rafmacalaba/datause-encoder-data huggingface dataset:
were removed from a facility. One-fourth had been mistreated in government medical facilities.<sup>46</sup> Many of the sampling issues that were faced in Nepal, were also faced in India but one additional issue becomes evident in this case:

- Given the constraints of the sampling method, only limited comparison to the general population is possible. Meaning that it is nearly impossible to conclude whether LGBTI people are worse off than non-LGBTIO people. For example, the study found that one-third of the respondents did not have access to a bank account but in order to understand whether this a problem specific to the LGBTI community a comparable data set for the general population would be needed. One way to respond to this is to include questions on SOGIE status in larger population surveys and thus reach both LGBTI and non-LGBTI people at the same time. But given the likely small number of LGBT people within an overall population, a large sample (of LGBTI and non-LGBTI) people would be needed to pick up enough LGBTI people to be able to undertake a meaningful analysis of the results – especially of the component L,G,B,T,I parts. Further, problems around having respondents reveal themselves and truthfully respond will remain and perhaps be heightened if the survey is not run by trusted local LGBTI partners. Respondents need to be sure that their privacy and security is ensured.<sup>47</sup> Hence, it could be more effective to include non-LGBTI people in research projects specific to the LGBTI community, using the same sampling strategy and similar questionnaires to generate comparable data sets. This approach was recently tested by the World Bank in <u>a survey in Thailand</u> (results forthcoming in Fall 2017).

# **Europe**

In 2012, the European Union Agency for Fundamental Rights (FRA) collected data on sexual <u>orientation, gender identity and expression in 28 European countries.</u><sup>48</sup> The online survey reached 93,079 respondents in total. The overarching goal was to explore how LGBT<sup>49</sup> people experience fundamental rights.<sup>50</sup> FRA designed the survey instrument in 27 languages in close cooperation with a scientific advisory board, made up of representatives from civil society as well as local and

> 46 Singh, Suneeta; Krishan, Anjali; Mishra, Chittaranjan; Ghai, Kavya. Experienced discrimination and its relationship with life chances and socio-economic status of sexual minorities in India. 47 Ibid.

> 48 At that time 27 EU member countries as well as Croatia, which officially joined in 2013, participated.

> 49 Intersex people were not specifically targeted by this survey.

> 50 FRA. 2012. EU LGBT Survey Technical Report.

15
In this paper, we estimate the total impact of changes in exports driven by foreign demand shocks, rather than tariff changes or import competition, on labor market outcomes including income and employment variables. This direct and indirect export-induced demand has been studied in Goutam et al. (2017) [5]; however, as only employment variables are in focus, many other questions regarding wages and heterogeneity have been left unanswered.

The paper proceeds as follows. Section 2 presents a conceptual framework that allows us to examine the local labor market repercussions of exports, incorporating a supply chain perspective. Section 3 discusses the data used and how we constructed the export exposure analysis using InputOutput linkages. Section 4 describes the empirical strategy and Section 5 presents the empirical results. Section 6 concludes.

# **2 Conceptual Framework**

We apply a standard shift-share approach that assesses the effects of trade shocks on labor markets. Relevant works in the literature include Autor, Dorn and Hanson (2013) and Dix-Carneiro and Kovak (2015) [3][2]. More directly related to exploring the effects of exports on local labor markets are studies by Robertson et. al. (2021) and G ́oes, Lopez-Acevedo, and Robertson (2023) [8] [4].

Unlike the papers above, however, our index in this paper is not one of export exposure, but one of total export receipts exposure. By exploiting the input-output structure of production, we account for both direct and indirect payments to factors of production, and use trade data to move closer to regional production, which is what we would ideally like to observe.

Let _s,d_ be industry index ( _s_ stands for source sector and _d_ stands for destination sector), and let γ _s,d_ denote the intermediate use shares of a good of industry _s_ in the production process of a good of industry _d_ . Under the assumptions of perfect competitive or monopolistic competitive product markets, a constant fraction of total sales will be paid to the factors of production. If domestic factor markets are competitive, there are no mark-ups or mark-downs on factor prices.

