Academy Schools and Their Introduction to English Education

Comments Welcome

The Effects of Moving to More Autonomous School Structures: Academy Schools and Their Introduction to English Education

Stephen Machin* and James Vernoit**

October 2010

* Department of Economics, University College London and Centre for Economic Performance, London School of Economics

** Centre for Economic Performance, London School of Economics

Abstract In this paper, we study a high profile case of where changing school structures resulted in schools having more autonomy and flexible governance, namely the introduction of academy schools to the English secondary school sector. We consider the impact of academy status on pupil intake, pupil performance and on pupil performance in neighbouring schools using a difference-in-difference approach comparing how these outcomes altered in academy schools compared to their (non-academy) predecessor school relative to changes in a set of matched comparison schools. Our results suggest that moving to the more autonomous academy school structure may well have yielded performance improvements, that the quality of intake went up and there is some limited evidence of positive spillovers to neighbouring schools. It seems that becoming an academy takes a while to yield such benefits. Meanwhile, in the controversial policy discussion about academies, our results (at least so far) do seem to place a relatively positive slant on the programme introduced by the Labour government of 1997-2010.

JEL Keywords: Academies; Pupil Intake; Pupil Performance. JEL Classifications: I20; I21; I28.

1. Introduction

Around the world, there is serious interest in what types of educational institutions deliver better outcomes to pupils. Nowhere is the debate more vociferous than for the case of schools. Policy focus in many countries has been placed on whether innovative schooling strategies can offset the problems that have been connected to low student achievement in state/public schools. Examples include learning lessons from the private sector (see

Chakrabarti and Peterson, 2008), giving more autonomy to schools (Clark, 2009), different forms of school accountability (Hanushek and Raymond, 2004) and more flexible (and extended) teaching times and curriculum innovation (e.g. Abdulkadiroglu et al, 2009).

A growing economics of education literature has presented empirical estimates of the impact of school type on pupil achievement. A quite sizable recent body of US work on charter schools (publically funded schools that have autonomy levels more like private schools) does find some evidence of achievement gains.1 In England, different school types seem important in enabling choice and competition to raise pupil performance.2 Almost everywhere in the developed world, increased parental demand for school choice and the high housing valuations induced by what parents view as better schools for their children

(Black and Machin, 2010) all point to a heightened focus on what kind of schools deliver better outcomes to children.

Some nations have been more innovative than others in their departure from the orthodox model of the local or community school. The charter school movement in the US has

1 This literature is not without controversy. Recent, typically small scale, quasi-experimental evaluations of charters in particular US cities (Boston and New York) does report positive impacts on educational achievement (see Abdulkadiroglu et al, 2009, Dobbie and Fryer, 2009, and Hoxby and Murarka, 2009). Wider coverage non-experimental evaluations produce more mixed results (CREDO, 2009). 2 See, for example, Gibbons, Machin and Silva (2008) find that in English community schools there is little scope for choice/competition to enhance performance, whilst such a possibility exists in faith schools where more autonomy in decision making is present. 1 spread across many states. In Sweden, a new type of private school has started operating in most of the country (self-titled ‘free schools’) that compete for students with public schools on an equal financial basis. These free-schools are privately managed, but receive full public funding which is calculated on the basis of the number of students that they enrol who live in their local area. In England, various different types of school have enjoyed popularity, but none more so than the case of academy schools that we study in this paper.

Academy schools, and their gradual introduction into the English Educational system, have generated a controversial area of schools policy ever since the first clutch of academies opened in September 2002. Academies are independent, non-selective, state- funded schools that fall outside the control of local authorities, managed by a private team of independent co-sponsors. The sponsors then delegate the management of the school to a largely self-appointed board of governors.3 Each governing body of the academy has the responsibility to employ all academy staff, agree levels of pay, agree on conditions of service with its employees, and decide on the policies for staffing structure, career development, discipline, and performance management. It is hypothesised that the combination of independence to pursue innovative school policies and curricula, with the experience of the sponsor, will enable the Academy to drive up the educational attainment of their pupils throughout the school.

