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Journal of Behavioral and Experimental Economics 55 (2015) 40–47
Contents lists available at ScienceDirect
Journal of Behavioral and Experimental Economics
journal homepage: www.elsevier.com/locate/socec
Recycling waste: Does culture matter?
Alessandro Crociataa,∗, Massimiliano Agovinob, Pier Luigi Sacco c
aDepartment of Philosophical, Educative and Economic-Quantitative Sciences, University “G. D’Annunzio” of Chieti-Pescara, Viale Pindaro,
42-65127 Pescara, Italy
bDepartment of Business and Economic Studies, University of Naples “Parthenope”, Via Generale Parisi, 13, 80133, Naples, Italy
cIULM University, Via Carlo Bo, 1-20143 Milan, Italy
article info
Article history:
Received 9 August 2013
Revised 20 January 2015
Accepted 20 January 2015
Available online 30 January 2015
Keywords:
Waste recycling
Pro-environmental household behavior
Culture
Social sustainability
Cross-sectional models
abstract
The aim of this study is to explore the relationship between culture and waste recycling, in order to provide
a possible estimation of the impact of cultural participation upon households’ behavior within the meta-
issue of sustainability. We look at the cognitive and social determinants of pro-environmental behavior. We
based the exploratory analysis on the Italian Multipurpose Survey on Households Daily Life Aspects 2007,
provided by ISTAT. We used data on household behaviors to highlight the determinants of waste recycling by
moving from a cultural–ecological standpoint. The analysis highlights a strong positive relation between the
propensity to take part in some cultural activities and the propensity to abide by waste recycling guidelines
and prescriptions. Our empirical results indicate that policies aiming to influence sustainable development
by fostering pro-environmental behaviors may be more effective when considering the cultural participation
dimension as a complementary factor.
© 2015 Elsevier Inc. All rights reserved.
1. Motivations and literature background
The blueprint for worldwide sustainable development put for-
wardbyAgenda21(UNCED, 1992) identified waste from domestic
sources as a major barrier to achieving environmental sustainability,
thus raising interest toward community attitudes in waste recycling
(Barr, 2007; Fiorillo, 2013). In this sense, waste recycling represents
a prominent indicator of environmental sustainability. For instance,
Kinnaman (2006),Martin, Williams, and Clark (2006) and van den
Bergh (2008), showed linkages between waste materials and land-
filling in terms of economic costs, health and environmental risks.
As resources decrease and waste increases, recycling has thus be-
come imperative, and a critical environmental practice. Within this
context, as Barr, Gilg, and Ford (2001) point out, the political agenda
of developed nations has been focused more and more on enabling
households to reach sustainable waste management targets, thereby
enhancing responsible waste behavior, such as effective recycling.
The community dimension of both awareness and action is to-
day well appreciated, but then, how is it possible to motivate peo-
ple to recycle and to improve the effectiveness and social relevance
of recycling practices? The issue has stimulated a stream of inter-
disciplinary research (economics, psychology, sociology, engineer-
ing, law, to list a few ones). The economics viewpoint, for instance,
∗Corresponding author. Tel.: +39 085 4537896.
E-mail addresses: crociata@gmail.com (A. Crociata), agovino.massimo@gmail.com
(M. Agovino), pierluigi.sacco@iulm.it (P.L.Sacco).
puts pricing schemes or incentives under the spotlight, including
monetary rewards (e.g., Curlee, 1986; Jenkins, Martinez, Palmer, and
Podolsky, 2003; Hage and Söderholm, 2008). Environmental psychol-
ogists concentrate upon altruistic motivations (e.g., De Young, 1986;
Tang, Chen, and Luo, 2011). Sociologists consider social pressures and
environmental constraints such us moral norms activated through
social interactions (e.g., Burn and Oskamp, 1986; Tonglet, Phillips,
and Read, 2004; Hage, Söderholm, and Berglund, 2009).Legalre-
searchers consider the effects of legal measures such as mandatory
recycling laws (e.g., Lanza, 1983; Hicks, Dietmar, and Eugster 2005;
Viscusi et al., 2013). Engineers compare the relative effects of alter-
native technologies, and the impact of their mechanical properties on
waste recycling systems (e.g., Noll, 1985; Duan et al., 2011).Public
pedagogues call for participation and learning processes in the con-
text of environmental and sustainable development education (e.g.
Van Poeck and Vandenabeele, 2012; Læssøe, 2010).Inordertoat-
tain a balanced, interdisciplinary point of view, Hornik et al. (1995)
conducted an extensive meta-analysis, and summarized the impact
of different variables by grouping them into five categories: Extrinsic
Incentives, Intrinsic Incentives, Internal Facilitators, External Facili-
tators, and Demographic Variables. Among the five meta-factors, the
strongest predictors of recycling turned out to be Internal Facilita-
tors. Consequently, this implied that consumer knowledge and edu-
cation should be the best way to tackle internal barriers to recycling
due to consumers’ ignorance. Some External Incentives, such as so-
cial influence and monetary rewards, also played a significant role,
even if the effect of the former seemed more conducive to long-term
http://dx.doi.org/10.1016/j.socec.2015.01.005
2214-8043/© 2015 Elsevier Inc. All rights reserved.
