Innovation and Top Income Inequality

Transcription

1 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

2 Introduction Innovation and Top Income Inequality Introduction Past decades have witnessed a sharp increase in top income inequality worldwide and particularly in developed countries Top Income Inequality April / 44

3 US MALE WAGE INEQUALITY, Source: Goldin and Katz (2008)

4 Percentile Share Income shares at the very top over last 100 years: US top 1% increases from 9% in 1978 to 22% in US Top 1% US Top 0.1% U.S. Top 1% U.S. Top 0.1% Source: Atkinson, Piketty & Saez; High Income Database

5 Percentile Share 25 Income shares at the very top: UK top 1% increases from 6% in 1978 to 14% in UK Top 1% 10 5 UK Top 0.1% 0 U.K. Top 1% U.K. Top 0.1% Source: Atkinson, Piketty & Saez; High Income Database

6 Introduction Introduction However no consensus has been reached as to the main underlying factors behind this surge in top income inequality In this lecture we shall argue that innovation is certainly one such factor and that it also affects social mobility. Top Income Inequality April / 44

7

8 Introduction Innovation and Top Income Inequality Introduction Three parts to the presentation: 1 Part 1: Model we develop a Schumpeterian model of innovation, top income inequality and social mobility 2 Part 2: Empirical analysis using US aggregate data we use cross-state panel data over the period to look at the effect of innovativeness on top income inequality. we use cross Commuting-zone data from Chetty et al (2015) to look 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 look at the social mobility of inventors versus non-inventors. Top Income Inequality April / 44

9 Introduction Innovation and Top Income Inequality Introduction Part 1 and Part 2 are drawn from Aghion-Akcigit-Bergeaud-Blundell-Hemous (2015) Part 3 is drawn from ongoing work by Aghion-Akcigit-Toivanen (2015) Top Income Inequality April / 44

10 Summary of Part 1 Innovation and Top Income Inequality Introduction We develop a simple Schumpeterian growth model where: 1 growth results from quality-improving innovations by incumbents or from potential entrants. 2 facilitating innovation increases top income shares as top incomes are earned by innovators Top Income Inequality April / 44

11 Summary of Part 1 Innovation and Top Income Inequality Introduction We develop a simple Schumpeterian growth model where: 1 growth results from quality-improving innovations by incumbents or from potential entrants. 2 facilitating innovation increases top income shares as top incomes are earned by innovators spurs social mobility as innovation entails creative destruction Top Income Inequality April / 44

12 Summary of Part 1 Innovation and Top Income Inequality Introduction The model predicts: 1 Innovation by entrants and/or incumbents increases top income inequality; 2 Innovation by entrants increases social mobility; 3 Entry barriers (e.g. from lobbying), lower the positive effects of entrants innovations on top income inequality and social mobility. Top Income Inequality April / 44

13 Summary of Part 2 Innovation and Top Income Inequality Introduction Our main empirical findings from cross-state panel regressions: 1 The top 1% income share is positively and significantly correlated with the state s degree of "innovativeness" 2 This at least partly reflects a causal effect of innovation on top incomes 3 Innovativeness is less positively correlated with broader measures of inequality. Top Income Inequality April / 44

14 Summary of Part 2 Innovation and Top Income Inequality Introduction From cross-section regressions performed at the CZ level: 1 Innovativeness is positively correlated with upward social mobility 2 The positive effects of innovativeness on social mobility, is driven mainly by entrant innovators and less so by incumbent innovators 3 The positive effects of innovation on the top 1% income share and on social mobility are both dampened in states with higher lobbying intensity Top Income Inequality April / 44

15 Introduction Relationship with existing literature The analysis in this paper relates to several strands of literature on income inequality and growth 1 Empirical literature on inequality and growth: Forbes (2000), Banerjee and 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 and Saez (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) Top Income Inequality April / 44

16 Introduction Outline Introduction Part 1: Model Part 2: Empirical analysis using US aggregate data Part 3: Empirical analysis using individual data Conclusion Top Income Inequality April / 44

17 Part 1: Model Model Population Discrete time; continuum of individuals of measure 2: half are capital (firm) owners and the rest works as production workers Each individual lives only for one period Every period, a new generation of individuals is born and individuals that are born to current firm owners inherit the firm from their parents The rest of the population works in production unless they successfully innovate and replace incumbents children. Top Income Inequality April / 44

