1

Do Taxes for Soda and Sugary Drinks Work?

Scanner Data Evidence from Berkeley, CA and Washington State*

Christian Rojas†

Emily Wang‡

First Draft: October 2017

Abstract

Curbing obesity through taxation of certain beverage products has been a priority in the policy agenda across many U.S. jurisdictions. We assess the effectiveness of this highly debated policy instrument through two measures of its impact: the pass-through rate (the extent to which the tax actually translates into a retail price increase) and the impact on consumption (volume sales). We evaluate the actual effect of two excise taxes on the beverages market: the sugar-sweetened-beverages (SSB) tax of 1¢ per ounce in the city of Berkeley that has been in effect since 2015 and the tax of 1/6¢ per ounce on carbonated drinks (soda) that the state of Washington imposed from July through December of 2010. We carry out the analysis with a barcode-level dataset containing price and volume sales information from a large number of retail outlets. Our identification relies on sales data from stores located in taxed areas as well as from stores in nearby localities. We find differences across the two tax events on pass-through: retail prices in Washington reacted sharply (by a larger magnitude than the tax) and promptly, whereas in Berkeley retail prices reacted only marginally (by less than 30% the magnitude of the tax). In terms of volume sales, we find a 5% volume reduction in Washington but fail to find any evidence of an effect in Berkeley. We discuss the possible reasons for the divergent effects.

Keywords: soda, sugar-sweetened beverages, excise tax, obesity, tax pass-through.

JEL classification: H22, L66, I18

* Nielsen data was provided by the Kilts Center for Marketing at the University of Chicago. We thank audiences at North Carolina State University and the University of Massachusetts Amherst for helpful comments. † Corresponding author. Associate Professor, Department of Resource Economics, University of Massachusetts Amherst. Contact: rojas@umass.edu. ‡ Assistant Professor, Department of Resource Economics, University of Massachusetts Amherst. Contact: emilywang@ umass.edu.

2

1. Introduction

Fighting obesity and the chronic illnesses associated with it has become a priority in the worldwide political agenda. Across 186 countries, the percentage of obese men rose from 3.2% in 1975 to 10.8% in 2014, and from 6.4% to 14.9% for women over the same time period (NCD Risk Factor Collaboration, 2016). These figures are much more alarming for developed countries. For instance, the percentage of obese adults in the U.S. increased from 30.5% in 2000 to 37.5% in 2014 and 13.9% to 17.2% among the youth (CDC, 2015). Obesity-related illnesses follow a concomitantly similar pattern. Across the globe, there are 7.5 million deaths associated with high blood pressure, 3.4 million with diabetes (high glucose), and 2.6 million with high cholesterol (WHO, 2009). Meanwhile, in the U.S., 37% of adults suffer from cardiovascular disease, 34% from hypertension, 16% from high cholesterol, and 11% from diabetes (USDA, 2010).

In addition to the cited higher mortality incidence, other justifications for public intervention include the increased medical expenses and the increased health insurance premiums derived from obesity, as well as the productivity losses in the labor market (Fletcher, 2011). The estimated figure of annual U.S. health care cost for obesity-related illnesses is $190.2 billion, or about 21% of annual U.S. medical expenditures (Cawley and Meyerhoefer, 2012).

Scientific evidence points to a link between soda consumption and obesity (Ludwig and Ebbeling, 2001; Apovian, 2004; Malik et al., 2006; Vartanian et al., 2007; Libuda and Kersting, 2009). This link is not surprising since soda is considered the largest source of calorie intake in the U.S. (Block and Willett, 2011; Wang et al., 2008; Block, 2004). As a consequence, one the most advocated policies in combatting the obesity epidemic has been soda taxation, in particular taxation of the so-called “sugar-sweetened beverages” (or SSB) segment.1 National and international organizations (e.g., American Academy of Pediatrics, American Public Health Association, Institute of Medicine, United Nations, and World Health Organization)2 as well as academics (e.g., Jacobson and Brownell, 2000; Brownell and Frieden, 2009) have proposed taxation of SSB as a main policy tool to combat the obesity epidemic.

While there seems to be a national and international policy consensus that taxing sugary drinks may be an effective means to curb obesity, the specifics of how such tax should be applied are somewhat less clear. However, there seems to be an emerging agreement in policy and academic discussions on three elements that seem crucial for the effectiveness of a tax. First, in order for the tax to be salient to the consumer decision, an excise tax is preferred over a value-added tax. The main hurdle with a value-added tax is that the resultant price increase is only perceived by the consumer upon checkout.3 Conversely, excise taxes are imposed on producers (typically distributors) and, to the extent that producers pass this

1 “Soda taxes” is typically used as a catchall term for these types of taxes. Strictly speaking, the definition of soda refers to any carbonated drink, which includes sugar-free drinks (i.e., diet soda). SSB, on the other hand, refers to beverages with added sugar (whether they are carbonated or not). In this paper, we study a soda (carbonated drinks) tax (Washington) as well as a SSB tax (Berkeley). 2 See Rudd Center (2012) and World Health Organization (2015). 3 Even in this case, it is likely that consumers might have difficulty discerning which portion of the total value-added tax paid in a particular transaction (which is likely to contain multiple items, not just soda) is attributable to soda.

3

tax hike downstream, the resulting price increase will be directly observed by consumers on the shelf price (e.g. Brownell et al., 2009; Rudd Center, 2012; Colantuoni and Rojas, 2015).4

A second element is the size of the tax. Research and policy advocates have argued that the response to the tax might be non-linear: consumers might react (i.e., reduce consumption) disproportionately more to a larger tax than to a relatively small one (e.g., Andredyeva et al., 2011; Rudd Center, 2012). For instance, in 2008 the Congressional Budget Office argued for a national 3 cents per ounce tax to fund health care reform. Using as an example the current price of a 12 pack of 12 ounce cans of Coca-Cola (approx. $4.6/pack, or $0.38 per can), this proposal would imply a tax of nearly 100%. More recently, penny-per-ounce proposals (which amount to roughly a 20% price increase) have become commonplace (Brownell et al., 2009; Rudd Center, 2012).

A third element in the discussion is the tax pass-through rate (a 100% pass-through rate means that consumers face a price increase equal to the tax increase; larger or smaller pass-through rates are known, respectively, as over- or under-shifting). The ultimate goal is to generate a price increase that can retract consumption. Since taxes impact prices only indirectly, a complete pass-through is not guaranteed: there is always the possibility (as we show later in one of the case studies) that firms may decide to absorb part (or all) of the tax hike.

As of 2014, 35 states had a soda tax in place. However, in all cases these taxes took the form of a value- added (or sales) tax (Bridging the Gap, 2014).5 Further, the average sales tax in these states is not particularly high (at 5.172%). As a consequence, despite the unified consensus for large excise taxes, the evidence on the effectiveness of this type of tax has remained largely elusive. Some studies have constrained their focus on the effectiveness of sales taxes (Powell and Chaloupka, 2009; Kim and Kawachi, 2006; Finkelstein et al., 2010; Sturm et al., 2010; Colantuoni and Rojas, 2015), finding an unsurprisingly insignificant impact on consumption. Other studies have relied on structural models with the objective of exploiting plausible exogenous price variation in the data that would provide researchers with reliable price elasticity estimates of the taxed beverages; with the estimated elasticity in hand, these studies form a prediction of what a sizeable excise tax might do to consumption (e.g., Andreyeva et al., 2011; Dharmasena and Capps, 2012; Wang, 2015; Wang et al., 2017).

Albeit potentially informative, structural models can have drawbacks. First, elasticity estimation depends heavily on the accuracy of the modeling assumptions (e.g. demand functional form, instrument validity, and model specification). Further, the counterfactual approach is only valid conditional on the tax having an effect on prices (an element already noted).

After many years of numerous failed proposals, several cities in the U.S. have started to enact legislation approving sizeable excise taxes on SSB. In early 2015, Berkeley became the first U.S. city to impose a one cent per ounce tax on SSB. Since then, four other cities: San Francisco, CA; Albany, CA; Oakland, CA; and Philadelphia, PA; and Cook County, IL have followed suit with similar initiatives, all of which will be effective in 2017. Other countries have adopted similar measures. In 2011, France imposed a €

4 In the majority of these states, soda is taxed at the sales tax set by the state for other goods and services thereby failing to make soda taxation “salient”. In addition, excise taxes are characterized for setting a rate on a per-unit of volume basis (e.g., 1 cent per ounce) rather than a per-value rate (e.g., 10% of price). 5 In 15 of these cases, the lack of tax salience is exacerbated by the fact that the tax applied to soda is the same as the one applied to all other goods and services subject to a state sales tax.

4

7.16/hectoliter tax (approximately 1/5 cent per ounce) and in 2014 Mexico started to collect a 1 peso/L tax (approximately 0.15 cents per ounce).

In this paper, we report estimates of the effect of the SSB tax imposed by the city of Berkeley, which was put into effect on March 1, 2015. Although the Berkeley tax was the first SSB excise tax to be imposed, it was not the first excise tax on the (non-alcoholic) beverage industry. In 2010, the state of Washington approved an excise tax on carbonated drinks (soda). The tax was quickly repealed after five months, following a ballot initiative sponsored by the soft drinks industry. This event is less known and has remained unstudied. We complement the Berkeley results with estimates from the Washington temporary excise tax (in the next paragraphs we explain why the Washington event is worthy of study). In both cases, we evaluate the effectiveness of the tax on two dimensions: (a) the extent to which the tax was actually passed through to consumers, and (b) the impact on consumption. In both cases, we use detailed transaction-level data collected from scanner devices in numerous stores located in both the affected areas as well as in nearby localities.

It is important to point out the differences between the Berkeley event and the Washington event at the outset. Some of these differences make Berkeley a preferable setting for evaluation purposes, but other differences make Washington an interesting case study. First, Washington imposed a tax on carbonated beverages, which includes both diet and regular version and thus excludes many (carbonation-free) sugary drinks. The reason for this difference is that the Washington tax was designed for revenue purposes; from this perspective, the Berkeley event is a true “sugary beverages” tax and therefore a potentially better candidate for evaluation purposes in the overarching policy discussions to improve health. Second, while in both cases the excise tax was imposed on distributors, the size of the tax in Washington was a fraction of that imposed by Berkeley (1/6 cent per ounce versus 1 cent per ounce); this sizeable difference would conceivably make the Berkeley event more salient in terms of a visible price increase to consumers (however, as we later show, this did not occur).

Third, as opposed to the Berkeley tax, the Washington tax was a large-scale state-wide event, affecting consumers from hundreds of localities in a large area at once. Further, the Washington tax was not publicized (if at all) prior to its approval. The tax was enacted by Legislature majority swiftly and quickly during a special session in April of 2010, taking the soda industry’s lobbying machinery by surprise (Washington Post, 2010). The soft drinks industry only reacted to the tax approval one month later by filing a repeal initiative to the Legislature. The soft drinks industry prevailed and, through ballot vote, the tax was removed effective December 1, 2010 after being in place for 5 months.

Despite neither having the sizable effect of the Berkeley event nor targeting the “ideal” set of products (i.e., all sugary drinks), the Washington experiment is attractive from an evaluation point of view for at least three reasons. First, in the Berkeley experiment, one might be concerned that the locations where soda taxes are being proposed/approved are not “random”: these localities may be precisely those where consumer awareness for obesity is already high and therefore consumption might not decrease as expected (consumption might already be low; alternatively, because of extensive media attention prior to approval, consumption may decrease even before the tax is put in place). Second, the application of a tax to a relatively small geographic area such as Berkeley makes cross-border shopping easier thereby undermining the policy objective of observing large price increases at the retail level: affected beverage companies (and stores) might be less willing to pass on the tax in order to avoid losing consumers to nearby (untaxed) stores (Cawley and Frisvold, 2017). Third, the unexpected nature of the tax in

5

Washington provides a sharper setup for identification since it is possible that other factors not related to the tax might have also changed in tandem during the Berkeley intervention (public awareness, firms advertising strategies, product repositioning); further, this could have occurred not only in Berkeley but also in nearby localities.

We find a systematic difference on the price pass-through rate between the two events. The average retail price of soda in Washington reacted sharply and promptly to the tax increase with a pass-through rate in the range of 104% and 109% (depending on the specification). While there is some notable heterogeneity in the price increase experienced by different products (in several cases we observe several products pass- through rates exceed 200%), the results indicate that essentially all taxed products experienced a significant increase in price.

Conversely, while there seems to be some evidence to support the fact that prices in Berkeley increased as a consequence of the tax, the magnitude can be considered moderate at best. Of the four specifications considered, only one yields a statistically significant price increase, with an implied pass-through rate of 24.4%.6 Further, our disaggregate analyses indicate that the limited price effect of the tax appears to be partially driven by price increases in a small subset of products. Taken together, the price results of our analyses indicate that consumers in Washington were more broadly exposed to a price increase (since the tax increased prices of products across the board). This is particularly striking given that Berkeley implemented a tax that was six times larger. Equally striking is the fact that the absolute magnitude of the average price increase was not too dissimilar across the two events.7

We find that overall consumption in Washington decreased (approximately) 5% as a result of the tax, although this effect is not always precisely estimated; interestingly, we find an important heterogeneity across products, with some brands experiencing volume reductions of nearly 30%, most products experiencing no reaction, and a few brands being positively affected. Although a subset of products appear to have experienced a decrease in volume sales,8 we do not find any evidence of a reduction in the overall consumption of SSB in Berkeley. Our Washington and Berkeley findings cast doubt on the effectiveness of soda/SSB taxes to curb consumption; on the other hand, the results (especially those in Berkeley) indicate that imposing excise taxes on beverages can be an effective revenue-generating tool for the state.

