INCOME INEQUALITY IN THE KNOWLEDGE ECONOMY1 Cristiano Antonelli a, b,
Matteo Tubiana a, c
a Dipartimento di Economia e Statistica Cognetti de Martiis, Università di Torino,
Lungo Dora Siena 100/A, 10153, Torino, Italy.
b Collegio Carlo Alberto. Piazza Vincenzo Arbarello, 8, 10122 Torino, Italy.
c Dipartimento di Scienze Aziendali, Economiche e Metodi Quantitativi, Università di
Bergamo. Via dei Caniana, 2, 24127 Bergamo, Italy.
Emails: cristiano.antonelli@unito.it; matteo.tubiana@unibg.it.
ABSTRACT. Advanced economies are characterised by the parallel increase of income inequality and of the role of knowledge intensive activities that substitute the manufacturing industry at the core of the system. Radical changes in the organization of the generation, appropriation and exploitation of technological knowledge increase the levels of knowledge rents. The shift to the knowledge economy triggers the polarization of labour markets between creative workers, able to participate into the rents associated with knowledge exploitation, and standard labour, exposed to the fall of employment in progressively de-unionized manufacturing industries. The theoretical framework introduced associates such knowledge-intensive structural change to the rising levels of income inequality. The empirical section provides robust support for this correlation estimating on the evidence of 20 OECD countries from 1990 to 2016 a negative sign for within income inequality regressed on the quota of KIBS and R&D investments.
JEL classification: P10, D24, O33
Keywords: Knowledge tradability; Knowledge appropriability; Knowledge-intensive business services; Trade-union bargaining power; Unemployment; Wealth inequality; Rent inequality; Income inequality.
1. INTRODUCTION
The historical analysis of the distribution of income shows a clear discontinuity in the long-term evolution at the end of the XX century. The long-term reduction of income inequality levelled off in most advanced countries in the last decades of the XX century. Since the beginning of the XXI century, there is abundant evidence of the beginning of a new trend towards increasing levels of inequality (Atkinson, Piketty, Saez, 2011). The new trend towards increasing levels of income inequality parallels the introduction and diffusion of new information and communication technologies (ICT) and the globalization of both product and financial markets. This trend shapes the shift of advanced economies away from the manufacturing industry as the pillar of their economic structure and the emergence of knowledge-intensive business services (KIBS) as the new key sectors (Atkinson and Piketty, 2007 and 2009; Piketty, 2014; Franzini and Pianta, 2016).
This paper provides a theory of the relations among these three dynamics – namely inequality, globalization and knowledge economy transition – and lays down a first empirical attempt to measure it. It articulates the hypothesis that the new tradability of knowledge as an economic good that can be exchanged as a service, capitalized in financial assets and intangible property rights, rather than embodied in other tangible goods, together with the radical structural change of the economic system towards a knowledge economy are the cause of the increasing levels of income inequality experienced by advanced countries.
We propose that the transition to the knowledge economy is based upon the increased levels of knowledge appropriability made possible by the use of ICT and the new organization of the production and exploitation of knowledge. In the knowledge economy, where knowledge is at the same time the key input and output, income inequality is increased by the high levels of knowledge rents and by the polarization of labour markets with the separation of creative workers, able to defend their wages at the pre-globalization levels and to participate into the rent associated with the exploitation of knowledge, from standard labour, exposed to the decline of the manufacturing industry and the fall of its employment (Autor, 2020; Ravaillon, 2018; Wessel, 2013; Crouch, 2019).
The shift to the knowledge economy can be regarded as a radical change in the economic structure of advanced countries. Following the way paved by Kuznets (1955), radical changes in the economic structure of economic systems are likely to affect income inequality with an inverted-U shape. Income inequality raises when the change in the structure of the system is radical and slows down eventually (Antonelli, 2019).
The new understanding of the central role of knowledge made possible by the new growth theory pushes to apply the recent advances of the economics of knowledge to try and understand the dynamics of income distribution that parallels the economic growth of advanced economies since their shift towards the knowledge economy (Aghion, Caroli, Garcia-Penalosa, 1999).
The current changes in the organization of the generation and exploitation of knowledge, and the increased levels of knowledge appropriability and tradability, instead, risk augmenting pre-existing wealth and rent inequalities and worsening labour remuneration selectively. The remaining of the paper will provide a more in-depth treatment of these arguments, exploiting the inheritance of the economics of knowledge extensively. In particular, Section 2 analyses the determinants and the effects of the new knowledge tradability. Section 3 presents an econometric analysis with OECD country-level data, illustrating the correlation between the phenomena at play. The conclusions summarise the results of the analysis. 2. THE NEW MECHANISMS OF KNOWLEDGE APPROPRIATION, TRADABILITY AND EXPLOITATION
2.1 The new mechanisms of new knowledge appropriation, tradability and exploitation
The limited appropriability of knowledge and the intrinsic information asymmetries that take place in its market exchange have traditionally curbed the viability of the markets for disembodied knowledge. In such markets, the prospective customer bears high levels of ex-ante risk. The vendor may try and sell a lemon. For the transaction to take place, it is consequently necessary that the vendor reveals all the details of the piece of knowledge to the vendor who needs to assess its actual content before
the transaction takes place. The risk, however, shifts from the customer to the vendor as soon as the details of the piece of knowledge are shared. The customer, who was – ex-ante – exposed to the opportunistic conduct of the prospective vendor, may – ex-post – implement an opportunistic behaviour, leave the knowledge marketplace and take advantage of the information disclosure without any payment. Now the risk has fully shifted to the vendor. The Chandlerian corporation, based upon the separation between ownership and control and the vertical integration of knowledge generation intra-muros enabled to handle the problems of the limited appropriability and tradability of knowledge (Lazonick, 2010).
The use of the wide range of ICT and the strengthening of IPRs have changed in depth the mechanisms of generation, appropriation, exploitation and market exchange of knowledge as a property right in the new markets for patents, as service in internal quasi-markets as well as in arm’s length transactions, and capitalized as an asset traded in financial markets. Let us analyse them in turn.
The new IPR regime. The enforcement in March 1994 of the Agreement of Trade-related Aspects of Intellectual Property Rights (TRIPs Agreement) and the sequence of patent reforms in the USA led to the strengthening of the IPRs regime and their globalization (Pagano and Rossi, 2009). The enhanced privatization of knowledge with the strengthening of the IPR regime can be regarded as one of the main institutional changes that characterise and favour the emergence of the new knowledge economy. Many have compared the current trends towards the reinforcement and extension of IPR to the enclosure of common land that preceded and actually enabled the Industrial Revolution. The enhanced privatization of knowledge is clearly necessary to support the new tradability of knowledge as a service. The strengthening of the IPR, in fact, reduces the risks of opportunistic behaviour associated with the information asymmetry in knowledge exchange and supports the specialization of firms in the generation and market exchange of knowledge as a service, rather than an input embodied in other goods (Gallini, 2002; Pagano, 2014; Aghion, Howitt, Prantl, 2015).
