rafmacalaba/data-use-mentions-extended huggingface dataset:
between April 2013 and March 2015. Even though it was not compulsory, participation in the behavioral accompanying measures was very high. The average attendance rate among cash transfer beneficiaries was 92%. Substantial participation of non-beneficiaries in assemblies held in beneficiary villages was also observed.<sup>16</sup> The follow-up survey was collected between mid-January and mid-May 2015. The survey again included two separate instruments: a household survey and a survey for children aged 6 to 59 months at followup. The household and child survey instruments were based on the baseline survey questionnaires. The child questionnaire included anthropometric measurements for children 6-59 months old as well as the test to measure cognitive development for children below 42 months. In addition, the strengths-anddifficulties questionnaire was introduced to measure socio-emotional development among children 2459 months old (Goodman, 1997).

The sampling strategy for the follow-up survey was designed to oversample actual program beneficiaries in the treatment groups. This was done to ensure sufficient statistical power to detect impacts of the behavioral intervention in the sub-sample of beneficiary households. Therefore, the follow-up sample was stratified based on the proxy means test score “predicted” from the baseline survey.<sup>17</sup> A total of 377 households from the baseline sample (with high predicted PMT scores, hence low likelihood of being selected as beneficiaries) were dropped from the follow-up sample. The distribution of these 377 households was balanced between the two treatment and control groups. Separately, an additional booster sample of 1,058 beneficiary households was randomly selected from the administrative database of beneficiaries and added to the follow-up sample. That booster sample is only added for the two treatment groups and used to estimate the value-added of the BCC component. Of the 5,011 households in the follow-up sample, 4,818 were tracked and interviewed, a response rate of 96.1%.<sup>18</sup> Table 1 summarizes the composition of the follow-up sample, which we discuss in more detail in the next section.

> 16
of them can undermine the quality of care mothers provide to their children, the information gathered from the questionnaire is unusual and quite useful. Henceforth, we will use the term _mental_ health to refer to both cases of depression and/or anxiety.

Using the responses to the questionnaire, we estimated three mental health indexes: the simple average of all items and two standardized items using factor analysis and principal components analysis. As we explain below, we use information on maternal mental health from the first round of the YL survey.

# **_D. External shocks_**

We exploit the availability of data on exposure to external shocks in the first round of the Peruvian YL. Caregivers were asked about events or changes that negatively affected the household welfare and that occurred since the mother of the index child was pregnant until the day of the interview. Survey respondents described the event and the enumerator classified it among the 14 categories. We group these categories into 6 groups of shocks, including natural disaster, crop or livestock loss, decrease in food availability, job or income loss, death or severe illness, and birth/new household member.<sup>12</sup>

# **2.2 Descriptive statistics**

Table 1 reports summary statistics of the variables used in this paper for the sample under analysis. We separate the variables in four panels by mother, child, household and community characteristics. Columns 1-3 present mean, standard deviation, and number of observations for the sample in the 2006/7 round. Similarly, columns 4-6 show same statistics for the third YL round (2009/10).

As presented in Panel A, mothers are 31-35 years old on average between the two rounds. Around 16% of them are of indigenous origin, and although 79% are literate, 57% has not completed primary school. Finally, 94% of mothers attended antenatal care while they were pregnant with the index child. In terms of children’s characteristics (Panel B), half
violent conflict suffer from two major data-related limitations. First, because the outbreak of violent conflict disrupts traditional data collection efforts, evaluating the immediate impacts of an active violent conflict proves difficult. Second, tracking the trajectory of outcomes associated with violent conflict may require highfrequency data that are not usually collected in conflict settings. This is particularly crucial for large-scale high-intensity violent conflicts that may evolve in ways that are difficult to foresee. In other words, these limitations lead to delays in reliable data collection, leaving most micro-level

> 4 Similarly, Leon (2012) and Bertoni et al. (2017) find negative impacts of conflict on human capital accumulation and schooling outcomes, respectively, in Peru and Nigeria; while Odozi and Oyelere (2019) examine the impacts of a broader range of conflicts on poverty outcomes.

> 5 Dabalen and Paul (2014) find evidence of reduced dietary diversity for the same period. 6 Other studies in Africa have focused on identifying causes of violent conflicts as in the case of increases in food price levels and extreme climatic conditions leading to socio-political unrest (e.g., Hsiang, 2011; Hendrix et al., 2012; Smith, 2014; Maystadt and Ecker, 2014; van Weezel, 2019). For the case of Ethiopia, Akresh et al. (2012) and Weldeegzie (2017) use panel data to estimate impacts of the 1998-2000 Ethiopia-Eritrea border conflict on child health and schooling outcomes of children.

