This paper uses cross-country panel data from 1950-2014 to investigate whether the rise in top income inequality can be explained by capital accumulation or innovation when accounting for cross-country differences. Pooled OLS and fixed effects models are used to analyze the relationship between top income inequality, measures of capital accumulation, and measures of innovation. The paper finds some evidence that capital accumulation drives top income inequality in highly competitive countries, consistent with Piketty's theory. However, there is stronger evidence that innovation rents drive top income inequality rather than capital accumulation, as measures of innovation like patents granted and book-to-market ratio are significantly related to higher top 1% and top 10% income shares. The relationship between innovation and top income
The effect of Economic Conditions on Net Greek MigrationEmily Marshall
This document provides an introduction, literature review, model specification, data description, empirical results, and conclusions regarding a study analyzing the effect of economic conditions on Greek net migration from 1991-2014. The study uses econometric techniques to model the relationship between key economic indicators like GDP, inflation, unemployment, interest rates, and dummy variables for economic crisis and EU membership, on net migration. The initial linear model showed some issues which were partially addressed by transforming it to a log-log model. Overall, the study found GDP and unemployment were statistically significant in explaining migration, and concluded that strengthening Greece's economic position could help stabilize net migration flows.
CO-EVOLUTIONARY COUPLING BETWEEN CAPTURED AND UNCAPTURED GDP CYCLES:CROSS LEA...IJMIT JOURNAL
A solution to the critical problem of a dilemma between R&D expansion and productivity decline that a
majority of information and communication technology (ICT) leaders have been confronting in the digital
economy is expected. It can be expected by a spinoff from economic functionality-seeking GDP-based coevolution cycle to supra-functionality beyond an economic value-seeking uncaptured GDP-driven coevolution cycle. However, the transformation dynamism remains a black box.
This document provides an overview and review for an exam covering macroeconomics chapters 10-16. It includes summaries of key topics like the problems of macroeconomics, stock markets, types of firms, unemployment, inflation, aggregate supply and demand modeling, monetary policy tools, and transmission mechanisms. The Federal Reserve and its role in using tools like open market operations, the discount rate, and reserve ratio to influence money supply and achieve its mandates of stable prices and maximum employment are discussed.
Session 4 d discussant iariw paper trappIARIW 2014
This paper develops a new panel dataset of labor income shares for 100 developing countries from 1990 to 2011. It improves upon previous estimates by combining macroeconomic data with information from social accounting matrices and micro-level studies. This allows the paper to better account for the labor income of self-employed individuals, an important issue in developing countries. The findings suggest the labor share has been declining in developing countries over the period studied. This decline carries implications for rising inequality according to the paper.
Trends in Information Technology for Economic & Social Development in 2020 - ...IJMIT JOURNAL
International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
The document discusses signs that the true unemployment rate in the US is higher than reported, around 6-7% rather than the quoted 4.3%. It notes weakness in the labor market that will likely lead to falling wages and payrolls over the next 6-8 months. It also discusses a stressed US consumer with rising debt delinquencies, indicating weaker discretionary spending and retail sales going forward. Several trades are recommended based on this outlook, including selling stocks, buying bonds, and selling retail and base metals ETFs.
This document analyzes trends in labor income shares and the substitution of ICT capital for routine labor between 1979-2012 in the US. It finds that the routine labor income share declined substantially while the ICT capital income share nearly doubled. It uses these trends to estimate an elasticity of substitution between 1.5-4.5 for ICT capital and routine labor. A calibrated model suggests the decline in the aggregate labor share was primarily due to automation of routine tasks, while changing specialization patterns primarily drove the reallocation of labor across occupations.
The document summarizes the 2014-2015 annual operating budget for Naperville, Illinois. It discusses how the city has been impacted by the economic recession but has improved its budget since 2008-2009. The budget was developed by the Finance Department to meet the fiscal needs of the city government. Key points of the budget include a decline in property values and taxes, issues with funding the electric utility, and a $9 million reduction due to pension contributions. The largest sources of general fund revenue are sales tax, property tax, and state income tax.
The effect of Economic Conditions on Net Greek MigrationEmily Marshall
This document provides an introduction, literature review, model specification, data description, empirical results, and conclusions regarding a study analyzing the effect of economic conditions on Greek net migration from 1991-2014. The study uses econometric techniques to model the relationship between key economic indicators like GDP, inflation, unemployment, interest rates, and dummy variables for economic crisis and EU membership, on net migration. The initial linear model showed some issues which were partially addressed by transforming it to a log-log model. Overall, the study found GDP and unemployment were statistically significant in explaining migration, and concluded that strengthening Greece's economic position could help stabilize net migration flows.
CO-EVOLUTIONARY COUPLING BETWEEN CAPTURED AND UNCAPTURED GDP CYCLES:CROSS LEA...IJMIT JOURNAL
A solution to the critical problem of a dilemma between R&D expansion and productivity decline that a
majority of information and communication technology (ICT) leaders have been confronting in the digital
economy is expected. It can be expected by a spinoff from economic functionality-seeking GDP-based coevolution cycle to supra-functionality beyond an economic value-seeking uncaptured GDP-driven coevolution cycle. However, the transformation dynamism remains a black box.
This document provides an overview and review for an exam covering macroeconomics chapters 10-16. It includes summaries of key topics like the problems of macroeconomics, stock markets, types of firms, unemployment, inflation, aggregate supply and demand modeling, monetary policy tools, and transmission mechanisms. The Federal Reserve and its role in using tools like open market operations, the discount rate, and reserve ratio to influence money supply and achieve its mandates of stable prices and maximum employment are discussed.
Session 4 d discussant iariw paper trappIARIW 2014
This paper develops a new panel dataset of labor income shares for 100 developing countries from 1990 to 2011. It improves upon previous estimates by combining macroeconomic data with information from social accounting matrices and micro-level studies. This allows the paper to better account for the labor income of self-employed individuals, an important issue in developing countries. The findings suggest the labor share has been declining in developing countries over the period studied. This decline carries implications for rising inequality according to the paper.
Trends in Information Technology for Economic & Social Development in 2020 - ...IJMIT JOURNAL
International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
The document discusses signs that the true unemployment rate in the US is higher than reported, around 6-7% rather than the quoted 4.3%. It notes weakness in the labor market that will likely lead to falling wages and payrolls over the next 6-8 months. It also discusses a stressed US consumer with rising debt delinquencies, indicating weaker discretionary spending and retail sales going forward. Several trades are recommended based on this outlook, including selling stocks, buying bonds, and selling retail and base metals ETFs.
This document analyzes trends in labor income shares and the substitution of ICT capital for routine labor between 1979-2012 in the US. It finds that the routine labor income share declined substantially while the ICT capital income share nearly doubled. It uses these trends to estimate an elasticity of substitution between 1.5-4.5 for ICT capital and routine labor. A calibrated model suggests the decline in the aggregate labor share was primarily due to automation of routine tasks, while changing specialization patterns primarily drove the reallocation of labor across occupations.
The document summarizes the 2014-2015 annual operating budget for Naperville, Illinois. It discusses how the city has been impacted by the economic recession but has improved its budget since 2008-2009. The budget was developed by the Finance Department to meet the fiscal needs of the city government. Key points of the budget include a decline in property values and taxes, issues with funding the electric utility, and a $9 million reduction due to pension contributions. The largest sources of general fund revenue are sales tax, property tax, and state income tax.
The U.S. industrial market experienced strong net absorption in Q3 2018, with overall vacancy remaining at historic lows despite increased construction. Demand was broad-based across regions and product types, with the South and West leading in absorption. Rents continued rising above 5% annually in over half of markets as demand outstripped supply in a tight market. The development pipeline expanded but speculative construction remains concentrated in top markets, indicating limited overbuilding risk through 2019.
2011 - Charting international labor comparisonsRichard Han
The document is a report published by the U.S. Bureau of Labor Statistics that provides international labor comparisons data for 2009 and earlier years. It features data on gross domestic product, labor force indicators, manufacturing costs and productivity, and consumer prices for various countries. The report aims to improve comparability of cross-country labor statistics by adjusting data to a common conceptual framework. It contains charts and tables presenting key labor market indicators for different economies.
1. The document discusses the difference between economic growth and development. Growth refers to increases in production, while development encompasses improvements in living standards.
2. It examines factors that influence economic growth according to neoclassical growth models, including capital accumulation, technological progress, savings rates, and human capital. The Solow and Romer growth models are described.
3. Empirical evidence suggests that differences in total factor productivity and institutional quality best explain variations in growth between rich and poor countries. Geography and institutions have significant impacts on development.
Misallocations go a long way. Firm-level evidence from PolandGRAPE
We analyze the link between prior resource misallocation and subsequent long-run economic growth. We use two unique and novel sources of data for Poland and measure misallocation inherited from the period of central planning, i.e. period where input prices did not determine the use of inputs at firm, industry and country level. We find that regions with more misaligned firms under central planning (especially in terms of under-capitalization) experienced lower economic growth once market mechanisms were reinstated. This result proves highly robust, even three decades since the market mechanisms were reinstated. To explain this finding, we assess regional, sectoral, and cohort dimension of the inputs misallocation. We show that under-capitalization was more prevalent that over-capitalization, and that it was due mostly to the firm and sector level variation in factor inputs. Given this insight, subsequent reallocation of the resources required shifting of inputs not only between firms, but also between sectors: a process which is relatively more prone to specificity and information frictions.
This document discusses measuring business demography at the regional level. It finds that new businesses make up on average 10% of all firms but there is substantial regional variation. New businesses are concentrated in urban and productive regions and are associated with better local governance, infrastructure, access to financing, and education. Exact location of employment is important to understand business dynamics' impact. The document also finds that business births can create up to 8% new employment and higher regional firm dynamics indirectly boost existing firm employment growth. Key regions driving business creation are capital regions. Further analysis of regional drivers of firm dynamics and improving measurement is discussed.
This document summarizes a research paper that investigates the impact of trade shocks on inequality in the US and other countries. It finds that:
1) Workers in US manufacturing sectors more exposed to trade shocks were more likely to be unemployed and experienced weaker wage growth, particularly those without a college degree.
2) However, there was no increase in inequality within sectors most exposed to trade shocks.
3) Internationally, various trade shocks were not generally correlated with rising inequality. Instead, historical top marginal tax rates had a strong correlation with the top 1% income share in different countries. Lower tax rates were associated with higher inequality.
TVA p3 The GLOBAL SOCIO ENVIRO ECONOMIC SystemPeter Burgess
The Global SOCIO-ENVIRO-ECONOMIC System is a very complex system and a significant challenge to manage in an effective way. While there has been progress in improving quality of life for many people using the metrics of money, the system leaves many behind. Worse, a system that only has focus on money economics means that progress is achieved at the expense of some segments of society and at a huge cost to the environment , neither of which are part of the dominant money metrics framework. True Value Accounting brings people (society) and planet (environment) into the accounting and accountability in a way that is as rigorous for these elements as it is for money.
The document discusses whether globalization is the primary cause of rising income inequality in developed countries. It presents several perspectives on this issue. The Heckscher-Ohlin model and evidence from scholars like Wood support the view that globalization increases inequality, but others like Krugman and the OECD argue technological progress is a more important factor. The IMF finds that both globalization and technology significantly contribute to rising inequality, and it is difficult to say which is the primary cause.
