Youth Crime in the Era of School Takeovers

Youth Crime in the Era of School Takeovers. Evidence from London Secondary School Academies*

Emma Duchini,† Victor Lavy,‡ Stephen Machin§

October 2020

Abstract

In this paper, we study the impact of London school takeovers on youth criminal activity. In the last 20 years more than 60 percent of English secondary schools have converted to academies. Upon conversion, academies acquire substantial freedom in terms of teacher hiring and pay, taught curriculum, parents’ involvement, and length and structure of the school day. On the one hand, increased autonomy over this bundle of management and teaching practices may help to reduce teenagers’ propensity to commit crime. On the other hand, if autonomy is not accompanied by an improvement in the school management, youth disruptive behavior may be unaffected or even increase upon conversion. Exploiting the variation across London boroughs and years in school takeovers, we find evidence that the first channel seems to prevail. A 10 percentage point increase in the share of secondary schools that are converter academies leads to a 3 percent significant reduction in juvenile property and violent crime, compared with the pre-treatment mean. Effects are larger among male teenagers and are driven by 15-17 year- old teenagers. Additional results suggest that an improvement in certified school quality and a strengthening of schools’ disciplinary ethos following conversion may help to explain our findings.

JEL codes: I28; J13; J18. Keywords: school autonomy; academies; youth crime.

*We are grateful to Claire Crawford, Gordon Dahl, Eric Hanushek, Olmo Silva, and Sascha Becker for their useful comments. We further thank Andrew Eyles, and Matteo Sandi for their constructive suggestions. We also acknowledge participants in the IX Barcelona Workshop on Economics of Education, III Dondena Workshop on Public Policy, V Lisbon Research Workshop on Economics, Statistics and Econometrics of Education, 2019 RES Conference, Tenth International Workshop on Applied Economics of Education (IWAEE), and 2019 EEA conference. Finally, we gratefully acknowledge financial support from the British Academy, and the Centre for Competitive Advantage in the Global Economy (CAGE). Please, do not cite this paper without the authors’ consent. All mistakes are our own. † University of Warwick, Department of Economics, Office S0.70, Coventry CV4 7AL, United Kingdom. Email: [email protected] ‡ University of Warwick, Department of Economics, Office S1.10, Coventry CV4 7AL, United Kingdom. Email: [email protected] § Department of Economics and Centre for Economic Performance, London School of Economics. Email: [email protected]

1 1 Introduction

In the school year 2015-16, 60 percent of English secondary state-funded schools and slightly less than 20 percent of primary schools were academies. Like charter takeovers in the United States, English academies are community schools that are converted into autonomous schools (Abdulka- diroglu˘ et al. 2016, Abdulkadiroglu˘ et al. 2011, Eyles and Machin 2015, Eyles et al. 2017b). Remarkably, no academy school existed at the beginning of the years 2000s. While conversion models have evolved over time,1 the common element of these schools is that, upon conversion, they acquire substantial freedom in terms of teacher hiring and pay, taught curriculum, parents’ involvement, and length and structure of the school day (Eyles et al. 2017a, Machin and Sandi 2018). In this paper, we focus on London secondary schools, and study the impact of school takeovers on youth criminal activity. Between 2006 and 2016, the British police detained 1.8 millions teenagers, accounting for one every six arrests in this period, while 10-17 years old represent less then 10 percent of the overall population.2 According to a report conducted by the UK Ministry of Justice in 2011, each young offender costs £ 8,000 per year to the criminal justice system (National Audit Ofﬁce 2011).3 As a comparison, OECD statistics show that, in 2014, the United Kingdom spent roughly the same amount of money per student enrolled in secondary school. A priori, it is not clear how school autonomy can affect youth crime. On the one hand, budgetary autonomy, parents’ involvement, and after-school support may help reduce youth crime, by lowering teenagers’ desire to engage in anti-social behavior, and keeping them busy with school activities (Jacob and Lefgren 2003, Berthelon and Kruger 2011, Deming 2011, Imberman 2011).

1As extensively documented in Eyles and Machin(2015), Eyles et al.(2017a), Eyles et al.(2017b) and Machin and Sandi(2018), this rapid expansion of the autonomous sector in the English school system comprises two distinctive batches. While the ﬁrst wave of “academisation”, begun in the 2002–03 school year, concerns mainly under- performing secondary schools located in disadvantaged areas, the second batch that originates from the Academies Act of May 2010, results in a mass “academisation” process and increased heterogeneity across these autonomous schools. 2Similarly, in the United States, criminal activity rises sharply in adolescence, reaches a peak between 16 and 18, and declines thereafter (Levitt and Lochner 2001). 3This includes the costs of police, courts, offender management teams, and custody, but excludes others such as the costs businesses and individuals incur in anticipation of crime.

2 On the other hand, crime is often a social activity (Bayer et al. 2009, Damm and Dustmann 2014), implying that certain types of crime, such as drug-related offenses, may potentially increase when teenagers spend more time together in school in a demanding environment (Jacob and Lefgren 2003, Kling et al. 2005, Weiner et al. 2009, Carrell and Hoekstra 2010, Imberman et al. 2012, Billings et al. 2013). In this paper, we shed light on these mechanisms by studying the relationship between the massive and staggered expansion of converter academies in London that begins in 2010-11 and the dynamic of youth crime.4 Our identification strategy is a difference-in-differences model that compares local authorities where school takeovers take place by 2011-2012, with local authorities where there is still no converter academy by 2012 but at least one opens in the next four years. In what follows, we will provide evidence to show that, prior to the start of the conversion period, the evolution of youth crime, and that of factors influencing both the probability of school conversion and the outcome of interest, are comparable across treated and control areas. To conduct our analysis, we use administrative data on youth violent crime, property crime and drug-related offences, which are provided by the UK Ministry of Justice. We focus on Lon- don over the period 2004/05-2011/12, as only the data for the capital are broken down by local authority.5 Moreover, since the financial year 2004-2005, the data are available by offender’s age (10-17), gender, and race, which are key dimension for the heterogeneity analysis.6 In detail, our outcomes are youth proven offenses per thousands of inhabitants in the relevant crime category and age range, where these comprise any offense that results in the young person receiving a caution or conviction. As for the school data, we use the school-level version of the National Pupil Database (NPD) providing a rich set of information on the universe of pupils enrolled in English schools,

4Two conversion models coexist in the English educational system. The “academisation” process began under the 1997-2010 Labour government as a remedial program for low-performing schools. Schools willing to gain more autonomy first had to identify a sponsor with a successful management record and willing to take up the administration of the school. For this reason, these school takeovers were named ”sponsored academies”. With the issue of the 2010 Academy Act, any high-performing school became eligible to acquire the status of a so-called ”converter academy”, without the need to find a sponsor. Up to 80 percent of academies today are converter academies. You can find more details on this in the next section. 5Note that a local authority corresponds to a borough, and here we will use the two terms at the time. 6A financial year in the United Kingdom goes from April of a certain year to March of the following one.

3 and link it to a data set built by the UK Department for Education that identifies which secondary schools convert into academies, and the type and timing of conversion.7 Our results show that, in treated boroughs compared with control ones, a ten percentage point increase8 in the share of converter academies among secondary schools leads to a reduction in both youth property and violent crime of around 3 percent, compared with the mean for the period where no converter academy existed. As for drug offenses, while point estimates suggest that the expansion of academies leads to an increase in this type of crime in treated boroughs in comparison with the control ones, this effect is not significant in our main specification. Further results show that the effect on violent crime is driven by male teenagers, who are also much more inclined to commit this crime to begin with. Along the age dimension, both the impact on property crime, and violent crime are larger for 15-17 years old. Finally, although the point estimates are of similar magnitude, the effects are more precisely estimated for white offenders, than for non-white teenagers. To better understand these results, we explore several mechanisms, including changes in the composition of the student population, improvements in pupils’ school performance, the management of school expenditures, changes in certified school quality, and the role of school disciplinary ethos. This analysis suggests that the effect of conversions on school quality, as measured by the scores that the Office for Standards in Education, Children’s Services and Skills (OFSTED) gives to schools, partly explains our results. In particular, while a small sample size limits the precision of our coefficients, point estimates suggest that upon conversion, schools improve along several dimensions, including overall effectiveness, students’ performance and behavior, and teaching quality. Moreover, by exploiting unique data on youth victimization rates by day and hour of the day, we find suggestive evidence that the expansion of academies leads to an increase in offenses committed during school time, and a decrease in those committed off schools. To us, this is consistent

7Note that we are soon going to gain access to the individual-level version of the NPD. Using the school level version is currently limiting our possibility of identifying the impact of school takeover on students’ performance and behavioral outcomes such absences or expulsions. However, where possible, we still provide results obtained from the analysis of aggregate data, and discuss the potential threats to identiﬁcation we face. 8Which roughly corresponds to a one-standard deviation increase.