The aim of this paper is to carefully appraise this hypothesis. It is important to find out whether or not the increased autonomy of academies does, in fact, result in superior academic performance for children who attend them. This matters both from an economic perspective which argues that more autonomy and flexible governance sharpens economic incentives, and from a public policy standpoint. On either side of the latter debate on

3 An academy usually has around thirteen governors, with seven typically appointed by the sponsor. 2 academies you will find, on one side, fierce supporters who passionately believe that academies will drive up educational performance, and on the other side, you will find fierce critics of Academy schools who claim the Academies do not work, and that they are just a way of (implicitly) privatising the state-education system in England.

The rest of the paper is devoted to this task. In the next section of the paper, we discuss the nature of secondary schooling in England, and document the rise of academies.

Section 3 describes the data and modelling strategy we adopt, and uses this discussion to formulate key hypotheses to be tested in the empirical work. Section 4 presents the results, whilst Section 5 concludes.

2. The Introduction of Academy Schools to English Education

Academies are a new type of secondary school, first introduced into the English education system in the early 2000s. In this section, we consider this introduction, discussing how it relates to the different school types in the English secondary school system and documenting the scale of the rise of academy schools.

School Types in England

The English education system has always been characterised by a strong voluntary movement which has actively been involved in the delivery of education. These voluntary organisations are typically religious, and prior to the 19th century, were the sole providers in the delivery of education in England. Since this time, the state sector has gradually taken up a more active involvement in providing the resources to the education system by first helping to fund these early schools, and then actively creating new schools that have no affiliation with the voluntary sector (now called community schools). However, the

3 commitment of the voluntary movement to education has never waned, and even today, a significant percentage of the schools in the English education system are provided on the basis of a partnership between the voluntary sector and the state sector. Today, these schools are named either as a voluntary aided school, voluntary controlled school or a foundation school.

In addition to the voluntary sector, there is also private sector involvement in

England’s education system. However, this has typically taken the form of private funded independent schools which run alongside state funded schools. During much of this time, there was little private sector involvement in the state funded schools (Machin and Wilson,

2008). However, since the 1988 Education Act, the UK government has actively encouraged schools, which are public-private ventures as a medium for delivering education (for more details, see Machin and Wilson, 2008). The passing of this act led to the creation of a new type of school, which enabled the private sector to work in partnership with the state sector within the same school. These new types of school were called city technology colleges (CTC). In more recent years, the academy school programme has been introduced and, in some important dimensions, can be thought of as a continuation of the CTC scheme.

Together, these different school types make up the English secondary education system. Table 1 shows a typology of English secondary schools, listing the different type of school available in the sector and the key features in terms of school autonomy and governance. The Table orders the school types by the amount of autonomy that their governing body/management body has in making schooling decisions, and how dependent they are on the state sector. At the top of the list are registered independent schools which

4 are able to charge fees. They also have a management body which determines the staffing decisions, the curriculum, the admissions and the policies that the school follows.

Registered independent schools are therefore much less dependent on the state sector than some other types of schools. Academy schools share some of the characteristics of independent schools such as being able to make staffing decisions, but they cannot charge fees, and have to follow the national curriculum in some core subjects. They are also all- ability schools, and therefore have much less scope to decide on their admissions. However, they are all specialist schools and are therefore able to select up to 10% of their intake, and these will be pupils who have demonstrated the necessary aptitude and enthusiasm in the specialism that the academy has decided to follow. As mentioned previously, CTCs are very similar to academy schools. However, the crucial difference is that they have to follow the national curriculum in all subjects (Whitty et al., 1993). It is therefore this extra autonomy that an Academy has to decide the majority of the curriculum that really makes them unique. As stated earlier, the voluntary-aided schools, foundation schools, and voluntary-controlled schools all operate based on a partnership between the state sector and voluntary sector. However, within each of the schools the voluntary sector has varying degrees of influence within the school to decide on the schools policies and this is made clear in Table 1. Finally, there are the traditional local community schools that are centrally organized through the local education authority, and have very little autonomy and rigid governance structures.

