Multinational Pricing: Lessons from IKEA · PDF fileMultinational Pricing: Lessons from IKEA ... - The IKEA catalog is the largest printed media in ... 2002 2003 2004 2005 2006 2007

Multinational Pricing:Lessons from IKEA

Marianne Baxter Anthony LandryBoston University The Wharton School

and NBER U. of Pennsylvania

Federal Reserve Bank of San FranciscoNovember 24, 2014

How Do Multinational Retailers Set Prices?

- Recent research emphasizes the central role played by largefirms with multiple products, both within countries (Midrigan(2011), Bernard et al. (2011)) and in dominating internationaltrade (Eaton et al. (2012)).

- In this paper, we shed light on the way in which a largemultinational retailer operates in a setting characterized bymultiple 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 modelswith one firm selling multiple products in multiple countries.

- We test our model against a novel database of IKEA products andcatalog prices: The entire population of products advertized inIKEA catalogs from seven different countries between 2002-15.

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3 New Multinational Pricing Facts

1. Price spell lengths differ across countries

- weaker currencies experience shorter price spells (e.g. Canadiandollar in early 2000s, British pounds since 2007)

2. Price adjustments are not synchronized across countries

- marginal cost (in source currency) is not the dominant forceaffecting the firm’s pricing decision

3. LOP deviations are pervasive

- price adjustments only slightly reduce LOP deviations

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Insights from a Multi-Product Menu-Cost ModelExchange 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 costsand relatively stable markups!

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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

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Roadmap


Part 2: Multinational Pricing Facts



Part 5: Conclusion

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The IKEA Database5 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 DatabaseThe 7 countries in our sample represents about 60% of IKEA’s revenue

Rest of Europe28%

Germany14%

France9%

Italy6%

Sweden6%

U.K.6%

Canada4%

U.S.12%

Russia7%

Asia8%

Share of IKEA's revenue per country, 2013

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The IKEA DatabaseIKEA products come from multiple sources

Sweden6%

Poland17%

Italy8%

Germany6%

Rest of Europe27%

China21%

Rest of Asia11%

North America4%

Purchasing per country, 2005-13 averages

Swedish krona6%

Polish zloty17%

euro41%

U.S. dollar36%

Corresponding invoicing currencies, 2005-13 averages

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The IKEA DatabaseBasic statistics, 2002-2015

Number of observations

Total number of observations 211,895

Average number of observations per catalog 2,162

Number of products and varieties

Total number of products 23,068

Total number of varieties (e.g. colors and finishes) 240

Total number of product-variety pairs 30,973

Geographical distribution of product-variety pairs

Share of product-variety pairs available in 7 countries 40%

Share of product-variety pairs available in 1 country 23%

Life of product-variety pairs

Life expectancy of product-variety pairs 2.1 years

Percentage of product-variety pairs living >1 year 42%number of price spells by life of products

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The IKEA DatabasePrice adjustment variables example

Canada’s Product: LACK table, Variety: black

0

5

10

15

20

25

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Pri

ce in

$C

A

Local-currency price x x x $19.99 $19.99 x $19.99 x $14.99 $7.99 $9.99 $9.99 $9.99 $9.99Price change indicator x x x x 0 x 0 x -1 -1 1 0 0 0

Size of price change (%) x x x x 0 x 0 x -25% -47% 25% 0 0 0Price duration x x x 1 2 x 4 x 1 1 1 2 3 4

Age x x x 1 2 3 4 5 6 7 8 9 10 11

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The IKEA DatabaseFrequencies, implied durations, and size of price adjustments

for product-variety pairs living at least 2 years, 2002-2015

Duration (in years) Size of price adjustmentmean median median size(+) median size(-)

CA 2.08 2 8.0% -12.2%DE 2.16 2 8.2% -11.8%FR 2.08 2 5.0% -14.1%IT 2.21 2 9.0% -15.2%SE 2.22 2 8.3% -10.5%UK 1.77 1 10.8% -10.5%US 2.24 2 12.8% -14.2%