The new IPR regime supports the growth of the new markets for knowledge. Next to the trade of knowledge as a service, knowledge is traded as a patent and a license (De Rassenfosse, Palangkaraya, Webster,
2016). The trade as IPRs is actually complementary to the trade of knowledge as a service, since the exchange of property rights is implemented by the provision of dedicated services that enable customers to take advantage of the proprietary knowledge that is acquired. In traditional IPRs markets, transactions took place mainly across borders between corporations active on the supply side, and foreign manufacturing firms on the demand side (Arora, Fosfuri and Gambardella, 2001; Branstetter, Fisman, and Foley 2006). In the new larger and denser markets for IPRs, transactions take place to a more significant extent within national borders between scientific entrepreneurship and small knowledge-intensive firms active on the supply side and corporations on the demand side (De Marco, Scellato, Ughetto, Caviggioli, 2017; Caviggioli and Ughetto, 2013; Monk, 2009).
In the new markets for proprietary knowledge, transactions based upon patents are more and more complemented by knowledge interactions whereby knowledge producers provide knowledge users not only with a licence but also with direct assistance. Such assistance enables to implement the actual and effective transfer of proprietary knowledge and, at the same time, provides vendors with the tools to keep under control its applications and eventual uses to generate additional knowledge. Licencing agreements are part of more extensive and articulated contracts that combine knowledge interactions and knowledge transactions and make possible for both parties to participate in the advantages of user-producer interactions. Customers can better access and use the licenced proprietary knowledge while vendors can take advantage of the learning processes that take place in the after-sale experience. Here, ICTs provide an array of new mechanisms based upon dedicated procedures and protocols that enable the selective access to specialised and proprietary databases so as to share the irreducible components of tacit knowledge and make the knowledge interaction more effective and yet possible. Digital trade of knowledge enables to increase its appropriation.
Venture capitalism and knowledge intensive assets. Knowledge is more and more traded through financial markets where knowledge is capitalised as a financial asset. On this regard, venture capitalism has been a major institutional innovation. It combines, in fact, the advantages of the Schumpeterian “innovative banker” with the merits of the corporation
while avoiding their respective shortcomings. Venture capitalists, like the innovative banker, perform the screening of new projects well beyond the limits of the internal competence of the incumbents, accessing the broad spectrum of professional and scientific competencies available in the marketplace. Like the corporation, it is able to mobilize competent managers to assist in the development of the start-up. Like the corporation, it can raise equity to fund innovations and actually participate not only in failures – as it is the case of the banker – but also to successes. The take-over of successful start-ups after their IPO in the stock markets becomes a significant source of new knowledge. After the take-over, the new small high-tech companies are delisted and become part of the corporation that, in so doing, acquires advanced technological competence and effective prototypes, well screened and tested with respect to both the engineering and the marketing side. The take-over of small high-tech firms substitutes intra-muros R&D activities within corporations. Most importantly, for the focus of this paper, it enables venture capitalists, including scientific entrepreneurs, to appropriate the economic value of knowledge, capitalised as an asset. The exploitation of knowledge capitalised as equity takes place in the stock markets that perform the new function of markets for knowledge embodied in knowledge intensive assets (Kortum and Lerner, 2000; Gompers and Lerner, 2004).
Internal knowledge markets. The manufacturing corporations of advanced countries became progressively global with an aggressive reorganization of the production process within global value chains (Amador and Cabral, 2016). Manufacturing activities were outsourced and delocalized in industrializing countries. Knowledge intensive activities were retained in their domestic locations. The internal division of labour within global value chains led to the reduction of manufacturing activities in advanced countries and the specialization of headquarters in the production of KIBS for the rest of the corporation. The exploitation of technological knowledge generated nearby headquarters took place within internal intermediary markets. Here, downstream users could access the proprietary knowledge by means of transfer mechanisms among related parties based upon internal licensing agreement, which would integrate both affiliates and third parties associated in outsourcing activities to the value chain. The globalization of the corporation had direct effects on the composition of
employment retained within headquarters that became knowledge intensive service providers.
Knowledge as a service. Knowledge is more and more sold as a service. In the new markets for knowledge as an intermediary input, customers do not purchase knowledge: they purchase the problem-solving capability of specialised knowledge-intensive agents, who can stock their knowledge and use it as an input in the generation of new idiosyncratic technological knowledge, strictly dedicated to the need and requirements of the customers (Goldfarb, Greenstein, Tucker, 2015). Digital technologies support the systematic search of existing knowledge, its active inclusion as an input in a recombinant knowledge generation process and its dedicated application to the specific needs of customers. The interactive use of large databases and broadband-based communication procedures such as Artificial Intelligence, Internet of Things, Big Data and Cloud Computing allows the effective organization of research platforms into which several specialised suppliers and customers can interact and exchange knowledge. The digital infrastructure enables to apply the mechanisms and the foundations of professional activities, traditionally practised in the provision of personal services in final markets, to new intermediary knowledge markets. In the new intermediary markets, the derived demand for knowledge of large manufacturing firms matches the supply provided by small service firms and scientific entrepreneurship specialised in the generation and market exchange of specific types of technological knowledge traditionally integrated within the corporation (Bauer, Latzer, 2016). The introduction of ICTs enables to reduce knowledge costs in small, specialised service firms below corporate levels. Small and highly specialised knowledge generation units, both private and public, enter the new intermediary markets for knowledge, as they are able to generate new knowledge at much lower costs than the large research departments of corporations. At the same time, ICTs exploitation increases the size of the international markets for selective knowledge-intensive services into which small and specialised firms can enter (Freund and Weinhold, 2002; Choi, 2010). A large part of the new trade of knowledge as a service is typically based upon repeated and intense user-producer interactions that enable the parties to specify their needs and apply their competence, respectively. Within KIBS, the generation and exploitation of knowledge are strongly associated via the enhanced levels of cumulability and
extensibility that, in turn, increase the levels of appropriability. The new dedicated and idiosyncratic knowledge, generated on purpose for a specific customer, enters and remains in the stock of internal knowledge of suppliers and becomes an additional input for its eventual recombinant generation and sale embodied in new knowledge services to other customers. Customers have not access to the stock of knowledge, whereas the providers retain the full command of the knowledge outputs and of the procedures and methodologies that enabled them to elaborate specific applications and solutions to the problems of the customers. Customers can purchase the applications of the “algorithms”, not the “algorithms” themselves. Appropriability is endogenous to both the generation and the trade of knowledge as a service (Gans and Stern, 2017). Vendors can appropriate large knowledge rents because they can take advantage of the dynamic increasing returns triggered by the limited exhaustibility of knowledge. The profitability of KIBS increases as they can keep selling specific knowledge and yet increasing their stocks of generic knowledge. 2.2 The effects of the new mechanisms of knowledge tradability, appropriation and exploitation
The new tradability of knowledge increases the layers of the knowledge value chain and changes its organization. It is no longer vertically integrated within the corporation, but it is stretched by the upstream entry of new specialised knowledge-intensive suppliers that sell, in new intermediary markets, knowledge as a service, supported by tightened IPR to downstream corporations. In turns, corporations use this knowledge to manage their organization and to feed the introduction of product and process innovations.