Academy Schools

Table 2 shows the pace of introduction of academies into English secondary education. The school typology of Table 1 is adopted, and the Table shows the number (and

5 percent) of schools from school years 2001/2 (when there were no academies) through to

2008/9. The Table shows a very marked change in the structure of schools in the English secondary sector through the 2000s. The first point of note is the decline of the traditional

('bog standard') community school. The share falls by 12 percentage points over less than ten school years. The second observation concerns the marked rise in schools with non- traditional structures, having much less LEA involvement and more autonomy with respect to decision-making and admissions. This manifests itself in a rise of the number of foundation schools and, most importantly for our analysis, of academies. By 2008/9 there were 130 academies, comprising 4.3 percent of secondary schools, and there is a commitment to there being many more in future (see Machin and Vernoit, 2010).

What is evident from Tables 1 and 2 is that there is now a significantly larger number and share of schools with more autonomy and flexibility on governance in the

English secondary school landscape. The flagship model is very clearly the academy model and trying to carefully evaluate its potential impact is an important task. We turn to this next in terms of formulating key hypotheses regarding the educational impact of academies.

3. Data, Modelling Approach and Initial Data Description

Data

The school-level characteristics we use in this paper are taken from the Edubase,

School Performance Tables (SPT), and Annual School Census (ASC) data sources. These data sources are collected by the Department for Education (DfE). Edubase consists of yearly data for all schools in England and Wales from the 1999/00 academic year onwards.

It contains information on the number of pupils who attend the school, the school type (e.g.

Academy, CTC, Foundation, etc.) and on the admissions policy of the school (e.g. comprehensive, selective, etc.). School Performance Tables were created in 1994, and contain yearly data on the performance and address details of secondary schools. In addition, we also use the school performance tables to get information on the attendance rates of the school (e.g the proportion of half days missed due to unauthorised absences). In

England all state maintained schools have a statutory requirement (Elias and Jones, 2006), under the Education Act 1996, to complete an Annual School Census. This source provides yearly school-level information on the total number of pupils, the percentage of pupils who are eligible for free school meals, the total number of qualified teachers, and the percentage of pupils with special educational needs, both with and without, a statement. It also provides information on the pupil-teacher ratio, and the percentages of pupils from different ethnic groups.

In addition to these three school level sources, we also use data contained within the

National Pupil Database (NPD). This is a centrally collected data source which consists of the Pupil-Level Annual School Census (PLASC) and Key Stage (KS) data files. The

PLASC data file contains background characteristics of each pupil, such as whether the pupil has Special Educational Needs (SEN), whether the pupil is eligible for free school meals (FSME), their first language, their gender and the ethnicity of the pupil. It also provides other background characteristics such as their school year group, the code of the school which the pupil attends, and the local education authority (LEA) of the school. This data is collected three times per year (in January, May, and September). However, we only use the year-on-year January collection (which is the most available and consistent). This

7 data first started being collected to the 2001/02 academic year, and we use this data source every year all the way up to the 2008/09 academic year.

All analysis undertaken here uses pupil test performance at KS2 (aged 10/11), KS3

(aged 13/14), and KS4 (aged 15/16). The KS2 data is available from the 1995/96 academic year, and we use the KS3 data from the 1998/99 academic year, and the KS4 data is available from the 2001/02 academic years. We match both the KS data and the PLASC data together using the anonymous unique pupil identifier which is contained in each of these data files. This NPD dataset is then matched to all annual school-level factors

(derived from the three data sources outlined above).