The retail market report summarizes 2015 trends in the Phoenix metro area. It notes that 65,700 jobs were added in 2015, home starts increased 70% year-over-year, and these economic gains are boosting consumer confidence. Retail vacancy rates declined to 9.3% while net absorption was 1.77 million square feet. Average rental rates increased to $14/sqft, up from $13.62/sqft in 2014. The report concludes that with continued job and housing growth, the retail sector is poised for growth in 2016.
The document outlines the AK model of economic growth proposed by Frankel in 1962 and evaluates its relevance to developing countries like Zimbabwe. The AK model emphasizes capital accumulation and assumes technological progress occurs endogenously through learning by doing. It predicts capital accumulation will increase productivity and growth. The model is relevant for developing countries in promoting industrialization and reducing unemployment. However, it may fail to achieve desired results as it does not consider other important factors like political stability and strong institutions. It is also too general and does not provide specific policy prescriptions tailored to individual country contexts.
Growth theory began with Solow (1956) and Swan (1956) formalizing an economic growth framework. Later models endogenized technical progress, including Arrow (1962) on learning by doing, Lucas (1988) on human capital accumulation, and Romer (1990) on R&D-driven innovation. The book provides an overview of these models and extensions, examining issues like the role of public policy, demographics, money, and sustainable development. It aims to demonstrate how growth theory tools can be applied to answer different economic questions.
The document summarizes the 2016 U.S. Goodwill Impairment Study. It finds that 2015 saw record levels of both goodwill added ($458 billion) and goodwill impaired ($57 billion) among U.S. public companies. Goodwill impaired more than doubled from 2014 levels, driven by increases in the energy, information technology, consumer discretionary, industrials, and utilities industries. The study also reports that 59% of public company respondents in a survey now use the optional qualitative goodwill impairment test, up from 29% in 2013. Furthermore, 82% of survey respondents supported proposed FASB changes to simplify the goodwill impairment test.
The document discusses increasing unemployment rates and reasons for unemployment. It notes private sector employment has been negative for 10 years, with 8 million jobs lost during the recession. Unemployment duration and rates are at record highs. Reducing unemployment will require stimulating all three engines of the economy - consumers, businesses, and government - as each are currently struggling. The document proposes a non-profit jobs program as a potential "fourth engine" to reduce unemployment.
1. The document discusses endogenous growth models and their representation of economic growth processes.
2. It addresses issues with decreasing marginal returns in endogenous growth models and introduces the Jones critique of these models.
3. Semi-endogenous growth models and the Jones model are presented as alternatives that account for decreasing marginal returns to R&D over time.
This document summarizes the goals and methodology of a study assessing the economic base of the Miami Valley region from 1970 to 2000. The study aimed to provide an in-depth review of the region's economy compared to Ohio and the US through analyzing indicators such as income, business establishments, and employment by industry. It conducted location quotient and shift-share analyses using 1970 and 2000 employment data to understand the region's economic base and changes over time due to reference area growth, industry mix, and regional shift effects. The full report is available online.
This document discusses endogenous growth theory and compares it to the neoclassical growth model. It summarizes two core endogenous growth models: the AK model and a "learning by doing" model. The AK model endogenizes long-run growth by having output be a linear function of capital alone. This causes savings rates to permanently impact the growth rate. The "learning by doing" model features knowledge spillovers that increase productivity based on average capital levels. Both models generate sustained long-run growth, unlike the neoclassical model. The document then discusses how international capital market integration can further boost growth by allowing more efficient global allocation of capital.
-U.S. Office Market Was Driven by the Tech
Sector in the Fourth Quarter of 2018
-Absorption exceeds construction completions, vacancy
declines and the pipeline grows
-Tech markets tighten
-Rents rise, but the pace slows:
The document discusses global cities and their competitiveness. It identifies seven types of global cities based on their population, GDP, talent, traded sectors, innovation, infrastructure, industry characteristics, and economic characteristics. The first type discussed are "Global Giants," which are the largest global cities by population and GDP that play a dominant role in the global economy.
This benchmarking study, developed by the Brookings Metropolitan Policy Program, provides the Greater Charlotte region with a framework and data to better understand its performance and position in the global economy, offering information and insights to help leaders more actively shape the region’s economic strategy.
This document provides an introduction to a dissertation that examines the relationship between trade liberalization, government debt, human capital, and income inequality using panel data econometrics. It aims to understand the determinants of global income inequality and account for rising inequality in developed countries. The analysis uses the consistent EHII index to measure household income inequality across 136 countries from 1968-2008. Static and dynamic panel techniques are employed to explore how macroeconomic variables like human capital, trade openness, government debt, inflation, and growth impact inequality. It also considers whether effects differ between developed and developing countries. The results seek to inform policy to reduce inequality and its associated social and economic issues.
Tourism has been found to contribute to decreasing income inequality according to some studies. The author analyzes the impact of tourism on income inequality using panel data and cross-country regression. Variables like the Gini coefficient, tourism's contribution to GDP, education levels, economic factors, and others are used. Fixed effects regression shows that as tourism's contribution to GDP increases by 1%, the Gini coefficient decreases by around 0.266, indicating tourism is associated with reduced income inequality. Some variables like inflation and female labor participation were also found to significantly impact income inequality.
The U.S. industrial market experienced strong net absorption in Q3 2018, with overall vacancy remaining at historic lows despite increased construction. Demand was broad-based across regions and product types, with the South and West leading in absorption. Rents continued rising above 5% annually in over half of markets as demand outstripped supply in a tight market. The development pipeline expanded but speculative construction remains concentrated in top markets, indicating limited overbuilding risk through 2019.
2011 - Charting international labor comparisonsRichard Han
The document is a report published by the U.S. Bureau of Labor Statistics that provides international labor comparisons data for 2009 and earlier years. It features data on gross domestic product, labor force indicators, manufacturing costs and productivity, and consumer prices for various countries. The report aims to improve comparability of cross-country labor statistics by adjusting data to a common conceptual framework. It contains charts and tables presenting key labor market indicators for different economies.
1. The document discusses the difference between economic growth and development. Growth refers to increases in production, while development encompasses improvements in living standards.
2. It examines factors that influence economic growth according to neoclassical growth models, including capital accumulation, technological progress, savings rates, and human capital. The Solow and Romer growth models are described.
3. Empirical evidence suggests that differences in total factor productivity and institutional quality best explain variations in growth between rich and poor countries. Geography and institutions have significant impacts on development.
Misallocations go a long way. Firm-level evidence from PolandGRAPE
We analyze the link between prior resource misallocation and subsequent long-run economic growth. We use two unique and novel sources of data for Poland and measure misallocation inherited from the period of central planning, i.e. period where input prices did not determine the use of inputs at firm, industry and country level. We find that regions with more misaligned firms under central planning (especially in terms of under-capitalization) experienced lower economic growth once market mechanisms were reinstated. This result proves highly robust, even three decades since the market mechanisms were reinstated. To explain this finding, we assess regional, sectoral, and cohort dimension of the inputs misallocation. We show that under-capitalization was more prevalent that over-capitalization, and that it was due mostly to the firm and sector level variation in factor inputs. Given this insight, subsequent reallocation of the resources required shifting of inputs not only between firms, but also between sectors: a process which is relatively more prone to specificity and information frictions.
This document discusses measuring business demography at the regional level. It finds that new businesses make up on average 10% of all firms but there is substantial regional variation. New businesses are concentrated in urban and productive regions and are associated with better local governance, infrastructure, access to financing, and education. Exact location of employment is important to understand business dynamics' impact. The document also finds that business births can create up to 8% new employment and higher regional firm dynamics indirectly boost existing firm employment growth. Key regions driving business creation are capital regions. Further analysis of regional drivers of firm dynamics and improving measurement is discussed.
This document summarizes a research paper that investigates the impact of trade shocks on inequality in the US and other countries. It finds that:
1) Workers in US manufacturing sectors more exposed to trade shocks were more likely to be unemployed and experienced weaker wage growth, particularly those without a college degree.
2) However, there was no increase in inequality within sectors most exposed to trade shocks.
3) Internationally, various trade shocks were not generally correlated with rising inequality. Instead, historical top marginal tax rates had a strong correlation with the top 1% income share in different countries. Lower tax rates were associated with higher inequality.
TVA p3 The GLOBAL SOCIO ENVIRO ECONOMIC SystemPeter Burgess
The Global SOCIO-ENVIRO-ECONOMIC System is a very complex system and a significant challenge to manage in an effective way. While there has been progress in improving quality of life for many people using the metrics of money, the system leaves many behind. Worse, a system that only has focus on money economics means that progress is achieved at the expense of some segments of society and at a huge cost to the environment , neither of which are part of the dominant money metrics framework. True Value Accounting brings people (society) and planet (environment) into the accounting and accountability in a way that is as rigorous for these elements as it is for money.
The document discusses whether globalization is the primary cause of rising income inequality in developed countries. It presents several perspectives on this issue. The Heckscher-Ohlin model and evidence from scholars like Wood support the view that globalization increases inequality, but others like Krugman and the OECD argue technological progress is a more important factor. The IMF finds that both globalization and technology significantly contribute to rising inequality, and it is difficult to say which is the primary cause.
The retail market report summarizes 2015 trends in the Phoenix metro area. It notes that 65,700 jobs were added in 2015, home starts increased 70% year-over-year, and these economic gains are boosting consumer confidence. Retail vacancy rates declined to 9.3% while net absorption was 1.77 million square feet. Average rental rates increased to $14/sqft, up from $13.62/sqft in 2014. The report concludes that with continued job and housing growth, the retail sector is poised for growth in 2016.
The document outlines the AK model of economic growth proposed by Frankel in 1962 and evaluates its relevance to developing countries like Zimbabwe. The AK model emphasizes capital accumulation and assumes technological progress occurs endogenously through learning by doing. It predicts capital accumulation will increase productivity and growth. The model is relevant for developing countries in promoting industrialization and reducing unemployment. However, it may fail to achieve desired results as it does not consider other important factors like political stability and strong institutions. It is also too general and does not provide specific policy prescriptions tailored to individual country contexts.
Growth theory began with Solow (1956) and Swan (1956) formalizing an economic growth framework. Later models endogenized technical progress, including Arrow (1962) on learning by doing, Lucas (1988) on human capital accumulation, and Romer (1990) on R&D-driven innovation. The book provides an overview of these models and extensions, examining issues like the role of public policy, demographics, money, and sustainable development. It aims to demonstrate how growth theory tools can be applied to answer different economic questions.
The document summarizes the 2016 U.S. Goodwill Impairment Study. It finds that 2015 saw record levels of both goodwill added ($458 billion) and goodwill impaired ($57 billion) among U.S. public companies. Goodwill impaired more than doubled from 2014 levels, driven by increases in the energy, information technology, consumer discretionary, industrials, and utilities industries. The study also reports that 59% of public company respondents in a survey now use the optional qualitative goodwill impairment test, up from 29% in 2013. Furthermore, 82% of survey respondents supported proposed FASB changes to simplify the goodwill impairment test.
