innovation and top income inequality - banque de france · innovation and top income inequality...
TRANSCRIPT
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
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
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
Innovation and Top Income Inequality Introduction
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.
()Innovation and Top Income Inequality April 2015 3 / 44
Innovation and Top Income Inequality Introduction
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.
()Innovation and Top Income Inequality April 2015 4 / 44
Innovation and Top Income Inequality Introduction
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)
()Innovation and Top Income Inequality April 2015 5 / 44
Innovation and Top Income Inequality Introduction
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
()Innovation and Top Income Inequality April 2015 6 / 44
Innovation and Top Income Inequality Introduction
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
()Innovation and Top Income Inequality April 2015 6 / 44
Innovation and Top Income Inequality Introduction
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.
()Innovation and Top Income Inequality April 2015 7 / 44
Innovation and Top Income Inequality Introduction
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.
()Innovation and Top Income Inequality April 2015 8 / 44
Innovation and Top Income Inequality Introduction
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
()Innovation and Top Income Inequality April 2015 9 / 44
Innovation and Top Income Inequality Introduction
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)
()Innovation and Top Income Inequality April 2015 10 / 44
Innovation and Top Income Inequality Introduction
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 April 2015 11 / 44
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.
()Innovation and Top Income Inequality April 2015 12 / 44
Innovation and Top Income Inequality Part 1: Model
ModelProduction
A final good is produced according to:
lnYt =∫ 1
0ln yitdi
Each intermediate is produced with a linear production function
yit = qit lit
()Innovation and Top Income Inequality April 2015 13 / 44
Innovation and Top Income Inequality Part 1: Model
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.
()Innovation and Top Income Inequality April 2015 14 / 44
Innovation and Top Income Inequality Part 1: Model
ModelR&D technology
By spending
CJ ,t (x) = θJx2
2Yt
an incumbent (J = I ) or entrant (J = E ) can innovate withprobability x .
()Innovation and Top Income Inequality April 2015 15 / 44
Innovation and Top Income Inequality Part 1: Model
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.
()Innovation and Top Income Inequality April 2015 16 / 44
Innovation and Top Income Inequality Part 1: Model
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).
()Innovation and Top Income Inequality April 2015 17 / 44
Innovation and Top Income Inequality Part 1: Model
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 ,
()Innovation and Top Income Inequality April 2015 18 / 44
Innovation and Top Income Inequality Part 1: Model
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 .
()Innovation and Top Income Inequality April 2015 19 / 44
Innovation and Top Income Inequality Part 1: Model
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).
()Innovation and Top Income Inequality April 2015 20 / 44
Innovation and Top Income Inequality Part 1: Model
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
}
()Innovation and Top Income Inequality April 2015 21 / 44
Innovation and Top Income Inequality Part 1: Model
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.
()Innovation and Top Income Inequality April 2015 22 / 44
Innovation and Top Income Inequality Part 1: Model
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
) .
()Innovation and Top Income Inequality April 2015 23 / 44
Innovation and Top Income Inequality Part 1: Model
ModelEquilibrium share of high mark up sectors
We have:µt = µ∗ = (1− z) x∗ + x̃∗
()Innovation and Top Income Inequality April 2015 24 / 44
Innovation and Top Income Inequality Part 1: Model
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.
()Innovation and Top Income Inequality April 2015 25 / 44
Innovation and Top Income Inequality Part 1: Model
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).
()Innovation and Top Income Inequality April 2015 26 / 44
Innovation and Top Income Inequality Part 1: Model
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.
()Innovation and Top Income Inequality April 2015 27 / 44
Innovation and Top Income Inequality Part 1: Model
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 April 2015 28 / 44
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.
()Innovation and Top Income Inequality April 2015 29 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate 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.
()Innovation and Top Income Inequality April 2015 30 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
Data and measurementInnovation
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.
()Innovation and Top Income Inequality April 2015 32 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
Data and measurementInnovation
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.
()Innovation and Top Income Inequality April 2015 33 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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
()Innovation and Top Income Inequality April 2015 34 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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
()Innovation and Top Income Inequality April 2015 35 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
Regression equation
Regressing top income inequality on innovativeness:
log(yit ) = A+ Bi + Bt + β1 log(innovi (t−1)) + β2Xit + εit .
()Innovation and Top Income Inequality April 2015 36 / 44
OLS regressions on patents per capita on top 1%
OLS regressions on various measures of innovation on top 1%
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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.
()Innovation and Top Income Inequality April 2015 37 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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.
()Innovation and Top Income Inequality April 2015 38 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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).
()Innovation and Top Income Inequality April 2015 39 / 44
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.
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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
()Innovation and Top Income Inequality April 2015 40 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
Extensions
The effect of innovativeness on social mobility
Entrant versus incumbent innovation
Lobbying as a dampening factor
()Innovation and Top Income Inequality April 2015 41 / 44
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.
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
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.
()Innovation and Top Income Inequality April 2015 42 / 44
Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data
Outline
Introduction
Part 1: Model
Part 2: Empirical analysis using US aggregate data
Part 3: Empirical analysis using individual dataConclusion
()Innovation and Top Income Inequality April 2015 43 / 44
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 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.
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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
1991 / 1999 top-10=0 top-10=1 Conditional Prob.top-10=0 41.95 19.61 31.86top-10=1 7.60 30.84 80.23
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
Transition Matrix by Gender
Table 3: Transitions 1991 to 1999 conditional on gender
Female
non-inventors inventors
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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
Transition Matrix by Age
Table 4: Transitions 1991 to 1999 by age (inventors only)
< median age
1991 / 1999 top-10=0 top-10=1 Conditional Prob.top-10=0 47.19 26.53 35.99top-10=1 5.10 21.17 80.56
> median age
1991 / 1999 top-10=0 top-10=1 Conditional Prob.top-10=0 38.98 14.29 26.83top-10=1 9.39 37.35 79.93
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
Transition Matrix by Innovation Quality
Table 5: Transitions 1991 to 1999 by quality of invention
< 20 citations
1991 / 1999 top-10=0 top-10=1 Conditional Prob.top-10=0 43.60 17.08 28.15top-10=1 8.15 31.18 79.29
≥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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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.
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
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)
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
Transition Matrix by Own Education
Table 6: Transitions 1991 to 1999 conditional on own education
education in 1991 < 16 years
non-inventors inventors
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
Innovation and Top Income Inequality April 2015
Innovation and Top Income Inequality Part 3: Empirical analysis using individual data
Transition Matrix by Firm Size
Table: Transitions 1991 to 1999 conditional on firm size
firm size in 1991 < median firm size in 1991
non-inventors inventors
1991/1999 top-10=0 top-10=1 Con Pr 1991/1999 0 1 Con Prtop-10=0 84.76 4.36 4.89 0 35.03 23.73 40.38top-10=1 3.21 7.67 60.50 1 5.08 36.16 87.68
firm size in 1991 ≥ median firm in size1991
1991/1999 top-10=0 top-10=1 Con Pr 1991/1999 0 1 Con Prtop-10=0 89.11 4.14 4.44 0 44.54 18.72 29.59top-10=1 2.08 4.67 69.19 1 8.09 28.65 77.98
Innovation and Top Income Inequality April 2015
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