Corporations rely more and more on external sources for their knowledge inputs. Research and development activities are more and more outsourced to specialised suppliers that act as providers of specialised knowledge inputs. On the one side, the radical change in knowledge tradability has positive effects on the efficiency of knowledge generation as it enables higher levels of division of labour and specialisation among a variety of specialised suppliers. The reduction of the adverse effects of the not-invented-here syndrome, limiting the capability of corporations to overcome the enclosures generated by the limited scope of their own competences, comes as a consequence. The increased tradability of
knowledge also has positive effects in terms of larger opportunities to use the market as a screening and selection device, better able than internal hierarchies to sort projects that have more opportunities to succeed.
The new tradability of knowledge has radical effects on its exploitation and appropriation, changing the distribution of knowledge rents along the value chains. KIBS can appropriate an increasing share of the profits associated with the generation and exploitation of knowledge. The limited exhaustibility of knowledge and its powerful effects in terms of extensibility and cumulability enables KIBS firms to retain an increasing share of the profits and rents stemming from the generation of knowledge. Profits shift upstream in the global value chains and concentrate in the KIBS industries of advanced countries. Manufacturing companies in advanced countries focus on the role of commercial platforms and retain a declining share of the overall profitability. The profits of manufacturing activities, left to industrializing countries, are squeezed (Amador and Cabral, 2016).
Within KIBS, the new tradability of knowledge as a service, capitalized as a financial asset and an IPR, benefits skilled workers of knowledge service firms the most. The higher levels of participation of KIBS workers to knowledge rents and the direct involvement of active entrepreneurs and shareholders in KIBS firms increase the levels of wealth inequality and contribute to enhancing the levels of rents, thus increasing income inequality. The direct participation of creative workers – employing various types of stock options and financial piece-rate systems – to the rents associated with the exploitation of knowledge reinforces the process. The new tradability of technological knowledge parallels the effects of globalization that engenders a radical structural change with a shift of advanced countries away from the manufacturing industry and the specialization in service activities (Autor et al. 2020). Manufacturing companies, more and more based in industrializing countries, rely on the KIBS rapidly growing in advanced countries that become knowledge economies, as shown in Figure 1 andFigure 2 (Antonelli and Fassio, 2016 and 2014).
In these figures, the share of employment accounted by manufacture declines for every advanced country, with the exception of the Czech Republic. Symmetrically, in the historical period analysed, the relevance of KIBS is increasing sharply in European regions: it climbs in Sweden from around 5% to almost 9%, in the UK from 7% to 10.6%, the Netherlands from 8% to almost 10%.
These changes, in turn, affect in-depth the income distribution with two different processes: i) the polarization of labour markets, caused by the decline of demand and increased unemployment of standard labour and augmented demand of creative; ii) the increase of profits associated to the generation, appropriation and exploitation of knowledge, mostly unshared with low-skilled workers. Respectively, both wage and rent (wealth) inequalities increase. Let us analyse each process in turn.
Figure 1. Employment share2 of KIBS and Manufacture all OECD
countries. Source: OECD.
Figure 2. Employment share of KIBS and Manufacture for some relevant OECD countries (Australia, Germany, Spain, France, Italy, Netherlands, Poland, Sweden, UK, USA). Source: OECD.
2 Drawing from OECD National Accounts tables, we exploit indexes of the share of employment (in persons) accounted by KIBS and Manufacture as a proxy for their economic centrality.
Wage Inequality. The demise of the Chandlerian corporation engenders the reduction of average wages at the system level: in large corporations, blue-collar workers did benefit of high unit wages, far above baseline wage levels in the economy at large, stemming from their high levels of unionization. In the corporation, in fact, unit wages did exhibit a strong positive relationship with firms’ size and profitability (Mueller, Ouimet, Simintzi, 2017; Card, Heining Kline, 2017; Farber et al., 2018). Within manufacture, instead of decreasing wages, we observe a contraction of employment. Indeed, the bargaining power of unionized labour, even if amid a declining trend across OECD countries (see Figure 3), defends sticky wages in standard labour (Figure 4). Overall, the share of workers protected by unions and benefitting of above-average wages declines.
We suggest that the new tradability of knowledge complements and supports the specialization of advanced economies in the generation and exploitation of technological knowledge, the exit from the manufacturing industries and the growth of KIBS sectors with major effects on the polarization of the labour markets in two sections: the market for creative labour and the market for standard labour as well as the shift upstream in the global value chains in the distribution of increased knowledge rents.
Figure 3. Employment and Average Wages on base 100 in 1990 (or at first later date available). KIBS and Manufacture comparison. Source: OECD.
Standard labour employed in the production of manufacturing goods is exposed to the stiff competition raised by the entry of new large, low-wage and labour abundant economies. Employment contraction is associated with sticky wages. The markets for creative labour, able to participate actively into the generation and exploitation of technological knowledge, on the opposite, are protected by international competition by the unique conditions of accessing and using the large stock of technological knowledge, cumulated through time and implemented by high-quality knowledge governance mechanisms. Such markets are characterized, on the demand side, by the positive effects exerted by the knowledge-intensive direction of structural change. The increased levels of knowledge derived demand have, indeed, positive effects on both job opportunities and wage levels. The increasing levels of wage inequality are reinforced by the upstream shift of profits in the global value chains, their concentration in the KIBS sectors and the participation of professional and scientific entrepreneurship in their appropriation.
Rent (wealth) inequality. The corporate economy was characterized by substantial levels of profit sharing among stakeholders. The organization of corporations was based upon several layers of management that enabled white collars to participate into the large markups (Garicano, 2000; Garicano, Rossi-Hansberg, 2006; Bloom, Garicano, Sadun, Van Reenen, 2014). The size of mark-ups associated to the persistent introduction of process and product innovations in oligopolistic product markets, well defended by relevant barriers to entry and imitation, were large but shared with stakeholders so that actual profits were lower.
Scientific and professional entrepreneurship and small knowledge service firms, instead, incur in much lower unit coordination costs as decision making is concentrated in small groups of active shareholders directly involved in management. The new tradability and appropriability of knowledge are at the origin of a radical structural transformation that weakens the mechanisms of income redistribution associated with the corporation. It enhances, instead, the mechanisms of income concentration based upon higher levels of profitability of small, knowledge-intensive private companies, where the classical separation between ownership and control exerted by the management shrinks, paving the way to new profiles of knowledge and scientific entrepreneurship able to appropriate
knowledge rents directly as profits (Audretsch and Link, 2018). In the knowledge economy, the share of income paid to capital is larger than in the corporate economy. As a consequence, income inequality increases through higher rent (wealth) inequality.
