Innovation and Top Income Inequality - Banque de France · Innovation and Top Income Inequality Philippe ... In this lecture we shall argue that innovation is certainly one such factor

Innovation and Top Income Inequality

Philippe Aghion (Harvard)Ufuk Akcigit (UPenn)

Antonin Bergeaud (Bank of France)Richard Blundell (UCL)David Hemous (INSEAD)

April 2015

Innovation and Top Income Inequality April 2015

Innovation and Top Income Inequality Introduction

Introduction

Past decades have witnessed a sharp increase in top income inequalityworldwide and particularly in developed countries

()Innovation and Top Income Inequality April 2015 2 / 44

US MALE WAGE INEQUALITY, 1937-2005

Source: Goldin and Katz (2008)

0

5

10

15

20

25

3019

18

19

20

19

22

19

24

19

26

19

28

19

30

19

32

19

34

19

36

19

38

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40

19

42

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44

19

46

19

48

19

50

19

52

19

54

19

56

19

58

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60

19

62

19

64

19

66

19

68

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70

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72

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74

19

76

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78

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80

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82

19

84

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86

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88

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90

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92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Perc

entile

Share

U.S. Top 1% U.S. Top 0.1%

US Top 1%

US Top 0.1%

Income shares at the very top over last 100 years:US top 1% increases from 9% in 1978 to 22% in 2012

Source: Atkinson, Piketty & Saez; High Income Database

0

5

10

15

20

2519

18

19

20

19

22

19

24

19

26

19

28

19

30

19

32

19

34

19

36

19

38

19

40

19

42

19

44

19

46

19

48

19

50

19

52

19

54

19

56

19

58

19

60

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

Perc

entile

Share

U.K. Top 1% U.K. Top 0.1%

UK Top 1%

UK Top 0.1%

Income shares at the very top: UK top 1% increases from 6% in 1978 to 14% in 2009

Source: Atkinson, Piketty & Saez; High Income Database


Introduction

However no consensus has been reached as to the main underlyingfactors behind this surge in top income inequality

In this lecture we shall argue that innovation is certainly one suchfactor and that it also affects social mobility.



Introduction

Three parts to the presentation:1 Part 1: Model−→ we develop a Schumpeterian model of innovation, top incomeinequality and social mobility

2 Part 2: Empirical analysis using US aggregate data−→ we use cross-state panel data over the period 1995-2010 to look atthe effect of innovativeness on top income inequality.−→ we use cross Commuting-zone data from Chetty et al (2015) tolook at the effect of innovativeness on social mobility.

3 Part 3: Empirical analysis using using individual data−→ we combine individual patenting with individual fiscal data to lookat the social mobility of inventors versus non-inventors.



Introduction

Part 1 and Part 2 are drawn fromAghion-Akcigit-Bergeaud-Blundell-Hemous (2015)

Part 3 is drawn from ongoing work byAghion-Akcigit-Toivanen (2015)



Summary of Part 1

We develop a simple Schumpeterian growth model where:1 growth results from quality-improving innovations by incumbents orfrom potential entrants.

2 facilitating innovation

increases top income shares as top incomes are earned by innovators

spurs social mobility as innovation entails creative destruction



Summary of Part 1

We develop a simple Schumpeterian growth model where:1 growth results from quality-improving innovations by incumbents orfrom potential entrants.

2 facilitating innovation

increases top income shares as top incomes are earned by innovatorsspurs social mobility as innovation entails creative destruction



Summary of Part 1

The model predicts:1 Innovation by entrants and/or incumbents increases top incomeinequality;

2 Innovation by entrants increases social mobility;3 Entry barriers (e.g. from lobbying), lower the positive effects ofentrants’innovations on top income inequality and social mobility.



Summary of Part 2

Our main empirical findings from cross-state panel regressions:1 The top 1% income share is positively and significantly correlated withthe state’s degree of "innovativeness"

2 This at least partly reflects a causal effect of innovation on top incomes3 Innovativeness is less positively correlated with broader measures ofinequality.



