rafmacalaba/data-use-mentions-extended huggingface dataset:
a straight-through processing basis, risks are concentrated in two areas. The first involves identifying and enrolling investors; the second occurs at the time of purchase.

# **Compliance with “know thy customer” rules**

The Treasury appoints participating dealers as agents to enroll retail investors. The account opening procedure follows standard bank account openings in that the applicant has to appear before the enrolling bank officer and bring suitable identification. The entire application process is done at a bank branch, with the information created and committed on an internal network.

Compliance with “know thy customer” rules is still rooted in physical identification. The Philippines does not have electronic identity databases that would allow banking or investment accounts to be created through electronic verification. Although physical identification may be tedious, it makes it harder to create phantom identities and accounts.

Electronic documents carry all the terms and conditions underpinning the program. These documents are digitally signed by the bank, and copies are stored with the Treasury and the bank. The Treasury needs copies because of its registrar functions, which include acting on ownership-related 41
including night-time lights, air pollution, and EVI (a vegetation index) measured from satellites, Google mobility data, and the popularity of Google search of certain keywords. These are listed in Table 2. Summary statistics of real-time indicators for wave 1 and all waves in 2020 are reported in Appendix Table 2A and 2B, respectively.

The measures of night-time lights, air pollution, and EVI are all obtained from Google Earth Engine. The night-time lights data are taken from the VIIRS monthly composites produced

> 4 Each survey wave is assigned to a unique month.

8
(with the exception of upper arm circumference in the baseline) tended to be lower than the population mean using the WHO standard (i.e., have negative _z_ -scores). All of these measures decreased between the baseline and the endline, reflecting the aging of the cohorts and the growth faltering that occurs during the first two years of life (Victora et al., 2010). There are no statistically significant differences in the anthropometric measures between the treatment and control groups.

# _8.9 Child Development_

Both the baseline and endline surveys included an “Ages and Stages” questionnaire, which measures child development across five domains: communication, gross motor skills, fine motor skills, problem solving, and personal-social development. At the baseline this questionnaire was administered to parents or caregivers of children ages 3 – 24 months. In the endline survey it was extended to children up to 60 months of age. This report focuses on the communication, motor skills, and personal-social development. These measures are of interest because a child’s health status might affect his or her development. The questions asked about each child were selected according to the child’s age in months. To make a comparison, the child development index for each skill is standardized by calculating _z_ -scores.<sup>19</sup> Table 8b shows greater improvement in communication (significant at the 10 percent level) for children in treatment communities.

To summarize, a comparison of raw outcomes in treatment and control communities shows greater toilet construction in treatment communities and declines in open defecation. In line with this, there is evidence of decreases in diarrhea prevalence in treatment communities, relative to control communities. The prevalence of mucus or blood in the stool has also decreased. Health outcomes further along the causal chain appear to have been largely unaffected by the TSSM program.

# **9. ESTIMATION STRATEGY AND RESULTS**

In this section, we estimate the “intention-to-treat” (ITT) of program impact. The ITT
# **Figure 2: Study Timeline**

<!-- Start of picture text --> First cases of Ebola in Outbreak<br>Sierra Leone, schools reaches Sierra Leone<br>EBOLA close peak declared Ebola-free<br>(May-Jun 2014) (Dec 2014)<br>OUTBREAK (Mar 2016)<br>Ebola reaches all Schools<br>14 Districts Reopen<br>(Oct 2014) (Apr 2015)<br>2013 2014 2015 2016<br>DATA Census Baseline survey ELA Monitoring and Endline survey<br>COLLECTION (Oct-Nov 2013) (Feb-May 2014) Community Leader (Feb-May 2016)<br>surveys<br>(Jun-Oct 2015)<br>Clubs established, Vocational training + Vocational training<br>ELA life skills training starts microcredit round 1 round 2<br>INTERVENTION (Aug 2014) (Q1 2015) (Q1 2016)<br><!-- End of picture text -->
significantly with the percentile – i.e., higher percentiles have significantly higher inflation rates.

Third, the difference between the inflation rates of the richest and the poorest is of a significant magnitude. For example, in Brazil (1988-1996) the difference between the inflation of the highest and lowest quintiles was close to 7 percentage points per year. In Colombia (19972003), Mexico (1996-2002) and Peru (2001-2003), the inflation differential was smaller but still noticeable, at .5-.7 percentage points per year. Similarly, the difference between average CPI inflation and the inflation of the lowest percentiles is not negligible either for the same periods – some .3 percentage points per year in Peru, .4 in Mexico and Colombia, and 4 percentage points in Brazil.

