«Healthy school meals and Educational Outcomes Michèle Belot Jonathan James Department of Economics Nuffield College University of Essex University ...»
The “Feed me Better” Campaign The British Chef Oliver started the campaign “Feed me Better” in 2004, drawing attention to the poor quality of meals offered in schools. The campaign was publicised through a TV documentary broadcast in February 2005 on one of UK channels (Channel 4). The programme featured mainly one school in Greenwich (Kidbrooke secondary school), the first school where the changes were implemented. The idea of the campaign was to drastically change the school meal menus in all schools of the borough of Greenwich, as an “experiment” that would serve as an example for the rest of the country.
Typically, the Local Education Authorities are in charge of allocating a budget to schools. Schools have contractual agreements with catering companies – the largest one in the UK at the time was Scholarest. These contracts are long-term contracts and short-term changes to menus are very difficult to implement. Oliver obtained the agreement of the Council of Greenwich to change the menus (provided the menus would stay within budget). The large majority of schools in the Greenwich area switched from their old menus to the new menus in the school year of 2004-2005.
Before the campaign, school meals were mainly based on low-budget processed food.
In the Appendix, we provide an example of menus as they were before and after the Jamie Oliver campaign.
The campaign mobilised a lot of resources, involved retraining the cooks (most cooks participated to a three-day boot camp organised by the Chef) and equipping the schools with the appropriate equipment. Clearly, the implementation has not been straightforward and it would have been very difficult for schools in other LEAs to have made these changes on such a large scale in such a short amount of time.
In September 2004 at the start of the autumn term Jamie hosted an evening for all the head teachers in which they were invited to take part in the experiment. 81 of the 88 head teachers signed up. The aim was to roll the scheme, which completely replaced the junk food with healthy alternatives, out in 6 weeks, so it commenced just after the half term-October 2004. The scheme was rolled out gradually across the borough, five schools at a time. By February 2005, more than 25 schools had removed all processed foods and implemented the new menus.5 The roll out had taken place fully by September 2005 with 81 of the 88 schools taking part in the scheme, with those unable to participate due to lack of kitchen facilities.
As part of the experiment the council increased the investment specifically into school meals: an initial increase in the school food budget by £628,850 was agreed in the February 2005 budget going to cover the cost of the extra staff hours that were needed for the preparation of the meals, equipment costs and promotion to the parents. By September 2007 a total £1.2 million had been invested in the experiment6.
In the pilot school of Kidbrooke, the healthy meals were initially being put alongside the original junk food. In most cases children preferred to stick to the junk food rather than opting for the healthy meals. This was not the case when the scheme was rolled out across the borough.
Source: www.greenwich.gov.uk Despite the initial difficulties of implementation, the evaluation of the campaign has been quite positive. The website of the “Heath Education Trust”7 for example mentions the following reactions: The Head teacher of Kidbrooke School said, “Because the children aren’t being stuffed with additives they’re much less hyper in the afternoons now. It hasn’t been an easy transition as getting older children to embrace change takes time”. One classroom teacher commented: “Children enjoy the food and talk about it more than they did in the past. They seem to have more energy and can concentrate for longer.” We have some information on the nutritional content of the meals offered to the children before the changes, although only through the TV programme. The Jamie Oliver team asked a nutritionist to analyse a sample of the pre-campaign meals. The meals were lacking fruit and vegetables, and the meat/fish was reconstituted, rather than fresh. Overall, the meals were lacking in basic nutrients, such as iron and vitamin C. Furthermore, the reform included removing all junk food.
4. Data, sample and descriptive statistics
4.1 Data and Sample We investigate the effects of the campaign on three outcome variables: Educational outcomes, absenteeism and take-up rates. We limit our analysis to primary schools, for two main reasons: 1) The recent economic literature has pointed to the importance of interventions in early childhood8, 2) primary school children are typically not allowed to leave the school during lunch time, while secondary children are.
Therefore, primary school children are less likely to have been able to substitute for school meals by alternative food (such as buying junk food in neighbouring outlets).
Since the number of junk outlets per secondary school is 36.7 on average in the Inner London area9, it is more challenging to identify with certainty the treated group.
Source: http://www.healthedtrust.com/ Heckman et al. (2006) who stresses the importance of early interventions even before the children enter school.
Source: School Food Trust; Inner London includes: Hammersmith and Fulham, Kensington and Chelsea, Westminster, Camden, Islington, City, Hackney, Tower Hamlets, Soutwark, Lambeth, Wandsworth, Lewisham and Greenwich; the number is calculated by dividing the total number of outlets in the area by the number of secondary schools in that area.
We use detailed individual data from the National Pupil Database (NPD), which matches information collected through the Pupil Level Annual Schools Census (PLASC) to other data sources such as Key Stage attainment.
The NPD contains information on key pupil characteristics. These include several variables such as ethnicity, a low-income marker and information on Special Education Needs (SEN), that we have matched with Key Stage 2 attainment records.
Key Stage 2 corresponds to the grades 3 to 6 in England; and all pupils take a standardized test at the end of the Key Stage (in year 6, typically at the age of 11).
The Key Stage 2 test has three main components: English, Maths and Sciences. We will consider these three components separately.
Our empirical analysis follows closely Machin and McNally (2008). We conduct two levels of analysis. We have school level data, that is, data aggregated at the school level on the levels attained by pupils (levels 3, 4 and 5); where level 4 is the national standard target as set by the government. We also use individual pupil data. In this case we have individual test scores. Rather than using the raw scores, we create a percentile rank score (as in Machin and McNally (2008)). This prevents any mark scheme changes from driving the results.
