Will a Fat Tax Work?
Romana Khan
Booth School of Business, University of Chicago, Chicago, IL
Kanishka Misra
London Business School, London, England
Vishal Singh
Stern School of Business, New York University, New York, NY
1
Will a Fat Tax Work?
Romana Khan1, Kanishka Misra2, Vishal Singh34
ABSTRACT
BACKGROUND
Of the many proposals to counter the obesity epidemic, the most contentious is the use of the “fat tax”.
Previous investigations of the efficacy of price-based initiatives in altering consumption behavior have
yielded contradictory findings.
METHODS
We use six years of milk sales data from 1,500 stores to investigate whether price incentives induce
substitution to healthier alternatives. Retail prices of milk are either uniform across fat content level, or
non-uniform - decreasing with fat content. The prevailing price structure is determined at the chain level,
and is independent of local demand conditions. This exogenous variation provides a natural quasiexperiment to analyze the impact of small price differences on substitution across fat content.
RESULTS
When prices are uniform, whole milk has the highest market share at 36.4%. Under non-uniform prices, 2%
milk is on average 14 cents cheaper than whole milk, and whole milk share falls to 29.7%. Under uniform
prices, whole milk share for the lowest income quartile exceeds the highest income quartile by 17
percentage points. As the whole milk premium over lower-fat alternatives increases, whole milk share for
both income groups falls, but the response is stronger for low income consumers. The discrepancy in
market shares between income groups disappears with a whole milk premium of 15-20%.
CONCLUSIONS
We find that within-category demand for milk is highly elastic suggesting that taxes, if reflected in shelf
prices, can serve as an effective mechanism to shift choices toward healthier options, particularly amongst
lower income consumers.
1
Booth School of Business, University of Chicago, Chicago, IL.
London Business School, London, England.
3
Stern School of Business, New York University, New York, NY.
4
To whom correspondence should be addressed: [email protected]
2
2
Obesity has reached epidemic proportions in the US, two-third of adults and one in three
children are overweight or obese (1) (2). Obesity has been linked to an increased risk of
conditions such as heart disease and diabetes (3), and is estimated to cause 112,000
deaths every year (4). In addition to compromising individual health, it imposes
significant externalities through productivity losses and healthcare costs. Estimates put
obesity-related medical expenditures as high as $147 billion per year, half of which are
paid by taxpayers in the form of Medicare and Medicaid (5). Estimates on the economic
costs related to productivity losses due to obesity are even higher. A recent report in the
Lancet estimates a loss of 1.7 to 3 million productive person-years in working US adults,
representing economic costs of $390-580 billion (6).
Given the individual and societal costs associated with obesity, the issue has received
attention from healthcare professionals, social scientists, and public officials. Its global
impact prompted the U.N. to highlight it as a risk factor in a high level meeting to address
the prevention and control of non-communicable diseases (7). Recommendations to curb
obesity rates have ranged from modification of food labels to educational programs
promoting healthier lifestyles (3) (8) (9). Among these interventions, the most
contentious is the use of the so-called “fat tax” to discourage consumption of unhealthy
products (10) (11). Proponents of the measure point to successes achieved in combating
tobacco use, and the potential to use tax revenues to offset other obesity-related costs
(12), (13). Ideological opposition has come on the grounds of personal responsibility
versus the role of government, as well as the regressive nature of the policy (14) (15)
(16). Recent attempts to impose taxes on carbonated and sweetened beverages faced stiff
opposition from the beverage industry that spent millions of dollars on lobbying and
3
advertising against the proposed taxes (17). For instance, in response to plans for a tax
on sugared beverages, Coca-Cola threatened to suspend investments planned in France
(18). Furthermore, a number of recent articles reviewing the existing empirical evidence
have raised skepticism on the efficacy of such fiscal interventions, with calls for
additional empirical research to guide policy (19) (20) (21).
