rafmacalaba/data-use-mentions-extended huggingface dataset:
others in their community as opposed to save for themselves, and preferences for the village to develop together versus separately).

**_Sociodemographics._** Most sociodemographic measures including Proxy Means Targeting (PMT) poverty score (Premand & Schnitzer, 2021), age, relationship to household head, and nomad status were collected in a census survey prior to the randomization.

# _Empirical strategy_

The analysis plans were pre-specified (https://www.socialscienceregistry.org/trials/3570). Deviations from this plan are also uploaded and primarily reflect changes to align analyses with the policy experiment, as published in Bossuroy et al. (2022).

To assess treatment effects at endline, our primary model compares each psychosocial intervention to a control condition that included no additional psychosocial intervention.<sup>18</sup> We control for a vector of stratification variables used in randomization, including timing of the program implementation (early versus late), policy experiment treatment arm (Psychosocial versus Full), and whether or not participants were randomly selected in the policy experiment sample for data collection (presented in Bossuroy et al., 2022). This model is represented as follows: 0 1 P P P 2 P P 0 ii ii ii ii ii YY = ββ + ββ TT. P _p-_ values and cluster robust + ββ TT. IIPPIIP ppP _p-_ PP + γγ XX + εε We present both robust _p- p-_ values. We present clustered standard errors to account for the fact that women were organized into groups as part of the program and thus their outcomes are likely to be correlated and because a group-level randomization was conducted on top of the individual-level randomization. However, given recent evidence (Abadie et al., 2022), these cluster robust _p_ -values may be overly conservative. For this reason, we present both clustered and non-clustered robust _p_ values and prioritize the latter for interpretation.

A second implication of this design is the potential for contamination of intervention content in case women were to discuss
this year is defined can be crucial for evaluating the macroeconomic trends around the onset of conflicts. To assess the robustness of the econometric estimates across various definitions of the onset of conflicts, which can affect both the starting dates as well as the number of conflicts, we utilize the following four alternative definitions of conflicts:

**Definition 1:** All conflicts with their corresponding starting dates. This definition includes all the conflicts involving developing economies reported by the UCDP/PRIO Armed Conflict Dataset. The starting years were retrieved from the data set and correspond to the date of the first recorded battle‐related death.

**Definition 2:** High intensity conflicts with their corresponding starting dates. This definition considers only conflicts that in at least one year since their onset experienced a minimum of 1,000 battle‐related deaths, which is the highest intensity level reported in the data set. If a country experiences more than one conflict in the same year, we considered in the analysis the episode with the highest level of intensity. The starting year, retrieved from the data set, corresponds to the date of the first battle‐related death recorded in the conflict, and thus the dates of the onsets of conflicts are the same as in definition 1 but the number of conflicts is lower than under definition 1.

**Definition 3:** High intensity conflicts with adjusted starting dates. This definition considers only conflicts that in at least one year since their onset experienced a minimum of 1,000 battle‐related deaths. But, in contrast with Definition 2, the starting date corresponds to the year when the threshold of 1,000 battle‐ related casualties in a year was reached.

**Definition 4:** All conflicts with starting years based on cumulative intensity. Considering the temporal dimension of the conflicts, this definition includes all the confrontations reported by the UCDP/PRIO Armed Conflict Dataset that since their onset exceed 1,000 battle‐related deaths. The starting date corresponds to the
description of the recent evolution in attitudes toward a set of public institutions in El Salvador. I also discuss the relevance of Nighttime Light Density as proxy for income across Salvadoran municipalities.

## _2.1 Attitudinal Data from Opinion Surveys_

In order to study Salvadorans’ attitudes towards public institutions over the 2004-2018 period, I use several waves of two opinion surveys, namely, LAPOP (9 waves) and Latinobar ́ometro (13 waves). Both surveys are implemented in El Salvador since the 1990s and focus primarily on citizen’s perceptions and attitudes toward different institutional actors. LAPOP is conducted every two years and interviews around 1,500 individuals in each wave whereas Latinobar ́ometro interviews around 1,000 Salvadorans every year. Both surveys provide information on a small set 4
assumption that Vietnam will fulfill its stated ambition to raise minimum wages to the level of the minimum monthly living standard in 2018. By way of comparison, the growth of real labor productivity in the state, non-state domestic and FDI sectors are also depicted. Finally, Figure 3 shows vertical lines to highlight three key years for Vietnam’s labor market and labor market policies: 2007 as the year of WTO accession, 2012 as the year of the passage of the new Labor Code and Trade Union Law, and 2015 as the year when the ASEAN Economic Community is supposed to be established and the movement of skilled labor across the ASEAN region is deregulated.