3
job holdings. Third, multi‐topic household surveys, such as LSMS‐ISA surveys, often include other modules such as the agricultural module (AG) and the non‐farm household enterprise (NFE) module which capture specific activities of the self‐employed (Box 1). This implies that collecting detailed information on self‐employment in agriculture and non‐farm household enterprises is not the main objective of the LSMS‐ISA labor module, as this information is already collected in other, dedicated modules.

10
# Survey design and administration

Designing a socio-economic survey including consumption measures for non-displaced households is challenging as the interview time is limited and one has to be very selective to restrict the number of questions in surveys. For FDPs, the challenge is exacerbated given the need to understand not only their present socio-economic well-being, but also their displacement trajectory as well as their aid dependency. In this context, spending 90 or more minutes on an elaborate consumption module might not be a priority. Instead, detailed information on displacement and aid is crucial and something that is not properly assessed in non FDPs surveys.

Fatigue from over-surveying is often cited as an anecdotal challenge in the context of FDPs, leading to survey non-response. Even though FDPs are often subject to intensive surveying by multiple agencies, survey non-response is not as high as for regular populations and the likelihood for a household to be interviewed multiple times is limited. Only censuses interview everyone in the population, they are often prohibitively expensive, and suffer from low data quality outstripping their size advantage. Thus, only few censuses – with very short questionnaires – are necessary for verification exercises. Hence, the chance of multiple interviews for the same households in a short period of time is low. However, refugees do complain about excessive surveying, possibly because contacts with interviewers are not just limited to sample surveys but also needed for a wide variety of purposes related to administering services to refugees.

FDPs are not only particularly vulnerable and need specific protection, they also often originate from a different region in the case of IDPs or country in the case of refugees. This creates several challenges in interviewing FDPs. The respondent might not speak the official language of the country or may not trust enumerators that come from different communities. This can be overcome by assigning purposefully enumerators to specific households to ensure that they speak the same language and share the same culture. However, enumerators should also have a similar cultural background as respondents. Di Maio and Fiala (2020) use a large-scale experiment in Uganda in which
|4.0|4.2|
|Consumption - government|13,931|1.1|2.2|2.0|1.9|1.6|1.3|1.2|0.9|0.9|1.5|
|Fixed investment - private|19,731|4.7|5.3|5.2|5.1|4.4|4.0|3.9|3.9|3.9|4.5|
|Fixed investment - government|1,924|4.0|4.4|4.3|4.2|4.1|3.9|3.9|3.9|3.9|4.1|
|Exports|25,005|0.6|3.4|3.6|3.7|4.3|4.8|5.1|5.4|5.3|4.4|
|Imports|58,474|2.5|3.8|3.7|3.8|3.8|3.8|3.9|4.0|4.0|3.8|
|GDP at factor cost|53,735|3.3|4.3|4.3|4.3|4.3|4.3|4.4|4.4|4.4|4.3|
|Total factor employment (index)||3.5|3.1|2.8|2.6|2.4|2.3|2.1|2.0|1.9|2.4|
|Total factor productivity (index)||-0.1|1.3|1.5|1.7|1.9|2.1
<!-- Start of picture text --> City Cost of water per cubic metre (US$)<br>House connections Public taps Water vendors<br>Bandung (Indonesia) 0.38 0.26 3.60<br>Bangkok (Thailand) 0.30 ~ 28.94<br>Chennai (India) 0.30 0.58 -<br>Chonburi (Thailand) 0.38 ~ 19.33<br>Colombo (Sri Lanka) 0.04 0.02 -<br>Dhaka (Bangladesh) — 0.08 0.84<br>Hanoi (Vietnam) 0.09 0.55 ~<br>Karachi (Pakistan) 0.10 — 1.14<br>Kathmandu (Nepal) 0.18 0.24 2.61<br>Lae (Papua New Guinea) 2.20 5.96 —<br>Malé (Maldives) 5.08 - 11.20<br>Manila (Philippines) 0.29 - 215<br>Mumbai (India) 0.07 0.07 0.50<br>Phnom Penh (Cambodia) 0.13 - 0.96<br>Port Vila (Vanuatu) 0.42 0.86 8.77<br>Seoul (South Korea) 0.25 14.13 2152<br>Shanghai (China) 0.08 0.06 -<br>Tashkent (Uzbekistan) 0.01 0.02 =<br>Thimphu (Bhutan) 0.03 0.05 -<br><!-- End of picture text --> Source: UN-HABITAT (2003) Water and Sanitation in the World's Cities. Local Action for Global Goals. Earthscan, London, Table 2.8. Based on consumer surveys by Asian Development Bank, reported in McIntosh, A.C. and Yfiiguez, C.E. (1997) Second Water Utilities
br>15.3<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>8.0 6.3 6.3<br>5.3<br>6.0<br>3.7<br>3.3 3.2<br>4.0 2.7 2.6 2.5 2.1 2.1 2.1 2.0 1.8 1.6 1.6 1.5 1.5<br>2.0<br>0.0<br><!-- End of picture text --> Source: European Banking Authority (EBA).

