Multinational Pricing:
Lessons from IKEA
Marianne Baxter
Boston University
and NBER
Anthony Landry
The Wharton School
U. of Pennsylvania
Federal Reserve Bank of San Francisco
November 24, 2014
How Do Multinational Retailers Set Prices?
- Recent research emphasizes the central role played by large
firms with multiple products, both within countries (Midrigan
(2011), Bernard et al. (2011)) and in dominating international
trade (Eaton et al. (2012)).
- In this paper, we shed light on the way in which a large
multinational retailer operates in a setting characterized by
multiple products distributed and priced in many countries.
- How Do Multinational Retailers Set Prices?
- We develop a partial equilibrium model of multinational pricing.
Our model is a simple extension of standard menu-cost models
with one firm selling multiple products in multiple countries.
- We test our model against a novel database of IKEA products and
catalog prices: The entire population of products advertized in
IKEA catalogs from seven different countries between 2002-15.
1 / 57
3 New Multinational Pricing Facts
1. Price spell lengths differ across countries
- weaker currencies experience shorter price spells (e.g. Canadian
dollar in early 2000s, British pounds since 2007)
2. Price adjustments are not synchronized across countries
- marginal cost (in source currency) is not the dominant force
affecting the firm’s pricing decision
3. LOP deviations are pervasive
- price adjustments only slightly reduce LOP deviations
2 / 57
Insights from a Multi-Product Menu-Cost Model
Exchange rate movements matter for the firm’s pricing decisions
- Our multi-product menu-cost model
- The multinational provides a plethora of products to subsidiaries
at a common marginal cost denominated in Source currency
- Subsidiaries maximize local-currency profits by setting prices
- We allow for variable markups to differ across subsidiaries
- Use actual data to test the model (e.g. a case study)
- Exchange rate movements impact IKEA’s pricing decision
- In our model, exchange rate movements either exacerbate or
offset marginal cost movements ⇒ decision to adjust prices
- If marginal costs are more volatile than exchange rates:
⇒ Cannot reproduce Fact #1 and #2
- If markups fluctuate too much:
⇒ Cannot reproduce Fact #1 and #2
The model that works best has persistent marginal costs
and relatively stable markups!
3 / 57
Roadmap
Part 1: The IKEA Database
Part 2: Multinational Pricing Facts
Part 3: A Multi-Product Menu-Cost Model
Part 4: Model Insights
Part 5: Conclusion
4 / 57
Roadmap
Part 1: The IKEA Database
Part 2: Multinational Pricing Facts
Part 3: A Multi-Product Menu-Cost Model
Part 4: Model Insights
Part 5: Conclusion
5 / 57
The IKEA Database
5 reasons to work with IKEA catalog prices
1. Size of the market
-
IKEA is the largest retailer of furniture and household furnishing
The IKEA catalog is the largest printed media in the world
The IKEA catalog is distributed online, by mail, and in stores
Main marketing tool (70% of the annual marketing budget)
2. Timing
- The IKEA catalog is distributed every summer
- Prices remain unchanged over the course of the catalog year
- Catalog prices are an excellent measure of transaction prices
3. One catalog per country
- National pricing policy: no within-country variations
4. Products are homogenous across countries
5. Each product z is manufactured in the same location
- For example, every Billy Bookcase is made in Falkoping, Sweden.
Why IKEA?
6 / 57
The IKEA Database
The 7 countries in our sample represents about 60% of IKEA’s revenue
Share of IKEA's revenue per country, 2013
Russia
7%
Asia
8%
Rest of Europe
28%
U.S.
12%
Canada
4%
U.K.
6%
Sweden
6%
Germany
14%
Italy
6%
France
9%
7 / 57
The IKEA Database
IKEA products come from multiple sources
Purchasing per country, 2005-13 averages
North America Sweden
4%
6%
Rest of Asia
11%
China
21%
Corresponding invoicing currencies, 2005-13 averages
Swedish krona
6%
Poland
17%
Polish zloty
17%
U.S. dollar
36%
Italy
8%
Germany
6%
Rest of Europe
27%
euro
41%
8 / 57
The IKEA Database
Basic statistics, 2002-2015
Number of observations
Total number of observations
Average number of observations per catalog
Number of products and varieties
Total number of products
Total number of varieties (e.g. colors and finishes)
Total number of product-variety pairs
Geographical distribution of product-variety pairs
Share of product-variety pairs available in 7 countries
Share of product-variety pairs available in 1 country
Life of product-variety pairs
Life expectancy of product-variety pairs
Percentage of product-variety pairs living >1 year
211,895
2,162
23,068
240
30,973
40%
23%
2.1 years
42%
number of price spells by life of products
9 / 57
The IKEA Database
Price adjustment variables example
Canada’s Product: LACK table, Variety: black
25
Price in $CA
20
15
10
5
0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Local-currency price
Price change indicator
Size of price change (%)
Price duration
Age
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
$19.99 $19.99
x
0
x
0
1
2
1
2
x
x
x
x
3
$19.99
0
0
4
4
x
x
x
x
5
$14.99 $7.99
-1
-1
-25% -47%
1
1
6
7
$9.99
1
25%
1
8
$9.99
0
0
2
9
$9.99
0
0
3
10
$9.99
0
0
4
11
10 / 57
The IKEA Database
Frequencies, implied durations, and size of price adjustments
for product-variety pairs living at least 2 years, 2002-2015
CA
DE
FR
IT
SE
UK
US
Average
Duration (in years)
mean
median
2.08
2
2.16
2
2.08
2
2.21
2
2.22
2
1.77
1
2.24
2
2.1
2
Size of price adjustment
median size(+) median size(-)
8.0%
-12.2%
8.2%
-11.8%
5.0%
-14.1%
9.0%
-15.2%
8.3%
-10.5%
10.8%
-10.5%
12.8%
-14.2%
9.1%
-12.5%
11 / 57
Roadmap
Part 1: The IKEA Database
Part 2: 3 Multinational Pricing Facts
Part 3: A Multi-Product Menu-Cost Model
Part 4: Model Insights
Part 5: Conclusion
12 / 57
Multinational Pricing Facts
Survival rates of price adjustments
- Questions:
- How long do price spells last?
