kyel research paper draft final

A Cross Country Analysis on Top Income Inequality

Kyel Governor

6/9/2016

This paper uses cross-country panel data from 1950-2014 to investigate if the rise in top income inequality can be explained by capital accumulation when innovation and cross-country differences are taken into account. Pooled OLS and fixed effects models are used to investigate the relationship. This paper finds some evidence in favour of capital accumulation driving top income inequality for highly competitive countries. However there is stronger evidence suggesting that innovation rents drive top income inequality rather than capital accumulation.

ContentsIntroduction.................................................................................................................................................2

1 Theory.................................................................................................................................................3

1.1 Piketty’s General Laws of Capitalism...........................................................................................3

1.2 Schumpeterian Growth Theory and Innovation Rents.................................................................4

2 Data and Measurement.......................................................................................................................5

2.1 Income Inequality........................................................................................................................6

2.2 Innovation....................................................................................................................................7

2.3 Piketty’s r > g and K over Output.................................................................................................9

2.4 Control Variables.........................................................................................................................9

2.5 Global Competitiveness Index (GCI).............................................................................................9

2.6 Table of Variables and Descriptions.............................................................................................9

3 Main Empirical Analysis: The effect of innovativeness and capital accumulation on income distribution................................................................................................................................................11

3.1 Estimation Strategy....................................................................................................................11

3.2 Piketty r > g and Capital Accumulation: Results from Pooled OLS and Fixed Effects Regressions11

3.3 Schumpeterian Growth Theory and Innovation Rents: Results from Pooled OLS and Fixed Effects Regressions................................................................................................................................16

3.3.1 Measure of Innovation: Patents Granted...........................................................................16

3.3.2 Measure of Innovation: Book to Market............................................................................20

3.4 Evidence of Piketty’s r>g and Schumpeter’s Innovation Rents: Interaction of Countries Grouped by the Global Competitiveness Index....................................................................................................23

3.4.1 GCI Grouping Table............................................................................................................24

3.4.2 Results...............................................................................................................................24

4 Discussion..........................................................................................................................................27

4.1 Reverse Causality –Innovation on Income Inequality OLS Regression and Panel VAR...............27

4.2 Pre and Post Y2K Regressions – Evidence of a New Economy...................................................31

5 Conclusion.........................................................................................................................................33

References.................................................................................................................................................34

Appendix...................................................................................................................................................36

1

IntroductionIncreasing top income inequality in developed countries has become a phenomenon in the world today and yet there is no general agreement on the root causes behind the rise in top income inequality. This paper debates whether top income inequality is driven by capital accumulation or innovation. Thomas Piketty has brought the issue of growth and income redistribution back to the forefront of economic debates with his (2014) book, Capital in the Twenty-First Century, where he argues that capital accumulation by the wealthiest top 1% is driving top income inequality. In short, he defends that wealth grows faster than economic output, centering his argument on the expression r > g (where r is the rate of return to capital and g is the economic growth rate), and so slower growth leads to a rapid increase in income inequality. Piketty’s more than six-hundred page publication in large part ignores cross-country differences and the endogenous evolution of technology and its relationship with top income inequality leaving his work susceptible to dispute (Acemoglu et al, 2015). In fact in the United States patenting and top 1% income share follow parallel evolutions indicating that innovation may play a role in explaining the rise in top income inequality (Aghion et al, 2015). This paper will use cross-country panel data from 1950-2014 to investigate if the rise in top income inequality can be explained by capital accumulation when innovation and cross-country differences are taken into account. Two different measures are used for innovation; total patents granted and the book to market ratio. The capital share of national income (CSNI) is measured by two variables; the capital stock to gross domestic product ratio and r minus g. Lastly, top income inequality is measured by the share of income held by the top 1% and the top 10%, and the Pareto Lorenz coefficient.

In summary, this paper finds some evidence validating Piketty’s argument that capital accumulation drives top income inequality for highly competitive1 countries. However there is stronger evidence suggesting that innovation rents drive top income inequality rather than capital accumulation. Excluding the least competitive countries, measures of innovation show a significant relationship with top income inequality where higher levels of innovation lead to higher top 1% and top 10% income shares which reflect innovation rents and thus is consistent with Schumpeterian growth theory. The model predicts that on average a 1% increase in patents granted leads to a 16.6% increase in the top 1% share of income and a 14.7% increase in the top 10% share of income, a result consistent with Aghion et al. (2015) U.S. cross-state analysis on innovation and top income inequality. The book to market ratio measure of innovation also shows that on average innovation increases the top 1% and top 10% share of income. After controlling for fixed effects, regressions show that innovation measures such as R&D expenditure and book to market ratio are significant in predicting top income inequality. Furthermore, results show no significant relationship between innovation or CSNI and the rest of the income distribution. When investigating whether income inequality causes innovation pooled OLS regressions show that on average the top 10% share of income increases innovation and a panel vector auto

1 As measured by the Global Competitive Index (2015-2016). See Section 3.4.

2

regression supports this result. Finally the paper finds that before the year 2000 there is no evidence of a positive relationship between CSNI and top income inequality and no relationship between innovation and top income inequality. However after the year 2000 there is a positive relationship for both CSNI and innovation with top income inequality.

This analysis has been motivated by Acemoglu and Robinson (2015) where they criticize general laws of capitalism proposed in Piketty’s (2014) recent work. Contrary to Piketty, they show that r > g has no relationship with top income inequality. My paper adds to their work on top income inequality by adding an analysis of innovation driven inequality. This paper closely follows Aghion, Akcigit, Bergeaud, Blundell and Hémous (2015) U.S. cross-state analysis of innovation on top income inequality except this paper takes a cross-country perspective and factors in the Pareto Lorenz Coefficient as a measures of income inequality. Like Aghion et al. (2015), the paper depends on the Schumpterian growth model presented in Aghion, Akcigit and Howitt (2013) in where innovation led growth is dependent on innovation rents which are represented by increases in top income inequality. Therefore this endogenous growth model leads the analysis on top income inequality and innovation. This growth model also allows that an increase in top income inequality causes an increase in innovation as mentioned by Tselosis (2011) and Zweimuller (2000). So our analysis investigates this likely event first by univariate regressions and then by a panel vector auto regression (VAR) on innovation and top income inequality motivated by Atems and Jones (2015). Finally the paper allows that there has been a recent shift to a new digital economy as presented in Stiroh’s (2004) review and so the return on innovation and human capital has dramatically increased. This hypothesis is supported by Hémous and Olsen (2014) in which they argue that the digital economy benefits high skilled labourers and innovators because of the automation of low skilled labour. This paper looks to investigate this hypothesis.

The paper is organized as follows. Section 1 of the paper reviews Piketty’s general laws of capitalism and Schumpterian growth theory where top income inequality is driven by quality-improving innovations. Section 2 introduces the data involved and describes the measures of innovation and income inequality used in the paper. Section 3 presents the main findings. Section 4 is a discussion on two extensions of the analysis: first, an investigation into a reverse causality where top income inequality drives innovation; and second, a look at pre Y2K and post Y2K regressions. Section 5 concludes.

1 Theory

1.1 Piketty’s General Laws of CapitalismPiketty proposes a theory on the long run proclivities of capitalism in Capital in the Twenty-first Century (2014). He relies on Marxian economics as premise to come to the conclusion that the future of capitalism is one where there is mass ownership of capital by the wealthiest and their main income comes from capital rents.

To come to his predictions, Piketty first presents two “fundamental laws”;

3

capital share of national income=r x ( KY ),

KY

=s / g,

where r is the real interest rate, K is the capital stock, Y is GDP, s is the saving rate and g is the growth rate of GDP. As derived in a Solow model the growth rate of capital stock K is equal to saving sY in a

closed economy and so the ratio KY over time will approach s/ g due to economic growth. Piketty then

combines the two equations to get;

capital share of national income=r x ( sg ) .Assuming r and s are approximately constant derives Piketty’s first general law;

1. The capital share of national income is high when growth is low.

His second general law states;

2. r > g,

which defines that the real interest rate r is always greater than the growth rate g in an economy. This law holds when a country is dynamically efficient, meaning that it has maximized consumption in every period over an infinite timeline, otherwise it may not be the case that the real interest rate exceeds the growth rate.

