Crowding-out Effect of Financial Incentives for Households to Recycle Waste

Abstract

Willingness of individuals to recycle their waste is driven by wide range of factors. These may be distinguished as intrinsic, for example perceived importance of recycling, and extrinsic, e. g. command-and-control interventions or financial incentives in a form of landfill taxes, deposits, charges, and fees. In order to increase the participation rate, specific policy measures and interventions are introduced. Some of these interventions, like educational campaigns, are focused on intrinsic motivation of residents, whereas some other measures are using financial stimuli to affect the peoples´ recycling behavior directly. In this respect, the crowding-out effect might occur when financial incentives might reduce the effect of intrinsic factors. This paper reports on responsiveness of residents to the direct and indirect incentives. Purpose of the analysis was to test the crowding-out hypothesis supposing that direct incentives are replacing the intrinsic motivation to recycle. Presented data based on nationwide survey (n=1.579) that was conducted in Czechia during 2017 confirms the hypothesis for large part of the population (50 %). However, it was also found-out that one fifth of the sample is responsive only to the direct incentives. Therefore, the crowding-out effect is not confirmed for segment of the population. It seems that direct and indirect incentives may not be mutually exclusive. For some individuals the direct and indirect measures might support each other and together may increase positive impact on recycling behavior. It is also highly recommended to consider the context within which the given measures are to be implemented. Under certain circumstances, such as high intrinsic motivation of residents, the launch of direct measures may not be reasonable.

Full text

CROWDING-OUT EFFECT OF FINANCIAL INCENTIVES
FOR HOUSEHOLDS TO RECYCLE WASTE
JIRI REMR
Institute for Evaluations and Social Analyses, Czech Republic
ABSTRACT
Willingness of individuals to recycle their waste is driven by a wide range of factors. These may be
distinguished as intrinsic, for example perceived importance of recycling, and extrinsic, e.g. command-
and-control interventions or financial incentives in a form of landfill taxes, deposits, charges, and fees.
In order to increase the participation rate, specific policy measures and interventions are introduced.
Some of these interventions, like educational campaigns, are focused on intrinsic motivation of
residents, whereas some other measures are using financial stimuli to affect people’s recycling behavior
directly. In this respect, the crowding-out effect might occur when financial incentives might reduce
the effect of intrinsic factors. This paper reports on responsiveness of residents to the direct and indirect
incentives. The purpose of this analysis was to test the crowding-out hypothesis supposing that direct
incentives are replacing the intrinsic motivation to recycle. The presented data is based on a nationwide
survey (n=1.579) that was conducted in the Czech Republic during 2017 and confirms the hypothesis
for a large part of the population (50%). However, it was also found that one fifth of the sample is
responsive only to the direct incentives. Therefore, the crowding-out effect is not confirmed for a
segment of the population. It seems that direct and indirect incentives may not be mutually exclusive.
For some individuals the direct and indirect measures might support each other and together may
increase positive impacts on recycling behavior. It is also highly recommended to consider the context
within which the given measures are to be implemented. Under certain circumstances, such as high
intrinsic motivation of residents, the launch of direct measures may not be reasonable.
Keywords: recycling, crowding-out effect, attitude, behavioral change, intrinsic motivation, waste
management, direct incentives.
1 INTRODUCTION
This paper compares the responsiveness of residents to direct (typically financial) and
indirect measures (usually in a form of educational campaigns). It also estimates the
usefulness of replacing the currently-used measures and policies aimed at affecting the
recycling behavior of households with new ones. In making such comparison, I hypothesize
that a crowding-out effect might occur when indirect measures are replaced with direct
incentives. However, there is a rival concept besides the crowding-out hypothesis that
considers the direct incentives as having an additional effect strengthening the efficacy of
indirect measures. According to the rival hypothesis, the direct incentives might also
encourage the different individuals (i.e. new segments of the population) that are resistant
toward indirect measures.
