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

HIV counseling and testing<sup>1</sup> is one of the pillars of HIV prevention (Potts et al. 2008). Recently, the U.S. Preventive Services Task Force released a draft recommendation statement suggesting that nearly everyone between the ages of 15-65 be screened for HIV (U.S. Preventative Services Task Force 2012). One argument for universal HIV testing is due to recent findings indicating that HIV-positive individuals who immediately receive antiretroviral therapy (ART) significantly reduce their transmission risk to uninfected partners and experience lower morbidity and mortality rates than those receiving delayed treatment (Cohen et al. 2011). A distinct argument suggests that HIV-positive individuals reduce risky sexual behaviors after learning their serostatus (Cassell and Surdo 2007; Gersovitz 2010; Fonner et al. 2012), indicating that testing itself would be a means of decreasing subsequent sexual transmission of disease.

Despite the belief that increasing awareness of HIV status has prevention benefits, there are no studies that have shown population level reductions in the incidence of HIV or other sexually transmitted infections (STIs) as a result of HIV testing (Potts et al. 2008).<sup>2</sup> Several studies in public health and economics have presented VCT impacts by HIV serostatus. These studies generally show no effects of testing on sexual behavior or seroconversion for HIV-negative individuals (Allen et al. 1992a; Weinhardt et al. 1999; Corbett et al. 2007; Thornton 2008; Fonner et al. 2012), but available evidence suggests that HIV-positive individuals reduce risky behaviors after testing (Allen et al. 1992b; Weinhardt et al. 1999; Boozer and Philipson 2000; Thornton 2008; Delavande and Kohler 2012; Fonner et al. 2012).<sup>3</sup>

> 1 HIV counseling and testing encompasses both voluntary counseling and testing (VCT) and home based HIV testing and counseling (HTC).

> 2 Furthermore, there are no randomized controlled trials (RCT) that directly assess subsequent HIV transmission for VCT vs. non-VCT. Kamb et al. (1998), using an RCT, assessed the effects of different modes of counseling for HIV-negative individuals on STI incidence, but everyone received an HIV test and learned their serostatus. More recently, Project Accept (HPTN 043) is assessing the effectiveness of community-based VCT relative to standard clinic-based VCT on community-level HIV incidence (http://www.cbvct.med.ucla.edu/overview.html). The Voluntary HIV-1 Counseling and Testing Efficacy Study Group (2000) found significant decreases in risky activity with non-primary partners for individuals and couples who received VCT.

> 3 de Paula et al. (2010) reports that an increase in the beliefs of being HIV-positive reduced extra-marital affairs among married men in Malawi. Beegle, Poulin and Shapira (2012), using randomly assigned VCT in Malawi, find no effects on either sexual behavior or economic outcomes. Haile (2011) examines randomly assigned testing in Ethiopia and also finds limited impacts on sexual behavior and if anything finds that getting a negative test actually leads to more risky behavior.

2
required by the model, closely ...ts the observed distributions. The distribution functions for productivity and demand shocks are given by G(') and Hj(v); j 2 fUS; EU g, respectively.

# 4.1 Estimation Strategy

We distinguish between what we take as given, the data, and the parameters to be estimated. For the analysis below we de...ne three kinds of ...rms: ...rms that sell to the US only (OUS ...rms), to the EU only (OEU ...rms), and to both the EU and US (AUS ...rms).

## 4.1.1 Trade Policy Data

We take the values for � (the per-unit cost of meeting ROOs), t (tari¤s), and � (transport costs) to be set at levels roughly in line with the speci...cs of the market. As ROOs involve using domestic cloth, which is about 20% more expensive than imported cloth and as roughly 75% of the cost is the cloth, we assume a 15% cost increase from meeting ROOs and so set � = :85 in wovens. As there are quotas in the US, ROOs must be met by all ...rms so that for the US market we cannot separately estimate documentation and ...xed costs. As ROOs are easy to document in the US since only assembly is required, we set d<sup>US</sup> = 0: We denote a license price associated with the US quotas (estimated at 7%) by � below.

Table 2: Trade Policy Parameters

||�|t|t<sup>ROO</sup>|�+�|
|---|---|---|---|---|
|EU|0.85|0.12|0|1.14|
|US|1|0.2|0.2|1.14 + 0.07|

In the EU, as only some ...rms meet ROOs, we can estimate d and f separately. Transport cost estimates for the apparel industry range from a low of about 8% to a high of roughly 14%.<sup>29</sup> Bangladesh has poor infrastructure in transportation and for this reason we use the upper end of the estimates in the literature and set transport costs to be 14% in our

> 29See World Bank (2005), p.110, and Gajewski and Riley (2006), p. 6.