A. Crociata et al. /Journal of Behavioral and Experimental Economics 55 (2015) 40–47 41
changes in behavior than the latter. In case of monetary incentives,
pro-environmental behavior usually lasts only as far as the incentive
is in place, and may even cause motivational crowding out when it
ceases (Frey and Jegen, 2001). Barr, Gilg, and Ford (2001) and Barr
(2007) developed a conceptual framework for understanding and an-
alyzing households’ attitudes toward waste management. In order to
establish a linkage between environmental attitudes and recycling
actions, they took into account three predictors: environmental val-
ues, situational variables, and psychological variables. Their findings,
that cover not only recycling activities but also minimization and
reuse of waste, point out that situational variables are significant in
shaping recycling behavior (more specifically, logistical factors such
as the presence of recycling services and facilities). The lack of facil-
ities as a barrier to waste management is a common finding in the
empirical literature (Coggins, 1994; Perrin and Barton, 2001; Omran
et al., 2009). Environmental values and psychological variables are
more relevant for minimization and reuse than for recycling, which
turns out to be perceived mainly as a normative behavior. Another
common predictor analyzed in the literature is the socio-economic
and demographic profile of recyclers. Belton et al. (1994) showed
that non-recyclers tend to be found among relatively young people
in low-status socio-economic groups. Perrin and Burton (2001) find,
accordingly, that the more mature, the better educated and the home-
owners are more likely to be recyclers. Samdahl and Robertson (1989)
found a positive association between higher education and recycling.
A link between higher socioeconomic status and recycling emerged in
Vining and Ebreo (2002). It seems, however, that the analysis of socio-
economic and demographic determinants of recycling is rather incon-
clusive (Guerin, Crete, and Mercier, 2001), in that other researchers
have come up with contradictory or non-significant results (McGuire,
1984; Oskamp et al., 1991; Valle et al., 2004). Literature results are
then somewhat mixed, and debate is still ongoing to single out the
most significant determinants of recycling (see Tang, Chen, and Luo,
2011 for a review). More recently, Miafodzyeva and Brandt (2013) car-
ried out a meta-analysis of results from previous studies on different
variables influencing the households’ recycling behaviors. They eval-
uated trends in research outputs in the period 1990–2010, and their
analysis classified variables affecting the recycling behavior of house-
holders into four theoretical groups: socio-psychological, technical-
organizational, individual socio-demographic and study-specific. The
strongest predictors of householders’ recycling behavior were identi-
fied as follows: convenience, moral norms, information and environ-
mental concern.
The overall picture that emerges from past and current published
research allows to conclude that:
(i) predictors of waste behavior seem to include a large array of
diverse variables, which capture the influence of a variety of
factors;
(ii) even though households are generally aware of recycling, such
awareness does not necessarily reflect into actual recycling
practice;
(iii) further research is needed to identify reliable recyclers profiles,
and to explore the role of underlying psychological, cultural
and social attitudes to recycling.
In view of the previous discussion, we believe that there is room
to delve a little deeper into the cognitive determinants of house-
holds recycling behavior. In particular, in this paper we examine the
role of a factor that has been entirely overlooked so far and is, to
our knowledge, pondered here for the first time in the literature on
waste recycling: namely, households’ cultural capital (Throsby, 1999 ,
2005). Cultural capital, as Throsby argues, comes in both tangible and
intangible forms. The stock of tangible cultural capital assets consists
of many different artifacts such as historical buildings and locations
with cultural significance (the so-called cultural heritage), as well as
objects such as artworks (paintings, sculptures, etc.), books, music,
video and multimedia, and so on. Intangible cultural capital includes
ideas, practices, beliefs, traditions and values, which carry special
significance and identity value for groups and communities. The un-
derlying hypothesis is that cultural capital fosters awareness on a
multitude of socially relevant issues, and therefore motivates indi-
viduals to take, consequently, more responsibility as to the pro-social
dimension of their daily acts. In the specific case of pro-social, envi-
ronmentally conscious behavior, people’s awareness may be solicited
directly, for instance, by reading books or watching movies which are
primarily focused on environmental issues, but also indirectly, as a
result of accessing e.g. emotionally engaging cultural contents which
generically stimulate the individual sense of responsibility, of social
and environmental connectedness, and so on; but also less targeted
cultural contents may have a relevant indirect effect on environmen-
tal responsiveness.
And then, can the cultural sphere have a sensible influence on
waste recycling behavior? In the affirmative case, given the incon-
clusiveness of the preexisting literature, this could be a powerful
argument for further investigation of the cultural/symbolic dimen-
sion of pro-social behavior, and of the environmentally related one
in particular. Moreover, the existence of a meaningful connection
between environmental issues and cultural participation would es-
tablish an intriguing and so far unexplored link between ecological
and cultural economics, that could be conducive to further, stimulat-
ing research. As we will show in the present paper, we believe that
this link is important, and that cultural capital may be an important
factor in understanding the determinants of recycling behavior, so
that further examination of the cultural-environmental link seems to
be warranted by the preliminary evidence provided here. Specifically,
by using the Italian Multipurpose Survey on Households Daily Life As-
pects 2007, provided by ISTAT, the paper contributes to the household
waste recycling literature by analyzing the role of non-economic fac-
tors in the household’s decision to sort and recycle domestic waste.