4 with academy conversions leading to increased reporting of offenses taking place in school, and an actual decrease in victimization rates outside of school time. In turn, this supports the hypothesis that school takeovers result in a strengthening of schools’ disciplinary ethos. Overall, these findings contribute to several strands of literature. First, this paper gives an important contribution to the increasing number of studies that look at the impact of charter-style schools (Bohlmark and Lindahl 2007, Clark 2009, Angrist et al. 2010, Angrist et al. 2013, Fryer Jr 2014, Dobbie and Fryer Jr 2015, Eyles and Machin 2015, Angrist et al. 2016, Eyles et al. 2017a, Machin and Sandi 2018, Dobbie and Fryer 2020). As this type of school becomes to spread out across several countries, we need to assess how they affect a full range of outcomes, even beyond school performance. Focusing on youth crime is especially relevant, given that it is a priori unclear how school autonomy may affect teenagers’ behavioral outcomes. Remarkably, to the best our knowledge, so far only Dobbie and Fryer Jr(2015) have studied the consequences that school autonomy has on youth criminal activity. Focusing on one single middle charter school from the Harlem Children’s Zone, and exploiting the lottery-based admission process, the authors find that, on top of gaining in school performance, male teenagers are less likely to be incarcerated six year after enrollment in this school, while no effect is detected on self-reported drug-consumption or criminal behavior. Although a few recent studies have shown that charter takeovers seem to be as effective as lottery-based charter (Abdulkadiroglu˘ et al. 2016), it is unclear whether results obtained from lottery-based samples could be extended to students who do not apply to charter schools. Moreover, as youth criminal activity peaks between 16 and 18, it is important to understand whether high-school school takeovers have an immediate impact during the period in which teenagers have a higher risk of engaging in crime. However, no evidence exists on this crucial stage of education, and our paper aims to fill this gap. Second, we add to the specific literature that studies the impact of education on crime (Jacob and Lefgren 2003, Lochner and Moretti 2004, Kling et al. 2005,Weiner et al. 2009, Berthelon and Kruger 2011, Deming 2011, Machin et al. 2011, Machin et al. 2012, Billings et al. 2013, Dobbie and Fryer Jr 2015), by digging into the mechanisms through which different school inputs can

5 affect youth criminal activities. The paper proceeds as follows. The next section describes the institutional context and, in particular, the different phases of the “academisation” of the English educational system. Section 3 introduces the data we use to conduct the analysis, and section4 discusses the empirical strategy. Section5 presents the main results and robustness checks. Section6 discusses heterogeneous results by offender’s gender, age, and race. Section7 discusses potential mechanisms to explain the main ﬁndings. Section8 presents the analysis on youth victimization data. Section9 concludes.

2 Institutional setting

At the beginning of the years 2000s no academy existed in the English educational landscape. However, as shown in Figure1, by the school year 2015-16, 60 percent of English secondary state-funded schools and slightly less than 20 percent of primary schools were academies. Un- like traditional community schools, academies are managed and run outside the local authority’s control. As extensively documented in Eyles and Machin(2015), Eyles et al.(2017a), Eyles et al. (2017b) and Machin and Sandi(2018), this rapid expansion of the autonomous sector in the English school system comprises two distinctive batches. While the ﬁrst wave of “academisation”, which begun in the 2002–03 school year, has concerned mainly under-performing secondary schools located in disadvantage areas, the second batch that originated from the Academies Act of May 2010 has resulted in a massive “academisation” process and increased heterogeneity across these autonomous schools. Documenting the extent of this heterogeneity is key to the understanding of the setting studied here. Till the beginning of the 2000s, the majority of English schools, both at primary and secondary level, were community schools operating under the control of local authorities. The re- minder of the state funded sector comprised mostly religious and foundation schools. These schools are usually run by an autonomous government body that employs the staff and sets the

6 entrance criteria.9 During this period, a mounting consensus emerged in the educational community that many secondary schools, and especially those located in poor urban neighborhoods, were failing to provide an adequate educational level to their pupils and were afflicted by severe discipline problems. With the precise aim of reforming these low-performing schools, the 1997-2010 Labour government mainly opted for boosting school autonomy, through the creation of sponsored academies. This name came from the fact that schools willing to gain more autonomy have first to identify a sponsor, usually an educational charity or business group. This entity, together with the school’s local authority, has to submit to the Secretary of State for Education a formal expression of interest to convert the existing school into an academy. Upon approval, and after a subsequent feasibility stage, the sponsor of the new academy can finally delegate its management to a largely self-appointed board of governors. Between the school years 2002-03 and 2009-10, around 15 percent of secondary schools - but no primary school - converted into an academy via this process. The election of a new Conservative government in 2010 marked the end of this first and relatively circumscribed phase of “academisation”, and the beginning of the second and massive wave. With the immediate issue of the Academies Act, the new government converted what had been conceived as a remedial programme for poorly performing schools into a larger injection of autonomy and competition at every level of the educational system. Academy conversion was no longer tied to school performance and the requirement to find a sponsor to become an academy was removed. Furthermore, even primary schools could convert into academies. As a result, as shown in Figure1, in just six years the share of academies among secondary schools increased by four times, and this new type of school started to spread across primary schools as well. Importantly, as favoured by the Academies Act, many of the schools converting into academies in the second phase were high-performing schools enrolling pupils with an advantageous background. Moreover, more than 80 percent of them converted without the support of a sponsor. For this reason, they are named ”converter” academies, rather than ”sponsored”.

9In case of over-subscription, for instance, Faith schools can select students based on religious afﬁliation. Yet, none of these school is allowed to select based on ability.

7 Let aside the conversion process, both types of academies enjoy a great deal of autonomy. Besides recruiting the staff, and managing school admissions as foundation and faith school do, they can choose to deviate from the national curriculum (except for core subjects), lengthen the school day and year, and set their own disciplinary code. Nonetheless, according to the evidence accumulated so far on these two “academisation” phases (Eyles and Machin 2015, Eyles et al. 2017a and Machin and Sandi 2018), we should dis- tinguish three groups of academies: the pre-2010 sponsored academies, the post-2010 sponsored academies, and the post-2010 converter academies. In particular, Eyles et al.(2017a) show that, while Labour-type sponsored academies in both regimes may be fairly comparable, the experience of sponsored and converter should be analyzed separately. According to the authors, for instance, converter academies tend to be originally high- performing schools enrolling advantaged pupils, while schools converted into sponsored academies tend to be low-performing schools located in disadvantaged areas, especially in the first wave of “academisation”. To conclude on the institutional setting, we briefly describe the structure of the English educational system, as this will also result to be important to have a clear understanding of our findings. Compulsory education in England is organized into Key Stages (KSs). Pupils enter school at age 4–5 and for two years are enrolled in the Foundation Stage. Then they progress to KS1, up until age 6–7, when they enter the last stage of primary education, called KS2. By the end of it, at age 10-11, students take KS2 standardized national tests, which assess their knowledge in English, mathematics and science. Next, they enter secondary school which lasts from age 11-12 to age 15-16, and is divided into KS3 (up to age 13-14) and KS4. At the end of this Stage, pupils sit ex- ternally marked academic (GCSE) and/or vocational (NVQ/BTEC) exams in a range of subjects, with English, maths and science being the only compulsory ones. Finally, after compulsory education, pupils can keep on studying in so-called sixth-form colleges, which normally last for two years, and then move to university. Around 93 percent of students keep on studying at upper secondary school level. Importantly, high schools usually offer sixth-form education in their premises

8 as well.

3 Data

Our objective is to study the relationship between the expansion of academies among English secondary schools and youth crime. To do so, we rely on the richness of data available for London, and exploit the massive and staggered expansion of converter academies across London boroughs. The UK Ministry of Justice (MoJ hereafter) provides youth justice statistics for London, broken down by borough of residence, since the financial year 2004-05.10 In detail, for each year and London borough, we know the number of proven offenses committed by individuals aged 10 to 17 who reside in this borough, defined by the MoJ as any offense that results in the young person receiving a caution or conviction. Moreover, these figures are also available by type of crime committed, gender, race, and offender’s age. The main limitation of this data set is that it is not available at the individual level, and hence cannot be matched with school records. However, it also offers several valuable features. First, the data collected are not self-reported, but come from police records and youth custody centers. As engagement in criminal activity is often under-reported in survey data, the administrative nature of these data is a strong advantage in this respect. Second, these statistics are available for up to 19 categories of crime.11 Although here, for comparability with other studies, we focus on three main classes of crime, namely drug offenses, property crime, and violent crime, this detailed

10Note that in the United Kingdom a ﬁnancial year goes from March of a certain year to March of the following one. The primary source for these data are Youth Offending Teams (YOTs), multi-agency teams made up of representatives from police, probation, education, health and social services, and specialist workers. In the year ending in March 2017, there were 152 YOTs across England and Wales, but only London has one for each of its boroughs. According to the deﬁnition provided by the Ministry of Justice, YOTs are organized geographically and independent of the police and courts. Their goal is to advocate for children and young people involved with the criminal justice system both in custody and in the community, run prevention programs aimed at keeping children and young people from criminal activity, and provide advice and guidance for children and young people and their families in court. Importantly, the data provided by these institutions are fairly comparable to the ones coming from police forces. 11In order, these comprise: Arson, Breach of Bail, Breach of Conditional Discharge, Breach of Statutory Order, Criminal Damage, Death Or Injury By Reckless Driving, Domestic Burglary, Drug Offenses, Fraud and Forgery, Motoring Offenses, Non Domestic Burglary, Other Offenses, Public Order, Racially Aggravated Offenses, Robbery, Sexual Offenses, Theft and Handling, Vehicle Theft Violence Against Person.