Average 2.1 2 9.1% -12.5%

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Roadmap


Part 2: 3 Multinational Pricing Facts



Part 5: Conclusion

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Multinational Pricing FactsSurvival 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 probabilitythat 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 pricesnumber of possible prices

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Multinational Pricing FactsSurvival rates of price adjustments, all observations 2002-2015

0.00

0.25

0.50

0.75

1.00

0 2 4 6 8 10 12 14year(s) since last price adjustment

Probabillity that a price spell survives x year(s)

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FACT #1Survival rates of price adjustments differ across countries

0.00

0.25

0.50

0.75

1.00


ca defr itse ukus


15 / 57

Multinational Pricing FactsSynchronization in price adjustments

- Are price adjustments synchronized across countries?

- The fraction of price increases/decreases in country Xconditional on observing a price increase/decrease in country Y

Example

Product Variety Price adj. indicator

Canada U.S.

LACK black 0 +1LACK white +1 +1LACK birch +1 -1LACK black-brown +1 0LACK red x +1

Fraction of positive price increases in Canada conditionalon observing a positive price increase in the U.S.: 50%

- Note: control for VAT changes

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FACT #2Price adjustments are not synchronized across countries

ca de fr it se uk us average

ca 0.22 0.25 0.26 0.36 0.40 0.54 0.34


ca 0.28 0.30 0.24 0.30 0.25 0.43 0.30

Fraction of positive price adjustments in row

conditional on observing a positive price adjustment in column

Fraction of negative price adjustments in row

conditional on observing a negative price adjustment in column

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ca 0.22 0.25 0.26 0.36 0.40 0.54 0.34

de 0.20 0.37 0.37 0.43 0.32 0.14 0.33 0.31

fr 0.19 0.46 0.49 0.40 0.40 0.28 0.37

it 0.15 0.31 0.28 0.21 0.22 0.21 0.23

se 0.31 0.37 0.35 0.30 0.35 0.24 0.32

uk 0.44 0.41 0.51 0.56 0.54 0.40 0.48

us 0.53 0.34 0.31 0.33 0.24 0.23 0.33


ca 0.28 0.30 0.24 0.30 0.25 0.43 0.30

de 0.26 0.45 0.32 0.34 0.42 0.29 0.35

fr 0.30 0.59 0.44 0.46 0.52 0.33 0.44

it 0.30 0.63 0.64 0.69 0.62 0.33 0.54

se 0.29 0.46 0.44 0.50 0.58 0.30 0.43

uk 0.31 0.41 0.41 0.34 0.42 0.32 0.37

us 0.33 0.21 0.23 0.18 0.21 0.23 0.23





all price adjustments (positive and negative)

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Multinational Pricing FactsLOP deviations

- How big are LOP deviations? Are LOP deviations smallerwhen price adjustments are synchronized across markets?

- The Law of One Price states that the exchange-rate adjustedprice 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,tPi,t(z)

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FACT #3LOP deviations are pervasive

ca de fr it se uk ca de fr it se uk

de 5.3% 2.7% 2.8% 2.9% 3.4% de 5.3% 2.8% 3.0% 3.0% 3.3%(4.5%) (3.4%) (3.5%) (3.4%) (3.5%) (3.8%) (3.6%) (3.6%) (3.4%) (3.4%)

fr 5.1% 2.7% 2.6% 2.8% 3.3% fr 5.0% 2.8% 2.5% 3.0% 3.1%(4.6%) (3.4%) (3.8%) (3.5%) (3.7%) (4.3%) (3.6%) (3.1%) (3.3%) (3.2%)

it 5.1% 2.8% 2.6% 2.8% 3.1% it 5.0% 3.0% 2.5% 3.1% 2.9%(4.7%) (3.5%) (3.8%) (3.5%) (3.5%) (4.4%) (3.6%) (3.1%) (3.6%) (3.4%)

