Notes

crisis will leave behind. To achieve this improvement will require strategic leadership informed by evidence and analysis of the main drivers of learning. Consolidating resilient education systems will require decision-makers to make the most of constrained financial resources during an economic downturn whose impacts in many settings are likely to be prolonged. SDI surveys can contribute through the evidence and lessons already produced. As the effort evolves, it will also support countries to continue transforming their education and other social service systems, applying new tools and methods to spark progress for students, teachers, families, and societies. The next chapter looks in detail at some of the measurement innovations that can drive this work.

1 | For simplicity, this book presents unweighted results. However, all messages extracted from the data are robust to using weights.

2 | Morocco is not included in this estimation because it is not part of Sub-Saharan Africa. This sample focuses on SDI surveys that were completed between 2012 and 2018. The 2010 Senegal and Tanzania pilot surveys are excluded because they were conducted at a smaller scale and are not fully comparable. Similar exercises such as the Systems Approach for Better Education Results (SABER)–Service Delivery surveys are not considered for this book, because they have not been fully harmonized with SDI data. Recently collected data that have not yet been validated will be included in future reports. When panel data are available for a given country, the latest year is used. The share of schoolchildren is estimated on the basis of each country’s latest available statistics from the World Bank (https://databank.worldbank.org/).

3 | The estimates of GDP per capita (based on purchasing power parity in current international dollars) come from World Bank Open Data, and the year of the survey is used for each country’s estimate. See https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD

4 | See appendix D for details on the methodological groundwork for the SDI teacher and student assessments.

5 | The sample of SDI countries in Bold et al. (2017) differs from the one used in this book. In particular, that paper uses data from the SDI pilots in Senegal and Tanzania in 2010 and from Tanzania in 2014, whereas this book excludes those data in favor of newer data for Tanzania (2016) and adds data for

Madagascar (2016), Morocco (2016), and Niger (2015). For more details on the selection of surveys for this book, see the discussion on sample, methods, and framework.

6 | The difference is 0.07 of a standard deviation in a regression of math test scores, controlling for student age and country fixed effects.

7 | For the language component of the student assessment, a subsample of students in some countries was tested in a language other than the language of instruction, typically the vernacular. Although this approach provides important insights for within-country analysis and policy recommendations, it makes language scores less comparable across countries. For that reason, the remainder of this subsection focuses only on students’ mathematics test scores when presenting cross-country comparisons. For further details, see box 3.1.

8 | In order to make test scores comparable across countries and time and to put them on the same scale, test scores are computed using psychometric linking methods from item response theory and then standardized to have mean = 0 and standard deviation = 1. Maximum likelihood estimates are

used throughout this book. For details on these methods, see Bau, Das, and Yi Chang (2021); Das and Zajonc (2010).

9 | In contrast, Harmonized Learning Outcomes rankings and differences in test scores for this group of countries show Kenya as having the highest performance and Niger as having the lowest performance (and a difference of 1.5 standard deviations).

10 | A simple ANOVA decomposition shows that, whereas country fixed effects explain up to 63.5 percent of the variation, the first principal component obtained through principal component analysis of school inputs and teacher characteristics accounts for 6.6 percent and 11.5 percent of the explained variation in the SDI sample.

11 | The characteristics that this chapter focuses on were selected mostly through a double-lasso regression on student test scores. A very similar combination of variables was prioritized by the first principal component obtained through principal component analysis as explaining the most variation within four categories of variables corresponding to the World Development Report 2018 framework.

12 | See the discussion in chapter 1.

13 | These averages are in line with the 23 percent and 44 percent reported in Bold et al. (2017). However, new countries provide different pictures. For instance, teacher absence from school and the classroom in Morocco seems to be low, at 3 percent and 4 percent, respectively, but only after excusing those who were initially reported as absent because they were working on a different shift. This correction substantially affects only the estimates for Morocco, which otherwise would have a teacher absence rate of 23 percent from both school and classroom. The earlier paper uses data from the SDI pilots in Senegal and Tanzania in 2010 and from Tanzania in 2014; this book excludes those data in favor of newer data for Tanzania (2016) and adds data from Niger (2015), Madagascar (2016), and Morocco (2016). This book also focuses on public schools (in the current subsection), whereas the results of Bold et al. (2017) include both public and private schools. Furthermore, the use of different weights for some countries and the reclassification of some teachers reported to be absent because the visit did not take place during their shift might create a small difference in the estimates. More details on how SDI countries were selected for this book can be found in the section

on samples, methods, and framework.

14 | It is difficult to gauge the reliability of this information and the degree to which it appropriately explains the bulk of teacher absences reported in the Morocco survey. Future SDI surveys could perform a follow-up investigation in a small sample of facilities to study the share of excused absences that are legitimate.

15 | A regression of assessment scores and education-level groups of teachers finds significant and positive, albeit very small, associations (2–4-percentage-point differences) between groups of teachers by level of education and teachers’ knowledge and pedagogical skills after controlling for country fixed effects, urban-rural locality, and teachers’ age and gender.

16 | A substantial number of such stories is documented in World Development Report 2018: Learning to

Realize Education’s Promise (World Bank 2018).