6 Assuming total assets are on average 2.8 times RWAs, based on a sample of internationally active European banks, and the estimated aggregate EBA capital shortage of Є114.7 billion.
and import transactions from January 2017 to October 2022 based on bills of lading (BoL). The data includes the name and address of the shipper (exporter) and the consignee (importer) of each shipment as well as HS8 product codes. Exporting and importing firms are identified by their names, as BoL data does not require inclusion of tax identification numbers.<sup>96</sup> We use machine learning and text analysis techniques to clean firm names and assign to each name a unique firm identifier, thus constructing a panel of Sri Lankan firms engaged in exports or imports. Appendix A.B.1 below provides details on our fuzzy matching algorithms.

The data is subject to a series of cleaning procedures as in Fernandes et al. (2016). Specifically, we drop observations for which trade value is zero or missing, product codes are missing or not in the HS 2017 revision list, partner country is missing or is Sri Lanka (which may indicate trans-

> 96While Panjiva adds a firm identifier to the BoL data that Flaaen et al. (2023) use for the United States, we found such identifier to be extremely unreliable for Sri Lankan firms. The same firm appearing with a different name spelling has a different identifier, and a given firm making an export transaction has a different identifier than the same firm making an import transaction. On any given day, the same firm transacting more than once could also have different Panjiva identifiers.
**<u>│</u> 15**

For selected indicators corresponding to fractions over employment (specifically the shares of skilled and semi-skilled workers in the workforce, employment expansion, and average wage) the average is taken over the population of workers rather than number of firms, using the weighted sum of employment E� in each subgroup (national total, foreign multinationals with 50 percent and 10 percent ownership) as the basis for the calculation:

# **Figure 1. Representativeness of Foreign MNEs in World Bank Enterprise Surveys**

<!-- Start of picture text --> Share of MNEs in Aggregate Output<br>70<br>60<br>50 Slovak<br>Republic<br>Czech<br>40 Romania Republic<br>Estonia<br>30 Poland<br>Latvia<br>Sweden<br>Croatia<br>20 Slovenia<br>Mexico<br>Indonesia<br>10<br>China<br>Russia<br>India<br>0<br>0 10 20 30 40 50 60 70<br>World bank Enterprise Surveys<br>OECD Estimate<br><!-- End of picture text --> _Source:_ Calculations on World Bank Enterprise Surveys; Cadestin et al., 2018 for OECD estimates based on official AMNE data and WIOD. _Note_ : Resampling weights have been used to generate shares of foreign multinationals in output in World Bank Enterprise Surveys.

The results are discussed in three parts: (i) the relationship between foreign ownership and the firm; (ii) cross-country differences found in the World Bank Enterprise Surveys; and (iii) implications of these differences for the local economy based on what is known.
(which in theory reflects productivity and hence wages), and the presence of other women in the household.<sup>7</sup> To explore the role of values in shaping women’s labor market decisions, we use two complementary data sources. The first is the Arab Barometer, which measures political, societal, and religious attitudes. The questionnaire distinguishes “Employed,” “Inactive,” and “Unemployed” individuals. While it contains basic demographic characteristics, it does not include some important data, such as household size, region, rural/urban household, or occupation. Importantly, it includes a question asking the level of agreement with the claim that “A married woman can work outside home if she wishes,” which we will use as a measure of the respondent’s attitudes toward women’s work. Interviewers conduct face-toface interviews in the respondent’s residence. There are four waves that include Morocco that are available: 2006-2009, 2012-2014, 2016-2017, and 2018-2019. The first three waves have a sample size of around 1,000 observations. The fourth one contains 1,560 women aged between 18 and 64.<sup>8</sup> The second data source is the World Values Survey, which aims to understand changes in beliefs, values, and motivations of people throughout the world. Morocco is included in three of the waves—2001, 2007, and 2011—each of which has about 1,200 observations, equally split between men and women, for a total of 3,648 individuals aged 18 or older. Questions measure cultural values, attitudes, and beliefs about diverse variables (such as gender, family, religion, poverty, education, health, and security). The surveys contain a labor status variable

> 7 Verme et al. (2016), facing the same limitation, argue that in informal economies earnings play a less relevant role in shaping labor supply decisions than in formal economies.

> 8 The unavailability of the
into our dataset. The tariff rates are shown in Table 4. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1% of GDP, which is consistent with collected revenues in Armenia.<sup>22</sup> Given that Armenia participates in preferential trade areas with the other CIS member states, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from CIS countries at zero. We then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the collected tariff rates for the regions in our model where positive tariff rates apply. The resulting adjusted tariff rates are also reported in Table 4.

