Groupe de REcherche en Droit, Economie, Gestion
UMR CNRS 7321
SELECTIVE SORTING OF WASTE: A STUDY
OF INDIVIDUAL BEHAVIORS
Documents de travail GREDEG
GREDEG Working Papers Series
Ankinée Kirakozian
GREDEG WP No. 2013-49
http://www.gredeg.cnrs.fr/working-papers.html
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Selective sorting of waste: A study of individual
behaviors
Ankinée Kirakozian∗
April 2, 2014
Abstract
Our paper aims at understanding the determinants of households’ selective waste sorting behavior. Based on data from an original survey
of the French PACA Region of 694 individuals, we first apply polychoric
principal components analysis to reduce the number of explanatory variables to a set of 7 factors. In a second step, we estimate the probability
to sort waste as a function of those factors, using a probit model. The results of our empirical analysis show that the expected relationship between
service quality and community recycling activity, and recycling, increases
when communities implement an efficient infrastructure. The environmental preferences variables confirm the findings in the literature: proenvironmental preferences increase recycling, lack of environmental preferences has negative impact on social influences. The social influence on
recycling behavior has been studied mostly by sociologists and psychologists
rather than economists. Our results show also that social influence has a
negative impact on recycling, which contrasts with most of the literature
which finds a positive relationship of social influence on pro-environmental
behavior.
Keywords : Recycling, Waste, Public Policy, Econometric Modeling
JEL codes: Q56, Q58, C51
1
Introduction
Environmental problems in the 21st century have become top priority for
the international community. The significant increase in wealth at the international level has been accompanied by an increase in the production and consumption of goods and services. The amount of product packaging has grown as
a result of offensive marketing methods, shorter product life-cycles, and multiple
complementary consumption goods. However, the amount of waste generated
∗ Université de Nice-Sophia Antipolis, France, and GREDEG CNRS UMR 7321. Email:
[email protected]
1
by packaging has been mostly overlooked despite its huge contribution to the
increase in household waste production.
In France, the waste management sector dominates national environmental
protection activities. In 2011, the cost of environmental protection was estimated at 46 billion euros. Spending on waste management accounted for 33%
of total spending, while the shares of other areas (air, noise, soil, biodiversity,
etc.) varied between 4% and 8%.
In France1 , the waste situation has become critical with waste volumes growing continuously. Waste management is at the core of current environmental policy. In the past, several economic policies have been implemented, but it was not
until the Grenelle environment meeting in 2007 (Grenelle de l’Environnement)
that a specific plan for waste management was formulated. The target was to
reduce the amount of waste going to landfill or incineration by 15%, and to
reduce waste production by 7% over 5 years. The national medium term target
is to reduce annual production of waste to 200kg per household. Thus, reducing
the production of packaging and increasing recycling have become priority areas.
However, there is a gap between policy objectives and actual implementation of
policies by local authorities.
In some French regions, the situation is particularly acute; for example, in
the Provence Alpes Côte d’Azur (PACA) Region in 2011 waste per inhabitant
(selective waste collection, waste, green waste and bulky waste) was 730kg,
compared to the average for French households of 592kg per annum. Recycled
waste shows a similar trend; it represented only 56kg per inhabitant for the
PACA region compared to 77kg nationally. Eighty percent of recycled waste
comes from packaging. Although significant progress has been made in recycling,
a considerable amount of waste is still burnt or land filled. To minimize these
types of disposal, it is important to make policy choices based on assessment
of consumer needs and behavior, and then to change their behavior to increase
attention to recycling.
Since the early 1980s, various types of public policies aimed at reducing
solid waste and increasing recycling have been formulated and implemented in
many countries of the world. Palmer et al. (1997) employ a theoretical model
and econometric simulation to show the impacts of various economic policy
options related to waste reduction. They compare three policies aimed at providing economic incentives for reducing municipal waste: a consignment system,
subsidy for recycling, and an advance fee for disposal. Sterner and Bartelings
(1999) analyze the cost of recycling and waste disposal in three Swedish communities using three different structures (weight-based fee, frequency-based fee,
and flat fee). Dijkgraaf and Gradus (2004) study different pricing systems in
Dutch municipalities (weight, frequency, volume and bag based systems). The
essential question is how to limit the amount of waste produced through the
introduction of various economic policies. Market instruments, such as taxes
or fees, and regulatory instruments, such as norms, have been at the center of
1 See “Evolution of the volume of waste in France and PACA Region” table 3 and 4 in
Appendix
2
the debate and standards and emission limits for firms have been set in order
to limit the production of waste at the source. However, the discussion quickly
moved to a market-based argument. Where a good has a waste component it
was straightforward to apply a direct tax or charge. However, the weakness of
taxes and inelastic demand limit the scope of these taxes on the overall volume
of waste. Other economic policies have been proposed alongside striving for
greater consumer (waste generator) awareness.
We need also to qualify and understand the role of public institutions in
the management of waste. Institutional mechanisms and organized collection
and treatment of waste by municipalities could have a significant impact on
overall waste management performance. Several studies, mainly conducted in
the United States, have sought to estimate the costs related to waste, and to
understand their evolution based on econometric models and panel data.
The national and regional trajectories explored in the field of waste management are numerous. However, there is a lack of consensus about the optimal
policy. The local contexts and consumers’ behavior vary but point to the importance of consumers for waste management.
The present paper aims to examine the factors that influence the behavior
of agents regarding waste sorting. We are interested in whether inhabitants of
the PACA Region have certain characteristics that result in poor waste sorting
behavior? Which public policies affect this behavior? Based on the results of
our econometric study, we propose innovative public policies that take account
of agents’ heterogeneity.
