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Determinants of household waste recycling behavior the case of ho chi minh city

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF HOUSEHOLD WASTE
RECYCLING BEHAVIOR
THE CASE OF HO CHI MINH CITY

BY

PHAN BÙI KHUÊ ĐÀI

MASTER OF ARTS IN DEVELOPMENT ECONOMICS


HO CHI MINH CITY, NOVEMBER 2015


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF HOUSEHOLD WASTE
RECYCLING BEHAVIOR
THE CASE OF HO CHI MINH CITY
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

PHAN BÙI KHUÊ ĐÀI

Academic Supervisor:
PROF. NGUYỄN TRỌNG HOÀI

HO CHI MINH CITY, NOVEMBER 2015


ABSTRACT
This paper investigates determinants of household waste recycling behavior for six
different materials: paper, carton, plastic, metal, glass and cloth using data from the
survey “Consumption behavior towards green growth in urban area of Viet Nam”,
funded by National Foundation for Science and Technology Development
(NAFOSTED) and logistic regression. My findings reveal that psychological factors
towards recycling generally appear to be statistically insignificant. Nevertheless,
concern about waste, awareness of inheritance for future generation and satisfaction
of waste condition at household’s residency explain their recycling behavior for
some materials. Another interesting finding is that the household’s belief of money


gained from waste recycling will lead to recycling paper, carton and plastic.
Furthermore, the results disclose that income and age in some cases affect the
recycling behavior negatively. Moreover, housing characteristics have impact on
recycling behavior positively in the case of metal, carton and paper recycling.
Key words: household waste recycling, recycling behavior, logistic model

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ACKNOWLEDGMENT
Firstly, I would like to express my sincere gratitude to my supervisor Prof.
Nguyen Trong Hoai who provided me with precious data set from the survey
“Consumption behavior towards green growth in urban area of Viet Nam”, funded
by National Foundation for Science and Technology Development (NAFOSTED)
and gave me valuable guidelines, comments and suggestions for the successful
completion of this study.
I would like to express my special appreciation to Dr. Truong Dang Thuy
whom I

have

learned

a

lot

from

his

enthusiastic guidance, useful

recommendations and inspiration. Besides, his friendly and inspiring approach has
given me a great deal of encouragements to overcome difficulties in the whole
research process.
I would like to express my thanks to Dr. Nguyen Hoang Bao and MA. Phung
Thanh Binh for introducing the logistic regression model at “Data analysis and
forecasting” course on December, 2014, hold by the Faculty of Development
Economics of HCMC, University of Economics.
I am also thankful to all lecturers and program administrators of the Vietnam
– The Netherlands Program for M.A. in Development Economics. They have given
me wonderful knowledge and helped me kindly during the course.
To all my friends in MDE Class 16, 19 and 20, especially MA. Nguyen Van
Dung (Class 16) and MA. Nguyen Quang Huy (Class 19) who gave me emotional
encouragement, I would like to express my sincere thanks.
Finally, I would like to express my deeply appreciation to my family and Mr.
Tran Kim Minh for spiritual support and love. In particular, I dedicate this thesis to
my beloved farther, Mr. Phan Van Bay.
HCMC, November 2015
Phan Bui Khue Dai

ii


TABLE OF CONTENTS
LIST OF TABLES ...........................................................................................................v
LIST OF FIGURES ........................................................................................................ vi
Chapter 1: INTRODUCTION .........................................................................................1
1.1. Problem statement .....................................................................................................1
1.1.1. Real world problem ............................................................................................ 1
1.1.2. Scientific problem............................................................................................... 1
1.2. Research objectives ...................................................................................................2
1.3. Research questions ....................................................................................................2
1.4. Research scope and data ............................................................................................2
1.5. The structure of this study .........................................................................................2
Chapter 2: LITERATURE REVIEW ...............................................................................4
2.1. Theoretical Review....................................................................................................4
2.2. Empirical Review ......................................................................................................5
2.2.1. Socio-Economic and Demographic characteristics ............................................ 5
2.2.2. Housing characteristics ....................................................................................... 9
2.2.3. Psychological factors towards recycling .......................................................... 10
Chapter 3: RESEARCH METHODOLOGY .................................................................15
3.1. Conceptual framework and the econometric model ................................................15
3.2. Data source ..............................................................................................................20
3.3. Methodology ...........................................................................................................20
Chapter 4: EMPIRICAL RESULTS ..............................................................................22
4.1. Descriptive Statistics ...............................................................................................22
4.1.1. Dependent variables ......................................................................................... 22
4.1.2. Independent variables ....................................................................................... 22
4.2. Bivariate analysis ....................................................................................................24
4.2.1. Metal recycling ................................................................................................. 24
4.2.2. Carton recycling ............................................................................................... 27
4.2.3. Paper recycling ................................................................................................. 29
4.2.4. Plastic recycling ................................................................................................ 32
4.2.5. Glass recycling ................................................................................................. 34
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4.2.6. Cloth recycling ................................................................................................. 36
4.3. Regression results ....................................................................................................38
Chapter 5: CONCLUSION AND POLICY RECOMMENDATION ...........................43
5.1. Conclusion ...............................................................................................................43
5.2. Policy recommendation ...........................................................................................44
5.3. Research limitation ..................................................................................................44
REFERENCES ...............................................................................................................45
APPENDICE ..................................................................................................................48

