以往對於消費者對基因改造食品偏好及願付價格估計之研究，如 Moon (2001)、Chern et al. (2002)和Fu (2004)，都是將兩個研究目的 分開個別處理，因此就統計的觀點來說，這樣的分析方式是比較缺乏 效率的。 本研究中提出一個整合式的分析方式，來分析台灣消費者對於基 因改造食品的偏好以及願付溢價。本方法除了增進統計效率外，也同 時納入願付價格分析中可能遇到因為受訪者對該財貨有強烈偏好，對 於該財貨有再高價格都願意支付的傾向，因此預期本方法將可提供更 高的準確度。 為了研究台灣民眾對基因改造鮭魚的認知行為，中央研究院調查 研究專題中心在2003 年進行了全台的電話訪問。而本研究發現台灣消 費者願意多付目前平均市價之12%來購買非基因改造飼料餵食之鮭 魚。此外，消費者本身對於基因改造科技的看法、對基因改造科技的 了解、以及消費者的年齡將扮演影響消費偏好以及願付溢價的高低之 重要因素。 / Previous researchers, such as Moon (2001), Chern et al. (2002), and Fu et al. (2004), attempted to analyze consumer preference toward genetically modi¯ed pro- duces and to estimate a willingness to pay for them. These two objectives, however, have been conducted separately and therefore are de¯cient in statistically e±ciency. An integrated technique is introduced to understand how consumers in Taiwan perceive genetically modi¯ed produce as well as how much they are willing to pay a premium simultaneously. Apart from improving statistical e±ciency, this newly proposed method also considers the possibility that consumers might be willing to any price if he/she strongly prefer this goods. Higher precision is expected under this scenario. To investigate how public perceive genetically modi¯ed salmon, Center for Sur- vey Research, Academia Sinica, conducted a telephone survey to Taiwanese con- sumers in 2003. Our study indicates that consumers are willing to pay a premium, about 12% of the average market price, for non-GM-fed salmon rather than GM-fed counter part. Moreover, the way consumers think of the risk regarding GM food, how much they know about GM food, and their age will all in°uence their consump- tion tendency and willingness to pay.

本研究的目的主要是想要探討台灣地區的人民對於基因檢測的意向及願付價格。資料來自於中央研究院所主導的一項電話訪問，其中有關願付價格的部分是透過條件評估法的方式來取得。針對願付價格的分析，我們藉由潛在變數模型將受訪者對於基因檢測的知識、態度和自我認知等資訊萃取出來，並視為新的解釋變數來進行分析。此外，僅完成單界詢價過程的受訪者的資訊也和提供完整回答的受訪者一併納入分析。 結果顯示一個人的性別、教育程度、宗教信仰傾向及對基因檢測的態度會顯著影響是否願意免費參加基因檢測的意願。而在詢價過程中，一開始受訪者被問及的金額和此人其基因檢測相關的知識程度影響了他（她）是否願意付錢參加基因檢測。至於在願意付合理價格的人們之中，他們的健康程度、收入和自家人的癌症病史則皆為影響價格高低的因素。 / This study is aimed to explore people’s intention and their willingness to pay (WTP) for genetic testing in Taiwan. A telephone survey using contingent valuation method (CVM) was conducted by the Academia Sinica to collect the data. There are three unique features that distinguish our data analysis approach from the others. First, the covariates related to a respondent’s knowledge, attitude and perception (KAP) on genetic testing are generated through the use of a latent trait model. Second, the information collected from a respondent who completed only the single-bounded part of the survey is also included in the analysis. Third, reasons given by a respondent is used to decide whether he/she is willing to pay a lower price or unwilling to pay any price. It is shown that one’s gender, education level, religious tendency and attitude all have significant impact on whether a respondent is willing to try a free genetic test. When it comes to pay for it, the initial bid asked and the degrees of knowledge affect his/her decision a lot. For those who are willing to pay a reasonable price for genetic testing, their WTP depend largely on their health conditions, incomes, and cancer histories.

本研究主要目的是想要探討台灣地區民眾對於維持公共電視的營運與發展之願付價格。資料來自於中央研究院人文社會科學研究中心調查研究專題中心所進行的一項公共電視願付價格調查，其中關於願付價格的部分是透過條件評估法的方式取得，受訪者隨機分配至兩個題組之一，其中A題組為考慮公共電視帶給家人的好處，而B題組為考慮公共電視帶給全國民眾及社會的好處。 建模時我們採用一或二要素混成模型，這是一個可以將不理性受訪者分離，僅針對理性受訪者的願付價格進行估計的一個模型。分析結果顯示考慮到公共電視帶給家人的好處時，年齡50歲以上的受訪者中存在著一群無論什麼價格都不願意支付或Ney-sayers的人，估計的比例大約為21.13%；而考慮到公共電視帶給全國民眾及社會的好處時，很少看文化教育節目的受訪者中也存在著一群無論什麼價格都不願意支付或Ney-sayers的人，估計比例大約為13.53%。 針對願意支付合理價格的受訪者配適加速失敗模型時，我們不僅就位置參數引進解釋變數，同時也引進解釋變數至尺度參數。分析結果顯示願意支付合理價格的受訪者，在考慮公共電視帶給家人的好處時，每年願意支付價格之平均值為1477元；而考慮到公共電視帶給全國民眾及社會的好處時，每年願意支付價格之平均值為1663元。顯示受訪者在考慮到全國民眾及社會的好處，願意付出較高的價錢。 / This study aimed to explore people’s willingness to pay (WTP) for maintaining the operation of Public Television Service (PTS) in Taiwan. The survey using contingent valuation method was conducted by the Center for Survey Research, Academia Sinica. The survey sample was split into two groups, A and B. Each group was presented with the same scenario but different scope of benefits. Group A considered the benefits that PTS might bring to one’s family, while Group B considered the benefits that PTS might bring to the whole society. The model used in this study was a one/two-component hybrid model, a model that is able to separate those who are willing to pay a reasonable price from these who are not, and obtain their mean WTP estimate. Multinomial logistic part of the model, indicated that for Group A, among those who were 50 years of age or older, about 24.48% were not willing to pay any price or Ney-sayers. And for Group B, among those who seldom watched cultural or educational programs, about 12.91% were not willing to pay any price or Ney-sayers. Appling accelerated failure time model to those who were willing to pay for reasonable prices enabled us to evaluate the WTP. We not only introduced explanatory variables in the location parameter but also the scale parameter. The estimated mean WTP for Group A was found to be NT$1477 per year, while the mean WTP for Group B was NT$1663 per year.

