UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM –THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AIRLINE CHOICE FOR DOMESTIC FLIGHTS IN VIETNAM: APPLICATION OF MULTINOMIAL LOGIT MODEL BY TRAN PHUOC THO MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, December 2016 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AIRLINE CHOICE FOR DOMESTIC FLIGHTS IN VIETNAM: APPLICATION OF MULTINOMIAL LOGIT MODEL A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRAN PHUOC THO Academic Supervisor: TRUONG DANG THUY HO CHI MINH CITY, December 2016 ACKNOWLEDGEMENT First of all, I would like to express my gratitude supervisor Dr Truong Dang Thuy of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics for his patience, enthusiasm, and immense knowledge He not only guided me to the right direction but also continuously supported in overcoming a lot of obstabcles in my research Second, I would like to thank all of the respondents for spending their time to answer the questions in my survey They contribute significantly in collecting data for my study Without their participation, I am sure that the survey could not be conducted successfully Finally, my sincere thanks also go to my family and my friends for encouraging me throughout two years of study as wel as throughout the process of researching and writing this thesis Thank you Tran Phuoc Tho December, 2016 I ABBREVIATIONS RUM Random Utility Model SP Stataed Preference RP Revealed Preference VNA Vietnam Airline VJ Vietjet Air BL Jetstar Pacific LCC Low cost carrier II ABSTRACT In 2015, Vietnam witnessed the booming of airline industry The participation of low cost carriers makes the airline market more and more competitive Understanding the behavior of passengers is essential for any carriers to make their strategic policies This study employs the multinomial logit model with the data of 122 respondents to investigate the impacts of characteristics of passengers as well as attributes of airlines on the airline choice The characteristics of passengers include age, gender, marital status, education, and income whereas the attributes of airlines consist of price, number of flights of airlines, punctuality, comfort of seat space, and quality of check in service A stated preference survey is conducted online from 16th to 23rd of October 2016 to collect the data of 122 respondents, who used to travel by air at least one time before They are required to finish three tasks The first task is providing their information, such as age, gender, marital status, education, and income The second one is evaluating about the quality of services of the three airlines, including Vietnam Airline, Vietjet, and Jetstar The final part is hypothetical scenarios of fifteen domestic routes given along with the prices of airlines for the respondents to choose one of the three airlines Jetstar is chosen as the base outcome, the results of multinomial logit model suggest that characteristics of airlines have relationships with the ratios of probability of chosing Vietnam Airline or Vietjet over probability of chosing Jetstar, except for the satisfaction of customers about staff at the check in counter When comparing one airline and the based airline (Jetstar), the attributes of the third airline is also necessary to be taken into consideration In general, a good judgment of service of an airline makes the odds ratios of that airline and the base increased In contrast, a good evaluation of the based carrier or of the other airline makes the odds ratios declined Besides that, income has positive association with probability of choice Vietnam Airline and Vietjet but negative relation with Jetstar, holding other variables constantly III TABLE OF CONTENTS Contents ACKNOWLEDGEMENT I ABSTRACT B TABLE OF CONTENTS: IV LIST OF TABLES VI LIST OF FIGURES VII INTRODUCTION 1.1 Problem statement a Overview of airline industry b Airline industry in Vietnam 1.2 Research objectives 1.3 Research questions 1.4 Scope of the thesis 1.5 Structure of thesis LITERATURE REVIEW 2.1 Theoretical review a Random Utility Model (RUM) b Reveal Preference & Stated Preference survey 2.2 Empirical review RESEARCH METHODOLOGY 13 3.1 Stated preference method 13 3.2 Questionnaire and survey process 14 3.3 Attributes of airlines 16 3.4 Model