Summary of Part 2

From cross-section regressions performed at the CZ level:1 Innovativeness is positively correlated with upward social mobility2 The positive effects of innovativeness on social mobility, is drivenmainly by entrant innovators and less so by incumbent innovators

3 The positive effects of innovation on the top 1% income share and onsocial mobility are both dampened in states with higher lobbyingintensity



Relationship with existing literature

The analysis in this paper relates to several strands of literature onincome inequality and growth

1 Empirical literature on inequality and growth: Forbes (2000), Banerjeeand Duflo (2003), Frank (2009)

2 Literature on skill-biased technical change: Katz and Murphy (1992),Krusell, Ohanian, Ríos-Rull and Violante (2000), Goldin and Katz(2008), Acemoglu, (1998, 2002 and 2007)

3 Literature on evolution of income and wealth inequality: Piketty andSaez (2003), Gabaix and Landier (2008), Piketty (2014)

4 Ongoing work on innovation and social mobility using individual data:Toivanen and Vaananen (2014), Bell et al (2015)



Outline

Introduction

Part 1: ModelPart 2: Empirical analysis using US aggregate data

Part 3: Empirical analysis using individual data

Conclusion


Innovation and Top Income Inequality Part 1: Model

ModelPopulation

Discrete time; continuum of individuals of measure 2:−→ half are capital (firm) owners and the rest works as productionworkers

Each individual lives only for one period

Every period, a new generation of individuals is born and individualsthat are born to current firm owners inherit the firm from their parents

The rest of the population works in production unless theysuccessfully innovate and replace incumbents’children.



ModelProduction

A final good is produced according to:

lnYt =∫ 1

0ln yitdi

Each intermediate is produced with a linear production function

yit = qit lit



ModelInnovation

When there is a new innovation in any sector i :

qi ,t+1 = ηHqi ,t .

If there is no new innovation in sector i in period t + 1, theincumbent’s technological lead shrinks to ηL where ηL < ηH .

If there is a new innovation in sector i , the previous technologybecomes fully available to every firm in the economy, therefore thetechnological lead remains ηH .

An incumbent can use lobbying to prevent entry by an innovator→ Lobbying is successful with probability z , in which case, theinnovation is not implemented.



ModelR&D technology

By spending

CJ ,t (x) = θJx2

2Yt

an incumbent (J = I ) or entrant (J = E ) can innovate withprobability x .



ModelTiming of events within each period

1 In each line i , a potential entrant spends Ct (xi ) and the offspring ofthe incumbent in sector i spends Ct (x̃i ) .

2 With probability (1− z) xi the entrant succeeds, replaces theincumbent and obtains a technological lead ηH ; with probability x̃ithe incumbent succeeds and improves its technological lead from ηLto ηH , with probability 1− (1− z) xi − x̃i , there is no successfulinnovation and the incumbent stays the leader with a technologicallead of ηL

3 Production and consumption takes place and the period ends.



ModelEquilibrium profits and wages

Marginal cost of production of intermediate producer i at time t :

MCit =wtqi ,t.

Hence the price charged at time t by intermediate producer i is:

pi ,t =wtηit ,qi ,t

where ηi ,t ∈ {ηH , ηL} depending on when the last innovationoccurred (recall that recent technologies have higher markups).



ModelEquilibrium labor demand and profits

Use the fact that in equilibrium

pi ,tyit ≡ Yt .

Equilibrium profits in sector i at time t:

πit = (pit −MCit )yit =ηit − 1

ηitYt ,



ModelEquilibrium profits

Hence profits are higher if the incumbent has recently innovated,namely:

πH ,t =ηH − 1

ηH︸︷︷︸≡πH

Yt > πL,t =ηL − 1

ηL︸︷︷︸≡πL

Yt .