- Are price spells’s behavior similar across countries?
- The survival function of price adjustments is the probability
that a price spell survives beyond a specific number of years
- We use the nonparametric Kaplan-Meier survival function,
which allows us to easily deal with censored data
- No difference in survival rates when censoring the last price spells
- Basic intuition:
Survival rate =
# of surviving prices
number of possible prices
13 / 57
Multinational Pricing Facts
Survival rates of price adjustments, all observations 2002-2015
0.00
0.25
0.50
0.75
1.00
Probabillity that a price spell survives x year(s)
0
2
4
6
8
10
year(s) since last price adjustment
12
14
14 / 57
FACT #1
Survival rates of price adjustments differ across countries
0.75
1.00
Probabillity that a price spell survives x year(s)
de
it
uk
0.00
0.25
0.50
ca
fr
se
us
0
2
4
6
8
10
year(s) since last price adjustment
12
14
15 / 57
Multinational Pricing Facts
Synchronization in price adjustments
- Are price adjustments synchronized across countries?
- The fraction of price increases/decreases in country X
conditional on observing a price increase/decrease in country Y
Example
Product
LACK
LACK
LACK
LACK
LACK
Variety
Price adj. indicator
black
white
birch
black-brown
red
Canada
0
+1
+1
+1
x
U.S.
+1
+1
-1
0
+1
Fraction of positive price increases in Canada conditional
on observing a positive price increase in the U.S.: 50%
- Note: control for VAT changes
16 / 57
FACT #2
Price adjustments are not synchronized across countries
Fraction of positive price adjustments in row
conditional on observing a positive price adjustment in column
ca
ca
de
0.22
fr
0.25
it
0.26
se
0.36
uk
0.40
us
0.54
average
0.34
Fraction of negative price adjustments in row
conditional on observing a negative price adjustment in column
ca
ca
de
0.28
fr
0.30
it
0.24
se
0.30
uk
0.25
us
0.43
average
0.30
17 / 57
FACT #2
Price adjustments are not synchronized across countries
Fraction of positive price adjustments in row
conditional on observing a positive price adjustment in column
ca
ca
de
fr
it
se
uk
us
0.20
0.19
0.15
0.31
0.44
0.53
de
0.22
0.37
0.46
0.31
0.37
0.41
0.34
fr
0.25
0.37
0.28
0.35
0.51
0.31
it
0.26
0.43
0.49
0.30
0.56
0.33
se
0.36
0.32
0.40
0.21
0.54
0.24
uk
0.40
0.14
0.40
0.22
0.35
us
0.54
0.33
0.28
0.21
0.24
0.40
0.23
average
0.34
0.31
0.37
0.23
0.32
0.48
0.33
Fraction of negative price adjustments in row
conditional on observing a negative price adjustment in column
ca
ca
de
fr
it
se
uk
us
0.26
0.30
0.30
0.29
0.31
0.33
de
0.28
0.59
0.63
0.46
0.41
0.21
fr
0.30
0.45
0.64
0.44
0.41
0.23
it
0.24
0.32
0.44
0.50
0.34
0.18
se
0.30
0.34
0.46
0.69
0.42
0.21
uk
0.25
0.42
0.52
0.62
0.58
0.23
us
0.43
0.29
0.33
0.33
0.30
0.32
average
0.30
0.35
0.44
0.54
0.43
0.37
0.23
all price adjustments (positive and negative)
18 / 57
Multinational Pricing Facts
LOP deviations
- How big are LOP deviations? Are LOP deviations smaller
when price adjustments are synchronized across markets?
