(Luận văn thạc sĩ) 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 i 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 Problem statement .1 1.1.1 Real world problem 1.1.2 Scientific problem 1.2 Research objectives 1.3 Research questions 1.4 Research scope and data 1.5 The structure of this study Chapter 2: LITERATURE REVIEW .4 2.1 Theoretical Review 2.2 Empirical Review 2.2.1 Socio-Economic and Demographic characteristics 2.2.2 Housing characteristics 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 iii 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 Description of the variables 16 Table Prevalence of recycling toward six materials 22 Table Descriptive statistics of numerical variables 23 Table Descriptive statistics of binary variables 24 Table A comparison between metal recycling and non-recycling in term of income, age and size of housing 24 Table A comparison between metal recycling and non-recycling in term of gender, type of housing and education 25 Table A comparison between carton recycling and non-recycling in term of income, age and size of housing 27 Table A comparison between carton recycling and non-recycling in term of gender, type of housing and education 27 Table 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 Conceptual framework 15 Figure A comparison between metal recycling and non-recycling in term of some selected psychological factors 26 Figure A comparison between carton recycling and non-recycling in term of some selected psychological factors 28 Figure A comparison between paper recycling and non-recycling in term of some selected psychological factors 31 Figure A comparison between plastic recycling and non-recycling in term of some selected psychological factors 33 Figure A comparison between glass recycling and non-recycling in term of some selected psychological factors 35 Figure A comparison between cloth recycling and non-recycling in term of some selected psychological factors 37 vi 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 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 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 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 Illinois tobit model Barr, S (2007) A sample of 673 respondents of Exeter, UK in 1999 The standardized regression model The frequency and willingness to recycling Halvorsen, B (2008) A sample of 1162 respondents between the age of 16 and 79 from the Norwegian population OLS, Ordered Probit Model The household recycling effort on the following fractions: paper and cardboard, drink cartons, glass, metal, plastic and organic waste Household Income (+) Nixon, H., & Saphores, J D M (2009) A sample of 1507 respondents in US Logistic regression model Decision to recycle Income (n.s.) Education (n.s.) Age (n.s.) 49 education (+ for storage space) container recycling) Home type (+ for recycling intention) Housing type (n.s.) Homeowner (+) Household size (+) (larger house) recycling bins (+) (for container recycling intensity) Acceptance of the norm (+) Good recycling facilities (+) Awareness of local recycling service and its convenience (+) Believing recycling contribute to a better environment (+) Living according to Golden Rule (+) Concern about oneself-respect (+) Perceiving recycling as mandatory (+) the money incentives (+) the number of fractions collected by the municipality (+) Perceive recycling barriers(-), Moral beliefs (+), Awareness of the consequences of recycling towards Hage, O., Söderholm, P., & Berglund, C (2009) Thanh, N P., Matsui, Y., & Fujiwara, T (2010) A sample of The ordered 2800 household Probit model members in different Swedish municipalities (Pitea, Huddinge, Vaxjo, Gothenburg) in 2006 A sample of 100 households in Can Tho city, Viet Nam in 2009 Lee, S., & A sample of Paik, H S 196 responses (2011) collected through a survey conducted in Seoul, Korea in June and July 2008 Sidique, S F., A sample of Joshi, S V., 774 What extent households recycle packaging waste (paper, plastic, glass and mental) without refund payment Gender (n.s.) Income (n.s.) Education (n.s) (except for paper packaging where the coefficient is negative and significant) Age (+) (for all packaging materials) Income (+ for food waste) Housing type (Department) (for metal) environment (+) Moral obligation (+ for glass, mental, paper) Perception of others’ recycling effort (+) Perception of Impact (+, particularly for plastic) The regression model The household solid waste generation rate per resident Correlations and multiple regression model Waste management behavior Older age (+) Bigger house (+) Higher income (for food (+) Gender (n.s) separation) Education (n.s) Higher concerns about environment (+) Higher interests to waste management (+) The pooled OLS model Residential recycling rate Age (+) Higher Education (+) Easy access to curbside recycling 50 Household size (-) (for plastic, food, metal, total waste) & Lupi, F (2010) Dalen, H M., & Halvorsen, B (2011) OECD, G H B (2011) observations representing 86 counties in Minnesota obtained from the Minnesota SCORE database and the US Census Bureau A sample of 10.000 respondents from 10 OECD countries (Norway, Sweden, Canada, France, Italy, The Netherlands, The Czech Republic, Mexico, Australia, Korea) in 2008 The data from the OECD survey per annum (percentage) Income (-) OLS The number of Age (+) (Male) materials Gender (-) recycled by the (Female) household (glass, plastic, aluminium, metal containers, paper/ cardboard, food, garden waste, batteries and pharmaceuticals The multivariate empirical Household recycling rate 51 services and The presence of recycling drop-off center (+) Educating the public (+) Detached house (+) (Female) Concern about waste generation (+) (female than male) Believing can contribute to a better environment (+) (male than female) Availability of recycling service (+) Individual’s analysis Afroz, R., A sample of Tudin, R., 402 households Hanaki, K., & in Dhaka City Masud, M M (2011) OLS regression and Logistic regression Model Willingness to minimize solid waste and (how often households recycled their solid waste) Bao, R (2011) The cross tabulation and Pearson chi-square statistic model Student’s attitude, knowledge and behavior about recycling Gellynck, X., Jacobsen, R., A sample of 1523 responses in Finnish, 116 in Swedish, 195 in English, conducted by the Student Village Foundation of Turku The final sample of 295 The empirical Each logit model, municipality 52 Middle income House size (n.s) group (+) Age (+) (Aging from 25 to 35) Average Income (+) environmental attitudes (+) (for glass, plastic and aluminium) Volumebased charges (n.s) Recycling promgrammes and Recycling collection systems (+) Environmental consciousness (+) Willing to separate the waste (+) No economic incentive () Establishment of a solid waste management (+) Sustainable development (+) Moral norm of responsibility (+) Lack of space (-) Do not have enough information (-) Other people’s behavior (n.s) Drop-off recycling services (+), & Verhelst, P municipalities (2011) in the Flemish Region in 2003 the binary logistic regression model Halvorsen, B (2012) OLS Ferrara, I., & Missios, P (2012) A sample of 10.251 respondents among 10 OECD countries: Australia, Canada, Czech Republic, France, Italy, Korea, Mexico, Norway, Netherland and Sweden A sample of 10.251 respondents from a cross section of 10 countries, namely: Ordered probit model has a probability of reaching the goal of 150 kg/capita residual household waste The number of materials recycled by the household Indicators for recycling particular materials (glass, plastic, aluminum, paper, and 53 Frequency of recycling of organic waste (+) Household Income (+) Detached house (+) Environmental concern (n.s.) Concern about climatic change (-) Member of environmental organization (+) Civic Duty (+) Concern about waste generation (+) Concern about water pollution (+) Gender (+) (Male, recycling participation and intensity for aluminum) Young Age (–) Ownership and size of residence (+) home size (captured by number of rooms) (+) (for all except for plastic and Concern for environmental problems (+) (for all except for paper) Attitude towards waste generation (n.s.) Individual’s Saphores, J D M., Ogunseitan, O A., & Shapiro, A A (2012) Pakpour, A H., Zeidi, I M., Emamjomeh, M M., Asefzadeh, S., & Pearson, H (2014) Anderson, B A., Romani, J H., Wentzel, M., Australia, Canada, Czech