Individual preference heterogeneity, targeting and welfare effects of soda taxes

Transcription

1 Individual preference heterogeneity, targeting and welfare effects of soda taxes Pierre Dubois, Rachel Griffith and Martin O Connell Institute for Fiscal Studies Lisbon, October / 44

2 Motivation: high and rising obesity rates Source: OECD 2 / 44

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5 Motivation Sugar consumption is far in excess of recommended levels across much of the developed (and increasingly in the developing) world Eating too much sugar is associated with diet related disease (e.g. obesity, type II diabetes) Evidence suggest 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 5 / 44

6 Motivation Tax is advocated as policy response to lower intake of (sugary) soda World Health Organization urged countries to introduce a tax on sugary drinks to reduce consumption among young people introduced in France, Mexico, the UK, Berkeley, San Francisco, Boulder, Philadelphia, Seattle Economic rationale large future costs of high sugar consumption are not fully taken account of at point of consumption corrective tax can help off-set this internality (e.g. Gruber and Koszegi (2004), O Donoghue and Rabin (2006)) Public health community have been influential in promoting soda taxes (appealing mainly to the impact taxes can have on lowering childhood obesity) 6 / 44

7 Is targeting sugar in soda sensible? Share of added sugar from soda (a) By age (b) By total added sugar in diet 7 / 44

8 Contribution We provide evidence on how effective soda taxes are in changing sugar consumption we estimate consumer demand in the drinks market and simulate impact of tax, accounting for pass-through We ask whether soda taxes are effective at targeting young people and high sugar consumers, and what are the distributional consequences Relative to existing work, we estimate consumer specific preferences, which allows us to ask whether soda taxes are effective at targeting the right people study individual behaviour, on-the-go, where purchases are made for immediate consumption 8 / 44

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 / 44

10 Demand model overview Model demand for drinks on-the-go discrete choice model, choice of drink conditional on buying a drink individual chooses: one of several soda products, some with sugar some 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) 10 / 44

11 Demand model 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) 11 / 44

12 Consumer utility 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 12 / 44

13 Preference heterogeneity 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 13 / 44

14 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 14 / 44

15 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 15 / 44

16 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) 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 16 / 44

17 Consumer age Age group < % of sample % of group that ever purchases soda Sugar from soda (g) / 44

18 Equivalized expenditure Decile of distribution of total equivalized grocery expenditure % of decile that ever purchases soda Sugar from soda (g) Income proxy 18 / 44

19 Drinks products Product Firm Brand Variety Size Market Price g sugar share ( ) per 100ml Sodas Coca Cola 54.7% Company Coca Cola 38.1% Sugary 330ml can 6.2% Sugary 500ml bottle 11.2% Diet 330ml can 7.1% Diet 500ml bottle 13.6% Fanta 5.9% Sugary 330ml can 0.9% Sugary 500ml bottle 4.5% Diet 500ml bottle 0.5% Cherry Coke 4.3% Sugary 330ml can 0.8% Sugary 500ml bottle 2.4% Diet 500ml bottle 1.1% Oasis 6.5% Sugary 500ml bottle 5.9% Diet 500ml bottle 0.5% Pepsico 15.1% Pepsi 15.1% Sugary 330ml can 1.6% Sugary 500ml bottle 3.5% Diet 330ml can 1.9% Diet 500ml bottle 8.2% GSK 11.7% Lucozade 7.4% Sugary 380ml bottle 3.8% Sugary 500ml bottle 3.6% Ribena 4.3% Sugary 288ml carton 1.1% Sugary 500ml bottle 2.4% Diet 500ml bottle 0.9% Non-sodas Fruit juice 330ml 4.0% Flavoured milk 500ml 2.2% Outside option Water 12.3% 19 / 44

20 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 20 / 44

21 Marginal preference distributions (a) Price Bivariate (b) Sugar (c) Soda 21 / 44

22 Preference variation with age (a) Sugar preference (b) Price preference 22 / 44

23 Preference variation with equivalized expenditure (a) Sugar preference (b) Price preference Coefficients 23 / 44

24 Price elasticities 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] 24 / 44

25 (Possible) incidental parameters problem 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) 25 / 44

26 Incidental parameters problem: sugar parameter (a) kernel density (b) bias by T (c) bias by age (d) bias by equivalized expenditure 26 / 44

27 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 27 / 44

28 Supply side model 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 / 44

29 Volumetric sugary soda tax 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 29 / 44

30 Tax pass-through Pass-through of tax to (sugary) soda price averages 130% over-shifting driven by strategic complementarities pass-through of small increase in a firm s marginal costs is around 60% Coca Cola Pass-through higher for 500ml bottle (160%) than 330ml cans (100%) high pass-through for bottles leads marginal consumers to switch away (often to cans) leaving infra-marginal consumers who have strong preferences Details 30 / 44

31 Impact of tax on equilibrium prices and shares Sugary Diet Sugary Outside soda soda alternatives option Tax (pence) price (pence) [12.60, 13.80] [-2.20, -1.60] share (p.p.) [-5.75, -5.25] [3.20, 3.60] [0.55, 0.65] [1.45, 1.55] 31 / 44

32 Impact on sugar Notes: The dotted lines show the 25th percentile (where the reduction is 22g), the median (65g) and the 75th percentile (156g). 32 / 44

33 Impact on sugar By age 33 / 44

34 Impact on sugar By equivalized expenditure poorest richest 34 / 44

35 Impact on sugar By age-equivalized expenditure youngest poorest oldest richest 35 / 44

36 Compensating variation By age 36 / 44

37 Compensating variation By equivalized expenditure poorest richest 37 / 44

38 Compensating variation By age-equivalized expenditure youngest poorest oldest richest 38 / 44

39 Consumer welfare Compensating variation is a consumer welfare measure based on decision utility If consumers suffer from internalities, it will not reflect any gain to the consumer resulting from less sugar leading to lower future health costs (those not taken into account at the point of consumption) Median compensating variation per 100g of sugar reduced is around 25% higher for youngest (< 22) and oldest group (60+) compared with those aged And is broadly constant across equivalized expenditure distribution 39 / 44

40 Reduction in sugar by total dietary sugar youngest low sugar oldest high sugar 40 / 44

41 Compensating variation by total dietary sugar youngest low sugar oldest high sugar 41 / 44

42 Switching to sugar in food 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 1% (for age< 22) to 8% (for age 51-60) smaller 42 / 44

43 Summary A tax on sugary soda achieves larger reduction in sugar consumption in young than old to a lesser extent, larger reduction in low income than high Compensating variation is higher among the young and poor However, they are groups we may expect to gain most from averted internalities The tax does not targeted individuals with high sugar diets they reduce consumption by the same amount as those with low sugar diets they have higher compensating variation if they gain sufficiently more from averted internalities (if internalities are sufficiently convex in total sugar) this group might still benefit 43 / 44

44 APPENDIX

45 Price variation of Coca Cola (a) 330ml can Back (b) 500ml bottle (c) Within brand price variation 44 / 44

46 Relationship between expenditure and income Back 44 / 44

47 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 / 44

48 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 44 / 44

49 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 44 / 44

50 Bivariate preference distributions (a) Price-soda Back (b) Price-sugar (c) Soda-sugar 44 / 44

51 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 / 44

52 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 / 44

53 Pass-through Diet products Back Tax Price Pass-through ( ) ( ) (%) Coca Cola Diet Coca Cola Diet Fanta Diet Cherry Coke Diet Oasis Diet Pepsi Diet Pepsi Diet Ribena Diet / 44