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 UPF, May / 40

2 Adult obesity has increased Source:WHO 2 / 40

3 Adult obesity has increased Source:WHO 2 / 40

4 Adult obesity has increased Source:WHO 2 / 40

5 Child obesity has increased Source:WHO 3 / 40

6 Child obesity has increased Source:WHO 3 / 40

7 Child obesity has increased Source:WHO 3 / 40

8 Sugar consumption is high UK teenagers consume over 3 times recommended level Source:NDNS 4 / 40

9 Source: Euromonitor 5 / 40

10 Source: Euromonitor 5 / 40

11 Drinks account for a large share of sugar particularly amongst teenagers Source:NDNS 6 / 40

12 Soft drinks account for the largest part Source:NDNS 7 / 40

13 Targeting sugar in soda looks sensible Share of added sugar from soda is high in younger consumers (a) UK (b) US Source: NDNS, NHANES 8 / 40

14 Targeting sugar in soda looks sensible also high in consumers with high total added sugar in diet (c) UK (d) US Source: NDNS, NHANES 9 / 40

15 Motivation Sugar consumption is far in excess of recommended levels across much of the developed world Eating too much sugar is associated with poor health outcomes (e.g. obesity, type II diabetes) poor long run social and economic 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 if they effectively target excess consumption 10 / 40

16 Soda Taxes Corrective taxes have long been seen as a tool to improve social welfare when consumption imposes costs on others externalities (Pigou, 1920) taxes on fuel, alcohol, tobacco, gambling are examples More recently have been advocated to reduce consumption that imposes costs on your future self (sin taxes) internalities Gruber and Koszegi, 2004; ODonoghue and Rabin, 2006; Haavio and Kotakorpi, 2011; Allcott, Mullainathan and Taubinsky, 2014,... Soda taxes are a leading example 11 / 40

17 Soda taxes Increased interest in soda taxes to reduce consumption of sugar introduced in France in 2012, Mexico in 2013, UK in 2016 four cities in California (Albany, Berkeley, Oakland, and San Francisco) Boulder, Philadelphia, Cook County in Illinois and the Navajo Nation World Health Organisation (WHO) in 2016 urged countries to tax sugary drinks to reduce sugar consumption, especially in children considerable evidence that sugar is over consumed, and that this leads to obesity, type 2 diabetes, heart disease, cancers, etc... is associated with poor mental health and poor school performance, particularly in children poor childhood nutrition thought to be an important determinant of later life health, social and economic outcomes and of presistent inequality 12 / 40

18 Our contribution Do soda taxes lead to reductions in sugar consumption in young and heavy sugar consumers? 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 us to estimate individual level preferences and importantly correlations in preference structures 13 / 40

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 14 / 40

20 Demand model 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) 15 / 40

21 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) 16 / 40

22 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 17 / 40

23 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 Choice probabilities 18 / 40

24 Identification of price coefficients 1. We treat retailer choice as exogenous consumer chooses a retailer for reasons other that their idiosyncratic preference shocks consumers face different price vectors in different retailers 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 19 / 40

25 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 / 40

26 Data We use novel data on food and drink purchases made on-the-go (collected by Kantar in the UK) around 50% of soda is purchased and consumed outside the home Individuals record all purchases from shops and vending machines at barcode level Participants belong to households in the Kantar Worldpanel records all grocery purchases made and brought into the home gives us a measure of total calories from added sugar 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 21 / 40

27 Consumer age Age group % of sample % of group that ever purchases soda Sugar from soda (g) / 40

28 Total dietary sugar Decile of distribution of share of calories from added sugar % of group that ever purchases soda Sugar from soda (g) 23 / 40

29 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% 24 / 40

30 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 25 / 40

31 Coefficient estimates - consumer specific more parameters 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 / 40

32 Marginal preference distributions Bivariate Incidental parameters correction (a) Price (b) Sugar (c) Soda 27 / 40

33 Preference variation with age (a) preferences for sugar (b) preferences for price (c) correlation 28 / 40

34 Preference variation with total dietary sugar (a) preferences for sugar (b) preferences for price (c) correlation 29 / 40

35 Outline 1. Motivation and contribution 2. Demand model 3. On-the-go data 4. Demand estimates 5. Soda tax simulation 6. Summary and conclusions 30 / 40

36 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 31 / 40

37 Simulation of soda tax Simulate impact of tax of 25p per litre (τ = 0.25) on sugary soda { p p jt = jt + τl j j Ω s p jt 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 pass-through of tax on sugary soda is 140% over-shifting driven by strategic complementarities between brands higher for 500ml bottle (150%) than 330ml cans (100%) high pass-through for bottles leads marginal consumers to switch away (often to cans) 32 / 40

38 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] 33 / 40

39 Reduction in sugar (g) from tax on sugary soda (a) by age (b) by total dietary sugar 34 / 40

40 Reduction in sugar (g) from tax on sugary soda by age and total dietary sugar youngest lowest 35 / 40

41 Compensating variation ( ) (a) by age (b) by total dietry sugar 36 / 40

42 Impact of tax on sugary soda (a) reduction in sugar (g) (b) compensating variation ( ) 37 / 40

43 Consumer welfare We want to weigh compensating variation against benefits from reduced consumption Reduction in costs from excess consumption that fall on the consumer themselves and externalities are very difficult to measure We can compute average saving from reduced sugar consumption that would be necessary to make consumers indifferent to tax e.g. per can of Coca Cola (36g sugar) 0.80 for those aged below for those in top decile of dietary sugar distribution By income 38 / 40

44 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 4% (for age< 22) to 11% (for age 51-60) smaller 39 / 40

45 Summary We simulate the effects of a tax on sugary soda we estimate consumer specific preferences we study drinks on-the-go We show a tax on sugary soda is well targeted at the 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 40 / 40

46 EXTRA SLIDES

47 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 40 / 40

48 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 40 / 40

49 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 40 / 40

50 Price variation of Coca Cola (a) 330ml can Back (b) 500ml bottle (c) Within brand price variation 40 / 40

51 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 40 / 40

52 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 40 / 40

53 Bivariate preference distributions (a) Price-soda Back (b) Price-sugar (c) Soda-sugar 40 / 40

54 (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) 40 / 40

55 Incidental parameters problem: sugar parameter (a) kernel density (b) bias by T Back (c) bias by age (d) bias by equivalized expenditure 40 / 40

56 Effects across total expenditure distribution Back 40 / 40