rafmacalaba/data-use-mentions-extended huggingface dataset:
individuals. Manley et al. (2013) undertake a systematic review and meta-analysis on conditional and unconditional cash transfers and child nutrition and come to similar conclusions.

A relevant concern is the existence of intra-household inequality. The unitary model of the household (characterized by a single utility function) has found little support empirically, and various alternatives have been proposed (as reviewed by Chiappori and Mazzocco, 2015, and Baland and Ziparo, 2017). These alternative models permit new sources of inequality within households, such as in reservation utility levels. An extensive literature details intra-household inequalities in resource allocations and outcomes (as reviewed in World Bank 2012). There are two direct policy implications: targeting poor households may well miss some significantly disadvantaged individuals and targeted households may not allocate the benefits to the neediest within the household. This paper addresses the first issue.

It is well recognized in principle that household-level consumption or income-based measures do not allow for inequality within the household. There is also (largely qualitative) evidence that certain individuals are poor and/or vulnerable, but do not live in households that would normally be considered poor and so are hidden from view in standard data sources on poverty. Differentiation between men and women has been widely documented in human capital, legal protection, constraints stemming from social norms, roles and responsibilities, and control over resources (Ezememari et al. 2002). For Africa, there is evidence that household shocks affect men and women differentially, with women bearing the brunt of negative shocks (Rose 1999; Dercon and Krishnan 2000). Such differentiation can be expected to have consequences for measures of poverty and inequality. In an important early example, Haddad and Kanbur (1990) find that such measures for the Philippines are appreciably underestimated using standard household-level data, although the “profiles”—the comparisons of these measures across subgroups such as urban and rural areas—were found to be quite robust. Using a survey for Senegal that (unusually) collected a relatively individualized measure of consumption, Lambert et
ten percent of a standard deviation.

Columns 3 and 4 of Table 8 check whether overrepresented areas are either poorer or more unequal than underrepresented areas. If this were true, then the higher transfers to these

> 26 The interaction term can be interpreted as the probability that two random voters vote for a party that is associated with a former non-democratic regime, because it is the probability that two people vote for the same party (the HH index) times the probability that a person votes for a party associated with a former non–democratic regime (the vote share going to this party).

> 27 Our data on government transfers covers Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Uruguay.

19
household income, and that the F-test of joint significance

> 7Our target sample size according to initial power calculations was 600. The reduced sample size reflects the early curtailing of the project by the MFI due to the onset of the COVID-19 pandemic in March 2020. Ex post power calculations suggest reasonable power for outcomes such as firm closure; but weaker power for noisier, continuous outcomes such as profits.

> 8The pre-analysis plan mentioned a sample of 124 women, of whom 63 were in the control group and 61 were in the treatment group. This was due to an administrative error in the first version of administrative data shared by the MFI, where seven women (6 control, 1 treatment) were marked as having their applications still in process when in fact the applications had been fully processed.

> 9We also conducted a short follow-up phone survey to understand the impacts of COVID-19 in April-May 2020, targeting just the firms in the RCT impact evaluation sample. The eventual sample for the phone survey included 101 of these firms. Of the 101 who participated, 65 women’s businesses were permanently or temporarily closed due to COVID-19, and hence they did not complete parts of the survey pertaining to recent business activity. This left only 36 women with measured business outcomes during the phone survey. We hence do not rely on this phone survey as a major data source for the impact evaluation.

9
else._|
||_I did some vocational training and I want to apply the skills I learned from it._|
|**_Tanza_**|**_nia_**<br>|
|<br>|_I just lost my job and I need to earn to support my family._<br>_I am a retiree and my family needs for more income. I have some savings that I used to_<br>_start my business._|
||_My business gives me a good opportunity to earn more. It does not require much capital,_<br>_plus I can be independent._|
||_My small business allows me to make some money and at the same do my house chores. I_<br>_can work at any time, whenever I want._|
|_Source_|_: Unpublished fieldwork transcripts. See World Bank 2011b and 2012, and Kweka and Fox, 2011_|

Qualitative data from fieldwork in four countries gives a more nuanced perspective, as the owners were able to speak for themselves (Box 2). They suggest that from an individual‟s perspective, both the exclusion view and the inclusion view may be relevant. In rural areas, where the alternative is either lower productivity agriculture or idleness (owing to seasonality factors, for example), the decision to start and maintain an enterprise may be a positive, albeit constrained, choice. In urban areas, starting an enterprise may be a positive decision to enter business for oneself, or it may be an alternative pursued as a second choice, in order to survive. In Rwanda, one trader reported that their economic activity was a plus for them and their families, but another expressed frustration with the limited options available within the sector. The desire to be independent, to have control over hours of work, tasks, and income - the key motivating factor cited all over the world by enterprise owners, large, small and
a mother works outside the home, her children suffer. This is slightly less agreement than the national average of 84% among Jordanian women, as measured in the World Value Survey (2018). Additionally, 44% of study participants reported believing that society judges a mother that works outside the home, and a third agreed that it would be better for women to stay at home to preserve their own value. Given that these interviews were conducted with women alone, these figures suggest that study participants themselves have views in favor of women staying at home and that they perceive society to hold even more conservative views.