A major obstacle to evaluating the impact of financial incentives is the lack of sufficient
evidence on how tax-based policy instruments may alter consumption behavior. There
have been three general approaches to provide guidelines on the likely impact of a fattax. The first involves manipulating prices in a controlled experimental setting to create
incentives to switch to healthier options. Results from both lab (22) and controlled field
experiments (23) (24) show that relative price reductions on healthier options are highly
effective in shifting demand toward them. Although useful, these studies are conducted in
highly controlled settings with potentially non-representative populations. The second
approach involves using secondary data and price elasticity estimates for a class of
products (e.g. sugared beverages) to simulate changes in demand under hypothetical
taxes (13) (25). Given that at the category level most food items tend to be relatively
price inelastic (26), the conclusion from this approach is that small taxes are not
sufficient to alter behavior (27). The findings from this approach are likely to be sensitive
to the type of econometric model used by the researcher, and are often based on
predicting the impact of hypothetical price increases outside the range of observed data.
Finally, a number of studies linking state level taxes (primarily for carbonated soft
drinks) directly to health outcomes and have found limited evidence of any association
(28) (29) (30). To understand this apparent discrepancy in findings, two aspects of
4
current tax policies on soft drinks and snacks merit mention. First, the taxes are levied on
the entire product class rather than on specific items, giving consumers limited incentives
to substitute within the category (e.g. regular to diet soda). Second, taxes are usually in
the form of post-purchase sales taxes, rather than reflected in shelf prices. Evidence
suggests that a majority of consumers do not take sales taxes into account when making
purchase decisions (31).
In this article we provide a large-scale empirical field study of the impact of price
differences across fat content on consumer demand for milk. Several aspects of the data
used in this study make it useful to address the general issue of price incentives (via
taxes) in altering consumption behavior. Milk is an ubiquitous commodity purchased by
virtually all US households. It is also among the top three leading sources of saturated fat
in the American diet (32). More interestingly, retail prices of milk in the US follow a
peculiar pattern whereby prices across Whole, 2%, 1%, and Skim are either uniform or
increasing with fat content. The price structure at a particular retail outlet is determined at
the chain level and is independent of the local demand conditions. This exogenous
variation (i.e. not correlated with underlying consumer preferences) provides an ideal
natural quasi-experimental setup to identify price induced substitution patterns across fat
content directly from the data, as opposed to imposing a model and simulating predicted
changes in demand. Finally, the broad spectrum of demographic profiles served by the
stores in the database allows us to measure price responsiveness for specific demographic
groups, and to predict the likely impact of taxes in inducing substitution to healthy
products for the most at-risk populations.
5
METHODS
STORE LEVEL SALES AND DEMOGRAPHIC DATA
Our analysis uses store level scanner data on plain milk sales provided by IRI (33). The
data covers a period of six years, from 2001 to 2006. We observe weekly sales, price, and
promotion information for each UPC. There are a total of 1,567 reporting stores from 101
chains, operating in 47 markets across 39 states. There are 416 counties represented in the
data, the population of which accounts for 47% of the total US population. The customer
base of each store is profiled with an extensive set of demographic variables which fall
under four categories: age, income, urban/suburban, and ethnicity. We collected
additional data to characterize local competition and cost factors (See Appendix for
details and summary statistics).
MILK CATEGORY DATA
Our analysis uses annual store level sales and price data of private label plain milk in the
128 oz plastic jug at the four major fat content levels: whole (3.5% fat), 2 %, 1% , and
skim (less than 0.5% fat). These four products represent 67% of the total volume share of
plain milk. The ubiquity and high market penetration of private label plain milk facilitates
comparison across a large number of stores and demographic profiles.
An important feature of the data is that the relative prices of milk across fat content vary
extensively across stores. In approximately one-third of stores, prices are uniform across
all fat content levels. Prices at the remaining stores span an array of non-uniform
structures which share two key features: (1) Whole milk is the most expensive type and
(2) Prices are decreasing with fat content. Whether prices are uniform or non-uniform, is
6
determined at the chain level and is independent of the local demand conditions (See
Appendix). This exogenous variation in price structure provides a natural quasiexperimental setup to compare the differences in shares across pricing structure.
