sample research paper about absenteeism

Report | Children

Student absenteeism : Who misses school and how missing school matters for performance

Report • By Emma García and Elaine Weiss • September 25, 2018

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A broader understanding of the importance of student behaviors and school climate as drivers of academic performance and the wider acceptance that schools have a role in nurturing the “whole child” have increased attention to indicators that go beyond traditional metrics focused on proficiency in math and reading. The 2015 passage of the Every Student Succeeds Act (ESSA), which requires states to report a nontraditional measure of student progress, has codified this understanding.

The vast majority of U.S. states have chosen to comply with ESSA by using measures associated with student absenteeism—and particularly, chronic absenteeism. This report uses data on student absenteeism to answer several questions: How much school are students missing? Which groups of students are most likely to miss school? Have these patterns changed over time? And how much does missing school affect performance?

Data from the National Assessment of Educational Progress (NAEP) in 2015 show that about one in five students missed three days of school or more in the month before they took the NAEP mathematics assessment. Students who were diagnosed with a disability, students who were eligible for free lunch, Hispanic English language learners, and Native American students were the most likely to have missed school, while Asian students were rarely absent. On average, data show children in 2015 missing fewer days than children in 2003.

Our analysis also confirms prior research that missing school hurts academic performance: Among eighth-graders, those who missed school three or more days in the month before being tested scored between 0.3 and 0.6 standard deviations lower (depending on the number of days missed) on the 2015 NAEP mathematics test than those who did not miss any school days.

Introduction and key findings

Education research has long suggested that broader indicators of student behavior, student engagement, school climate, and student well-being are associated with academic performance, educational attainment, and with the risk of dropping out. 1

One such indicator—which has recently been getting a lot of attention in the wake of the passage of the Every Student Succeeds Act (ESSA) in 2015—is student absenteeism. Absenteeism—including chronic absenteeism—is emerging as states’ most popular metric to meet ESSA’s requirement to report a “nontraditional” 2 measure of student progress (a metric of “school quality or student success”). 3

Surprisingly, even though it is widely understood that absenteeism has a substantial impact on performance—and even though absenteeism has become a highly popular metric under ESSA—there is little guidance for how schools, districts, and states should use data about absenteeism. Few empirical sources allow researchers to describe the incidence, trends over time, and other characteristics of absenteeism that would be helpful to policymakers and educators. In particular, there is a lack of available evidence that allows researchers to examine absenteeism at an aggregate national level, or that offers a comparison across states and over time. And although most states were already gathering aggregate information on attendance (i.e., average attendance rate at the school or district level) prior to ESSA, few were looking closely into student-level attendance metrics, such as the number of days each student misses or if a student is chronically absent, and how they mattered. These limitations reduce policymakers’ ability to design interventions that might improve students’ performance on nontraditional indicators, and in turn, boost the positive influence of those indicators (or reduce their negative influence) on educational progress.

In this report, we aim to fill some of the gaps in the analysis of data surrounding absenteeism. We first summarize existing evidence on who misses school and how absenteeism matters for performance. We then analyze the National Assessment of Educational Progress (NAEP) data from 2003 (the first assessment with information available for every state) and 2015 (the most recent available microdata). As part of the NAEP assessment, fourth- and eighth-graders were asked about their attendance during the month prior to taking the NAEP mathematics test. (The NAEP assessment may be administered anytime between the last week of January and the end of the first week of March, so “last month” could mean any one-month period between the first week of January and the first week of March.) Students could report that they missed no days, 1–2 days, 3–4 days, 5–10 days, or more than 10 days.

We use this information to describe how much school children are missing, on average; which groups of children miss school most often; and whether there have been any changes in these patterns between 2003 and 2015. We provide national-level estimates of the influence of missing school on performance for all students, as well as for specific groups of students (broken out by gender, race/ethnicity and language status, poverty/income status, and disability status), to detect whether absenteeism is more problematic for any of these groups. We also present evidence that higher levels of absenteeism are associated with lower levels of student performance. We focus on the characteristics and outcomes of students who missed three days of school or more in the previous month (the aggregate of those missing 3–4, 5–10, and more than 10 school days), which is our proxy for chronic absenteeism. 4 We also discuss data associated with children who had perfect attendance the previous month and those who missed more than 10 days of school (our proxy for extreme chronic absenteeism).

Given that the majority of states (36 states and the District of Columbia) are using “chronic absenteeism” as a metric in their ESSA accountability plans, understanding the drivers and characteristics of absenteeism and, thus, the policy and practice implications, is more important than ever (Education Week 2017). Indeed, if absenteeism is to become a useful additional indicator of learning and help guide effective policy interventions, it is necessary to determine who experiences higher rates of absenteeism; why students miss school days; and how absenteeism affects student performance (after controlling for factors associated with absenteeism that also influence performance).

Major findings include:

One in five eighth-graders was chronically absent. Typically, in 2015, about one in five eighth-graders (19.2 percent) missed school three days or more in the month before the NAEP assessment and would be at risk of being chronically absent if that pattern were sustained over the school year.

About 13 percent missed 3–4 days of school in 2015; about 5 percent missed 5–10 days of school (between a quarter and a half of the month); and a small minority, less than 2 percent, missed more than 10 days of school, or half or more of the school days that month.
We find no significant differences in rates of absenteeism and chronic absenteeism by grade (similar shares of fourth-graders and eighth-graders were absent), and the patterns were relatively stable between 2003 and 2015.
While, on average, there was no significant change in absenteeism levels between 2003 and 2015, there was a significant decrease over this period in the share of students missing more than 10 days of school.

Absenteeism varied substantially among the groups we analyzed. In our analysis, we look at absenteeism by gender, race/ethnicity and language status, FRPL (free or reduced-price lunch) eligibility (our proxy for poverty status), 5 and IEP (individualized education program) status (our proxy for disability status). 6 Some groups had much higher shares of students missing school than others.

Twenty-six percent of IEP students missed three school days or more, compared with 18.3 percent of non-IEP students.
Looking at poverty-status groups, 23.2 percent of students eligible for free lunch, and 17.9 percent of students eligible for reduced-price lunch, missed three school days or more, compared with 15.4 percent of students who were not FRPL-eligible (that is, eligible for neither free lunch nor reduced-price lunch).
Among students missing more than 10 days of school, the share of free-lunch-eligible students was more than twice as large as the share of non-FRPL-eligible students (2.3 percent vs. 1.1 percent). Similarly, the share of IEP students in this category was more than double the share of non-IEP students (3.2 percent vs. 1.5 percent).
Perfect attendance rates were slightly higher among black and Hispanic non-ELL students than among white students, although all groups lagged substantially behind Asian students in this indicator.
Hispanic ELL students and Asian ELL students were the most likely to have missed more than 10 school days, at 3.9 percent and 3.2 percent, respectively. These shares are significantly higher than the overall average rate of 1.7 percent and than the shares for their non-ELL counterparts (Hispanic non-ELL students, 1.6 percent; Asian non-ELL students, 0.6 percent).

Absenteeism varied by state. Some states had much higher absenteeism rates than others. Patterns within states remained fairly consistent over time.

In 2015, California and Massachusetts were the states with the highest full-attendance rates: 51.1 and 51.0 percent, respectively, of their students did not miss any school days; they are closely followed by Virginia (48.4 percent) and Illinois and Indiana (48.3 percent).
At the other end of the spectrum, Utah and Wyoming had the largest shares of students missing more than 10 days of school in the month prior to the 2015 assessment (4.6 and 3.5 percent, respectively).
Five states and Washington, D.C., stood out for their high shares of students missing three or more days of school in 2015: in Utah, nearly two-thirds of students (63.5 percent) missed three or more days; in Alaska, nearly half (49.6 percent) did; and in the District of Columbia, Wyoming, New Mexico, and Montana, nearly three in 10 students were in this absenteeism category.
In most states, overall absenteeism rates changed little between 2003 and 2015.

Prior research linking chronic absenteeism with lowered academic performance is confirmed by our results. As expected, and as states have long understood, missing school is negatively associated with academic performance (after controlling for factors including race, poverty status, gender, IEP status, and ELL status). As students miss school more frequently, their performance worsens.

Overall performance gaps. The gaps in math scores between students who did not miss any school and those who missed three or more days of school varied from 0.3 standard deviations (for students who missed 3–4 days of school the month prior to when the assessment was taken) to close to two-thirds of a standard deviation (for those who missed more than 10 days of school). The gap between students who did not miss any school and those who missed just 1–2 days of school was 0.10 standard deviations, a statistically significant but relatively small difference in practice.
For Hispanic non-ELL students, missing more than 10 days of school harmed their performance on the math assessment more strongly than for the average (0.74 standard deviations vs. 0.64 on average).
For Asian non-ELL students, the penalty for missing school was smaller than the average (except for those missing 5–10 days).
Missing school hindered performance similarly across the three poverty-status groups (nonpoor, somewhat poor, and poor). However, given that there are substantial differences in the frequency with which children miss school by poverty status (that is, poor students are more likely to be chronically absent than nonpoor students), absenteeism may in fact further widen income-based achievement gaps.

What do we already know about why children miss school and which children miss school? What do we add to this evidence?

Poor health, parents’ nonstandard work schedules, low socioeconomic status (SES), changes in adult household composition (e.g., adults moving into or out of the household), residential mobility, and extensive family responsibilities (e.g., children looking after siblings)—along with inadequate supports for students within the educational system (e.g., lack of adequate transportation, unsafe conditions, lack of medical services, harsh disciplinary measures, etc.)—are all associated with a greater likelihood of being absent, and particularly with being chronically absent (Ready 2010; U.S. Department of Education 2016). 8 Low-income students and families disproportionately face these challenges, and some of these challenges may be particularly acute in disadvantaged areas 9 ; residence in a disadvantaged area may therefore amplify or reinforce the distinct negative effects of absenteeism on educational outcomes for low-income students.

A detailed 2016 report by the U.S. Department of Education showed that students with disabilities were more likely to be chronically absent than students without disabilities; Native American and Pacific Islander students were more likely to be chronically absent than students of other races and ethnicities; and non-ELL students were more likely to be chronically absent than ELL students. 10 It also showed that students in high school were more likely to miss school than students in other grades, and that about 500 school districts reported that 30 percent or more of their students missed at least three weeks of school in 2013–2014 (U.S. Department of Education 2016).

Our analysis complements this evidence by adding several dimensions to the breakdown of who misses school—including absenteeism rates by poverty status and state—and by analyzing how missing school harms performance. We distinguish by the number of school days students report having missed in the month prior to the assessment (using five categories, from no days missed to more than 10 days missed over the month), 11 and we compare absenteeism rates across grades and across cohorts (between 2003 and 2015), as available in the NAEP data. 12

How much school are children missing? Are they missing more days than the previous generation?