**Digital Practices:** Consistent with high social media usage rates in Jordan, 85% of participants say that they use Facebook daily or weekly. The average participant estimates that 84% of her acquaintances use Facebook daily or weekly, suggesting common knowledge around the platform’s high usage rates. Despite widespread acceptance around being on the platform, only 58% of women said they use their full names on Facebook, and only 11% said they use their real pictures. When participants were asked whether they would be willing to reveal their identity on a business page to increase perceived trustworthiness online, only 64% agreed to reveal their names, and only 31% said they would be willing to appear on the page to advertise it through informational videos. These preferences from the sample are consistent with practices adopted by female users in public trading groups on Facebook, as outlined in section 2.2. Indeed, women in the sample also seem to value their privacy in virtual spaces and to conceal parts of their identities online to protect it.

> 17At the national level, 37% of Jordanian women have less than a high school degree (26% in the sample), 36% have a high school degree (47% in the sample), and 27% have more than a high school degree (27% in the sample) (Arab Barometer, 2018-19 survey).

12
The study uses individual data from the Demographic and Health Surveys (DHS) which are large scale, repeated cross-sectional household surveys that use standardised questionnaires which are comparable across countries.

# Intimate partner violence

Since 1998 the DHS has included a standardised domestic violence module which asks women if they have ever experienced specific acts of physical, emotional and sexual abuse. Since 2005, the DHS also asks women if they experienced any of these three forms of violence in the twelve months preceding the survey date. The study analyses reports of abuse _in the last twelve months_ so as to exploit the timing of unemployment shocks. A potential concern with self-reported measures of IPV is that women may systematically underreport its incidence (Kishor and Johnson, 2004). The DHS surveys attempt to minimize this through methods of women selection, interviewer training, and interview technique. Specifically, only one eligible woman per household is selected for the domestic violence module so as to reduce the likelihood that others in the household are aware of the nature of the questions. Women are asked the questions only towards the end of the interview so that a rapport has been built up between interviewer and respondent before the questions are posed, questions on IPV are only asked if absolute privacy is ensured, and translators are avoided to ensure privacy. Finally, interviewers are provided with extensive training regarding the appropriate way to ask questions of such a sensitive nature.<sup>16</sup> Another possible concern is that reporting behavior changes endogenously with male vs female empowerment. It seems plausible that secular (permanent) changes in relative unemployment rates will empower or disempower women to report violence. However, it is less plausible that

> 16 Some recent, limited, evidence from Peru suggests that underreporting may occur for highly educated women (Aguero and Frisancho, 2017), but it is unclear how generalizable this is for other country contexts. In any case, in all cases, this study controls for education in our analysis. The estimates should remain unbiased and consistent with possible measurement error
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

Indicator 10 - Day Consecutive trips generated by a Proxy for daily mobility Origin Destination sequence of records, counting across regions plus the Matrix the number of times each pair approximate duration of appears and time spent at both stay in a location. origin and destination regions.

a Source: Authors’ adaptation based on the World Bank COVID19 Mobility Task Force repository on GitHub.

# **C. Application in The Gambia**

This paper makes use of the proposed indicators from the World Bank COVID-19 Mobility Task Force and applies them to the country context of The Gambia to analyze patterns of human mobility during COVID-19. More specifically:

- Indicator 3 shows changes in the population distribution over time. As subscribers are accounted for in every region in which they use their phones, it overestimates subscribers who visit multiple regions in a day when computed at the administrative unit level. The value of this indicator can be also affected by load sharing where several cell towers jointly cover a certain area due to network optimization. This impact is mitigated by the cell-tower clustering, which was described as part of data pre-processing in the previous section. We compute this indicator at the national level for examining how the number of active subscribers as a whole country changes over time and for adjusting the result of other indicators.

- Indicator 6 illustrates changes in the location of residency, which could infer the incidence of migration over the data period. For mapping the residential distribution, there are various methodologies and algorithms (Deville et al., 2014) (Ahas, Silm, Järv, Saluveer, & Tiru, 2010), which provide more accurate estimates compared to the proposed method. We consider it still useful for detecting a flexible home location reflecting weekly changes as the estimation result is used at the administrative unit level. In addition, the proposed method is relevant under resource-scarce settings as it is not computationally intensive.