se 5.2% 2.9% 2.8% 2.8% 3.3% se 5.6% 3.0% 3.0% 3.1% 3.2%(4.7%) (3.4%) (3.5%) (3.5%) (3.6%) (4.5%) (3.4%) (3.3%) (3.6%) (3.4%)

uk 4.7% 3.4% 3.3% 3.1% 3.3% uk 4.7% 3.3% 3.1% 2.9% 3.2%(4.5%) (3.5%) (3.7%) (3.5%) (3.6%) (4.0%) (3.4%) (3.2%) (3.4%) (3.4%)

us 4.3% 4.1% 3.9% 4.0% 3.9% 3.9% us 4.1% 3.7% 3.9% 4.1% 3.5% 3.6%(4.2%) (4.0%) (4.1%) (4.3%) (4.1%) (4.0%) (4.2%) (3.5%) (3.9%) (4.0%) (3.6%) (3.8%)

average 4.9% 3.2% 3.1% 3.1% 3.1% 3.4% average 4.9% 3.1% 3.0% 3.1% 3.1% 3.2%(4.5%) (3.6%) (3.7%) (3.7%) (3.6%) (3.7%) (4.2%) (3.5%) (3.4%) (3.5%) (3.5%) (3.4%)

Absolute LOP deviations, all observationsmeans and (standard deviations) means and (standard deviations)

Absolute LOP deviations, REPRICED IN BOTH COUNTRIES

Leaving in Canada!

Cavallo, Neiman, and Rigobon

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FACT #3Price adjustments only slightly reduce LOP deviations


de 5.3% 2.7% 2.8% 2.9% 3.4% de 5.3% 2.8% 3.0% 3.0% 3.3%(4.5%) (3.4%) (3.5%) (3.4%) (3.5%) (3.8%) (3.6%) (3.6%) (3.4%) (3.4%)

fr 5.1% 2.7% 2.6% 2.8% 3.3% fr 5.0% 2.8% 2.5% 3.0% 3.1%(4.6%) (3.4%) (3.8%) (3.5%) (3.7%) (4.3%) (3.6%) (3.1%) (3.3%) (3.2%)

it 5.1% 2.8% 2.6% 2.8% 3.1% it 5.0% 3.0% 2.5% 3.1% 2.9%(4.7%) (3.5%) (3.8%) (3.5%) (3.5%) (4.4%) (3.6%) (3.1%) (3.6%) (3.4%)

se 5.2% 2.9% 2.8% 2.8% 3.3% se 5.6% 3.0% 3.0% 3.1% 3.2%(4.7%) (3.4%) (3.5%) (3.5%) (3.6%) (4.5%) (3.4%) (3.3%) (3.6%) (3.4%)

uk 4.7% 3.4% 3.3% 3.1% 3.3% uk 4.7% 3.3% 3.1% 2.9% 3.2%(4.5%) (3.5%) (3.7%) (3.5%) (3.6%) (4.0%) (3.4%) (3.2%) (3.4%) (3.4%)

us 4.3% 4.1% 3.9% 4.0% 3.9% 3.9% us 4.1% 3.7% 3.9% 4.1% 3.5% 3.6%(4.2%) (4.0%) (4.1%) (4.3%) (4.1%) (4.0%) (4.2%) (3.5%) (3.9%) (4.0%) (3.6%) (3.8%)

average 4.9% 3.2% 3.1% 3.1% 3.1% 3.4% average 4.9% 3.1% 3.0% 3.1% 3.1% 3.2%(4.5%) (3.6%) (3.7%) (3.7%) (3.6%) (3.7%) (4.2%) (3.5%) (3.4%) (3.5%) (3.5%) (3.4%)

Absolute LOP deviations, all observationsmeans and (standard deviations) means and (standard deviations)

Absolute LOP deviations, REPRICED IN BOTH COUNTRIES

Leaving in Canada!