In 2015, almost one in five, or 19.2 percent of, eighth-grade students missed three or more days of school in the month before they participated in NAEP testing. 13 About 13 percent missed 3–4 days, roughly 5 percent missed 5–10 days, and a small share—less than 2 percent—missed more than 10 days, or half or more of the instructional days that month ( Figure A , bottom panel). 14

How much school are children missing? : Share of eighth-grade students by attendance/absenteeism category, in the eighth-grade mathematics NAEP sample, 2003 and 2015

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Source: EPI analysis of National Assessment of Educational Progress microdata, 2003 and 2015

On average, however, students in 2015 did not miss any more days than students in the earlier period; by some measures, they missed less school than children in 2003 (Figure A, top panel). While the share of students with occasional absences (1–2 days) increased moderately between 2003 and 2015, the share of students who missed more than three days of school declined by roughly 3 percentage points between 2003 and 2015. This reduction was distributed about evenly (in absolute terms) across the shares of students missing 3–4, 5–10, and more than 10 days of school. But in relative terms, the reduction was much more significant in the share of students missing more than 10 days of school (the share decreased by nearly one-third). We find no significant differences by grade ( Appendix Figure A ) or by subject. Thus, we have chosen to focus our analyses below on the sample of eighth-graders taking the math assessment only.

Which groups miss school most often? Which groups suffer the most from chronic absenteeism?

Absenteeism by race/ethnicity and language status.

Hispanic ELLs and the group made up of Native Americans plus “all other races” (not white, black, Hispanic, or Asian) are the racial/ethnic and language status groups that missed school most frequently in 2015. Only 39.6 percent (Native American or other) and 41.2 percent (Hispanic ELL) did not miss any school in the month prior to the assessment (vs. 44.4 percent overall, 43.2 percent for white students, 43.5 percent for black students, and 44.1 percent for Hispanic non-ELL students; see Figure B1 ). 15

Which groups of students had the highest shares missing no school? : Share of eighth-graders with perfect attendance in the month prior to the 2015 NAEP mathematics assessment, by group

Source: EPI analysis of National Assessment of Educational Progress microdata, 2015

Asian students (both non-ELL and ELL) are the least likely among all racial/ethnic student groups to be absent from school at all. Two-thirds of Asian non-ELL students and almost as many (61.6 percent of) Asian ELL students did not miss any school. Among Asian non-ELL students, only 8.8 percent missed three or more days of school: 6.1 percent missed 3–4 days (12.7 percent on average), 2.1 percent missed 5–10 days (relative to 4.8 percent for the overall average), and only 0.6 percent missed more than 10 days of school (relative to 1.7 percent for the overall average). Among Asian ELL students, the share who missed three or more days of school was 13.3 percent.

As seen in Figure B2 , the differences in absenteeism rates between white students and Hispanic non-ELL students were relatively small, when looking at the shares of students missing three or more days of school (18.3 percent and 19.1 percent, respectively). The gaps are somewhat larger for black, Native American, and Hispanic ELL students relative to white students (with shares missing three or more days at 23.0, 24.0, and 24.1 percent, respectively, relative to 18.3 percent for white students).

Which groups of students had the highest shares missing three or more days? : Share of eighth-graders missing three or more days of school in the month prior to the 2015 NAEP mathematics assessment, by group

Notes: This chart represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school. Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Among students who missed a lot of school (more than 10 days), there were some more substantial differences by race and language status. About 3.9 percent of Hispanic ELL students and 3.2 percent of Asian ELL students missed more than 10 days of school, compared with 2.2 percent for Native American and other races, 2.0 percent for black students, 1.4 percent for white students, and only 0.6 percent for Asian non-ELL students (all relative to the overall average of 1.7 percent) (see Figure B3 ).

Which groups of students had the highest shares missing more than 10 days? : Share of eighth-graders missing more than 10 days of school in the month prior to the 2015 NAEP mathematics assessment, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Absenteeism by income status

The attendance gaps are even larger by income status than they are by race/ethnicity and language status (Figures B1–B3). Poor (free-lunch-eligible) students were 5.9 percentage points more likely to miss some school than nonpoor (non-FRPL-eligible) students, and they were 7.8 percentage points more likely to miss school three or more days (23.2 vs. 15.4 percent). 16 Among somewhat poor (reduced-price-lunch-eligible) students, 17.9 percent missed three or more days of school. The lowest-income (free-lunch-eligible) students were 4.1 percentage points more likely to miss school 3–4 days than non-FRPL-eligible students, and more than 2.4 percentage points more likely to miss school 5–10 days ( Appendix Figure B ). Finally, and most striking, free-lunch-eligible students—the most economically disadvantaged students—were more than twice as likely to be absent from school for more than 10 days as nonpoor students. In other words, they were much more likely to experience extreme chronic absenteeism. Figures B1–B3 show that the social-class gradient for the prevalence of absenteeism, proxied by eligibility for free or reduced-price lunch, is noticeable in all absenteeism categories, and especially when it comes to those students who missed the most school.

Absenteeism by disability status

Students with IEPs were by far the most likely to miss school relative to all other groups. 17 The share of IEP students missing school exceeded the share of non-IEP students missing school by 7.7 percentage points (Figure B1). More than one in four IEP students had missed school three days or more in the previous month (Figure B2). About 15.5 percent of students with IEPs missed school 3–4 days (vs. 12.4 percent among non-IEP students); 7.3 percent missed 5–10 days; and 3.2 percent missed more than 10 days of school in the month before being tested (Appendix Figure B; Figure B3).

Absenteeism by gender

The differences by gender are slightly surprising (Figures B1–B3). Boys showed a higher full-attendance rate than girls (46.6 vs. 42.1 percent did not miss any school), and boys were no more likely than girls to display extreme chronic absenteeism (1.7 percent of boys and 1.6 percent of girls missed more than 10 days of school). Boys (18.2 percent) were also slightly less likely than girls (20.2 percent) to be chronically absent (to miss three or more days of school, as per our definition).

Has there been any change over time in which groups of children are most often absent from school?

For students in several groups, absenteeism fell between 2003 and 2015 ( Figure C1 ), in keeping with the overall decline noted above. Hispanic students (both ELL and non-ELL), Asian non-ELL students, Native American and other race students, free-lunch-eligible (poor) students, reduced-priced-lunch-eligible (somewhat poor) students, non-FRPL-eligible (nonpoor) students, and IEP students were all less likely to miss school in 2015 than they were over a decade earlier. For non-IEP and white students, however, the share of students who did not miss any school days in the month prior to NAEP testing remained essentially unchanged, while it increased slightly for black students and Asian ELL students (by about 2 percentage points each).

How much have perfect attendance rates changed since 2003? : Percentage-point change in the share of eighth-graders who had perfect attendance in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

As seen in Figure C2 , we also note across-the-board reductions in the shares of students who missed three or more days of school (with the exception of the share of Asian ELL students, which increased by 1.7 percentage points over the time studied). The largest reductions occurred for students with disabilities (IEP students), Hispanic non-ELL students, Native American students or students of other races, free-lunch-eligible students, and non-FRPL-eligible students (each of these groups experienced a reduction of at least 4.4 percentage points). 18 For all groups except Asian ELL students, the share of students missing more than 10 days of school ( Figure C3 ) also decreased (for Asian ELL students, it increased by 1.3 percentage points).

How much have rates of students missing three or more days changed since 2003? : Percentage-point change in the share of eighth-graders who were absent from school three or more days in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: This chart represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school. Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

How much have rates of students missing more than 10 days changed since 2003? : Percentage-point change in the share of eighth-graders who were absent from school more than 10 days in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

In order to get a full understanding of these comparisons, we need to look at both the absolute and relative differences. Overall, the data presented show modest absolute differences in the shares of students who are absent (at any level) in various groups when compared with the averages for all students (Figures B1–B3 and Appendix Figure B). The differences (both absolute and relative) among student groups missing a small amount of school (1–2 days) are minimal for most groups. However, while the differences among groups are very small in absolute terms for students missing a lot of school (more than 10 days), some of the differences are very large in relative terms. (And, taking into account the censoring problem mentioned earlier, they could potentially be even larger.)

The fact that the absolute differences are small is in marked contrast to differences seen in many other education indicators of outcomes and inputs, which tend to be much larger by race and income divisions (Carnoy and García 2017; García and Weiss 2017). Nevertheless, both the absolute and relative differences we find are revealing and important, and they add to the set of opportunity gaps that harm students’ performance.

Is absenteeism particularly high in certain states?

Share of students absent from school, by state and by number of days missed, 2015.

Notes: Based on the number of days eighth-graders in each state reported having missed in the month prior to the NAEP mathematics assessment. “Three or more days” represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school.

Over the 2003–2015 period, 22 states saw their share of students with perfect attendance grow. The number drops to 15 if we count only states in which the share of students not missing any school increased by more than 1 percentage point. In almost every state (44 states), the share of students who missed more than 10 school days decreased, and in 41 states, the share of students who missed three or more days of school also dropped, though it increased in the other 10. 19 Louisiana, Massachusetts, Nevada, Indiana, New Hampshire, and California were the states in which these shares decreased the most, by more than 6 percentage points, while Utah, Alaska, and North Dakota were the states where this indicator (three or more days missed) showed the worst trajectory over time (that is, the largest increases in chronic absenteeism).

Is absenteeism a problem for student performance?

Previous research has focused mainly on two groups of students when estimating how much absenteeism influences performance: students who are chronically absent and all other students. This prior research has concluded that students who are chronically absent are at serious risk of falling behind in school, having lower grades and test scores, having behavioral issues, and, ultimately, dropping out (U.S. Department of Education 2016; see summary in Gottfried and Ehrlich 2018). Our analysis allows for a closer examination of the relationship between absenteeism and performance, as we look at the impact of absenteeism on student performance at five levels of absenteeism. This design allows us to test not only whether different levels of absenteeism have different impacts on performance (as measured by NAEP test scores), but also to identify the point at which the impact of absenteeism on performance becomes a concern. Specifically, we look at the relationship between student absenteeism and mathematics performance among eighth-graders at various numbers of school days missed. 20

The results shown in Figure D and Appendix Table 1 are obtained from regressions that assess the influence of absenteeism and other individual- and school-level determinants of performance. The latter include students’ race/ethnicity, gender, poverty status, ELL status, and IEP status, as well as the racial/ethnic composition of the school they attend and the share of students in their school who are eligible for FRPL (a proxy for the SES composition of the school). Our results thus identify the distinct association between absenteeism and performance, net of other factors that are known to influence performance. 21

In general, the more frequently children missed school, the worse their performance. Relative to students who didn’t miss any school, those who missed some school (1–2 school days) accrued, on average, an educationally small, though statistically significant, disadvantage of about 0.10 standard deviations (SD) in math scores (Figure D and Appendix Table 1, first row). Students who missed more school experienced much larger declines in performance. Those who missed 3–4 days or 5–10 days scored, respectively, 0.29 and 0.39 standard deviations below students who missed no school. As expected, the harm to performance was much greater for students who were absent half or more of the month. Students who missed more than 10 days of school scored nearly two-thirds (0.64) of a standard deviation below students who did not miss any school. All of the gaps are statistically significant, and together they identify a structural source of academic disadvantage.

