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Do soda taxes affect the consumption and health of school-aged children?
Evidence from France and Hungary∗

Selina Gangl†

†University of Fribourg (Switzerland), Department of Economics

August 27, 2024

###### Abstract

This paper examines the effect of two different soda taxes on consumption
behaviour and health of school-aged children in Europe: Hungary imposed a
Public Health Product Tax (PHPT) on several unhealthy products in 2011. France
introduced solely a soda tax, containing sugar or artificial sweeteners, in
2012. In order to exploit spatial variation, I use a semi-parametric
Difference-in-Differences (DID) approach. Since the policies differ in Hungary
and France, I analyse the effects separately by using a neighbouring country
without a soda tax as a control group. The results suggest a counter-intuitive
positive effect of the tax on soda consumption in Hungary. The reason for this
finding could be the substitution of other unhealthy beverages, which are
taxed at a higher rate, by sodas. The effect of the soda tax in France is as
expected negative, but insignificant which might be caused by a low tax rate.
The body mass index (BMI) is not affected by the tax in any country.
Consequently, policy makers should think carefully about the design and the
tax rate before implementing a soda tax.

Keywords: Soda tax, consumption, health, semi-parametric difference-in-
differences, HBSC
JEL Classification: H25, I12, I18, L66

∗ I have benefited from comments by seminar participants at the Swiss Public
Health Conference 2020, the EuHEA PhD Student-Supervisor & Early Career
Researcher Conference 2020, the Annual Congress of the Verein für
Socialpolitik 2020, and a research seminar at the University of Fribourg. I
thank participants, in particular Edel Doherty, Reiner Eichenberger, Martin
Huber, Marco Portmann, Giannina Vaccaro, Christian Zihlmann, and two anonymous
reviewers of SMYE 2021 for their helpful comments. Addresses for
correspondence: Selina Gangl, University of Fribourg, Bd. de Pérolles 90, 1700
Fribourg, Switzerland<EMAIL_ADDRESS>Declaration of conflicts of
interest: none. Corresponding author: Selina Gangl.

##  1 Introduction

Childhood obesity is a worldwide problem and implies risks like staying obese
in adulthood as well as developing non-communicable diseases (WHO, 2020).
Therefore, ways and means are searched to mitigate or even prevent childhood
obesity. Since an association between childhood obesity and soda consumption
exists, the intake of sugar-sweetened drinks among children should be reduced
(James and Kerr, 2005). For example, a substitution of sugar-sweetened drinks
by sugar-free drinks showed a decrease in weight among children (de Ruyter et
al., 2012).

Hungary and France have implemented a tax on sodas, yet the design and the tax
rate differed among the countries: Hungary introduced a broad tax on unhealthy
products, in which sugar-sweetened sodas were taxed by converted 1.83
Eurocents per litre111converted into Euros at the rate on 01.09.2011 from 2011
on (Ecorys, 2014). One year later, France imposed a soda tax of 7.16 Eurocents
per litre but included sodas with artificial sweetener too (Ecorys, 2014).
These taxes are designed to increase the price and aim to decrease the
consumption of sodas in the population. The existing literature focuses on the
consumption behaviour of the household, whereas little is known about the
response of children to a soda tax (Wilson and Hogan, 2017).

Hence, this paper analyses the effect of a soda tax on consumption behaviour
and the body mass index (BMI) of school-aged children in Hungary and France.
Since the policies differ among the countries, I analyse the effect
separately. In the first analysis, Hungary forms the treatment group, while
neighbouring country Croatia, which does not levy a soda tax, serves as the
control group. In the second analysis, France constitutes the treatment group
and Spain, the control group without a soda tax. Methodologically, I exploit
spatial variation of the tax and use a semi-parametric Difference-in-
Differences (DiD) approach to evaluate the policy. This method uses inverse
probability weighting (IPW) to control for differences in observable
characteristics between the treatment and control group as well as over time.

Since this paper aims at a cross-country comparison, I use data from the
cross-national survey Health-Behaviour in School-Aged Children (HBSC) which
ensures that the same question is asked in each country. This survey is
conducted in cooperation with the World Health Organization (WHO) Europe and
takes place on a quadrennial basis. In the setting of this natural experiment,
the year 2010 constitutes the pre-treatment period and 2014 post-treatment
period. Furthermore, I use the survey years 2006 and 2010 as pre-treatment
years to provide evidence for the parallel trend assumption.

The results suggest a counter-intuitive positive and significant effect of the
tax on soda consumption in Hungary. Considering the nature of the tax might
explain this finding. Since the prices of other unhealthy products, like
energydrinks, increase as well, the substitution behaviour of children could
explain this result. Soda consumption is not affected by the tax in France. A
reason for this finding might be the low soda tax rate of 7 Eurocents per
litre. Children’s body mass index is not influenced by the tax in any country.

This paper relates to two strands of literature. The first one addresses the
impact of a soda tax on adults or households in different countries and
several U.S. jurisdictions. For example, Falbe et al. (2016) focus on poorer
districts in North California and study the effect of a converted 31 Eurocents
per litre222converted into litre and Euros (at the rate on 01.03.2015) soda
tax in Berkeley in 2015, compared to similar areas without such a tax in
Oakland and San Francisco. They find a decrease in soda consumption by 21%
after the tax implementation and an increase in water consumption by 63%. In
Philadelphia, the soda tax amounts to converted 49 Eurocents per
litre333converted into litre and Euros (at the rate on 01.01.2017) in 2017.
Zhong et al. (2018) find that Philadelphians, compared to citizens of close
and similar cities without such a tax, drink 40% fewer sodas directly after
the tax implementation, yet the effect diminishes one year later (Zhong,
2020). However, Wilson and Hogan (2017) criticize that the soda tax can be
easily circumvented, if the tax is implemented very locally, such as in a
particular city. For this reason, Wilson and Hogan (2017) caution against
over-interpreting the results of these studies.