- The Law of One Price states that the exchange-rate adjusted
price of a product z is the same across all countries. This is
Pi,t (z) =
Pj,t (z)
Si,j,t
for all i, j, t
- Si,j,t is June’s nominal exchange rate (monthly average)
between country i and j
- LOP deviations, net of VATs (the price received by IKEA):
LOP =
Pj,t (z)
Si,j,t Pi,t (z)
19 / 57
FACT #3
LOP deviations are pervasive
Absolute LOP deviations, all observations
de
ca
5.3%
means and (standard deviations)
de
fr
it
2.7%
2.8%
(4.5%)
fr
it
se
uk
us
average
(3.4%)
uk
3.4%
(3.5%)
(3.4%)
(3.5%)
2.6%
2.8%
3.3%
(3.8%)
(3.5%)
(3.7%)
2.8%
3.1%
(3.5%)
(3.5%)
5.1%
2.7%
(4.6%)
(3.4%)
5.1%
2.8%
2.6%
(4.7%)
(3.5%)
(3.8%)
5.2%
2.9%
2.8%
2.8%
(4.7%)
(3.4%)
(3.5%)
(3.5%)
3.3%
3.4%
3.3%
3.1%
3.3%
(3.5%)
(3.7%)
(3.5%)
(3.6%)
4.1%
3.9%
4.0%
de
it
se
3.9%
uk
3.9%
(4.2%)
(4.0%)
(4.1%)
(4.3%)
(4.1%)
(4.0%)
4.9%
3.2%
3.1%
3.1%
3.1%
3.4%
(4.5%)
(3.6%)
(3.7%)
(3.7%)
(3.6%)
(3.7%)
ca
5.3%
means and (standard deviations)
de
fr
it
2.8%
3.0%
(3.8%)
fr
(3.6%)
4.7%
(4.5%)
4.3%
Absolute LOP deviations, REPRICED IN BOTH COUNTRIES
se
2.9%
us
average
(3.6%)
se
3.0%
uk
3.3%
(3.6%)
(3.4%)
(3.4%)
2.5%
3.0%
3.1%
(3.1%)
(3.3%)
(3.2%)
5.0%
2.8%
(4.3%)
(3.6%)
5.0%
3.0%
2.5%
(4.4%)
(3.6%)
(3.1%)
5.6%
3.0%
3.0%
3.1%
(4.5%)
(3.4%)
(3.3%)
(3.6%)
3.1%
2.9%
(3.6%)
(3.4%)
3.2%
(3.4%)
4.7%
3.3%
3.1%
2.9%
3.2%
(4.0%)
(3.4%)
(3.2%)
(3.4%)
(3.4%)
4.1%
3.7%
3.9%
4.1%
3.5%
3.6%
(4.2%)
(3.5%)
(3.9%)
(4.0%)
(3.6%)
(3.8%)
4.9%
3.1%
3.0%
3.1%
3.1%
3.2%
(4.2%)
(3.5%)
(3.4%)
(3.5%)
(3.5%)
(3.4%)
Leaving in Canada!
Cavallo, Neiman, and Rigobon
20 / 57
FACT #3
Price adjustments only slightly reduce LOP deviations
Absolute LOP deviations, all observations
de
ca
5.3%
means and (standard deviations)
de
fr
it
2.7%
2.8%
(4.5%)
fr
it
se
uk
us
average
(3.4%)
uk
3.4%
(3.5%)
(3.4%)
(3.5%)
2.6%
2.8%
3.3%
(3.8%)
(3.5%)
(3.7%)
2.8%
3.1%
(3.5%)
(3.5%)
5.1%
2.7%
(4.6%)
(3.4%)
5.1%
2.8%
2.6%
(4.7%)
(3.5%)
(3.8%)
5.2%
2.9%
2.8%
2.8%
(4.7%)
(3.4%)
(3.5%)
(3.5%)
3.3%
3.4%
3.3%
3.1%
3.3%
(3.5%)
(3.7%)
(3.5%)
(3.6%)
4.1%
3.9%
4.0%
de
it
se
3.9%
uk
3.9%
(4.2%)
(4.0%)
(4.1%)
(4.3%)
(4.1%)
(4.0%)
4.9%
3.2%
3.1%
3.1%
3.1%
3.4%
(4.5%)
(3.6%)
(3.7%)
(3.7%)
(3.6%)
(3.7%)
ca
5.3%
means and (standard deviations)
de
fr
it
2.8%
3.0%
(3.8%)
fr
(3.6%)
4.7%
(4.5%)
4.3%
Absolute LOP deviations, REPRICED IN BOTH COUNTRIES
se
2.9%
us
average
(3.6%)
se
3.0%
uk
3.3%
(3.6%)
(3.4%)
(3.4%)
2.5%
3.0%
3.1%
(3.1%)
(3.3%)
(3.2%)
5.0%
2.8%
(4.3%)
(3.6%)
5.0%
3.0%
2.5%
(4.4%)
(3.6%)
(3.1%)
5.6%
3.0%
3.0%
3.1%
(4.5%)
(3.4%)
(3.3%)
(3.6%)
3.1%
2.9%
(3.6%)
(3.4%)
3.2%
(3.4%)
4.7%
3.3%
3.1%
2.9%
3.2%
(4.0%)
(3.4%)
(3.2%)
(3.4%)
(3.4%)
4.1%
3.7%
3.9%
4.1%
3.5%
3.6%
(4.2%)
(3.5%)
(3.9%)
(4.0%)
(3.6%)
(3.8%)
4.9%
3.1%
3.0%
3.1%
3.1%
3.2%
(4.2%)
(3.5%)
(3.4%)
(3.5%)
(3.5%)
(3.4%)
Leaving in Canada!
