Factors of consumers choices a reaveled preferences analysis for 3 in 1 coffee

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FACTORS OF CONSUMER’S CHOICES: A REVEALED PREFERENCE ANALYSIS FOR 3IN1 COFFEE BY NGUYEN VAN VIEN MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2016 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 FACTORS OF CONSUMER’S CHOICES: A REVEALED PREFERENCE ANALYSIS FOR 3IN1 COFFEE A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN VAN VIEN Academic Supervisor: TRUONG DANG THUY HO CHI MINH CITY, November 2016 ACKNOWLEDGEMENT I would first like to thank my thesis supervisor Dr Truong Dang Thuy of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics He consistently allowed this paper to be my own work, but steered me in the right the direction whenever he thought I needed it I acknowledge the contribution of Dr Nguyen Ba Thanh (IUH) as the second reader of this thesis, and I am gratefully indebted to him for his very valuable advices on building idea for this thesis I would like to express my gratitude to the VNP officers who were involved in my thesis process by updating thesis schedule and providing good condition for my research process Without their passionate participation, the thesis process could not have been successfully conducted Finally, thanks are also due to my classmates for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them Thank you Nguyen Van Vien Ho Chi Minh City, November 2016 Page i ABSTRACT 3in1 coffee is known as an important product of instant coffee market in Vietnam, especially in Ho Chi Minh City The reason of that comes from the benefits which 3in1 coffee brings to consumers in term of convenience, product quality, and appropriate price In the above context of 3in1 coffee market, the main objective of this study is to identify the determinants of consumer’s choices in 3in1 coffee market such as price, main ingredients, packaging, manufacturer, discount, and weight promotion This study is a practical research with the basis of random utility theory Specifically, empirical result is produced from the estimation of conditional logit model for the dataset which is collected from consumers in Ho Chi Minh City in 2016 The survey process relies on the revealed preference method with several additional hypothetical scenarios The main finding of this study emphasizes the importance of main ingredients, packaging, and manufacturers of 3in1 coffee in consumer’s choices It is recognized that price may not matter consumer’s choices However, consumers love discount and weight promotion In addition, several manufacturers enjoy positive marginal utility of price for consumers while the others enjoy the negative one On the other hand, price changes may give small effects on choice probability of 3in1 coffee products According to those empirical findings, implications have been employed for manufacturers in order to understand more about 3in1 coffee market, widen their market share, and increase their profits Page ii TABLE OF CONTENT Chapter Page Acknowledgement i Abstract ii Table of content iii List of tables v List of figures vi Introduction 1.1 Research problem 1.2 Research objective 1.3 Scope of study 1.4 Thesis structure Literature review 2.1 Random utility theory 2.2 Random utility model forms 2.3 Random utility model for beverage or food 11 2.4 The investigation of coffee’s attribute 12 2.5 Consumer’s social-demographic characteristics 16 Research methodology 18 3.1 Revealed preference method 18 3.2 Attributes of coffee 20 3.3 Choice set .21 3.4 Questionnaire .23 3.5 Survey process .24 3.6 Model specification .25 Data and empirical result 30 4.1 Data 30 4.2 Empirical result 39 4.2.1 Determinants of consumer’s choices for 3in1 coffee 39 Page iii 4.2.2 Price and consumer’s utility of 3in1 coffee by