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FACT #3LOP deviations (relative to Germany) for all product-variety pairs

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3CA/DE

0.1

.2.3

.4.5

Fra

ctio

n-.3 -.2 -.1 0 .1 .2 .3

FR/DE

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3IT/DE

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3SE/DE

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3UK/DE

0.1

.2.3

.4.5

Fra

ctio

n-.3 -.2 -.1 0 .1 .2 .3

US/DE

22 / 57

FACT #3Price adjustments only slightly reduce LOP deviations

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3CA/DE

0.1

.2.3

.4.5

Fra

ctio

n-.3 -.2 -.1 0 .1 .2 .3

FR/DE

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3IT/DE

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3SE/DE

0.1

.2.3

.4.5

Fra

ctio

n

-.3 -.2 -.1 0 .1 .2 .3UK/DE

all repriced0

.1.2

.3.4

.5F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3US/DE

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Roadmap





Part 5: Conclusion

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A Multi-Product Menu-Cost ModelThe 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 marginalcost 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

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IKEA IKEA IKEA IKEA IKEA IKEA IKEACanada (CA) Germany (DE) France (FR) Italy (IT) Sweden (SE) U.K. (UK) U.S. (US)

SourceIKEA Canada pays

𝝍𝝍𝒕𝒕(𝒛𝒛)𝑺𝑺𝒄𝒄𝒄𝒄,𝒕𝒕

26 / 57


Canadian German French Italian Swedish British AmericanConsumers Consumers Consumers Consumers Consumers Consumers Consumers



Canadian consumers pay 𝑷𝑷$𝒄𝒄𝒄𝒄,𝒕𝒕(𝒛𝒛)

27 / 57


Canadian German French Italian Swedish British AmericanConsumers Consumers Consumers Consumers Consumers Consumers Consumers



Canadian consumers pay 𝑷𝑷$𝒄𝒄𝒄𝒄,𝒕𝒕(𝒛𝒛)

Canadian consumers pay 𝑷𝑷𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆,𝒕𝒕(𝒛𝒛)



Canadian consumers pay 𝑷𝑷𝒌𝒌𝒆𝒆𝒆𝒆𝒌𝒌𝒄𝒄,𝒕𝒕(𝒛𝒛)

Cn consumers pay 𝑷𝑷𝒑𝒑𝒆𝒆𝒆𝒆𝒌𝒌𝒑𝒑,𝒕𝒕(𝒛𝒛)

adonsumers pay 𝑷𝑷$𝒆𝒆𝒖𝒖,𝒕𝒕(𝒛𝒛)

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A Multi-Product Menu-Cost ModelDemand structure

- An important feature of our dataset is the pervasive nature oflaw of one prices deviations, even when price adjustmentsare synchronized across subsidiaries

- We allow for variable markups to differ across subsidiaries byusing the non-constant demand elasticity schedule proposed byKlenow and Willis (2006)

ci ,t(z) =(

1− γ ln(Pi,t(z)Pi,t

))ε/γci ,t

where the price elasticity of the desired markup depends onproduct z ’s price relative to the aggregate price level,

Γ = γ

ε−1+γ ln

(Pi,t (z)

Pi,t

)- Constant markup arises when γ → 0

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A Multi-Product Menu-Cost ModelProfit 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 ’sprice after observing the product state Ωi ,t(z). Accordingly, thevalue of each product z is

vi ,t(z) = maxvai ,t(z), v ci ,t(z)

where

v ai,t(z) = maxP∗

i,t(z)

πi,t(P

∗i,t(z)|Ωi,t(z)) + βEtvi,t+1 − ξ

v ci,t(z) = πi,t(Pi,t−1(z)|Ωi,t(z)) + βEtvi,t+1

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A Multi-Product Menu-Cost ModelParameter values

Parameter Valueε, elasticity of demand 3.5γ, super-elasticity of demand 1ξ, menu cost (fraction of annual revenues) 0.0034β, annual discount factor 0.96

ε = 3.5 ⇒ steady-state markup ε/(ε− 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: IfPi,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: IfPi,t(z)Pi,t