18 Part 1: Model Model Production A final good is produced according to: ln Y t = 1 0 ln y it di Each intermediate is produced with a linear production function y it = q it l it Top Income Inequality April / 44

19 Part 1: Model Model Innovation When there is a new innovation in any sector i : q i,t+1 = η H q i,t. If there is no new innovation in sector i in period t + 1, the incumbent s technological lead shrinks to η L where η L < η H. If there is a new innovation in sector i, the previous technology becomes fully available to every firm in the economy, therefore the technological lead remains η H. An incumbent can use lobbying to prevent entry by an innovator Lobbying is successful with probability z, in which case, the innovation is not implemented. Top Income Inequality April / 44

20 Part 1: Model Model R&D technology By spending C J,t (x) = θ J x 2 2 Y t an incumbent (J = I ) or entrant (J = E ) can innovate with probability x. Top Income Inequality April / 44

21 Part 1: Model Model Timing of events within each period 1 In each line i, a potential entrant spends C t (x i ) and the offspring of the incumbent in sector i spends C t ( x i ). 2 With probability (1 z) x i the entrant succeeds, replaces the incumbent and obtains a technological lead η H ; with probability x i the incumbent succeeds and improves its technological lead from η L to η H, with probability 1 (1 z) x i x i, there is no successful innovation and the incumbent stays the leader with a technological lead of η L 3 Production and consumption takes place and the period ends. Top Income Inequality April / 44

22 Part 1: Model Model Equilibrium profits and wages Marginal cost of production of intermediate producer i at time t : MC it = w t q i,t. Hence the price charged at time t by intermediate producer i is: p i,t = w t η it, q i,t where η i,t {η H, η L } depending on when the last innovation occurred (recall that recent technologies have higher markups). Top Income Inequality April / 44

23 Part 1: Model Model Equilibrium labor demand and profits Use the fact that in equilibrium p i,t y it Y t. Equilibrium profits in sector i at time t: π it = (p it MC it )y it = η it 1 η it Y t, Top Income Inequality April / 44

24 Part 1: Model Model Equilibrium profits Hence profits are higher if the incumbent has recently innovated, namely: Y t > π L,t = η L 1 Y t. π H,t = η H 1 η H }{{} π H η L }{{} π L Top Income Inequality April / 44

25 Part 1: Model Model Income inequality Let µ t denote the fraction of high-mark-up sectors Entrepreneur share is: entrepreneur_share t = Y t w t Y t = 1 µ t η H 1 µ t η L Thus the entrepreneur share is increasing in the fraction of high-mark-up sectors µ t. µ t in turn depends upon innovation intensities by entrants and incumbents (x and x). Top Income Inequality April / 44

26 Part 1: Model Model Equilibrium innovation investments The offspring of a previous period s incumbent solves: { xπh Y t + (1 x (1 z) x ) π L Y t + (1 z) x w t max x x θ 2 I 2 Y t A potential entrant solves: x max {(1 z) xπ H Y t + (1 x (1 z)) w t 2 } θ E x 2 Y t }. Top Income Inequality April / 44

27 Part 1: Model Model Equilibrium innovation investments Nash equilibrium (x, x ) where x and x are decreasing functions of (θ E, θ I ) Higher entry barriers (higher z) discourage entrant innovation. Top Income Inequality April / 44

28 Model Innovation and Top Income Inequality Part 1: Model More formally: and x = x = π ( H π L 1 = 1 ) 1 θ I η L η H θ I ( ( ) π H 1 η L ηl η x ) (1 z) ( H ). θ E (1 z) 2 1 η 1 L η H Top Income Inequality April / 44

29 Part 1: Model Model Equilibrium share of high mark up sectors We have: µ t = µ = (1 z) x + x Top Income Inequality April / 44

30 Part 1: Model Model Equilibrium income shares The entrepreneur and labor income shares in equilibrium are: entrepreneur_share t = 1 1 ( ) ((1 z) x + x ). η L η L η H and wage_share t = w t = 1 ( 1 1 ) ((1 z) x + x ) Y t η L η L η H Thus any change (e.g lower R&D costs) which fosters innovation by incumbents or entrants also increases the entrepreneur share of income. This effect is lower when barriers to entry (z) are larger. Top Income Inequality April / 44

31 Part 1: Model Model Social mobility Probability that worker offspring is also a worker: Hence we define social mobility as Ψ = 1 x (1 z). M = 1 Ψ = x (1 z), which is increasing in the innovation rate x but less so the higher entry barriers (i.e the higher z). Note that a reduction in the incumbent s R&D costs will also foster social mobility (general equilibrium effect). Top Income Inequality April / 44