The document discusses increasing unemployment rates and reasons for unemployment. It notes private sector employment has been negative for 10 years, with 8 million jobs lost during the recession. Unemployment duration and rates are at record highs. Reducing unemployment will require stimulating all three engines of the economy - consumers, businesses, and government - as each are currently struggling. The document proposes a non-profit jobs program as a potential "fourth engine" to reduce unemployment.
1. The document discusses endogenous growth models and their representation of economic growth processes.
2. It addresses issues with decreasing marginal returns in endogenous growth models and introduces the Jones critique of these models.
3. Semi-endogenous growth models and the Jones model are presented as alternatives that account for decreasing marginal returns to R&D over time.
This document summarizes the goals and methodology of a study assessing the economic base of the Miami Valley region from 1970 to 2000. The study aimed to provide an in-depth review of the region's economy compared to Ohio and the US through analyzing indicators such as income, business establishments, and employment by industry. It conducted location quotient and shift-share analyses using 1970 and 2000 employment data to understand the region's economic base and changes over time due to reference area growth, industry mix, and regional shift effects. The full report is available online.
This document discusses endogenous growth theory and compares it to the neoclassical growth model. It summarizes two core endogenous growth models: the AK model and a "learning by doing" model. The AK model endogenizes long-run growth by having output be a linear function of capital alone. This causes savings rates to permanently impact the growth rate. The "learning by doing" model features knowledge spillovers that increase productivity based on average capital levels. Both models generate sustained long-run growth, unlike the neoclassical model. The document then discusses how international capital market integration can further boost growth by allowing more efficient global allocation of capital.
-U.S. Office Market Was Driven by the Tech
Sector in the Fourth Quarter of 2018
-Absorption exceeds construction completions, vacancy
declines and the pipeline grows
-Tech markets tighten
-Rents rise, but the pace slows:
The document discusses global cities and their competitiveness. It identifies seven types of global cities based on their population, GDP, talent, traded sectors, innovation, infrastructure, industry characteristics, and economic characteristics. The first type discussed are "Global Giants," which are the largest global cities by population and GDP that play a dominant role in the global economy.
This benchmarking study, developed by the Brookings Metropolitan Policy Program, provides the Greater Charlotte region with a framework and data to better understand its performance and position in the global economy, offering information and insights to help leaders more actively shape the region’s economic strategy.
This document provides an introduction to a dissertation that examines the relationship between trade liberalization, government debt, human capital, and income inequality using panel data econometrics. It aims to understand the determinants of global income inequality and account for rising inequality in developed countries. The analysis uses the consistent EHII index to measure household income inequality across 136 countries from 1968-2008. Static and dynamic panel techniques are employed to explore how macroeconomic variables like human capital, trade openness, government debt, inflation, and growth impact inequality. It also considers whether effects differ between developed and developing countries. The results seek to inform policy to reduce inequality and its associated social and economic issues.
Tourism has been found to contribute to decreasing income inequality according to some studies. The author analyzes the impact of tourism on income inequality using panel data and cross-country regression. Variables like the Gini coefficient, tourism's contribution to GDP, education levels, economic factors, and others are used. Fixed effects regression shows that as tourism's contribution to GDP increases by 1%, the Gini coefficient decreases by around 0.266, indicating tourism is associated with reduced income inequality. Some variables like inflation and female labor participation were also found to significantly impact income inequality.
Fiscal space and the composition of public finances - Jean-Marc Fournier, OECDOECD Governance
This presentation was made by Jean-Marc Fournier, OECD, at the 9th Annual Meeting of the OECD network of Parliamentary Budget Officials and Independent Fiscal Institutions held in Edinburgh, Scotland, on 6-7 April 2017.
This document presents a study that develops a unified measure of inclusive growth by integrating economic growth performance and income distribution outcomes in emerging markets over three decades. The study applies a macroeconomic social mobility function concept to measure inclusive growth in a way that focuses on both inequality and the pace of growth. The results indicate that macroeconomic stability, human capital, and structural changes promote inclusive growth, while financial deepening and technological change show no clear effects. Globalization in the forms of foreign direct investment and trade openness can foster greater inclusiveness.
This document summarizes research on economic convergence and growth. It reviews a core article by Barro and Sala-i-Martin that found evidence of convergence across U.S. states, with poorer states growing faster than richer ones. The document also reviews several other studies on factors influencing convergence and growth, such as policies, technology diffusion, education levels, and trade liberalization. Many findings support the idea that convergence exists, though some studies also found evidence of increasing divergence among certain countries or regions.
This document is a working paper that studies the overstatement of GDP growth in autocratic regimes compared to democracies. The author uses nighttime light data from satellites as a proxy for economic activity that is less prone to manipulation than GDP figures reported by governments. Regression analyses show that the elasticity of GDP to changes in nighttime lights is systematically higher in more authoritarian countries, suggesting autocracies exaggerate yearly GDP growth by about 35%. This paper aims to provide more credible estimates of economic performance in non-democratic countries.
Comparative Advantage and Intellectual Property Rights: Some Evidences from C...Peter Zhen Ye
This document summarizes a working paper presented at the 6th Annual Conference of the EPIP Association on intellectual property rights and development. It analyzes trade data from UNCTAD on creative industries in 103 countries to assess trade dependency ratios and revealed comparative advantage. Key findings include that many European and small island economies have above average trade dependency on creative industries. The document also lists the top countries by trade dependency on creative goods, with Afghanistan, UAE, Singapore, Switzerland and Moldova among the top 10. The analysis aims to further study relationships between intellectual property rights and revealed comparative advantage across countries and sectors.
劉遵義 Income inequality under economic globalisation(final) 20150414Andy Kuo
The document discusses income inequality under economic globalization. It examines how globalization and technological changes have contributed to rising income inequality within and between countries. While globalization has helped lift many people out of poverty, it has also been a factor in increasing national income disparities and declining wages in some developed countries. The Gini coefficient is presented as a common measure of income inequality, with higher values indicating greater inequality.
El jueves 17 de mayo del 2018 se organizó una Mesa Redonda en la Fundación Ramón Areces, en la cual se habló sobre las subidas de tipos en la era Trump y la nueva globalización.
How has Income Inequality and Wage Disparity Between Native and Foreign Sub-p...Dinal Shah
This study uses repeated cross-sectional data from 2007 to 2015 to examine in
detail what impact the 2008 recession had on income inequality and wage disparity
in the UK and on its sub-populations. The findings suggest inequality in the UK has
been decreasing since 2007, but that the foreign sub-population experiences a
greater degree of inequality than the native sub-population. Additionally, foreign and
native labour are imperfect substitutes where the wages of foreign workers are
greatly more susceptible to economic shocks effecting the UK’s labour market. To
the best of my knowledge this is the first paper to conduct research into income
inequality between native and foreign workers.
This document discusses the drivers of inequality and presents both orthodox and emerging views. The orthodox view is that rising inequality is inevitable due to technological change and globalization, but this view is inadequate. The emerging view is that inequality results from growing economic power asymmetries, weakened labor protections, tax changes benefiting the wealthy, the outsized influence of the financial sector, privatization, and macroeconomic policies favoring stability over full employment. The document argues that policy interventions can help reduce inequality by strengthening collective bargaining, reforming banks, raising taxes on the wealthy, focusing economic development on stable jobs, and adopting macroeconomic policies promoting both stability and full employment.
Medef - La France peut-elle retrouver le chemin d'une croissance forte ?Lionel Sanchez ✔
- The document discusses the challenges posed by secular stagnation and the need for stronger policy action to escape the low-growth trap. It notes that exceptionally low interest rates create both financial distortions and opportunities for fiscal initiatives.
- To boost growth, the document argues for targeted structural reforms combined with collective fiscal initiatives focused on quality investment in infrastructure and human capital. Maintaining open markets through domestic policies that support workers is also key.
- Escaping the low-growth trap requires weaving together fiscal, structural, and trade policies to strengthen both short-term and long-term growth in an inclusive manner.
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La diseguaglianza economica dal 1900 ad oggiLavoce.info
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Session 8 c diewert discussion of feenstra inklaar and timmerIARIW 2014
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Estimating the stock of public capital in 170 countries May 2021.pdfAbdelmalekBOUMDIR1
Estimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of puEstimating the stock of public capital in 170 countries May 2021.pdfEstimating the stock of public capital in 170 countries May 2021.pdf
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Disparity in growth rates among countriesSparsh Banga
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1 10The Real Economy in the Long RunECO372The.docxhoney725342
Team D has been tasked with finding the best location outside the US to open a new manufacturing facility. The report analyzes Mexico based on several economic criteria. It finds that Mexico has high productivity growth linked to total factor productivity. Government policies that encourage investment, education, and trade can boost productivity. Mexico's financial system matches savings with investment, fueling capital growth. Relocating risks like operational, financial, and communication issues can be reduced through expert advice and cultural preparation. Mexico has a large labor force and relatively low current unemployment projected to decline further. Overall Mexico presents a favorable environment for the new facility.
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1. A Cross Country Analysis
on Top Income Inequality
Kyel Governor
6/9/2016
Thispaperusescross-countrypanel datafrom1950-2014 to investigateif the rise intopincome
inequalitycanbe explainedbycapital accumulationwheninnovationandcross-countrydifferencesare
takenintoaccount. PooledOLSandfixedeffectsmodelsare usedtoinvestigatethe relationship. This
paperfindssome evidence infavourof capital accumulationdriving topincome inequalityforhighly
competitivecountries.Howeverthere isstrongerevidence suggestingthatinnovationrentsdrivetop
income inequalityratherthancapital accumulation.
2. 1
Contents
Introduction.......................................................................................................................................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 effectof innovativenessandcapital accumulationonincome
distribution ......................................................................................................................................11
3.1 Estimation Strategy............................................................................................................11
3.2 Pikettyr> g and Capital Accumulation:ResultsfromPooledOLSandFixedEffectsRegressions
11
3.3 SchumpeterianGrowthTheoryandInnovationRents:ResultsfromPooledOLSandFixed
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’sr>gand Schumpeter’sInnovationRents:Interactionof 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
3. 2
Introduction
Increasingtopincome inequality indevelopedcountries hasbecome aphenomenoninthe worldtoday
and yetthere isno general agreement onthe rootcauses behind the rise in topincome inequality.This
paperdebateswhethertopincome inequalityisdrivenby capital accumulationorinnovation.Thomas
Piketty hasbroughtthe issue of growthand income redistribution backtothe forefrontof economic
debateswithhis (2014) book, Capitalin the Twenty-FirstCentury,where he argues thatcapital
accumulationbythe wealthiesttop1%is drivingtopincome inequality. Inshort, he defendsthat wealth
growsfasterthan economicoutput,centeringhisargument onthe expressionr> g (where ris the rate
of returnto capital andg isthe economicgrowthrate),andso slowergrowthleadsto a rapidincrease in
income inequality. Piketty’smore thansix-hundredpage publication inlarge partignores cross-country
differencesand the endogenousevolutionof technology anditsrelationshipwith topincome inequality
leavinghis worksusceptibletodispute (Acemogluetal,2015). Infact in the UnitedStates patentingand
top 1% income share followparallel evolutions indicatingthatinnovationmayplayarole inexplaining
the rise in top income inequality (Aghionetal,2015). Thispaperwill use cross-countrypanel datafrom
1950-2014 to investigate if the rise in topincome inequality canbe explainedby capital accumulation
wheninnovationandcross-countrydifferences are takenintoaccount.Twodifferentmeasuresare used
for innovation;total patentsgranted andthe booktomarketratio. The capital share of national income
(CSNI) ismeasuredby twovariables;the capital stocktogrossdomesticproduct ratioand r minusg.
