rafmacalaba/data-use-mentions-extended huggingface dataset:
# **EXECUTIVE SUMMARY**

## **Introduction**

In 1993, the United Nations General Assembly defined violence against women as “ _any act of_ **_genderbased violence_** _that results in, or is likely to result in, physical, sexual or psychological harm or suffering to women”_ (United Nations, 1993). The United Nations referred to “gender-based” violence to acknowledge that such violence is rooted in gender inequality and is often tolerated and condoned by laws, institutions and community norms; it is not only a manifestation of gender-inequality, but often serves to enforce it (Heise, Ellsberg and Gottemoeller, 1999). Gender-based violence comes in many forms throughout the life cycle; this review focuses on the two most common types: physical intimate partner violence and sexual violence against women by any perpetrator.

A growing body of international research documents the magnitude and patterns of gender-based violence. Worldwide, men experience higher levels of physical violence than women as a result of war, gang-related activity, street violence, and suicide, while women and girls are more likely to be assaulted or killed by someone they know, such as an intimate partner (Heise and Garcia Moreno, 2002). Population-based surveys have found that between 10-70% of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). The bonds of family, economic and emotional dependence that accompany intimate partner violence make prevention and protection particularly complex.

In addition, a substantial proportion of girls and women experience child sexual abuse, rape and other forms of sexual coercion in virtually every setting of the world. Population-based studies report rates of forced sexual debut among sexually experienced young people ranging from 7% in New Zealand to 46% in the Caribbean (Heise and Garcia Moreno, 2002). Contrary to popular belief, most victims know the perpetrators, who often include intimate partners, stepfathers, fathers, other relatives, authority figures, teachers, and acquaintances (
Where _Wj_ is average income level for an employed newly educated, _uj_ is average unemployment, _gj_ denotes real income growth rate and τ stands for average marginal tax rate. An implicit assumption is that the expected income of unemployed workers is zero.

The cost function _Cj(t)_ measures lost income from a reduction in time on the labor market due to studies and tuition cost, and is defined as: Where _Tj_ is tuition cost and _Pj_ is part time work.

# **III-2 APPLYING THE INTERNAL RATE OF RETURN WITH AVAILABLE DATA**

Applying the above model to available data inherently involves some trade-offs. This section describes these trade-offs, the sources of data and the parameters that will form our base scenario for estimating the returns to schooling. Further, we vary the key parameters to observe the impact on the profitability of re-entering the education system. These alternative scenarios will be applied separately to the base case scenario.

The most important constraint for accurate estimations is available data. A longitudinal dataset spanning a lifetime would provide the ideal information to observe the impact of improved education credentials at a point in time of their professional career on labor market earnings. However, such a dataset does not exist for Colombia, and would require around 50 years to establish. In the absence of such a dataset, we estimate the rates of returns by simulating re-entries into the education system. Hence, we estimate foregone income and wage benefits based on the currently observable wage profiles.

For individual data on age, gender, income and education, the study relies on the national quarterly household survey, _Encuesta de Hogares_ , from the national institute of statistics, DANE. Unless noted otherwise, the observations are for 6 stacked surveys from September of 1995 to September 2000. To ensure consistency in data the sample is restricted to the seven largest metropolitan areas of Colombia, which cover 67 percent of 8
**Figure 6. Actual and benchmarked value of innovation output index: developing East Asian countries and top performers, 2017**

<!-- Start of picture text --> 80 Top 5 countries<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Observed value<br>predicted value(linear)<br>predicted value(quadratic)<br><!-- End of picture text --> Source: Own elaboration based on GII dataset and WDI dataset Note: Bars show the observed innovation output index value. Dots show the benchmark predicted by a (linear) regression with (the log of) GDP per capita adjusted for (2011) purchasing power parity as the explanatory variable. The regression used all countries available in the GII and WDI dataset.