In order to analyse the impact of Academies on the schools which are in the vicinity of an Academy School we define a ‘Neighbouring School’ as any maintained secondary school which is within a 3-mile radius of an academy which has opened over the years

2001/02 to 2008/09. For this analysis our control group consists of ‘Neighbouring Schools’ to Academies which open after the 2008/09 academic year. For all schools that we have classified as ‘neighbouring-schools’, we calculate an academy-on effect from the year when the first academy starts operating within 3 miles of it. We are therefore assuming that there is a flat Academy effect, which means more than one Academy School close by is of no more benefit to the neighbouring school than having just one Academy close by. This classification of a ‘Neighbouring School’ is somewhat arbitrary. However, this distance is large enough to mean that there are a large enough number of schools classified as

‘neighbouring schools’ and yet small enough to mean that there may be spill-over effects from the Academy School to the ‘neighbouring school’.

Dataset Construction

We are able to analyse changes in the pupil-intake of the secondary schools over the

8 PLASC waves by making use of the pupil year group and school code information which is contained within this data source. This allows us to see which pupils are entering year 7 of each school per year. We are able to look at the ‘intake-quality’ of each secondary school by looking at their linked in KS2 results (matched using the anonymous unique pupil identifier). The KS2 exams are taken the year before entering secondary school, and we have therefore matched the background characteristics of the pupils in year 7 at secondary school over 2001/02 to 2008/09 with their corresponding end of primary school attainment over 2000/01 to 2007/08. This therefore allows us to see the academic achievement of the pupil before they enter secondary school and provides an indication of the secondary schools ‘intake-quality’.

We analyse the secondary school performance of the school by looking at their KS4 results. However, to get a richer data source we match each pupil’s KS4 results to their corresponding KS3 results (taken two years before) and KS2 results (taken five years before). We therefore match the KS4 results over 2001/02 to 2008/09 to their KS3 results over 1999/00 to 2006/07 to their KS3 results over 1996/97 to 2003/04.

However, our unit of analysis for this paper is at the school-level rather than the individual pupil. It is therefore necessary to use the PLASC school code (for the analysis on the pupil-intake), and the KS4 school code (for the analysis of the secondary school performance) to collapse this data to the level of each individual school. This means all the pupil characteristics are collapsed to averages for each individual school. We then add to each of these datasets the school-level characteristics from the Edubase, School

Performance Tables (SPT) and ASC data sources, and we keep only the schools and years which have complete data from every source.

Outcomes of Interest

To summarise, we investigate three outcomes of interest: i) pupil intake - measured by the key stage 2 test performance (total key stage 2 points score in year 6, the final year of primary school) of children enrolling in academies (in the first year of secondary school, year 7); ii) pupil performance - measured by GCSE performance (the proportion of children in the school achieving five or more A*-C GCSE passes) in the final year (year 11); iii) pupil performance in ‘neighbouring schools’ - also measured by year 11 GCSE performance.

Modelling Approach

Our interest is therefore to gauge the impact of academy status on the pupil intake of the school, their pupil performance and whether there is any impact on ‘neighbouring schools’. We set up our analysis in a difference-in-difference setting looking at what happened to the outcomes of interest for schools converted to academy schools (A) before and after conversion relative to a set of control schools. The key parameter of interest is the coefficient δ in the equation

J (1) yit αi αt Ai*PolicyOnt λ jXjit uit j 0 where y is the outcome of interest in school i in year t, X denotes a set of control variables,

αi and αt are respectively school and school year fixed effects and u is an error term.

Identification of the academy effect depends critically on the definition of control schools. This is a serious issue in the case of academies because, in their predecessor form,

10 many (especially the earlier conversions) were very much poorly performing problem schools, often being the worst in their local education authority. It is thus important to check (as we do below) that the chosen control group looks similar to the predecessor school. Possibly even more important than this, one can argue that schools likely to be given academy status are a selected set of schools with particular unobservable attributes likely to make them more amenable to becoming academies (e.g. they have the type of school ethos that is more in line with the academy model).

