Discussion Paper No. 840

MORE APPLES LESS CHIPS?
THE EFFECT OF SCHOOL FRUIT SCHEMESON THE CONSUMPTIONOF JUNK FOOD
Giorgio Brunello
Maria De Paola
Giovanna Labartino

April 2012

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

More Apples Less Chips?
The Effect of School Fruit Schemes on the Consumption of Junk Food

Giorgio Brunello (University of Padova, IZA and Cesifo)
Maria De Paola (University of Calabria)
Giovanna Labartino (IRVAPP)

Abstract

We use scanner data of supermarket sales to investigate the effects of the EU School Fruit
campaign, conducted in a sample of primary schools in the city of Rome during 2010 and 2011,
on the consumption of unhealthy snacks. We allocate supermarkets to treatment and control
groups depending on whether they are located or not near treated schools and estimate the causal
effect of the program by comparing the changes in the sales of snacks in treated stores with thechanges in control stores. We find evidence that the campaign reduced the consumption of
unhealthy snacks bought in stores located in high income areas. No effect is found in poorer
areas. Repeated treatment does not strengthen the effects of the program.

Keywords: EU School Fruit campaign, junk food, RomeJEL Code: I18
Acknowledgements: we are grateful to Giovanni Anania, Daniele Fabbri, Mario Mazzocchi,
Lorenzo Rocco and Vincenzo Scoppa for comments and suggestions. Giorgio Brunello and
Giovanna Labartino gratefully acknowledge financial support from Fondazione Cariparo. The
usual disclaimer applies.

1Introduction

Policy interventions to counter increasing child obesity in developed countries can beclassified as information and market intervention measures. Information measures include
information campaigns, advertising regulations, labelling rules and nutritional education
programs in schools (see Mazzocchi, Traill and Shogren, 2009). These policies are expected toaffect behaviour because they increase awareness and knowledge of the health consequences of
eating habits.
Examples of education programs in schools are the 5-a-day fruit and vegetable campaigns
carried out in several countries and the EU School Fruit Scheme, which was launched at the endof 2007 and implemented in the following years in a number of member countries. Empiricalstudies that have tried to evaluate the impact of these policies have found some positive effects,
especially in the short run. In a recent review of this literature, Mazzocchi, Traill and Shogren,2009, conclude that ”... the balance of evidence on the effectiveness of information campaigns
and social marketing is that they raise awareness but do not affect behaviour ...” (p.119).
Fruit and vegetable campaigns typically combine the distribution of these products inschools with the dissemination of information on correct health lifestyles, which favours theconsumption of fruit and vegetables at the expense of junk food such as unhealthy salty andsweet snacks (snacks high in added sugar and/or saturated fat).
While most empirical studies have analyzed whether and how these campaigns have affected
the consumption of fruit and vegetables, little has been done to investigate their effects on the
consumption of junk food. Yet an increase in the intake of fruits need not suffice to combat
overweight and obesity if this increase is not accompanied by a reduction in the intake of high in - calories junk food. Clearly, fruit and vegetable campaigns which increase the intake of theseproducts at the expense of junk food are more valuable for public health than campaigns that
only affect the consumption of fruit and vegetables.
In this paper, we study the effects of the European School Fruit campaign on the
consumption of unhealthy salty and sweet snacks by using data on the daily sales of theseproducts in a sample of supermarkets located in the city of Rome1. Although the consumption offruit and vegetables in Italy is higher than in Northern European countries, poor nutrition is an
important policy issue in this country as well, especially for young people. In Italy, the1

Unfortunately, our data do not include information on the sale of fruit and vegetables. In Italy, at least 40 percentof the purchases of these products occurs in local markets and at small greengrocers (data from the “Osservatorio
dei Consumi Ortofrutticoli delle famiglie italiane”). Therefore, even if we had such data, they would provide arather partial information on overall consumption.

2

percentage of obese children has increased from 9 to 12.5 percent in less than ten years and close
to one quarter of all children are overweight (National Health Institute, 2010).
Our empirical strategy is a standard differences-in-differences technique, which combines
treated schools with neighbouring food retail stores belonging to two large national supermarket
chains. We consider as treated the stores located within a radius of 500 meters from treatedschools and as controls the stores located outside such radius. For both groups of stores, we
compare the daily sales of a broad variety of sweet and salty snacks before and after theeducation campaign.
The key assumption required for this strategy to work is that the majority of households
living in a given area send their children to nearby primary schools and shop at nearby
supermarkets. Under such assumption, the effects of the policy show up by comparing the salesof snacks by local and distant supermarkets before and after the treatment. Because we cannot
exclude that some households shop also farther away or send their children to more distantschools, and that local schools attract pupils from outside the neighbourhood, our estimates must
be interpreted as a lower bound of the true effect. Better strategies would compare consumption
before and after the treatment by the households sending their children to treated and control
primary schools. The household data required for such comparison must have detailed
information on consumption patterns as well as information on the primary school wherechildren go. Typically, these data are not available.
When we focus on the initial treatment (Spring 2010), we find that the campaign has
reduced the consumption of unhealthy snacks in treated stores – relative to control stores - by 4.6
percent. This effect is small and not statistically significant, in line with the assessment byMazzocchi and co-authors. There is also evidence, however, that the impact of the campaign isheterogeneous across locations and store types. When we distinguish between stores located in
wealthier and poorer areas of the city, we find that the campaign has been effective in reducing
the consumption of snacks only in the treated stores located in wealthier areas. In these stores,
the consumption of junk food declined after the treatment by 12 percent, a sizeable andstatistically significant effect. In contrast, we do not find any statistically significant decline in
the consumption of snacks in the treated stores located in poorer areas.
We further distinguish between regular and discount stores and find that the negative effectof the campaign on the consumption of snacks in wealthier areas of the city is even larger (13.1%) when we focus on regular stores. The effect is statistically not different from zero inother stores. These heterogeneous effects are not driven by differences in price behavior, as wefind that the campaign does not affect the pricing of snacks in treated supermarkets.
3

