Markups, Market Power and Implications

Jan De Loecker1 Jan Eeckhout2

1KU Leuven, NBER and CEPR2University College London and UPF

Bruegel

Background

• Secular trends: concentration, profits, investment, labor market.

• Market Power: common cause?

• Until recently little known:

1. Measurement: markups – not concentration ratios (HHI),

2. Data: long panel of relevant firms,

3. Focus on aggregates or case-studies.

The topic of conversation: markups?

• Recent attention micro-based aggregate markups in US, and other

regions – e.g. De Loecker and Eeckhout (2017, 2018).

• Debate at various high-level policy circles (ECB Sintra, Jackson Hole,

etc.) and among antritrust professionals, about the rise (or not) of

markups and therefore market power.

• Potentially large implications for studying aggregates:

• Labor market outcomes: wages, labor share and income inequality.

• Role of competition and trade policy.

• Inflation or lack thereof: rising markups and no inflation?

• Productivity growth measurement.

Roadmap

1. Methodology and data sources

2. Major patterns

3. Macroeconomic implications

4. Potential drivers

Data

• Listed firms (Compustat and Worldscope)

• Census: US and rolling across 10+ other countries

• Discuss pro and cons.

Methodology and data sources

• Challenging to measure (economic) profits and therefore market power.

• Markups, Pc , are good indicator but how to correctly measure it?

• Production Approach

1. Rely on firm-level production, revenue and expenditure data.

2. Compare cost and revenue share:

wL

PQ︸︷︷︸data

6= wL

cQ︸︷︷︸model

3. Consider aggregate across industry/region.

Producer Behavior

• Define markup µ = Pλ or

µit = θVitPitQit

PVit Vit

.

depending on Sales Sit = PitQit and expenditure share θVit , which is

specific to technology

• DLE and DLEU consider two distinct technologies.

Mt =∑i

sitµit (1)

United States (Compustat & US Census)

Secular Increase since 1980: 40 pts

.1.2

1.3

1.4

1.5

1.6

1960 1970 1980 1990 2000 2010

1

1.1

1.2

1.3

1960 1970 1980 1990 2000 2010

Driver aggregate pattern: superstar firms

• Aggregate useful for macro-analysis and policy discussion, hides

massive heterogeneity.

• Visualize by showing percentiles (sales-weighted) and markup

distribution over time.

Markup Percentiles and dispersion

All Action in Upper Half Distribution

1

1.5

2

2.5

1960 1970 1980 1990 2000 2010

AverageP90P75P50

0

.5

1

1.5

2

1 2 3

20161980

• Robust across industries and countries.

Reallocation: natural consequence

1.20

1.30

1.40

1.50

1980 1990 2000 2010

Markup (benchmark)WithinReallocationNet Entry

Markup = Market Power?

Profit Rate: + 7 ppt

0

.02

.04

.06

.08

.1

1960 1970 1980 1990 2000 2010

Markup = Market Power?

External validity Market Cap-sales

.5

1

1.5

Mar

ket V

alue

Sha

re

1.2

1.3

1.4

1.5

1.6

Mar

kup

1960 1970 1980 1990 2000 2010

MarkupMarket Value Share

Measurement and aggregation (DLE 2018)

1. Variable (COGS) versus (quasi)fixed (SGA):

• Combine COGS and SGA relies on perf. substitutes and flexibility

(variable) yielding net-profit rate (excl capital)

• Implied profits require netting out fixed costs (SGA)

2. Aggregation: using representative firm framework is invalid and

counterfactual. Using correct scale economies and heterogeneity in

underlying markup distribution yields observed aggregate profit share

when using:

Πs =∑i

si1

µi− F

S

Pictures from the US: measurement

• Sum all of COGS and SGA (adv, marketing, RD, brand value, CEO

comp.) into input bundle and apply DLW:

0

.02

.04

.06

.08

.1

profi

t rat

e

1.08

1.1

1.12

1.14

1.16

1.18

tau

1960 1970 1980 1990 2000 2010

tauprofit rate .1

.12

.14

.16

.18

profi

t rat

e (w

ithou

t Cap

ital)

1.08

1.1

1.12

1.14

1.16

1.18

tau

1960 1970 1980 1990 2000 2010

tauprofit rate (without Capital)

Pictures from the US: aggregation

0

.1

.2

.3

.4

1980 1990 2000 2010

Avg, No FCAvg, FCAggr, No FCAggr, FC (PF1)Profit Rate

Global Markup

Secular Increase since 1980: +50 pts

1.1

1.2

1.3

1.4

1.5

1.6

1980 1990 2000 2010

GLOBAL

Global Markup

Densities

0.5

11.5

2

1 2 3

20161980

.75

11.

251.

51.

752

Mar

kup

2016

.75 1 1.25 1.5 1.75 2Markup 1980

0.0110.0100.0090.0080.0070.0050.0040.0030.0020.0010.000

Evolution of Elasticities and Cost Shares

pVVpVV+rK

and θV

.82

.84

.86

.88

.9

.92

1960 1970 1980 1990 2000 2010

Elasticity VCost Share V

Markup Continents

1

1.2

1.4

1.6

1.8

1

1.2

1.4

1.6

1.8

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

EUROPE NORTH AMERICA SOUTH AMERICA

ASIA OCEANIA AFRICA

Markup for Individual Countries

>1.75(1.5,1.75](1.25,1.5][1,1.25]No data

Markup for Individual Countries

2016 change?