specification 18 DATA & EMPIRICAL RESULTS 23 4.1 Data 23 4.2 Empirical results 31 a Controlling variables 35 b Attributes of airline 37 IV c Effect of different routes 38 CONCLUSION 41 REFERENCES i APPENDIX v V LIST OF TABLES Table 3.1 Summary of hypothetical scenarios in survey: 15 Table 3.2 Attributes of airline: 17 Table 3.3 Prices and numbers of flights by routes of carriers 20 Table 3.4 Description of variables: 21 Table 4.1 Social demographic characteristics 27 Table 4.2 Estimation results of multinomial logit model 32 VI LIST OF FIGURES Figure 3.1 The screen of the online survey 16 Figure 4.1 Airline Choice for Destinations 24 Figure 4.2 Frequency Of Income 25 Figure 4.3 Willingness to pay for routes 26 Figure 4.4 Check-In Service Evaluation 28 Figure 4.5 Cabin Crew Service Evaluation 28 Figure 4.6 Food & Drink Onboard Evaluation 29 Figure 4.7 Inflight Seat Space Evaluation 29 Figure 4.8 On-time Performance Evaluation 30 Figure 4.9 Schedules Delay Evaluation 30 Figure 4.10 Predicted probability of airline choice and income 35 Figure 4.11 Predicted probability of airline choice and age 36 VII CHAPTER INTRODUCTION 1.1 Problem statement a Overview of airline industry In 2015, the world’s aviation industry achieved the highest net profit in history, 33 billion dollars It is nearly double when compared to a net profit of 17.4 billion dollars in 2014 Particularly, the aviation industry in Asia Pacific obtained net profit of more than 5.8 billion dollars In addition, region of Asia Pacific accounted for 31% of global passengers, while Europe and North America is 30% and 26%, respectively It is noted that low cost carrier has transported over 950 million passengers, approximately 28% of those who are scheduled passengers (IATA report, 2016) According to The International Air Transport Association (IATA), number of air travelers is forecasted to increase nearly double, from 3.8 billion in 2016 to 7.2 billion in 2035 IATA also announces the five fastest growing markets that have the most additional passengers per year for over the next 20 years, including China, US, India, Indonesia, and Vietnam In detail, Vietnam may have 112 million new passengers for a total of 150 million Moreover, IATA also stated that Vietnam is one of the seven countries which have fastest growth in aviation industry Besides that, Vietnam Government pays much attention to infrastructure which is one of the most critical components of air transport sector Vietnam’s planning is to have 26 airports by 2020; particularly Long Thanh International Airport will be ready by 2020 b Airline industry in Vietnam The Vietnam airline industry, which was administered by Ministry of Transport and Civil Aviation Authority of Vietnam, has witnessed rapid growth in 2015 compared to the figures in 2014 The whole market served 40.1 million of passengers and transported 771 thousand tons of cargo In particular, transportation of domestic carriers is 31.1 million passengers, increased by 21% This positive sign with the falling of crude oil price of 30% in 2015 are stimulus for airline carriers to continue reducing fares in order to meet the demand of transportation of passengers + | 1.87 1.87 | 64 59.81 61.68 | 2.80 64.49 | 38 35.51 100.00 + Total | 107 100.00 tab choice4 choice4 | Freq Percent Cum + | 3.74 3.74 | 43 40.19 43.93 | 40 37.38 81.31 | 20 18.69 100.00 + Total | 107 100.00 sum wtp4 Variable | Obs Mean Std Dev Min Max -+ -wtp4 | 86 659965.1 249824.5 280000 2000000 tab purpose5 purpose5 | Freq Percent Cum + | 3.70 3.70 | 66 61.11 64.81 | 4.63 69.44 | 33 30.56 100.00 + Total | 108 100.00 tab choice5 choice5 | Freq Percent Cum + | 7.41 7.41 | 11 10.19 17.59 | 36 33.33 50.93 | 53 49.07 100.00 + Total | 108 100.00 sum wtp5 Variable | Obs Mean Std Dev Min Max -+ -wtp5 | 84 680702.4 275521.8 280000 2000000 tab purpose6 purpose6 | Freq Percent Cum + | 12 11.11 11.11 | 59 54.63 65.74 | 8.33 74.07 | 28 25.93 100.00 + Total | 108 100.00 tab choice6 xxiv choice6 | Freq Percent Cum + | 10 9.26 9.26 | 21 19.44 28.70 | 28 25.93 54.63 | 49 45.37 100.00 + Total | 108 100.00 sum wtp6 Variable | Obs Mean Std Dev Min Max -+ -wtp6 | 79 766012.7 275570.9 380000 2000000 tab purpose7 purpose7 | Freq Percent Cum + | 41 38.32 38.32 | 35 32.71 71.03 | 10 9.35 80.37 | 21 19.63 100.00 + Total | 107 100.00 tab choice7 