Our study is the first to report results for the Washington event. The Berkeley tax has been studied before, but the scope has been limited. Two studies have analyzed the tax effect of retail prices alone (Falbe et al., 2015; Cawley and Frisvold, 2017) and one has focused solely on consumption (Falbe et al., 2016). The most recent study (Silver et al., 2017) focuses, as we do, on both price and volume effects.

Both Falbe et al. (2015) and Cawley and Frisvold (2017) surveyed store prices of certain brands in their most popular sizes, both before as well as after the imposition of the tax. The two studies are broadly similar in design and in results. In both cases, the data employed comprises brands belonging to a variety

6 Two of the other three specifications yield price increases of near zero; the remaining specification implies a pass-through rate of 16.2% but it is not statistically significant. 7 The most favorable specification in Berkeley implies an absolute average price increase of 24.4 c/oz. The most favorable specification in Washington implies an absolute average price increase of 18.2 c/oz (109% pass-through x 0.167 c/oz). 8 The most notable case is 12 packs, which seem to have experienced an important reduction in volume sales.

6

of taxed and untaxed beverages categories and both studies employ prices in nearby stores (not affected by the tax) as controls.9 Both analyses find (as we do below) that the tax was not fully passed through to consumers and that the pass-through rate was similar: 47% and 43.1%, respectively.

Falbe et al. (2016) collected self-reported consumption data through intercept surveys administered near the highest foot-traffic intersections of neighborhoods in Berkeley, San Francisco, and Oakland characterized by low income and a large fraction of African-American and Latino populations. According to the authors, consumption of SSB in these neighborhoods might have decreased by 24% as a consequence of the tax. An important limitation of this study, beyond the usual measurement error concerns in survey data, is the possibility of a significant and systematic under-reporting of consumption of SSB: in the after-tax survey, interviewees were asked about their consumption habits of a product that had recently been in the public spotlight (highly debated and publicized) for its possible negative health effects.

Silver et al. (2017) analyzed barcode data from two supermarket chains (three stores in Berkeley and six stores in neighboring cities) and found a pass-through rate of 67% across all taxed products.10 The authors also reported a 9.6% reduction in the number of ounces of taxed products purchased in a given customer transaction (a checkout). Given the limited set of stores, it is not clear to what extent these results are representative of Berkeley as a whole. Further, the volume results (measured at the ounces/transaction level) cannot directly be used to infer whether overall purchases (and hence consumption) decreased.

Our study constitutes an improvement over prior (largely survey-based) work. First, we are able to analyze both price pass-through and consumption effects of the tax simultaneously. Second, we are able to measure the effects of the tax in a comprehensive manner by including: (a) a large pool of stores from multiple chains, and (b) all product variants, at the barcode level, of affected beverage categories. Third, the richness of the data allows us to test the robustness of our results to multiple specification variants and to utilize and assess multiple possible controls (thereby providing us with an opportunity to obtain a credible identification strategy).

The paper proceeds as follows. First, we provide an overview of the two tax events we study. We then provide a description of the data and an initial visual and descriptive preamble of our identification strategy and results. We then discuss the method and results of our study. The last section concludes.

2. The Tax Events

Washington

Governor Chris Gregoire proposed a 5 cent per can (5/12 cent per ounce) soda tax in February of 2010 as one of several measures to partially cover the looming $2.8 billion budget deficit faced by the state of Washington (population: 7.06 million). The proposal was stalled and eventually it was not voted on by the state Legislature. In April, during the special session period, governor Gregoire took up the issue again,

9 One difference between the studies is that Cawley and Frisvold (2017) include more stores and more sizes. Falbe et al. (2015) include two control cities (Oakland and San Francisco) instead of one (San Francisco), but focus only on certain neighborhoods. Also, Falbe et al. (2015) focus on neighborhoods with certain demographics and income level, as in Falbe et al. (2016). 10 The paper also reports results based on survey measures (price surveys from stores and self-reported consumption data). These results are generally in line with (and suffer from the same limitations as) the earlier literature reviewed.

7

this time proposing a 2 cent per can (1/6 cent per ounce) tax on carbonated drinks.11 As opposed to the first version of the tax, this time the proposal remained largely secretive right until noon on the day when it was voted on (The Spokesman Review, 2010). According to press articles, this tax was the least publicized of all taxes discussed by the Legislature that year (The Spokesman Review, 2010). There were no committee hearings. With the Legislature at a cross-roads scrambling to finance its deficit before the special session period ended, and the swift and unexpected proposal by the governor to tax soda, the measure was approved by the House on April 12, 2010 and became effective on July 1, 2010. As stated earlier, this tax did not affect some beverages that are now targeted by SSB taxes (e.g., sugar-added juices) and impacted some beverages that do not contain added-sugar (diet soda).

One factor contributed to the largely non-existent opposition by the soda industry to the proposed tax. Although largely secretive, news of a potential soda tax did leak to a few bottlers and distributors. Representatives from these companies made it to the Capitol to voice their opposition to the tax on the weekend prior to voting day. To appease these companies’ concerns, Legislature representatives assured that it was highly unlikely that these companies would be affected since there was a provision in the proposal to exempt all bottlers and distributors with sales under $10 million. Upon approval, a language glitch was discovered: the exemption applied to bottlers but not distributors. Since sales of soda to retailers in Washington is carried out by distributors (not bottlers), the exemption had no practical effect.

After failing to get the language fixed in the bill, on May 10 the industry submitted an initiative to the State of Washington to repeal the tax. The American Beverage Association’s (ABA) spent over $1 million to collect signatures to obtain approval for the initiative (Washington Post, 2010). On July 2, the initiative was accepted by the State and was slated to be voted on by the public on November 2. Prior to the ballot date, the ABA spent over $16 million on an anti-tax campaign, making it the most expensive initiative in Washington State history up to that date (Ballotpedia, 2010). The industry obtained the desired outcome and the tax was removed effective December 1, 2010.

Berkeley

Although more than a dozen states and several cities proposed SSB tax legislation between 2013 and 2014, only one proposal was successful: measure D in Berkeley, California (population: 117,000). The measure contemplated a one-cent-per-ounce excise tax on the distribution of SSB. The tax was specifically targeted to all beverages containing added sugar (i.e., excluding diet products and including non-carbonated but sugar-rich beverages).12 As opposed to Washington, the Berkeley tax was highly publicized by both sides before the initiative was approved, as well as during the entire period leading up to the ballot. An estimated $1 million was spent by supporters and another $2.5 million by the industry (Dugdale, 2015). After a long and bitter campaign, 76% of the electorate decided to support measure D on November 4, 2014.

The new tax was to become effective January 1, 2015, but its implementation was delayed until March 1 (roll-out to small businesses was delayed further). Although there was no explicit earmarking of the tax revenue for specific health-related programs, measure D contained a provision to create an expert panel

11 The measure was accompanied by taxes on other products such as beer, cigarettes, candy, and gum. 12 Flavored milk is also excluded from the tax.

8

to provide recommendations as to how the city should provide funds for programs aimed at reducing SSB consumption and reduce their negative health effects.

3. Data and Descriptive Evidence

Our main source is the Nielsen retail scanner database, provided through the Kilts Center for Marketing at the University of Chicago. The database consists of weekly pricing as well as volume information generated by point-of-sale systems (scanners) from over 35,000 stores (grocery stores, drug stores, mass merchandisers, etc.) comprising more than 90 retail chains across all U.S. markets. The database is currently available from 2006 until 2015.13 We use data from January 2009 through December 2012 for Washington and data from January 2014 through December 2015 for Berkeley. Nielsen provides information on the 3-digit zip code of the store, which allows us to identify (treatment and potential control) stores in both tax events. In Berkeley, we identify a “treated” store if it is located in the 947 3-digit zip code; Washington stores use the 980 through 994 3-digit zip code.

The scanner devices record information for each Universal Product Code (UPC). A UPC (or barcode) is the most disaggregate information products have; within each brand (e.g., “Pepsi”) there are multiple (usually dozens) of UPCs, each corresponding to a size-units pair (e.g., “12 ounce can-12 units” or “2L-1 unit”). The database contains weekly product information for approximately 2.5 million UPCs (including food and non-food items) and covers more than half the total sales volume of U.S. grocery and drug stores (more than 30% of mass merchandising sales volume). An observation in the data provides the number of ounces (as well as the average price) sold for a UPC in a week at a particular store in the country. Thus, Nielsen’s dataset has three dimensions: temporal (week), geographic (store) and product (UPC). Our difference-in-difference analysis focuses on UPCs affected by the tax in the treatment region and their counterparts in the control region (for comparison purposes, we also report some difference-in-difference (DID) results using UPCs in beverage categories not affected by the tax).

For the entire analysis in the paper, we aggregate the temporal dimension up to the month level. We treat all stores in the treated/control area as being in the same “region” (in the case of Washington a “region” is a state, in the case of Berkeley a “region” is defined by a 3-digit zip code). In addition to carrying out regressions that include all UPCs, we also report separate regressions for selected UPCs (most popular brands and sizes). For example, we analyze the price of a 2L Coke in both the taxed region (Washington or Berkeley) and compare it to the price of a 2L Coke in neighboring regions not affected by the tax.

Most of our regressions are carried out at the UPC-store-month level. We also report regressions at the region-month level.14 Studying the impact at different levels of aggregation allows us to investigate the robustness of our results. Further, disaggregate results can uncover possible heterogeneity effects across brands and sizes. In this section, we focus on graphical and descriptive evidence at the region-month level, and relegate more disaggregate results to the following section.

We use stores in the state of Oregon as controls for Washington; stores in Berkeley’s eight neighboring 3-digit zip codes are used as controls in the Berkeley analysis. We discuss in section 3.1 the choice of the control regions in both events. We use a total of 1,067 stores for the Washington event (685 in 13 Each January an additional year of data is released. The last release (2015) was made available in January 2017. 14 An observation in a region-month regression corresponds to the average price (per ounce) across all UPCs (and stores) in a region; in the case of volume, it corresponds to the cumulative number of ounces sold across all UPCs and stores in a region.

9

Washington State and 382 stores in Oregon), and 436 stores for the Berkeley tax event (10 in the treatment group and 426 in the control group).15 Our selection criteria produce a total of 16,645,094 UPC-store-month observations in the analysis of the Washington event (10,776,699 observations in Washington and 5,868,395 counterpart observations in Oregon) and 1,851,810 UPC-store-month observations for the Berkeley event (34,931 taxed observations and 1,816,879 in control stores).

Measure D in Berkeley taxes all products that contain any “added caloric sweetener,” which includes sucrose, fructose, glucose, other sugars, and high fructose corn syrup. The measure excludes from the tax beverages that contain sugar in their natural form (fruit and vegetable juices) as well as milk products and infant formula. To determine whether a UPC is affected by the tax, we employ Gladson’s nutrition database. This database contains detailed information on each UPC’s nutritional content (including any form of added caloric sweetener). For those cases in which a UPC is not contained in Gladson’s database, we obtain nutrition information directly from web searches. This procedure yields a total of 922 taxed UPCs and 1,310 untaxed UPCs.

3.1 Preliminary Graphical Evidence

The next figures present preliminary graphical evidence on the plausible effects of the two tax events. These graphs correspond to data at the most aggregate level: all UPCs across all stores in a region-month. Each figure displays data for both the treatment region as well as the control region.

Figure 1 presents results for Washington. The left panel displays (average) price results while the right panel displays (aggregate) volume results. The months in which the tax was in effect are contained between the red vertical bars. To ease graphical comparison of series across the treatment and control regions, we normalize both variables (price and volume) using January 2009 levels (volume and price are equal to 1 on January 2009). This normalization is particularly important for volume since Washington is a much larger state and consequently records much higher baseline volume sales. 16

The right panel in Figure 1 presents the total volume sales across all taxed UPCs (regular and diet soda). The left panel reports the monthly volume-weighted average price per ounce (in cents) across all taxed UPCs. To obtain a volume-weighted average price, we use each UPC’s volume sold in each store-month as the weight. Using a weighted price is advantageous for three reasons. First, a volume-weighted average price is a more precise estimate of the average overall price effectively paid by consumers, which is ultimately what matters from a practical point of view. Second, this measure allows us to account for the skewness we observe in the popularity of beverage products: a few UPCs (typically the lower-priced ones, such as a 2L Coke) capture an important fraction of sales while dozens of UPCs (many of them highly-priced ones, such as energy drinks) capture a near-negligible fraction of sales; an unweighted average price would give both popular and unpopular products identical weight. Finally, to the extent that consumers are able to engage in price “arbitrage” (e.g., waiting for a sale period to stockpile, or seeking for a store that has the lowest price), a weighted average price would account for the effect of such behavior. This is particularly important since it has been argued that a possible downside of a soda (or an SSB) tax is that its effectiveness can be compromised by the presence of “sale-sensitive” consumers (or

15 The 3-digit zip code areas used as controls (and corresponding number of stores) are: 940 (68); 941 (83); 943 (4); 944 (11); 945 (198); 946 (23); 948 (8); and 949 (31). 16 In the regression analysis, we use the original scale of the data for price and use the natural logarithm of volume.

