1 Economics of Education Review 31 (2012) Contents lists available at SciVerse ScienceDirect Economics of Education Review jou rnal h omepa g e: Early to rise? The effect of daily start times on academic performance Finley Edwards Department of Economics, Colby College, 8800 Mayflower Hill, Waterville, ME 04901, United States a r t i c l e i n f o Article history: Received 25 March 2011 Received in revised form 18 June 2012 Accepted 6 July 2012 JEL classification: I2 Keywords: Start times Test scores Education production function Middle school Sleep a b s t r a c t Local school districts often stagger daily start times for their schools in order to reduce busing costs. This paper uses data on all middle school students in Wake County, NC from 1999 to 2006 to identify the causal effect of daily start times on academic performance. Using variation in start times within schools over time, the effect is a two percentile point gain in math test scores roughly fourteen percent of the black white test score gap. I find similar results for reading scores and using variation in start times across schools. The effect is stronger for students in the lower end of the distribution of test scores. I find evidence supporting increased sleep as a mechanism through which start times affect test scores. Later start times compare favorably on cost grounds to other education interventions which result in similar test score gains Elsevier Ltd. All rights reserved. 1. Introduction What time should the school day begin? There is considerable variation in daily start times, both across the nation and within individual communities, with some schools beginning at 7:30 a.m. or earlier, and others beginning at 9:00 a.m. or later. 1 The issue of timing is by no means trivial. Districts stagger the start times of different schools in order to reduce the number of buses used and thus reduce transportation costs. However, if beginning the school day early in the morning has a negative impact on academic performance, staggering start times may not be justified on a cost benefit basis. In recent years, school start times have received considerable attention in the popular press (see, for example, Kalish (2008) and Trudeau (2007)). Proponents of later start times argue that students in early starting schools do not receive enough sleep and that performance can be increased by beginning the school day at a later time. Tel.: addresses: 1 Throughout this paper, all times are a.m., unless otherwise indicated. Several school districts have responded by delaying the start of their school day, and a 2005 congressional resolution (H. Con. Res. 200) was introduced by Rep. Zoe Lofgren, recommending that all secondary schools start at 9:00 or later nationwide. Despite this attention, the relationship between start times and academic performance is not well understood. This paper uses data on all middle school students in Wake County, North Carolina from 1999 to 2006 to examine how start times affect the performance of middle school students (grades 6 8) on standardized tests. Wake County is uniquely suited for this purpose because it has considerable variation in start times across schools within a single large school district, as well as variation in start times within schools over time. The differences in start time across schools are a result of the bus schedule. As the student population has grown, the school district has changed the start times for many individual schools in order to maintain a balanced bus schedule, generating variation in start times within schools. Using both sources of variation in start times, I find that a 1 h delay in start time increases standardized test scores on both math and reading tests by three percentile points. Since start times may be correlated with other characteristics that determine test scores, I also /$ see front matter 2012 Elsevier Ltd. All rights reserved.