= 10% ⇒ markup is <25%

ξ = 0.0034 from Midrigan (2011) and Stella (2014)

Variable markups and Gamma

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A Multi-Product Menu-Cost ModelMarginal costs process

We estimate the marginal cost process by extracting the commontime effect of individual products advertized in the catalogs:

1. Estimate the following fixed-effects regression on each productz’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) = ρψΨt−1(z) + et(z)

Results: ρψ = 0.947 and σeψ = 0.081

Caveats: sticky prices and non-CES demand imply that the timeeffects can no longer be literally taken to measure marginal costs

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A Multi-Product Menu-Cost ModelExchange rates process

- The nominal exchange rates between the Source and thesubsidiaries evolve as

Si ,t = ρSSi ,t + eS,t

- We calibrate the model to match the features of developedeconomies’ 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

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A Multi-Product Menu-Cost ModelAggregate prices process

- The aggregate price levels evolve as

(Pi ,t − Pi ) = ρP(Pi ,t−1 − Pi ) + (1− ρP)φQi ,t

where

Pi = ε/(ε− 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 exchangerate pass-through into the price level (the consensus is 0.2-0.4)

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Roadmap





Part 5: Conclusion

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Model InsightsResults #1: ↑ γ ⇒ ↑ price stickiness

Mean duration (in years)data model

γ = 0 γ = 1 γ = 3

CA 2.1 2.6 2.4 3.7DE 2.2 2.8 2.7 3.9FR 2.1 2.8 2.7 3.9IT 2.2 2.7 2.7 3.9SE 2.2 2.7 2.6 3.8UK 1.8 2.4 2.4 3.7US 2.2 2.6 2.5 3.4

Average 2.1 2.6 2.5 3.7

size of price adjustments

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Model InsightsResults #1: ↑ γ ⇒ ↓ survival rates dispersion

0.00

0.25

0.50

0.75

1.00


ca defr itse ukus


Data, 2002-2015

0.00

0.25

0.50

0.75

1.00


ca defr itse ukus


Model, 2002-2015

Implications of ↑ γ

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0.00

0.25

0.50

0.75

1.00


ca defr itse ukus


Data, 2002-20150.

000.

250.

500.

751.

00


ca defr itse ukus


Model, 2002-2015

Implications of ↑ γ

37 / 57

Model InsightsResults #2: Price adjustments are not synchronized


ca 0.22 0.25 0.26 0.36 0.40 0.54 0.34

de 0.20 0.37 0.37 0.43 0.32 0.14 0.33 0.31

fr 0.19 0.46 0.49 0.40 0.40 0.28 0.37

it 0.15 0.31 0.28 0.21 0.22 0.21 0.23

se 0.31 0.37 0.35 0.30 0.35 0.24 0.32

uk 0.44 0.41 0.51 0.56 0.54 0.40 0.48

us 0.53 0.34 0.31 0.33 0.24 0.23 0.33


ca 0.63 0.62 0.63 0.62 0.67 0.74 0.65

de 0.57 0.97 0.96 0.73 0.67 0.57 0.74

fr 0.56 0.97 0.97 0.72 0.67 0.56 0.74

it 0.56 0.95 0.96 0.71 0.67 0.56 0.74

se 0.62 0.80 0.79 0.79 0.67 0.61 0.71

uk 0.59 0.66 0.65 0.66 0.59 0.63 0.63

us 0.69 0.58 0.58 0.58 0.57 0.67 0.61



DATA, 2002-2015

MODEL, 2002-2015

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Model InsightsResults #2: Price adjustments are not synchronized