STATISTICAL ANALYSIS
The focal outcome variable is the market share of whole milk in each store-year (see
Appendix for distribution). In the regression analyses, we use the logit transformation of
whole milk share (
) as the dependent variable:
. In the first model, the
predictor variables include the prices of whole, 2%, 1% and skim milk, and demographic
controls. The parameter estimates are used to compute estimates of average price (own
and cross) elasticity, which provides a unit free measure of consumer price sensitivity.
Response to a relative price premium (or discount) could vary with the level of discount
in a non-linear fashion. For example, consumers may respond to price only when the
price differences exceed a particular threshold level. To address this, we estimate a
second model where we use the price ratio of whole to 2% milk (the closest substitute) as
a measure of the premium for whole milk. To identify the potential non-linearities, we
discretize the price ratio using a sequence of dummy variables to indicate when the price
ratio is between: (1.01 and 1.05); (1.05 and 1.10); (1.10 and 1.15); (1.15 and 1.20); and
greater than 1.20. The base, where the price ratio equals 1, represents the situation where
prices of whole and 2% milk are equal.
Response to the premium is also likely to vary with demographic characteristics. Given
the higher prevalence of obesity among lower income groups (1), we investigate whether
the price incentive is equally effective across income levels. For income level, we create
7
a second set of dummy variables to indicate the top, middle-two, and bottom quartiles of
per capita income. The third model uses interactions between the set of price ratio and
income dummy variables to capture how response to the price premium varies across
income groups. We also include interactions between the price ratio and other
demographic variables.
To demonstrate robustness of our findings, we conduct further analysis (shown in the
Appendix) to replicate our results with a non-parametric matching approach, quantile
regressions, and allowing for endogenous prices.
RESULTS
MARKET SHARES AND PRICES, BY PRICE STRUCTURE
Figure 1 plots the average prices and market shares under uniform and non-uniform
prices in our data. When prices are uniform, whole milk has the highest market share at
36.4%. Under non-uniform prices, where 2% milk is on average 14 cents cheaper than
whole milk, the market share of whole milk falls to 29.7%. The majority of movement in
market share is to 2% milk, which has the highest market share under non-uniform prices.
While aggregate differences in market share indicate a high level of responsiveness to
price, they do not control for important factors such as demographics.
REGRESSION RESULTS
Table 1 shows the first model results from regressing whole milk market share on the
price of whole milk and its substitutes: 2%, 1% and skim milk, while controlling for the
demographic characteristics surrounding each store. The price coefficients indicate that
an increase in the price of whole milk decreases the share of whole milk, while increases
8
in the prices of its lower fat substitutes result in higher shares for whole milk. Evaluated
at the mean, the own price elasticity for whole milk is -2.73, implying that a 1% increase
in price for whole milk will reduce its share by 2.73%. The closest substitute for whole
milk is 2% milk, with a cross-price elasticity of 1.86, while 1% and skim milk are weaker
substitutes, with cross price elasticities of 0.74 and 0.70, respectively. This is consistent
with the summary analysis in Figure 1, where the majority of movement in market share
of whole milk under non-uniform pricing is to the 2% option. The estimates of the
demographic variables are consistent with our expectations.
The results of the second and third model are best understood by plotting the implied
market shares at different levels of the price ratio (See Appendix for the table of results
and discussion). Figure 2 plots the model-based average market shares of whole milk as
the price ratio between whole milk and 2% milk increases. The plot shows that as the
premium of whole milk increases, the market share of whole milk falls. However the
response is highly non-linear, with a decreasing marginal impact of increases in the price
premium. The majority of the shift in market share away from whole milk is achieved
with a price difference of just 5-10%.