**Figure 7. Relative importance of enabling factors to explain innovation output index across all countries**

<!-- Start of picture text --> (absolute contributions to R-squared; 90percent percentile<br>confidence intervals with median)<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Median CI lower CI upper<br><!-- End of picture text --> Source: Own elaboration based on GII dataset and WDI dataset Note: Bars show the share of total explained variance of innovation output index by individual regressor variables (GDP per capita, and all sub components grouped under each enabling factor: Institutions, Human capital and research, Infrastructure, Market sophistication, and Business sophistication). The percentage contributions are calculated as Owen values for each group of enabling factor. While the goodness of fit (R2) decomposition does not have standard errors, bootstrapping is applied to help attach greater reliability to comparisons of importance. The
after dwelling sizes and housing locations adjustment, utilities within each income class are equal and spatial inequalities disappear. However, in the medium run, we find evidence of persisting spatial inequalities, especially within income class 2.

# 7. Aggregated impact on emissions

Table 8 shows the impact of the fuel price increase on passenger-km for each transportation mode, as well as the further decomposition between the variations due to modal shift and the variations due to workplace-residence distance changes. The last column estimates the impact on emissions, using emissions per km data from WWF (2016).

Scenario 1, in which only private cars are impacted by the fuel price increase, is more efficient in terms of emissions mitigation than scenario 2, in which private cars, buses, and minibuses/taxis are impacted. This result, which implies that taxing all polluting transportation modes does not necessarily lead to higher emissions reduction, can seem counterintuitive. There are two reasons for this. The first is that there are more modal shifts in scenario 1 than in scenario 2. Indeed, in scenario 1, taxing private cars leads to a strong reduction in their modal share, to the profit of buses and minibuses/taxis, which are less fuel consuming travel modes. In scenario 2, as private cars, minibuses/taxis and buses/taxis are taxed, the modal share of private cars is actually increasing: as buses and minibuses/taxis are slow, a tax makes them really unattractive, leading to a modal shift to the profit of private cars, more expensive due to the tax but also faster than other modes. This effect depends on the relative increase in private cars transportation costs compared to the relative increase in buses and minibuses due to the tax: it depends on the share of variable monetary cost among total transportation costs, which varies with income group and location. The second reason is that the decomposition of passenger-km variations between modal shares variations and average distances variations shows that passenger-km variations are mostly driven by modal shift and not by passenger-km variations
but simply stopped responding to quizzes out of lack of interest or motivation to search for phone signal. Figure A1 shows that the number of treated individuals participating in weekly quizzes evolved similarly for the three thematic variations.

# **2.3 Data**

Baseline data were collected in the fall of 2014 using a short household survey instrument. It captured information on household composition, socio-economic status, cell phone usage, social interactions and economic activities, as well as child-specific information regarding health practices, preschool participation, nutritional intake, early childhood practices and attitudes. The baseline was used to identify for each child a "mother" and a "father" figure, i.e. the child’s biological mother and father, or in their absence, the individuals with similar caregiver roles, as defined by the household.

A follow-up survey was implemented in July and August 2015, approximately nine months after households had started receiving the text messages. The duration of exposure is similar to other early childhood interventions where impacts on ECD outcomes were found in nearby municipalities in Nicaragua (Macours et al., 2012).<sup>12</sup> The follow-up survey targeted children in sample households, as well as the main caregiver. We limit the analysis to children under 7 years of age at follow-up (2,803 children), in order to use the full battery of tests, as detailed below. Questions on early childhood practices and attitudes were asked separately to mother and father figures.

The child-level follow-up instrument includes a number of tests to assess early childhood development. First, the four sub-scales of the Denver Developmental Screening Test (Frankenburg and Dodds (1967)) were used to assess social-personal, language, fine motor, and gross motor skills for all children between 12 and 84 months of age. We use a modified version of the Denver previously used as part of a national early childhood stimulation program in Nicaragua.