ca 0.28 0.30 0.24 0.30 0.25 0.43 0.30

de 0.26 0.45 0.32 0.34 0.42 0.29 0.35

fr 0.30 0.59 0.44 0.46 0.52 0.33 0.44

it 0.30 0.63 0.64 0.69 0.62 0.33 0.54

se 0.29 0.46 0.44 0.50 0.58 0.30 0.43

uk 0.31 0.41 0.41 0.34 0.42 0.32 0.37

us 0.33 0.21 0.23 0.18 0.21 0.23 0.23


ca 0.73 0.73 0.73 0.68 0.74 0.72 0.72

de 0.52 0.97 0.96 0.75 0.58 0.48 0.71

fr 0.52 0.97 0.97 0.74 0.57 0.47 0.71

it 0.52 0.96 0.97 0.74 0.58 0.47 0.71

se 0.50 0.77 0.77 0.77 0.55 0.46 0.64

uk 0.73 0.80 0.80 0.80 0.74 0.69 0.76

us 0.86 0.79 0.79 0.79 0.73 0.83 0.80



DATA, 2002-2015

MODEL, 2002-2015

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Model InsightsResults #3: LOP deviations are pervasive


de 5.3% 2.7% 2.8% 2.9% 3.4% de 5.3% 2.8% 3.0% 3.0% 3.3%(4.5%) (3.4%) (3.5%) (3.4%) (3.5%) (3.8%) (3.6%) (3.6%) (3.4%) (3.4%)

fr 5.1% 2.7% 2.6% 2.8% 3.3% fr 5.0% 2.8% 2.5% 3.0% 3.1%(4.6%) (3.4%) (3.8%) (3.5%) (3.7%) (4.3%) (3.6%) (3.1%) (3.3%) (3.2%)

it 5.1% 2.8% 2.6% 2.8% 3.1% it 5.0% 3.0% 2.5% 3.1% 2.9%(4.7%) (3.5%) (3.8%) (3.5%) (3.5%) (4.4%) (3.6%) (3.1%) (3.6%) (3.4%)

se 5.2% 2.9% 2.8% 2.8% 3.3% se 5.6% 3.0% 3.0% 3.1% 3.2%(4.7%) (3.4%) (3.5%) (3.5%) (3.6%) (4.5%) (3.4%) (3.3%) (3.6%) (3.4%)

uk 4.7% 3.4% 3.3% 3.1% 3.3% uk 4.7% 3.3% 3.1% 2.9% 3.2%(4.5%) (3.5%) (3.7%) (3.5%) (3.6%) (4.0%) (3.4%) (3.2%) (3.4%) (3.4%)

us 4.3% 4.1% 3.9% 4.0% 3.9% 3.9% us 4.1% 3.7% 3.9% 4.1% 3.5% 3.6%(4.2%) (4.0%) (4.1%) (4.3%) (4.1%) (4.0%) (4.2%) (3.5%) (3.9%) (4.0%) (3.6%) (3.8%)

average 4.9% 3.2% 3.1% 3.1% 3.1% 3.4% average 4.9% 3.1% 3.0% 3.1% 3.1% 3.2%(4.5%) (3.6%) (3.7%) (3.7%) (3.6%) (3.7%) (4.2%) (3.5%) (3.4%) (3.5%) (3.5%) (3.4%)


de 4.3% 2.4% 4.1% 2.7% 6.1% de 3.4% 0.2% 0.2% 2.3% 5.5%

(3.6%) (1.0%) (1.3%) (3.0%) (4.8%) (2.9%) (0.7%) (0.9%) (2.8%) (4.2%)

fr 4.2% 2.4% 3.1% 2.7% 6.1% fr 3.4% 0.2% 0.2% 2.4% 5.6%(3.6%) (1.0%) (1.1%) (3.1%) (4.8%) (2.9%) (0.7%) (0.8%) (2.8%) (4.2%)

it 4.2% 4.1% 3.1% 2.7% 6.2% it 3.4% 0.2% 0.2% 2.4% 5.7%(3.6%) (1.3%) (1.1%) (3.0%) (4.8%) (2.9%) (0.9%) (0.8%) (2.8%) (4.3%)