In our theoretical framework, the new forms of knowledge tradability and the enhanced levels of knowledge appropriability, based upon ICTs and the new institutional set-up, are part of a broader structural change where knowledge is the central input and output. A structural change that reshapes the organization of technological knowledge generation and exploitation at the system level and entails the demise of the corporation as the core institutional mechanism not only for the generation and exploitation of knowledge but also for the distribution of extra-profits and rents to the working class, and the emergence of the KIBS sector, based upon small firms and scientific entrepreneurship, with lower but resilient wages, large profits and high levels of capitalization of knowledge as a financial asset.
The new tradability of knowledge, hence, has direct effects on the levels of knowledge appropriability, increasing knowledge rents and the polarization of labour markets, differently remunerating creative and standard workers, with a consequential increase in the levels of wage inequality. The increase in both wage and rent inequalities reinforces the increase in income inequality.
3. EMPIRICAL ANALYSIS 3.1 Data
We build a panel dataset of countries borrowing from two sources. Namely, i) the Standardized World Income Inequality Database (SWIID 8.1, Solt, 2016) which is a valuable and extensive source for income inequality data (in the forms of Gini indexes3) normalized across countries
and sources; and ii) various tables from the OECD repository.
3 From the word of the SWIID creator, Frederick Solt: «I think the clearest explanation of the Gini index is that it is half the average difference in income between all pairs of units—say, households—as a percentage of the mean income of those units. Okay, I said “clearest,” not necessarily “clear.” Anyway, it has a theoretical range of 0 (all households have the same income) to 100 (one household has all the income and the rest have none), but Ginis below 20 or above 60 are rare in the real world. There are good reasons to prefer other measures of inequality, and there are many options, but the Gini is by far the most widely available» (https://fsolt.org/blog/2017/07/28/the-swiid-source-data.html)
SWIID is the output of a strenuous effort to generate freely available data on inequality covering the maximum range of countries and years, through harmonization and imputation of various data sources4. We exploit it to
define our dependent variable: income inequality. As Florida and Mellander (2016) report, income and wage inequality describe different geographical distributions since they reflect different but interlinked phenomena. Income inequality, indeed, is determined both by wage and rent inequalities. Income inequality is, therefore, a proper measure to investigate our research questions.
The Gini index is just one of the many measures used to grasp inequality. We opted for it in order to maximize the length and breadth of our panel, given that we want to explore the transitions of economies from one regime to another. Figure 6 in the Appendix shows the correlations between some indexes of inequality: the Gini on disposable and market income from SWIID and other three indexes from OECD – the Palma Ratio, the Percentile Ratio and the Gini, all of them computed on disposable income. Different measures of inequality computed on the same type of income are highly and positively correlated, whereas the correlation between Ginis on market and disposable income is average intensity. In the econometric analysis that will follow, we go for the Gini on market income as the main dependent variable. Indeed, the difference between market and disposable income is the mediation of the welfare state redistributing income within the population. At this stage, we are interested in the unmediated, direct effect of the knowledge economy transition in income, duly accounting for state-level idiosyncrasies.
Figure 5 describes the market and disposable income inequality trends for a panel of OECD countries. It portraits a generalized condition of rising inequality but qualified by a certain degree of heterogeneity from country to country. Most of the plotted countries experience rising inequality from
4 Jenkins (2015) examines two major sources of inequality data, namely SWIID and WIID. He expresses a conditional favor for the use of the latter for comparative studies. SWIID, indeed, is the output of a not completely clear multiple imputation methodologies on WIID raw data, pointing to maximize the countries time-series and making them comparable, properties that are not provided by WIID. WIID, instead, leaves to the researcher the duty to match and harmonize the series, with a large amount of discretion. Even though we are thankful for the insightful exercise, we do not agree with Jenkins conclusions in two regards. First, as he brings evidence, empirical analysis from WIID and SWIID for developed countries, whose data availability is larger, is consistent. Second, we think that relying on the research efforts of established and trustable academics and institutions is necessary and advisable for new, parsimonious, frontline research insights to be provided.
the ‘80s onwards, on average, as documented on the regional scale by Rosés and Wolf (2018). France and Italy exhibit the renown U-shaped trend (Piketty and Saez, 2003). As announced, market and disposable income inequality follow very similar trajectories for most countries, but figures in the lower panel exhibit more pronounced shapes. This indicates that redistribution policies are at work.
The increasing centrality of KIBS seems to provide a reliable proxy of the emergence of the knowledge economy. Therefore, we extract the share of persons active in KIBS sectors over the total economy from the OECD National Accounts tables.
Another available measure of a country’s knowledge intensity is R&D investments as a portion of GDP. The amount of resources devoted to research is undoubtedly correlated with the emergence of knowledge-based firms, but the two are not measuring the same phenomenon. Indeed, R&D investments, both from the business and the public sectors, may take place – actually took place – even without incurring in a knowledge-dedicated sector. KIBS firms now perform an increasing share of business related R&D investments.
A feature undoubtedly related to the potential of knowledge resources in a country is the endowment of human capital. The variable is taken from the Penn World Tables (Feenstra et al., 2015) and is a combination of workers’ years of schooling and assumed return from education.
We are aware that the dynamics of knowledge production and use can account for only a fraction of a vast, structural phenomenon as inequality, which is mostly feeding itself. In the framework we proposed, the globalization of product and financial markets covers a pivotal role as a trigger of the knowledge transition of industrialized countries. We measure
it as exports plus imports relative to GDP, extracted from the World Bank’s World Development Indicators. To comply with the argument put forward above about the distributional struggles arising from de-unionization, we add the coverage of unions on employment, or union density, to the list, as well as the unemployment rate. Moreover, we use the log of GDP per capita at PPP to control for countries’ economic development. All these variables come from the OECD datawerahouse. To make the investigation more robust, we follow Tridico (2018) and acknowledge the importance of labour policies. In particular, the OECD Employment Protection Legislation index (EPL) provides salient information about laws governing individual and collective dismissals, i.e. labour market flexibility. Tridico (2018) documents the expanding flexibility of OECD labour markets and its negative impact on redistribution. Furthermore, as Dorn (2016)’s report suggests, the demographic structure of a population may affect inequality even if the real income distribution keeps constant among adult workers. Hence, we control for population density, elderly dependency ratio (share of over 65 on 15-64 persons) and age-adjusted mortality rate (as an indicator of societal life quality), once again from the OECD repositories.
We collect data on all these variables for OECD countries. However, in the principal analysis, we retain only countries we think are, or potentially may be, experiencing a knowledge economy transition. Data availability for these variables is not homogeneous, which makes of our dataset a strongly unbalanced panel covering at most 20 OECD countries5 for a
maximum of 27 years from 1990 to 2016. Table 4 and Table 5 in the Appendix provide a brief description of these variables and summary statistics.
3.2 Empirical strategy and results
As discussed in Stockhammer (2017) and Florida and Mellander (2016), there is a large number of contributions spending efforts to explain the determinants of (income, wage, regional) inequality (see e.g. Piketty and Saez, 2003 and Rosés and Wolf, 2018). The present work investigates the role of the changes in the organization of the generation, appropriation and
5 The longest sample of countries involved in the main empirical exercises include: Austria, Australia, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, New Zealand, Poland, Portugal, Spain, Sweden, United Kingdom, United States. Depending on the covariates, the list of countries shrinks.
exploitation of technological knowledge and in the economic structure of advanced economies.