There is little literature that relates to France and Hungary which are
analysed in this paper. The soda tax literature based on France reports a
pass-through of the soda tax to the consumer between 39% (Etilé et al., 2018)
and 100% (Capacci et al. (2019) and Berardi et al. (2016)). Capacci et al.
(2019) analyse the number of purchased sodas at the household level in France
compared to close regions in Italy. They apply a differences-in-differences
approach and find a very small but imprecisely estimated decrease in soda
consumption and explain their result with the low soda tax level. Bíró (2016)
evaluates the effect of the unhealthy product tax in Hungary but sodas are
excluded from the analysis due to data restrictions. Kurz and König (2021)
examine both the soda tax in France and the PHPT in Hungary using data on
purchased sodas and apply a synthetic control group analysis. Their findings
suggest a slight decline in soda consumption in France and a decrease in
Hungary in the short run that fades away two years later. However, these
results are imprecisely estimated due to the small number of observations.

The second strand of literature discusses the effect of the soda tax on
children’s and adolescents’ outcomes in other countries than France and
Hungary. The evidence is mixed in these studies: Fletcher et al. (2010) finds
a small decline in soda consumption of children and adolescents due to the
implementation of a soda tax across all U.S. states, which had an average soda
tax between 1.5% and 2.3%. Whereas Sturm et al. (2010) ascertain that a low
soda tax ($\leq$ 4%) has neither an effect on soda consumption nor on obesity
rates of children in the U.S. states. In Philadelphia, a tax of converted 49
Eurocents per litre444converted into litre and Euros (at the rate on
01.01.2017) does not affect children’s soda consumption (Cawley et al., 2019).
However, subgroups like overweighted children, children from families with low
incomes (Sturm et al., 2010) or children with a high soda consumption prior to
the reform (Cawley et al., 2019) are more likely to react to a soda tax.
Regarding the effect of the soda tax on BMI, Powell et al. (2009) find no
significant change among adolescents in different U.S. states. In Peru, a
price war among the manufactures lead to a reduction of soda prices at the end
of the nineties, which increased the obesity rate among children (Ritter,
2018).

To the best of my knowledge, this is the first paper analysing the impact of
two different soda taxes on soda consumption and health of school-aged
children. A cross-country comparison is difficult to draw because countries
collect their data by themselves with different questions and methods (Jou and
Techakehakij, 2012). Using the health behaviour in school-aged children (HBSC)
dataset enables me to draw a cross-country comparison.

The remainder of this paper is organized as follows: In the next section, I
provide information about the implementation of the soda tax in France and
Hungary. Thereafter, I present the data source, descriptive statistics, and
define the subsample. Then, I discuss the empirical strategy and show the
results. Finally, I conclude the empirical analysis.

##  2 Institutional background: Soda taxes

Soda taxes represent a policy tool to combat sugar intake on a country and
local level. Several U.S. cities, as well as various countries, have
implemented a tax on sugar-sweetened sodas in recent years (Allcott et al.,
2019). The first two countries which imposed a soda tax in Europe were Finland
in 1940 followed by Norway in 1981 (Asen, 2019).

Thirty years after Norway, Hungary levied a ”Public Health Product Tax” (PHPT)
on salted snacks, condiments, flavoured alcohol, fruit jams, confectionery,
energy drinks, and also on sugar-sweetened beverages (Ecorys, 2014). Every
product category reveals a different tax level, even among the sugar-
containing beverages exist differences: Syrups are taxed by 200 Hungarian
Forint (HUF)555equals 73 Eurocents on 01.09.2011. per litre, whereas other
sugar-sweetened sodas are taxed by 7 HUF666equals 2.2 Eurocents on 01.01.2012
per litre. Additionally, the original tax level for sodas amounted to 5 HUF in
2011 and was increased to 7 HUF in 2012 (Bíró, 2016). This soda tax only
affects sodas exceeding a sugar content of 8 grams per 100 millilitres, sodas
with less sugar are not taxed. To make this threshold more apparent, an
original Coca-Cola contains more than 10 grams of sugar per 100
millilitres.777https://www.coca-cola.co.uk/our-business/faqs/how-much-sugar-
is-in-coca-cola, last retrieved on 07.11.2021. The reason for the
implementation of this tax was a public health crisis (WHO, 2015). Non-
communicable diseases have been a widespread cause of premature death, whereby
the main risk factor was unhealthy diet (Institute for Health Metrics and
Evaluation, 2010). One year prior to the implementation of the health policy,
the share of overweight adults888Persons 18 years and older with a body mass
index equal or bigger than 25. amounted to 61.7% (WHO, 2017c) which exceeded
the European average of 58.7% (WHO, 2017d). Likewise, the share of obese
adults was higher (25.3%) (WHO, 2017a) than the European average (22.3%) (WHO,
2017b).

According to the law, the consumer bears the tax burden, by paying retail
price including the soda tax (Ecorys, 2014). To answer the question whether
the soda tax was passed through to the consumer, I tracked the development of
the annual average price of two litres of Coca-Cola. Table 2.1 reports a price
increase of Coca-Cola from the soda tax implementation in 2011 onwards. With
this data at hand, I calculated the yearly price increase and the price index
based on the previous year. The price of two litres of Coca-Cola rose from the
pre-treatment year 2010 to the implementation year 2011 by 21 HUF. To see
whether the price increase was driven by inflation or the soda tax, I compared
the Coca-Cola price index with the consumer price index (CPI) of non-alcoholic
beverages. The price of Coca-Cola increased more than the price of non-
alcoholic beverages in general, which points to the pass-through of the soda
tax. In 2012, the tax was slightly raised from 5 HUF to 7 HUF per litre and
comparing the two different price indexes shows an even greater difference
than in the year before. The price indexes started to converge in the year
2013 and approximated each other in 2014.