32 Part 1: Model Model Predictions Entrant and incumbent innovation increase top income inequality; Entrant innovation increases social mobility; Entry barriers lower the positive effects of entrant innovation on top income inequality and social mobility. Top Income Inequality April / 44

33 Part 1: Model Outline Introduction Part 1: Model Part 2: Empirical analysis using US aggregate data Part 3: Empirical analysis using individual data Conclusion Top Income Inequality April / 44

34 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 , a time range imposed upon us by the availability of patent data. Top Income Inequality April / 44

35 Part 2: Empirical analysis using US aggregate data Data and measurement Inequality Data on share of income owned by the top 1% and the top 10% of income distribution are drawn from the US State-Level Income Inequality Database (Frank, 2009). from that data source, we also gather information on Atkinson Index, Theil Index and the Gini Index. In every US state, the top 1% income share has increased between 1975 and 2010 the unweighted mean value was around 8% in 1975 and reached 21% in 2007 before slowly decreasing to 16.3% in Top Income Inequality April / 44

36 Part 2: Empirical analysis using US aggregate data Data and measurement Innovation When looking at cross state or more local levels, the US patent offi ce (USPTO) provides complete statistics for patents granted between the years 1975 and For each patent, it provides information on the state of residence of the patent inventor, the date of application of the patent and a link to every citing patents granted before For patents with multiple inventors, we assume that they are split evenly among inventors and thus we attribute only a fraction of the patent to each inventor. We follow Jaffe, Hall and Trajtenberg (2001) to address the issue of truncation bias in both the number of patents and the number of citations. Top Income Inequality May / 43

37 Data and measurement Innovation Part 2: Empirical analysis using US aggregate data The USPTO classification considers three types of patents according to the offi cial documentation: 1 Utility patents that are used to protect a new and useful invention, or an improvement to an existing process. 2 Design patents that are used to protect a new design of a manufactured object. 3 Plant patents that protect some new varieties of plants. The first type accounts for more than 90% of all patents at the USPTO and it is the only type of patents for which we have complete data. We thus focus on utility patents, in line with the patenting literature. Top Income Inequality April / 44

38 Data and measurement Innovation Part 2: Empirical analysis using US aggregate data There is a substantial amount of variation in innovativeness both across states and over time. 1 Between 1975 and 1990: Delaware, Connecticut, New Jersey and Massachusetts were the most innovating states, whereas Arkansas, Mississippi and Hawaii were the least innovative states with less than 0.05 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 1000 inhabitants. Top Income Inequality April / 44

39 Data and measurement Quality of innovation Part 2: Empirical analysis using US aggregate data 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 5 years 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 given year 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 (at least one) before 2014 Top Income Inequality April / 44

40 Part 2: Empirical analysis using US aggregate data Data and measurement Control 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 Top Income Inequality April / 44

41 Regression equation Innovation and Top Income Inequality Part 2: Empirical analysis using US aggregate data Regressing top income inequality on innovativeness: log(y it ) = A + B i + B t + β 1 log(innov i(t 1) ) + β 2 X it + ε it. Top Income Inequality April / 44

42 OLS regressions on patents per capita on top 1%

43 OLS regressions on various measures of innovation on top 1%

44 Part 2: Empirical analysis using US aggregate data Instrumentation First instrument Following Aghion et al (2004), we consider the time-varying State composition of the appropriation committees of the Senate and the House of Representatives. A Committee member often push towards subsidizing research education in her State, in order to increase her chances of reelection in that State. a state with one of its congressmen seating on the committee is likely to receive more funding for research education, which should increase its innovativeness in following years. Top Income Inequality April / 44

45 Part 2: Empirical analysis using US aggregate data Instrumentation Second instrument Second instrument based on knowledge spillovers. The idea is to instrument innovation in a state by the sum of innovation intensities in other states weighted by the relative innovation spillovers from these other states. Top Income Inequality April / 44

46 Part 2: Empirical analysis using US aggregate data Instrumentation Second instrument More formally, if m(i, j, T ) is the number of citations from a patent in state i, to a patent of state j over period , and if innov(j, t) denotes our measure of innovativeness in state j at time t, then we posit: w i,j = k =i m(i, j, T ) m(i, k, T ) and Y i,t = j =i w i,j innov(j, t 1). Top Income Inequality April / 44

47 IV regressions with first instrument (Appropriation Committee)

48 IV regressions with second instrument (Spillover)

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

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

51 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.