10

“storers”) who might (at least partially) dodge the effect of the tax by intensifying their purchases during sale periods or at lower-priced stores (e.g., Wang et al., 2017).17

An interesting feature of the Washington event is that there are three time periods: pre-tax (before), tax (during), and post-tax (after). The post-tax data in Washington is a valuable feature as it allows us to further scrutinize (and validate) the appropriateness of our chosen control group (Oregon): the common-trends assumption cannot only be checked during the pre-event period but also after the tax was removed. Across the two panels, there appears to be strong (visual) support for the common-trends assumption needed for identification of the difference-in-difference estimator (before as well as after the tax); while the common-trends assumption seems to hold in both series (price and volume), the similarity in movement is particularly salient in volume sales where the timing and size of the cycles in the variable are essentially indistinguishable across treatment and control groups.

There are a few additional features that can be discerned from the data. First, the effect of the tax on price seems evident. One can see a sizeable increase in the price of the taxed products. Second, there appears to be an impact on volume sales although it seems to be smaller in size compared to the price effect (we confirm this graphical assessment in the econometric analysis).

Figure 1: Preliminary Graphical Evidence of Tax Effects on Price and Volume, Washington State

Figure 2 displays weighted price (left panel) and volume series for Berkeley. The series correspond to data from all taxed products (SSB). Normalization of the series uses January 2014 as the basis. The control region corresponds to a synthetic control that employs all eight 3-digit zip codes that surround Berkeley (we later provide details of these control areas, as well as how the synthetic control is constructed). In general, we can see that the synthetic control is a reasonable counterfactual, especially in the volume series. This graphical evidence does not seem to suggest that Berkeley experimented a decrease in the overall volume of SSB. In term of price, the gap of the series between Berkeley and the synthetic control is larger after the imposition of the tax suggesting that there might have been an effect; we note, however, that this gap starts to widen roughly three months before the tax is imposed.

17 To be sure, later in the paper, we also present robustness results using a simple price average as the unit of analysis.

11

Figure 2: Preliminary Graphical Evidence of Tax Effects on Price and Volume of all SSB, Berkeley v. Synthetic Control

3.2 Descriptive Evidence

Table 1 contains price and volume descriptive statistics (average and standard deviation) for both events (Washington in Panel A, Berkeley in Panel B). For each event, we present separate statistics for the treatment region and for the control region (as before, we use Oregon as a control for Washington and the synthetic control for Berkeley). The first row in each panel reports statistics for all taxed products (all soda in Washington and all SSB in Berkeley); the second row presents regular soda (the largest taxed beverage category in both events). To descriptively gauge the effect of the tax, we further segment the statistics into two periods: before tax and during tax. In the case of Washington, the before period is January 2009 through May 2010 while the during period is from June 2010 until November 2010. For Berkeley, these two periods are, respectively, January 2014 to February 2015 and March 2015 to December 2015. Note that for the case of Washington we also have an “after” period (December 2010 onwards); to simplify the descriptive analysis, we do not consider that period here, but use it in the regression analysis.

The objective of Table 1 is to summarize the graphical evidence presented in subsection 3.1, and to provide some evidence on the effect of the tax on the largest beverage category affected in both events (regular soda). To be precise, the average and standard deviations that we report on the first row of the tables (all taxed products), are calculated using the plotted values shown in Figures 1 and 2.21

21 This corresponds to 23 values for the Washington event (18 before and 6 after) and 24 values for the Berkeley event (14 before and 10 after). Including data from the period after the tax was removed (and using it as part of the “untaxed” observations) does not alter the conclusions of this preliminary analysis.

12

Table 1: Average Prices and Volume Before and During the Tax Period (Standard Deviation)

Panel A: Washington Event Washington Oregon (Control) Normalized (Weighted) Average Monthly Price Per Ounce Before During Diff. Before During Diff. All Taxed Products (Diet + Regular Soda)

1.0254 (0.0329)

1.1001 (0.0198) 0.0747 1.0420

(0.0330) 1.0541 (0.0200) 0.0121

Regular Soda 1.0209 (0.0330)

1.0918 (0.0220) 0.0709 1.0416

(0.0332) 1.0509 (0.0222) 0.0093

Normalized Average Monthly Volume Sales Before During Diff. Before During Diff. All Taxed Products (Diet + Regular Soda)

0.8444 (0.1268)

0.8144 (0.1139) -0.0300 0.8562

(0.1319) 0.8722 (0.1414) 0.0160

Regular Soda 0.8452 (0.1285)

0.8023 (0.1156) -0.0429 0.8571

(0.1326) 0.8609 (0.1428) 0.0038

Number of observations: 18 in Before period and 6 in During period.

Panel B: Berkeley Event Berkeley Synthetic Control Normalized Average Price Per Ounce Before After Diff. Before After Diff. All Taxed Products (SSB) 1.0435

(0.0283) 1.1284 (0.0220) 0.0849 1.0373

(0.0318) 1.1313 (0.0171) 0.0940

Regular Soda (Taxed Products)* 1.0388 (0.0395)

1.1321 (0.0138) 0.0933 1.0163

(0.0369) 1.1247 (0.0097) 0.1084

Normalized Average Monthly Volume Sales Berkeley Synthetic Control Before After Diff. Before After Diff. All Taxed Products (SSB) 1.2534

(0.2082) 1.1908 (0.1945) -0.0626 1.1325

(0.1918) 1.0712 (0.1570) -0.0613

Regular Soda (Taxed Products)* 1.1669 (0.1492)

1.1528 (0.1390) -0.0142 1.0929

(0.1409) 1.0987 (0.1275) 0.0059

Number of observations: 14 in Before period and 10 in During period. *There are a few soda products that do not contain added sugar and are therefore not taxed (excluded from this group).

In the case of Washington, the reported differences imply a 6.26% price increase for all soda products (6.16% for the regular soda segment). This increase, as we later explain in more detail, implies an average pass-through rate (across all taxed UPCs) of 110%.22 In terms of volume, the figures in the table suggest that consumption of soda in Washington decreased by 4.6% (4.59% for regular soda). Standard deviation figures, however, suggest that the price estimates are likely to be more precisely estimated than volume estimates (this is confirmed later in our regression analysis).

In the case of Berkeley, the reported means for all SSB imply that price experienced a negligible effect; in fact, the synthetic control shows a slightly higher increase in price than Berkeley (about 1% for all SSB and 1.5% for regular soda), opposite of what would be expected. Consistent with the price results, we observe

22 As we later document, the price increase exhibits significant heterogeneity across products, some surpassing a 200% pass-through rate.

13

that volume might have experienced a minor (possibly statistically insignificant) effect (-0.13% in all SSB and -2.01% for regular soda).

3.3 Choice of Controls

Given the nationwide nature of our data, we have multiple candidate regions that can serve as a control for the treated region. We highlight ethnic, economic and educational similarities as important elements for choosing appropriate controls since there is evidence that suggests that these variables can be correlated with obesity rates (which is, in turn, known to be linked to sugar consumption). In this section, we present the analysis used to determine the most suitable control region(s) for each of the two events.

Washington

The two obvious control choices are the states of Idaho and Oregon. The demographics presented in table 2 suggest that Oregon is more similar to Washington than Idaho is. Of the three states, Washington is the most densely populated and urbanized, it has the highest median income and educational attainment, and possesses the least homogeneous ethnic composition. Conversely, Idaho is a more sparsely and rurally populated state (see also Figure 3), with a significantly lower median income and education attainment, and has the least ethnically diverse population. These demographics suggest that Oregon would be a more appropriate control state.

Further, Washington and Oregon share an important geographical feature. The Cascade Range is a large mountainous body that naturally divides each of these two states into a western and an eastern region. Interestingly, the population in both of these states is largely concentrated to the west of the Cascade Range (Figure 3). More importantly, the Cascade Range creates two distinctly different climatic regions in each state,23 a feature that can have important consequences for demand conditions for beverages. Conversely, Idaho is located in its entirety to the east of the Cascades, with all their population facing climatic conditions that are importantly different (i.e., drier, colder winters and warmer summers) than those faced by cities located near the ocean (where the large majority of the population in Washington lives).

Table 2: Demographics of States considered in the Washington Event Variable Washington Oregon Idaho Population 6,724,540 3,831,074 1,683,140 % White (excludes Hispanics) 72.5 78.5 84 % African American 3.6 1.8 0.6 % Hispanic 11.2 11.7 11.2 % Asian 7.2 3.7 1.2 Med. HH Income [$000/year] 58.1 60.8 51.6 % At least Bachelor Degree 32.9 30.8 25.9 Density (per sq. mi.) 101.2 39.9 19 % Urban population 84.1 81.0 70.6

Source: U.S. Census Bureau (www.census.gov), 2010

23 The two climatic regions exist in both states, but are more clearly delineated in Washington. See the Western Regional Climatic Center (http://www.wrcc.dri.edu).

14

Finally, a graphical comparison of the trends exhibited by the price and volume series suggests that, visually speaking, Washington (when compared with Idaho) shares more similar market conditions with Oregon. To illustrate this point with an example, Figure 4 displays the weighted average price for the most popular product in the sample (2L Coke). The left panel compares Washington with Oregon; the right panel uses Idaho as the control. It can clearly be seen that common trends assumption is more likely to hold for Oregon.

We interpret the graphical evidence in Figure 1, as well as the analysis throughout this subsection, as strong evidence in favor of the common trends assumption (crucial for identification) when using Oregon as a control. This leads us to conclude that constructing a synthetic control does not seem necessary or particularly useful in the Washington event.

Figure 3: Population Density, Inhabitants per Square Mile

Figure 4: Evolution of Weighted Average Price (2L Coke), Washington and Oregon v. Washington and Idaho

15

Berkeley

There is a total of nine 3-digit zip code areas that are used in the analysis. The 947 code corresponds to Berkeley24, while codes 940, 941, 943, 944, 945, 946, 948 and 949 are all neighboring areas that can serve as potential controls (see Figure 5).25 In the analysis below, when feasible, we construct a synthetic control using all eight 3-digit zip areas that surround Berkeley. The advantage of a synthetic control is that it uses information from all control areas by attributing a larger weight to the regions where market conditions resemble more closely those of the treatment region. However, the synthetic control requires that data be aggregated at the region-month level; we thus employ a synthetic control as our preferred counterfactual only in these types of regressions.

Since a large portion of our analysis uses UPC-store-month level regressions, we need to choose one (or more) specific control region(s) for this portion of the analysis. Based on geographic proximity, the 3 most obvious control candidates are San Francisco (941), Oakland (946) and Richmond (948). The remaining 4 areas are likely less ideal for being more distant to Berkeley; further, as Table 3 suggests, the demographics of these 4 (more distant and, in some cases, very broad) areas appear to be less similar to Berkeley than are 941, 946 and 948.

Of the three potentially superior control candidates (941, 946 and 948), we consider San Francisco to be a preferable choice for the following reasons. First, the immediate neighbors Oakland and Richmond have demographic (i.e., ethnic/racial) characteristics that are significantly dissimilar to those observed in Berkeley. In Berkeley, 54.7% of the population is White, whereas in both Richmond and Oakland this percentage is half or less: 27.4% in Oakland, 18.2% in Richmond. The proportion of African Americans in Richmond is nearly 4 times larger than in Berkeley, whereas the proportion of Hispanics in Oakland is 2.7 times larger than in the treatment city. While there are some differences in ethnical composition between Berkeley and San Francisco, they are less severe.

24 Zip code boundaries do not necessarily coincide with those of a city (or a county). In the case of Berkeley, the 3-digit zip code 947 contains all the territory occupied by the city of Berkeley; in addition, the vast majority of the territory covered by 947 corresponds to area where the city of Berkeley resides. This 3-digit zip code also contains, in its periphery, portions of the territory occupied by the cities of Albany, Richmond (both in the north), and Oakland (in the south and west). For our purposes, this overlap does not compromise identification since all stores located in the 3-digit-zip code 947 in our data are in the city of Berkeley. 25 Since these 3-digit codes do not correspond to a specific city (in some cases, such as 940, 945 and 949, more than a dozen cities are contained in a single 3-digit code), we label them either using the name of the most important city in that 3-digit zip code, or (in the case of large areas) a name that is usually associated to that region (i.e., “Peninsula” for 940, “East Bay” for 945 and “Marin” county for 949).

