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, 14 (4), e1002283
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Changes in Prices, Sales, Consumer Spending, and Beverage Consumption One Year After a Tax on Sugar-Sweetened Beverages in Berkeley, California, US: A Before-And-After Study

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Changes in Prices, Sales, Consumer Spending, and Beverage Consumption One Year After a Tax on Sugar-Sweetened Beverages in Berkeley, California, US: A Before-And-After Study

Lynn D Silver et al. PLoS Med.

Abstract

Background: Taxes on sugar-sweetened beverages (SSBs) meant to improve health and raise revenue are being adopted, yet evaluation is scarce. This study examines the association of the first penny per ounce SSB excise tax in the United States, in Berkeley, California, with beverage prices, sales, store revenue/consumer spending, and usual beverage intake.

Methods and findings: Methods included comparison of pre-taxation (before 1 January 2015) and first-year post-taxation (1 March 2015-29 February 2016) measures of (1) beverage prices at 26 Berkeley stores; (2) point-of-sale scanner data on 15.5 million checkouts for beverage prices, sales, and store revenue for two supermarket chains covering three Berkeley and six control non-Berkeley large supermarkets in adjacent cities; and (3) a representative telephone survey (17.4% cooperation rate) of 957 adult Berkeley residents. Key hypotheses were that (1) the tax would be passed through to the prices of taxed beverages among the chain stores in which Berkeley implemented the tax in 2015; (2) sales of taxed beverages would decline, and sales of untaxed beverages would rise, in Berkeley stores more than in comparison non-Berkeley stores; (3) consumer spending per transaction (checkout episode) would not increase in Berkeley stores; and (4) self-reported consumption of taxed beverages would decline. Main outcomes and measures included changes in inflation-adjusted prices (cents/ounce), beverage sales (ounces), consumers' spending measured as store revenue (inflation-adjusted dollars per transaction) in two large chains, and usual beverage intake (grams/day and kilocalories/day). Tax pass-through (changes in the price after imposition of the tax) for SSBs varied in degree and timing by store type and beverage type. Pass-through was complete in large chain supermarkets (+1.07¢/oz, p = 0.001) and small chain supermarkets and chain gas stations (1.31¢/oz, p = 0.004), partial in pharmacies (+0.45¢/oz, p = 0.03), and negative in independent corner stores and independent gas stations (-0.64¢/oz, p = 0.004). Sales-unweighted mean price change from scanner data was +0.67¢/oz (p = 0.00) (sales-weighted, +0.65¢/oz, p = 0.003), with +1.09¢/oz (p < 0.001) for sodas and energy drinks, but a lower change in other categories. Post-tax year 1 scanner data SSB sales (ounces/transaction) in Berkeley stores declined 9.6% (p < 0.001) compared to estimates if the tax were not in place, but rose 6.9% (p < 0.001) for non-Berkeley stores. Sales of untaxed beverages in Berkeley stores rose by 3.5% versus 0.5% (both p < 0.001) for non-Berkeley stores. Overall beverage sales also rose across stores. In Berkeley, sales of water rose by 15.6% (p < 0.001) (exceeding the decline in SSB sales in ounces); untaxed fruit, vegetable, and tea drinks, by 4.37% (p < 0.001); and plain milk, by 0.63% (p = 0.01). Scanner data mean store revenue/consumer spending (dollars per transaction) fell 18¢ less in Berkeley (-$0.36, p < 0.001) than in comparison stores (-$0.54, p < 0.001). Baseline and post-tax Berkeley SSB sales and usual dietary intake were markedly low compared to national levels (at baseline, National Health and Nutrition Examination Survey SSB intake nationally was 131 kcal/d and in Berkeley was 45 kcal/d). Reductions in self-reported mean daily SSB intake in grams (-19.8%, p = 0.49) and in mean per capita SSB caloric intake (-13.3%, p = 0.56) from baseline to post-tax were not statistically significant. Limitations of the study include inability to establish causal links due to observational design, and the absence of health outcomes. Analysis of consumption was limited by the small effect size in relation to high standard error and Berkeley's low baseline consumption.

Conclusions: One year following implementation of the nation's first large SSB tax, prices of SSBs increased in many, but not all, settings, SSB sales declined, and sales of untaxed beverages (especially water) and overall study beverages rose in Berkeley; overall consumer spending per transaction in the stores studied did not rise. Price increases for SSBs in two distinct data sources, their timing, and the patterns of change in taxed and untaxed beverage sales suggest that the observed changes may be attributable to the tax. Post-tax self-reported SSB intake did not change significantly compared to baseline. Significant declines in SSB sales, even in this relatively affluent community, accompanied by revenue used for prevention suggest promise for this policy. Evaluation of taxation in jurisdictions with more typical SSB consumption, with controls, is needed to assess broader dietary and potential health impacts.