> 21 http://www.armstat.am/file/article/ft_2nish_07_14.pdf 22 For the year 2008, aggregate data from Armenia show that tariff collections are 1% of GDP.

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online appendix

**Administrative use of EBM** The primary purpose of EBM is to support the self-enforcing design of the VAT (Pomeranz, 2015). Through the creation of a paper trail, EBM increases the information with which the RRA can validate declarations and undertake audits. In Rwanda, VAT input credits must be supported by EBM receipts, creating an incentive for the buyer to both request and associate their TIN with a purchase receipt. The nature of also creates sources of bias in our data; firms may seek to evade tax by over-reporting their taxable inputs or under-reporting their taxable output. Similarly, firms may under-report their turnover by neglecting to issue an EBM receipt to a (non-VAT) buyer.

# **A.1.1 The EBM Receipt**

As described in Section 2.1, we employ electronic billing machine data from EBM devices in Rwanda to isolate product prices and sales. We present a sample EBM receipt in Figure A1. EBM II data is directly transmitted to and securely stored as data tables. We leverage the following variables from the data:

- The price of the product

- The quantity of the products sold

- Units can be integers, or fractions (e.g., 0.200 of “Gouda cheese”)

- Negative unit values are often used by businesses to capture cancellations (note “VOID”)

- A text description of the product (e.g., “Plain Bread”)

- The taxpayer’s identification number (TIN), and the SDC used to issue the receipt

A3
a stronger impact in troubled times, when investors are more anxious, support this interpretation (Garcia, 2013).

This paper relates to the growing body of research investigating the link between the news media, investor sentiment, and asset prices, in particular Tetlock (2007) and Garcia (2013). We contribute to this literature in several ways. To our knowledge, we are the first to assess the link between news sentiment and equity returns in a large sample of advanced countries and emerging countries using a large dataset of news.<sup>2</sup> Extending the analysis to other countries allows us to isolate the contribution of global sentiment and highlight its differential effect. Finally, we are the first to assess the effect of sentiment shocks on high frequency capital flows data. Beyond validating the results obtained on prices, our results cast a light on _who_ are the investors most sensitive to sudden changes in sentiment. More generally, we are closely connected to the vast empirical literature on investor sentiment that has focused on how to measure investor sentiment and to quantify its effects on a variety of financial market outcomes (See Baker and Wurgler (2007) for a review). With respect to that literature, we confirm the value of media text (and tone) in in capturing investors’ sentiment.

> 2 For instance, Garcia (2013) and Tetlock (2007) use one column in one newspaper per day to capture US news sentiment, representing roughly 30.000 and 3.000 articles respectively. For the US only, we use 1.8 million articles.
<sup>11</sup> To calculate this, we divide the per-student gains by the per-student costs, so the figure shows LAYS gained per student per $100 spent per student. Typical spending in education systems ranges between $208 per student in Sub-Saharan Africa to $7,908 in East Asia in primary school in terms of 2013 PPP USD (Bashir et al. 2018). Therefore, cost-effectiveness per $100 per student is a relevant unit for many systems, along the lines of status-quo spending benchmarks, even at the lower tail of spending. Of course, the share of overall investment that $100 represents will depend substantially on context.

The top performers, ranked by mean effect size, are: targeted information campaigns on benefits, costs and quality; interventions to target teaching instruction by learning level rather than grade (such as “Teaching at the Right Level” interventions and tracking interventions); improved pedagogy in the form of structured lesson plans with linked materials and monitoring (which includes combination interventions such as Tusome in Kenya); community involvement in school

> 11In Appendix C we complement our data with data from the 2023 GEEAP updated report (Akyeampong et al., 2023) and show cost-effectiveness results in Figure C1.

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0<br>.6<br>CCDF<br>.4 Difference<br>-.1<br>.2<br>0 -.2<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C7. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Public assistance indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C7.1. FOSD: Counting Vector

<!-- Start of picture text --> C7.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1 .15<br>.8 .1<br>.6<br>.05<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.05<br><!-- End of picture text --> C7.3. SOSD: Social Sustainability Index <!-- Start of picture text --> C7.4. Rank correlations<br><!-- End of picture text --> 54
with MFN tariff rates at the eight digit level taken from

the website of the Kenyan government: <u>www.kra.go.ke/customs/customsdownloads.php. These tariff</u> rates were then aggregated to the sectors of our model, using simple averages.

We obtained data on the total taxes on imports and the total value of imports and took the ratio to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent.<sup>23</sup> That is,

> 22 To avoid double counting we exclude from COMESA the members of the East African Customs Union. 23 Economic Survey (2006, pp. 103, 115).

22