rafmacalaba/data-use-mentions-extended huggingface dataset:
. As a result, a center that meets the minimum requirements under the Indonesia ECED Standard receives a score of 2 on the ECERS-R scale rather than the “minimal” score of 3 as defined by the ECERS-R.

In Figure 4, we show the quality of preschool in Indonesia using two definitions of “minimal”—one using an ECERS-R score cutoff of 2 as in the Indonesia ECED Standard and one using an ECERS-R score cutoff of 3. The graph shows that using a context-specific definition of minimal standard presents a strikingly different picture than the ECERS-R definition. 81.0% of centers in rural Indonesia meet the national requirements for preschool quality while only 42.7% meet the ECERS-R minimum score. Given the resource-constrained environment in rural villages in Indonesia, we believe that using the Indonesia definition of minimal quality may be more useful to frame the conversation for national policy makers as well as the early childhood community in Indonesia. It is worth noting that the results in Figure 4 suggest that poor, rural communities are generally making efforts to meet the national standards. Despite these efforts, nearly 20% of early childhood services still need to achieve even a minimum requirement of quality as defined at the national level. Over 57%of centers fall short of meeting minimum requirements of quality at an internationally acceptable level. As the government seeks to expand access to preschool services more broadly it will need to increase its focus on enforcing its minimum service standards.

# **8. Discussion and Conclusion**

Our analysis of early childhood education quality in Indonesia makes three important contributions to the literature. We find that:

- i. Classroom observations of ECED quality are subject to considerable measurement error. Not correcting for this leads to an underestimation of the association between quality and child development outcomes. Correcting for measurement error using an instrumental variables approach (with the first rater’s score as an instrument for the second rater’s score) allows us to improve the use of ECERS-R as a predictor of child development outcomes;

14
from survey data**

_Dropout rate_ is the percentage of students that dropped out among those who were supposed to graduate the academic year before the survey.

_Extra time to graduate (ETG)_ is the average percentage of additional time that students take to graduate relative to the theoretical duration of the program. For example, if a program is supposed to last two years and students take, on average, three years to graduate, then ETG=50% (=1 extra year *100 / 2 years). To calculate ETG, we asked directors (i) the average time it took the last cohort to graduate from the program and (ii) the theoretical duration of the program.

57
register later|0.12|0.10|0.03|0.22|0.18|
|Seller didn’t have certificate|0.02|0.03|0.04|0.00|0.02|
|DLO is too far|0.01|0.01|0.01|0.01|0.01|
|Conflict on the parcel|0.01|0.01|0.01|0.01|0.01|
|Other reason|0.38|0.25|0.67|0.22|0.21|
|No. of ownedparcels|12,798|3,574|3,501|3,234|2,489|

_Source:_ Own computation from 2015 round of Rwanda Land Tenure Regularization Survey a Includes newly acquired parcels located outside the cell boundary or with missing detailed information on subsequent registration. b Restricted only to parcels acquired within the cell of sampled villages.

21
observation and pre-knowledge of realistic price ranges for each commodity during the time period. Outliers were identified with Tukey outlier detection method (Tukey, 1977; Dastjerdy, et al., 2023) by calculating the interquartile range and applying fence-rule to filter values. Spurious datapoints (1.3% of the entire dataset) were excluded from further analysis. Second, the intraday crowdsourced and enumerator-submitted prices were computed into daily, weekly, and monthly averages for each commodity type/sub-type. To assess the degree of closeness between the prices from both sources, we conducted pairwise Pearson’s correlation analysis, and fitted a linear trend between paired price datasets, as described in other literatures (Taylor, 1990 and Senthilnathan, 2019). While the correlation coefficient (R) determines the existence and direction of a relationship between the prices, the coefficient of determination (r<sup>2</sup> ) indicates the pattern and strength of the relationship, based on the co-variability of both prices (Equation 1 and 2). R ranges from -1 to +1, indicating strongly negative to strongly positive relationship, respectively; whereas r<sup>2</sup> ranges from 0 to 1, directly indicating the degree of variance explained between the prices. The correlative and regression analysis were performed between the crowdsourced and enumerator prices at daily, weekly, and monthly timesteps, using relevant R packages and functions.

Also, following previous literature (Xu, et al., 2017; Snedecor & Cochran, 1989), we conducted paired sample student-t tests to assess if the differences between price means were significant between the data sources during the period. The tests were conducted based on the null hypotheses that mean prices from one data source is not significantly different from the other. Inferential test statistics were based on a confidence level threshold (α) of 0.05, and the acceptance or rejection of the null hypotheses was based on final _p_ -values.

