rafmacalaba/datause-encoder-data huggingface dataset:
# OPPORTUNITIES FOR EXPANDING FINANCIAL INCLUSION

_The Global Findex data point to several promising opportunities for expanding financial inclusion. These fall into two broad categories: expanding account ownership among the unbanked and increasing the use of accounts among those who already have one. But before exploring these opportunities, this chapter first takes a step back to look at who the unbanked are and what reasons they report for not having an account and to assess how those who have an account use it._

## Who the unbanked are

> FIGURE 4.1

Globally, 2 billion adults remain unbanked. South Asia and East Asia and the Pacific together account for more than half the world’s unbanked adults. South Asia, home to about 625 million adults without an account, has about 31 percent of the global total; East Asia and the Pacific, with 490 million unbanked adults, accounts for about 24 percent (figure 4.1). This is no surprise, since these two regions are home to the developing world’s three most populous countries—China, India, and Indonesia. Indeed, these three countries together account for 38 percent of the world’s unbanked (figure 4.2). India is home to 21 percent of the world’s unbanked adults and about two-thirds of South Asia’s. China accounts for 12 percent of the world’s unbanked and Indonesia for 6 percent; together they account for three-quarters of the unbanked in East Asia and the Pacific.

Te world’s unbanked adults by region Adults without an account (%), 2014 <!-- Start of picture text --> Sub-Saharan Africa 17<br>East Asia Latin America<br>& Pacific & Caribbean 10<br>24<br>Europe &<br>Central Asia 5<br>Other<br>economies 4<br>Middle East 4<br>High-income<br>South Asia 31 OECD economies 3<br><!-- End of picture text --> _Note: “_ Other economies” include high-income non-OECD economies, Algeria, and Tunisia. _Source:_ Global Findex database.

> FIGURE 4.2

Share of the world’s unbanked adults in China, India, and Indonesia <!-- Start of picture text --> Adults without an account (%), 2014<br><!-- End of picture text --> <!-- Start of picture text --> India 21<br>Rest of China 12<br>the world<br>Indonesia 6<br>Source: Global Findex database.<br><!-- End of picture text --> Women make up 55 percent of the world’s unbanked adults: 1.1 billion. And adults in the poorest 40 percent of households within economies make up half: 1 billion. These shares vary little across developing regions.

Why do almost 40 percent of adults around the world remain unbanked? The Global Findex survey asked adults without an account at a financial institution why they do not have one, providing insights into where policy makers might be able to remove barriers to financial inclusion.<sup>1</sup>

> THE GLOBAL FINDEX DATABASE 59
<!-- Start of picture text --> Citizenship- 36.8<br>fo)<br>a2 Country of birth- paa<br>8 7<br>Reason(s) for migration-j ‘<br>0| 10| 20| 30|<br>% of datasets<br><!-- End of picture text -->

<!-- Start of picture text --> usual residencePlace of |<br>5 displacement<br>> most recent displacement<br>lo)5 habitual residPlac e nce of | _<br>oO displacement”Date of first<br>Main reason(s) for _<br>first displacement<br>0 25 50 75<br>% of datasets<br><!-- End of picture text -->
<!-- Start of picture text --> EU-15 Post-2004 Enlargements<br>2©<br>22<br>2 ©<br>&<br>= Q 2<br>° o> o>)<br>Oo<br>£<br>‘aoo—~ yn2 N2 |<br>Ox<br>oO -<br>a= wo wo<br>ay ° °<br>O2 é<br>2R<br>_ —_—— —~———_|-_}—~_ ~~~ __ bE<br>A °<br>©22<br>©= =<br><x<br>2 ©<br>12 ©<br>% %<br>AT BE DE DK EL ES FI FR IE IT LU NL PT SE UK BG CY CZ EE HR HU LT LV MT PL RO SI SK<br>Country Country<br><!-- End of picture text -->
export provided by Hausmann, Hwang and Rodrik (2007)<sup>2</sup> . It is more of an indicative measure mirroring the concept of EPXY of Hausmann, Hwang and Rodrik (2007) since we don’t have the finer classification of services as compared to those for goods and also availability of data for various types of services is not easily available for most countries<sup>3</sup> . We plot the trend sophistication for selected emerging economies in Figure 8. Starting from a very similar level of sophistication, India has taken off exponentially since 2000. The result is largely driven by large share of modern computer and IT exports from India which is a very high skill product. In terms of sophistication of service exports, India is much ahead of rest of the East Asian economies.

