rafmacalaba/data-use-mentions-extended huggingface dataset:
This paper uses data from the Colombian DHS for 2005, 2010, and 2015.<sup>4</sup> Surveys are representative of the female population ages 13-49 at the national, urban, and rural levels. They collect information on health outcomes and socio-economic characteristics. The most recent waves include attitudes towards gender equality, women’s role in society, GBV, and intra-household decision-making. The DHS employs two-stage sampling designs. Primary sampling units (PSUs) or clusters are sampled in the first stage, and households in the second stage. This design results in a multilevel data set. The sample consists of 37,211 households (41,344 women) in 2005, 51,447 households (53,521 women) in 2010, and 44,614 households (38,718 women) in 2015. Men were interviewed in the 2015 round, but they are not included in the analysis because of the limited set of questions on attitudes and behaviors in the questionnaire.

The surveys allow for the direct identification of displaced household members. Questions ask whether the respondent lived in one or more places in the last 5 years, the date of migration, and the reason for migrating.<sup>5</sup> Displaced households are defined as those that had at least one member who was forced to flee due to conflict. This is a reasonable assumption for Colombia, where nearly 91 percent of IDPs migrate with all household members (Ibáñez, 2008). Furthermore, to facilitate the provision of reparations and other entitlements, as the IDP status is attached to a household, it is transmitted across generations (Sarzin, 2017; Shultz et al., 2014). IDPs were oversampled in the three survey waves and they represent 7 percent of the individuals who migrated internally each survey-year.

# **_5.1. Measurement of gender norms_**

Gender norms relate to multiple spheres of life. This study focuses on norms around reproductive health, economic opportunity, mobility, violence against women
1). Not surprisingly, households that report having suffered from theft or violence also have higher perceptions of insecurity. Similarly, conflict intensity is higher in provinces experiencing a larger number of conflict-related incidents. While these two correlation coefficients involve indicators from the same database, there is also a relatively high and statistically significant correlation between conflict intensity reported by the provincial database and the perception of insecurity estimated based on the NRVA survey. This pattern is corroborated by households’ perception of security from the Survey of the Afghan People.

# **4. Modeling conflict intensity**

The econometric specification of Model 1 builds on the analyses by Biddle, Friedman, and Shapiro (2012) and Hultman, Kathman, and Shanon (2013). The model focuses on changes in conflict intensity at the local level and how they are affected by changes in the presence of troops on the ground. Conflict intensity is measured by the number of casualties. To allow for richer dynamics, the specification considers two periods: the current year and the previous year. It also differentiates between international troops and Afghan troops, as their effectiveness at reducing conflict may be different. Troops and casualties are deflated by provincial population.

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, may also result in underreporting of income.

**Our data and method used in this note are also not good at capturing the underreporting of income** . **at the top of the distribution because top earners tend to be underrepresented in the survey data** While surveys can provide helpful information about income trends and distributions, they may not accurately capture the incomes of the wealthiest individuals, leading to potential gaps and biases in the data. Wealthy individuals may be missing from the sample (due to coverage errors, sparseness, or unit non-response) or -- even if they are included-- the income information is missing (due to item nonresponse), underreported or censored (Lustig, 2019; Ravallion, 2022; Yonzan et al., 2022) To somehow control for this problem, we restrict the distribution of survey income and imputed income at the 99<sup>th</sup> percentile of the income in the EU-SILC (see Bendek & Lelkes, 2011). In this case, the mean income in the EU-SILC is 11,231 EUR, and the mean imputed tax income is 10,193 EUR. The average

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- Juanmarti Mestres, Arnau, Guillem López Casasnovas, and Judit Vall Castelló (2021), “The Deadly Effects of Losing Health Insurance.” _European Economic Review_ 131.

- Kam, Cindy D., and Marc J. Trussler (2017), “At the Nexus of Observational and Experimental Research: Theory, Specification, and Analysis of Experiments with Heterogeneous Treatment Effects.” _Political Behavior_ 39(4): 789–815.

