1

Price Nudge for Obesity

Romana Khan1, Kanishka Misra

2, Vishal Singh

34

ABSTRACT

BACKGROUND

Of the many proposals to reverse the obesity trend, the most contentious is the use of price-based

interventions such as the “fat tax”. Previous investigations of the efficacy of such initiatives in altering

consumption behavior have yielded contradictory findings.

METHODS

We use six years of point-of-sale scanner data for milk from a sample of over 1,700 supermarkets across

the US to investigate the potential of small price incentives in inducing substitution to healthier

alternatives. We exploit a peculiar pricing pattern for milk in the US whereby prices in some geographical

regions are uniform across Whole, 2%, 1% and Skim milk; while in other regions they are decreasing with

the fat content level. The prevailing price structure is determined at a regional level and is independent of

local demand conditions. This exogenous variation in price structure provides a natural quasi-experiment to

analyze the impact of small price differences on substitution across fat content. Detailed demographics are

used to evaluate price sensitivity and substitution patterns for different socio-economic groups.

RESULTS

When prices are uniform across fat content, there is a large discrepancy in the market shares for the high-

calorie whole milk between low- and high-income households (53% vs. 26%). In markets where milk

prices are non-uniform, a gallon of 2% milk is on average 14 cents (5%) cheaper than whole milk. This

small price difference results in a significant drop in market share for whole milk, particularly for the low-

income consumers. With a price gap of just 15%, the market share of whole milk in low-income

neighborhoods drops by more than half to 25%. As a benchmark, the market share of regular (vs. diet)

carbonated soft drinks for low-income consumers continues to be higher (75% vs. 55% for high-income) in

all markets.

CONCLUSIONS

Small price differences, if reflected in shelf prices at the point-of-purchase, can act as nudges to alter

consumer behavior and induce substitution to healthier options, particularly amongst the at-risk low-income

households.

1 Ozyegin University, Istanbul, Turkey

2 London Business School, London, England.

3 Stern School of Business, New York University, New York, NY.

4 To whom correspondence should be addressed: vsingh@stern.nyu.edu

2

Obesity in the US is growing at an alarming rate with two-third of adults and one in three

children overweight or obese (1) (2). Obesity has been linked to an increased risk of heart

disease, diabetes, and excess mortality (3) (4) leading to forecasts of lowered future life

expectancies (5) (6). Furthermore, obesity and associated negative health outcomes show

a marked socioeconomic gradient, with significantly higher rates among racial/ethnic

minorities and the poor (7) (8) (9). Although a myriad of biological and environmental

factors mediate the observed escalation and disparities in obesity rates, growing evidence

points to a chronic imbalance in energy expenditure and dietary intakes as one of the

leading causes (10) (11) (12). Since obesity also imposes significant externalities

through productivity losses and healthcare costs (13) (14), the issue has received attention

from healthcare professionals, social scientists, and public officials. Recommendations to

curb obesity rates have ranged from modification of food labels, access to healthier food

in low-income neighborhoods, regulations on food marketing to children, and broad

educational programs promoting healthier lifestyles (3) (10) (15).

Given the complexity of the problem, reversing the obesity trend is likely to involve

multifaceted strategies at several levels (3) (16). Among all the proposed interventions,

the most contentious is the use of the so-called “fat tax” to discourage consumption of

unhealthy products (17) (18). A major advantage of such point-of-purchase intervention

is the cost effectiveness in reaching a large population base (19) (20) (16). Proponents of

the measure also point to successes achieved in combating tobacco use, and the potential

to use tax revenues to offset other obesity-related costs (21), (22). However, the measure

faces stiff opposition from the food industry5. Ideological opposition has also come on

5 For instance, the beverage industry spent millions of dollars on lobbying and advertising against recent

attempts to impose taxes on carbonated and sweetened beverages (52).

3

the grounds of personal responsibility versus the role of government, as well as the

regressive nature of the policy (23) (24) (25). Furthermore, a number of academic studies

have raised skepticism on the efficacy of such fiscal interventions, with calls for

additional empirical research to guide policy (26) (27) (28).