9 classification allows us to gain a lot of insights into the dynamics of youth criminal activity. Figure2 provides an overview of the data. To ease comparability, we consider youth crime rates, defined as proven offenses per thousands of inhabitants in the relevant age, gender and race cell. Borough-level yearly population data are taken from the Office for National Statistics’ website.12 The main types of criminal activity that young people commit are Criminal Damage, Drug offenses, Motoring offenses, Robbery, Theft, and Violence against the Person. Males are also five times more likely to commit crime than females, and non-white are two times more likely to engage in criminal activity than white youth. Finally, criminal activity is 6 times more intense among teenagers aged 15-17 than at younger ages. Next, Figure3 compares the evolution of youth crime in London, to that of other big English cities, over the period 2004-05 to 2015-16. The sum of drug offenses, property and violent crime are considered.13 Remarkably, youth crime substantially drops across all major English cities over the period considered. Moreover, while youth crime is lower in London than in other cities at the beginning of the period, over time, the two series converge, which suggests that focusing on London may be informative for other urban areas as well. Data on London schools come from the National Pupil Database (NPD). This data set is managed by the Department for Education (DfE hereafter) that uses it to compile schools’ performance data every year. For all pupils enrolled in state-maintained primary and secondary schools, it provides information on their socio-economic background - gender and race, free-school meal eligibility, special education needs status - as well as their attainment at various stages of the educational system. Moreover, it also gives details regarding students’ school characteristics, such as school type or institutional arrangement. To gain additional information on academies, we have linked it to an external file compiled by the DfE and providing details such as the date when con-

12A final remark on the timing of our data. Youth crime data are reported over a a financial year, that goes from March of year y to March of year y+1. To construct crime rates, we associate the crime data for the period 03/y to 03/y+1, to the population figures for year y. More importantly, we relate youth crime for the period 03/y to 03/y+1 to the share of converter academies in the school year 09/y to 06/y+1. 13In detail, we define property crime as the sum of proven offenses related to burglary - domestic and non-domestic - criminal damage, and theft - theft and handling plus vehicle theft. Violent crime comprises robbery and violence against the person.

10 version takes place, whether they convert into sponsored or converter, and the identiﬁer of the predecessor school.14 Importantly, so far, we only have access to the school-level version of the NPD from 2001-02 to 2015-16. Table1 describes the expansion of academies among London secondary schools since the school year 2004-05. As schools start to convert into sponsored academies already from 2001-02, out of the 31 boroughs of London,15 7 have at least one sponsored academy in 2004-05, 8 others experience this type of school takeover in the ”Labour phase”, and 10 see a secondary school converting into a sponsored academy only in the post-2010 phase. As for converter academies, none of the 31 boroughs experience this type of school takeover till the school year 2009-10; in the next two years 21 boroughs see at least one school acquiring the status of converter academy, while 6 other boroughs only experience this type of school takeover between 2012-13 and 2015- 16, and 4 have no converter academy throughout the period considered here. Following Eyles et al.(2017a), and Machin and Sandi(2018), we want to compare the dynamics of youth crime only across boroughs that eventually experience the same type of school takeovers. While the absence of crime data for the years prior to the expansion of sponsored academies hinders the possibility of conducting this analysis for the case of sponsored academies, we can use the MoJ data to identify the impact of London converter academies. Table2 provides a comparison of London secondary schools that eventually become converter academies, with other secondary schools located in the rest of the country, in the year 2009- 10, right before the start of the expansion of the converter model. As in other parts of England, these schools tend to enroll pupils who perform better than the typical English student, and are less likely to receive free school meal. However, as London has always hosted a more diverse population compared to the rest of the country, schools that become converter academies also tend

14Importantly, we define as opening year of an academy the first full school year that it operates as an academy. This implies for instance that a school converted in September 2011 would be considered to operate as an academy from the 2011-12 school year, while a school opening in October 2011 would only be classified as academy from the school year 2012-13. 15Note that London is actually composed of 33 boroughs. However, Kingston and Richmond upon Thames report youth crime data together, and the same is true for the case of Tower Hamlets and the City of London. As a result, we have 31 observations per year and crime category.

11 to have a lower share of English native students and a higher proportion of non-white pupils, in comparison to both the typical English school, and the average school that will acquire converter status. Moreover, converter academies represent three quarters of all academies, in London as in the entire England.

4 Empirical strategy

To identify the impact of the expansion of academies on youth crime, we focus our attention on the 27 boroughs that eventually see at least one secondary school becoming a converter academy, and deﬁne as treatment group the 21 where at least one school takeover takes place between 2010-11 and 2011-12, and as control group the 6 boroughs where schools start converting into converter academies only in the following four years.16 Figures4 and5 report the geographical distribution of these two groups on a map of London, as well as the expansion of academies among secondary schools over these two years. Having deﬁned the treatment and control groups in this way, we estimate the following regression model over the period 2004-05 to 2011-12:

0 Y outhCrimeRatebt = γb + δt + β ShareAcademiesbt + Xbtπ + ubt. (1)

Here ShareAcademiesbt is our main regressor of interest and measures the share of secondary schools in borough b and school year t that are converter academies; γb are borough ﬁxed effects that should capture the impact on criminal activity of any time-invariant characteristics of the borough, such as the presence of an historical monument that represents a touristic attraction; and δt are school-year ﬁxed effects, which should capture the effect of temporal shocks common

16In detail, the treatment group comprises the following boroughs: Barnet, Bexley, Brent, Bromley, Ealing, Enﬁeld, Greenwich, Hackney, Hammersmith and Fulham, Harrow, Havering, Hillingdon, Hounslow, Kingston and Richmond, Lambeth, Newham, Redbridge, Southwark, Sutton, Wandsworth, Westminster. The control group includes: Barking and Dagenham, Croydon, Haringey, Kensington and Chelsea, Tower Hamlets and City of London, Waltham Forest. Finally, the four boroughs that never experience the opening of a converter academy in the period under study are: Camden, Islington, Lewisham, and Merton.

12 to all boroughs, such as the financial crisis or the Olympic games. In what follows, we will present both specifications with and without borough-level time-varying controls. These include the share of unemployed workers and the share of white individuals from the Annual Population Survey, 2001 Census share of highly educated individuals interacted with year fixed effects, and political party in power taken from the London Data Store. As for the outcome variable, we focus on the three main classes of crime, namely drug offenses, property crime, and violent crime, as well as the sum of these three categories. Moreover, in the main specification, we consider offenses committed by 10-17 years old, as data by gender and race are only available over this age range.17 Finally, we employ wild bootstrapped standard errors clustered at the borough level, and use population weights in our regressions. This identification strategy relies on the assumption that, conditional on these covariates, the variation in the timing of school conversions is substantially idiosyncratic. To start providing evidence in favor of this assumption, Appendix Table A.1 shows that the share of converter academies in a borough is not correlated with other factors that could affect the outcomes of interest, such the share of sponsored academies, house prices, or the borough-level unemployment rate. To provide further support for the assumption that the timing of school conversions is basically idiosyncratic, in the next section we will show that the evolution of the outcomes of interest does not start diverging in treated and control boroughs before the occurrence of school takeovers.

5 Main ﬁndings

Table3 reports the results of the estimation of regression1 on youth crime. Every two columns correspond to a different outcome, being respectively violent crime, property crime, drug offenses, or the sum of the three. For each outcome, the estimates displayed in the first column refer to a specification without borough-level time-varying controls, while those reported in the second column come from a model that includes them. We only show the coefficient of our main regressor of

17However, we will also provide a breakdown of our results by more detailed age groups.

13 interest, that is the share of converter academies among secondary schools in a borough. Accord- ing to these results, the expansion of converter academies across London boroughs signiﬁcantly decreases both youth violent and property crime. In detail, a ten percentage point increase in the share of converter academies among the secondary schools of a London borough18 leads to a decrease in both property and violent crime of around 3 percent compared with the pre-treatment mean. On the other hand, the expansion of academies seems to increase drug-related offenses, though the estimates are not robust to the inclusion of time-varying controls. As a result, total crime declines in treated boroughs in comparison with control ones.19 In Table4, we also show that the effect on violent crime is driven by a reduction in robbery. As for property crime, Table5 indicates that the overall effect results from a reduction in burglary and criminal damage. Appendix Table A.3 further complements these results by showing that the expansion of academies does not signiﬁcantly affect other components of youth crime.

Robustness checks. To support the validity of our identiﬁcation strategy, we perform event-study exercises by estimating the following regression:

+1 X CrimeRatebt = γb + δt + βk F stT akeoverbt + ubt, (2) k=−7

where we take the event year -1 as reference point. Note also that the coefﬁcients β−7 and β1 can only be estimated for the 6 boroughs that experience their ﬁrst school takeover in the school year

2010-11. Figure6 plots the estimated βk. As we can see, none of the leads is signiﬁcant in the case of violent and property crime, while only one is signiﬁcant in the case of drug-related offenses. While this could be a matter of chance, the decreasing pattern displayed in the case of property

18This roughly corresponds to the average growth of converter academies over the treatment period. 19To complement these results, Appendix Table A.2 shows unconditional difference-in-differences estimates together with their group and time components. All three crime outcomes decrease in treated borough in the post- treatment period compared with the pre-treatment years. While the overall effect on violent crime may have been magniﬁed by a positive trend in this outcome for the control group, property crime is decreasing in both treated and control group but relative more in the former. Finally, while drug-related offenses are decreasing in both treated and control group from the pre- to the post-treatment period, the fall is smaller in treated boroughs, which contributes to explain the positive coefﬁcient we obtain on this outcome.

14 crime may be more worrisome. However, when looking at the evolution of each component of property crime in Figure7, we can see that the negative trend is mostly driven by theft, which is not significantly affected by the expansion of academies. The effect of academies is instead clearly visible for the case of burglary, while that on criminal damage is less precisely estimated. As for the components of violent crime, the negative effect on robbery stands out in the dynamic specification presented in Figure8. To provide further evidence that our estimates do not capture the effect of differential pre- trends, in Table6 we progressively restrict the pre-treatment period. The table reads as follows. Each row refers to a different outcome, being this violent crime in the first row, property crime in the second, and drug-related offenses in the third one. Then, for each outcome, in the first column we report the main estimates, in the second one we restrict the pre-treatment period from 6 to 5 years, and then we proceed by eliminating another year up to the last column when we are left with 2 pre-treatment years. Remarkably, in all three cases, the estimates on the impact of converter academies are very stable across the different columns. Overall, these robustness checks suggest that our findings are not capturing pre-existing diverging trends of youth criminal activity in treated and control boroughs.