se 4.0% 2.7% 2.7% 2.7% 5.7% se 3.3% 2.3% 2.4% 2.4% 5.0%(3.8%) (3.0%) (3.1%) (3.0%) (5.0%) (3.3%) (2.8%) (2.8%) (2.8%) (4.5%)

uk 7.4% 6.1% 6.1% 6.2% 5.7% uk 6.5% 5.5% 5.6% 5.7% 5.0%(6.8%) (4.8%) (4.8%) (4.8%) (5.0%) (6.1%) (4.2%) (4.2%) (4.3%) (4.5%)

us 7.8% 6.1% 6.1% 6.2% 6.1% 4.8% us 7.0% 5.5% 5.5% 5.6% 5.4% 4.2%(5.7%) (6.2%) (6.2%) (6.2%) (6.0%) (4.6%) (5.3%) (5.8%) (5.8%) (5.8%) (5.7%) (4.2%)

average 5.3% 4.3% 4.1% 4.4% 4.0% 5.8% average 4.5% 2.7% 2.8% 2.8% 3.5% 5.2%(4.5%) (3.3%) (3.2%) (3.3%) (4.0%) (4.8%) (3.9%) (2.9%) (2.8%) (2.9%) (3.7%) (4.3%)

MODEL, 2002-2015

Absolute LOP deviations, all observations Absolute LOP deviations, REPRICED IN BOTH COUNTRIESmeans and (standard deviations) means and (standard deviations)

DATA, 2002-2015

LOP distributions

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Model InsightsComparing parameter values

Modelγ = 0 γ = 1 γ = 3

Within countriesaverage duration X X ×durations dispersion X X ×

Across countriessyncronization X X ×

Average LOP dev.all observations × X Xrepriced × X X

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Conclusion

- We provide a set of empirical facts to guide the development ofinternational pricing models

- To this end, we develop a partial equilibrium model ofmultinational pricing. Our model is a simple extension ofstandard menu-cost models with multiple products.

- Exchange rate movements impact IKEA’s pricing decision

- In our model, exchange rate movements either exacerbate oroffset 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 costsand relatively stable markups!

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BACKUP SLIDES

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Why IKEA?IKEA’s global revenue was e28.5 billion in 2013

making it one of the top 30 largest global retailers

4

29

1994 2013

Revenuein billions of Euros

44 / 57

Why IKEA?over 300 stores in 40 countries

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Why IKEA?IKEA is unique among multinational retailers with its annual catalogs of

local-currency prices that are guaranteed to hold for 1 year

Where the everyday begins

$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.

THE PRICES IN THIS CATALOG CAN ONLY GET LOWER UNTIL JUNE 2015, NEVER HIGHER.

back to the presentation

46 / 57

The IKEA DatabaseBasic statistics, 2002-2015

Life (years) Fraction Median # price adjustments

2 16% 1

3 9% 2

4 6% 2

5 4% 2

6 2% 2

7 2% 3

8 2% 4

9-13 1% 3

14 0% 5

back to presentation

47 / 57



ca 0.35 0.41 0.43 0.49 0.48 0.67 0.47

de 0.32 0.52 0.47 0.46 0.33 0.41 0.42

fr 0.36 0.66 0.73 0.68 0.58 0.39 0.57

it 0.35 0.62 0.69 0.63 0.52 0.37 0.53

se 0.44 0.57 0.61 0.61 0.55 0.37 0.52

uk 0.55 0.52 0.61 0.59 0.60 0.51 0.56

us 0.60 0.36 0.36 0.38 0.33 0.32 0.39

Fraction of price adjustments in row

conditional on observing a price adjustment in column


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LOP Deviations Fun FactsHow much would you save if you could travel the globe?