manufacturers 46 4.2.3 Price change and choice probability 49 4.2.4 Marginal utility of price for respondents 52 4.2.5 Discount and weight promotion and consumer’s utility for 3in1 coffee 54 4.2.6 Manufacturers, social-demographic characteristics and consumer’s choices for 3in1 coffee .55 Conclusion 58 Reference vii Appendix xi Page iv LIST OF TABLES Table 2.1 Importance of factor on consumer’s coffee preferences 14 Table 3.1 List of suggested attributes 21 Table 3.2 Volume share and value share of main manufacturers 22 Table 3.3 List of all available 3in1 coffee products 23 Table 3.4 Variable description 26 Table 4.1 District, super-market, and the number of respondents 31 Table 4.2 Descriptive statistics of the sample 32 Table 4.3 Frequency of social-demographic characteristics 34 Table 4.4 Factors of consumer’s choices for 3in1 coffee 42 Table 4.5 Marginal utility of price for respondents by alternative 53 Table A.1 All 19 alternatives and their attributes xii Table A.2 Price fluctuation among super-markets xxi Table A.3 Consumer’s choices change among various choice scenarios xxii Table A.4 Regression result of equation (3.1) and (3.3) (specific choice set) xxiii Table A.5 Regression result of equation (3.1) and (3.3) (single variable) xxiv Page v LIST OF FIGURES Figure 4.1 Frequency of choice of each alternative by gender 35 Figure 4.2 Alternative and gender, occupation, income, and frequency of 3in1 coffee consumption of respondents 37 Figure 4.3 Change of consumer’s choices in various choice scenarios 38 Figure 4.4 Price and consumer’s utility by manufacturer 48 Figure 4.5 Price changes and choice probabilities of alternative 2, 8, 15, 18 in case of all choice scenarios 51 Figure A.1 All 19 alternatives for survey process xi Figure A.2 Consumer’s utility along current price values .xxv Page vi CHAPTER 1: INTRODUCTION 1.1 Research problem In recent years, coffee is an important product of industry sector, agriculture sector, and service sector in Vietnam According to historical data of International Coffee Organization (ICO), beside Brazil, Vietnam is one of the key countries in coffee production and consumption in the world with 19.4 percent of total coffee production, and 4.5 percent of domestic consumption Annual yield of Vietnam coffee production increases by 21 times in the period 1990 – 2014 (ICO, 2015) The volume of coffee export in Vietnam contributes more than 2.4 percent of GDP in 2012 (Vietnam Ministry of Industry and Trade, 2012) Based on the report of AC Nielsen (2015), 3in1 coffee market in Vietnam are contributed by many manufacturers of which five main manufacturers are Vinacafe, Nestlé, Trung Nguyen, Fes Vietnam, and Tran Quang In 2015, total volume share of five main manufacturers is 88 percent (Vinacafe: 38%, Nestlé: 19%, Trung Nguyen: 14.6%, Fes Vietnam: 4.2%, Tran Quang: 12.2%) compared to about 99 percent in 2014 Moreover, in term of value share, 3in1 coffee products comprise 83 percent of total value of instant coffee market In term of package, bag, box, and sachet are three main kinds of package with 99.9 percent of volume share Therefore, it is concluded that total demand of 3in1 coffee is relatively high compared to other instant coffee products and the competition among manufacturers is also intense in order to capture more market share Due to the high demand of consumers, especially young consumers, many manufacturers have diversified their 3in1 coffee products in term of brands, prices, segments, packages, pack size, promotion, main ingredients For example, five main manufacturers including Vinacafe, Nestlé, Trung Nguyen, Fes Vietnam, and Tran Quang provide 27 different kinds of 3in1 coffee in term of main ingredients, packaging, and brands Moreover, the competition among these manufacturers is also reflected in the aspect of prices and