# **Data Availability**

Generally, the
, Olarreaga and Payton 2010), and the effects we identify even if statistically robust are quantitatively small.

The paper is organized as follows. Section 2 discusses the recent literature related to this paper. Section 3 presents qualitative evidence based on a recent survey of African exporters conducted by the World Bank that motivates the consequent analysis and presents a brief description of the data. Section 4 discusses the estimation strategy and the result, Section 5 3
Based on these results, it seems that both RD and DID estimates may be picking up, at least partially, something other than just the effect of the program. In the best case scenario, the RD estimates on formalization rates should be seen as upper bound effects of the SIMPLES program.

# **5. Conclusion**

The paper revisited the impact of the tax simplification program for SMEs (SIMPLES) in Brazil to reconcile the conflict of findings and conclusions in Fajnzylber et al. (2011) and Monteiro and Assunção (2012). It then checked the validity of the identification strategy used in both studies, but the focus was on the RD as a credible design to assess the impact of this program. The paper used the ECINF data from 1997 as in the original papers, and found that the main reason for disagreement between the studies was to do with the month used as cutoff point to identify the ‘pre and-post-dummy’ variable. While Fajnzylber et al. used the date the provisional measure was sent to the Congress to vote, Monteiro and Assunção (2012) used the date the law was enacted. The difference is just one month, but it seems to be key to explain the difference in findings between the two papers. Firms that formalized in November 1996 apparently anticipated the approval of the law by the Congress. This cluster of firms that came into business in November 1996 seems to drive most of the large results found by Fajnzylber et al. (2011).

To check whether the jump in formalization rates was observed among young firms, the study undertook a couple of validity tests that have become standard among RD design practitioners. A McCrary density test to check for perfect manipulation of the assignment variable and a simple test of difference in means were performed to check balance around the cutoff using different bandwidth sizes. The McCrary test pointed to a perfect manipulation of the assignment variable around the cutoff point. This result usually suggests that ‘treated’ and ‘control’ firms can be systematically different
The education system in Bhutan consists of general education, monastic education and non-formal education. The CEQ analysis only considers general education as it represents the formal education system and accounts for an overwhelming majority of government education spending. General education spans all stages of education from early childhood care education to tertiary/technical/vocational education. Most schools in Bhutan are public and provide everything free of charge, from textbooks to boarding facilities. In FY2016-2017, 5.9 percent of GDP was spent on education (0.9 percent of GDP on tertiary education).<sup>10</sup> While public education is provided for free, students are encouraged to share a small portion of cost based on their financial ability.

### **_Health Spending_**

Health care in Bhutan is almost entirely funded and delivered by the Royal Government of Bhutan (RGoB) health institutions. Large investments in health care helped greatly to improve access and utilization in the past. Private health care services are limited to a few pharmacies and selective diagnostic centers, mostly in urban areas. Bhutanese can access public health services free of charge with only a few exceptions.<sup>11</sup> Some services not covered include private cabin facility at government hospitals, cosmetic dental care, and drugs outside the national essential drug list. Specialized health services which are not available in the country are referred to hospitals in India at the cost of the government. In FY2015-16, the total health expenditure incurred by the government was around 3 percent of GDP.<sup>12</sup>

# 3. Data Sources and Methodology

## 3.1. Data sources

The main source of data is the Bhutan Living Standards Survey 2017 (BLSS). The survey is representative at the national and district (“dzongkhag”) level and covered 11,660 households in both rural and urban areas in 2017. Detailed information was collected on individual demographics, education, and health; household expenditure; housing; access to services;
4.3 considers an alternative account of stunting in India: could it be a mere effect of short mothers causing restricted fetal growth, reflecting historical conditions and not a healthy environment that might exist today? Finally, section 4.4 proceeds with the decomposition, applying linear and non-parametric approaches to explain the India-Africa gap.

# **4.1 Effects of village sanitation: A negative externality**

As a first step towards explaining the height difference between Indian and African children, this section verifies that village-level open defecation predicts children’s height within each region. As in section 3, I use household-level DHS data to find the fraction of households in a PSU reporting open defecation, which I treat as an estimate of village-level open defecation.