- Indicator 7 demonstrates changes in levels of mobility
that facilities-based providers of basic telecom services must operate as a joint venture with a foreign equity limit of 49 percent.

**Figure 3: STRI for Telecommunications - Doha Offers, ‘Best PTA’, TPP Commitments and Applied Policy of 2015**

<!-- Start of picture text --> Telecom: Doha, PTA, TPP and applied policy<br>BRN MYS CHL SGP MEX CAN VNM USA PER JPN AUS NZL<br>Doha PTA<br>TPP Policy '15<br>Note: Absense of a bar means there are no restrictions (STRI score is zero)<br>100<br>75<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: The World Bank TPP Database, 2016

# **Retail Distribution**

No country made TPP commitments that went beyond its applied policies. Many countries – Australia, Chile, Japan, Mexico, New Zealand, Peru and United States -maintain no explicit restrictions and their TPP countries reflect that. Four countries made TPP commitments that went beyond Doha offers and the rest maintained what they already offered during the Doha negotiations. Chile and Brunei did not include the retail sector in their Uruguay Round commitments or Doha offer, but in the TPP, these countries have placed no limitations on retailing.

Most countries’ TPP commitments reflect their best commitments under their PTA, except that Malaysia and Singapore’s TPP commitments are more restrictive than their AFAS commitments. Malaysia’s TPP commitment lists several limitations: for example, foreigners are not allowed to operate supermarkets or mini markets and no less than 30 per cent of the equity in hypermarkets, superstores and convenience stores is to be held by the Bumiputera (i.e. native Malay).<sup>15</sup> Singapore

> 15 Other restrictions include the following: All distributive trade companies with foreign equity are required to appoint Bumiputera
learning outcomes are comparable to those obtained for school readiness across each of the model specifications (i)– (iii).

In specification (iv), contemporaneous developmental outcomes are included in the model, and the wealth gradient ceases to be significant. These results suggest that the school-age developmental outcomes account for the remaining associations between learning outcomes and wealth, suggesting a close mapping of our school-based outcomes with our contemporaneous developmental outcomes (sustained attention, receptive vocabulary, and cognitive composite).

Table 6: Magnitudes of SES gradient for school learning outcomes (math and literacy)

|||Base|(i)|(ii)|(iii)|(iv)|
|---|---|---|---|---|---|---|
|Math|Q5 vs Q1|1.315|0.904|0.684|0.485|0.213|
|||[0.979, 1.651]|[0.569, 1.239]|[0.319, 1.05]|[0.126, 0.845]|[-0.074, 0.5]|
||R<sup>2</sup>|0.11|0.148|0.163|0.173|0.26|
||N. obs|1003|997|909|906|906|
|Literacy|Q5 vs Q1|1.317|0.74|0.519|0.395|-0.039|
|||[0.954, 1.68]|[0.372, 1.108]|[0.116, 0.921]|[-0.015, 0.806]|[-0.401, 0.323]|
||R<sup>2</sup>|0.
1|
|Nepal|79.8|80.0|81.7|80.5|79.9|79.9|
|Pakistan|13.4|12.5|16.0|19.3|23.9|24.6|
|China|72.7|72.2|70.7|66.5|63.5|63.9|
|Arab World|20.8|20.5|20.9|21.4|22.7|23.3|
|European Union|45.7|46.3|47.8|48.8|50.2|50.8|
|United States|56.4|57.8|59.0|58.3|57.6|56.3|

Source: ILOSTAT 2015 Moreover, the FLFP rate dropped from 49.0 percent to 37.8 percent in rural areas between 200405 and 2009-10 (NSSO, 2011), despite an impressive annual GDP growth rate of around 8.6 percent, and an annual population growth rate of 1.74 percent. The same pattern continued into the most recent round of the National Sample Survey (NSSO) in 2011-12. Among the Asian 2
The Rural Agriculture and Fishery Census (RAFC) includes all households in rural areas, and is conducted every five years. While the agricultural census and the population census have a range of variables in common (demographics, education, dwelling unit characteristics and asset ownership), there are also some important differences.

Firstly, the agricultural census only covers rural households such that the small area poverty and inequality estimates represent the rural population of Vietnam. Estimates based on the population census represent the entire population.

Secondly, the agricultural census includes a selection of specific variables that are particularly informative of rural livelihoods and which are not available in the population census. These include variables on rice cultivation, aquatic cultivation, household ownership of farming tools and machinery. These variables are important correlates of the household’s agricultural activities that will directly affect the household’s income.

