The new direction of technological change in the global economy

Journal Pre-proof

THE NEW DIRECTION OF TECHNOLOGICAL CHANGE IN THE GLOBAL ECONOMY Cristiano Antonelli , Christophe Feder PII: DOI: Reference:

S0954-349X(18)30422-3 https://doi.org/10.1016/j.strueco.2019.09.013 STRECO 866

To appear in:

Structural Change and Economic Dynamics

Received date: Revised date: Accepted date:

17 December 2018 12 September 2019 29 September 2019

Please cite this article as: Cristiano Antonelli , Christophe Feder , THE NEW DIRECTION OF TECHNOLOGICAL CHANGE IN THE GLOBAL ECONOMY, Structural Change and Economic Dynamics (2019), doi: https://doi.org/10.1016/j.strueco.2019.09.013

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

THE NEW DIRECTION OF TECHNOLOGICAL CHANGE IN THE GLOBAL ECONOMY1 Cristiano Antonelli, Dipartimento di Economia e Statistica, Università di Torino and BRICK (Bureau of Research in Innovation Complexity and Knowledge), Collegio Carlo Alberto. Christophe Feder, Dipartimento di Scienze Economiche e Politiche, Università della Valle d’Aosta and BRICK (Bureau of Research in Innovation Complexity and Knowledge), Collegio Carlo Alberto. Abstract The direction and rate of technological change in the global economy exhibit contrasting trends. Since the end of the XX century, the rate of technical change measured by productivity growth has been stronger if its direction is more labor-intensive. This puzzling finding can be explained using an interpretative framework that integrates the induced technological change and localized knowledge approaches with the Schumpeterian creative response. It is hypothesized that the globalization of both product and financial markets induces a creative response based on localized knowledge which accounts for the new knowledge- and labor-intensive direction of technological change that is more effective and productivityenhancing than capital-intensive innovations. Structural equation modeling confirms the hypotheses in 55 countries during the years 1995-2014. We find that human capital is particularly relevant in the OECD countries, while knowledge spillovers matter in the non-OECD countries. KEY-WORDS: Globalization; Localized technological Learning; Schumpeterian creative response; SEM.

knowledge;

JEL CODES: O33

1

The authors acknowledge useful comments from the anonymous referees and the editor on a preliminary version as well as the funding of the research project PRIN 20177J2LS9.

1

1. Introduction The empirical evidence shows that factor shares are far from being the “magic constant” advocated by Robert Solow (1958) and Nicholas Kaldor (1961). Rather, the heterogeneity of the levels and rates of changes in factor shares is very high and, in some cases, quite dramatic. In the last 50 years in most of the advanced countries, factor shares have changed hugely, and there is a growing empirical literature documenting the variability of factor shares from the final decades of the 20 th century (Blanchard, 1997; Bassanini and Manfredi, 2012). However, these trends are not clear and quite heterogeneous (Elsby et al., 2013; Autor et al., 2017; Antonelli and Feder, 2019). The evidence on factor shares provided by the Penn World Table, suggests that country trends show wide differences. For example, in Japan, Spain, Portugal, and Israel the share of labor decreased fairly steadily in the period 1995 to 2014 while in the US, Switzerland, the UK, Korea, China, India, and Turkey following an increase in the mid-1990s, the share of labor declined after the turn of the century. In Italy and Sweden, it increased quite sharply from the end of the 1990s, and in France, Finland, and Germany the share of labor has increased from 2008. The empirical evidence shows that a variety of processes have affected the rate and the direction of technological change in the global economy. It should be noted that since the beginning of the 21 st century, total factor productivity (TFP) has increased at faster rates in several of the advanced countries where the labor share has increased. The somewhat puzzling new evidence calls for some effort to understand its determinants. The present paper uses the Schumpeterian framework to propose an explanation of these huge differences. Textbook economics sees technology as exogenous with the level of output elasticity of each factor matching its income share. Heterodox economics regards income distribution as the outcome of rivalry between capital and labor (Dosi and Nelson, 2010). In the induced technological change approach, the changes in factor shares reflect the introduction of new directed technologies triggered by changes in factor markets and the efficient direction of technological change biased towards the cheaper factor (Acemoglu, 1998; Ruttan, 2001; Aghion and 2