<!-- Start of picture text --> Figure 8: Service Sophistication Index (EXPY) for Select Emerging Economies, 1990-2007<br>17000<br>16000<br>15000<br>14000<br>13000<br>12000<br>11000<br>10000<br>9000<br>8000<br>7000<br>1990 1993 1996 1999 2002 2005<br>Malaysia China Korea India Singapore<br>Service EXPY<br><!-- End of picture text --> _Source_ : IMF Balance of Payments 2008. _Note_ : EXPY is calculated as described in text.

2 We calculate the PRODY and EXPY for services on the similar lines of Hausmann, Hwang and Rodrki (2007). More formally, the PRODY and EXPY measures are calculated in the following way: _X_ country i and _i_ is the value-added share of commodity j in the country’s overall export basket. While PRODY is a measure of productivity level associated with a product while EXPY is the measure of the productivity level of a country’s export basket. Thus it can be interpreted as sophistication level of a country’s export basket. For details see Hausmann, Hwang and Rodrik (2007).

3 Due to data and classification issues we are forced to use ten different categories of services as income weights to come up with this measure as opposed to thousands of commodities categories used by Hausmann, Hwang and Rodrik (2007).

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the of 6-9. We selected households from the list after ages randomly eligible randomly allocating the school-catchment areas to control and treatment groups. Selected households in treatment and control communities were visited by the survey firm for baseline data collection. A month after the baseline, community mobilizers visited households to invite them to the community screenings. The invitation was for a 6-9 year old and their parents.

The baseline was collected between December 2018 and April 2019, the interventions were staggered and took place between April-September 2019. Follow-up (endline) data collection started in March 2020, although data collection was stopped three weeks later due to COVID-19 shutdowns. We managed to interview 21% of our trial participants prior to COVID-19 closures. We resumed endline data collection in September 2020 and completed it in December of that year. Figure 2 shows the timeline of the study’s data collection activities and of the interventions.

An experienced survey firm (Hanovia Ltd) conducted the trial’s data collection. With a support letter from the federal and state ministries of education of Kano and Jiwaga states, the survey firm sought approval from the community leader to provide access to all 128 study communities and to interview selected households to collect listing, baseline and endline data. Baseline data were collected for 9,393 households that were randomly selected from the listing. To maximize the likelihood of finding adults for interviewing and children for the was asked to visit to three times selected testing, survey firm up households, local markets or businesses to find study participants. Attrition rates at follow up were very low: only 3% of main respondents and 5% of target children were not found endline. Most children who were not found had or mi- during passed away grated to another village or state to attend Almajiranci (Islamic school), a religious and non-formal system of education practiced in northern Nigeria.

# **5.1.1 Main outcome measures**

The main outcomes are the percent of children not attending school and learning outcomes (literacy and numeracy test scores) as pre-registered in the AEA registry of randomized control trials, see footnote 8. For both baseline and follow-up data collection we interviewed the household head and tested on literacy/numeracy skills a 6-9 year old child (referred as ‘target child’, as this child was invited to the community screenings and targeted by the interventions’ messaging) and a 6-12 year old sibling (referred as One child was selected at baseline if the house- ‘sibling’). per age group randomly hold had more than one child in each age category. Assignment was stratified by gender of the target child. To analyze the mechanisms through which our interventions may 14
In the first stage, we regress the take-up rate on the pre-primary access indicator defined at the community-cohort level. In the second stage, we estimate the model described above but specifying the fraction of children attending the pre-primary at the cohortcommunity level as the main independent regressor and instrumenting this variable with the pre-primary access dummy.<sup>9</sup>

# **5 Results**

## **5.1 Main impacts**

We first examine if and how the opening of pre-primaries in rural communities affects primary school progression by age 12. Table 2 presents the corresponding ITT estimates of the average effect of interest. The baseline specification described above is presented in column (1). We find no statistically significant effects associated with having access to a pre-primary on enrollment rates (Panel A). However, we do find statistically significant positive effects on adequate school progression (Panel B). The results indicate that opening a pre-primary in these rural communities increased by approximately 2.4 percentage points the proportion of children in grade 6 by age 12.

In columns (2) to (4), we test the robustness of these results using alternative specifications. Column (2) presents results when controlling for department-linear time trends and column (3) when controlling for differential municipality-linear time trends. We exploit the matched Census 1994 and 2002 data and control for time-varying controls in column (4). The baseline estimates are robust to these alternative specifications.