The more frequently students miss school, the worse their performance : Performance disadvantage experienced by eighth-graders on the 2015 NAEP mathematics assessment, by number of school days missed in the month prior to the assessment, relative to students with perfect attendance in the prior month (standard deviations)

Notes: Estimates are obtained after controlling for race/ethnicity, poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL (a proxy for school socioeconomic composition). All estimates are statistically significant at p < 0.01.

The results show that missing school has a negative effect on performance regardless of how many days are missed, with a moderate dent in performance for those missing 1–2 days and a troubling decline in performance for students who missed three or more days that becomes steeper as the number of missed days rises to 10 and beyond. The point at which the impact of absenteeism on performance becomes a concern, therefore, is when students miss any amount of school (vs. having perfect attendance); the level of concern grows as the number of missed days increases.

Gaps in performance associated with absenteeism are similar across all races/ethnicities, between boys and girls, between FRPL-eligible and noneligible students, and between students with and without IEPs. For example, relative to nonpoor (non-FRPL-eligible) students who did not miss any school, nonpoor children who missed school accrued a disadvantage of -0.09 SD (1–2 school days missed), -0.27 SD (3–4 school days missed), -0.36 SD (5–10 school days missed), and -0.63 SD (more than 10 days missed). For students eligible for reduced-price lunch (somewhat poor students) who missed school, compared with students eligible for reduced-price lunch who did not miss any school, the gaps are -0.16 SD (1–2 school days missed), -0.33 SD (3–4 school days missed), -0.45 SD (5–10 school days missed), and -0.76 SD (more than 10 days missed). For free-lunch-eligible (poor) students who missed school, relative to poor students who do not miss any school, the gaps are -0.11 SD (1–2 school days missed), -0.29 SD (3–4 school days missed), -0.39 SD (5–10 school days missed), and -0.63 SD (more than 10 days missed). By IEP status, relative to non-IEP students who did not miss any school, non-IEP students who missed school accrued a disadvantage of -0.11 SD (1–2 school days missed), -0.30 SD (3–4 school days missed), -0.40 SD (5–10 school days missed), and -0.66 SD (more than 10 days missed). And relative to IEP students who did not miss any school, IEP students who missed school accrued a disadvantage of -0.05 SD (1–2 school days missed), -0.21 SD (3–4 school days missed), -0.31 SD (5–10 school days missed), and -0.52 SD (more than 10 days missed). (For gaps by gender and by race/ethnicity, see Appendix Table 1).

Importantly, though the gradients of the influence of absenteeism on performance by race, poverty status, gender, and IEP status (Appendix Table 1) are generally similar to the gradients in the overall relationship between absenteeism and performance for all students, this does not mean that all groups of students are similarly disadvantaged when it comes to the full influence of absenteeism on performance. The overall performance disadvantage faced by any given group is influenced by multiple factors, including the size of the group’s gaps at each level of absenteeism (Appendix Table 1), the group’s rates of absenteeism (Figure B), and the relative performance of the group with respect to the other groups (Carnoy and García 2017). The total gap that results from adding these factors can thus become substantial.

To illustrate this, we look at Hispanic ELL, Asian non-ELL, Asian ELL, and FRPL-eligible students. The additional penalty associated with higher levels of absenteeism is smaller than average for Hispanic ELL students experiencing extreme chronic absenteeism; however, their performance is the lowest among all groups (Carnoy and García 2017) and they have among the highest absenteeism rates.

The absenteeism penalty is also smaller than average for Asian non-ELL students (except at 5-10 days); however, in contrast with the previous example, their performance is the highest among all groups (Carnoy and García 2017) and their absenteeism rate is the lowest.

The absenteeism penalty for Asian ELL students is larger than average, and the gradient is steeper. 22 Asian ELL students also have lower performance than most other groups (Carnoy and García 2017).

Finally, although there is essentially no difference in the absenteeism–performance relationship by FRPL eligibility, the higher rates of absenteeism (at every level) for students eligible for free or reduced-price lunch, relative to nonpoor (FRPL-ineligible) students, put low-income students at a greater risk of diminished performance due to absenteeism than their higher-income peers, widening the performance gap between these two groups.

Conclusions

Student absenteeism is a puzzle composed of multiple pieces that has a significant influence on education outcomes, including graduation and the probability of dropping out. The factors that contribute to it are complex and multifaceted, and likely vary from one school setting, district, and state to another. This analysis aims to shed additional light on some key features of absenteeism, including which students tend to miss school, how those profiles have changed over time, and how much missing school matters for performance.

Our results indicate that absenteeism rates were high and persistent over the period examined (2003–2015), although they did decrease modestly for most groups and in most states. Unlike findings for other factors that drive achievement gaps—from preschool attendance to economic and racial school segregation to unequal funding (Carnoy and García 2017; García 2015; García and Weiss 2017)—our findings here seem to show some positive news for black and Hispanic students: these students had slightly higher perfect attendance rates than their white peers; in addition, their perfect attendance rates have increased over time at least as much as rates for white students. But with respect to the absenteeism rates that matter the most (three or more days of school missed, and more than 10 days of school missed), black and Hispanic students still did worse (just as is the case with other opportunity gaps faced by these students). Particularly worrisome is the high share of Hispanic ELL students who missed more than 10 school days—nearly 4 percent. Combined with the share of Hispanic ELL students who missed 5–10 school days (nearly 6 percent), this suggests that one in 10 children in this group would miss school for at least a quarter of the instructional time.

The advantages that Asian students enjoy relative to white students and other racial/ethnic groups in academic settings is also confirmed here (especially among Asian non-ELL students): the Asian students in the sample missed the least school. And there is a substantial difference in rates of absenteeism by poverty (FRPL) and disability (IEP) status, with the difference growing as the number of school days missed increases. Students who were eligible for free lunch were twice as likely as nonpoor (FRPL-ineligible) students to be absent more than 10 days, and students with IEPs were more likely than any other group to be absent (one or more days, that is, to not have perfect attendance).

Missing school has a distinct negative influence on performance, even after the potential mediating influence of other factors is taken into account, and this is true at all rates of absenteeism. The bottom line is that the more days of school a student misses, the poorer his or her performance will be, irrespective of gender, race, ethnicity, disability, or poverty status.

These findings help establish the basis for an expanded analysis of absenteeism along two main, and related, lines of inquiry. One, given the marked and persistent patterns of school absenteeism, it is important to continue to explore and document why children miss school—to identify the full set of factors inside and outside of schools that influence absenteeism. Knowing whether (or to what degree) those absences are attributable to family circumstances, health, school-related factors, weather, or other factors, is critical to effectively designing and implementing policies and practices to reduce absenteeism, especially among students who chronically miss school. The second line of research could look at variations in the prevalence and influence of absenteeism among the states, and any changes over time in absenteeism rates within each state, to assess whether state differences in policy are reducing absenteeism and mitigating its negative impacts. For example, in recent years, Connecticut has made reducing absenteeism, especially chronic absenteeism, a top education policy priority, and has developed a set of strategies and resources that could be relevant to other states as well, especially as they begin to assess and respond to absenteeism as part of their ESSA plans. 23

The analyses in this report confirm the importance of looking closely into “other” education data, above and beyond performance (test scores) and individual and school demographic characteristics. The move in education policy toward widening accountability indicators to indicators of school quality, such as absenteeism, is important and useful, and could be expanded to include other similar data. Indicators of bullying, school safety, student tardiness, truancy, level of parental involvement, and other factors that are relevant to school climate, well-being, and student performance would also merit attention.

Acknowledgements

The authors gratefully acknowledge John Schmitt and Richard Rothstein for their insightful comments and advice on earlier drafts of the paper. We are also grateful to Krista Faries for editing this report, to Lora Engdahl for her help structuring it, and to Julia Wolfe for her work preparing the tables and figures included in the appendix. Finally, we appreciate the assistance of communications staff at the Economic Policy Institute who helped to disseminate the study, especially Dan Crawford and Kayla Blado.

About the authors

Emma García is an education economist at the Economic Policy Institute, where she specializes in the economics of education and education policy. Her areas of research include analysis of the production of education, returns to education, program evaluation, international comparative education, human development, and cost-effectiveness and cost-benefit analysis in education. Prior to joining EPI, García was a researcher at the Center for Benefit-Cost Studies of Education, the National Center for the Study of Privatization in Education, and the Community College Research Center at Teachers College, Columbia University, and did consulting work for the National Institute for Early Education Research, MDRC, and the Inter-American Development Bank. García has a Ph.D. in economics and education from Teachers College, Columbia University.

Elaine Weiss served as the national coordinator for the Broader, Bolder Approach to Education (BBA) from 2011 to 2017, in which capacity she worked with four co-chairs, a high-level task force, and multiple coalition partners to promote a comprehensive, evidence-based set of policies to allow all children to thrive. She is currently working on a book drawing on her BBA case studies, co-authored with Paul Reville, to be published by the Harvard Education Press. Weiss came to BBA from the Pew Charitable Trusts, where she served as project manager for Pew’s Partnership for America’s Economic Success campaign. Weiss was previously a member of the Centers for Disease Control and Prevention’s task force on child abuse and served as volunteer counsel for clients at the Washington Legal Clinic for the Homeless. She holds a Ph.D. in public policy from the George Washington University and a J.D. from Harvard Law School.

Appendix figures and tables

Are there significant differences in student absenteeism rates across grades and over time : shares of fourth-graders and eighth-graders who missed school no days, 1–2 days, 3–4 days, 5–10 days, and more than 10 days in the month before the naep mathematics assessment, 2003 and 2015, detailed absenteeism rates by group : shares of eighth-graders in each group who missed school no days, 1–2 days, 3–4 days, 5–10 days, and more than 10 days in the month before the naep mathematics assessment, 2015, the influence of absenteeism on eighth-graders' math achievement : performance disadvantage experienced by eighth-graders on the 2015 naep mathematics assessment, by group and by number of days missed in the month prior to the assessment, relative to students in the same group with perfect attendance in the prior month (standard deviations).

*** p < 0.01; ** p < 0.05; * p < 0.1

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines). Estimates for the “All students” sample are obtained after controlling for race/ethnicity, poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL (a proxy for school socioeconomic composition). For each group, controls that are not used to identify the group are included (for example, for black students, estimates control for poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL; etc.)

1. See García 2014 and García and Weiss 2016.

2. See ESSA 2015. According to ESSA, this nontraditional indicator should measure “school quality or student success.” (The other indicators at elementary/middle school include measures of academic achievement, e.g., performance or proficiency in reading/language arts and math; academic progress, or student growth; and progress in achieving English language proficiency.)

3. Thirty-six states and the District of Columbia have included student absenteeism as an accountability metric in their states’ ESSA plans. This metric meets all the requirements (as outlined in ESSA) to be considered a measure of school quality or student success (valid, reliable, calculated the same for all schools and school districts across the state, can be disaggregated by student subpopulation, is a proven indicator of school quality, and is a proven indicator of student success; see Education Week 2017). See FutureEd 2017 for differences among the states’ ESSA plans. See the web page “ ESSA Consolidated State Plans ” (on the Department of Education website) for the most up-to-date information on the status and content of the state plans.