- Laban, Cornelis J. et al. (2004), “Impact of a Long Asylum Procedure on the Prevalence of Psychiatric Disorders in Iraqi Asylum Seekers in The Netherlands.” _Journal of Nervous and Mental Disease_ 192(12): 843–51.

- Leeper, Thomas J., Sara B. Hobolt, and James Tilley (2020), “Measuring Subgroup Preferences in Conjoint Experiments.” _Political Analysis_ 28(2): 207–21.

- Marbach, Moritz, and Guido Ropers (2019), “Not in My Backyard: Do Increases in Immigration Cause Political Violence?” _SSRN Electronic Journal_ . https://papers.ssrn.com/abstract=3310352 (October 20, 2021).

- McLaren, Lauren (2003), “Anti-Immigrant Prejudice in Europe: Contact, Threat Perception, and Preferences for the Exclusion of Migrants.” _Social Forces_ 81(3): 909–36.

- ———. (2015), Immigration and Perceptions of National Political Systems in Europe _Immigration and Perceptions of National Political Systems in Europe_ . Oxford University Press.

- Migración Colombia (2020), “Mas de 1 Millon de Venezolanos Estarian Radicados En Colombia - Migración Colombia.” _Viernes, 03 Abril 2020_ . https://www.migracioncolombia.gov.co/noticias/mas-de-1-millon-825-mil-venezolanosestarian-radicados-en-colombia (April 22, 2021).

- Mutz, Diana C (2011), Population-Based Survey Experiments _Population-Based Survey Experiments_ . Princeton: Princeton University Press.

- Ratkovic, Marc (2021), “Subgroup Analysis: Pitfalls, Promise,
of a significant share of total exposure to individual entities), and systematic risk, such as geographic concentration, industry concentration, and general economic volatility. Additionally, the deposit insurance scheme<sup>39</sup> and the new bank recovery and resolution framework are included in the analysis as well.

# **_Credit risk quantification_**

SNDO calculates expected losses or market values for guarantees/on-lending.<sup>40</sup> To calculate expected losses when a rating methodology has been applied to analyze a beneficiary, SNDO needs to estimate PDs and LGDs. Default probabilities are derived from the respective default rate tables of rating agencies based on historic data for similar types of entities. LGDs are also derived from rating agencies’ information on historic bond and loan recoveries. LGDs are differentiated by the bond/loan seniority stipulated in the respective agreement. If SNDO requires collateral to underwrite a guarantee/onlend, LGD estimates may be adjusted accordingly. The valuation of collateral may be outsourced to third parties. Additionally, SNDO sometimes adjusts LGD estimates based on a qualitative assessment.

If a simulation model has been used to estimate the distribution of a loss function, expected losses are inferred from the frequency of losses resulting in default. LGDs can be inferred from the severity of losses in default events.

To conduct a market value assessment, SNDO compares yields to maturity for corporate bonds and comparable government bonds. Yields of corporate bonds are taken from corporates with the same credit rating as the guarantee beneficiary’s internal credit rating as assessed by SNDO. Differences in yield hence reflect credit and liquidity premia. SNDO also studies credit default swaps (CDS) to perform market value assessments.

# **_Application in risk management_**

SNDO applies various risk mitigation tools to manage the fiscal risk stemming from exposure to credit risk from guarantees and on-lending, including guarantee fees, partial guarantee coverage, appropriations to a notional reserve account, and risk reporting.<sup>41</sup> Sweden has currently not set
# **4 Could an Increase in Green Trade Affect Workers?**