iv


LIST OF TABLES
Table 1. Description of the variables ............................................................................. 16
Table 2. Prevalence of recycling toward six materials .................................................. 22
Table 3. Descriptive statistics of numerical variables .................................................... 23
Table 4. Descriptive statistics of binary variables.......................................................... 24
Table 5. A comparison between metal recycling and non-recycling in term of
income, age and size of housing ..................................................................................... 24
Table 6. A comparison between metal recycling and non-recycling in term of
gender, type of housing and education ........................................................................... 25
Table 7. A comparison between carton recycling and non-recycling in term of
income, age and size of housing ..................................................................................... 27
Table 8. A comparison between carton recycling and non-recycling in term of
gender, type of housing and education ........................................................................... 27
Table 9. A comparison between paper recycling and non-recycling in term of
income, age and size of housing ..................................................................................... 29
Table 10. A comparison between paper recycling and non-recycling in term of
gender, type of housing and education ........................................................................... 30
Table 11. A comparison between plastic recycling and non-recycling in term of
income, age and size of housing ..................................................................................... 32
Table 12. A comparison between plastic recycling and non-recycling in term of
gender, type of housing and education ........................................................................... 32
Table 13. A comparison between glass recycling and non-recycling in term of
income, age and size of housing ..................................................................................... 34
Table 14. A comparison between glass recycling and non-recycling in term of
gender, type of housing and education ........................................................................... 34
Table 15. A comparison between cloth recycling and non-recycling in term of
income, age and size of housing ..................................................................................... 36
Table 16. A comparison between cloth recycling and non-recycling in term of
gender, type of housing and education ........................................................................... 36
Table 17. Parameter estimates for the logit models ....................................................... 39
v


LIST OF FIGURES
Figure 1. Conceptual framework .................................................................................... 15
Figure 2. A comparison between metal recycling and non-recycling in term of some
selected psychological factors ........................................................................................ 26
Figure 3. A comparison between carton recycling and non-recycling in term of
some selected psychological factors .............................................................................. 28
Figure 4. A comparison between paper recycling and non-recycling in term of some
selected psychological factors ........................................................................................ 31
Figure 5. A comparison between plastic recycling and non-recycling in term of
some selected psychological factors .............................................................................. 33
Figure 6. A comparison between glass recycling and non-recycling in term of some
selected psychological factors ........................................................................................ 35
Figure 7. A comparison between cloth recycling and non-recycling in term of some
selected psychological factors ........................................................................................ 37

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Chapter 1: INTRODUCTION
1.1. Problem statement
1.1.1. Real world problem
In Vietnam, the household waste is a pressing environment issue in recent years.
Ministry of Natural Resources and Environment (2014) indicated that total solid
waste from household was 12.8 million tons per year and would reach to 22 million
per year by 2020. Increasing amounts of household waste may lead to the risk of
heavy environmental pollution and the seriously impact on public health. In the
general context, Ho Chi Minh City is facing many challenges in waste management.
Every day, the city has more than 7,000 tons of garbage and costs each year up to
235 billion VND to handle. Thus, in order to recommend government in the design
of effective policies to minimize waste, it is necessary to understand household
waste recycling behavior. The study analyzes the determinants of household waste
recycle behavior with the scope of research in Ho Chi Minh City.
1.1.2. Scientific problem
Currently, there are few studies on the issue of household behavior in Vietnam,
mainly towards green consumption (water use; energy use; recycling and transport
choice). Of which, there is only one study of Luu Bao Doan and Nguyen Trong
Hoai (2015) on household waste recycling behavior. Using structural equation
modeling, this study indicated that recycling related to the attitude of the household
towards recycling. Further, general concern and knowledge of environment do not
have direct relations with the behavior of interest to recycle.
Therefore, the study with logit models may contribute to academic knowledge on
recycling behavior in the context of a developing country like Vietnam.
Furthermore, this study can provide policy makers with a new perspective on the
nature of recycling behavior, the relationship between this behavior and such factors
as psychological factors of the environment in general and waste recycling in
particular, so that the authority can consider appropriate tools to adjust citizen's
behavior towards recycling more.