Section 2 reviews the waste management literature; Section 3 provides the
results of a survey on consumption patterns and consumer choices in the PACA2
region (France). The survey results provide unique and original data on individual behavior and the preferences of households, and also their views concerning
the infrastructures established by their communities. Section 4 presents an
econometric model of individual selective sorting and Section 5 provides some
concluding remarks.
2
The economics, sociology and psychology literature on waste management
This review of the literature on the management of solid waste is organized
according to four themes: economic instruments, information and equipment
policies, residential conditions and environmental preferences, and social influence. These themes provide the basis for the hypotheses we test the econometric
analysis.
Economic instruments such as monetary incentives affect the benefits and
costs of different individual choices. Financial taxes are often considered as
2 Provence
Alpes Côte d’Azur, a region of France
3
complementary to incentive fees or taxes. The former are used to finance the
costs of waste management, the latter to encourage individuals to change their
behaviors. Incentive fees act to reduce pollution by taxing polluters for their
pollution (Pigou, 1920). A tax incentive to pollute less (produce less waste)
provides an option for those individuals who would rather pay the tax than
change their behavior. Incentive fees (pay-as-you-throw) seek to change the
behavior of households while supporting the management of household waste
services. Miranda et al. (1994) classify countries according to their recycling
programs. Their results showed that a direct payment imposed on households
allows a more efficient waste disposal system and increases the amount of recycled waste. Incentive fees are at odds with the traditional system of financial
taxes which would apply a single rate per household regardless of the quantity
of waste generated by each household. Studies show that the amount of waste
generated by households is decreased with the imposition of a user fee accompanied by programs that increase public awareness of waste issues. Most economic
studies agree that a flat-rate pricing system independent of the amount of waste
produced is undesirable. The basic choice is between an “input tax” and a
“downstream tax” (Bartelings et al., 2004). An input tax could consist of a
deposit system or waste tax in order to internalize the costs of waste treatment
in the price of the product. An “output tax” could be implemented as a system
of tariff rates in which the amount of the tax depends on the real quantity of
waste generated or indicators such as the number of household members. A
downstream tax is an incentive tax. For Billitewski (2008) and Reichenbach
(2008), incentive fees measure the amount of waste generated by each individual, and calculate the costs of its management. A downstream tax can educate
individual waste producers who are taxed according to the amount of waste
they throw away. The more that people act responsibly by sorting their waste,
the less they will be obliged pay. However, this solution generates negative externalities because individuals who are taxed according to the amount of waste
they produce may be incited to illegally dump their waste to avoid paying its
real costs. Fullerton and Kinnaman (1996), Bartelings et al., (2004) put the
positive effects of this incentive into perspective by showing that a reduction
in the amount of waste collected might be due to antisocial behavior. Studies
show that we can expect significant levels of illegal disposal in response to the
waste policy based on price.
This leads us to our first hypothesis,
Hypothesis 1: Tax policy negatively influences sorting behavior.
In addition to waste management policy, communities are implementing information and equipment policies to support and encourage recycling. Studies
show that waste generated by households may be limited by user fees if they
are accompanied by programs that increase public awareness of waste issues.
For example, the study by Inver and Kashyap (2007) shows that information
policies are less efficient than incentive policies. However, their effect lasts even
after they have been withdrawn, which is not the case with incentive policies.
4
Information policies have a smaller but longer lasting effect. Several studies
show also that information and knowledge are essential to increase recycling.
Granzin and Olsen (1991) show that the most frequent recyclers are those who
spend more time learning and accumulating knowledge about environmental
problems from various sources (books, magazines, newspapers, television, etc.).
Generally, specific knowledge on waste sorting and recycling is positively correlated with selective sorting behavior (Oskamp et al., 1991). Research by De
Young (1988-1989) shows that level of knowledge differentiates recyclers and
non-recyclers. Recyclers are better informed about the subject. De Young
shows that non-recyclers explain their non-participation in recycling as due to
lack of information about how to sort. Information policies are needed, but in
the absence of a suitable infrastructure to facilitate recycling, sorting will not
increase. Knussen et al. (2004) show that facilitation increases sorting behavior.
They discuss the perception that sorting requires specific resources. Peretz et al.
(2005) finds that more convenient recycling programs and higher income lead
to higher recycling rates. Foltz (1999) considers the effect a reduction in the
amount of effort required in relation to increased selective sorting. For example,
the distance that the waste has to be transported to recycle can be reduced by
eliminating the need to sort, and implementing curbside collection of recyclable
materials. Berger (1997) shows that easy access to a recycling point is an intermediate between socioeconomic factors and recycling practices. Other studies,
such as Guagnano et al. (1995), show that behavioral factors associated with
external conditions act to influence behavior. Their main results are that the
existence of recycling bin is positively correlated with sorting behavior. Similarly, Ebreo and Vining (1990) shows that the lack of equipment has a negative
influence on the adoption of recycling behavior. Abbott et al. (2011) show that
recycling performance improved in the UK with the introduction of curbside
collection, which eases sorting. However, they show also that there are differences between local authorities which are free to implement different recycling
policies (frequency of collection, size and type of container, etc.).
These results lead to our second and third hypotheses:
Hypothesis 2: Collectivity support positively influences sorting behavior.
Hypothesis 3: Container available positively influences recycling.
Location also has an impact on the availability and practicality of sorting equipment. Many studies (McEvoy, 1972; Samdahl and Robertson, 1989;
Schwartz and Miller, 1994; Zimmer et al., 1994) find a positive relation between
residential location and concern for the environment. Zimmer et al. (1994)
demonstrate that urban dwellers are more likely to show concern for environmental issues. Berger (1997) shows that the size of the residential area is positively related to sorting activity.