Figure 3 plots the regression-based market shares for whole milk for the top and bottom
income quartiles at different levels of the whole milk premium. Under uniform prices, the
discrepancy between income groups is large - whole milk share for the lower income
exceeds the higher income group by 17 percentage points. As the whole milk premium
increases, the share for both income groups falls, but the response is stronger for the
lower income quartile, driven by higher price sensitivity for this group. At a premium of
5-10%, the market share for low income falls from 43% to 29%, while for high income it
9
falls from 26% to 18%. The discrepancy between income groups continues to fall as the
premium increases, and disappears with a premium of 15-20%.
DISCUSSION
This study shows that price incentives are highly effective in inducing consumers to
switch to lower fat options.
The key finding is that influencing choice through price
mechanisms can be achieved with relatively small price differences, with the majority of
shifts in demand achieved with premiums of just 5-10%. The results also provide strong
evidence that policies based on price incentives can be particularly useful in shifting the
purchases of lower income consumers, who are most vulnerable to obesity.
The market share changes (Figures 1, 2 and 3) and elasticity estimates (Table 1)
presented here are higher in magnitude compared to those reported in previous research
for milk (16) and food products in general (26). Note however that our analysis is focused
on within category elasticity (i.e. substitution between products in the category) which
tends to be significantly higher than price elasticity at the category level (34). In general,
identification of direct substitution across fat or diet attributes is difficult since products
within a brand (e.g. Coke and Diet Coke) typically have the same price points with
coordinated short-term promotions. This is important because evaluating the feasibility of
price based instruments such as taxes or subsidies critically depends on elasticity
estimates. For example, previous research based on dairy data concludes that even a 50%
tax would have limited impact in altering consumption (14). The unique advantage of our
10
data is the cross-sectional variation in pricing structure observed across retailers which
allows us to empirically identify the substitution across fat content.
The results presented here suggest a selective taxation mechanism to alter the relative
prices of healthy and unhealthy products within a category (e.g. diet versus regular soda,
baked versus fried chips). Excise taxes on targeted products (e.g 1 cent per ounce on
beverages with added caloric sweeteners) have been proposed by health policy advocates
(18), and were recently under consideration in the state of New York (35). Tax policies
designed to alter the relative prices of healthy versus unhealthy products can also mitigate
regressive impacts by incentivizing consumers to switch to healthier subsidized options.
Our results suggest that relatively small price differences (5-10% in the case of milk) can
induce substantial shifts in demand, particularly amongst the more price sensitive lower
income consumers. However, the differences in relative prices need to be reflected in
shelf prices at the point of purchase. Our findings also lend credence to the potential
effectiveness of Walmart's recent announcement to join Michelle Obama's anti-obesity
program, by making healthy choices more affordable and eliminating the price premium
for 'better-for-you' products (items containing less sodium, sugar and fats) (36).
There are of course several caveats to the study. First, our analysis is focused on a single
product category. In particular, substitution between whole and low-fat milk may be
different from substitution between diet and regular soda, or baked and fried potato chips.
An advantage of fluid milk is that the wholesale cost for the least healthy option (whole
milk) is the highest (See Appendix), which supports the implementation of the suggested
pricing strategy. Second, using data from a single category does not allow us to
understand consumption across categories. For instance, substitution to the healthier
11
option in the targeted category may be compensated for by over-consumption of high
calorie products in other food categories. Third, we note that our data does not separate
consumers in the USDA's WIC program which provides government support for low
income mothers, infants and children (up to 5 years of age) who are at nutritional risk.
Consumers in this program are price insensitive; therefore, excluding them from our
analysis would only make our results stronger. We believe that the WIC program will
play a critical role for the successful implementation of a fat-tax, as it ensures that those
at nutritional risk do not pay higher prices.
Despite these shortcomings, our study
provides strong empirical support to the previously reported findings from controlled lab
experiments, that relative price reductions on healthier options are highly effecting in
shifting demand toward them (22) (23) (24).
12
Figure 1. Market share and price by milk type, under uniform and non-uniform price
structures.