ModelIncome inequality

Let µt denote the fraction of high-mark-up sectors

Entrepreneur share is:

entrepreneur_sharet =Yt − wtYt

= 1− µtηH− 1− µt

ηL

Thus the entrepreneur share is increasing in the fraction ofhigh-mark-up sectors µt .−→ µt in turn depends upon innovation intensities by entrants andincumbents (x and x̃).



ModelEquilibrium innovation investments

The offspring of a previous period’s incumbent solves:

maxx̃

{x̃πHYt + (1− x̃ − (1− z) x∗)πLYt + (1− z) x∗wt

−θIx̃ 22 Yt

}.

A potential entrant solves:

maxx

{(1− z) xπHYt + (1− x (1− z))wt − θE

x2

2Yt

}



ModelEquilibrium innovation investments

Nash equilibrium (x∗, x̃∗) where x∗ and x̃∗ are decreasing functions of(θE , θI )

Higher entry barriers (higher z) discourage entrant innovation.



Model

More formally:

x̃∗ =πH − πL

θI=

(1

ηL− 1

ηH

)1θI

and

x∗ =

(πH − 1

ηL+(1

ηL− 1

ηH

)x̃∗)(1− z)

θE − (1− z)2(1

ηL− 1

ηH

) .



ModelEquilibrium share of high mark up sectors

We have:µt = µ∗ = (1− z) x∗ + x̃∗



ModelEquilibrium income shares

The entrepreneur and labor income shares in equilibrium are:

entrepreneur_sharet = 1−1

ηL+

(1

ηL− 1

ηH

)((1− z) x∗ + x̃∗).

and

wage_sharet =wtYt=1

ηL−(1

ηL− 1

ηH

)((1− z) x∗ + x̃∗)

Thus any change (e.g lower R&D costs) which fosters innovation byincumbents or entrants also increases the entrepreneur share ofincome.

This effect is lower when barriers to entry (z) are larger.



ModelSocial mobility

Probability that worker’offspring is also a worker:

Ψ = 1− x∗ (1− z) .

Hence we define social mobility as

M = 1−Ψ = x∗ (1− z) ,

which is increasing in the innovation rate x∗ but less so the higherentry barriers (i.e the higher z).

Note that a reduction in the incumbent’s R&D costs will also fostersocial mobility (general equilibrium effect).



ModelPredictions

Entrant and incumbent innovation increase top income inequality;

Entrant innovation increases social mobility;

Entry barriers lower the positive effects of entrant innovation on topincome inequality and social mobility.



Outline

Introduction

Part 1: Model

Part 2: Empirical analysis using US aggregate dataPart 3: Empirical analysis using individual data

Conclusion


Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data

Data and measurement

Our core empirical analysis is carried out at US state level.

Our dataset covers the period 1975-2010, a time range imposed uponus by the availability of patent data.



Data and measurementInequality

Data on share of income owned by the top 1% and the top 10% ofincome distribution are drawn from the US State-Level IncomeInequality Database (Frank, 2009).−→ from that data source, we also gather information on AtkinsonIndex, Theil Index and the Gini Index.

In every US state, the top 1% income share has increased between1975 and 2010−→ the unweighted mean value was around 8% in 1975 and reached21% in 2007 before slowly decreasing to 16.3% in 2010.



Data and measurementInnovation

When looking at cross state or more local levels, the US patent offi ce(USPTO) provides complete statistics for patents granted betweenthe years 1975 and 2010.

For each patent, it provides information on the state of residence ofthe patent inventor, the date of application of the patent and a linkto every citing patents granted before 2010.

For patents with multiple inventors, we assume that they are splitevenly among inventors and thus we attribute only a fraction of thepatent to each inventor.

We follow Jaffe, Hall and Trajtenberg (2001) to address the issue oftruncation bias in both the number of patents and the number ofcitations.