52 Magnitude of the effects Part 2: Empirical analysis using US aggregate data When measured by the number of patent per capita, innovativeness accounts on average for about 17% of the total increase in the top 1% income share between 1975 and 2010 according to either IV regression Top Income Inequality April / 44

53 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 Top Income Inequality April / 44

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

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

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

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

58 Part 2: Empirical analysis using US aggregate data Summarizing Part 2 We have analyzed the effect of innovation-led growth on top incomes and 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 from innovativeness to top income shares. When measured by the number of patent per capita, innovativeness accounts on average across US states for about 17% of the total increase in the top 1% income share between 1975 and Top Income Inequality April / 44

59 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 data Conclusion Top Income Inequality April / 44

60 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

61 Part 3: Empirical analysis using individual data Data The data used now includes 1 all inventors in our data (i.e., individuals who obtained a USPTO patent ) that work in firms that participate in the R&D survey. 2 The original inventor sample consists of some 75% of all Finnish inventors of USPTO patents that could be matched to the Finnish employer-employee data. 3 The 884 inventors in the current data are circa 38% of the 2328 inventors in the full data. 4 a random sample of (almost) 100K control individuals from those same firms. 5 These individuals represent some 5% of the Finnish working age population. In 1991, we have individuals in our sample of whom 843 obtain at least one USPTO patent between 1990 and For 1999, we have individuals of whom 882 have obtained at least one USPTO patent between 1990 and Innovation and Top Income Inequality April 2015

62 Part 3: Empirical analysis using individual data Wage Income Growth (1) Wage Income Growth ( ) by Percentiles non inventors inventors income percentiles Innovation and Top Income Inequality April 2015

63 Part 3: Empirical analysis using individual data Wage Income Growth (2) Wage Income Growth ( ) by Percentiles non inventors inventors income percentiles Innovation and Top Income Inequality April 2015

64 Part 3: Empirical analysis using individual data Capital vs Labor Income in 1999 Inventor/Non inventor Ratio by Type of Income in capital income ratio wage income ratio Innovation and Top Income Inequality April 2015

65 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= top-10= inventors 1991 / 1999 top-10=0 top-10=1 Conditional Prob. top-10= top-10= Innovation and Top Income Inequality April 2015

66 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/Pr top10= top10= top10= top10= Father s education 12 years 91 / C/Pr 91 / 99 top-10=0 top-10=1 C/Pr top10= top10= top10= top10= Innovation and Top Income Inequality April 2015

67 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 / Con Pr 91 / 99 top-10=0 top-10=1 Con Pr top10= top-10= top10= top-10= Male 91 / Con Pr 91 / 99 top-10=0 top-10=1 Con Pr top10= top-10= top10= top-10= Innovation and Top Income Inequality April 2015

68 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= top-10= > median age 1991 / 1999 top-10=0 top-10=1 Conditional Prob. top-10= top-10= Innovation and Top Income Inequality April 2015

69 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= top-10= citations 1991/1999 top-10=0 top-10=1 Conditional Prob. top-10= top-10= Innovation and Top Income Inequality April 2015

70 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 citations citations citations citations polynomial in Ln(wage) in controls YES YES YES father s educ. NO YES NO nobs R-sq 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 father s educ. = 45 field and level of education dummies for the father. Innovation and Top Income Inequality April 2015

71 Part 3: Empirical analysis using individual data Labor Income in 1999 Percentage Increase in Wage (relative to 0 cited) citation counts Innovation and Top Income Inequality April 2015

72 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/ Con Pr top-10= top-10= education in years 1991/ Con Pr 1991/ Con Pr top-10= top-10= Innovation and Top Income Inequality April 2015

73 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/ Con Pr top-10= top-10= firm size in 1991 median firm in size /1999 top-10=0 top-10=1 Con Pr 1991/ Con Pr top-10= top-10= Innovation and Top Income Inequality April 2015

74 Conclusion Innovation and Top Income Inequality 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 make it to top income brackets 2 Analyze the direct and indirect contribution of inventions to top income inequality: the labor and capital incomes of inventors, the value of firms created by inventors, how the invention affects the top incomes of people working with the inventor. 3 Policy implications: e.g., how do we factor in *innovation* when designing 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 between innovation, top income inequality, and social mobility. Top Income Inequality May / 43