4. There is no precise official definition that identifies how many missed days constitutes chronic absenteeism on a monthly basis. Definitions of chronic absenteeism are typically based on the number of days missed over an entire school year, and even these definitions vary. For the Department of Education, chronically absent students are those who “miss at least 15 days of school in a year” (U.S. Department of Education 2016). Elsewhere, chronic absenteeism is frequently defined as missing 10 percent or more of the total number of days the student is enrolled in school, or a month or more of school, in the previous year (Ehrlich et al. 2013; Balfanz and Byrnes 2012). Given that the school year can range in length from 180 to 220 days, and given that there are about 20–22 instructional days in a month of school, these latter two definitions imply that a student is chronically absent if he or she misses between 18 and 22 days per year (depending on the length of the school year) or more, or between 2.0 and about 2.5 days (or more) per month on average (assuming a nine-month school year). In our analysis, we define students as being chronically absent if they have missed three or more days of school in the last month (the aggregate of students missing “3–4,” “5–10,” or “more than 10 days”), and as experiencing extreme chronic absenteeism if they have missed “more than 10 days” of school in the last month. These categories are not directly comparable to categories used in studies of absenteeism on a per-year basis or that use alternative definitions or thresholds. We purposely analyze data for each of these “days absent” groups separately to identify their distinct characteristics and the influence of those differences on performance. (Appendix Figure B and Appendix Table 1 provide separate results for each of the absenteeism categories.)

5. In our analysis, we define “poor” students as those who are eligible for free lunch; we define “somewhat poor” students as those who are eligible for reduced-price lunch; and we define “nonpoor” students as those who are not eligible for free or reduced-price lunch. We use “poverty status,” “income status,” “socioeconomic status” (“SES”), and “social class” interchangeably throughout our analysis. We use the free or reduced-price lunch status classification as a metric for individual poverty, and we use the proportion of students who are eligible for FRPL as a metric for school poverty (in our regression controls; see Figure D). The limitations of these variables to measure economic status are discussed in depth in Michelmore and Dynarski’s (2016) study. FRPL statuses are nevertheless valid and widely used proxies of low(er) SES, and students’ test scores are likely to reflect such disadvantage (Carnoy and García 2017).

6. Under the Individuals with Disabilities Education Act (IDEA), an IEP must be designed for each student with a disability. The IEP “guides the delivery of special education supports and services for the student” (U.S. Department of Education 2000). For more information about IDEA, see U.S. Department of Education n.d.

7. Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status.

8. The U.S. Department of Education (2016) defines “chronically absent” as “missing at least 15 days of school in a year.” Ready (2010) explains the difference between legitimate or illegitimate absences, which may respond to different circumstances and behaviors. Ready’s findings, pertaining to children at the beginning of school, indicate that, relative to high-SES students, low-SES children with good attendance rates experienced greater gains in literacy skills during kindergarten and first grade, narrowing the starting gaps with their high-SES peers. No differences in math skills gains were detected in kindergarten.

9. U.S. Department of Education 2016. This report uses data from the Department of Education’s Civil Rights Data Collection 2013–2014.

10. The analysis finds no differences in absenteeism by gender. It is notable that the Department of Education report finds that ELL students have lower absenteeism rates than their non-ELL peers, given that we find (as described later in the report) that Asian ELL students have higher absenteeism rates than Asian non-ELL students and that Hispanic ELL students have higher absenteeism rates than Hispanic non-ELL students. It is important to note, however, that the data the Department of Education analyze compared all ELL students to all non-ELL students (not only Asian and Hispanic students separated out by ELL status), and thus our estimates are not directly comparable.

11. Children in the fourth and eighth grades were asked, “How many days were you absent from school in the last month?” The possible answers are: none, 1–2 days, 3–4 days, 5–10 days, and more than 10 days. An important caveat concerning this indicator and results based on its utilization is that there is a potential inherent censoring problem: Children who are more likely to miss school are also likely to miss the assessment. In addition, some students may be inclined to underreport the number of days that they missed school, in an effort to be viewed more favorably (in social science research, this may introduce a source of response-bias referred to as “social desirability bias”). Although we do not have any way to ascertain the extent to which these might be problems in the NAEP data and for this question in particular, it is important to read our results and findings as a potential underestimate of what the rates of missingness are, as well as what their influence on performance is.

12. One reason to look at different grades is to explore the potential connection between early absenteeism and later absenteeism. Ideally, we would be able to include data on absenteeism from earlier grades in students’ academic careers since, as Nai-Lin Chang, Sundius, and Wiener (2017) explain, attendance habits are developed early and often set the stage for attendance patterns later on. These authors argue that detecting absenteeism early on can improve pre-K to K transitions, especially for low-income children, children with special needs, or children who experience other challenges at home; these are the students who most need the social, emotional, and academic supports that schools provide and whose skills are most likely to be negatively influenced by missing school. Gottfried (2014) finds reduced reading and math achievement outcomes, and lower educational and social engagement, among kindergartners who are chronically absent. Even though we do not have information on students’ attendance patterns at the earliest grades, looking at patterns in the fourth and eighth grades can be illuminating.

13. Students are excluded from our analyses if their absenteeism information and/or basic descriptive information (gender, race/ethnicity, poverty status, and IEP) are missing.

14. All categories combined, we note that in 2015, 49.5 percent of fourth-graders and 55.6 percent of eighth-graders missed at least one day of school in the month prior. Just over 30 percent of fourth-graders and 36.4 percent of eighth-graders missed 1–2 days of school during the month.

15. In the sample, 52.1 percent of students are white, 14.9 percent black, 4.5 percent Hispanic ELL, 19.4 percent Hispanic non-ELL, less than 1 percent Asian ELL, 4.7 percent Asian non-ELL, and 3.8 percent Native American or other.

16. Of the students in the sample, 47.8 percent are not eligible for FRPL, 5.2 percent are eligible for reduced-price lunch, and 47.0 percent are eligible for free lunch.

17. In the 2015 eighth-grade mathematics sample, 10.8 percent of students had an IEP.

18. For students who were eligible for reduced-price lunch (somewhat poor students), shares of students absent three or more days also decreased, but more modestly, by 3.3 percentage points.

19. Number of states is out of 51; the District of Columbia is included in the state data.

20. The results discussed below cannot be interpreted as causal, strictly speaking. They are obtained using regression models with controls for the relationship between performance and absenteeism (estimates are net of individual, home, and school factors known to influence performance and are potential sources of selection). However, the literature acknowledges a causal relationship between (high-quality) instructional time and performance, in discussions about the length of the school day (Kidronl and Lindsay 2014; Jin Jez and Wassmer 2013; among others) and the dip in performance children experience after being out of school for the summer (Peterson 2013, among others). These findings could be extrapolable to our absenteeism framework and support a more causal interpretation of the findings of this paper.

21. Observations with full information are used in the regressions. The absenteeism–performance relationship is only somewhat sensitive to including traditional covariates in the regression (not shown in the tables; results available upon request). The influence of absenteeism on performance is distinct and is not due to any mediating effect of the covariates that determine education performance.

22. Asian ELL students who miss more than 10 days of school are very far behind Asian ELL students with perfect attendance, with a gap of more than a standard deviation. This result needs to be interpreted with caution, however, as it is based on a very small fraction of students for whom selection may be a concern, too.

23. The data used in our analysis are for years prior to the implementation of measures intended to tackle absenteeism. See Education Week 2017. Data for future (or more recent) years will be required to analyze whether Connecticut’s policies have had an effect on absenteeism rates in the state.

Balfanz, Robert, and Vaughan Byrnes. 2012. The Importance of Being in School: A Report on Absenteeism in the Nation’s Public Schools . Johns Hopkins University Center for Social Organization of Schools, May 2012.

Carnoy, Martin, and Emma García. 2017. Five Key Trends in U.S. Student Performance: Progress by Blacks and Hispanics, the Takeoff of Asians, the Stall of Non-English Speakers, the Persistence of Socioeconomic Gaps, and the Damaging Effect of Highly Segregated Schools . Economic Policy Institute, January 2017.

Education Week. 2017. School Accountability, School Quality and Absenteeism under ESSA (Expert Presenters: Hedy Chang and Charlene Russell-Tucker) (webinar).

Ehrlich, Stacy B., Julia A. Gwynne, Amber Stitziel Pareja, and Elaine M. Allensworth with Paul Moore, Sanja Jagesic, and Elizabeth Sorice. 2013. Preschool Attendance in Chicago Public Schools: Relationships with Learning Outcomes and Reasons for Absences . The University of Chicago Consortium on Chicago School Research, September 2013.

ESSA. 2015. Every Student Succeeds Act of 2015 , Pub. L. No. 114-95 § 114 Stat. 1177 (2015–2016).

FutureEd. 2017. Chronic Absenteeism and the Fifth Indicator in State ESSA Plans . Georgetown University.

García, Emma. 2014. The Need to Address Noncognitive Skills in the Education Policy Agenda . Economic Policy Institute, December 2014.

García, Emma. 2015. Inequalities at the Starting Gate: Cognitive and Noncognitive Skills Gaps between 2010–2011 Kindergarten Classmates . Economic Policy Institute, June 2015.

García, Emma, and Elaine Weiss. 2016. Making Whole-Child Education the Norm. How Research and Policy Initiatives Can Make Social and Emotional Skills a Focal Point of Children’s Education . Economic Policy Institute, August 2016.

García, Emma, and Elaine Weiss. 2017. Education Inequalities at the School Starting Gate: Gaps, Trends, and Strategies to Address Them . Economic Policy Institute, September 2017.

Gottfried, Michael A. 2014. “Chronic Absenteeism and Its Effects on Students’ Academic and Socioemotional Outcomes.” Journal of Education for Students Placed at Risk 19, no. 2: 53–75. https://doi.org/10.1080/10824669.2014.962696 .

Gottfried, Michael A., and Stacy B. Ehrlich. 2018. “Introduction to the Special Issue: Combating Chronic Absence.” Journal of Education for Students Placed at Risk 23, no. 1–2: 1–4. https://doi.org/10.1080/10824669.2018.1439753 .

Jin Jez, Su, and Robert W. Wassmer. 2013. “The Impact of Learning Time on Academic Achievement.” Education and Urban Society 47, no. 3: 284–306. https://doi.org/10.1177/0013124513495275 .

Kidronl, Yael, and Jim Lindsay. 2014. The Effects of Increased Learning Time on Student Academic and Nonacademic Outcomes: Findings from a Meta-Analytic Review . REL 2014-015. Regional Educational Laboratory Appalachia.

Michelmore, K., and S. Dynarski. 2016. The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Student Achievement . National Bureau of Economic Research Working Paper no. 22474.