The results of comprehensive survey research are presented in this paper. Besides the
description of recycling behavior and identification of its key determinants a specific module
was devoted to the perceived significance of direct incentives and to other motivations to
recycle that respondents might have. In spite of the discussions in scientific literature, no
policy instruments for affecting the recycling behavior are widely recommended. These
questions are not exclusive to waste management system in the Czech Republic and therefore
I believe that the findings could inspire also the others.
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doi:10.2495/WM180251

2 TYPES OF INCENTIVES
Stakeholders responsible for waste management systems (i.e. the municipalities) use a wide
range of measures and policies aimed at affecting the recycling behavior of households and
increasing participation rate. These interventions might have a form of command-and-control
stimuli such take-back schemes, as well as market-based incentives (e.g. landfill taxes,
deposits, charges, and fees). Such measures are intended to change the behavior of
individuals directly, while other measures work indirectly. Indirect measures, which may
come in the form of educational campaigns, are used primarily to challenge the intrinsic
motivations of individuals and build awareness with the expectation that such attitudinal
change will be followed by a change in behavioral patterns.
Financial incentives such as deposit-refunds or marginal pricing were found by some
scholars to be ineffective [1]–[4]. Moreover, Vining and Ebreo [5] stated that it is not
financial rewards but desirable environmental results, which drives people to recycle. De
Young [6] added that some people recycle for their personal satisfaction and not for a
financial reward. The main obstacle to participation in recycling is then a lack of knowledge
as De Young [6], Valerio et al. [7], Hahn and Stavins [8], McDonald and Oates [9] or Ewing
[10] found. McDonald and Oates [9] or Oskamp et al. [11] stated that knowledge about
recycling has been identified as the key difference between recyclers and non-recyclers (the
amount of information about recycling was different between these two groups).
In practice, the stakeholders responsible for waste management systems often consider
replacing currently-used measures with different ones, anticipating the need to improve
effectiveness of their waste management systems and seeking to increase participation rate.
However, the key question is whether such changes might yield the desired improvement.
3 THE CASE OF THE CZECH REPUBLIC
According to current legislation (Section 17 of the Czech Waste Act No. 185/2001 Coll.),
residents are obliged to recycle all waste produced in the households. However, only 14% of
the population is aware about this provision. Moreover, enforcing the legal obligation to
recycle is hardly feasible; especially among those living in condominiums, it is practically
impossible to identify who recycles and who does not. From the perspective of effectiveness
it is important to highlight that legal obligation (even enforced by sanctions) is not an
indispensable prerequisite for recycling. As Abbott et al. [12] or Kirakozian [3] reported,
when social norms are internalized by individuals, sanctions are not necessary.
Direct incentives are used only by some municipalities, so they have only marginal impact
on the overall participation rate. On the other hand, indirect measures are a key driver of
recycling. Since 2003, nationwide educational campaign has worked to increase residents’
awareness of recycling and to change their behavior to favor more recycling. The nationwide
campaign has been supplemented by regional campaigns that usually share the same message
but use local media and specifically communicate the practical information concerning
recycling such as schedule of waste collection or types of waste suitable for recycling.
To summarize, the current participation rate of 72% [13] has been achieved by indirect
measures rather than by the direct ones.
4 METHOD USED IN STATISTICAL ANALYSIS
4.1 Description of variables
Two rival constructs were defined, the first of which is responsiveness to indirect measures
whereas the second represents responsiveness to direct ones. Further analysis examines how
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the two types of responsiveness relate to each other and compares the profiles of respondents
who are responsive to each measure. Finally, an important descriptor is the variable that
reflects current recycling habits.
4.1.1 Responsiveness to indirect measures
Responsiveness to indirect measures reflects the intrinsic motivations of individuals to
recycle. Previous studies [3], [12], [14], [15] have shown that such motivations are associated
with the amount of information that an individual has about recycling [16], with the degree
of routinization, i.e. to what extent recycling is embedded in people’s lives [17], [18], and
with individual responsibility for the environment [19]. Therefore, the construct is based on
three following variables.
The first variable reflects the respondents’ interest in receiving more information on
recycling. In this question, an emphasis was put on practical rather than general information.