There are multiple mechanisms for why green goods trade could affect labor markets differently. Sector-specific skills needs for the production process involving green goods are not evenly distributed across genders and skill groups. Green industries often require specialized skills and knowledge related to sustainability and environmental technologies. The availability of these skills and the preferences of female workers towards these sectors can influence the share of female workers within green industries. If women possess the necessary skills and are more inclined towards environmentally focused sectors, the share of female workers in these industries may be relatively higher. For example, a study by International Renewable Energy Agency shows that renewable energy employs about 32% women, compared to 22% in the energy sector overall (IRENA, 2019).<sup>2</sup> Moreover, the skill level required in the production of green goods could be higher. The 2022 LinkedIn Global Green Skill Report (LinkedInEconomicGraph, 2022) found that in 2021 there were only 62 women for every 100 men considered green talent — a number that has been stagnant since 2015.<sup>3</sup> The sorting pattern of workers into industries with a higher share of green goods exports can be influenced by social norms and stereotypes that shape career choices and the representation of different genders and skill levels. For instance, the perception of green industries as environmentally conscious and socially responsible may attract a specific pool of workers, such as skilled college graduates (OCDE, 2014).<sup>4</sup> The expansion of green goods exports has the potential to increase the demand for labor across all types of industries, and it can be particularly advantageous for previously underrepresented groups. As the demand for labor in the green goods sector grows, the likelihood of employment opportunities for these underrepresented groups also increases like any other

> 2https://www.irena.org/publications/2019/Jan/Renewable-Energy-A-Gender-Perspective

> 3https://economicgraph.
habits (Carins and Rundle-Thiele 2014) and reducing social stigma in different areas (for a complete review, see Shawky et al. 2019).<sup>6</sup> However, the effectiveness of such campaigns has been, to our knowledge, entirely restricted to assessing on-platform engagement metrics or short-term “Brand Lift” surveys,<sup>7</sup> which may not be informative about medium-term behavioral change and public health outcomes.

We contribute to the existing literature by measuring the effects of a social media campaign on malaria-related behaviors and incidence, using both self-reported surveys and objective health facility data for up to five months post-advertisement exposure. Our study provides new and substantial evidence that social media can be an important channel in fighting malaria and similar diseases through the promotion of preventative behaviors and response actions. In this respect, our research complements evaluations of traditional interventions conducted in the field to combat malaria, including initiatives to subsidize and distribute antimalarials, rapid tests, and bed nets. (Cohen et al. 2015; Tarozzi et al. 2014). We show that digital ads that remind individuals to use bed nets and timely seek treatment in case of symptoms can be a cost-effective way of reducing malaria incidence. Moreover, in line with previous studies (Lambrecht and Tucker 2019; Sapiezynski et al. 2022), our analysis of the mechanisms sheds light on the importance of accounting for algorithmic targeting when delivering ads with public health objectives, as populations that are more at risk of the disease are also less likely to engage with the ads.

In this respect, our study demonstrates the importance of data in targeting users and tracking them to measure ad effectiveness, contributing to the ongoing debate on privacy and the use of thirdparty data in digital marketing (Acquisti et al. 2016; Tucker et al. 2018). The constraints imposed by privacy regulations, such as the GDPR, CCPA, and recent changes by Apple and Google, pose significant challenges (Goldfarb and Tucker 2011 _b_ ; Johnson et al
transactional systems. Four countries had adopted connected systems by 2014, but data on procurement competitiveness is not available for them, and they are excluded from our analysis.

Figure 1 shows the total number of e-filing and e-procurement systems adopted each year, by functionality. Overall, 125 countries implemented e-filing systems and 73 countries did not implement any system; 142 countries implemented e-procurement systems and 56 countries did not implement any system during the period 1990–2014.

To estimate the impact of e-government, we focus on tax compliance costs, public procurement competitiveness, and corruption measures at the country and firm levels. Tax compliance costs at the country level are the number of tax payments and the time required to prepare and pay taxes, which are available on an annual basis from the _Doing Business_ (DB) database.<sup>6</sup> These costs are estimated for a “typical” medium-sized manufacturing firm by in-country experts based on existing regulations and

> 6 One caution is that the number of tax payments might be somewhat endogenous with respect to e-filing implementation, as according to its definition “the number of payments takes into account electronic filing. Where full electronic filing and payment is allowed and it is used by the majority of medium-size businesses, the tax is counted as paid once a year even if filings and payments are more frequent.” (Source: <u>http://www.doingbusiness.org/methodology/paying-taxes). For completeness, we nevertheless consider this</u> variable in the analysis.