2 F. Edwards / Economics of Education Review 31 (2012) estimate the effect using only variation in start times within the same schools over time, and find a two percentile point improvement in math and a one and a half percentile point improvement in reading. The effect of start times on academic performance is robust to different specifications and sources of variation. The magnitude of the effect is similar to the difference in test scores for one additional year of parental education. The impact of later start times on test scores is persistent. Conditional on a high school fixed effect, a 1 h later start time in grade eight is associated with an increase in test scores in grade ten similar in magnitude to the increase in grade eight. Quantile regression results show that the effect of later start times is stronger for the lower end of the distribution of test scores, suggesting that later start times may be particularly effective in meeting minimum competency requirements. Public attention on school start times largely focuses on high school start times. In contrast, this paper examines how start times affect middle school students, in order to examine possible mechanisms through which later start times might raise student performance. The rationale typically given for start times affecting academic performance is primarily biological. Earlier start times may result in fewer hours of sleep, since students may not fully compensate for earlier rising times with earlier bed times. In particular, adolescents have difficulty adjusting to early bed times due to the timing of the release of the hormone melatonin (Dahl & Lewin, 2002). A reduced amount of sleep has been demonstrated to reduce students cognitive ability (Meijer, Habekothe, & Van Den Wittenboer, 2000), which in turn could reduce learning, resulting in lower test scores. Since most students enter adolescence during their middle school years, the adolescent hormone explanation predicts that the effect of later start times will be larger for older students. I find evidence supporting this explanation: among middle school students, the impact of start times is greater for older students. However, I also find evidence of other potential mechanisms as well. Students who begin school later have fewer absences, watch less television and spend more time on homework each week. These factors may also explain why later-starting students have higher test scores. Later start times also have the potential to be a cost effective method of increasing test scores. For example, Krueger (1999) finds similar increases in test scores from smaller class sizes, but at cost an order of magnitude larger than the cost of changing start times. 2. Why might start times be important? One reason that schools begin the school day at different times is the use of tiered busing systems. Many school districts stagger the times their schools begin the school day so that the same bus and driver can serve multiple schools. For example, a school district might start a high school at 7:30, a middle school at 8:15 and an elementary school at 9:00. Staggering start times in this manner has the potential to drastically reduce transportation costs compared to an alternative schedule where all three schools start at 8:15. Only one nationally representative dataset records school start times: The 2001 Before- and After-School Programs and Activities section of the National Household Education Survey (ASPA-NHES), conducted by the National Center for Educational Statistics. This dataset does not contain any information on academic achievement, so it cannot be used to measure the impact of start times on student performance. However, it is useful in establishing the variation of start times across the nation and for comparing the data used in this paper to national norms. Table 1 lists summary statistics for middle school start times for the national sample in Nationally, the median middle school student begins school at 8:00. Over one fourth of students begin school at 8:30 or later, while more than 20 percent begin at 7:45 or earlier. In addition to the national sample, Table 1 also lists statistics for middle school start times in Wake County from 1999 to As would be expected when comparing a specific district to the national distribution, start times in Wake County are more concentrated than they are nationwide. In Wake County, 53.1 percent of middle school students start school at 7:30, and another 22.4 percent begin at 8:15. In the national sample, only 26.7 percent of students start at the national modal time of 8:00. More importantly, start times are consistently earlier in Wake County than nationwide: the median Wake County student begins school earlier than over 90 percent of students nationwide. Since the marginal impact of start times on academic performance may decrease (or increase) for later start times, care should be taken in imputing the conclusions reached in this paper to schools that have a later start time. Put another way, the gain from later start times found here derive largely from changes from 7:30 to 8:15. The same gains may not occur from changing start times from 8:30 to 9:15, for example. Another source of information on nationwide start times is a survey of high school start times by Wolfson and Carskadon (2005). They randomly selected 4116 schools and asked them to report start times retrospectively since While the survey deals with high schools instead of middle schools and has response problems (fewer than ten percent of schools selected are included in their final sample), it does provide useful information about the types of schools that begin early or late. They find that large schools (>1000 students) begin on average 15 min earlier than small (<1000 students) schools, and that rural schools begin 15 min later than urban or suburban schools. Schools that are not part of a tiered busing system begin on average 15 min later than schools with a two-tiered system and 20 min later than schools using a three-tiered system. Their findings are consistent with the WCPSS being an urban/suburban school district with a three-tiered busing system and having somewhat earlier start times than the national sample. There is credible evidence (Baroni et al., 2004; Danner & Phillips, 2008) that students who begin the school day earlier sleep less. To obtain the same amount of sleep as students who have a later start time, early-starting students would need to go to sleep earlier. Activities such as sports, work, family and social schedules may make it difficult for students to adjust the time they go to bed. In addition, the onset of puberty brings two factors that can make this adjustment particularly difficult for adolescents: an increase in the needed amount of sleep and a