Table 2.1: Price development of Coca-Cola and consumer price index (CPI) of
non-alcoholic beverages

| 2010 | 2011 | 2012 | 2013 | 2014
---|---|---|---|---|---
Coca-Cola average price (2l) | 295 HUF | 316 HUF | 351 HUF | 366 HUF | 373 HUF
Coca-Cola price increase (2l) | 0 HUF | 21 HUF | 35 HUF | 15 HUF | 7 HUF
Coca-Cola price index | 100 | 107.1 | 111.1 | 104.3 | 102.0
CPI of non-alcoholic beverages | 100.1 | 101.9 | 105.9 | 101.9 | 100.5

Source: Data concerning the Coca Cola annual average price and the CPI of non-
alcoholic beverages stems from the Hungarian Central Statistical Office. The
Coca-Cola price increase and index is self-calculated.

A beverage tax was implemented in France in January 2012 and it was originally
intended to apply to sugar-sweetened sodas only. However, this was, according
to Ecorys (2014), not possible, because the customs codification classifies
sodas with added sugar and sweetener into the same category. Whereas Le Bodo
et al. (2019) reports that sodas with artificial sweeteners were included in
the tax to generate higher tax revenues for the farm sector. Consequently,
both kinds of sodas are taxed, independently of their quantity of sugar or
sweetener. The tax increased over time from 7.16 Eurocents per litre in 2012,
to 7.31 Eurocents in 2013, and reached 7.45 Eurocents in 2014 (Ecorys, 2014).
The aim of the soda tax is twofold: Firstly, it is designed to collect
additional revenue for the health (Ecorys, 2014) and farm sector Le Bodo et
al. (2019). Secondly, the soda tax is supposed to reduce the obesity rate
among French citizens (Ecorys, 2014). One year prior to the implementation of
the health policy, France revealed a share of overweight adults exceeding the
European average by almost 2 percentage points (WHO (2017c) and WHO (2017d)).
Every fifth adult had a BMI equal to or over 30 which indicates obesity among
the WHO definition, yet the share of obese adults in France is lower than the
European average (WHO (2017a) and WHO (2017b)). Moreover, the National
Nutrition and Health Programme 2011 formulated the goal to decrease the share
of children drinking more than half a glass of soda a day by at least a
quarter in the following five years (Ministère du Travail, de l’Emploi et de
la Santé, 2011). The discussion about the implementation of the soda tax
lasted from 2005 to 2011. In August 2011 the tax was decided unexpectedly, the
implementation followed five months later (Le Bodo et al., 2019). The pass-
through of the soda tax to the consumer is reported between 39% (Etilé et al.,
2018) and 100% (Capacci et al. (2019) and Berardi et al. (2016)).

Table 2.2 highlights the main differences in the taxes in Hungary and France.
Hungary’s Public Health Product Tax includes a bunch of unhealthy products and
not only sodas. However, exclusively sugar-sweetened sodas exceeding the
threshold of 8 g sugar per 100 millilitres are taxed, whereas sodas with less
sugar or artificial sweetener are exempt from the tax. France taxes sugar-
sweetened soft drinks (regardless of sugar content) and soft drinks with
artificial sweeteners. The tax level is different among the countries, yet
both countries raised the tax after implementation.

Table 2.2: Comparison of taxes in Hungary and France

| Hungary | France
---|---|---
Implementation year | 2011 | 2012
Tax | Unhealthy product tax | Beverage tax
Products | Sodas | Sodas
| Syrups |
| Energy drinks |
| Confectionery |
| Salted snacks |
| Condiments |
| Flavoured alcohol |
| Fruit jams |
Kind of sodas | Sugar-sweetened | Sugar-sweetened
|  | or artificial sweetener
Threshold of sugar |  $>$ 8 grams per 100 millilitres | None
Tax level in implementation year | 5 HUF ($\sim$ 1.8 Eurocents) | 7.16 Eurocents
Tax level in evaluation year (2014) | 7 HUF ($\sim$ 2.4 Eurocents) | 7.45 Eurocents
Tax level increase | 40% | 4%

Source: The information concerning Hungary and France stems from Ecorys
(2014), the table was created by myself.

##  3 Hypothesis Development

Soda taxes aim to raise the price and to decrease the consumption of sugar-
sweetened beverages (Wilson and Hogan, 2017). In theory, manufacturers collect
the retail price including the tax from the customer and transfer it to the
state. In practice, the manufacturers might decide to reduce their margin and
to pass only part of the tax to the customer. Empirical evidence ranges from a
partly pass-through of 39% (Etilé et al., 2018) to 100% (Capacci et al. (2019)
and Berardi et al. (2016)) in France. Table 2.1 reveals a price increase of
Coca-Cola greater than the inflation after the implementation of the soda tax
in Hungary. Consequently, the soda prices increased for the consumer in both
countries. Figure 3.1 illustrates the increase in the soda price due to the
positive pass-through of the tax.

Soda tax \+  Pass-through $>$ 0% $\Rightarrow$ Soda price $\uparrow$

Figure 3.1: Soda price development

Whether the increased price leads to a decrease in demand depends on the price
elasticity of sodas (Wilson and Hogan, 2017). Sodas are goods with a rather
high price elasticity because they do not belong to staple food. Hence, I
expect a reduction of soda consumption as shown in Figure 3.2).

Soda price $\uparrow$ \+  High price elasticity $\Rightarrow$ Soda consumption
$\downarrow$

Figure 3.2: Reduction of soda consumption

In a next step, this hypothesis is adjusted to the subgroup of school-aged
children which is presented in Figure 3.3. The crucial question is: How do
children get mainly access to sugar-sweetened sodas? Either parents provide
these beverages or children spend (part of) their pocket money on sodas. In
the first case, children’s soda consumption should also decrease when
household consumption decreases due to the soda price increase. In the second
case, children are even more affected by a price increase, because of their
limited budget. Hence, I expect a decrease in soda consumption among children.