choice7 | Freq Percent Cum + | 35 32.71 32.71 | 14 13.08 45.79 | 47 43.93 89.72 | 11 10.28 100.00 + Total | 107 100.00 sum wtp7 Variable | Obs Mean Std Dev Min Max -+ -wtp7 | 66 1067879 298980.4 100000 2000000 tab purpose8 purpose8 | Freq Percent Cum + | 22 20.75 20.75 | 50 47.17 67.92 | 5.66 73.58 | 28 26.42 100.00 + Total | 106 100.00 tab choice8 choice8 | Freq Percent Cum + | 21 19.81 19.81 | 11 10.38 30.19 | 30 28.30 58.49 | 44 41.51 100.00 + Total | 106 100.00 xxv sum wtp8 Variable | Obs Mean Std Dev Min Max -+ -wtp8 | 70 742300 223554 200000 1500000 tab purpose9 purpose9 | Freq Percent Cum + | 28 26.42 26.42 | 44 41.51 67.92 | 8.49 76.42 | 25 23.58 100.00 + Total | 106 100.00 tab choice9 choice9 | Freq Percent Cum + | 28 26.42 26.42 | 8.49 34.91 | 60 56.60 91.51 | 8.49 100.00 + Total | 106 100.00 sum wtp9 Variable | Obs Mean Std Dev Min Max -+ -wtp9 | 64 744203.1 253346.2 200000 1500000 tab purpose10 purpose10 | Freq Percent Cum + | 0.93 0.93 | 74 69.16 70.09 | 1.87 71.96 | 30 28.04 100.00 + Total | 107 100.00 tab choice10 choice10 | Freq Percent Cum + | 1.87 1.87 | 21 19.63 21.50 | 36 33.64 55.14 | 48 44.86 100.00 + Total | 107 100.00 sum wtp10 Variable | Obs Mean Std Dev Min Max -+ -wtp10 | 86 1014953 313390.3 400000 2500000 tab purpose11 xxvi purpose11 | Freq Percent Cum + | 29 27.62 27.62 | 36 34.29 61.90 | 13 12.38 74.29 | 27 25.71 100.00 + Total | 105 100.00 tab choice11 choice11 | Freq Percent Cum + | 29 27.62 27.62 | 21 20.00 47.62 | 19 18.10 65.71 | 36 34.29 100.00 + Total | 105 100.00 sum wtp11 Variable | Obs Mean Std Dev Min Max -+ -wtp11 | 67 1709090 3529667 400000 3.00e+07 tab purpose12 purpose12 | Freq Percent Cum + | 28 25.93 25.93 | 53 49.07 75.00 | 4.63 79.63 | 22 20.37 100.00 + Total | 108 100.00 tab choice12 choice12 | Freq Percent Cum + | 27 25.00 25.00 | 72 66.67 91.67 | 8.33 100.00 + Total | 108 100.00 sum wtp12 Variable | Obs Mean Std Dev Min Max -+ -wtp12 | 71 713802.8 190168.1 400000 1400000 tab purpose13 purpose13 | Freq Percent Cum + | 20 19.05 19.05 | 51 48.57 67.62 | 7.62 75.24 | 26 24.76 100.00 + Total | 105 100.00 tab choice13 xxvii choice13 | Freq Percent Cum + | 22 20.95 20.95 | 13 12.38 33.33 | 32 30.48 63.81 | 38 36.19 100.00 + Total | 105 100.00 sum wtp13 Variable | Obs Mean Std Dev Min Max -+ -wtp13 | 70 944914.3 557787.9 400000 5000000 tab purpose14 purpose14 | Freq Percent Cum + | 33 31.13 31.13 | 42 39.62 70.75 | 7.55 78.30 | 23 21.70 100.00 + Total | 106 100.00 tab choice14 choice14 | Freq Percent Cum + | 30 28.30 28.30 | 4.72 33.02 | 20 18.87 51.89 | 51 48.11 100.00 + Total | 106 100.00 sum wtp14 Variable | Obs Mean Std Dev Min Max -+ -wtp14 | 64 1189844 1427163 500000 1.20e+07 tab purpose15 purpose15 | Freq Percent Cum + | 23 22.33 22.33 | 46 44.66 66.99 | 10 9.71 76.70 | 24 23.30 100.00 + Total | 103 100.00 tab choice15 choice15 | Freq Percent Cum + | 26 25.24 25.24 | 66 64.08 89.32 | 11 10.68 100.00 + Total | 103 100.00 xxviii sum wtp15 Variable | Obs Mean Std Dev Min Max -+ -wtp15 | 69 845072.5 917101.6 400000 8000000 tab regular_purpose regular_pur | pose | Freq Percent Cum + | 53 43.44 43.44 | 16 13.11 56.56 | 53 43.44 100.00 + Total | 122 100.00 sum business Variable | Obs Mean Std Dev Min Max -+ -business | 52 41.92308 26.44183 100 sum travel Variable | Obs Mean Std Dev Min Max -+ -travel | 51 56.86275 25.43542 100 Regression results of model mlogit choice age male single schoolyear income job_emp ontvn_pun ontvj_pun ontbl_pun seavn_ufr seavj_ufr seabl_ufr chevn_u > fr chevj_ufr chebl_ufr ,base(3) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: log log log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = = = = = Multinomial logistic regression Log likelihood = -549.51358 -622.1505 -553.83835 -549.84222 -549.58388 -549.53002 -549.51669 -549.5141 -549.51364 -549.51359 -549.51358 Number of obs LR chi2(30) Prob > chi2 Pseudo R2 = = = = 605 145.27 0.0000 0.1168 -choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -1 | age | -.128752 0537763 -2.39 0.017 -.2341516 -.0233523 male | 7188646 3888461 1.85 0.064 -.0432598 1.480989 single | -1.386741 3345177 -4.15 0.000 -2.042383 -.7310979 schoolyear | -.3158129 1685835 -1.87 0.061 -.6462304 0146046 income | 0512001 0279901 1.83 0.067 -.0036595 1060596 job_emp | -.4076625 354437 -1.15 0.250 -1.102346 2870213 ontvn_pun | -.1210241 