Nai-Lin Chang, Hedy, Jane Sundius, and Louise Wiener. 2017. “ Using ESSA to Tackle Chronic Absence from Pre-K to K–12 ” (blog post). National Institute for Early Education Research website, May 23, 2017.

National Center for Education Statistics (NCES), National Assessment of Educational Progress (NAEP). Various years. NAEP microdata (unpublished data).

Peterson, T.K., ed. 2013. Expanding Minds and Opportunities: Leveraging the Power of Afterschool and Summer Learning for Student Success . Washington, D.C.: Collaborative Communications Group.

Ready, Douglas D. 2010. “Socioeconomic Disadvantage, School Attendance, and Early Cognitive Development: The Differential Effects of School Exposure.” Sociology of Education 83, no. 4: 271–286. https://doi.org/10.1177/0038040710383520 .

U.S. Department of Education. 2000. A Guide to the Individualized Education Program . Office of Special Education and Rehabilitative Services, July 2000.

U.S. Department of Education. 2016. Chronic Absenteeism in the Nation’s Schools: An Unprecedented Look at a Hidden Educational Crisis (online fact sheet).

U.S. Department of Education. n.d. “ About IDEA ” (webpage). IDEA (Individuals with Disabilities Education Act) website . Accessed September 19, 2018.

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Teacher absenteeism, improving learning, and financial incentives for teachers

Cases/Trends
Published: 15 November 2022
Volume 52 , pages 343–363, ( 2022 )

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Margo O’Sullivan 1

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We know that learning is in crisis. We know that teachers are key to addressing the crisis. Yet, the significant investments in supporting teachers to improve learning have not enabled improved learning outcomes. This article examines a key reason for this: teacher absenteeism. Poor teacher motivation is highlighted as an explanation for teacher absenteeism, with poor remuneration emerging as teachers’ main reason for not attending school and/or class. This article explores the use of financial incentives, which have been sidelined within the education aid architecture, to improve teacher motivation, address teacher absenteeism, and improve learning. It distils the successes and lessons learned from the research literature, which can be used to devise a framework to guide financial-incentive-focused strategies. The framework is currently informing a research-based intervention in schools in Uganda that is using a cost-effective mobile-phone-based and teacher-motivation-focused strategy and tools to improve learning.

Approaches to Improving Teacher Quality and Effectiveness: What Works?

School-Based Teacher Educators: A Scottish Manifesto

A Cost Analysis of Traditional Professional Development and Coaching Structures in Schools

Avoid common mistakes on your manuscript.

Nine out of 10 children are not learning (World Bank, 2019 ). Beeharry ( 2021 ) used this statistic to introduce his highly regarded paper appealing to the global education aid community to prioritize foundational literacy and numeracy (FLN) in using education aid funding. Girindre’s ( 2021 ) paper led to an excellent set of 20 reflection pieces presented at a Centre Global for Development (CGD) symposium on May 19, 2021 (Hares & Sandefur, 2021 ). When reading these reflection pieces, I was struck by the dearth of focus on teachers, who are the frontline workers in the war against learning poverty. Only three of the papers mentioned teachers (Baron, 2021 ; McClean, 2021 ; Schipper et al., 2021 ). In light of teachers’ critical role in improving learning, in particular FLN, this is a concern. It also does not reflect the renewed focus, during Covid-19, on the role of teachers and the various interventions that emerged to support teachers—in particular, building teacher capacity (Burns, 2021 ). Almost every initiative and intervention in recent years has incorporated teacher education, which has increasingly focused on realizing the improved teachers’ pedagogical practices that research has found to best enable children’s learning to improve (Evans & Acosta, 2021 ; Snilstveit et al., 2015 ). “Developing countries spend many millions annually” (World Bank, 2018 , p. 131), yet learning is still in crisis.

In this article, I focus on two areas, which I call the “elephants in the classroom”, related to teachers and improving learning: teacher absenteeism and its detrimental impact on learning poverty, and motivational financial performance incentives for teachers. These will be explored through a presentation of the evidence and on the research and experience informing an intervention in Uganda that seeks to address these elephants in the classroom and ultimately improve learning outcomes much more cost effectively and sustainably than many of the current modalities being used within our education aid architecture. The research-based intervention is using a cost-effective mobile-phone-based and teacher-motivation-focused strategy and tools to improve learning. Currently, the mobile-phone-based tools for the intervention are being devised. For the purposes of this article and the intervention, learning encompasses foundational literacy and numeracy (FLN) and 21st-century learning skills.

The article is in several parts. After this introduction, it examines the overwhelming evidence on what works best in improving learning—namely, pedagogy and how best to support teachers to implement proven best-practice pedagogies (Education Commission, 2020 ; Evans & Acosta, 2021 ; Snilstveit et al., 2015 ). For the purposes of this article, pedagogy is the observable act of teaching, together with its attendant discourse on educational theories, values, evidence, and justifications (Alexander, 2009 ). I then explore the elephant in the classroom (i.e., teacher absenteeism) by examining the extent of the issue, its strong relationship with teacher motivation, and efforts to address it. The use of motivational financial performance incentives emerges as a potentially universally effective approach to addressing teacher absenteeism and ultimately improving learning. I review the literature and studies, whose results were mixed, of teacher motivational financial performance incentives interventions in developing countries. In light of the sidelining of teacher incentive approaches within the education aid architecture, I was surprised to find many examples of successes with the approach, even in those studies deemed unsuccessful, in improving learning and addressing teacher absenteeism. I purport that the small-scale nature of most of the interventions, as well as the seemingly myriad of issues emerging, even in those that were successful, is one explanation for the sidelining of the approach. I call for a reexamination of the approach to, at a minimum, raise it within the education aid architecture as a potentially effective approach—to not “throw the baby out with the bathwater”.

This leads into the next section of the article, a further analysis of the studies to distill best practices and lessons, which have been collated into a framework of six areas to guide and inform financial incentive interventions going forward. I present one such intervention in Uganda, with a framework as well as a change theory related to the humanity of teachers (Fullan, 1991 ) informing it. The research-based intervention involves three phases and is currently in the first phase. The intervention seeks to demonstrate that it is more cost-effective to use funding to financially incentivize teachers to ultimately improve learning outcomes than to use business-as-usual strategies (e.g., the newly emerging digital and innovative strategies), which fail to take teacher agency and their humanity into account. In conclusion, the article supports Girindre’s ( 2021 ) call for the prioritization of teachers in funding allocation decisions. Such prioritization would enable a focus on FLN, taking into account the humanity of teachers and addressing their levels of motivation.

Teachers and pedagogy proven to improve learning

Research definitively demonstrates that the highest aid strategy returns are pedagogy and teacher focused (i.e., what goes on in classrooms and what goes on between teachers and learners), using the various out-of-school interventions that emerged during Covid-19 school closures. The evidence is overwhelming that teachers are key to improving the quality of education and ultimately learning. And key to teachers improving learning is best practice pedagogy (Education Commission, 2020 ; Evans & Popova, 2016 ; Education Commission, 2018 ; World Bank, 2018 ). Snilstveit et al.’s ( 2015 ) definitive analysis of research from 78,000 papers, as well as Evans and Acosta’s ( 2021 ) analysis of 145 empirical studies from 2014 onward (within which 64% were government-implemented programs), highlighted pedagogy interventions as critical to improving learning.

Programs seeking to improve pedagogy had an impact greater than the equivalent of an extra half year of business-as-usual schooling and also had an 8% increase in the present discounted value of lifetime earnings (Evans & Yuan, 2017 ). In the United States, students with great teachers advance 1.5 grade levels or more in one school year, compared with just 0.5 grade levels for those with ineffective teachers (Hanushek, 1992 ; Rockoff, 2004 ). Shanghai topped Programme for International Student Assessment (PISA), thanks to policies that ensured every classroom had a prepared, supported, and motivated teacher (Liang et al., 2016 ). Hence, the focus within the education aid architecture should be placed on developing teacher capacity.

Almost every initiative and intervention in recent years has incorporated teacher professional development that seeks to realize improved pedagogical practices and ultimately children’s learning. A survey of in-service teacher training in 38 countries found that 91% of teachers had participated in the previous 12 months (Strizek et al., 2014 ). Two out of three World Bank projects with an education component in the last decade incorporated teacher professional development. UNICEF also supports teacher professional development in most of its 193 country offices and is currently carrying out research into the effectiveness of its annual investment in teacher education and training.

We also know from evidence what works best in teacher professional development to ultimately improve learning. It is most effective when it (a) targets teachers’ capacity gaps; (b) is aligned with practices associated with better student performance, often around specific pedagogical techniques (e.g., focused on FLN); and (c) includes follow-up coaching, as one-off workshops rarely bring about a change in practices. Practicality, specificity, and continuity are key to effective professional development (Conn, 2017 ; Darling-Hammond, et al. 2009 ; Popova et al., 2016 , 2017 , 2018 ; Walter & Briggs, 2012 ; Yoon et al., 2007 ; Zhang et al., 2016 ). Related to specificity, research has found that focusing on just one new pedagogical technique at a time and providing teachers with explicit guidance are supportive. “For effective teacher training, design it to be individually targeted and repeated, with follow-up coaching—often around a specific pedagogical technique” (World Bank, 2018 , p. 18).

However, the huge investments in teacher education—both financial and human, often seeking to bring about large-scale sustained improvements—have little to show for them (Popova et al., 2018 ). The returns are, at best, minimal and are mostly project/intervention dependent. The literature is littered with the failures of innumerable teacher education projects.

Is there another way to support teachers within government systems that is more cost-effective and sustainable, leads to implementation of what teachers learn, and improves students’ learning? The Power Teachers motivation-focused intervention, to be implemented in Uganda in 2022, seeks to provide another way. The intervention’s name, Power Teachers, reflects the focus of this intervention on teachers themselves and on putting power into their hands to bring about improved learning. It is informed by research, lessons learned, experience, and partnership. It begins with the elephant in the classroom: teacher absenteeism.

The elephant in the classroom: Teacher absenteeism

Amidst all the business-as-usual strategies within the current education aid architecture (e.g., teacher professional development, development of policies and curricula, advocacy campaigns, capacity building, and commissioning research and think pieces) that seek to improve teacher capacity and children’s learning, is the elephant in the classroom: teacher absenteeism. We can devise more reforms, innovations, and business-as-usual interventions to improve quality and learning; however, if teachers are not in school—or when in school, not in class teaching—failure is inevitable. Teachers’ skills do nothing for learning unless teachers choose to apply them in the classroom (World Bank, 2018 , p. 22).

UNICEF Innocenti’s ( 2020 ) recently published study on teacher absenteeism in 19 countries in Eastern and Southern Africa found teacher absenteeism rates ranging from 15% to 45%. This is a crisis issue, yet these findings, which reflect findings from earlier studies, have not received significant attention. The study examined factors affecting the various forms of teacher attendance, which include being at school, being punctual, being in the classroom, and teaching when in the classroom. Teacher absenteeism and reduced time on task wastes valuable financial resources, shortchanges students, and is one of the most cumbersome obstacles to improving learning.