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1.2. Research objectives
The objective of the study is to identify factors affecting household’s waste
recycling behavior in Ho Chi Minh City towards six different materials: metal,
carton, paper, plastic, glass and cloth.
1.3. Research questions
Main question: What factors affect household waste recycling behavior in Ho Chi
Minh City?
Sub questions:
i. Do Socio-Economic and Demographic Characteristics affect household
waste recycling behavior in Ho Chi Minh City?
ii. Do Primary Residence Characteristics affect household waste recycling
behavior in Ho Chi Minh City?
iii. Do Psychological factors of the environment and waste recycling affect
household waste recycling behavior in Ho Chi Minh City?
1.4. Research scope and data
The study will investigate determinants of household waste recycling behavior for 6
different materials: paper, carton, plastic, metal, glass and cloth by using data from
the survey “Consumption behavior towards green growth in urban area of Viet
Nam”, funded by National Foundation for Science and Technology Development
(NAFOSTED). The survey was conducted in Ho Chi Minh City on April and May
2014, including 200 households from District 1, 3, 4, 9, Binh Thanh, Go Vap, Phu
Nhuan and Thu Duc. To collect data, investigators directly contacted the household
head for an interview, clearly explained the questions and choices, then recorded the
respondents' feedback.
1.5. The structure of this study
Five chapters will be constructed in this study as follows:
Chapter 1: Introduction. This is the beginning section of thesis, which consists of
the research topic and problem statement. The research objectives, research
questions and the research scope and data are also presented in this chapter. The
final section will provide the structure of the research.
2


Chapter 2: Literature review. This chapter provides with theoretical and empirical
reviews related to household waste recycling behavior. The first section will review
a related theory explaining recycling behavior, which is Random Utility Theory.
The empirical researches indicate that there are three main groups of factors that
affect individual’s behavior of recycling. They are socio-economic and
demographic characteristics, housing characteristics and psychological factors
towards recycling. Furthermore, these factors impact positively or negatively on
recycling behavior depending on the circumstances and the material as well.
Chapter 3: Methodology. This chapter presents research methods and conceptual
framework.
Chapter 4: Empirical results. This chapter starts with descriptive statistic and then
provides a bivariate relationship between household recycling behavior and some
important determinants. Finally, the regression results and interpretation are
presented.
Chapter 5: Conclusions. This chapter summarizes the findings and concludes with
some policy implication and research limitations.

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Chapter 2: LITERATURE REVIEW
This chapter provides with theoretical and empirical reviews related to household
waste recycling behavior. The first section will review random utility theory. The
empirical researches indicate that there are three main groups of factors that affect
individual’s behavior of recycling. They are socio-economic and demographic
characteristics, housing characteristics and psychological factors towards recycling.
Furthermore, these factors impact positively or negatively on recycling behavior
depending on the circumstances and the material as well.
2.1. Theoretical Review
A related theory explaining recycling behavior is Random Utility Theory by
Marschak (1960). This theory indicated that an individual utility could be defined
by two components: deterministic components and random components. So, the
total utility of a household i recycling waste are the sum of the two utility
components:
U1i = V1i + e1i
Where, V1i can be approximated by a linear function of recycling in the vector of Xi
and the population utility weights for each attribute in the vector i : V1i = 1iX1i. In
additional, e1i is random utility component.
Similarly, the total utility of a household i with non – recycling:
U0i = V0i + e0i
Where, V1i can be approximated by a linear function of recycling in the vector of Xi
and the population utility weights for each attribute in the vector i : V0i = 0iX0i. In
additional, e0i is random utility component.
The probability that a household recycles can be expressed as the probability that
the utility associated with recycling is higher than the utility of non – recycling:
Pr(recycling) = Pr (U1i > U0i)
Or Pr(recycling) = Pr (V1i + e1i > V0i + e0i) = Pr (e1i - e0i > V0i - V1i)
Or Pr(recycling) = Pr (e1i - e0i > 0iX0i- 1iX1i)