Thus, separately for each region _r_ Africa and India, I estimate: where _i_ indexes individual children under 5, _v_ are villages (rural PSUs), _c_ are country-years (DHS surveys) in Africa and states in India, and _r_ are regions (India or Africa). The dependent variable, _heightivcr_ is the height of child _i_ in standard deviations, scaled according to the WHO 2006 reference chart. The independent variable _open defecation_<sup>_v_</sup> _vcr_<sup>isthecom-</sup> puted fraction 0 to 1 of households reporting open defecation in the child’s village (again, implemented as rural primary sampling unit), with a quadratic term included in some specifications. Household-level open defecation _open defecation_<sup>_i_</sup> _ivcr_<sup>isanindicator,0or1,forthe</sup> child’s household. Including both household and village-level open defecation tests whether one household’s open defecation involves negative externalities for other households.<sup>21</sup> In other words, is it only a household’s own sanitation that matters, or do other households’ sanitation matter, even controlling for one’s own?

> 21G ̈unther and Fink’s (2010) working
economies.<sup>4</sup> The information from PovcalNet is combined with the World Development Indicators (WDI), which is the World Bank’s principal collection of development indicators compiled from officially recognized international sources.

For the micro-analysis, disposable household income data was drawn from the Luxembourg Income Study (LIS).<sup>5</sup> Spanning five decades, the LIS is the largest available income database of harmonized microdata collected from about 50 countries around the world.<sup>6</sup> The countries’ data sets are harmonized into a common framework and contain household- and person-level data on labor income, capital income, pensions, public social benefits (excluding pensions) and private transfers, as well as taxes and contributions, demography, employment, and expenditures. Sixteen countries were selected (four developing and twelve developed countries), which were considered to be representative of the groups outlined in the introduction, notably<sup>7</sup> : India (2011) for the group South Asia and MENA; Brazil (2013), Chile (2013), and Uruguay (2013) for the LAC group; Belgium (2013), France (2010), Italy (2014), Greece (2013), and Spain (2013) for the EuroMediterranean cluster; the United Kingdom (2013), Ireland (2014) and the Netherlands (2013) for the Northern European non-social democratic group; and, finally, Sweden (2005), Denmark (2013), Finland (2013) and Norway (2013) for the social-democratic Northern Europe group, i.e. the Scandinavians.

As mentioned before, we also conducted a preliminary analysis on a group of MENA countries that was not included in the LIS data set, e.g. Morocco, Jordan and Egypt.<sup>8</sup> The comparison with

> 4 We follow the most recent (2020) World Bank classification that uses the Atlas method to estimate the
**UP household survey:** A comprehensive assessment at baseline (spring 2016) and a followup survey (summer 2018) were conducted to measure primary and secondary outcomes of interest for participating households in the treatment and control groups. This instrument was divided into two parts: Lady of the Household (LHH; approximately 2 hours) or primary women, and the Man Head of the Household (MHHH; approximately 45 mins). The woman in the household with the most knowledge and decision power completed the LHH survey. When she was not also the head of the household, the MHHH survey was administered to the man head. Where no lady of the household existed, the men household heads completed the LHH survey modules. They are referred in the analysis as primary woman and primary man, respectively.

**Saliva collection/Cortisol tests:** Saliva samples were collected to measure cortisol levels, a neurobiological marker of stress, at follow-up, using SaliCap tubes. A single saliva sample was collected at the end of each survey from both the primary woman and the primary man (if there was one). All samples were kept at 4 ° C for no longer than necessary before freezing them at or below -20 ° C. The samples were then shipped to Dresden Lab Services (Germany) where they were assayed for cortisol using a standard chemiluminescent immunoassay.

**Village and market surveys:** Additionally, two shorter instruments were used: One for village leaders to record basic village-level data such as infrastructure, violence, and other village shocks, and the other at the district market level to collect data on food prices for calculating consumption expenditures from quantities. Field supervisors visited the largest market in each of the four districts and collected sales prices for all food items found in the household consumption survey. These instruments were administered in parallel with the household surveys.

Primary Outcome Measures We generate primary outcome measures as follows:

1. **Consumption** : Total value of food for the last
(2005) have documented a decrease in rural poverty in India due to an expansion of bank branches in rural areas, although these findings have been questioned (Panagariya 2006 and Kochar 2011). Within the limitations of country level data, the IMF has related financial inclusion with a number of macroeconomic outcomes, including economic growth, stability and equality (Sahay et al. 2015). Their analysis suggests that financial inclusion can be positively related to these outcomes but that the relationship may depend on factors such the level of per capita income or quality of the regulatory environment. Yet, so far there is no rigorous research showing a direct impact of financial inclusion on economic growth and inequality at the country level.

