rafmacalaba/data-use-mentions-extended huggingface dataset:
designed/implemented software interventions is echoed in other studies (Banerjee et al. 2007; Carrillo, Onofa, and Ponce 2011). In a meta-review of all rigorously evaluated education interventions, McEwan (2015) finds the largest effects for interventions involving instructional technology. In a similar review, Conn (2014) considers “pedagogical interventions” to be the most effective subset of education interventions, and many of these pedagogical interventions are in fact computer-assisted learning programs.

In this paper, we examine the potential effectiveness of digital technologies for student learning by focusing on non-interactive eReaders. The experiment is explicitly designed to disentangle the content effect from the reciprocity effect. Given that the eReaders are non-interactive, we are only able to examine a very limited technology enabled pedagogical intervention with no room for active feedback or targeting of content. We test the impact of providing eReaders with or without curriculum content. In a third arm, we also examine whether provision of other remedial content can usefully enhance learning outcomes. The paper measures the impact of digital technology on both learning and participation in the next level of secondary schooling.

We conducted a nested randomized control trial (RCT) in which eReaders with different types of content were provided to Grade 8 students in Lagos, Nigeria. A study sample of 497 students were randomly assigned to one of the following groups - a control group that received nothing and three treatment groups that each received eReaders with either: a) only non-curriculum reading material; b) non-curriculum reading material _and_ curriculum textbooks; and c) non-curriculum material, curriculum textbooks _and_ supplementary curriculum-relevant material. We examine short-term student outcomes after a six-month exposure to eReaders and longer-term outcomes obtained from a student tracking survey 2.5 year after the experiment.

For the purposes of benchmarking the impact of eReaders on school participation against a nontechnology intervention, we implement a low-cost ‘information’ intervention (Jensen 2010). A random sub-sample of treatment students was assigned–orthogonal to eReader randomization–to receive an information intervention. Students assigned to the information treatment group
. for NB in NL vil.|0.125|12.583|-0.758|-0.199|-0.320|-0.340|
|P-val of B = NB in L villages|_0.000_|_0.525_|_0.552_|_0.118_|_0.759_|_0.981_|
|Model|Tobit|Tobit|OLS|OLS|OLS|OLS|
|Percentage of censured observations|0.597|0.038|||||
|Number of households|828|547|920|828|381|299|

Note: This table reports the estimation of the following specifications: _y ij = α ij + β 1 Beneficiary ij + β 2 Lottery j + β 3 Lottery j *Beneficiary ij + ε ij_ , where _i_ indexes the individual (household) and _j_ indexes the village. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. _Lottery j_ is a dummy that takes value of 1 if beneficiaries in village _j_ were chosen using a lottery. The models presented here are not adjusted for any baseline covariates. Column 1 reports a percentage of production that is sold. Column 2 reports on log of total household income, including non-agricultural activities. Dependent variable in Column 3 is constructed using the households reported worry for scarcity of food, number of meals per day, size of meals, and consumption of least preferred foods; higher values represent higher food security. The dependent variable in all columns 4-6 is a standardized index of agricultural outcomes and it was constructed base on questions asked on follow-up household survey. The index components are (i) percentage of production that is sold and (ii) food security index, where higher values represent higher food security. In Column 4, we use the sample of households interviewed at follow-up with non-missing values of the dependent variables in Column 1 and 3.
0.352|0.109|0.312|
|HH has internet|0.077|0.266|0.076|0.266|
|HH has calculator|0.826|0.379|0.811|0.392|
|Aspires to attend college|0.385|0.487|0.315|0.465|
|Studies English|0.117|0.321|0.098|0.298|
|_School Characteristics_|||||
|Student teacher ratio|25.203|7.363|24.927|7.251|
|Has computer|0.485|0.5|0.497|0.5|
|More than 1 grade per classroom|0.26|0.439|0.27|0.444|
|Proportion of teachers with university|0.313|0.274|0.304|0.278|

Source: Authors calculations: ENLACE 2007-2008 merged with context questionnaires and school characteristics.

