How well targeted are soda taxes?

Transcription

1 How well targeted are soda taxes? Pierre Dubois, Rachel Griffith and Martin O Connell Institute for Fiscal Studies Imperial, February / 28

2 Motivation Sugar consumption is far in excess of recommended levels across much of the developed world Detail Eating too much sugar is associated with externalities diet related disease (e.g. obesity, type II diabetes) too much sugar early in life is associated with poorer long run outcomes Consumption of soda has been highlighted as a major driver of high levels of dietary sugar, particularly in young Corrective tax can help off-set these externalities 2 / 28

3 Targeting sugar in soda looks sensible Share of added sugar from soda, by age (a) UK (b) US 3 / 28

4 Targeting sugar in soda looks sensible Share of added sugar from soda, by total added sugar in diet (c) UK (d) US 4 / 28

5 Our contribution How effective soda taxes are in changing sugar consumption of the targeted population we estimate consumer demand in the UK drinks market and simulate impact of tax, accounting for pass-through Relative to existing work, we study on-the-go purchases for immediate consumption consumption of soda out of the home is very common ( 50%) data include purchases of teenagers longitudinal data allows estimation of individual level preferences relaxing common restrictions on preference structures 5 / 28

6 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 6 / 28

7 Demand model Detail Model demand for drinks on-the-go discrete choice model, choice of drink conditional on buying a drink individual chooses: one of several branded soda products, sugar and diet non-soda drinks with sugar non-soda drinks without sugar (mainly water) show robustness to upper stage model of choice to buy a drink, chocolate bar, or other (non-sugary) snack Estimate individual specific demands exploit long panel, allow preferences for price, sugar and soda to be individual specific other preferences vary by demographic group (gender-age) 7 / 28

8 Identification 1. We treat region and retailer choice as exogenous consumer chooses a retailer for reasons other that their idiosyncratic preference shocks (conditional on retailer-soda, retailer-non-soda and retailer-outside option effects) decision leads consumers to face different price vectors 2. We exploit non-linear pricing across container-sizes we control for time (demographic) varying shocks to brand demand and use differential price variation over time within brands Variation 8 / 28

9 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 9 / 28

10 Data Around 50% of soda is purchased and consumed outside the home We use novel data on food and drink purchases made on-the-go (collected by Kantar in the UK) Individuals record, at barcode level, all purchases from stores and vending machines Participants belong to households in the Kantar Worldpanel records all grocery purchases made and brought into the home means we have measures of overall household spending and diet for individuals in our sample Sample of 5,400 individuals from June 2009-October 2012 observe each person making a food/drink purchase on at least 25 days and 81 on average 10 / 28

11 Consumer age Age group % of sample % of group that ever purchases soda Sugar from soda (g) / 28

12 Total dietary sugar Decile of distribution of share of calories from added sugar % of group that ever purchases soda Sugar from soda (g) 12 / 28

13 Drinks products Product Firm Brand Variety Size Market Price g sugar share ( ) per 100ml Sodas Coca Cola 51.1% Company Coca Cola 36.1% Regular 330ml can 7.4% Regular 500ml bottle 8.8% Diet 330ml can 8.4% Diet 500ml bottle 11.5% Fanta 5.7% Regular 330ml can 1.1% Regular 500ml bottle 4.1% Diet 500ml bottle 0.5% Cherry Coke 3.5% Regular 330ml can 0.9% Regular 500ml bottle 2.0% Diet 500ml bottle 0.7% Oasis 5.9% Regular 500ml bottle 5.4% Diet 500ml bottle 0.5% Pepsico 17.1% Pepsi 17.1% Regular 330ml can 1.8% Regular 500ml bottle 4.0% Diet 330ml can 2.3% Diet 500ml bottle 9.0% GSK 11.8% Lucozade 7.6% Regular 380ml bottle 4.4% Regular 500ml bottle 3.1% Ribena 4.3% Regular 288ml carton 1.2% Regular 500ml bottle 2.2% Diet 500ml bottle 0.9% Non-sodas Fruit juice 330ml 3.6% Flavoured milk 500ml 2.3% Outside option Water 14.0% 13 / 28

14 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 14 / 28

15 Marginal preference distributions Bivariate Coefficients (a) Price (b) Sugar (c) Soda 15 / 28

16 Preference variation with age (a) preferences for sugar (b) preferences for price (c) preference correlation 16 / 28

17 Preference variation with total dietary sugar (a) preferences for sugar (b) preferences for price (c) preference correlation 17 / 28

