. the area to the left of 2068 kcal/day in the cumulative standard normal distribution.

# PANEL B: MODULE-SPECIFIC MEAN

|Module|mean<br>(_μ_*)|CV|z-score<br>Eq (1)|proportion<br>hungry*|
|---|---|---|---|---|
|1. Long list of 58 food items; 14 day recall|1,793|0.35|0.59|0.72|
|2. Long list of 58 food items; 7 day recall|2,129|0.32|0.06|0.53|
|3. Short list of 17 food items; 7 day recall^|2,066|0.26|0.13|0.55|
|4. Long list of 58 food items; usual 12 month recall|1,909|0.35|0.41|0.66|
|5. 14 day household diaries with frequent visits|2,413|0.27|-0.45|0.33|
|6. 14 day household diaries with infrequent visits|2,520|0.25|-0.69|0.24|
|7. 14 day individual diaries with frequent visits|2,677|0.26|-0.89|0.19|

* The proportion hungry is derived as the _p_ -value from the _z_ -score, i.e. the area to the left of 2068 kcal/day in the cumulative standard normal distribution.

22
differences between real GDP<sup>e</sup> (using ICP data) and real GDP<sup>o</sup> (using national accounts data) is eliminated, and the year-to-year growth of real GDP from the national accounts is used to impute real GDP in the PWT for all years in-between the benchmark years. The fact that the distinction between real GDP<sup>e</sup> and GDP<sup>o</sup> is not clearly made is a limitation of previous versions of the PWT that future revisions intend to improve upon.<sup>5</sup>

# **3. Measurement of Real GDP**

Suppose there are i =1,...,M final goods, such as the categories of goods currently collected by the ICP, of which the first M0 are non-traded. These final goods may also be used as intermediate inputs, and there are another i =M+1,...,M+N goods that are exclusively intermediate inputs; for convenience we treat these all as traded internationally. To treat domestic demand, trade and production in a consistent framework, an input-output analysis must be used. In this framework the fundamental equality is between total demand and total supply of each good. For each country j = 1,...,C, denote final demand by qij, intermediate demand by zij, output by yij, exports by xij and imports by mij, for i = 1,...,M+N. We assume that all of these quantities are _nonnegative,_ but many can be zero: in particular, the intermediate inputs i = M+1,...,M+N have qij = 0, and the non-traded goods = 1,...,M0 have xij = mij = 0. Total demand in country j is given by qij + xij + zij, and total supply by yij + mij. Hence the equality between demand and supply is: Re-arranging terms, we obtain: where we refer to yij – zij as “net output” of each good, i.e. gross output minus intermediate demand.

Multiplying by prices and summing over goods i = 1,...,M+N, nominal GDP can be measured either from the expenditure side (left-hand side of (4)) or from the production side

> 5  For example the title of the Phase III report of the ICP was _World Product and Income_ (Kravis, Heston and Summers, 1982). This clearly implied a treatment of GDP converted at PPPs of countries as interchangeable with what is termed _output_ and _expenditure_ in this paper. Further the provision of output per worker figures in PWT, though provided because users were taking per capita real GDP as a labor productivity measure, clearly implies that

7
as a whole, over 2,000 parameters are reported, leading to considerable overlap with the substances reported for the TRI. Some of the more important additional parameters are Biological Oxygen Demand (BOD, a measure of the amount of oxygen consumed in the biological processes that break down organic matter in water), Total Suspended Solids (TSS), pH and temperature. The length of the time series varies regionally, the longest being about ten years. However the data are most complete from 1987 onwards, following the most recent modification of the database.

# 2.2 The Human Health and Ecotoxicity Database (HHED)

The EPA's HHED contains a number of indices of toxicological potency. No single index is considered sufficient to characterize all the factors relevant to a chemical's toxic potential under different circumstances, so different indices have been developed for specific applications. For example the Reportable Quantity (RQ) index is designed to guide the reporting of accidental releases required under CERCLA, whereas the Threshold Planning Quantity (TPQ) index was developed to meet the emergency response planning requirements of SARA Title III, Section 2.

|

For the purposes of risk-screening the HHED aggregates the toxicity values for ten indices into three toxicological potency groups. Table 2.1 indicates the mapping of threshold figures onto toxicological potency groups for four of the ten indices. In a number of cases the differences in the criteria used to develop the indices cause the same chemical to be rated 8