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We assume that error terms of alternatives do not correlate with each other and they
have the same variance and follow logistic distribution. In this case the probability
that a household chooses to recycle is a logit probability:
Pr(recycling ) 

e 1i X1i
e 0 i X 0 i

2.2. Empirical Review
Environmental degradation could destroy our natural habitat and recycling used
materials is argued to be a better solution to make the environment cleaner,
conserve materials, save energy and reduce garbage in landfills (Fiorillo, 2013). A
great deal of research and empirical studies have been carried out in order to
investigate the necessity and important role of recycling toward our life through the
following dependent variable: the number of materials recycled by the household or
individual (Dalen et al., 2011; Halvorsen, 2012), what kind of materials recycled
most and the factors influencing household recycling behavior (Halvorsen, 2008;
Hage et al., 2009).
The recent studies have investigated many kinds of household waste such as e-waste
(Dwivedy et al., 2013; Saphores et al., 2012), solid waste (Afroz et al., 2011; Thanh
et al., 2010). However, there are generally five most-examined materials as
followed: glass, paper, food, plastic and aluminum (Ferrara and Missios, 2012;
Fiorillo, 2013). In addition to that, there are three main factor groups influencing
household waste recycling behavior, namely: (i) Socio-economic and demographic
characteristics, (ii) Housing characteristics and (iii) Psychological factors towards
recycling (Ferrara and Missios, 2012; Fiorillo, 2013; Nixon et al., 2009; Hage et al.,
2009; Afroz et al., 2011; Dalen et al., 2011).
2.2.1. Socio-Economic and Demographic characteristics
Household Income
Income which is studied in most of researches relating to recycling behavior plays a
crucial role in affecting household recycling behavior of both household and
individual. Many studies have shown a significant positive relationship between
higher household income and recycling behavior (Halvorsen, 2008; Lee et al., 2011;
5


Ferrara and Missios, 2012; Fiorillo, 2013; Halvorsen, 2012; Hui Zhao et al., 2013).
Halvorsen (2008 and 2012) surveyed 1.162 and 10.251 households respectively
from Norway and 10 various OEDC countries in 2008 and indicated that the higher
income respondents have, the more likely they are to recycle household waste. In
2011, Lee et al. showed that higher income creates a higher incentive for
households to participate in both food separation and recycling through a survey
including 196 responses conducted in Seoul, Korea. Ferrara and Missios (2012)
used the sample of 10.251 respondents from 10 OEDC countries to conclude that
very richer households or individuals are more likely to take part in glass recycling
as well as recycle at a larger ratio of glass, plastic and aluminum. One year later,
Fiorllo (2013) conducted an empirical study of 47.643 households and found out
that people with high income tend to recycle all materials included paper, glass,
plastic and aluminum excepting for food waste. In the meanwhile, Hui Zhao et al.
(2013) researched a data of 500 questionnaires collected in Qingdao to prove the
positive correlation between higher income and recycling behavior. Moreover,
analyzing a data of 402 households in Dhaka City, they summarized that the
frequency of solid waste recycling behavior is influenced positively by the middle
of income group. These results implied that households on low and middle income
tend to take advantage of all materials to minimize the cost of buying new things.
Several other researchers have suggested a negative or insignificant correlation
between income and household waste recycling behavior (Hage et al., 2008;
Shaufique et al., 2010). Using a sample of 2800 household members in 4 different
Swedish municipalities in 2006, Hage et al. (2009) evaluated that income is not a
determinant of household recycling behavior of packaging waste which is similar to
the finding of Nixon et al. (2009) that examined households’ attitudes towards
recycling. On the other hand, Rafia et al. (2011) did not see the correlation between
income and recycling behavior in the direct manner. More interestingly,
Shaufique et al. (2010) demonstrated that income has a negative impact on
residential recycling rate per annum. Based on a data set of 774 respondents
representing 86 counties in Minnesota, the study of Shaufique et al. (2010)
calculated that if annual income per capita increase 1000 dollar then there will be a
6