To get round these problems we use a set of comparison schools that, in our sample, are standard state schools, but who are to become academies after the sample ends. They do appear to be well matched pre-academy status. Table 3 shows a set of balancing tests based on comparing means of academy and control schools before schools became academies. In all but one case (the proportion of non-white pupils) there is no difference in pre-treatment characteristics. The one difference where the non-white proportion is significantly higher in academy versus control schools is probably not surprising when one bears in mind that many of the original academies, set up in the earlier years, were located in urban, more disadvantaged areas.

Table 4 describes the sample of academy and control (to be academy beyond the sample) schools that we use in our empirical modelling. We consider 115 academy schools and compare them to the control group of 113 future academy schools. When the number of schools is broken down by cohort, it very clearly shows the heightening pace of academy introduction over the 2000s.

4. Empirical Results

This section reports on the empirical findings. We first look at the impact of academy status on pupil intake and achievement, we then consider spill-overs on to neighbouring schools.

Academies and Pupil Intake

The hypothesis that academy status alters quality of the pupil intake is considered in results from difference-in-difference models reported in Table 5. As already noted in the previous section of the paper, quality of the pupil intake is measured by the average key stage 2 performance of the pupils admitted to the first year of secondary school in the relevant year. The Table reports four specifications, beginning with the raw difference-in- difference in column (1), adding time-varying controls in column (2), allowing cross-cohort heterogeneity in column (3) and finally restricting the column (3) specification to effects for 'early' and 'late' academy conversions in column (4).

The estimated coefficients in the Table do uncover significantly higher key stage 2 test scores for newly enrolled academy pupils. Column (1) shows the key stage 2 total points score to be 1.47 points higher (or around 2 percent higher relative to the mean). On addition of the controls in column (2), this falls to 1.15 points (or around 1.6 percent of the mean). The specifications in columns (3) and (4) make it evident that the quality of intake measured by primary school test scores generally increases by more in the earlier academy conversions. In column (4) the 'early' cohort conversions (cohorts 1 to 4, in school years

2002/3 to 2005/6) saw a 1.97 point improvement, as compared to an improvement of just less than half of that (0.84) in the 'later' conversions (cohorts 5 to 7, in school years 2006/7 to 2008/9).

Academies and Pupil Achievement

That intake levels seemed to rise begs the question as to whether overall, end of secondary school achievement was enhanced? This is considered in Table 6, which has the same structure as Table 5, but instead looks at difference-in-difference estimates of the impact on GCSE performance. In column (1), there is a marginally significant improvement of 0.02 in the proportion of children in the school achieving five or more good GCSE passes. However, this is halved and rendered statistically insignificant on inclusion of the controls in column (2).

Consideration of cohort-specific and 'early'/'late' cohort differences, respectively in columns (3) and (4), reveals a striking finding. Only the earlier academy conversions were beneficial to GCSE performance. According to the column (4) specification, the proportion of children in the school achieving five or more good GCSE passes rose by a strongly significant 0.058 in 'early' academies, whilst the effect is zero in 'late' academies.

Whether, this reflects a real difference, or that it takes time for academy status to yield educational improvements remains an open question that we will be able to address as and when more data becomes available. However, it does seem (unlike the earlier much shorter term work of Machin and Wilson, 2008) that given enough time to embed the new methods of school organization and autonomy the academies set up under the Labour government may have delivered improved educational performance.

Academies and Neighbouring Schools

What about possible beneficial competition effects on neighbouring schools, or detrimental effects on such schools due to changing pupil intakes? Table 7 reports estimates of the impact of having an academy set up within three miles to explore possible beneficial

13 or detrimental spillover effects. There is some evidence of a positive spillover in the earlier cohorts where performance was enhanced, but a zero spillover in the later academy conversions. This seems more in line with competition enhancing effects resulting from academy introduction, at least where the conversion to academies actually promoted superior pupil performance as compared to the predecessor school.