In particular, we emphasize the importance of cultural consumption
on the recycling decisions of individuals who regularly carry out care-
fully sorted waste collection and disposal. In addition, we address the
problem of self-selection of individuals, due to the practical difficul-
ties encountered in making separate waste disposal, e.g. individuals
who do not sort out waste because the recycling bins are difficult to
reach. In this case, we implement a probit model à la Heckman. The
remainder of the paper is organized as follows. In Section 2,wedis-
cuss why culture could be a relevant determinant of waste recycling.
In Section 3, we introduce the econometric framework and discuss
our strategy. We present our data in Section 4. We then discuss our
results in Section 5, and provide concluding comments in Section 6.
2. Cultural access and attitude toward recycling
The recent literature provides us with several hints as to why and
how culture acts as a powerful driver of sustainable development.
Sacco and Crociata (2013) present a conceptual framework for the
design of culture-driven development strategies, and for the evalua-
tion of the multidimensional effects of culture; see also Sacco, Ferilli,
and Tavano Blessi (2014). Even within this framework, however, no
attempt has been made so far at exploring the relationship between
culture and the ecological dimension. Here, we focus upon the re-
lationship between cultural participation and recycling behavior, by
looking at the cognitive and social determinants of pro-environmental
behavior and its connections to cultural, social and human capital
components. The cultural economics literature widely acknowledges
that culture is an asset that generates forms of social value that are
complementary to economic value (Throsby, 2005, p. 3). Investigat-
ing the peculiarities of intangible cultural capital (according to the
Throsby definition, quoted above), Hutter (1996) argues that culture
can play an important role in shaping up a collective identity within
a community, thereby solidifying binding social ties and contributing
42 A. Crociata et al. /Journal of Behavioral and Experimental Economics 55 (2015) 40–47
to the enforcement of social norms. In fact, cultural access is not a
mere act of consumption such as the purchase of any other good or
service. The very sense of a cultural experience is that of questioning
existing conventions and meanings, of inquiring about one’s place
in the world and in the society, and of re-framing one’s knowledge
and belief systems into new coordinates (e.g. Boyd, 2009). More-
over, access to these meanings largely depends on the individual
backgroundofexperiences(Pine and Gilmore, 2011), skills (Wright,
1975), capabilities (Sen, 2000) and allocation of one’s cognitive sur-
plus across time and space (Shirky, 2010). Matarasso (1997) shows
how widespread cultural access promotes sociability through the cre-
ation of shared languages and imageries, and sets the conditions for
better human development. More generally, cultural participation
functions as a platform for social regeneration, networking, and co-
hesion (e.g. Everingham, 2003). Beyond its commodity dimension,
cultural experience plays the role of a cognitive and even motivational
premise to all forms of consumption. It is a form of identity building
processes, thereby producing new, distinctive forms of human and
social capital (Jenkins, 2008) and causing spillovers in diverse fields
such as innovation, welfare, social cohesion, and so on. Most cultural
experiences are also a clear instance of a relational good (Uhlaner,
1989), a form of social production that is in turn adding up to the
stock of social capital (Antoci, Sacco, and Vanin, 2007). Although the
link between cultural participation and environmentally responsible
behavior has not been investigated so far, there is no reason to rule
out in principle that the pro-social implications of cultural experience
translate into stronger sustainability concerns, and recycling attitudes
in particular. Understanding the relationship between pro-social en-
vironmental behavior, quality of life, and well-being of the current and
future generations is not an easy task, and this is all the more true for
specific aspects such as waste recycling habits. The open-mindedness
and curiosity that comes with sustained cultural participation might
provide a sensible contribution to a more careful consideration of the
consequences of everyday choices, such as deciding whether or not
to walk the distance to the recycle bin whenever appropriate. To test
this intuition, we now investigate the relationship between recycling
habits and cultural consumption.
3. Econometric strategy
As the recycle decision is the outcome of a dichotomous choice
(whether or not to recycle), the most suitable model for empirical
investigation necessarily belongs to the field of models with binary
variables. In particular, in the study of the relationship between cul-
tural consumption and waste collection and disposal, it is likely to
stumble upon problems of self-selection of individuals, due to the
practical difficulties encountered in actually carrying out differenti-
ated waste recycling. The choice of whether or not sorting out waste
collection and disposal does not only depend on cultural and socio-
economic factors. In particular, if the choice of whether sorting out
waste or not depends on the practical difficulty to reach and use the
appropriate bins, we face a double problem of self-selection:
1 the recycling variable that is built tends to exclude people who,
for example, carry out waste collection only for certain types of
waste;
2 the presence of problems related to the practical difficulty of
reaching and using the recycling bins influences the probability
of the choice to carry out waste collection.
The first problem could be eliminated by considering a recycling
variable for each specific type of waste, reducing in this way the self-
selection problem.1In fact, the variable that we will use to measure
the difficulty of carrying out waste collection and disposal does not
1In particular, it is possible to posit that the collection and disposal of waste is
easier for certain types of waste (e.g., organic, plastic, paper), because the municipality
discriminate for types of waste, and for this reason we have cho-
sen a measure of recycling that only considers people who make
the collection of all types of waste. We tackle this potential problem
of self-selection by adopting Heckman two-step selection strategy
(Heckman, 1979). It is a methodology which helps us to assess the
impact of cultural consumption on recycling, after accounting for the
possibility of selection of individuals due to the practical difficulty of
effectively recycling waste.