promotions Price increase could help manufacturers enjoy the benefits from the increase of profit; however, they may also suffer the decrease of quantity sold Beside price change, manufacturers could conduct promotion activities in order to increase the number of consumers who know about their products or their brand names Promotion activities are conducted through many forms such Page as weight promotion, additional sachets, or a gift of related product, for example, spoon, plastic cup, or glass cup Thus, two important questions are raised that: (1) What are important factors which affect consumer’s choices?, and (2) Do price changes and promotions possibly help one manufacturer to gain consumers from the others? 1.2 Research objective According to Batsell and Louviere (1991), experimental methods relied on the framework of both econometric analysis and psychometric analysis and it is the most popular method to researches about consumer’s preferences Experimental methods explain consumer’s preferences through the process of identifying the range of significant factors, generating hypothetical profiles, collecting consumer’s choices, and analyzing choice data The datasets of experimental methods are collected from two main survey methods: revealed preference and stated preference methods Two main survey methods provide a wide application in understanding consumer’s preferences For example, Durevall (2007) investigated that decreasing price of coffee has less impact on coffee demand in the long term due to the combination of consumer’s preferences and population structure in Sweden In addition, Wolf et al (2011) suggested that the interaction of product attributes also have significant impact on consumer’s preferences, beside prices Therefore, the aim of this study is to achieve three research objectives: (1) Identifying the key determinants of consumer’s choices for 3in1 coffee, (2) Determining relationship between price and consumer’s utility for 3in1 coffee, (3) Evaluating the impact of discount, promotion, and price changes on consumer’s choices First, beside price, several factors are claimed to be reliably important and influence the consumer’s choices Thus, identifying these factors provides deeply understanding about consumer’s preference in order to suggest both implication for instant coffee market and development strategy for manufacturers Second, it is said that determining the relationship between price and consumer’s utility plays an important role in finding out the effect of price change on consumer’s utility or choice probability of products for each manufacturer This finding gives manufacturers an evaluation about their advantages or disadvantages to get higher profits in the 3in1 coffee market compared to the competitors Finally, this study also Page // Conditional logit model: squared variables / is not a valid command name r(199); clogit choice price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group(id) robust Iteration 0: log pseudolikelihood = -538.10755 Iteration 1: Iteration 2: log pseudolikelihood = -537.96638 log pseudolikelihood = -537.96628 Iteration 3: log pseudolikelihood = -537.96628 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -537.96628 Number of obs Wald chi2(12) = = 3,448 44.68 Prob > chi2 Pseudo R2 = = 0.0000 0.0439 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | price2 | 0748233 -.0003747 0376358 0002315 1.99 -1.62 0.047 0.106 0010586 -.0008285 1485881 0000791 bitter | bitter2 | -4.825643 -1.665778 7.318675 7.985068 -0.66 -0.21 0.510 0.835 -19.16998 -17.31622 9.518696 13.98467 sweet | fat | 1.389632 2.709244 3048544 6112958 4.56 4.43 0.000 0.000 7921283 1.511126 1.987136 3.907362 weight_alt | no_sachet | -.0252337 3630109 0110873 1746734 -2.28 2.08 0.023 0.038 -.0469644 0206573 -.003503 7053644 pap_pack | vinacafe | -.2761876 8.559927 2477704 1.963215 -1.11 4.36 0.265 0.000 -.7618087 4.712097 2094334 12.40776 trungnguyen | nestle | 3.183092 3.387774 1.179897 1.425301 2.70 2.38 0.007 0.017 870536 5942357 5.495649 6.181311 - test price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) ( 2) [choice]price = [choice]price2 = Page xxxii ( 3) [choice]bitter = ( 4) ( 5) [choice]bitter2 = [choice]sweet = ( 6) ( 7) [choice]fat = [choice]weight_alt = ( 8) ( 9) [choice]no_sachet = [choice]pap_pack = (10) (11) [choice]vinacafe = [choice]trungnguyen = (12) [choice]nestle = chi2( 12) = Prob > chi2 = 44.68 0.0000 // Conditional logit model: all kinds of variables / is not a valid command name r(199); clogit choice price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction', group(id > ) robust Iteration 0: log pseudolikelihood = -530.78387 Iteration 1: Iteration 2: log pseudolikelihood = -524.03165 log pseudolikelihood = -523.07611 Iteration 3: Iteration 4: log pseudolikelihood = -523.00657 log pseudolikelihood = -523.00589 Iteration 5: log pseudolikelihood = -523.00589 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -523.00589 Number of obs Wald chi2(28) = = 3,448 Prob > chi2 Pseudo R2 = = 0.0705 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | price2 | 0105693 -.0003766 056875 0003131 0.19 -1.20 0.853 0.229 -.1009037 -.0009904 1220424 0002371 Page xxxiii bitter | 24.94274 15.21993 1.64 0.101 -4.887769 54.77326 bitter2 | sweet | -40.74544 0735659 18.68836 6308328 -2.18 0.12 0.029 0.907 -77.37397 -1.162844 -4.116921 1.309976 fat | weight_alt | 1177642 0541594 1.234397 0365326 0.10 1.48 0.924 0.138 -2.30161 -.0174432 2.537138 125762 no_sachet | pap_pack | -1.103827 -.4327863 6433185 2763634 -1.72 -1.57 0.086 0.117 -2.364708 -.9744487 1570541 1088761 vinacafe | trungnguyen | 1.883888 -1.696056 3.378918 2.298462 0.56 -0.74 0.577 0.461 -4.73867 -6.200959 8.506446 2.808848 nestle | privin | 3831005 0279594 1.734024 0273143 0.22 1.02 0.825 0.306 -3.015524 -.0255757 3.781725 0814945 pritru | prines | 1449513 1213597 0593004 0444217 2.44 2.73 0.015 0.006 0287247 0342949 261178 2084246 priin2 | priin3 | 0079212 0050583 0108327 0140365 0.73 0.36 0.465 0.719 -.0133106 -.0224527 029153 0325692 priin4 | priin5 | 0261014 -.0404617 0165183 0329138 1.58 -1.23 0.114 0.219 -.0062739 -.1049715 0584767 0240482 priin6 | priin7 | 0191906 -.0343395 0169313 0263455 1.13 -1.30 0.257 0.192 -.0139942 -.0859757 0523753 0172967 priin10 | prioc2 | 003507 -.0122084 0117998 0152003 0.30 -0.80 0.766 0.422 -.0196202 -.0420005 0266342 0175837 prioc3 | prioc4 | 0215448 -.0002668 0143125 0166422 1.51 -0.02 0.132 0.987 -.0065071 -.0328849 0495968 0323513 prioc5 | prioc6 | -.0026192 -.1965365 0154504 0244599 -0.17 -8.04 0.865 0.000 -.0329015 -.2444771 027663 -.1485959 prioc7 | prioc8 | -.0212758 0038037 0112908 0177165 -1.88 0.21 0.060 0.830 -.0434053 -.03092 0008538 0385274 primal | priday | 0186226 -.0159314 0090569 014777 2.06 -1.08 0.040 0.281 0008714 -.0448939 0363738 013031 priwee | -.0138459 0152013 -0.91 0.362 -.0436399 015948 - test price price2 bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction' ( 1) [choice]price = ( 2) ( 3) [choice]price2 = [choice]bitter = ( 4) ( 5) [choice]bitter2 = [choice]sweet = ( 6) ( 7) [choice]fat = [choice]weight_alt = ( 8) ( 9) [choice]no_sachet = [choice]pap_pack = (10) (11) [choice]vinacafe = [choice]trungnguyen = Page xxxiv (12) [choice]nestle = (13) (14) [choice]privin = [choice]pritru = (15) (16) [choice]prines = [choice]priin2 = (17) (18) [choice]priin3 = [choice]priin4 = (19) (20) [choice]priin5 = [choice]priin6 = (21) (22) [choice]priin7 = [choice]priin10 = (23) (24) [choice]prioc2 = [choice]prioc3 = (25) (26) [choice]prioc4 = [choice]prioc5 = (27) (28) [choice]prioc6 = [choice]prioc7 = (29) (30) [choice]prioc8 = [choice]primal = (31) (32) [choice]priday = [choice]priwee = Constraint dropped Constraint dropped Constraint 25 dropped Constraint 30 dropped chi2( 28) = 9122.09 Prob > chi2 = 0.0000 clear // Second case: hypothetical choice scenarios / is not a valid command name r(199); use temp_data.dta drop if time == (3,743 observations deleted) Page xxxv // Conditional logit model: single variables / is not a valid command name r(199); clogit choice price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: Iteration 1: log pseudolikelihood = -1122.5249 log pseudolikelihood = -1120.2138 Iteration 2: Iteration 3: log pseudolikelihood = -1120.2131 log pseudolikelihood = -1120.2131 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1120.2131 Number of obs = 2,094 Wald chi2(12) Prob > chi2 = = 70.24 0.0000 Pseudo R2 = 0.0411 (Std Err adjusted for clustering on id) -| choice | Coef Robust Std Err z P>|z| [95% Conf Interval] -+ -price | -.0172831 027035 -0.64 0.523 -.0702708 0357046 weight | discount | -.0182327 0224552 0088621 0039187 -2.06 5.73 0.040 0.000 -.035602 0147748 -.0008633 0301356 bitter | sweet | 5.535878 -.7926451 3.263817 4965164 1.70 -1.60 0.090 0.110 -.8610858 -1.765799 11.93284 1805092 fat | weight_alt | -1.358692 0017038 1.015161 0148487 -1.34 0.11 0.181 0.909 -3.348372 -.0273991 6309871 0308067 no_sachet | pap_pack | -.0040357 -.2883857 2442787 5083474 -0.02 -0.57 0.987 0.571 -.482813 -1.284728 4747417 7079569 vinacafe | trungnguyen | -3.628694 -1.96607 3.146857 1.568099 -1.15 -1.25 0.249 0.210 -9.796421 -5.039487 2.539033 1.107347 nestle | -1.522152 1.734 -0.88 0.380 -4.920729 1.876425 - test price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle Page xxxvi ( 1) ( 2) [choice]price = [choice]weight = ( 3) ( 4) [choice]discount = [choice]bitter = ( 5) ( 6) [choice]sweet = [choice]fat = ( 7) ( 8) [choice]weight_alt = [choice]no_sachet = ( 9) (10) [choice]pap_pack = [choice]vinacafe = (11) (12) [choice]trungnguyen = [choice]nestle = chi2( 12) = Prob > chi2 = 70.24 0.0000 // Conditional logit model: squared variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group( > id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -1120.4226 Iteration 1: Iteration 2: log pseudolikelihood = -1117.6378 log pseudolikelihood = -1117.6373 Iteration 3: log pseudolikelihood = -1117.6373 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1117.6373 Number of obs Wald chi2(14) = = 2,094 70.27 Prob > chi2 Pseudo R2 = = 0.0000 0.0433 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ Page xxxvii price | -.0598778 0925234 -0.65 0.518 -.2412203 1214647 price2 | weight | 0002984 -.0185582 0005589 0088612 0.53 -2.09 0.593 0.036 -.000797 -.0359258 0013939 -.0011906 discount | bitter | 0223737 1.258177 003906 13.22888 5.73 0.10 0.000 0.924 0147181 -24.66995 0300293 27.18631 bitter2 | sweet | 4.48238 -.778222 15.12241 5177499 0.30 -1.50 0.767 0.133 -25.15699 -1.792993 34.12175 2365491 fat | weight_alt | -1.480806 0036457 1.040222 0232498 -1.42 0.16 0.155 0.875 -3.519604 -.0419231 5579916 0492146 no_sachet | pap_pack | -.0382134 -.2828311 3757582 5225289 -0.10 -0.54 0.919 0.588 -.7746859 -1.306969 698259 7413066 vinacafe | trungnguyen | -4.544782 -2.230604 3.338794 1.998066 -1.36 -1.12 0.173 0.264 -11.0887 -6.146741 1.999134 1.685532 nestle | -2.265243 2.424922 -0.93 0.350 -7.018004 2.487518 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = ( 2) ( 3) [choice]price2 = [choice]weight = ( 4) ( 5) [choice]discount = [choice]bitter = ( 6) ( 7) [choice]bitter2 = [choice]sweet = ( 8) ( 9) [choice]fat = [choice]weight_alt = (10) (11) [choice]no_sachet = [choice]pap_pack = (12) (13) [choice]vinacafe = [choice]trungnguyen = (14) [choice]nestle = chi2( 14) = Prob > chi2 = 70.27 0.0000 // Conditional logit model: all kinds of variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt Page xxxviii no_sachet pap_pack vinacafe