Piketty’s third general law proposes that;

3. Income inequality tends to rise whenever r > g.

The logic behind this proposition is that capital income grows approximately at the rate r where national income grows at the rate g and since capital income is distributed mainly to the wealthiest; their incomes grow faster than the per capita income thus resulting in capital-driven income inequality.

According to Piketty’s theory top income inequality rises as the capital share of national income increases because capital income is mainly distributed among the wealthiest. Assuming r is approximately constant, capital share of national income will rise as K/Y increases (change in capital exceeds change in GDP) and/or as g decreases making the distance between r and g greater. This paper tests both hypotheses in order to substantiate Piketty’s general laws of capitalism.

1.2 Schumpeterian Growth Theory and Innovation RentsThe baseline Schumpeterian growth model as developed in Aghion et al. (2015) offers a theoretical framework for how innovation increases top income inequality. The main predictions of the model as it relates to this paper are;

4

Innovation increases top income inequality.

5

Higher innovation rent leads to increased levels of innovation.2

To come to these predictions the baseline Schumpeterian growth model is developed as follows. Given discrete time, the economy is populated by two types of individuals every period; capital owners who own the firms and the production workers. Individuals live only for one period and in the subsequent period a new generation is born with those who are born to firm owners inheriting their parents’ firm and the rest work as production workers unless they successfully innovate and takeover an incumbent.

The following Cobb-Douglas technology production function defines the production of final goods Y t:

lnY t=∫0

1

ln y¿di

y¿=q¿l¿

where y¿ is a vector of intermediate inputs used in final production, l¿ is the labour used to produce the intermediate inputs, and q¿ is the labour productivity; all at time t. Whenever innovation takes place in any sector i in period t the innovator gains a technological lead of ηH over the competition and so the innovator produces the intermediate input y¿ with the production function:

y¿=ηHq i ,t−1l¿

If no new innovations occur in the following period, the innovator’s technology is partly imitated by competitors and so the innovator’s technological lead decreases to ηL where1<ηL<ηH .

Solving the model yields the two following equations pertinent to the paper’s analysis;

MC ¿=wt /qi , t, and, pi ,t=wtη¿ /qi ,t

where ηi , t ϵ {ηH , ηL}, MC¿ is the marginal cost of producing the intermediate input, w t is the wages paid to labour involved in producing the intermediate input i, and pi ,t is the price charged for the intermediate input; all at time t. Thus the profit earned by the innovator is given by;

π¿=(p¿−MC¿ ) y¿=(η¿−1 )Y t

η¿>0

The profits earned by innovators in this model are innovation rents. Furthermore if the cost of innovation is allowed in this model then the cost of innovation for entrants can be greater than the cost of innovation for incumbents (Aghion et al., 2015). This leads to entry barriers and thus reduces innovative activity since entrants receive less profit from innovation the higher the cost of innovation is for them.

2 This result is further explored in Section 4.2 where the marginal effect of innovation on income inequality is stronger after the IT market crashed in 2000.

6

2 Data and MeasurementThe paper uses cross-country unbalanced panel data covering the period 1950-2014. Missing values between data points are approximated by a linear interpolation. The 27 countries covered are as follows: Argentina, Australia, Canada, China, Colombia, Denmark, Finland, France, Germany, India, Indonesia, Ireland, Italy, Japan, Korea, Malaysia, Netherlands, New Zealand, Norway, Portugal, Singapore, South Africa, Spain, Sweden, Switzerland, United Kingdom, and United States.

2.1 Income InequalityThe data on the share of income held by the top 1%, top 10%, and on the Pareto Lorenz Coefficient was obtained from the World Top Income Database (Alvaredo, Atkinson, Piketty, and Saez’s World Top Incomes Database at http://topincomes.parisschoolofeconomics.eu/). These are our top income inequality measures. Data on the share of income owned by the bottom 40% was obtained from the World Bank Databank (http://databank.worldbank.org/data). The share of income owned by the middle 40%-90% was obtained by subtracting the top 10% and bottom 40% share from 100.

Figure1 shows the evolution of top 1% income inequality for France, Germany, South Africa, Sweden, and United States. The graph clearly illustrates significant cross country differences however a significant upward trend is noticeable for all countries around after 1990. Figure 2 shows the top 10%, middle 40%-90%, and bottom 40% income shares over time for the United States and it is evident that top income shares increase at the expense of the middle class.

510

1520

Top

1% In

com

e S

hare

1950 1960 1970 1980 1990 2000 2010 2020Year

France GermanySouth Africa SwedenUnited States

Top 1% Income Share, 1960-2014

Figure 1

7

http://topincomes.parisschoolofeconomics.eu/

1020

3040

50

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Year

Top 10% Share of Income Mid 40%-90% Share of IncomeBottom 40% Share of Income

United States Income Distribution 1950 - 2014

Figure 2

2.2 InnovationWe obtain patents granted by the USPTO and EPO to obtain total of patents granted per country by inventor’s country of residence. This data was obtained from the OECD database (http://stats.oecd.org/). The data covers the time period 1999-2014. The book to market ratio (b/m) was created by dividing capital stock by market capitalization. Capital stock (K) was obtained from Penn World Tables version 8.1 (www.ggdc.net/pwt) and market capitalization was obtained from the World Bank Databank. This proxy for the book to market ratio works well. The correlation between the actual U.S. book to market ratio with K over market capitalization is equal to 0.9452. A low book to market ratio value implies that a valuation above the worth of physical assets. A market valuation above physical assets implies valuable intangible assets such as innovative activities and so movement in the book to market ratio is a valid measure of innovation.

Figure 3 shows the evolution of patents granted for France, Germany, South Africa, Sweden, and United States while Figure 4 illustrates the dynamic relationship between book to market and patents granted for the United States. Figure 4 gives evidence of a relationship between the two measures of innovation especially when comparing the three year lag of patents granted with the book to market ratio. An increase in patents granted (lagged 3 years) leads to a fall in the book to market ratio.

8

http://www.ggdc.net/pwt

050

100

150

Gra

nted

pat

ents

(in

1000

s)

2000 2005 2010 2015Year

France GermanySouth Africa SwedenUnited States

Total Patents Granted, 1999-2014

Figure 3

.02

.025

.03

.035

.04

Boo

k to

Mar

ket

8010

012

014

016

0G

rant

ed P

aten

ts (1

000s

)

2000 2005 2010 2015Year

Granted patents (in 100s)Granted patents (in 100s), Lagged 3 yearsBook to Market

United States, Book to Market vs Patents Granted

Figure 4

9

2.3 Piketty’s r > g and K over OutputData on real interest rates (r) were retrieved from the World Bank Databank and data on GDP was obtained from Penn World Tables version 8.1 and Madison’s dataset (http://www.ggdc.net/maddison/maddison-project/data.htm). The GDP growth rate (g) is computed and the variable r > g is computed by simply subtracting g from r. Our capital to output ratio (K/Y) is also computed using K and GDP data.

2.4 Control Variables The paper controls for the size of the financial sector and the size of the government sector because they are likely to have direct effects on innovation and top income share (Aghion et al, 2015). Furthermore the analysis controls for macroeconomic variables such as population growth, the employment rate, and human capital; and Research and Development (R&D) variables such as R&D expenditure per capita and number of R&D researchers. Controlling for the employment rate is mainly to reduce time varying cross country differences in order to have more efficient results. Human capital is controlled for since this likely has an effect on income inequality measures and innovation. Finally R&D is controlled for since it is correlated with patents granted and income inequality measures; also R&D may be considered a measure of innovation.