Thus, we estimate a probit model whose form is:
PrYj=1|Xj=α+βXj+δLj+εj(1)
where Yjis the dependent variable of the model, which equals 1 in
the event of recycling, and 0 otherwise, for every jth individual, Xja
set of socio-demographic and cultural factors, and Lja set of variables
related to different cultural consumptions. These two sets of covari-
ates would yield coefficients estimated for the individuals who en-
counter no difficulty in recycling, and decide to do it (or otherwise).
The class of all such individuals clearly differs from the more gen-
eral one of all recyclers, whence estimates would be biased. Thus, it
seemed necessary to resort to the correction introduced by a two-step
probit model à la Heckman, which estimates two equations simulta-
neously: a “difficulty of carrying out waste collection” equation and
a “recycling” equation. Heckman (1979) showed that, under a series
of conditions,2,3the estimation produced by a model with the above
structure does not produce biased coefficients. The model proposed
is thus a bivariate probit in the following form:
Pr Dj=1|Zj=α+γZj+εj(2)
Pr Yj=1|Xj=α+βXj+δLj+λj+uj(3)
where Djis the dichotomous variable of the outcome equation, which
equals 1 if the individual has no difficulty in carrying out recycling,
and 0 otherwise; Zjis the set of covariates; Yjis the dichotomous
variable of the selection equation defined as above; Xjand Ljare
identically defined as above; λjis the inverse Mills ratio, obtained by
first-stage regression, which allows the self-selection problem to be
taken into account.
4. Data and descriptive statistics
We used micro data from the Multipurpose Survey on House-
holds – Daily Life Aspects 2007 (ISTAT, 2007), a survey con-
ducted by the National Institute of Statistics in 2007 on 19,170
Italian households for a total of 48,253 individuals, on many as-
pects of their daily life, satisfaction, and habits. Data were col-
lected both at individual and household level. For our purposes,
questions concerning waste recycling programs and attitude to-
ward them refer to the head of the household, while ques-
tions regarding cultural consumption refer to each individual. This
of residence prepares bins placed near the home of residence, whereas for other waste
recycling (e.g. medicines, clothes, batteries), it becomes more difficult. For the latter
types, people have to reach some points of the city where the municipality has prepared
the appropriate bins. Consequently, the place of residence is important for purposes of
recycling.
2The procedure à la Heckman assumes that the errors of the two equations
are normally distributed with zero mean and variance, and are correlated among
themselves:(ε,u)∼N(0,0,σ2
ε,σ2
u,ρεu)independent of the set of covariates X,Land Z.
It is possible to test the null hypothesis that the two errors are not correlated: H0:ρ
=0 with a specific Wald test. Rejecting the hypothesis H0of zero correlation, it can be
stated that in the model there is no problem of self-selection and the estimators are
not biased. Finally, for the goodness of the estimates, as suggested by Heckman (1979),
it is necessary that in the selection equation there is at least a variable included in Xj
or Ljand not present in Zjof the first-stage equation.
3The two-step model is generally more stable when the data are problematic. It
even tolerates estimates of ρless than −1 and greater than 1. For these reasons, the
two-step model may be preferred when exploring a large dataset. Still, if the maximum
likelihood estimates cannot converge, or converge to a value of ρthat is at the boundary
of acceptable values, there is scant support for fitting a Heckman selection model on
the data (Manning, Duan, and Rogers, 1987).
A. Crociata et al. /Journal of Behavioral and Experimental Economics 55 (2015) 40–47 43
Table 1
Descriptive statistics.
Variable Mean Standard Observations
deviation
Recycling equation
Dependent variable
Recycling habits 0.25 0.44 14,970
Independent variables
Socio-demographic characteristics
Male 0.71 0.45 19,170
Age 35–44 0.20 0.40 17,714
Age 45–54 0.20 0.40 17,714
Age 55–64 0.20 0.40 17,714
More than 64 0.39 0.49 17,714
Central Italy 0.18 0.39 19,170
Southern Italy 0.38 0.48 19,170
High school diploma 0.28 0.45 19,170
Bachelor’s degree 0.10 0.29 19,170
Cultural consumptions
Newspapers 0.65 0.48 19,170
Theatre 0.17 0.37 19,170
Cinema 0.34 0.48 19,170
Museum and exhibitions 0.24 0.42 19,170
Opera and classical music 0.09 0.28 19,170
Other music 0.15 0.35 19,170
Archaeological and
monuments sights
0.19 0.40 19,170
Books 0.35 0.48 19,170
Social capital
Political parties 0.06 0.23 19,170
Ecological associations 0.02 0.14 19,170
Difficulty of carrying out collection of waste equation
Dependent variable
Difficulty to recycle 0.78 0.41 18,735
Independent variables
Bins 0.22 0.41 15,208
Poor health 0.14 0.34 18,699
selection leaves us with 19,170 individuals. Table A1 in Appendix A
shows definitions and measurement of variables used in the
econometric analysis. Summary statistics are reported in Table 1.
Household members are asked about the recycling habits of the
family for each material (paper, glass, medicinal, batteries, cans,
plastics and organic), with the following possible answers: Yes,
always –Yes, sometimes –Never. Responses are re-coded into a binary
variable which is equal to 1 in cases of “Yes, always”and0otherwise.