Alternative specifications. Table7 compares the estimates from the main specification, displayed in Panel A, with those obtained from unweighted regressions, reported in Panel B. While the impact on total crime becomes marginally insignificant, overall the results change little across the two specifications, which suggest that the reduction in youth crime is not concentrated in larger boroughs, but common to all treated ones. Next, in Table8, we compare the main results with those obtained by estimating the following specification:

0 Y outhCrimeRatebt = γb + δt + β AtLeastOneAcademybt + Xbtπ + ubt, (3)

where AtLeastOneAcademybt is a dummy variable that is equal to one from the ﬁrst year a

15 borough experiences an academy conversion onward. Point estimates from this alternative speci- ﬁcation go in the same directions as our main results, but the impact on property crime and total crime fall shortly to be signiﬁcant. Intuitively, this suggests that the intensity of the treatment is relevant to explain the impact of academies on youth crime.

6 Subgroup analysis

In Table9 we explore whether our results vary by gender. The effect on violent crime is completely driven by male teenagers, with a p-value on the test for the equality of coefficients across gender equal to 0.006. This is consistent with the fact that boys are also more likely to commit this crime to begin with. As for the impact on property crime, although boys are 3 to 4 times more likely to engage in criminal activity than girls, the effect on this outcome comes from both male and female offenders, and the two coefficients are not statistically different from each other. In the case of drug offenses, although neither the coefficient on boys nor the one on girls come out significant, any positive effect seems to be concentrated among male offenders. Note that already prior to the appearance of converter academies, female teenagers committed almost no drug-related offense. Turning to the effects by offender’s age, Table 10 shows that both the decrease in violent crime and the fall in property crime are driven by 15 to 17 years old, with a p-value on the test for the equality of coefficients across subgroup equal, respectively, to 0.006 and 0.004. This result is consistent with the fact that 15-17 years old are 5 times more likely than young people aged 10-14 to commit crime to begin with. Finally, in Table 11, we compare the impact on white and non-white teenagers. While effects are more precisely estimated in the case of white teenagers, the point estimates for all outcomes are very similar and non-statistically different between the two groups.20

20Note that to perform the test on the equality of coefﬁcients across subgroups, we ﬁrst stack the data for the two subgroups together and then run a fully interacted model that recovers the effect on each subgroup.

16 7 Potential mechanisms

So far, we have seen that the expansion of converter academies across London boroughs appears to affect youth criminal activity. To better understand these results, we explore several mechanisms, including changes in the composition of the student population, improvements in pupils’ school performance, the role of school discipline, changes in certiﬁed school quality, and the management of school expenditures. All the data used in this section are publicly available and can be downloaded from the DfE’s website.

Students’ composition. To investigate to what extent academy conversion changes pupils’ intake, we compare the evolution of students’ composition in secondary schools that acquire the status of converter academies between 2010-11 and 2011-12, with that of schools that only become converter academies after 2011-12, by estimating the following regression:

P upilCharacteristicsst = γs + δt + β ConverterAcademyst + ust, (4) on, respectively, the share of students receiving free school meal, the proportion of English native pupils, and the fraction of white British students.21 Here s stands for school and t for school year.

γs and δt are, respectively, school and school-year ﬁxed effects. The main regressor of interest is

ConverterAcademyst, which is a dummy variable taking value 1 from the year a school acquires the status of converter academy onwards. Finally, we cluster the standard errors at the school level. Columns 1-3 of Table 12 show that, while the acquisition of the status of converter academy signiﬁcantly increases the share of English native students in the school, it has no impact on any other characteristics of the student population. To complement these ﬁndings, we augment regres- sion4 with the leads and lags of academy conversions:

21Data on these outcomes are publicly available and can be found at https://www.gov.uk/government/collections/statistics-school-and-pupil-numbers.

17 +1 X P upilCharacteristicsst = γs + δt + βk AcademyConversionst + ust. (5) k=−7

The results of this exercise, reported in the top three graphs of Figure9, show no sign of clear differential pre-trends in these variables between treated and control schools. This supports the hypothesis that, when comparing schools that eventually convert into academies, the time of conversion does not seem to relate to changes in the student composition. Nor, overall, these dimensions seem to be affected by the conversion. We now focus on students’ performance along both cognitive and non-cognitive outcomes.

Students’ performance and disciplinary outcomes. While studies conducted at the national level point to a limited impact of converter academies on students’ performance, here we estimate this effect for the case of London academies (Andrews et al. 2017). The second channel we consider in this paragraph concerns academies’ disciplinary ethos. Upon conversion, academies may have used their autonomy and more direct involvement of parents to adopt stricter disciplinary standards. One way to investigate this is to study their impact on disciplinary outcomes, such as unauthorized absences and expulsions.22 Importantly, on the one hand, a tougher disciplinary code may decrease these instances, but on the other hand, it could trigger an increase in reporting absences and more frequent exclusions. To investigate these mechanisms, we estimate regression4 on the share of students obtaining at least a C in GCSE exams - the standard outcome to measure students’ performance until 2016 in the UK context - the share of unauthorized absence sessions over all potential school sessions in an year, and the share of temporary and permanent exclusions in an year over all students in the school.23 Note that to take into account any change in the composition of the student population,

22As above, these data are publicly available. Data on pupils’ absences can be accessed from https://www.gov.uk/government/collections/statistics-pupil-absence. Data on pupils’ exclusions can be downloaded from https://www.gov.uk/government/collections/statistics-exclusions. 23Note that disciplinary outcomes are only available since the school year 2006-07. Moreover, the share of unauthorized absence sessions are missing for 0.002 percent of the sample, the share of temporary exclusions for 2 percent

18 we should ideally estimate these regressions at the student level as done in Eyles and Machin (2015), Eyles et al.(2017a), Eyles et al.(2017b) and Machin and Sandi(2018). 24 While waiting to be able to access the individual-level version of these data, we present here the results of the analysis conducted at the school level. As we have just shown that school conversions do not seem to change students’ composition substantially in the context of London academies, conducting the analysis at the school level should not affect much our ability to identify causal effects. Columns 4-7 of Table 12 present the results of this analysis. While on average, disciplinary outcomes do not seem to be affected by a school conversion, this seems to signiﬁcantly decrease students’ performance.25 To better understand these results, we consider the dynamic evolution of these outcomes in the bottom graphs of Figure9. Clearly, here we can see a decreasing trend in students’ performance in treated schools compared to control ones prior to conversion, which may in part explain the conversion itself. To take into account these differential pre-trends, we check that, at borough level, school performance alone is not signiﬁcantly correlated to youth crime. Moreover, controlling for it in our main regressions does not affect the estimates of the impact of school conversions on youth crime - and these results are available upon request.

School quality indicators. While the analysis of students’ performance and behavioral outcomes has given mixed results, it is possible that the school conversion triggers an improvement in school quality, even though this is not immediately visible along these measures. To investigate this possibility, we study the impact of academy conversions on ofﬁcial and standardized indicators of school quality. OFSTED is the authority designated to rate schools’ quality. Its inspectors peri- of the sample, and that of permanent exclusions for 18 percent of the sample. Importantly, note that the probability that this outcome is missing only has a 0.02 positive correlation with the probability that the school is in the treatment group. 24Speciﬁcally, we should combine the difference-in-differences analysis with an instrumental variable approach. To control for selection into the treatment, we should focus exclusively on students who were already enrolled in the school prior to conversion, or legacy-enrolled students. To control for selection out of the treatment, we should instrument the school where the student is actually enrolled with academy status. 25In Appendix Table A.4 we also report the impact of academy conversions on unauthorized absences by reason of the absence. While overall unauthorized absences are not affected, those due to holidays or a late arrival at schools seem to increase following the school conversion. These results are consistent with an increased reporting of certain students’ absences due to the adoption of a stricter disciplinary ethos after conversion. In turn, this could help understand the impact of academies on youth crime.

19 odically visit schools and issue scores regarding several dimensions, such as overall effectiveness, pupils’ outcomes and behavior, and teaching and management quality.26 In Table 13, we report the estimated effect of academy conversions on each of these scores, obtained by running regression 4 on these outcomes. As we observe OFSTED scores both before and after academy takeovers only for a limited number of London schools, we also report the results on all English schools that eventually become academies over the period considered.27 Point estimates for both samples suggest that the conversion triggers an improvement along all these dimensions. Though the coef- ﬁcients are not precisely estimated, this analysis points to a potential beneﬁcial effect of autonomy on school quality, which may contribute to explain how the expansion of academies leads to a reduction in youth crime.28

School funding and expenditure. To conclude the analysis of potential mechanisms, we consider the impact of academy conversions on school funding and expenditure.29 To carry out this part of the analysis, we use publicly available data on community schools and academies, and closely follow Eyles et al.(2017b) who have studied this dimension for the case of primary school academies. A few elements are important to consider here. First, while around 10 percent of community schools’ income is retained by local authorities for central services, academies receive their funding directly from the government. As such, we should expect a mechanical increase in school funding upon conversion. Second, contrary to community schools who file their return on a financial year basis (April Y to March Y+1), academies do this over the academic year (September Y to August Y+1). More importantly, academies that convert between March and August can file a return for more than 12 months. As we do not observe which academies do this, we decide to exclude from the main analysis those schools that convert in the period March-August.