Our second outcome measure is absenteeism at the school level, measured by the percentage of half days missed (the data has been extracted from the DCSF publication tables)10. We have two levels of absenteeism, authorised and unauthorised. Authorised absences are those where the pupil has received permission from the school to miss the time from school. This is typically, although not exclusively, because of illness. Unauthorised absences include absences that have not been permitted by the school; this would in most cases include no illness based absences. Hence although we do not have any direct measures of health, authorised absenteeism is our closest proxy.
Finally, we investigate the effect of the campaign on take-up rates of school meals, for children who are eligible for free school meals (provided by the DCSF). There is no public information available on the take-up rate for all children, so this measure is the closest indicator we have to assess the effect of the campaign on take-up.
We concentrate the analysis on the school years from 2002 to 2007, and exclude the year 2005, because changes in menus were introduced in the course of the year 2004Note that we do not have information about the exact timing of these changes in each school and even if we would have this information, differences in timing are unlikely to be exogenous. Thus, we prefer to exclude the whole school year 04-05 from the analysis.
We use five neighbouring Local Education Countries as controls for the analysis. The campaign was implemented in one borough only, the idea being to use this as an experiment for the whole country. Of course, Greenwich has specific characteristics;
it is in the neighbourhood of London and is a relatively poor area. There are potentially a large number of possible controls though and we chose to use as controls LEAs that resemble Greenwich most in terms of health indicators (obesity rates), socio-economic characteristics, such as the proportion of whites, proportion of households living in social housing and the unemployment rate. Figure 1 shows the geographical location of these LEAs and Table 1 presents summary neighbourhood statistics of these LEAs. Note that we will also conduct a robustness analysis where we will extend the control group to other LEAs in the London area (see Section 5.2 e)).
4.2 Descriptive statistics Table 2 compares control and treatment schools on a number of observable characteristics, as well as educational outcomes, before and after the campaign.
Although we have chosen the control LEAs for their similarities with Greenwich, there are a number of notable differences worth pointing out. The percentage of white pupils is higher in Greenwich than in the control areas. The reverse is true for the percentage of pupils speaking English as their first language (this specific difference will be alleviated in the robustness analysis with the extended control group). On the other hand, indicators of social deprivation, such as the Income Deprivation Affecting Children Index and the percentage of pupils eligible for free school meals are comparable in the treatment and control groups. Importantly for our analysis, these indicators are quite similar before and after the campaign.
Turning to educational outcomes, we find that most indicators do increase between 2004 and 2006, both in the treatment schools and in Greenwich. Looking at the raw means, we see a slight relative improvement in performance in Greenwich in comparison to other LEAs.
We now turn to a more detailed empirical analysis.
5.1 Empirical strategy As in Machin and McNally (2008), we estimate a difference-in-differences model on school level outcomes and individual outcomes. We estimate the following model at
the school level:
Yslt = α + β Greenwichl + γ Greenwichl* Post-2005t + ϕ Zst + λ Zs + + πt Tt + ρlt + εist Where Yslt denotes the outcome variable for school s in LEA l in year t; Greenwich is a dummy variable equal to 1 for the LEA of Greenwich and 0 for the five neighbouring LEAs; Post-2005 is a dummy variable equal to 1 for school years 2004and 2006-07 and 0 for school years 2002-03, 2003-04, Z is a vector of school characteristics; T is a set of yearly dummies; and εist is an error term. In addition to the Machin and McNally (2008) specification, we also allow for LEA specific trends (captured by the parameters ρl).
γ is our main coefficient of interest. It shows how pupil performance changed in Greenwich schools in comparison to other LEAs. If the campaign had a positive effect on diet and performance, we should find a positive coefficient.
Secondly, we estimate the following model with individual data:
Yislt = α + β Greenwichl + γ Greenwichl* Post-2005t + Xist’δ + λZst + πt Tt + ρlt + εist Where Yislt denotes the outcome variable for pupil i in school s and LEA l ; and X is a vector of pupil characteristics. Again, γ is our main coefficient of interest.
a) Effect on educational outcomes We first study the effect of the campaign on school-level outcomes, more precisely, on the percentage of pupils reaching (1) level 3 or more, (2) level 4 or more or (3) level 5 in English, maths and science respectively.
We start with the analysis based on school-level data. The results for the different specifications are presented in Table 3. We find that Key stage 2 results are significantly improved, specifically in English and Science. We find a significant effect of the interaction dummy on the percentage of pupils reaching level 4 in English and on the percentage of people reaching level 5 in Science. The effects are quite large: We find that the percentage of pupils reaching level 4 or more in English increased by 4.5 percentage points and the percentage of pupils reaching level 5 for science increased by 6 percentage points. We should point out that the coefficients are close to zero for the percentage of pupils reaching level 3 and above, and positive for levels 4 and 5. However, the standard errors are quite large, and we cannot rule out small (or even negative) effects, as we can also not rule out relatively large effects.
The bottom of Table 3 reports the results of DD estimates based on pupil level data.
We find that the results significantly improved in English. Again, the coefficients are positive for test scores in Maths and Science as well, but the standard errors are large and we cannot reject that they have not been affected. Note that the dependent variable here is the test score result, thus the picture suggests that even though we cannot reject that the Science test scores did not change on average, it seems that they have improved at the top of the distribution, which enabled some pupils to reach level 5 instead of level 4.
Overall the results so far show some evidence that educational outcomes improved in the Greenwich area relatively to other neighbouring LEAs. The estimated coefficients are relatively high, but so are the standard errors. Thus, a careful conclusion is to note that there is some evidence pointing in the direction of a positive effect. This is quite noteworthy though, given that these effects are within a relatively short horizon and given that the campaign was not directly targeted at improving educational outcomes.