Equation (1) portrays in a formula the main relationships we have in mind:
Ginimktit=∝+ KEit+Xit+εit
Where i and t stand for country and year subscripts, KE is a matrix filled by the knowledge economy transition measure – the centrality of KIBS and BERD expenditures on GDP – and matrix X contains a set of control variables progressively plugged into the model. To protect our estimates from omitted variables, we proceed with a two-ways FE estimation, therefore controlling for both the unobserved time-invariant heterogeneity at the country level and the endogenous time effect, usually referred as business cycle effect. All covariates enter the regression at the same point in time of the dependent variable. Table 1 reports the first set of results. The first column introduces the knowledge economy variables only, which are positive and significant. In the second round, variables accounting for the distributional issues related to employment comes in: the estimated parameters are significant and with expected signs. In the third column, variables related to the structure of the economy step in but are not much significant. Finally, the fourth column introduces the demographic variables: the strongest, and significant, (negative) effect comes from the elderly dependence ratio6. This first set of regressions, already introducing
a wide range of controls, does confirm the expected positive correlation between the knowledge economy transition and income inequality within advanced countries.
6 The VIF of the relevant variables (KIBS % and BERD expenditures) for these regressions is satisfactory, well below 10. Indeed, the highest value is that of KIBS (~5) followed by GERD (~2.3). However, the VIF for KIBS is pushed up by the introduction of population density rather than by GERD, even though the two are correlated at 0.43.
Table 1. Two-ways Fixed Effect estimator7.
Dependent variable:
(Log of) Gini on Market Income
(1) (2) (3) (4) BERD on GDP 0.027*** 0.026*** 0.030*** 0.024** (0.007) (0.006) (0.007) (0.008) KIBS % (Persons) 0.009* 0.012* 0.016*** 0.015*** (0.004) (0.004) (0.004) (0.004) Openness -0.0001 -0.0001 -0.0002 (0.0001) (0.0001) (0.0002) Union density -0.001** -0.001** -0.001* (0.0004) (0.0005) (0.0005) Unempl rate 0.003*** 0.004*** 0.005*** (0.0005) (0.001) (0.001) HC 0.062. 0.063* (0.033) (0.030) GDP pc 0.037 0.066* (0.026) (0.030) EPL 0.020 0.035* (0.016) (0.015) Pop density -0.001* (0.0003)
Elderly dep. ratio 0.003**
(0.001)
Mortality 0.001
(0.001)
Observations 393 376 343 339
R2 0.089 0.110 0.295 0.320
7 An R2 of 0.32 may appear low respect to a model with such a wide range of variables. However, one has to remind
this is a within R2, that is, it finds the total sum of squares on the demeaned outcome variable. The overall R2, obtained
through a LSDV with the very same model, scores as high as 0.93. Considering that inequality is a inherent, path-dependent phenomenon, we think a 0.32 within-R2 is a satisfactory score.
Adjusted R2 -0.035 -0.024 0.172 0.194
Note: . p<0.1; * p<0.05; ** p<0.01; *** p<0.001
Table 2 and Table 3 report a few robustness checks, with two different estimators. The first column in Error: Reference source not found reproduces the last one in Table 1 as a reference to the main results. The second column tests the same model on all available OECD observations, including considerably more countries8. The most relevant differences are
that, in Table 2, the coefficient of BERD considerably increases in size, whereas Openness and Mortality get statistical significance.
The third column introduces a different operationalization of KIBS sectors. Indeed, Rodriguez and Ballesta (2010) provide a review of some contributions pursuing a definition of KIBS and provide a table for grouping ISIC Rev. 4 codes into a KIBS category. Their suggestion is to include classes “62”, from “69” to “74” and “78”. However, data availability for the Rodriguez and Ballesta (2010) classification is scarce because many national statistical offices aggregate close classes in their national accounts. As Antonelli and Fassio (2014) did in a similar work using ISIC Rev. 3 categories, we included some additional classes to maximize the number of observations. Hence, the classes exploited to define the KIBS relevance in this paper are “62”, “63” and from “69” to “75”. The third column uses the Rodriguez and Ballesta (2010) stricter definition of KIBS sectors. The results are robust.
Finally, the fourth column switches from the Gini on market to disposable income. As argued, both choices are legitimate, market income being an unmediated income measure, whereas disposable income reflects already the redistributive action of welfare states. In any case, the effect of BERD is much larger in size. Moreover, the Gini on disposable income exhibits more path dependence than that on market income.
In Table 3Error: Reference source not found we try to account for one potential sources of relevant omitted variables. We provide a more fine-grained accounting of institutional characteristics of a country (even though we already control for labour market stringency and country-level invariant features). We borrow from Botero et al. (2004) their set of indexes collecting the set of laws governing four crucial aspects of labour: individual and collective employment, social security and civil rights. It implies the introduction of time-invariant variables, undoable in a FE
regression setting9. For this purpose, Table 3Error: Reference source not
found exploits the Within-Between Random Effect estimator (Bell, Jones, 2015). The rationale is to consider the data as hierarchical – two levels: country and time – and estimate a random intercept model to account for the multilevel structure. Variables enter the regression as yearly deviations from the country mean (the shape of one-way FE model). Year dummies are used too in order to control for common shocks. Other than deviations from the mean, addressing the within variance component, the researcher can insert variables country averages, grasping the between component, or any other level-invariant characteristic. Estimation happens via ML optimization.
The first column in Table 3Error: Reference source not found reproduces, once again, the last one in Table 1 as a reference to the main results. Then, the second column introduces the labour regulation indexes. Even though KIBS and BERD coefficients’ shrinks in size and significance, they resist the new fine-grained controls. Regarding column two, Social security laws is the only statistically significant variable, displaying an inequality-protecting effect. The remaining three columns repropose the robustnesses illustrated for Table 2, with the alternative Withing-Between RE estimator and the labour regulation indexes. No relevant issues are spotted.
These results confirm the hypotheses but it seems appropriate to take into account the limitations of our investigation.
9 Both the Within and the LSDV estimators call out the time-invariant country-specific components by demeaning variables or absorbing them through individual dummies.
Table 2. Two-ways FE estimator. Robustness checks.