Cavallo, Neiman, and Rigobon
21 / 57
FACT #3
.4
Fraction
.2
.3
.1
Fraction
.3
.2
.3
-.3 -.2 -.1 0 .1
SE/DE
.2
.3
-.3 -.2 -.1 0 .1
FR/DE
.2
.3
-.3 -.2 -.1 0 .1
UK/DE
.2
.3
.2
.3
-.3 -.2 -.1 0 .1
US/DE
.2
.3
.4
0
0
.1
.1
Fraction
.2
.3
Fraction
.2
.3
.4
.5
.4
Fraction
.2
.3
.1
0
-.3 -.2 -.1 0 .1
IT/DE
.5
.2
.5
-.3 -.2 -.1 0 .1
CA/DE
0
0
0
.1
.1
Fraction
.2
.3
.4
.4
.5
.5
.5
LOP deviations (relative to Germany) for all product-variety pairs
22 / 57
FACT #3
0 .1
CA/DE
.2
-.3 -.2 -.1
0 .1
SE/DE
.2
.3
Fraction
.3
.4
.2
.1
-.3 -.2 -.1
0 .1
FR/DE
.2
.3
0
.1
UK/DE
.2
.3
-.3 -.2 -.1
0 .1
IT/DE
.2
.3
-.3 -.2 -.1
0 .1
US/DE
.2
.3
.4
Fraction
.3
.2
.1
-.2
-.1
0
-.3
.3
all
0
0
.1
.1
Fraction
.2
.3
.4
Fraction
.2 .3 .4
.5
.5
.5
-.3 -.2 -.1
0
0
0
.1
.1
Fraction
.2
.3
Fraction
.3
.2
.4
.4
.5
.5
.5
Price adjustments only slightly reduce LOP deviations
repriced
23 / 57
Roadmap
Part 1: The IKEA Database
Part 2: 3 Multinational Pricing Facts
Part 3: A Multi-Product Menu-Cost Model
Part 4: Model Insights
Part 5: Conclusion
24 / 57
A Multi-Product Menu-Cost Model
The multinational,
- operates in country i through subsidiary i
- produces all products z in the Source country
- provides product z to all subsidiaries at a common marginal
cost ψt (z) in Source currency Si,t
The subsidiaries,
- maximize local-currency profits πi,t (z) on each product
- set local-currency prices Pi,t (z)
- pay a fixed cost ξ to adjust prices
The consumers,
- purchase z from the subsidiary i at price Pi,t (z)
- no supply- or demand-driven complementarity across products
25 / 57
Source
IKEA Canada pays
𝝍𝝍𝒕𝒕 (𝒛𝒛)
𝑺𝑺𝒄𝒄𝒄𝒄,𝒕𝒕
IKEA
IKEA
IKEA
IKEA
IKEA
IKEA
IKEA
Canada (CA)
Germany (DE)
France (FR)
Italy (IT)
Sweden (SE)
U.K. (UK)
U.S. (US)
26 / 57
Source
IKEA Canada pays
𝝍𝝍𝒕𝒕 (𝒛𝒛)
𝑺𝑺𝒄𝒄𝒄𝒄,𝒕𝒕
IKEA
IKEA
IKEA
IKEA
IKEA
IKEA
IKEA
Canada (CA)
Germany (DE)
France (FR)
Italy (IT)
Sweden (SE)
U.K. (UK)
U.S. (US)
Canadian consumers pay
𝑷𝑷$𝒄𝒄𝒄𝒄,𝒕𝒕 (𝒛𝒛)
Canadian
German
French
Italian
Swedish
British
American
Consumers
Consumers
Consumers
Consumers
Consumers
Consumers
Consumers
27 / 57
Source
IKEA Canada pays
𝝍𝝍𝒕𝒕 (𝒛𝒛)
𝑺𝑺𝒄𝒄𝒄𝒄,𝒕𝒕
IKEA
IKEA
IKEA
IKEA
IKEA
IKEA
IKEA
Canada (CA)
Germany (DE)
France (FR)
Italy (IT)
Sweden (SE)
U.K. (UK)
U.S. (US)
Canadian
consumers pay
Cn consumers pay adonsumers
Canadian
consumers
pay
Canadian consumers
Canadian
pay consumers
Canadian
pay
consumers
pay
𝑷𝑷𝒌𝒌𝒌𝒌𝒌𝒌𝒌𝒌𝒌𝒌,𝒕𝒕 (𝒛𝒛)
pay 𝑷𝑷$𝒖𝒖𝒖𝒖,𝒕𝒕 (𝒛𝒛)
𝑷𝑷𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆,𝒕𝒕 (𝒛𝒛)
𝑷𝑷$𝒄𝒄𝒄𝒄,𝒕𝒕 (𝒛𝒛)
𝑷𝑷𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆,𝒕𝒕 (𝒛𝒛)
𝑷𝑷𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆,𝒕𝒕 (𝒛𝒛)
𝑷𝑷𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑,𝒕𝒕 (𝒛𝒛)
Canadian
German
French
Italian
Swedish
British
American
Consumers
Consumers
Consumers
Consumers
Consumers
Consumers
Consumers
28 / 57
A Multi-Product Menu-Cost Model
Demand structure
- An important feature of our dataset is the pervasive nature of
law of one prices deviations, even when price adjustments
are synchronized across subsidiaries
- We allow for variable markups to differ across subsidiaries by
using the non-constant demand elasticity schedule proposed by
Klenow and Willis (2006)
/γ
P (z)
ci,t
ci,t (z) = 1 − γ ln i,t
Pi,t
where the price elasticity of the desired markup depends on
product z’s price relative to the aggregate price level,
Γ=
γ
−1+γ ln
Pi,t (z)
Pi,t
- Constant markup arises when γ → 0
29 / 57
A Multi-Product Menu-Cost Model