Conflict of interest statement

BMP is on an NAS committee focused on preschool beverage consumption, chairs the Choices International Foundation scientific committee, has been a co-investigator of one random controlled trial funded by Nestle’s Water USA, but has never consulted for them. BMP presented a paper on SSB global trends in a symposium at the British Nutrition Society symposium sponsored by Danone Waters. LDS is a volunteer board member of the Center for Science in the Public Interest and has worked as a consultant, both paid and volunteer, for the World Health Organization, and organizations which have advocated for sugar sweetened beverage taxes. LDS has also donated to Berkeley’s Measure D and advocated for its approval. SWN is on the expert advisory committee for the Philadelphia sweetened beverage tax evaluation project that is being conducted by researchers at University of Pennsylvania and Harvard University.

Figures

Fig 1
Fig 1. Berkeley sugar-sweetened beverage tax implementation and evaluation timeline.
Fig 2
Fig 2. Store price survey mean (95% CI) beverage price changes (cents per ounce) in Berkeley stores.
Top: price change between December 2014 (round 1) and March 2016 (round 3). Bottom: price change between December 2014 (round 1) and June 2015 (round 2). Sample limited to 55 product types with 313 prices across stores that were collected in all three rounds of the store price survey; of these, 56% were prices for taxed beverages and 44% for untaxed beverages. Prices account for inflation. Values in bold italics show the price difference between taxed and untaxed beverages. *Statistically significant difference between prices in later round (March 2016 or June 2015) compared to December 2014 at p < 0.05 using paired t-tests. **Statistically significant difference between prices in later round (March 2016 or June 2015) compared to December 2014 at p < 0.01 using paired t-tests. Statistically significant difference of price of taxed beverages compared to untaxed beverages at p < 0.05 (unpaired t-tests since taxed and untaxed beverage items are different). Source: store price survey data collected by Public Health Institute.
Fig 3
Fig 3. Point-of-sale model adjusted beverage prices (cents per ounce) in Berkeley versus non-Berkeley stores (sales unweighted).
Fixed effects models account for the month-year (indicator variables), store located or not located in Berkeley, interaction of Berkeley store and month-year, and an indicator variable of underreported sales data from each store in particular month. Prices account for inflation. Vertical lines demarcate the pre-tax period (January 2013–December 2014), the ambiguous period (January–February 2015), and the post-tax period (March 2015–February 2016). Full sales-unweighted results can be found in S8 Table. Full sales-weighted results can be found in S9 Table. **Statistically significant difference between the Berkeley and non-Berkeley prices for March–December 2015 at p < 0.01. Source: point-of-sale data from two chains of large supermarkets in the Bay Area obtained by the Public Health Institute.
Fig 4
Fig 4. Point-of-sale adjusted mean daily volume of beverages sold (ounces per transaction) in Berkeley versus non-Berkeley stores.
(A) Point-of-sale taxed beverage volume sold (ounces per transaction).(B) Point-of-sale untaxed beverage volume sold (ounces per transaction).(C) Point-of-sale taxed and untaxed beverage volume sold (ounces per transaction).(D) Percent change in post-tax untaxed beverage sales (ounces per transaction) in relation to counterfactual in Berkeley and non-Berkeley stores. Models account for store ID, month, year, day of week, holiday and holiday eve, number of transactions (linear and quadratic), a post-tax indicator, and interactions of store ID with the post-tax indicator, month, and year variables, correcting the standard errors by clustering the analyses at the city level. Back-transformation uses Duan smearing. Model n = 10,152. Vertical lines demarcate the pre-tax period (January 2013–December 2014), the ambiguous period (January–February 2015), and the post-tax period (March 2015–February 2016). To derive the counterfactuals, we predicted the volume of taxed and untaxed beverages sold if the post-tax indicator = 0 for March 2015–February 2016. Full results can be found in S10 and S11 Tables. *Statistically significant difference between the counterfactual and observed volumes sold during the entire post-tax period at p < 0.05. **Statistically significant difference between the counterfactual and observed volumes sold during the entire post-tax period at p < 0.01. Source: point-of-sale data from two chains of large supermarkets in the Bay Area obtained by the Public Health Institute.
Fig 5
Fig 5. Point-of-sale adjusted mean store revenue/consumer spending (dollars per transaction) in Berkeley versus non-Berkeley stores.
Models account for store ID, month, year, day of week, holiday and holiday eve, a post-tax indicator, and interactions of store ID with the post-tax indicator, month, and year variables, correcting the standard errors by clustering the analyses at the city level. Revenues account for inflation. Vertical lines demarcate the pre-tax period (January 2013–December 2014), the ambiguous period (January–February 2015), and the post-tax period (March 2015–February 2016). To derive the counterfactuals, we predicted the volume of taxed and untaxed beverages sold if the post-tax indicator = 0 in March 2015–February 2016. **Statistically significant difference between the Berkeley and non-Berkeley store revenues during the post-tax period at p < 0.01. Source: point-of-sale data from two chains of large supermarkets in the Bay Area obtained by the Public Health Institute.

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