**Figure 3 – Rising faster than productivity: Time series of statutory minimum wages and labor productivity**

<!-- Start of picture text --> 600<br>WTO Establishment of ASEAN<br>accession Economic Community<br>500<br>Minimum wage<br>region 1<br>Minimum wage<br>400 region 2<br>Passage of new Labor Minimum wage<br>Code and Trade region 3<br>Union Law Minimum wage<br>300 region 4<br>Labor<br>producitivity state<br>Labor productivity<br>200 non-state domestic<br>Labor productivity<br>FDI<br>100<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Year<br>labor productivity (GNI deflator); 2006=100<br>Real minimum wage (CPI 2005; private domestic sector) and real<br><!-- End of picture text -->

**Source: World Bank staff calculations based on MOLISA, VGCT, GSO, World Bank WDI and IMF WEO data. Notes: “**  **” denotes projections. Depicted are non-government sector minimum wages.**

As is evident from Figure 3, Vietnam’s non-government domestic sector minimum
Policy Research Working Paper 6072

# **Abstract**

Using micro-level data on mutual funds from different financial centers investing in equity and bonds, this paper analyzes how investors and managers behave and transmit shocks across countries. The paper shows that the volatility of mutual fund investments is quantitatively driven by investors through injections of capital into, or redemptions out of, each fund, and by managers changing the country weights and cash in their portfolios. Both investors and managers respond to returns and crises, and substantially adjust their investments accordingly. These mechanisms generated large capital reallocations during the global financial crisis. Their behavior tends to be pro-cyclical, reducing their exposure to countries experiencing crises and increasing it when conditions improve. Managers actively change country weights over time, although there is significant short-run “pass-through,” meaning that price changes affect country weights. Consequently, capital flows from mutual funds do not seem to stabilize markets and instead expose countries to foreign shocks.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at craddatz@worldbank.org and sschmukler@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
of the proceeds from debt issues is typically used to repay maturing debt and, therefore, only a fraction of debt issues can be considered new financing. Henderson et al. (2006) try to adjust the data on debt issues to take this fact into account and conclude that, even with these adjustments, debt issues constitute a much larger source of new capital than equity issues at the aggregate level.

> 4 Black and Munro (2010) use unit record data for bond issuance by corporate residents of Australia; Hong Kong SAR, China; Korea; Japan; and Singapore to analyze firms‘ decision to issue debt abroad, and Eidenmüller et al. (2010) analyze the choice of issuer location in the European corporate debt market during the period 1980-2008. 5 A broader literature studies the aggregate effects of financial integration on economic growth, investment, the cost of capital, and financial development. See, among many others, Levine and Zervos (1998), Edison et al. (2002), and Bekaert et al. (2005, 2006).

6
*Measurements of the business environment**

We first discuss the logic of the _Doing Business_ (DB) and the Global Competitiveness (GC) data collection and the pros and cons of these data sets. The World Bank _Doing Business_ project provides objective measures of business regulations for local firms in 190 economies. Each indicator in _Doing Business_ is calculated from several objective questions based on case scenarios with specific assumptions. Instead of surveying firms, the _Doing Business_ project collects the data from legal professionals, such as lawyers, judges, public officials, and other professionals (that is, accountants, architects, and engineers). In addition, about two-thirds of the data are based on a reading of the law because the primary goal of the project is to measure what is regulated by the law of a certain country.

> 2 Thus wholly state-owned firms are not included in the sample.

> 3 For a more detailed description of the WBES, see http:\\www.enterprisesurveys.org.

> 4 For each dependent variable, the number of observations differs. The figure of 80,000 is for the dependent variable of employment growth, which has fewer missing observations. The 709 cities in 128 countries refers to the sample where the issue of missing data does not arise.

7
nominal rates of assistance” using a database created by Anderson and Nelgen (2013), although this database applies only to agricultural products, where distortions are most prevalent.

Assessing the drivers of participation in GVC sectors then hinges on identifying so‐called “GVC products” based on a list of traded products most likely to be included in cross‐border production networks. The specific lists utilized, described below in the data section, are isolated to manufacturing sectors, where a predominant set of opportunities for developing countries exists. Being a snapshot of GVC activities at a point in time, agri‐food and services cross‐border value chains are not included, even though these chains constitute smaller, but increasingly important, opportunities for developing countries.