Howitt, 2006). Globalization has added to the secular increases in wages and has induced the introduction of capital-intensive technologies (Leamer, 1996; Slaughter, 1999; Karabarbounis and Neiman, 2014). This paper investigates the idea that since the end of the 1990s the increases in the share of labor positively affects the technological change: the rate of growth in TFP is stronger in countries where technological change has been more labor-intensive and the share of labor in income has been larger. This paper articulates and tests the hypothesis that changes in the depth and dynamics of induced technological change, and the sources of technological knowledge are the consequence of the emergence of a knowledge economy based on the central role of technological knowledge as both an input and an output. The new knowledge economy is both the cause and the consequence of a creative response based on localized learning processes and the introduction of knowledge- and labor-intensive and localized technological change. This process has been initiated in the high-income countries but also applies to low- and middle-income countries due to the powerful effects of directed knowledge externalities from international knowledge spillovers. Using the Penn World Table database, we test our hypotheses for 55 countries during the period 1995-2014. We find that the effects of international knowledge spillovers and of human capital are mainly relevant for poor and rich countries, respectively. In the advanced economies, knowledge embodied primarily in skilled and creative labor has become the cheaper factor (Autor et al., 2003; Autor, 2015). In the new knowledge economies, knowledge is generated mostly by learning processes that are localized in the limited technical space in which firms have accumulated experience and competence. When wages increase, localized knowledge triggers the introduction of new knowledgeand labor-intensive technologies. In knowledge economies, the direction of technological change has changed from being capital-intensive to being based on skilled labor. Knowledge is the key input that will allow the advanced economies to regain their competitive edge by exploiting the knowledge embodied in skilled labor, which is better able to learn and to accumulate competence. 3

Exploration of the determinants of the direction of technological change allows an assessment of the limitations of the standard assumptions -based on Euler’s theorem- about the mechanisms of the income distribution. When both factor and product markets are in equilibrium, income shares are determined exclusively by the output elasticity of production factors dictated by the state of the technology. However, when product and factor markets are shaped by substantial imperfections, the share of income paid to capital defers to the equilibrium level (Bivens and Mishel, 2015). The share of labor in income is also augmented by the effects of income distribution policies aimed at reducing income inequality and poverty (Schwellnus et al., 2017). The Schumpeterian framework of the creative response implemented by the localized technological change approach provides the theoretical tools to capture the determinants of the direction of technological change when knowledge plays a central role. According to this approach, technological change is spurred by changing factor costs but is localized due to the part played by learning processes and the constraints this imposes on the generation of technological knowledge. In these conditions, learning is directed towards the introduction of new technologies which are knowledge- and labor-intensive. The larger the endowment of human capital supporting the generation of knowledge, the stronger will be this effect. The evidence captured by the Leontief paradox according to which high wages countries are specialized not in capital-intensive technologies but in knowledge- and labor-intensive technologies, identified the early signs of a long-term process that now appears to become established. When technological change is labor-intensive and based on the central role of learning processes in the generation of new technological knowledge, TFP growth rates are higher (Leontief, 1953; Ranis et al., 2000). The new direction of technological change applies not only to high-income countries but also to low- and medium-income countries because of the pervasive effects of directed international spillovers. Followers benefit from these international spillovers; however, their effects are not neutral. Imitation of technologies introduced by the leaders has a twofold effect: i) it allows low-cost access to the technological knowledge generated by third parties, but ii) it enforces a bias based on the factor market conditions 4

