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Price Nudge for Obesity(with Romana Khan & Kanishka Misra)
Why Intervene?
• Dire consequences of obesity to Individual– Increased risks of type 2 diabetes, hypertension,
cardiovascular diseases, cancer, gallbladder disease, osteoarthritis, disabilities, psychosocial problems ...Estimated 112,000 deaths every year
Externalities: Significant economic implications costing $150 billion p.a.– Medical costs: half on Medicare and Medicaid– Additional productivity loss
Vishal Singh, Stern School of Business, NYU 2
How to Intervene?
Disclosure & Education Limiting choices (zoning and prohibition) Marketing regulation (Limiting messages) Surveillance (data provision)
Taxation– “Fat Taxes” or “Junk Food Tax”– Already in place in many states– Soda tax (mean rate 5.2%) in 33 US states– A sugar based tax has been proposed
o Bans/Regulations
Vishal Singh, Stern School of Business, NYU 3
Problems with “Twinkie” Tax
o Ideological
Highly Regressive
Will it Work?
o Will it get Implemented?o Strong Industry Opposition
Will it Work?Previous Evidence
o Field Work Econometric/data problemsFocus on Sales TaxIndustry Funded
Experimental Work Lab/Cafeteria/Vending MachinesSmall non-representative samples
This Paper: Quasi Natural Experiment
Whole milk 2% milk 1% milk Skim milk$2.40
$2.50
$2.60
$2.70
$2.80
$2.90
$3.00
$2.91 $2.91 $2.91 $2.90$2.87
$2.73$2.71
$2.60
Uniform Price Non-Uniform Price
Depending on where you live and what supermarket chain you patronize, you see one of these patterns.
Milk Pricing in the US
Milk Pricing in the US
Vishal Singh, Stern School of Business, NYU 7
Non Flat PricingPrimarily Non-FlatMixedPrimarily FlatFlat PricingNo 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
DATA
1800 + supermarkets
6 Years weekly data
UPC level sales, price, promotion etc.
Counties represent approximately 50% of the population
About Tableau maps: www.tableausoftware.com/mapdata
Flat
Mixed
Non-Flat
B: Distributions of weekly milk prices for selected cities in flat and nonflat markets. Prices are dollars per gallon for whole milk(pink) and 2% milk (blue) between 2001-2006.
A: Geographic distribution of flat and non-flat price structure. In mixed states, both flat and non-flat pricing stores are present, but price structure is consistent across stores within a chain.
a) Comparison of Demographic Profile between Flat and NonFlat Stores
Flat stores Non-Flat 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 % of explained variation
accounted for by:
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
Is Pricing Structure Exogenous?
Med. Income Poverty % Children % Elderly % College
NonFlat
Flat
$48K
$48K
10.2
10.1
7%
7%
12%
12%
29%
30%
Pop. Density $ Hourly Wage Retailers in 5 mi. Discounters in 10 mi. % White
NonFlat
Flat
3.6K
3.1K
$19
$17
7.0
7.1 3.6
3.7 77%
78%
C: Distribution of ‘percent of stores with flat prices’ within a chain, and within a chain-state. The majority of chains are either always flat (=1) or alwaysnon-flat (=0), with fewer chains adopting mixed policies.
B: Variance Decomposition: Dependent variable is the price ratio of whole to 2% milk. The table shows the percent of explained variation accounted forby demographics, competitive factors, milk marketing order fixed effects and chain fixed effects.
Does it Change Behavior?
Low Income High Income
Flat Non Flat Flat Non Flat
10% 11%
10%11%
27%
38%
53%
40%
25% 27%
19%19%
30%
33%
26%21%
A: Large Change in Whole Milk Share in Low IncomeSubstituition to 2% Milk
Skim
1%
2%
Whole
Low Income High Income
Flat Non Flat Flat Non Flat
Diet Soda Shares Across Stores
Median 26%
Median 45%
B: Distribution of Diet Soda ShareNo Differences in Flat/Non Flat
Flat
Non Flat
No Unobserved Differences in Taste
Correlation of Shares & SEC
9.0 9.5 10.0 10.5 11.0 11.5
Log Income Capita
0% 10% 20% 30% 40% 50% 60% 70% 80%
% College
0%
20%
40%
60%
80%
Share Whole Milk
0%
10%
20%
30%
40%
50%
60%
70%
Share Diet Soda
C: Correlation of Socio-economics & Markets Shares Pricing structure only impacts milk (more so at low income/education)
Key Message: (1) Impact of pricing structure higher on low SEC, (2) Shares of soda suggest no systematic taste differences between flat and non-flat markets.
NonFlat Flat
Large Response to Small Price Changes
Recommendations
• Small price gaps that are reflected at the point of purchase– Mitigates the regressive nature of taxes
$1.05
$.95
$2.05
$1.95
NOTE: Approximately half of the total grocery sales are on promotion
2002 2003 2004 2005 2006
0
200
400
600
800
1000
Unit Sold
(A) Weekly Sales at Store # 204353
2002 2003 2004 2005 2006
$1.00
$2.00
$3.00
$4.00
$5.00
Price
(A) Weekly Prices at Store # 204353
Figure 6: Pricing and Sales Patterns of Carbonated Beverages in the US. (A) The graphs are for the highest selling UPCs for Coke & Pepsi (Reg. 12-pack Cans).
(B) Across all stores/weeks in the data (500K + obs.), promotions account for over 90% of the total sales for both pro..
COKEPEP..
40%
60%
80%
100%
(B) % of Total Sales on Promotion (Distribution across 1800 stores)
54%
48%
52%
46%
COKE
PEPSI
Altering the Food Subsidies