From these results we can formulate hypothesis 4:
Hypothesis 4: Residential conditions affect recycling.
Many authors consider altruistic behavior in discussing pro-environmental
5
attitudes. De Young (1985) finds that intrinsic motivation and personal satisfaction ate the most frequent reasons given for choosing recycling. This suggests
that people act in a good way not in expectation of a reward but for the personal
satisfaction it brings. De Young and Kaplan (1986) show that people interested
in ecology are guided not by economic incentives when recycling but rather by
the feeling that what they do is useful and beneficial to society.3 Abbott et al.
(2013) show that the “warm-glow”, which is the personal satisfaction an individual derives from an activity independent of any consideration of the result
(Andreoni, 1990), is a determinant of recycling behavior. Hopper and Nielsen
(1991) show that recycling behavior is altruistic behavior guided by personal
standards. McCarty and Shrum (2001) invoke the concepts of individualism and
collectivism. They show that individualism is negatively correlated to beliefs
about the difficulties associated with recycling, while collectivism is positively
correlated to beliefs about the importance of recycling. Collectivist (altruistic)
individuals accord high importance to recycling because they think about the
future benefits of recycling to society. Individualists accord little importance to
recycling because they focus only on the benefit to themselves in the short-term.
Schultz and Oskamp (1996) show, in the case of a program of experimental recycling, that environmental attitudes are positively correlated with participation.
They insist on the essential role of recycling efforts in the conversion of attitudes
into actual behaviors. The idea is that if the amount of effort required to recycle
is high, only those with strong pro-environmental attitudes are likely to do it.
Conversely, when the amount of effort required to recycle is small, then a slight
or medium environmental concern may be sufficient to achieve the behavior.
This leads to two further hypotheses:
Hypothesis 5a : A “pro-environmental attitude” is positively correlated
with selective sorting behavior.
Hypothesis 5b: A “pro-environmental attitude” is positively correlated
with selective sorting behavior.
The concept of social influence has been developed mainly by sociologists
and psychologists; there is no empirical research on the economic impact of
the social environment on recycling behavior. Several studies (Cheung et al.,
1999; Kestemont et al., 2001), suggest a major influence of social pressure on
consumer engagement in pro-environmental behaviors such as selective sorting.
Ajzen and Fishbein (1980) define the subjective standard in the theory of reasoned action. They find a perceived social norm or social pressure measured as
the beliefs of individuals regarding the expectations of various social referents
(family, neighbors, friends) about their behavior, as well as incentives to comply.
Hopper and Nielsen (1991) explore the idea that selective sorting is a form of
altruistic behavior guided by norms. They demonstrate that recycling behavior
3 This refers to the crowding out effect. Ballet et al. (2007) define this crowding out effect
as a reduction in the voluntary contribution of the individual after a state intervention. They
show that a convergence effect occurs when individuals increase their voluntary contribution
following a state intervention.
6
is compatible with Schwartz’s altruism model, according to which behavior is
influenced by social norms, personal norms, and awareness of consequences. Recycling is costly in time and energy for the individual. There is no immediate or
individual reward from recycling but it is beneficial for society, especially in the
future. Hopper and Nielsen show that a program that involves “block leaders”,
of residents who encourage their neighbors to recycle, influences altruistic norms
and increases recycling behavior. According to Benabou and Tirole (2006), although some people are truly altruistic, others see good deeds (e.g., charitable
donations) as an investment in their social image to establish or maintain social
esteem; they are concerned about what others think of them. Ellingsen et al.’s
(2010) guilt averse model works in a similar direction; they propose that people care about what others expect of them and develop a sense of guilt if their
behavior falls below expectations. Abbott et al. (2013) show that social norms
have an effect on recycling behavior. They recommended resorting to measures
to enable the social norm, rather than imposing recycling levels on individuals.
For instance, by setting up a program of curbside collection, recycling is more
visible to neighbors, thereby promoting the social norm to recycle. Hornik et
al. (1995) demonstrate the strong relationship between social influence and the
propensity to recycle. They show that the social influence of neighbors, friends,
and family members encourages recycling behavior. They define social influence
as the support of friends, neighbors and family members for recycling.
From these results we can formulate hypothesis 6.
Hypothesis 6 : The variable “social influence” is positively correlated with
selective sorting behavior.
The results for socio-economic characteristics are more mixed results and
sometimes contradictory.
Results for the influence of age are mixed. Some studies show that older
people tend to recycle more (Granzin and Olsen, 1991) although Oskamp et al.
(1991) find no correlation between age and sorting behavior.
The results are similar for gender: some studies show that women are more
involved in sorting (Granzin and Olsen, 1991; Stern et al., 1995), and some find
no relation (Ebreo and Vining, 1990).
In relation to income, Granzin and Olsen (1991) find no significant relation
between income and adoption of sorting behavior although Ebreo and Vining
(1990), Oskamp et al. (1991), and Berger (1997) highlight a positive significant
relation with individual income.
Finally, Berger (1997) finds a positive and significant relation between education and sorting behavior while Granzin and Olsen (1995), Ebreo and Vining
(1990) and Oskamp et al. (1991), find no significant relation.
7
3
3.1
A survey of consumption patterns and consumer choices in the PACA region.
Data and survey description
This paper proposes an analysis based on a survey of consumption patterns
and waste management in the region PACA Region (France). The survey was
conducted between August 15, 2012 and January 15, 2013. It provides data on
the behavior of 496 individuals related to waste management. The objective of
the survey was to investigate the determinants of recycling behavior.