Data on individual household members, however, is only collected for members aged 15 or older (the population census covers all household members). To ensure consistency between the variables from the census and the survey, household members aged 14 or younger were dropped from the latter. Also, the head of household is not identified in the agricultural census.

Finally, the codes that identify communes, districts and provinces did not provide a perfect match between the census and the survey. We managed to resolve this problem by using the names of both the provinces and districts to merge data from different sources.

# _II.2 Poverty line_

In the report we use two poverty lines: one for expenditure and one for income. Household members are classified as poor if their per capita expenditure (income) is below the expenditure (income) poverty line.

GSO calculated the expenditure poverty line with technical support from the World Bank in Vietnam. The expenditure poverty line is designed to measure the price of a consumption basket that meets pre-specified nutritional needs and essential non-food expenditures that include clothing and housing. For 2006, the expenditure poverty line was equal to 2,560,000 VND/
# **4.3 Simulating a 10 percent increase in sugar prices**

First, we provide an estimate of how a 10 percent increase in the world price of sugar would affect wages of those currently employed.<sup>20</sup> We use the actual survey data to calculate the predicted change in wages for each person employed and aggregate the results for various groups. The results by sector and education level are presented in Table 8. Again, workers in sugar growing and sugar processing stand to gain the most. Even with imperfect transmission (recall that in many states a 10 percent increase in the world price would translate into less than 10 percent increase in the local price) this change would bring a 2 to 4 percent wage increase, depending on the level of education. Workers in the service sector that have low level of education would on average lose 0.5 percent in wages, while well-educated service workers would experience a small gain (0.8 percent).

The first column of Table 9 provides an estimate of the percentage change in household income brought by the change in expected wages of those currently employed (as in Table 8), by income quintile. The average individual in all quintiles benefits from the 10 percent increase in world sugar prices, but the wage increases are larger for individuals in the top income quintile. Conditional on being employed, the average individual in the top two income quintiles experiences an increase in income which is 17 percent higher than the one experienced by the average individual in the bottom two income

> 20 Note that all changes in income provided in this section are based on a somewhat arbitrary 10% increase in the international sugar price. Dividing the percentage income changes reported here by 10 would produce easily interpretable income elasticities with respect to the world price of sugar. In other words, the percentage change in income is linear in the percentage change in world prices and therefore to obtain the percentage change in income after an _x_ % change in world prices,
One challenge in assembling micro-LAYS estimates is how to handle a study that reports impacts on both participation and learning. If we sum the estimates, we will double-count in cases where gains in learning resulted directly from gains in participation or where gains in learning led to gains in participation (e.g., because students had a greater incentive to attend schools that delivered more learning). As an alternative to adding the two estimates, we could choose to use only estimates from either participation or learning. However, under this approach we would be assuming that one is the central output, and that the other outcome dimension is largely captured within that central output. Instead, for the purposes of this paper, we use the LAYS impact that is greater—whether that was obtained through schooling or learning increases—for each evaluation. This approach places _a priori_ equal weight on schooling and learning, introduces no new assumptions, and avoids double-counting.

# **3 Data and Analysis Framework**

We compare impact estimates from over 150 evaluations of education interventions in 46 countries using a unified measure. In our comparison, we highlight findings from a subset of studies that have comparable cost data<sup>8</sup> and that therefore allow us to compare cost-effectiveness of interventions. We examine how many LAYS each policy or intervention delivers; how cost-effective those gains are; and how much of the gap between quality-adjusted years of schooling and actual years of schooling that intervention would close if it were scaled up, assuming that the effectiveness of the intervention remained constant. This assumption of scalability is not trivial, given that effectiveness at system scale is often substantially lower than effectiveness in even a large pilot study; we therefore carry out this calculation as a calibration exercise rather than a simulation exercise.

We start with over 300 studies compiled from a database produced by Evans and Yuan (2019), which draws studies from a wide range of reviews (Evans & Yuan, 2019; Ganimian & Murnane, 2016; Glewwe et al.
**<u>Table A.4. RDD regressions of individual’s health and children’s education using VHLSSs 2004 to 2008</u>**

||||De<br>|pendent variabl<br>|es<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Having<br>health<br>insurance<br>(yes=1,<br>no=0)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|The annual<br>number of<br>inpatient<br>healthcare<br>visits|The number<br>of completed<br>schooling<br>years<br>(children<br>aged 6-17)|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program|0.1991***|-0.2849***|-0.2718***|-0.0130|0.0840|0.0230|0.6334**|
||(0.0651)|(0.0877)|(0.0845)|(0.0174)|(0.2655)|(0.0386)|(0.2507)|
|(Poverty rate – 50)|-0.0046|0.0132|0.0109|0.0022|-0.