16

Figure 5: Potential 3-Digit-Zip-Code Control Areas

Berkeley (947) and 940 (Peninsula)

Berkeley (947) and 941 (San Francisco)

Berkeley (947) and 943 (Palo Alto)

Berkeley (947) and 944 (San Mateo)

Berkeley (947) and 945 (East Bay)

Berkeley (947) and 946 (Oakland)

Berkeley (947) and 948 (Richmond)

Berkeley (947) and 949 (Marin)

17

Table 3: Demographics of 3-Digit Zip Code Areas Considered in the Berkeley Event

Berkeley

(947) Peninsula

(940)

San Francisco

(941)

Palo Alto

(943)

San Mateo (944)

East Bay

(945) Oakland

(946) Richmond

(948) Marin (949)

Pop. [000] 112.6 762.1 802.2 106.4 127.8 227 414.5 150.6 315.4 % White (excludes hisp.) 54.7 41.8 41.9 44.9 45.4 42.4 27.4 18.2 71.2 % African American 10 2.2 6.1 6.18 2.3 8.2 27.1 22.9 1.7 % Hispanic 10.8 22.5 15.1 22.2 21.8 23.2 24.5 41.4 18.1 % Asian 19.3 29.3 33.3 20.9 25.1 21.4 17.1 14.1 5.5 Med. HH Inc. [000/year] 66.2 103.9 81.3 102.8 98.1 85.3 59.2 53.6 88.5 % At least Bachelor Deg. 70.9 51.3 53.8 64.9 50.7 37.7 41.1 23 48.6 Source: U.S. Census Bureau (www.census.gov)

Second, choosing immediate neighbors (such as Oakland and Richmond) as controls has the potential downside that these areas might not have been completely isolated from the effects of the tax. If cross-border shopping is important, then, to the extent that the tax propelled some consumers to seek lower prices in nearby non-taxed areas, Oakland and Richmond would be inappropriate counterfactuals due to this possible spillover effect (the measured effect of the tax on the treated region would be a mixture of the true effect of the tax on the treated region and the effect of the tax on the control region).

Third, many policies are not randomly implemented, so one might worry about policy endogeneity: an unobservable factor in Berkeley’s consumption of SSB could be correlated with the policy decision. If this is the case, a potential concern is that the control group may be inherently different than the treatment group (i.e., the control group does not have the unobservable component that produces an inclination to adopt the policy); this situation would violate the orthogonality condition and result in a biased estimate of the tax effect. Choosing San Francisco over Oakland and Richmond as controls may alleviate this concern. The reason is that San Francisco considered a similar ballot as Berkeley at exactly the same time (November 2014). San Francisco considered a 2 cent/oz. tax on SSB, and received majority support (56%); however, the proposed policy was defeated because San Francisco requires 2/3 of the vote to approve an initiative of this type (in Berkeley, where simple majority is needed, the tax was approved with 76% of the vote). To the extent that both Berkeley and San Francisco were “similar” in their tendency to adopt a policy to reduce SSB consumption (and that this enters the demand for SSB in an unobservable way), the policy endogeneity concern would be reduced.26

Figure 6 compares the (weighted average) price series of Berkeley to each of the eight possible control areas (similar figures for volume series can be found in the Appendix). These plots are consistent with our assessment regarding the most suitable control area: the graph where support for the common trends assumption is (arguably) clearer is San Francisco. Despite the apparent superiority of San Francisco as a control region, and to add confidence to our main findings, we verify the robustness of our UPC-store-month regressions by using all eight areas as a “pooled” control region.

26 This argument is also used by Cawley and Frisvold (2017) for their choice of San Francisco as a control city.

18

Figure 6: Evolution of Average Weighted Price (All Taxed Products) for Each Potential 3-Digit-Zip-Code Control AreaBerkeley (947) and 940 (Peninsula)

Berkeley (947) and 941 (San Francisco)

Berkeley (947) and 943 (Palo Alto)

Berkeley (947) and 944 (San Mateo)

Berkeley (947) and 945 (East Bay)

Berkeley (947) and 946 (Oakland)

Berkeley (947) and 948 (Richmond)

Berkeley (947) and 949 (Marin)

19

4. Model and Results

4.1 Model

Our main specification uses a standard difference-in-difference estimator:

𝑌𝑌𝑏𝑏𝑏𝑏𝑏𝑏 = 𝜃𝜃 + 𝛽𝛽𝐷𝐷𝑏𝑏𝑡𝑡𝑡𝑡𝑡𝑡𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡𝑏𝑏 + 𝛾𝛾𝐷𝐷𝑑𝑑𝑑𝑑𝑡𝑡𝑑𝑑𝑡𝑡𝑑𝑑 + 𝛼𝛼𝐷𝐷𝐷𝐷𝐷𝐷�𝐷𝐷𝑏𝑏𝑡𝑡𝑡𝑡𝑡𝑡𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡𝑏𝑏 ∗ 𝐷𝐷𝑑𝑑𝑑𝑑𝑡𝑡𝑑𝑑𝑡𝑡𝑑𝑑�+ 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏

where the subscript 𝑏𝑏 denotes the unit of analysis (at the most disaggregated level an individual UPC; at the most aggregated level, all taxed UPCs), 𝑚𝑚 indicates the geographic unit (a store in disaggregated regressions; a region in aggregate regressions), and 𝑡𝑡 represents time (month). The variable 𝑌𝑌 denotes the outcome variable (price or volume), 𝐷𝐷𝑏𝑏𝑡𝑡𝑡𝑡𝑡𝑡𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡𝑏𝑏 is a dummy variable equal to 1 if the observation is in the treatment region and 0 otherwise, and 𝐷𝐷𝑑𝑑𝑑𝑑𝑡𝑡𝑑𝑑𝑡𝑡𝑑𝑑 is a dummy variable equal to 1 during the period when the tax was in place. In UPC-store-month regressions we include 3-digit zip code, brand (e.g., Coke, Pepsi, etc.) and size (e.g., 2L, 1L) fixed effects (for expositional ease, these fixed effects are not shown in the baseline equation displayed above).

When the outcome variable (𝑌𝑌) is price, we define it in terms of price per ounce; this allows us to directly use the 𝛼𝛼𝐷𝐷𝐷𝐷𝐷𝐷 coefficient to infer the pass-through rate: a coefficient equal to the tax (i.e., 0.1667 for Washington and 1 for Berkeley) implies 100% pass-through, values above (below) the tax represent tax over-shifting (under-shifting). When the outcome variable is volume, we define it as the natural logarithm of the cumulative volume sales at the corresponding level of aggregation (e.g., in region-month regressions, we sum the volume across all UPCs in that category across all stores in a region-month). Thus, in volume regressions, we can interpret the 𝛼𝛼𝐷𝐷𝐷𝐷𝐷𝐷 coefficient as an elasticity estimate (i.e., percentage change in volume as a result of the tax).27

We assume that the residual 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏 is orthogonal to the explanatory variables but allow for correlation across observations within clusters. We choose to cluster at the store level in our UPC-store-month regressions; the inclusion of fixed effects at the zip code, brand, and size levels in these regressions control (although not fully) for common shocks across observations thereby reducing the need of clustering at these three levels.28 Clustering in our region-month regressions is problematic due to the existence of too few clusters (in our case, two clusters) whereby clustering techniques produce unreasonably small standard errors (Cameron and Miller, 2015); in these regressions we report the usual standard errors. We do note, however, that an advantage of region-month regressions is that they alleviate concerns regarding the possible presence of serial correlation across UPCs and/or stores that might not be addressed by our clustering and/or fixed effects used in UPC-store-month regressions.

27 Strictly speaking, because we are using the difference of the variable in natural log format, the percentage change in the variable is given by 𝑒𝑒𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 − 1, where 𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 is the DID estimate. For small enough 𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 (as is the case here), 𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 is quite a good approximation of 𝑒𝑒𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 − 1. Using the logarithmic transformation on volume also has the benefit of transforming the series of control and treatment areas to a similar scale (e.g., when using the untransformed series, total aggregate consumption in Berkeley is significantly smaller than in a large control area such as San Francisco, which can lead to erroneous conclusions in DID analyses). 28 We experimented with a variety of clustering and fixed effects combinations without any change to our conclusions.

20

Synthetic Control

We use the synthetic control method laid out by Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2014). We use all eight available 3-digit zip code areas to form the synthetic control for Berkeley. Our general approach is to use untaxed products to create the synthetic control weights. Specifically, we use three predictor variables: untaxed products’ volumes, untaxed products volume-weighted prices and untaxed products’ average (unweighted) prices. We use these same three variables to construct the synthetic control group in both the price and volume regressions.29

4.2 Regression Results

In the case of Washington, we use two different 18-month time windows as the “untaxed” period: January 2009–June 2010 (before the tax was in place) and December 2010–May 2012 (after the tax was removed); the “during” period is July 2010–November 2010. In the case of Berkeley, we employ the 14 months prior to the tax as the untaxed period (January 2014–February 2015) and the 10 months for which we have data as the “during” period (March 2015–December 2015).

For both cases, we organize results in two groups: overall results for all taxed products and results for selected products (i.e., popular brands and sizes). Overall results group all taxed UPCs. In overall regressions, we entertain specifications at the UPC-store-month level, as well as aggregate specifications at the region-month level. As stated earlier, aggregate specifications alleviate potential concerns of serial correlation across UPCs and/or stores not accounted for brand, size, and zip code fixed effects. For completeness and comparison purposes (in the overall results analysis), we also report separate regressions for all untaxed UPCs as well as for all products in the main taxed category: soda. In the case of Berkeley, we use the synthetic control in region-month regressions and San Francisco as a control in UPC-store-month regressions. For completeness and robustness purposes, we also report results using a pooled control specification (i.e., using all eight 3-digit zip code areas as a control).

For regressions on selected products, our unit of analysis is a single UPC (i.e. a brand -size -unit combination: e.g., Coca-Cola, 12 oz, 12 units) in a store-month. We select the most popular taxed UPCs. To select the most popular taxed UPCs, we first choose Nielsen’s beverage categories with the largest reported volume that contain UPCs that were affected by the tax;30 Table 4 displays these beverage categories. The table groups the categories depending on whether or not they are affected by the tax in either of the two events.31 From these categories, we select the taxed UPCs with the largest volume sales (Table 5). In the case of Washington, the selected taxed UPCs account for approximately 61% of all volume across all taxed products; this figure is 40% in the Berkeley analysis. In these regressions of selected

29 In the results section (region-month regressions), we present regressions on all taxed products (all SSB) as well as on the largest taxed category (soda). The procedure to create the synthetic control for SSB is the one just described. For soda regressions, we use a similar procedure, with the difference that instead of using all non-taxed products to create predictor variables, we use several non-taxed categories: milk, apple juice, grape juice, lemon/lime juice, pineapple juice, non-taxed soda products, and bottled water. 30 Nielsen refers to these categories as “product module codes” (PMC). 31 Note that some of these categories have labels that would suggest all products in the category are untaxed (i.e., vegetable juices, bottled water); this is not the case as there are brands in these categories which do contain added sugar (see Table 5).

21

products we use San Francisco as the control area for the Berkeley analysis; however, we also report robustness results from pooling all eight zip code areas as a single control group.

Table 4: Beverage Categories from which Selected Taxed Products are Chosen

Beverage Categories Washington Soda, Diet Soda

Berkeley

Soda, Fruit Drinks (Cranberry); Fruit Drinks (Frozen); Fruit Drinks (Canned); Fruit Drinks (Other); Fruit Juice (Nectar), Vegetable Juice*, Bottled Water*

*There are products in these categories that are sugar-sweetened (therefore taxed).

Table 5: Selected UPCs (brand-size-unit combination) in Each Beverage Category

Beverage Category Selected UPCs (Brand-Size-Unit)*

Soda Brands: Private Label, Coca-Cola, Pepsi, Mountain Dew, Dr. Pepper, Sprite, A&W Size (Units): 12 oz (12), 12 oz (24), 1L (1), 20 oz (1), 2L (1)

Diet Soda Brands: Private Label, Coke, Pepsi, Dr. Pepper, Coke Zero, Coca-Cola Caffeine Free, Mountain Dew, A&W Size (Units): 12 oz (12), 12 oz (24), 1L (1), 20 oz (1), 2L (1)

Fruit Drinks (Cranberry)

Brand-Size (Units): Private Label-64 oz (1), Ocean Spray-64 oz (1), Simply Cranberry-64 oz (1)

Fruit Drinks (Frozen)

Brand-Size (Units): Private Label-12 oz (1), Hawaii’s Own-12 oz (1), Minute Maid-12 oz (1)

Fruit Drinks (Canned)

Brand-Size (Units): Arizona-12 oz (1), Minute Maid-23 oz (1), Rockstar Recovery-12 oz (12)

Fruit Drinks (Other) Brand-Size (Units): Gatorade-20 oz (8), Tampico-128 oz (1), Capri Sun 6-oz (10) Fruit Juice (Nectar) Brand-Size (Units): Kern’s-59 oz (1), Sun Tropics-64 oz (1), Jumex-11.3 oz (1)

Vegetable Juice Brand-Size (Units): V8 Splash-64 oz (1), Clamato-64 oz (1), Faraon-50.7 oz (1)

Bottled Water Brand-Size (Units): Glaceau Vitamin Water-20 oz (1), Glaceau Vitamin Water Zero-20 oz (1), Vita Coco-33.8 oz (1)

* Units refers to the number of items in the package (e.g. 12 oz (12) is a 12 pack of 12 oz cans).

4.2.1 Washington

Overall Results

Table 6 contains region-month and UPC-store-month results for volume-weighted price and for (log of) volume. We report results for the entire set of products affected by the tax (All Soda), as well as for each of the two soda categories (Regular Soda and Diet Soda), separately. For comparison purposes, we also report the DID estimate for all untaxed beverages. First, the most consistent result across the two specifications (region-month and UPC-store-month regressions) is that of a statistically significant (and similar) increase in price; this magnitude ranges between 0.163 and 0.179 cents per ounce implying a pass-through rate between 98% and 107%, which we interpret as, by and large, evidence for over-shifting. There appear to be no important differences in the pass-through rate between the two types of soda categories affected by the tax. The weighted average price across all UPCs in both soda segments is ¢

22

2.99/oz,32 implying a (weighted) price increase of 5.88% (0.176/2.99).33 In robustness checks (Section 4.3), we report results using the unweighted price series (and confirm these conclusions).