We proceed by examining the ToT average effects of attending a pre-primary on primary school progression in Table 3. Because the average take-up rate is about 60 percent for a given cohort at age 6, the estimated effects are roughly 1.67 times the ITT estimates of Table 2. The results indicate no significant effects on enrollment rates, but show a positive statistically significant effect on adequate progression of about 4.2 percentage points. As before, the results are robust to changes in the empirical specification used.

Expanding access to pre-primary education seems to improve adequate progression, while not changing enrollment rates in primary school. Taken together this evidence suggests that repetition rates are reduced by this intervention. This is important in Guatemala, where repetition rates in early grades are high (about 30 percent in first grade and 10-15 percent in later grades). However, the magnitude of the effect on adequate progression (about 2.4 percentage points) seems fairly modest when considering that only

> 9To estimate treatment-on-the treated estimates we assume that pre-primary attendance in nonbeneficiary communities is zero. In the case this is not true, our estimates of the effect of attending a pre-primary would be downward biased and, hence, the estimates can be interpreted as a lower bound of the true

10
greater percentage of SLPP party members are both associated with more unfavorable perception of the quality of government health care provision.

The negative coefficient for chiefly contact indicates that a higher amount of contact between chiefs and councilors is associated with worsening perceptions of the quality of government health services. As median contact moves from its lowest value (1.8 times per month) to its highest value (11 times per month), this is associated with decrease of 0.58 points in the five-point scale (a move from 3.4 to 2.8). In contrast, the negative coefficient for the chiefly disputes variable suggests that frequent disputes increase the likelihood that respondents, on average, report an improvement in the quality of public health care. As the percentage of disputes moves from its lowest value per council (0 percent) to its highest value (17 percent), we see an associated 0.4 point increase on the response scale (moving from 3.1 to 3.5). Figure 3 below plots the average effect size as well as the council specific intercepts for each these measures of councilor/chief interactions.

**Predicted Values DV: 2011 EA Mean Perceptions of Gov. Health Services**

<!-- Start of picture text --> 2 4 6 8 10 0.00 0.05 0.10 0.15<br>Median Contact with Mean % Disputes with<br> Chiefs by Council Chiefs by Council<br>4.0 4.0<br>3.5 3.5<br>3.0 3.0<br>2.5 2.5<br>EA Perceptions of Gov. Health Services EA Perceptions of Gov. Health Services<br>2.0 2.0<br><!-- End of picture text -->

**Figure 3:** Predicted values of median contact with chiefs (left) and mean percentage of disputes with chiefs (right) on EA-level mean perceptions of improvements of government health services in the past year. Mean predicted intercept in red and council specific intercepts in black.

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The Adult Literacy and Life Skills Survey (ALL) is a more recent (2003), large-scale cooperative effort undertaken by governments, national statistics agencies, research institutions and multilateral agencies. The ALL study builds on the International Adult Literacy Survey (IALS), the world’s first internationally comparative survey of adult skills undertaken. The foundation skills measured in the ALL survey include prose literacy, document literacy, numeracy, and problem solving. Additional skills assessed indirectly include familiarity with and use of information and communication technologies.

Both data sets include three scales as measures of skill: prose literacy, document literacy and quantitative literacy, each in the 0-500 range. Prose literacy tests the understanding and use of texts such as editorials, news stories, fiction and poems. Document literacy tests skills required to locate and use information contained in a variety of formats, such as job applications, payroll forms, maps and tables. Quantitative literacy tests skills required in making calculations after locating numbers embedded in printed materials; examples of such calculations include determining the interest on a loan, calculating a tip and balancing a checkbook.

# **4. Results**

The working sample in this multi-country study includes males employed for wages between the ages of 22 and 65. Empirical results are restricted to males, as earnings function estimates for females would hinge on the extent of selectivity bias<sup>§§</sup> . The dependent variable in the earnings regressions is the logarithm of the hourly wage, calculated using information on yearly earnings from wages, hours worked per week and weeks worked per year. In the estimation of earnings functions, gross

> §§ Potential selectivity could be serious, given that in some of the countries female labor force participation is low (for example, it is 57 % in Switzerland (It.), 58 % in Italy and only 36 % in Chile).