6. Conclusions

From an economic perspective, the main rationale for changing school structures to promote more autonomy and flexible governance is that this can generate economic incentives for head teachers, teachers and pupils to do better. In this paper, we consider a high profile case of where such effects have been hypothesised to occur, namely the introduction of academy schools to the English secondary school sector. We consider the impact of academy status on pupil intake, pupil performance and on pupil performance in neighbouring schools using a difference-in-difference approach comparing how these outcomes altered in academy schools compared to their (non-academy) predecessor school status relative to changes in a set of matched comparison schools.

Our results suggest that moving to the more autonomous academy school structure may well have yielded performance improvements, that the quality of intake went up and there is some limited evidence of positive spillovers to neighbouring schools. It seems that becoming an academy takes a while to yield such benefits, and the dynamics of this process is something we plan to study in future work. Meanwhile, in the controversial policy discussion about academies, our results (at least so far) do seem to place a relatively positive slant on the programme introduced by the Labour government of 1997-2010.

References

Abdulkadiroglu, A., J. Angrist, S. Dynarski, T. Kane and P. Pathak (2009) Accountability and Flexibility in Public Schools: Evidence From Boston's Charters and Pilots, National Bureau of Economic Research Working Paper 15549.

Black, S. and S. Machin (2010) 'Housing Valuations of School Performance, in Hanushek, E., S. Machin and L. Woessmann (eds.) Handbook of the Economics of Education, Volume 3, North Holland.

Chakrabarti, R. and P. Peterson (eds.) (2008) School Choice International, MIT Press, Cambridge, MA.

Clark, Damon (2009) The Performance and Competitive Effects of School Autonomy, Journal of Political Economy, 117, 745-83.

CREDO (2009) Multiple Choice: Charter Performance in Sixteen States, Center for Research on Education Outcomes, Stanford University.

Department for Education and Skills (2007) What are Academies?, http://www.standards.dfes.gov.uk/academies/what_are_academies/?version=1.

Dobbie, W. and R. Fryer (2009) Are High Quality Schools Enough to Close the Achievement Gap? Evidence From a Social Experiment in Harlem, National Bureau of Economic Research Working Paper 15473.

Elias, P. and P. Jones (2006) ‘Administrative data as a research source: a selected audit’, Economic & Social Research Council Regional Review Board Report 43/06, Warwick Institute for Employment Research.

Gibbons, S., S. Machin and O. Silva (2008) Choice, Competition and Pupil Achievement, Journal of the European Economic Association, 6, 912-47.

Hanushek, E. and M. Raymond (2004) The Effect of School Accountability Systems on the Level and Distribution of Student Achievement, Journal of the European Economic Association, 2 , 406-415.

Hoxby, C. and S. Murarka (2009) Charter Schools in New York City: Who Enrolls and How They Affect Student Achievement, National Bureau of Economic Research Working Paper 14852.

Machin, S. and J. Vernoit (2010) Academy Schools - Who Benefits?, Centrepiece, Centre for Economic Performance, London School of Economics.

Machin, S. and J. Wilson (2008) Public and Private Schooling Initiatives in England: The Case of City Academies, in R. Chakrabarti and P. Peterson (eds.) School Choice International, MIT Press, Cambridge, MA.

Whitty, G., T. Edwards and S. Gewirtz (1993) Specialisation and Choice in Urban Education: The City Technology College experiment. London, Routledge.