CA DE FR IT SE UK US

2002 -23% -12% -13% -14% -6% -29% -28%

2003 -18% -10% -10% -12% -6% -17% -24%

2004 -17% -11% -11% -12% -6% -10% -15%

2005 -14% -15% -22% -17% -19% -12% -15%

2006 -22% -15% -18% -13% -22% -17% -14%

2007 -29% -15% -18% -12% -20% -16% -13%

2008 -30% -16% -16% -10% -17% -19% -13%

2009 -24% -24% -23% -13% -20% -11% -9%

2010 -34% -18% -17% -17% -8% -16% -19%

2011 -47% -14% -13% -12% -13% -15% -24%

2012 -30% -21% -17% -10% -24% -13% -9%

2013 -28% -11% -12% -11% -15% -19% -18%

2014 -25% -13% -17% -13% -18% -15% -16%

2015 -22% -13% -17% -12% -16% -19% -16%

Average -26% -16% -17% -13% -17% -16% -15%

How much would a consumer in country X save

if she could travel and buy the cheapest products?


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0.1

.2.3

.4F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3CA/DE

0.1

.2.3

.4F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3FR/DE

0.1

.2.3

.4F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3IT/DE

0.1

.2.3

.4F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3SE/DE

0.1

.2.3

.4F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3UK/DE

2002-08 2009-150

.1.2

.3.4

Fra

ctio

n-.3 -.2 -.1 0 .1 .2 .3

US/DE


50 / 57

A Multi-Product Menu-Cost ModelExample of marginal costs, exchange rates, and prices

Product #7 in the U.S., 2002-2014

0.6

0.8

1

1.2

1.4

1.6

1.8

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

marginal cost local-currency price ($)

price level exchange rate (Krona/$) index

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A Multi-Product Menu-Cost ModelExample of marginal costs, exchange rates, and prices

Product #7 in the U.S., 2002-2014

0.6

0.8

1

1.2

1.4

1.6

1.8

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 ModelParameter values

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.40.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

relative price

prof

it m

axim

izin

g m

arku

p

Gamma = 0Gamma = 1Gamma = 3


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Size of price adjustment (+)data model

γ = 0 γ = 1 γ = 3

CA 8.0% 12.2% 9.5% 6.8%DE 8.2% 10.8% 8.1% 5.4%FR 5.0% 10.8% 8.1% 5.4%IT 9.0% 10.8% 8.1% 5.4%SE 8.3% 10.8% 8.1% 5.4%UK 10.8% 10.8% 8.1% 5.4%US 12.8% 13.5% 9.5% 6.8%

Average 9.1% 11.4% 5.8% 5.8%


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Size of price adjustment (-)data model

γ = 0 γ = 1 γ = 3

CA -12.2% -12.2% -9.5% -6.8%DE -11.8% -10.8% -8.1% -5.4%FR -14.1% -10.8% -8.1% -5.4%IT -15.2% -10.8% -8.1% -5.4%SE -10.5% -10.8% -8.1% -5.4%UK -10.5% -12.2% -9.5% -6.8%US -14.2% -14.9% -10.8% -6.8%

Average -12.5% -11.8% -8.9% -6.0%


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0.00

0.25

0.50

0.75

1.00


ca defr itse ukus


γ = 00.

000.

250.

500.

751.

00


ca defr itse ukus


γ = 3


56 / 57

Model InsightsResults #3: LOP deviations are pervasive

0.1

.2.3

.4.5

.6.7

.8.9

1F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3CA/DE

0.1

.2.3

.4.5

.6.7

.8.9

1F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3FR/DE

0.1

.2.3

.4.5

.6.7

.8.9

1F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3IT/DE

0.1

.2.3

.4.5

.6.7

.8.9

1F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3SE/DE

0.1

.2.3

.4.5

.6.7

.8.9

1F

ract

ion

-.3 -.2 -.1 0 .1 .2 .3UK/DE

all repriced0

.1.2

.3.4

.5.6

.7.8

.91

Fra

ctio

n-.3 -.2 -.1 0 .1 .2 .3

US/DE


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Documents

Multinational Pricing: Lessons from IKEA · PDF fileMultinational Pricing: Lessons from IKEA ... - The IKEA catalog is the largest printed media in ... 2002 2003 2004 2005 2006 2007