rafmacalaba/datause-encoder-data huggingface dataset:
stage of pregnancy, women are also more likely to be aware that they are pregnant and can recognize miscarriage as such.

ENADID data virtually replicates the epidemiologically predicted proportion of naturally occurring clinical miscarriages. Of all pregnancies reported in 2005, 7.05 percent ended in clinical miscarriages (Table 3). This number just about accounts for biologically inevitable miscarriages – being too low to also include abortions reported as miscarriages. We conclude that women who resort to clandestine abortion do not report a pregnancy at all, as opposed to reporting such a pregnancy as having terminated in miscarriage.

# **3.3. How to account for women who chose to abort, but had a miscarriage prior to executing this option?**

Another challenge to the identification strategy is that, among women who miscarried, there is a fraction who would have aborted had miscarriage not occurred before they acted on their decision. These women may be different from women who chose to carry the pregnancy to term, and therefore will not constitute a valid comparison group. To derive an unbiased estimate of the effect of early childbearing, we need to compare the outcomes of women who had children to the outcomes of women who miscarried, but who intended to carry the pregnancy to term, or (using earlier terminology) whose latent status is _B*_ . Hotz et al. (2005) use data on reported abortions to correct their estimates for the possibility that among women who miscarried, some had latent status _A*_ . However, as shown above, ENADID 2006 does not contain reliable information on abortions.

To account for the possibility that women who miscarried may include women with latent status _A*_ , we will take advantage of the fact that ENADID includes the number of months of pregnancy when a miscarriage occurred. If we define latent status as a permanent characteristic that does not vary with time, women with latent status _A*_ know that they want to abort soon after they discover that they are pregnant. They have many incentives to act on their decision as early as possible: abortions are safer and emotionally easier when carried out early (Bartlett et al., 2004); furthermore, it is also easier to conceal a pregnancy during the first months.

9
The results of the analysis of efficiency with monetary inputs are shown in Table 5. These results reveal the sensitivity of the efficiency measures with respect to sampling variation. The biascorrected efficiency (BC) reveals that differences in measurement efficiency are of a different magnitude than when the original efficiency scores (N) are considered. For all of the manufacturing sectors, the efficiency declines slightly.

Table 5 Descriptive statistics of the output and inputs used by sector and country <u>(year 2005).</u>

|**Variables/industry**|**Beverages**|**Food**|**Garments**|**Metals and**<br>**machinery**|
|---|---|---|---|---|
|**Mean**<br>|||||
|Sales (Y)|2476.644|895.9026|592.492|1030.763|
|Materials Cost (X1)|1282.234|453.563|242.7717|497.1371|
|Energy Cost (X2)|136.3903|50.45932|35.12418|53.18183|
|Personal Cost(X3)|209.1959|80.92757|151.6997|149.9907|
|**Median**|||||
|Sales (Y)|196.418|262.3651|118.1627|82.81187|
|Materials Cost (X1)|85.13541|116.6796|46.78612|37.21292|
|Energy Cost (X2)|9.772637|12.94952|6.90287|5.241257|
|Personal Cost(X3)|22.49096|23.00957|27.17094|14.67552|
|**Standard deviation**|||||
|Sales (Y)|9214.583|1604.977|1536.927|3170.787|
|Materials Cost (X1)|5205.379|856.9301|664.3403|1578.464|
|Energy Cost (X2)|595.0032|87.42706|104.0559|154.6308|
|Personal Cost(X3)|641.1238|154.4191|427.9902|445.9945|
|**Maximum**|||||
|Sales (Y)|88827.03|8673.232|13709.87|30116.26|
|Materials Cost (X1)|52686.09|5364.427|5982.488|15777.23|
|Energy Cost (X2)|6747.266|587.511|875.7089|1700.407|
|Personal Cost(X3)|5275.788|773.6096|3373.633|4774.785|
|**Minimum**|||||
|Sales (Y)|2.154254|0.509635|0.2548175|0.3011479|
|Materials Cost (X1)|0.4787231|0.2162088|0.1235479|0.1389914|
|Energy Cost (X2)|0.2393615|0.0386087|0.0154435|0.0154435|
|Personal Cost(X3)|0.4015306|0.1081044|0.0540522|0.0540522|

Source: Authors‘ calculation from the Investment Climate Survey Databank, World Bank Having found that there exists wide variation in technical efficiency among the sample firms it is important and useful to understand the impact of the overdraft facility policy.