5092773 -0.24 0.812 -1.119189 8771411 ontvj_pun | 7380261 4632657 1.59 0.111 -.169958 1.64601 ontbl_pun | -.7476221 3873305 -1.93 0.054 -1.506776 0115316 seavn_ufr | -3.067038 1.576288 -1.95 0.052 -6.156506 0224306 xxix seavj_ufr | -.5615809 6252579 -0.90 0.369 -1.787064 6639021 seabl_ufr | 1.74697 501263 3.49 0.000 7645128 2.729428 chevn_ufr | 14.85398 684.1408 0.02 0.983 -1326.037 1355.745 chevj_ufr | -.6734434 644489 -1.04 0.296 -1.936619 5897318 chebl_ufr | 799236 6819827 1.17 0.241 -.5374255 2.135898 _cons | 8.443488 3.289067 2.57 0.010 1.997034 14.88994 -+ -2 | age | -.0489542 0417112 -1.17 0.241 -.1307065 0327982 male | 8915023 2912341 3.06 0.002 320694 1.462311 single | -.2756376 276419 -1.00 0.319 -.8174089 2661336 schoolyear | -.4305385 134274 -3.21 0.001 -.6937108 -.1673663 income | 0699513 0226868 3.08 0.002 025486 1144166 job_emp | -.1445924 27533 -0.53 0.599 -.6842292 3950445 ontvn_pun | -.7497854 3713861 -2.02 0.043 -1.477689 -.0218819 ontvj_pun | 1.067782 3442481 3.10 0.002 3930682 1.742496 ontbl_pun | -.5883789 3024722 -1.95 0.052 -1.181214 0044558 seavn_ufr | -5.450222 1.371941 -3.97 0.000 -8.139177 -2.761266 seavj_ufr | -1.659381 5209029 -3.19 0.001 -2.680331 -.6384297 seabl_ufr | 2.307086 4322562 5.34 0.000 1.459879 3.154293 chevn_ufr | 14.35631 684.1405 0.02 0.983 -1326.534 1355.247 chevj_ufr | -.4631225 5471183 -0.85 0.397 -1.535455 6092096 chebl_ufr | -.5325138 5988922 -0.89 0.374 -1.706321 6412934 _cons | 8.591626 2.650361 3.24 0.001 3.397013 13.78624 -+ -3 | (base outcome) - set more off mfx, predict(p outcome(1)) Marginal effects after mlogit y = Pr(choice==1) (predict, p outcome(1)) = 18629451 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -age | -.0146098 01328 -1.10 0.271 -.040642 011423 27.2479 male*| 0122022 20562 0.06 0.953 -.390796 415201 193388 single*| -.2051721 2073 -0.99 0.322 -.611479 201135 671074 school~r | -.004713 11011 -0.04 0.966 -.220533 211107 16.4182 income | 0007489 01785 0.04 0.967 -.034234 035731 12.7438 job_emp*| -.0487886 06247 -0.78 0.435 -.17122 073642 67438 on~n_pun*| 0570763 26025 0.22 0.826 -.452997 56715 829752 on~j_pun*| -.0063692 25886 -0.02 0.980 -.513727 500988 168595 on~l_pun*| -.0538499 06868 -0.78 0.433 -.18847 08077 26281 se~n_ufr*| -.1517674 13559 -1.12 0.263 -.417525 11399 02314 se~j_ufr*| 0473397 60636 0.08 0.938 -1.1411 1.23578 145455 seab~ufr*| -.0009746 44565 -0.00 0.998 -.874441 872491 241322 ch~n_ufr*| 1848875 20038 0.92 0.356 -.20786 577635 01157 ch~j_ufr*| -.0527894 06676 -0.79 0.429 -.18364 078062 06281 cheb~ufr*| 2203737 5034 0.44 0.662 -.766273 1.20702 057851 -(*) dy/dx is for discrete change of dummy variable from to mfx, predict(p outcome(2)) Marginal effects after mlogit y = Pr(choice==2) (predict, p outcome(2)) = 5381167 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -age | 0007397 0613 0.01 0.990 -.119406 120885 27.2479 male*| 136338 42877 0.32 0.751 -.704039 976715 193388 single*| 0913006 6459 0.14 0.888 -1.17463 1.35724 671074 school~r | -.0753494 17909 -0.42 0.674 -.426361 275662 16.4182 income | 0122535 02899 0.42 0.673 -.044564 069071 12.7438 job_emp*| 0067056 20149 0.03 0.973 -.388217 401628 67438 on~n_pun*| -.1671592 20949 -0.80 0.425 -.577747 243429 829752 on~j_pun*| 173172 47653 0.36 0.716 -.760815 1.10716 168595 xxx on~l_pun*| -.0790073 35526 -0.22 0.824 -.775298 617283 26281 se~n_ufr*| -.5529706 1.06026 -0.52 0.602 -2.63104 1.5251 02314 se~j_ufr*| -.3390786 10414 -3.26 0.001 -.543181 -.134976 145455 seab~ufr*| 3235905 94405 0.34 0.732 -1.5267 2.17389 241322 ch~n_ufr*| 1253804 20077 0.62 0.532 -.268116 518877 01157 ch~j_ufr*| -.0590606 32379 -0.18 0.855 -.69368 575559 06281 cheb~ufr*| -.2235072 48713 -0.46 0.646 -1.17826 731248 057851 -(*) dy/dx is for discrete change of dummy variable from to mfx, predict(p outcome(3)) Marginal effects after mlogit y = Pr(choice==3) (predict, p outcome(3)) = 27558879 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -age | 0138701 04992 0.28 0.781 -.083962 111702 27.2479 male*| -.1485403 62419 -0.24 0.812 -1.37192 1.07484 193388 single*| 1138715 44505 0.26 0.798 -.758416 986159 671074 school~r | 0800625 28556 0.28 0.779 -.479616 639741 16.4182 income | -.0130023 0464 -0.28 0.779 -.103941 077936 12.7438 job_emp*| 0420831 16236 0.26 0.795 -.276141 360307 67438 on~n_pun*| 1100829 45545 0.24 0.809 -.782588 1.00275 829752 on~j_pun*| -.1668028 72392 -0.23 0.818 -1.58567 1.25206 168595 on~l_pun*| 1328572 40406 0.33 0.742 -.659092 924806 26281 se~n_ufr*| 7047379 1.18585 0.59 0.552 -1.61948 3.02896 02314 se~j_ufr*| 291739 55388 0.53 0.598 -.793839 1.37732 145455 seab~ufr*| -.3226159 1.38504 -0.23 0.816 -3.03725 2.39202 241322 ch~n_ufr*| -.310268 02215 -14.01 0.000 -.353681 -.266855 01157 ch~j_ufr*| 11185 33222 0.34 0.736 -.539289 762989 06281 cheb~ufr*| 0031335 11564 0.03 0.978 -.223513 229781 057851 -(*) dy/dx is for discrete change of dummy variable from to test age male single schoolyear income job_emp ontvn_pun ontvj_pun ontbl_pun seavn_ufr seavj_ufr seabl_ufr chevn_ufr chevj_ > ufr chebl_ufr ( 1) ( 2) ( 3) ( 4) ( 5) ( 6) ( 7) ( 8) ( 9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) [1]age = [2]age = [3]o.age = [1]male = [2]male = [3]o.male = [1]single = [2]single = [3]o.single = [1]schoolyear = [2]schoolyear = [3]o.schoolyear = [1]income = [2]income = [3]o.income = [1]job_emp = [2]job_emp = [3]o.job_emp = [1]ontvn_pun = [2]ontvn_pun = [3]o.ontvn_pun = [1]ontvj_pun = [2]ontvj_pun = [3]o.ontvj_pun = [1]ontbl_pun = [2]ontbl_pun = [3]o.ontbl_pun = [1]seavn_ufr = [2]seavn_ufr = [3]o.seavn_ufr = [1]seavj_ufr = [2]seavj_ufr = xxxi (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) (44) (45) [3]o.seavj_ufr = [1]seabl_ufr = [2]seabl_ufr = [3]o.seabl_ufr = [1]chevn_ufr = [2]chevn_ufr = [3]o.chevn_ufr = [1]chevj_ufr = [2]chevj_ufr = [3]o.chevj_ufr = [1]chebl_ufr = [2]chebl_ufr = [3]o.chebl_ufr = Constraint dropped Constraint dropped Constraint dropped Constraint 12 dropped Constraint 15 dropped Constraint 18 dropped Constraint 21 dropped Constraint 24 dropped Constraint 27 dropped Constraint 30 dropped Constraint 33 dropped Constraint 36 dropped Constraint 39 dropped Constraint 42 dropped Constraint 45 dropped chi2( 30) = Prob > chi2 = 114.36 0.0000 Regression results of model mlogit choice pricevn pricevj pricebl freqvn freqvj freqbl age male single schoolyear income job_emp ontvn_pun ontvj_pun on > tbl_pun seavn_ufr seavj_ufr seabl_ufr chevn_ufr chevj_ufr chebl_ufr ,base(3) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: log log log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = = = = = Multinomial logistic regression Log likelihood = -418.6353 -560.61005 -428.20342 -419.40993 -418.70388 -418.6478 -418.63813 -418.63597 -418.63542 -418.63532 -418.6353 Number of obs LR chi2(42) Prob > chi2 Pseudo R2 = = = = 530 283.95 0.0000 0.2533 -choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -1 | pricevn | -.8021864 1386516 -5.79 0.000 -1.073939 -.5304343 pricevj | -.4075871 2611863 -1.56 0.119 -.9195028 1043285 pricebl | 1.035526 2473849 4.19 0.000 5506609 1.520392 freqvn | -.2740774 1296258 -2.11 0.034 -.5281392 -.0200155 freqvj | 307144 115898 2.65 0.008 0799881 5342999 freqbl | 1534777 1473208 1.04 0.298 -.1352658 4422212 age | -.1993709 0604912 -3.30 0.001 -.3179315 -.0808104 male | 920857 4303017 2.14 0.032 0774813 1.764233 single | -1.698606 377738 -4.50 0.000 -2.438958 -.9582527 schoolyear | -.5329384 1896671 -2.81 0.005 -.9046791 -.1611977 income | 0841102 0321291 2.62 0.009 0211384 1470821 job_emp | -.471115 3953027 -1.19 0.233 -1.245894 303664 xxxii ontvn_pun | -.5073396 5721376 -0.89 0.375 -1.628709 6140295 ontvj_pun | 1.229474 5168964 2.38 0.017 216376 2.242573 ontbl_pun | -.9918319 4308216 -2.30 0.021 -1.836227 -.1474371 seavn_ufr | -5.055117 1.833997 -2.76 0.006 -8.649684 -1.460549 seavj_ufr | -.7057249 7018883 -1.01 0.315 -2.081401 6699508 seabl_ufr | 2.462795 5683816 4.33 0.000 1.348788 3.576803 chevn_ufr | 14.15057 459.9414 0.03 0.975 -887.3179 915.6191 chevj_ufr | -.7703578 7299505 -1.06 0.291 -2.201034 6603189 chebl_ufr | 4017757 7431443 0.54 0.589 -1.05476 1.858312 _cons | 17.2852 3.949192 4.38 0.000 9.544927 25.02548 -+ -2 | pricevn | -.3948781 1100561 -3.59 0.000 -.610584 -.1791722 pricevj | -1.10252 2287903 -4.82 0.000 -1.550941 -.6540996 pricebl | 1.416351 2151645 6.58 0.000 9946368 1.838066 freqvn | -.3748977 1118604 -3.35 0.001 -.5941401 -.1556553 freqvj | 3087313 1011275 3.05 0.002 1105251 5069375 freqbl | 1393903 1359135 1.03 0.305 -.1269953 4057759 age | -.1041325 0510823 -2.04 0.041 -.204252 -.0040131 male | 1.271407 3596302 3.54 0.000 5665447 1.976269 single | -.5970448 339786 -1.76 0.079 -1.263013 