# _B. Descriptive variables on outcomes_

Table 3 shows the proportion of students enrolled after sixth grade, which we take as a baseline for our study. We present the proportion enrolled in seventh grade, ninth grade and twelfth grade, by gender and for the samples of schools in medium to very high marginalized areas and for students in indigenous primary schools during sixth grade. The Table shows enrollment, proxied by the proportion of students taking the ENLACE in that year. According to the Table, about 60% of males and 64% of female sixth graders are captured in the seventh grade ENLACE applied in the next academic year and about 30% of males and 32% of females are captured as taking the twelfth grade ENLACE. As mentioned before, this is an underestimate of enrollment 16
from BRAC|0.007|25.580|
||(0.006)|(37.423)|
|9. Mediator: received help from GDBC committee|0.005|-3.210|
||(0.003)|(17.737)|
|10. Mediator: received microcredit from BRAC|-0.002|-60.597**|
||(0.004)|(29.875)|

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

**Notes:** The table shows the results from mediation analysis following Gelbach (2016). In each panel, the first row shows the ITT impact based on specification (1) estimated with OLS, corresponding to the estimates in Table 3 (in the paper) columns 5 and 6. The second row shows the ITT impact once the mediators are controlled for?these are having received enterprise support (asset, training and cash transfer) from BRAC, having received any other type of support from BRAC (e.g. health or education support, sanitary latrine, tubewell), having received sanitary latrinne or tubewell from BRAC, having received health support from BRAC (for medical care of any household member), having received any help with school expenses from BRAC, having received any help with transportation costs from BRAC, having received any support from the GDBC (Village Poverty Alleviation) committee and having received any microcredit from BRAC. Row 3 shows the total mediated effect, and rows 4 - 10 show the contribution of each mediator. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the
In 2005, 6.1 million persons had been displaced in Sudan (including areas that are now part of South Sudan).<sup>8</sup> In 2019, the year the survey data used in this paper were collected, the number of IDPs was still estimated at 1.87 million, with 446,441 of these located in North Darfur. In that year, 54,028 people returned to their homes in North Darfur (UN OCHA, 2019), but lack of protection, risks of gender-based violence, political uncertainty, and lingering disputes over land still prevent most IDPs from returning (Elzarov, 2019).

Since 2017, the numbers of new displacements have been relatively low, but the manifold challenges discussed above make it difficult to build sustainable livelihoods for IDP and non-IDPs, leaving many extremely vulnerable and strongly dependent on aid (UN OCHA, 2020). Many IDPs are also vulnerable to mental health issues, such as posttraumatic stress disorder, which constrains livelihood strategies available to IDPs even further (Hamid and Musa, 2010).

The majority of displaced households arrived at their destination with very little and needed to build new livelihoods, which in some contexts led to a diversification of livelihood activities (Saeed Ali, 2014). Furthermore, the structure of many families changed due to displacement, which also shapes livelihood choices and sometimes forces women to take on new roles (Young and Jacobsen, 2013).

In addition to factors characterizing the displaced population (displacement characteristics), existing reports emphasize that displacement status also plays a key role for the livelihood strategies of the displaced. Compared to their places of origin, employment opportunities might increase (George et al., 2019), but institutional and safety challenges prevail in many places, which often leaves women struggling to find decent employment and having to work informally, without any protection and safety (Young et al., 2005; George et al., 2019). Thus, identifying and addressing gender-specific issues is considered an integral
respondents who moved to areas that, while administratively recognized within Tanzania as cities, have a population below 500,000 (Arusha, Tanga and Mbeya). Results do not change

> 4For Mwanza this is the sum of Nyamagana Municipal Council (363,452) and Ilemela Municipal Council (343,001). More detailed analysis using 2002 census data (Wenban-Smith and Ambroz, 2014) showed that while the municipal districts of Mwanza counted a population of 596,885, only 65% of these lived in strictly urban wards (others lived in rural or mixed wards). The current census does not yet disclose the new categorizations, but applying the same rural-urban ratio of 65% to the current census figure gives us an urban population of 456,631. This estimate lies under 500,000, but we decided to treat Mwanza as a city based on the district-level figures.