Our evidence also suggests that program effectiveness tends to diminish over time. When
we combine the initial treatment in 2010 with the second treatment in 2011, we find that the sizeof the overall effect is smaller than the effect of the initial treatment, suggesting that an importantpart of the latter is temporary: as treated consumers get used to the treatment, they partially but
not entirely revert to old consumption habits.
If the effects identified by our empirical approach were spurious and did not genuinely
reflect the causal impact of the School Fruit campaign, we should find similar effects on
unrelated products. Yet, in a placebo experiment using the sales of liquid detergents, which areunlikely to be affected by the campaign, we find no statistically significant effect of thetreatment both in the full sample and in the sub-groups of stores located in different areas. We
conclude that our estimates are unlikely to capture spurious effects.
A relatively large literature has documented the positive relationship between higher levelsof education and better health (see Grossman, 2006, for a survey and Brunello, Fabbri and Fort,
2011, for a recent contribution covering several European countries). As suggested by Cutler and
Lleras-Muney (2010), better educated individuals may be more prone to follow healthy
behaviours, better informed of the health consequences of bad eating habits and, as aconsequence, more sensitive to programs reinforcing this information. Alternatively, individuals
with a poor socio-economic background may be more affected by problems of self - control andtime inconsistencies, which deter them from translating in good health behaviours theinformation received through the program (Scharff 2009, Komls et al. 2004).
Since consumers shopping in supermarkets located in high income areas tend to have higher
income and better education than consumers shopping in poorer areas and in discount stores, ourresults can be interpreted as suggesting that the School Fruit campaign has reduced theconsumption of junk food in the sub-group of the population who is less likely to be exposed to
overweight and obesity problems, but has been not effective at all for the sub-group more at risk.
Whether this interpretation holds with better (but yet unavailable) data which include
information on household consumption patterns as well as on the schools where children go is an
open question that we leave to future research.
The paper is organized as follows. Section 2 presents a brief review of the relevant empiricalliterature, Section 3 describes the School Fruit campaign as implemented in the primary schoolsof the city of Rome. Sections 4 and 5 introduce the data and the empirical setup. Results arepresented and discussed in Section 6. Section 7 is devoted to a few robustness checks.
Conclusions follow.

4

2. Previous Literature

Since policies that address obesity are fairly recent, empirical evaluations of programs aimed
at improving the food habits of children are still relatively rare. In this section, we provide ashort review of this literature, without pretending to be exhaustive. A strand of the literature has
focused on the effect of school food programs, which impose nutritional requirements to combat
childhood obesity. A number of recent studies have analysed the effects of two well known
programs implemented in US: the School Breakfast Program (SBP) and the National School
Lunch Program (NSLP). Results are, however, rather inconclusive: once non-random selection istaken into account, it emerges that the while the SBP has produced beneficial effects, the NSLP
has been detrimental (Schanzenbach, 2009; Bhattacharya, Currie and Haider, 2006; Millimet et al.,2010).

Another strand has investigated the effects of information dissemination on health. Since
consumers generally lack information on the nutritional value and caloric content of the foodproducts they consume and are not fully aware of the health consequences of poor eating habits,
disseminating information has been included among the objectives of many public programs.
These policies include mandatory nutritional labelling and mandatory posting of nutrient content
on menus. Again, results are mixed. On the one hand, Bollinger et al. (2010) use a difference-indifferences estimation strategy and show that mandatory calorie posting affects consumer
behaviour and reduces the average calories of chosen food in a well know fast food chain. On
the other hand, Variyam (2008), Drichoutis et al. (2009) and Elbel et al. (2011) show that thesepolicies do not produce significant effects on consumer food choices and the body mass index.
Among the factors contributing to undermine the effects of information dissemination campaigns,
a crucial role is played by food industry advertising, which typically affect food preferences(Lobstein and Dibb, 2005; Chou et al. 2008).

Closely related to our work are the papers investigating the effects on consumers of
information campaigns promoting the consumption of fruit and vegetables. An important
contribution in this area is Capaci and Mazzocchi, 2011, who study the 5-a-day information
campaign in the UK and find evidence of positive but small effects on fruit and vegetables
consumption, which are partially offset by price increases. Pomerleau et al, 2005, review theevidence on the effectiveness of programs promoting fruit and/or vegetables consumption
inadults and report small increases in intakes. De Sa and Lock, 2008, examine 23 studies
assessing the impact of school campaigns on children and conclude that these schemes are
effective in increasing both knowledge and intake. Finally, Velde and co-authors, 2008, evaluate5the Pro Children campaigns aimed at promoting the intake of fruit and vegetables in Norway,
The Netherlands and Spain and find that these policies are promising but tend to have short term
effects and to work especially for fruit.
By and large this literature evaluates the impact of fruit and vegetables campaigns on the
consumption of these products but fails to investigate whether the increase in this consumption
has been accompanied by the reduction in the intake of unhealthy and calorie intensive junkfood, such as salty and sweet snacks, which has strongly increased over time and has today avery important role in the daily diet of children, with negative effects on their health and bodyweight. Piernas and Popkin, 2010, for instance, show that more than 27 percent of US children's
intake of daily calories derives from snacks (mainly salty and high fat snacks). Kerr et al. 2009,
on the other hand, use British and Irish data to show that the consumption of snacks issignificantly higher among adolescents in 2005 than in 1997. The upward trend in thecontribution of snacks to the daily energy intake is confirmed by Ovaskainen et al., 2006, forFinland, by Bellisle et al., 2003, for France and by Marques-Vidal, 2006, for Portugal.