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# **References**

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- [9] Angelucci, M., O. Attanasio and V. Di Maro (2012), “The Impact of Oportunidades on Consumption, Savings and Transfers”, _Fiscal Studies_ , 33 (3): 305-334.

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- Hanushek, E. A., Woessmann, L., 2011. The Economics of International Differences in Educational Achievement. North Holland, Chapter 2 in _Handbook of the Economics of Education_ , vol. 3, edited by Eric A. Hanushek, Stephen J. Machin and Ludger Woessmann.

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- United Nations, 2015. The millennium development goals report 2015. 2015 TIME FOR GLOBAL ACTION FOR PEOPLE AND PLANET.

- World Bank, June 2010. Solar lighting for the base of the pyramid: Overview of an emerging market.

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Domestic violence is a risk factor and it is strongly linked to other risk factors and negative outcomes. In Argentina, 10 percent of youth have experienced domestic violence.<sup>8</sup> Figure 3.11 shows a correlation between domestic violence and negative outcomes for the case of Argentine youth. Youth who experience domestic violence are more likely to dropout of school than their peers from non-violent households or dropouts are more likely to be exposed to domestic violence; there is likely reverse causality. On the one hand it may be that youth from a violent home find it harder to handle stressful situations, do their homework or pay attention in school and this leads them to drop out. On the other hand it may be that youth who have dropped out of school have disappointed their parents, who react with violence, or that for example a negative income shock has required the young person to leave school to work and now lives in a more troubled situation to which violence becomes a vent of frustration. Also, youth from violent households have their sexual initiation at an earlier stage in life (before age 18) than youth from non-violent households, increasing the risk of teenage pregnancy or STDs. Thus, domestic violence is correlated with negative outcomes. In addition, one in a hundred youth have had forced sex.

Figure 3.11: Domestic Violence vs. School Dropout and Early Sexual Initiation, Argentina 2005 <!-- Start of picture text --> 80.0<br>Dropout<br>70.0<br>Early sexual initiation<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Female Male Female Male<br>From Violent Household Non-violent Households<br>Note : Early sexual initiation is for the age group 15─18 years.<br>Source : Own calculations on ESCVJA (four urban areas)<br>Percent<br><!-- End of picture text -->

# **4. Cluster Analysis**

The entire data sample is used for one cluster analysis rather than splitting the data by certain characteristics. While it potentially is interesting to divide the data by, for

> 8 In the USA, 75 percent of violent juvenile offenders have been abused by family members. More than half of all prisoners come from single parent families, and 60 percent of juveniles in custody have parents who abuse alcohol or drugs. Moreover, nearly a third of adult prisoners have a brother who has been convicted (Ayres 1998). Clearly, a supportive family is immensely important for positive outcomes while the lack thereof can lead to devastating behaviors.

16
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Policy Research Working Paper 9363

# **Abstract**

This paper analyzes bank stock prices around the world to assess the impact of the COVID-19 pandemic on the banking sector. Using a global database of policy responses during the crisis, the paper also examines the role of financial sector policy announcements on the performance of bank stocks. Overall, the results suggest that the crisis and the countercyclical lending role that banks are expected to play have put banking systems under significant stress, with bank stocks underperforming their domestic markets and other non-bank financial firms. The effectiveness of policy interventions has been mixed. Measures of liquidity support, borrower assistance, and monetary easing moderated the adverse impact of the crisis, but this is not true for all banks or in all circumstances. For example, borrower assistance and prudential measures exacerbated the stress for banks that are already undercapitalized and/or operate in countries with little fiscal space. These vulnerabilities will need to be carefully monitored as the pandemic continues to take a toll on the world’s economies.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and 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/prwp. The authors may be contacted at ademirguckunt@worldbank. org, apedrazamorales@worldbank.org, and cruizortega@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
Secondly, this research provides unique panel data through a natural experiment that helps to minimize the endogeneity aspect related to most of the studies on such subject (property rights).

Third, many aspects have been applied to try to explain the reasons for child labor. The most common are income, poverty, parents’ level of education, parents’ previous child labor experience, credit constraints, and others. This paper intends to provide an additional aspect that can be used as part of the explanation for the causes of child labor.

Last, but not least, this paper provides an initial impact measure, in terms of applied public policy, for the " _Papel Passado_ " program and gives a partial feedback for policymakers about the effects of land titling in variation of child labor force participation. Certainly, reducing child labor force participation is one of the main goals of federal and local governments. Social programs such as PETI ( _Programa de Erradicação de Trabalho Infantil_ ), an initiative that focuses on providing education opportunities for children engaged in labor activities and extra income for their poor families, is a great example of government's concern. Understanding the potential positive effects of land titling and property rights in such subject could be valuable to make any effort related to child labor participation stronger and more effective.