2.5 Global Competitiveness Index (GCI)The paper groups countries based on their GCI obtained from the World Economic Forum Global Competitiveness Report (http://reports.weforum.org/global-competitiveness-report-2015-2016/) in order to see the relationship of CSNI and innovation on top income inequality across different economic stages. The GCI is a composite of sub-indexes such as institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, goods market efficiency, labour market, financial market development, technological readiness, market size, business sophistication, and innovation. The table in Section 3.4.1 shows how countries are grouped.

2.6 Table of Variables and DescriptionsVariable Number of

Observations (annual)

Mean Standard Deviation

Time Span

Retrieval Description

Income Inequality VariablesTop 1% Share of Income (%)

1177 12.057 3.425 1950-2014

World Top Incomes Database

Share of national income held by the

top 1% earnersTop 10% Share of

Income (%)

962 36.6 6.809 1950-2014

World Top Incomes Database

Share of national income held by the

top 10% earnersPareto Lorenz

Coefficient1068 1.844 .224 1950-

2014World Top Incomes Database

Measure of the income distribution

Piketty and Innovation Variables

10

http://reports.weforum.org/global-competitiveness-report-2015-2016/

http://www.ggdc.net/maddison/maddison-project/data.htm

Patents Granted

(in 1000s)

416 8.872 22.643 1999-2014

OECD Database

Number of patents granted by the EPO

and USPTOMarket

Capitalization (% of GDP)

799 93.502 45.774 1975-2014

World Bank

Databank

Share price x number of shares outstanding

for listed domestic companies

r (%) 946 2.736 3.554 1961-2014

World Bank

Databank

Real interest rate

g (%) 1620 1.224 2.103 1951-2010

Madison’s dataset

GDP growth rate

K (in mil. 2005US$)

1644 8724524 1.27e+07 1950-2011

Penn World Tables

Capital Stock

GDP (in mil. 2005US$)

1644 2856685 4178096 1950-2011

Penn World Tables

Real GDP

Control VariablesR&D per

capita expenditure (%

of GDP)

346 42289.59 20903.95 1996-2010

World Bank

Databank

Expenditures for research and

development both public and private

Number of R&D

researchers

391 3345.405 1427.127 1996-2013

World Bank

Databank

Professionals engaged in the creation of new

knowledge, products, processes, methods,

or systemsIndex of

Human Capital per person

1644 3.133 .337 1950-2011

Penn World Tables

Based on years of schooling and returns

to educationShare of

Government Consumption

(%)

1644 .139 .034 1950-2011

Penn World Tables

Share of government consumption at

current PPPs

Services per capita (% of

GDP)

1014 68.641 8.288 1960-2014

World Bank

Databank

Services correspond to ISIC divisions 50-99

(includes financial)Employment to population

ratio, 15+, total (%) (national

estimate)

817 58.957 8.937 1980-2014

World Bank

Databank

Proportion of a country's population

that is employed

Population growth (annual

1484 .989 .467 1950-2011

Penn World Tables

Population growth rate

11

%)Global

Competitiveness Index

(1-7 (best))

N/A N/A N/A 2015-2016

World Economic

Forum

Integrates macroeconomic and

micro/business aspects of

competitiveness into a single index

3 Main Empirical Analysis: The effect of innovativeness and capital accumulation on income distribution

3.1 Estimation StrategyThe main empirical analysis involves looking at whether Piketty’s variables, r-g and capital accumulation measured by K/Y, or innovation measured by b/m and total patents granted are responsible for increases in top income shares. The main estimated equation is:

log ( y¿ )=A+Bi+B t+B1 j (Piketty j )+B2 log (innovation )+B3 iGCI k−1∗log (innovation )+B4 jiGCI k−1∗(Piketty j )+B5X¿+ε¿

where y¿ is the measure of inequality, Bi is a country fixed effect, Bt is a year fixed effect, Piketty j is a vector of Piketty variables which include r-g (j=1) and K/Y (j=2), innovation is innovativeness measured by b/m or total patents granted, GCI k−1 are GCI dummies and X is a vector of control variables.

In Section 3.2 B2, B3 and B4 are set to zero and a series of Piketty regressions are estimated by pooled OLS and fixed effects. The restriction on B2 is removed in Section 3.3 and the model is estimated again by pooled OLS and fixed effects. In Section 3.4 all restrictions are removed and the model is estimated by fixed effects. All results are cluster bootstrapped and include time dummies.3

3.2 Piketty r > g and Capital Accumulation: Results from Pooled OLS and Fixed Effects RegressionsThe results from a series of Piketty regressions are presented below in Table 1. In (3) and (4) of Table 1 it is assumed that all capital markets are open and cross country variation in r is zero and so r is normalized to 0, therefore we have r-g = - g. The regressions show no significant relationship between the top 1% share of income and r > g for pooled OLS and fixed effects estimates as seen in Acemoglu and Robinson (2015). Results from Table 1 illustrate that cross country differences have a strong influence on Piketty’s r > g since fixed effects coefficients are significantly less than pooled OLS coefficients. The last column in Table 1 estimates that the coefficient on Piketty’s K/Y is significant but negative in a fixed effects regression. This implies that an increase in CSNI decreases top income inequality which contradicts Piketty’s theory.

3 Additional regressions are kept in the appendix.

12

To continue with the results found in (6) of Table 1 the next two table shows the results from regressions against different controls and for different time spans. The results show that K/Y remains negative and significant after adding the controls however over the period 1999-2010 it becomes insignificant as shown in (4) of Table 3. Over the same period in (3) of Table 3 r > g is positive and significant.

Table 1

Piketty's r > g and Capital Accumulation1961-2010 1951-2010 1961-2010

(1) (2) (3) (4) (5) (6)Top 1% Share of Income (Log)

Top 1% Share of Income (Log)





r - g 0.00921(1.34)

-0.00246(-1.02)

0.0103(1.43)

0.00122(0.48)

normalizing r=0 (r-g=-g)

0.00818(0.96)

-0.00155(-0.79)

K/Y (Log) -0.125(-0.64)

-0.536**

(-2.36)Country Fixed Effects

No Yes No Yes No Yes

Observations 829 829 1339 1339 829 829R2 0.284 0.202 0.293R2 overall 0.233 0.194 0.173R2 between 0.152 0.173 0.0217R2 within 0.561 0.462 0.617# of countries in regression

27 27 27 27 27 27

t statistics in parentheses

Cluster Bootstrapped Standard Errors* p < 0.1, ** p < 0.05, *** p < 0.01

13

Table 2

Piketty's r > g and Capital Accumulation with different controls1961-2010

(1) (2) (3) (4)Top 1% Share

of Income (Log)

Top 1% Share of Income

(Log)


(Log)


(Log)r - g 0.00184

(0.77)0.00221(0.92)

0.00120(0.47)

0.00259(1.15)

K/Y (Log) -0.498**

(-2.46)-0.487**

(-2.56)-0.559**

(-2.45)-0.506***

(-2.85)

Index of human capital per person (Log)

-0.752*

(-1.82)-0.605(-1.55)

Share of Government Consumption

-1.930***

(-3.22)-1.762***

(-3.21)

Population growth (annual %)

-0.0182(-0.89)

-0.0369*

(-1.94)Country Fixed Effects

Yes Yes Yes Yes

Observations 829 829 829 829R2 overall 0.193 0.358 0.155 0.331R2 between 0.0544 0.214 0.0120 0.187R2 within 0.643 0.651 0.618 0.672# of countries in regression

27 27 27 27



14

Table 3

Piketty's r > g and Capital Accumulation over different time spans1980-2010 1999-2010

(1) (2) (3) (4)Top 1% Share

of Income (Log)


(Log)


(Log)


(Log)r - g 0.0106

(1.34)0.000222

(0.09)0.0151*

(1.83)-0.00308(-1.52)

K/Y (Log) -0.0937(-0.48)

-0.394**

(-2.21)-0.0992(-0.45)

-0.0203(-0.09)

Country Fixed Effects

No Yes No Yes

Observations 681 681 235 235R2 0.284 0.101R2 overall 0.200 0.0174R2 between 0.0449 0.0110R2 within 0.624 0.165# of countries in regression

27 27 27 27



In Table 4 all the control variables are included and the Piketty variables are regressed on several measures of top income inequality. The regressions show that increasing r-g increases the top 1% share of income as seen in (1) of Table 4 but when country fixed effects are included the coefficient becomes insignificant. In (6) of Table 4 K/Y has a significant and positive relationship with the Pareto Lorenz coefficient and so an increase in K/Y decreases income inequality which is what was found in Table 1 to 3. This leads to conflicting results in terms of analyzing Piketty’s argument. Favourable results for Piketty are found in the pooled OLS regressions but not in fixed effects regressions.