Dependent variable:
(Log of) Gini on Market Income (Log of) Gini on Disposable Income
(1) (2) (3) (4) BERD on GDP 0.024** 0.034*** 0.035*** 0.051*** (0.008) (0.007) (0.010) (0.010) KIBS % (Persons) 0.015*** 0.015*** 0.013** (0.004) (0.003) (0.005) KIBS % (Persons, 2nd) 0.008** (0.003) Openness -0.0002 -0.001*** -0.0002 -0.0001 (0.0002) (0.0001) (0.0002) (0.0002) Union density -0.001* -0.001 -0.001* -0.002* (0.0005) (0.0004) (0.001) (0.001) Unempl rate 0.005*** 0.004*** 0.004*** 0.003** (0.001) (0.001) (0.001) (0.001) HC 0.063* -0.001 0.065* -0.032 (0.030) (0.025) (0.031) (0.036) GDP pc 0.066* 0.025 0.058. 0.050 (0.030) (0.026) (0.031) (0.038) EPL 0.035* 0.030* 0.030. 0.036* (0.015) (0.012) (0.016) (0.014) Pop density -0.001* -0.0003 -0.001. -0.001* (0.0003) (0.0003) (0.0003) (0.0004)
Elderly dep, ratio 0.003** 0.003* 0.003. 0.004*
(0.001) (0.001) (0.001) (0.002) Mortality 0.001 0.003** 0.002 0.006*** (0.001) (0.001) (0.001) (0.002) Observations 339 419 309 339 R2 0.320 0.317 0.316 0.316 Adjusted R2 0.194 0.201 0.181 0.189 Note: . p<0.1; * p<0.05; ** p<0.01; *** p<0.001
Table 3. Within-Between Random Effect Estimator. Robustness checks.
Dependent variable:
(Log of) Gini on Market Income Disposale Income(Log of) Gini on
(1) (2) (3) (4) (5)
BERD on GDP 0.024*** 0.017** 0.029*** 0.027*** 0.041***
(0.007) (0.006) (0.005) (0.007) (0.008) KIBS % (Persons) 0.013*** 0.012** 0.012*** 0.010*
(0.003) (0.004) (0.003) (0.005)
KIBS % (Persons, 2nd definition) 0.007**
(0.003) Openness -0.0001 -0.0001 -0.001*** -0.0001 -0.0001 (0.0002) (0.0002) (0.0001) (0.0002) (0.0002) Union density -0.001** -0.001* -0.001* -0.001** -0.002** (0.0005) (0.0005) (0.0003) (0.001) (0.001) Unempl rate 0.005*** 0.004*** 0.003*** 0.003*** 0.002** (0.001) (0.001) (0.0005) (0.001) (0.001) HC 0.078** 0.041 -0.005 0.053. -0.058 (0.029) (0.028) (0.022) (0.029) (0.038) GDP pc 0.063** 0.050* -0.007 0.044. 0.038 (0.022) (0.023) (0.020) (0.025) (0.030) Pop density -0.001* -0.001. -0.0002 -0.0004 -0.001* (0.0003) (0.0003) (0.0003) (0.0003) (0.0004) Elderly dep, ratio 0.003*** 0.003** 0.003** 0.002. 0.004**
(0.001) (0.001) (0.001) (0.001) (0.001)
Mortality 0.001 0.001 0.004** 0.002 0.006**
(0.002) (0.002) (0.001) (0.002) (0.002)
Employment laws -0.070 -0.180* -0.090 -0.559**
(0.074) (0.078) (0.077) (0.190) Social security laws -0.345* -0.333* -0.390** -0.308
(0.144) (0.141) (0.150) (0.369)
Civil rights 0.012 -0.141 0.026 0.268
(0.084) (0.088) (0.083) (0.216) Collective relations laws 0.080 0.212* 0.094 0.456.
(0.094) (0.100) (0.093) (0.243)
Observations 339 353 440 321 353
Log Likelihood 874.491 898.211 1,105.322 812.913 791.480 Akaike Inf. Crit. -1,672.982 -1,714.421 -2,128.643 -1,543.826 -1,500.959 Bayesian Inf. Crit. -1,527.594 -1,555.896 -1,961.085 -1,389.197 -1,342.434 Note: . p<0.1; * p<0.05; ** p<0.01; *** p<0.001
Estimation produced with R package lme4 Country and year random intercepts
We might conjecture that there are chances for some common trends to exist, which might yield a correlation where there is none, instead. Therefore, we test for the presence of a unit root in the measures for inequality, KIBS and GERD quota, but the variables look stationary10.
Nonetheless, we experiment with an Error Correction Model, estimated through OLS, taking into account the presence of a co-integration between the variables at stake. However, results (Table 6 in the Appendix) are not robust: the autoregressive component of the model absorbs most of the effects and BERD expenditures on GDP are barely significant in the long run. We tested a dynamic model, augmented by the first lag of our dependent variable, with a the two-steps, difference-GMM estimator. Similarly to the ECM case, results are not robust (Table 6 in the Appendix): the KIBS centrality index is only slightly significant; however, the autoregressive component is not.
We acknowledge that our empirical models do not provide support for a causality claim, but we believe that the theoretical argument, together with the empirical evidence provided by the FE and RE models, are sufficient to set a discussion on the issues raised. Indeed, the FE and RE models implemented altogether point into the same direction, that of a correlation between KIBS centrality and BERD expenditures on GDP with market income inequality measured with a Gini index. This correlation is robust to a vast set of control variables embracing the most renown factors affecting income distributions within countries – globalization, union coverage, unemployment and labour regulations – and others we considered relevant – structural and demographic features and long-term inequality.
4. CONCLUSIONS
This paper has emphasized the role of the changes in the organization of the generation, exploitation and appropriation of technological knowledge stressing their relationship with income inequality.
This paper argued that the new tradability of knowledge is part of a broader process of radical structural change engendered by the
10 We perform the Im et al. (2003) (IPS) and Maddala and Wu (1999) (MADWU) panel unit root tests, available for unbalanced panel data in the plm R package, version 2.2-3. The IPS test on BERD investments on GDP does not reject the null of a unit root, whereas the MADWU test does. We then perform the same tests on the residuals of the Gini index regressed on BERD: both reject the null.
globalization of product and capital markets and the consequent changes in the international division of labour. The new tradability of knowledge complements and supports the specialization of advanced countries in the generation and exploitation of knowledge. The diffusion of ICT, the new Intellectual Property Right regime and the growth of the KIBS sectors are crucial ingredients and aspects of the shift towards a knowledge-intense economy of the advanced countries. ICT diffusion and application, together with the new institutional context, radically affected the way of producing, exploiting and exchanging knowledge. The use of ICT enables to reduce the traditional limits of knowledge tradability, stemming from its limited appropriability and information asymmetries, with the introduction of new protocols and procedures that enable to trading knowledge as a service, a property right and capitalized as an asset traded in financial markets, rather than embodied in tangible goods.