# **1. Child Labor Force Participation: The Economic Context**

Investing in and focusing on human capital development is a critical factor to increase economic growth, as stated by Becker and Lewis (1973), and given such a key assumption, The United Nations Millennium Development Goals include eliminating child labor as a crucial step into a better and equal world.

According to the International Labour Organization (2002), 246 million children and teenagers between 5 and 17 years old are engaged in child labor around the world. Furthermore, 75.0% of those children work for their own family activities. Asia, Africa and Latin America are the continents with the most the child labor in the world. Asia has the highest number of children in terms of volume but Africa is the leader relative to the total size of the work force.

In Brazil, data from PNAD (IBGE, 2007) has shown that from a 44.7 million population between 5 and 17 years old, 10.8% (4.8 million) are directly involved in child labor. The worst region of Brazil regarding this subject is the Northeast, with 13.4% of the 5-17 year old population working; Southeast holds the lowest average (7.9%). These statistics represent a positive evolution over the last 4 years (see Figure 3 below).

5
# **5.1. National‐level analysis for poverty and exposure to floods**

# **5.1.1. Flood risks (with and without climate change)**

For the entire country of Vietnam, at the district level, we estimate the total number of people and the share of the population who are exposed to floods. In the results presented, we examine the four scenarios for the 25‐year, 50‐year, 100‐year and 200‐year return period flood – a historical scenario, and three scenarios representing future climate: a low, medium, and high scenarios.

We aggregate the results at the country level.<sup>3</sup> A third (33%) of today’s population is already exposed to a 25‐year flood in Vietnam, assuming no protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well‐protected areas. In these well‐protected areas, our flood maps may over‐estimate the flood hazard.

When including climate change, this percentage increases by 13‐27%, depending on the severity of sea level rise. This increase in exposure is due to the concentration of the population in coastal areas. For the 50‐year flood, more than a third (38%) of today’s population is already exposed. Given climate change, this number is expected to increase by 7‐21% (resulting in overall exposure of between 40 and 48%) for the same return period (50‐year). For a 100‐ and 200‐year flood under a high climate scenario, more than half of the population is exposed.

Climate change impacts can be seen in these exposure numbers – for instance, a 50‐year flood with medium climate change impacts has the same exposure of a 200‐year historical flood (at 44%), while almost half the country’s population (48%) is exposed to a 50‐year flood with high climate impacts. Full results are presented in Table 3.

_Table 3. Population exposed to flood risk in Vietnam, across the 16 flood hazard scenarios examined._

|**Scenario**|**Exposure**||**Return**|**period**||
|---|---|---|---|---|---|
|||25|50|100|200|
|Hitil|Estimated population exposed (m)|30.17|34.30|38.35|40.43|
|sorca|Percentage of today’spopulation|33%|38%|42%|44%|
|li|Estimated population exposed (m)|34.78|36.87|40.91|42.32|
|Low cmate<br>h|Percentage of today’s population|38%|40%|45%|46%|
|cange|Increase due to climate change|13%|7%|6%|4%|
|Medium|Estimated population exposed (m)|38.03|40.22|43.34|45.16|
|climate|Percentage of today’s population|42%|44%|48%|50%|
|change|Increase due to climate change|21%|15%|11%|10%|
||Estimated population exposed (m)|41.46|43.36|46.13|48.72|
|High climate<br>h|Percentage of today’s population|46%|48%|51%|53%|
|cange|Increase due to climate change|27%|21%|17%|17%|

3 Results presented are similar to a previous study analyzing the exposure to a 100‐year return period flood without climate change impacts, which finds 40 million people to be exposed to that event (Jongman et al. 2014).

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The 190 hours of technical assistance will begin in the second half of 2018. 49 percent of the firms in the Benefits 2 (full intervention group) exported in 2017.

We want to know how much you think this will CHANGE for the group getting offered the full intervention compared to getting offered just the diagnostic and trade fair, over the first 12 months since firms start their implementation. For example, if you think there is a 15 percent chance the intervention will increase the percent offirms exporting by 8 percentage points, put 3 stones in the box under 8, and allocate your remaining stones according to what else you think is likely. Where a single number is written (e.g. 7, you should think of it as the interval [7.0 to 7.99] BELIEFS ABOUT THE IMPACTON THE PERCENT OF FIRMS THAT EXPORT

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