2010
DOI: 10.1002/agr.20269
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Organic and conventional milk purchase behaviors in Central Ohio

Abstract: The authors use weekly milk scanner data from six stores of a national supermarket chain to investigate empirically the purchasing patterns of suburban and inner-city residents for conventional and organic milk. They disaggregate conventional milk products into four categories based on fat content (whole, 2%, 1%, and skim); organic milk is disaggregated into these same categories, but for empirical estimation, some categories are combined. Their descriptive statistics show that suburban consumers, relative to … Show more

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Cited by 14 publications
(27 citation statements)
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References 12 publications
(18 reference statements)
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“…Demand for organic milk seems to be even less elastic than demand for conventional milk. This finding is consistent with the work of Chang et al () and Monier et al (), but conflicts with the elasticity estimates of all the other studies.…”
Section: Resultssupporting
confidence: 82%
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“…Demand for organic milk seems to be even less elastic than demand for conventional milk. This finding is consistent with the work of Chang et al () and Monier et al (), but conflicts with the elasticity estimates of all the other studies.…”
Section: Resultssupporting
confidence: 82%
“…The results indicated an elastic demand for both organic and conventional milk. In contrast, Chang, Hooker, Jones, and Sam () showed an inelastic demand for both milk types. They analyzed retail scanner data of six stores in central Ohio comparing suburban and inner city areas.…”
Section: Literature Reviewmentioning
confidence: 91%
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“…Furthermore, the basics cluster was characterized by respondents who reported a generally lower income, lower age, and less focus on diet and exercise compared with other groups. The similarities between the value and basics clusters with respect to their conjoint results suggest that demographics may not be an effective determinant of fluid milk purchase for all consumer groups (Zepeda et al, 2003;Chang et al, 2011).…”
Section: Conjoint Surveymentioning
confidence: 99%
“…Although an expanding market would indicate growing demand, one cannot directly link changes in SRP into changes in demand. Thus although sales (scanner) data (e.g., Chang, Hooker, Jones, & Sam, ; Levaggi & Brentari, ; Martinez‐Garmendia, ; Schollenberg, ;) remain the gold standard for hedonic studies at the consumer level, this analysis of SRP is intended to explore manufacturer valuation directly (Carew, Florkowski, & Smith, ) and thereby only provides indirect insight into customer/consumer valuation. But although SRP may not be a perfect substitute for point‐of‐sale data, it not only comes reasonably close to what market price would actually be (assuming producer knowledge of consumer WTP), it also tells us what firms think about their consumer base before observing their actual purchase—an interesting and useful glimpse into the thought process of the firm that has applications from traditional regression analysis to game theory experiments.…”
Section: Introductionmentioning
confidence: 99%