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Table 4

Piketty's r > g and Capital Accumulation – Top Income Inequality1980-2010





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)

r - g 0.00904**

(2.06)0.000327

(0.15)0.00433(1.03)

-0.000450(-0.32)

-0.00850**

(-1.99)-0.000450

(-0.32)

K/Y (Log) -0.116(-0.67)

-0.380(-1.38)

-0.110(-0.65)

0.110(0.46)

0.179(1.13)

0.379**

(1.99)


-0.680*

(-1.75)-0.673(-1.06)

-0.293(-0.89)

-0.132(-0.24)

0.234(0.69)

0.197(0.42)


-4.549***

(-3.89)-1.896***

(-2.68)-2.050***

(-2.73)-0.232(-0.45)

2.592***

(3.43)1.172**

(2.01)


0.0549(1.05)

-0.0361(-1.60)

-0.00665(-0.13)

-0.0144(-0.72)

-0.0144(-0.33)

0.0125(0.58)

Services per capita (% of GDP)

-0.00134(-0.13)

-0.00357(-0.08)

-0.00235(-0.28)

-0.00361(-0.07)

0.00128(0.19)

-0.00387(-0.20)

Employment to population ratio, 15+, total (%) (national estimate)

-0.00987*

(-1.75)-0.00485(-0.79)

-0.00943**

(-2.21)-0.00218(-0.76)

-0.00173(-0.28)

0.00206(0.40)




26 26 23 23 24 24

16



3.3 Schumpeterian Growth Theory and Innovation Rents: Results from Pooled OLS and Fixed Effects Regressions So far our analysis provides conflicting evidence for Piketty’s argument on CSNI driving top income inequality. Based on fixed effects the analysis is in agreement with Acemoglu and Robinson (2015). In this section innovation is allowed in the model and is first measured by total patents granted and then by book to market. The results met from both measures support Schumpeterian growth theory but does not debunk Piketty’s argument for capital accumulation increasing top income inequality.

3.3.1 Measure of Innovation: Patents GrantedTable 5 displays the results when total patents granted is included in the regression. The pooled OLS regression on top 10% share of income shows significant results finding a 1% increase in patents granted increases the top 10% share of income by 6.6%. Also in the table, (1) shows r-g is positive and significant and K/Y becomes insignificant for all regressions giving evidence that innovation is an omitted variable in Section 3.2 regressions.

The results in Table 5 give some evidence in support of Schumpeterian growth theory and innovation rents and evidence against Piketty’s supposition. However the regressions in Table 5 cover a short time span, 1999-2010, and so general inferences cannot be drawn.

To ignore the insignificant coefficients on patents granted in (1) and (2) of Table 5 would be unjust since the top 1% share of income is one of the income inequality measures of interest. There is an apparent omitted variable which is R&D activity since this is very likely correlated with patents granted and can likely be correlated with our dependent variables. R&D activity approximates the total cost of innovations. According to Schumpeterian growth theory as outlined in Section 1.2, innovators will produce an innovation if the expected total profit4 from an innovation is equal to or greater than the expected cost of innovation. If an innovation, measured by patents granted, occurs then the innovator has incurred the cost of innovation which is approximated by R&D activity. Therefore R&D activity affects patents granted where higher R&D activity leads to an increase in patents granted. R&D activity can also affect innovation rents since the cost of innovation has an impact on innovation rents. R&D activity can thus have a positive effect on innovation rents if the return on R&D activity is positive and a negative effect if the return on R&D activity is negative.

Table 6 controls for R&D activity and provides a significant and twice as large coefficient for patents granted on the top 10% share of income pooled OLS regression than what was presented in Table 5, however regressions on the top 1% share of income remain insignificant.5 The key result in Table 6 is in (1) where r-g remains positive and significant in explaining the top 1% share of income.

4 Profit as defined by the equation π¿=(p¿−MC¿ ) y¿ in Section 1.2. Expected total profit is the expected present value of the stream of all profits earned up to time T. See Aghion, Akcigit and Howitt (2013).

17

Table 5 – Check appendix Table A1 for complete list of excluded countries.

Innovation Measure: Patents Granted – Top Income Inequality1999-2010





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)

Patents Granted (Log)

0.0243(0.45)

0.0368(0.55)

0.0664**

(2.19)0.0410(0.79)

-0.00404(-0.15)

-0.0186(-0.63)

r - g 0.0107*

(1.85)-0.00403(-1.29)

0.00422(0.83)

-0.000130(-0.05)

-0.00636(-1.38)

0.00313**

(2.36)

K/Y (Log) -0.156(-0.62)

-0.0816(-0.19)

-0.234(-1.07)

-0.0103(-0.05)

0.119(0.70)

-0.0280(-0.11)


-0.811(-0.99)

1.784(1.05)

-0.594(-0.98)

0.831(1.01)

0.106(0.23)

-0.665(-1.02)


-3.994**

(-2.24)-0.151(-0.13)

-1.206(-0.99)

-0.508(-1.41)

2.163**

(2.25)-0.370(-0.79)


0.0954(1.28)

-0.0113(-0.28)

0.0551(1.07)

-0.000113(-0.01)

-0.0214(-0.55)

-0.000475(-0.02)


-0.00186(-0.09)

0.0961(1.49)

0.00370(0.31)

0.0391(1.20)

0.00350(0.39)

-0.0680***

(-3.05)


-0.00779(-0.95)

0.00377(0.38)

-0.0100(-1.56)

-0.00575(-1.36)

-0.00516(-0.72)

-0.00766(-1.43)



Observations 220 220 202 202 202 202

5 By adding R&D activity to the regressions a few countries were dropped however Table 5 results were consistent when the same countries were excluded and therefore the results are not tainted by any selection bias.

18

R2 0.516 0.574 0.388R2 overall 0.0623 0.00183 0.000000567R2 between 0.0437 0.000400 0.00327R2 within 0.255 0.270 0.370# of countries in regression

25 25 23 23 24 24



Table 6 - Check appendix Table A1 for complete list of excluded countries.