The questionnaire focused on three main household waste sorting activities. The first part deals with consumption patterns and consumers’ knowledge
about environmental practices and the importance of the environment in their
purchasing decisions. The second part focused on respondents selective sorting
behavior, its context (the different options available for waste collection, public
policies, information on selective sorting from local authorities, etc.), and their
views on public policies, and especially waste policy. The third part of the questionnaire asked about the general characteristics of respondents’ (date of birth,
place of residence, income, etc.).4 .
We built an initial sample of 6,000 representative individuals based on the
distribution of individuals in the PACA Region (in terms of population), socioprofessional category (corresponding to regional data provided by INSEE), and
gender distribution. We obtained 694 responses and 496 complete responses
from the initial sample of 6,000 individuals. We have therefore chosen our
sample to be representative of gender. Our sample is balanced in relation to
gender: 50.4% of respondents are women (against 52.1% according to INSEE,
2012 statistics) and 49.6 are men (against 47.9% for INSEE statistics).
3.2
Preliminary statistics
The on line survey covers the six departments in the region with strong representation of the department “Alpes-Maritimes” (41,1%). Waste sorting was
reported by 76% of respondents. However, in terms of recycled material, 84%
is glass and only 54% is organic waste. This difference may be due to the
sorting/collection facilities; 91% of respondents have a garbage bin and 80% a
recycling bin. In relation to garbage collection stations (GCS), 76% of respondents consider them efficient. Among those who consider them inefficient, 24%
said they were too far from their homes, 16% say that they were often full up,
and only 6% said that more of them were needed. The propensity to sort waste
is lower in younger people (under 25 years) and increases with age. We note
4 To increase the number of respondents, we asked local authorities (municipalities), political parties, universities and other local organizations to help disseminate the survey. Some
advertised it through their local newspapers or websites ; others used their social networks
to encourage people to participate in the online survey. We also contacted political organizations and asked them to inform their members about the survey ; two major parties responded
favorably.
8
that sorting behavior also increases with income: households with the highest
income sort more. There is a high propensity to recycle (94%) among people
living in rural areas, and also among people who live in houses - 71% of organic
waste is recycled by people who live in houses, and 79% if they are located in
an urban area. Overall recycling of organic waste is 50%. It seems that living
conditions are important for the recycling of waste. Finally, our results show
that individuals rarely sort only one type of waste. The highest sorting rates
are for glass; the practice has been in place for a long time and has become a
habit. Among respondents who said they sorted waste, the majority sort all
types, and do it regularly.
Figure 1: Recycling by Housing-Area and type of Housing
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4
4.1
Empirical evidence
Polychoric principal components analysis
The literature review showed that the determinants of selective sorting behavior include specific public policies (taxes, penalties, information, deposit policy,
infrastructure, communication, availability of waste containers), individual preferences (pro-environment, no-environmental), individual behavior (social influence), and residence-related characteristics (place of residence, type of housing).
All these elements were included in the 23 questions in our questionnaire5 .
Before the probit analysis which tests the propensity to use selective sorting, we conducted a polychoric principal components analysis (Kolenikov and
Angeles, 2004). The initial step is implementation of factor analysis. Factor
analysis provides an empirical base by creating fewer but independent variables
from the many highly correlated variables.
This technique also reduces the problem of multicollinearity among the explanatory variables since although the variables included in these factors are
5 See “Questions of survey of consumption patterns and in the PACA Region” table 5 in
Appendix
10
correlated the factors are not. These new variables are the “principal components” or “factor axes”. The factor analysis results in 7 homogenous factor
groups based on the 23 variables extracted from our questionnaire.
The first factor “collectivity support” includes all means put in place by the
community to inform people of the local waste infrastructure, and how to sort
waste (recycling and sorting guidelines, advertising campaigns) which provide
both positive and negative signals – the latter referring to the inefficiency of
garbage collection stations (too far away, too full).
The second axis refers to “pro-environmental attitudes” and individual environmental preferences based on the variables: "environmental impact" - attention paid to the environmental impact of products purchased, "pay" - ability
to pay more for environmentally friendly products, "environmental sacrifice" –
willingness to make daily sacrifices in order to promote environmental protection, "changing ones consumption at higher cost" - the capacity to change one’s
pattern of consumption to protect the environment even if it costs more.
The third factor is "social influence". This is the influence of the sorting behavior of friends, family, and neighbors on individual behavior, and the influence
of their opinions on individual sorting behavior.
The fourth factor is "living conditions" which includes type of housing
(apartment or house), location (rural or urban), and the possession or not of a
composter.
The fifth factor is "tax policy" and represents the impact of introduction of
a tax policy on garbage collection, on individual behavior.
The sixth factor, "non-environmental," expresses the opposite preferences.
This factor includes variables for lack of concern over environmental issues. "Environmental indifference" refers to individuals who believe that environmental
consequences are so far removed in time that there is no reason to worry; "financial gain" corresponds to individuals who think that acting for the environment
is only worthwhile if there is immediate financial gain; "environmental interests" reflects the view that the population generally is overly concerned about
the environment.
The last factor “container” refers to materials made available by the municipalities to individual households in the form of containers for household garbage
and sorting. Below we present the results of polychoric principal component
analysis:
11
Figure 4: Results of Polychoric principal component analysis
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4.2
Econometric analysis
Having determined the factors we can estimate their impact on the probability
that the individual sorts waste selectively, using a probit approach. Our estimation of the determinants of recycling behavior is in line with the following
model:
Recyclingi = β0 +β1 Collectivity − supporti + β2 Containeri
+β3 Housing − conditionsi + β4 P ro − Envirt − attitudei
+β5 N o − environmentalyi + β6 Social − inf luencei
+β7 T ax − policyi + Xi + ui
(1)
6 See
“Factor loadings” figure 5 in the Appendix.