For all the spells under consideration, Table 8 summarizes the inflation rates by quintile, and their respective deviation from standard CPI inflation. For the most part, the table conveys the same message as Figure 1 above. In the episodes considered, inflation has consistently been anti-rich, with poorer households experiencing lower inflation rates than richer ones. The only exception was Mexico in 1994-1996.

For comparison, Table 8 also reports the growth rate of real household consumption by quintile, computed using each percentile’s consumption price index. This provides a rough

> 6 Due to unavailability of expenditure surveys for the exact years on which official CPIs are based, we use the expenditures from the survey closest to the CPI base year in each country in order to mimic the base-period households’ baskets. In particular, we use as base periods 1996, 1997, 2002 and 1995 for Brazil, Colombia, Mexico and Peru respectively (the official base periods are Dec 1993, Dec 1998, Jun-Dec 2002 and 1994 respectively).

7
fixed effects, while εijt is the error term. Firm’s GVC participation here is defined as the average of its intermediates’ import share and the exporting intensity. The export intensity, in turn, is defined as the ratio of exports over sales.

Table 11 shows that sectoral FDI has a positive effect on firm-level labor productivity, and this effect is weakened by the GVC participation of the firm. This somewhat mirrors the

> 5 For the measurement of FDI here, we tried both number of FDI projects and the dollar value because the fDi Markets FDI project data is more accurate for recent years than the dollar-value FDI data, which is more of an estimate for recent years. The results are qualitatively similar. Moreover, our current data goes to 2017 so revisions have likely ameliorated this problem at the time of writing.

> 6 This control variable is important because it captures the direct firm-level effect of being the recipient of foreign financing—and at the same time whether the firm is mostly a foreign-owned affiliate (share over 50 percent) or a local firm. Recall that the key result of our regressions is the effect of the indirect ‘spillover’ effects. Direct financing to one firm in that city/sector, for worker training or machines for example, is thus captured by the foreign ownership share variable.
involved consultations with statistical offices across several countries, evaluation of questionnaires that have previously been used to survey informal businesses, and application of the extensive experience of the team that implements the World Bank Enterprise Surveys (WBES) of formal firms. This trial-and-error learning process led to the following principles when designing the appropriate questionnaire.

Informal businesses should not be interviewed using questionnaires designed for registered businesses. The primary reason for this is that terminology and definitions used in such questionnaires reflect experiences of legal, registered entities, and may not be suited for informal businesses. Accounting and technical terms commonly used in formal business surveys, such as 'equity', 'income statement', or capacity utilization' must be avoided. It is important to recognize that informal businesses operate in ways that are fundamentally different from formal businesses. Therefore, not only the terms used in the questionnaire, but also concepts need to be adapted to ensure that pertinent measures are being collected. For example, when measuring labor inputs, it is important to capture non-remunerated labor or support from family, so survey questions must be worded accordingly and/or added.

The interviewees of the Informal Sector Enterprise Surveys tend to have lower levels of education than the typical respondent of a formal business survey, and some may even be unable to read or write. Consequently, direct yes/no questions are preferable, and commonly used aids for interviewing, such as 'show cards', should be avoided. The language used as well as the question structure should be simple, straightforward, and able to be read quickly. This means that questions must be formulated in ways that require little or no explanation. Questions that require great cognitive effort, such as those involving counterfactuals or vignettes scenarios, are avoided.

To maximize the effectiveness of the questionnaire, questions requiring mathematical computations or complex numerical responses should be minimized, and those requiring responses in percentages should be avoided. Importantly, since many informal businesses may not keep any written accounts of their activities, it is not advisable to
> 46Since HIES is a cross-sectional survey, I observe single-year measures of the earnings. Such short-run measures of workers’ earnings may include both measurement error and transitory fluctuations in earnings. Thus, I select a period to observe the representativeworkers when their earnings are most likely to accurately reflect permanent earnings, ages 30-50 (the prime earnings years). _t_ = 0 is related to age 30 and _T_ = 24 is related to age 54.

> 47For example for future income I consider (i) median/average of (all/treated) workers’ income in the last year of sanctions (year 2013), and (ii) median/average of (all/treated) workers’ income before the sanctions (year 2006).

22
, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses.

Table D.20. Cooperatives

||Has a|Producer belongs|Commercial producer|Subsistence producer|Producer belongs|
|---|---|---|---|---|---|
||cooperative|to a cooperative|belongs to cooperative|belongs to cooperative|to association|
||(1)|(2)|(3)|(4)|(5)|
|Guerrilla control|0.00545|0.00226|0.00879|-0.00301|-0.00480|
||(0.00354)|(0.00960)|(0.0158)|(0.00429)|(0.00579)|
|Observations|929|2,400|2,400|2,400|2,400|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.00900|0.0520|0.0750|0.00800|0.0110|

_Note:_ The table presents the results of equation 1 for outcomes related to cooperatives. Information was taken from the Agrarian Census of 2007. The unit of observation is at the census tract level. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses.