26Data on OFSTED reports are also publicly available, and can be found on its website at https://www.gov.uk/government/organisations/ofsted/about/statistics. 27As in the case of London schools, all the English schools that convert into academies by 2011-12 are considered treated, while those that convert by 2015/16 are assigned to the control group. 28Note that we cannot perform event-study exercises for these outcomes as we observe them at most once in the pre-treatment period. 29As all data used until here, school expenditure data can be downloaded from the link on school performance statistics indicated above.

20 The results from our preferred sample are reported in Table 14. As anticipated, total income increases by around 8 percent, while school expenditures increase by up to 13 percent. However, the increase in expenditure seems to be mostly allocated to running costs, rather than other items such as teaching staff or learning resources. While Table 15 shows that this does not signiﬁcantly affect expenditure shares, this analysis suggests that the management and allocation of school resources can hardly explain the results we have found on youth crime.

8 Analysis by day and time of the day

To complement the results discussed until here, we exploit a unique data set on borough-level offenses broken down by day of the week and hour of the day. These data have been provided by the London Metropolitan Police (MET hereafter) and measure offences where a young person (10-17 years old) is the victim. While these are not necessarily committed by young people, the literature has shown that young people are often victims of their own peers (Weiner et al. 2009). For privacy reasons, the data are grouped in two-year intervals, but crucially for us, we have them for both the pre and post-treatment period. In particular, we observe offenses involving young people every day and hour of the day in each London borough over the time intervals 2005- 06, 2007-08, 2009-10, 2011-12. Appendix Figures A.2 and A.3 provide a detailed representation of the data by treated and control groups and pre- and post-treatment periods. All ﬁgures are normalized by 10-17 years-old borough-period population counts. As we can see, during week days these offenses peak right after the end of the school day (2 pm) where they reach 3 cases per 1000 inhabitants, while during the weekend, they are overall lower but more spread out from the afternoon until midnight. These dynamics are similar across treated and control groups, both in the pre and post-treatment periods. To study the impact of academies on day and time of the day where offenses involving young people are committed, we proceed as follows. First, we consider separately offenses committed during week days and in the weekend. Then, for what concerns week days, we consider separately

21 offenses committed during school time (8-2 pm), those committed in the after-school time (2-6), in the pre-school time (7-8 am), and in the rest of day. Finally, as we only observe one post-treatment period, we estimate the following regression:

0 Y outhV ictimizationRatebt = γb + δt + β AtLeastOneAcademybt + Xbtπ + ubt, (6) where b stands for borough, and t for a two-year period. As above, time-varying controls include the share of white population, the share the share of unemployed workers, 2001 Census share of highly educated individuals interacted with period ﬁxed effects, and share of time a political party is in power over the two-year period. The main regressor of interest is AtLeastOneAcademybt, a dummy that is equal to 1 if a borough experiences at least one school conversion over the two years 2011 and 2012. Table 16 reports the results of this analysis. While the sample size limits our ability to ﬁnd precise results, the point estimates suggest that the expansion of academies leads to an increase in victimization rates during week days, and a decrease in victimization rates during the weekend. Focusing on different parts of the day in week days, results in columns 3 to 12 point to an increase in offense rates during the school time, and a decrease during after-school and pre-school time. Somehow surprisingly victimization rates seem to increase during the rest of the day. Overall, these results are consistent with academies leading to an increased reporting of offenses during school time, and an actual decrease of victimization rates outside of school time. In turn, this supports the hypothesis that academy conversions are accompanied by a strengthening of the school disciplinary ethos, and subsequent decrease in youth crime.

9 Summary and concluding remarks

In light of the rapid expansion of autonomous schools in many countries, it is especially important to establish what impact this institutional model has on pupils’ outcomes, even beyond their educational achievement. In this paper, we study the impact of English secondary school academies

22 on youth criminal activity. In the last 20 years more than 60 percent of English secondary schools have converted to academies. Upon conversion, academies acquire substantial freedom in terms of teacher hiring and pay, taught curriculum, parents’ involvement, and length and structure of the school day. A priori, it is not clear how school autonomy can affect youth crime. On the one hand, budgetary autonomy, parents’ involvement, and after-school support may help to reduce youth crime, by lowering teenagers’ desire to engage in anti-social behavior, and keeping them busy with school activities (Jacob and Lefgren 2003, Berthelon and Kruger 2011, Deming 2011, Imberman 2011). On the other hand, crime is often a social activity (Bayer et al. 2009, Damm and Dustmann 2014), implying that certain types of crime, such as drug-related offenses, may potentially increase when teenagers spend more time together in school in a demanding environment. (Jacob and Lefgren 2003, Kling et al. 2005, Weiner et al. 2009, Carrell and Hoekstra 2010, Imberman et al. 2012, Billings et al. 2013). Exploiting the variation across London boroughs and years in school takeovers, we find evidence that the first channel seems to prevail. A 10 percentage point increase in the share of secondary schools that are converter academies leads to a 3 percent significant reduction in juvenile property and violent crime, compared with the pre-treatment mean. Effects are larger among male teenagers and are driven by 15-17 year-old teenagers. Additional results suggest that an improvement in certified school quality and a strengthening of schools’ disciplinary ethos following conversion may contribute to explain our main findings. To conclude, a few considerations are worth mentioning. First, this paper identifies the short- run effect of school takeovers on youth crime. Damm and Dustmann(2014) find that growing up in a high crime neighborhood increases youth criminal activity. Hence, the long-run effects of reducing local juvenile crime may be even more beneficial, both for the affected teenagers and their social communities. Second, this study focuses on London, which implies that our findings may be especially relevant for an urban context. As youth crime is more frequent in large cities, our results become particularly remarkable.

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26 10 Graphs and tables

Figure 1: Expansion of academies among English schools

Expansion of academies among secondary schools

.2 Share of secondary schools

2001-022002-032003-042004-052005-062006-072007-082008-092009-102010-112011-122012-132013-142014-152015-16

Expansion of academies among primary schools

.15

.05 Share of primary schools

2001-022002-032003-042004-052005-062006-072007-082008-092009-102010-112011-122012-132013-142014-152015-16

Source: UK Department for Education. Notes: These graphs represent the expansion of academies over time in English secondary and primary schools.

27 Figure 2: Youth crime in London

Youth crime categories - London - 2005-2016

2 Crime per thousand inhabitants

Arson Robbery Public Order Breach of Bail Vehicle Theft Other Offences Drug Offences Reckless Driving Sexual FraudOffences & Forgery Theft & Handling Domestic Burglary Criminal Damage Motoring Offences

Non Domestic Burglary Breach of Statutory Order Violence Against Person Racially Aggravated Offences Breach of Conditional Discharge

Youth crime by gender - London - 2005-2016 Youth crime by age - London - 2005-2016

100 120

80 100

80 60

60 40 40 20 Crime per thousand inhabitants Crime per thousand inhabitants 20

0 0 Male 15 16 17 Female 10-14

Source: UK Ministry of Justice. Notes: This ﬁgure displays summary statistics on youth crime in London. Crime rates are deﬁned as number of offenses per thousand of inhabitants in the relevant age-gender-race bracket.

28 Figure 3: Youth crime in England

Trends in drug, property, and violent crime 10-17 years old

Crime per thousand inhabitants aged 10-17 0 2004-05 2007-08 2010 Academy act 2013-15 2015-16

School year

London Birmingham, Liverpool, Manchester

Source: UK Ministry of Justice. Notes: This ﬁgure compares the evolution of youth crime in London, with that of other big English cities. Crime data per thousand of inhabitants aged 10-17 are reported. The crime categories considered are drug-offenses, property crime, and violent crime. The red line refers to London, while the blue one represents the average crime rate across Birmingham, Liverpool, and Manchester.

29 Figure 4: Geographical distribution of treated and control boroughs

Treated, control and excluded boroughs

No converter academy Converter academies btw 2010-11 and 2011-12 Converter academies after 2011-12

Source: UK Department for Education. Notes: This ﬁgure reports the geographical distribution of treated and control boroughs. The treated boroughs are Barnet, Bexley, Brent, Bromley, Ealing, Enﬁeld, Greenwich, Hackney, Hammersmith and Fulham, Harrow, Havering, Hillingdon, Hounslow, Kingston and Richmond, Lambeth, Newham, Redbridge, Southwark, Sutton, Wandsworth, Westminster. The control group includes Barking and Dagenham, Croydon, Haringey, Kensington and Chelsea, Tower Hamlets and City of London, and Waltham Forest. Finally, the four boroughs that never experience the opening of a converter academy in the period under study are Camden, Islington, Lewisham, and Merton.

30 Figure 5: Expansion of academies between 2010-11 and 2011-12

Distribution of converter academies in 2011 Distribution of converter academies in 2012

Highest quantile 3rd quantile Share of converter academies 2nd quantile No converter academies Lowest quantile

Source: UK Department for Education. Notes: This ﬁgure reports the expansion of converter academies in London between 2010-11 and 2011-12. In detail, the left-hand-side graph shows the geographical distribution of academies in 2010-11, while the right-hand-side graph reports that for 2011-12. The treated boroughs are Barnet, Bexley, Brent, Bromley, Ealing, Enﬁeld, Greenwich, Hackney, Hammersmith and Fulham, Harrow, Havering, Hillingdon, Hounslow, Kingston and Richmond, Lambeth, Newham, Redbridge, Southwark, Sutton, Wandsworth, Westminster. The control group includes Barking and Dagen- ham, Croydon, Haringey, Kensington and Chelsea, Tower Hamlets and City of London, and Waltham Forest. Finally, the four boroughs that never experience the opening of a converter academy in the period under study are Camden, Islington, Lewisham, and Merton.