Innovation Measure: Patents Granted controlling for R&D – Top Income Inequality1999-2010

19





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)


0.105(1.39)

0.0367(0.54)

0.122***

(2.90)0.0447(0.68)

0.0459(0.94)

-0.00978(-0.33)

R&D per capita expenditure (Log)

-0.362(-1.51)

0.136(1.28)

-0.355***

(-2.58)0.0360(0.37)

-0.211(-1.34)

-0.103(-1.54)

R&D researchers (Log)

0.246(1.00)

-0.108(-1.18)

0.283**

(2.50)-0.0496(-0.75)

0.130(0.86)

0.0746(1.16)

r - g 0.0129**

(2.01)-0.00294(-0.89)

0.00455(0.97)

-0.000283(-0.10)

-0.00665(-1.44)

0.00304*

(1.96)

K/Y (Log) -0.141(-0.54)

-0.0495(-0.12)

-0.196(-1.03)

0.0442(0.20)

0.131(0.71)

-0.0704(-0.28)


-0.950(-0.91)

2.372(1.60)

-0.582(-1.03)

1.078(1.05)

-0.0223(-0.04)

-0.949(-1.12)


-4.648***

(-2.74)-0.289(-0.55)

-1.801*

(-1.90)-0.328(-0.92)

1.910**

(2.00)-0.456(-0.75)


0.107(1.34)

-0.0106(-0.27)

0.0654(1.40)

-0.000541(-0.03)

-0.0164(-0.46)

-0.00186(-0.09)


0.00965(0.44)

0.0937*

(1.78)0.00941(0.77)

0.0383(1.09)

0.0102(0.84)

-0.0650**

(-2.47)


-0.00521(-0.51)

-0.00369(-0.60)

-0.00973(-1.50)

-0.00499(-1.22)

-0.00343(-0.44)

-0.00421(-0.89)

Country Fixed No Yes No Yes No Yes

20

EffectsObservations 209 209 197 197 192 192R2 0.625 0.707 0.484R2 overall 0.0597 0.00287 0.0153R2 between 0.0231 0.000901 0.0176R2 within 0.359 0.274 0.411# of countries in regression

24 24 23 23 23 23



3.3.2 Measure of Innovation: Book to MarketAs stated in the previous subsection, there is a short time span of available data for patents granted and so the advantage gained from using the book to market ratio as a measure of innovation is a longer time span. The disadvantage, however, is that b/m is an indirect measure of innovative activity since it does not directly capture innovative activity like the more commonly used measure patents granted, but instead it approximates the aggregate value of innovation. According to Schumpeterian growth theory as presented in Section 1.2, the value of an innovation decreases over time which corresponds to an increasing b/m. If a new innovation occurs in a given period then the value of that innovation is greater than the one that preceded it and so there will be a corresponding decrease in the b/m.

The results from regressing income inequality measures on b/m are displayed in Table 7. The regressions in Table 7 cover the period 1980-2010 which is the same period covered in the Piketty regressions in Section 3.2. The table predicts that both r-g and b/m contribute to the top 1% share of income however r-g affects the top 1% share of income in a pooled OLS regression whereas b/m affects the top 1% share of income in a fixed effects regression. Between the results in Section 3.3.1 and Table 7 there is some evidence that innovation increases top income inequality and also that in a pooled OLS there is a positive and significant relationship between r-g and the top 1% share of income.

Kitchen sink regressions6 on top income inequality are performed and the results are captured in Table 8. The most significant results are found for pooled OLS regression on the top 1% and top 10% share of income. Both innovation measures are significant in the correct direction indicating that innovation increases top income inequality and more so for the top 1% share of income than the top 10% share of income. Furthermore the Piketty variable r-g becomes insignificant. Table 8 provides some more evidence that innovation rents explain top income inequality rather than the Piketty variables. However the results are not robust to country fixed effects.


Innovation Measure: Book to Market – Top Income Inequality1980-2010

6 Kitchen sink regression includes all variables of interest and controls.

21





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)

Book to Market (Log)

-0.0447(-0.89)

-0.0578*

(-1.78)-0.0542(-1.43)

-0.0149(-0.80)

0.000460(0.01)

0.00975(0.51)

r - g 0.00763*

(1.65)-0.000391

(-0.11)0.00436(1.08)

0.00150(0.67)

-0.00202(-0.45)

0.00163(0.54)


-1.051**

(-2.36)-1.220**

(-2.04)-0.299(-1.00)

-0.340(-0.71)

0.215(1.00)

0.517(1.12)


-3.669***

(-3.18)-1.340(-1.50)

-1.765**

(-2.08)-0.0889(-0.18)

1.852***

(3.06)1.320(1.40)


0.0436(0.91)

-0.0137(-0.55)

-0.0215(-0.41)

-0.0109(-0.56)

-0.0186(-0.50)

0.000551(0.02)


-0.00677(-0.93)

-0.0146(-0.29)

-0.00271(-0.39)

-0.00653(-0.12)

0.00373(0.75)

0.00459(0.20)


-0.00382(-0.77)

-0.00816(-1.26)

-0.00966**

(-1.98)-0.00413*

(-1.78)-0.00723**

(-1.99)0.00440(0.76)




25 25 23 23 23 23



22


Kitchen Sink – Top Income Inequality1999-2010





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)


-0.138*

(-1.72)-0.0608(-0.89)

-0.116*

(-1.93)-0.0338(-1.07)

0.0521(0.90)

-0.0184(-0.43)


0.166**

(2.33)0.0408(0.50)

0.147***

(3.93)0.0562(0.85)

0.0199(0.40)

0.00391(0.11)


-0.659***

(-2.66)0.0508(0.24)

-0.517***

(-4.04)-0.0274(-0.24)

-0.0921(-0.55)

-0.189*

(-1.75)


0.413*

(1.90)-0.0785(-0.64)

0.368***

(3.69)-0.0358(-0.56)

0.0778(0.56)

0.0833(0.95)

r - g 0.0111(1.48)

-0.00428(-0.78)

0.00280(0.67)

-0.0000555(-0.02)

-0.00373(-0.69)

0.00365(1.61)


-0.873(-1.06)

2.269(1.26)

-0.256(-0.51)

0.952(1.02)

-0.103(-0.23)

-0.828(-0.70)


-3.661**

(-2.15)-0.144(-0.23)

-1.400(-1.37)

-0.265(-0.72)

1.362*

(1.67)-0.0669(-0.11)


0.0658(0.92)

-0.00620(-0.18)

0.0324(0.70)

0.00180(0.11)

0.00152(0.05)

0.00275(0.13)


0.0162(0.98)

0.113**

(2.15)0.0152(1.43)

0.0475(1.61)

0.00586(0.62)

-0.0610**

(-2.01)

23


0.00337(0.45)

-0.00219(-0.27)

-0.00837(-1.45)

-0.00390(-0.82)

-0.00734(-1.18)

-0.00424(-0.81)




24 24 23 23 23 23



3.4 Evidence of Piketty’s r>g and Schumpeter’s Innovation Rents: Interaction of Countries Grouped by the Global Competitiveness Index The countries in the panel have very different economies and so such differences should be considered. The fixed effects estimator is appropriate for capturing these differences. However it is important to recognize that different economies will react differently to changes in innovation and Piketty variables. Rather than interacting each country with each measure of innovation and each Piketty variable the countries are first grouped in terms of their competitiveness as measured by the GCI and then the interaction between the variables of interest and the GCI groups are estimated. Four groups are created as seen in the table below where GCI0 is the group with the most competitive countries and GCI3 is the group with the least competitive countries.

3.4.1 GCI Grouping TableGlobal Competitive Index (2015-2016) Categories

Label Value Countries Total

24

GCI0 GCI ≥ 5.5 Switzerland, Singapore, United States, Germany, Netherlands, Japan, Finland

7

GCI1 5.4 ≥ GCI ≥ 5 Sweden, United Kingdom, Norway, Denmark, Canada, New Zealand, Malaysia, Australia, France,

Ireland, Korea Rep.

11

GCI2 4.9 ≥ GCI ≥ 4.5 China, Spain, Indonesia, Portugal, Italy 5GCI3 4.4 ≥ GCI South Africa, India, Columbia, Argentina 4

3.4.2 ResultsTable 9 displays results from fixed effects regressions7 showing that an increase in r>g increases the top 1% share of income for GCI0 countries and that the effect of an increase in r>g is nearly zero or negative for most non-GCI0 countries. Also the table shows that the innovation measure patents granted has no effect on top income inequality but R&D expenditure does. Excluding patents granted and treating R&D as a measure of innovation, (5) and (6) show that the coefficient on R&D expenditure per capita is significant and positive for the top 1% and top 10% share of income for GCI0 countries but for the top 10% share of income is nearly zero or negative for all other countries.