Global Average 1.59 +0.52

Europe 1.64 +0.66

1 Denmark 2.84 +1.95

2 Switzerland 2.72 +1.63

3 Italy 2.46 +1.46

4 Belgium 2.06 +1.03

5 Greece 1.80 +0.85

6 United Kingdom 1.68 +0.74

7 Norway 1.60 +0.74

8 Ireland 1.82 +0.66

9 France 1.50 +0.53

10 Sweden 1.31 +0.50

11 Netherlands 1.52 +0.47

12 Finland 1.36 +0.44

13 Austria 1.33 +0.41

14 Spain 1.34 +0.33

17 Germany 1.35 +0.29

16 Portugal 1.19 –0.06

North America 1.76 +0.63

1 United States 1.78 +0.63

2 Canada 1.53 +0.61

3 Mexico 1.55 +0.17

Africa 1.38 +0.32

1 South Africa 1.34 +0.07

2016 change?

Asia 1.45 +0.43

1 South Korea 1.48 +0.72

2 Hong Kong 1.65 +0.41

3 India 1.32 +0.34

4 Japan 1.33 +0.30

5 Indonesia 1.50 +0.22

6 Thailand 1.44 +0.21

7 Malaysia 1.33 +0.03

8 Pakistan 1.17 –0.01

9 Taiwan 1.24 –0.15

10 Turkey 1.16 –0.32

11 China 1.41 –0.45

12 Philippines 1.50 –0.77

Oceania 1.55 +0.56

1 Australia 1.57 +0.57

2 New Zealand 1.35 +0.37

South America 1.59 +0.01

1 Argentina 1.45 +0.64

2 Colombia 1.56 +0.41

3 Brazil 1.61 –0.01

4 Peru 1.64 –0.04

5 Venezuela 1.47 –0.46

6 Chile 1.37 –2.25

North America, Africa, Oceania

1

1.5

2

1

1.5

2

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

CANADA UNITED_STATES MEXICO

SOUTH_AFRICA AUSTRALIA NEW_ZEALAND

South America

1

2

3

4

1

2

3

4

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

ARGENTINA BRAZIL CHILE

COLOMBIA PERU VENEZUELA

Asia

.5

1

1.5

2

.5

1

1.5

2

.5

1

1.5

2

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

CHINA HONG_KONG INDIA INDONESIA

JAPAN MALAYSIA PAKISTAN PHILIPPINES

SOUTH_KOREA TAIWAN THAILAND TURKEY

Europe

11.52

2.53

11.52

2.53

11.52

2.53

11.52

2.53

1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010

AUSTRIA BELGIUM DENMARK FINLAND

FRANCE GERMANY GREECE IRELAND

ITALY NETHERLANDS NORWAY PORTUGAL

SPAIN SWEDEN SWITZERLAND UNITED_KINGDOM

Change in Markup

>1(0,1][-1,0]<-1No data

Listed firms: Representative?

• US census confirm Compustat (logic)

• China census confirm Worldscope

• Program in progress cross-country census data

China

1

1.2

1.4

1.6

1.8

1980 1990 2000 2010

CHINA

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����

���

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08

���� ���� ���� ���� ����\HDU

Macroeconomic Implications

Decline in Labor Share

µit = θVitPitQit

PVit Vit

V=L⇒ wtLitSit

=θLitµit

.54

.56

.58

.6

.62

Labo

r sha

re (K

N)

.6

.7

.8

.9

1

Mar

kup

(Inve

rse)

1980 1990 2000 2010

Markup (Inverse)Labor share (KN)

For same reason: decline in Capital Share

Summary of Facts

1. Increase in Markup since 1980: + 0.40 / 0.30

2. Mostly within industry (in all; no particular industries)

3. Only in the upper half of Markup distribution (especially at top)

4. Pure markup growth and reallocation across firms.

5. Total profits +7 ppt

Implications

• Intuition model: 2 to 1 firm →• Total production declines and price increase,

• Demand for labor goes down, wages decrease,

• Average firm size increase.

• Labor market:

• Declining labor share

• Decline in (Low-skill) Wages and increasing inequality

• Decline in Labor Force Participation and Reallocation

• Decline in Output Growth

• Decline entry and investment?

MLN USD question: Source?

• Heavily debated with and without facts: competition policy.

• My interpretation (so far):

1. Technological change:

• IT applications, rise fixed factors, automation, AI, etc.

2. Globalization: market size and cost reductions.

• Demand (expansion) and cost channels (GVC, outsourcing)

• Case-study: India (1985-1992) – De Loecker and Goldberg.

Trade liberalization

• Natural experiments to simulate effects of opening up markets.

• Case of India: BOP crisis and IMF steps in: overnight tariff cuts.

• Conventional wisdom: pro-competitive effect reduces markups.

• We find increasing markups due to incomplete pass-through:

• Lower tariffs implies cost reductions,

• Not fully passed-on to final product price

• Results confirmed in other settings (e.g. Chinese import comp.).

• Globalization: allows top firms to expand more, while reducing cost.

Concluding remarks

• Large secular change in firm performance with potential massive

implication for society at large.

• Global debate across academics, industry, and policy makers.

• Evidence-based debates are critical.