We do not consider the separate collection for each waste material,
but construct a variable that captures people who carry out the col-
lection for all types of waste without distinguishing by type of waste.
Restricting our analysis to the head of the family, our sample will
be composed of individuals all over 18 years old. For this reason, we
can expect them to be more responsible in the management of the
house (and also in the recycling) with respect to younger individuals
who are more dependent on their parents. As we can see in Table 1,
the percentage of individuals who claim to always do recycling is
very low (only 25%). As a measure of cultural capital, we considered
several cultural consumptions as in Grossi et al. (2010 and 2011)and
in Crociata, Agovino, and Sacco (2014). Regarding cultural consump-
tions, the information is available at the individual level. In particular,
all individuals aged more than 5 are asked about their frequency of
cultural access to certain activities:
– weekly newspapers reading ((1) never, (2) yes, every one or two
days, (3) yes, every three or four days, (4) yes, every five or six
days, (5) yes, every day). In this case we used responses (2), (3),
(4) and (5) and created a dummy variable if the respondent reads
newspaper at least once a week;
– yearly attendance of theater, cinema, museums and exhibitions,
opera and classical music, other music, archaeological and mon-
uments sights ((1) never, (2) 1–3 times, (3) 4–6 times, (4) 7–12
times, (5) more than 12 times). Also in this case, for each item of
cultural consumption, we used responses (2), (3), (4) and (5) and
created a dummy variable if the respondent benefits from these
cultural consumptions at least once a year;
– only in the case of books, the information is dichotomous (yes, I
have read books in the last 12 months; no, I have not read the
books in the last 12 months).
In this case, we observe that the cultural goods with easier access
are the ones mainly consumed, i.e. newspapers, books and cinema, re-
spectively, with 65%, 35% and 34%. On the contrary, consumer goods
with more difficult access (in geographical and expense terms) are
the ones less consumed, e.g. museums and exhibitions, archaeologi-
cal and monuments sights, theatre, other music, opera and classical
music, respectively, with 24%, 19%, 17%, 15% and 9%. The particularly
high consumption of newspapers could be justified by their relatively
easier and less expensive retrieval compared to alternative cultural
goods, and by their availability in certain places (e.g. barber and hair-
dresser shops, medical waiting rooms, bars, etc.), without necessarily
purchasing them. Digital platforms further facilitate ease of access,
and provide an abundance of free content. Social behavior is mea-
sured through two social capital variables (see Fiorillo, 2013):
– participation in meetings of political parties in the last 12 months;
– participation in meetings of ecological associations in the last
12 months.
Table 1 shows that 2% and 6% of respondents participate, respec-
tively, in meetings of political parties and ecological associations. We
do not control for other social capital variables because of the level
of incompleteness of information concerning such variables. In addi-
tion, we control for many socio-demographic characteristics such as
gender, age, education and place of residence of respondents. Regard-
ing these characteristics, Table 1 shows that 70% of the respondents
is male, while 28% of the respondents have undergraduate education
(completed high school). The largest group of individuals (39%) is aged
more than 64 years old. Finally, 38% of the respondents lives in South-
ern Italy. The variables presented above represent the covariates in
the equation that allows us to estimate the impact of culture on re-
cycling, controlling for socio-demographic factors and cultural con-
sumptions. In the methodological section, we have highlighted the
possibility of a self-selection problem, and for this reason we have
explained how the way to overcome this problem is the Heckman
two-step selection strategy. This method requires at first to estimate
the equation that measures the impact of culture on recycling, and
then the estimation of the equation associated with the latent vari-
able that allows us to check for self-selection and to calculate Mill’s
ratio. In our case, such variable is the practical difficulty associated to
carrying out waste collection and disposal. In particular, household
members are asked about the difficulty to recycle, with the following
possible answers: (1) No difficulty,(2)Some difficulty,(3)Great diffi-
culty. Responses are re-coded into a binary variable which is equal 1
in the case “No difficulty” and 0 otherwise. Table 1 shows that 78% of
respondents had no difficulty in recycling. The difficulties to recycle
may be due to:
– the absence of bins in the area where the family lives;
– a poor health which makes it difficult to perform the daily activi-
ties, including recycling.
These variables are the covariates that we will use to explain the
practical difficulties associated to recycling. In particular, each indi-
vidual answers to the question about the presence/absence of bins
in the area where the family lives ((1) Yes, they are easily accessible,
(2) Yes, but they are difficult to reach,(3)No). We used responses (1)
and (2) and created a dummy variable for recycling bins. They appear
to be rather uncommon in Italy: only 22% of respondents confirm
44 A. Crociata et al. /Journal of Behavioral and Experimental Economics 55 (2015) 40–47
presence of recycling bins in the area where they live. As to health
status, the questionnaire includes an inquiry upon chronic illnesses
or permanent disabilities that reduce personal autonomy and require
the help of other people for the needs of daily life in the home or out-
side the home ( (1) No,(2)Yes, occasionally,(3)Yes, always). We used
responses (2) and (3) and created a dummy variable for poor health.
In this case, we observe that 14% of respondents report to have bad
health.