Second, we find a reduction in the consumption of taxed products that ranges between 4.1% and 6%, depending on the specification and the beverage category. However, this effect is only statistically significant in the UPC-store-month regressions. The overall price increase reported in the prior paragraph, together with the volume reduction observed for All Soda in the region-month regression (-5.7%), imply a price elasticity of soda of -0.97 (although imprecisely estimated because of the lack of statistical significance of the volume coefficient).

Table 6: Overall DID Results, Washington

Dependent Variable: Region-Month Regressions UPC-Store-Month Regressions‡ Untaxed

Beverages Regular

Soda Diet Soda

All Soda Untaxed Beverages

Regular Soda

Diet Soda All Soda

Volume-Weighted Average Price, cents/oz

[pass-through rate]

-0.011 (0.139)

N.A.

0.179*** (0.088) [107%]

0.174** (0.077) [104%]

0.176*** (0.080) [105%]

-0.011 (0.007)

N.A.

0.163*** (0.008) [98%]

0.179*** (0.006) [107%]

0.171*** (0.006) [102%]

Log Volume 0.001

(0.087) -0.060 (0.092)

-0.052 (0.090)

-0.057 (0.089)

-0.013*** (0.005)

-0.041*** (0.000)

-0.047*** (0.006)

-0.043*** (0.000)

# Obs 82 82 82 82 7,967,168 5,240,085 3,437,822 8,677,907 ‡UPC-store-month regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.

While the literature reports a wide range of elasticity estimates for soft drinks, results typically fall on the price-inelastic range (-0.8 to -1; see Andreyeva et al., 2010).34 Estimates by Yale University’s Rudd Center for Food Policy and Obesity suggest that for every 10% increase in price, consumption would decrease by 7.8% (Brownell and Frieden, 2009). Thus, the (imprecisely estimated) elasticity derived from the Washington event is generally consistent with the findings in prior literature.

Results on Selected Products

We carry out regressions for each UPC, both for weighted price as well as for (log of) volume. Table 7 presents the results for price while Table 8 displays volume results. Both tables organize selected products into the two selected beverage categories (diet soda and regular soda). Each entry in the table displays the estimated DID coefficient in the regression, information regarding statistical significance, and the number of observations used. The gray-colored cells denote the regression results from pooling UPCs (across brands and/or across sizes), whereas the last column and row of Table 7 report the implied pass-through rates of the pooled regressions.

32 This figure corresponds to the constant obtained in the All Soda region-month regression (not reported). 33 For this exercise, we use the region-month results (All Soda), as these provide a more accurate measure of the

overall average weighted price across UPCs. The reason is that the weighing in UPC-store-month regressions only accounts for the different volumes a UPC experiences across weeks in a store. This can be misleading for the purposes of estimating overall price increase of soda since the regression treats the price of a popular UPC-store combination (e.g. 2L in the largest store in Seattle) with the same importance in the regression as an unpopular one (e.g. 20 oz Fanta in a convenience store in Spokane). Conversely, a weighted price observation in region-month regressions accounts for differences in volumes across UPCs, stores and weeks.

34 Some authors report price elastic demand (e.g. Dharmasena and Capps, 2011; -1.90).

23

Table 7: UPC-Store-Month DID Price Results for Washington, Selected Products, [# obs.]

Beverage Category: Diet Soda Priv. Label Diet Coke Diet

Pepsi Diet Dr. Pepper

Coke Zero

Coke Caff. Free

Diet Mtn. Dew

Pepsi Caff. Free

2L 0.164*** 0.166*** 0.221*** 0.124*** 0.167*** 0.179*** 0.261*** 0.248*** [24,239] [42,679] [42,694] [39,486] [35,491] [31,544] [30,013] [30,123]

12 pack 0.132*** 0.063*** 0.111*** 0.069*** 0.068*** 0.075*** 0.098*** 0.114***

[23,153] [43,119] [43,088] [41,308] [40,398] [38,574] [37,124] [35,986]

24 pack - 0.167*** 0.191*** 0.193*** 0.204*** 0.177*** 0.198*** 0.202***

[17,080] [22,521] [18,579] [14,772] [9,598] [14,095] [18,487]

20 oz 0.370*** 0.420*** 0.338*** 0.288*** 0.421*** 0.505*** 0.364*** 0.466***

[19,309] [43,055] [43,149] [42,258] [40,870] [19,068] [38,776] [14,494]

1L 0.169*** 0.120*** 0.174*** 0.138*** 0.100 - 0.033 -

[27,620] [32,281] [31,659] [8,398] [5,737] [3,393]

All Sizes 0.213*** 0.197*** 0.211*** 0.166*** 0.205*** 0.216*** 0.235*** 0.227*** [94,325] [178,214] [183,111] [150,029] [137,268] [98,784] [123,401] [99,090]

Pass-Through 128% 118% 126% 99% 123% 129% 141% 136%

Table 7, continued

Beverage Category: Regular Soda Pass-Through Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W All Brands

2L 0.093*** 0.159*** 0.224*** 0.231*** 0.134*** 0.156*** 0.153*** 0.177*** 106% [36,651] [42,745] [42,734] [42,455] [42,381] [42,501] [42,043] [567,779]

12 pack 0.154*** 0.060*** 0.107*** 0.121*** 0.098*** 0.074*** 0.099*** 0.094*** 56%

[23,284] [43,117] [43,063] [42,840] [42,528] [42,903] [41,159] [581,644]

24 pack - 0.220*** 0.197*** 0.194*** 0.199*** 0.147*** - 0.191*** 114%

[17,474] [22,638] [22,348] [20,200] [16,294] [214,230]

20 oz 0.420*** 0.421*** 0.325*** 0.343*** 0.313*** 0.419*** 0.477*** 0.383*** 229%

[20,387] [43,056] [43,150] [43,101] [43,041] [42,907] [38,281] [534,902]

1L 0.099*** 0.133*** 0.146*** 0.153*** 0.110*** 0.114*** 0.350*** 0.177*** 106%

[33,411] [33,959] [34,730] [34,207] [29,066] [21,520] [7,258] [303,239]

All Sizes 0.174*** 0.203*** 0.203*** 0.213*** 0.173*** 0.202*** 0.243*** 0.204*** [113,736] [180,351] [186,315] [184,951] [177,216] [166,125] [128,878] [2.201 MM]

Pass-Through 104% 122% 122% 128% 104% 121% 146% 122% Notes: All regressions include (3-digit) zip code fixed effects. Pooled size regressions (second-to-last row) contain size fixed effects; pooled brand regressions (second-to-last column) contain brand fixed effects; the overall pooled regression includes brand and size fixed effects. Standard errors are clustered at the store level. ***, ** and * significant at 1%, 5% or 10% (respectively).

24

Table 8: UPC-Store-Month DID Volume Results for Washington, Selected Products, [# obs.]

Beverage Category: Diet Soda Priv. Label Diet Coke Diet Pepsi Diet Dr.

Pepper Coke Zero Coke Caff.

Free Diet Mtn.

Dew Pepsi Caff.

Free

2L -0.018 0.006 -0.079*** 0.06** -0.002 -0.07* -0.222*** -0.093** [24,239] [42,679] [42,694] [39,486] [35,491] [31,544] [30,013] [30,123]

12 pack -0.079*** 0.042* 0.016 0.051** 0.017 -0.035 -0.087** -0.023

[23,153] [43,119] [43,088] [41,308] [40,398] [38,574] [37,124] [35,986]

24 pack - -0.044 -0.260*** -0.245*** -0.123*** -0.065 -0.175*** -0.197***

[17,080] [22,521] [18,579] [14,772] [9,598] [14,095] [18,487]

20 oz -0.102* -0.048*** -0.004 0.012 -0.056*** 0.057 0.013 0.090* [19,309] [43,055] [43,149] [42,258] [40,870] [19,068] [38,776] [14,494]

1L -0.026 0.098*** 0.097*** 0.074 0.144 - 0.230** -

[27,620] [32,281] [31,659] [8,398] [5,737] [3,393]

All Sizes -0.053** 0.008 -0.032*** 0.009 -0.015 -0.036 -0.093*** -0.059*** [94,325] [178,214] [183,111] [150,029] [137,268] [98,784] [123,401] [99,090]

Table 8, continued

Beverage Category: Regular Soda Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W All Brands

2L 0.054 0.024 -0.049*** -0.079*** -0.006 0.034* -0.006 -0.026*** [36,651] [42,745] [42,734] [42,455] [42,381] [42,501] [42,043] [567,779]

12 pack -0.071*** 0.034* -0.011 -0.024 0.008 0.016 -0.026 -0.007

[23,284] [43,117] [43,063] [42,840] [42,528] [42,903] [41,159] [581,644]

24 pack - -0.074*** -0.287*** -0.243*** -0.275*** -0.063* - -0.153***

[17,474] [22,638] [22,348] [20,200] [16,294] [214,230]

20 oz -0.105*** -0.029*** 0.003 -0.007 -0.022* -0.019 -0.057*** -0.025***

[20,387] [43,056] [43,150] [43,101] [43,041] [42,907] [38,281] [534,066]

1L -0.006 0.075*** 0.040** 0.087*** 0.059* 0.155*** -0.025 0.062***

[33,411] [33,959] [34,730] [34,207] [29,066] [21,520] [7,258] [303,239]

All Sizes -0.025 0.008 -0.041*** -0.039*** -0.029*** 0.0136 -0.029** -0.0204*** [113,736] [180,351] [186,315] [184,951] [177,216] [166,125] [128,878] [2.201 MM]

Notes: see Table 7.

25

The main findings can be summarized as follows. First, non-pooled price regressions always yield positive DID coefficients, and in all but two non-pooled cases (out of 70) these estimated coefficients are statistically significant at the 1% level; further, there is a generalized over-shifting of the tax, with a combined (pooled regression) pass-through estimate of 122%. Conversely, volume results are more mixed: the estimated DID coefficient is negative in 43 out of 70 non-pooled regressions, and (when negative) it is statistically significant (at 10% or better) in 25 instances; in addition, the volume effect is positive and statistically significant (at 10% or better) in 11 of the non-pooled regressions.

Third, there appears to be an important heterogeneity in the estimated coefficients, especially across different sizes. Regressions that pool different brands of the same size (last column of regression results) indicate that the pass-through rate fluctuates from 56% (12 pack) to 229% (20 oz); similarly, the volume effect fluctuates from a positive and statistically significant 6.2% (1L) to a negative and statistically significant -15.3% (24 pack). There is some heterogeneity across brands (last row of regression results), but the variation is less pronounced: pass-through rates vary between 99% (Diet Dr. Pepper) and 146% (A&W) and volume effects range from 1.39% (Sprite, not statistically significant) to -5.9% (Pepsi Caffeine Free). Despite the noted heterogeneity, there appears to be no systematic difference in results between products in the diet and regular categories.

Given the overall weighted average price of soda for the selected products ($2.76/oz; not shown in tables), the price increase caused by the tax (0.204 cents/oz; overall pooled regression) implies a price increase of 7.4% (in these selected products). Conversely, the overall pooled regression for volume suggests a reduction of 2.04% in the consumption of the most popular soda products. Compared with the results for the overall group of taxed products presented in the prior section (region-month specification), top soda products experienced a larger price effect (0.204 v. 0.18 cents per ounce) but a considerably smaller consumption effect (-2.05%, bottom right cell table 8; -4.4%, UPC-store-month volume coefficient for All Soda in Table 6). This is consistent with the notion that less popular soda products face a greater price elasticity. The conclusions in this section are confirmed in robustness analyses, which we report in section 4.3.

4.2.2 Berkeley

Overall Results

Table 9 presents the overall results in two panels. Panel A reports the region-month DID coefficients, while Panel B reports the UPC-store-month results. As stated earlier, the preferred control in region-month specifications is the synthetic series while San Francisco is the preferred control in UPC-store-month regressions. For comparison (robustness) purposes, we also report the results using all eight 3-digit zip code areas (pooled control) in both panels. In addition to the specification that quantifies the effect of tax on all taxed products (All SSB), we also report results on the most popular beverage category affected by the tax (regular soda) as well as results for all untaxed beverages.

Overall, the results are largely indistinguishable from zero (only 4 out of 24 estimated coefficients are significant, and one of these occurs in a specification for untaxed products). The only three coefficients in specifications that consider taxed products appear in price regressions (all in region-month specifications). None of the volume coefficients are statistically significant.