Profit and cost structure
- The multinational collects profits from subsidiaries
- Each subsidiary contemporaneous profit is
πi,t (Pi,t (z)|Ωi,t (z)) = Pi,t (z) −
ψt (z)
Si,t
ci,t (z)
- Each subsidiary decides whether or not to adjust product z’s
price after observing the product state Ωi,t (z). Accordingly, the
value of each product z is
a
c (z)
(z), vi,t
vi,t (z) = max vi,t
where
a
∗
∗ (z) πi,t (P
vi,t
(z) = maxPi,t
i,t (z)|Ωi,t (z)) + βEt vi,t+1 − ξ
c
vi,t
(z) = πi,t (Pi,t−1 (z)|Ωi,t (z)) + βEt vi,t+1
30 / 57
A Multi-Product Menu-Cost Model
Parameter values
Parameter
, elasticity of demand
γ, super-elasticity of demand
ξ, menu cost (fraction of annual revenues)
β, annual discount factor
Value
3.5
1
0.0034
0.96
= 3.5 ⇒ steady-state markup /( − 1) of 40%
- IKEA’s average gross profit margin (2008-2013) is 42.6%
- U.S. furniture industry average (2002-2013) is 38.4%
γ = 1: If
Pi,t (z)
Pi,t
= 10% ⇒ markup is 35% instead of 40%
- FYI: IKEA’s average operating margin (2008-2013) is 31.9%
- Literature usually use γ > 3: If
Pi,t (z)
Pi,t
= 10% ⇒ markup is <25%
ξ = 0.0034 from Midrigan (2011) and Stella (2014)
Variable markups and Gamma
31 / 57
A Multi-Product Menu-Cost Model
Marginal costs process
We estimate the marginal cost process by extracting the common
time effect of individual products advertized in the catalogs:
1. Estimate the following fixed-effects regression on each product
z’s price denominated in $U.S., pi,t (z):
lnpi,t (z) = Ψt (z) + θi (z) + βi (z)lnSi,US,t + ui,t
2. Estimate an AR(1) regression on the panel of time effects:
Ψt (z) = ρψ Ψt−1 (z) + et (z)
Results: ρψ = 0.947 and σeψ = 0.081
Caveats: sticky prices and non-CES demand imply that the time
effects can no longer be literally taken to measure marginal costs
32 / 57
A Multi-Product Menu-Cost Model
Exchange rates process
- The nominal exchange rates between the Source and the
subsidiaries evolve as
Si,t = ρS Si,t + eS,t
- We calibrate the model to match the features of developed
economies’ exchange rates:
4-year half life → ρS = 0.877 and σeS = 0.084
- Simulate 5,000 products z from 1979 to 2015, where
-
41% (2,050 products) are invoiced in euros
36% (1,800 products) are invoiced in U.S. dollars
17% (850 products) are invoiced in Polish zlotys
6% (300 products) are invoiced in Swedish kronor
⇒ Compare model to data’s statistics from 2002 to 2015
33 / 57
A Multi-Product Menu-Cost Model
Aggregate prices process
- The aggregate price levels evolve as
(Pi,t − Pi ) = ρP (Pi,t−1 − Pi ) + (1 − ρP )φQi,t
where
Pi = /( − 1) represents the steady-state aggregate price level
Qi,t represents country i trade-weighted exchange rate
- The inertia in the price level, ρP = 0.36, comes from an AR(1)
regression on de-trended U.S. CPI data
- The parameter φ = 0.3 governs the level of long-run exchange
rate pass-through into the price level (the consensus is 0.2-0.4)
34 / 57
Roadmap
Part 1: The IKEA Database
Part 2: 3 Multinational Pricing Facts
Part 3: A Multi-Product Menu-Cost Model
Part 4: Model Insights
Part 5: Conclusion
35 / 57
Model Insights
Results #1: ↑ γ ⇒ ↑ price stickiness
CA
DE
FR
IT
SE
UK
US
Average
Mean duration (in years)
data
model
γ=0 γ=1 γ=3
2.1
2.6
2.4
3.7
2.2
2.8
2.7
3.9
2.1
2.8
2.7
3.9
2.2
2.7
2.7
3.9
2.2
2.7
2.6
3.8
1.8
2.4
2.4
3.7
2.2
2.6
2.5
3.4
2.1
2.6
2.5
3.7
size of price adjustments
36 / 57
Model Insights
Results #1: ↑ γ ⇒ ↓ survival rates dispersion
0.75
1.00
Probabillity that a price spell survives x year(s)