5. Econometric results
The individual decision to recycle is the outcome of a complex
choice behind which there may be many motivations. It is the fruit
of a set of factors ranging from personal characteristics, educational
curriculum, and other factors as cultural consumptions.
In Appendix B,Table B1, we report the estimates produced by the
uncorrected model (Eq. (1)), whereas Tables 2 and 3illustrate the
Table 2
Results from first-stage equations of probit models à la Heckman.
Variables Marginal effects z-value Sign.
Bins 0.737 35.07 ∗∗∗
Poor health −0.212 −7.26 ∗∗∗
Constant 0.483 33.80 ∗∗∗
Number of observations 13,164
Notes: Regressors’ legend: see Table A1 in Appendix A. The standard errors are cor-
rected for heteroskedasticity. The symbols ∗∗∗,∗∗,∗denote that the coefficient is
statistically different from zero at 1%, 5% and 10%, respectively.
Table 3
Results from selection equations of probit models à la Heckman.
Variables Marginal effects z-value Sign.
Socio-demographic characteristics
Male 0.029 3.100 ∗∗∗
Age 35–44 (Reference group:
18–34)
0.080 2.390 ∗∗
Age 45–54 0.093 2.770 ∗∗
Age 55–64 0.112 3.290 ∗∗∗
more than 64 0.077 2.280 ∗∗
Central Italy (Reference group:
North Italy)
−0.290 −25.420 ∗∗∗
Southern Italy −0.296 −31.390 ∗∗∗
High school diploma (Reference
group: completed compulsory
education)
0.024 2.230 ∗∗
Bachelor’s degree 0.089 5.540 ∗∗∗
Cultural consumptions
Newspapers 0.013 5.340 ∗∗∗
Theatre −0.003 −0.220
Cinema 0.034 3.200 ∗∗∗
Museum and exhibitions 0.040 3.040 ∗∗
Opera and classical music 0.015 0.900
Other music 0.018 1.340
Archaeological and monuments
sights
0.009 0.670
Books 0.045 4.440 ∗∗∗
Social capital
Political parties 0.047 2.650 ∗∗
Ecological associations 0.104 3.340 ∗∗∗
Constant 0.436 12.60 ∗∗∗
Number of observations 13164
Mills ratio 1.234 7.952 ∗∗∗
Wald test (p-value) 0.000
Log-likelihood −12113.41
AIC 13858.35
BIC 14009.02
Notes: The dependent variable household recycling takes value 1 if the household
head always recycles. Regressors’ legend: see Table A1 in Appendix A. The standard
errors are corrected for heteroskedasticity. The symbols ∗∗∗,∗∗,∗denote that the
coefficient is statistically different from zero at 1%, 5% and 10%, respectively.
results achieved by Heckman’s (1979) two-step estimation model4
Theresultsofthefirst-stageequation(Eq. (2)) are shown in Table 2.
In particular, we regress the dichotomous variable Dj, which assumes
the value of 1 if the individual has no difficulty in carrying out recy-
cling and 0 otherwise, on bins and poor health. We report marginal
effects that are useful for a more immediate interpretation of esti-
mated coefficients. The coefficients have the expected signs and are
all significant at 1%. In particular, the presence of bins in the area
where the household lives increases the probability of having no dif-
ficulty in recycling. The magnitude of the marginal effect is very high
(74%). Finally, the presence of chronic illness or permanent disability
decreases by 21% the probability of having no difficulty in recycling.
Table 3 shows the results of the second-stage equation (the so-
called selection equation) (Eq. (3))5. The Wald test is reported at the
bottom of Table 3 to verify the correlation of errors, as specified in
Heckman’s hypothesis. As it may be observed, the null hypothesis is
rejected with significance of 1%. It may thus be concluded that the
errors are significantly correlated among themselves, as required by
Heckman’s hypothesis. The estimates show primarily that an older
age increases the probability of recycling. In particular, we observe
that individuals in the age group 55–64 have the highest probability of
recycling (11.2%). Such probability decreases for people aged 64 and
over. The lower probability among elderly people is due to several
causes, e.g. reduced autonomy for the onset of age-related diseases
and disabilities. The coefficient of the gender variable indicates that
men have a 3% higher recycling probability than their women coun-
terparts. The probability of recycling for people who live in Central
and Southern Italy decreases by 29%. Thus place of residence of the
respondent is one of the most significant and important quantitative
coefficients in the specifications. This result tells us that Northern
Italy (reference group) is characterized by increased sensitivity to en-
vironmental issues, presenting a greater propensity for recycling. The
virtuous process that characterizes this macro-area makes it a bench-
mark for the Central and Southern Italy. The variables related to edu-
cation have estimates that are significant and with the expected signs.
In particular, we show that being graduates or PhDs increases the re-
cycling probability by 9%. These results suggest a positive correlation
between education and recycling behavior, and are consistent with
previous studies (Hong, Adams, and Love, 1993; Jenkins et al., 2003;
Fiorillo, 2013). Table 2 shows a positive correlation (statistically sig-
nificant at 1% and 5%) between social capital variables and the choice
of the household head to always recycle. Participation in ecological
associations entails a 10% higher probability of recycling. Participation
in the meetings of political parties causes a 5% increase of the prob-
ability to always recycle. A plausible reason for these findings points
to the argument that politically interested people are well-informed
and have a high level of current knowledge about what is going on
in politics (Torgler and Garcìa-Vali ˇ
nas, 2006). Hence, politically inter-
ested people may be well informed about environmental issues and
4As preliminary step, we implement some tests to verify the presence of multi-
collinearity in the covariates. The tests indicate that there are no problems of multi-
collinearity for all covariates. In particular, we observe that the VIF is always less than
10, the tolerance index (1/VIF) and eigenvalue are far from zero, and the conditional
index is always lower than 30. The results of these tests are available from the authors
upon request.