0.2 percentage point decline in the rate of recycling. It was also an average income
group that raises the likelihood of reaching the aim of 150kg/capita residual
household waste of each municipality (Gellynck et al., 2011).
Gender
There are a great number of studies citing gender as a determinant of household
waste recycling behavior (Sterner and Bartelings, 1999; Saphores et al., 2012;
Fiorillo, 2012; Ferrara and Missios, 2012). Sterner and Bartelings (1999) analyzed
nearly 600 samples in Tvaaker which were gathered in 1994 to assess respondents’
willingness to pay for the attention of the waste and recycling issue. The variable
gender has a significant indication, which means that women are more probable to
pay and pay more than men. Similarly, Saphores et al. (2011) concerned that
women are easier to engage in e-waste recycling and be willing to recycle e-waste at
drop-off center than men after testing a data of 2136 households from a 2006
national survey of US. Noticeably, Ferrara and Missios (2012) and Fiorillo (2013)
proved the significant positive sign between gender and household recycling
behavior of five following materials: glass, plastic, paper, food and aluminum
waste. However, whilst Ferrara and Missios suggested that men are willing to
recycle and recycle more aluminum than women, Fiorillo found out that women are
ready to recycle all materials than men. These findings implied that the gender’s
impact on recycling trend maybe dependent on kind of waste materials as well as
survey area.
In contrast, basing on a survey of 10.000 households from 10 various OECD
countries, Dalen and Halvorsen (2011) indicated that women do not intend to
embark on recycling activities. Besides, the gender factor has also been examined in
a lot of researches, but the correlation is insignificant or very slight (Hage et al.,
2009; Lee et al., 2011; Pakpour et al., 2013; Ayalon et al., 2013; Huffman et al.,
2014).
Education
Many sudies showed that the role of education in recycling behavior is very
diferrent. Some scientific researches recognized the positive effect of education
level on practicing recycling behavior (Amy W. and Gosselin, 2005; Shaufique,
7


V.Joshi, Lupi, 2010; Dwivedy and Mittal, 2013; Pakpour et al., 2013; Lange et al.,
2014). Ando (2005) used the probit and double-censored tobit model to examine
recycling rates of 214 multifamily dwellings in Urbana, Illinois before concluding
that number of years of education present a positive correlation with container
recycling rate. Significantly, Anderson et al. (2013) applied the logistic regression
model in order to analyze a sample from the 2003 - 2006 and suggested education
level of household head plays a crucial role in making the decision to recycle.
Education also showed a negative impact on recycling behavior for monetary
reasons.
On the other hand, Sterner and Bartelings (1999) found a negative correlation of age
with household’s willingness to pay for caring for recycling issues which imply that
people with less education are willing to pay more. Similarly, Hage et al. (2009)
showed that education has a negative influence on paper recycling. However, some
empirical studies investigated that there is no impacts of education on recycling
behavior (Nixon et al., 2009; Lee et al., 2011; Ferrara and Missios, 2012; Byrne and
O’regan, 2014; Hui Zhao et al., 2013).
Age
Almost recent empirical studies have presented a positive correlation between age
and household waste recycling behavior (Ando et al., 2005; Shaufique et al., 2010;
Saphores et al., 2012; Pakpour et al., 2013; Hui Zhao et al., 2013; Lange et al.,
2014). As for Hage et al. (2009), age was deemed to be a positive determinant for
all packaging materials recycled by individuals and households. In the same pattern,
Ayalon, Sharon and Shechter (2013), through a survey of 12.000 households in
2011, proved age influence positively on household separation and recycling
behavior except for food and garden. Whilst Lee et al. (2011) presented the
importance of individuals being older in the Korean household recycling behavior,
Dalen et al. (2011) mentioned older women are easier to intend to recycle. Afroz et
al. (2011) and Fiorillo (2013) also found that respondent from 25 to 35 or from 51
to 60 years old will tend to engage in recycling activities for all materials.
However, a few recent studies showed negative relationship between age and
household waste recycling. According to Ferrara and Missios (2012), the younger
8