Lastly, top income inequalityismeasuredbythe share of income heldbythe top1% and the top 10%,
and the ParetoLorenzcoefficient.
In summary,thispaperfinds some evidence validatingPiketty’sargumentthatcapital accumulation
drivestopincome inequality forhighly competitive1
countries.Howeverthere isstrongerevidence
suggestingthatinnovationrentsdrivetopincome inequalityratherthancapital accumulation.Excluding
the leastcompetitivecountries, measuresof innovation show asignificantrelationshipwith topincome
inequality where higherlevelsof innovationleadto highertop 1% and top10% income shareswhich
reflectinnovationrentsandthus isconsistentwithSchumpeteriangrowththeory. The modelpredicts
that on average a 1% increase inpatentsgrantedleadstoa 16.6% increase in the top1% share of
income anda 14.7% increase inthe top 10% share of income,aresultconsistentwithAghionetal.
(2015) U.S. cross-state analysisoninnovationandtopincome inequality. The booktomarketratio
measure of innovation alsoshowsthatonaverage innovationincreases the top1% and top 10% share of
income.Aftercontrollingforfixedeffects,regressionsshow that innovationmeasuressuchas R&D
expenditure andbooktomarketratio are significantinpredictingtopincome inequality. Furthermore,
resultsshownosignificantrelationshipbetween innovation orCSNI andthe restof the income
distribution. Wheninvestigatingwhetherincome inequality causesinnovation pooledOLSregressions
showthat on average the top10% share of income increasesinnovation andapanel vectorauto
regressionsupportsthisresult.Finally the paperfindsthatbefore the year2000 there is no evidence of
a positive relationshipbetween CSNI andtopincome inequalityandnorelationshipbetweeninnovation
1 As measured by the Global Competitive Index (2015-2016).See Section 3.4.
4. 3
and topincome inequality.However afterthe year2000 there isa positive relationship forbothCSNI
and innovationwithtopincome inequality.
ThisanalysishasbeenmotivatedbyAcemogluandRobinson(2015) where they criticize general lawsof
capitalismproposedinPiketty’s(2014) recentwork. Contrary to Piketty,theyshow thatr> g has no
relationship withtopincome inequality. Mypaperadds to theirworkontop income inequalityby
addingan analysisof innovationdriveninequality.ThispapercloselyfollowsAghion,Akcigit, Bergeaud,
Blundell and Hémous(2015) U.S. cross-state analysisof innovationontopincome inequality exceptthis
papertakesa cross-country perspectiveandfactorsin the ParetoLorenz Coefficientasa measuresof
income inequality.Like Aghionetal.(2015),the paper dependsonthe Schumpteriangrowthmodel
presentedinAghion,AkcigitandHowitt (2013) in where innovationledgrowthis dependenton
innovation rentswhichare representedbyincreasesintopincome inequality.Therefore this
endogenous growthmodelleadsthe analysisontopincome inequalityandinnovation. Thisgrowth
model also allowsthatanincrease in topincome inequality causesanincrease ininnovation as
mentionedby Tselosis(2011) and Zweimuller(2000).So our analysisinvestigatesthislikelyevent firstby
univariate regressions andthenby apanel vectorauto regression (VAR) oninnovationandtopincome
inequality motivatedbyAtemsandJones(2015).Finally the paperallows thatthere hasbeen arecent
shifttoa newdigital economy aspresentedin Stiroh’s(2004) review andsothe return on innovation
and humancapital has dramaticallyincreased.Thishypothesisissupportedby HémousandOlsen (2014)
inwhichtheyargue that the digital economybenefitshighskilledlabourers andinnovatorsbecause of
the automationof lowskilledlabour.Thispaperlookstoinvestigate thishypothesis.
The paper isorganizedasfollows. Section1of the paperreviewsPiketty’s general lawsof capitalismand
Schumpteriangrowththeorywhere topincome inequalityisdrivenbyquality-improvinginnovations.
Section2 introducesthe datainvolvedanddescribesthe measuresof innovationandincome inequality
usedinthe paper.Section3 presentsthe mainfindings.Section4isa discussionontwoextensionsof
the analysis:first,aninvestigationintoareverse causalitywhere topincome inequalitydrives
innovation;andsecond, alookat pre Y2K andpost Y2K regressions.Section5concludes.
1 Theory
1.1 Piketty’s General Laws of Capitalism
Pikettyproposesatheoryon the longrun proclivitiesof capitalismin Capitalin the Twenty-firstCentury
(2014). He reliesonMarxianeconomics aspremise tocome to the conclusionthat the future of
capitalismisone where there ismassownershipof capital bythe wealthiestandtheirmainincome
comesfromcapital rents.
To come to his predictions, Piketty firstpresents two“fundamental laws”;
𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 = 𝑟 𝑥 (
𝐾
𝑌
),
𝐾
𝑌
= 𝑠/𝑔,
5. 4
where 𝑟 isthe real interestrate, 𝐾 isthe capital stock, 𝑌 isGDP, 𝑠 is the savingrate and 𝑔 isthe growth
rate of GDP. As derivedinaSolowmodel the growthrate of capital stock 𝐾 isequal to saving 𝑠𝑌 ina
closedeconomyandso the ratio
𝐾
𝑌
overtime will approach 𝑠/𝑔due to economicgrowth. Pikettythen
combinesthe twoequationstoget;
𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 = 𝑟 𝑥 (
𝑠
𝑔
).
Assuming 𝑟 and 𝑠 are approximatelyconstant derivesPiketty’sfirstgeneral law;
1. The capital share of national income ishigh whengrowth islow.
His secondgeneral lawstates;
2. r > g,
whichdefinesthatthe real interestrate r isalwaysgreaterthanthe growthrate g inan economy. This
lawholdswhena countryisdynamicallyefficient,meaningthatithas maximizedconsumption inevery
periodoveraninfinite timeline,otherwise itmaynotbe the case that the real interestrate exceedsthe
growthrate.
Piketty’s thirdgeneral law proposesthat;
3. Income inequalitytendsto rise wheneverr > g.
The logicbehindthispropositionisthat capital income growsapproximatelyatthe rate r where national
income growsat the rate g and since capital income is distributedmainlytothe wealthiest;their
incomesgrowfasterthanthe per capita income thusresultingincapital-drivenincome inequality.
Accordingto Piketty’stheorytopincome inequalityrisesasthe capital share of national income
increases because capital incomeismainlydistributedamongthe wealthiest. Assumingris
approximately constant,capital share of national incomewillrise asK/Yincreases(change in capital
exceedschange in GDP) and/oras g decreases makingthe distance between randg greater. Thispaper
testsbothhypotheses inordertosubstantiate Piketty’sgenerallawsof capitalism.
1.2 Schumpeterian Growth Theory and Innovation Rents
The baseline Schumpeteriangrowthmodel asdevelopedinAghionetal.(2015) offersa theoretical
frameworkforhowinnovationincreasestopincome inequality. The mainpredictionsof the model asit
relatestothispaperare;
Innovation increasestopincome inequality.
Higherinnovationrentleadstoincreasedlevelsof innovation.2
2 This resultis 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. 5
To come to these predictionsthe baseline Schumpeteriangrowthmodelisdevelopedasfollows. Given
discrete time,the economyispopulatedbytwotypesof individualseveryperiod;capital ownerswho
ownthe firmsand the productionworkers. Individualslive onlyforone period andinthe subsequent
perioda newgenerationisbornwiththose whoare bornto firmowners inheritingtheirparents’firm
and the restwork as productionworkersunlesstheysuccessfully innovate andtakeoveranincumbent.
The followingCobb-Douglastechnologyproductionfunction definesthe productionof finalgoods 𝑌𝑡:
𝑙𝑛𝑌𝑡 = ∫ 𝑙𝑛
1
0
𝑦𝑖𝑡 𝑑𝑖
𝑦𝑖𝑡 = 𝑞𝑖𝑡 𝑙𝑖𝑡
where 𝑦𝑖𝑡 isa vector of intermediateinputsusedinfinal production, 𝑙𝑖𝑡 isthe labourusedtoproduce
the intermediateinputs,and 𝑞𝑖𝑡 isthe labourproductivity; all attime t.Wheneverinnovationtakes
place inany sector 𝑖 inperiod 𝑡 the innovatorgainsa technologicalleadof 𝜂 𝐻 overthe competition and
so the innovatorproducesthe intermediateinput 𝑦𝑖𝑡 withthe productionfunction:
𝑦𝑖𝑡 = 𝜂 𝐻 𝑞𝑖,𝑡−1 𝑙𝑖𝑡
If no newinnovationsoccurinthe followingperiod,the innovator’stechnologyispartlyimitatedby
competitorsandsothe innovator’stechnological leaddecreasesto 𝜂 𝐿 where 1 < 𝜂 𝐿 < 𝜂 𝐻.
Solvingthe model yieldsthe twofollowingequationspertinenttothe paper’sanalysis;
𝑀𝐶𝑖𝑡 = 𝑤 𝑡/𝑞𝑖, 𝑡, and, 𝑝𝑖,𝑡 = 𝑤 𝑡 𝜂𝑖𝑡/𝑞𝑖, 𝑡
where 𝜂𝑖,𝑡 𝜖 {𝜂 𝐻, 𝜂 𝐿}, 𝑀𝐶𝑖𝑡 isthe marginal cost of producingthe intermediate input, 𝑤 𝑡 isthe wagespaid
to labourinvolvedinproducingthe intermediate input 𝑖,and 𝑝𝑖,𝑡 isthe price charged forthe
intermediate input;all attime t. Thusthe profitearnedbythe innovatorisgivenby;
𝜋𝑖𝑡 = ( 𝑝𝑖𝑡 − 𝑀𝐶𝑖𝑡) 𝑦𝑖𝑡 =
( 𝜂𝑖𝑡 − 1) 𝑌𝑡
𝜂𝑖𝑡
> 0
The profitsearnedbyinnovatorsinthismodel are innovationrents.Furthermore if the costof
innovationisallowedinthismodel then the costof innovationforentrantscanbe greaterthan the cost
of innovationforincumbents (Aghionetal.,2015). This leads toentrybarriersand thusreduces
innovative activity since entrantsreceive lessprofitfrominnovationthe higherthe costof innovationis
for them.
2 Data and Measurement
The paper uses cross-country unbalanced panel datacoveringthe period1950-2014. Missingvalues
betweendatapointsare approximatedbyalinearinterpolation.The 27countriescoveredare as
follows:Argentina,Australia,Canada,China,Colombia,Denmark,Finland,France,Germany,India,
7. 6
Indonesia,Ireland,Italy,Japan,Korea,Malaysia,Netherlands,New Zealand,Norway,Portugal,
Singapore,SouthAfrica,Spain,Sweden,Switzerland,UnitedKingdom, and UnitedStates.