These results show that after controlling for fixed effects and innovation the Piketty variable r>g is not significant in explaining top income inequality for the most competitive economies. This is evidence against Piketty’s theory on capital driven top income inequality. Furthermore the results give some justification for Schumpeterian growth theory and innovation rents.


Global Competitive Index Interaction Results – GCI0 is basePiketty: r>g, K/Y

1980-2010Patents Granted

1999-2010R&D: Expenditure

1996-2010(1) (2) (3) (4) (5) (6)

Top 1% Top 10% Top 1% Top 10% Top 1% Top 10%

7 Pooled OLS regressions are included in Appendix Table A6.

25

Share of Income (Log)






r - g 0.0110*

(1.88)0.00121(0.46)

0.00141(0.23)

0.00425(0.93)

0.000675(0.20)

0.000542(0.19)

GCI1 x r - g -0.0131*

(-1.67)-0.00312(-0.82)

-0.00830(-0.97)

-0.00704(-1.35)

-0.00926*

(-1.65)-0.00542(-1.47)

GCI2 x r - g -0.0146**

(-2.38)-0.00193(-0.51)

-0.00354(-0.19)

-0.00251(-0.25)

-0.00247(-0.49)

-0.00217(-0.64)

GCI3 x r - g -0.0104(-1.49)

0.00176(0.57)

-0.00243(-0.36)

-0.00352(-0.63)

-0.00221(-0.70)

-0.00384(-1.29)


0.0951(0.67)

0.111(1.28)

GCI1 x Patents Granted (Log)

-0.0752(-0.48)

-0.104(-1.17)


-0.121(-0.70)

-0.114(-1.18)


-0.101(-0.69)

-0.0783(-0.90)


0.168*

(1.67)0.0993(1.01)

0.571***

(2.72)0.357***

(2.69)

GCI1 x R&D per capita expenditure (Log)

-0.363(-1.59)

-0.326**

(-2.42)


-0.519*

(-1.94)-0.377**

(-2.21)


-0.334(-1.41)

-0.407***

(-3.82)

26

K/Y (Log) -0.717(-1.39)

0.216(0.77)

0.0334(0.07)

0.106(0.47)

0.344(0.96)

0.353(1.64)

GCI1 x K/Y (Log)

0.373(0.60)

-0.107(-0.20)

GCI2 x K/Y (Log)

0.467(0.57)

-0.656(-1.03)

GCI3 x K/Y (Log)

-0.232(-0.23)

0.0525(0.16)


-0.0977(-1.12)

-0.0333(-0.53)

-0.121(-0.99)

-0.0380(-0.55)


-0.756(-1.14)

-0.0911(-0.17)

2.286(1.47)

0.942(0.95)

0.835(0.82)

1.048*

(1.74)


-1.722**

(-1.97)-0.287(-0.61)

-0.444(-0.69)

-0.561(-1.50)

-1.108(-1.64)

-0.327(-0.79)


-0.0411*

(-1.66)-0.0112(-0.64)

-0.00980(-0.26)

-0.00161(-0.11)

-0.0399(-1.34)

-0.0222*

(-1.69)


-0.00545(-0.13)

-0.00182(-0.03)

0.0972*

(1.89)0.0401(1.04)

0.0673(1.09)

0.0431(1.01)


-0.00515(-0.79)

-0.00183(-0.72)

-0.00368(-0.56)

-0.00576(-1.40)

0.00617(1.09)

0.000102(0.02)


Yes Yes Yes Yes Yes Yes

Observations 502 444 209 197 278 255R2 overall 0.0403 0.167 0.0433 0.000163 0.00318 0.00781R2 between 0.000164 0.0887 0.0153 0.00104 0.0127 0.00819R2 within 0.662 0.558 0.382 0.351 0.540 0.543# of countries 26 23 24 23 25 23

27

in regressiont statistics in parentheses


4 Discussion

4.1 Reverse Causality –Innovation on Income Inequality OLS Regression and Panel VARIt is worthwhile to investigate the regression of innovation on income inequality in order to truly investigate Schumpeterian growth theory. According to theory, higher top income inequality should lead to greater innovative activity since inventors receive larger innovation rents and thus have a greater incentive to innovate. If this is the case then the coefficient on patents granted in our regressions on top income inequality measures in Section 3.3.1 can likely suffer from endogeneity caused by reverse causality since patents granted is a measure of innovative activity. The regressions on book to market in Section 3.3.2 avoid some of this problem because book to market is a measure of the value of innovative activities rather than innovative activity.

Table 10 displays the results when patents granted is regressed on income inequality measures. An increase in the top 10% share of income significantly increases patents granted as shown in (3) of Table 10. This result suggests that higher top 10% share of income increases innovative activity. However, the most significant result from the table is that R&D expenditure per capita has a significantly strong positive effect on patents granted which remains robust after accounting for country fixed effects.


Reverse Causality – Patents Granted on Top Income Inequality1999-2010

(1) (2) (3) (4) (5) (6)Patents Granted (Log)






28


0.950(1.55)

0.157(0.69)


2.817**

(2.43)0.492(0.96)

Pareto Lorenz Coefficient (log)

1.450(0.94)

-0.137(-0.55)


2.138***

(3.00)0.447(1.33)

2.279***

(3.61)0.779***

(2.77)2.184***

(2.76)0.562*

(1.68)


-0.567(-0.76)

0.443*

(1.73)-1.058*

(-1.74)0.334(1.62)

-0.549(-0.60)

0.413(1.51)

r - g 0.00561(0.32)

-0.00712*

(-1.71)0.00865(0.40)

-0.00606(-0.94)

0.0266(1.30)

-0.00836*

(-1.75)

K/Y (Log) 0.00361(0.00)

0.986(1.04)

0.471(0.56)

0.522(0.74)

-0.289(-0.26)

1.325(1.32)


1.281(0.39)

1.307(0.43)

1.718(0.62)

2.140(0.56)

0.393(0.11)

2.737(0.80)


1.413(0.30)

2.711(1.06)

2.328(0.54)

1.768(0.69)

-5.480(-0.97)

2.885(0.86)


-0.270(-0.96)

0.0272(0.36)

-0.338*

(-1.83)-0.00215(-0.03)

-0.169(-0.58)

0.0380(0.46)


-0.186***

(-3.89)-0.0567(-0.51)

-0.154***

(-3.24)-0.0490(-0.57)

-0.196***

(-3.35)-0.0676(-0.59)

Employment to population ratio, 15+, total (%) (national

-0.00384(-0.13)

-0.0236(-1.04)

0.0242(0.70)

-0.0132(-0.60)

-0.00461(-0.12)

-0.0325(-1.37)

29

estimate)Country Fixed Effects



24 24 23 23 23 23



To further investigate the causal relationship between top income inequality and innovation the paper conducts two panel VAR estimation; the first between the top 1% share of income and b/m, and the second between the top 10% share of income and b/m. The number of lags chosen is four, based on what was done in Atems and Jones (2015). The results from granger causality tests shown in Figure A1 in the appendix support what was found in the preceding regressions; increases in the top 10% share of income causes increases in innovation. Figure 5 and Figure 6 show selected impulse response functions from both multivariate regressions.

30

-2

-1.5

-1

-.5

0

0 5 10

bm : top1incIR

F

stepimpulse : response

Figure 5 – Impulse Response Function: Impulse (Book to Market), Response (Top 1% Share of Income)

-.005

0

0 5 10

top10inc : bm

IRF

stepimpulse : response

Figure 6 - Impulse Response Function: Impulse (Top 10% Share of Income), Response (Book to Market)

31

4.2 Pre and Post Y2K Regressions – Evidence of a New EconomySo far the analysis has consistently given different results between regressions with time spans starting from and after 1997 to 2010 and regressions where the time period covered extend from the 1980s to 2010. Therefore this section further explores the difference in results. Table 11 displays results for regression over the full period, pre-2000 and post-2000.

