Ornamental Plants in the United States: An Econometric Analysis of a Household‐Level Demand System

Abstract

This study provides an empirical analysis of demand for a large group of ornamental plants using a theorybased demand model. Specifically, consumer preferences are represented by the Almost Ideal Demand System where allowance is made for demand censoring. Given that revealed-preference data are usually limited to certain ornamental plants and geographical locations, we exploit unique hypothetical purchase data collected via an online survey regarding 16 annual, perennial, and foliage plants from across the United States. The effect of various socio-economic and demographic factors on demand for plants is quantified. Our findings indicate that ornamental plants are predominantly price-elastic with foliage plants being more price-responsive vis-a-vis other plant categories. Further, a majority of plants are expenditureelastic with the estimates of foliage plants manifesting the greatest variability. Finally, plants in the same category appear to be closer substitutes. Results benefit ornamental plant industry stakeholders as they determine the best pricing strategies in their specific markets. C 2016 Wiley Periodicals, Inc.

Pollinator-friendly Plants: Reasons for and Barriers to Purchase

“…However, retailers who are not able to create increased label value may be blunting sales volume by charging premiums. Given recent findings that ornamental plant demand is elastic (Hovhannisyan and Khachatryan, 2017), increasing prices would result in lower revenue overall with an elastic demand curve. Thereby, pricing needs to be considered in conjunction with efforts to promote the pollinator-friendly plant label.…”

Examining Demand Elasticities in the U.S. Differentiated Yogurt Market

“…They derive an extension of the AIDS model -the quadratic almost ideal demand system (QUAIDS) which also includes a higher-order total expenditure term. In this study, we estimate the demand parameters and the price and income elasticities using the QUAIDS model which has been broadly used recently by demand analysis studies of fish and meat (Lambert et al, 2006); food (Abdulai & Aubert, 2004;Bopape, 2006;Hoang, 2018), yogurt (Davis et al, 2010), wine (Cembalo et al, 2014), and ornamental plants (Hovhannisyan & Khachatryan, 2017).…”

“…However, this procedure is less efficient than MLE (Yen and Lin, 2006). Nevertheless it remains an attractive alternative and it is still widely used in empirical literature (Sckokai & Moro, 2009;Schrock, 2012;Khaliukova, 2013;Hailu et al, 2014;Hovhannisyan & Khachatryan, 2017;Chen et al, 2018) due to its simplicity. Therefore, we apply this procedure in this paper.…”

“…Decision-makers in the dairy sector, both public and private, require contemporaneous demand analysis (Maynard & Veeramani, 2003) especially after the change in per capita consumption of major dairy products in the last decades. The decision to alter the price of a product depends on both the own-price elasticity and the cross-price elasticity of a product (Hovhannisyan & Khachatryan, 2017). How consumers respond to price changes is an important question for retailers to manage and develop their future marketing strategies to maximize their profit.…”

Empirical Evidence of Changing Food Demand and Consumer Preferences in Russia

“…2 Specifically, we modify the GEASI model by incorporating a time transition function in the spirit of Ohtani and Katayama (1986) to examine potential structural preference shifts. While retaining all of the desirable properties of the previous workhorse demand models such as the Almost Ideal Demand System (AIDS) (Deaton and Muellbauer, 1980;Hovhannisyan and Khachatryan, 2017), the GEASI system offers a number of unique advantages due to its ability to: (i) account for unobserved consumer heterogeneity, (ii) allow for unrestricted Engel curves whose structure is determined by data rather than being imposed a priori (Pendakur, 2009;Samuelson, 1948;Stone, 1954), (iii) account for potential pre-commitment bias in empirical settings, where pre-committed quantities constitute an integral part of consumer demand (Hovhannisyan and Shanoyan, 2019;Rowland, Mjelde, and S. Dharmasena, 2017), and (iv) assure invariance of elasticity estimates to the data measurement units (Alston, Chalfant, and Piggott, 2001).…”