2.1 Income Inequality
The data on the share of income heldbythe top 1%,top 10%, and on the ParetoLorenzCoefficient was
obtained fromthe World Top Income Database (Alvaredo,Atkinson,Piketty,andSaez’sWorldTop
IncomesDatabase at http://topincomes.parisschoolofeconomics.eu/).These are ourtopincome
inequalitymeasures. Dataonthe share of income ownedbythe bottom40% was obtainedfromthe
WorldBank Databank (http://databank.worldbank.org/data).The share of income ownedbythe middle
40%-90% wasobtainedbysubtractingthe top10% and bottom40% share from100.
Figure1showsthe evolutionof top1%income inequality forFrance,Germany,SouthAfrica,Sweden,
and UnitedStates. The graphclearlyillustratessignificantcrosscountrydifferences howevera
significantupwardtrendisnoticeable forall countriesaroundafter1990. Figure 2 showsthe top 10%,
middle 40%-90%,andbottom 40% income sharesovertime forthe UnitedStates and itis evidentthat
top income sharesincrease atthe expenseof the middle class.
Figure 1
5
101520
1950 1960 1970 1980 1990 2000 2010 2020
Year
France Germany
South Africa Sweden
United States
Top 1% Income Share, 1960-2014
8. 7
Figure 2
2.2 Innovation
We obtainpatentsgranted bythe USPTO and EPOto obtain total of patentsgranted percountryby
inventor’scountryof residence. Thisdatawasobtainedfromthe OECDdatabase
(http://stats.oecd.org/).The datacoversthe time period1999-2014. The bookto marketratio(b/m) was
createdby dividingcapital stockbymarketcapitalization. Capital stock (K) wasobtainedfromPenn
WorldTables version8.1 (www.ggdc.net/pwt) andmarketcapitalizationwasobtainedfrom the World
Bank Databank. Thisproxyfor the bookto marketratio workswell.The correlationbetweenthe actual
U.S. bookto market ratio withKovermarketcapitalizationisequal to0.9452. A low bookto market
ratiovalue impliesthatavaluationabove the worthof physical assets.A marketvaluationabove
physical assets impliesvaluableintangible assetssuchasinnovativeactivitiesandsomovementinthe
bookto marketratio isa validmeasure of innovation.
Figure 3 showsthe evolutionof patentsgrantedforFrance,Germany,SouthAfrica,Sweden,andUnited
Stateswhile Figure 4illustrates the dynamicrelationshipbetween booktomarketand patentsgranted
for the UnitedStates.Figure 4 gives evidence of arelationship between the twomeasuresof innovation
especiallywhencomparing the three yearlagof patentsgrantedwith the bookto marketratio.An
increase inpatentsgranted (lagged3years) leadstoa fall inthe book to marketratio.
1020304050
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Top 10% Share of Income Mid 40%-90% Share of Income
Bottom 40% Share of Income
United States Income Distribution 1950 - 2014
9. 8
Figure 3
Figure 4
0
50
100150
2000 2005 2010 2015
Year
France Germany
South Africa Sweden
United States
Total Patents Granted, 1999-2014
.02.025.03.035.04
BooktoMarket
80
100120140160
2000 2005 2010 2015
Year
Granted patents (in 100s)
Granted patents (in 100s), Lagged 3 yearsBook to Market
United States, Book to Market vs Patents Granted
10. 9
2.3 Piketty’s r > g and K over Output
Data on real interestrates (r) were retrievedfromthe WorldBankDatabankand data on GDP was
obtainedfromPennWorldTablesversion8.1and Madison’sdataset
(http://www.ggdc.net/maddison/maddison-project/data.htm).The GDPgrowthrate (g) iscomputed
and the variable r> g is computedbysimplysubtractinggfromr. Our capital to outputratio (K/Y) isalso
computedusingKand GDP data.
2.4 Control Variables
The paper controlsforthe size of the financial sector andthe size of the governmentsectorbecause
theyare likelyto have directeffectsoninnovationandtopincome share (Aghionetal,2015).
Furthermore the analysiscontrolsfor macroeconomicvariablessuchaspopulationgrowth,the
employmentrate, andhumancapital;andResearchand Development(R&D) variables suchasR&D
expenditure percapitaandnumberof R&D researchers. Controllingforthe employmentrate ismainly
to reduce time varyingcrosscountrydifferencesinordertohave more efficientresults. Humancapital is
controlled forsince thislikelyhasaneffectonincome inequalitymeasuresandinnovation. FinallyR&D
iscontrolledforsince itiscorrelatedwithpatentsgrantedandincome inequalitymeasures;alsoR&D
may be consideredameasure of innovation.
2.5 Global Competitiveness Index (GCI)
The paper groupscountriesbasedontheirGCI obtainedfromthe WorldEconomicForum Global
CompetitivenessReport (http://reports.weforum.org/global-competitiveness-report-2015-2016/) in
orderto see the relationshipof CSNIandinnovation ontopincome inequality acrossdifferenteconomic
stages.The GCI is a composite of sub-indexes suchasinstitutions,infrastructure, macroeconomic
environment,healthandprimaryeducation,highereducationand training, goodsmarketefficiency,
labourmarket,financial marketdevelopment,technological readiness,marketsize,business
sophistication,andinnovation. The table inSection3.4.1showshow countriesare grouped.
2.6 Table of Variables and Descriptions
Variable Number of
Observations
(annual)
Mean Standard
Deviation
Time
Span
Retrieval Description
Income Inequality Variables
Top 1% Share
of Income (%)
1177 12.057 3.425 1950-
2014
World Top
Incomes
Database
Share of national
income held by the
top 1% earners
Top 10%
Share of
Income (%)
962 36.6 6.809 1950-
2014
World Top
Incomes
Database
Share of national
income held by the
top 10% earners
Pareto Lorenz
Coefficient
1068 1.844 .224 1950-
2014
World Top
Incomes
Database
Measure of the
income distribution
Piketty and Innovation Variables
Patents
Granted
416 8.872 22.643 1999-
2014
OECD
Database
Number of patents
granted by the EPO
11. 10
(in 1000s) and USPTO
Market
Capitalization
(% of GDP)
799 93.502 45.774 1975-
2014
World
Bank
Databank
Share price x number
of sharesoutstanding
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 Variables
R&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 systems
Index of
Human Capital
per person
1644 3.133 .337 1950-
2011
Penn
World
Tables
Based on years of
schoolingandreturns
to education
Share 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 ISICdivisions50-99
(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
Global N/A N/A N/A 2015-
2016
World
Economic
Integrates
macroeconomic and
12. 11
Competitivene
ss Index
(1-7 (best))
Forum micro/business
aspects of
competitiveness into
a single index
3 Main Empirical Analysis:The effect of innovativeness and capital
accumulationon income distribution
3.1 Estimation Strategy
The main empirical analysis involveslookingatwhetherPiketty’svariables,r-g andcapital accumulation
measuredbyK/Y,or innovationmeasuredby b/mandtotal patentsgranted are responsible for
increasesintopincome shares. The mainestimatedequationis:
log( 𝑦𝑖𝑡) = 𝐴 + 𝐵𝑖 + 𝐵𝑡 + 𝐵1𝑗( 𝑃𝑖𝑘𝑒𝑡𝑡𝑦𝑗) + 𝐵2 𝑙𝑜𝑔(𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛) + 𝐵3𝑖 𝐺𝐶𝐼 𝑘−1 ∗ 𝑙𝑜𝑔(𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛)
+ 𝐵4𝑗𝑖 𝐺𝐶𝐼 𝑘−1 ∗ ( 𝑃𝑖𝑘𝑒𝑡𝑡𝑦𝑗) + 𝐵5 𝑋𝑖𝑡 + 𝜀𝑖𝑡
where 𝑦𝑖𝑡 isthe measure of inequality, 𝐵𝑖 isacountry fixedeffect, 𝐵𝑡 isa yearfixedeffect, 𝑃𝑖𝑘𝑒𝑡𝑡𝑦𝑗 isa
vectorof Pikettyvariableswhich include r-g(j=1) andK/Y(j=2), 𝑖𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛isinnovativeness measured
by b/mor total patentsgranted, 𝐺𝐶𝐼 𝑘−1 are GCI dummies and 𝑋 isa vector of control variables.
In Section 3.2 𝐵2, 𝐵3 and 𝐵4 are setto zero and a seriesof Pikettyregressionsare estimatedby pooled
OLS and fixedeffects.The restrictionon 𝐵2 isremoved inSection3.3and the model isestimatedagain
by pooledOLS andfixedeffects. InSection3.4all restrictionsare removedandthe model isestimated
by fixedeffects. All resultsare clusterbootstrappedand include time dummies.3
3.2 Piketty r > g and Capital Accumulation: Results from Pooled OLS and
Fixed Effects Regressions
The resultsfroma seriesof Pikettyregressionsare presentedbelowinTable 1.In (3) and(4) of Table 1 it
isassumedthat all capital marketsare openand cross countryvariationinr iszero andso r is
normalizedto0, therefore we have r-g=- g. The regressionsshow nosignificantrelationshipbetween
the top 1% share of income andr > g for pooledOLSand fixedeffectsestimates asseeninAcemogluand
Robinson(2015). ResultsfromTable 1 illustrate thatcrosscountrydifferences have astronginfluence
on Piketty’s r> g since fixedeffectscoefficientsare significantlylessthanpooledOLScoefficients.The
lastcolumnin Table 1 estimatesthatthe coefficientonPiketty’sK/Yissignificantbutnegativeinafixed
effectsregression.Thisimpliesthat anincrease inCSNIdecreasestopincome inequalitywhich
contradictsPiketty’s theory.
To continue withthe resultsfoundin(6) of Table 1 the nexttwotable shows the resultsfrom
regressions againstdifferentcontrolsandfordifferenttimespans. The resultsshow that K/Yremains
negative andsignificantafteraddingthe controls howeveroverthe period1999-2010 itbecomes
3 Additional regressionsarekept in the appendix.
13. 12
insignificantasshownin(4) of Table 3. Overthe same periodin(3) of Table 3 r > g ispositive and
significant.
Table 1
Piketty's r > g and Capital Accumulation
1961-2010 1951-2010 1961-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
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 829 829
R2 0.284 0.202 0.293
R2 overall 0.233 0.194 0.173
R2 between 0.152 0.173 0.0217
R2 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
14. 13
Table 2
Piketty's r > g and Capital Accumulation with different controls
1961-2010
(1) (2) (3) (4)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(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 829 829 829 829
R2 overall 0.193 0.358 0.155 0.331
R2 between 0.0544 0.214 0.0120 0.187
R2 within 0.643 0.651 0.618 0.672
# of countries in
regression
27 27 27 27
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
15. 14
Table 3
Piketty's r > g and Capital Accumulation over different time spans
1980-2010 1999-2010
(1) (2) (3) (4)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(Log)
Top 1% Share
of Income
(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 681 681 235 235
R2 0.284 0.101
R2 overall 0.200 0.0174
R2 between 0.0449 0.0110
R2 within 0.624 0.165
# of countries in
regression
27 27 27 27
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
In Table 4 all the control variables are includedandthe Pikettyvariablesare regressedonseveral
measuresof topincome inequality.The regressions show thatincreasingr-gincreasesthe top1% share
of income asseenin (1) of Table 4 butwhencountryfixedeffectsare includedthe coefficientbecomes
insignificant.In(6) of Table 4 K/Y has a significantandpositiverelationshipwiththe ParetoLorenz
coefficientandsoan increase inK/Ydecreasesincome inequality whichiswhatwasfoundinTable 1 to
3. This leadstoconflictingresultsintermsof analyzingPiketty’sargument.Favourable resultsforPiketty
are foundinthe pooledOLS regressionsbut notinfixedeffectsregressions.