# 5.2 Propensity Score Matching Estimation.

To better construct the match, table 6 below shows tests for mean differences between supported and non-supported groups for the impact and other key variables. Identification of the matching technique to create the propensity score requires that the impact variables are independent of the treatment conditional on the propensity score. Few statistically significant differences between participants and non-participants were observed, with the exception of the large and younger firms being more prevalent among those receiving overdraft facilities.

18
**Figure 1. Physical/Manual and F2F intensity, by GDP per capita**

<!-- Start of picture text --> (a) Physical/Manual Intensity vs. GDP (PIAAC) (b) F2F Intensity vs. GDP (PIAAC)<br>0.5 0.5<br>TUR NZL<br>0.4 0.4 FIN<br>USA<br>0.3 0.3 GBR SWE NOR<br>ECU MEX 0.2 CAN DNK SGP<br>0.2 PER IRL<br>USA 0.1<br>0.10 9 9.5 10KAZGRCPOLCHL RUS SVKLTUHUNSVNKOR10.5CZENZLGBR ITA IRL DEU SWENLDAUTDNK11 NOR 11.5 -0.10 9 ECU 9.5PER MEX10 CHLPOLGRCESTRUS SVNCZEKOR10.5ESPISR JPNFRABELDEUAUT NLD 11 11.5<br>-0.1 EST ESP CAN -0.2 TUR ITA<br>FRA BEL<br>-0.2 SGP -0.3 KAZ SVK<br>ISR FIN HUN<br>-0.3 -0.4 LTU<br>JPN<br>-0.5 GDP per capita PPP (log)<br>-0.4 GDP per capita PPP (log)<br>(c) Physical/Manual Intensity vs. GDP (STEP) (d) F2F Intensity vs. GDP (STEP)<br>0.5 0.3<br>LKA<br>COL<br>0.4 0.2<br>SLV<br>0.3 LAO ARM MKD<br>SLV 0.1<br>0.2 BOL UKR KOS<br>0 KEN GEO SRB<br>7.5 8 8.5 9 9.5 10<br>0.1 COL VNM<br>GHA<br>-0.1<br>GHA<br>0 BOL ARM<br>7.5 8 8.5 9 UKR 9.5 10 LKA<br>KEN PHL -0.2<br>-0.1 PHL<br>-0.2 VNM GEO KOS MKD SRB -0.3<br>LAO<br>-0.3 GDP per capita PPP (log) -0.4 GDP per capita PPP (log)<br>F2F index<br>Physical/Manual index<br>F2F index<br>Physical/Manual index<br><!-- End of picture text --> Note: The vertical axis measures the corresponding task index, in standard deviations from the mean for all PIAAC/STEP countries. GDP per capita PPP comes from the World Development Indicators (WDI) and corresponds to the same year of the respective PIAAC and STEP surveys.

The fact that the intensity of jobs on physical/manual tasks tends to decline with economic development, and that the intensity on F2F tasks shows the opposite pattern suggests that two opposing forces are at play when shaping the relationship between WFH measures and GDP per capita. However, F2F occupations also tend to be more intensive in ICT use. As seen in Figure 2, countries such as Singapore or the United States that have jobs more intensive in F2F tasks also use more ICT at work than countries such as Lithuania or Kazakhstan. That is, several of the tasks embedded in such occupations are more prone to be performed remotely. In other words, ICT use at work would tend to weaken the effect of F2F intensity on WFH measures. This correlation between F2F tasks and ICT use at work can also be observed within countries (see Figure A 1 in Appendix 2).