are less likely to recycle except plastic waste. Anderson et al. (2013) stated that age
of non – African household head has a negative effect on their recycling behavior.
While Sterner and Bartelings (1999) also demonstrated an inverse relationship
between age and recycling, there are many researches not finding any connection of
two above factors (Nixon et al.,2009; Byrne and O’regan, 2014).
2.2.2. Housing characteristics
House type
The relationship between home type with the intention of household recycling
behavior has been examined. Ando and Gosselin (2005) indicated that multifamily
dwellings living in the apartment having adequate storage space will achieve higher
recycling rate. According to Barr (2007), home type was deemed to be positively
impact on recycling intention through applying the standardized regression model to
analyze a data of 673 respondents of Exeter. Dalen and Halvorsen (2011) realized
that women living in detached house will be more likely to recycle. In 2012,
Halvorsen also found the similar impact of detached house on the number of
materials recycled. In another recent study, Dwivedy and Mittal (2013) illustrated
that among 150 respondents from India who living in an apartment will be more
willing to take part in e-waste recycling.
In contrast, using ordered probit analysis, Hage et al. (2009) proposed that
household living in an apartment tend to recycle less metal waste than others.
Moreover, Ferrara and Missios (2012) indicated that household living in a detached
or semi-detached is less probable to recycle. There are a few of the studies not
showing any relationship between two above variables (Nixon et al.,2009; Byrne
and O’regan, 2014).
House size
The correlation between house size and recycling behavior is investigated
differently among studies. Through a survey of 1507 households in the US, Nixon
et al., (2009) suggested that bigger house is really a determinant stimulating
homeowner to recycle. Lee et al. (2011) presented the similar implication that
people living in larger house are more probable to participate in food separation.
Interestingly, Ferrara and Missios (2012) applied the ordered probit analysis to point
9


out that size of residence will impact positively on household recycling and waste
prevention. The more rooms household owns, the more probable it is to engage in
recycling all materials except for plastic and aluminum waste. Afterwards, Fiorillo
(2013) suggested that people living in a house having from 1 to 5 rooms promote
the likelihood of recycling all materials except for food waste. However, another
empirical investigation did not propose any relationship between home size and
recycling behavior (Afroz et al., 2011).
2.2.3. Psychological factors towards recycling
With reference to empirical investigations, there is a great deal of studies depicting
the effect of psychological factors towards recycling behavior. The first and
foremost one would be Sterner and Bartelings (1999) suggesting that people
showing their concern toward the importance of waste are easier to pay for taking
care of household waste, whereas, respondents reporting difficulty of recycling
different materials are less likely to pay attention to recycling problem together with
recycling behavior.
Regarding Bruvoll et al. (2002), it was clear that there is an intense positive
relationship between sorting behavior and households’ attitudes toward sorting.
After examining psychological factors of 1162 interviewees in 1999, namely:
Perceiving sorting as mandatory, environmental considerations and moral
requirements, the authors pointed out that individuals concerning more about
natural habitats as their own and civic responsibility will definitely contribute to
classifying behavior.
In reference to Barr (2007), the most striking finding was that individuals who
considered recycling to be the norm of the society are more probable to recycle than
others. It was also assumed that recycling willingness and intensity are able to be
stimulated by providing households convenient recycling facilities. For instance, if
respondents perceive the presence of drop-off locations or feel easy to access
curbside recycling services, it is certain that residential recycling rate per annum
will increase substantially (Shaufique et al., 2010, X. Gellynck et al., 2011)
Furthermore, the habit of recycling organic waste encouraged municipalities to
minimize their domestic waste.
10


In 2008, Halvorsen applied ordinary least square estimation (OLS) to evaluate the
number of fractions recycled by the household and pointed out that belief of
recycling is the strongest factor. In another later study, Halvorsen and Dalen (2011)
used the same dependent variable is the number of materials recycled by the
household. This study described that women’s recycling efforts are more
encouraged by their concern about waste generation, whilst men respond more to
the beliefs of better environment. Furthermore, Halvorsen (2012) illustrated that
people in an environmental organizations will tend markedly to recycle more than
others. In addition, respondent concerned about waste generation, water pollution
and believing of recycling as their civic duty is more probable to recycle. However,
in 2012, Halvorsen also indicated that people who have concern about climatic
change will concentrate their efforts on other green-friendly activities rather than
recycling.
According to Nixon et al. (2009), respondents will recycle more if it is believed that
recycling behavior is the determinant of reducing the use of landfills significantly
and conserving natural resources effectively. Moreover, households’ recycling
participation was also proven to arise from environmental benefits rather than
economic benefits. Noticeably, this study demonstrated that people concurring with
how internal values and morals affecting pro-environmental behavior or feeling a
moral obligation to recycle are willing to recycle and recycle more by 7.2 times than
those dissenting with the above statement. Interestingly, the results of the study also
showed a positive relationship between information sources and how information
influences the intention to recycle, namely: respondents who receive recycling
information from print and family/friends, print and work/school are more likely to
recycle by 7.3 and 5.0 times respectively. Also, they implied that the more
information resources households receive, the more likely they are to engage in
recycling behavior.
In 2009, Hage et al. showed a highly positive correlation between moral obligation
and recycling behavior which means that households who are aware of their
personal responsibility will do recycle all materials, particularly paper, glass and
metal. It is assumed that the perception of others’ recycling effort, concern about the
11