12
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Table 1 summarizes the independent variables used in the econometric model
Table 1: Variable definitions and sources
Since our analysis is made on a cross-sectional sample, we need to add a
series of variables to control for individuals’ socio-economic characteristics (age,
sex, income, socio-professional category) based on questionnaire responses.
The results are presented in Table 1.
The results of our econometric estimates show the correlations between our
independent variables and the dependent variable.
First, regarding individuals’ environmental preferences ("Pro environmental
attitude" and "non-environmental"), both variables have a significant impact
on our dependent variable. "Pro-environmental attitude" has a positive and significant impact on recycling behavior. Looking at the marginal effects, we note
that a 1% increase in "pro-environmental attitude" increases the probability
of sorting by 7.1%. This positive relation between pro-environmental attitude
and recycling behavior is in line with the results in Schultz and Oskamp (1996).
They suggest that their findings are related to important constraints associated
with recycling. Further, our "non-environmental" variable has a negative impact on recycling behavior; more precisely, a 1% increase in non-environmental
behavior reduces the adoption of recycling behavior by 3, or 46%.
We can therefore Hypotheses 5a ("pro-environmental attitude" variable is
positively correlated with recycling behavior) and its corollary 5b.
The variables related to the implementation of local public policies ("collectivity support" and "containers") are positively and significantly associated
with recycling behavior. However, the "tax policy" variable has no significant
impact.
"Collectivity support" variable is positively correlated with sorting behavior.
If the infrastructure provided by the authorities increases by 1%, the probability of adopting a sorting behavior increases by 6.79%. Local governments are
providing more information about the waste management services available; information is crucial to achieve optimal sorting. Individuals need to know the
routines and the locally available facilities. The works of De Young (1988-1989)
and Ebreo and Vining (1990) show that complexity can have a negative influence on sorting behavior. This might be due to lack of knowledge or information
about sorting. Questions such as “how to sort?”, “where to sort?” and “why we
should sort?” need to be addressed, usually achieved through awareness campaigns organized by national institutions such as ADEME in France and local
communities. The objective of these campaigns is to educate people and change
their behavior. Communication can be focused, for example on the benefits of
recycling and / or the disadvantage of not doing it (Lord and Putrevu, 1998).
Perrin (2004) provides evidence of successful communication campaigns related
to curbside recycling while Knussen et al. (2004) show that, to be efficient,
information policies need to be complemented by an adequate recycling infrastructure to enable sorting behavior.
The "container" variable, that is, the presence of garbage and recycling
bins in the individual’s building, positively influences sorting behavior. The
availability of a container increases sorting behavior adoption by 9.18%. Whilst
this may seem obvious, possession of these containers is not systematic and some
14
Table 2: Probit regression
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15
buildings do not have storage space for garbage. This applies particularly to
old buildings and those in old town centers. In these cases individuals have to
expend more effort to dispose of their rubbish. They are obliged to store it to
avoid daily travel for recycling. Guagnano, Stern and Dietz (1995) show that
having a nearby garbage bin and recycling bin positively influences the adoption
of a sorting behavior. Some newer buildings have facilities for containing waste
but not sorting; municipalities usually supply rubbish bins for free to residents
following application from the building trustee or house owner. According to
our respondents, many households do not have sorting containers.
Finally, our econometric estimation shows that the variable "tax policy" is
not significant and has no influence on individual sorting although the sign is
positive. Note that all municipalities in the region practice the same policy
of "billing" (i.e. a flat tax); thus, all users pay the same amount for waste
management. Under this tax regime an individual who recycles pays as much
as a person who does not.
In the case of sorting policies, local governments expect people to understand
that their participation in the program positively impacts on the collective welfare. Because of limited personal gains from sorting, free-riding behaviors may
hamper the effectiveness of these policies (Pieters, 1991). Incentive-driven policies such as pricing policies for waste management mean that free-riders are
penalized financially (Maystre et al., 1994; Bartelings et al., 2004; Billitewski,
2008; Reichenbach, 2008). If the community implements a pricing policy for
waste management, imposing new constraints on agents, not all individuals
perceive and react to these obligations in the same way. The obligation may
generate negative behavior for some individuals who resent being told how to
behave. Before selective sorting became more generalized, individuals were not
concerned with waste management policy. It is necessary for individuals to understand the importance of their role in this process. Information and communication policies focus on the importance of sorting (using financial and ecological
arguments), as well as the process of sorting (how to do it) both of which are
needed to reduce the gap between awareness and behavior change.
The results for the impact of public policies support hypotheses 2 and 3:
there is a positive correlation between collectivity support and sorting behavior, and there is a positive influence of availability of containers on recycling
behavior. We find no support for hypothesis 1 on the impact of tax policy.
Our analysis also considers social influence to identify social norms. Our results show that social influence has a significant and negative effect on recycling.
This means that the individual behavior of recycling is negatively influenced by
neighbors. Social influence can be considered from two perspectives: first, the
way neighbors behave and second, the way that neighbors perceive the individual behavior.
Our econometric results reveal a negative and significant impact of social influence. Indeed, if the "social influence" variable increases by 1%, the probability
of adopting recycling behavior decreases by 2.56%. According to Benabou and
Tirole’s (2006) social esteem model, individuals care about what others think
16
about them. They feel pleased if others admire them, and feel ashamed in the
opposite case.
An individual (recycler or not) can modify his/her behavior to conform to
the behavior of neighbors, in order to feel like the “others”. However, PACA
residents are far below the national average for recycling, so the social norm in
PACA is not to recycle.