8
WPS3378

# **Pro-growth, pro-poor: Is there a tradeoff?**

J. Humberto Lopez<sup>*</sup> The World Bank (PRMPR)

## **Abstract**

Is a pro-growth strategy always the best pro-poor strategy? To address this issue, this paper provides an empirical evaluation of the impact of a series of pro-growth policies on inequality and headcount poverty. We rely on a large macroeconomic data set and estimate dynamic panel models that allow us to differentiate between the short- and longrun impacts of the policies under consideration on growth, inequality and poverty. Our findings indicate that regardless of their impact on inequality, all the pro-growth policies we consider lead to lower poverty levels in the long run. However, we also find evidence indicating that some of these policies may lead to higher inequality and, under plausible assumptions for the distribution of income, to higher poverty levels in the short run. These findings would justify the adoption of a pro-growth policy package as the center of any poverty reduction strategy together with pro-poor measures that complement such a package by offsetting potential short-run increases in poverty.

## World Bank Policy Research Working Paper 3378, August 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* This paper has been prepared in the context of the "Pro-poor Growth" program sponsored by the World Bank's PREM Poverty Group. I would like to thank Oya Celasum, Aart Kraay, Ugo Paniza, Cico Ferreira, Tito Cordella, Ulrich Lachler and seminar participants at the World Bank, IMF, and IDB for comments received and to Peter Bocock for editorial assistance. Address for correspondence: MSN MC4-415, The World Bank, 1818 H Street, Washington, DC 20433. Email: <u>Hlopez@worldbank.org. Tel: 1+202-473-</u> 4909.
decision-making (e.g. World Bank, 2014), while the important topic of entrepreneurship has been relatively neglected (Naudé, 2013).

# **2.2 The View of Development Economics from Textbooks**

Our surveys reveal that the three most widely-used textbooks for teaching development economics at the undergraduate and masters levels are Todaro and Smith’s (2014) _Economic Development_ (first published in 1993 and now in its twelfth edition), Ray’s (1998) _Development Economics_ , and Banerjee and Duflo’s (2011) _Poor Economics._ These books differ substantially in their approach to the subject; their emphasis on theory, empirics, and stylized facts; the topics they cover (which we compare in Table 1); and the approach they take to student learning. This differs from the case of introductory and intermediate micro and macro-economics, in which Allgood et al. (2015) note that the choice of textbook is probably inconsequential for learning given the similarity of content.

# **2.2.1 Economic Development**

Todaro and Smith (2014) define development economics as “the study of how economies are transformed from stagnation to growth and from low-income to high-income status, and overcome problems of absolute poverty”. They have the broadest topic coverage of the three textbooks, covering a number of macro, trade, and environmental issues not discussed in the other books. The teaching approach taken is a “problem- and policy-oriented” approach and relies heavily on case studies and stylized facts. Evidence here is presented largely as a set of motivating examples and facts with which to discuss a topic, rather than as explicit tests of specific models or hypotheses.

The structure of the text and review sections at the end of each chapter suggest a learning focus of attaining a general understanding of key topics together with rote-learning of definitions, with the questions for discussion and concepts for review at the end of each chapter asking students to give an account of the concepts seen and policies proposed in the preceding sections. Examples are questions like “Outline the range of major policy options for a developing country to alter and modify its size distribution of national income. Which policies do you believe are absolutely essential? Explain your answer.” “List and briefly describe the principal causes of high population growth in developing countries and the major consequences.” The book itself does not ask students to apply economic knowledge to an analysis of new problems, but gives students concepts they can use in doing so. There are no empirical exercises or attempts to develop data analysis skills. Theory is taught in general and graphical terms, with no equations.

6
calculate the propensity of every country in the destination subregion to accept migrants from elsewhere in the world. All of the propensities sum to one. These shares are multiplied by the total migrant stock figures provided in the Trends in International Migrant Stock database to calculate the bilateral numbers.

# _III. RELIABILITY OF THE ESTIMATES_

The previous section described the challenges in constructing the matrices and the range of measures used to generate the missing observations. This section highlights the extent to which the estimates are based on the underlying raw data and their reliability.