18 Price elasticities Incidental parameters correction Effect of 1% price increase on: Own cross demand for: Total demand sugary diet sugary demand soda soda alternatives Coca Cola [-2.65, -2.55] [0.25, 0.26] [0.08, 0.08] [0.05, 0.06] [0.01, 0.01] Coca Cola [-1.88, -1.66] [0.34, 0.38] [0.11, 0.12] [0.16, 0.19] [-0.07, -0.07] Coca Cola Diet [-2.52, -2.42] [0.08, 0.08] [0.29, 0.30] [0.01, 0.01] [0.01, 0.01] Coca Cola Diet [-1.58, -1.39] [0.10, 0.12] [0.34, 0.38] [0.04, 0.06] [-0.05, -0.05] Soda [-0.35, -0.33] [0.68, 0.82] [-0.28, -0.26] Sugary soda [-0.77, -0.70] [0.47, 0.52] [0.57, 0.67] [-0.16, -0.15] 18 / 28

19 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 19 / 28

20 Simulation of soda tax We specify supply behaviour as Nash-Bertrand Supply model pass-through of increase in marginal costs is around 60% Coca Cola Simulate a tax on sugary soda of 25p per litre pass-through is 140% on sugar soda over-shifting driven by strategic complementarities higher for 500ml bottle (150%) than 330ml cans (100%) high pass-through for bottles leads marginal consumers to switch away (often to cans) Details 20 / 28

21 Impact of tax on equilibrium prices and shares Sugary Diet Sugary Outside soda soda alternatives option Tax (pence) price (pence) [14.05, 15.90] [-3.66, -2.58] share (p.p.) [-5.48, -4.96] [2.92, 3.30] [0.53, 0.62] [1.45, 1.59] 21 / 28

22 Impact of tax on sugary soda (a) by age (b) by total dietary sugar 22 / 28

23 Impact of tax on sugary soda by age and total dietary sugar lowest youngest 23 / 28

24 Compensating variation (a) by age (b) by total dietry sugar 24 / 28

25 Compensating variation youngest lowest 25 / 28

26 Impact of tax on sugary soda (a) impact of tax on sugar (b) compensating variation 26 / 28

27 Consumer welfare We want to weight compensating variation against benefits from reduced consumption Size of externalities, and costs from excess consumption that fall on the consumer themselves are very difficult to measure We can compute average saving that would be necessary to make consumers indifferent to tax e.g. per can of Coca Cola 0.80 for those aged below for those in top decile of dietary sugar distribution By income Robustness 27 / 28

28 Summary We estimate consumer specific preferences on-the-go for drinks Approach captures arbitrary relationship between tax predictions and individual attributes We show a tax on sugary soda is well targeted at young because of the correlation between sugar preferences and price sensitive for young consumers But less effective at targeting older consumers with high levels of total dietary sugar because of the lack of correlation between sugar preferences and price sensitive for older heavy sugar consumers 28 / 28

29 EXTRA SLIDES

30 Share of total calories from added sugar (c) US Back (d) UK 65% of people are above 10% calories from added sugar (recommended max in US) 70% are above 10% 90% are above 5% (recommended max in UK) Source: NHANES (US), LCFS (UK) 28 / 28

31 Demand model Back Consumers, i = 1,..., N, choose which drink to purchase (while on-the-go) We observe each consumer on many choice occasions, t = 1,..., T Products include j = 0,..., J sugary sodas diet sodas alternative drinks such as fruit juice or flavoured milk the outside option (bottled water) i Each product j belongs to a brand b(j) Each consumer i belongs to demographic (age-sex) group d(i) 28 / 28

32 Consumer utility Back Utility consumer i gets on choice occasion t from choosing product j 0 is U ijt = α i p jrt + β i s j + γ i w j + δ d(i) z j + ξ d(i)b(j)t + ζ d(i)b(j)r + ɛ ijt ɛ ijt i.i.d. type I extreme value shock p jrt price of product j at time t in region r s j = 1 if sugary; w j = 1 if soda z j pack size effects ξ d(i)b(j)t : demographic-brand-time shock ζ d(i)b(j)r : demographic-brand-retailer shock Utility from choosing outside option is U i0t = ξ d(i)0t + ζ d(i)0r + ɛ i0t 28 / 28

33 Preference heterogeneity Back Price (α i ), sugar (β i ) and soda (γ i ) preferences are consumer specific We treat α = (α 1,..., α N ), β = (β 1,..., β N ) and γ = (γ 1,..., γ N ) as parameters to estimate use large T dimension of data to recover estimates of (α, β, γ) and large N dimension to construct nonparametric estimate of f (α i, β i, γ i ) Key advantage is we can allow for any arbitrary relationship between (α i, β i, γ i ) and any individual level attribute Contrasts with random coefficient models which restrict relationship when integrating across random effects 28 / 28

34 Consumer distastes Back Large T dimension of data allows us to distinguish between: Individuals that: never buy soda when buying drinks, sometimes buy soda when buying drinks, always buy soda And individuals that, when buying soda: sometimes buy sugary and sometimes diet variety always buy sugary variety always buy diet variety We incorporate into choice probabilities Details 28 / 28