We introduced a theoretical framework where the increasing levels of income inequality are a direct consequence of the new specialization of advanced countries of in the knowledge generation and exploitation based on the KIBS industries and the consequent polarization of labour markets. The new role of small knowledge-intensive firms, forming the KIBS industry with the emergence of new intermediary markets for knowledge as a service, have been paralleling the declining role of the Chandlerian corporation in knowledge generation and exploitation. The increased levels of appropriability and tradability of technological knowledge trigger increasing levels of rent inequality that add to the wage inequality associated with the polarization of labour markets. Creative workers engaged in the generation and exploitation of knowledge are able to defend the levels of their wages and to participate into the rents associated with the exploitation of knowledge, as they act as gatekeepers to access the large stock of technological knowledge rooted in the advanced economic systems – providing substantial barriers to entry. The knowledge-intensive direction of structural change supports the increase of the derived demand for creative labour with increasing opportunities for the employment of skilled labour and resilient wages. Standard labour, instead, is fully exposed to the competitive pressure of the exports of low-wage, labour abundant countries and experiences a systematic decline of job opportunities and contraction of employment. We posited that this dynamics strongly correlates with the increasing levels of income
inequality for its positive effects on the increase of knowledge rents, on the one hand, and the negative ones on the levels of unionization, on the other. Exploiting available data mostly from the OECD data warehouse and the Standardized World Income Inequality Database, we brought evidence of a robust and novel correlation that confirms the validity of the hypotheses. Even though no causality is claimed in the paper, quite an effort has been made to stress the main correlation with a heterogeneous set of controls and various model specifications.
The present analysis dives into the interplay between structural change and income distribution. We stress the emerging limits in the ability of the society to share and distribute the returns from the generation of technological knowledge that are now better appropriated but only by a small portion of stakeholders.
ACKNOWLEDGMENTS
The authors acknowledge the comments of the referees and the director of this journal, many attendants to the international conference “Innovation and industrial economics” held at the Nanjing University, June 2018 and the 20th AISSEC Conference “Rise and Decline of Economies: A Comparative Perspective”, Collegio Carlo Alberto, Torino, October 2018 and Guido Pialli. The funding of the research project PRIN 20177J2LS9 is also acknowledged.
REFERENCES
Aghion, P., Caroli, E., Garcia-Penalosa, C. (1999), Inequality and economic growth: The perspective of the new growth theories, Journal of Economic Literature 37(4), 1615-1660.
Aghion, P., Howitt, P., Prantl, S. (2015), Patent rights product market reforms and innovation, Journal of Economic Growth 20(3), 223-262. Amador, J., Cabral, S. (2016), Global value chains: A survey of drivers and measures, Journal of Economic Surveys 30(2), 278–301.
Antonelli, C. (2019), The knowledge growth regime: A Schumpeterian approach. Palgrave MacMillan, London.
Antonelli, C., Fassio, C. (2014), The economics of the light economy. Globalization, skill biased technological change and slow growth, Technological Forecasting & Social Change 87, 89-107.
Antonelli, C., Fassio, C. (2016), Globalization and the knowledge economy, Economic Development Quarterly 30(1), 3-14.
Arora, A., A. Fosfuri, and A. Gambardella (2001), Markets for technology: The economics of innovation and corporate strategy. MIT Press, Cambridge.
Atkinson, A. (2015), Inequality. What can be done? Harvard University Press, Cambridge.
Atkinson A.B., Piketty, T. (eds.) (2007), Top incomes over the Twentieth century: A contrast between continental European and English speaking countries. Oxford University Press, Oxford.
Atkinson A.B., Piketty, T. (eds.) (2009). Top Incomes over the Twentieth century: A global perspective. Oxford University Press, Oxford.
Atkinson, A.B., Piketty, T., Saez, E. (2011), Income inequality in the long run history, Journal of Economic Literature 49(1), 3-71.
Audretsch, D.B., Link, A. (2018), Sources of knowledge and entrepreneurial behavior, Toronto University Press, Toronto.
Autor, D., Dorn, D., Hanson, G.H., Pisano, G., Shu, P. (2020), Foreign competition and domestic innovation: Evidence from US patents, American Economic Review, forthcoming.
Bauer, J.M., Latzer, M. (eds.) (2016), Handbook on the economics of the internet, Edward Elgar, Cheltenham.
Bell, A., Jones, K. (2015), Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data, Political Science Research Methods 3 (1), 133-153.
Bellemare, M. F., Masaki, T., Pepinsky, T. B. (2017), Lagged explanatory variables and the estimation of causal effect, The Journal of Politics 79(3), 949-963.
Bloom, N., Garicano, L., Sadun, R., Van Reenen, J. (2014), The distinct effects of information technology and communication technology on firm organization, Management Science 60, 2859-2885.
Botero, J. C., Djankov, S., La Porta, R., Lopez-de-Silanes, F., Shleiffer, A. (2004), The regulation of labour, Quarterly Journal of Economics 119(4), 1339-1382.
Branstetter, L. G., R. Fisman, and C. F. Foley (2006), Do stronger intellectual property rights increase international technology transfer? empirical evidence from U.S. firm level panel data, Quarterly Journal of Economics 121(1), 321-349.
Card, D., Heining, J., Kline, P. (2013), Workplace heterogeneity and the rise of West German wage inequality, Quarterly Journal of Economics 128(3), 967-1015.
Caviggioli, F., Ughetto, E. (2013), The drivers of patent transactions: Corporate views on the market for patents, R&D Management 43(4) 318-332.
Choi, C. (2010), The effect of Internet on service trade, Economics Letters 109, 102-104.
Crouch, C. (2019), Inequality in post-industrial societies, Structural Change and Economic Dynamics 51, 11-23.
De Marco, A., Scellato, G., Ughetto, E., Caviggioli, F. (2017), Global markets for technology: Evidence from patent transactions, Research Policy 46(9), 1644-1654.
De Rassenfosse, G., Palangkaraya, A., Webster, E. (2016), Why do patents facilitate trade in technology? Testing the disclosure and appropriation effects, Research Policy 45(7), 1326-1336.
Dorn, F. (2016), On data and trends in income inequality around the world, CESifo DICE Report, 4/2016.
Farber, H.S., Herbst, D., Kuziemko, I., Suresh, N. (2018), Unions and inequality over the twentieth century: New evidence from survey data, NBER Working Paper 24587
Feenstra, R.C., Inklaar, R., Timmer, M.P. (2015), The next generation of the Penn World Table, American Economic Review 105, 3150–3182.
Florida, R., Mellander, C. (2016), The geography of inequality: Difference and determinants of wage and income inequality across US metros, Regional Studies 50(1) 79-92.
Franzini, M., Pianta, M. (2016), Explaining inequality. Routledge, London.
Freund, C., Weinhold, D. (2002), The Internet and international trade in services, American Economic Review 92(2), 236–240.
Gallini, N. 2002. The economics of patents: Lessons from recent U.S. patent reform, Journal of Economic Perspectives 16, 131–154.
Gans, J.S., Stern, S. (2017), Endogenous appropriability, American Economic Review 107(5) 317-321.
Garicano, L (2000), Hierarchies and the organization of knowledge in production, Journal of Political Economy 108, 874-904.
Garicano, L., Rossi-Hansberg, E. (2006), Organization and inequality in a knowledge economy, Quarterly Journal of Economics 121 (4), 1383-1435. Goldfarb, A., Greenstein, S., Tucker, C. (2015), Economic analysis of the digital economy, University of Chicago Press, Chicago.
Gompers, P.A., Lerner, J. (2004), The venture capital cycle, MIT Press, Cambridge.