Access to sodas Scenario 1: Parents Household consumption $\downarrow$
Children’s soda consumption $\downarrow$ Scenario 2: Pocket money Soda price
$\uparrow$ Children’s soda consumption $\downarrow$ Figure 3.3: Children’s
access to sodas

The second hypothesis addresses the effect of the soda tax on children’s BMI
and depends on the substitution behaviour of the children: If children consume
less sugar due to a lower soda intake and this amount of sugar is not
substituted by other sugar-containing products, the average BMI of children
might decline. For example, a substitution of sugar-sweetened drinks by sugar-
free drinks pointed to a decrease in weight among children (de Ruyter et al.,
2012). However, if children substitute the saved sugar with other sugary
products, the soda tax does not affect the BMI. Since Powell et al. (2009)
find no significant effect of a soda tax on children’s BMI in the U.S., I
expect at least not an increase in BMI due to the soda tax.

##  4 Data

To undertake a cross-country comparison, I use data about the health behaviour
in school-aged children (HBSC). This survey is conducted in cooperation with
the World Health Organization (WHO) Europe on a quadrennial basis since 2001.
The advantage of this survey is the use of the same questionnaire in each
country. One question captures the frequency of sugar-sweetened soda
consumption on a 7-point scale from ”never” to ”more than once daily”. Another
section addresses body measures like children’s height and weight. Based on
this information, the body mass index (BMI) is calculated and reveals whether
a child is overweight. The random sampling is made class-wise which implies a
repeated cross-sectional design.

Regarding the sample restriction, I excluded pupils with missing observations
in the dependent variable like soda consumption and body mass index. In the
survey year 2006 is no information about the BMI available, so I calculate it
by the following formula $\text{BMI}=\text{bodyweight}/\text{(body height in
m)}^{2}$.999https://projekte.uni-
hohenheim.de/wwwin140/info/interaktives/bmi.htm Furthermore, I have excluded
children with missing information about their age, sex, TV consumption on
weekdays, and having their own bedroom. Moreover, observations are excluded
from the sample that do not inform about the number of computers in the
household, ownership of a car, family wealth, and whether the mother or the
father lives in the main home.

An appropriate control group is similar to the treatment group but without the
implemented policy. This approach enables us to attribute the change in the
outcome to the implemented policy in the treatment group (Taillie et al.,
2017). Therefore, I use a neighbouring country, without a soda tax in force,
as a control group. As Table 4.1 shows, Croatia constitutes the control group
for Hungary and Spain for France. Hungary has implemented the tax in 2011 and
France followed one year later, yet the data is available every four years.
Therefore, the survey year 2010 constitutes the pre-treatment period and 2014
the post-treatment period. In the treatment as well as in the control group
are 11 to 15 years old surveyed pupils.

Table 4.1: Treatment and control groups Panel A: Hungary and Croatia
---
| Treatment | Control
| Country | Survey year | Country | Survey year
Pre-treatment | Hungary | 2010 | Croatia | 2010
Post-treatment | Hungary | 2014 | Croatia | 2014
Panel B: France and Spain
| Treatment | Control
| Country | Survey year | Country | Survey year
Pre-treatment | France | 2010 | Spain | 2010
Post-treatment | France | 2014 | Spain | 2014

Table 4.2 reports the descriptive statistics for the treated children living
in France and the untreated children living in Spain separately. For either
group, the mean and the standard deviation (std.dev) of the variables are
provided. The last two columns contain the mean differences across groups as
well as the p-values. In France, the share of boys and girls in the survey is
almost balanced and children are on average 13.5 years old. The children watch
on average 2 hours TV on a weekday and the majority has their own bedroom.
Most children live together with their mother and father at home. The family
posses on average 1.7 cars and 2.3 computers, and reports well-being between
”quite well of” and ”average”. All these before mentioned control variables
are statistically significantly different across children in France and Spain.
The lower part of Table 4.2 presents the descriptive of the outcome variables.
The frequency of consumed sodas is measured as a categorical variable, a value
of four corresponds to a soda consumption on two to four days a week. A body
mass index (BMI) of 19 is within the normal
range.101010https://www.stanfordchildrens.org/en/topic/default?id=determining-
body-mass-index-for-teens-90-P01598 The mean differences of the two outcome
variables are statistically significant between children in France and Spain.
Regarding the sample size, 8,821 children participated in the survey in France
and 9,744 children participated in Spain which sums up to 18,565 observations
in total.

Table 4.2: Descriptive statistics: France (treated) and Spain (non-treated)

| Treated | Non-treated |  |
---|---|---|---|---
| mean | std.dev | mean | std.dev | mean difference | p-value
| Time |  |
Year | 2011.97 | 2.00 | 2012.32 | 1.97 | -0.35 | 0.00
| Control variables |  |
Child characteristics |  |  |  |  |  |
Female (Dummy) | 0.51 | 0.50 | 0.52 | 0.50 | -0.02 | 0.04
Age (in years) | 13.54 | 1.65 | 13.68 | 1.61 | -0.14 | 0.00
TV consumption on a weekday (categorical) | 2.09 | 1.77 | 2.00 | 1.60 | 0.09 | 0.00
Household characteristics |  |  |  |  |  |
Mother living at main home (Dummy) | 0.92 | 0.27 | 0.96 | 0.20 | -0.04 | 0.00
Father living at main home (Dummy) | 0.75 | 0.44 | 0.83 | 0.37 | -0.09 | 0.00
Number of family cars | 1.69 | 0.53 | 1.54 | 0.58 | 0.15 | 0.00
Own bedroom (Dummy) | 0.85 | 0.36 | 0.83 | 0.38 | 0.02 | 0.00
Number of computers per family | 2.30 | 0.80 | 2.23 | 0.83 | 0.07 | 0.00
Family well-off (categorical) | 2.28 | 0.85 | 2.93 | 0.50 | -0.65 | 0.00
| Outcome variables |  |
Frequency of sodas (categorical) | 3.97 | 1.87 | 3.85 | 1.76 | 0.12 | 0.00
Body mass index (BMI) | 18.97 | 3.18 | 19.94 | 3.30 | -0.97 | 0.00
Number of observations | 8,821 |  | 9,744 |  |  |

Source: Health behaviour of school-aged children (HBSC)