Traditionally, scientists assume that social influence positively impacts on
people’s recycling behavior. However, our study reveals the contrary. This result
is surprising since most respondents declared themselves to be recyclers. For
individual recyclers we assume that this result is due to the negative influence of
their non-recycling neighbors. Indeed, individuals might feel discouraged from
recycling and may stop recycling because they think it is futile in the face of
neighbors’ behavior.
The variables related to type and location of residence are important for sorting as confirmed by our econometric results. Indeed, we note that the "housing
conditions" variable is positive and significant. An individual living in a rural
area or in a house which has a composter has a greater probability of sorting.
When this variable increases by 1%, recycling behavior increases by 14.14%.
Waste sorting requires organization but also requires the equipment needed to
separate its different components. Individuals living in houses recycle more,
perhaps because they have more room to store sorting containers than those
living in apartments. This confirms the results in Zimmer et al. (1994) who
show a link between location of residence and environmental concern. The authors show that individuals living in rural areas are more likely to be concerned
about environmental issues. In addition, shared recycling bins may become
“polluted” with non-recyclable waste if some residents do not adhere to or do
not know the right recycling behavior. This suggests that an individual who
recycles is more likely to do so if he does not share a waste bin: we know what
is in our rubbish but we do not know what is in other people’s. Also, people
living in houses may be less influenced by neighbors’ negative behavior. An
additional constraint for apartment dwellers is that collection equipments may
be located in another building. Collective housing rarely provides composters,
which reduces the probability to recycle.
Finally, our results show that the socio-economic characteristics have no
impact on the adoption of recycling behavior.
We also tested the quality of adjustment of our model and its degree of
prediction. The adjustment-quality test shows that 88.51% of our predictions
are good. Goodness-of-fit test allows us to accept the initial assumption of a
good fit. In order to test the robustness of our model we performed a logit
7
which confirms the results obtained using the probit. Taken together, these
tests confirm the quality of the model.
7 See
“Robustess test : Logit” table 6 in Appendix
17
5
Conclusion and remarks
The adoption of recycling behavior allows consumers to indicate their knowledge
about the impact of waste on the environment. Refusal to comply with recycling behavior means that consumers do not feel concerned about the increasing
amounts of waste. Some respondents indicated that they would be willing to
change their behavior if it did not involve too much additional cost and effort.
Therefore, we have four types of consumers: the "green consumer" who acts to
preserve the environment, the “blue consumer” who is interested in the environment but does not recycle because neighbors either do not recycle or recycle
carelessly (put material in the wrong containers), the "yellow consumer" who
is not concerned about environmental issues but also is not keen for this to be
known to neighbors and friends, and the "red consumer" who is not convinced
about the need to recycle and is not concerned about environmental issues.
The impact of different policies will differ for each type of consumer. For example, a green consumer will likely be more receptive to the introduction of an
informational policy (sorting information), while a red consumer will be more
responsive to the implementation of a tax policy (incentive). Information on
sorting allows green consumers to increase their knowledge about sorting, while
a red consumer sees it of no practical interest since he does not sort. However,
implementation of an incentive policy will have an impact on the red consumer;
even if he/she decides not to change his/her behavior the policy has a direct
impact (he/she will pay more for not changing behavior). An efficient policy
for one group may be ineffective for others, which is why it is necessary to have
diversified instruments that affect all consumers.
Our results show that social influence plays a crucial role in the compliance
with a of recycling behavior. The neighborhood leader in Hopper and Nielsen
(1991) promotes recycling behavior. We believe that "green consumers" could
act as neighborhood leaders to inform their neighbors about the means available
to them, and educate them about how to sort their waste. These neighborhood
leaders could interact with local authorities to obtain the appropriate waste
management equipment.
Equipment policy (e.g. a nearby container) promotes recycling behavior.
There may be lack of space for storing containers, or no collective request from
the community for a sorting container. The authorities should identify areas
where sorting behavior is low, and check whether containers are available to
these households. They could provide containers or increase recycling garbage
collection stations for buildings where there is a storage problem. The referent
neighborhood could play a key role.
Information policies are effective and should be maintained, however, they
must be combined with efficient equipment policy. If there is a known infrastructure but it is consistently defective (too full, too far away) this will discourage
yellow consumers. All types of policies must be increased to facilitate increased
recycling.
The results for market instruments and especially tax policy are interesting –
18
they have no significant effect on recycling behavior. We suggest implementation
of incentive policies. However, we cannot confirm that an incentive policy would
be effective; our recommendation is based on the results in the literature. To
demonstrate the impact of market instruments on recycling behavior, our results
would need to be compared with the results for a community with an established
incentive policy. This would show whether or not people are more likely to
recycle in the case of a tax directly related to the cost of their individual behavior
(the amount of waste they produce).
The variety and complexity of policy instruments for waste management do
not allow us to say one instrument is superior to another. The information and
equipment policies related to economic instruments such as the flat tax, or other
incentives (Gunningham and Grabosky, 1998) show that the economics literature considers these instruments separately rather than as complementary. In
reality, different policies coexist, and therefore comparison of the effectiveness
of separate economic policies seems inappropriate. All waste policy instruments
have advantages and disadvantages. This is because the instruments do not
work in the same way on individuals with different preferences and priorities.
It would seem more appropriate to consider a combination of several instruments, to combine the strengths of each of these separate policies. In all cases,
consumer choice and complementarity among different public policies are key to
the success of optimal waste management policies. Finally, although beyond the
scope of this study, the amount of waste could be reduced by reducing product
packaging.