31 Figure 6: Dynamic impact of converter academies on youth crime

Total crime

Estimated coefficient -4

-8 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Violent crime Property crime

6 6

3 3

0 0

Estimated coefficient -3 Estimated coefficient -3

-6 -6 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1

Year Year

Drug-related offences

Estimated coefficient -2

-4 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Source: UK Department for Education and UK Ministry of Justice. Notes: These graphs report the dynamic response of youth crime to school takeovers, obtained via the estimation of regression2. Each graph also displays 95% conﬁdence intervals associated to wild-bootstrap standard errors clustered at borough level.

32 Figure 7: Dynamic impact of converter academies on youth crime

Property crime

Estimated coefficient -3

-6 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Burglary Criminal Damage

2 4

1 2

0 0

Estimated coefficient -1 Estimated coefficient -2

-2 -4 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1

Year Year

Theft

Estimated coefficient -3

-6 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Source: UK Department for Education and UK Ministry of Justice. Notes: These graphs report the dynamic response of each component of property crime to school takeovers, obtained via the estimation of regression2. Each graph also displays 95% conﬁdence intervals associated to wild-bootstrap standard errors clustered at borough level.

33 Figure 8: Dynamic impact of converter academies on youth crime

Violent crime

Estimated coefficient -3

-6 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Robbery Violence Against Person

4 4

2 2

0 0

Estimated coefficient -2 Estimated coefficient -2

-4 -4 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1

Year Year

Source: UK Department for Education and UK Ministry of Justice. Notes: These graphs report the dynamic response of each component of violent crime to school takeovers, obtained via the estimation of regression2. Each graph also displays 95% conﬁdence intervals associated to wild-bootstrap standard errors clustered at borough level.

34 Figure 9: Dynamic impact on pupils’ intake, performance and disciplinary outcomes

Share of fsm eligible students Share of English native students Share of white British students

.06 .2 .06

.04 .15 .04

.02 .1 .02

0 .05 0

-.02 0 -.02 Estimated coefficient Estimated coefficient Estimated coefficient

-.04 -.05 -.04

-.06 -.1 -.06 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1

Year Year Year

Share of students achieving 5 A*-C in final exams

.15

.05

0 Estimated coefficient

-.05

-.1 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Share of unauthorized absences Share of fixed-term exclusions Share of permanent exclusions

.01 .06 .3

.25

.005 .03 .2

.15

0 0 .1

.05

Estimated coefficient -.005 Estimated coefficient -.03 Estimated coefficient 0

-.05

-.01 -.06 -.1 -5 -4 -3 -2 -1 0 1 -5 -4 -3 -2 -1 0 1 -5 -4 -3 -2 -1 0 1

Year Year Year

Source: UK Department for Education. Notes: This graph reports the dynamic response of pupils’ intake, performance and disciplinary outcomes to a school acquiring converter academy status. These results are obtained via the estimation of regression5. Each graph also displays 95% conﬁdence intervals associated to standard errors clustered at school level.

35 Table 1: Expansion of academies among London secondary schools

Boroughs Average Boroughs Average School with >= 1 share of with >= 1 share of year sponsored sponsored converter converter academy academies academy academies (1) (2) (3) (4) (5) 2004-05 7 0.02 0 0 2005-06 9 0.03 0 0 2006-07 12 0.04 0 0 2007-08 14 0.06 0 0 2008-09 14 0.07 0 0 2009-10 15 0.08 0 0 2010-11 18 0.1 6 0.02 2011-12 19 0.1 21 0.23 2012-13 20 0.11 25 0.37 2013-14 21 0.12 26 0.41 2014-15 24 0.13 26 0.42 2015-16 25 0.14 27 0.43 Total number of boroughs 31 31

Source: UK Department for Education. Notes: This table describes the expansion of academies over time across London boroughs. In detail, the ﬁrst column indicates the school year, the second column reports the number of boroughs with at least one sponsored academy in the corresponding school year, the third column displays the average share of sponsored academies across all London secondary schools, while the last two columns present these ﬁgures for converter academies.

36 Table 2: London secondary schools vs. the rest of England

All All English London English London English London sponsored sponsored converter converter schools schools academies academies academies academies (1) (2) (3) (4) (5) (6) Share of students achieving 5 A*-C 0.76 0.77 0.61 0.68 0.82 0.81 0.17 0.18 0.27 0.24 0.12 0.14 Share of FSM students 0.12 0.19 0.19 0.19 0.08 0.10 0.10 0.13 0.11 0.12 0.08 0.10 Share of English native students 0.86 0.61 0.85 0.75 0.88 0.83 0.20 0.24 0.20 0.24 0.17 0.22 Share of white British students 0.78 0.35 0.78 0.61 0.80 0.73 0.25 0.25 0.25 0.33 0.23 0.29 Community school 0.44 0.37 0.45 0.42 0.39 0.39 0.50 0.48 0.50 0.49 0.49 0.49 Foundation school 0.31 0.28 0.20 0.24 0.43 0.39 0.46 0.45 0.40 0.43 0.50 0.49 Voluntary Aided School 0.16 0.26 0.04 0.14 0.15 0.17 0.37 0.44 0.19 0.35 0.36 0.38 Voluntary Controlled School 0.03 0.01 0.01 0.01 0.03 0.03 0.17 0.10 0.11 0.11 0.17 0.17 Share of academies in the Borough 0.05 0.08 0.08 0.08 0.04 0.05 0.08 0.12 0.10 0.11 0.07 0.08 Observations 2,348 388 521 56 1,079 167

Source: UK Department for Education. Notes: This table compares London secondary schools to secondary schools located in the rest of England. All ﬁgures refer to 2010. Columns 3 and 6 focus on schools that will acquire the status of converter academy from 2010-11 onwards.

37 Table 3: Impact of converter academies on youth crime

Violent Violent Property Property Drugs Drugs Sum of Sum of crime crime crime crime offenses offenses the three the three (1) (2) (3) (4) (5) (6) (7) (8) Share of converter academies -2.217∗ -2.921∗∗ -3.575∗ -3.674∗∗ 1.151∗ 0.968 -4.641 -5.627∗ (1.2) (1.23) (2.04) (1.86) (.67) (.78) (3.26) (3.26) Year fe Y Y Y Y Y Y Y Y Borough fe Y Y Y Y Y Y Y Y Time-varying controls N Y N Y N Y N Y Observations 216 216 216 216 216 216 216 216 Adjusted R2 0.861 0.866 0.748 0.752 0.744 0.763 0.821 0.830 Pre-treatment mean 9.44 9.44 10.58 10.58 4.34 4.34 24.36 24.36

Source: UK Department for Education and UK Ministry of Justice. Notes: The table reports the impact of converter academies on different categories of crime, obtained via the estimation of regression1. Each regression includes borough and school-year FE. Time-varying borough-level controls include share of white population, share of unemployed workers, 2001 Census share of highly-educated individuals interacted with year FE, and dummies for political party in power. The estimation sample comprises the 27 boroughs of London that experience at least one school takeover between 2010-11 and 2015-16. The estimation period goes from 2004-05 to 2011-12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. All regressions are estimated with 10-17 population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

38 Table 4: Impact of converter academies on violent crime

Any violent crime Robbery Violence against a person (1) (2) (3) Share of converter academies -2.921∗∗ -1.778∗∗ -1.143 (1.23) (.9) (.77) Observations 216 216 216 Adjusted R2 0.866 0.838 0.810 Pre-treatment mean 9.440 3.030 6.410

Source: UK Department for Education and UK Ministry of Justice. Notes: This table reports the impact of converter academies on each component of violent crime, obtained via the estimation of regression1. Each regression includes borough and school-year FE, and time-varying borough-level controls. These comprise share of white population, share of unemployed workers, 2001 Census share of highly-educated individuals interacted with year fe, and dummies for political party in power. All regressions are estimated with 10-17 gender-speciﬁc population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

39 Table 5: Impact of converter academies on property crime

Any property crime Burglary Criminal damage Theft (1) (2) (3) (4) Share of converter academies -3.674∗∗ -0.944∗∗ -2.212∗∗∗ -0.518 (1.86) (.47) (.86) (1.45) Observations 216 216 216 216 Adjusted R2 0.752 0.485 0.711 0.723 Pre-treatment mean 10.58 1.21 2.45 6.92

Source: UK Department for Education and UK Ministry of Justice. Notes: This table reports the impact of converter academies on each component of property crime, obtained via the estimation of regression1. Each regression includes borough and school-year FE, and time-varying borough-level controls. These comprise share of white population, share of unemployed workers, 2001 Census share of highly-educated individuals interacted with year fe, and dummies for political party in power. All regressions are estimated with 10-17 gender-speciﬁc population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

40 Table 6: Impact of converter academies on youth crime - restricting the pre-treatment period

2004-05 to 2011-12 2005-06 to 2011-12 2006-07 to 2011-12 2007-08 to 2011-12 2008-09 to 2011-12 (1) (2) (3) (4) (5) Violent crime Share of converter academies -2.921∗∗ -2.695∗∗ -2.638∗∗ -2.396∗∗ -2.327∗∗ (1.23) (1.24) (1.04) (1.18) (1.17)

Property crime Share of converter academies -3.674∗∗ -3.930∗∗ -4.307∗∗∗ -4.472∗∗∗ -4.593∗∗∗ (1.86) (1.71) (1.5) (1.36) (1.36)