# _Categorizing the Methods Used_

Nine main methods were used to generate the cells:

1. _Pure raw:_ Derived directly from the raw bilateral census data.

2. _Raw scaled:_ Based on the underlying raw bilateral data scaled to the UN numbers.

3. _Pure remainder:_ Assigned directly from the disaggregation of aggregate categories applying one of the propensity measures.

4. _Remainder scaled:_ Based on disaggregations using one of the propensity measures and then scaled to the UN numbers.

5. _R&R_ [ _Raw and Remainder combined not scaled:_ Based primarily on bilateral raw data and to which disaggregations of certain aggregate categories were added.

6. _R&R_ [ _Raw and Remainder combined_ ] _scaled:_ Similar to R&R not scaled except that the resulting value was scaled to the UN numbers.

7. _Pure interpolation:_ Calculated solely by interpolating missing end and middle censuses, but not scaled to the UN data.

8. _Interpolation and scaled:_ Both interpolated and scaled, for countries with poor data or for cells calculated by interpolating missing and end decades which then had to be scaled.

9. _Missing:_ For countries for which bilateral data were missing for every census round, such as Somalia.

The data used in the first six methods are from the raw census data. The data for the last three methods are missing because of omissions in the underlying data and need to be filled. Therefore, varying percentages of observations in each decade are assigned by the methods described (table 4). In 1960, 59 percent of observations are directly assigned from the raw bilateral data or from one of the disaggregations of the aggregate raw data (the first six categories). By 2000, this proportion rises to 69 percent. However, these observations account for some 84 percent of the total number of international migrants in 1960 (table 5). This proportion rises to 86 percent by 2000 because a small number of corridors (cells) account for a large proportion of global migration stocks. The bulk of the remaining international migrants are assigned on the basis of interpolation.

15
Pages

# Tableof Contents

|1.<br>Intro|duction...<br>..<br>.<br>2.<br>ee<br>ee<br>ew<br>ee<br>ee<br>et<br>ee<br>ee<br>ee<br>a|
|---|---|
|2. Buildi|ng Blocks for Plant Level Databases<br>.........3|
|2.1|US EPA Emissions Databases<br>.............3|
||2.1.1<br>The Toxic Release Inventory<br>(TRI)<br>.....<br>4<br>2.1.2<br>Aerometric Information Retrieval System<br>(AIRS) a<br>2.1.3<br>National Pollutant Discharge Elimination|
||System<br>(NPDES)<br>..............7|
|2.2|The Human Health and Ecotoxicity Database<br>(HHED)<br>..<br>8|
|2.3|The Longitudinal Research Database<br>(LRD)<br>...-....9|
|3.<br>Pollut|ion Intensity Index Construction ..........<br>1|
|3.2.|The Conceptual Goal ........-.4......6..<br>«22|
|3.2.|Operational Complexities<br>.............<br>12|
||3.2.1<br>Merger of the EPA and LRD files.<br>....<br>13|
||3.2.2<br>The Choice of<br>a Numerator ........<br>13|
||3.2.3.<br>The Choice of a Demominator .......<br>14|
||3.2.4<br>Alternative Estimates of<br>Sectoral|
||Pollution Intensities ..........<br>16|
||3.2.5.<br>Remapping US Facilities to 4-digit ISIC<br>.<br>18|
|4.<br>Constr|uction of a Toxic Pollution Risk Intensity Index ..<br>19|
|4.1.|Calculation of Risk-Weighted and Unweighted<br>Releases and Transfers<br>..............<br>19|
|4.2.|Scaling by Shipment Value to Give Pollution|
||Intensity...<br>....-.<br>ee<br>1<br>ee<br>ee<br>ee<br>ee<br>ee<br>ODD|
|4.3.|Results<br>.<br>.<br>.<br>2.<br>1.<br>1.<br>ee<br>ew<br>ew<br>ee<br>ee<br>ee<br>ee<br>ee<br>ee<br>2D|
|4.4|Variation Across<br>Indices<br>.............<br>27|
|5. Altern|ative Estimates,<br>Choice of Denominators,<br>and Medium-|
|Spec|ific Indices of Pollution Intensities<br>......<br>.<br>35|
|5.1|Alternative Estimates<br>of Sectoral Pollution|
||Intensities<br>.<br>.<br>.....<br>6<br>2.<br>ee<br>ee<br>ew<br>ee<br>ew<br>eee)<br>88|
|5.2|Different Measures of Activity<br>..........<br>39|
|5.3|Medium-SpecificIntensities............<br>40|
**-** **<u>Table 6: Country level poverty status at $1.90 PPP</u>**