3. The School Fruit Campaign

The EU School Fruit campaign was launched in October 2007 to increase children’s intakeof fruit and vegetables and to promote healthy diet habits and more balanced nutrition patterns.
The campaign consists of a) distributing products to children at school; b) providing information
on a correct and healthy diet to children, teachers and parents.
In Italy the campaign was implemented by the Italian Ministry of Agriculture jointly with
the Ministry of Education and Research. The target group consisted of children aged six toeleven and attending primary school. Implementation started in the Spring of 2010 and continued
in the following years, involving about 5000 primary schools all over the country. Participationof schools to the program was on a voluntary basis, broadly on a “first come first served” basis.
Schools participating to the first treatment could and typically did join the treatment in thefollowing year, with the only restriction that priority was given to new entrants in the scheme.
The program consisted in the periodical distribution of fruits or vegetables to children during
school hours. The period when the distribution took place varied by region, but in most cases
started in February and ended in June, when the school year ended. While the varieties of fruitand vegetables distributed were slightly different across regions, the following products were
distributed across all schools: oranges, strawberries, kiwi, apples, pears, carrots, fennels andcherry tomatoes. During the treatment period established by the program, every child had to6

consume fruit and vegetables at least 20 times and consumption was expected to include at least
4 different fruit and vegetable products. The program also required teachers to match the
distribution of products with information on the benefits of an healthy diet. To favor thiscombination, the distribution of products never occurred during lunch breaks. To enhance theeffectiveness of the campaign, children were also exposed to complementary activities, aimed atpromoting awareness of healthy food habits. These actives included visits to farms, working inthe school garden, games associated to the consumption of fruit and vegetables, and homework
activities focusing on the healthy properties of fruit and vegetables.
In the first two years of the campaign close to 870.000 children were treated in Italy, whichcorresponds to about 20% of the population of pupils enrolled in primary schools. In this paper,
we focus on the pupils of the primary schools located in Rome, who were exposed to treatment
in 2010 and 2011. During the first year, 100 out of 579 schools in the capital city were treated2.
During the second year, this number increased to 174, with 72 schools participating in both
years3.
We match treated and untreated schools with supermarkets belonging to two national chains,
for which we have data on daily sales of a large variety of salty and sweet snacks high in addedsugar and/or saturated fat. Matching is based on distance: we define as treated the stores locatedwithin a given radius from a treated school, and as controls the stores located outside suchradius. This matching strategy implies that we only consider the sub-sample of treated schools
which is located close to at least one supermarket in our list of stores.
As discussed more in detail below, we perform this match for 2010 and 2011 and end up
with 44 stores, 15 belonging to the treatment group and 29 to the control group. By selecting forthe treatment group the stores located near schools treated both in 2010 and in 2011, we cancompare the effects of the first and the second treatment. We exclude from our sample the only
store located near a school treated in 2010 but not in 2011, and the stores located near to schools
treated in 2011 but not in 2010, because our data end in September 2011, too early to capture thepost-treatment effects.

4. The Data

Compared to fruit and vegetables, which are sold in Italy also by small greengrocers andlocal markets, processed food - including salty and sweet snacks - is bought mainly in
2
3

Data from the “Ufficio Scolastico Regionale” of Lazio, the region where Rome is located.
All the classes in each participating school were involved in the program.

7

supermarkets4. We use scanner data on the daily sales of sweet and salty snacks in the stores of
two large Italian supermarket chains (DESPAR and Tuo Discount) operating nationwide. These
stores sell different varieties of sweet and salty snacks. We focus on unhealthy sweet and salty
snacks with a high content of added sugar and/or saturated fats, such as crisps and pastries, andexclude low calorie snacks. For each variety, we have information on the daily price and quantity
sold, the daily stock5 and total daily revenues by store. Overall, there are 182 varieties, 96 in the
category “sweet snacks” and 86 in the category "crisps". Close to one quarter of these varieties
are commercialized as premium products of well known brand manufacturers, and the remaining
three quarters are sold at lower prices as generic products with a local brand or no brand at all.
For each variety, we measure the daily quantity sold in pieces. We control in our regressions
for the different formats and weights of each variety by using variety dummies. Table 1 showsthat the average daily quantity of snacks sold per store during the selected period (2009-2011)
was equal to 76.54 pieces (19.25 kilograms) in the 15 stores assigned to the treatment (“treatedstores”) and to 110.2 pieces per day (28.16 kilograms) in the 29 stores assigned to control(“control stores”). The average price per piece sold was €1.43 (1.51 and 1.36 respectively fortreated and control stores), and average daily sales of all products were €7,949 for treated storesand €10,153 for control stores.