A major obstacle to evaluating tax-based policy instruments is the lack of sufficient

evidence on how price interventions may alter consumption behavior. There have been

three general approaches to provide guidelines on the issue. The first involves using price

elasticity estimates for a class of products (e.g. sugared beverages) to simulate changes in

demand under hypothetical taxes (22) (29). Given that most food items tend to be

relatively price inelastic (30), the conclusion from this approach is that small taxes are

not sufficient to alter behavior (31)6. The second approach involves directly linking the

current level of state taxes (primarily for carbonated soft drinks) to health outcomes, and

has found limited evidence of any association (32) (33) (34). Finally, the third approach

involves manipulating prices in a controlled experimental setting (e.g. lab or cafeteria) to

create incentives to switch to healthier options. In contrast to field studies, results from

controlled experiments show that relative price reductions on healthier options are highly

effective in shifting demand toward them (35) (36) (37).

To understand this apparent discrepancy in findings, a few aspects of current tax policies

on soft drinks and snacks merit mention. First, the current levels of state taxes are

significantly lower than the price manipulations in controlled experiments (35) (36) (37).

Second, the current taxes are levied on the entire product class (e.g. carbonated

beverages) rather than on specific items, giving consumers limited incentives to substitute

6 Note that the conclusions drawn from these studies are sensitive to the type of econometric model used by

the researcher, and are often based on predicting the impact of a hypothetical price increase outside the

range of observed data.

4

within the category (e.g. regular to diet soda). Finally, 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 (38).

The objective of this article is to demonstrate the potential of price-based interventions in

altering consumption behavior. Unlike previous experimental studies that are conducted

with small non-representative populations, this article utilizes a large scanner database

from over 1,700 supermarkets across the US. We exploit a peculiar pattern of milk

pricing in the US whereby depending on the geographical region and retail chain, prices

across Whole, 2%, 1%, and Skim are either uniform or increasing with fat content. The

price structure at a particular retail outlet (whether or not to charge uniform prices across

fat) is independent of the local demand conditions. This exogenous variation (i.e. not

correlated with underlying consumer preferences) provides a quasi-experimental setup to

identify price-induced substitution patterns across fat content without imposing any

modeling assumptions. We show that at equal prices across alternatives, low-SEC

households consume a significantly larger proportion of high-calorie products compared

with the high-SEC households. However, with small price differences, low-income

households readily switch to healthier option. Our results suggest that at the point-of-

purchase, small price differences can act as nudges (39) to induce substitution to healthier

options, particularly amongst the at-risk low-income households.

5

METHODS

DATA DESCRIPTION

The cornerstone of our empirical strategy relies on a comprehensive scanner data

provided by IRI (40). 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 1,708 stores belonging to 101 supermarket chains. There are 447 counties represented

in the data, the population of which accounts for over 50% of the total US population.

The customer base of each store is profiled with an extensive set of demographic

variables using zip code data from US Census. The demographics include variables such

population density, age distribution, income, education, and ethnicity.

Milk Category

Milk is a ubiquitous commodity dominated by retailer private labels that accounts for

over 75% of the market share. It is sold in four major fat content levels: whole (3.5% fat),

2 %, 1%, and skim (less than 0.5% fat). Our analysis uses store level sales and price data

of private label plain milk in the 128 oz plastic jug. The four selected products represent

67% of the total volume share of plain milk. We restrict the analysis to a single size and

brand to avoid aggregation biases and facilitate comparison across stores and

demographic profiles. Robustness of results by including all products in the category are

reported the appendix.

Pricing of Milk in the US

Dairy pricing in the US comprises a complicated combination of market forces and

various government regulations. Approximately two-third of the total milk in the US are

regulated through federal milk marketing orders (FMMO) that were established after the

6

great depression. FMMO’s set the minimum farm prices of milk that processors and

manufacturers must pay depending on one of the 10 geographic regions (CRS policy

reports). In addition, certain large milk producing states such as California (that accounts

for approximately 20% of the milk produced in the US) operate under their own state

regulations rather than federal rules. Although retail prices are largely unregulated, they

too display a unique pricing pattern based on geographical regions. In particular, retail

prices in certain markets are uniform across Whole, 2%, 1% and Skim milk; while they

are declining with fat content in other regions7. Note that declining prices with fat reflect

the underlying wholesale costs of milk - since butterfat is an expensive component, the

cost of milk increases with its fat content.

