Elsevier

Social Science & Medicine

Volume 225, March 2019, Pages 98-107
Social Science & Medicine

Sugar-sweetened beverage price elasticities in a hypothetical convenience store

https://doi.org/10.1016/j.socscimed.2019.02.021Get rights and content

Highlights

  • Sugar-sweetened beverage (SSB) price increases reduce SSB purchases among consumers.

  • SSB price changes have similar purchase impacts across age, gender and income groups.

  • More frequent SSB consumers are less responsive to SSB price changes.

  • SSB pricing policies may benefit consumers suffering greatest health effects less.

Abstract

An increase in sugar-sweetened beverage (SSB) prices has been suggested to reduce SSB intake to improve population health. Using a discrete choice experiment, we tested the effect of price changes on beverage choices in an online hypothetical convenience store setting amongst 1,008 Australian adults in May to June 2016. From this we calculated pre-packaged beverage price elasticities overall and for health policy-target consumer subgroups; and identified consumer subgroups likely to be most reactive to beverage price changes. Using mixed logit analysis, we found similar price elasticities for age, gender and income groups. More frequent SSB consumers tended to be less sensitive to SSB price changes. Latent class analysis revealed five consumer groups, none of which fit the desirable policy-target of highly price sensitive, frequent SSB consumers. An improved understanding of responsiveness to beverage price changes and consumer preferences could improve predictions of whose health is likely to benefit most from pricing interventions.

Introduction

One policy-active area of public health is sugar-sweetened beverage (SSB) purchase and consumption. SSBs are generally described as non-alcoholic water-based beverages with added sugar and may include cordials, fruit drinks, flavoured milks, sports drinks, energy drinks, vitamin waters, sweetened ice teas, as well as non-diet sodas or ‘soft drinks’. These products offer minimal nutritional benefits and their consumption is associated with obesity, Type 2 Diabetes Mellitus and dental decay (Malik et al., 2010; Trumbo and Rivers, 2014). Purchasing decisions for these products vary within and across consumer groups, including by age, gender, income, frequency of purchase, and race/ethnicity (Australian Bureau of Statistics, 2014; Etilé and Sharma, 2015; Han and Powell, 2011, 2013).

Over recent years there has been an acceleration in the number of governments considering or implementing health-motivated beverage pricing changes, including SSB taxes (Backholer et al., 2016a), and health-related food taxes (Cornelsen et al., 2014). Recently the World Health Organisation (WHO) urged governments to adopt fiscal policies to improve the healthiness of food and beverage intake (World Health Organisation, 2015; World Health Organization, 2016). Health-motivated SSB taxes are designed to discourage SSB consumption to reduce energy and added sugar intakes (World Health Organisation, 2015; World Health Organization, 2016).

In this paper, we focus on four particular gaps in the literature, which need to be addressed to inform evidence-based policy developments: (i) estimates of beverage price elasticities over a wide range of price levels; (ii) price elasticity estimates for policy-relevant subgroups; (iii) investigation of clustering of consumer preferences and behavioural responses to beverage pricing interventions; and (iv) examination of price elasticities in a convenience store setting.

“[Price] elasticities give the percentage change in demand for good per (marginal) percentage change in the price of good κ" (Mas-Colell et al., 1995). This can be the change in demand for a beverage in response to changes in its price (own-price elasticity (OPE)) or change in demand for a beverage in response to change in the price of another beverage (cross-price elasticity (CPE)). Accurate price elasticity estimates are required to predict both changes in purchasing, and the effect on health-related outcomes such as weight gain, which can be extrapolated to estimate population level health effects such as obesity rates (Veerman et al., 2016). Own and cross-price elasticity estimates for policy-relevant population subgroups (e.g. based on age, gender, income and frequency of SSB consumption) would improve predictions of differential intervention effects and thereby assist in determining who is likely to be most responsive to pricing interventions (Nghiem et al., 2013). Further, better understanding the clustering of consumer preferences and behavioural responses to beverage pricing interventions could improve predictions of whose health is likely to benefit most from pricing interventions.

Natural experiments provide important and externally valid insights into the effects of a price shock such as an SSB tax on entire populations (Craig et al., 2012). A growing number of real-world SSB tax evaluations suggest that SSB taxes are associated with decreases in SSB purchases (Caro et al., 2018; Colchero et al., 2016, 2017; Silver et al., 2017). However, natural experiments do not allow estimation of price elasticities over a range of prices and therefore cannot be used to calculate price elasticities and cannot be used for testing yet-to-be implemented policy proposals, such as combined SSB taxes and non-SSB subsidies (Craig et al., 2012).

To date, price elasticity studies have predominantly used historical purchasing data, based on time series, household surveys, or scanner panel data (Andreyeva et al., 2010 Feb; Escobar et al., 2013; Harding and Lovenheim, 2017; Powell et al., 2013; Sharma et al., 2014). They have found a wide range of population beverage price elasticity estimates for different beverage groups. For example, the most recent review in 2014 found an OPE for non-diet soda of −1.25 (range −0.71 to −2.26). Elasticity estimates from historical purchasing data are subject to several issues, including: elicitation from a narrow range of price levels leading to extrapolation of elasticity estimates beyond the data price variation, reducing generalisability of the results (Nghiem et al., 2013); and exclusive focus on supermarket settings. The focus to date on purchases from supermarkets overlooks approximately a quarter of off-trade (non-restaurant) purchases of ready-to-drink beverages from non-supermarket sources such as small grocery stores, convenience stores and vending machines (Euromonitor International, 2015).