5
35 marketing models that vary by type of management structure employed to handle the project, type of products marketed, type of distribution methods used, and by retail outlets chosen as points of purchase.

Five recent reviev articles summarizing CSH experiences were cited in the introduction to this study. The most recent (Sheon, Schellstede and Derr, 1987) indicates that CSM has been tried in almost 30 countries and that about one third of these countries currently has a CSH program. Programs in Colombia, Jamaica, Bangladesh, Egypt, and Nepal covered more than twenty percent of contracepting couples in 1984 according to Binnendijk (1986). These may be overstatements, however, because they are generated from program data and not from demand studies. Because program data rely on vholesale level volumes and because of vastage and disappearance of CSH products beyond that point, the retail volumes are likely to be lower.

Social marketing programs often aim to exploit existing distribution networks to expand points of purchase and reach more potential contraceptive users. A current example of hov private networks can be used is the SOMARC project in Zimbabve, launched in 1986, seeking broader distribution of the condom. A commercial distributor, Geddes Ltd. that already distributes deodorants to supermarkets is going to distribute condoms to petrol stations, bars, supermarkets, and bottle stores.

The condom has been the most common socially marketed product, vith oral contraceptives, vaginal spermicides, injectables and IUDs following in order of number of programs that have used them. The first CSH program had its inception in 1967 in India vith Nirodh, the name chosen for the socially marketed condom. Not only has there been increasing product diversification in socially marketing attempts, but programs have tried to reach self-sufficiency and have added more brands of the same product; high and low dose pills or lubricated and unlubri-

**ml.tpsffadly** PluainJ **Paper** ('bole
Cappellari (2019) approach is based on family triads with father and two children in a family. We take the oldest two children from those families where the number of children is more than 2. To ensure that the siblings are not too far apart, we follow Bingley and Cappellari (2019) and restrict their age gap to a maximum of 12 years. The intergenerational transmission is estimated as the average of the persistence between father and the rst child, and between father and the second child in the sample. The birth cohorts are dened based on the birth year of the older sibling in a household. For the estimation of the share of intergenerational component, we do not impose the stationary distributions assumption across generations used by Bingley and Cappellari (2019) as this assumption is rejected by our data.<sup>29</sup> We also nd that the estimated share can be more than 100 percent if we incorrectly impose stationary distribution assumption within the Bingley and Cappellari (2019) approach. The estimates from the Mazumder (2008) method for the share of intergenerational component are implemented using Restricted Maximum Likelihood (REML) in a mixed eects model.

# (5) Evidence: Geography of Sibling Correlation and Evolution over Three Birth Cohorts

# (5.1) Geography of Sibling Correlation across Regions and Nations

The estimates suggest that there are substantial regional variations in intergenerational educational mobility as measured by sibling correlation. Figure 1 presents the average sibling correlation estimates for six regions of the world. The country specic estimates are reported in Table 1. The estimates suggest that intergenerational educational mobility for the 1960s-1990s birth cohorts is the lowest in the Latin American and Caribbean countries with an average sibling correlation of 0.65.<sup>30</sup> Compare this with an average of 0.41 for developed countries noted before. This evidence on Latin America and Caribbean is interesting as the countries in this region also experienced some of the highest income inequality during this period (De Ferranti (
questions and did more tests, they were not more likely to recommend the correct treatment. Another health study<sup>21</sup> found that providers with dual public‐private practices performed better in their private practice than in their public practice, suggesting financial incentives may play a role – a theme explored in a separate note in this series.