Drug offenses Share of converter academies 0.968 1.248 1.390 1.637∗ 1.248 (.78) (.86) (.87) (.96) (.82) Observations 216 189 162 135 108 41 Source: UK Department for Education and UK Ministry of Justice. Notes: The table reports the impact of converter academies on youth crime, obtained via the estimation of regression1. The ﬁrst column reports the main results, while in the following columns we progressively restrict the pre-treatment period. All regressions include borough-level time- varying controls, borough and year FE. Wild-bootstrap standard errors clustered at the borough level displayed in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Table 7: Impact of converter academies on youth crime - no weights

Violent Violent Property Property Drugs Drugs Total Total crime crime crime crime offenses offenses crime crime (1) (2) (3) (4) (5) (6) (7) (8) With population weights Share of converter academies -2.217∗ -2.921∗∗ -3.575∗ -3.674∗∗ 1.151∗ 0.968 -4.641 -5.627∗ (1.2) (1.23) (2.04) (1.86) (.67) (.78) (3.26) (3.26) Adjusted R2 0.861 0.866 0.748 0.752 0.744 0.763 0.821 0.830 Without population weights Share of converter academies -2.096 -3.167∗∗ -3.521 -3.869∗∗ 1.388∗ 0.856 -4.229 -6.180 (1.35) (1.42) (2.19) (1.93) (.78) (.82) (3.98) (3.9) Adjusted R2 0.861 0.866 0.748 0.752 0.744 0.763 0.821 0.830 Year fe Y Y Y Y Y Y Y Y Borough fe Y Y Y Y Y Y Y Y Time-varying controls N Y N Y N Y N Y Observations 216 216 216 216 216 216 216 216 Pre-treatment mean 9.44 9.44 10.58 10.58 4.34 4.34 24.36 24.36

Notes: The table reports the impact of converter academies on different categories of crime, obtained via the estimation of regression1. Regressions in the top panel are estimated with 10-17 population weights, while those in the bottom panel are unweighted regressions. Each regression includes borough and school-year FE. Time-varying borough-level controls include share of white population, share of unemployed workers, 2001 Census share of highly- educated individuals interacted with year FE, and dummies for political party in power. The estimation sample comprises the 27 boroughs of London that experience at least one school takeover between 2010-11 and 2015-16. The estimation period goes from 2004-05 to 2011-12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

42 Table 8: Impact of converter academies on youth crime - different speciﬁcations

Violent Violent Property Property Drugs Drugs Total Total crime crime crime crime offenses offenses crime crime Share of converter academies -2.921∗∗ -3.674∗∗ 0.968 -5.627∗ (1.23) (1.86) (.78) (3.26) >= 1 converter academy -0.800∗ -0.864 0.233 -1.431 (.47) (.7) (.24) (1.07) Observations 216 216 216 216 216 216 216 216 Adjusted R2 0.866 0.864 0.752 0.747 0.763 0.762 0.830 0.828 PreTreatmentMean 9.44 9.44 10.58 10.58 4.34 4.34 24.36 24.36

Source: UK Department for Education and Ministry of Justice, 2004-05 to 2011-2012. Notes: The table reports the impact of converter academies on different categories of crime. Estimates in the top panel are estimated using regression1, while those in the bottom panel are estimated via regression3. Each regression includes borough and school-year FE. Time-varying borough-level controls include share of white population, share of unemployed workers, 2001 Census share of highly-educated individuals interacted with year FE, and dummies for political party in power. The estimation sample comprises the 27 boroughs of London that experience at least one school takeover between 2010-11 and 2015-16. The estimation period goes from 2004-05 to 2011-12. All regressions are estimated with 10-17 population weights. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

43 Table 9: Heterogeneous effect by gender

Entire sample Boys Girls (1) (2) (3) Violent crime Share of converter academies -2.921∗∗ -5.043∗∗∗ -0.444 (1.23) (1.56) (1.15) Pre-treatment mean 9.44 14.06 3.91

Property crime Share of converter academies -3.674∗∗ -5.183∗ -2.229∗∗ (1.86) (2.96) (1) Pre-treatment mean 10.58 15.08 5.17

Drug offenses Share of converter academies 0.968 1.725 0.147 (.78) (1.45) (.17) Pre-treatment mean 4.34 7.8 0.42 Observations 216 216 216

Source: UK Department for Education and UK Ministry of Justice. Notes: This table reports the impact of converter academies on youth crime by gender, obtained via the estimation of regression1 on each subgroup separately. Each regression includes borough and school- year FE, and time-varying borough-level controls. These comprise share of white population, share of unemployed workers, 2001 Cen- sus share of highly-educated individuals interacted with year fe, and dummies for political party in power. All regressions are estimated with 10-17 gender-speciﬁc population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

44 Table 10: Heterogeneous effect by offender’s age

10-17 10-14 15-17 (1) (2) (3) Violent crime Share of converter academies -2.921∗∗ -0.614 -6.724∗∗∗ (1.23) (.80) (2.56) Pre-treatment mean 9.44 4.18 18.37

Property crime Share of converter academies -3.674∗∗ -1.242 -7.647∗∗ (1.86) (1.87) (3.2) Pre-treatment mean 10.58 4.57 20.62

Drug offenses Share of converter academies 0.968 0.234 2.076 (.78) (.39) (1.76) Pre-treatment mean 4.34 0.44 10.96 Observations 216 216 216

Source: UK Department for Education and UK Ministry of Justice. Notes: This table reports the impact of converter academies on youth crime by age, obtained via the estimation of regres- sion1 on each subgroup separately. Each regression includes borough and school-year FE, and time-varying borough-level controls. These comprise share of white population, share of unemployed workers, 2001 Census share of highly-educated individuals interacted with year fe, and dummies for political party in power. All regressions are estimated with 10-17 gender-speciﬁc population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

45 Table 11: Heterogeneous effect by race

Entire sample White Other ethnicities (1) (2) (3) Violent crime Share of converter academies -2.921∗∗ -3.554 5.942 (1.23) (2.53) (7.62) Pre-treatment mean 9.44 15.18 28.21

Property crime Share of converter academies -3.674∗∗ -7.593∗ -5.011 (1.86) (4.49) (12.16) Pre-treatment mean 10.58 23.81 21.13

Drug offenses Share of converter academies 0.968 3.491∗ 5.970 (0.78) (1.99) (3.88) Pre-treatment mean 4.34 8.58 11.31 Observations 216 216 216

Source: UK Department for Education and UK Ministry of Justice. Notes: This table reports the impact of converter academies on youth crime by ethnicity, obtained via the estimation of regression1 on each subgroup separately. Each regression includes borough and school-year FE, and time-varying borough-level controls. These comprise share of white population, share of unemployed workers, 2001 Census share of highly- educated individuals interacted with year fe, and dummies for political party in power. All regressions are estimated with 10-17 gender-speciﬁc population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

46 Table 12: Student composition, performance, and behavior - school level analysis

Student composition Performance and behavior

Share FSM Share English Share White Share Share Share Share students native British students unauthorized temporary permanent students students with A*-C absences exclusions exclusions (1) (2) (3) (4) (5) (6) (7) Converter academy -0.00847 0.0196∗ -0.0123 -0.0350∗ 0.00136 -0.00108 0.00000168 (0.00568) (0.0112) (0.00797) (0.0186) (0.000913) (0.00623) (0.000400) Observations 1343 1343 1343 1343 1005 988 835 Adjusted R2 0.960 0.939 0.981 0.752 0.728 0.692 0.535 Pre-treatment Mean 0.15 0.71 0.50 0.86 0.009 0.061 0.002

Source: UK Department for Education, 2004-05 to 2011-2012. Notes: The table reports the impact of a school conversion on students’ composition, performance and disciplinary outcomes, obtained from the estimation of regression4. The outcome is displayed at the top of each column. Each regression includes school and school-year FE. The estimation sample comprises London schools that acquire the status of converter academy between 2010-11 and 2015-16. The estimation period goes from 2004-05 to 2011-12. In columns 5-7, the outcomes are only available from 2006-07. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Standard errors clustered at the school level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

47 Table 13: Probability of obtaining outstanding OFSTED score - school level analysis

Overall Pupil Pupil Teaching Management effectiveness outcomes behavior quality quality England London England London England London England London England London (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Converter academy 0.101 0.282 0.0960 0.280 0.00713 0.0146 0.0729 0.0753 0.0202 0.0146 (0.0775) (0.330) (0.0716) (0.329) (0.0159) (0.0367) (0.0568) (0.108) (0.0330) (0.0367) Observations 1600 176 1600 176 1600 176 1600 176 1600 176 Pre-Treatment Mean 2.050 2.150 2.130 2.280 1.810 1.890 2.170 2.280 1.920 1.970

Source: UK Department for Education, 2004-05 to 2011-2012. Notes: The table reports the impact of a school conversion on certiﬁed school quality as measured by OFSTED scores. These estimates obtained from the estimation of regression4. The outcome is displayed at the top of each column. Each regression includes school and school-year FE. The estimation sample comprises London schools that acquire the status of converter academy between 2010-11 and 2015-16, and receive an OFSTED inspection between 2004-05 and 2011-12. The pre-treatment mean refers to the mean 48 of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Standard errors clustered at the school level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Table 14: School expenditure and income pp - school-level analysis

Income Expenditure

Total Granted Total Teaching Other Other Learning and income income expenditure staff staff running costs ICT resources (1) (2) (3) (4) (5) (6) (7) Converter academy 0.0816∗∗∗ 0.0478∗∗∗ 0.138∗∗∗ 0.0972 0.0606 0.204∗∗∗ 0.0922 (0.0157) (0.0141) (0.0167) (0.0775) (0.0626) (0.0701) (0.0988) Observations 884 884 884 884 884 884 884 Adjusted R2 0.930 0.937 0.926 0.783 0.769 0.746 0.620 Pre-Treatment Mean 5835.2 5634.4 5397.6 3527.7 556.8 658.0 332.7