Table 4.3: Descriptive statistics: Hungary (treated) and Croatia (non-treated)

| Treated | Non-treated |  |
---|---|---|---|---
| mean | std.dev | mean | std.dev | mean difference | p-value
| Time |  |
Year | 2011.74 | 1.98 | 2011.64 | 1.97 | 0.09 | 0.00
| Control variables |  |
Child characteristics |  |  |  |  |  |
Female (Dummy) | 0.52 | 0.50 | 0.51 | 0.50 | 0.00 | 0.64
Age | 13.59 | 1.65 | 13.68 | 1.66 | -0.10 | 0.00
TV consumption on a weekday (categorical) | 2.04 | 1.65 | 2.48 | 1.74 | -0.43 | 0.00
Household characteristics |  |  |  |  |  |
Mother living at main home (Dummy) | 0.95 | 0.23 | 0.98 | 0.13 | -0.04 | 0.00
Father living at main home (Dummy) | 0.74 | 0.44 | 0.94 | 0.24 | -0.20 | 0.00
Number of family cars | 1.04 | 0.71 | 1.34 | 0.59 | -0.30 | 0.00
Own bedroom (Dummy) | 0.73 | 0.44 | 0.67 | 0.47 | 0.07 | 0.00
Number of computers per family | 1.83 | 0.87 | 1.73 | 0.85 | 0.10 | 0.00
Family well-off (categorical) | 2.40 | 0.83 | 2.15 | 0.91 | 0.25 | 0.00
| Control variables |  |
Frequency of sodas (categorical) | 4.03 | 1.98 | 3.94 | 1.84 | 0.09 | 0.00
Body mass index (BMI) | 19.54 | 3.49 | 19.90 | 3.24 | -0.36 | 0.00
Number of observations | 7,544 |  | 9,919 |  |  |

Source: Health behaviour of school-aged children (HBSC)

Table 4.3 reports the descriptive statistics for the second country-pair.
Children living in Hungary belong to the treatment group and children living
in Croatia form the control group. TV consumption is slightly higher among the
children in Croatia and it is more likely that the father lives in the main
household in Croatia. Families in Croatia have a higher probability to have
more than one car in comparison to families in Hungary, whereas children in
Hungary are more likely to have their own bedroom. The number of computers at
home is slightly higher in Hungary than in Croatia, whereas the families in
Croatia score higher in being well off. Children in Hungary consume slightly
more frequently sodas than children in Croatia. The BMI is slightly higher in
Croatia than in Hungary. There are 7,544 treated children in Hungary and 9,919
non-treated children in Croatia, so 17,463 children in total.

##  5 Econometric approach

In this chapter, I discuss the Difference-in-Differences (DiD) strategy for
identifying the Average Treatment Effect on the Treated (ATET) (see e.g.
Lechner (2010)), i.e. among children living in Hungary or France in 2014. The
potential outcome Y (e.g. frequency of consumed sodas) depends on the time
period t $\in$ {0, 1} and the potential treatment state d $\in$ {0, 1} . The
notation $Y_{t}^{d}$ indicates the potential outcome in the potential
treatment state d and in time period t. For example, the potential outcome of
the treatment group (d = 1) in the pre-treatment period (t = 0) is represented
by $Y^{1}_{0}$. This notation facilitates to state the identifying assumptions
of the DiD framework, see Lechner (2010):

The first assumption, formulated in equation 5.1, implies the exogeneity of
the covariates (X). This assumption would be violated if the soda tax affects
the characteristics of the children or the household. Time-independent
covariates, like gender, cannot be affected by the soda tax because they are
constant over time. Time-dependent variables may be affected by the treatment,
especially if these variables are measured after the implementation of the
soda tax. Since I use repeated cross-sections, the covariates are measured in
2014, whereas the soda tax is in force since 2011 or 2012 respectively.
However, it is rather unlikely that the soda tax affects, for example,
children’s TV consumption or whether the mother lives at the main home or not.

$\begin{array}[]{l}X^{1}=X^{0}=X;\;\forall x\in\chi.\end{array}$ (5.1)

The main identifying assumption in the context of DiD is the common trend
assumption, formally stated in equation 5.2. Intuitively speaking, the soda
consumption and the BMI of children living in Hungary and Croatia, would
follow the same time trend in the absence of the soda tax.111111This
assumption must hold for France and Spain too. For this reason, I need to
control for child and household covariates that would lead to different time
trends. For example, boys and children from low-income families consume more
sugar-sweetened sodas than girls and children from more privileged families
(Schröder et al., 2021), hence the time trend differs between these groups. I
provide a placebo test conditional on covariates using unaffected periods in
Table 6.2 in Section 6 to support this assumption.

$\begin{array}[]{l}E[Y^{1}_{0}|X=x,D=1]-E[Y^{0}_{0}|X=x,D=1]=\\\
E[Y^{1}_{0}|X=x,D=0]-E[Y^{0}_{0}|X=x,D=0]=\\\
E[Y^{1}_{0}|X=x]-E[Y^{0}_{0}|X=x];\;\forall x\in\chi.\end{array}$ (5.2)

A further assumption rules out an anticipatory effect ($\theta$) of the policy
in the pre-treatment period (t = 0) as formulated in equation 5.3.
Accordingly, children in the treated countries Hungary and France must not
anticipate the effect of the soda tax in 2010 and their soda consumption must
not change prior to the implementation of the tax. Since the tax was discussed
from 2005 to 2011 in the parliament in France, it might have raised the
awareness of unhealthy beverages among the French children. However, the
decision to pass this law was unexpected and the implementation time of five
months was rather short (Le Bodo et al., 2019). In Hungary, the law was passed
one and a half months before it came into force (Ecorys, 2014), which
represents also a short period for anticipatory behaviour.