Table 1.Descriptive statistics
Variables
Percent in high income areas
Percent of discount stores
Percent of branded products on sale
Average daily sales (euro)
Average quantity of sweet and salty snacks sold per day (inpieces)Average quantity of sweet and salty snacks sold per day (in kg.)
Minimum house price in the area (euro)
Average price per piece (euro)(1)
Treated group(15 stores)
46.6
53.3
25.7
7,949
76.54

(2)
Control group(29 stores)
55.2
72.4
14.7
10,153
110.2

19.25
4,320
1.51

28.16
4,230
1.36

These data have the advantage of providing very detailed day to day information on the
consumption of snacks. This advantage comes at the price of not having information on theindividual characteristics of customers, including income and education, which are likely to4

According to the National Statistical Institute, 2010, about 70 percent of Italian households buy processed food insupermarkets.
5
We set quantities sold to zero in a given day if the variety was available in the store on that day but did not sell and to
missing if the variety was not on supply.

8

affect the effectiveness of the treatment. On the one hand, more educated parents may have abetter understanding of the aims of the School Fruit campaign and therefore reduce to a higher
extent the household consumption of unhealthy snacks. On the other hand, wealthier consumers,
who generally can afford an healthier diet, may react less to the program as they already eat
enough fruits and vegetables and fewer snacks.
To overcame this lack of information on the characteristics of consumers, we infer socioeconomic conditions from individual choices. Different consumers select different type of stores.
In particular, we expect that while wealthier individuals shop mainly in regular stores, poorer
consumers buy to a larger extent in discount stores. In addition, since consumers tend to buy
food products in the area where they live, we classify locations into wealthier and poorer byusing local house prices6. As shown in Table 1, the percentage of discounts is 53.3 and 72.4
percent of treated and control stores respectively. Moreover, 46.6 and 55.2 percent of treated andcontrol stores are located in wealthier areas, where house prices are above the average price inthe city (and equal to €4,320 for squared meter)7.

5. The Empirical Strategy

According to the National Survey on the Daily Life of Italian Households (ISTAT 2009),
close to 36 percent of primary school children in Italy walk to school, and the median time tocover the distance from home to school is 10 minutes. If an adult can walk on average 80 meters
per minute, a child aged 6 to 11 can walk 50 meters. This suggests that a significant percentage
of primary school children live within a radius of 500 meters from their primary school.
Assuming that their parents shop for food in the neighbourhood, this radius can be used toclassify supermarkets into two groups: the group of stores located near treated schools, and thegroup located relatively far from these schools. We define a supermarket as treated if it is locatedwithin the 500 meters radius of a treated school, and as control if it is located without that radius.
This classification does not require that control stores are unaffected by the policy. Rather, it
assumes that treated stores are affected to a larger extent than control stores because of theirproximity to affected customers. Control stores would be unaffected if: a) the households
sending their children to treated primary schools shop exclusively in the neighbourhood of theseschools; b) the households sending their children to untreated schools shop exclusively in stores
6

House prices for different neighborhoods in the city of Rome are from the Italian Ministry of the Economy(http://www.agenziaterritorio.it/).
7
Of the total 44 stores in our sample, 23 are in low income areas and 29 are discount shops; 17 of these shops are in
low-income areas, which contain 4 regular shops (out of 15).

9

outside the 500 meters radius which includes treated schools. Both conditions are unlikely tohold for all households. However, as long as shopping for food takes place mainly near the placeof residence, the comparison of consumption in treated and control stores produces a lower
bound estimate of the effect of the policy8.
Since our dataset covers the period from the 1st January 2009 to the 18th September 2011,
and the program was first implemented in February 2010, before this date none of the stores inour sample was exposed to the program. On the other hand, after February 1st 2010, treatedstores were exposed to a higher extent than control stores, due to their close location to treatedschools. Using a differences-in-differences approach, we estimate the causal effect of the
program on the consumption of snacks by comparing the changes in the sales of snacks inducedby the treatment in treated and control stores.
The program affects sales both directly and indirectly, by affecting the prices of snacks.
Since the policy relevant effect is the gross effect, which combines direct and indirect effects, we
exclude prices from the regressions of quantities sold on the treatment and other covariates.
However, we also check whether the program has had a differential effect on the prices of snacks
by running regressions of prices on treatment and covariates9. We estimate the following reduced
form regression model10
Yijt   0  1Tt   2Tt * TS j   3timet   4timet * TS j   5 X jt   i   j   ijt[1]

where Yijt is the quantity (in pieces) of snack i sold by store j in day t; the dummy Tt (treatment)takes the value one from February 2010 onwards and zero otherwise; the dummy TS j is equal to
one for treated stores and to zero for control stores; time is a linear time trend; X is a vector of

8

In the rather extreme and implausible case when most of the shopping for food occurs far away from the place ofresidence, we expect to find that – if the policy is effective – the consumption of snacks declines to a higher extent
in the sample of control stores.
9
In Section 7 we estimate by instrumental variables a regression of quantities on prices and other covariates andfind that the net effect (conditional on prices) is similar to the gross (unconditional) effect.
10
Equation [1] is obtained by substituting the price equation
Pijt   0  1Tt   2Tt * TS j   3timet   4timet * TS j   5 X jt  vijt

in the demand equation
Yijt   0  1Tt   2Tt * TS j   3timet   4timet * TS j   5 X jt   6 Pjt   ijt

and by adding supermarket and snack dummies. The reduced form impact of the program on the sale of snacks isequal to  2   2   6 *  2 .