26

The results from Table 9 suggest that a price reaction was observed as a consequence of the tax. However, the implied pass-through rate (not shown in table) is quite low: it ranges from 23.7% (region-month, synthetic, regular soda) to 29.7% (region-month, pooled controls, regular soda) for the three statistically significant coefficients. When looking at all taxed products (All SSB), the price coefficient is statistically significant only in one of the four specifications considered and implies a pass-through rate of 24.4% (pooled control, region-month); two of the other three specifications yield price increases of near zero, while the remaining result (synthetic, region-month) implies a pass-through rate of 16.2% but (not precisely estimated).35 This moderate evidence for a price increase in Berkeley suggests that the Berkeley intervention was largely unsuccessful in creating the incentive necessary for consumers to reduce consumption.36

Table 9 (Panel A): Overall DID Results, Region-Month Regressions, Berkeley

Dep. Var.: Control: Synthetic Control: all 3-digit zip areas

Untaxed Beverages

Regular Soda

All SSB Untaxed Beverages

Regular Soda

All SSB

Weighted Average Price (¢/oz)

0.162 (0.10)

0.237*** (0.08)

0.162 (0.09)

-0.081 (0.09)

0.297*** (0.08)

0.244** (0.10)

Log(Volume) 0.0002 (0.08)

-0.018 (0.07)

0.002 (0.09)

0.022 (0.08)

-0.018 (0.07)

-0.019 (0.09)

# Obs 48 48 48 48 48 48 Table 9 (Panel B): Overall DID Results, UPC-Store-Month Regressions, Berkeley‡

Dep. Var.: Control: San Francisco Control: all 3-digit zip areas

Untaxed Beverages

Regular Soda

All SSB Untaxed Beverages

Regular Soda

All SSB

Weighted Average Price (¢/oz)

-0.098*** (0.02)

-0.079 (0.08)

-0.037 (0.05)

-0.029 (0.02)

-0.034 (0.07)

0.048 (0.05)

Log(Volume) -0.03 (0.04)

-0.026 (0.03)

-0.012 (0.03)

-0.021 (0.03)

-0.009 (0.03)

-0.012 (0.03)

# Obs 330,599 145,517 229,238 1,917,350 958,306 1,458,604 ‡UPC-store-month regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.

35 We note that the price coefficient for both soda and all SSB is only significant in region-month regressions. Further, this coefficient is an order of magnitude smaller in UPC-store-month regressions (and negative in three out of four cases). These seemingly inconsistent results are explained by the weighing mechanism. In UPC-store-month regressions, weighing only plays a significant role (i.e., produces a weighted average price that could be significantly different than the unweighted average price) to the extent that price varies significantly across weeks within a UPC-store-month. In region-month regressions, weighing plays a much larger role since the average weighted price is obtained by considering the volume of each UPC in a store-week. Thus, a popular product, such a 2L Coke, would weigh heavily on an observation at the region-month level, but not at the UPC-store-month level. 36 In robustness checks, we carry out regressions using unweighted price as a dependent variable (see Section 4.3 and Appendix Table A.6). In those regressions, the price effect that we find in region-month weighted price regressions (Table 9A) is no longer sizeable or statistically significant. This finding suggests that the price increase that we document in Table 9A is explained by consumers’ tendency to purchase pricier products after the tax was imposed, and not by an increase in the shelf price (which would be reflected in the unweighted price regressions). This adds support to our conclusion that the price effect that we document for Berkeley is modest at best.

27

Consistent with the price results, volume regressions do not show any statistically significant effect. The estimated DID coefficients (in volume regressions that consider taxed products) are negative in all but one case; however, the economic significance of these coefficients appears to be marginal (implying a volume reduction no greater than 2.6%).

Results on Selected Products

Tables 10 and 11 report, respectively, the price and volume regression results using San Francisco as a control area (robustness checks report results for all control areas). Each table displays results by grouping selected products by Nielsen’s beverage category; Panel A of the tables contains results for products in the soda category, while Panel B displays DID coefficients for selected products in all other beverage categories. Results from pooling the selected products are displayed in the bottom-right portion of Panel B.

Table 10 (Panel A): UPC-Store-Month Price Results for Berkeley, Selected Soda Products, [# obs.]

Category: Regular Soda

Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W

2L 0.051*** 0.069 0.006 0.005 0.125*** 0.070 0.072

[1,726] [2,152] [2,123] [1,792] [1,934] [2,096] [1,935]

12 pack -0.006 0.189* 0.260** 0.199 0.139 0.175 0.164

[504] [1,340] [1,150] [963] [1,029] [1,131] [868]

20 oz 0.384 0.051 0.0467 0.343*** 0.121 0.049 0.210 [888] [2,170] [2,170] [2,157] [2,166] [2,162] [2,120]

1L 0.122 0.122 0.137 0.093 0.126 0.314 0.0638 [588] [887] [725] [600] [628] [533] [38]

Notes: Control: San Francisco

Table 10 (Panel B): UPC-Store-Month Price Results for Berkeley, Other Selected Products, [# obs.] Category: Fruit Drinks (Cranberry) Category: Fruit Juice (Nectar)

Priv. Label Ocean Spray Simply Cranberry Jumex Kern’s Sun Tropics 0.235* 0.135 -0.224 0.262** -0.102 -0.061

[691] [1,782] [538] [504] [485] [210] Category: Fruit Drinks (Frozen) Category: Vegetable Juice

Priv. Label Hawaii’s Own Minute Maid Clamato Faraon V8 Splash -1.418*** 0.026 1.203 0.026 -0.153 -0.113

[147] [456] [276] [1,691] [571] [130] Category: Fruit Drinks (Canned) Category: Bottled Water

Arizona Minute Maid Rockstar Recovery Glaceau Water Glaceau Zero Vita Coco 0.058 -0.196 0.098 0.006 -0.465 -0.103

[2,080] [268] [2,046] [2,539] [750] [1,101] Category: Fruit Drinks (Other) Pooled Results

Gatorade Tampico Capri Sun Panel A (soda) Panel B (other) Panel A+B (soda+other) 0.143* 0.049 -0.343 0.117* 0.023 0.094** [2,177] [857] [3,287] [38,559] [22,586] [61,145]

Notes: The overall pooled regressions (bottom right, panel B) include brand and size fixed effects. Standard errors are clustered at the store level. ***, ** and * significant at 1%, 5% or 10% (respectively). Control: San Francisco

28

All but one soda product (out of 28 selected), show a positive price coefficient. However, only five of these coefficients are significant at 10%, and their magnitude suggests significant under-shifting: the largest implied pass-through rate is 34.3% (Mountain Dew, 20 oz). Price results are even more mixed for products in other beverage categories: 10 (out of 21) coefficients are negative (one significant at 1%) and only 4 of the positive coefficients show statistical significance at 10% or better. As in the soda products, the statistically positive coefficients suggest incomplete pass-through (at most 26.2%; Jumex, Fruit Juice Nectar). Pooled regressions show a statistically significant pass-through of 11.7% for the selected soda products and of 9.4% for all selected UPCs (soda + other).

Table 11 (Panel A): UPC-Store-Month Volume Results for Berkeley, Selected Soda Products, [# obs.]

Category: Regular Soda

Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W

2L 0.037 -0.091* -0.087 -0.100 0.013 0.001 -0.151** [1,726] [2,152] [2,123] [1,792] [1,934] [2,096] [1,935]

12 pack -0.183 -0.476*** -0.331** -0.247* -0.136 -0.384** -0.272

[504] [1,340] [1,150] [963] [1,029] [1,131] [868]

20 oz -0.936 0.041 0.184* -0.113 0.0226 0.019 -0.076

[888] [2,170] [2,170] [2,157] [2,166] [2,162] [2,120]

1L -0.324 -0.278** -0.080 0.081 -0.011 -0.072 0.554

[588] [887] [725] [600] [628] [533] [38] Notes: Control: San Francisco

Table 11 (Panel B): UPC-Store-Month Volume Results for Berkeley, Other Selected Products, [# obs.] Category: Fruit Drinks (Cranberry) Category: Fruit Juice (Nectar)

Priv. Label Ocean Spray Simply Cranberry Jumex Kern’s Sun Tropics -0.158 -0.272** 0.136 -0.207** 0.063 0.280

[691] [1,782] [538] [504] [485] [210] Category: Fruit Drinks (Frozen) Category: Vegetable Juice

Priv. Label Hawaii’s Own Minute Maid Clamato Faraon V8 Splash 0.253 -0.364*** -0.205** 0.161 -0.082 -0.556 [147] [456] [276] [1,691] [571] [130]

Category: Fruit Drinks (Canned) Category: Bottled Water Arizona Minute Maid Rockstar Recovery Glaceau Water Glaceau Zero Vita Coco

-0.183*** 0.158 -0.074 -0.107 -0.664 0.231 [2,080] [268] [2,046] [2,539] [750] [1,101]

Category: Fruit Drinks (Other) Pooled Results Gatorade Tampico Capri Sun Panel A (soda) Panel B (other) Panel A+B (soda+other)

-0.044 -0.029 -0.016 -0.105* -0.062 -0.083** [2,177] [857] [3,287] [38,559] [22,586] [61,145]

Notes: The overall pooled regressions (bottom right, Panel B) include brand and size fixed effects. Standard errors are clustered at the store level. ***, ** , and * significant at 1%, 5%, or 10% (respectively). Control: San Francisco

Volume results are also mixed, although more coefficients are negative than positive (20 out 28 in soda and 14 out 21 in other categories). However, only 12 of the 34 negative coefficients are statistically significant at 10%. A group of products that seems to have been consistently affected by the tax is 12-packs in the soda category, where the largest coefficient reaches 0.476 (equivalent to a 60% volume

29

reduction).37 This large impact on 12-packs is reflected on the (weakly) statistically significant result of the pooled regression for soda (-10.5%) and the pooled result for all selected products (-8.3%).38

Price and volume results from this selected group of UPCs are generally consistent with the overall results reported before. Price coefficients in overall UPC-store-month regressions (see Table 9B) for all SSB are smaller (and even negative) than the coefficients obtained when pooling the selected UPCs (see bottom right cell, Table 10B), suggesting that UPCs not included in Table 10 experienced (on average) smaller price increases (or even price decreases) compared to the UPCs studied here. A similar pattern is observed for volume results: the pooled regression for the selected “soda+other” UPCs (see bottom right cell, Table 11B) indicates a larger drop in volume than that observed in the overall results (see Table 9B, all SSB volume results), suggesting that less popular UPCs experienced a much smaller volume decrease (or none at all) with respect to the more popular UPCs in Table 11.

4.3 Robustness Checks

We perform a battery of alternative specifications designed to provide additional robustness support for our findings. All of these results are reported in tables that can be found in the Appendix. Specifically, we carry out: (a) regressions using unweighted average prices (Tables A.1 and A.5, Washington; Tables A.2 and A.6, Berkeley), and (b) pooled control (i.e., using all possible control areas) specifications for Berkeley (Tables A.3 and A.4). These alternative specifications do not modify our overall conclusions in a significant way.39

One potential problem that could arise in our setup is the issue of serially correlated errors. As Bertrand, Dufflo, Mullainathan (2004) show, bootstrapping or clustering methods may not work well and suggest collapsing the pre- and post-data to a single observation in order to remove any temporal correlation in the error term. We carry out this procedure by carrying out specifications at the UPC-store-pre/post level for both Washington and Berkeley. Since the tax in Washington was temporary, and we have observations before and after the tax enactment, we collapse data into the “untreated” time period by lumping observations prior to the tax together with observations after the tax was removed (we do this for two time spans: 5 months prior and after and 18 months prior and after). These results (see Appendix Tables A.7 and A.8), do not modify our overall conclusions.

5. Conclusion

We study the price and purchase effect of two taxes on beverages. One tax (Washington) was much smaller (¢1/6 per oz) and not intended to reduce consumption of sugary drinks, the other (Berkeley) was six times as large and was intended to reduce consumption of sugar-added beverages. We find that the

37 For DID coefficients of this magnitude, the interpretation of the coefficient as an elasticity is no longer a reasonable approximation; the 60% reported is obtained by applying the exact formula (i.e., 𝑒𝑒𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 − 1). 38 Using the exact 𝑒𝑒𝛼𝛼�𝐷𝐷𝐷𝐷𝐷𝐷 − 1 formula, these values are 11% and 9.7%, respectively. 39 We find one difference in region-month price regressions for Berkeley. The positive and statistically significant coefficients recorded for weighted price in table 9A (regular soda and SSB) are no longer significant (or positive in some cases) when unweighted prices are used (in the appendix, see table A.6). This suggests that the price increase that resulted from the tax was entirely due to consumers shifting consumption away from least expensive products (or, alternatively, searching less for discounted products), rather than from an increase in the shelf price. This results suggests that the moderate pass-through for Berkeley that we report finding in this study is likely an overstated result.

30

tax that did not pursue a large price increase and a reduction in consumption was more successful in doing both: retail prices increased by a larger magnitude than the tax itself and consumption appears to have dropped (arguably) moderately. The Berkeley tax was largely unsuccessful as it was able to increase the price to consumers by less than 25% of its intended magnitude. Further, even though the absolute magnitude of the price increase in both events is relatively similar (with Berkeley being slightly larger), the consumption effect in Berkeley was essentially non-existent. These results cast doubt about the effectiveness of SSB taxation.

The evidence presented by these two tax events suggests that a crucial element for the success of sugary drinks taxation is the ability to impose taxes on large territories. As argued in prior research, one reason for this drawback is that cross-border shopping becomes more likely and pervasive if the taxed area is small, thereby undermining the desired price effect sought by the tax. Further, we conjecture that taxes may have the desired effect if applied on areas least likely to approve these types of taxes by popular vote (where awareness about the negative effects of sugary drinks consumption is already high); this argument is consistent with our finding that the tax in Washington appears to have decreased consumption (where the tax was not pursued because of or motivated by health concerns). Under this view, a federal initiative seems like a worthwhile and effective effort.