6
Table 1. O,srsctaat;ics of St"i I;t%; Pr l"ald ; .

|Progrem<br>_Tore_or<br>thister|when did it<br>finish?|Eucl;ange<br>Rat.<br>Pined**as.**<br>CreelI|Income<br>Pol.cies|Multiple<br>or U.nf; ad|Fiscal|In.<br>t;<br>aI<br>R*sor.**<br>(months of<br>laporta)|Reducti<br>Infla<br>Fro|on i.<br>tion,<br>To|_Commercial_<br>Poi Cy|Precod.d by<br>#loetery<br>F.".caM.-Ow.|.anto<br>Sh%ock<br>or<br>_C-adue1_|IfatI.<br>o"<br>(Anerage 12<br>MiotO. prior<br>to prom,..)|Precedwid<br> <br>by lb1..<br>-<br>Decaluait-|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Israel<br>_3rdQ._65|Not yt|Fidsod<br>infrequent<br>adjustments|Y--<br>(ehock)|Official<br>and<br>parallel|_f; ip_<br>initial<br>adjustment|3.2|21.2|* I|NoSpecial|No||327 7|Yun|
|Austral<br>3dQ.03|3rdQ.U6|Fi.od|**Yoe**<br>(shock)|Official<br>an1ld<br>pralll|largo<br>transitory<br>adjustment|5.3|24.9|2.6|No|No|Cradual|10824|Yeo|
|Crusado<br>lot_Q._**U4**|4thQ<br>**66**|Fined|**Ye**<br>(ehok)|official<br>and<br>parallel<br>(lerpg<br>praom**u-)**|no<br>adju*tment|10.4|11.1|1 7|No|NO|Shock|237.2|No|
|Monico<br>let_9_|Not yet<br>lot Q. of<br>_._**66**|Fined<br>(lot<br>yqarj<br>thon<br>ye:.1~~Cro<br>thn|Yoe<br>=S" I I<br>:i|NO|_YeT._<br>Main<br>~~~Adju<br>Prior<br>to<br>Program|12.4<br>stment|8.2<br>|2.6|Trade<br>A<br>Capcita<br>AccopntalAjem<br>Liberal-<br>ijation|F,ecal<br>1j0on|Notlet|131 8|Y-s|
|Chile<br>3rd_Q._76|3rid_Q._<br>2|Crewl, thon<br>Preennounced<br>then Fined|NO|Unified|**Yen**|2.6|11.2|6.5|TrAdad<br>Capital<br>Account<br>Liberalized|Yoe|Shock|249.37|YTo|
|Uruguay<br>4thQ.78|4th_Q._62|Proannounced|No|Unified|Yes|5.9|3.4|4.6|Libo,al.xiid<br>_Trade_end<br>Capital<br>Accounts|Preceded by<br>SmellIFiscal<br>Aejuetisent|Shock|42 3|No|
|Argentine<br>4th_Q._7S|let_Q._I1|Preannouncod|No|Unifild|**luod**<br>**ate**<br>Stable<br>sudgint<br>Deficit|16.7|8.1|6.6|Yes|Yon|Shock|1607|No|
|Argentina<br>3rd_Q._73|2nd_Q._75|Fined|**Yeo**|**_Three_**<br>Enchang.<br>Rot|**E.pneionary**|3.5|5.7|0.8|No|No|Shock|754|No|
|Argentin<br>2nd_Q._67|3Srd_Q._70|Fined|Yoe<br>(Oreduel)|Unifiod<br>Eachango<br>Rote|Fiscal<br>Adjustment|1.9|2.5|2.7|Incenti,we<br>for<br>capital inflame|No|Cradual|27 4|Yun|
|2rnail<br>2nd_Q._64stop|3rld_Q._**6t**<br>|**FPoed***/<br>Devaluation|Yoe<br>(Gradual)|Official<br>adAdjustment<br>Paralll|Fiscal<br>|0.9|6.4|4.2|No|NO|Cradual<br>and<br>succoessful|96 3|YTw|
|Uruguay<br>2ndl. **6O**|lot_Q._72|neaad|Yos<br>(Shock)|Officibl<br>end<br>Parallel|Fiscal<br>preced#d<br>deterioration<br>later<br>on|4.1|9.5|1.Y|No|Yoe,<br>C**anntha**<br>earl er|Shock|163 C.|Yn|
|ist||||||||||||||
|Argentine<br>3rd_Q._59|2nd_Q._62|Fined|No|Lnified<br>Eschange<br>Reta|Initial<br>edjuetment<br>deteriorated<br>lst*r on|5.7|9.5|1.9|Incentins<br>for<br>Foreign<br>In,estment|Yoe (hf<br>Program<br>amonthe<br>before|Cradual|136 1|Yo-|

* A shock usually includee O si-ultoneou* froox* of **magma.** prices and the enchange rats.
for MoLSA is correctly referred by the PPMT. Referral failure – the probability that ineligible households are refereed. Failure to refer – the probability that a household in the humanitarian database who is eligible for MOLSA is not referred by PPMT.