The utilization of gross exports from standard trade data stands in contrast with other studies on GVC participation, which utilize value‐added data from input‐output (I‐O) tables. These I‐O databases, including the World Input‐Output Database (WIOD), UNCTAD’s EORA database, and OECD‐WTO Trade in Value Added (TiVA), cover only a subset of countries participating in GVCs. Some studies on drivers of GVC participation utilize an I‐O based “GVC participation index” by De Backer and Miroudot (2013), which sums the import content of exports and the exports of intermediate inputs (goods and services) used in third countries’ exports, taken as a share of gross exports. The use of trade data in this study allows for measurement of GVC participation at a higher level of disaggregation and country coverage. The use of gross exports also puts the focus in this study on the extent of participation in global value chains rather than the amount of value created in global value chains, an equally important issue associated with upgrading in production networks.

# III. Framework and data

## A. Capabilities that matter for GVC participation

Employing the above methodology, we focus on a set of capabilities that are most common in the
effects on how men and women benefit. For example, where parents do not enroll girls in school, for whatever reason, societal roles would lower the impact of education benefits for women. Girls receive less education if household dynamics make them drop out of school earlier than boys; spending more on education would not necessarily correct this inequity. Government spending on health and education could also have different effects on men and women due to the gender division of labor in production and social reproduction (Johannes and Noula 2011; Enríquez and Elson 2012 **;** Stewart 2018). Difficulty in accessing social services (due to income as well as mobility, norms, information or other barriers), user fees for public services (e.g., for hospital visits), and informal taxes on accessing public goods could have more impact on girls and women than on boys and men <mark>(Enríquez and Elson 2012).</mark> Different fiscal policies are proposed to address gender inequality through opportunities and the advancement of women in areas such as education, health, and economic empowerment (Kolovich 2018; Stewart 2018). Governments can integrate gender analysis into fiscal systems by ensuring that the systems contain neither explicit nor implicit negative bias; are responsive in meeting the needs and priorities of both men and women and males and females; or are transformative by contributing to shifts in and transformation of gender roles and power dynamics (Aziz, Norman, and Athene 2016). However, evidence to support this policy position is sparse. A comprehensive review of several gendered fiscal incidence studies found that most focus on measuring gender equity in government spending on education and health but neglect to assess the combined effect of both taxes and transfers (Greenspun and Lustig 2013). Although the few studies to date provide useful insights into the degree, if any, of gender equity implied by specific taxes and transfers or 2
language skills of these children (Arenas Arroyo and Schmidpeter, 2022). Moreover, Santillano _et al._ (2020) also find that in the United States, enrollment of Hispanic immigrant children in the Head Start program—which provides comprehensive early childhood education, health, and nutrition—decreases by 10% after a law enforcement raid. Primarily, this decline occurs because parents are deterred from appearing in public spaces.

Concerning the education outcomes of immigrant children, Amuedo-Dorantes and Lopez (2015) and Amuedo-Dorantes and Lopez (2017) find that in U.S. areas with intensified immigration law enforcement, the probability of school-year repetition and dropping out for Hispanic children between ages 14 and 18 years increases by 14% and 18%, respectively. Likewise, Dee and Murphy (2020) find that new ICE partnerships lead to a 10% decrease in the Hispanic immigrant student population within a span of two years. Furthermore, the learning environment plays an important role in immigrant children’s motivation, focus, and parental involvement. Amuedo-Dorantes _et al._ (2023) use data from the “Between the Lines” project to show that immigrant students who are enrolled in U.S. school districts implementing safe-zone policies<sup>2</sup> demonstrate improvements in several outcomes. For instance, they are 17% less likely to report difficulties in concentrating during school hours, 65% more inclined to express diligence in their studies, and 31% more optimistic about their prospects of achieving higher education, relative to other students. Additionally, these policies enhance caregiver participation in parent-teacher meetings by 34%.

Similar results are observed in the health care domain. Particularly, Watson (2014) finds that an increase in federal immigration enforcement acts leads to a decrease in Medicaid enrollment and usage among children of non-citizens, even if the children themselves are U.S. citizens. Additionally, Amuedo-Dorantes _et al._ (2022) find that more intense local U.S. immigration enforcement measures, particularly during the
or in the compensation package for employment—from evasion of payment, such as theft of electricity or non‐payment without disconnection. No household survey enabled probing of this question at that level of detail.

- In Mali, Niger, and Zambia, the survey asked if the rent included electricity. In South Africa, the survey asked if rent or free rental included “electricity, water, etc.” without isolating electricity. In South Africa, the survey also asked how much the household had spent on utilities if water and electricity bills were bundled.

- In Mali and Niger, the survey asked if the household received free “water‐electricity” as part of employment compensation, without isolating electricity.