environmental impacts of waste disposal, beliefs of their recycling efforts are
crucial incentives for respondents to engage in recycling, too. In other words,
individuals who perceive the negative externality arising from desist from recycling
will be willing to recycle more their packing waste, especially plastic materials.
Conversely, households who are not aware of the implication of their throwing
away packaging waste with the environment seem not to engage in recycling
efforts.
Another recent study, Lee et al. (2011) applied the new environmental paradigm
(NEP) index to analyze the respondent’s environmental attitudes and behavior.
According to that, people showing higher concerns about the environment implied
higher degrees of recycling participation. Moreover, household’s attitude, higher
agreement and interest in waste management had a significant positive impact on
food classifying and recycling behavior.
Afterwards, Afroz et al. (2011) showed a positive and correlation between the
household’s attitude and recycling behavior. Specifically, environmental awareness,
willingness to separate and minimize the household waste and respondent’s belief
toward solid waste management practices are the determinants encouraging them to
produce less waste and recycle more.
Besides that, Bao (2011) analyzed a sample of 1523 students in Turku through
Pearson chi-square statistic and suggested positive, relevant implication of recycling
and psychological factors, namely: respondents reported higher concern about
sustainable development and believing Recycling assists to conserve the
environment will show higher degrees of desire toward recycling. Additionally,
individuals accepting moral norms as well as considering recycling as their
responsibility are more willing to take part in recycling. Finally, recycling behavior
is able to be stimulated by giving individuals more information on waste separation,
making recycling more convenient and guiding them the destination of separated
waste.
In 2011, the report of OECD about green household behavior showed availability of
convenient recycling service and the characteristics of recycling collection services
also impact positively on recycling behavior. Curbside recycling facility and drop12


off system increase aluminum recycling rate by 34 percent. This scientific source
also proposed that recycling intention and participation for glass, plastic and
aluminum are promoted markedly by individuals’ environmental attitudes.
Recycling programs are also cited as positive factors impacting on the likelihood of
household recycling decision, particularly aluminum waste.
One year later, Saphores et al. pointed out moral beliefs are considered as the most
statistically significant variables toward recycling behavior followed by e-waste
recycling convenience and mandatory recycling at work or school respectively. In
other words, households perceiving their responsibility as voluntary or mandatory
and being aware of ease of recycling will tend to recycle e-waste more considerably
than others. Furthermore, recycling participation can be encouraged by providing
households knowledge about the potential of danger of e-waste.
In addition, Ferrara and Missios (2012) found evidences proving that respondents
reported higher concern about environmental problems are at higher levels of
participation of glass, plastic, aluminum and food waste recycling. Besides, the
individuals’ environmental attitude variables are assumed to be the determinants
promoting glass, plastic and aluminum waste recycling. Whether and what extent to
which recycling is considered to be beneficial for the environment need taking into
account as positive factors toward recycling behavior.
In 2013, Fiorillo indicated that respondents stating no dirtiness problems at the
residence will increase the probability of recycling glass, paper and plastic waste. If
a household claims that there is no pollution around area they live, his/her
likelihood of recycling is lower than others, especially for paper, plastic and
aluminum waste. Conversely, if an individual perceives the habitat being clean day
by day, he/she then tries to keep and encourage recycling behavior continually.
Moreover, Pakpour et al. (2013) used data from 1782 households of 8 urban health
centers in the Qazvin city to emphasize the importance of individuals’ attitude,
subjective rules, perceived behavioral control, moral obligation and self-cognition
towards waste recycling behavior. According to that, households who reported
higher concern to these above variables will stimulate recycling considerably.
13