()Innovation and Top Income Inequality May 2015 31 / 43



The USPTO classification considers three types of patents accordingto the offi cial documentation:

1 Utility patents that are used to protect a new and useful invention, oran improvement to an existing process.

2 Design patents that are used to protect a new design of amanufactured object.

3 Plant patents that protect some new varieties of plants.

The first type accounts for more than 90% of all patents at theUSPTO and it is the only type of patents for which we have completedata.−→ We thus focus on utility patents, in line with the patentingliterature.




There is a substantial amount of variation in innovativeness bothacross states and over time.

1 Between 1975 and 1990: Delaware, Connecticut, New Jersey andMassachusetts were the most innovating states, whereas Arkansas,Mississippi and Hawaii were the least innovative states with less than0.05 patents per thousands inhabitants

2 Between 1990 and 2009, the most innovative states were Idaho,Vermont, Massachusetts, Minnesota and California, whereas Arkansas,West Virginia and Mississippi all had less than 0.06 patents per 1000inhabitants.



Data and measurementQuality of innovation

Four measures of innovation quality, aggregated at the state level:1 3, 4 and 5 year windows citations counter−→ the number of citations received within no more than 3, 4 or 5years after the application date

2 Is the patent among the 5% most cited in the year by 2010−→ dummy variable equal to one if the patent applied for in a givenyear belong to the top 5% most cited patents.

3 Total corrected citation counter−→ the number of times a patent has been cited

4 Has the patent been renewed−→ dummy variable equal to one if the patent has been renewed (atleast one) before 2014



Data and measurementControl variables

Output gap to control for the business cycle

Share of state GDP accounted for by the financial sector

Size of the government sector

GDP per capita

Growth of total population



Regression equation

Regressing top income inequality on innovativeness:

log(yit ) = A+ Bi + Bt + β1 log(innovi (t−1)) + β2Xit + εit .


OLS regressions on patents per capita on top 1%

OLS regressions on various measures of innovation on top 1%


InstrumentationFirst instrument

Following Aghion et al (2004), we consider the time-varying Statecomposition of the appropriation committees of the Senate and theHouse of Representatives.

A Committee member often push towards subsidizing researcheducation in her State, in order to increase her chances of reelectionin that State.−→ a state with one of its congressmen seating on the committee islikely to receive more funding for research education, which shouldincrease its innovativeness in following years.



InstrumentationSecond instrument

Second instrument based on knowledge spillovers.−→ The idea is to instrument innovation in a state by the sum ofinnovation intensities in other states weighted by the relativeinnovation spillovers from these other states.



InstrumentationSecond instrument

More formally, if m(i , j ,T ) is the number of citations from a patentin state i , to a patent of state j over period 1975-1984, and ifinnov(j , t) denotes our measure of innovativeness in state j at time t,then we posit:

wi ,j =m(i , j ,T )

∑k 6=im(i , k,T )

and Yi ,t = ∑j 6=iwi ,j ∗ innov(j , t − 1).


IV regressions with first instrument (Appropriation Committee)

IV regressions with second instrument (Spillover)

IV regressions of innovation on various measure of inequality (2 instruments)

IV regressions of innovation on top 1% at various lag (2 instruments)

IV regressions of innovation on top 1% with additional controls for financial sector and oil (2 instruments)

Col 2: remove NY, DE, CT and SD (highest shares of financial sector). Col 3: remove all patents from financial-related IPC classes. Col 6: remove all patents from oilrelated IPC classes.


Magnitude of the effects

When measured by the number of patent per capita, innovativenessaccounts on average for about 17% of the total increase in the top 1%income share between 1975 and 2010 according to either IV regression



Extensions

The effect of innovativeness on social mobility

Entrant versus incumbent innovation

Lobbying as a dampening factor


CZ level: Effect of innovation on social mobility. OLS regressions

CZ level: New Entrants VS Incumbent innovation, effect on social mobility. OLS regressions

State level: New Entrants VS Incumbent innovation, effect on top 1%. OLS regressions

Effect of lobbying on new entrant and incumbent innovation on top 1% and social mobility. IV regressions for col 3, OLS for others.