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on the productivity of manufacturing firms.** Generally, high FDI barriers prevent countries from reaping productivity spillover effects upstream and downstream in the economy (Javorcik, 2004a; 2004b; Javorcik and Spatareanu, 2005) since foreign know‐how and technology do not flow to local firms. Such spillovers tend to be felt by local suppliers (i.e. vertically) rather than in the same sector where the FDI takes place (i.e. horizontally), although results depend on the country of analysis. FDI barriers in Turkey thus not only limit the entry of more productive foreign service providers, but also productivity‐enhancing spillovers to local suppliers of those service providers. This could have a negative productivity impact on manufacturing firms, particularly those that rely more heavily on efficient establishment and post‐manufacturing services.

# **_Domestic regulatory barriers_**

**Services suppliers, whether foreign or domestic, are affected by domestic regulatory barriers.** For instance, state‐owned enterprises, monopolies or other forms of state‐ownership can limit the entry of private firms, both domestic and foreign (Van der Marel, 2012). In this case, domestic regulatory barriers also become a trade barrier. This section examines Turkey’s domestic regulatory measures based on the OECD’s Product Market Regulation (PMR) database as well Turkey’s services trade policy regime using the OECD Services Trade Restrictiveness Index (STRI).

**Higher levels of domestic regulatory restrictions in Turkey are mainly in post‐manufacturing and back‐ office services.** Figure 4.4 shows the level of domestic regulatory restrictions of selected services in different production stages. The largest restrictions are in rail and road transport (post‐manufacturing

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# **-** **<u>Appendix table A4: Comparing PSG and non PSG participants based on farm survey data</u>**

||<br> <br>|<br>**Beneficiaries o**<br>|<br>**f PSG progra**<br>|<br>**m**<br>|**Non-be**<br>|**neficiaries**<br>|
|---|---|---|---|---|---|---|
||**Total**|**<20 ha**|**20-50 ha**|**50-120 ha**|**< 120 ha**|**> 120 ha**|
|**Panel A: Farm characteristics**|||||||
|Total area cultivated (ha)|82.0|14.0|74.5|107.0|94.4|596.4|
|<br>Members working full time on farm|1.76|1.86|1.68|1.74|2.01|2.33|
|Members working part-time on farm|1.04|1.19|1.10|0.98|1.07|0.47|
|Permanent employees|1.08|0.17|1.81|1.20|2.45|13.88|
|<br>Have tractor < 40 hp|0.22|0.28|0.20|0.20|0.25|0.29|
|Have tractor 40-120 hp|0.71|0.38|0.72|0.82|0.66|0.82|
|<br>
takes advantage of unique micro data, which comprises stop-by-stop shipping data in the Central Asia Regional Economic Cooperation (CAREC) member countries, including Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, collected by the Asian Development Bank (ADB).<sup>2</sup> The data is focused on longdistance truck shipments and covers a long period of time from 2010 to 2020, which allows us to evaluate the impacts of long-term infrastructure investment made in the region on freight costs.

> 2 See ADB (2014, 2020) for more details. I would like to express my special thanks to the CAREC Corridor Performance Measurement and Monitoring (CPMM) team for sharing relevant raw data.
by this problem, this strongly support the view that dierences in natural hedge matter for the composition of a country's exports.

# 4.3 Robustness

The baseline results were obtained measuring the correlation of the international prices and nominal exchange rates, as well as the volatility of the exchange rate using the growth rates of each series to remove trends. Under the assumption that the series are dierence stationary (in logs), this procedure correctly removes trends, but it may still lead to spurious correlations if the growth rates are trending, which could happen over short periods of time. An alternative procedure would be to de-trend the series by ltering their long run trends using a Hodrick-Prescott lter (HP lter). This more complex detrending procedure allows for changes in the low frequency trends followed by the series.<sup>10</sup> To check for the importance of the choice of detrending procedure I also estimated the regressions for the global share specication (Eq. [7]) using standard deviations and correlations computed from HP ltered series.<sup>11</sup> The results, reported in Table 6, are somewhat smaller but very much in line with those obtained in the baseline regressions, both in sign and magnitude.