Ayalon et al. (2013) analyzed the survey of OECD and suggested that households
reported higher concern about environmental issues are less likely to refuse to
participate in waste recycling. This study stressed the positive impacts of
environmental motivations toward recycling in all countries surveyed except for
Israel citizen. Obviously, being aware of the recycling service available like dropoff center will encourage individuals to recycle more.
Moreover, Anderson et al. (2013) investigated the decision to recycle among urban
South Africans and demonstrated that households who see littering as a community
issue are more willing to recycle than others do not. The two main factors, including
greater awareness of environmental concern and greater access to recycling
facilities play an important role in mitigating household waste and rising waste
recycling rate. Hui Zhao et al. (2013) also stated that environmental concern had a
positive effect on recycling behavior.
In 2014, Byrne and O’regan have constructed statements to evaluate the reason why
or why not recycling was considered as a daily routine. Statistics showed that
almost respondents had positive recycling habits and individual’s recycling efforts
would create a significant incentive for others to recycle. In some cases, households
suggested that they will tend to recycle if received more information and knowledge
about recycling facilities supplied by waste collectors. In another latest study,
Huffman et al. (2014) supported the view that there is a strict interaction between
social norms and recycling attitudes as well as self-reported recycling behavior. In
other words, individuals who perceive a high social effect are more likely to
announce that they will engage in waste recycling.
The appendix 1 provides the summary of above empirical studies on household
waste recycling behavior in a more visual way.

14


Chapter 3: RESEARCH METHODOLOGY
This chapter presents research methods and conceptual framework.
3.1. Conceptual framework and the econometric model
Conceptual framework
Based on the theoretical and empirical reviews, a conceptual framework is built as
the Figure 1 following. The empirical researches indicate that there are three main
groups of factors that affect individual’s behavior of recycling. They are socioeconomic

and

demographic

characteristics,

housing

characteristics

and

psychological factors towards recycling.
Figure 1. Conceptual framework
Socio-Economic and
Demographic
Characteristics
 Age
 Gender
 Education
 Household Income
Household waste
recycling
behavior

Psychological factors towards
Recycling
 Concern about waste
 Household’s awareness of impacts
on environment
 Willingness to protect the
environment
 Household’s satisfaction of waste
condition at residency
 Belief of economic benefits of
recycling

Housing characteristics
 House type
 House size

The econometric model
Using Stata, six logistic regression models are estimated for household waste
recycling behavior towards six different materials: metal, carton, paper, plastic,
glass and cloth. Household recycling behavior is investigated through the question:
“Which of the following materials does your family usually recycle or collect for
vendors?” The possible answer to each material is “yes” or “no”. The response for
15


that material is coded into a binary variable which means 1 in case of “yes” and 0
otherwise.
Table 1. Description of the variables
No

Variables

Description

Dependent variables
Dummy variable, = 1 if household
1

plastic

recycles plastic bottles and utensils, = 0
otherwise
Dummy variable, = 1 if household

2

metal

recycles containers and utensils made of
iron, steel, stainless steel, aluminum, = 0
otherwise

3

glass

Dummy variable, = 1 if household
recycles glass objects, = 0 otherwise
Dummy variable, = 1 if household

4

paper

recycles paper and newspaper, = 0
otherwise

5

carton

6

cloth

Dummy variable, = 1 if household
recycles carton, = 0 otherwise
Dummy variable, = 1 if Household
recycles old clothes, = 0 otherwise

Independent variables
Socio-Economic and Demographic Characteristics
1

age

2

gender

Age of the respondent
Gender of the respondent (1= male. 0 =
female)
Education

3

education

of

the

respondent

(1=

completed vocational school, college,
university; 0 = no education, completed
elementary school, secondary school, high
16


school)
4

ln_income

Logarithm of total average income per
month (in million dongs)

Housing characteristics
5

house_size

6

house_type

Size of house (in squared meter – m2)
Type of house (1 = detached or semidetached house; 0 = apartment)

Psychological factors towards Recycling
Dummy variable, = 0 if the respondent’s
degree of concern over waste management
7

concern

“No idea” or “Not concerned” or “Fairly
concerned” ; = 1 if the respondent’s
degree of concern over waste management
“Concerned” or “Very Concerned”.
Dummy variable, = 0 if the respondent’s
awareness

degree

of

environmental

impacts on human life “No idea” or
8

life

“Disagree” or “Fairly Agree”; = 1 if the
respondent’s

awareness

degree

of

environmental impacts on human life
“Agree” or “Strongly Agree”.
Dummy variable, = 0 if the respondent’s
awareness

degree

of

environmental

impacts on future generation “No idea” or
9

inheritance

“Disagree” or “Fairly Agree”; = 1 if the
respondent’s

awareness

environmental

impacts

degree
on

of

future

generation “Agree” or “Strongly Agree”.
10

longevity

Dummy variable, = 0 if the respondent’s
awareness
17

degree

of

environmental


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