Three additional tests were conducted for children between 36 and 84 months old: 1)
# LINKING PARTICIPATORY POVERTY ASSESSMENTS TO POLICY AND POLICYMAKING: EXPERIENCE FROM VIETNAM

By Carrie Turk, World Bank

# **Introduction**

It is estimated that in the mid-1980's seven out of every ten Vietnamese were living in poverty. A little more than a decade later - a decade of reforms and rapid economic growth - the incidence of poverty has halved. Latest household survey data suggests that 37% of the population is now under the poverty line and 15% of the population is so poor that consumption is inadequate to meet nutritional needs. Social and other indicators have also improved significantly. But many of the newly non-poor households are still nearly poor, hovering close to the poverty line. Their vulnerability to shocks and crises coupled with the recent slowdown in economic growth means that the gains in poverty reduction cannot yet be considered robust.

1999 was an important year for poverty-related research and policy development in Vietnam. Household survey data from the second Vietnam Living Standards Survey had been collected by the General Statistics Office (GSO) the previous year and became available for analysis in 1999. In addition, four participatory poverty assessments (PPAs) were implemented in Vietnam during the year. _The Vietnam-Sweden Mountain Rural Development Program_ carried out a PPA in two Districts of Lao Cai Province, an upland area with a high proportion of ethnic minorities living in remote villages. _ActionAid_ coordinated a PPA in six Districts of Ha Tinh Province in the north central coastal region, a typhoon-prone area with very poor natural endowments. _Oxfam GB_ carried out a PPA in two Districts of Tra Vinh Province, a coastal region with a large ethnic minority population and growing problems of landlessness. In Ho Chi Minh City, _Save the Children Fund (UK)_ coordinated a study of three poor, urban Districts of Vietnam's most prosperous city .

This case study describes the work that took place collaboratively between Government agencies, donors and NGOs in implementing the PPAs. Although there has been a considerable amount of qualitative information on poverty produced in Vietnam over the past 8-10
.<sup>7</sup> Countries are surveyed every three to four years but not simultaneously. To ensure data consistency and inter-country comparability we only use data from countries in the standardized dataset 2006-2010 which contains data for 100 countries.<sup>8</sup> The number of firms surveyed in each country depends on the size of the economy with more firms

> 6 There are other categories such as insurance companies or pension funds that we do not include in our analysis. 7 Only private sector firms are surveyed; fully state-owned firms are excluded.

> 8 Due to changes in the questionnaire data from the earlier years cannot be easily compared to data collected in the more recent years. In the six instances where multiple years of data are available for a given country, we keep only the most recent year of data.

7
approaches for evaluating energy storage systems for grid applications. Renewable and Sustainable Energy Reviews 107, 516–534. https://doi.org/10.1016/j.rser.2019.02.016 ‐ Chen, Z., Marin, G., Popp, D. and Vona, F., Green Stimulus in a Post pandemic Recovery: the Role of Skills for a Resilient Recovery. _Environmental and Resource Economics_ (2020) 76:901–911. <u>https://doi.org/10.1007/s10640-020-00464-7</u> Cystat (2020a). <u>GDP growth rate, 2</u><sup>nd</sup> <u>quarter 2020. Statistical Service of Cyprus, online data.</u> Cystat (2020b). Mean annual consumption expenditure per household by main categories of goods <u>and services, district, and urban/rural residence. Statistical Service of Cyprus, online data.</u> DFID (United Kingdom Department for International Development), 2019. <u>Synthesis Report on the</u> “ ” <u>Fourth Roundtable Discussion on Strategic Energy Planning , August.</u> EEA (European Environment Agency), 2020. _The European Environment – State and outlook 2020_ , Copenhagen. doi: <u>10.2800/96749</u> Elliott, R. J., Schumacher, I., & Withagen, C., 2020. Suggestions for a Covid-19 post-pandemic research agenda in environmental economics. _Environmental and Resource Economics_ 76(4), 1187-1213. <u>https://doi.org/10.1007/s10640-020-00478-1</u> Engström G., Gars J., Jaakkola N., Lindahl T., Spiro D. and van Benthem A.A., 2020. What Policies Address Both the Coronavirus Crisis and the Climate Crisis? _Environmental and Resource Economics_ 76:789
|1,578|2,603|6,587|39.3|40.3|54.0|53.9|
|Lower middle income,<br>excl. India|192|257|516|1,537|352|461|737|2,366|54.4|55.8|70.0|65.0|
|BRICS 5 countries|168|459|1,043|5,563|695|1,774|2,710|11,609|24.2|25.8|38.5|47.9|

Source: World Bank WDI database.