3 972 F. Edwards / Economics of Education Review 31 (2012) Table 1 Nationwide and wake county student start time statistics. Percentiles 10% 25% 50% 75% 90% Nationwide 7:35 7:55 8:00 8:30 8:45 Wake County, NC 7:30 7:30 7:30 8:15 8:15 S. Dev. (min) Mode Percentage at mode Sample size Nationwide : % 4568 Wake County, NC : % 173,791 Students are in grade 6 8. Nationwide data comes from the ASPA-NHES (2001). Wake County data is pooled data from 1999 to change in the natural timing of the sleep cycle. Hormonal changes, in particular the secretion of melatonin, shift the natural circadian rhythm of adolescents, making it increasingly difficult to fall asleep early in the evening. It is well established in the physiological literature that less sleep is associated with a decrease in cognitive performance, both in a laboratory settings and through self-reported sleep habits (Pilcher & Huffcutt, 1996). 2 Specific to academic achievement, numerous studies have reported a negative correlation between self-reported hours of sleep and grades among both middle and high school students. 3 Since students who start school earlier typically sleep less, and less sleep is associated with decreased academic performance, one would expect that students in earlystarting schools would perform worse on standardized tests. However, there is little empirical evidence directly linking school start times and academic performance. Allen (1992) and Wolfson, Spaulding, Dandrow, and Baroni (2007) examine a small number of schools and find a positive correlation between later start times and student grades. This approach is inherently limited; any increase in performance could reflect other unobserved factors rather than the impact of a later start time. The study most widely cited in the popular press (Wahlstrom, 2000) examined two Minnesota school districts (Minneapolis Public Schools and Edina) which changed the start times in their secondary schools to start over an hour later. 4 Wahlstrom is concerned with the impact of start times on a wide range factors, including attendance, sleep behavior, school discipline and extracurricular activities, in addition to academic performance. The impact of a delayed start time is measured by comparing the mean grade obtained by high school students during the three years prior to the start time change and mean grades from three years after the change. Wahlstrom finds a positive but statistically insignificant increase in mean grades. Recent papers by Hinrichs (2011), Carrell, Maghakian, and West (2011), and Wong (2011) avoid many of the 2 Laboratory studies tend to focus on large amounts of sleep loss. The amount of sleep lost from starting school earlier would be much less. Laboratory results should be viewed as establishing a credible relationship between sleep and cognitive performance. 3 See Wolfson and Carskadon (2003) for a survey of these studies. 4 Edina school district changed their start times from 7:25 to 8:30 and Minneapolis changed from 7:15 to 8:40. issues of extant studies. Hinrichs uses individual ACT data on students in the Minneapolis metro area for the same policy change as Wahlstrom. However, he includes additional school districts, allowing him to control for secular time trends. Hinrichs also uses school-level data on Kansas assessment tests in the 10th and 11th grades. Using a variety of specifications, he finds no effect of later start times in any of his specifications. Carrell et al. (2011) examine how the time of the first class of the day affects college freshman. They use data from the United States Air Force Academy, where freshman are randomly assigned to class periods. 5 They find that a 1 h delay in the first class of the day increases grades by 0.15 standard deviations. Wong (2011) uses cross sectional data and finds effects ranging from 0.02 to 0.08 standard deviations for a 1 h later start time, although his results generally lack statistical precision. A related literature examines how time of day affects student performance. Dills and Hernandez-Julian (2008) find that college students receive higher grades in classes that meet later in the day. Cortes, Bricker, and Rohlfs (2012) examine students in Chicago public high schools and find that grades are reduced in first-period classes. This paper complements existing studies in several ways. First, I am better able to control for unobservable factors by using multiple sources of variation in start times, both across schools and within schools over time. Second, I examine students in middle school (6th 8th grades), while existing studies tend to examine high school students, often in a single grade. If adolescent hormones determine how start times affect academic performance, middle school students may respond differently than high school students. Third, I use standardized test scores instead of letter grades or grade point average. Fourth, I examine changes in start times that occurred in different years for different schools, and are not intended to increase student achievement. This feature of the data makes Hawthorne effects 6 unlikely and will allow me to separate out year effects. Lastly, my unique data set allows me to examine several specific mechanisms through which start times may affect academic performance, in particular whether 5 Some students have a first period class and others do not, but all students must attend mandatory breakfast prior to the first class period. 6 A Hawthorne effect occurs when subjects modify their behavior because they are being studied, and not because of a change in an explanatory variable.