trungnguyen nestle `intera > ction', group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -1105.6076 Iteration 1: Iteration 2: log pseudolikelihood = -1035.2261 log pseudolikelihood = -1033.1658 Iteration 3: Iteration 4: log pseudolikelihood = -1032.9919 log pseudolikelihood = -1032.9576 Iteration 5: Iteration 6: log pseudolikelihood = -1032.9501 log pseudolikelihood = -1032.9483 Iteration 7: Iteration 8: log pseudolikelihood = -1032.948 log pseudolikelihood = -1032.9479 Iteration 9: log pseudolikelihood = -1032.9479 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -1032.9479 Number of obs Wald chi2(33) = = 2,094 Prob > chi2 Pseudo R2 = = 0.1158 (Std Err adjusted for clustering on id) -| Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | price2 | -.1820351 000095 1141533 0006539 -1.59 0.15 0.111 0.884 -.4057714 -.0011866 0417012 0013766 weight | discount | -.0166574 0264571 0093158 0038075 -1.79 6.95 0.074 0.000 -.0349159 0189944 0016012 0339197 bitter | bitter2 | 11.60804 -14.33617 22.34722 27.61913 0.52 -0.52 0.603 0.604 -32.1917 -68.46866 55.40779 39.79632 sweet | fat | -1.751805 -3.498232 9461934 1.892311 -1.85 -1.85 0.064 0.065 -3.60631 -7.207092 1027002 2106288 weight_alt | no_sachet | 0536203 -1.063678 0512912 9210173 1.05 -1.15 0.296 0.248 -.0469086 -2.868839 1541492 7414826 pap_pack | vinacafe | -.4006129 -8.889098 5616754 6.074351 -0.71 -1.46 0.476 0.143 -1.501476 -20.79461 7002506 3.016412 trungnguyen | nestle | -6.272952 -5.136031 4.093952 3.50599 -1.53 -1.46 0.125 0.143 -14.29695 -12.00764 1.751047 1.735583 privin | pritru | 01992 100401 073974 0953109 0.27 1.05 0.788 0.292 -.1250664 -.0864049 1649064 2872068 prines | priin2 | 0997277 -.0854952 0761846 0434129 1.31 -1.97 0.191 0.049 -.0495914 -.170583 2490468 -.0004075 priin3 | priin4 | -.0227783 0448272 0413039 0787783 -0.55 0.57 0.581 0.569 -.1037324 -.1095753 0581758 1992298 Page xxxix priin5 | -.1771435 1502122 -1.18 0.238 -.471554 117267 priin6 | priin7 | -.0047118 -.0112734 1207281 2541146 -0.04 -0.04 0.969 0.965 -.2413344 -.5093288 2319109 4867821 priin10 | prioc2 | 1.158004 -.0047272 0871971 0899093 13.28 -0.05 0.000 0.958 987101 -.1809462 1.328907 1714917 prioc3 | prioc4 | 1349585 0593048 0587326 0470941 2.30 1.26 0.022 0.208 0198447 -.0329979 2500724 1516074 prioc5 | prioc6 | 0929088 -2.174753 0562369 5324643 1.65 -4.08 0.099 0.000 -.0173135 -3.218363 2031311 -1.131142 prioc7 | prioc8 | -1.581307 -.0588697 1365646 0456308 -11.58 -1.29 0.000 0.197 -1.848969 -.1483045 -1.313645 030565 primal | priday | 0296589 1109244 024396 0612533 1.22 1.81 0.224 0.070 -.0181564 -.0091298 0774743 2309786 priwee | 1034773 0693351 1.49 0.136 -.032417 2393716 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction' ( 1) [choice]price = ( 2) ( 3) [choice]price2 = [choice]weight = ( 4) ( 5) [choice]discount = [choice]bitter = ( 6) ( 7) [choice]bitter2 = [choice]sweet = ( 8) ( 9) [choice]fat = [choice]weight_alt = (10) (11) [choice]no_sachet = [choice]pap_pack = (12) (13) [choice]vinacafe = [choice]trungnguyen = (14) (15) [choice]nestle = [choice]privin = (16) (17) [choice]pritru = [choice]prines = (18) (19) [choice]priin2 = [choice]priin3 = (20) (21) [choice]priin4 = [choice]priin5 = (22) (23) [choice]priin6 = [choice]priin7 = (24) (25) [choice]priin10 = [choice]prioc2 = (26) (27) [choice]prioc3 = [choice]prioc4 = Page xl (28) [choice]prioc5 = (29) (30) [choice]prioc6 = [choice]prioc7 = (31) (32) [choice]prioc8 = [choice]primal = (33) (34) [choice]priday = [choice]priwee = Constraint dropped chi2( 33) = 2.1e+09 Prob > chi2 = 0.0000 clear // Third case: all choice scenarios / is not a valid command name r(199); use temp_data.dta // Conditional logit model: single variables / is not a valid command name r(199); clogit choice price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: Iteration 1: log