50%
$3.00
$2.91
$2.91
$2.91
$2.90
$2.87
45%
$2.90
$2.80
Average Market Share
36.4%
$2.71
$2.70
36.3%
$2.60
35%
30%
$2.60
29.6%
29.7%
$2.50
$2.40
25%
Price per gallon
$2.73
40%
$2.30
20%
17.7%
18.6%
16.2% 15.5%
15%
$2.20
$2.10
10%
$2.00
Whole milk
2% milk
Uniform Share
Uniform Price
1% milk
Skim milk
NonUniform Share
Non-Uniform Price
13
Figure 2. Whole milk market share, by level of whole milk price premium over 2%
milk. Estimates are based on regression results in table S5. Vertical bars show 95%
confidence intervals.
35%
34%
Whole milk market share
30%
29%
24%
24%
23%
19%
19%
18%
14%
0-1% (Uniform)
1-5%
5-10%
10-15%
15-20%
>20%
Percentage price premium of whole milk over 2% milk
14
Figure 3. Whole milk market share by income group, by level of whole milk price
premium over 2% milk. Estimates are based on regression results in table S6. Vertical
bars show 95% confidence intervals.
45%
43%
Low Income
High Income
40%
37%
Whole milk market share
35%
30%
29%
27%
25%
26%
21%
23%
20%
20%
21%
17%
18%
19%
15%
10%
0-1% (Uniform)
1-5%
5-10%
10-15%
15-20%
>20%
Percentage price premium of whole milk over 2% milk
15
Table 1. Response of whole milk market share to price, and implied own and cross price
elasticities. OLS estimates with standard errors. * indicates significant at 1% level.
Dependent variable: ln(Whole share/1-Whole share)
Intercept
Parameter
Std. Error
-1.695 *
0.047
Whole milk price
2% milk price
1% price
Skim price
-1.352
0.959
0.382
0.372
*
*
*
*
0.046
0.085
0.060
0.052
Per capita income
% Age<5
% Age>55
Population density
% White
-0.249
0.043
0.123
0.026
-0.412
*
*
*
*
*
0.008
0.010
0.009
0.008
0.009
Adjusted r-square
Number of observations
Elasticity of Whole Milk
wrt price of:
Estimate Std. Error
-2.73
1.86
0.74
0.70
0.093
0.166
0.117
0.098
0.534
6835
16
References
1. Ogden CL, Carroll D. Prevalence of overweight, obesity and extreme obesity among
adults: United States, Trends 1976-1980 through 2007-2008. National Center for Health
Statistics 2010.
2. —. Prevalence of obesity among children and adolescents: United States, Trends 19631965 through 2007-2008. National Center for Health Statistics 2010.
3. U.S. Department of Health and Human Sevices. The Surgeon General's Vision for a
Healthy and Fit Nation. Office of the Surgeon General 2010.
4. Flegal KM, Graubard B, Williamson D, Gail M. Excess deaths associated with
underweight, overweight, and obesity. Journal of the American Medical Association
2005; 293: 1861-7.
5. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending
attributable to obesity: payer-and service-specific estimates. Health Affairs 2009; 28:
822-31.
6. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic
burden of the projected obesity trends in the USA and the UK. Lancet 2011; 378: 815-25.
7. Political Declaration of the High-Level Meeting of the General Assembly on the
Prevention and Control of Non-communicable Diseases. United Nations General
Assembly. September 16, 2011 ( Accessed October 2, 2011 at
http://www.un.org/ga/search/view_doc.asp?symbol=A%2F66%2FL.1&Lang=E)
8. Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: Where do we
go from here? Science 2003; 299: 853-5.
9. Battle EK, Brownell KD. Confronting a rising tide of eating disorders and obesity:
treatment vs. prevention and policy. Addictive Behaviors 1996; 21:755-65.
10. Brownell KD, Get slim with higher taxes. New York Times. December 15, 1994:
A29.
11. Waist banned: Does a tax on junk food make sense? The Economist. July 30, 2009.
12. Jacobsen M, Brownell KD. Small taxes on soft drinks and snack foods to promote
health. American Journal of Public Health 2000; 90:854-7.