90
# **3.2.1 Electricity prices**

To construct electricity prices, we combine EAIM information on plant-level electricity expenditure with state-level data on number of users, values and volumes of electricity sales, and state-month-specific electricity tariffs from the CFE. First, using the information on value and volume of consumption at the tariff-municipalitymonth level from Secretar ́ıa de Energ ́ıa (Ministry of Energy, SENER), we calculated average tariffs paid as value over volume (pesos/KwH) and average sales as sales over number of users (pesos/user). Secondly, we analyzed this information and defined a lower and an upper limit of electricity bills for each municipalitytariff level. On the basis of these lower and upper limits, we assigned a tariff level to each establishment based on its expenditure on electricity. We assigned four different electricity tariffs (two for medium-sized companies and two for large companies). Once we defined the tariff-level that each plant faces, we used that to compute a weighted price using the plant-specific monthly production shares from the EMIM in 2009 as weights. For firms that entered the survey after 2009, we used the first year in which the firm appears to compute weights.<sup>26</sup> where _Pit_<sup>_e_istheaverageelectricitypricethatplant</sup><sup>_i_facesinyeart;</sup><sup>_yim_0isthe</sup> revenue of _i_ in month _m_ in year 0 and _Pimt_<sup>_e_isthepricethat</sup><sup>_i_facesinmonth</sup><sup>_m_</sup> and year _t_ .<sup>27</sup>

# **3.2.2 Fuels prices**

To construct plant-level fuel prices, we followed four steps. First, using consumption data from SENER, we identified the four main types of fossil fuel used in manufacturing. These are natural gas, diesel, petroleum coke, and coal and account for 85% of other fuels consumption, and 68% of total energy consumption

>
32 Table 3. Expenditures on subsidized food by expenditure quintile group in urban Egypt, 1997

|Subsidized food|Mean per<br>Lowest<br>Quintile<br>[1]|capita expe<br>Second<br>[21|nditures pe<br>Third<br>[3]|r week (inL<br>Fourth<br>[4]|E)aon subsi<br>Highest<br>Quintile<br>[5]|dized food by:<br>Average|
|---|---|---|---|---|---|---|
|Baladi bread|0.96|0.94|0.95|0.86|0.73*|0.88|
|Baladi wheatflour|0.10|0.13|0.12|0.06|0.09|0.10|
|Edible oil|0.08|0.10|0.12|0.09|0.07|0.09|
|Sugarb|0.08|0.09|0.11|0.10|0.10|0.10|
|Total expenditures on subsidized|||||||
|food byquintile|1.22|1.26|1.30|1.11|0.99|1.17|
|Percent of total expenditures<br>on subsidized food by quintile|20.75|21.43|22.11|18.88|16.83|100.00|
|Percent of total population<br>in quintile|20.00|20.00|20.00|20.00|20.00|100.00|

Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,078. Urban population is divided into quintile
and income. A similarly stable, or even negative, trend was observed for gender attitudes relating to these categories.

This persistence of gender disparities and conservative attitudes is particularly remarkable when contrasted with the sustained economic development observed in the last two or more decades in most South Asian countries. Gender equality in the region does not seem to be an automatic byproduct of economic development, indicating that restrictive gender norms are constraining progress in gender outcomes. Stylized fact 4 highlights exactly this. Attitudes are related to gender outcomes, and conservative gender attitudes are linked to worse gender outcomes. However, gender norms are still poorly understood and measured, generally, with inadequate proxies. Attitudes may not always correspond with the norms prevalent in society, and do not adequately indicate the strength of a norm (which can potentially override individual attitudes in influencing behavior). To properly address these normative barriers, future research should be focused to: (i) measure gender norms more systematically, (ii) identify causal links between norms and outcomes, and (iii) inform norm sensitive policies and interventions.

# **7. References**

- <mark>Abramsky, T., Watts, C. H., Garcia-Moreno, C., Devries, K., Kiss, L., Ellsberg, M., ... & Heise, L. (2011). What factors are associated with recent intimate partner violence? Findings from the WHO multi-country study on women's health and domestic violence. BMC public health, 11(1), 1-17.</mark>

- <mark>Acemoglu, D., & Jackson, M. O. (2017). Social norms and the enforcement of laws. Journal of the European Economic Association, 15(2), 245-295.</mark>

- <mark>Afridi F., Dinkelman T., Mahajan K. (2018). Why are Fewer Married Women Joining the Work Force in India? A Decomposition Analysis over Two Decades.
accept government’s right to make people pay taxes. These results suggest that the more effective and fair the government, the more likely that its population will accept the government’s right to make people pay taxes, my indicator for legitimating beliefs. With the available African data, I cannot identify how respondents’ beliefs about taxation translate into actual compliance behavior.