An earlier study in seven African countries found that, on average, primary students received less than 2.5 hours of teaching per day, less than half the intended instructional time (Education Commission, 2018 ). This does not take into account the quality of the instructional time and whether it enabled children’s learning. Across seven African countries, more than one in five teachers (23%) were absent from schools on unannounced visits by survey teams, with only 55% in classrooms and 45% actually teaching in classrooms (Bold et al., 2017 ; Figure 1 ).

Teacher absenteeism from schools and classrooms in Africa

The World Development Report (World Bank, 2018 ) collated evidence from countries globally and highlighted that the situation in Africa (Bold et al., 2017 ) is also reflected in countries in South America, South Asia, and the Middle East (Figure 2 ).

A lot of official teaching time is lost

Anecdotal evidence and unpublished evidence also reflect these findings. These include donor and civil society reports and Ministry of Education and Sports (MoES) monitoring reports. For example, Uganda’s annual Sector Wide Approaches (SWAP) monitoring, which has taken place annually since 2008, found rates of teacher absenteeism between 17% and 30%. Moreover, not only were teachers absent, but head teacher absenteeism was also a major issue and was believed to encourage teacher absenteeism.

With teachers’ pay comprising at least 80% of recurrent budgets in most countries, this is a leading cause of inefficiency and wastage (Bold et al., 2017 ; Figure 3 ).

The gains to be had from efficiency

Muralidharan et al. ( 2017 ) calculated the fiscal cost of teacher absenteeism at $1.5 billion each year in India and Uganda. Muralidharan revisited the rural villages in Uganda and India surveyed by Chaudhery et al. ( 2006 ) and found only a modest reduction in teacher absenteeism, from 26.3% to 23.7%, on average. In 2006, Uganda’s rate was 26%, and India’s 19%.

A number of reasons can explain this systemic teacher absenteeism. The most often cited reasons include poor accountability of managers and inspectors, illness, and poor working and living conditions of teachers. The lack of focus from governments, donors, and the various platforms within the education aid architecture must also be considered factors. A key and overarching factor in teacher absenteeism is the disconnect between the demands of the profession and systemic support to teachers to meet these demands. Teachers are expected to perform as professionals, but the education systems fail to treat them as such or to provide a professional culture for them. Pay, respect, and working conditions are poor and have declined over the last few decades (World Bank, 2018 ). Systems still want more from teachers, especially during the ongoing Covid-19 pandemic, but teachers also deserve and want more from the systems that employ them (Evans & Yuan, 2017 ; World Bank, 2018 ). Many teachers in Africa have to deal on a daily basis with large classes; long working hours, including sometimes double shifts; duties outside classrooms; poor housing; lack of school infrastructure and equipment; hungry children, lack of parental engagement; and a salary that is inadequate to support their families, often requiring teachers to take on other work to supplement their salaries. Yet they are expected to implement many innovations, new curriculum reforms, and employ new pedagogies. Covid-19 added further demands on these teachers.

Agnes, a teacher in rural Eastern Uganda, encapsulates the challenges faced by many teachers in low-income countries. Agnes has been teaching lower primary classes in a small rural school for 13 years, working in very difficult conditions and only able to afford poor living conditions in a shared house 5 km from the school. She is a qualified teacher and attends in-service teacher training a few times a year. “The government expects us to use new methods, but they don’t support us or appreciate us. Just look at our poor salary, I can barely feed my children on the salary”. She explained that she and many of her colleagues feel demoralized, with some of them regularly absent from school as a result [meeting with author, February 2019].

Teachers’ general feelings of dissatisfaction affect their attendance at work. Ejere ( 2010 ) termed this absenteeism a “repulsive strategy”, which teachers use to respond to their difficult conditions and marginalization in schools. A TISSA study of teachers in Uganda in 2013 supported this. The study highlighted the key reason for teacher absenteeism as poor teacher motivation. The TISSA study found that most teachers (84%) indicated they would like to leave the profession within 2 years; 59% indicated that if they were to start their career again, they would not choose teaching. Bennett and Akeampong’s ( 2007 ) notable 12-country case study research into teacher motivation highlighted what they termed a “teacher motivation crisis”; a sizeable number of the millions of primary teachers in the 12 countries had low levels of job satisfaction and were poorly motivated, leading to “many tens of millions of children not being taught properly and are not receiving even a minimally acceptable education” (p. viii).

We still have a teacher motivation crisis; however, it is not receiving the attention it deserves, especially in light of the critical role of teachers in improving learning. More recently, Evans and Yuan’s ( 2018 ) work on teachers’ working conditions further highlighted the huge issues with poor teacher motivation. They used Maslow’s hierarchy of needs to present issues effecting teacher motivation and found that, for teachers in developing countries, physiological and safety needs were significant. These needs included compensation, living conditions, workload, work environment, and fundamental preparation.

Compensation and teachers’ pay have emerged in many studies as a critical factors in poor teacher motivation (Bennett & Akeampong, 2007 ; Evans & Yuan, 2018 ; TISSA, 2013 ). Bennett and Akeampong’s ( 2007 ) found that poor teacher salaries “more than anything else, is the key factor undermining teacher morale and motivation” (p. viii). This has led to a number of interventions that seek to use motivational financial performance incentives to improve teacher motivation, address teacher absenteeism; and ultimately, improve teacher performance and learning outcomes.

Motivational financial performance incentives

Efforts have been made in developing countries to address teacher absenteeism, mostly focused on strengthening accountability systems, and in recent years, also on piloting the provision of teacher housing, additional pay for teachers in hard-to-reach areas, provision of in-service education and training, automated salary systems direct to teachers’ bank accounts, and timely monthly salary payments. Unfortunately, none have had a sustained impact on decreasing absenteeism and improving teacher performance and children’s learning.

On a positive note, another intervention, motivational financial performance incentives—both to reduce absenteeism and to improve learning outcomes—has had some success in developing countries, unlike results in developed countries, which were not positive. For example, in the United States, Fryer ( 2011 ) and Goodman and Turner ( 2010 ) in New York and Springer (2010) in Tennessee found no improvement in test scores when motivational financial performance incentives for teachers were piloted. In developing countries, three studies reviewed by Snilstveit et al. ( 2015 ) found the positive effects of decreasing absenteeism and improving learning.

Successful financial performance incentive interventions in developing countries include a 30-month intervention in 57 non-formal education centers in India, which found that motivational financial performance incentives led to absenteeism decreasing from 44% to 19%, and students scoring higher on tests at the end of the program. It was also demonstrated to be cost-effective, at a total cost of $6 per child, and the cost of increasing test scores by 0.1 standard deviation was only $3.58 (Duflo et al., 2012 ). Another successful intervention took place in 300 schools in four districts over 5 years in Andhra Pradesh (Muralidharan, 2011 ). The intervention was guided by four Ps: presence, preparation, pedagogy, and performance. The impact on improved learning in mathematics and language was much larger than the effects found for most other interventions in developing countries. Of particular interest was the finding that students also performed better in core subjects, which were not the initial focus of the study—specifically, science and social studies. The study highlighted that teacher’s performance bonus pay motivated them to pay special attention to weaker children, provide and correct homework, conduct extra classes after school, and use methods other than rote learning.

In Rwanda, a novel experimental design separated the impact of performance pay on recruitment and on effort and found favorable effects on both, with a significant net increase in student test scores (Leaver et al., 2015 , 2019 ; Zeitlin et al., 2017 ). Popova et al. ( 2018 ) found that linking salary and incentives was the most effective design for professional development. In Tanzania, researchers tested two alternative financial incentive designs—one was a pay-for-percentile system in which a teacher’s bonus was based on students’ ranks against other students with similar baseline scores; in the other program, a teacher’s bonus was based on students achieving benchmark proficiency levels, which the authors argued was easier to implement and gave teachers clearer targets. Both designs boosted test scores, but the latter program had larger impacts at a lower cost (Mbiti et al., 2019b ).

Other studies on financial performance incentive to improve teacher motivation in developing countries had mixed results (Evans & Acosta, 2021 ). For example, a pay-for-performance program in Uganda had test score impacts only for the subset of students who attended schools that had books (Gilligan et al., 2018 ). In Kenya, using contracts that were renewable, based on performance, to hire teachers also boosted students’ learning, although an effort to scale up those contracts nationwide did not result in learning gains, potentially due to a combination of political opposition, reduced monitoring, and delayed salaries (Bold et al., 2017 ). A study in Tanzania found that paying performance-based bonuses to teachers had positive impacts on students’ learning in only one of the two tests administered (Mbiti et al., 2019a ). Of note is the finding that teachers supported these programs in Tanzania, both in theory and in practice, reporting higher levels of satisfaction in schools that had performance pay (Mbiti et al., 2019b ).

A framework for motivational financial performance incentives for teachers

In light of the many successful teacher financial incentive interventions, as well as the successes within interventions that had mixed findings, I wondered why teacher performance financial incentive approaches have not emerged as an accepted, and at least potentially effective, approach within the education aid architecture’s approaches and strategies. I purported that the small-scale nature of most of the interventions, as well as their focus on the issues emerging even in those interventions that were successful, is one explanation for this. I also questioned the extent to which the failure of interventions to take on board lessons learned from issues in studies, as well as best practices that emerged, has led to this sidelining of motivational financial performance incentives for teachers.

Upon further analysis of the best practices and the lessons learned from the interventions, I found that many were similar across interventions, and I used these to develop a framework to guide a teacher financial performance incentive intervention in Uganda. The framework can usefully inform other interventions involving motivational financial performance incentives. It is hoped that it will also spark reflection and debate within the education aid architecture community and with country government officials responsible for improving education quality and learning—in other words, debate that focuses especially on the capacity of teacher financial incentive strategies to address teacher absenteeism, develop teacher capacity, and ultimately improve learning outcomes.

The framework presented in Figure 4 distills the best practices and lessons learned into six main areas. Some of the lessons learned are relevant to more than one of the six areas; however, for the purposes of this article, they are presented in the area in which they have most impact. It needs to be noted that my own decades’ long experience working with teachers in low-income countries, as well as many colleagues within her professional network globally with similar experience, also informed the framework and indicated that financial performance incentive approaches, especially in light of the ongoing highlighting of teacher compensation as a key motivational factor, needs more attention and focus.

Framework for motivational financial performance incentives for teachers

Metrics and measurement systems and tools

All the interventions reviewed highlighted issues with the systems and tools used to track teachers’ attendance and assess learning—in particular, related to tool robustness, cost, and reliance on a large number of personnel to support and manage it. For example, the NGO implementing the non-formal education project in 57 NFE centers in India used cameras with tamper-proof and time data functions (to prevent corruption) to track teachers’ attendance (Duflo, 2007 ). A child photographed the teacher with the students at the beginning and end of the school day, the teacher’s salary was based on their attendance, and they were fined for days they missed. However, the project was not found to be replicable as it was too expensive to roll out; was outside the government system; and was subject to other risks, such as camera theft, lack of consistent electricity, inability to capture and save feeds, inconsistent functioning of the cameras, and very labor intensive in the support required of the NGO managing the project. Also, even when teachers were in school, it was difficult to ascertain if they were actually teaching and teaching effectively. Issues related to potential issues about children effectively reporting on their teachers, though not measured in the study, are relevant and must be taken into account in any financial incentive intervention. Children should never report on teachers.