4 F. Edwards / Economics of Education Review 31 (2012) the adolescent hormone hypothesis explains the effect of start times on academic performance. 3. Data and institutional background The data set used in this paper is combined from two sources. The first source is administrative data for every student in North Carolina between 1999 and The data contain detailed demographic variables for each student as well as end of grade test scores in both reading and math. 8 The raw test scores vary considerably by year, so I use the statewide data to construct percentile scores for each student within their grade and the current year. 9 The second source of data is the start time for each school in the Wake County Public School System (WCPSS), by year. 10 Start times were matched to the school code for each student in the administrative data. There are several factors that make these data well suited for examining the effect of start times on academic performance. Examining a single school district avoids problems resulting from correlation of start times with unobserved characteristics at the district level. Since start times are generally determined at the district level, it may be that well-run districts tend to have earlier (or later) start times, or they may be more (or less) likely to change the start times of their schools. The WCPSS is also large enough to measure effects precisely. It has considerable variation in start times both across schools and within schools over time. By focusing on middle school students, I am able to test the adolescent hormones hypothesis of why later start times affect test scores. The WCPSS is the eighteenth largest public school district in the United States with 120,504 students (kindergarten through twelfth grade) in the school year. It encompasses all public schools in Wake County, a mostly urban and suburban county that includes the cities of Raleigh and Wake Forest. Start times for schools in the district are proposed by the transportation department (which also determines bus schedules) and are approved by the school board. While the school 7 The administrative data were provided by the North Carolina Education Research Center. 8 The data also contain information on teachers, potentially allowing me to control for teacher and classroom characteristics. However, I am unable to link students to the teacher data set for roughly 28% of my sample. In addition, the teacher that is linked is the teacher who supervised the exam, which may not be the teacher the student had for the school year. I construct a class size index which is the number of students coded with the same teacher, class period, and year. For 15% of students matched to teachers, the class size was over 50, suggesting for many students the teacher linked to their record was not their true teacher, but rather a supervisor of a test for multiple classes. As a result, I do not include teacher or class characteristics in the results shown. Results are generally similar when teacher characteristics are included. 9 Specifically, I pool the raw test scores for all students in North Carolina in a given year and grade. I then assign each student in Wake County a percentile rank based on where their raw test score falls in the statewide distribution. Percentile scores were constructed separately for math and reading. An alternate normalization would be to construct Z-scores by subtracting the statewide mean and dividing by the statewide standard deviation. Results using Z-scores are presented in Table 7 and are very similar to those for percentile ranks. 10 The start time data were provided by the WCPSS transportation department. Number of Schools :30 7:45 8:00 8:15 8:30 8:45 Start Time Fig. 1. Histogram of middle school start times. Source: Author s calculations from data provided by the WCPSS transportation department. Figure includes all non-magnet middle schools in Wake County, NC from 1999 to Each school appears once per school year. board could in theory make changes to the bell schedule, they did not do so during the sample period of The practice of combining school start times and bus scheduling is supported by the Operations Research literature concerning the school bus problem, where it is well established that costs can be reduced up to 30% if start times and bus routes are chosen simultaneously (Fugenschuh, 2009; Keller & Muller, 1979). Since 1995, WCPSS has operated under a three-tiered system. While there is some variation in the exact start times, most Tier I schools begin at 7:30, Tier II schools at 8:15 and Tier III at 9:15. Tiers I and II are composed primarily of middle and high schools, and Tier III is composed entirely of elementary schools. Fig. 1 is a histogram of the distribution of start times of middle schools for the pooled sample. 11 Just over half of middle schools begin at 7:30, with substantial numbers of schools beginning at 8:00 and 8:15 as well. All of the schools have the same length of the school day. Wake County is divided into 1134 student assignment nodes. Each node is associated with an elementary, middle and high school that is the base school for all students living within the node. Nodes are matched to schools based on facility utilization, distance and diversity. Each year, as the population changes and new schools are built, a limited number of nodes are reassigned to new schools. Nodes remain with a given school for a minimum of three years between reassignments. A majority (72.5% in 2006) of students attend their base school. Alternatively, students may choose to apply to attend a magnet school Most schools are represented seven times (once for each year) although sometimes with different start times. 12 A small number (<5%) of students transfer to a base school other than the one they are assigned. Such transfers are only allowed for exceptional