# **POLICY ISSUES**

This strong overall trade performance—with some exceptions, for example Sub-Saharan Africa in manufactures-- raises a lot of questions about the factors responsible, the implications for the architecture of the trading system as well as its sustainability.

How much of this growth was due to the developing countries own policies? To what extent improved market access resulted from the implementation of multilateral trade liberalization following the Uruguay Round Agreements as well as the establishment of numerous regional preferential arrangements? Following the
Figure 3: Weather-Based Crop Insurance: Flow Chart of a portfolio approach to ratemaking <!-- Start of picture text --> Weather data<br>Unified calculation framework<br>With data de-trending, if<br>appropriate<br>BC: Historical Burn Costs BCC: Burn Cost Cap<br>= total claim payout / total sum insured = X percentile historical BC<br>Calculated for year of each product for which Calculated for each product, with interpolation where<br>data is available necessary (cf. Excel function PERCENTILE)<br>PBR: Product Base Rate<br>Classification of products into = all year historical average of capped BCs<br>Balance Back Collectives = AVERAGE(MIN(BCC,BCyear))<br>WABC: Weighted Average over all products<br>Classification of products into Risk Collectives<br>in Balance Back Collective of the all year<br>historical average of uncapped BCs for each<br>product<br>WBR: Weighted Base Rate<br>WABR: Weighted average BR for = weighted average of PBR for all products in the<br>Balance Back Collective same Risk Collective.<br>BBF: Balance Back Factor =<br>WABC/WABR<br>Z: Credibility Factor =N/(N+K) where N is #years used<br>in PBR and K is a statistical parameter<br>Pure Premium Rate (PPR) = BR x BBF<br>BR: Base Rate = PBR*Z+WBR*(1-Z)<br>Calculated for each product<br>CL: Catastrophe Load, based on estimated<br>Risk Loaded Rate (RLR) = PPR + CL PML for the entire expected portfolio<br>Commercial Premium<br>= RLR x Administrative Load<br><!-- End of picture text --> 16
Business Size, Development, and Inequality in Latin America: A Tale of One Tail Marcela Eslava<sup>∗</sup> Marcela Mel ́endez<sup>†</sup> Laura Tenjo<sup>‡</sup> Nicol ́as Urdaneta<sup>§</sup> _Keywords: business size, developing economies, firm-level datasets, self-employment, micro-enterprises, income inequality, Latin America._ _JEL Codes: O54, E24, J21, J46_

- ∗Universidad de Los Andes, Bogot ́a, meslava@uniandes.edu.co

- †The World Bank, mmelendez@worldbank.org

- ‡The World Bank, lauratenjo@worldbank.org

- §Duke University and Universidad de Los Andes,n.urdaneta@duke.edu

We are extremely thankful to Gabriela Bojanini and Andr ́es Soto for their excellent data management and processing work. We thank audiences at the Latin American and Caribbean Economic Review (LACIR) meetings, the 2022 LACEA Annual Meeting, the LACEA Network on Inequality, Universidad de Los Andes, Universidad de Chile, CAF’s research seminar, and the World Bank for useful comments.
<!-- Start of picture text --> MHHH-FHHH gaps in per capita land owned (cond_) vs. GDP per captta<br>APA<br>ol<br>=><br>2 ai NIC<br>=<br>a<br>=<br>=5S avNM am ----AGCTM oo eo4 BGRTT<br>£7 7 «4mm S6BcHs “den oO ALB<br>co ao<br>= aa<br>i=3<br>£ «BOL<br>——<br>co 8<br>oo<br>ow a NAL<br>6.5 ri f.5 8 8.5 9<br>log of GDP per capita (PPP, constant US$ 2005)<br>Source: RIGA data.<br><!-- End of picture text -->
Lau, Lawrence J., Dean T. Jamison, and F. F. Louat. 1991. "Education and Productivity in Developing Countries: An Aggregate Production Function Approach." Policy Research Working Paper 612. Office of the Vice President, Development Economics, World Bank, Washington, D.C.