$\begin{array}[]{l}\theta_{0}(x)=0;\;\forall x\in\chi.\end{array}$ (5.3)

The last assumption is known as the common support assumption and is
formulated in equation 5.4. It ensures that for each child in Hungary in the
year 2014, another child exists with the same characteristics in the following
three groups: i) Hungary in 2010, ii) Croatia in 2010, and iii) Croatia in
2014.121212This assumption holds for France in 2014 too. In this case, the
three groups are i) France in 2010, ii) Spain in 2010, and iii) Spain in 2014.
Under assumptions 5.1 \- 5.4, the ATET is identified.

$\begin{array}[]{l}P[TD=1|X=x,(T,D)\in{(t,d),(1,1)}]<1;\\\
\;\forall(t,d)\in\\{(0,1),(0,0),(1,0)\\};\;\forall x\in\chi.\end{array}$ (5.4)

A standard DiD approach models a linear relationship between the policy and
the outcome, in this case, the outcome variable is continuous. The variable
”Frequency of sodas” is measured as a categorical variable in the HBSC
dataset. Therefore, this variable is a limited dependent variable, implying a
non-linear relationship between the policy and the outcome. However,
considering the non-linearity may lead to the violation of the identifying
assumption of the DiD, the common trend assumption (Lechner, 2010). To deal
with this issue, I use a semi-parametric approach to model the relationship in
a more flexible way than a parametric approach.

Equation 5.5 describes the identification of the semi-parametric ATET based on
inverse probability weighting (Huber, 2019). The outcome variable Y is
multiplied by an inverse probability weight, where $\Pi$ gives the share of
treated observations in the post-treatment period and $\rho_{d,t}(X)$ is the
probability of being in the treatment state d and in the time period t,
conditional on covariates X. This propensity score is estimated by probit.

$\begin{array}[]{l}E\left[\left\\{\frac{DT}{\Pi}-\frac{D(1-T)\rho_{1,1}(X)}{\rho_{1,0}(X)\Pi}-(\frac{(1-D)T\rho_{1,1}(X)}{\rho_{0,1}(X)\Pi})-\frac{(1-D)(1-T)\rho_{1,1}(X)}{\rho_{0,0}(X)\Pi})\right\\}Y\right],\\\
where\ \Pi=Pr(D=1,T=1),\ \rho_{d,t}(X)=Pr(D=d,T=t|X).\end{array}$ (5.5)

To ensure that the common trend assumption holds, I include the following
covariates (X) in the estimation: On the individual level, I control for age
and sex of the child, because older children reveal a different soda
consumption than younger children and boys differ in their consumption
behaviour from girls (Vereecken et al., 2005). Since TV consumption was
associated with soda consumption (see for example Andreyeva et al. (2011) and
Grimm et al. (2004)), I control for television consumption on a weekday. On
the household level, I take into account several characteristics: Firstly, I
control for the household structure, in particular, whether the mother or the
father lives in the same household as the child. Secondly, I control for the
wealth of the family, because it is associated with different soda consumption
levels (Drewnowski et al., 2019). I use the following proxies for family’s
wealth: Ownership of a family car, number of computers in the household, well-
off of the family, a dummy indicating whether the child has his/her own
bedroom. Furthermore, soda consumption increases with wealth in Eastern
European countries, whereas it decreases in Western European countries
(Vereecken et al., 2005). Country-specific characteristics, like the growth of
the Gross Domestic Product (GDP), may affect the soda consumption of its
inhabitants and thus bias the results. Controlling for country-specific
covariates could serve as a solution for this problem, yet this is not
possible because of the multi-collinearity with the treatment. Therefore, I
inspect the GDP growth of each country pair in Section 6.

For the estimation, I use the didweight command of the causalweight package in
R, with the default number of bootstrap replications of 1,999 to calculate the
standard errors, and the default trimming rule of 0.05 to drop observations
with an extreme propensity score from the sample. Furthermore, I use no
clusters in the analysis. Clustered standard errors would help to get
consistent standard errors given the number of clusters is large enough.
However, in this setting there is only one country in the treatment group. I
do not use clustered standard errors on the country level because whenever
bootstrap does not draw the one treated country, the observation is dropped.
In the Appendix, school-year specific clustering is used as a robustness
check. Since the didweight command is designed for one pre- and one post-
treatment-period, I use the survey years 2010 and 2014 in the estimation.
Several pre-treatment years are available to test the parallel trend
assumption. I use the survey years 2006 and 2010 and run the estimation with a
fake treatment in the latter.

##  6 Results

This chapter provides the estimated results as well as the sensitivity
analysis. Table 6.1 presents the effects of the implemented tax, the standard
errors are estimated by bootstrap, the p-values are obtained from t-tests.
Panel A reports the effect of the policy package in Hungary, whereas Croatia
constitutes the control group. The findings point at the first glance to a
counter-intuitive positive and significant (p $<$ 0.01) effect of the tax on
consumption behaviour among school-aged children.131313The effect size is not
directly interpretable because soda consumption is measured as a categorical
variable. Since a range of products are taxed, the substitution of sugar-
sweetened products might drive this result. For example, a survey among adults
who changed their nutritional behaviour due to the PHPT suggested that 52%
substituted energy drinks with sodas (Martos et al., 2016), which might be
driven by the higher tax on energy drinks.141414250 HUF/l if taurin $>$ 100 mg
per 100 ml or 40 HUF/l if no taurin but methylxanthine $>$ 15 mg per 100 ml
see Bíró (2015). However, another driver of this result might be the relative
stronger GDP growth in Hungary compared to Croatia (see Figure 2.B.1 in the
Appendix), i.e. children in Hungary could spend more money for sodas.151515Due
to the multicollinearity with the treatment, I cannot control for the GDP
growth. The second outcome, children’s BMI is not affected by the tax in
Hungary. Panel B in Table 6.1 reports the effects of the soda tax on the
frequency of sugar-sweetened soda consumption and the BMI for French children
(treated) and Spanish children (untreated). The effects have the expected
negative sign but are insignificant. This result is consistent with analyses
of large quantities of soft drinks purchased at the aggregate level such as
households (Capacci et al., 2019) and industry (Kurz and König, 2021), which
find only a small and imprecisely estimated decrease in soft drinks purchased.
But Table 2.B.2 shows larger GDP growth in Spain than in France over time
which might affect the result.