Table 1 - Typology of English Secondary Schools

Characteristics of School Governance

Non-LEA Majority Sponsor Governing Body Admissions Appointed Maintained by Non- responsible for most Authority Governing Body LEA School policies Fee-Charging

Registered independent schoola      Academy schoolb      City Technology Collegec      Voluntary aided d school      Foundation schoole      Voluntary controlled schoolf      Community schoolg          

Notes: a - Registered independent schools are independent of the Local Education Authority (LEA), and are fee-charging. b - Academy schools are all ability independent specialist schools, which do not charge fees, and are not maintained by the Local Education Authority (LEA). Academies only have to follow the national curriculum in English, Maths, Science and ICT [DfES, 2007] . They are established by Sponsors from business, faith or voluntary groups, who work in partnership with central government. Sponsors and the DfE provide the capital costs for the Academy. Running costs are met by the DfE in accordance with the number of pupils, at a similar level to that provided by LEAs for maintained schools serving similar catchment areas. c - City Technology Colleges are all ability independent schools, which do not charge fees, and are not maintained by the Local Authority (LA Education Authority (LEA). They have a curriculum which has a strong technological, scientific and practical bias (in addition to following the national curriculum) [see Whitty et al., 1993]. They are established by Sponsors from business, faith or voluntary groups, who work in partnership with central government. Sponsors and the DfE provide the capital costs for the CTC. Running costs are met by the DfE in accordance with the number of pupils, at a similar level to that provided by LEAs for maintained schools serving similar catchment areas. d - Voluntary aided schools are maintained by the Local Education Authority (LEA). The foundation (generally religious) appoints most of the governing body. The governing body is then responsible for admissions, employing the school staff, and the foundation will normally own the school’s land and buildings (apart from the playing fields which are normally owned by the LA). e - Foundation schools are maintained by the Local Education Authority (LEA). The foundation (generally religious) appoints some – but not most – of the governing body. The governing body is then responsible for admissions, employing the school staff, and either the foundation or the governing body will own the school’s land and buildings. f - Voluntary controlled schools are maintained by the Local Education Authority (LEA). The foundation (generally religious) appoints some – but not most – of the governing body. The LA continues to be the admissions authority. The governing body will employ school staff, and the foundation will normally own the school’s land and buildings (apart from the playing fields which are normally owned by the LEA). g - Community schools are maintained by the Local Education Authority (LEA). The LEA is responsible for admissions, employing the school staff, and it also owns the school’s land and buildings.

Table 2 - Introduction of Academy Schools, 2001/2-2008-9

Number (Percent) of Schools by Type

2001/2 2002/3 2003/4 2004/5 2005/6 2006/7 2007/8 2008/9

Academy school 0 (0.0) 3 (0.1) 12 (0.4) 17 (0.5) 27 (0.9) 46 (1.5) 83 (2.7) 130 (4.3) City Technology College 15 (0.5) 15 (0.5) 14 (0.5) 14 (0.5) 11 (0.4) 10 (0.3) 5 (0.2) 3 (0.1)

Community school 2030 (64.4) 1982 (63.8) 1959 (63.2) 1958 (63.0) 1891 (61.3) 1857 (60.7) 1764 (56.7) 1594 (52.4)

Foundation school 499 (15.8) 499 (16.1) 501 (16.2) 502 (16.1) 546 (17.7) 548 (17.9) 657 (21.1) 726 (23.9) Voluntary aided school 510 (16.2) 514 (16.5) 517 (16.7) 523 (16.8) 519 (16.8) 512 (16.7) 517 (16.6) 508 (16.7) Voluntary controlled school 97 (3.1) 96 (3.1) 95 (3.1) 95 (3.1) 89 (2.2) 87 (2.8) 85 (2.7) 82 (2.7)

Total 3151 3109 3098 3109 3083 3060 3111 3043

Notes: Source – School Performance Tables.

Table 3 – Characteristics of Pre-Treatment Matched Academy Schools (Treatment Group) with Matched Future Academy Schools (Control Group)

Proportion Proportion Proportion non- % half days Proportion Key stage 2 % SEN, no % SEN, eligible taking FSM white missed getting 5 or points score statement statement FSM (unauthorised) more A*-C GCSEs

Academies 0.281 0.208 0.203 2.489 0.286 75.629 21.373 2.774 Controls (Future Academies) 0.277 0.201 0.160 2.449 0.286 76.055 20.651 2.627 Difference (Standard -0.004 -0.007 0.043 0.105 -0.000 0.246 0.723 0.148 Error) (0.007) (0.006) (0.014)** (0.38) (0.007) (0.125) (0.614) (0.107)

Notes: ** denotes significant at the 5% level.