de
it
uk
0.00
0.25
0.50
ca
fr
se
us
0
2
4
6
8
10
year(s) since last price adjustment
12
14
Data, 2002-2015
37 / 57
Model Insights
Results #1: ↑ γ ⇒ ↓ survival rates dispersion
1.00
Probabillity that a price spell survives x year(s)
0.75
0.75
1.00
Probabillity that a price spell survives x year(s)
ca
fr
se
us
de
it
uk
0.00
0.00
0.25
0.50
de
it
uk
0.25
0.50
ca
fr
se
us
0
2
4
6
8
10
year(s) since last price adjustment
12
14
0
Data, 2002-2015
2
4
6
8
10
year(s) since last price adjustment
12
14
Model, 2002-2015
Implications of ↑ γ
37 / 57
Model Insights
Results #2: Price adjustments are not synchronized
Fraction of positive price adjustments in row
conditional on observing a positive price adjustment in column
ca
ca
de
fr
it
se
uk
us
0.20
0.19
0.15
0.31
0.44
0.53
ca
ca
de
fr
it
se
uk
us
0.57
0.56
0.56
0.62
0.59
0.69
de
0.22
0.37
0.46
0.31
0.37
0.41
0.34
fr
0.25
0.37
de
0.63
fr
0.62
0.97
0.97
0.95
0.80
0.66
0.58
0.28
0.35
0.51
0.31
0.96
0.79
0.65
0.58
DATA, 2002-2015
it
se
0.26
0.36
0.43
0.32
0.49
0.40
0.21
0.30
0.56
0.54
0.33
0.24
MODEL, 2002-2015
it
se
0.63
0.62
0.96
0.73
0.97
0.72
0.71
0.79
0.66
0.59
0.58
0.57
uk
0.40
0.14
0.40
0.22
0.35
us
0.54
0.33
0.28
0.21
0.24
0.40
average
0.34
0.31
0.37
0.23
0.32
0.48
0.33
us
0.74
0.57
0.56
0.56
0.61
0.63
average
0.65
0.74
0.74
0.74
0.71
0.63
0.61
0.23
uk
0.67
0.67
0.67
0.67
0.67
0.67
38 / 57
Model Insights
Results #2: Price adjustments are not synchronized
Fraction of negative price adjustments in row
conditional on observing a negative price adjustment in column
ca
ca
de
fr
it
se
uk
us
0.26
0.30
0.30
0.29
0.31
0.33
ca
ca
de
fr
it
se
uk
us
0.52
0.52
0.52
0.50
0.73
0.86
de
0.28
0.59
0.63
0.46
0.41
0.21
de
0.73
0.97
0.96
0.77
0.80
0.79
fr
0.30
0.45
0.64
0.44
0.41
0.23
fr
0.73
0.97
0.97
0.77
0.80
0.79
DATA, 2002-2015
it
se
0.24
0.30
0.32
0.34
0.44
0.46
0.69
0.50
0.34
0.42
0.18
0.21
MODEL, 2002-2015
it
se
0.73
0.68
0.96
0.75
0.97
0.74
0.74
0.77
0.80
0.74
0.79
0.73
uk
0.25
0.42
0.52
0.62
0.58
us
0.43
0.29
0.33
0.33
0.30
0.32
average
0.30
0.35
0.44
0.54
0.43
0.37
0.23
us
0.72
0.48
0.47
0.47
0.46
0.69
average
0.72
0.71
0.71
0.71
0.64
0.76
0.80
0.23
uk
0.74
0.58
0.57
0.58
0.55
0.83
39 / 57
Model Insights
Results #3: LOP deviations are pervasive
DATA, 2002-2015
Absolute LOP deviations, all observations
de
fr
it
se
uk
us
average
Absolute LOP deviations, REPRICED IN BOTH COUNTRIES
means and (standard deviations)
ca
de
fr
it
5.3%
2.7%
2.8%
se
2.9%
uk
3.4%
(4.5%)
(3.4%)
(3.5%)
(3.4%)
5.1%
2.7%
(4.6%)
(3.4%)
(3.5%)
2.6%
2.8%
3.3%
(3.8%)
(3.5%)
(3.7%)
5.1%
2.8%
2.6%
2.8%
3.1%
(4.7%)
(3.5%)
(3.8%)
(3.5%)
(3.5%)
5.2%
2.9%
2.8%
2.8%
3.3%
(4.7%)
(3.4%)
(3.5%)
(3.5%)
(3.6%)
4.7%
3.4%
3.3%
3.1%
3.3%
(4.5%)
(3.5%)
(3.7%)
(3.5%)
(3.6%)
de
fr
it
se
uk
4.3%
4.1%
3.9%
4.0%
3.9%
3.9%
(4.2%)
(4.0%)
(4.1%)
(4.3%)
(4.1%)
(4.0%)
4.9%
3.2%
3.1%
3.1%
3.1%
3.4%
(4.5%)
(3.6%)
(3.7%)
(3.7%)
(3.6%)
(3.7%)
ca
5.3%
means and (standard deviations)
de
fr
it
2.8%
3.0%
(3.8%)
us
average
(3.6%)
5.0%
2.8%
(4.3%)
(3.6%)
(3.6%)
se
3.0%
uk
3.3%
(3.4%)
(3.4%)
2.5%
3.0%
3.1%
(3.1%)
(3.3%)
(3.2%)
5.0%
3.0%
2.5%
3.1%
2.9%
(4.4%)
(3.6%)
(3.1%)
(3.6%)
(3.4%)
5.6%
3.0%
3.0%
3.1%
3.2%
(4.5%)
(3.4%)
(3.3%)
(3.6%)
(3.4%)
4.7%
3.3%
3.1%
2.9%
3.2%
(4.0%)
(3.4%)
(3.2%)
(3.4%)
(3.4%)
4.1%
3.7%
3.9%
4.1%
3.5%
3.6%
(4.2%)
(3.5%)
(3.9%)
(4.0%)
(3.6%)
(3.8%)
4.9%
3.1%
3.0%
3.1%
3.1%
3.2%
(4.2%)
(3.5%)
(3.4%)
(3.5%)
(3.5%)
(3.4%)
ca
3.4%
de
MODEL, 2002-2015
de
ca
4.3%
de