5An important ingredient in the selectivity-corrected models is the instrument
which permits model identification. In order to check the strength of the instrumental
variables, we proceed as follows: we estimate the recycling equation adding the “diffi-
culty to recycle variable “(Dj) among regressors. Then, assuming endogeneity problems
in Djwe implement an IV estimator, where we instrument Djwith bins and poor health
(see Table A1 in Appendix A for variables’ legend). Finally, we run the F-test suggested
by Staiger and Stock (1997) to verify the strength of the instrumental variables. In our
case, the F-statistic for joint significance of the instruments in the first stage of the
endogenous variable on the instruments and all other exogenous variables is 137.41,
well above the threshold of 10 suggested by Staiger and Stock (1997). Thus, we can
conclude that our instrumental variables are not weak. For reasons of space we do not
show the estimates results; interested readers can request them to the authors.
A. Crociata et al. /Journal of Behavioral and Experimental Economics 55 (2015) 40–47 45
problems, which have now a permanent place in the political agenda,
and may have greater willingness to participate in recycling actions.
This justification seems to apply also in the case of ecological asso-
ciations. Generally, the participation in associations, political parties,
trade unions, etc. pursuing certain goals, raises awareness about the
corresponding defining issues, the causes that generated them, and
the initiatives undertaken to address and solve such issues. Regarding
the cultural consumption variables, we show that the cultural goods
that influence the probability to always recycle are the ones character-
ized by higher diffusion, and cheaper cost. In particular, reading books
and newspapers increases the recycling probability by 4.5% and 1.3%,
respectively. Probability to recycle increases by 3.4% if a person goes
to cinema. Museums and exhibitions attendance is the only cultural
experience characterized by relatively more difficult access which is
still significant at 5%, and increases the recycling probability by 4%. In
summary, we observe that cultural consumptions are relevant in the
decision to carry out recycling. In particular, they contribute to the
shaping of social awareness and attention for environmental issues,
and concern for the perspectives of future generations. Moreover, it is
interesting to point out that the more common and cheaper cultural
goods are the ones that have the largest impact on the decision of
individuals to always recycle.
The estimated coefficients of cultural consumptions with the two-
step probit model à la Heckman turn out to be larger than the ones
in the uncorrected model (see Appendix B,Table B1). This shows the
presence of downward bias caused by a problem of self-selection. Fur-
thermore, the Mills ratio coefficient is positive and significant at 1%.
This means that there is an underestimation in the choice to carry out
recycling, if we do not consider the selective problem of individuals
having difficulty in waste collection and disposal.
Finally, by comparing Eq. (3) (Table 3)withEq. (1) (Appendix B,
Table B11), we observe that (3) minimizes the AIC and BIC crite-
ria and maximizes log-likelihood (Akaike, 1973). Consequently, the
model that best estimates data is the correct one for the auto-selection
problem (Eq. (3)).
6. Concluding remarks
This paper addresses the question of the impact of cultural con-
sumption on households’ recycling behavior, asking in particular
whether cultural access works as an effective predictor of recycling ac-
tivities. To our knowledge, such question has never been posed before
in the literature, but nevertheless it has some logical ground once one
suitably considers the role of cultural access in fostering all forms of
pro-social behavior. In particular, participation in various forms of cul-
tural experiences provides opportunities for mind-opening discovery
and interaction, encouraging the development of knowledge-oriented
dispositions, intellectual curiosity, and better awareness about the re-
latedness of everyday choices and long-term social outcomes. In our
research, we find that the relationship between cultural access and
pro-social waste recycling attitudes is strong and statistically mean-
ingful for our large Italian population sample. Our findings, should
they turn out to be robust, could pave the way to possible future syn-
ergies between cultural and environmental policies, a possibility of
particular interest in view of the increasing emphasis placed on smart
growth strategies, and a totally overlooked option so far.
In particular, our article adds to the literature in at least two as-
pects. First, we investigate the impact of culture on recycling de-
cisions. So far, no authors have taken care to focus upon this rela-
tionship. Second, we document how cultural consumptions have a
different impact on two distinct categories of individuals from the
viewpoint of our analysis. Individuals who always carry out recycling
are characterized by a relatively stronger interest in cultural experi-
ences, and in particular they show an interest in various fields of cul-
ture (lovers of variety). In contrast, individuals who only sometimes
recycle are relatively less interested in cultural experiences and more
selective (not lovers of variety). Although an intuitive interpretation of
such results can be worked out, developing a choice-theoretic model
that explains in detail the relationship between cultural curiosity, re-
cycling behavior and other forms of pro-social behavior could open
up new avenues for theory, applied research, and policy design.
As to next steps of analysis, on the one side, it is necessary to
check to what extent our results replicate in different socio-economic
contexts, e.g. in other European countries, as well as in non-European
ones. Also regional studies could be of interest, to capture whether
such results are sensitive to specific socio-economic environments.