We conduct several analyses to understand the underlying factors that account for

observed differences in pricing patterns across stores. In Figure1 we plot the geographical

distribution of retail chains that charge uniform or non-uniform prices, and observe

regional patterns somewhat in line with FMMO regional jurisdictions. In Table 1 we

provide a comparison of the average demographic profiles served by uniform and non-

uniform stores and find no significant differences in the characteristics of the customer

base. Finally, we regress the price ratio of whole to 2% milk (i.e., the price premium of

whole milk over 2%) on demographics that capture the local demand characteristics,

measures of the competitive environment, regional fixed effects for FMMO’s, and chain-

specific fixed effects. Looking at the variance decomposition, it is evident that demand

conditions facing the store have limited explanatory power, with chain and marketing

order fixed effects explaining almost all of the variance. Note that if the decision to

7 In the article we use “uniform” and “flat” pricing interchangeably to refer to retail chains that charge same

prices across fat content.

7

charge uniform or non-uniform prices were based on the underlying demand conditions,

we would expect retail chains to vary their strategy across stores based on the

demographic or competitive situation facing the stores. We find no evidence for this in

our data8.

STATISTICAL ANALYSIS

This exogenous variation in price structure provides a quasi-experimental setup to

examine the impact of small price changes on substitution patterns across fat content.

We formulate this as a regression model where the focal outcome variable is the market

share of whole milk in each store. We consider three alternative models, with the logit

transformation of whole milk share ( ) as the dependent variable:

. The first

model is specified as

Here the independent variables include the prices of whole ( , 2%( , 1% (

and skim ( milk, and demographic (D) controls. The parameter estimates are used to

compute average price (own and cross) elasticity, which provides a unit free measure of

consumer price sensitivity.

Second, since the response to a relative price premium (or discount) could vary in a non-

linear fashion, we estimate a second model where we use the price ratio of whole to 2%

milk (the closest substitute) to create a sequence of dummy variables. These variables

indicate the level of price premium for whole milk over 2%: No price premium ( ), up

to 5%( ), 5% to 10%( ), 10% to 15%( ), and greater than 15% ( ). The

equation for this regression model is:

8 Additional robustness checks are provided in the appendix.

8

The model above captures the idea that consumers may respond only when the price

differences exceed a certain threshold. Finally, since the response to price differences is

likely to vary with demographic characteristics, we estimate a modification of equation 2

by interacting price with income. This is of particular interest given the higher prevalence

of obesity among lower income groups (1). We create a second set of dummy variables

based on income quintiles and interact these with the set of price dummy variables (as in

equation (2)).

To demonstrate robustness of our findings, we conduct further analyses (shown in the

Appendix) to replicate our results with (a) a non-parametric matching approach, (b)

quantile regressions, (c) allowing for endogenous prices and (d) a utility based aggregate

discrete choice model.

RESULTS

PRELIMINARY ANALYSIS

We begin with summary results to compare the market shares for whole, 2%, 1%, and

skim milk under uniform and non-uniform pricing. As a benchmark, we also provide

market shares for diet vs. regular soft drinks, which are computed by aggregating the total

sales in ounces for diet and regular soda (across all brands/sizes/flavors) at each store. As

seen in Figure 2, the pricing structure has a significant impact on market shares. Under

non-uniform pricing, the price of whole milk is on average 14 cents (5%) higher than the

price of 2% milk. This price difference is accompanied by a significant drop in market

9

share of whole milk from 37% in uniform pricing stores to 30% in non-uniform pricing

stores (p < 0.05). Most of the substitution is towards the closest (in terms of fat content)

substitute 2% milk that sees a significant increase in market share from 29% in uniform

pricing stores to 37% in non-uniform stores. We find no significant difference in the

shares for 1% and skim milk across the two formats. Note that there is no difference (not

statistically significant) in market shares for diet vs. regular soda across the two pricing

structures. This suggests that (1) consumers do not substitute high calorie milk for high

calorie soda in non uniform pricing stores, and (2) there are no significant differences in

preference for lower calorie products between the customers of uniform and non-uniform

pricing stores.