16. 15
Table 4
Piketty's r > g and Capital Accumulation – Top Income Inequality
1980-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
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)
Index of
human capital
per person
(Log)
-0.680*
(-1.75)
-0.673
(-1.06)
-0.293
(-0.89)
-0.132
(-0.24)
0.234
(0.69)
0.197
(0.42)
Share of
Government
Consumption
-4.549***
(-3.89)
-1.896***
(-2.68)
-2.050***
(-2.73)
-0.232
(-0.45)
2.592***
(3.43)
1.172**
(2.01)
Population
growth (annual
%)
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)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 502 502 444 444 450 450
R2 0.713 0.597 0.539
R2 overall 0.509 0.283 0.366
R2 between 0.468 0.233 0.227
R2 within 0.642 0.538 0.659
# of countries
in regression
26 26 23 23 24 24
t statistics in parentheses
17. 16
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
3.3 Schumpeterian Growth Theory and Innovation Rents: Results from
Pooled OLS and Fixed Effects Regressions
So far our analysisprovides conflictingevidence forPiketty’sargumentonCSNI drivingtopincome
inequality. Basedonfixedeffectsthe analysisis inagreementwithAcemogluandRobinson(2015).In
thissectioninnovationisallowedinthe model andisfirstmeasuredby total patentsgranted andthen
by bookto market.The resultsmetfrombothmeasuressupport Schumpeteriangrowththeorybutdoes
not debunkPiketty’sargumentforcapital accumulationincreasingtopincome inequality.
3.3.1 MeasureofInnovation:PatentsGranted
Table 5 displays the results whentotal patentsgrantedis includedinthe regression. The pooledOLS
regression ontop10% share of income showssignificantresults findinga1% increase inpatentsgranted
increasesthe top10% share of income by6.6%. Alsointhe table,(1) shows r-gis positive andsignificant
and K/Y becomesinsignificantforall regressions givingevidence thatinnovationisanomittedvariable in
Section3.2 regressions.
The resultsinTable 5 give some evidence insupportof Schumpeteriangrowththeoryandinnovation
rentsand evidence againstPiketty’s supposition.Howeverthe regressionsinTable 5covera short time
span, 1999-2010, and so general inferencescannotbe drawn.
To ignore the insignificantcoefficientsonpatentsgrantedin (1) and(2) of Table 5 wouldbe unjust since
the top 1% share of income is one of the income inequalitymeasuresof interest. There isanapparent
omittedvariable whichis R&Dactivitysince thisisverylikelycorrelatedwithpatentsgrantedand can
likelybe correlatedwithourdependentvariables. R&Dactivityapproximatesthe total costof
innovations.AccordingtoSchumpeteriangrowththeoryasoutlinedinSection1.2,innovatorswill
produce an innovation if the expectedtotal profit4
fromaninnovation isequal toorgreaterthan the
expected costof innovation. If aninnovation,measuredbypatentsgranted,occurs thenthe innovator
has incurredthe cost of innovation whichisapproximatedbyR&Dactivity. Therefore R&Dactivity
affectspatentsgrantedwhere higherR&Dactivityleadstoan increase inpatentsgranted.R&Dactivity
can alsoaffectinnovationrents since the costof innovationhasanimpacton innovationrents. R&D
activitycan thushave a positive effectoninnovationrentsif the returnonR&D activityispositive anda
negative effectif the returnonR&D activityisnegative.
Table 6 controlsforR&D activityand providesasignificantand twice aslarge coefficientforpatents
grantedon the top 10% share of income pooledOLSregression thanwhatwaspresentedinTable 5,
4 Profitas defined by the equation 𝜋𝑖𝑡 = ( 𝑝𝑖𝑡 − 𝑀𝐶𝑖𝑡
) 𝑦𝑖𝑡 in Section 1.2. Expected total profitis the expected
present valueof the stream of all profits earned up to time T. See Aghion, Akcigitand Howitt (2013).
18. 17
howeverregressionsonthe top1% share of income remaininsignificant.5
The keyresultinTable 6is in
(1) where r-gremains positive andsignificantinexplainingthe top 1% share of income.
Table 5 – Check appendix Table A1 for complete list of excluded countries.
Innovation Measure: Patents Granted – Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
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)
Index of
human capital
per person
(Log)
-0.811
(-0.99)
1.784
(1.05)
-0.594
(-0.98)
0.831
(1.01)
0.106
(0.23)
-0.665
(-1.02)
Share of
Government
Consumption
-3.994**
(-2.24)
-0.151
(-0.13)
-1.206
(-0.99)
-0.508
(-1.41)
2.163**
(2.25)
-0.370
(-0.79)
Population
growth
(annual %)
0.0954
(1.28)
-0.0113
(-0.28)
0.0551
(1.07)
-0.000113
(-0.01)
-0.0214
(-0.55)
-0.000475
(-0.02)
Services per
capita (% of
GDP)
-0.00186
(-0.09)
0.0961
(1.49)
0.00370
(0.31)
0.0391
(1.20)
0.00350
(0.39)
-0.0680***
(-3.05)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
-0.00779
(-0.95)
0.00377
(0.38)
-0.0100
(-1.56)
-0.00575
(-1.36)
-0.00516
(-0.72)
-0.00766
(-1.43)
5 By addingR&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 arenot tainted by any selection bias.
19. 18
Country Fixed
Effects
No Yes No Yes No Yes
Observations 220 220 202 202 202 202
R2 0.516 0.574 0.388
R2 overall 0.0623 0.00183 0.000000567
R2 between 0.0437 0.000400 0.00327
R2 within 0.255 0.270 0.370
# of countries
in regression
25 25 23 23 24 24
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
20. 19
Table 6 - Check appendix Table A1 for complete list of excluded countries.
Innovation Measure: Patents Granted controlling for R&D – Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (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)
Index of
human capital
per person
(Log)
-0.950
(-0.91)
2.372
(1.60)
-0.582
(-1.03)
1.078
(1.05)
-0.0223
(-0.04)
-0.949
(-1.12)
Share of
Government
Consumption
-4.648***
(-2.74)
-0.289
(-0.55)
-1.801*
(-1.90)
-0.328
(-0.92)
1.910**
(2.00)
-0.456
(-0.75)
Population
growth (annual
%)
0.107
(1.34)
-0.0106
(-0.27)
0.0654
(1.40)
-0.000541
(-0.03)
-0.0164
(-0.46)
-0.00186
(-0.09)
Services per
capita (% of
GDP)
0.00965
(0.44)
0.0937*
(1.78)
0.00941
(0.77)
0.0383
(1.09)
0.0102
(0.84)
-0.0650**
(-2.47)
Employment
to population
ratio, 15+,
total (%)
-0.00521
(-0.51)
-0.00369
(-0.60)
-0.00973
(-1.50)
-0.00499
(-1.22)
-0.00343
(-0.44)
-0.00421
(-0.89)
21. 20
(national
estimate)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 209 209 197 197 192 192
R2 0.625 0.707 0.484
R2 overall 0.0597 0.00287 0.0153
R2 between 0.0231 0.000901 0.0176
R2 within 0.359 0.274 0.411
# of countries
in regression
24 24 23 23 23 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
3.3.2 MeasureofInnovation:Bookto Market
As statedinthe previoussubsection,there isashorttime spanof available dataforpatentsgrantedand
so the advantage gainedfromusingthe booktomarket ratioas a measure of innovationisalongertime
span. The disadvantage,however,isthat b/misan indirectmeasure of innovative activity since itdoes
not directly capture innovative activitylike the more commonlyusedmeasure patentsgranted, but
insteaditapproximates the aggregate value of innovation. AccordingtoSchumpeteriangrowth theory
as presentedinSection1.2,the value of an innovationdecreasesovertime whichcorrespondstoan
increasingb/m.If a newinnovation occurs ina givenperiod thenthe value of thatinnovation isgreater
than the one that precededit andso there will be acorrespondingdecreasein the b/m.
The results fromregressingincome inequalitymeasureson b/mare displayedin Table 7.The regressions
inTable 7 coverthe period1980-2010 whichisthe same periodcoveredinthe Pikettyregressions in
Section3.2. The table predicts thatboth r-g andb/m contribute to the top 1% share of income however
r-g affectsthe top1% share of income ina pooledOLSregressionwhereasb/maffectsthe top1% share
of income ina fixedeffectsregression.Betweenthe resultsinSection3.3.1and Table 7 there is some
evidence thatinnovation increasestopincome inequalityandalsothatina pooledOLSthere isa
positive andsignificantrelationship betweenr-gandthe top 1% share of income.
Kitchensinkregressions6
ontopincome inequalityare performedandthe resultsare capturedinTable
8. The mostsignificantresultsare foundfor pooledOLS regressionon the top1% and top10% share of
income.Bothinnovationmeasuresare significantinthe correctdirectionindicatingthatinnovation
increasestopincome inequalityandmore sofor the top 1% share of income thanthe top 10% share of
income. Furthermore the Pikettyvariable r-gbecomesinsignificant. Table 8providessome more
evidence thatinnovationrentsexplain topincome inequalityratherthanthe Pikettyvariables.However
the resultsare not robustto countryfixedeffects.
6 Kitchen sink regression includes all variables of interestand controls.
22. 21
Table 7 - Check appendix Table A1 for complete list of excluded countries.
Innovation Measure: Book to Market – Top Income Inequality
1980-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
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)
Index of
human capital
per person
(Log)
-1.051**
(-2.36)
-1.220**
(-2.04)
-0.299
(-1.00)
-0.340
(-0.71)
0.215
(1.00)
0.517
(1.12)
Share of
Government
Consumption
-3.669***
(-3.18)
-1.340
(-1.50)
-1.765**
(-2.08)
-0.0889
(-0.18)
1.852***
(3.06)
1.320
(1.40)
Population
growth
(annual %)
0.0436
(0.91)
-0.0137
(-0.55)
-0.0215
(-0.41)
-0.0109
(-0.56)
-0.0186
(-0.50)
0.000551
(0.02)
Services per
capita (% of
GDP)
-0.00677
(-0.93)
-0.0146
(-0.29)
-0.00271
(-0.39)
-0.00653
(-0.12)
0.00373
(0.75)
0.00459
(0.20)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
-0.00382
(-0.77)
-0.00816
(-1.26)
-0.00966**
(-1.98)
-0.00413*
(-1.78)
-0.00723**
(-1.99)
0.00440
(0.76)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 433 433 408 408 388 388
R2 0.723 0.593 0.600
R2 overall 0.582 0.430 0.370
R2 between 0.444 0.362 0.155
R2 within 0.670 0.550 0.648
# of countries
in regression
25 25 23 23 23 23
23. 22
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
Table 8 - Check appendix Table A1 for complete list of excluded countries.