(3.6%)
fr
it
se
uk
us
average
4.2%
2.4%
(3.6%)
(1.0%)
fr
2.4%
it
4.1%
se
2.7%
uk
6.1%
(1.0%)
(1.3%)
(3.0%)
(4.8%)
3.1%
2.7%
6.1%
(1.1%)
(3.1%)
(4.8%)
4.2%
4.1%
3.1%
2.7%
6.2%
(3.6%)
(1.3%)
(1.1%)
(3.0%)
(4.8%)
4.0%
2.7%
2.7%
2.7%
(3.8%)
(3.0%)
(3.1%)
(3.0%)
5.7%
de
(2.9%)
fr
it
se
(5.0%)
7.4%
6.1%
6.1%
6.2%
5.7%
(6.8%)
(4.8%)
(4.8%)
(4.8%)
(5.0%)
uk
7.8%
6.1%
6.1%
6.2%
6.1%
4.8%
(5.7%)
(6.2%)
(6.2%)
(6.2%)
(6.0%)
(4.6%)
5.3%
4.3%
4.1%
4.4%
4.0%
5.8%
(4.5%)
(3.3%)
(3.2%)
(3.3%)
(4.0%)
(4.8%)
us
average
3.4%
0.2%
(2.9%)
(0.7%)
fr
0.2%
it
0.2%
se
2.3%
uk
5.5%
(0.7%)
(0.9%)
(2.8%)
(4.2%)
0.2%
2.4%
5.6%
(0.8%)
(2.8%)
(4.2%)
3.4%
0.2%
0.2%
2.4%
5.7%
(2.9%)
(0.9%)
(0.8%)
(2.8%)
(4.3%)
3.3%
2.3%
2.4%
2.4%
(3.3%)
(2.8%)
(2.8%)
(2.8%)
5.0%
(4.5%)
6.5%
5.5%
5.6%
5.7%
5.0%
(6.1%)
(4.2%)
(4.2%)
(4.3%)
(4.5%)
7.0%
5.5%
5.5%
5.6%
5.4%
4.2%
(5.3%)
(5.8%)
(5.8%)
(5.8%)
(5.7%)
(4.2%)
4.5%
2.7%
2.8%
2.8%
3.5%
5.2%
(3.9%)
(2.9%)
(2.8%)
(2.9%)
(3.7%)
(4.3%)
LOP distributions
40 / 57
Model Insights
Comparing parameter values
Model
γ=0 γ=1 γ=3
Within countries
average duration
durations dispersion
X
X
X
X
×
×
Across countries
syncronization
X
X
×
Average LOP dev.
all observations
repriced
×
×
X
X
X
X
41 / 57
Conclusion
- We provide a set of empirical facts to guide the development of
international pricing models
- To this end, we develop a partial equilibrium model of
multinational pricing. Our model is a simple extension of
standard menu-cost models with multiple products.
- Exchange rate movements impact IKEA’s pricing decision
- In our model, exchange rate movements either exacerbate or
offset marginal cost movements
- If marginal costs are more volatile than exchange rates
⇒ Cannot reproduce Fact #1 and #2
- If markups fluctuate too much
⇒ Cannot reproduce Fact #1 and #2
The model that works best has persistent marginal costs
and relatively stable markups!
42 / 57
BACKUP SLIDES
43 / 57
Why IKEA?
IKEA’s global revenue was e28.5 billion in 2013
making it one of the top 30 largest global retailers
Revenue
in billions of Euros
29
4
1994
2013
44 / 57
Why IKEA?
over 300 stores in 40 countries
45 / 57
Why IKEA?
IKEA is unique among multinational retailers with its annual catalogs of
local-currency prices that are guaranteed to hold for 1 year
3999/3pcs
$
ALVINE KVIST full/queen duvet cover set
Includes full/queen duvet cover and two
queen pillowcases. 100% cotton. Imported.
White/gray 201.596.35 Available in other sizes.
Prices vary.
Where the
everyday begins
THE PRICES IN THIS CATALOG CAN ONLY GET LOWER UNTIL JUNE 2015, NEVER HIGHER.
back to the presentation
46 / 57
The IKEA Database
Basic statistics, 2002-2015
Life (years)
2
3
4
5
6
7
8
9-13
14
Fraction
16%
9%
6%
4%
2%
2%
2%
1%
0%
Median # price adjustments
1
2
2
2
2
3
4
3
5
back to presentation
47 / 57
FACT #2
Price adjustments are not synchronized across countries
Fraction of price adjustments in row
conditional on observing a price adjustment in column
ca
ca
de
fr
it
se
uk
us
0.32
0.36
0.35
0.44
0.55
0.60
de
0.35
0.66
0.62
0.57
0.52
0.36
fr
0.41
0.52
0.69
0.61
0.61
0.36
it
0.43
0.47
0.73
0.61
0.59
0.38
se
0.49
0.46
0.68
0.63
0.60
0.33
uk
0.48
0.33
0.58
0.52
0.55
0.32
us
0.67
0.41
0.39
0.37
0.37
0.51
average
0.47
0.42
0.57
0.53
0.52
0.56
0.39
back to presentation
48 / 57
LOP Deviations Fun Facts
How much would you save if you could travel the globe?
How much would a consumer in country X save
if she could travel and buy the cheapest products?