Source: UK Department for Education, 2004-05 to 2011-2012. Notes: The table reports the impact of a school conversion on school income and expenditure. These estimates obtained from the estimation of regression4. The outcome is displayed at the top of each column. Each regression includes school and school-year FE. The estimation sample comprises London schools that acquire the status of converter academy between 2010-11 and 2015-16, and are assumed to ﬁle their returns over a 12-month period. The estimation period includes school years 2004-05 to 2011-12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Standard errors clustered at the school level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

49 Table 15: Impact of conversions on expenditure shares - school level analysis

Teaching staff Learning and ICT resource Other expenditures (1) (2) (3) Converter academy -0.00121 0.00128 -0.0000741 (0.0286) (0.00674) (0.0267) Observations 884 884 884 Adjusted R2 0.244 0.300 0.324 Pre-Treatment Mean 0.66 0.06 0.28

Source: UK Department for Education, 2004-05 to 2011-2012. Notes: The table reports the impact of a school conversion on school income and expenditure. These estimates obtained from the estimation of regression4. The outcome is displayed at the top of each column. Each regression includes school and school-year FE. The estimation sample comprises London schools that acquire the status of converter academy between 2010-11 and 2015-16, and are assumed to ﬁle their returns over a 12- month period. The estimation period includes school years 2004-05 to 2011-12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Standard errors clustered at the school level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

50 Table 16: Impact of converter academies on on youth victimization rates

Week Week Weekend Weekend School School After-school After-school Pre-school Pre-school Rest of Rest of day day time time time time time time school day school day (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) >= 1 converter academy 3.385 2.856 -1.320 -1.397 1.209 1.169 -0.228 -0.384 0.0195 -0.0161 2.321 1.987 (6.86) (6.14) (2.74) (2.09) (2.23) (2.14) (2.93) (2.41) (0.11) (0.12) (2.98) (2.78) Observations 108 108 108 108 108 108 108 108 108 108 108 108 Adjusted R2 0.920 0.923 0.934 0.939 0.880 0.880 0.918 0.921 0.616 0.628 0.905 0.907 Pre-Treatment Mean 96.48 96.48 34.43 34.43 26.29 26.29 37.77 37.77 1.22 1.22 41.39 41.39

Source: UK Department for Education and London MET Police, 2004-05 to 2011-2012. Notes: The table reports the impact of converter academies on youth victimization rates, obtained via the estimation of regression6. Each regression includes borough and two-year period FE. Time-varying borough-level controls include share of white population, share of unemployed workers, 2001 Census share of highly educated interacted with period FE, and share of time a political party is in power over the two-year period. The estimation sample comprises the 27 boroughs of London that experience at least one school takeover between 2010-11 and 2015-16. The estimation period goes from 2004-05 to 2011-12. All regressions are estimated with 10-17 population weights.

51 The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in the two-year period 2009-10, just before the start of the expansion of converter academies. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 11 Appendix

Figure A.1: Dynamic impact on school income and expenditure

Per pupil income Per pupil grant funding Per pupil expenditure

.2 .2 .2

.15 .15 .15

.1 .1 .1

.05 .05 .05

0 0 0 Estimated coefficient Estimated coefficient Estimated coefficient

-.05 -.05 -.05

-.1 -.1 -.1 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1

Year Year Year

Per pupil expenditure on teaching staff

-.1 Estimated coefficient

-.2

-.3 -7 -6 -5 -4 -3 -2 -1 0 1

Year

Per pupil cost on other staff Other running costs per pupil Per pupil expenditure on learning and ICT resources

.4 .2

.1 .4 .2

0 .2 0 -.1 0 Estimated coefficient Estimated coefficient Estimated coefficient -.2 -.2 -.2

-.3 -.4 -.4 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1 -7 -6 -5 -4 -3 -2 -1 0 1

Year Year Year

52 Figure A.2: Youth victimization rates by day and hour - pre-treatment period

Pre-treatment period

Monday Tuesday Wednesday

3 3 3

2 2 2 1 1 1 Offense rate Offense rate Offense rate 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Thursday Friday Saturday

3 3 3

Sunday

2 1 Offense rate 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Treatet boroughs Control boroughs

Source: UK Department for Education and London MET Police. Notes: This graph reports pre-treatment period youth victimization rates by day and hour of the day. The purple bar refers to treated boroughs, while the green one refers to control boroughs.

53 Figure A.3: Youth victimization rates by day and hour - pre-treatment period

Post-treatment period

Monday Tuesday Wednesday

3 3 3

Thursday Friday Saturday

3 3 3

Sunday

2 1 Offense rate 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Treatet boroughs Control boroughs

Source: UK Department for Education and London MET Police. Notes: This graph reports post-treatment period youth victimization rates by day and hour of the day. The purple bar refers to treated boroughs, while the green one refers to control boroughs.

54 Table A.1: Presence of converter academies and other factors

(1) (2) (3) Share of sponsored academies House prices Unemployment rate Share of converter academies -0.0403 -0.0337 -0.00770 (.05) (.04) (.02) Observations 216 208 216

Source: UK Department for Education, Annual Population Survey, and Land Registry. Notes: The table reports the relationship between the expansion of academies and different borough- level characteristics, obtained via the estimation of regression1. Each regression includes borough and school-year FE and time-varying controls. Time-varying borough-level controls include share of white population, 2001 Census share of highly-educated individuals interacted with year FE, and dummies for political party in power. The estimation sample comprises the 27 boroughs of London that experience at least one school takeover between 2010-11 and 2015-16. House prices are not available for the Borough of Westminster. The estimation period goes from 2004-05 to 2011-12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. All regressions are estimated with 10-17 population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

55 Table A.2: Unconditional difference-in-difference

(1) (2) (3) (4) (5) Treated Control Row Unconditional Sd Boroughs Boroughs difference Diff-in-diff Robbery and violence before 2010-11 9.74 11.33 -1.59 1.22 Robbery and violence after 2009/10 9.51 11.65 -2.14 1.54 Column Difference -0.23 0.32 -0.55 0.73 Burglary and Crim. Damage before 2010/11 13.36 14.01 -0.66 1.34 Burglary and Crim. Damage after 2009/10 8.04 10.21 -2.16 0.91 Column Difference -5.31 -3.81 -1.50 0.80 Drugs Offences before 2010/11 4.03 4.67 -0.64 0.61 Drugs Offences after 2009/10 3.60 4.07 -0.47 0.66 Column Difference -0.43 -0.60 0.17 0.50 Total crime before 2010/11 27.13 30.01 -2.89 2.36 Total crime after 2009/10 21.16 25.93 -4.77 2.55 Column Difference -5.97 -4.09 -1.88 1.43

Source: UK Department for Education and Ministry of Justice. Notes: The table shows the unconditional difference-in-differences estimates together with their time and group components. Treated boroughs are those 21 London local authorities that experience at least one school takeover between 2010-11 and 2011-12. Control boroughs are those 6 London local authorities that experience at least one school takeover between 2012-13 and 2015-16. The estimation period goes from 2004-05 to 2011-12. *** p<0.01, ** p<0.05, * p<0.1.

56 Table A.3: Impact of converter academies on main outcomes and other offenses

(1) (2) (3) (4) Violent crime Property crime Drugs Other offenses Share of converter academies -2.921∗∗ -3.674∗∗ 0.968 2.097 (1.23) (1.86) (.78) (1.5) Year FE Y Y Y Y Borough FE Y Y Y Y Time-varying controls Y Y Y Y Observations 216 216 216 216 Adjusted R2 0.866 0.752 0.763 0.780 Pre-treatment mean 9.44 10.58 4.34 9.89

Source: UK Department for Education and UK Ministry of Justice. Notes: The table reports the impact of converter academies on different categories of crime, obtained via the estimation of regression1. Each regression includes borough and school-year FE and time-varying controls. These include the share of white population, share of unemployed workers, 2001 Census share of highly-educated individuals interacted with year FE, and dummies for political party in power. The estimation sample comprises the 27 boroughs of London that experience at least one school takeover between 2010-11 and 2015-16. The estimation period goes from 2004-05 to 2011-12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. All regressions are estimated with 10-17 population weights. Wild-bootstrap standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

57 Table A.4: Impact of converter academies on students’ unauthorized absences

Share unauthorized absences

Any type Holidays Late arrival Other reasons (1) (2) (3) (4) Converter academy 0.00136 0.000210∗ 0.000307∗ 0.000478 (0.000913) (0.000117) (0.000156) (0.000880) Observations 1005 1005 1005 1005 Adjusted R2 0.728 0.644 0.38 0.704 Pre-treatment Mean 0.009 0.001 0.001 0.008

Source: UK Department for Education, 2004-05 to 2011-2012. Notes: The table reports the impact of a school conversion on unauthorized absences, by reason of the absence. These estimates are obtained from the estimation of regression4. The outcome is displayed at the top of each column. Each regression includes school and school-year FE. The estimation sample comprises London schools that acquire the status of converter academy between 2010-11 and 2015-16, and are assumed to ﬁle their returns over a 12- month period. The estimation period includes school years 2004-05 to 2011- 12. The pre-treatment mean refers to the mean of the outcome variable in treated boroughs in 2009-10, just before the start of the expansion of converter academies. Standard errors clustered at the school level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.