19
6
Appendix
Table 3: Evolution of the volume of waste in municipalities of PACA Region
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Table 4: Evolution of the volume of waste in municipalites of PACA Region
2005
2007
2009
2011
Household Recycled Household Recycled Household Recycled Household Recycled
Waste
Waste
Waste
Waste
Waste
Waste
Waste
Waste
(kg/hab) (kg/hab) (kg/hab) (kg/hab) (kg/hab) (kg/hab) (kg/hab) (kg/hab)
Alpes-de-HauteProvence
Alpes-Maritimes
Bouches-duRhône
Hautes-Alpes
Var
Vaucluse
621,63
50,28
599,23
77,28
653,35
59,47
678,55
58,72
708,6
43,74
721,26
57,24
729,64
61,52
770,35
65,57
721,69
35,11
635,83
39,59
695,45
40,94
698,18
42,21
716,6
84,9
710,51
83,15
759,51
86,69
750,97
91,06
722,7
50,1
669,05
55,75
830,71
63,69
764,31
63,42
652,18
49,21
660,11
49,36
682,24
57,51
711
59,49
20
What attention give yourself to the environmental impact of products you buy?
How about the following ?
Acting for the environment is worth while if it won me money
Financial gains
I am willing to pay more for products that respect the environment
Pay
We are too much worried concerning environmentally issue
Environmental interest
I am willing to make sacrifice in my life every day to promote environmental protection
Environmental sacrifice
The impacts of climate change are so distant in time that I have no reason to worry
Environmental indifference
That you sort your waste?
Recycling
In your home or neighborhood, what means do you have to manage your waste?
Garbadge can
Can
Recycling bin
Recycling bin
Composter
Composter
What are the resources put in place by your municipality to inform you on how to sort?
Visit recycling coatch
Recycling coatch
Creation of sorting brochure
Sorting brochure
Advertising Campaign
Advertising campaign
In the case where, you are not completely satisfied by the means implemented by your community this is explained by?
Garbadge collection station are too far from home
Garbadge Collection Station far
Garbadge collection station are often full
Garbadge Collection Station full
Do you think tax policy implemented by your municipality is effective on yourself?
Policy tax on myself
Do you think tax policy implemented by your municipality is effective on others?
Policy tax on other
How about the following ?
I think the fact that my neighbors sort, have or may have an influence on my willingness to sort
Influence of neighbors
Loved ones tell me that I should sort
Opinion of loved
My neighbors tell me that I should sort
Opinion of neighbors
I like to do what my neighbors or my family think I should do
Influence of friends and loved
You are
Gender
How age range you belong you
Do you live?
Do you live?
What’s your level of education
In which tranche is your wage?
10
18
18.2
18.5
18.7
18.8
18.9
20
25
25.1
25.2
25.3
27
27.1
27.2
27.3
29
29.1
29.2
31.1
31.2
39
39.1
39.2
39.3
39.4
40
41
44.1
44.2
49
50
Variables names
21
Wage
Education
Habitat area
Housing
Age
Environmental impact
Change behavior to higher cost
Questions
Would you be willing to change your modes of consumption (higher cost) to preserve the
environment?
6.3
Question
Modalities
This is absolutely false = 1
This is false = 2
It is neither true nor false = 3
It is true = 4
This is very true = 5
Male = 1 ; Female = 2
Under 25 years = 1
Between 25 and 50 years = 2
Between 50 and 75 years = 3
Over 75 years = 4
Rural area = 1 ; Urbain area = 0
Detached housing = 1 ; Group housing = 0
No diploma = 1
General certificate of secondary education = 2
School leaving certificate = 3
Short higher education diploma = 4
Long higher education diploma = 5
Under 1000 € = 1
Between 1000 and 1500 € = 2
Between 1500 and 2000 € = 3
Over 2000 € = 4
Yes = 1 ; No = 0
Yes = 1 ; No = 0
Yes = 1 ; No = 0
Yes = 1 ; No = 0
Totally disagree = 1
Somewhat disagree = 2
Neither agree nor disagree = 3
Somewhat agree = 4
Totally agree = 5
Yes = 1 ; No = 0
No importance = 1
A moderate importance = 2
Great importance = 3
Yes = 1 ; No = 0
Table 5: Question in the survey on consumption patterns in the PACA Region
Table 6: Robustness test : Logit
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22
Figure 5: Factor loadings
23
References
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[2]
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[9]
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[10] Cheung S., Chan D., et Wong Z., (1999), “Reexaminig the theory of planned
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DOCUMENTS DE TRAVAIL GREDEG PARUS EN 2013
GREDEG Working Papers Released in 2013
2013-01
Raphaël Chiappini
Persistence vs. Mobility in Industrial and Technological Specialisations: Evidence from 11 Euro Area Countries
2013-02
Kevin D. Hoover
Was Harrod Right?
2013-03
Kevin D. Hoover
Man and Machine in Macroeconomics
2013-04
Isabelle Corbett-Etchevers & Aura Parmentier-Cajaiba
Toying with Regulation: ‘Strategizing Tools’ as Organizational Bricolage
2013-05
Aura Parmentier-Cajaiba
Research Diary Mapping: Enhancing Reflectivity in Process Research
2013-06
Richard Arena
Sraffa’s and Wittgenstein’s Crossed Influences: Forms of Life and Snapshots
2013-07
Christophe Charlier & Sarah Guillou
Distortion Effects of Export Quota Policy: An Analysis of the China - Raw Materials Dispute
2013-08
Cristiano Antonelli & Alessandra Colombelli
Knowledge Cumulability and Complementarity in the Knowledge Generation Function
2013-09
Marco Grazzi, Nadia Jacoby & Tania Treibich
Dynamics of Investment and Firm Performance: Comparative Evidence from Manufacturing Industries
2013-10
Anna Calamia, Laurent Deville & Fabrice Riva
Liquidity in European Equity ETFs: What Really Matters?