Table 6.1: Empirical results

Panel A: Hungary and Croatia
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.35 | 0.07 | 0.00 | 18,712
Body Mass Index (BMI) | 0.12 | 0.13 | 0.36 | 17,553
Panel B: France and Spain
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | -0.08 | 0.08 | 0.31 | 20,951
Body Mass Index (BMI) | -0.07 | 0.15 | 0.66 | 18,723

Note: Standard errors are estimated by bootstrap.

A downside of non-clustered standard errors in a DiD setting is the
possibility of underestimating the standard errors (Bertrand et al., 2004). To
check whether the empirical results in Table 6.1 are robust, I have re-
estimated the results with clustered standard errors. The most conservative
approach would be to cluster standard errors on an aggregate level. However,
it is not possible to cluster on the country level in this setting, because of
the few numbers of treated countries. Whenever cluster bootstrap does not draw
the treatment group, the procedure does not work. Hence I cluster on the next
lowest level which has variation in the data: the school-year level. Table
2.B.1 in the Appendix shows that the clustered standard errors are almost
equal to the robust standard errors in Table 6.1.

The identifying assumption of the DiD approach is the parallel trend
assumption, implying that, conditional on the covariates, the treatment and
control group follow the same time trend in the absence of the treatment. This
assumption is not testable, yet it is possible to conduct a placebo test to
support this assumption: I use the two pre-treatment periods 2006 and 2010 and
pretend that in the latter a ’fake treatment’ was implemented. Table 6.2
reports large p-values in both panels, which supports the parallel trend
assumption.

Table 6.2: Unaffected periods

Panel A: Hungary and Croatia
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.10 | 0.08 | 0.23 | 19,069
Body Mass Index (BMI) | -0.06 | 0.15 | 0.67 | 17,949
Panel B: France and Spain
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.09 | 0.08 | 0.26 | 24,919
Body Mass Index (BMI) | 0.01 | 0.15 | 0.96 | 21,826

Note: Standard errors are estimated by bootstrap.

As a robustness test, I use another neighbouring country as an alternative
control group in each panel: I substitute Croatia with Slovakia and Spain with
Switzerland. Table 2.B.2 and Table 2.B.3 in the Appendix report the
descriptive statistics of the children and the household for these groups.
Table 2.B.4 in the Appendix shows the placebo test of the unaffected periods
for Hungary and Slovakia (Panel A) and France as well as Switzerland (Panel B)
separately. It suggests that the parallel trends assumption holds for both
Panels, except for the outcome variable BMI in Panel A. Finally, Table 6.3
reports the results for robustness test. Even if the control group changes we
find a highly statistically significant positive effect on the frequency of
consumed sodas in Hungary. In France, the results on the soda consumption and
the BMI have a negative sign, yet they are insignificant as in the main
results in Table 6.1. Figure 2.B.3 and 2.B.4 in the Appendix report a parallel
GDP growth of each country pair prior to the measured effect in 2014.
Therefore, GDP growth can be excluded as a driver for the increase in soda
consumption in Hungary.

Table 6.3: Robustness test

Panel A: Hungary and Slovakia
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.48 | 0.09 | 0.00 | 15,425
Body Mass Index (BMI) | -0.13 | 0.15 | 0.38 | 14,059
Panel B: France and Switzerland
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | -0.04 | 0.07 | 0.52 | 22,986
Body Mass Index (BMI) | -0.13 | 0.11 | 0.24 | 20,258

Note: Standard errors are estimated by bootstrap.

##  7 Conclusion

This paper examines the effect of two different health policies on sugar-
sweetened soda consumption behaviour and body mass index (BMI) of school-aged
children in Europe. Hungary has implemented a Public Health Product Tax (PHPT)
on several unhealthy products, including sugar-sweetened sodas, in 2011, while
France only taxes sodas, containing sugar and artificial sweetener, since
2012. Methodologically, I apply a Difference-in-Differences (DiD) approach to
evaluate this natural experiment and use neighbouring countries without such a
soda tax as a control group. I analyse the effect in Hungary and France
separately, because of the different policy designs. Since the frequency of
soda consumption is measured by a categorical scale, I use a semi-parametric
method to estimate the effect in a flexible way. To the best of my knowledge,
this is the first paper analysing the impact of two different soda taxes on
the consumption and health of school-aged children.

The results suggest that the PHPT had a statistically significant effect (p
$<$ 0.01) on soda consumption of school-aged children in Hungary, yet the sign
is unexpectedly positive. An explanation for this counter-intuitive result
might be the substitution behaviour among children, as the price of other
unhealthy products, such as energy drinks or syrups, are taxed even higher. In
France, the soda consumption of school-aged children is not affected by the
soda tax. This result is in line with the analyses of Capacci et al. (2019)
and Kurz and König (2021), who use soda sales data at a more aggregated level
and find a very small, but not robust effect on the quantity of purchased
sodas. Capacci et al. (2019) explain this finding by the very low tax level of
7.16 Eurocents per litre.

Moreover, I analyse the effect on children’s BMI and find neither in France
nor in Hungary a statistically significant effect. This finding is consistent
with Powell et al. (2009) who analyses the effect of soda tax on BMI among
adolescents. Regarding the sensitivity analysis, I run a placebo test with two
unaffected periods. The results suggest an insignificant effect, which
supports the parallel trend assumption. The results are robust to an
alternative control group.

Consequently, policy makers should think carefully about the design and the
tax rate before implementing a soda tax. The availability of data about
children’s quantity of soda consumption would help to estimate the effect of
the soda tax in a more precise way.

##  Appendix A Acknowledgment

HBSC is an international study carried out in collaboration with WHO/EURO. The
International Coordinator of the 2005/06 survey was Prof. Candace Currie and
the Data Bank Manager was Prof. Oddrun Samdal.