Another education measurement tool, EduTrak, developed in 2012 in Peru and Uganda, used mobile technology to gather education data in remote communities, including data about teachers’ and students’ attendance, timely delivery of school materials, Water, Sanitation and Hgyiene (WASH) facilities and use, and school maintenance. Students, teachers, and communities were all involved in data collection. However, from my experience with EduTrak in Uganda, as well as unpublished memos, there were many system and tool issues—for example, issues with the timing of automated requests for information (e.g., requests coming during the night and waking participants), with charging the phones, and with the trustworthiness of the data. Significant issues were related to participants’ motivation to use the tool. This is especially related to change theories.

An account of a small-scale teacher financial incentive and attendance project using WhatsApp is more inspiring (Nedungadi et al., 2017 ). The project involved 19 teachers in educational village centers in Uttarakhand. The project team devised a special app to prevent corruption, to be used on teachers’ cheap smartphones with 2G sim cards. The app had time and date stamped photograph functions. The key issue with this small project, however, was the large human resources needed by the NGO to analyze the photographs; thus, the intervention would not be feasible on a larger scale and within government systems.

Of particular importance is getting the teacher financial incentive system and tools right in relation to assessing learning. The interventions with the most positive results fairly rewarded teachers for improved learning outcomes and included learning assessment data collection from the outset. The latter included gathering baseline learner assessment data and regular assessments throughout the intervention, using tools such as Early Grade Reading Assessment (EGRA); paper and pen administered by a team of assessors; or a digital test, which would involve bringing laptops to schools for assessment (e.g., the Standardized Testing and Reporting [STAR] assessment tool, which tests five subjects in 1 hour). Using national assessment data (e.g., primary leaving examinations [PLE]) as well as national assessments of lower primary classes (e.g., the national assessment of progress in education [NAPE] in Uganda, which assesses children’s learning in primary 2 and primary 6) emerged as also useful. Ultimately, key to success is to ensure that the incentives for improved learning outcomes are fairly linked to teachers’ performance and effort as well as to school-wide incentives. The latter includes a school-wide incentive for all teachers and the head teacher, as well as for inspectors and in-service teacher educators, if national examinations achieve either a pre-agreed benchmark or an improvement of baseline national assessments at the beginning of the intervention (World Bank, 2018 ). Motivating individual teachers’ efforts, which emerged as a key lesson, was also related to benchmarking, whereby teachers whose students achieved benchmark proficiency levels in specific subjects received a financial incentive at the end of every term and/or year, and was effective (Evans et al., 2021 ; Mbiti et al., 2019b ).

Cost, which is a cross-cutting factor in all six areas of the framework, is especially relevant here—both the cost of measurement systems and of tools for learning outcomes, and costs focused on addressing teacher absenteeism and/or improving teacher performance. There needs to be careful balancing of systems and tools and the cost of their implementation, in order to enable the best possible improvement of learning outcomes within reasonable and sustainable budgets. With the ongoing emergence of new technology, especially the mobile-phone-based technology increasingly accessible to teachers in low-income countries, cost can be significantly reduced.

Gaming and corruption

Gaming issues emerged in all teacher financial incentive interventions, especially those involving use of learner assessment tests to determine teachers’ pay. In Chile, pay incentives linked to a 1-year tournament led to a lot of cheating on the test (Rau & Contreras, 2009 ). In a pilot project in 12 preschools in Kenya, teachers were eligible for bonuses of up to 85% of their salary, based on attendance. An evaluation found no effect on teachers’ attendance as head teachers, who were tasked with the monitoring role, routinely paid the entire bonus to teachers, even when absent (Glewe et al., 2011 ). Other gaming examples included reclassifying students as special needs to exclude them from testing or just excluding weak students from tests (this was addressed through penalties for students enrolled at the beginning of the year not taking the tests), teaching to the tests (this was addressed through assessing free-response questions and questions designed to test conceptual understanding), boosting carolific content of meals on test day, and outright cheating (Muralidharan, 2011 ).

Design of teacher financial incentive formulae

The importance of effectively determining the financial incentive amount cannot be underestimated. Muralidharan ( 2017 ) highlighted that it is critical to design the bonus formulae well and to make sure that these designs reflect insights from economic theory. Glewe et al. ( 2010 ) highlighted the need to carefully assess the dynamics of behavioral patterns when examining performance-related reward schemes. Also, it is critical to take incentive theory (e.g., penalizing attempts to corrupt/game it, and reward gains) seriously at all points. Motivation theories are also important; however, research has found that theories highlighting that extrinsic external incentives can crowd out intrinsic motivation do not apply in most developing country contexts. Muralidharan and Sundararaan ( 2011 ) found that motivational financial performance incentives increased intrinsic motivation in contexts such as India and Africa, where career prospects are not differentiated of based on effort, where norms of teacher effort in the public sector are quite low anyway, and where best practice teaching not very different from teaching to the test.

Political landscape

Another overarching lesson gleaned from the studies of financial incentive interventions is the criticality of mapping at the outset the political landscape and taking political context and competing agendas into account. It is also important to factor in intertemporal choices and discriminatory social norms. Of particular importance are political economy and competing interests, which keep developing countries in a low-learning trap. Actors stuck in low-learning traps characterized by low accountability and high inequality

lack either the incentives or the support needed to focus on learning impact. Instead, they are constantly pressured to deliver other services for more powerful players. As actors juggle multiple objectives, relying on each other in an environment of uncertainty, low social trust, and risk aversion, it is often in the interest of each to maintain the status quo – even if society, and many of these actors, would be better off if they could shift to a higher quality equilibrium. (World Bank, 2018 , p. 15)

When planning any intervention, but especially one that seeks to improve learning, it is important to bear in mind that all actors have other goals—some stated, some not—and to try to preempt those not stated, such as improved learning in government schools affecting private tuition, per diems from training, job security, patronage, favoritism, and not rocking the boat by performing too well. Otherwise, a situation emerges, such as that in Indonesia, which completely derailed a well-intentioned intervention. Between 2006 and 2015, the Indonesian government tried to increase teacher capacity by nearly doubling the salaries of teachers upon certification; however, political pressures watered down the certification process and left only the pay increase in place. Unfortunately, improvements in teacher performance and in learning did not occur (De Ree et al., 2017 ; World Bank, 2018 ).

Government teacher support and accountability systems

Ultimately, most interventions seek to find a long-term solution to specific issues in a country’s education sector, and this requires working within and strengthening government systems to implement the intervention. An issue for many financial incentive interventions is the failure to effectively work with the government and within its systems. A positive example emerged from Chile, where the use of government national school assessments was found useful to the success of their teacher pay program (Rau & Contreras, 2009 ). However, too few positive examples of interventions working successfully with governments and within their systems have been reported.

All countries have support and accountability systems for teachers, involving head teachers, school inspectors, and systems for teacher support, such as the coordinating center tutors (CCTs; in-service teacher educators) in Uganda. Of critical importance in teacher financial performance incentive interventions is to include teacher support personnel, especially head teachers. The literature is awash with evidence highlighting the importance of head teachers in improving learning, especially when they use pedagogical leadership strategies involving regular lesson observations and provision of feedback to support improvement of teachers’ practices.

Digital teacher professional development

Globally, the emerging interest in digital approaches to improve education quality, which has been sharpened with Covid-19 and the need to rapidly test a variety of remote and blended learning strategies, is also having an impact on digital tools and approaches for teacher professional development, especially the use of mobile phone technology. Prior to Covid, mobile phones were only used in a few ways to support teacher professional development. For example, the World Bank’s TEACH found teacher training on text as successful as face-to-face / in-person training. When the effectiveness of the various digital teacher support interventions that were rapidly implemented during Covid-19 have been assessed, more evidence-informed examples of best teacher digital support strategies and tools will emerge. A significant number of interventions have been documented at this stage, especially by multilateral agencies and platforms, as well as civil society surveys. The publication of the largest online survey of teachers and technology during Covid-19 (Pota et al., 2021 ) is particularly useful; its results can inform teacher financial performance incentive interventions. We eagerly await the 2023 Global Education Monitoring Report , which will focus on technology in education, with background papers already being prepared (Burns, 2021 ).

Currently, only one initiative can be identified that seeks to link digital teacher professional development with monetary incentives. Nedungadi et al.’s ( 2017 ) WhatsApp pilot intervention study required 19 teachers, before they received certain monetary incentives, to send daily reports to coordinators via WhatsApp of what was taught in class. The coordinators also regularly sent small modules as pedagogical support. Key issues with this innovative system, however, are that it operates outside government systems and relies on significant support human resources to manage it.

The World Bank ( 2018 ) suggested that performance pay be linked to professional performance and improved learning needs and to straightforward actions teachers can take. We know from the literature what these actions are and what works. Note that, in spite of some of the issues with Bridge Academy and other private school initiatives, their digital approaches, including scripted lesson plans with concrete steps for teachers to support teacher training in developing countries, are emerging as effective (He et al., 2008 ; Lucas et al., 2014 ). This provides specific guidance crucial for low-skilled and poorly educated teachers who may lack the ability to be effective when motivated by monetary incentives ().

The Power Teachers intervention in Uganda

The financial incentive framework for teachers (Figure 4 ) is currently being used to inform an intervention focused on mobile-phone-based teacher motivation in Uganda. Change theories also inform the intervention. Fullan’s ( 1991 ) work on effective education change theories, models, and strategies is still relevant here, in particular his highlighting of the critical role of taking the realities at a “classroots” (Hawes & Stephens, 1990 ) level into account. These include, for example, teachers’ personal objective and subjective realities and the realities related to teacher motivation, such as low pay and poor housing. This latter reality, however, continues to be sidelined in the development of teacher-focused education interventions. Too many education interventions fail to fully bear in mind the human motivation aspect, a key part of theories of change (Fullan, 2015 ). Related to this is the failure of interventions to involve teachers more in new practices, innovations, and reforms they are required to implement. The Uganda intervention’s name, Power Teachers, reflects its focus on teachers themselves and putting the power into their hands to bring about improved learning. They are provided with an incentive to do this; however, ultimately, the power rests with the teachers themselves, and they are provided with some agency to bring about improved learning.

The research-based Power Teachers intervention has three phases:

Phase 1: Development of mobile-phone-based tools (2021–2022)

Mobile-phone-based teacher absenteeism tool

Mobile-phone-based professional development tool

Learning assessment tool (may not be mobile phone based)

Phase 2: Piloting in up to 10 schools in Uganda (2022)

Phase 3: Implementation in at least 100 schools in Uganda (2023–2025)

The intervention aims to address teacher absenteeism, develop teachers’ capacity, and improve learning, at a much lower cost than that of the current strategies supported by the education aid architecture. The project also seeks to demonstrate that it is more cost-effective to use funding to financially incentivize teachers to ultimately improve learning outcomes than to use business-as-usual strategies, which include newly emerging digital and innovative strategies that fail to take teacher agency and their humanity into account. The intervention is being conducted in primary schools; however, it is also suitable for secondary schools.

