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They are the driving forces of a firm's incentive to adopt quality/safety standards./3 which is a vector of logistic coefficients.The intercepts vary between categories and satisfy the constraints α1≤α2≤……≤αn-1.It is assumed that the data are categorized independent of each other.Using the ordinal logistic setting,it is possible to estimate the relative odds of being in each category for firms which have a particular characteristic to those which do not after taking into account the effect of all other explanatory variables.The logistic coefficients represent the estimated increase of probabilities in each category of adoption intensity ha the particular characteristics.
We estimate the probability of adopting a quality/safety standard.Our model draws upon the methods of Hassan et al.(2006).Firstly,we use a binomial logistic model to identify the factors that differentiate between adopters.A dichotomous variable takes value 0 for standards "less" adopters (zero or one standard) and 1 for "more" adopters (two or more standards).Secondly,it is also important to understand to what extent a firm would implement food safety and quality standards.Ordered logistic analysis was then used to identify the difference between high-degree adopters and low-degree adopters.The adopting magnitude can be measured by a three-category scale ranging from "Low" to "Medium" and "High".Respondents that stated that no standards were implemented were classified as Low degree.Respondents that stated that one or two standards were implemented were classified as Medium degree.Respondents with three or more standards were classified as High degree.Therefore,ordered and binomial logistic models are specified as shown in Table 4.3.