Distinguishing general and practical (or instrumental) information comes from Barr [20],
who argued that general knowledge comprises information about the waste management
system, its usefulness, and reminds individuals to separate their waste whereas instrumental
knowledge covers practical information related to recycling within the given circumstances.
Therefore, instrumental information focuses on types of materials collected, on localization
of containers, etc. Previous studies [21], [22] found that instrumental knowledge is a better
predictor of recycling than the general knowledge. The second variable is an agreement with
statement: “Recycling is part of my lifestyle.” This variable measures the extent of adoption
of behavioral patterns associated with recycling. It represents the extent to which recycling
is embedded in the respondent´s life. This variable has proven to be valid and it is considered
a strong predictor of recycling [23]. The third variable tackles the issue of personal
responsibility for waste. It is indicated by an agreement with statement: “Recycling my waste
is solely my own duty.” This attitude reflects strong intrinsic motivation to recycle.
In their original form, all three variables form the Likert scale with four options ranging
from “definitely agree” (1) to “definitely disagree” (4). Values of all variables were
summarized and finally divided into three categories reflecting the degree of responsiveness.
Agreement with the given variables reflects strong intrinsic motivation to recycle, or more
precisely, high responsiveness to indirect measures. Table 1 shows that two fifths (41%) of
respondents are highly responsive to indirect incentives on the other hand, one fifth (21%) of
all respondents has low intrinsic motivation to recycle. Among them, we cannot rely on their
self-motivation to recycle and therefore their willingness to recycle has to be increased by
other stimuli.
4.1.2 Responsiveness to direct measures
Similarly as in the case of indirect measures, three independent variables were used to
indicate responsiveness to direct incentives. The rationale behind this construct stems from
the desire of respondents to receive any kind of financial benefit as a reward or compensation
for their recycling effort. The construct is therefore based on an overall acceptance of
receiving a financial reward for recycling. The other two variables reflect the two major
Table 1: Responsiveness to indirect incentives.
Codin
g
Frequenc
y
Mean Std. Deviation
1 = Hi
g
h 41%
1.8 0.759
2 = Moderate 38%
3 = Low 21%
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approaches that are usually discussed [24], i.e. entitling recyclers to discount from fees, and
price premium (or fine) that the non-recyclers must to pay.
The first variable comes from the agreement with statement that “People should receive a
financial reward for their recycling efforts.” The second indicator represents an interest of
respondents in receiving a discount as a reward for recycling. The third component of this
construct is an agreement with the statement: “Those who do not recycle should pay more
than those who recycle.” Agreement with these statements reflects a strong craving for
financial reward and thus high responsiveness to direct measures.
Similarly as in case of indirect measures, all three questions form the Likert scale with
four options ranging from “definitely agree” (1) to “definitely disagree” (4). They were
summarized and finally divided into three groups reflecting the degree of responsiveness. As
Table 2 indicates, 44% of respondents are highly responsive to direct measures. On the other
hand, 17% are not driven by direct incentives.
4.1.3 Declared recycling
Self-reported data on recycling behavior was also collected. It is a common approach to study
recycling; the same method can be found in Derksen and Gartrell [25], Miliute-Plepiene et
al. [26] or Fullerton and Kinnaman [27]. However, this approach has some limitations (see
[28]) because of overestimation of declared recycling, typically caused by a social
desirability effect. Since exact data related to actual recycling are not available, the focus on
self-reported recycling is acceptable.
Table 3 brings data for the analyzed sample showing that 44% declare they regularly
recycle, 37% do so occasionally and 19% declare that they do not recycle at all. Such findings
are consistent with other data, especially on participation rate, which is 72% [13]. The higher
self-reported data are caused by the aforementioned social desirability effect.
4.2 Sample, sampling technique and data collection method
The target population is the general population of the Czech Republic aged 18–74 comprising
only Czech residents living permanently in the Czech Republic.