of the leaders. Directed knowledge externalities spilling over from the new knowledge-intensive technologies introduced in advanced countries trigger the knowledge-intensive direction of the catching-up in the industrializing economies. The paper is organized as follows. Section 2 summarizes the debate, introduces the Schumpeterian framework, implements an interpretative framework, and outlines the hypotheses to be tested. Section 3 presents econometric evidence. Section 4 concludes. 2. The theoretical framework To understand the puzzling evidence, it is necessary to present the building blocks (section 2.1), to combine them in an interpretative framework (section 2.2), and, finally, to identify the key research hypotheses (section 2.3). 2.1. The building blocks 2.1.1 The traditional wisdom on the induced technological change approach The induced technological change approach explores the determinants of the direction of technological change and the hypothesis that the rate and the direction of technological change are influenced if not determined by the changing conditions in factor markets. Specifically, the standard induced technological change approach hypothesizes that an increase in the relative cost of labor with respect to capital user costs, should “induce” the introduction of labor-saving technological change characterized by a lower output elasticity of labor and a higher output elasticity of capital. The starting point can be found in the adaptation to neoclassic production theory of the Marxian intuition elaborated by John Hicks: Changed relative prices will stimulate the search for new methods of production which will use more of the now cheaper factor and less of the expensive one. (…) A change in the relative prices of the factors of production is itself a spur to invention, and to invention of a particular kind directed to economising the use of a factor which has become relatively expensive. (Hicks [1932] 1963:120, 124-5). 5

The above quote states that when the relative price of the factors changes, greater use is made of the cheaper factor. However, it reveals substantial ambiguity about the definition of the cheaper factor: is it at the absolute or the relative level? In other words, it is unclear whether the direction of technological change emerges when a factor becomes cheaper than before, or when a factor becomes cheaper than some other factor. The first part of the text seems to suggest that all increases in wages –for given levels of capital user costs- which take place in a labor abundant country will stimulate the introduction of labor-saving technological change. However, the second part seems to suggest that only wage increases, that make labor more expensive than capital, will stimulate the introduction of new laborsaving technologies. The literature has implemented the first interpretation and proposed a range of arguments and analytical tools (Fellner, 1961, 1966). The Kennedy-Von Weiszäcker-Samuelson line of analysis proposed the “invention possibility frontier” and suggested that all changes in the slope of the isocost would lead to the introduction of biased technological changes (Kennedy, 1964, 1966, 1967, 1973). This approach enabled Samuelson to show that the retro-effects of the introduction of biased technological change on the derived factor demand and hence on their cost eventually would lead the system to an equilibrium point where the output elasticities of the production factors are eventually equal (Samuelson, 1965, 1966). More recently, Acemoglu revived the induced technological change approach to analyze the dynamics of the US labor markets in the last decades of the 20th century. These were characterized by a sharp increase in the supply of skilled labor and a consequent decline in its relative cost and increasing levels of capital output elasticity in an economy where labor costs had been higher than capital user costs for some time (Griliches, 1969; Acemoglu, 1998, 2002, 2010). The distinctions elaborated by Acemoglu (2007) between weak and strong and absolute and relative bias are particularly relevant in this context. Acemoglu’s applied work confirms that the induced technological change approach is a powerful economic investigative tool, and especially when considering the actual “hedonic” cost of labor as an input. The strong increase in human capital levels experienced in the transition from a 6