Impact by Income Groups

As noted in the introduction, obesity rates in the US are significantly higher among lower

socio-economic status households. In Figure 3 we show the county level correlations

between market shares of low-fat milk, diet soda, obesity rates, and income. Data on

obesity rates are derived from the Behavioral Risk Surveillance Survey (BRFSS)

conducted by the Center of Disease Control (CDC)9. Consistent with the previous

literature, we find high correlation between obesity rates and socio-economic

characteristics. Although a variety of factors may account for these differences, previous

literature has noted lower relative prices of (unhealthier) energy-dense food and the lack

of access to healthy alternatives (such as fresh fruits and vegetables) in lower income

neighborhoods as important reasons for the observed relationship. Our results (albeit in a

narrower context of just two product categories) suggest that even at equal prices and

9 The figure uses data from 2006 to match with the time frame of the market share data.

10

same availability, lower SEC households consume a significantly higher proportion of

high-calorie products10

.

In Figure 4, we show the differences in market share for milk and soda between flat and

non-flat stores, for the highest and lowest income quintiles. Although the market shares

for whole milk drop for both income groups under non-flat pricing, the change is

significantly larger in the low-income quintile. Given higher obesity rates among lower

income consumers, the higher response amongst low income consumers to small price

differences may have significant policy implications. For soda, low income consumers

have a higher share of regular (higher calorie) soda relative to high income consumers.

Since there are no price differences between diet vs. regular soda, we observe no

difference in market shares between flat and non-flat stores. This suggests that consumers

across all income groups do not seem to substitute across categories for calories.

REGRESSION RESULTS

Next, we turn to regression results based on the three models discussed above. The first

two columns in Table 2 show the 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

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 -3.04, implying that a 1% increase

in price for whole milk will reduce its share by about 3%. The closest substitute for

whole milk is 2% milk, with a cross-price elasticity of 1.14, while 1% (cross price

10

Product assortment in all stores in our data contains low-fat options for both milk and soda.

11

elasticity 0.40) and skim milk (not significant) are weaker substitutes. This is consistent

with the summary analysis in Figure 2, 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 for model 2 are provided in the third and forth columns of Table 2. The

estimates show that as the premium of whole milk over 2% 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 approximately 10%

(about 27c per gallon).

The results of the third model are displayed by plotting the implied market shares at

different levels of the price ratio for low and high income consumers (See Appendix for

the table of results and discussion). Under uniform prices, the discrepancy between

income groups is large - whole milk share for the lower income (52%) is more than

double the higher income group (25%). As the whole milk premium increases, the share

for both income groups falls, but the response is stronger for the lower income quartile.

At a premium of 5-10%, the market share for low income falls from 52% to 36%, while

for high income it falls from 25% to 17%. The discrepancy between income groups

continues to fall as the premium increases, and is statistically insignificant with a

premium of more than 15%.

12

DISCUSSION

The market share changes (Figures 2, 4, 5) and elasticity estimates (Table 2) presented

here are higher in magnitude compared to those reported in previous research for milk

(25) and food products in general (30). For example, previous research based on dairy

data concludes that even a 50% tax would have limited impact in altering consumption

(14). Note however two key differences in our analysis: first, our identification relies on

cross-sectional variation in price structures as opposed to time series movements in price.

Although milk prices are volatile, relative prices across options (e.g. whole vs. 2%) at a

given store do not change11

. Second, we focus 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 (41). These differences are important because

evaluating the feasibility of price based instruments such as taxes or subsidies critically

depends on elasticity estimates.

Our results suggest that influencing choice through price mechanisms can be achieved

with relatively small price differences, particularly amongst the low-income population.

The findings are in line with a growing body of experimental evidence that shows that

small, often unnoticed nudges (39) such as trimming the portion size (42) (43) or minor

changes in product accessibility (44) (45) can lead to significant changes in consumption

behavior. Furthermore, evidence suggests that relatively small changes in energy intakes

can accumulate and lead to substantial changes in prevalence of obesity (10) (12) (46). In

the current context of milk (which is among the top three leading sources of saturated fat

11

In the appendix we show this more precisely by parsing out the identification from times series vs. cross

sectional variations in price and shares.

13

in the American diet (47)), we find that the majority of shifts in demand toward the

healthier option can achieved with a price gap of just 5-10%. Whether similar substitution

effects can be achieved between say, diet and regular soda, or baked and fried potato

chips, remains an open question. Identification of direct substitution across fat or diet

attributes in field data is difficult since products within a brand (e.g. Coke and Diet Coke)

typically have the same price points and coordinated promotions. Recent experimental

work on changing the relative prices of regular versus diet soda in hospital cafeteria

indicates that price manipulations can be effective (48).