Table 4 – Sample of Academy Schools, by Cohort, and Future Academy Schools

Number of Schools

Academies 115 Controls (Future Academies) 113 Academies, Cohort 1 3 Academies, Cohort 2 9 Academies, Cohort 3 5 Academies, Cohort 4 10 Academies, Cohort 5 17 Academies, Cohort 6 30 Academies, Cohort 7 41

Table 5 - Academy Schools and Pupil Intake (Key Stage 2 Total Points Score)

Key Stage 2 Test Scores

(1) (2) (3) (4)

Academy 1.472 (0.436) 1.145 (0.405) Academy, Cohort 1 3.051 (0.505) Academy, Cohort 2 3.832 (0.982) Academy, Cohort 3 3.041 (1.158) Academy, Cohort 4 0.776 (1.512) Academy, Cohort 5 2.202 (0.757) Academy, Cohort 6 0.445 (0.591) Academy, Cohort 7 0.092 (0.528) Academy, Early 1.970 (0.935) Academy, Late 0.839 (0.401)

School Fixed Effects Yes Yes Yes Yes Control Variables No Yes Yes Yes Year Dummies Yes Yes Yes Yes

R-Squared 0.788 0.808 0.813 0.809 Sample Size 1579 1579 1579 1579

Notes: Robust standard errors in parentheses. Control variables are: % of pupils eligible for Free-School-Meals (FSM), % of pupils who are White-Ethnic, Ratio of total pupils to qualified teachers, % of pupils with Special Educational Needs (SEN) & have a statement, % of pupils with Special Educational Needs(SEN) & don’t have a statement. Early comprises cohorts 1-4. Late comprises cohorts 5-7.

Table 6 - Academy Schools and GCSE Performance

Key Stage 4 Test Scores

(1) (2) (3) (4)

Academy 0.020 (0.011) 0.012 (0.011) Academy, Cohort 1 0.088 (0.043) Academy, Cohort 2 0.055 (0.037) Academy, Cohort 3 0.069 (0.040) Academy, Cohort 4 0.053 (0.020) Academy, Cohort 5 0.047 (0.034) Academy, Cohort 6 -0.019 (0.022) Academy, Cohort 7 -0.034 (0.014) Academy, Early 0.058 (0.016) Academy, Late -0.004 (0.014)

School Fixed Effects Yes Yes Yes Yes Control Variables No Yes Yes Yes Year Dummies Yes Yes Yes Yes

R-Squared 0.788 0.794 0.800 0.797 Sample Size 1579 1579 1579 1579

Notes: As for Table 5.

Table 7 - Neighbouring Schools (Within 3 Miles) GCSE Performance

Key Stage 4 Test Scores

(1) (2) (3) (4)

Nr_Academy 0.004 (0.003) 0.004 (0.003) Nr_Academy, Cohort 1 0.014 (0.010) Nr_Academy, Cohort 2 0.025 (0.007) Nr_Academy, Cohort 3 0.017 (0.008) Nr_Academy, Cohort 4 0.001 (0.007) Nr_Academy, Cohort 5 0.002 (0.006) Nr_Academy, Cohort 6 -0.012 (0.007) Nr_Academy, Cohort 7 -0.009 (0.007) Nr_Academy, Early 0.014 (0.004) Nr_Academy, Late -0.006 (0.004)

School Fixed Effects Yes Yes Yes Yes Control Variables No Yes Yes Yes Year Dummies Yes Yes Yes Yes

R-Squared 0.937 0.938 0.938 0.938 Sample Size 8068 8068 8068 8068

Notes: As for Table 5.