11
# Table A9. Common Support, Outcome: Female Top Manager

|**(**<br>Treatment<br>assignment|**a) GVC1, All regio**<br>Common<br>support<br>On support|**ns**<br>Total|**(b) GVC4, All regions**<br>Treatment<br>assignment<br>Common<br>support<br>On support|
|---|---|---|---|
|Untreated<br>Treated|67,059<br>17,220|67,059<br>17,220|Untreated<br>Treated<br>76,103<br>1,857|
|Total|84,279|84,279|Total<br>77,960|

|Treatment<br>assignment|Common<br>support<br>On support|Total|
|---|---|---|
|Untreated|76,103|76,103|
|Treated|1,857|1,857|
|Total|77,960|77,960|

|**(**<br>Treatment<br>assignment|**c) GVC1, MENA re**<br>Common<br>support<br>On support|**gion**<br>Total|**(d**<br>Treatment<br>assignment|**) GVC4, MENA re**<br>Common<br>support<br>On support|**gion**<br>Total|
|---|---|---|---|---|---|
|Untreated<br>Treated|9,246<br>2,200|9,246<br>2,200|Untreated<br>Treated|10,033<br>187|10,033<br>187|
|Total|11,446|11,446|Total|10,220|10,220|

Table A10. Common Support, Outcome: Number of Full-Time Female Employees

|**(**<br>Treatment<br>assignment|**a) GVC1, All regio**<br>Common<br>support<br>On support|**ns**<br>Total|**(b**<br>Treatment<br>assignment|**) GVC4, All region**<br>Common<br>support<br>On support|**s**<br>Total|
|---|---|---|---|---|---|
|Untreated<br>Treated|35,900<br>5,207|35,900<br>5,207|Untreated<br>Treated|28,901<br>334|28,901<br>334|
|Total|41,107|41,107|Total|29,235|29,235|
|**(**|**c) GVC1, MENA re**|**gion**|**(d**|**) GVC4, MENA re**|**gion**|
|Treatment<br>assignment|Common<br>support<br>On support|Total|Treatment<br>assignment|Common<br>support<br>On support|Total|
|Untreated<br>Treated|3,954<br>622|3,954<br>622|Untreated<br>Treated|3,101<br>35|3,101<br>35|
|Total|4,576|4,576|Total|3,136|3,136|

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# **Table O1: Table 3 Panel B with alternate measures of relative earnings/hours**

|||Days|Hours<br>||Days|Hours|
|---|---|---|---|---|---|---|
|||worked|worked||worked|worked|
|Dependent variable|1(Work)|last<br>week|last<br>week|1(Work)|<br>last<br>week|last<br>week|
|_Panel A: Relative pay_|||||||
|Other worker sick|0.039|0.095|1.46|‐0.054|‐0.363|‐0.105|
||[0.053]|[0.420]|[3.629]|[0.140]|[0.411]|[3.915]|
|Other worker sick X Female|‐0.08|‐0.102|‐2.342|0.024|0.589|‐5.38|
||[0.077]|[0.513]|[4.719]|[0.223]|[1.545]|[11.690]|
|Above median baseline earnings in household|0.093|0.54|5.8||||
|X Other worker sick|[0.066]|[0.482]|[4.403]||||
|Above median baseline earnings in household|‐0.097|‐0.813|‐6.112||||
|X Other worker sick X Female|[0.095]|[0.682]|[1.125]<br>||||
|Relative baseline earnings in household||||0.137|0.749|4.62|
|X Other worker sick||||[0.122]|[0.621]|[5.633]|
|Relative baseline earnings in household||||‐0.148|‐1.078|‐0.042|
|X Other worker sick X Female||||[0.213]|[1.462]|[11.074]|
|Observations|3951|3948|3948|3860|3857|3857|
|R‐squared|0.507|||0.507|||
|_Panel B: Relative earnings potential within household_|||||||
|Other worker sick|0.045|0.12|2.453|‐0.01|‐0.203|‐1.246|
||[0.055]|[0.272]|[2.105]|[0.119]|[0.670]|[5.978]|
|Other worker sick X Female|‐0.11|‐0.45|‐3.561|‐0.177|‐0.766|‐0.984|
||[0.089]|[0.475]|[3.837]|[0.179]|[1.070]|[8.282]|
|Above median baseline usual hours of work|0.094|0.584|4.733||||
|in HH X Other worker sick|[0.063]|[0.380]|[4.034]||||
|Above median baseline usual hours of work|‐0.036|‐0.035|‐4.419||||
|in HH X Other worker sick X Female|[0.120]|[0.631]|[5.757]||||
|Relative usual hours of work in HH at baseline||||0.091|0.555|5.493|
|X Other worker sick||||[0.102]|[0.594]|[6.141]|
|Relative usual hours of work in HH at baseline||||0.058|0.308|‐4.255|
|X Other worker sick X Female||||[0.151]|[0.931]|[7.868]|
|Observations|3951|3948|3948|3951|3948|3948|
|R‐squared|0.507|||0.511|||