This paper suggests a selective taxation mechanism by altering the relative prices of

healthy and unhealthy products in a way that those changes are reflected in shelf prices at

the point of purchase. Tax policies designed to alter the relative prices within narrowly

defined food products can also mitigate regressive impacts by incentivizing consumers to

switch to relatively cheaper and healthier options12

. Similar proposals of excise taxes on

only targeted products (e.g. a 1 cent per ounce tax on beverages with added caloric

sweeteners) have been made by health policy advocates (18), and were recently under

consideration in the state of New York (49). An example of price based initiatives from

the business sector is Walmart's recent announcement of making healthy choices more

affordable and eliminating the price premium for 'better-for-you' products (50). At a

broader level, the issue relates to imbalances in the current agricultural policies of

subsidizing certain crops and sectors of the food industry (51) (52). Given the magnitude

of the problem, reversing the obesity trend is likely to involve multifaceted efforts from

individuals, public policy officials, and the food industry.

12

In the appendix, we estimate use a utility based demand model and compute welfare implications of

various tax and subsidy options in the milk category.

14

In closing we note several caveats to the study. As noted above, our evidence of

behavioral changes due to small price differences is from a single product category. Taste

preferences and substitution patterns between high-calorie and healthier options may be

quite different in other food products. In addition, unlike milk where the cost for the least

healthy option (whole milk) is the highest, healthier options (e.g. organic) in other food

products may be more costly to produce. Finally, data from a single category does not

allow us to understand consumption across categories. Although our use of soda

consumption as a benchmark seems to suggest otherwise, we cannot rule out that

substitution to the healthier option in the targeted category may not be compensated by

over-consumption of other unhealthy foods. Despite these shortcomings, our study

provides strong empirical support to the previously reported findings from controlled lab

experiments (35) (36) (37) on the efficacy of price interventions in altering consumption

behavior.

15

Figure 1: Geographical Distribution of Pricing Structure for Milk, with locations of

Federal Milk Marketing Orders (FMMO).

Non Flat Pricing

Primarily Non-Flat

Mixed

Primarily Flat

Flat Pricing

No Data Available

Southeast FMMO

Pennsylvania: Large

milk producer. State

regulations.

Uniform/Non-Uniform price

structure is consistent across

stores within a chain, even in

mixed states.

Upper Midwest FMMO:

Wisconsin is 2nd largest producer

Central FMMO

Northeast FMMO

MidEast

FMMO

California: Largest

milk producer.

Not part of a federal

order. State regulation.

16

Figure 2: Market shares and prices for milk (compared to market share for soda) in

Uniform and Non-Uniform pricing stores. p-values represent the significance level for a

t-test to test equal share in Uniform and Non-Uniform Stores (n.s stands for not

significantly different at 10% level.)

37.1%

30.3%

15.3%

17.2%

34.8%

29.0%

36.8%

15.5%

18.7%

35.3%

$2.95 $2.95 $2.95$2.92

$2.82

$2.68 $2.66

$2.55

$1.00

$1.50

$2.00

$2.50

$3.00

10%

15%

20%

25%

30%

35%

40%

45%

50%

Whole Milk Share (p<0.05)

2% Milk Share (p<0.05)

1% Milk Share (n.s.)

Skim Milk Share (p<0.05)

Diet Soda Share

(n.s.)

Pri

ce p

er

gallon

Avera

ge M

ark

et

Sh

are

Uniform Share (n=627)

NonUniform Share(n=1081)

17

Figure 3: Correlation between county market shares, obesity rates, and demographics.

Obesity data for county is obtained from the BRFSS survey conducted by the Center of

Disease Control (CDC). Low-fat milk is the sum of Skim and 1%. In the figure, College-

Dummy (=1) indicates counties where < 30% of the population have a college degree. All

correlations reported in the table are statistically significant at .05.

Low-Fat Milk Share Diet-Soda Share County Obesity Median Income Percent College

Low-Fat Milk Share 1.00

Diet-Soda Share 0.84 1.00

County Obesity -0.45 -0.43 1.00

Median Income 0.50 0.54 -0.68 1.00

Percent College 0.53 0.51 -0.65 0.84 1.00

18

Figure 4: Market shares for milk and soda in stores with Flat or Non-Flat pricing of milk.

High- and Low-incomes refer to the top and bottom income quintiles respectively. For

milk, low-fat is sum of shares for Skim and 1% milk.

