Topics for Today

Topics for Today Benefits of dramatic new products Radically declining prices

The history of lighting Digital prices revisited

Radically increased assortment The Long Tail Consumer surplus in detail

Just Google It…..

Milestones in the History of Light

Evolution

Open fire

Fat-burning lampSesame oil 1750 BC

Tallow candles40.3 cents per 1000 L-H 1800

Kerosene lamp4.0 cents 1855

Gas lighting5.0 cents 1875

Edison lighting9.2 1883.6 cents 1920

Compact fluorescent .12 cents 1992

LED lighting.04 cents 2004

Real Price of Lighting

Fuel-based:Light 1: Kerosene, Gas, & Electricity.

Light 2: Light 1 early, then electricity after 1940.

True price: Characteristics based. Price per 1,000 Lumens.

W. Nordhaus

Leads to Much Heavier Usage

Spending can rise before falling, but

consumption eventually grows

dramatically.

Measurements of growth in real

wages have been biased low. Just due to lighting: 7 percent higher -

$275 billion in year 1992 compared to

1800.

Nordhaus says total bias in measurements is much higher: New goods make us much richer

What about price + assortment?

Slides with logo courtesy of Erik Brynjolfsson, M.I.T.

Lower Prices on the Internet Theory:

Increased competition (Bakos 1997) Increased operational efficiency

“The average book may sit on the shelf of a store for six months or a year before it is bought. The cost of this inventory in a chain of hundreds of stores is huge. Amazon can keep just one or two copies in its warehouse — and still make the title available to the whole country — and restock as quickly as customers buy books.” (Saul Hansel in the New York Times)

Evidence: Book and CD prices on Internet 6-16% lower than prices

in physical stores (Brynjolfsson and Smith 2000)


“The Internet is providing access for people who just can’t find the book they are looking for in a store.”

Nora Rawlinson, editor Publishers Weekly, in Investors Business Daily.


Locating and Delivery Backlist Books

Barnes and Noble Total time: 59.5 minutes Total delay: 8 days Total expense: $37.45

BN.com Total time: 2.5

minutes Total delay: 3 days Total expense: $31.99


Product Variety Comparison

Wal-Mart stocks six times as many SKUs online versus in superstore


Signals of Benefit “The Internet is making backlist books more

accessible for readers…The Internet has really stimulated sales” Frank Urbanowski, MIT Press, noting 12% increase in

backlist sales ’97 & ‘98 Amazon advertising slogan “World’s Largest Selection”

…however no systematic measure

Goal: Measure consumer value of increase product selection and convenience


Selected New Goods Literature Hicks (1942): Compensating Variation Hausman (1981): Closed Form Solution to

Hicksian Demand Hausman (1997a): CV for New Goods using

“virtual price” Hausman and Leonard (2001): Separate

variety result (availability of new goods) and price effect (changes in prices of existing goods)


16% price drop => $150 million welfare gain Welfare from increased variety 6 times larger than from

lower prices

Compare Consumer Surplus from Price Drop and New Good

P

Q

P

Qobscure

p0 p1

q0 q1

p0

q1 q0

p1

This gain can be much bigger.


New Goods Applications Brynjolfsson (1995): IT Investments Hausman (1997a): Telephone messaging services

and Cellular Services Hausman (1997b): Apple Cinnamon Cheerios Nevo (2002): Specialty breakfast cereals Goolsbee and Petrin (2001): Direct Broadcast

Satellite Petrin (2001): Automobiles


Model Following Hausman Leonard (2001)

CV=e(pco,p*,u1)-e(pc1,p,u1) Note that pco=pc1

CV=e(pc,p*,u1)-e(pc,p,u1) Log-Linear Demand (Brynjolfsson 1995,

Hausman 1997a, Hausman 1997b, Hausman and Leonard 2001) X(p,y)=Apy


Model, cont. Hicksian CS for price change from p* to p

Assume income effect = 0

yyxpxpyCV

)1/(1

)1(110

* )(1

1

111xp

CV


Economic Estimates Aggregate elasticity of demand P*Q of new product Application area: Books