|Country|Survey<br>year|Poverty<br>rate (%),<br>per capita|Poverty<br>rate (%),<br>square root|Millions<br>of poor,<br>per capita|Millions of<br>poor,<br>square root|Square-root equivalent<br>poverty line (2011<br>USD PPP)|
|---|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Nigeria|2018.75|39.1|39.1|78.0|78.1|4.871|
|Mali|2009.89|50.3|50.3|7.5|7.5|6.713|
|India|2011.5|22.5|22.5|282.9|282.9|4.343|
|Pakistan|2018.5|4.4|4.4|9.5|9.5|5.202|
|Tajikistan|2015|4.1|4.1|0.3|0.3|5.46|
|Indonesia|2017|4.5|4.5|11.8|11.8|3.958|
|Yemen,|||||||
|Rep.|2014|18.3|18.3|4.7|4.7|5.473|
|Colombia|2017|4.0|4.0|2|2|3.942|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) Notes: The survey years with decimals are the cases where the survey spans two consecutive calendar years. By</u> convention, the decimal indicates the share of the survey conducted in the second year. For example, 75% of the Nigeria survey was conducted in 2019.

35
<!-- Start of picture text --> Rural poverty ratio and rural per-capita expenditure, 2009-10<br>60.0<br>wharChhat,<br>50.0<br>= avo : <9 zs<br>=rt5= 30.0 Orissa® SPoseweAssamoMlaha.<br>= Kam? Raj® Gu.<br>t2 20.0 a+ ——<br>2 TN 2 Haryana<br>“Punjab<br>10.0 = Kerala<br>oO<br>oO 200 400 600 S00 1ono 1200 1400 Ino0) | LRO0 | SOK<br>Rural per-capita expenditure in current rupees in 2009-10<br>* Rural poverty ratio §==§ —Expon, (Rural poverty ratio)<br><!-- End of picture text -->

<!-- Start of picture text --> Urban poverty ratio and urban per-capita expenditure, 2009-10<br>45.0<br>40.0 * hilar<br>33.0<br>200 Nias * UP<br>2,2.eCE250 Chhat,*- Orissa.ast~ @ Assam we® Haryana<br>200 * Raj. oo<br>2150| Gujs AP‘Tanah<br>= *1N<br>10.0 . ® Kerala<br>5.0<br>0.0<br>0 500 1000 1500 2000 2300<br>Urban per-capita expenditure in current rupees in 2009-10<br>* Urhan poverty ratio §=—Expon, (Lirban poverty ratio)<br><!-- End of picture text -->
Most studies comparing attendees of preschools of any type (e.g. formal or informal) with nonattendees have found higher scores on some measure of child development, such as literacy, vocabulary, math, and quantitative reasoning, teacher assessments at the end of the year, and/or on subsequent school performance (for reviews, see (Engle et al., 2011; Rao et al., 2015)).<sup>4</sup> Beyond these comparisons, there is also a growing literature and emphasis on quality, and what variables are most important for developing and supporting a young child’s abilities (Behrman et al., 2013; Britto et al., 2014).

The evaluation of Chile’s _Un Buen Comienzo_ (A Good Start) is the first large-scale, randomized study of an effort to improve the quality of preschool education in South America (Yoshikawa et al., 2015). It is a two-year program that provides teacher training and professional development to prekindergarten and kindergarten teachers. After exposure to the teacher-training intervention, many classroom characteristics and teacher behaviors showed significant improvements. There were no effects on children’s language or literacy skills, however. The authors interpret these findings as a consequence of the low intensity of, and insufficient exposure of the children to, the training program.