manufacturing to a knowledge economy can be regarded as a powerful factor in the reduction of “hedonic” wages. 2.1.2 Localized technological knowledge In the traditional induced technological change approach, little attention is paid to the constraints raised by the generation of the technological knowledge required to introduce the new directed technologies. The shift in the economics of knowledge from analysis of the economic properties of knowledge as an economic good to the investigation of the generation of knowledge makes it possible to identify the central role of learning (Arrow, 1962; Penrose, 1959). The generation of knowledge is the result of both a top-down process in which scientific knowledge precedes its eventual application as a source of technological knowledge and a bottom-up process in which technological knowledge is the outcome of the accumulation of competence and tacit knowledge based on learning. Both processes are necessary and are complementary. Analysis of the knowledge generation mechanisms had a major impact on both the theory of the firm leading to the so-called “resource-based theory of the firm” following Edith Penrose and the economics of growth and the notion of localized technological knowledge. Technological knowledge is localized because it is acquired mainly if not exclusively by learning by doing, learning by using, and learning by interacting. Firms can learn and build technological competence based on their learning processes. However, these learning processes are confined to the space of the techniques they have been practicing: firms cannot learn about techniques that they have never used. The very notion of learning restricts the assumption that the full array of techniques can be implemented. Localized technological knowledge cannot easily be extended and applied far from its original locus of accumulation. Individual firms cannot encompass the full codified base of scientific knowledge, or use a topdown deductive process to extract the full range of potential new applications characterizing the full range of production techniques. 7

Competence and localized learning in the generation of new knowledge constrain technological change within a narrow technical corridor (Atkinson and Stiglitz, 1969; Stiglitz and Greenwald, 2014; Acemoglu, 2015). To increase the rate of competence accumulation, firms pay labor more than its marginal productivity as an incentive to improve learning rates (Stiglitz, 1974). Technological knowledge is embodied more and more in human rather than in fixed capital, also induced by the introduction of information and communication technologies (ICTs) which facilitate learning and the generation of technological knowledge and accumulation of tacit competence stored in digitalized procedures. 2.1.3 The Schumpeterian creative response The Schumpeterian notion of creative response provides the platform to articulate a consistent framework that allows a comprehensive and coherent account of the new direction of induced technological change based on an appreciation of the endogenous determinants of the new laborintensity of technological change (Schumpeter, 1947). According to the Schumpeterian creative response framework, innovation is endogenous to economic systems if the mismatch between expected and actual product and factor market conditions coupled with access to knowledge at prices below equilibrium levels enables the firm to implement a creative response. Firms caught in out-of-equilibrium conditions try to react to product market changes brought by globalization, and factor market changes brought by wage increases, with a creative reaction and the introduction of new technologies that change the existing isoquant mapping. A creative response requires the presence of knowledge externalities since they are at the origin of productivity-enhancing technological change. In turn, knowledge externalities are made available by both the system in which the firm is embedded and the internal -localized- learning processes which allow accumulation of technological competence and tacit knowledge which are indispensable for the generation of new technological knowledge.

8

If the determinants of the generation of new technological knowledge are internal learning processes, the creative response is localized within the limited range of the techniques in which the firm has the competence and its stock of technological knowledge. Creative response based on the competence accumulated via learning processes induces the firms -that try and cope with both the changes in product markets and the increase of wages- to bias technological change towards a skilled labor-intensive direction. Technological change is localized and hence is labor-intensive because tacit knowledge and competence acquired via localized learning processes are the primary input required for the generation of the technological knowledge needed to implement the creative response. Technological change can be strongly localized, which allows the introduction of new technologies only within a specific technical range, i.e. along the isocline which links the technique in place at each point in time to the origin. In this case, the new technology will be more intensive in the factor that has become relatively more expensive, e.g. it will be more labor-intensive if labor costs increase. Alternatively, it can be weakly localized, which allows the introduction of new technologies in a broader technical context. In this second case, the change in relative labor costs has the twin effect of changing both the technique with an increase of capital intensity and the technology with an increase in the labor output elasticity. The standard economics textbook setup for an analysis of the equilibrium conditions illustrates this point. Starting with a simple Cobb-Douglas aggregate production function, , and its cost equation, , the equilibrium is achieved when: (1)