5 974 F. Edwards / Economics of Education Review 31 (2012) Table 2 Changes in start times. New start time 7:30 7:35 7:45 8:00 8:05 8:15 8:25 8:45 7: :35 1 7:45 Old start time 8: :05 8:15 2 8:25 1 8:45 1 Source: Author s tabulation. Each cell contains the number of schools that changed their start time from the given old start time to the new start time. In addition to these changes eleven schools started at the same time in each sample year, of these eight started at 7:30 and one each at 7:45, 8:00, and 8:05. Magnet schools use a specialized curriculum and typically have smaller enrollments. Magnet school admittance is determined by lottery. 13 Bus transportation is provided to students whether they attend the base school or a magnet school. Since buses serving magnet school must cover a larger geographic area, ride times tend to be longer for magnet school students. As a result, almost all magnet schools begin at the earliest start time. For example in 2004, nine out of ten magnet schools began at 7:45 or earlier compared with nine out of sixteen base schools. Students at magnet schools tend to have higher test scores, which may cause a spurious negative relationship between start times and test scores. Furthermore, since students can choose to apply to magnet schools, it is possible that they chose a magnet school partially based on start time. For these reasons, I exclude magnet schools from my sample. Five schools began magnet programs during the sample period. These schools are included in the sample prior to becoming magnet schools and excluded after. Results including magnet schools are presented in Table 7. Results including magnet schools are similar to the main results, but with slightly lower magnitudes. Over the seven years examined in this paper, WCPSS grew from 20,530 students enrolled in twenty-two middle schools during the school year to 27,686 students enrolled in twenty-eight middle schools in In addition to population growth, the WCPSS increased the number of magnet schools from five to thirteen by opening three new magnet schools and converting five existing non-magnet schools into magnet schools. Three new non-magnet schools were also opened. As a result of population growth, the transportation department changed start times to accommodate new schools and increased enrollment at existing schools. An explanation of the need to change start times is given in the minutes of the Board of Education: The proposed bell schedule modifications open and close schools at times the Transportation Department circumstances and for grandfathered students who live in a reassigned node. Bus transportation is not provided for transfer students. I do not have information on which students have transferred to a different base school than the one to which they are assigned. 13 Preference within the lottery is given to some students based on sibling enrollment, distance and diversity. can more effectively and efficiently provide bus service to students. Advantages of the bell schedule modifications include long-term savings in operating costs, better utilization of equipment, and the ability to provide better service. It is important to note that new school openings will dictate a need to change opening and closing times. (Wake County Board of Education, 2004) Existing non-magnet schools had their start times change fourteen times in the sample, with some schools starting earlier and others later. In the portion of my analysis that uses school or student fixed effects, it is these changes that identify the effect of later start times. Table 2 tabulates the changes in start times. Each cell contains the number of schools that switched from the corresponding old start time to the corresponding new start time. Four schools changed their start time two or three times. Those schools appear more than once in the table. There were a total of fourteen changes in start times by nine schools. Seven of those changes were of 30 min or more. Eleven schools did not change their start time in the sample period, and so do not appear in the table. Of the eleven, eight started at 7:30 and one each at 7:45, 8:00, and 8:05. Table 3 lists means of selected demographic variables by start time for 2000 and for Panel A includes student characteristics, while Panel B includes (unweighted) school characteristics. 14 For the purposes of tabulation, I separate start times into two groups: Tier I (7:30 7:45) and Tier II (8:00 8:45). Buses that serve Tier I schools would have time to serve two additional schools, but buses that serve Tier II schools would only have time for one more school. For almost all of the student demographics, there are differences between earlier and later starting schools in The differences are generally small in magnitude, but precisely measured. For example, 24% of students in Tier I schools are black, compared to 21% of Tier II students. Students in earlier starting schools are more likely to be female, belong to an ethnic minority, be eligible for free lunches and have less educated parents. In 2006, the demographic characteristics of the two tiers are generally closer together, but several differences still remain. 14 Tabulations for other years in the sample are similar to 2000 and 2006, but are not presented.