On the other hand, one can interpret our results, especially those in Berkeley, as successful if the measure’s main objective is to raise revenue with the purpose of combating obesity through other means (e.g., obesity prevention programs in vulnerable populations, health awareness campaigns, etc.). The public debate, however, indicates that SSB taxation measures are expected to have an effect on consumption. The results of our work may help study and evaluate these initiatives from a different perspective.

31

References

Abadie, A., and Javier Gardeazabal. 2003. “The Economic Costs of Conflict: A Case Study of the Basque Country.” American Economic Review. 93(1), 113–132.

Abadie, A., A. Diamond, and J. Hainmueller. 2014. “Comparative Politics and the Synthetic Control Method.” American Journal of Political Science, 59(2), 495–510.

Andreyeva, T., Long, M., and K.D. Brownell. 2010. “The impact of food prices on consumption: a systematic review of research on price elasticity of demand for food.” American Journal of Public Health, 100(2), 216–222.

Andreyeva, T., Chaloupka, F.J., and K.D. Brownell. 2011. “Estimating the potential of taxes on sugar-sweetened beverages to reduce consumption and generate revenue.” Preventive Medicine, 52 (6), 413–416.

Apovian, C. M. 2004. “Sugar-sweetened soft drinks, obesity, and type 2 diabetes.” JAMA: The Journal of the American Medical Association 292(8), 978-979.

Ballotpedia. 2010. accessed April 1, 2017:

https://ballotpedia.org/Washington_Repeal_Tax_Law_Amendments,_Initiative_1107_(2010)

Bertrand, D., Mullainathan. 2004. “How Much Should We Trust Differences-in-Differences Estimates?” Quarterly Journal of Economics, 119: 249–275

Block, G. 2004. “Foods contributing to energy intake in the U.S.: data from nhanes iii and nhanes 1999-2000.” Journal of Food Composition and Analysis 3(17).

Block, J. P. and W. C. Willett. 2011. “Can taxing sugary soda influence consumption and avoid unanticipated consequences?” Choices 3(26).

Bridging the Gap Program. 2014. University of Illinois at Chicago, available at: www.bridgingthegapresearch.org

Brownell, K.D., and T. Frieden. 2009. “Ounces of Prevention — The Public Policy Case for Taxes on Sugared Beverages.” New England Journal of Medicine, (360), 1805-1808.

Brownell, K.D., Farley, T., Willett, W.C., Popkin, B.M., Chaloupka, F.J., Thompson, J.W., and D.S. Ludwig. 2009. “The public health and economic benefits of taxing sugar-sweetened beverages.” New England Journal of Medicine, 361, 1599-1605.

Cameron, D. and D. Miller. 2015. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, 50(2), 317-372.

32

Cawley, J. and D. Frisvold 2017. “The Pass-Through of Taxes on Sugar-Sweetened Beverages to Retail Prices: The Case of Berkeley, California.” Journal of Policy Analysis and Management. 36 (2), 303–326

Cawley, J. and C. Meyerhoefer. 2012. “The medical care costs of obesity: an instrumental variables approach.” Journal of Health Economics 31(1), 219-230.

Center for Disease Control (CDC), 2015. Adult Obesity Facts. Accessed April 1, 2017: https://www.cdc.gov/obesity/data/adult.html

Colantuoni, F. and C. Rojas. 2015. “The Impact of Soda Sales Taxes on Consumption: Evidence from Scanner Data.” Contemporary Economic Policy, 33, 714-730.

Dharmasena, S., and O. Capps. 2012. “Intended and Unintended Consequences of a Proposed National Tax on Sugar-Sweetened Beverages to Combat the U.S. Obesity Problem.” Health Economics, 21 (6), 669-694.

Donald, S. and K. Lang. 2007. “Inference with Difference-in-Differences and other Panel Data,” Review of Economics and Statistics, 89(2), 221-233.

Dugdale, E. 2015. “Berkeley soda tax raises $116,000 of revenue in first month.” Berkeleyside, May 18. Accessed on June, 15, 2017: http://www.berkeleyside.com/2015/05/18/berkeley-soda-tax-raises-116000-revenue-in-first-month/

Falbe, J., H. Thompson, C. Becker, N. Rojas, C. McCulloch, and K. Madsen, 2015. “Impact of the Berkeley Excise Tax on Sugar-Sweetened Beverage Consumption” American Journal of Public Health, 106(10), 1865-1871.

Falbe, J., H., N. Rojas, A. Grummon, and K. Madsen, 2016. “Impact of the Berkeley Excise Tax on Sugar-Sweetened Beverage Consumption” American Journal of Public Health, 105(11), 2194-2201.

Finkelstein, E. A., C. Zhen, J. Nonnemaker, and J. E. Todd. 2010. “Impact of targeted beverage taxes on higher-and lower-income households.” Archives of Internal Medicine, 170(22), 2028-2034.

Fletcher, J. 2011. “Soda taxes and substitution effects: will obesity be affected?” Choices 26(3).

Jacobson, M., and K. Brownell. 2000. “Small taxes on soft drinks and snack foods to promote health.” American Journal of Public Health, 90 (6), 854–857.

Kim D., and I. Kawachi I. 2006. “Food taxation and pricing strategies to “thin out” the obesity epidemic.” American Journal of Preventive Medicine, 30, 430–437.

Libuda, L. and M. Kersting. 2009. Soft drinks and body weight development in childhood: is there a relationship? Current Opinion in Clinical Nutrition & Metabolic Care 12(6).

33

Ludwig, D. S. and C. B. Ebbeling. 2001. “Type 2 diabetes mellitus in children.” JAMA: the Journal of the American Medical Association 286(12), 1427-1430.

Malik, V. S., M. B. Schulze, and F. B. Hu. 2006. “Intake of sugar-sweetened beverages and weight gain: a systematic review.” The American Journal of Clinical Nutrition 84(2), 274-288.

NCD Risk Factor Collaboration. 2016. “Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants.” Lancet, 387, 1377–1396.

Powell, L. and F. Chaloupka. 2009. “Food Prices and Obesity: Evidence and Policy Implications for Taxes and Subsidies.” The Milbank Quarterly, 81(1), 229-257.

Rudd Center, 2012. “Sugar-Sweetened Beverage Taxes: An Updated Policy Brief”. Accessed on September 15, 2017: http://www.uconnruddcenter.org/files/Pdfs/Rudd_Policy_Brief_Sugar_Sweetened_Beverage_Taxes.pdf

Silver, L., S. Wen Ng, S. Ryan-Ibarra, L. Smith Taillie, M. Induni, D. Miles, J. Poti and B. Popkin. 2017. “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.” PLOS Medicine, available on-line at: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002283

Sturm, R., Powell, L.M., and J.F. Chriqui. 2010. “Soda taxes, soft drink consumption, and children's body mass index.” Health Affairs, 2010, 29(5), 1052–1058.

The Spokesman Review, 2010. “Spin Control: Drafting in dark messed up soda tax,” by J. Camden. Accessed on April 1, 2017: http://www.spokesman.com/stories/2010/may/02/drafting-in-dark-messed-up-soda-tax/

U.S. Department of Agriculture and U. S. Department of Health and Human Services. 2010. Dietary guidelines for Americans, 2010, 7th ed. Washington, DC: U.S. Government Printing Office.

U.S. Census Bureau. 2010. Various statistics accessed at: www.census.gov

Vartanian, L.R., Schwartz, M.B, and K.D. Brownell. 2007. “Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis.” American Journal Public Health, (97), 667-675.

Wang, Y. C., S. N. Bleich, and S. L. Gortmaker. 2008. “Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices among U.S. children and adolescents, 1988-2004.” Pediatrics 121(6), 1604-1614.

Wang, E. Y. 2015. The impact of soda taxes on consumer welfare: implications of storability and taste heterogeneity. RAND Journal of Economics, 46(2), 409-441.Wang et al., 2017

34

Wang, E., F. Colantuoni and C. Rojas. 2017. “Heterogeneous behavior, obesity and storability in soft drink consumption: A dynamic demand model,” American Journal of Agricultural Economics, 99(1), 1:33.

Washington Post, 2010, “Soda taxes fizzle in wake of industry lobbying,” by Joey Peters, July 13, 2010. http://www.washingtonpost.com/wp-dyn/content/article/2010/07/13/AR2010071303494.html, accessed April 1, 2017.

World Health Organization. 2009. Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva, Switzerland: Author.

World Health Organization. 2015. Using price policies to promote healthier diets. Geneva, Switzerland: Author.

35

Appendix (for online publication)

List of cities by 3-digit zip code (and corresponding county) in and around Berkeley

940 San Mateo County: Belmont, Brisbane, Burlingame, Hillsborough, Colma, Daly City, El Granada, Half Moon Bay, La Honda, Loma Mar, West Menlo Park, Menlo Park, Atherton, Portola Valley, Millbrae, Montara, Moss Beach, Pacifica, Redwood City, Pescadero, Emerald Hills, Woodside, San Bruno, San Carlos, San Gregorio, South San Francisco Santa Clara County: Los Altos, Los Altos Hills, Moffett Field, Mountain View, Sunnyvale, Onizuka Afb

941 San Francisco County: San Francisco 943 Santa Clara County: Palo Alto, East Palo Alto, Stanford 944 San Mateo County: San Mateo, Foster City 945 Alameda County: Alameda, Fremont, Hayward, Castro Valley, Livermore, Mount Eden, Newark,

Pleasanton, Dublin, San Leandro, San Lorenzo, Sunol, Union City Napa County: American Canyon, Vallejo, Angwin, Calistoga, Napa, Oakville, Pope Valley, Rutherford, St. Helena, Deer Park, Yountville Contra Costa County: Blackhawk, Danville, Alamo, Antioch, Bethel Island, Brentwood, Byron, Discovery Bay, Canyon, Clayton, Concord, Pleasant Hill, Crockett, Diablo, El Cerrito, Rodeo, Hercules, Knightsen, Lafayette, Pacheco, Martinez, Moraga, Spanish Flat, Oakley, Orinda, Pinole, Bay Point, Pittsburg, Port Costa, San Ramon, Walnut Creek

Solano County: Benicia, Birds Landing, Fairfield, Suisun City, Rio Vista, American Canyon, Vallejo

946 Alameda County: Oakland, Piedmont, Emeryville 947 Alameda County: Berkeley, Albany, Kensington 948 Contra Costa County: North Richmond, Point Richmond, Richmond, El Sobrante, Hilltop Mall, San Pablo 949 Marin County: San Rafael, Greenbrae, Kentfield, San Rafael, Belvedere Tiburon, Belvedere,

Tiburon Marin County, Dogtown, Bolinas, Corte Madera, Dillon Beach, Fairfax, Forest Knolls, Inverness, Lagunitas, Larkspur, Marshall, Mill Valley, Novato, Nicasio, Olema, Penngrove, Point Reyes Station, Ross, San Anselmo, San Geronimo, San Quentin, Muir Beach, Sausalito, Stinson Beach, Tomales, Woodacre

Sonoma County: Bodega, Bodega Bay, Cotati, Rohnert Park, Valley Ford, Petaluma

Source: http://www.ciclt.net/sn/clt/capitolimpact/gw_state.aspx?state=ca&stfips=06&stname=California

36

Figure A.1: Evolution of Volume (All Taxed Products) for Each Potential 3-Digit-Zip-Code Control Area Berkeley (947) and 940 (Peninsula)

Berkeley (947) and 941 (San Francisco)

Berkeley (947) and 943 (Palo Alto)

Berkeley (947) and 944 (San Mateo)

Berkeley (947) and 945 (East Bay)

Berkeley (947) and 946 (Oakland)

Berkeley (947) and 948 (Richmond)

Berkeley (947) and 949 (Marin)

37

Table A.1: UPC-Store-Month DID Unweighted Price Results for Washington, Selected Brands, [# obs.]