為了處理在願付價格的研究中，極端受訪者對於估計結果所造成的誤差。本文沿用Tsai(2005)所建議採用的三要素混合模型，將受訪者區分為價格再高都願意支付、願意支付合理價格以及價格再低都不願意支付等三種類型。在評估願付價格時，以加速失敗模型(accelerated failure time model，簡稱AFT model)針對願意支付合理價格的受訪者進行估計，並且在考慮不同起價點可能會造成不同程度的起價點偏誤(starting point bias)或是定錨效果(anchoring effect)的情形下，提出一個起價點偏誤調整模型來做探討。我們並以CVDFACTS中的高血壓之願付價格資料進行實證分析。分析結果發現，教育程度越高的男性對於能降低高血壓病患罹患心臟血管相關疾病之新藥願意付較高的金額。此外我們也發現在此筆資料中，不同起價點確實會造成不同程度的偏誤，經由偏誤調整後會得到較高的願付金額。 / A study of willingness-to-pay often suffers from the bias introduced by extreme respondents who are willing to or not willing to pay any price. To overcome the problem, a three-component model proposed by Tsai (2005) is adopted. Under such a circumstance, respondents are classified into three categories, i.e. respondents who are willing to pay any price, unwilling to pay any price, or willing to pay a reasonable price. The willingness-to-pay for those subjects who are willing to pay a reasonable price is again modeled by an accelerated failure time model (AFT model). In this study, we, however, propose an unified model that allows us to look into the issue related to starting point bias and anchoring effect, simultaneously. Willingness-to-pay for cardiovascular disease treatment from a longitudinal follow-up survey- CVDFACTS, is investigated using the new model. Through the use of the model, we are able to detect the effects of starting point biases, and make a proper adjustment accordingly. Our analysis indicates that male respondents with higher education level have an inclination to pay higher price for the new treatment. Besides, we also discover that starting point bias does exist in this dataset.

本文主要著重在處理條件評估法下，「不知道」受訪者的回應。當「不知道」受訪者的產生機制並未符合完全隨機時，考量他們的真實意向就顯得極為重要。 文中使用中央研究院生醫所在其研究計畫「竹東及朴子地區心臟血管疾病長期追蹤研究」（CardioVascular Disease risk FACtor Two-township Study，簡稱CVDFACTS）第五循環中的研究調查資料。 　　由於以往的文獻對於「不知道」受訪者的處理，皆有不足之處。如Wang (1997)所提出的方法，就只能針對某種特定的「不知道」受訪者來做處理；而Caudill and Groothuis (2005)所提的方法，由於將「不知道」受訪者的差補與願付價格的估計分開，亦使其估計結果不具備一些好的性質。在本文中，我們提出一個能同時處理「不知道」受訪者且估計願付價格的方法。除了使得統計上較有效率外，也保有EM演算法的一個特性：願付價格模型中的估計參數為最大概似估計值。此外，在加入三要素混合模型(Tsai (2005))後，我們也可避免用到極端受訪者的訊息去差補那些「不知道」受訪者的意向。 　　在分析願付價格的過程中，我們發現此筆資料的「不知道」受訪者，其產生的機制為隨機，而非為完全隨機，這意謂著不考量「不知道」受訪者的分析結果，必定會產生偏差。而在比較有考量「不知道」受訪者與沒有的情況後，其結果確實應證了我們的想法：只要「不知道」受訪者不是完全隨機產生的，那麼不考量他們必定會產生某種程度的偏差。 / This paper investigates how to deal with “Don’t Know” (DK) responses in contingent valuation surveys, which must be taken into consideration when they are not completely at random. The data we use is collected from the fifth cycle of the Cardiovascular Disease Risk Factor Two-township Study (CVDFACTS), which is a series of long-term surveys conducted by the Institute of Biomedical Sciences, Academia Sinica. Previous methods used in dealing with DK responses have not been satisfactory because they only focus on some types of DK respondents (Wang (1997)), or separate the imputation of DK responses from the WTP estimation (Caudill and Groothuis (2005)). However, in this paper, we introduce an integrated method to cope with the incomplete data caused by DK responses. Besides being more efficient, the single-step method guarantees maximum likelihood estimates of the WTP model to be obtained due to the good property that the EM algorithm possesses. Furthermore, by adding the concept of the three-component mixture model (Tsai (2005)), some extreme information are drawn out when imputing the DK inclinations. In this hypertension data, the mechanism of the DK responses is “Don’t know at random”, which means the analysis of DK-dropped results in a bias. By using our method, the difference between DK-dropped and DK-included is actually revealed, which proves our suspicion that a DK-dropped analysis is accompanied by a biased result when DK is not completely at random.