Summarizing Part 2

We have analyzed the effect of innovation-led growth on top incomesand on social mobility.

We found positive and significant correlations between (entrant)innovation, top income shares and social mobility.

Our instrumentation at cross-state level suggested a causality frominnovativeness to top income shares.

When measured by the number of patent per capita, innovativenessaccounts on average across US states for about 17% of the totalincrease in the top 1% income share between 1975 and 2010.



Outline

Introduction

Part 1: Model

Part 2: Empirical analysis using US aggregate data

Part 3: Empirical analysis using individual dataConclusion


Living ”American Dream” in Finland:The Social Mobility of Innovators

Philippe Aghion Ufuk Akcigit Otto Toivanen

Harvard UPenn KU Leuven

April 2015


Innovation and Top Income Inequality Part 3: Empirical analysis using individual data

Data

The data used now includes1 all inventors in our data (i.e., individuals who obtained a USPTO

patent 1990 - 1999) that work in firms that participate in the R&Dsurvey.

2 The original inventor sample consists of some 75% of all Finnishinventors of USPTO patents that could be matched to the Finnishemployer-employee data.

3 The 884 inventors in the current data are circa 38% of the 2328inventors in the full data.

4 a random sample of (almost) 100K control individuals from those samefirms.

5 These individuals represent some 5% of the Finnish working agepopulation.

In 1991, we have 82 184 individuals in our sample of whom 843obtain at least one USPTO patent between 1990 and 1999.

For 1999, we have 94 806 individuals of whom 882 have obtained atleast one USPTO patent between 1990 and 1999.



Wage Income Growth (1)

0.39

0.25

0.230.22 0.22 0.22 0.22 0.22

0.2

0.4

0.27

0.310.3

0.31

0.290.28

0.24

0.42

10 20 30 40 50 60 70 80 90income percentiles

Wage Income Growth (1990‐1999) by Percentilesnon‐inventors inventors



Wage Income Growth (2)

0.2 0.2 0.2 0.21 0.26

0.420.49

0.63

1.65

3.25

90 92 94 96 98income percentiles

Wage Income Growth (1990‐1999) by Percentilesnon‐inventors inventors



Capital vs Labor Income in 1999

5.745.44

6.336.50

7.12

1.93 2.022.18

3.56

5.11

90 92 94 96 98

Inventor/Non‐inventor Ratio by Type of Income in 1999

capital income ratio wage income ratio



Transition Matrix

Table 1: Transitions 1991 to 1999

non-inventors

1991 / 1999 top-10=0 top-10=1 Conditional Prob.top-10=0 88.05 4.17 4.51top-10=1 2.34 5.45 69.96

inventors




Transition Matrix by Father’s Education

Table 2: Transitions 1991 to 1999 conditional on father’s education

Father’s education < 12 years

non-inventors inventors

91 / 99 top10=0 top10=1 C/Pr 91 / 99 top10=0 top10=1 C/Prtop10=0 86.55 5.13 5.60 top10=0 44.81 19.10 29.88top10=1 2.41 5.91 71.03 top10=1 6.84 29.25 81.07

Father’s education ≥12 years

91 / 99 0 1 C/Pr 91 / 99 top-10=0 top-10=1 C/Prtop10=0 88.24 4.05 4.39 top10=0 39.24 20.85 34.70top10=1 2.36 5.35 69.30 top10=1 8.07 31.84 79.78



Transition Matrix by Gender

Table 3: Transitions 1991 to 1999 conditional on gender

Female


91 / 99 0 1 Con Pr 91 / 99 top-10=0 top-10=1 Con Prtop10=0 95.73 2.02 2.07 top-10=0 67.78 11.11 14.08top10=1 0.87 1.38 61.33 top-10=1 1.11 20.00 94.74