10

additional covariates – which includes total daily sales and seasonal month dummies;  i is a setof snack dummies,  j a set of supermarket dummies and  ijt is the error term11.
Because the participation of schools to the campaign was on a voluntary basis, the allocationof stores to treatment and control groups in our data is non random. We control for time invariant
unobservable store characteristics with the store fixed effects  j , and for time variant unobservables with store –specific daily sales. The key coefficient of interest is  2 , whichmeasures the differential effect of the treatment on the sample of treated and control stores, or2  ( E[Yijt | Tt  1, TS j  1]  E[Yijt | Tt  0, TS j  1])  ( E[Yijt | Tt  1, TS j  0]  E[Yijt | Tt  0, TS j  0]) [2]

where E is the expectation operator. If the School Fruit campaign has been effective in reducing
the consumption of snacks in the stores located near treated schools relative to the consumption
in stores located farther from those schools, we should find that  2  0 .

6. The Effects of the Campaign on the Sales and Prices of Snacks

6.a The Effects of the Initial Treatment

During our sample period, the School Fruit campaign was implemented in the primaryschools of Rome twice, from February 2010 to the end of school year (in June 2010) and fromFebruary 2011 to the end of school year in June 2011. Our selected sample of schools andassociated supermarkets was either treated twice or never. Since the effect of the program maychange over time either because individuals get used to it and, as a consequence, tend to be lessaffected, or because repeated treatment reinforces their response, we initially focus on the effectof the first treatment and then consider the overall response.
To investigate the effect of the initial treatment, we estimate [1] on the sub-period runningfrom 1st January 2009 to 31st January 2011, just before the start of the second treatment. Table 2reports the fixed effects estimates when the dependent variable is the number of pieces ofdifferent varieties of snacks sold by each store per day. The Table is organized in 5 columns. The
first column presents the estimates for the full sample; columns (2) and (3) show separate
estimates for the stores located in areas with higher than median or at most median house prices,11

In a sensitivity we have added day of the week dummies to the vector of controls. While sales increase on
Saturdays, our effect of interest remains substantially unchanged.

11and columns (4) and (5) distinguish between regular stores in wealthier areas and other stores. In
all specifications, standard errors are heteroskedasticity consistent and clustered at the store
level12.
Focusing on the full sample (column (1)), our estimates show a declining trend in the daily
consumption of snacks and an upward shift in consumption during the post-treatment period.
The combination of these two effects imply that predicted average sales of snacks at the end of2010 were more or less equal to average sales one year later. Compared to the control group, thesales of snacks in the treated group declined mildly (-4.6 percent) after the treatment, although
the estimated difference is not statistically significant.
The statistically insignificant differences-in-differences effect could be driven by thepresence of important heterogeneity in the stores included in our sample. To investigate whether
this is the case, we re-estimate (1) on the two sub-samples of stores located in low income andhigh income areas, where the former are defined as the areas with minimum house prices below
or equal to median minimum house prices, and the latter are defined as the areas with higher than
median minimum house prices. As shown in columns (2) and (3), the differential effect ispositive but not statistically significant in low income areas and negative and statisticallysignificant in high income areas. This finding suggest that the School Fruit campaign has beeneffective in reducing the relative sales of snacks only in the treated supermarkets located in highincome areas. When evaluated at sample mean average sales, this relative reduction is equal to
12 percent, a sizeable effect. Under the assumption that these stores are visited mainly by local
residents, this finding suggests that the campaign has been particularly effective on high income
consumers13.
Stores in wealthier areas include both regular and discount stores. Since we expect higher
income consumers to shop mainly in regular stores, we distinguish between these stores in highincome areas and other stores (stores in low income areas and discounts in high income areas).
As shown in columns (4) and (5), the relative effect of the campaign on the consumption of
unhealthy food has been negative and sizeable in the former group of shops (-12.2 percent) andclose to zero in the latter group.

12

The presence of serial correlation in our data requires that we cluster standard errors. We choose to cluster at thestore level, as suggested by Bertrand, Duflo and Mullainathan, 2004 and Angrist and Pischke, 2009.
13
Our estimates also suggest that the sale of snacks increases with total daily sales and is lowest during the summer
months.

12

Table 2. Initial treatment: February 2009 to January 2011. Quantities sold of sweet and salty snacks.
Treatment group: stores located within 500 meters from treated schools. Control Group: stores distant atleast 500 meters from treated schools
Variables

Daily sales
Time trend
Trend * treatedTreatment
Treatment * treatedObservations
R-squared
Semi-elasticity(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular stores inhigh income areas(5)
Other stores

0.001***[0.000]-0.002***[0.000]0.000[0.000]0.754***[0.138]-0.110[0.082]0.002***[0.000]-0.001**[0.000]0.001***[0.000]0.282[0.172]-0.280***[0.086]0.001***[0.000]-0.003***[0.000]-0.000[0.000]0.788***[0.131]
0.102[0.123]0.001***[0.000]-0.002***[0.000]0.001**[0.000]0.666***[0.152]-0.240***[0.039]0.001***[0.000]-0.003***[0.000]0.000[0.000]0.798***[0.174]-0.006[0.115]

1,221,791
0.374-0.0465

612,761
0.411-0.122572,9340.380
0.0405334,524
0.299-0.131

855,500
0.392-0.00222

Note: each regression includes store, month and product dummies. Standard errors are clustered at thestore level. One, two and three stars for statistical significance at the 10, 5 and 1 percent level ofconfidence.