Beverage Category: Diet Soda Priv. Label

(Store Brand) Diet Coke Diet

Pepsi Diet Dr. Pepper

Coke Zero

Coke Caff. Free

Diet Mtn. Dew

Pepsi Caff. Free

2L 0.167*** 0.158*** 0.228*** 0.134*** 0.155*** 0.166*** 0.273*** 0.273*** [24,239] [42,679] [42,694] [39,486] [35,491] [31,544] [30,013] [30,123]

12 pack 0.136*** 0.075*** 0.136*** 0.108*** 0.068*** 0.086*** 0.122*** 0.139***

[23,153] [43,119] [43,088] [41,308] [40,398] [38,574] [37,124] [35,986]

24 pack 0.183*** 0.192*** 0.188*** 0.212*** 0.164*** 0.192*** 0.194*** [17,080] [22,521] [18,579] [14,772] [9,598] [14,095] [18,487]

20 oz 0.379*** 0.418*** 0.339*** 0.289*** 0.422*** 0.511*** 0.361*** 0.454***

[19,309] [43,055] [43,149] [42,258] [40,870] [19,068] [38,776] [14,494]

1L 0.169*** 0.119*** 0.174*** 0.137*** 0.096 0.032

[27,620] [32,281] [31,659] [8,398] [5,737] [3,393]

All Sizes 0.216*** 0.198*** 0.220*** 0.179*** 0.203*** 0.214*** 0.243*** 0.239*** [94,325] [178,214] [183,111] [150,029] [137,268] [98,784] [123,401] [99,090]

Pass-Through 129% 119% 132% 107% 122% 128% 146% 143%

Table A.1, continued

Beverage Category: Regular Soda Pass-Through Priv. Label

(Store Brand) Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W All Brands

2L 0.0899*** 0.153*** 0.231*** 0.239*** 0.142*** 0.150*** 0.182*** 0.181*** 108% [36,651] [42,745] [42,734] [42,455] [42,381] [42,501] [42,043] [567,779]

12 pack 0.156*** 0.071*** 0.131*** 0.140*** 0.117*** 0.084*** 0.115*** 0.111*** 66%

[23,284] [43,117] [43,063] [42,840] [42,528] [42,903] [41,159] [581,644]

24 pack 0.234*** 0.197*** 0.197*** 0.194*** 0.156***

0.194*** 116%

[17,474] [22,638] [22,348] [20,200] [16,294] [214,230]

20 oz 0.417*** 0.419*** 0.327*** 0.344*** 0.312*** 0.419*** 0.476*** 0.383*** 229%

[20,387] [43,056] [43,150] [43,101] [43,041] [42,907] [38,281] [534,902]

1L 0.167*** 0.129*** 0.146*** 0.152*** 0.109*** 0.112*** 0.345*** 0.142*** 85%

[33,411] [33,959] [34,730] [34,207] [29,066] [21,520] [7,258] [303,239]

All Sizes 0.193*** 0.204*** 0.210*** 0.219*** 0.178*** 0.202*** 0.257*** 0.210*** [113,736] [180,351] [186,315] [184,951] [177,216] [166,125] [128,878] [2.201 MM]

Pass-Through 116% 122% 126% 131% 107% 121% 154% 126% Notes: Pooled size regressions (second-to-last row) contain size fixed effects; pooled brand regressions (second-to-last column) contain brand fixed effects; the overall pooled regression includes brand and size fixed effects. Standard errors are clustered at the store level. ***, **, and * significant at 1%, 5% or 10% (respectively).

38

Table A.2 (Panel A): UPC-Store-Month Unweighted Price Results for Berkeley, Selected Soda Products, [# obs.]

Category: Regular Soda

Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W

2L 0.044*** 0.060 -0.025 0.007 0.084 0.067 0.045

[1,726] [2,152] [2,123] [1,792] [1,934] [2,096] [1,935]

12 pack 0.009 0.152* 0.263*** 0.135 0.131 0.157* 0.099 [504] [1,340] [1,150] [963] [1,029] [1,131] [868]

20 oz 0.388 0.050 0.0456 0.170** 0.135** 0.039 0.219 [888] [2,170] [2,170] [2,157] [2,166] [2,162] [2,120]

1L 0.219 0.125*** 0.137*** 0.098*** 0.120*** 0.311*** 0.054 [588] [887] [725] [600] [628] [533] [38]

Notes: Control: San Francisco

Table A.2 (Panel B): UPC-Store-Month Unweighted Price Results for Berkeley, Other Selected Products, [# obs.]

Category: Fruit Drinks (Cranberry) Category: Fruit Juice (Nectar) Priv. Label Ocean Spray Simply Cranberry Jumex Kern’s Sun Tropics

0.233* 0.159** -0.223 0.328** -0.086 -0.025 [691] [1,782] [538] [504] [485] [210] Category: Fruit Drinks (Frozen) Category: Vegetable Juice

Priv. Label Hawaii’s Own Minute Maid Clamato Faraon V8 Splash -1.386*** -0.146 0.777 0.037 -0.126 -0.081

[147] [456] [276] [1,691] [571] [130] Category: Fruit Drinks (Canned) Category: Bottled Water

Arizona Minute Maid Rockstar Recovery Glaceau Water Glaceau Zero Vita Coco 0.049 -0.260 0.156 -0.072 -0.126 -0.069

[2,080] [268] [2,046] [2,539] [750] [1,101] Category: Fruit Drinks (Other) Pooled Results

Gatorade Tampico Capri Sun Panel A (soda) Panel B (other) Panel A+B (soda+other) 0.071 0.056 -0.322 0.108 0.025 0.091*

[2,177] [857] [3,287] [38,559] [22,856] [61,145] Notes: The overall pooled regressions (bottom right, panel B) include brand and size fixed effects. Standard errors are clustered at the store level. ***, ** , and * significant at 1%, 5% or 10% (respectively). Control: San Francisco

39

Table A.3 (Panel A): UPC-Store-Month Price Results for Berkeley, Selected Soda Products, [# obs.]

Category: Regular Soda

Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W

2L 0.064*** 0.030 0.006 -0.007 0.027 0.024 0.137**

[8,503] [10,099] [10,038] [9,502] [9,791] [10,011] [9,791]

12 pack -0.0267 -0.008 0.107 0.107 0.035 -0.014 0.088

[4,582] [9,289] [9,057] [8,556] [8,830] [9,050] [8,556]

20 oz 0.212 -0.016 -0.130 0.014 0.014 -0.007 0.153

[3,752] [10,118] [10,116] [10,053] [10,073] [10,106] [9,880]

1L -0.178 0.105 0.124 0.056 0.118 0.288*** -0.198** [5,588] [5,326] [5,023] [4,153] [4,179] [2,870] [249]

Notes: Control: Pooled across all eight 3-Digit Zip Code Areas

Table A.3 (Panel B): UPC-Store-Month Price Results for Berkeley, Other Selected Products, [# obs.] Category: Fruit Drinks (Cranberry) Category: Fruit Juice (Nectar)

Priv. Label Ocean Spray Simply Cranberry Jumex Kern’s Sun Tropics 0.263* 0.004 -0.097 0.311*** -0.305* 0.005 [2,321] [7,436] [5,169] [4,416] [4,846] [1,269]

Category: Fruit Drinks (Frozen) Category: Vegetable Juice Priv. Label Hawaii’s Own Minute Maid Clamato Faraon V8 Splash

-1.590*** -0.019 1.191 -0.116 -0.117 -0.159 [1,478] [4,279] [2,625] [9,151] [5,398] [1,025] Category: Fruit Drinks (Canned) Category: Bottled Water

Arizona Minute Maid Rockstar Recovery Glaceau Water Glaceau Zero Vita Coco 0.023 -0.188 0.789* -0.005 0.009 0.175

[9,102] [2,677] [11,010] [12,701] [2,923] [6,085] Category: Fruit Drinks (Other) Pooled Results

Gatorade Tampico Capri Sun Panel A (soda) Panel B (other) Panel A+B (soda+other) 0.124* -0.012 -0.342 0.072 0.048 0.071

[13,091] [6,283] [19,362] [217,022] [132,647] [349,669] Notes: The overall pooled regressions (bottom right, panel B) include brand and size fixed effects. Standard errors are clustered at the store level. ***, **, and * significant at 1%, 5%, or 10% (respectively). Control: Pooled across all eight 3-Digit Zip Code Areas

40

Table A.4 (Panel A): UPC-Store-Month Volume Results for Berkeley, Selected Soda Products, [# obs.]

Category: Regular Soda

Priv. Label Coke Pepsi Mtn. Dew Dr. Pepper Sprite A & W

2L 0.044 -0.158*** -0.057 -0.024 0.023 -0.075 -0.12**

[8,503] [10,099] [10,038] [9,502] [9,791] [10,011] [9,791]

12 pack -0.139 -0.213* -0.226** -0.227 -0.081 -0.162 -0.082 [4,582] [9,289] [9,057] [8,556] [8,830] [9,051] [8,556]

20 oz -0.684 0.025 0.182* -0.058 0.086 0.009 -0.067 [3,752] [10,118] [10,116] [10,053] [10,073] [10,106] [9,880]

1L -0.682** -0.359*** -0.159 -0.162 -0.021 -0.116 0.166

[5,588] [5,326] [5,023] [4,153] [4,179] [2,870] [249] Notes: Control: Pooled across all eight 3-Digit Zip Code Areas

Table A.4 (Panel B): UPC-Store-Month Volume Results for Berkeley, Other Selected Products, [# obs.] Category: Fruit Drinks (Cranberry) Category: Fruit Juice (Nectar)

Priv. Label Ocean Spray Simply Cranberry Jumex Kern’s Sun Tropics -0.002 -0.130 0.065 -0.243 0.115 0.031 [2,321] [7,436] [5,169] [4,416] [4,846] [1,269]

Category: Fruit Drinks (Frozen) Category: Vegetable Juice Priv. Label Hawaii’s Own Minute Maid Clamato Faraon V8 Splash

0.427 -0.367*** -0.292*** 0.129 -0.215 0.026 [1,478] [4,279] [2,625] [9,151] [5,398] [1,025] Category: Fruit Drinks (Canned) Category: Bottled Water

Arizona Minute Maid Rockstar Recovery Glaceau Water Glaceau Zero Vita Coco -0.178*** 0.078 -0.179 -0.127 -0.680* 0.116

[9,102] [2,677] [11,010] [12,701] [2,923] [6,085] Category: Fruit Drinks (Other) Pooled Results

Gatorade Tampico Capri Sun Panel A (soda) Panel B (other) Panel A+B (soda+other) -0.029 0.282*** -0.073 -0.051 -0.078* -0.055*

[13,091] [6,283] [19,362] [217,022] [132,647] [349,669] Notes: The overall pooled regressions (bottom right, panel B) include brand and size fixed effects. Standard errors are clustered at the store level. ***, **, and * significant at 1%, 5%, or 10% (respectively). Notes: Control: Pooled across all eight 3-Digit Zip Code Areas

41

Table A.5: Overall DID Unweighted Price Results, Washington

Dependent Variable: Region-Month Regressions UPC-Store-Month Regressions‡ Untaxed

Beverages Regular

Soda Diet Soda

All Soda Untaxed Beverages

Regular Soda

Diet Soda All Soda

Unweighted Average Price, cents/oz

[pass-through rate]

-0.032 (0.242)

N.A.

0.167*** (0.05)

[100%]

0.215 (0.149) [128%]

0.181*** (0.073) [108%]

-0.011 (0.007)

N.A.

0.167*** (0.007) [100%]

0.187*** (0.006) [112%]

0.176*** (0.006) [105%]

# Obs 82 82 82 82 7,967,168 5,240,085 3,437,822 8,677,907 ‡UPC-store-month regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.

Table A.6 (Panel A): Overall DID Unweighted Price Results, Region-Month Regressions, Berkeley

Dep. Var.: Control: Synthetic Control: all 3-digit zip areas

Untaxed Beverages

Regular Soda

All SSB Untaxed Beverages

Regular Soda

All SSB

Unweighted Average Price (¢/oz)

0.083 (0.11)

-0.129 (0.08)

-0.089 (0.111)

-0.031 (0.113)

-0.029 (0.08)

0.039 (0.10)

# Obs 48 48 48 48 48 48

Table A.6 (Panel B): Overall DID Unweighted Price Results, UPC-Store-Month Regressions, Berkeley‡

Dep. Var.: Control: San Francisco Control: all 3-digit zip areas

Untaxed Beverages

Regular Soda

All SSB Untaxed Beverages

Regular Soda

All SSB

Unweighted Average Price, cents/oz

-0.105*** (0.03)

-0.102 (0.09)

-0.047 (0.05)

-0.026 (0.02)

-0.039 (0.08)

0.052 (0.05)

# Obs 330,599 145,517 229,238 1,917,350 958,306 1,458,604 ‡UPC-store-month regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.

42

Table A.7 (Panel A): Overall DID Results, UPC-store-pre/post Regressions, Washington‡ (5 month collapsing)

Dependent Variable: Untaxed Beverages

Regular Soda

Diet Soda All Soda

Volume-Weighted Average Price, cents/oz

[pass-through rate]

-0.003 (0.015)

N.A.

0.151*** (0.009) [90%]

0.170*** (0.008) [102%]

0.158*** (0.008) [94%]

Log Volume -0.002 (0.007)

-0.033*** (0.000)

-0.041*** (0.01)

-0.036*** (0.001)

# Obs 469,048 438,270 272,017 710,287 ‡Regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.

Table A.7 (Panel B: Overall DID Results, UPC-store-pre/post Regressions, Washington‡ (18 month collapsing)

Dependent Variable: Untaxed Beverages

Regular Soda

Diet Soda All Soda

Volume-Weighted Average Price, cents/oz

[pass-through rate]

0.022 (0.015)

N.A.

0.171*** (0.009) [102%]

0.181*** (0.008) [108%]

0.174*** (0.008) [104%]

Log Volume 0.008

(0.009) -0.039***

(0.001) -0.035***

(0.01) -0.038***

(0.001) # Obs 469,048 438,270 272,017 710,287

‡Regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.

Table A.8: Overall DID Results, UPC-Store-pre/post Regressions, Berkeley‡

Dep. Var.: Control: San Francisco Untaxed

Beverages All SSB

Weighted Average Price (¢/oz)

-0.032 (0.05)

-0.013 (0.05)

Log(Volume) -0.06 (0.04)

-0.052 (0.04)

# Obs 27,791 19,530 ‡UPC-store-month regressions include zip code (3-digit), brand and size fixed effects; standard errors, in parentheses, clustered at the store level.