One reason why the relationship between financial inclusion and inequality and macroeconomic growth is not yet well understood is data availability. Establishing such a relationship requires a sufficiently long time-series on financial inclusion measures. Analysis of the factors shaping macroeconomic growth and inequality often requires decades of data. Until very recently, data on financial inclusion on a comparable, global level have not been available, limiting the ability to assess its impact.<sup>2</sup> Data on financial inclusion collected by financial institutions have been available for select economies starting as early as 2004 as part of the IMF’s Financial Access Survey.<sup>3</sup> There was no comparable global demand-side data on financial inclusion collected from the perspective of individuals until the World Bank launched its first Global Findex database in 2011 (Demirguc-Kunt et al., 2015). Another reason the connection between financial inclusion and macroeconomic outcomes remains unclear is that national policies aimed at increasing financial inclusion are for the most part very recent, and assessing their impact on country-level growth and inequality will take time.

The paper proceeds as following: Section 2 provides a description of account ownership around the world. Section 3 discusses the evidence on the benefits of financial inclusion organized around four major types of formal financial products: payments, savings, credit, and insurance
1.25|0.83|0.17|
|Bangladesh|4.83|5.46|9.60|3.13|-|-|1.14|0.28|1.51|1.27|
|Sri Lanka|2.37|3.34|4.17|3.23|1.76|0.10|-|-|0.41|22.15|
|Nepal|3.84|8.50|0.40|1.08|6.66|7.30|0.06|0.51|-|-|
|Maldives|2.19|0.64|0.86|0.95|N/A|N/A|10.01|11.73|NA|-|
|Bhutan|-|4.03|0.07|0.17|0.58|1.71|N/A|N/A|N/A|1.18|
|US|4.99|1.53|1.46|2.37|3.13|2.59|16.02|2.58|37.31|0.70|
|UK|0.76|0.56|0.81|0.97|0.76|1.23|0.80|1.49|2.05|0.30|

Note: Calculations are based on data from the IMF Direction of Trade Statistics Overall, the trade intensity indices and their movements from pre-reform to post-reform periods do not demonstrate a consistent positive relationship for all countries in the South Asia region. A positive increase in the TII appears only between India and Nepal; in other cases, the bilateral relationships are asymmetric and/or in decline. The evidence also supports the earlier observation that South Asia does not appear to fit the
measures than those hired before—either in the pooled model or in the country-specific models (Table 3). If anything, the estimates suggest that there is a positive relationship with being a postFPE hire. In the pooled model (Column 1 of Table 3) the effect of being a post-FPE hire is statistically significantly positive on the use of good pedagogical practices and negative on school absence (a pattern that is repeated in some of the individual countries such as Kenya, and Tanzania). Perhaps surprisingly, the only somewhat consistent result across countries from this analysis is that teachers hired post-FPE are less likely to have been absent on the day of the unannounced visit.

# 5. Discussion and conclusion

This analysis set out to explore the extent to which the effects of the rapid scale-up in the hiring of teachers associated with FPE reforms in Sub-Saharan Africa—and the potentially lower “quality” of those teachers, due to potentially lowering of standards required to rapidly recruit substantial numbers of teachers—are still detectable at the classroom level 5 to 16 years (depending on the country) after the reform. Administrative data confirm the rapid increase in the number of primary school teachers across these countries, with inflections at the time of FPE for several. In many of the countries, the years which current grade 4 teachers report as having been their first also reflect an inflection at the time of FPE.

Comparing the learning outcomes of students whose teachers were hired in the years before and after FPE suggests that there is, on average, a learning deficit for the latter—although, depending on the bandwidth used, the effects are not always statistically significantly different from 0. Pooled across all the countries in the study, the deficit is on the order of 0.2 standard deviation on the language test, and 0.1 standard deviation on the math test (test scores have mean 0 and standard deviation 1 across all test-taking students in these countries). Deficits in language are common across almost all the countries (Uganda is the
**Table 7. Output percentage. Youths from 18-35 years of age of ENCEL 2017.**

||||**Stratum**<br>|**of the so**<br>|**n or daugh **<br>|**ter(n= 3,**<br>|**084)**<br>||
|---|---|---|---|---|---|---|---|---|
|||**NM**|**Commerc**|**e HM**|**LMM**|**LMS**|**Agriculture**|**Total**|
||NM|26|8.2|16.4|12.3|16.4|20.5|100|
||Commerce|9.1|18.2|15.9|19.3|15.9|21.6|100|
|**Stratum of**<br>**the**|HM|8|11.8|30.4|13.5|19.8|16.5|100|
|<br>**provider**|LMM|11|11|19.7|26.2|14.2|17.8|100|
||LMS|9.2|15.8|15.8|8.8|23.8|26.7|100|
||Agriculture|9.2|11.4|11.2|10.6|14.6|43|100|
||Total|9.7|11.9|14.2|12.5|15.9|35.9|100
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