Hartmann, D., Guevara, M. R., Jara-Figueroa, C., Aristarán, M., & Hidalgo, C. A. (2017), Linking economic complexity, institutions, and income inequality, World Development, 93, 75-93.
Im, K. S., Pesaran, M. H., Shin, Y. (2003), Testing for unit roots in heterogeneous panels, Journal of Econometrics 115(1), 53-74.
Khan, Z.B., Sokoloff, K.L. (2008), Historical perspectives on patent systems in economic development, in Netanel, N. (ed.), The development agenda: Global intellectual property and developing countries. Oxford University Press, New York.
Kortum, S., Lerner, J. (2000), Assessing the contribution of venture capital to innovation. RAND journal of Economics, 674-692.
Kuznets, S. (1955) Economic growth and income inequality, American Economic Review 45, 1–28.
Jenkin, S.P. (2015), World income inequality: An assessment of WIID and SWIID, Journal of Economic Inequality 13, 629-671.
Lazonick, W. (2010), The Chandlerian corporation and the theory of innovative enterprise, Industrial and Corporate Change, 19, 2, 317-349.
Maddala, G. S., Wu, S. (1999), A comparative study of unit root tests with panel data and a new simple test, Oxford Bulletins of Economics and Statistics 61, 631-652.
Monk, A.H.B. (2009), The emerging market for intellectual property: drivers, restrainers and implications, Journal of Economic Geography 9(4) 469-491.
Mueller, H.M., Ouimet, P.P., Simintzi, E. (2017), Wage inequality and firm growth, American Economic Review 107(5) 379-383.
Pagano, U. (2014), The crisis of intellectual monopoly capitalism, Cambridge Journal of Economics 38(6), 1409-1429.
Pagano, U., Rossi, M.A. (2009), The crash of the knowledge economy, Cambridge Journal of Economics 33(4), 665-683.
Palokangas, T. (1989), Union power in the long run reconsidered, Scandinavian Journal of Economics 91, 625-635.
Palokangas, T. (1996), Endogenous growth and collective bargaining, Journal of Economic Dynamics and Control 20, 925-944.
Piketty, T. (2014), Capital in the XXI Century, Harvard University Press, Cambridge.
Piketty, T., Saez, E. (2003), Income inequality in the United States, 1913– 1998, Quarterly Journal of Economics, CXVIII, 1-39
Ravallion, M. (2018), Inequality and globalization: A review essay, Journal of Economic Literature 56(2) 620-642.
Reed, W. R. (2015), On the practice of lagging variables to avoid simultaneity, Oxford Bulletin of Economics and Statistics 77(6), 897-905. Rodriguez, M., Ballesta, J.A.C. (2010), Are knowledge-intensive business services so “hard” innovators? Some insights using Spanish microdata. Journal of Innovation Economics & Management, 41–65.
Rodrik, D. (1997), Has globalization gone too far, Columbia University Press, New York.
Rosés, J.R. and Wolf, N. (2018), Regional economic development in Europe, 1900-2010: A description of the patterns. CEPR Discussion Papers 12749.
Solt, F. (2016), The standardized world income inequality database, Social Science Quarterly 97, 1267–1281.
Stockhammer, E. (2017), Determinants of the wage share: A panel analysis of advanced and developing economies, British Journal of Industrial Relations 55, 3–33.
Tridico, P. (2018), The determinants of income inequality in OECD countries, Cambridge Journal of Economics 42, 1009–1042.
Wessel, T. (2013), Economic change and raising income inequality in the Oslo region: The importance of Knowledge-Intensive-Business Services, Regional Studies 47 (7) 1082-1094.
APPENDIX
Figure 6. Correlation between indexes measuring inequality. Source: OECD and SWIID.
Table 4. Variables description. Source: own elaboration.
Variable Source Type Description
Gini Mkt SWIID Dependent Gini on market income
KIBS % OECD NationalAccounts Main explanatory accounted by KIBS onShare of Persons total economy
BERD on GDP OECD Main explanatory expenditure in R&DBusiness-related
Openness World Bank WDI Main control Export plus importsrelative to GDP
Unempl rate OECD Main control Unemployment rate ofthe population
Union density OECD Main control Coverage of unions onemployment
HC Penn World Tables Main control
combination of workers’ years of schooling and assumed
return from education
GDP pc OECD Main control GDP at PPP per capita
EPL OECD Robustness control Index of stringency ofthe labour market
Pop density OECD Robustness control Population density
Elderly dep ratio OECD Robustness control Share of over 65 on 15-64 persons
Mortality rate OECD Robustness control Age-adjusted mortalityrate
Employment laws Botero et al. (2004) Robustness control individual employmentAggregate index on relations
Collective relations laws Botero et al. (2004) Robustness control collective employmentAggregate index on relations
Social security laws Botero et al. (2004) Robustness control
Aggregate index on social security and health expenditures
Table 5. Summary statistics of models variables. Source: own elaboration.
Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max
Gini market income 659 46.625 4.461 29.100 44.900 49.250 53.600
KIBS share (Persons) 418 6.531 1.871 2.356 5.123 7.724 11.730
BERD on GDP 574 1.282 0.785 0.081 0.703 1.795 3.721 Openness 691 72.630 34.875 16.014 51.347 84.652 226.041 Unemployment 641 7.816 4.187 1.777 4.820 9.577 27.466 Union density 604 33.282 20.292 7.800 18.300 44.200 87.400 GDP PPP pc 600 10.209 0.397 8.684 9.967 10.496 11.113 HC index 600 3.177 0.354 1.940 2.974 3.458 3.734 Pop. density 661 154.288 143.353 2.220 27.250 230.910 515.230
Elderly dep. ratio 660 22.869 5.093 7.390 19.133 25.892 45.180
Standard. mortality 599 8.924 1.597 4.530 7.760 9.980 15.260
EPL 559 2.122 0.851 0.257 1.560 2.679 4.833
Employment laws 696 0.510 0.205 0.161 0.329 0.708 0.809
Social security laws 696 0.736 0.071 0.624 0.676 0.786 0.873
Civil rights 696 0.660 0.114 0.468 0.565 0.755 0.848
Table 6. GMM and ECM estimations. Only main regressors displayed.
Dependent variable:
(Log of) Gini on
Market Income (Log of) ∆Gini on Market Income
difference GMM ECM
(1) (2)
(Log of) Gini on Market Income (L1) 0.298 -4.164**
(0.329) (1.390) KIBS % (Persons) 0.026* (0.013) BERD on GDP -0.011 (0.047) ∆BERD on GDP 0.463 (0.290) BERD on GDP (L1) 0.355* (0.181) ∆ KIBS % (Persons) 0.088 (0.142) KIBS % (Persons) (L1) -0.066 (0.078) Observations 296 300 R2 0.263 F Statistic 3.658*** (df = 23; 236) Note: . p<0.1; * p<0.05; ** p<0.01; *** p<0.001
GMM and ECM estimations produced with R package plm