35 Choice probabilities Back For a consumer we observe buying sugary soda, diet soda and a non-soda drink She chooses between the set of sodas and non-sodas, Ωi = Ω a Ωn And has a finite sugar and soda preference For this consumer the choice probability for j 0 is: P it (j) = exp(α i p jrt + β i s j + γ i w j + η ijrt ) exp(ξ d(i)0t + ζ d(i)0r ) + k Ω i exp(α i p jrt + β i s j + γ i w j + η ikrt ) where η ijrt = δ d(i) z j + ξ d(i)b(j)t + ζ d(i)b(j)r 28 / 28

36 Choice probabilities Back For a consumer we observe buying sugary soda, a non-soda drink but no diet soda She chooses between the set of sugary sodas and non-sodas, Ω i = Ω s Ωn And has a finite soda preference, but negatively infinite preference for diet For this consumer the choice probability for j 0 is: P it (j) = exp(α i p jrt + γ i w j + η ijrt ) exp(ξ d(i)0t + ζ d(i)0r ) + k Ω i exp(α i p jrt + γ i w j + η ikrt ) where η ijrt = δ d(i) z j + ξ d(i)b(j)t + ζ d(i)b(j)r 28 / 28

37 Price variation of Coca Cola (a) 330ml can Back (b) 500ml bottle (c) Within brand price variation 28 / 28

38 Relationship between expenditure and income Back 28 / 28

39 Coefficient estimates - consumer specific Back Moments of distribution of consumer specific preferences Variable Estimate Standard error Price Mean Standard deviation Skewness Kurtosis Soda Mean Standard deviation Skewness Kurtosis Sugar Mean Standard deviation Skewness Kurtosis Price-Soda Covariance Price-Sugar Covariance Soda-Sugar Covariance / 28

40 Coefficient estimates - females Back Consumer group specific preferences Estimate Standard Estimate Standard Variable error error Female - <40 Female ml carton ml bottle ml bottle Fanta Cherry Coke Oasis Pepsi Lucozade Ribena Fruit juice Flavoured milk Time-demographic-brand effects Retailer-demographic-brand effects Yes Yes 28 / 28

41 Coefficient estimates - males Back Consumer group specific preferences Estimate Standard Estimate Standard Variable error error Male - <40 Male ml carton ml bottle ml bottle Fanta Cherry Coke Oasis Pepsi Lucozade Ribena Fruit juice Flavoured milk Time-demographic-brand effects Retailer-demographic-brand effects Yes Yes 28 / 28

42 Bivariate preference distributions (a) Price-soda Back (b) Price-sugar (c) Soda-sugar 28 / 28

43 (Possible) incidental parameters problem Back Our maximum likelihood parameters may suffer from an incidental parameters problem Even if both N and T, if N and T grow at the same rate our estimator will be asymptotically biased We explore severity of bias using split sample jackknife procedure suggested in Dhaene and Jochmans (2015) 28 / 28

44 Incidental parameters problem: sugar parameter (a) kernel density (b) bias by T (c) bias by age (d) bias by equivalized expenditure 28 / 28

45 Supply side model Back Soda manufacturers, f {1,..., F } set prices in Nash-Bertrand game first-order conditions for firm f is, for all j F f, q jt (p t ) + k F f (p kt c kt ) q kt(p t ) p jt = 0 Where: qjt (p t ) = i P it(j) is market demand cjt is marginal cost Assuming observed prices are equilibrium of this game, can invert first-order conditions to obtain marginal costs 28 / 28

46 Volumetric sugary soda tax Back Simulate impact of tax of 25p per litre (τ = 0.25) on sugary soda p jt = { p jt + τl j p jt j Ω s j Ω d Ωn Vector of equilibrium producer prices, p jt, for all firms, satisfy q jt (p t ) + k F f ( p kt c kt ) q kt(p t ) p jt = 0 j F f 28 / 28

47 Pass-through of marginal cost shock Back Pass-through (%) Coca Cola Coca Cola Coca Cola Diet Coca Cola Diet Fanta Fanta Fanta Diet Cherry Coke Cherry Coke Cherry Coke Diet Oasis Oasis Diet / 28

48 Pass-through Sugary products Back Tax Price Pass-through ( ) ( ) (%) Coca Cola Coca Cola Fanta Fanta Cherry Coke Cherry Coke Oasis Pepsi Pepsi Lucozade Lucozade Ribena Ribena / 28

49 Effects across total expenditure distribution Back 28 / 28

50 Switching to sugar in food Back A possible consequence of a soda tax is people switch to sugar in food We consider a two-stage choice model stage one: choose from a set of confectionery products, or a non sugary snack, or to opt for a drink stage two: if drink is chosen in stage one, decide which one to select Full preferences heterogeneity we estimate in stage two influences stage one Simplifying assumption is idiosyncratic drinks shocks not known at stage one For soda consumers, implies sugar reductions is 4% (for age< 22) to 11% (for age 51-60) smaller 28 / 28