pseudolikelihood = -2501.6695 log pseudolikelihood = -2501.4993 Iteration 2: log pseudolikelihood = -2501.4993 Conditional (fixed-effects) logistic regression Page xli Log pseudolikelihood = -2501.4993 Number of obs = 5,542 Wald chi2(12) Prob > chi2 = = 75.28 0.0000 Pseudo R2 = 0.0391 (Std Err adjusted for clustering on id) -| choice | Coef Robust Std Err z P>|z| [95% Conf Interval] -+ -price | 0066835 0112857 0.59 0.554 -.0154361 028803 weight | discount | 0593704 0282543 009092 007887 6.53 3.58 0.000 0.000 0415504 0127959 0771904 0437126 bitter | sweet | -2.822397 5363297 1.337308 2195279 -2.11 2.44 0.035 0.015 -5.443472 106063 -.2013225 9665965 fat | weight_alt | 9210026 -.0129203 4605888 006082 2.00 -2.12 0.046 0.034 0182652 -.0248407 1.82374 -.0009998 no_sachet | pap_pack | 1841745 -.187218 0937489 2263209 1.96 -0.83 0.049 0.408 0004299 -.6307987 367919 2563627 vinacafe | trungnguyen | 2.623756 651586 1.489892 7966701 1.76 0.82 0.078 0.413 -.296378 -.9098588 5.543889 2.213031 nestle | 4707784 9282148 0.51 0.612 -1.348489 2.290046 - test price weight discount bitter sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = ( 2) ( 3) [choice]weight = [choice]discount = ( 4) ( 5) [choice]bitter = [choice]sweet = ( 6) ( 7) [choice]fat = [choice]weight_alt = ( 8) ( 9) [choice]no_sachet = [choice]pap_pack = (10) (11) [choice]vinacafe = [choice]trungnguyen = (12) [choice]nestle = chi2( 12) = Prob > chi2 = 75.28 0.0000 Page xlii // Conditional logit model: squared variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle, group( > id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -2500.9698 Iteration 1: Iteration 2: log pseudolikelihood = -2500.7156 log pseudolikelihood = -2500.7156 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -2500.7156 Number of obs = 5,542 Wald chi2(14) Prob > chi2 = = 75.01 0.0000 Pseudo R2 = 0.0394 (Std Err adjusted for clustering on id) -| choice | Coef Robust Std Err z P>|z| [95% Conf Interval] -+ -price | 0253697 0304712 0.83 0.405 -.0343529 0850922 price2 | weight | -.0001242 0592947 0001902 009103 -0.65 6.51 0.514 0.000 -.0004969 0414531 0002486 0771363 discount | bitter | 0282588 -4.721002 0078728 5.254564 3.59 -0.90 0.000 0.369 0128285 -15.01976 0436891 5.577754 bitter2 | sweet | 2.285835 5842252 5.945856 2395073 0.38 2.44 0.701 0.015 -9.367829 1147996 13.9395 1.053651 fat | weight_alt | 1.011793 -.0163849 506466 0089098 2.00 -1.84 0.046 0.066 019138 -.0338477 2.004448 0010779 no_sachet | pap_pack | 2411902 -.1997144 1393015 2272208 1.73 -0.88 0.083 0.379 -.0318358 -.6450591 5142162 2456302 vinacafe | trungnguyen | 2.796552 5015338 1.673858 9565249 1.67 0.52 0.095 0.600 -.4841496 -1.373221 6.077254 2.376288 nestle | 337785 1.191139 0.28 0.777 -1.996804 2.672374 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle ( 1) [choice]price = Page xliii ( 2) [choice]price2 = ( 3) ( 4) [choice]weight = [choice]discount = ( 5) ( 6) [choice]bitter = [choice]bitter2 = ( 7) ( 8) [choice]sweet = [choice]fat = ( 9) (10) [choice]weight_alt = [choice]no_sachet = (11) (12) [choice]pap_pack = [choice]vinacafe = (13) (14) [choice]trungnguyen = [choice]nestle = chi2( 14) = Prob > chi2 = 75.01 0.0000 // Conditional logit model: all kinds of variables / is not a valid command name r(199); clogit choice price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `intera > ction', group(id) robust note: multiple positive outcomes within groups encountered Iteration 0: log pseudolikelihood = -2466.0592 Iteration 1: Iteration 2: log pseudolikelihood = -2433.778 log pseudolikelihood = -2430.5099 Iteration 3: Iteration 4: log pseudolikelihood = -2429.7732 log pseudolikelihood = -2429.7334 Iteration 5: Iteration 6: log pseudolikelihood = -2429.7332 log pseudolikelihood = -2429.7332 Conditional (fixed-effects) logistic regression Log pseudolikelihood = -2429.7332 