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. 1993. "The Ultimate Sources of Economic Growth." In Adam Szirmai, Bart Vanark, and Dirk Pilat, eds. Explaining Economic
2020). TFP quartiles are calculated based on the year-specific distribution of TFP, using the entire linked employer-employee database. The heterogeneity indicators refer to twelve months before giving birth, except for the drug spending indicators, which refer to _−_ 24 to _−_ 13 months (before pregnancy) or to _−_ 9 to _−_ 4 months (during pregnancy) before childbirth. The ownership, TFP, and industry indicators are not observed for the smallest (single-bookkeeping) firms. The sample is women employed in the private sector who gave birth between age 20 and 40 and have non-zero wage between thirty and four months before giving birth. (Number of individuals in panel (a): childbirth 2012-2014— 8,019; childbirth 2015-2017—5,823. Number of individuals in panel (b): childbirth 2012-2014—46,801; childbirth 2015-2017—33,289.) 17
initial months of the shock. WW2 had a particularly lasting impact, as market liquidity never fully recovered after 1945, with average bid-ask spreads remaining at a level far above those in the 1920s. Also, idiosyncratic crises affected aggregate market liquidity, for example the Ottoman and Egyptian defaults in the 1870s, the Russian default in 1998, or the Argentine defaults in the 1890s and early 2000s.

Second, total returns tend to be higher when bid-ask spreads are high, except for the first months of major shocks, when returns collapse while bid-ask spreads spike. On average, higher risk is associated with lower market liquidity. Appendix B6 confirms this using bond-level data, as average returns are significantly higher for less liquid bonds. The difference between the most liquid and least liquid bonds is large and statistically significant, especially in the historical sample, which is in line with Alquist (2010) and Chavaz and Flandreau (2017), who also find a substantial sovereign bond liquidity premium. An indepth analysis of sovereign bond market liquidity over the full 200 years is left for future research.

# **5. Investor performance around debt crises: Returns, defaults, and haircuts**

This section studies the link between bond returns, defaults, and haircuts, by combining the data on debt restructurings and creditor losses from Section 3 with those on bond prices and returns from Section 4.

Figure 11: Bond returns around sovereign defaults <!-- Start of picture text --> 175<br>Upper 75%<br>150<br>125 Average<br>cumulative<br>return<br>100<br>Lower 25%<br>75<br>50<br>-2 -1 t 1 2 3 4 5 6<br>Years around default<br>Start of holding period: 2 years before default<br>Cumulative total return index<br><!-- End of picture text --> _Note:_ This figure shows total cumulative returns
. Pelletier and others (1994) applied to prospective surveys in Ethiopia, Guatemala, India, and Malawi a new methodology for determining the association of malnutrition and mortality by the severity of malnutrition. They demonstrated that 42–57 percent of deaths of children ages 6–59 months were associated with malnutrition‘s potentiating effects on infectious disease, 76–89 percent of them attributable to mild to moderate malnutrition. Analysis of data for 53 developing countries for the 1980s found that about 56 percent of child deaths were associated with malnutrition. The proportion is close to 67 percent for India, with 73–74 percent of it attributable to mild to moderate malnutrition (Pelletier and others 1995).

While this approach is based on weight for age and not height-for-age, it makes a good starting point for a simulation of imputed height-for-age, based on the assumption that 67 percent of deaths in children up to age 36 months in India were related to malnutrition (the upper bound for that association; see bottom panel of table 2). The height-for-age of living children by gender in a particular year was used to impute the average height-for-age of children whose deaths were not associated with malnutrition. Of deaths among children related to malnutrition, 70 percent were assumed to have occurred among children with moderate to mild malnutrition, and an average height to 7