Relatively “mature” Internet market Can compare to existing research on

gains from lower prices (Brynjolfsson and Smith 2000)


Wholesale Elasticity Under A1-3, aggregate retail elasticity = wholesale

elasticity. Publishers control pricing, so can use Lerner Index:

Publishers place gross margins between 58-64% => elasticity between –1.56 and –1.72 AAP ~ 58%, MIT Press ~ 63%, Technical Publisher ~ 64%,

Trade Publisher ~ 60% Brynjolfsson, Dick, and Smith (2002); Goolsbee and

Chevalier (2002) have consistent (or lower) estimates

1

p

mcp


Revenue From Obscure Titles

0

1

2

3

4

5

6

7

8

9

10

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000

Rank

Sale

s

What proportion of sales at Amazon.com are from obscure books? Use rank, assume Pareto relationship between rank and sales: Q= a*

Rankb


Proportion of Sales in Obscure


How Much Consumer Surplus? Comparing Amazon to off-line retailers (e.g. B&N

Superstore): The total increase in Consumer Surplus from access to

Amazon’s greater selection was about $1.03 billion in the year 2000

Vs. about $150 million gain from 16% lower prices

Implication: Amazon’s benefits to consumers via selection dwarf the benefits from lower prices


We can simulate this.

Extensions Time effects

Increase as more consumers gain access to Internet and comfort with ordering online.

Only one market CDs, movies, electronics, shareware, eBay, radio

programming, markets for advice, labor markets (Monster.com), dating services, …

Only one channel Impact on sales in physical stores…?


Conclusions and Implications

The major consumer benefit of Internet retailing is access to a broader variety of new goods and services

In book market, the surplus from greater product variety is many times larger than the surplus from lower prices

Effects may be even larger in other markets: CDs, DVDs, electronics, shareware, markets for advice, labor markets

(Monster.com), dating services, eBay, online music, … Effects are likely to grow over time

Increase in sales of obscure products => increased viability for production of additional obscure products

Improved technologies for recommendation, search and delivery


Backup Slides

Aggregate Elasticity of Demand

Ideally one would estimate this directly or through an experiment.

Difficult to get aggregate elasticity. Individual retailer estimates give upper bound on elasticity.

pw1, qw1

.

.

.

pw2, qw2

pwN, qwN

Publisher

Retailer 1

c

pr1, qr1

Retailer 2

Retailer NprN, qrN

pr2, qr2


Proportion of Sales in Obscure: Formula

1),(

)1(

)1()1(

1

1

1

2

22

2

2

N

xN

dtt

dtt

NxrN

N

x


Data Major publisher. 321 titles, 861 observations on

Amazon rank and publisher sales to Amazon. 3 weeks, Summer 2001. Weekly sales: <1 to 481 Weekly sales rank (average) = 238 to 961,367

ln(Q)=ln(a)+b ln(Rank)+ a=37,274, b=-0.871 R2=.801 Rank=10 => 5,000 sales/week;

Rank=100,000 => 1.6 sales/week


Elasticity Assumptions (A1) Wholesale price constant across firms (pwi=pw)

Clay, Krishnan, Wolff (2001) ABA lawsuits

(A2) Stable relationship between wholesale and retail price (pw=ki pri) Wholesale price set from list. Rare to make list price changes 88.5% of 23,744 books sold at Amazon were even

percentage of list (A3) Wholesale and retail quantity equal (qr=qw)

Books sold on consignment Returns with no penalty


Sensitivity Our estimates with respect to industry estimates

Total Amazon sales = 99.4 million books/year vs. industry estimate of 105 million

Experiments: know initial sales/rank, order 5-6 copies in 1 hour. Get final rank. Can estimate b. Goolsbee and Chevalier (2002): -.855 Weingarten (2001): -.952 Poynter (2000): -.834 Our own experiment (2002): -.916


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