The above assumes that poor service quality is indeed the reason why getting children into school does not automatically ensure learning and why getting patients in to health facilities does not automatically

> 19 Mohanan M, Vera‐Hernández M, Das V, Giardili S, Goldhaber‐Fiebert JD, Rabin TL, Raj SS, Schwartz JI, and Seth A. The Know‐Do Gap in Quality of Health Care for Childhood Diarrhea and Pneumonia in Rural India. _JAMA Pediatrics_ 2015; 169(4): 349‐57.

> 20 Das J, Holla A, Das V, Mohanan M, Tabak D, and Chan B. In Urban and Rural India, A Standardized Patient Study Showed Low Levels of Provider Training and Huge Quality Gaps. _Health Affairs_ 2012; 31(12): 2774‐84.

> 21 Das J, Holla A, Mohpal A, and Muralidharan K. Quality and Accountability in Health Care Delivery: Audit‐Study Evidence from Primary Care in India. _American Economic Review_ 2016; 106(12): 3765‐99.

10
try of new US exporters of electronics products into the EU. Schmidt and Steingress (2019) quantify the effect of the harmonization of standards on French firm exports. Exploiting harmonization events based on a novel cross-country standard equivalences database, they show the introduction of harmonized standards increases trade through entry and higher exports by existing exporters.<sup>9</sup> Our contribution to this literature is to show the heterogeneous effect of provisions for the harmonization of SPS in PTAs on firm exports, namely the stronger benefit for exports of small firms relative to large firms.

A third, smaller literature examines the effects of regulatory trade barriers on firm-level trade. Fontagné et al. (2015) estimate the impact of SPS concerns raised in the WTO Committee on SPS - which are SPS measures perceived as de facto trade barriers - on French firms’ exports using customs data. They show that SPS concerns reduce firms’ exports at the extensive margin (the probability of exporting) and at the intensive margin (export value) but increase firms’ export prices. The negative impact of SPS concerns is lower for larger firms exporting to multiple destinations. Fontagné and Orefice (2018) focus on TBT concerns raised in the WTO committee on TBT and show they have a negative effect on French firms’ export participation that is magnified for multi-destination firms able to divert their exports towards destinations with no TBT. Fernandes et al. (2019) estimate the effect of product standards (on pesticides) on firms’ export decisions using customs data for 42 developing countries. They show that an increase in the stringency of product standards in the destination country, relative to the exporting country, lowers firms’ probability of exporting, deters exporters from entering new markets, and fosters exit from existing markets. Smaller exporters are more affected in their market entry and exit decisions by the relative stringency of standards than larger exporters. Macedoni and Weinberger (2019) use a large panel of countries to show that countries have fewer exporters selling to destinations with larger numbers of standards
Preliminary demographic analysis reveals that over a quarter of Armenian households are composed of a male and female couple with children, with other adults also living in the household (Fuchs and Gonzalez 2022). Most single-headed households – where the self-identified household head is not living with a spouse – are sustained by females; single female-headed households account for almost one-fifth of all households.

## a. Definition of Household Types

Three typologies were tested for the fiscal incidence analysis (see Table 3). Ultimately the T3 category was used most often while T2 was used in some analyses due to sample size limitations.

# Table 3. Household Types Based on Armenia ILCS 2020

Demographics + Income (proxy for intrahousehold roles and bargaining power)

- (T1) A. (Labor) earners are a female majority • Calculated from the gender of Gender of B. (Labor) earners are a male majority household members who report household earners C. Equal number of male and female positive labor earnings in labor markets labor earners • Labor earnings include salaries and D. No (labor) earners in the household self-employed earnings; exclude incomes from agricultural sales and own consumption

- (T2) A. Larger share of female earnings • Calculated over total labor earnings Gender sustaining (>60% of household labor earnings) reported by gender of household household earnings B. Larger share of male earnings members (>60% of household earnings) • Labor earnings include salaries and

- Labor earnings include salaries and self-employed earnings; exclude incomes from agricultural sales and own consumption