Kitchen Sink – Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (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)
Patents
Granted (Log)
0.166**
(2.33)
0.0408
(0.50)
0.147***
(3.93)
0.0562
(0.85)
0.0199
(0.40)
0.00391
(0.11)
R&D per
capita
expenditure
(Log)
-0.659***
(-2.66)
0.0508
(0.24)
-0.517***
(-4.04)
-0.0274
(-0.24)
-0.0921
(-0.55)
-0.189*
(-1.75)
R&D
researchers
(Log)
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)
Index of
human capital
per person
(Log)
-0.873
(-1.06)
2.269
(1.26)
-0.256
(-0.51)
0.952
(1.02)
-0.103
(-0.23)
-0.828
(-0.70)
Share of
Government
Consumption
-3.661**
(-2.15)
-0.144
(-0.23)
-1.400
(-1.37)
-0.265
(-0.72)
1.362*
(1.67)
-0.0669
(-0.11)
Population
growth (annual
%)
0.0658
(0.92)
-0.00620
(-0.18)
0.0324
(0.70)
0.00180
(0.11)
0.00152
(0.05)
0.00275
(0.13)
24. 23
Services per
capita (% of
GDP)
0.0162
(0.98)
0.113**
(2.15)
0.0152
(1.43)
0.0475
(1.61)
0.00586
(0.62)
-0.0610**
(-2.01)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
0.00337
(0.45)
-0.00219
(-0.27)
-0.00837
(-1.45)
-0.00390
(-0.82)
-0.00734
(-1.18)
-0.00424
(-0.81)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 195 195 189 189 178 178
R2 0.666 0.744 0.463
R2 overall 0.0505 0.0166 0.00676
R2 between 0.0324 0.00713 0.0112
R2 within 0.368 0.286 0.442
# of countries
in regression
24 24 23 23 23 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
3.4 Evidence of Piketty’s r>g and Schumpeter’s Innovation Rents:
Interaction of Countries Grouped by the Global Competitiveness Index
The countriesinthe panel have verydifferenteconomies andsosuch differencesshouldbe considered.
The fixedeffectsestimatorisappropriate forcapturingthese differences.Howeveritisimportantto
recognize thatdifferenteconomieswillreactdifferentlytochangesininnovationandPikettyvariables.
Ratherthan interactingeachcountrywitheachmeasure of innovationandeachPikettyvariable the
countriesare firstgroupedintermsof theircompetitivenessasmeasuredbythe GCI and then the
interactionbetweenthe variablesof interestandthe GCIgroupsare estimated.Fourgroupsare created
as seeninthe table belowwhere GCI0isthe group withthe mostcompetitive countriesandGCI3isthe
groupwiththe leastcompetitive countries.
25. 24
3.4.1 GCIGroupingTable
Global Competitive Index (2015-2016) Categories
Label Value Countries Total
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 5
GCI3 4.4 ≥ GCI South Africa, India, Columbia, Argentina 4
3.4.2 Results
Table 9 displays resultsfromfixedeffectsregressions7
showingthatanincrease in r>g increases the top
1% share of income forGCI0 countriesand thatthe effectof anincrease inr>g is nearlyzero or negative
for mostnon-GCI0countries.Alsothe table showsthat the innovationmeasure patentsgranted hasno
effectontopincome inequalitybutR&Dexpenditure does.ExcludingpatentsgrantedandtreatingR&D
as a measure of innovation,(5) and(6) show that the coefficienton R&D expenditure percapitais
significantandpositive forthe top1% and top10% share of income forGCI0 countriesbutfor the top
10% share of income is nearlyzeroornegative forall othercountries.
These resultsshowthat aftercontrollingforfixedeffects andinnovation the Pikettyvariable r>g isnot
significantinexplainingtopincome inequalityforthe most competitive economies.Thisisevidence
againstPiketty’stheoryoncapital driventopincome inequality. Furthermore the resultsgive some
justificationforSchumpeteriangrowththeory andinnovationrents.
7 Pooled OLS regressions areincluded in Appendix Table A6.
26. 25
Table 9 - Check appendix Table A1 for complete list of excluded countries.
Global Competitive Index Interaction Results – GCI0 is base
Piketty: r>g, K/Y
1980-2010
Patents Granted
1999-2010
R&D: Expenditure
1996-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
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)
Patents
Granted (Log)
0.0951
(0.67)
0.111
(1.28)
GCI1 x Patents
Granted (Log)
-0.0752
(-0.48)
-0.104
(-1.17)
GCI2 x Patents
Granted (Log)
-0.121
(-0.70)
-0.114
(-1.18)
GCI3 x Patents
Granted (Log)
-0.101
(-0.69)
-0.0783
(-0.90)
R&D per
capita
expenditure
(Log)
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)
GCI2 x R&D
per capita
expenditure
-0.519*
(-1.94)
-0.377**
(-2.21)
27. 26
(Log)
GCI3 x R&D
per capita
expenditure
(Log)
-0.334
(-1.41)
-0.407***
(-3.82)
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)
R&D
researchers
(Log)
-0.0977
(-1.12)
-0.0333
(-0.53)
-0.121
(-0.99)
-0.0380
(-0.55)
Index of
human capital
per person
(Log)
-0.756
(-1.14)
-0.0911
(-0.17)
2.286
(1.47)
0.942
(0.95)
0.835
(0.82)
1.048*
(1.74)
Share of
Government
Consumption
-1.722**
(-1.97)
-0.287
(-0.61)
-0.444
(-0.69)
-0.561
(-1.50)
-1.108
(-1.64)
-0.327
(-0.79)
Population
growth (annual
%)
-0.0411*
(-1.66)
-0.0112
(-0.64)
-0.00980
(-0.26)
-0.00161
(-0.11)
-0.0399
(-1.34)
-0.0222*
(-1.69)
Services per
capita (% of
GDP)
-0.00545
(-0.13)
-0.00182
(-0.03)
0.0972*
(1.89)
0.0401
(1.04)
0.0673
(1.09)
0.0431
(1.01)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00515
(-0.79)
-0.00183
(-0.72)
-0.00368
(-0.56)
-0.00576
(-1.40)
0.00617
(1.09)
0.000102
(0.02)
Country Fixed Yes Yes Yes Yes Yes Yes
28. 27
Effects
Observations 502 444 209 197 278 255
R2 overall 0.0403 0.167 0.0433 0.000163 0.00318 0.00781
R2 between 0.000164 0.0887 0.0153 0.00104 0.0127 0.00819
R2 within 0.662 0.558 0.382 0.351 0.540 0.543
# of countries
in regression
26 23 24 23 25 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
4 Discussion
4.1 Reverse Causality –Innovation on Income Inequality OLS Regression and
Panel VAR
It isworthwhile toinvestigatethe regressionof innovationonincome inequality inordertotruly
investigateSchumpeteriangrowththeory.Accordingtotheory,highertopincome inequalityshouldlead
to greaterinnovative activitysince inventors receive largerinnovationrentsandthus have a greater
incentive toinnovate. If this isthe case then the coefficientonpatentsgrantedin ourregressionsontop
income inequalitymeasures inSection3.3.1can likelysufferfromendogeneitycaused byreverse
causalitysince patentsgrantedisameasure of innovative activity. The regressionsonbooktomarketin
Section3.3.2 avoid some of this problembecause booktomarketis a measure of the value of innovative
activitiesratherthaninnovative activity.
Table 10 displaysthe resultswhenpatentsgrantedisregressedonincome inequalitymeasures. An
increase inthe top10% share of income significantlyincreasespatentsgrantedasshownin (3) of Table
10. Thisresultsuggeststhathighertop 10% share of income increasesinnovativeactivity.However,the
mostsignificantresultfromthe table isthatR&D expenditure percapitahasa significantlystrong
positive effectonpatentsgranted whichremainsrobustafteraccountingfor countryfixedeffects.
29. 28
Table 10 - Check appendix Table A1 for complete list of excluded countries.
Reverse Causality – Patents Granted on Top Income Inequality
1999-2010
(1) (2) (3) (4) (5) (6)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Patents
Granted
(Log)
Top 1% Share
of Income
(Log)
0.950
(1.55)
0.157
(0.69)
Top 10% Share
of Income
(Log)
2.817**
(2.43)
0.492
(0.96)
Pareto Lorenz
Coefficient
(log)
1.450
(0.94)
-0.137
(-0.55)
R&D per capita
expenditure
(Log)
2.138***
(3.00)
0.447
(1.33)
2.279***
(3.61)
0.779***
(2.77)
2.184***
(2.76)
0.562*
(1.68)
R&D
researchers
(Log)
-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)
Index of human
capital per
person (Log)
1.281
(0.39)
1.307
(0.43)
1.718
(0.62)
2.140
(0.56)
0.393
(0.11)
2.737
(0.80)
Share of
Government
Consumption
1.413
(0.30)
2.711
(1.06)
2.328
(0.54)
1.768
(0.69)
-5.480
(-0.97)
2.885
(0.86)
Population
growth (annual
%)
-0.270
(-0.96)
0.0272
(0.36)
-0.338*
(-1.83)
-0.00215
(-0.03)
-0.169
(-0.58)
0.0380
(0.46)
Services per -0.186*** -0.0567 -0.154*** -0.0490 -0.196*** -0.0676
30. 29
capita (% of
GDP)
(-3.89) (-0.51) (-3.24) (-0.57) (-3.35) (-0.59)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00384
(-0.13)
-0.0236
(-1.04)
0.0242
(0.70)
-0.0132
(-0.60)
-0.00461
(-0.12)
-0.0325
(-1.37)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 209 209 197 197 192 192
R2 0.925 0.930 0.923
R2 overall 0.793 0.707 0.806
R2 between 0.744 0.685 0.765
R2 within 0.642 0.764 0.651
# of countries
in regression
24 24 23 23 23 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
To furtherinvestigatethe causal relationshipbetween topincome inequality andinnovation the paper
conductstwo panel VARestimation;the firstbetweenthe top1% share of income and b/m, andthe
secondbetweenthe top10%share of income and b/m.The numberof lagschosenisfour,basedon
whatwas done inAtemsand Jones(2015). The resultsfromgrangercausalitytests showninFigure A1in
the appendix support whatwas foundinthe precedingregressions;increasesinthe top10% share of
income causesincreasesininnovation. Figure 5and Figure 6 show selected impulseresponse functions
fromboth multivariate regressions.
32. 31
4.2 Pre and Post Y2K Regressions – Evidence of a New Economy
So far the analysishasconsistentlygivendifferentresultsbetweenregressionswithtime spansstarting
fromand after1997 to 2010 and regressionswherethe time periodcoveredextendfromthe 1980s to
2010. Therefore thissectionfurtherexploresthe difference inresults.Table 11displaysresultsfor
regressionoverthe full period, pre-2000 andpost-2000.
The year 2000 can be consideredaturningpointineconomichistory.The endof the dot-combubble
had begunandit putout many incumbentsinthe relativelyyounginformationtechnology(IT) industry.