Working with the MoES in Uganda, the initiative will provide bonus payments of between 10% and 20% of their salaries to teachers, head teachers, inspectors, in-service teacher trainers, and district education officers, with payments triggered by teachers’ and head teachers’ attendance at school, and when

they are in class teaching,

they complete proven best practice mobile-phone-based teacher training courses,

they upload evidence of implementation of new skills learned from courses, and

their students’ learning improves.

The intervention takes the political landscape into account and also works with and within government systems, two other areas of the framework. It supports MoES Uganda to implement the Teacher Incentive Framework (TIF; MoES, 2018 ) and Teacher Policy (MoES, 2019 ), with the former being devised in response to the issues with teacher absenteeism, as well as enabling improved teachers’ performance and students’ learning. The TIF, similar to research discussed by Evans and Yuan ( 2018 ), uses Maslow’s hierarchy of needs to illustrate teachers’ motivation issues in Uganda: “Teachers who are tired and hungry and excessively preoccupied with meeting their basic livelihood needs, are unlikely to be involved in professional development activities, nor will society attach much prestige and recognition to such teachers” (MoES, 2018 , p. 15). The TIF also notes that, in developed countries, teachers value intrinsic motivation (e.g., a sense of accomplishment) more, but in emerging economies, where teachers still struggle with basics of survival, they are bound to appreciate extrinsic rewards more than intrinsic ones (Muralidharan & Sundararaan, 2011 ). The TIF framework, based on Vegas and Umansky’s ( 2005 ) teacher motivation framework, is centered on four teacher incentive strategies: professional rewards, financial incentives, accountability pressures, and clarity of expectations of teachers.

The Power Teachers intervention is research based, using a three-phase, sequential, mixed methods design building on mixed-method approaches (Cohen et al., 2017 ; Creswell and Creswell, 2017 ; Haßler et al., 2020 ). In phases 1 and 2, a design-based implementation research approach (Penuel et al., 2011 ) is employed. This enables ongoing refinement of the tools and their implementation in schools in preparation for a larger-scale rollout in phase 3. It also enables the determination of the financial incentive amount, the bonus formulae area in TIF. In phases 1 and 2, using both quantitative and qualitative methods, data is collected in several cycles and analyzed immediately, with the results used to optimize the tools and the intervention implementation, as well as to refine the research instruments. Data collection tools include lesson observations, interviews, teacher portfolios, focus groups, questionnaires, and learner assessment tools. The mobile phone tool will also enable significant data to be gathered in real time regarding teacher absenteeism, teachers’ participation in professional development, and teachers’ implementation of what they learn. This in-built data collection system is being developed with MoES and other stakeholders, who will be able to access the data on teachers’ attendance and capacity building efforts on an ongoing basis.

Currently, the mobile-phone-based tool is in development, using lessons learned from the teacher motivational financial performance incentives framework. These lessons include:

The intervention will involve MoES Uganda personnel and work within MoES systems—in particular, Teacher Education, Monitoring and Evaluation (M&E) and policy and planning systems, as well as district education systems. The latter is critical to ensuring that issues informing the framework (e.g., teachers being moved, children enrolled not sitting the test) are addressed through an agreed-upon intervention contract.

The intervention will use robust low-cost solar-powered and battery friendly smartphones, with the tools pre-uploaded. Teachers will pay back the cost of the smartphones with their incentives over the duration of the project; if a phone is lost or stolen, they need to continue to pay for the lost one as well as a replacement.

The intervention will use the most cost-effective and least corruptible geo stamp, time stamp, and facial recognition software.

The tool system being devised will enable, as much as possible, automatic digital monitoring and support to address the major issues of large support personnel highlighted in research studies.

The intervention will work with the inspectors and Coordinating Centre Tutors (CCTs) to do spot checks of the data the tools provide, especially regarding teachers being in class and their professional development implementation evidence. They will be provided with motivational financial performance incentives for this. Similarly, head teachers will be involved.

To reduce mobile data costs, much of the tools’ activities will be available offline; mobile data will only be required to send teacher absenteeism evidence, professional development course certificates, and evidence of implementation.

To motivate teachers’ attendance, when evidence (i.e., using geo, facial recognition and time software) of teachers’ in-class teaching is digitally shared via the mobile phone tool, a financial incentive payment is automatically sent to teachers’ mobile phone money platforms. Some additional triggered requests are also sent to teachers periodically to enable verification, as well as some spot checks from time to time.

To support teachers’ professional development, best-practice mobile-phone-based teacher courses that focus on developing teachers’ improved pedagogy, developed by partners (name withheld for anonymity), have been provided for use in this intervention. Teachers can complete the courses, including assessments, offline. Upon successful completion of the assessments, they can send, using mobile data, the certificate for verification and subsequent automatic incentive payments to their mobile money platform. The professional development part of the tool will have pre-uploaded relevant documents, such as policies, memos, and best practices, as well as daily teaching tips. The tips will be automatically released at the beginning of every week, and at the end of the week, when the system proves they have read them, teachers will receive a financial incentive. The tool will have the capacity for teacher groups and collaboration; however, this is beyond the funding available for phases 1 and 2.

To prove that teachers are using what they learn in the professional development courses in their classrooms, they have to upload evidence of this; this evidence then triggers a mobile money payment to the teacher. The evidence uploads are predefined and can be digitally assessed to reduce costs. The intervention uses random selection of evidence to test the extent to which the evidence teachers are sending in is valid.

The mobile-phone-based tool is also being devised for head teachers, inspectors, and CCTs to encourage their support of teachers and the improvement of learning outcomes. This is beyond the scope of this article.

The third and critical part of the intervention is the improvement of learning outcomes. The intervention is currently in the initial stages of developing the strategy and tools for this. It will use both government system assessments, the PLE, and where applicable, the NAPE as well as EGRA-type assessments currently being devised. Lessons in the framework inform the learning assessment strategy and tool, including penalties for removing learners from the tests, and designing tests that do not enable teaching to the tests (Muralidharan, 2011 ). The project will also use a waiting-list approach to build in a comparison group of teachers so the learning outcomes of students taught by teachers participating in the pilot can be compared with the outcomes of students taught by teachers who were not involved in the pilot but will participate in the intervention after the pilot has been evaluated.

Because the ultimate focus of the teacher financial performance incentive interventions examined in this article and the Power Teachers intervention in Uganda is to improve children’s learning, the opening statement of this article—namely, that nine out of 10 children are not learning (World Bank, 2019 )—must also conclude it. Covid-19 has further sharpened the need to address the global learning crisis, exacerbated by forced school closures. Data from 149 countries found a 4-month learning loss by October 2019 for the world’s poorest children, just 6 months into the pandemic (UNESCO et al., 2020 ). Improving learning must remain at the top of all global agendas.

The introduction also highlighted Beeharry’s ( 2021 ) call to those within the education aid architecture to use available funding to target the achievement of FLN and thus address the learning crisis. This article concludes by calling for this funding to target teachers and enable them to effectively improve learning outcomes, especially FLN. We know from research that teachers, and especially pedagogy, are key to improving learning. We also know from research the pedagogical practices and teacher professional development strategies that work best. Yet, so much investment in supporting teachers has not improved learning. I examined a key reason for the failure of these investments: teacher absenteeism. Recent studies highlight that teacher absenteeism is itself a crisis, with the latest research in eight countries in Africa finding absenteeism rates of between 15% and 45% (UNICEF, 2020 ). If teachers are not in class, learning outcomes cannot improve. I then went on to examine the reasons for teacher absenteeism, highlighting poor teacher motivation as a key reason, with teacher compensation emerging as the most significant cause of poor teacher motivation. In light of the key role of motivation, too many Education interventions fail to fully bear in mind the human aspect, the motivation aspect, a key part of theories of change. We need to address this gap going forward.

I then focused on the second (the first being teacher absenteeism) of two elephants in the classroom: motivational financial performance incentives. In presenting the evidence of various interventions that used financial incentives for teachers, I questioned the absence of this from the current education aid architecture repertoire of effective approaches to improving education quality and children’s learning. The research literature of teacher financial incentive interventions highlights the success of the approach, both in successful interventions and those deemed unsuccessful but with some successful elements. Yet, financial incentive approaches seem to have been dismissed as ineffective. This led to the development of a motivational financial performance incentives framework to spark debate about the potential of the approach as well as to inform future interventions, including an intervention currently in its initial stages in Uganda.

I then asked the question “Is there another way to support teachers within government systems that is more cost-effective and sustainable, leads to implementation of what teachers learn, and improves students’ learning?” I proposed that teacher motivational financial performance incentive approaches, informed by lessons learned and best practices from studies of incentive interventions, which are encapsulated in the teacher financial performance incentive framework, provide the other way. The research-based three-phase Power Teachers intervention in Uganda, currently in phase 1, will gather evidence throughout the intervention, as well as from its final evaluation, that will highlight if this approach is the other way, or at least another way, that ultimately improves learning. We must continue the war against poor learning outcomes; the quality of the lives of too many children are at stake.

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Title: The effect of absenteeism on student's academic performance in grade 10 emerald in Malanday National High School Background of the Study

Absence nowadays cannot be separated from the individual who was called a student. This absence of phenomena, it is easy to see even likened it has become a necessity in the nature of students. So it is not surprising if this habit continues to fall proceeds among school students as examples of primary, secondary and even at university level, the problem still persists. Other than that, the sector is also facing the field of employment in the same problems that their employer impasse in the absence of these phenomena solution. University Selangor (UNISEL) at Shah Alam, Faculty of Business also facing the same thing, the absence of student in class getting worse. They are a few among of student sometimes attend a class a few time in a week, other than that the attendances of student in class based on the subject that they like. These scenarios actually should not be happened, because as a student they already know and thinks to differentiate between the good thing and the worse. Besides that, they also well know the impact and affect while they absent from a class. According to the (Marburger, 2001) states that the difficulty inferring the effect of absenteeism on performance because, once a student is absent in a class

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For the effect of absenteeism to student individual learning performance, both teacher and student population agreed that students perform poor in class followed by social relation with classmates and social relation with teachers. While for the effect of absenteeism to school performance all variables voiced out a poor result as a main factor.
Factors Affecting the absenteeism in Philippine ...
The researcher aims to determine the reasons of absenteeism among their school's learners. METHODS. This is a qualitative-descriptive research that maps out the reasons of the 26 students who are identified frequently absent. Data collection is done through in-depth interviews and document analysis of the pupils' daily attendance.
Absenteeism Problems And Costs: Causes, Effects And Cures
Some sources including Statistics Canada cite that absenteeism approximates 15-20 percent of payroll (direct and indirect) costs. This is significant. Canada Newswire stated on May 23, 2008 that ...