,

where is the capital stock and is its unitary user cost; is the labor and is its unitary cost; and are the elasticities of capital and labor, respectively. The increase in can be handled through an increase in combined with the introduction of biased technological change, i.e. a change to  However, because creative reactions rely mainly on the generation of 9

technological knowledge based on learning processes, localized technological change exerts its effect primarily on the increase of and can change the original only to a limited extent. Hence, in the global economy, following an increase in relative wages, the new equilibrium can be achieved only through the introduction of directed labor-intensive technological change shaped by an increase in and to a limited extent an increase in  2.2. The interpretative framework The theoretical framework based on the integration of localized technological knowledge into the Schumpeterian creative response allows the articulation of an interpretative framework to identify the hypotheses which can be tested using the empirical evidence. The globalization of both product and financial markets triggered radical changes to the economic structure of advanced countries involving a decline in manufacturing and new specialization in knowledge-intensive industries which now account for a large share of economic activity. The advanced countries have built a competitive advantage in the global economy based on their capability to generate and exploit knowledge both as an output and an input. The globalization of financial markets has reduced the competitive advantage stemming from access to cheaper capital and is pushing firms in the advanced countries to rely on their learning-based competences to generate new technological knowledge. Technological knowledge has become the key source of the advanced countries’ competitive advantage. A creative response can be implemented only if the technological knowledge can be accessed, generated, and used at below equilibrium costs (Antonelli, 2017, 2018, 2019). This radical structural change has been made possible by the new tradability of knowledge embodied in equity and capitalized as a financial asset, and in knowledge-intensive industries, based on the systematic use of information and communication technologies which provide the platforms required to generate, exchange, and exploit knowledge as an economic good. This structural change is the cause and the consequence of reduced fixed capital-intensity in advanced economies which are better 10

able to increase their TFP, the increased role of intangible assets and the skilled labor which embodies the knowledge and enables its accelerated accumulation using structured learning processes. The new labor-intensive direction of technological change is central to this process (Syverson, 2017). Since the emergence of the Leontief paradox, it has been clear that the competitive advantage and international specialization of high-wage countries were based not on the capital-intensity of their production processes but on the role of knowledge and competence (embodied in skilled labor) as critical production factors. Recent advances in the economics of knowledge offer clues to the resolution of the Leontief paradox. New evidence on the changing specialization of advanced and industrializing countries in the new global economy confirms the limits of the traditional induced technological change hypothesis which predicts that technological change will be directed towards the introduction of capitalintensive technologies, due to increases in wages. The advanced economies are coping with accelerated globalization, which is threatening their performance. It is also triggering a creative response that can lead to the introduction of radical technological innovations if it is based on the knowledge externalities enabled by the stock of quasi-public knowledge embodied in their systems (Bloom et al., 2016; McMillan et al., 2014). The accumulation of a stock of technological competence required to generate new technological knowledge can take place only in the limited technical ray defined by the equilibrium conditions at each time. The stock of technological competence based on learning processes is an indispensable and strictly complementary input –alongside codified internal and external knowledge- for the generation of new knowledge required to support a creative response. In the knowledge economy, when wages increase, the localization of technological competence limits the mobility of firms in the technical space of the map of isoquants and induces the introduction of new technologies that increase the output elasticity of labor and maintain existing levels of factor-intensity. The labor-intensive direction of technological change has a positive effect in terms of TFP growth because it enables faster accumulation and generation of new technological knowledge. 11