- C. No earners in the household

Demographics + Income (proxy for intrahousehold roles and bargaining power) + Presence of children and elderly (proxy for care needs and domestic responsibilities)

- (T3) • Typology (3) of gender sustaining Gender sustaining household labor earnings household earnings • Combined with the presence of + presence of retirees who may contribute to children and domestic tasks and care elderly • Combined with the presence of dependent household members:

- Calculated from gender and age of all
time, suggesting substantial within-PSU variation (see appendix A). Thus, the comparisons between box and household survey data are most accurately interpretable with summary statistics at a higher level of aggregation.

**Table 2: Descriptive Statistics from Survey and SSBs**

||Al|l day||Bet|ween 1|8:00 an|d 22:00|
|---|---|---|---|---|---|---|---|
|Mean|Med.|SD|Obs.|Mean|Med.|SD|Obs.|
|**(A) Unconditional**<br>**(A1) SSB Obs level:** _psu-by-month, averaged across b_<br> <br>|_oxes with_<br>|_in each_<br>|_psu_<br>|||||
|One if outage<br>0.88|1.00|0.31|1776|0.77|1.00|0.41|1776|
|Total hours/day<br>2.19|0.52|3.78|1776|0.32|0.04|0.62|1776|
|<br>**(A2) SSB Obs level:** _psu-by-date, averaged across bo_|_xes within_|_each ps_|_u_|||||
|One if any outage<br>0.28|0.00|0.45|96644|0.14|0.00|0.35|96644|
|Total hours/day<br>2.29|0.00|6.20|96644|0.34|0.00|1.05|96644|
|**(A3) Household Data Obs level:** _household-by-m_|_onthly-ro_|_und_||||||
|One
</sup> _ω_ is a vector of estimated coefficients on the product characteristics themselves. _β_ ,<sup>_−→_</sup> _β_ and<sup>_−→_</sup> _Z_<sup>_k_</sup> _j_<sup>areasde-</sup> 30In some instances, the PR data report trade flows arriving from an origin, even though our corresponding data shows no exports of that product from that origin in that year. In these cases we add the PR trade flow to total exports, and include a dummy variable indicating that we made this transformation.

22
In scaling debt by GDP, this approach implicitly focuses on the concept of the debt burden, which captures the ability of borrowers economy-wide to service their debt. In principle, a sharp increase in the debt burden, as measured by the debt-to-GDP ratio, could mechanically reflect: an output collapse; deflation; an exchange rate depreciation that raises the domestic currency value of debt; or a large increase in borrowing. Regardless of the underlying cause, a rise in the debt burden makes it more challenging for the economy to service debt and makes the debt burden more likely to become a source of financial stress.

In practice, output contractions were a source of increased debt-to-GDP ratios in a minority of the rapid debt accumulation episodes identified here (one-third of government debt episodes and two-fifths of private debt episodes). Sharp currency depreciations (in currency crises) have been associated with larger debt buildups during debt accumulation episodes, but such depreciations have typically happened before (usually two years before) debt peaks. The increase in debt during the year of the currency crisis has accounted for only between one-tenth (private debt episodes) and one-quarter (government debt episodes) of the total debt buildup during episodes involving currency crises.

Episodes associated with financial crises . Financial crises (banking, sovereign debt, or currency crises) are defined as in Laeven and Valencia (2018). Data for currency crises are extended to 2018 using the same methodology as Laeven and Valencia (2018).<sup>3</sup> A rapid debt accumulation episode is identified as having been associated with a financial crisis (of any type) if such a crisis occurred at any point between the start of the episode and the year of the episode’s peak debt-to-GDP ratio or within two years of the peak debt-to-GDP ratio. Appendix 3 lists financial crises in EMDEs. Some debt accumulation episodes were associated with multiple financial crises. For example, Mexico’s government debt accumulation episode of 1980-87 spanned a banking crisis in 1981, and currency and debt crises in