Elsevier

Labour Economics

Volume 41, August 2016, Pages 61-76
Labour Economics

Ill Communication: Technology, distraction & student performance

https://doi.org/10.1016/j.labeco.2016.04.004Get rights and content

Highlights

  • We investigate the impact of schools banning mobile phones on student test scores.

  • We implement a difference in differences (DID) strategy.

  • We combine a survey of school policies and England's National Pupil Database.

  • There is an increase in student performance after schools bans mobile phones.

  • These effects are driven by the previously lowest-achieving students.

Abstract

This paper investigates the impact of schools banning mobile phones on student test scores. By surveying schools in four English cities regarding their mobile phone policies and combining it with administrative data, we adopt a difference in differences (DID) strategy, exploiting variations in schools' autonomous decisions to ban these devices, conditioning on a range of student characteristics and prior achievement. We find that student performance in high stakes exams significantly increases post ban, by about 0.07 standard deviations on average. These increases in performance are driven by the lowest-achieving students. This suggests that the unstructured presence of phones has detrimental effects on certain students and restricting their use can be a low-cost policy to reduce educational inequalities.

Introduction

Technological advancements are commonly viewed as leading to increased productivity. Numerous studies document the benefits of technology on productivity in the workplace and on human capital accumulation.1 There are, however, potential drawbacks to new technologies, as they may provide distractions and reduce productivity. Mobile phones can be a source of great disruption in workplaces and classrooms, as they provide individuals with access to texting, games, social media and the Internet. Given these features, mobile phones have the potential to reduce the attention students pay to classes and can therefore be detrimental to learning.

There are debates in many countries as to how schools should address the issue of mobile phones. Some advocate for a complete ban while others promote the use of mobile phones as a teaching tool in classrooms. This debate has most recently been seen with the Mayor of New York removing a ten year ban of phones on school premises in March 2015, stating that abolition has the potential to reduce inequality (Sandoval et al., 2015).2 Despite the extensive use of mobile phones by students and the heated debate over how to treat them, the impact of mobile phones on secondary school student performance has not yet been academically studied.

In this paper, we estimate the effect of schools banning mobile phones on student test scores. This differs from other technology in schools research in that it examines the removal of an unstructured piece of technology, rather than a technology introduction. The lack of consensus regarding the impact of mobile phones means that there is no UK government policy about their use in schools. This has resulted in schools having complete autonomy on their mobile phone policy, and so have differed in their approaches. We exploit these differences through a difference in differences (DID) estimation strategy. We compare the gains in test scores across and within schools before and after mobile phone bans are introduced, where previously there was no stated policy.3

In order to do this, we generated a unique dataset on the history of mobile phone and other school policies from a survey of high schools in four English cities (Birmingham, London, Leicester and Manchester), carried out in spring of 2013. This is combined with administrative data on the complete student population from the National Pupil Database (NPD). From this, we know the academic performance of all students since 2001, and so use differences in implementation dates of mobile phone bans to measure their impact on student performance. Moreover, the NPD tracks students over time, which allows us to account for prior test scores along with a set of pupil characteristics including gender, race, ever eligible for free school meals (FSM), and special educational needs (SEN) status. Although we do not know which individuals owned mobile phones, it is reported that over 90% of teenagers owned a mobile phone during this period in England; therefore, any ban is likely to affect the vast majority of students (Ofcom, 2006, Ofcom, 2011).4 Even if a student does not own a phone themselves their presence in the classroom may cause distraction.

We find that following a ban on phone use, student test scores improve by 6.41% of a standard deviation. This effect is driven by the most disadvantaged and underachieving pupils. Students in the lowest quintile of prior achievement gain 14.23% of a standard deviation, while, students in the top quintile are neither positively nor negatively affected by a phone ban. The results suggest that low-achieving students have lower levels of self-control and are more likely to be distracted by the presence of mobile phones, while high achievers can focus in the classroom regardless of the mobile phone policy. This also implies that any negative externalities from phone use do not impact on the high achieving students. Schools could significantly reduce the education achievement gap by prohibiting mobile phone use in schools. We find the impact of banning phones for these students equivalent to an additional hour a week in school (Lavy, 2016), or to increasing the school year by five days (Hansen, 2011). We include several robustness checks such as event studies, placebo bans, tests for changes in student intake and a range of alternative outcome measures.

The rest of the paper is organized as follows: Section 2 discusses the related literature; Section 3 provides a description of the data, survey and descriptive statistics; Section 4 presents the empirical strategy; Section 5 is devoted to the main results and heterogeneity of the impacts; Section 6 provides a series of robustness checks; and Section 7 concludes with policy implications.

Section snippets

Related literature

Our paper is related to the literature on technology used on student outcomes. There is a growing literature on the impact of technology on student outcomes, which has yet to reach a consensus. Fairlie and Robinson (2013) conduct a large field experiment in the US that randomly provides free home computers to students. Although computer ownership and use increase substantially, they find no effects on any educational outcomes. Similar findings have occurred in recent randomized control trials

Student characteristics and performance

The NPD is a rich education dataset of the complete public school population of England.7 It contains information on student performance and schools attended, plus a range of student characteristics such as gender, age, ethnicity, FSM eligibility and SEN status. Each student is allocated an individual identifier, which allows for the student to be tracked over time and across schools.

Empirical strategy

We estimate the impact of a mobile phone ban on student achievement, exploiting differences in the timing of the introduction of policies across different schools. Eq. (1) presents our baseline specification:Yist=β0+β1Banst+μs+γt+εistwhere Yist is the test score of student i in high school s in year t. Our primary measure of student performance is test score at age 16.14

Main results

Table 4 presents estimates of the impact of a mobile phone ban on individual student performance. There are five columns, which account for more potential confounders as one moves from left to right. Column 1 is the most basic specification that only accounts for the across-school and across-year mean differences in test scores. Here we find a positive relationship between the introduction of a mobile phone ban and student test scores of 5.67% of a standard deviation.

However, we may be

Event studies & placebo tests

To discern whether the effects are driven by the mobile phone bans themselves and not unobserved shocks to the treated schools, we conduct several falsification tests. One crucial assumption to obtain unbiased estimates of β1 is if Cov( Banist, εist) = 0. We provide evidence that this assumption is likely to hold. If schools that introduced a mobile phone ban were improving regardless, then these gains could be falsely attributed to the policy and we would have an upward biased result.

We check for

Conclusion

This paper investigates the impact of restricting mobile phone use in schools on student productivity. We combine survey data on mobile phone policies in schools in four cities in England with administrative data on student achievement to create a history of student performance in schools. By exploiting differences in implementation dates, our results indicate that there is an improvement in student performance in schools that have introduced a mobile phone ban, which is driven by previously

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    We would like to thank Andriana Bellou, Vincent Boucher, Dave Card, David Karp, Briggs Depew, Christian Dustman, Ozkan Eren, Baris Kaymak, Stephen Machin, Naci Mocan, Ismael Yacoub Mourifie, Daniel Parent, Shqiponja Telhaj, Felix Weinhardt, and seminar participants at SOLE/EALE, AEFP, APPAM, RES, IAWEE, University of Montreal and the University of Texas at Austin for comments and discussions. We would also like to thank Guillaume Cote, Fan Duan and Vlad Khripunov for excellent research assistance. Any remaining errors are our own.

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