# **7.1.1 Control Variables**

Although I control for several socio-demographic variables, very few are significant at the p _<_ 0.05 level. Gender, age, ownership of a radio and television, use of the internet, and employment status are not significant at the p _<_ 0.05 level. Respondents who live in an urban rather than a rural area are more likely to be willing to defer to the tax department (p _<_ 0.05), as are respondents who use a mobile phone every day (p _<_ 0.05). Respondents who have experience with paying property taxes or fees (p _<_ 0.001) are also more likely to be willing to defer to the tax department. Yet, there is no association between experience with paying income taxes and the willingness to defer to the tax department. Although including random intercepts for ethnicity improves the model’s fit, the intra-class correlation for ethnicity is quite small. Ethnic affiliation only explains 1 percent of the variation in deference to the tax department.

Another step in the quantitative analysis is to test whether including country-level indicators improves the model’s fit beyond individual socioeconomic characteristics and perceptions of government effectiveness and fairness. I estimate coefficients for each of the following indicators for 2007: GDP per capita; population size; government consumption as a share of GDP; Freedom Houses measures of civil liberties and political rights; and World Bank Governance Indicators, government effectiveness and corruption. Not one of these indicators is significant at the p _<_ 0.05 level when I include the variables
classmates, or where peers had negative returns prior to the course, have the highest returns in their first year of trading–they outperform new investors from other courses and experienced students from their own classroom. Overall, the results are consistent with the hypothesis that selective communication encourages more market entry among uninformed investors.

To directly test how communication in the classroom is related to market entry and performance, we asked former students if they had any discussions about stock trading with classmates during the course.<sup>8</sup> Using the sample of survey respondents, we confirm our main findings. Students that report investment conversations with classmates in courses where peers had positive returns are more likely to start trading, but they underperform in their first year after the course.

A potential explanation for low returns among new investors might correspond to mean reversion in stock prices.<sup>9</sup> For example, if individuals systematically buy the best-performing stocks of their peers and large price increases precede reversals, new investors would display poor performance. To examine this possibility, we use the sample of market entrants and study the relation between an individual’s stock selection and the stock selection decision made by experienced classmates. We document a strong positive relation between the stock purchases of new investors and purchases of experienced students after the course. As new investors try to look for investment opportunities, we show that instead of following past trades from classmates or stocks with high returns in the peers’ portfolio prior to the class, they seem to select stocks in which peers are making new purchases.

Peer effects in stock selection have been widely documented in multiple social environments and are not surprising in our setting, but our evidence sheds new light on the role of information transmission bias. The mechanism is summarized as follows. Recent positive returns from experienced classmates attract new investors. Such outcomes, however, are the result of portfolios with high idiosyncratic volatility. Due

> 8 Details of the electronic survey are presented in Section 3.

>
peated sampling context.<sup>12</sup> As discussed, the model based Stress scenario, notwithstanding its benefits, is susceptible to the assumed parametric structure that is imposed on the data. We thus also propose an alternative non-parametric approach to construct the Stress scenario next.

# **3.3. Stress Scenario Based on International Experience**

There can exist circumstances when it is not possible or acceptable to employ a model based Stress scenario, due either to the lack of available data to formulate a sufficiently adequate statistical model, or due to a less favorable view of statistical models inside the policy environment in general. With this in mind, we propose an alternative model-free approach, which we label ” _Stress International_ ” (or _Stress Int._ for short). This approach looks at the actual historical evidence of countries that experienced a financial crisis in the past. Crisis periods are identified using the banking and currency crises dating database compiled by Laeven and Valencia (2008).<sup>13</sup> Given the crisis dates, we extract real GDP growth, CPI inflation, lending rates and changes in the exchange rate from the World Bank and the IMF’s IFS databases for the countries (and years) that went through a financial crisis, and then look at the values of these macroeconomic variables during the crisis years. There are a total of 161 countries,

> 12Note that the construction of the Stress scenario based on the adverse forecast confidence interval in (3) above relies on the assumption of _Ut_ being multivariate normal. This may or may not be appropriate. Should it not be appropriate, one can resort to re-sampling techniques such as bootstrapping to compute, say, 1000 bootstrapped forecast values, and then use the value corresponding to the 1<sup>_st_</sup> percentile as a stress value for real GDP growth and the 99<sup>_th_</sup> percentile for the lending rate.

> 13Note at this point that there are other