- In South Africa, the survey asked if the household received free electricity and its value. Low‐ consumption or poor households (depending on the municipality) can obtain certain amounts free of charge and pay for the rest. Therefore, answering yes to the question does not mean no payment to the utility was required. It is also possible that some households would report stolen electricity as free electricity. In Uganda and Zambia, the survey did not ask about free electricity, but the household was asked to value electricity received without making payment. Neither survey asked why free electricity was available.

- In Sierra Leone, the survey asked if the household had paid electricity bills (either bills addressed to the household or shared with others) over the past 12 months.

Asking if the household had made payment at any point over a long time period, such as 12 months in Sierra Leone, is far more likely to catch payments than asking about the past 30 days, when the household may have missed a payment, pre‐paid enough in advance, or pay less frequently than once a month. Households with missing values (missing answers) may not have answered the question despite having paid (for example, the respondent may have had trouble recalling the amount paid), but all missing values were assigned zero
tion**||||
|Overall|492|65%|464|61%|506|67%|583|61%|
|High income|186|88%|172|81%|168|79%|189|71%|
|Upper middle income|129|60%|113|52%|129|60%|139|51%|
|Lower middle income|117|55%|121|57%|132|62%|148|56%|
|Low income|60|50%|58|48%|77|64%|107|71%|
|||**Governme**|**nt spending**|**on social pro**|**tection**||||
|Overall|||||21|3%|102|11%|
|High income|||||1|0%|11|4%|
|Upper middle income|||||5|2%|33|12%|
|Lower middle income|||||10|5%|37|14%|
|Low income|||||5|4%|21|14%|

When we look at the disaggregation by region, for health we do find coverage that is close to universal for all the regions (Table 3). This may be related to decades-long international efforts to collect health expenditure data (Bui et al. 2015). That is not the case for education, where the trend in data availability is particularly worrying for Latin America (LAC) and Middle East and North Africa (MENA). It is encouraging, however, to observe a significant positive trend in data availability for South Asia and SubSaharan Africa. Importantly, for education, the shifts in data availability are so large that comparisons of mean spending values are not particularly informative since data availability might
-0.311**|
||||(0.040)|||(0.116)|
|Treatment_×_Gave any transfers|||-0.024|||-0.036|
||||(0.050)|||(0.137)|
|Value of transfers given||||-0.266||1.497*|
|||||(0.346)||(0.749)|
|Treatment_×_Value of transfers given||||0.468||0.565|
|||||(0.461)||(0.995)|
|Received any transfer from an employer|||||-0.001|-0.003|
||||||(0.020)|(0.020)|
|Treatment_×_Received transfer from employer|||||-0.024<br>(0.025)|-0.027<br>(0.025)|
|Attrition in control|0.162|0.162|0.162|0.162|0.162|0.162|
|F-test (p-value)||||||0.842|
|Adjusted R-squared|0.004|0.005|0.003|0.003|0.003|0.007|
|Observations|7953|7953|7953|7953|7953|7953|

**Source:** Author’s analysis based on original survey data.

**Notes:** The dependent variable is a dummy variable equal to 1 if the respondent has not been surveyed at midline and/or endline survey waves. The sample includes all ultra-poor households. All specifications control for subdistrict fixed effects, a dummy variable (DV) equal to 1 if the control variable is missing and
6 above 50 cents per piece.<sup>7</sup> This may help to explain why the United States only mandated universal home delivery in 1958, when letter volumes were above 300 per person per year (Campbell, 2004).

**Figure 2:  Estimated Relationship Between Costs per Letter and Volumes**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>0 50 100 150 200 250 300 350 400 450 500<br>pieces per capita<br>cost per piece<br><!-- End of picture text --> Table 3 suggests the breakdown of costs of US-quality delivery per piece of mail in a system of universal service in an economy where postal volumes per capita are low, based on data from the Cohen et. al. study and the World Bank. As seen, delivery would be expensive—over $0.76 per letter in a country with mail volumes of 10 per capita. Even without delivery to the door, the costs are high—over $0.60 per letter.<sup>8</sup> Again, this data only suggests orders of magnitude, as it is based on United States’ postal data adjusting for input costs—and the USPS is an operation in a very different (broadly more positive) environment from most developing countries. Nonetheless, the point is clear that letter delivery becomes considerably more expensive in lowvolume environments, not merely because of rising costs of home delivery, but also because the other fixed costs of the network are divided up amongst far fewer stamp purchases. And the reality for a great majority of poor countries is that total mail volumes will be small enough to suggest severe scale diseconomies—mail delivery will be comparatively very expensive to provide to all, let alone the difficult-to-serve.

> 7 Cohen et