Since wealthier individuals are more likely to buy premium products of brand manufacturers,
while poorer consumers buy to a larger extent unbranded or low – brand products, we have also
distinguished between varieties of snacks with a recognized national brand and with a local
brand or no brand at all. Since wealthier individuals are more likely to buy premium products ofbrand manufacturers, while poorer consumers buy to a larger extent unbranded or low – brandproducts, we have also distinguished between varieties of snacks with a recognized national
brand and with a local brand or no brand at all. We find (results not reported but available uponrequest) that the School Fruit campaign has reduced the consumption of branded varieties intreated stores relative to control stores. This reduction is statistically significant and close to 13
percent. If the preference for branded goods depends of income and education, these results
confirm that wealthier consumers are relatively more affected by the campaign14.
Very similar results to those reported in Table 2 are obtained when we exclude from oursample the summer period (July and August), when schools are closed and families with children
may be on vacation and live outside the treated areas (estimates not reported but available uponrequest). Again, we find that the School Fruit campaign has induced a reduction in the sales of
14

In additional sensitivity analysis (not reported but available upon request), we have also distinguished by type ofsupermarket and area and estimated separate regressions for regular supermarkets in high income areas and discountsupermarkets in low income areas. In line with previous results, we find a negative effect of the campaign in theformer group of stores (-14.1 percent) and no significant effect in the latter group.
13

unhealthy snacks only in the treated stores located in high income areas and in treated regularstores.
In the presence of a campaign favouring the consumption of fruit and vegetables andpromoting healthy lifestyles, retailers in treated stores could have reacted to the policy by
reducing the price of snacks, so as to offset the reduction in the quantities sold. Alternatively,
awareness of the fact that reducing prices could not increase the consumption of informed
customers, retailers could have raised prices after the treatment so as to increase revenue from
customers with a low demand elasticity. We look at price behaviour in treated and control storesby estimating a version of equation [1] where the dependent variable is the price per piece sold.
As shown in Table 3, there is no evidence that – relative to the control group - retail prices intreated stores have been significantly affected by the policy.

Table 3. Initial treatment, February 2009 to January 2011. Prices of sweet and salty snacks. Treatmentgroup: stores located within 500 meters from treated schools. Control Group: stores distant at least 500
meters from treated schools.
Variables

Daily sales
Time trend
Trend * treatedTreatment
Treatment * treatedObservations
R-squared
Semi-elasticity(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular stores inhigh income areas(5)
Other stores-0.000[0.000]0.000***[0.000]0.000[0.000]0.001[0.003]0.003[0.005]0.000*[0.000]0.000***[0.000]0.000[0.000]0.000[0.005]0.011[0.007]-0.000***[0.000]0.000***[0.000]-0.000[0.000]0.004[0.003]-0.002[0.003]0.000[0.000]0.000**[0.000]0.000[0.000]0.007[0.010]-0.002[0.007]-0.000***[0.000]0.000***[0.000]0.000[0.000]0.004*[0.002]-0.001[0.002]

1,221,791
0.969
0.00235

612,7610.968
0.00708

572,934
0.972-0.00150

334,5240.946-0.000978

855,500
0.977-0.000858

Note: see Table 2

6.b The Overall Response

In this sub-section we investigate the overall effect of the treatment by estimating equation[1] on the entire period from 1st January 2009 to 18th September 2011. During this period, treatedschools where involved twice in the School Fruit campaign, from February to the end of school
14

(June) both in 2010 and in 2011. We estimate the same specifications shown in Table 2 andreport our results in Table 4.
When we consider the entire sample, we find that the differential effect of the treatment on thesample of treated stores is negative, imprecisely estimated and smaller than the effect induced by
the initial treatment. Smaller effects are obtained also when we divide stores according to theirlocation (columns (2) and (3)) and type (columns (4) and (5)). In particular, we find that therelative effect of the campaign on the consumption of unhealthy food has been negative andequal to -8.2 and -6.9 percent in shops located in wealthier areas and in regular shops located inthese areas.
These results suggest that the effects on the consumption of snacks induced by the initial
treatment are larger than those generated by repeating the treatment. This can happen either
because consumers get used to the program and pay less attention to its contents when it isrepeated, or because they revert over time at least in part to their old eating habits15.

Table 4. Repeated treatment, February 2009 to September 2011. Sweet and salty snacks. Treatmentgroup: stores located within 500 meters from treated schools. Control Group: stores distant at least 500
meters from treated schools.
Variables

Daily sales
Time trend
Trend * treatedTreatment
Treatment * treatedObservations
R-squared
Semi-elasticity(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular storesin high income
areas

(5)
Discount
stores in low
income areas

0.001***[0.000]-0.001***[0.000]0.000[0.000]0.130**[0.049]-0.087[0.073]0.002***[0.000]-0.001***[0.000]0.001**[0.000]0.189***[0.066]-0.185**[0.086]0.001***[0.000]-0.001***[0.000]-0.000[0.000]0.028[0.055]0.073[0.101]0.001***[0.000]-0.000**[0.000]0.000[0.000]0.167**[0.064]-0.128*[0.063]0.001***[0.000]-0.001***[0.000]0.000[0.000]0.116*[0.058]-0.040[0.105]

1,581,658
0.353-0.0378

800,700
0.395-0.0821749,1310.352
0.0300444,7110.307-0.0693

1,109,509
0.369-0.0157

Note: see Table 2

15

To check whether the smaller effects found for repeated treatment are due to a reduction in snack prices, which
may have undermined the effect of the program, we have replicated regressions reported in Table 3, considering thewhole period covered by our data. Again, prices do not seem to have reacted to the treatment.