The year 2000 can be considered a turning point in economic history. The end of the dot-com bubble had begun and it put out many incumbents in the relatively young information technology (IT) industry. Between 1990 and 2000 there was heavy investment into the IT industry which led to an IT stock bubble which is commonly referred to as the dot-com bubble.8 The end of the dot-com bubble resulted in less incumbent firms in the IT industry and excess IT infrastructure, which gave rise to lower entry barriers and thus increasing competition and innovative activities in the IT industry. The post 2000 IT industry differs from other industries because it requires little capital, little labour, and high ability workers (high levels of human capital) for firms to be successful. The dot-com bubble enabled digitalization which has transformed the advanced economies into a digital economy (Stiroh, 2004). Hémous and Olsen (2014) suggest that the digital economy creates larger human capital and innovation rents resulting in higher income inequality.

Before 2000 innovation plays no role in explaining increases in top income inequality and human capital is significant and negatively correlated with the top 1% share of income as shown in Table 11. However after 2000 Table 11 shows that innovation increases top 1% and top 10% share of income for GCI0 countries while human capital becomes insignificant with a positive coefficient. Table 11 also shows that the coefficient on r-g has become significant and positive for GCI0 countries post 2000 which gives some indication for Piketty’s hypothesis that the future of capitalism tends to capital accumulation driven income inequality. The increasing significance of innovation and human capital in Table 11 provides evidence that increases in innovation and human capital rents explains recent trends in top income inequality.

8 See “The Boom and Bust in Information Technology Investment” by Mark Doms. http://www.frbsf.org/economic-research/files/er19-34bk.pdf.

32


Y2K Regressions: Book to Market pre and post 2000 – GCI0 is base1980-2010 1980-2000 2000-2010








-0.0812(-1.10)

-0.00325(-0.14)

-0.0534(-0.66)

0.00927(0.26)

-0.116**

(-2.03)-0.0465*

(-1.70)

GCI1 x Book to Market (Log)

0.0228(0.29)

-0.0242(-1.12)

0.0208(0.22)

-0.0142(-0.44)

0.0380(0.51)

-0.00547(-0.20)


0.100(0.67)

-0.000403(-0.00)

0.0941(0.35)

0.0140(0.14)

0.00673(0.02)

-0.0175(-0.15)


-0.0920(-1.10)

0.0488(1.34)

0.131(0.94)

-0.0409(-0.72)

0.0340(0.31)

0.0986***

(2.58)

r - g 0.0102**

(2.38)0.00288(1.21)

0.00590(0.97)

-0.00126(-0.57)

0.00517(1.02)

0.00670*

(1.74)

GCI1 x r - g -0.0144**

(-2.07)-0.00281(-0.79)

-0.0119(-1.56)

-0.000886(-0.28)

-0.0129(-1.43)

-0.00878*

(-1.94)

GCI2 x r - g -0.0162(-0.76)

-0.00102(-0.06)

-0.0206(-0.55)

-0.000654(-0.04)

-0.00319(-0.06)

-0.000171(-0.01)

GCI3 x r - g -0.0110**

(-2.31)-0.0000623

(-0.02)-0.00789(-0.99)

-0.000938(-0.30)

-0.00707(-1.46)

-0.00882***

(-2.86)


-1.210**

(-1.99)-0.349(-0.70)

-1.723**

(-2.42)-0.572(-1.51)

1.391(0.91)

0.544(0.73)


-1.344(-1.59)

0.0252(0.05)

-0.568(-0.45)

0.338(0.47)

-0.351(-0.64)

-0.495(-1.47)

Population growth (annual

-0.0136(-0.59)

-0.0115(-0.59)

0.0154(0.13)

-0.00795(-0.22)

-0.00565(-0.22)

-0.00421(-0.53)

33

%)


-0.0132(-0.30)

-0.00716(-0.12)

-0.00184(-0.03)

0.00168(0.04)

0.121**

(2.21)0.0414*

(1.83)


-0.00741(-1.16)

-0.00374*

(-1.70)-0.0106(-1.27)

-0.00385(-1.37)

0.000607(0.12)

-0.00283(-0.89)


Yes Yes Yes Yes Yes Yes

Observations 433 408 271 256 183 172R2 overall 0.606 0.0668 0.502 0.264 0.0470 0.171R2 between 0.572 0.0191 0.263 0.212 0.0418 0.103R2 within 0.692 0.567 0.589 0.479 0.435 0.391# of countries in regression

25 23 22 21 24 22



5 ConclusionThis paper has analyzed the effect of CSNI and innovation on top income inequality. The results show that neither CSNI nor innovation affects the rest of the income distribution as measured by the Pareto Lorenz coefficient. It is found that innovation is significant in explaining top income inequality where an increase in innovation increases the top 1% and top 10% share of income as predicted in Schumpeter’s growth model; however these results do not hold for the least competitive economies. Furthermore regressions predict that increases in the top 10% share of income lead to increases in innovative activity which further validates Schumpeterian growth theory. In support of Piketty the paper finds evidence that as an economy becomes more competitive r-g becomes more significant in explaining top income inequality trends. Lastly, results indicate that human capital rents might play a significant role in explaining top income inequality trends in the future.

The limitations realized in this paper are a few. The paper was limited to a relatively small number of observations for the analysis but this issue was mitigated by using pairs-cluster bootstrap procedure which has better finite sample properties than clustered standard errors. Also in terms of observations, the paper was limited to 27 countries with most of the observations coming from OECD countries in Europe and North America and thus presenting significant selection bias, yet the analysis is largely based on top income inequality of advanced economies and therefore being restricted to these countries is

34

acceptable. Lastly the time span presented significant challenges which the paper attempted to overcome by introducing book to market as a measure of innovation.

ReferencesAcemoglu, J., Robinson, J. A. (2015).The Rise and Decline of General Laws of Capitalism Journal of

Economic Perspectives, 29(1), 3-28. Retrieved from http://economics.mit.edu/files/11348

Aghion, P., Akcigit, U., Bergeaud, A., Blundell, R. W., & Hemous, D. (2015).Innovation and top income

inequality C.E.P.R. Discussion Papers, CEPR Discussion Papers: 10659. Retrieved from

http://sfx.scholarsportal.info/guelph/docview/1700663907?accountid=11233

Aghion, P., Akcigit, U., & Howitt, P. (2013).What Do We Learn From Schumpeterian Growth

Theory? National Bureau of Economic Research, Inc, NBER Working Papers: 18824. Retrieved from

http://www.nber.org/papers/w18824

Atems, B., & Jones, J. (2015). Income inequality and economic growth: A panel VAR approach. Empirical

Economics, 48(4), 1541-1561. doi:http://dx.doi.org/10.1007/s00181-014-0841-7

Falkinger, J., & Zweimuller, J. (1997). The impact of income inequality on product diversity and

economic growth. Metroeconomica, 48(3), 211-237. Retrieved from


Hémous, D., & Olsen, M. (2014). The rise of the machines: Automation, horizontal innovation and

income inequality C.E.P.R. Discussion Papers, CEPR Discussion Papers: 10244. Retrieved from


Hsing, Y. (2005). Economic growth and income inequality: The case of the US.International Journal of

Social Economics, 32(7), 639-647. Retrieved from


Lee, N., & Rodriguez-Pose, A. (2013). Innovation and spatial inequality in europe and USA. Journal of

Economic Geography, 13(1), 1-22. Retrieved from


Raff, Daniel M G (Review of: Scherer,Frederic M., & Perlman, M., eds). (1993). Review of:

Entrepreneurship, technological innovation, and economic growth: Studies in the schumpeterian

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http://economics.mit.edu/files/11348

tradition. Journal of Economic Literature, 31(4), 2017-2018. Retrieved from


Stiroh,Kevin (Review of: Greenan, Nathalie, L'Horty, Y., & Mairesse, J., eds). (2004). Review of:

Productivity, inequality, and the digital economy: A transatlantic perspective. Journal of Economic