_Notes: Other worker sick = 1 if another household member reported missing work for an entire week that she/he was planning to work that week and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Usual earnings and usual hours of work from reports in baseline survey. All specifications include individual fixed effects. Column 1 estimated by OLS and columns 2 and 3 least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, city X week‐of‐interview dummies, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
**Appendix Table 2: Dietary metrics for school-age and adolescent children – definitions and data source characteristics**

|**Dietary intake variables**|**Definition**|**Data source**|**Year**|**Data**<br>**availability**|**Notes**|
|---|---|---|---|---|---|
|**Frequency of carbonated**<br>**soft drink consumption**|Average number of times per day<br>adolescents (12-17 years) reported<br>consuming carbonated soft drinks|Data collected from<br>Global School-based<br>Student Health|year of<br>survey<br>varies by|N=63<br>countries<br>with data|Assesses behavioural risk<br>factors and protective factors in<br>10 key areas using a self-|
|**Frequency of fruit**<br>**consumption**|Average number of times per day<br>adolescents (12-17 years) reported<br>consuming fruit|Surveys<br>https://www.foodsyst|country<br>(range 2009<br>to 2015)||administered questionnaire<br>administered in schools|
|**Frequency of vegetable**<br>**consumption**|<br>Average number of times per day<br>adolescents (12-17 years) reported<br>consuming vegetables|emsdashboard.org/|||‘_during the past 30 days, how_<br>_many times per day do you_<br>_usually drink/eat..._’|
|**Frequency of fast food**<br>**consumption**|Average number of times per week<br>adolescents (12-17 years) reported<br>consuming fast food||||Meta-analysis byBeal et al.<br>2019|
|**At least daily carbonated**<br>**soft drink consumption**|<br>Proportion of adolescents (12-17<br>years) who reported consuming<br>carbonated soft drinks at least 1<br>time per day over the last 30 days|||||
|**At least weekly fast food**<br>**consumption**|Proportion of adolescents (12-17<br>years) who reported consuming fast<br>food at least 1 time per week|||||
|**Less than daily fruit**<br>**consumption**|Proportion of adolescents (12-17<br>years) who reported consuming<br>fruit less than 1 time per day over<br>the last 30 days|||||
|**Less than daily vegetable**<br>**consumption**|Proportion of adolescents (12-17<br>years) who reported consuming<br>vegetables less than 1 time per day<br>overthelast 30 days|||||

40
# **C Additional material**

## **C.1 Summary Statistics**

Figure C.4: Fixed and Variable Charges of the Property Tax Bill, by Deciles <!-- Start of picture text --> 100<br>34.7<br>80<br>47.9<br>54.1<br>58.4 62.4 66.2 70.3<br>74.5<br>60 79.0<br>86.1<br>40<br>65.3<br>52.1<br>45.9<br>20 41.6 37.6 33.8 29.7<br>25.5<br>21.0<br>13.9<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles (assessed value)<br>Fixed charge Variable charge<br>Proportion (%), 2019<br><!-- End of picture text --> _Note:_ This figure shows the relative importance of variable and fixed components of the property tax bill by deciles of assessed values in 2019. According to legislation in 2019, variable charges for residential properties were 0.5% of assessed value, and fixed charges amounted to a flat fee of AR$ 167 (AR$ 90 for security services, AR$ 52 for maintenance services, and AR$ 25 for health services).

Figure C.5: Dynamics for payments on-time <!-- Start of picture text --> % paid on time<br>1<br>Women<br>.9 Men<br>Co-Owned<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>2019m1 2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7 2023m1<br>Billing Period<br><!-- End of picture text --> _Note:_ This figure presents a time series for the fraction of residential bills that were paid on time. We define a bill paid on time as any bill that was registered up to the 27th day of the due month and bills that were paid in an annual payment at once.

C-5