15

7. Sensitivity and placebo estimates

In this section we focus on the initial treatment and verify whether our results are robust to:a) the inclusion of store – specific linear trends; b) marginal changes in the definition of thecontrol and treatment groups. We also check the validity of our identification strategy by runninga “placebo” regression of the sales of liquid detergents on the treatment and additional controls.
If our empirical strategy is valid, we expect the selected treatment to affect the consumption of
snacks but to have no effect whatsoever on unrelated products. Finally, we augment equation [1]with the product price of each variety. Since the latter is typically jointly determined with thequantity sold, we use an instrumental variables approach and instrument the current price of eachvariety with the variety – specific average price during the previous week.

7.a Store specific time trends

A typical and restrictive assumption of differences – in – differences models is common
consumption trends for treated and control stores in the absence of treatment. In our empirical
specification, we allow for trends to differ across the two groups of stores. A more flexible
specification replaces group-specific trends with store – specific trends. This is done in Table 5,
where we show that results are very similar to those obtained with group – specific trends.
Table 5. Initial treatment, February 2009 to January 2011. Sweet and salty snacks. Treatment group:
stores located within 500 meters from treated schools. Control Group: stores distant at least 500 metersfrom treated schools. With store – specific trends(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular storesin high income
areas

(5)
Discount stores in
low income areas

0.001***[0.000]-0.002***[0.000]0.747***[0.137]-0.110[0.082]0.002***[0.000]-0.001**[0.000]0.288[0.171]-0.284***[0.086]0.001***[0.000]-0.003***[0.000]0.781***[0.131]0.103[0.123]0.001***[0.000]-0.002***[0.000]0.668***[0.151]-0.241***[0.040]0.001***[0.000]-0.002***[0.000]0.788***[0.174]-0.003[0.115]Observations

1,221,791

612,761

572,934

334,524855,500

R-squared
Semi-elasticity

0.375-0.04640.412-0.1240.381
0.04110.299-0.1320.393-0.00124

Variables

Daily sales
Time trend
Treatment
Treatment * treatedNote: see Table 2
167.b Marginal changes in the definitions of treated and control groups

We have allocated stores to the treated and control groups depending on whether theirdistance from treated schools was lower or higher than a critical threshold. We have identify thisthreshold with 500 meters. In this sub-section, we alter this threshold in two different ways: first,
we reduce it to 400 meters; second, we allocate to the control group only those stores which are
distant at least 750 meters from treated schools. By choosing a lower threshold, we reduce thenumber of treated supermarkets (from 15 to 9); by increasing the minimum distance from treatedschools, we introduce a sharper distinction between treated and control stores and reduce thenumber of control stores (from 29 to 18)
Table 6 presents the estimates when the threshold is 400 meters and when the control groupis located at least 750 meters from treated schools. In the former case, we find that thedifferential effect of the treatment is negative and somewhat larger than in Table 2. There is also
evidence that this effect is statistically significant only for stores located in wealthier areas, again
in line with the findings of Table 2. As shown in the table, removing from the sample thosestores which are located between 500 and 750 meters from treated schools have marginal effects
on the relevant estimated effects, which remain negative and statistically significant for stores
located in high income areas.

Table 6. Initial treatment, February 2009 to January 2011. Sweet and salty snacks. Changes in the thresholddistance from treated schools.
Variables(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular storesin high income
areas

(5)
Other
stores-0.146*[0.085]-0.278**[0.098]0.000[0.147]-0.199***[0.053]-0.078[0.127]-0.055[0.091]-0.218**[0.091]

0.139[0.143]-0.242***[0.049]0.070[0.118]Threshold: 400 meters
Treatment * treatedThreshold: 750 meters forcontrol stores
Treatment * treatedNote: see Table 2

7.c. Placebo Regressions

17

The estimated differences-in-differences effects of the School Fruit campaign on the
consumption of snacks in treated stores could reflect genuine economic responses by consumers
as well as placebo effects. If these effects were driving our results, we should find them also for
products that are wholly unrelated to the treatment under study. To verify whether placebo
effects are driving our results, we estimate Eq. (1) using liquid detergents rather than snacks as
the consumption item. In the absence of these effects, sales of this product should not be affectedby the campaign. As shown by Table 7, in no specification do we find evidence that thetreatment has had a statistically significant differential impact on treated supermarkets. We
conclude from this that our estimates of the effects of the treatment on the consumption of
snacks are not a placebo effect.

Table 7. Initial treatment, February 2009 to January 2011. Liquid detergents. Treatment group: stores
located within 500 meters from treated schools. Control Group: stores distant at least 500 meters fromtreated schools.
Variables

Daily sales
Minimum local house
priceTime trend
Trend * treatedTreatment
Treatment * treatedObservations
R-squared
Semi-elasticity(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular storesin high income
areas

(5)
Other stores

0.000***[0.000]-0.037***0.001***[0.000]-0.015***0.000***[0.000]-0.021***0.000***[0.000]-0.007***0.000**[0.000]-0.061***[0.002]-0.002***[0.000]0.000[0.000]0.691***[0.106]-0.001[0.044][0.004]-0.001***[0.000]0.000**[0.000]0.367***[0.126]-0.040[0.049][0.001]-0.002**[0.001]-0.000[0.000]0.516**[0.237]0.062[0.048][0.000]-0.001***[0.000]0.000*[0.000]0.407***[0.116]-0.042[0.065][0.008]-0.002***[0.001]-0.000[0.000]0.712***[0.173]0.043[0.037]