Literature, 42(1), 218-219. Retrieved from


Tselios, V. (2011). Is inequality good for innovation? International Regional Science Review, 34(1), 75-

101. Retrieved from http://sfx.scholarsportal.info/guelph/docview/855184089?accountid=11233

Voitchovsky, S. (2005). Does the profile of income inequality matter for economic growth?:

Distinguishing between the effects of inequality in different parts of the income

distribution. Journal of Economic Growth,10(3), 273-296. doi:http://dx.doi.org/10.1007/s10887-

005-3535-3

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innovation-driven growth. Journal of Economic Growth, 5(2), 185-206. Retrieved from


Appendix

Table A1

Tables Excluded CountriesTable 1 None

36


Table 2 NoneTable 3 NoneTable 4 (1)-(2) India

(3)-(4) Argentina, Columbia, India, Indonesia (5)-(6) Argentina, Finland, India

Table 5 (1)-(2) India, Argentina(3)-(4) Argentina, Columbia, India, Indonesia

(5)-(6) Argentina, Finland, IndiaTable 6 (1)-(2) India, Argentina, Indonesia

(3)-(4) Argentina, Columbia, India, Indonesia (5)-(6) Argentina, Finland, India, Indonesia

Table 7 (1)-(2) India, Indonesia(3)-(4) Argentina, Columbia, India, Indonesia (5)-(6) Argentina, Finland, India, Indonesia

Table 8 (1)-(2) India, Argentina, Indonesia(3)-(4) Argentina, Columbia, India, Indonesia (5)-(6) Argentina, Finland, India, Indonesia

Table 9 (1) India(2) Argentina, Columbia, India, Indonesia

(3) India, Argentina, Indonesia(4) Argentina, Columbia, India, Indonesia

(5) India, Indonesia(6) Argentina, Columbia, India, Indonesia

Table 10 (1)-(2) India, Argentina, Indonesia(3)-(4) Argentina, Columbia, India, Indonesia (5)-(6) Argentina, Finland, India, Indonesia

Table 11 (1) India, Indonesia(2) Argentina, Columbia, India, Indonesia

(3) India, Indonesia, Canada, Columbia, China(4) India, Argentina, Indonesia, Canada, Columbia,

(5) India, Indonesia, Portugal(6) Argentina, Columbia, India, Indonesia, Portugal

Table A2

Innovation Measure: Patents Granted1999-2014





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)

Patents -0.00792 0.0428 0.0317 0.0354 -0.00540 -0.0320

37

Granted (Log) (-0.25) (0.71) (1.58) (0.88) (-0.40) (-1.02)Country Fixed Effects



26 26 23 23 25 25



Table A3

Innovation Measure: Patents Granted1999-2010





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)


-0.0120(-0.29)

0.0495(0.84)

0.0456*

(1.95)0.0507(1.30)

-0.00219(-0.12)

-0.0266(-0.75)

r - g 0.0199*

(1.89)-0.00269(-0.98)

0.00872(1.13)

0.000323(0.14)

-0.00735(-1.10)

0.00171*

(1.68)

K/Y (Log) -0.140(-0.59)

-0.161(-0.45)

-0.243(-1.11)

-0.0480(-0.26)

0.123(1.00)

0.0507(0.22)




26 26 23 23 25 25



38

Table A4

Innovation Measure: Book to Market1980-2014





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)


-0.0395(-0.46)

-0.0139(-0.51)

-0.101*

(-1.90)-0.00175(-0.14)

0.0150(0.42)

0.00322(0.16)



Observations 685 685 640 640 621 621R2 0.225 0.256 0.302R2 overall 0.216 0.0911 0.295R2 between 0.128 0.00617 0.0537R2 within 0.225 0.525 0.426 0.535# of countries in regression

25 25 23 23 23 23



Table A5

Innovation Measure: Book to Market1980-2010





Pareto Lorenz

Coefficient (log)

Pareto Lorenz

Coefficient (log)

Book to -0.121** -0.0251 -0.107* -0.00251 0.0143 0.00401

39

Market (Log) (-2.18) (-0.81) (-1.91) (-0.18) (0.43) (0.24)

r - g 0.00489(0.77)

-0.00121(-0.32)

0.00334(0.68)

0.000753(0.38)

0.00150(0.34)

0.00239(0.97)




25 25 23 23 23 23



Table A6

Global Competitive Index Interaction Results – GCI0 is base

Piketty: r>g, K/Y1980-2010

Patents Granted1999-2010

R&D: Expenditure1996-2010







r - g 0.00770(0.95)

-0.00149(-0.30)

0.0179*

(1.85)0.00484(0.73)

0.0184(1.39)

0.00694(0.91)

GCI1 x r - g -0.00442(-0.42)

0.00360(0.57)

-0.0187(-1.46)

-0.00863(-0.96)

-0.0230(-1.63)

-0.00849(-1.08)

GCI2 x r - g -0.000829(-0.08)

0.000689(0.09)

-0.00864(-0.26)

-0.00378(-0.30)

-0.0155(-0.95)

-0.00931(-0.86)

GCI3 x r - g -0.00816(-0.94)

0.00431(0.64)

-0.0174(-1.26)

-0.0155*

(-1.82)-0.0140(-0.95)

-0.00329(-0.33)


0.101(0.86)

0.115**

(2.35)

GCI1 x Patents 0.0126 0.0545

40

Granted (Log) (0.12) (1.02)


-0.00923(-0.05)

-0.0609(-0.81)


-0.142(-0.89)

-0.229**

(-2.52)


-0.234(-0.85)

-0.271**

(-1.98)-0.0521(-0.30)

-0.103(-0.78)


0.00147(0.08)

-0.00572(-0.60)


-0.0257(-0.68)

-0.00836(-0.39)


0.0259(0.50)

0.0412(1.19)

K/Y (Log) -0.0854(-0.46)

-0.00443(-0.03)

-0.121(-0.38)

0.0256(0.13)

-0.0316(-0.13)

-0.117(-0.58)

GCI1 x K/Y (Log)

-0.0255(-0.19)

-0.0882(-1.07)

GCI2 x K/Y (Log)

-0.275*

(-1.73)-0.181**

(-2.17)

GCI3 x K/Y (Log)

0.186(0.92)

0.328(1.14)


0.188(0.65)

0.155(1.31)

0.0935(0.48)

0.227*

(1.79)

Index of human capital

-0.913**

(-2.16)-0.304(-0.79)

-0.601(-0.47)

0.00836(0.02)

-0.857(-0.77)

-0.278(-0.44)

41

per person (Log)


-4.068***

(-3.06)-1.684**

(-2.07)-3.354*

(-1.93)-1.339(-1.63)

-4.414**

(-2.32)-1.792(-1.62)


0.0276(0.47)

-0.0221(-0.46)

0.137*

(1.87)0.0605(1.62)

0.0564(0.91)

0.0311(0.65)


-0.00345(-0.33)

-0.00414(-0.61)

0.0110(0.32)

0.0195(1.22)

-0.00774(-0.53)

-0.0131(-1.37)


-0.00971*

(-1.74)-0.00850**

(-2.26)-0.00523(-0.57)

-0.00649(-1.32)

-0.00746(-0.81)

-0.00715(-1.24)


No No No No No No

Observations 502 444 209 197 278 255R2 0.783 0.717 0.695 0.839 0.686 0.726# of countries in regression

26 23 24 23 25 23


Cluster Bootstrapped Standard Errors

* p < 0.1, ** p < 0.05, *** p < 0.01

42

ALL 14.438 4 0.006 bm 14.438 4 0.006 top1inc ALL 6.120 4 0.190 top1inc 6.120 4 0.190 bm Equation \ Excluded chi2 df Prob > chi2

ALL 6.517 4 0.164 bm 6.517 4 0.164 top10inc ALL 11.512 4 0.021 top10inc 11.512 4 0.021 bm Equation \ Excluded chi2 df Prob > chi2

Figure A1 – Granger Causality Tables

43

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