Beverages in convenience store settings generally have a higher unit price per volume than supermarket settings (Euromonitor International, 2015), and often have smaller individual beverage sizes dominating offerings. Further, supermarket estimates may be reflective of ‘stockpiling’ or ‘forward-purchasing’ behaviour in which consumers purchase more of a product when it is less expensive, in expectation that they will buy less during more expensive periods (Mela et al., 1998). By contrast, on-the-go beverage purchasing, such as in convenience stores, may plausibly be associated with greater impulse purchasing, but this has not been studied to date. Hence differences in alternatives available, alternative attributes (including price and volume), and choice purchasing context (‘task’), may all affect preferences, price elasticities, and purchasing patterns (Yale and Venkatesh, 1986). This may therefore also affect response to potential pricing policies, warranting further investigation in these settings.

Moreover, there have been very few studies on drivers of heterogeneous price elasticities of demand or response to SSB pricing policies amongst population subgroups. Limited evidence from historical purchasing data suggests mixed results on relative SSB price sensitivity across income groups (Backholer et al., 2016b), and that more frequent SSB consumers may have lower SSB price sensitivity than less frequent consumers (Etilé and Sharma, 2015; Finkelstein et al., 2013), particularly among older consumers (Dubois et al., 2017). The scarcity of micro-level data limits both this subgroup analysis and the examination of individual-level price responsiveness. Finally, to our knowledge, no other work has identified latent-class derived consumer group reactiveness to beverage price changes until now.

Discrete choice experiments (DCEs) offer a number of advantages in the exploration of consumer reactiveness to public health interventions in a hypothetical choice scenario. This includes the ability to explore elasticities over a wider range of price levels than usually observed in market data and the avoidance of multicollinearity by design. There have been no published DCEs to date examining packaged beverage purchases, however there is emerging work in this field in children (Yang et al., 2016) and using somewhat related methods in adults (Zizzo et al., 2016). There is a related literature using online randomised control trials comparing responses of participant groups to hypothetical price increases on either both unhealthy food and beverages (Epstein et al., 2010; Nederkoorn et al., 2011), or on SSBs only (Bollard et al., 2016; Waterlander et al., 2014). These experiments have shown lower numbers of calories purchased when unhealthy food and/or beverages are subjected to price increases. While RCTs have strong causal properties, as all SSBs were subjected to the same price increase in each treatment scenario, these experiments have not allowed simulation of scenarios where different beverages are subject to price increases, or allowed estimation of substitution behaviour between beverage alternatives (i.e. accurate price elasticity estimation). Lastly, these works have exclusively been carried out in supermarket settings and have included limited population subgroup analyses, mainly due to small sample sizes.

In this study, our overarching purpose was to determine the effect of changing beverage prices on beverage purchases at an aggregate level and in different consumer groups. We therefore aimed to: (i) determine pre-packaged beverage price elasticities for the overall sample and for common policy-target consumer subgroups, including by age, gender, SSB consumption frequency and income level; and (ii) identify consumer subgroup clusters that are likely to be most reactive to beverage price changes.

Section snippets

Overview

To answer these research questions we undertook an online computer-based DCE with a nationally representative sample of the Australian general public. To address the first aim we use a mixed logit model which allows for accurate estimation of unrestricted substitution patterns (Revelt and Train, 1998), which is important for predicting the likely health outcomes of pricing policies, since health outcomes differ due to nutrient and energy content, both of which differ across beverage

Study population

Between May and June 2016, 1,008 eligible adults completed the DCE drawn from an online panel. The sample was representative of the Australian population in age, gender (Australian Bureau of Statistics, 2011) and income tertiles (Australian Bureau of Statistics, 2013) (Table 2). Median [Interquartile range] survey completion time was 16.5 [12.1, 24.2] minutes.

Mixed logit results for pre-specified consumer subgroups

Goodness of fit increased progressively from Model A to B to C (Table 3). Due to superior goodness-of-fit, Model C was used for

Discussion

In this study, we have advanced the literature on the generation of beverage OPEs and CPEs in a number of important respects. Using a DCE, we have been able to look at larger price variation than is usually found using historical purchasing data, thereby allowing generalization to policy-relevant magnitudes of beverage price changes. We have uniquely focused on beverage pricing responses within a convenience store setting. To our knowledge, we are the first to investigate clustering of consumer

Acknowledgements

This research was funded by a Monash University Faculty of Business and Economics Interdisciplinary Grant. The funder had no role in the collection, analysis or interpretation of data. MB is supported by an Australian Government Research Training Program Scholarship and Deakin University. AP and KB are supported by Deakin University. EL is supported by an Australian Research Council fellowship. The authors thank Peter Sivey for his feedback on the manuscript and Hong Il Yoo, Yuanyuan Gu, Gang

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