The sampling technique applied was the multistage random procedure using random
route. Since no adequate sampling frame (register or list of residents) was available, primary
sampling units were selected. Subsequently, within each primary sampling unit, addresses
were identified and households were selected. Finally, the interviewers visited the pre-
selected addresses, attempted to contact the households, identified the prospective respondent
Table 2: Responsiveness to direct incentives.
Codin
g
Frequenc
y
Mean Std. Deviation
1 = Hi
g
h 44%
1.73 0.734
2 = Moderate 39%
3 = Low 17%
Table 3: Respondents recycling.
Codin
g
Frequenc
y
1 = Rec
y
cle re
g
ularl
y
44%
2 = Rec
y
cle occasionall
y
37%
3 = Do not rec
y
cle at all 19%
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using the Kish table [29] and invited the relevant individual to participate. Altogether 186
primary sampling units throughout the Czech Republic were selected; within each of these
units a maximum of 20 addresses were identified. Interviewers contacted 3.148 households
and performed 1.611 interviews (response rate was 51.2%). However, due to incompleteness
of some of these interviews, when respondents refused to provide key socio-demographic
data, the datafile comprised 1.579 cases used for analysis. Fieldwork took place during March
2017 and average duration of an interview was approx. 35 minutes. From all 1.611
interviews, 20% were supervised by check-backs and verified in terms of compliance with
ethical standards (especially confidentiality, informed consent, and non-maleficence).
5 RESULTS AND DISCUSSION
5.1 Comparison of responsiveness to direct and indirect incentives
From survey data it is obvious that the responsiveness to direct and indirect incentives is
similar, even though the responsiveness to direct measures is higher. Table 4 shows that
among the whole sample, 69% of respondents declared their interest in receiving financial
reward as a compensation for their recycling effort. At the same time, 63% of respondents’
recycling behavior is solely driven by intrinsic motivation. The identified difference is
statistically significant and therefore the very first conclusion would be that an interest in
direct incentives is stronger than the interest in indirect measures.
However, from Table 4 it is also obvious that for 50% of all respondents, both types of
measures are effective. These respondents, who represent the largest segment, are primarily
driven by intrinsic motivation but they call for financial reward as well. For these r espondents
the crowding-out hypotheses is relevant. The past behavior of this segment already proved
that indirect measures are strong enough to drive recycling behavior. The vast majority of
respondents in this segment (94%) declare that they recycle and they would do so even
without financial reward. As already explained, there are no such measures implemented in
the Czech Republic. Therefore, changing the logic of waste management system and
introducing the direct measures would therefore gain only marginal effect on participation
rate.
The second segment (19%) represents those who are responsive to direct incentives but
they do not seem to be responsive to indirect measures. Therefore, the crowding-out effect
will not appear within this segment. When these people are resistant to indirect measures,
targeting them by indirect stimuli might lead to suboptimal results, i.e. participation rate
would not increase. This estimate is confirmed by current data showing that 31% of non-
recyclers are within this segment, which is a significantly higher share than in all other
segments.
Table 4: Responsiveness-based segments.
Responsiveness to
indirect incentives Total
Hi
g
hLow
Responsiveness to direct incentives Hi
g
h50% 19% 69%
Low 13% 18% 31%
Total 63% 37% 100%
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Table 5: Comparison of selected socio-demographic characteristics.
Variables Responsiveness to
direct incentives
Responsiveness to
indirect incentives
Achieved
education
Elementar
y
19%10%
Vocational 32% 36%
Secondar
y
38%39%
Universit
y
12%15%
Total 100% 100%
Type of residence
Famil
y
house 32% 42%
Apartment house 22% 21%
Block of flats 46% 37%
Total 100% 100%
Size of settlement
Less than 3,000 inhabitants 25% 29%
3,000
–
23,999 inhabitants 33% 35%
24,000 inhabitants or more 42% 36%
Total 100% 100%
Note: [χ2=6.208, df=3, p=0.102, Mann-Whitney U=18708.000, p=0.099]; [χ2=4.782, df=2, p=0.092, Mann-Whitney
U=18348.000, p=0.035]; [χ2=1.544, df=2, p=0.462, Mann-Whitney U=19388.000, p=0.217].