13. Brownell KD, Farley T, Willet WC et al. The public health and economic benefits of
taxing sugar-sweetened beverages. New England Journal of Medicine 2009; 361: 15991605.
17
14. Epstein RA. Let the Shoemaker Stick to His Last: A defense of the 'old' public health.
Perspectives in Biology and Medicine 2003; 46: S138-59.
15. McGranahan L, Schanzenbach D. Who would be affected by soda taxes? Essays on
Issues, Federal Reserve Bank of Chicago March 2011, 284.
16. Chouinard HC, Davis DE, LaFrance JT, Perloff JM. Fat taxes: big money for small
change. Forum for Health Economics and Policy 2007; 10: Article 2.
17. Bauerlein V, McKay B. Soda tax uncaps a fight. Wall St Journal. May 23, 2010.
18. Boxell J, Rappeport A. Coke in French 'sugar' tax U-turn. Financial Times.
September 8, 2011.
19. Thow AM, Jan S, Leeder S, Swinburn B. The effect of fiscal policy on diet, obesity
and chronic disease: a systematic review. Bulletin of the World Health Organization
2010; 88: 609-14.
20. Powell LM, Chaloupka F. Food prices and obesity: evidence and policy implications
for taxes and subsidies. Millbank Quarterly 2009; 87: 229-57.
21. Cash S, Lacanilao R. Taxing food to improve health: economic evidence and
arguements. Agricultural and Resource Economics Review 2007; 36: 174-82.
22. Epstein LH, Dearing KK, Roba LG, Finkelstein EA. The influence of taxes and
subsidies on energy purchased in an experimental purchasing study. Pscychological
Science 2010; 21: 406-14.
23. French S. Pricing effects on food choices. Journal of Nutrition 2003; 133: 841S-843S.
24. French SA, Jeffrey RW, Story M et al. Pricing and promotion effects on low-fat
vending snack purchases: the CHIPS study. American Journal of Public Health 2001; 91:
112-117.
25. Finkelstein EA, Zhen C, Nonnemaker J, Todd JE. Impact of targeted beverage taxes
on higher- and lower-income households. Archives of Internal Medicine 2010; 170:
2028-32.
26. Andreyeva T, Long MW, Brownell KD. The impact of food prices on consumption: a
systematic review of research on the price elasticity of demand for food. American
Journal of Public Health 2010; 100: 216-22.
27. Kuchler F, Tegene A, Harris JM. Taxing Snack Foods: What to expect for diet and
tax revenues. Current Issues in Economics of Food Markets, USDA Economic Research
Service 2004; Agriculture Information Bulletin No. 747-08.
18
28. Powell LM, Chriqui JF, Chaloupka FJ. Associations between state-level soda taxes
and adolescent body mass index. Journal of Adolescent Health 2009; 45: S57-S63.
29. Sturm R, Powell LM, Chriqui JF, Chaloupka FJ. Soda taxes, soft drink consumption,
and children's body mass index. Health Affairs 2010; 29:1052-1058.
30. Fletcher J, Frisvold D, Tefft N. The effects of soft drink taxes on child adolescent
consumption and weight outcomes.Journal of Public Economics 2010; 94: 967-974.
31. Chetty R, Looney A, Kroft K. Salience and taxation: theory and evidence. American
Economic Review 2009; 99: 1145–77.
32. Dietary Guidelines for Americans, 2010, 7th Edition. U.S. Department of Agriculture
and U.S. Department of Health and Human Affairs. 2010.
33. Bronnenberg B. Kruger M, Mela C. Database paper - the IRI marketing data set.
Marketing Science 2008; 27: 745-8.
34. Bijmolt T, vanHeerde H, Pieters R. New empirical generalizations on the
determinants of price elasticity. Journal of Marketing Research 2005; 42: 141-56.
35. Berger J. New strategy for soda tax gives diet drinks a break. New York Times. May
19, 2010.
36. Stolberg SG. Walmart shifts strategy to promote healthy foods. New York Times.
January 20, 2011.
19