**<u>Step 6. Disaggregated analysis:</u>** The disaggregated analysis is conducted using summary statistics. In the first analysis, we calculate the share of cross eligible households across a population of interest, such as displaced and returnee households, female-headed households, households with high disability and chronic illness, households with no working members, and households in different governorates. In the second disaggregated analysis, we calculate the share of (cross) eligible households that are willing to be referred to the government support.

# 4. Results

# 4.1 The PMT score distribution

We apply the MoLSA PMT, Legacy VA, SEVAT, and the two PPMT models to the data collected from the field test. The correlation and kernel density of the PMT scores are shown in Table A1 and Figure A1 in the Annex. The MoLSA-PMT score ranged from 3.9 to 6.4, with a mean of 5, indicating that a larger share of the population lies at the right-hand (richer) side of the distribution. The MOLSA-PMT scores are expressed in a natural log and the IQD equivalent of the PMT would be calculated by an exponential function of the score, as shown in Table 2. The SEVAT score and the PPMTs show similarity in distribution with the MoLSA-PMT score. However, the Legacy-PMT has a unique distribution, ranging from 0 and 52, with 19.6 as the mean. The two PPMT distributions are wider than those of the MoLSA-PMT and SEVAT but lean towards the left-hand (poorer) side. While the SEVAT and the two PPMTs positively correlate with the MOLSA-PMT, the legacy PMT is negatively correlated with the MOLSA-PMT. This is expected as they use widely different targeting variables.

# 4.2 PMT cutoffs and eligibility proportion

Table 2 shows the cutoffs of each targeting model and the share of the population that are eligible under different program eligibility criteria. The result shows that in the humanitarian targeting system, more households are classified as vulnerable, as such are eligible for humanitarian support in contrast to the number of households classified as poor and eligible for social protection by the government’s PMT model. For example, about 63 percent of the sample are considered vulnerable and would be eligible for either 1, 2, or 3 times of assistance using the SEVAT model, and about 60 percent would be eligible in the Legacy VA model. However, the MOLSA-PMT model classifies only 21 percent of the sample to be living 14
per hour than do non-home-based workers in Brazil, Ecuador, and Mexico, respectively, with selection corrected estimates presented in the parentheses.<sup>23</sup> The wage premium to worksite wages is larger among women than men, where women who work from their homes earn approximately 26 (11), 41 (21), and 27 (29) percent less than non-homebased Brazilian, Ecuadorian and Mexican women. Men’s discrepancies, on the other hand, are in the range of a 0-17 percent premium to non-home-based work in all three countries.<sup>24</sup> While children and a spouse decrease the earnings for working women in general, only the presence of a spouse decreases the wages for home-based working women even more. The coefficient estimates for the interaction of the _home_ and the _married_ variable is negative in all three countries and significant in Brazil and Mexico. However, the interaction term between _home_ and the child variables is not significant for any country. The wages of men who worked from and outside of their homes did not differ by marital or child status.

Perhaps the gap for women reflects a compensating wage differential. Qualitative interview data in Latin America show us that women prefer home-based work due to the flexibility and opportunity to fulfill both home and market responsibilities, so maybe women who work outside the home are compensated for the foregone goods and services (for auto-consumption) that they would have produced had they not had to spend time traveling to work or could take breaks from market work to perform domestic work

> 23 When breaking apart the sample into type of home-based worker, Mexican and Ecuadorian home-based wage workers earned 38 and 61 percent less than wage workers outside of the home, with the differential between home-based and other self-employed was only half as large.  In Brazil, the differential for home - based and outside workers was the same for wage and self-employed workers (regression results available from authors upon request).

> 24 The exception is the wages of Ecuadorian men, with a 50 percent difference.

21
at the impact of the reform in this light, but rather use the reform itself as an instrument to estimate the causal effect of education on earnings of the group affected by the 1981 education reform, as well as the effect of the reform on quality of learning.