The third segment (13%) represents people with contrary attitudes toward recycling
compared to the previous segment. These respondents are responsive to indirect measures
but they also show low responsiveness to direct incentives. If these people recycle, they do
so because of intrinsic motivation without needing any financial rewards and therefore the
crowding-out effect might appear among these respondents. Implementation of the direct
incentives would be redundant and it would only replace the intrinsic motivation with
financial reward. It is worth mentioning that 94% of respondents within this segment declare
they already recycle.
The fourth segment representing 18% of the total sample are those who show reluctance
to both types of measures. For these individuals it will be a challenge to find the relevant
motivational driver; this segment is heterogeneous in terms of determinants affecting the
perception of recycling and intention to recycle.
Further analysis is focused on the comparison of the segments two and three; it also aims
to profile both segments and to find significant differences that might help to understand the
context and circumstances concerning the responsiveness to different kinds of incentives.
5.2 Detailed analysis of responsiveness
Within the segment of those who are responsive to direct incentives, the most responsive are
the people with lower education and generally with lower socio-economic status. The impact
of socio-demographic characteristics on recycling has been explored in various studies and
as Vicente and Reis [30] stated the results are inconsistent. Low correlation of socio-
demographic characteristics with recycling behavior is confirmed by e.g. Oskamp et al. [11]
and Barr [20]. Nevertheless, Barr [20] admits that some relationships between social-
demographic characteristics and recycling do exist. Similarly, Schultz and Zelezny [31], who
studied environmental concerns of households and their socio-demographic characteristics
identified many statistically significant associations. The data that I have analyzed confirms
the results of studies of Barr [20] or Schultz and Zelezny [31], showing in Table 5 that some
of the socio-demographic characteristics might serve as reliable predictors of recycling.
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Table 5 also shows that those who are most responsive to direct incentives are the
individuals who live in larger cities and those who reside in blocks of flats or condominiums.
However, these types of residence offer the lowest chances to implement direct measures
because in such residential areas it is difficult to differentiate which households recycle and
which do not with necessary high precision and certainty.
5.3 Expected effectiveness of direct and indirect incentives
Residents who are responsive to the direct incentives may represent a promising amount of
households. In case of The Czech Republic, it might be approximately 500.000 households.
However, it would be naïve to expect that for all of those households a lack of financial
reward is the only barrier to start recycling. Detailed analysis of the self-reported data
summarized in Table 6 shows that individuals in this segment produce the least amount of
waste. Whereas those who are responsive to indirect measures estimate the overall production
of waste within their households to 51 kg per month, respondents in the other segment
provide the mean estimate of only 43 kg. Table 6 also shows that the share of recyclers is
significantly higher among individuals who are responsive to indirect measures (90% when
54% recycles regularly and another 36% at least occasionally). Within the other segment of
the 69% of recyclers, 27% recycle regularly and 42% occasionally. Differences in recycling
behavior between both segments is also documented by the number of recycled commodities
and volatility of recycling when those who are responsive to indirect incentives (driven by
their intrinsic motivation supported with the educational campaigns) are more zealous about
recycling than the other segment. Therefore, the direct incentives launch might have only
marginal effect on recycling.
Table 6: Comparison of recycling behavior.
Variables Responsiveness to
direct incentives
Responsiveness
to indirect
incentives
Reported monthly amount of waste
(in k
g
per household) 43 51
Recycling
Re
g
ularl
y
27% 54%
Occasionall
y
42% 36%
N
ot at all 31% 10%
Total 100% 100%
Number of recycled
commodities*
Onl
y
some selectivel
y
77% 50%
All possible 24% 50%
Total 100% 100%
Volatility of recycling*
Low 63% 76%
Hi
g
h 38% 24%
Total 100% 100%
Note: * Base represents only those who recycle.