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
CA
-23%
-18%
-17%
-14%
-22%
-29%
-30%
-24%
-34%
-47%
-30%
-28%
-25%
-22%
DE
-12%
-10%
-11%
-15%
-15%
-15%
-16%
-24%
-18%
-14%
-21%
-11%
-13%
-13%
FR
-13%
-10%
-11%
-22%
-18%
-18%
-16%
-23%
-17%
-13%
-17%
-12%
-17%
-17%
IT
-14%
-12%
-12%
-17%
-13%
-12%
-10%
-13%
-17%
-12%
-10%
-11%
-13%
-12%
SE
-6%
-6%
-6%
-19%
-22%
-20%
-17%
-20%
-8%
-13%
-24%
-15%
-18%
-16%
UK
-29%
-17%
-10%
-12%
-17%
-16%
-19%
-11%
-16%
-15%
-13%
-19%
-15%
-19%
US
-28%
-24%
-15%
-15%
-14%
-13%
-13%
-9%
-19%
-24%
-9%
-18%
-16%
-16%
Average
-26%
-16%
-17%
-13%
-17%
-16%
-15%
back to presentation
49 / 57
.3
Fraction
.2
.1
Fraction
.2
.2
.3
-.3 -.2 -.1
0 .1
SE/DE
.2
.3
-.3 -.2 -.1
0 .1
FR/DE
.2
.3
0
.1
UK/DE
.2
.3
.4
0 .1
IT/DE
.2
.3
-.3 -.2 -.1
0 .1
US/DE
.2
.3
0
-.3
-.2
-.1
2002-08
0
0
.1
.1
Fraction
.2
.3
.1 Fraction
.2
.3
.3
Fraction
.2
-.3 -.2 -.1
.4
0 .1
CA/DE
.4
-.3 -.2 -.1
0
0
0
.1
.1
Fraction
.2
.3
.3
.4
.4
.4
Cavallo, Neiman, and Rigobon
2009-15
back to presentation
50 / 57
A Multi-Product Menu-Cost Model
Example of marginal costs, exchange rates, and prices
Product #7 in the U.S., 2002-2014
1.8
1.6
1.4
1.2
1
0.8
0.6
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
marginal cost
local-currency price ($)
price level
exchange rate (Krona/$) index
51 / 57
A Multi-Product Menu-Cost Model
Example of marginal costs, exchange rates, and prices
Product #7 in the U.S., 2002-2014
1.8
1.6
1.4
1.2
1
0.8
0.6
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
marginal cost
local-currency price ($)
price level
exchange rate (Krona/$) index
52 / 57
A Multi-Product Menu-Cost Model
Parameter values
0.8
Gamma = 0
Gamma = 1
Gamma = 3
profit maximizing markup
0.7
0.6
0.5
0.4
0.3
0.2
0.1
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
relative price
back to presentation
53 / 57
Model Insights
Results #1: ↑ γ ⇒ ↑ price stickiness
CA
DE
FR
IT
SE
UK
US
Average
Size of price adjustment (+)
data
model
γ=0 γ=1 γ=3
8.0% 12.2% 9.5% 6.8%
8.2% 10.8% 8.1% 5.4%
5.0% 10.8% 8.1% 5.4%
9.0% 10.8% 8.1% 5.4%
8.3% 10.8% 8.1% 5.4%
10.8% 10.8% 8.1% 5.4%
12.8% 13.5% 9.5% 6.8%
9.1% 11.4% 5.8% 5.8%
back to presentation
54 / 57
Model Insights
Results #1: ↑ γ ⇒ ↑ price stickiness
CA
DE
FR
IT
SE
UK
US
Average
Size of price adjustment (-)
data
model
γ=0
γ=1
γ=3
-12.2% -12.2% -9.5% -6.8%
-11.8% -10.8% -8.1% -5.4%
-14.1% -10.8% -8.1% -5.4%
-15.2% -10.8% -8.1% -5.4%
-10.5% -10.8% -8.1% -5.4%
-10.5% -12.2% -9.5% -6.8%
-14.2% -14.9% -10.8% -6.8%
-12.5% -11.8% -8.9% -6.0%
back to presentation
55 / 57
Model Insights
Results #1: ↑ γ ⇒ ↓ survival rates dispersion
1.00
Probabillity that a price spell survives x year(s)
0.75
0.75
1.00
Probabillity that a price spell survives x year(s)
ca
fr
se
us
de
it
uk
0.00
0.00
0.25
0.50
de
it
uk
0.25
0.50
ca
fr
se
us
0
2
4
6
8
10
year(s) since last price adjustment
12
14
0
γ=0
2
4
6
8
10
year(s) since last price adjustment
12
14
γ=3
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56 / 57
Model Insights
-.3 -.2 -.1
0 .1
SE/DE
.2
.3
Fraction
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
-.3 -.2 -.1
0 .1
FR/DE
.2
.3
Fraction
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
0 .1
CA/DE
Fraction
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
-.3 -.2 -.1
-.3 -.2 -.1
.2
.3
all
0 .1
UK/DE
.2
.3
repriced
-.3 -.2 -.1
0 .1
IT/DE
.2
-.3 -.2 -.1
0 .1
US/DE
.2
.3
Fraction
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Fraction
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Fraction
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Results #3: LOP deviations are pervasive
.3
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