# **Data**

The International Adult Literacy Survey (IALS) was carried out in 20 countries<sup>1</sup> between 1994 and 1998, a project undertaken by the governments of the countries and three intergovernmental organizations<sup>2</sup> . It is a carefully designed, innovative survey of adult populations, and goes beyond just measuring literacy capabilities to assessing how these capabilities are applied to everyday activities. The IALS was followed by an extensive quality review (see Murray _et al_ . 1998) which, after comparing the distribution of the characteristics of the actual and weighted samples, concluded that the actual and weighted samples were comparable to the overall populations of the IALS countries. The questionnaire also included questions about labor market status, earnings, education as well as demographic characteristics.

The data includes three scales as measures of literacy skills: prose literacy, document literacy and quantitative literacy, each in the 0-500 range. Prose literacy tests the understanding and use of texts such as editorials, news stories, fiction and poems. Document literacy tests skills required to locate and use information contained in a variety of formats, such as job applications, payroll forms, maps and tables. Quantitative literacy tests skills required in making calculations

> 1 The countries are: Belgium, Canada, Chile, Czech Republic, Denmark, Finland, Germany, Hungary, Ireland, Italy, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovenia, Sweden, Switzerland, the United Kingdom and the United States.

> 2 OECD, European Union and UNESCO.

6
WPS3893

# **Constraints to Growth and Job Creation in Low-Income Commonwealth of Independent States Countries**<sup>**1**</sup>

# **Paolo Verme**<sup>**2**</sup>

## World Bank Policy Research Working Paper 3893, April 2006

_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._

> 1 Paper prepared as a background paper for the report “Enhancing Job Opportunities: Eastern Europe and the Former Soviet Union”, World Bank, Washington, D.C.

> 2 Consultant, World Bank and Contract Professor, University of Torino and Bocconi University, Milan (paolo.verme@unito.it). I am grateful to Katya Zykova for research assistance and to the participants of a workshop held in Milan in May 2004 in the framework of the ECA Labor Market study including Tito Boeri, Pietro Garibaldi, Paloma Lopez-Garcia., Philip O'Keefe, Jan Rutkowski, Stefano Scarpetta, and Milan Vodovipec.
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 51 For this, data on local input and output levels and input prices are needed. However, international surveys on factor use or costs, such as material surveys, for dike construction are currently absent (Jonkman et al. 2013). In some cases, such information is seen as commercially sensitive and hence even if collected it is not in the public domain. Some case studies are nonetheless available that provide illustrations of factor use for construction of coastal protection measures (e.g. Dijkman 2007). However, case study information cannot be generalized across countries, as this is largely explaining the variation in unit costs, and input data are largely lacking for sea dikes.

## **A.2.4.2 Meta-analysis of case studies**

A meta-analysis as in Lenk et al. (2016), may be improved by more case studies, as well as by inclusion of country characteristics that have been omitted. However, this is a large effort, as available case studies are based on grey literature. Currently, the number of case studies remains insufficient for out-of-sample predictions of unit costs, and the sample of case studies needs to be increased.

# **A.2.5.       Other cost issues for further research**

## **A.2.5.1 Total costs**

Dronkers et al. (1990) apply dike length multipliers to coastline length "as the crow flies", to estimate total costs. This method is obsolete, and the applied dike length multipliers are likely to be inaccurate. Total cost estimates may not only be improved by updating unit cost estimates, but also by replacing previous dike length approximations by country totals of segment lengths as is already done in DIVA. However, the base coastline could be reviewed to test coastal lengths, especially where there are large populations and hence potential demands for defenses.