2013-11
Lauren Larrouy
Bacharach’s ‘Variable Frame Theory’: A Legacy from Schelling’s Issue in the Refinement Program?
2013-12
Amel Attour
Adoption et modèles de diffusion régionale de l’innovation dans les gouvernements locaux: le cas du développement de l’e-Gouvernement en Lorraine
2013-13
Anaïs Carlin, Sébastien Verel & Philippe Collard Modeling Luxury Consumption: An Inter-Income Classes Study of Demand Dynamics and Social Behaviors
2013-14
Marie-José Avenier & Catherine Thomas Designing a Qualitative Research Project Consistent with its Explicit or Implicit
Epistemological Framework
2013-15
Amel Attour & Maëlle Della Peruta Le rôle des connaissances architecturales dans l’élaboration de la plateforme technologique d’un écosystème en émergence: le cas des plateformes NFC
2013-16
Evelyne Rouby & Catherine Thomas
Organizational Attention Elasticity: An Exploratory Case of Cement Production
2013-17
Małgorzata Ogonowska & Dominique Torre
Residents’ Influence on the Adoption of Environmental Norms in Tourism
2013-18
Isabelle Salle & Pascal Seppecher
Social Learning about Consumption
2013-19
Eve Saint-Germes & Sabrina Loufrani-Fedida
L’instrumentation de la GTEC au service de l’articulation entre compétences individuelles et employabilité : le cas de la plateforme eDRH06
2013-20
Francesco Quatraro & Marco Vivarelli
Entry and Post-Entry Dynamics in Developing Countries
2013-21
Dorian Jullien, Judith Favereau & Cléo Chassonnery-Zaïgouche
Rationality and Efficiency: From Experimentation in (recent) Applied Microeconomics to Conceptual Issues
2013-22
Nabila Arfaoui, Eric Brouillat & Maïder Saint-Jean
Policy Design, Eco-innovation and Industrial Dynamics in an Agent-Based Model: An Illustration with the REACH Regulation
2013-23
Marc Deschamps
Pourquoi des politiques de concurrence ?
2013-24
Raphaël Chiappini
Do Overseas Investments Create or Replace Trade? New insights from a Macro-Sectoral Study on Japan
2013-25
Jordan Melmiès
Industrial Seigniorage, the Other Face of Competition
2013-26
Frédéric Marty
As-Efficient Competitor Test in Exclusionary Prices Strategies: Does Post-Danmark Really Pave the Way towards a More Economic Approach?
2013-27
Alfredo Medio
Simple and Complex Dynamics: A Hidden Parameter
2013-28
Giorgia Barboni & Tania Treibich
First Time Lucky? An Experiment on Single versus Multiple Bank Lending Relationships
2013-29
Michele Bernini, Sarah Guillou & Flora Bellone
Firms’ Leverage and Export Quality: Evidence from France
2013-30
Michele Bernini & Tania Treibich
Killing a Second Bird with One Stone? Promoting Firm Growth and Export through Tax Policy
2013-31
Marc Deschamps
L’articulation économie, droit et politique dans la pensée ordolibérale
2013-32
Sophie Pommet
The Impact of Venture Capital Investment Duration on the Survival of French IPOs
2013-33
Sandye Gloria-Palermo
In Search of the Right Tool: From Formalism to Constructivist Modelling
2013-34
Benjamin Montmartin & Nadine Massard
Is Financial Support for Private R&D Always Justified? A Discussion Based on the Literature on Growth
2013-35
Olivier Bruno & Alexandra Girod
Procyclicality and Bank Portfolio Risk Level under a Constant Leverage Ratio
2013-36
Mario Amendola, Jean-Luc Gaffard & Fabrizio Patriarca
Inequality, Debt and Taxation: The Perverse Relation between the Productive and the Non-
Productive Assets of the Economy
2013-37
Jean-Luc Gaffard
La macroéconomie à l’épreuve des faits
2013-38
Flora Bellone & Jérémy Mallen-Pisano
Is Misallocation Higher in France than in the United States?
2013-39
Sandye Gloria-Palermo
Equilibrium versus Process: A Confrontation between Mainstream and Austrian Ontology
2013-40
Nathalie Lazaric & Alain Raybaut
Do Incentive Systems Spur Work Motivation of Inventors in High Tech Firms ? A Group-Based Perspective
2013-41
Michaël Assous
Solow’s Struggle with Medium-Run Macroeconomics: 1956-1995
2013-42
Frédéric Marty
L’évolution des conditions de financement des contrats de partenariats public-privé : quels
impacts de la crise financière ?
2013-43
Peter T. Bryant, Nathalie Lazaric & Moustapha Niang
Routines Resistance: How Conflicts within Transactive Memory Obstruct Routinization
2013-44
Perry Mehrling
MIT and Money
2013-45
Alfredo Medio
Insolvency Traps and Multiple Equilibria Complex Dynamics in a Simple Bond Market
2013-46
Dorian Jullien
Intentional Apple-choice Behaviors: When Amartya Sen Meets John Searle
2013-47
Dorian Jullien
Asian Disease-type of Framing of Outcomes as an Historical Curiosity
2013-48
Marc Deschamps
Pour une hiérarchisation et une nouvelle forme de rédaction des décisions de l’Autorité de la concurrence
2013-49
Ankinée Kirakozian
Selective Sorting of Waste: A Study of Individual Behaviors