40%

53%

21%26%

38%

27%

33%30%

22% 20%

46% 45%

0%

25%

50%

75%

100%

Non-Flat (n=231)

Flat (n=112)

Non-Flat (n=220)

Flat (n=121)

Low Income High Income

Market Shares for Milk by Income

Whole Milk Share 2% Milk Share Low Fat Share

74% 75%

56% 55%

26% 25%

44% 45%

0%

25%

50%

75%

100%

Non-Flat (n=231)

Flat (n=112)

Non-Flat (n=220)

Flat (n=121)

Low Income High Income

Market Shares for Soda by Income

Regular Soda Share Diet Soda Share

19

Figure 5: Whole milk market share by income group, by level of whole milk price

premium over 2% milk. High- and Low-incomes refer to the top and bottom per capita

income quintiles respectively. Estimates are based on regression results in table S6.

Vertical bars show 95% confidence intervals.

52%

43%

36%

31%

25%25%

20%

17%

19%

15%

0%

10%

20%

30%

40%

50%

60%

Flat Upto 5% 5% to 10% 10% to 15% More than 15%

Whole

mil

k m

arket s

hare

Percentage price premium of whole milk over 2% milk

Implied Whole Milk Market Shares by Income Group

Low Income

High Income

20

Table 1: Factors accounting for the variation in price structure across stores. (a)

Comparison of demographic profiles between Uniform and non-Uniform stores. (b)

Regression of the price ratio of whole to 2% milk on demographics, competition, milk

marketing order fixed effects and chain fixed effects; with variance decomposition

showing the explained variation accounted for by each factor.

a) Comparison of Demographic Profile between Uniform and NonUniform Stores

Mean Std Dev Mean Std Dev p-value

Low income 18% 38% 21% 41% 0.08

High income 19% 39% 20% 40% 0.60

% Poverty 2% 1% 2% 1% 0.22

% Children 4% 1% 4% 1% 0.62

% College 39% 49% 41% 49% 0.58

% White 78% 19% 77% 19% 0.49

% Elderly 12% 4% 12% 5% 0.32

Population density 0.12 0.31 0.13 0.18 0.52

(b) (1) Regression of (Price Whole/ Price 2%) milk, and (2) Variance Decomposition

(1) (2)

Estimate Std Error

Intercept 1.0393 (0.006)

Median Income -0.0017 (0.002) 0.06%

% HH Kids -0.0003 (0.001) 0.00%

% College -0.0005 (0.002) 0.01%

% White -0.0014 (0.001) 0.09%

Population Density -0.0003 (0.001) 0.00%

Wage 0.0028 (0.002) 0.14%

All retailers within 5 miles -0.0002 (0.001) 0.00%

Discount retailers within 10 miles -0.0021 (0.001) 0.18%

Marketing Order Fixed Effects Included 15.44%

Chain Fixed Effects Included 84.07%

R square 0.658

Uniform stores Non-Uniform stores

% of explained variation accounted

for:

21

Table 2: Parameter estimates for main regression models to establish that whole milk

share does respond to changes in prices. Model 1 includes the estimated own and cross

price elasticity estimates for whole milk. Model 2 includes the estimated whole milk

share with the 95% confidence intervals. Standard errors in parentheses. ** represents significant at 5% and * represents significant at 10%.

Dependent variable: ln(Whole milk share/1-Whole milk share)

Model 1 Model 2

Price parameter Parameter Estimates

Implied Elasticity of

Whole Milk Parameter Estimates

Implied whole milk

share [95% C.I.]

Prices charged

Whole milk price -1.61 (0.11)** -3.14 (0.22)**

2% milk price 1.20 (0.21)** 1.14 (0.20)**

1% price 0.95 (0.15)** 0.40 (0.06)**

Skim price -0.08 (0.13) -0.04 (0.06)

Whole milk price premium

Flat -0.59 (0.03)** 36% [35%, 37%]

Upto 5% -0.86 (0.03)** 30% [28%, 31%]

5% to 10% -1.20 (0.03)** 23% [22%, 24%]

10% to 15% -1.27 (0.05)** 22% [20%, 24%]

More than 15% -1.60 (0.09)** 17% [14%, 19%]

Controls: Income, Education, Race, Age, and Density Income, Education, Race, Age, and Density

R-square 0.77 0.76

Number of observations 1,708 1,708

22

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