Number of obs = 5,542 Wald chi2(30) Prob > chi2 = = Pseudo R2 = 0.0667 (Std Err adjusted for clustering on id) Page xliv | Robust choice | Coef Std Err z P>|z| [95% Conf Interval] -+ -price | price2 | -.0546263 -.0000673 0432129 0002372 -1.26 -0.28 0.206 0.777 -.139322 -.0005322 0300694 0003977 weight | discount | 0594902 0294846 009122 0075459 6.52 3.91 0.000 0.000 0416114 0146949 077369 0442743 bitter | bitter2 | 26.89982 -39.12604 10.82788 13.44546 2.48 -2.91 0.013 0.004 5.677562 -65.47866 48.12207 -12.77342 sweet | fat | -.7765076 -1.679608 4583982 9151699 -1.69 -1.84 0.090 0.066 -1.674952 -3.473308 1219364 1140918 weight_alt | no_sachet | 0657071 -1.274387 0268564 4686745 2.45 -2.72 0.014 0.007 0130695 -2.192972 1183447 -.3558018 pap_pack | vinacafe | -.3550718 -3.684817 2298852 2.688282 -1.54 -1.37 0.122 0.170 -.8056385 -8.953752 0954949 1.584119 trungnguyen | nestle | -4.195729 -2.759318 1.629328 1.405234 -2.58 -1.96 0.010 0.050 -7.389154 -5.513526 -1.002305 -.0051102 privin | pritru | 0195945 1435674 0230591 0410234 0.85 3.50 0.395 0.000 -.0256004 063163 0647894 2239718 prines | priin2 | 1239666 -.0003728 0314023 0091668 3.95 -0.04 0.000 0.968 0624193 -.0183394 1855139 0175938 priin3 | priin4 | 0077191 0216312 0112433 0164455 0.69 1.32 0.492 0.188 -.0143173 -.0106013 0297555 0538637 priin5 | priin6 | -.0633063 0029581 0440288 0116245 -1.44 0.25 0.150 0.799 -.1496011 -.0198256 0229886 0257417 priin7 | priin10 | -.0312546 0161567 0399962 010547 -0.78 1.53 0.435 0.126 -.1096457 -.0045149 0471364 0368284 prioc2 | prioc3 | -.0201671 0292238 0170412 0155413 -1.18 1.88 0.237 0.060 -.0535673 -.0012366 0132331 0596842 prioc4 | prioc5 | 0075925 0038381 0139319 0142507 0.54 0.27 0.586 0.788 -.0197135 -.0240927 0348984 0317689 prioc6 | prioc7 | -.3216017 -.1084955 0325991 0169672 -9.87 -6.39 0.000 0.000 -.3854947 -.1417507 -.2577086 -.0752403 prioc8 | primal | -.0025634 0116561 017934 0066991 -0.14 1.74 0.886 0.082 -.0377135 -.0014739 0325866 0247861 priday | priwee | -.0046483 -.0010328 0140565 0146459 -0.33 -0.07 0.741 0.944 -.0321985 -.0297382 0229019 0276727 - test price price2 weight discount bitter bitter2 sweet fat weight_alt no_sachet pap_pack vinacafe trungnguyen nestle `interaction' ( 1) ( 2) [choice]price = [choice]price2 = ( 3) ( 4) [choice]weight = [choice]discount = Page xlv ( 5) [choice]bitter = ( 6) ( 7) [choice]bitter2 = [choice]sweet = ( 8) ( 9) [choice]fat = [choice]weight_alt = (10) (11) [choice]no_sachet = [choice]pap_pack = (12) (13) [choice]vinacafe = [choice]trungnguyen = (14) (15) [choice]nestle = [choice]privin = (16) (17) [choice]pritru = [choice]prines = (18) (19) [choice]priin2 = [choice]priin3 = (20) (21) [choice]priin4 = [choice]priin5 = (22) (23) [choice]priin6 = [choice]priin7 = (24) (25) [choice]priin10 = [choice]prioc2 = (26) (27) [choice]prioc3 = [choice]prioc4 = (28) (29) [choice]prioc5 = [choice]prioc6 = (30) (31) [choice]prioc7 = [choice]prioc8 = (32) (33) [choice]primal = [choice]priday = (34) [choice]priwee = Constraint dropped Constraint dropped Constraint 16 dropped Constraint 33 dropped chi2( 30) = 4015.81 Prob > chi2 = 0.0000 clear ******************************************************************************** Page xlvi ... (2 014 , 2 015 ) Manufacturer Value share, % 2 014 2 015 38 .7 24 .1 29 .1 19 .3 30.0 18 .3 2.2 8.6 Table 3. 3 presents the list of 19 kinds of 3in1 coffee They are not all kinds of available 3in1 coffee in. .. various packaging Moreover, Table A. 1 presents a summary of all 19 alternatives and their attributes such main ingredients, packaging, and manufacturer Table 3. 3 List of all available 3in1 coffee. .. Manufacturer Vinacafe Nestlé 10 11 12 13 14 15 Trung Nguyen 16 17 18 Fes Vietnam 19 3. 4 Brand Vinacafe Chất Wake-up Nescafe red Nescafe green G7 Maccoffee Product Vinacafe Gold original Vinacafe