##  Appendix B Appendix

Figure 2.B.1: GDP growth in Hungary and Croatia

Source: The World
Bank161616https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end=2014&locations=HU-
HR&start=2010, last accessed: 10.11.2021.

Figure 2.B.2: GDP growth in France and Spain

Source: The World
Bank171717https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end=2014&locations=FR-
ES&start=2010, last accessed: 10.11.2021.

Table 2.B.1: Results with school-year specific clusters

Panel A: Hungary and Croatia
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.35 | 0.10 | 0.01 | 18,712
Body Mass Index (BMI) | 0.12 | 0.15 | 0.42 | 17,553
Panel B: France and Spain
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | -0.08 | 0.09 | 0.36 | 20,951
Body Mass Index (BMI) | -0.07 | 0.15 | 0.67 | 18,723

Note: Bootstrapped and clustered standard errors.

Table 2.B.2: Descriptive statistics: Hungary (treated) and Slovakia (non-
treated)

| Treated | Non-treated |  |
---|---|---|---|---
| mean | std.dev | mean | std.dev | mean difference | p-value
| Time |  |
year | 2011.74 | 1.98 | 2011.53 | 1.94 | 0.21 | 0.00
| Control variables |  |
Female | 0.52 | 0.50 | 0.53 | 0.50 | -0.01 | 0.37
Age | 13.59 | 1.65 | 13.98 | 1.35 | -0.39 | 0.00
TV consumption on a weekday | 2.04 | 1.65 | 2.64 | 1.77 | -0.60 | 0.00
Mother living at main home (Dummy) | 0.95 | 0.23 | 0.96 | 0.19 | -0.02 | 0.00
Father living at main home (Dummy) | 0.74 | 0.44 | 0.88 | 0.33 | -0.14 | 0.00
Number of family cars | 1.04 | 0.71 | 1.24 | 0.66 | -0.20 | 0.00
Own bedroom (Dummy) | 0.73 | 0.44 | 0.58 | 0.49 | 0.15 | 0.00
Number of computers per family | 1.83 | 0.87 | 1.95 | 0.87 | -0.12 | 0.00
Family well-off | 2.40 | 0.83 | 2.07 | 0.82 | 0.33 | 0.00
| Outcome variables |  |
Frequency of sodas | 4.03 | 1.98 | 4.36 | 1.89 | -0.33 | 0.00
Body mass index (BMI) | 19.54 | 3.49 | 19.54 | 3.07 | 0.00 | 0.99
| 7,544 |  | 6,419 |  |  |

Source: Health behaviour of school-aged children (HBSC)

Table 2.B.3: Descriptive statistics: France (treated) and Switzerland (non-
treated)

| Treated | Non-treated |  |
---|---|---|---|---
| mean | std.dev | mean | std.dev | mean difference | p-value
| Time |  |
year | 2011.97 | 2.00 | 2011.93 | 2.00 | 0.04 | 0.15
| Control variables |  |
Female | 0.51 | 0.50 | 0.50 | 0.50 | 0.01 | 0.16
Age | 13.54 | 1.65 | 13.58 | 1.57 | -0.04 | 0.08
TV consumption on a weekday | 2.09 | 1.77 | 1.50 | 1.35 | 0.59 | 0.00
Mother living at main home (Dummy) | 0.92 | 0.27 | 0.97 | 0.18 | -0.05 | 0.00
Father living at main home (Dummy) | 0.75 | 0.44 | 0.81 | 0.39 | -0.06 | 0.00
Number of family cars | 1.69 | 0.53 | 1.46 | 0.60 | 0.24 | 0.00
Own bedroom (Dummy) | 0.85 | 0.36 | 0.87 | 0.33 | -0.03 | 0.00
Number of computers per family | 2.30 | 0.80 | 2.41 | 0.76 | -0.11 | 0.00
Family well-off | 2.28 | 0.85 | 2.50 | 0.84 | -0.22 | 0.00
| Outcome variables |  |
Frequency of sodas | 3.97 | 1.87 | 4.11 | 1.85 | -0.14 | 0.00
Body mass index (BMI) | 18.97 | 3.18 | 19.01 | 3.04 | -0.04 | 0.38
| 8,821 |  | 11,352 |  |  |

Source: Health behaviour of school-aged children (HBSC)

Table 2.B.4: Unaffected periods

Panel A: Hungary and Slovakia
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.06 | 0.09 | 0.51 | 16,012
Body Mass Index (BMI) | -0.33 | 0.14 | 0.02 | 14,866
Panel B: France and Switzerland
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | -0.01 | 0.06 | 0.84 | 22,845
Body Mass Index (BMI) | -0.13 | 0.09 | 0.15 | 20,646

Note: Standard errors are estimated by bootstrap.

Table 2.B.5: Unaffected periods with school-year specific clusters

Panel B: France and Switzerland
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | -0.01 | 0.07 | 0.86 | 22,845
Body Mass Index (BMI) | -0.13 | 0.11 | 0.21 | 20,646

Note: Bootstrapped and clustered standard errors.

Table 2.B.6: Results with school-year specific clusters

Panel A: Hungary and Slovakia
---
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | 0.48 | 0.11 | 0.00 | 15,425
Body Mass Index (BMI) | -0.13 | 0.18 | 0.46 | 14,059
Panel B: France and Switzerland
|  Effect |  Standard error |  P-value |  Number of observations
Frequency of sodas | -0.04 | 0.07 | 0.57 | 22,986
Body Mass Index (BMI) | -0.13 | 0.11 | 0.24 | 20,258

Note: Bootstrapped and clustered standard errors.

Figure 2.B.3: Parallel GDP growth in Hungary and Slovakia

Source: The World
Bank181818https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end=2014&locations=HU-
SK&start=2010, last accessed: 29.03.2021.

Figure 2.B.4: Parallel GDP growth in France and Switzerland

Source: The World
Bank191919https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end=2014&locations=HU-
SK&start=2010, last accessed: 29.03.2021.

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