0689236 schoolyear | -.6778039 1701958 -3.98 0.000 -1.011382 -.3442262 income | 1098154 0281505 3.90 0.000 0546415 1649893 job_emp | -.1662351 3396259 -0.49 0.625 -.8318896 4994195 ontvn_pun | -1.140356 4666985 -2.44 0.015 -2.055068 -.2256437 ontvj_pun | 1.64604 4313013 3.82 0.000 8007047 2.491375 ontbl_pun | -.8232627 3756338 -2.19 0.028 -1.559491 -.087034 seavn_ufr | -7.593768 1.687385 -4.50 0.000 -10.90098 -4.286554 seavj_ufr | -1.750564 6293878 -2.78 0.005 -2.984141 -.5169863 seabl_ufr | 3.148256 5225939 6.02 0.000 2.123991 4.172522 chevn_ufr | 14.09736 459.9409 0.03 0.976 -887.3703 915.565 chevj_ufr | -.7264155 6737862 -1.08 0.281 -2.047012 5941812 chebl_ufr | -.905836 6944085 -1.30 0.192 -2.266852 4551797 _cons | 15.77698 3.507014 4.50 0.000 8.903358 22.6506 -+ -3 | (base outcome) - mfx, predict(p outcome(1)) Marginal effects after mlogit y = Pr(choice==1) (predict, p outcome(1)) = 22090092 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -pricevn | -.0930302 02348 -3.96 0.000 -.139044 -.047017 10.9543 pricevj | 0555763 32352 0.17 0.864 -.578506 689658 7.82343 pricebl | 0167073 31379 0.05 0.958 -.598311 631726 7.79049 freqvn | -.0044191 08446 -0.05 0.958 -.169963 161125 6.02264 freqvj | 0176551 05464 0.32 0.747 -.08944 12475 6.01509 freqbl | 010519 0275 0.38 0.702 -.043376 064414 3.7283 age | -.0224379 00825 -2.72 0.007 -.038607 -.006269 27.2736 male*| -.0024972 25753 -0.01 0.992 -.507242 502247 single*| -.244603 09094 -2.69 0.007 -.422842 -.066363 666038 school~r | -.0144288 14446 -0.10 0.920 -.29756 268702 16.4208 income | 0019531 02377 0.08 0.935 -.044634 04854 12.7132 job_emp*| -.0640085 06564 -0.98 0.329 -.192661 064643 669811 on~n_pun*| 053184 27114 0.20 0.844 -.47825 584618 833962 on~j_pun*| -.0039863 30578 -0.01 0.990 -.603311 595339 169811 on~l_pun*| -.0773583 09592 -0.81 0.420 -.265366 110649 267925 se~n_ufr*| -.2144704 27878 -0.77 0.442 -.76087 331929 022642 se~j_ufr*| 0336459 50123 0.07 0.946 -.948743 1.01603 14717 seab~ufr*| 0013332 45854 0.00 0.998 -.897391 900057 243396 ch~n_ufr*| 1013105 19509 0.52 0.604 -.281058 483679 013208 ch~j_ufr*| -.0540442 11936 -0.45 0.651 -.287988 1799 066038 cheb~ufr*| 1893167 4845 0.39 0.696 -.760286 1.13892 064151 -(*) dy/dx is for discrete change of dummy variable from to mfx, predict(p outcome(2)) xxxiii Marginal effects after mlogit y = Pr(choice==2) (predict, p outcome(2)) = 51621706 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -pricevn | -.0071401 30357 -0.02 0.981 -.602136 587856 10.9543 pricevj | -.2288619 18434 -1.24 0.214 -.590155 132431 7.82343 pricebl | 2356314 41725 0.56 0.572 -.582162 1.05343 7.79049 freqvn | -.062372 1121 -0.56 0.578 -.282092 157348 6.02264 freqvj | 0420771 12202 0.34 0.730 -.197078 281232 6.01509 freqbl | 0173094 06479 0.27 0.789 -.109676 144295 3.7283 age | -.0032709 07605 -0.04 0.966 -.152328 145786 27.2736 male*| 19072 4052 0.47 0.638 -.603458 984898 single*| 0736779 61379 0.12 0.904 -1.12932 1.27668 666038 school~r | -.1085003 21512 -0.50 0.614 -.530128 313128 16.4208 income | 0178336 03396 0.53 0.599 -.048723 08439 12.7132 job_emp*| 0145681 18928 0.08 0.939 -.356423 385559 669811 on~n_pun*| -.2117619 29709 -0.71 0.476 -.794047 370523 833962 on~j_pun*| 2291238 5216 0.44 0.660 -.793191 1.25144 169811 on~l_pun*| -.1055995 34945 -0.30 0.763 -.790505 579306 267925 se~n_ufr*| -.5444404 76899 -0.71 0.479 -2.05162 962744 022642 se~j_ufr*| -.3359625 10007 -3.36 0.001 -.532096 -.139829 14717 seab~ufr*| 3837577 81801 0.47 0.639 -1.21951 1.98703 243396 ch~n_ufr*| 1992475 19588 1.02 0.309 -.184663 583158 013208 ch~j_ufr*| -.108122 27826 -0.39 0.698 -.653508 437264 066038 cheb~ufr*| -.2591687 30676 -0.84 0.398 -.860406 342068 064151 -(*) dy/dx is for discrete change of dummy variable from to mfx, predict(p outcome(3)) Marginal effects after mlogit y = Pr(choice==3) (predict, p outcome(3)) = 26288202 -variable | dy/dx Std Err z P>|z| [ 95% C.I ] X -+ -pricevn | 1001703 2893 0.35 0.729 -.466848 667189 10.9543 pricevj | 1732856 5009 0.35 0.729 -.808454 1.15502 7.82343 pricebl | -.2523387 72802 -0.35 0.729 -1.67922 1.17455 7.79049 freqvn | 0667911 19355 0.35 0.730 -.312566 446148 6.02264 freqvj | -.0597322 17305 -0.35 0.730 -.398897 279433 6.01509 freqbl | -.0278284 08407 -0.33 0.741 -.192602 136945 3.7283 age | 0257089 07465 0.34 0.731 -.120605 172023 