285,059
0.206-0.000729

145,4430.293-0.0441

123,9520.153
0.0739453,5310.307-0.0666

1,051,804
0.367
0.0428

Note: see Table 2

7.d. Controlling for Product Prices

In this final sub-section we try to decompose the total effect of the School Fruit campaign on
quantities sold into the effect mediated by product prices and the net residual effect (conditionalon these prices). Since current prices are jointly determined with quantities, we use an
18

instrumental variable approach, which consists of instrumenting current prices with their averagevalues during the previous week, which we treat as pre-determined with respect to current
quantities. Table 8 reports our results. As expected, we find that higher prices reduce quantities
sold. Perhaps more interestingly, the evidence suggests that the conditional effects of thetreatment are broadly similar to the total effects estimated in Table 2. By implication, the effects
mediated by price changes are very small and can be disregarded. These effects are small not
because the price elasticity of demand is small – quite the contrary, estimated elasticities areabove 1 in absolute value – but because prices respond very little to the treatment, as alreadyshown in Table 3.
Table 8. Initial treatment, February 2009 to January 2011, including product prices. Sweet and salty snacks.
Treatment group: stores located within 500 meters from treated schools. Control Group: stores distant atleast 500 meters from treated schools. IV estimates.
Variables

PricesDaily sales
Time trend
Trend * treatedTreatment
Treatment * treatedObservations
R-squared
Semi-elasticity
Price elasticity(1)
All stores(2)Stores in highincome areas(3)
Stores in low
income areas(4)
Regular storesin high income
areas

(5)
Discount
stores in low
income areas-2.880***[0.495]0.001***[0.000]-0.002***[0.000]0.000[0.000]0.734***[0.138]-0.109[0.079]-2.068***[0.308]0.002***[0.000]- 0.001*[0.001]0.001***[0.000]0.297[0.176]-0.271***[0.084]-4.296[0.903]0.001***[0.000]-0.002**[0.000]-0.000[0.000]0.753[0.129]0.088[0.075]-1.781***[0.305]0.001***[0.000]-0.002***[0.000]0.001***[0.000]0.738***[0.165]-0.264***[0.051]-4.473***[0.815]0.001***[0.000]-0.002***[0.001]0.000[0.000]0.759***[0.172]-0.017[0.112]12002130.384-0.046-1.7986025720.416-0.118-1.4105644240.399
0.035-2.3303282040.317-0.144-1.8208425740.403-0.006-2.230Note: see Table 2

Concluding remarks

Dietary habits and preferences largely form during childhood and hence teaching kids good
habits is crucial, as they will carry these into later life. For this reason most of the projects aimed
at increasing fruit and vegetable consumption are school based. However, little is known about
the effects of these policies.
In this paper we have examined the impact of the European School Fruit campaign, which
combines the distribution of free fruit and vegetables to school children with their education on
19

the contents of an healthy diet and with different activities, including school gardening andcooking classes. Rather than evaluating the impact of the consumption of fruit and vegetables, as
done by the scarce empirical literature, we have investigated whether this campaign has
contributed to reduce the consumption of unhealthy junk food, which includes salty and sweet
snacks and contributes to overweight and obesity problems in primary school children.
Our focus on the consumption of junk food has been guided both by data availability – wedon’t have information on the sale of fruit and vegetables – and by the following consideration: acampaign which increases the consumption of fruit and vegetables but has no effect on the
consumption of junk food is less effective on obesity than a campaign which has a negative
effect on the consumption of junk food.
We have looked at the impact of the School Fruit Scheme in the city of Rome, for which wehave daily supermarket data on the sale of sweet and salty snacks. We have assigned
supermarkets to treatment and control groups depending on their proximity to treated schools
and we have compared the quantities sold by treated and control stores before and after thetreatment. We have found that the campaign has had heterogeneous effects: on the one hand, it
has substantially reduced the (relative) sale of snacks by treated stores located in high income
areas; on the other hand, it has had no (statistically) significant effect on the (relative) quantities
sold by stores located in low income areas.
How do we explain these heterogeneous effects? A candidate explanation is that an income
effect is at work in poorer areas: as treated schools deliver free fruits and vegetables, poorer
families use the saved income to buy more snacks. We find this candidate explanation difficult tobelieve for two reasons: first, the expected income effect is small; second, the higher real income
saved by the provision of free fruit and vegetables can be devoted to buy consumption items thanhave nothing to do with salty and sweet snacks.
Failure to affect shops located in poorer areas could be due to policy design. For instance,some of the initiatives taken by the campaign require the availability of a computer at home,which is less likely in poorer areas. Alternatively, less well off parents living and shopping inthese areas may have less time to spend with their children, and therefore may be less affected intheir consumption decisions.
Finally, the policy may be effective but the research design adopted in this paper may fail tocapture true effects. This would happen, for instance, if households with poor socio-economic
conditions living near treated schools have a low opportunity cost of time and shop far awayfrom their place of residence in search of better bargains. If this is the case, the reduction in the
consumption of snacks would spread from treated to control stores, with no significant
20differential effect. This drawback of our research design can only be overcome by comparing
consumption before and after the treatment by the households sending their children to treatedand control primary schools. The household data required for such comparison must include
detailed information on consumption patterns as well as information on the primary school
where children go. Since these data are not currently available, we must leave this alternative
research strategy to future research.

21

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