Republic, France, Italy, Korea, Mexico, Netherlands, Norway and Sweden in 2008 A sample of 2136 respondents from the 2006 national survey of US food) and proportions of materials recycled as captured by integers to (for all except for plastic) Education (n.s.) Income (+) (for glass recycling) The ordered logit model Household willingness to recycle e-waste at drop-off centers and ewaste recycling Gender (+) Education (+) Age (+) but play only a minor role Income (n.s.) A sample of 1782 households of urban health centers in the city of Qazvin The hierarchical multiple regression model The household waste recycling behavior Age (+) Education (+) Gender (n.s.) The data are from the 2003, 2005, 2006 General The logistic regression model Perceptions of littering as a community problem and the Education of the household head (+) (for White, Asian 54 aluminium) environmental Detached or semi- Attitude (+) (for detached (-) glass, plastic, aluminium) Environmental concern (+) Moral beliefs (+) Recycling convenience (+) Prior e-waste recycling behavior (+) Knowledge of potential toxicity (+) Moral Obligation (+) Perceived behavioral control (+) Selfidentity (+) Past recycling behavior (+) Attitude (+) Awareness of environmental concern (+) Greater access to recycling & Phillips, H E (2013) Household Surveys conducted by Statistics South Africa decision to recycle Fiorillo, D (2013) A sample of 47 Probit Model 643 observations in 1998 and 2000 surveyed annually by the Italian Central Statistical Office Household recycling behavior on five different materials: paper, glass, plastic, aluminium and food waste Zhao, H H., Gao, Q., Wu, Y P., Wang, Y., & Zhu, X A sample of 500 questionnaires collected in Recycling behavior Correlation analysis and multiple regression 55 and Colored household except for African household) Education (-) (recycling for monetary reasons) Age of household head (+) (for nonAfrican household) (–) (for African household) Gender (+) (Female, for all materials) Age (+) (51-60, for all materials) Low education (-) Income (+) (for all except for food waste) Education level (n.s) Income (+) Age (+) Gender (n.s) facilities (+) Household characteristics (n.s.) No traffic problems (+) (for all), No pollution (-) (for plastic, paper, aluminium) No dirtiness problems (+) (for paper, plastic, glass) Civil house (+) (for paper, glass, plastic) Using behavior (+) Environmental concern (+) D (2014) Dwivedy, M., & Mittal, R K (2013) Ayalon, O., Brody, S., & Shechter, M (2013) Huffman, A H., Van Der Werff, B R., Henning, J B., & WatrousRodriguez, K (2014) Qingdao, Shandong Peninsula in 2009 A sample of 150 respondents from India A sample of about 12000 households obtained from the 2011 Survey on Environmental Policy and Individual Behavior Change (EPIC) A sample of 118 undergraduate students from the southwest model The logistic regression model Willingness to participate in ewaste recycling Education (+) Home type (+) (apartment) Economic benefit (+) Descriptive analysis model The determinants of household separation and recycling Home-owner (+) The moderated regression model Recycling attitudes and how various motivating factors are differently important to self-reported and observed recycling Gender (n.s) Age (+) (except for food and garden waste separation and recycling in Korea, Japan where this trend declines with respondent’s age) Gender (n.s.) Awareness of service availability for recyclables (+) Concern for environmental issues (+) Awareness of drop-off center (+) Being beneficial for environment (+) (for all countries involved except for Israel) Social norms (+) (for recycling attitudes and self-reported recycling behavior) 56 Lange, F., Brückner, C., Kröger, B., Beller, J., & Eggert, F (2014) Byrne, S., & O’Regan, B (2014) A sample of 306 participants was collected in and around the city of Braunschweig, Lower Saxony, Germany in 2013 A sample of 509 respondents from schools in Limerick City and County Hierarchical linear regression model Frequency analysis, cross tabulation and chisquared tests behavior The citizen’s tendency to participate in recycling Reasons for recycling 57 Age (+) Education (+) Age (n.s.), Education (n.s) Perceived distance (-) Homeowner (+) Home type (n.s.) Other’s recycling (+) Appendix Stata result for the logit model of metal recycling Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -133.43779 -107.5385 -106.73273 -106.71619 -106.71618 Logistic regression Number of obs LR chi2(17) Prob > chi2 Pseudo R2 Log likelihood = -106.71618 metal Coef ln_income age gender education house_size house_type concern life inheritance longivity tradeoff energy water waste transportation waste_condition money _cons -.2670899 -.0388664 -.0067328 -.4926176 0270576 -.1203363 -.4831615 -.3058462 5.510227 -1.365895 -.4007055 -.9581487 -1.90381 8825093 -.0019414 -.8626452 7587567 2835366 Std Err .2788463 0154525 3546765 4108376 0073315 4461672 4260293 1.078311 2.212599 1.475605 4602194 1.288968 1.831914 7152291 4617951 3866979 4290437 1.893725 58 z -0.96 -2.52 -0.02 -1.20 3.69 -0.27 -1.13 -0.28 2.49 -0.93 -0.87 -0.74 -1.04 1.23 -0.00 -2.23 1.77 0.15 P>|z| 0.338 0.012 0.985 0.231 0.000 0.787 0.257 0.777 0.013 0.355 0.384 0.457 0.299 0.217 0.997 0.026 0.077 0.881 = = = = 194 53.44 0.0000 0.2003 [95% Conf Interval] -.8136186 -.0691528 -.7018859 -1.297845 0126881 -.9948079 -1.318164 -2.419296 1.173613 -4.258027 -1.302719 -3.484479 -5.494296 -.5193139 -.9070431 -1.620559 -.0821535 -3.428096 2794389 -.00858 6884203 3126093 0414271 7541352 3518405 1.807604 9.846841 1.526237 501308 1.568181 1.686676 2.284333 9031604 -.1047312 1.599667 3.995169 Appendix Stata result for the logit model of carton recycling Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -135.48927 -116.34657 -115.96002 -115.95898 -115.95898 Logistic regression Number of obs LR chi2(17) Prob > chi2 Pseudo R2 Log likelihood = -115.95898 carton Coef ln_income age gender education house_size house_type concern life inheritance longevity tradeoff energy water waste transportation waste_condition money _cons -.3205379 -.0191955 -.4563607 -.3928354 0137528 4198167 1441283 -.5587165 1.721 -.172574 6102813 -.4424882 -1.27992 -.5195681 1.205399 0607028 1.182649 -.4283735 Std Err .2677887 0148742 3409785 3885789 0061665 4395486 3925798 1.049904 1.508431 1.201615 4420507 987251 1.219796 6358451 4441119 3712259 4249725 1.535823 59 z -1.20 -1.29 -1.34 -1.01 2.23 0.96 0.37 -0.53 1.14 -0.14 1.38 -0.45 -1.05 -0.82 2.71 0.16 2.78 -0.28 P>|z| 0.231 0.197 0.181 0.312 0.026 0.340 0.714 0.595 0.254 0.886 0.167 0.654 0.294 0.414 0.007 0.870 0.005 0.780 = = = = 196 39.06 0.0018 0.1441 [95% Conf Interval] -.845394 -.0483483 -1.124666 -1.154436 0016667 -.4416826 -.625314 -2.61649 -1.23547 -2.527696 -.2561223 -2.377465 -3.670675 -1.765802 3349561 -.6668866 3497184 -3.438531 2043182 0099573 211945 3687653 0258389 1.281316 9135707 1.499057 4.677469 2.182548 1.476685 1.492488 1.110835 7266654 2.075843 7882921 2.01558 2.581784 Appendix Stata result for the logit model of paper recycling Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -114.39822 -98.953431 -98.265192 -98.262503 -98.262503 Logistic regression Number of obs LR chi2(17) Prob > chi2 Pseudo R2 Log likelihood = -98.262503 paper Coef ln_income age gender education house_size house_type concern life inheritance longevity tradeoff energy water waste transportation waste_condition money _cons -.256894 -.0325088 -.1372261 4419989 0130001 -.5011199 1.063535 3058261 1.29805 -.2686426 -.5634145 -1.222496 1.762552 1998088 -.1266324 -.6631461 1130211 8156038 Std Err .2887125 0162763 3819872 4406529 0068531 5109673 395 8951642 1.333591 1.304176 4760188 1.401814 1.515041 6717344 4839569 4207552 4535882 1.536051 60 z -0.89 -2.00 -0.36 1.00 1.90 -0.98 2.69 0.34 0.97 -0.21 -1.18 -0.87 1.16 0.30 -0.26 -1.58 0.25 0.53 P>|z| 0.374 0.046 0.719 0.316 0.058 0.327 0.007 0.733 0.330 0.837 0.237 0.383 0.245 0.766 0.794 0.115 0.803 0.595 = = = = 196 32.27 0.0139 0.1410 [95% Conf Interval] -.8227602 -.0644098 -.8859073 -.4216649 -.0004318 -1.502597 2893489 -1.448663 -1.31574 -2.824781 -1.496394 -3.97 -1.206873 -1.116766 -1.075171 -1.487811 -.7759954 -2.195002 3089722 -.0006078 6114551 1.305663 026432 5003577 1.83772 2.060316 3.91184 2.287496 3695652 1.525008 4.731978 1.516384 8219057 1615189 1.002038 3.826209 Appendix Stata result for the logit model of plastic recycling Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = = = = = -119.04377 -103.40512 -102.57851 -102.57128 -102.57128 Logistic regression Number of obs LR chi2(17) Prob > chi2 Pseudo R2 Log likelihood = -102.57128 plastic Coef ln_income age gender education house_size house_type concern life inheritance longevity tradeoff energy water waste transportation waste_condition money _cons -.7636964 -.013394 -.1116593 -.0106815 0071988 1.150108 -.6536497 4979596 -1.901115 1.699145 7219153 -1.048607 -1.062145 0639164 -.2667513 4872445 8993118 3.146401 Std Err .3069352 0157898 3694109 4167778 0065792 450388 4699738 1.120628 1.802345 1.342049 4593658 1.140675 1.562251 6852111 4731652 4013549 3969072 1.833028 61 z -2.49 -0.85 -0.30 -0.03 1.09 2.55 -1.39 0.44 -1.05 1.27 1.57 -0.92 -0.68 0.09 -0.56 1.21 2.27 1.72 P>|z| 0.013 0.396 0.762 0.980 0.274 0.011 0.164 0.657 0.292 0.205 0.116 0.358 0.497 0.926 0.573 0.225 0.023 0.086 = = = = 196 32.94 0.0115 0.1384 [95% Conf Interval] -1.365278 -.0443414 -.8356914 -.827551 -.0056961 2673641 -1.574781 -1.69843 -5.433646 -.9312229 -.1784252 -3.284289 -4.1241 -1.279073 -1.194138 -.2993967 121388 -.4462675 -.1621145 0175535 6123728 806188 0200937 2.032853 2674819 2.694349 1.631416 4.329512 1.622256 1.187076 1.99981 1.406905 6606353 1.273886 1.677236 6.73907 Appendix Stata result for the logit model of glass recycling note: inheritance != predicts failure perfectly inheritance dropped and 10 obs not used Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = -104.04144 = -95.563812 = -95.220976 = -95.21988 = -95.21988 Logistic regression Log likelihood = Number of obs LR chi2(16) Prob > chi2 Pseudo R2 -95.21988 glass Coef ln_income age gender education house_size house_type concern life inheritance longevity tradeoff energy water waste transportation waste_condition money _cons -.5794502 0008655 -.065797 -.0169152 0124688 379766 1320166 1524019 -1.475476 6386823 -1.708408 -.4965143 1100461 7070966 -.2241362 7250377 1.038229 Std Err .3131399 0164981 3910419 4321947 0067766 523597 4475454 1.302981 (omitted) 1.389319 544561 1.002502 1.274054 7447053 5269461 4094869 5022737 2.22464 62 z P>|z| = = = = 186 17.64 0.3452 0.0848 [95% Conf Interval] -1.85 0.05 -0.17 -0.04 1.84 0.73 0.29 0.12 0.064 0.958 0.866 0.969 0.066 0.468 0.768 0.907 -1.193193 -.0314701 -.8322251 -.8640012 -.0008132 -.6464653 -.7451562 -2.401395 0342928 0332011 7006311 8301708 0257508 1.405997 1.009189 2.706198 -1.06 1.17 -1.70 -0.39 0.15 1.34 -0.55 1.44 0.47 0.288 0.241 0.088 0.697 0.883 0.180 0.584 0.149 0.641 -4.198491 -.4286377 -3.673275 -2.993614 -1.349549 -.3256988 -1.026716 -.2594007 -3.321985 1.247538 1.706002 2564593 2.000585 1.569642 1.739892 5784434 1.709476 5.398443 Appendix Stata result for the logit model of cloth recycling note: inheritance != predicts failure perfectly inheritance dropped and 10 obs not used Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood = -120.36305 = -112.05903 = -111.97702 = -111.977 = -111.977 Logistic regression Log likelihood = Number of obs LR chi2(16) Prob > chi2 Pseudo R2 -111.977 cloth Coef ln_income age gender education house_size house_type concern life inheritance longevity tradeoff energy water waste transportation waste_condition money _cons -.2600265 -.0063013 -.3784648 0006767 0016776 -.6105796 -.3044719 -1.978829 5880294 -.0425627 -1.207674 1.542721 -.55821 572286 5860081 4071505 1.48723 Std Err .2708486 014647 352804 3923531 0063171 4303647 3944475 1.241583 (omitted) 1.304175 4395127 9507015 1.252913 609485 4579311 3832362 4266327 2.159339 63 z P>|z| = = = = 186 16.77 0.4005 0.0697 [95% Conf Interval] -0.96 -0.43 -1.07 0.00 0.27 -1.42 -0.77 -1.59 0.337 0.667 0.283 0.999 0.791 0.156 0.440 0.111 -.79088 -.0350088 -1.069948 -.7683213 -.0107037 -1.454079 -1.077575 -4.412288 2708269 0224063 3130184 7696747 014059 2329197 468631 4546299 0.45 -0.10 -1.27 1.23 -0.92 1.25 1.53 0.95 0.69 0.652 0.923 0.204 0.218 0.360 0.211 0.126 0.340 0.491 -1.968107 -.9039918 -3.071015 -.9129439 -1.752779 -.3252425 -.165121 -.4290342 -2.744996 3.144166 8188664 6556665 3.998386 6363586 1.469815 1.337137 1.243335 5.719457 ... 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? ... 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... 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?