The wage increases in advanced countries accelerated the exits from the manufacturing industry which was exposed to strong competition from industrializing countries with access not only to cheap labor but also the credit and equity provided at low costs by international financial institutions as a result of the financial market globalization. The delocalization of manufacturing away from the advanced countries towards the industrializing economies has been both accelerated and implemented also by the strong flows of foreign direct investment which have become effective finance, technology, and competence vectors. Global firms produce in industrializing countries and export manufactured products to advanced countries. Non-equity-based outsourcing from the advanced countries further increases both the activity of global platforms and ultimately the delocalization of manufacturing towards the industrializing countries (Kemeny and Rigby, 2012). The decline in the manufacturing industry based on large corporations and its integration with knowledge-intensive sectors has strong implications for the aggregate direction of technological change. Knowledge- and laborintensive industries are substituting for capital-intensive manufacturing industries. Following Bogliacino et al. (2013) and Kleinknecht et al. (2014), the new specialization of advanced countries is based more and more on the integration of the old capital-intensive manufacturing industries with the new knowledge-intensive ones. The increased knowledge-intensity of production processes is a consequence of the search for production factors that are locally abundant and cheaper. Advanced countries are recognizing that the huge stocks of knowledge based on localized competences accumulated through structured learning processes both within and between firms and institutions, can be the source of renewed competitive advantage. The stocks of quasi-public knowledge accumulated in the advanced countries through highly localized learning processes are far larger than those in the industrializing countries, and the costs of accessing and using this knowledge to generate new technologies are far lower. Consequently, the introduction of technological change is geared towards knowledge-intensive activities that are localized in a narrow technical space defined by the techniques in place. The increasing knowledge12

intensity is causing the increase in TFP: technological knowledge is being acquired and used at costs that are below equilibrium and yield relevant pecuniary knowledge externalities. The fixed capital-intensity of the new knowledge-intensive industries is low and is much lower than the fixed capital-intensity of the manufacturing industries. At the same time, the knowledge-intensity of the labor employed in knowledge-intensive industries is high and is much higher than the levels in the manufacturing industries. As a result, integration of the old capital-intensive manufacturing industries with the new knowledge-intensive industries has remarkable effects on the aggregate evidence due to the decline in fixed capital-intensity and the increase in knowledge-intensity (Slaughter, 1999). In countries where the direction of technological change is knowledge- and labor-intensive, the wage productivity nexus is confirmed by the rate of increase in TFP (Farber et al., 2018). Countries better able to take advantage of the learning capabilities of their workforce and to accumulate a larger stock of quasi-public tacit knowledge are also better placed to support the creative responses of their firms and to increase the overall efficiency of the production process. The wage-labor-productivity nexus has been revived by the change in the mix of factor that shapes its dynamics. Alongside the positive effects of high wages on aggregate demand and the consequent demand-pull stressed by Boyer (1988), the increased productivity is now associated with the increase in wages. Indeed, the new key role of learning in the accumulation of competence and knowledge is to the recovery of a competitive edge by the advanced economies. The new evidence on the knowledge- and labor-intensive direction of technological change in the advanced countries allows reconsideration of the arguments in the debate over international knowledge spillovers and global convergence according to which poor countries or regions tend to converge toward rich ones (Barro and Sala-i-Martin, 1992, 1995), and puts greater emphasis on the direction of technological change. The convergence debate focuses on the rate of technological change of followers in the global economy. According to the so-called convergence, the rate of growth is faster in poor countries, which can 13

benefit from the knowledge externalities spilling over from the advanced countries that introduced the new technologies. There is an extensive empirical literature confirming higher rates of growth when the level of integration in the global economy is higher (Cameron et al., 2005), and measuring the efforts to absorb and adopt the new technologies spilling from advanced countries by domestic R&D expenditure, human capital, and inflow of foreign direct investment. The convergence literature stresses the limits imposed on convergence by asymmetric access to financial markets and lower levels of capital endowment among followers. Followers could take advantage of the technological spillovers of advanced countries but would be “forced” to adopt capital-intensive technologies that are more capital-intensive than local factor endowments would recommend (Aghion and Howitt, 2006). In this case, the excess capital-intensive direction of the technologies spilling from advanced countries would be harmful to growth in the industrializing countries (Bernard and Jones, 1996a, 1996b). The proposed argument can be synthesized as follows: i) the globalization of product market exposes manufacturing firms of advanced countries to a severe competition; ii) the globalization of financial markets limits competitive advantage based on the introduction of capital-intensive technologies; iii) the twin effects of globalization induce firms to implement a creative response and cope with the new product and factor market conditions by introducing productivity-enhancing innovations; which iv) parallel the shift away from manufacturing to new industries that are more and more reliant on knowledge as both an output and an input; where v) knowledge is localized by learning processes in the limited technical ray adopted at each point in time; so that vi) creative reaction can only occur if the firm retains the techniques in place before the increase in wages; vii) by introducing knowledge-intensive technologies; which viii) support the increase in TFP. The new knowledge-intensive direction of the technological change at work in the advanced economies changes the context of the debate and calls attention to the central role of knowledge as a driver of technological change not just in the advanced countries but also in the industrializing countries. The new analysis of the middle-income trap and the dynamics of catch-up stress the role of technological knowledge as a key factor if 14