## **A2.5.2 Other measures for unit costs**

Investment flexibility under sea level rise uncertainty is not addressed in the concept of unit costs, as gradual implementation of the work is not considered (Dronkers et al., 1990). However, the fixed cost component of structural flood protection investments is essential for economic decision-making, and it motivates anticipatory adaptation for the case of flood prevention by dike construction (van Dantzig 1956; Eijgenraam et al. 2016). It may therefore
<!-- Start of picture text --> (a) On-budget, 48 Countries (b) Donors, 48 countries<br>0.70% 0.70%<br>0.60% 0.60%<br>a 0.50% a 0.50%<br>6 0.40% & 0.40%<br>5 0.30% 5 0.30%<br>* 0.20% * 0.20%<br>0.10% 0.10%<br>0.00% 0.00%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>(c) Private sector, 48 countries (d) China, 35 countries, 2010-17<br>0.70% 0.70%<br>0.60% 0.60%<br>o 0.50% o 0.50%<br>6 0.40% 8 0.40%<br>8 0.30% S 0.30%<br>0.20%0.10% _— 0.20%0.10% —aa—=<br>0.00% 0.00%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->
security is low compared to the liquidity of its ADR, funds are expected to hold a higher fraction of their investment in that firm in the form of ADRs (see H3). To examine the impact of these liquidity measures on the funds’ investment allocation between the ADR and the domestic security we estimate a model of the following form:

- _ADR Differential_ = β 0 + β 1( _Log Market Cap_ ) + β 2 ( _Number of Analysts_ ) + β 3 ( _Accounting Quality_ ) + β 4 ( _Liquidity_ − _Underlying_ ) + β 5 ( _Liquidity_ - _Relative_ ) + β 6 ( _GDP Per Capita_ ) + β 7 ( _MarketCap GDP_ ) + β 8 ( _Turnover Country_ )+ β 9 ( _Transaction Efficiency_ ) + β 10 ( _Settlement Efficiency_ ) + β 11( _FX Volatility_ ) + β 12 ( _French_ ) + β 13 ( _German_ )+ β 14 ( _Transition_ ) (5)

Equation 5 is based on the specification used to measure the impact of country level factors on the _ADR_Differential_ (equation 4). In addition to the country characteristics we now include the following variables:

- _Liquidity-Underlying:_ This is the ratio of the daily average volume of underlying traded (for 2001) to average number of underlying outstanding (simple average of 2000 and 2001 reported numbers). The data is obtained from Datastream.

- _Liquidity-Relative_ : This is calculated by dividing the average daily trading volume of ADRs (converted into the equivalent underlying by using the ADR to underlying ratio) to the average trading volume of the underlying. Thus a high value for Liquidity-Relative implies that the ADR security is more liquid than the underlying. The trading volume data is obtained from Bloomberg.

While intuitively straightforward, data collection on each of these liquidity variables posed some special challenges. The underlying security is not always the common stock. Issuers from Brazil and Mexico almost always have multiple classes of shares. For example, Brazilian firms issue ADRs that have preferred shares as the underlying security. The preferred share is the main security for domestic investors, while the common stock is held 29
rainfall indicates the presence of nonlinearities. However, the magnitude of the coefficient is too small for it to have a noticeable impact.

The last column in Table 3 includes the interaction of the weather shock with a households’ access to health facilities. For this purpose, we construct a dummy variable that captures how easy is for a woman to access to a health clinic in the case of need for medical treatment.<sup>23</sup> The results show that, as expected, having relatively easy access to a medical facility significantly increases children weight-forheight Z-scores. The positive coefficient of the interaction of the current shock with clinic implies that the presence of the clinic helps reduce the impact of the shock on the WHZ. In addition, the positive coefficient of the interaction with the last rainy season shock implies that the presence of the clinic reinforces the net effect of the income and disease environment effect, probably due to the reduction of the environment disease subcomponent. Although the coefficients on the interactions have meaningful interpretations their signs are not statistically significant.

# Table 4

Two remaining questions are whether these impacts differ across boys and girls, and across age groups. We first examine differences across ages in the impact of precipitation on weight-for-height in Table 4. It is interesting to note that the effect of past rainfall is significant across ages and it is of similar magnitude. In contrast the effect of current rainfall shocks is significant for one and two years old, but not for the older children. As previously mentioned, the income effect may be stronger for girls than for boys if girls are discriminated in favor of boys in times of economic hardship. However, our results do not support this claim. Boys seem to benefit to a greater extent by past rainfall shocks, girls seem to do worse than boys when diseases are more likely, i.e. when current rainfall is higher than normal. However, most of the coefficients across age groups are not significantly different from zero.

# **Incidence of Diarrhea**

The adverse effect of current rainfall on weight-for-height seems to be caused by the disease environment. To further explore this issue we analyze the effect that current rainfall shocks have on the

> 23 The DHS provides valuable information about access to medical facilities and medical treatments. In particular, women respond that distance to a health facility is either “not a big problem” or “a big problem” when seeking medical treatment.

23