Between1990 and 2000 there washeavyinvestmentintothe ITindustrywhichledtoan IT stock bubble
whichiscommonlyreferred toasthe dot-combubble.8
The endof the dot-combubble resultedinless
incumbentfirmsinthe ITindustryandexcessITinfrastructure,whichgave rise tolowerentrybarriers
and thusincreasingcompetitionandinnovativeactivitiesinthe ITindustry. The post2000 IT industry
differsfromotherindustriesbecauseitrequireslittle capital,littlelabour,andhighabilityworkers(high
levelsof humancapital) forfirmstobe successful.The dot-combubbleenableddigitalizationwhichhas
transformed the advancedeconomiesintoadigital economy(Stiroh,2004). HémousandOlsen(2014)
suggestthatthe digital economycreateslargerhumancapital andinnovationrents resultinginhigher
income inequality.
Before 2000 innovationplaysnorole inexplainingincreases intopincome inequality andhumancapital
issignificantandnegativelycorrelatedwiththe top1% share of income asshowninTable 11. However
after2000 Table 11 showsthat innovationincreasestop1% and top10% share of income forGCI0
countries while humancapital becomesinsignificantwithapositivecoefficient.Table 11also showsthat
the coefficienton r-ghas become significantandpositiveforGCI0countriespost2000 whichgivessome
indicationforPiketty’shypothesis thatthe future of capitalismtendsto capital accumulationdriven
income inequality.The increasingsignificanceof innovationandhumancapital inTable 11 provides
evidence thatincreasesininnovationandhumancapital rents explainsrecenttrendsintopincome
inequality.
8 See “The Boom and Bust in Information Technology Investment” by Mark Doms. http://www.frbsf.org/economic-
research/files/er19-34bk.pdf.
33. 32
Table 11 - Check appendix Table A1 for complete list of excluded countries.
Y2K Regressions: Book to Market pre and post 2000 – GCI0 is base
1980-2010 1980-2000 2000-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Book to
Market (Log)
-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)
GCI2 x Book
to Market
(Log)
0.100
(0.67)
-0.000403
(-0.00)
0.0941
(0.35)
0.0140
(0.14)
0.00673
(0.02)
-0.0175
(-0.15)
GCI3 x Book
to Market
(Log)
-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)
Index of
human capital
per person
(Log)
-1.210**
(-1.99)
-0.349
(-0.70)
-1.723**
(-2.42)
-0.572
(-1.51)
1.391
(0.91)
0.544
(0.73)
Share of
Government
Consumption
-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)
34. 33
%)
Services per
capita (% of
GDP)
-0.0132
(-0.30)
-0.00716
(-0.12)
-0.00184
(-0.03)
0.00168
(0.04)
0.121**
(2.21)
0.0414*
(1.83)
Employment
to population
ratio, 15+,
total (%)
(national
estimate)
-0.00741
(-1.16)
-0.00374*
(-1.70)
-0.0106
(-1.27)
-0.00385
(-1.37)
0.000607
(0.12)
-0.00283
(-0.89)
Country Fixed
Effects
Yes Yes Yes Yes Yes Yes
Observations 433 408 271 256 183 172
R2 overall 0.606 0.0668 0.502 0.264 0.0470 0.171
R2 between 0.572 0.0191 0.263 0.212 0.0418 0.103
R2 within 0.692 0.567 0.589 0.479 0.435 0.391
# of countries
in regression
25 23 22 21 24 22
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
5 Conclusion
Thispaperhas analyzedthe effectof CSNI andinnovationon topincome inequality.The resultsshow
that neitherCSNI norinnovationaffects the restof the income distributionasmeasuredbythe Pareto
Lorenzcoefficient.Itisfoundthatinnovation issignificantinexplainingtopincome inequality where an
increase ininnovationincreasesthe top1% andtop 10% share of income aspredictedinSchumpeter’s
growthmodel;howeverthese resultsdonotholdforthe leastcompetitive economies. Furthermore
regressions predictthatincreasesinthe top10% share of income leadtoincreases ininnovativeactivity
whichfurthervalidatesSchumpeteriangrowththeory. Insupportof Pikettythe paperfinds evidence
that as an economybecomesmore competitive r-gbecomesmore significantinexplainingtopincome
inequality trends.Lastly,resultsindicate that humancapital rents mightplay asignificantrole in
explainingtopincome inequalitytrends inthe future.
The limitations realizedinthispaperare a few.The paperwas limited toarelativelysmall numberof
observationsforthe analysisbutthisissue wasmitigatedbyusingpairs-clusterbootstrapprocedure
whichhas betterfinite sample propertiesthanclusteredstandarderrors.Alsointermsof observations,
the paperwas limitedto27 countrieswithmostof the observationscomingfrom OECDcountriesin
Europe and NorthAmericaandthus presentingsignificantselectionbias,yetthe analysisislargelybased
on topincome inequalityof advancedeconomies andtherefore beingrestrictedtothese countriesis
35. 34
acceptable.Lastlythe time spanpresented significantchallengeswhichthe paperattemptedto
overcome byintroducingbooktomarketas a measure of innovation.
References
Acemoglu, 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
http://sfx.scholarsportal.info/guelph/docview/56703414?accountid=11233
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
http://sfx.scholarsportal.info/guelph/docview/1660008993?accountid=11233
Hsing, Y. (2005). Economic growth and income inequality: The case of the US.International Journal of
Social Economics, 32(7), 639-647. Retrieved from
http://sfx.scholarsportal.info/guelph/docview/56597962?accountid=11233
Lee, N., & Rodriguez-Pose, A. (2013). Innovation and spatial inequality in europe and USA. Journal of
Economic Geography, 13(1), 1-22. Retrieved from
http://sfx.scholarsportal.info/guelph/docview/1314321790?accountid=11233
36. 35
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
tradition. Journal of Economic Literature, 31(4), 2017-2018. Retrieved from
http://sfx.scholarsportal.info/guelph/docview/56619242?accountid=11233
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
http://sfx.scholarsportal.info/guelph/docview/56460277?accountid=11233
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
Zweimuller, J. (2000). Schumpeterian entrepreneurs meet engel's law: The impact of inequality on
innovation-driven growth. Journal of Economic Growth, 5(2), 185-206. Retrieved from
http://sfx.scholarsportal.info/guelph/docview/57019108?accountid=11233
37. 36
Appendix
Table A1
Tables Excluded Countries
Table 1 None
Table 2 None
Table 3 None
Table 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, India
Table 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
38. 37
Table A2
Innovation Measure: Patents Granted
1999-2014
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (Log)
-0.00792
(-0.25)
0.0428
(0.71)
0.0317
(1.58)
0.0354
(0.88)
-0.00540
(-0.40)
-0.0320
(-1.02)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 303 303 284 284 276 276
R2 0.0320 0.113 0.0600
R2 overall 0.000542 0.110 0.0281
R2 between 0.000126 0.0913 0.000743
R2 within 0.150 0.130 0.233
# of countries
in regression
26 26 23 23 25 25
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
Table A3
Innovation Measure: Patents Granted
1999-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Patents
Granted (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)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 229 229 210 210 211 211
R2 0.131 0.248 0.103
39. 38
R2 overall 0.000312 0.156 0.00727
R2 between 0.0000748 0.103 0.00183
R2 within 0.164 0.152 0.223
# of countries
in regression
26 26 23 23 25 25
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
Table A4
Innovation Measure: Book to Market
1980-2014
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (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)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 685 685 640 640 621 621
R2 0.225 0.256 0.302
R2 overall 0.216 0.0911 0.295
R2 between 0.128 0.00617 0.0537
R2 within 0.225 0.525 0.426 0.535
# of countries
in regression
25 25 23 23 23 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
40. 39
Table A5
Innovation Measure: Book to Market
1980-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Pareto
Lorenz
Coefficient
(log)
Pareto
Lorenz
Coefficient
(log)
Book to
Market (Log)
-0.121**
(-2.18)
-0.0251
(-0.81)
-0.107*
(-1.91)
-0.00251
(-0.18)
0.0143
(0.43)
0.00401
(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)
Country Fixed
Effects
No Yes No Yes No Yes
Observations 595 595 570 570 550 550
R2 0.351 0.268 0.324
R2 overall 0.294 0.0835 0.319
R2 between 0.164 0.000296 0.0331
R2 within 0.591 0.511 0.574
# of countries
in regression
25 25 23 23 23 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01
Table A6
Global Competitive Index Interaction Results – GCI0 is base
Piketty: r>g, K/Y
1980-2010
Patents Granted
1999-2010
R&D: Expenditure
1996-2010
(1) (2) (3) (4) (5) (6)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
Top 1%
Share of
Income
(Log)
Top 10%
Share of
Income
(Log)
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)
41. 40
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)
Patents
Granted (Log)
0.101
(0.86)
0.115**
(2.35)
GCI1 x Patents
Granted (Log)
0.0126
(0.12)
0.0545
(1.02)
GCI2 x Patents
Granted (Log)
-0.00923
(-0.05)
-0.0609
(-0.81)
GCI3 x Patents
Granted (Log)
-0.142
(-0.89)
-0.229**
(-2.52)
R&D per
capita
expenditure
(Log)
-0.234
(-0.85)
-0.271**
(-1.98)
-0.0521
(-0.30)
-0.103
(-0.78)
GCI1 x R&D
per capita
expenditure
(Log)
0.00147
(0.08)
-0.00572
(-0.60)
GCI2 x R&D
per capita
expenditure
(Log)
-0.0257
(-0.68)
-0.00836
(-0.39)
GCI3 x R&D
per capita
expenditure
(Log)
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 0.186 0.328
42. 41
(Log) (0.92) (1.14)
R&D
researchers
(Log)
0.188
(0.65)
0.155
(1.31)
0.0935
(0.48)
0.227*
(1.79)
Index of
human capital
per person
(Log)
-0.913**
(-2.16)
-0.304
(-0.79)
-0.601
(-0.47)
0.00836
(0.02)
-0.857
(-0.77)
-0.278
(-0.44)
Share of
Government
Consumption
-4.068***
(-3.06)
-1.684**
(-2.07)
-3.354*
(-1.93)
-1.339
(-1.63)
-4.414**
(-2.32)
-1.792
(-1.62)
Population
growth (annual
%)
0.0276
(0.47)
-0.0221
(-0.46)
0.137*
(1.87)
0.0605
(1.62)
0.0564
(0.91)
0.0311
(0.65)
Services per
capita (% of
GDP)
-0.00345
(-0.33)
-0.00414
(-0.61)
0.0110
(0.32)
0.0195
(1.22)
-0.00774
(-0.53)
-0.0131
(-1.37)
Employment to
population
ratio, 15+, total
(%) (national
estimate)
-0.00971*
(-1.74)
-0.00850**
(-2.26)
-0.00523
(-0.57)
-0.00649
(-1.32)
-0.00746
(-0.81)
-0.00715
(-1.24)
Country Fixed
Effects
No No No No No No
Observations 502 444 209 197 278 255
R2 0.783 0.717 0.695 0.839 0.686 0.726
# of countries
in regression
26 23 24 23 25 23
t statistics in parentheses
Cluster Bootstrapped Standard Errors
* p < 0.1, ** p < 0.05, *** p < 0.01