Another direction of some relevance is to investigate whether and
how propensity to recycle can be influenced, from an urban policy
perspective, by the joint availability, position and characteristics of
recycle bins and cultural facilities: are recycle bins more used if there
are cultural facilities in the neighborhood with easy and cheap access?
On the other side, our results seem to show that not all forms of
cultural participation have the same effect on recycling attitudes, and
it will be important to understand why, what are the critical elements
that explain such differences, and whether their impact depends on
the specific socio-economic environments – e.g. whether the most
effective forms of cultural access for recycling behavior vary across
countries and regions or remain stable throughout.
As it happens when breaking new ground, a remarkable amount
of work in different directions is then called for to develop a sound
understanding of such phenomena, and questions largely overcome
results. But we feel that, however preliminary, our results open up
interesting opportunities for (interdisciplinary) research and policy
design. We look forward to them with interest and curiosity.
Appendix A.
Table A1
Detailed description of variables.
Variable Description
Recycling equation
Dependent variable
Recycling habits Family accustomed to doing recycling,
1=yes, always.
Independent variables
Socio-demographic characteristics
Male Gender of the respondent, 1 =male.
Reference group: female.
Age 35–44 Age of the respondent, 1 =age between 31
and 40. Reference group: 18–34.
Age 45–54 Age of the respondent, 1 =age between 45
and 54.
Age 55–64 Age of the respondent, 1 =age between 55
and 64.
More than 64 Age of the respondent, 1 =64 years and
older.
Central Italy Place of residence of the respondent,
1=Central Italy. Reference group: North
Italy
Southern Italy Place of residence of the respondent,
1=Southern Italy.
Until compulsory schools Education of respondent, 1 =no education,
completed elementary school (5 years)
and completed junior high school
(8 years). Reference group.
High school diploma Education of respondent, 1 =completed
high school (13 years).
Bachelor’s degree Education of respondent, 1 =university
degree and/or doctorate (18 years and
more).
Cultural consumptions
Newspapers Whether the respondent reads newspaper at
least once a week; 1 =yes.
Theatre Whether the respondent goes to the theatre
at least once a year; 1 =yes.
Cinema Whether the respondent goes to the cinema
at least once a year; 1 =yes.
(continued on next page)
46 A. Crociata et al. /Journal of Behavioral and Experimental Economics 55 (2015) 40–47
Table A1 (continued)
Variable Description
Museum and exhibitions Whether the respondent visits museum and
exhibitions at least once a year; 1 =yes.
Opera and classical music Whether the respondent goes to the opera
and listen classical music at least once a
year; 1 =yes.
Other music Whether the respondent listens other music
at least once a year; 1 =yes.
Archaeological and
monuments sights
Whether the respondent visits
archaeological sites and monuments
sights at least once a year; 1 =yes.
Books Whether the respondent has read books in
last 12 months; 1 =yes.
Social capital
Political parties Whether the respondent attended meetings
of political parties in the last 12 months;
1=yes.
Ecological associations Whether the respondent attended meetings
in ecological associations in the last
12 months; 1 =yes.
Difficulty of making the collection of waste equation
Dependent variable
Difficulty to make the recycling Whether the respondent has no difficulty in
making the recycling; 1 =no difficulty.
Independent variables
Bins Presence in the area where the household
lives of bins; 1 =yes.
Poor health Whether the respondent has a chronic
illness or permanent disability; 1 =yes.
Appendix B.
Table B1
Results from the uncorrected model (Eq. (1)).
Variables Marginal effects z-value Sign.
Socio-demographic characteristics
Male 0.105 3.650 ∗∗∗
Age 35–44 (Reference group:
18–34)
0.402 3.420 ∗∗∗
Age 45–54 0.436 3.71 ∗∗∗
Age 55–64 0.482 4.080 ∗∗∗
More than 64 0.347 2.950 ∗∗∗
Central Italy (Reference group:
North Italy)
−0.967 −25.980 ∗∗∗
Southern Italy −0.943 −33.230 ∗∗∗
High school diploma (Reference
group: until compulsory schools)
−0.050 −1.580
Bachelor’s degree 0.245 4.980 ∗∗∗
Cultural consumptions
Newspapers 0.010 3.460 ∗∗∗
Theatre −0.051 −1.310
Cinema 0.004 3.300 ∗∗∗
Museum and exhibitions 0.035 2.460 ∗∗
Opera and classical music 0.052 1.100
Other music 0.084 2.130 ∗∗
Archaeological and monuments
sights
0.005 0.220
Books 0.025 4.190 ∗∗∗
Social capital
Political parties 0.155 2.940 ∗∗
Ecological associations 0.259 3.000 ∗∗∗
Constant −0.770 −6.45 ∗∗∗
Number of observations 13812
Pseudo R20.121
Log-likelihood −15113.41
AIC 24276.83
BIC 24463.96
Notes: The dependent variable household recycling takes value 1 if the household
head always recycles. Regressors’ legend: see Table A1 in Appendix A. The standard
errors are corrected for heteroskedasticity. The symbols ∗∗∗,∗∗,∗denote that the
coefficient is statistically different from zero at 1%, 5% and 10%, respectively.
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