A potential concern with the results is that extreme and potentially unreliable observations for the correlation may be behind the ndings. The baseline sample already dropped country-industries with correlations larger than 0.9 in absolute value, with correlations computed using too few annual observations (less than 8), as well as countries that exported too few goods and industries exported by few countries. Still, to check the role of extreme observations, the regressions in Table 7 impose further constraints on those variables. Column (1) drops country-industries with correlations above 0.8 (in absolute value), Column (2) drops those computed with less than 12 annual observations, and Column (3) drops sectors exported by less than 20 countries and countries that export less
significant reduction was partially offset by the increase in jobs offering fewer hours per week and lower wages, or a combination of both.

The worsening of socio-economic conditions demands a government's rapid response. To do so, representative information on migrants and host communities is vital for evidence-based policy design. However, two main challenges are associated with conducting a representative survey of the host and migrant populations. Even though Ecuador has a reliable and updated sampling frame for the resident population, the total number of Venezuelans and their geographical location in the country were not available at the time of the survey.<sup>7</sup> The official data provide only the net flows of migrants who entered and exited the country through an official gate. However, no official estimates exist for those migrants who did not register their entry into the country to add up to the total Venezuelan migrant population. In 2019, Ecuador was in a weak position relative to its neighboring countries in the availability of such data. Peru gathered the National Census in 2017, and Colombia had collected not only the 2018 National Census but also a national migratory registry.

Unlike other Latin American host countries, Venezuelan migrants were expected to be spatially clustered in Ecuador throughout the corridor. For instance, while in Peru, over 84 percent of migrants are in Lima, in Ecuador, it is estimated that only around 60 percent of Venezuelan migrants live in five main cities – Quito, Guayaquil, Cuenca, Manta and Santo Domingo.<sup>8</sup> This phenomenon is likely standard in this type of population's movements, a fact that has significant implications on both the survey's intended analyses and its sampling design.

From an analytical standpoint, assessing the impact of immigrants on the host population depends on their spatial proximity and their relative density. From the sampling viewpoint, the clustering of immigrants would help to implement a design that has proven to be successful in conducting similar surveys elsewhere. For instance, such as those addressed to immigrants
# **1. INTRODUCTION**

The World Bank Group’s (WBG) twin goals of ending extreme poverty and promoting shared prosperity are at the forefront of the WBG’s operations. The shared prosperity goal, defined as fostering income growth of the bottom 40 percent, reflects the fact that as countries grow, the benefits of the growth may not necessarily be widely shared among the population. Nations with a widening gap between those who can and cannot access opportunities in life have difficulty sustaining economic growth and social stability over time. Shared prosperity therefore focuses on two, often competing, concerns, expanding the size of the economy and sharing it among all members of society, particularly the poor (World Bank, 2015a). Furthermore, the indicator of shared prosperity features prominently as target 10.1 of one of the Sustainable Development Goals of reducing inequality within and among countries. The concept and measurement of shared prosperity may therefore gain traction beyond the Word Bank Group’s operational work in the coming years.

Unlike ending extreme poverty, the shared prosperity goal is country specific without a specific target at the global level. Good performance depends on whether consumption of the bottom 40 percent of the population is positive and how it compares with growth of the population from other parts of the distribution.

The frequency and quality of data on poverty and shared prosperity are of utmost importance to successfully monitor the twin goals and to set effective policies for a country’s poverty reduction program. Shared prosperity measures consumption or income growth rates from representative household surveys and requires a comparable welfare aggregate at two points in time. This makes it particularly demanding in terms of data availability and quality (World Bank, 2015a).

Countries should make numerous methodological choices to measure shared prosperity. The impact of these choices on the shared property indicator is an empirical question. This issue becomes even more important when the monitoring of shared prosperity is done at the global level. For example, the World Bank constructs corporate numbers on shared prosperity based on