Male

91 / 99 0 1 Con Pr 91 / 99 top-10=0 top-10=1 Con Prtop10=0 84.37 5.22 5.83 top-10=0 39.37 20.76 34.53top10=1 3.07 7.34 70.51 top-10=1 8.35 31.52 79.06



Transition Matrix by Age

Table 4: Transitions 1991 to 1999 by age (inventors only)

< median age


> median age




Transition Matrix by Innovation Quality

Table 5: Transitions 1991 to 1999 by quality of invention

< 20 citations


≥20 citations

1991/1999 top-10=0 top-10=1 Conditional Prob.top-10=0 35.78 38.53 51.85top-10=1 2.75 22.94 89.30



Labor Income in 1999

Table 6: Ln(wage) in 1999

Logwage top-10% in 1999(1) (2) (3)

patent count -0.1132 -0.0516 -0.03310.0438 0.0326 0.02440.0098 0.1135 0.1745

citations 1-9 0.1456 0.0594 0.09870.0664 0.0581 0.03880.0284 0.307 0.0109

citations 10-19 0.2725 0.2375 0.18030.1358 0.1658 0.06290.0448 0.152 0.0042

citations 20-29 0.4176 0.3975 0.23040.1483 0.1538 0.08030.0049 0.0098 0.0041

citations 30- 0.869 0.7862 0.33130.1913 0.2038 0.09930.000 0.0001 0.0008

polynomial in Ln(wage) in 1991 3 3 3controls YES YES YES

father’s educ. NO YES NOnobs 75233 13634 75262R-sq. 0.40 0.39 0.42

NOTES: numbers presented are coefficient, robust s.e., and p-value.Controls include third order polynomial in age; a gender dummy;a dummy for having Finnish as mother tounge; 45 field and level of educ dummies;a dummy for being an entrepreneur in 1991; and tenure in current job in 1991.father’s educ. = 45 field and level of education dummies for the father.



Labor Income in 1999

0.15

0.27

0.42

0.87

1‐9 10‐19 20‐29 30+citation counts

Percentage Increase in Wage (relative to 0‐cited)



Transition Matrix by Own Education

Table 6: Transitions 1991 to 1999 conditional on own education

education in 1991 < 16 years


1991/1999 top-10=0 top-10=1 Con Pr 1991/1999 0 1 Con Prtop-10=0 95.87 1.57 1.61 0 77.05 9.84 11.32top-10=1 1.28 1.28 50.00 1 4.10 9.02 68.80

education in 1991≥ 16 years

1991/1999 0 1 Con Pr 1991/1999 0 1 Con Prtop-10=0 71.91 9.57 11.75 0 37.11 21.32 36.49top-10=1 4.60 13.92 75.16 1 8.03 33.55 80.69



Transition Matrix by Firm Size

Table: Transitions 1991 to 1999 conditional on firm size

firm size in 1991 < median firm size in 1991



firm size in 1991 ≥ median firm in size1991



Innovation and Top Income Inequality

Conclusion

Overall, our findings suggest avenues for further research on(innovation-led) growth, inequality and social mobility.

1 Analyze how factors such as innate ability, parental education/income,and firms characteristics affect the probability for an inventor to makeit to top income brackets

2 Analyze the direct and indirect contribution of inventions to topincome inequality: the labor and capital incomes of inventors, the valueof firms created by inventors, how the invention affects the top incomesof people working with the inventor.

3 Policy implications: e.g., how do we factor in *innovation* whendesigning tax policy and combining with entry policy, patent policy,...to achieve more inclusive innovation-driven growth?

4 Go deeper into how institutions affect the relationship betweeninnovation, top income inequality, and social mobility.

()Innovation and Top Income Inequality May 2015 43 / 43

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Innovation and Top Income Inequality - Banque de France · Innovation and Top Income Inequality Philippe ... In this lecture we shall argue that innovation is certainly one such factor