followers are to participate fully in the convergence (Lee, 2013; Baldwin, 2016). The new approach to convergence stresses the role played by the direction of technological change to support its rate. Followers need to increase the labor-intensive direction of their technological change for two distinct but complementary reasons: i) the increase in the share of knowledge embodied in skilled labor is strictly necessary to increase their command of the value chains and to upgrade their role; ii) the new knowledge externalities spilling from the labor-intensive technologies introduced by advanced countries favor their introduction even if knowledge is relatively scarce and less abundant (Rodrik, 2013). 2.3 The testable hypotheses The interpretative framework obtained by including in the Schumpeterian creative response the hypothesis of localized technological knowledge provides the basis for a set of hypotheses which can be tested empirically: 1) the income shares of the production factors reflect both the state of the technology and the distribution among the stakeholders of the gross profit margins earned in inefficient product markets; 2) the introduction of technological change is the outcome of a creative response engendered by the reduced performance of firms exposed to the new global competition, and the increase in wages; 3) localized knowledge is the new source of competitive advantage for the advanced countries which is enhanced by the reduced barriers to access to capital-abundant domestic financial brought about by the globalization of financial markets; 4) technological change is localized, and hence is intrinsically knowledgeand labor-intensive because localized bottom-up knowledge generation processes based on learning are indispensable and strictly complementary for the generation of the required new technological knowledge: the introduction of new knowledge-intensive technologies can take place only within the limited technical ray defined by the ex-ante equilibrium conditions; 5) the dynamics of income share may reflect the implementation of economic policies directed at reducing levels of income inequality; 15

6) the stronger the labor-intensive direction of technological change, the higher will be the levels of TFP both for augmented rates of accumulation of knowledge based on learning and the demand-pull effect of the increased share of labor triggered by economic policy. 3. The econometric analysis The neoclassical literature assumes that the factor output elasticity is homogenous and constant in space and time (Cobb and Douglas, 1928) and that a variation in factor costs affects factor-intensity but not the direction of technological change. In contrast, the induced technological change approach builds on the hypotheses that changes to factor costs affect output elasticity but not factor-intensity and that wage increases induce an increase in capital output elasticity. Finally, the localized technological change hypothesis suggests that increases in wages induce an increase in the output elasticity of labor due to the central role of learning. Figure 1 shows that from 1995 to 2014, both the output elasticity of labor and its trend are highly differentiated across space: its values range from 0.25 to 0.74, and each path is country-specific. The traditional induced technological change approach assumes that the direction of technological change depends on the changing ratio of factor costs. Moreover, according to the traditional induced technological change hypothesis, the dynamics of factor output elasticities are determined by the dynamics of the factor costs so that the direction of technological change should be shaped by the increase in the (relatively) cheaper factor. [Figure 1] The new induced technological change hypotheses state that in the knowledge economy the increase in wage induces a labor-intensive direction of technological change which contrasts with the traditional assumptions about the capital-intensive direction of technological change at work when economic systems are based on the manufacturing industry. Figure 2 shows the relationship between the output elasticity of labor and exhibits a clear positive trend that supports predictions related to the