[t=-1.717, df=291.297; p=0.087]; [χ2=41.437, df=2, p=0.000, Mann-Whitney U=13583.500, p=0.000]; [χ2=23.616,
df=1, p=0.000, Mann-Whitney U=9355.000, p=0.000]; [χ2=6.542, df=1, p=0.011]; Mann-Whitney U=10846.500,
p=0.217].
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Table 7: Comparison of attitudes towards recycling.
Variables Responsiveness to
direct incentives
Responsiveness to
indirect incentives
Self-evaluation of
recycling
Excellen
t
3% 22%
Goo
d
36% 34%
Acceptable 42% 35%
Poo
r
13%6%
Wea
k
6% 3%
Total 100% 100%
Awareness about
positive impacts of
recycling
Hi
g
h 35% 50%
Moderate 27% 27%
Low 38% 23%
Total 100% 100%
Satisfaction with
containers localization
Satisfie
d
73% 86%
Dissatisfie
d
27% 14%
Total 100% 100%
Reported containers’
overfullness
Often or sometimes 76% 61%
Rarel
y
or neve
r
24% 39%
Total 100% 100%
Note: [χ2=31.058, df=4, p=0.000, Mann-Whitney U=9201.500, p=0.000]; [χ2=12.913, df=2, p=0.002, Mann-
Whitney U=15526.500, p=0.000]; [χ2=7.910, df=1, p=0.005, Mann-Whitney U=13431.000, p=0.005]; [χ2=9.122,
df=1, p=0.003, Mann-Whitney U=12684.000, p=0.003].
Apart from the declared behavioral differences, there are also substantial gaps in attitudes
toward recycling between the two compared segments as indicated in Table 7. Most
significant differences are observed in self-evaluation of recycling and in awareness about
positive impacts of recycling. Respondents who are responsive to indirect measures consider
their recycling as excellent in approximately one fifth of cases whereas with the other
segment there are only 3% of respondents with such self-perception. Table 7 also shows that
shares of those who declare that their recycling is either poor or week are doubled among
people responsive to direct incentives compared to individuals from the other segment. As
for the awareness about positive impacts of recycling, high awareness was identified among
half of the segment responsive to indirect measures compared to 35% in the other segment.
It was also observed that significantly more individuals in this segment consider other
issues as a barrier for their recycling. These objections focus on the waste management
system’s infrastructure and performance. As Table 7 documents, 27% of those who are
responsive to direct incentives and 14% in the other segment are dissatisfied with containers’
proximity; more people responsive to direct incentives also point out that containers are
sometimes or often overfilled.
6 CONCLUSIONS
Direct incentives do affect the decision-making of a major part of the population. However,
its effect is not exclusive – a similar (or even better) result might be achieved by utilization
of indirect measures. Moreover, people driven by direct incentives produce less waste and
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are more reluctant toward recycling and therefore, an increase in effectiveness of overall
waste management system should not be expected.
Realistically, direct and indirect incentives may not be mutually exclusive. For some
individuals these two measures might support each other and together might increase positive
impact on recycling behavior. The results obtained in my study confirmed the need for ad-
equate policies to increase participation rate, particularly amongst those who are not willing
to bear recycling on their minds. Of the currently available measures, indirect incentives such
as educational campaigns seem to produce an adequate impact on recycling.
Segmentation is essential in this respect: no measures affect the behavior of each member
of the target group in the same way. Direct incentives might yield desired behavior (change)
among some residents but may not be effective for the others. It is therefore essential to
analyze attitudes and patterns of behavior within specific segments of the population.
It is also important to consider the desired goals associated with the given measures. Is it
increasing the participation rate? Or increasing the effectiveness of waste management? The
given incentives that are effective in increasing the participation rate might not be effective
when the aim is to improve recycling effectiveness.
Finally, it is highly recommended to consider the context within which the given me asures
are analyzed. Merit of the presented case is a high participation rate (72%) that was built with
the use of indirect measures. Under such circumstances, it seems unreasonable to switch
suddenly and in a blanket manner to direct measures. However, it may be effective within
specific municipalities where the participation rate is low or where it is difficult and
expensive to reach the population with indirect incentives.
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