27.2736 male*| -.1882228 65035 -0.29 0.772 -1.46289 1.08645 single*| 170925 54502 0.31 0.754 -.897304 1.23915 666038 school~r | 122929 35547 0.35 0.729 -.573773 819631 16.4208 income | -.0197868 05724 -0.35 0.730 -.13197 092396 12.7132 job_emp*| 0494404 15882 0.31 0.756 -.261832 360713 669811 on~n_pun*| 1585779 55214 0.29 0.774 -.923602 1.24076 833962 on~j_pun*| -.2251375 81303 -0.28 0.782 -1.81865 1.36837 169811 on~l_pun*| 1829578 42598 0.43 0.668 -.651938 1.01785 267925 se~n_ufr*| 7589108 1.0462 0.73 0.468 -1.29161 2.80943 022642 se~j_ufr*| 3023167 48167 0.63 0.530 -.641732 1.24637 14717 seab~ufr*| -.3850909 1.27015 -0.30 0.762 -2.87454 2.10436 243396 ch~n_ufr*| -.300558 02778 -10.82 0.000 -.355013 -.246103 013208 ch~j_ufr*| 1621662 35896 0.45 0.651 -.541385 865717 066038 cheb~ufr*| 069852 22766 0.31 0.759 -.376358 516062 064151 -(*) dy/dx is for discrete change of dummy variable from to test pricevn pricevj pricebl freqvn freqvj freqbl age male single schoolyear income job_emp ontvn_pun ontvj_pun ontbl_pun s > eavn_ufr seavj_ufr seabl_ufr chevn_ufr chevj_ufr chebl_ufr ( ( ( ( ( ( 1) 2) 3) 4) 5) 6) [1]pricevn = [2]pricevn = [3]o.pricevn [1]pricevj = [2]pricevj = [3]o.pricevj 0 = 0 = xxxiv ( 7) ( 8) ( 9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) (44) (45) (46) (47) (48) (49) (50) (51) (52) (53) (54) (55) (56) (57) (58) (59) (60) (61) (62) (63) [1]pricebl = [2]pricebl = [3]o.pricebl = [1]freqvn = [2]freqvn = [3]o.freqvn = [1]freqvj = [2]freqvj = [3]o.freqvj = [1]freqbl = [2]freqbl = [3]o.freqbl = [1]age = [2]age = [3]o.age = [1]male = [2]male = [3]o.male = [1]single = [2]single = [3]o.single = [1]schoolyear = [2]schoolyear = [3]o.schoolyear = [1]income = [2]income = [3]o.income = [1]job_emp = [2]job_emp = [3]o.job_emp = [1]ontvn_pun = [2]ontvn_pun = [3]o.ontvn_pun = [1]ontvj_pun = [2]ontvj_pun = [3]o.ontvj_pun = [1]ontbl_pun = [2]ontbl_pun = [3]o.ontbl_pun = [1]seavn_ufr = [2]seavn_ufr = [3]o.seavn_ufr = [1]seavj_ufr = [2]seavj_ufr = [3]o.seavj_ufr = [1]seabl_ufr = [2]seabl_ufr = [3]o.seabl_ufr = [1]chevn_ufr = [2]chevn_ufr = [3]o.chevn_ufr = [1]chevj_ufr = [2]chevj_ufr = [3]o.chevj_ufr = [1]chebl_ufr = [2]chebl_ufr = [3]o.chebl_ufr = Constraint dropped Constraint dropped Constraint dropped Constraint 12 dropped Constraint 15 dropped Constraint 18 dropped Constraint 21 dropped Constraint 24 dropped Constraint 27 dropped Constraint 30 dropped Constraint 33 dropped Constraint 36 dropped Constraint 39 dropped Constraint 42 dropped Constraint 45 dropped xxxv Constraint Constraint Constraint Constraint Constraint Constraint 48 51 54 57 60 63 dropped dropped dropped dropped dropped dropped chi2( 42) = Prob > chi2 = 165.07 0.0000 Regression results of model mlogit choice age male single schoolyear income job_emp ontvn_pun ontvj_pun ontbl_pun seavn_ufr seavj_ufr seabl_ufr chevn_u > fr chevj_ufr chebl_ufr i.route ,base(3) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: log log log log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood = = = = = = = = = = = Multinomial logistic regression Log likelihood = -431.84992 -622.1505 -449.69888 -433.82939 -432.25687 -431.94668 -431.86977 -431.85425 -431.85091 -431.85015 -431.84996 -431.84992 Number of obs LR chi2(58) Prob > chi2 Pseudo R2 = = = = 605 380.60 0.0000 0.3059 -choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -1 | age | -.1811777 0600444 -3.02 0.003 -.2988626 -.0634927 male | 9397308 4326547 2.17 0.030 0917432 1.787718 single | -1.616548 3742645 -4.32 0.000 -2.350093 -.8830035 schoolyear | -.520062 1881876 -2.76 0.006 -.8889029 -.1512211 income | 0690313 0306474 2.25 0.024 0089634 1290992 job_emp | -.4505836 3931871 -1.15 0.252 -1.221216 320049 ontvn_pun | -.4602768 5682666 -0.81 0.418 -1.574059 6535053 ontvj_pun | 1.238742 517637 2.39 0.017 2241926 2.253292 ontbl_pun | -.9704523 4276319 -2.27 0.023 -1.808595 -.1323092 seavn_ufr | -4.223738 1.755162 -2.41 0.016 -7.663794 -.7836831 seavj_ufr | -1.079087 6943315 -1.55 0.120 -2.439952 2817777 seabl_ufr | 2.443061 565407 4.32 0.000 1.334884 3.551239 chevn_ufr | 15.72724 992.8868 0.02 0.987 -1930.295 1961.75 chevj_ufr | -.6624632 7138519 -0.93 0.353 -2.061587 7366609 chebl_ufr | 4746387 7411332 0.64 0.522 -.9779557 1.927233 | route | | 1.39835 790964 1.77 0.077 -.1519106 2.948611 | 1.603503 8567626 1.87 0.061 -.0757206 3.282727 | 8739126 6660526 1.31 0.189 -.4315265 2.179352 | -2.24029 750184 -2.99 0.003 -3.710623 -.769956 | -1.514101 6953573 -2.18 0.029 -2.876977 -.1512261 | -.5276611 9018291 -0.59 0.558 -2.295214 1.239892 | -1.632277 7276718 -2.24 0.025 -3.058487 -.2060664 | 0334244 849398 0.04 0.969 -1.631365 1.698214 10 | -1.476278 7062102 -2.09 0.037 -2.860425 -.0921317 11 | -.6749237 6701478 -1.01 0.314 -1.988389 6385418 12 | -14.28611 595.4546 -0.02 0.981 -1181.356 1152.783 13 | -1.945669 7592555 -2.56 0.010 -3.433783 -.4575555 14 | -3.194515 9404909 -3.40 0.001 -5.037844 -1.351187 15 | -14.50155 603.2766 -0.02 0.981 -1196.902 1167.899 | _cons | 14.23841 3.800749 3.75 0.000 6.789078 21.68774 xxxvi -+ -2 | age | -.074368 0491107 -1.51 0.130 -.1706233 0218873 male | 1.28544 3497642 3.68 0.000 5999148 1.970965 single | -.4396151 32745 -1.34 0.179 -1.081405 2021752 schoolyear | -.6266078 1589623 -3.94 0.000 -.9381682 -.3150474 income | 0919254 0256839 3.58 0.000 0415859 1422648 job_emp | -.137871 325044 -0.42 0.671 -.7749454 4992035 ontvn_pun | -1.078421 4479268 -2.41 0.016 -1.956342 -.2005009 ontvj_pun | 1.678437 4195578 4.00 0.000 8561191 2.500755 ontbl_pun | -.8944695 3587346 -2.49 0.013 -1.597576 -.1913625 seavn_ufr | -7.361364 1.591848 -4.62 0.000 -10.48133 -4.241399 seavj_ufr | -2.229705 6001461 -3.72 0.000 -3.40597 -1.05344 seabl_ufr | 3.161322 508484 6.22 0.000 2.164711 4.157932 chevn_ufr | 15.81308 992.8867 0.02 0.987 -1930.209 1961.835 chevj_ufr | -.5719712 6266648 -0.91 0.361 -1.800212 6562694 chebl_ufr | -.8328744 6801713 -1.22 0.221 -2.165986 5002369 | route | | 9952084 7073272 1.41 0.159 -.3911274 2.381544 | 6540068 7917693 0.83 0.409 -.8978325 2.205846 | -.9201655 6215827 -1.48 0.139 -2.138445 2981141 | -2.590467 5940379 -4.36 0.000 -3.75476 -1.426174 | -2.253939 5905633 -3.82 0.000 -3.411422 -1.096456 | 3077771 6644242 0.46 0.643 -.9944704 1.610025 | -1.955144 5856652 -3.34 0.001 -3.103027 -.8072614 | 5286468 6892502 0.77 0.443 -.8222589 1.879552 10 | -1.768509 5675276 -3.12 0.002 -2.880843 -.6561754 11 | -2.710413 6629501 -4.09 0.000 -4.009772 -1.411055 12 | 8035548 6822166 1.18 0.239 -.5335653 2.140675 13 | -2.294942 6024989 -3.81 0.000 -3.475818 -1.114066 14 | -3.719887 7031357 -5.29 0.000 -5.098008 -2.341767 15 | 5398403 6544236 0.82 0.409 -.7428063 1.822487 | _cons | 13.62863 3.231403 4.22 0.000 7.295199 19.96207 -+ -3 | (base outcome) test age male single schoolyear income job_emp ontvn_pun ontvj_pun ontbl_pun seavn_ufr seavj_ufr seabl_ufr chevn_ufr chevj_ > ufr chebl_ufr ( 1) ( 2) ( 3) ( 4) ( 5) ( 6) ( 7) ( 8) ( 9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) [1]age = [2]age = [3]o.age = [1]male = [2]male = [3]o.male = [1]single = [2]single = [3]o.single = [1]schoolyear = [2]schoolyear = [3]o.schoolyear = [1]income = [2]income = [3]o.income = [1]job_emp = [2]job_emp = [3]o.job_emp = [1]ontvn_pun = [2]ontvn_pun = [3]o.ontvn_pun = [1]ontvj_pun = [2]ontvj_pun = [3]o.ontvj_pun = [1]ontbl_pun = [2]ontbl_pun = [3]o.ontbl_pun = [1]seavn_ufr = [2]seavn_ufr = xxxvii (30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) (44) (45) [3]o.seavn_ufr = [1]seavj_ufr = [2]seavj_ufr = [3]o.seavj_ufr = [1]seabl_ufr = [2]seabl_ufr = [3]o.seabl_ufr = [1]chevn_ufr = [2]chevn_ufr = [3]o.chevn_ufr = [1]chevj_ufr = [2]chevj_ufr = [3]o.chevj_ufr = [1]chebl_ufr = [2]chebl_ufr = [3]o.chebl_ufr = Constraint dropped Constraint dropped Constraint dropped Constraint 12 dropped Constraint 15 dropped Constraint 18 dropped Constraint 21 dropped Constraint 24 dropped Constraint 27 dropped Constraint 30 dropped Constraint 33 dropped Constraint 36 dropped Constraint 39 dropped Constraint 42 dropped Constraint 45 dropped chi2( 30) = Prob > chi2 = 125.09 0.0000 xxxviii ... Education Income Description = Vietnam Airline = Vietjet = Jetstar Airfare of VNA Airfare of VJ Airfare of BL Number of flights of a route in a day of VNA Number of flights of a route in a day of VJ... to fly The choice set in this survey is three airlines that are operating in Vietnam, including Vietnam Airline, Vietjet Air, and Jetstar Pacific By employing the multinomial logit model with... difference of assumptions of the distribution of the error terms causes many forms of choice models According to Train (2009), the main models include logit, GEV, probit and mixed logit model First, logit