Another approach to promoting early child development revolves around support of parents.<sup>5</sup> Most existing studies of home-visiting studies in low- or middle-income countries (LMICs) have been smaller efficacy trials, though some recent papers have examined programs at scale. For example, a home visiting program in Pakistan utilized community health workers and demonstrated improved child development outcomes (Yousafzai et al., 2014). A recent scaled-up program in the Caribbean delivered parenting support messages within primary care clinics and showed benefits to child development (Chang 4 For example, preschool attendance compared with non-attendance has been associated with better cognitive performance among preschool children in Mozambique (Martinez et al., 2012); reduced dropout and grade repetition among children in Uruguay (Berlinski et al., 2008); better school performance among third graders in Argentina (Berlinski et al., 2009); and improved 4<sup>th</sup> grade math scores in a national sample in Brazil (Rodrigues et al., 2010). 5 Meta-analyses of parenting and home visiting programs from high- and low- income countries have found that the most effective parenting programs included systematic training methods, a structured, evidence-based curriculum built on a strong, theory-driven approach (Engle et al., 2011; Segal et al., 2012).

3
|||||||Technology|Adoption||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MScoreoverall<br>Log(Employees)|0.286°**<br>(0.075)|0.175***<br>(0.087)<br>0.045**<br>(0.018)|0.051***<br>(0.018)|0.044**<br>(0.018)|0.509"**<br>(0.106)|0445***<br>(0.111)<br>0.043<br>(0.029)|0.049*<br>(0.028)|0.048*<br>(0.029)|
|Log(Cap/Employees)||0.015*<br>(0.009)|0.014"<br>(0.009)|0.015*<br>(0.009)||0.007<br>(0.011)|0.006<br>(0.011)|0.008<br>(0.011)|
|Log(Mat/Employees)||0.013<br>(0.014)|0.015<br>(0.015)|0.012<br>(0.014)||0.027<br>(0.017)|0.030°<br>(0.017)|0.028"<br>(0.017)|
|DuighEdu||0.076**<br>(0.030)|0.079***<br>(0.030)|0.078**<br>(0.031)||—0.033<br>(0.043)|—0.036<br>(0.043)|—0.025<br>(0.043)|
|MScoreyponitoring|||0.002<br>(0.081)||||0.350°**<br>(0.103)||
|MScorerez||||0.194°*<br>(0.078)||||0.313°**<br>(0.086)|
|R-Square|0.05|0.19|0.18|0.20|0.02|0.10|0.10|0.10|
|N|727|554|554|554|727|693|693|693|
|Region Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Notes: Columns (1) — (4) use innovation|dummy as|the depend|ent variable.|It takes a|value 1<br>if a|firm register|s itself for|a trademark, patent, utility|
|models, and industrial designs. Columns (|5) - (8) use|a a techno|logy adoption|dummy|(it indicates|whether a f|irm has ado|pted sophisticted technology|
|for quality control) as the dependent vari|able. ‘MSco|reQyeraii’|is the overall|managem|ent score of|a firm as ca|lculated fol|lowing Bloom et al. (2018).|
|‘MScoreMonitoring’ 1s the data driven|performanc|e monitorin|g managemen|t score. ‘|MScorey¢.7’|is the target|s and incen|tives management score.|
|‘Employees’ is the total employees ofa fi<br>capital.‘Mat/Employees’istheperempl|rm. ‘Cap/E<br>oyeemater|mployees’ <br>ialsconsu|is the capital <br>medbyafirm.|per emplo<br>‘DyighE|yee of a fir<br>du’takesa|m. We use fi<br>value1ifmo|xed assets o<br>rethan20|f a firm as an indicator for<br>%workersofafirmhavea|
|<br>higher education degree. Robust standard|<br> errors are|<br>in the paren<br>s|<br>thesis. Interc<br>ignificance,re|<br>epts incl<br>spectively|<br>uded but not <br>.|<br> reported. *,|<br>~*,*** deno|<br>tes 10%, 5% and 1% level of|