6 F. Edwards / Economics of Education Review 31 (2012) Table 3 Means by start time for 2000 & Tier I Tier II Tier I Tier II Panel A: Student characteristics Female (0.005) (0.006) (0.006) (0.006) Age (0.009) (0.012) (0.013) (0.013) Black (0.004) (0.005) (0.005) (0.005) Hispanic (0.002) (0.003) (0.003) (0.003) Free lunch eligible (0.004) (0.005) (0.005) (0.005) Parent s education (0.02) (0.03) (0.03) (0.04) Math score (0.285) (0.37) (0.347) (0.336) Reading score (0.278) (0.362) (0.336) (0.325) Number of students 10, Panel B: School characteristics % Black (0.034) (0.046) (0.050) (0.054) % Hispanic (0.006) (0.009) (0.016) (0.017) % Free lunch eligible (0.060) (0.080) (0.051) (0.055) Avg. parent education (0.238) (0.322) (0.364) (0.420) Pupil/teacher ratio (0.47) (0.63) (0.38) (0.40) Enrollment (87.6) (118.5) (49.0) (52.4) Avg. math score (2.59) (3.51) (4.22) (4.51) Avg. reading score (2.45) (3.31) (3.01) (3.22) Start time 7:32 8:01 7:30 8:14 (0:01) (0:02) (0:01) (0:01) Schools Tier I: 7:30 7:45. Tier II: 8:00 8:45. Standard deviations in parentheses. Panel B is unweighted. Since the characteristics more prevalent among Tier I students are generally believed to be associated with lower test scores, the simple correlation between test scores is unlikely to represent a causal effect. Moreover, if students in early-starting schools have other characteristics that are correlated with test scores, controlling for the observed characteristics may not be sufficient to give an unbiased estimate of the effect of start times. The school characteristics in each tier are much more similar to each other than the student characteristics are. 15 In particular, schools in both tiers have similar enrollments and pupil to teacher ratios. For none of the variables are the differences between the two tiers statistically significant at conventional levels. Even for variables that are statistically different in panel A, the corresponding percentages in Panel B are not statistically different. For the portion of my analysis that identifies the effect of later start times using only the variation in start times within schools, it is important to know the characteristics of schools that experience a change in start time. In particular, students in schools that change to an earlier start time may be different than students in schools that change to a later start time. Table 4 gives the means of selected demographic characteristics for students in schools that experienced a change in start time. Columns 1 3 give results for schools that changed from a later start time to an earlier one. Column 1 is the year before the change, column 2 the first year with the new start time, and column 3 the difference between columns 1 and 2. Columns 4 6 give results for schools that changed from an earlier start time to a later one. Column 4 is the year before the change, column 5 the first year with the new start time, and column 6 the difference between columns 4 and 5. Lastly, column 7 gives the difference between columns one and two. For both schools moving to an earlier start time and those moving to a later start time, students are similar before and after the change in terms of gender, race, eligibility for free lunch and parent s education. Students in schools that change to an earlier start time have higher test scores before the change, and students in schools that change to a later start time have higher test scores after the change, although the differences are not statistically significant. Students in schools that change to an earlier start time have similar characteristics to students in schools that change to a later start time in the year prior to the change. A comparison of Tables 3 and 4 indicates that schools which change start times differ from those that do not change start times. In particular, students in schools which change start times are less likely to be black, Hispanic or eligible for free lunch. 4. The relationship between start time and performance The unconditional relationship between test scores and start times can be seen in Figs. 2 and 3. These figures present the empirical cumulative distribution function of test scores in schools with earlier (Tier I) and later (Tier II) start times. For both the reading and math tests, the cumulative distribution for late-starting schools first-order stochastically dominates the distribution for early-starting schools; for every percentile a greater proportion of students score at or below that percentile in the early-starting schools than in later starting schools. 16 For example, 45% of students in early-starting schools have math test scores at or below the 50th percentile, while only 36% of students at late-starting schools score at or below the 50th percentile. These figures suggest that later start times positively impact student performance. Since there are other differences between early- and late-starting schools, the simple relationship between test scores and start times should not 15 The differences in the percentage characteristics in panel B and the corresponding variables in panel A is mainly a result of Panel B being unweighted. 16 In an alternate specification (not shown), I restricted the estimates to schools that started at either 7:30 or 8:15 (roughly 75% of the sample). Results were highly similar to those in Figs. 2 and 3.