16

new induced technological change hypotheses (Berman et al., 1998; Berman and Machin, 2000).2 [Figure 2] 3.1 Database and descriptive statistics Using the Penn World Table database, we test these hypotheses for 55 countries in the period 1995-2014.3 The time period analyzed is due to the difficulties related to accurately measure the output elasticity of labor in previous periods for a sufficiently large number of countries (Feenstra et al., 2013; Inklaar and Timmer, 2013). The database includes 29 OECD countries and 26 non-OECD countries.4 Following Feenstra et al. (2015), we use comparable variables to measure the GDP, , with the output-side real GDP at current PPPs (in millions of 2011$); the capital, , with the capital stock at current PPPs (in millions of 2011$); the labor, , with the number of persons engaged (in millions); the output elasticity of labor, , with the share of labor compensation in GDP at current national prices; and the human capital, , using an index based on years of schooling and returns to education. From , we can estimate the direction of technological change, whether labor- or capital-intensive (Acemoglu, 2015). When increases the direction is labor-intensive, and when decreases the direction of the technological change is capitalintensive. As shown in Figure 1, the share of labor changes a lot over time and space. Though human capital is a measure of the level of education and not of skills, it is used as a proxy for the country’s knowledge and learning with the result that the estimated relationship between TFP and knowledge is potentially underestimated. To estimate knowledge spillovers, we assume that they are associated with catch-up, , (Keller, 2004) and that the closer the country to the technological frontier, the more easily it will be able to take advantage of 2

Extending the analysis also before 1995 does not change the finding. https://www.rug.nl/ggdc/productivity/pwt/ 4 We use OECD and non-OECD countries to proxy respectively for rich and poor countries. The OECD countries are: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Mexico, the Netherlands, New Zealand, Norway, Portugal, the Republic of Korea, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the United States. The non-OECD countries are: Argentina, Bolivia, Brazil, Bulgaria, China, Colombia, Costa Rica, Croatia, Cyprus, Dominican Republic, Ecuador, Guatemala, Honduras, Hong Kong, India, Ivory Coast, Macao, Nicaragua, Panama, Paraguay, Peru, Romania, South Africa, Trinidad and Tobago, Uruguay, and Venezuela. 3

17

knowledge spillovers.5 We measure catch-up at time for the country as its distance from the level of the US GDP at time , i.e. . Penn World Table data allow arithmetic calculation of the slope of the isocost, , at each point in time, assuming the equilibrium conditions described by (1) and the hypothesis of constant returns to scale. 6 Data on capital stock, the slope of the isocost, and the income share of production factors allow the use of the standard measure of TFP (Solow, 1957).7 For each country, TFP in 1995 is normalized to 1. Using structural equation modeling (SEM), first, we test the extent to which technological change is strongly or weakly localized, i.e. whether the slope of the isocost affects both factor output elasticity and intensity. We next assess income share determined by the induced technological change hypothesis and the difference with its real share to identify the outof-equilibrium conditions. We use these instrumented variables to test the Schumpeterian hypothesis of the theoretical income share of labor, the outof-equilibrium conditions, human capital, and catch-up with TFP as the dependent variable. Table 1 provides descriptive statistics. Note the high level of heterogeneity in the TFP, human capital, and values in the database. Moreover, we observe that the is an appropriate normalization of catch-up because almost all other GDPs are below this value. [Table 1] 3.2 Empirical results 3.2.1 The determinants of income shares According to textbook economics and the Euler theorem, the distribution of income across production factors is determined exclusively by the exogeneity of the technology. The Euler theorem applies when both 5

There are other important technology transmission channels that we do not deal with in this paper (Montobbio and Sterzi, 2011). 6 The nature of the database means that in the Euler theorem is directly observable while is the result of a mathematical procedure. However, the validity of the results does not differ significantly from the reverse estimation procedure. 7 We use the standard methodology rather than the Penn World Table methodology because the latter inserts human capital directly into the TFP measure with the results that the following estimates would be biased (Inklaar and Timmer, 2013).

18

product markets are in competitive equilibrium with no profit and when factor market wages and rental costs are also in equilibrium, i.e. they are identified by the relationship between the supply of the production factors and their marginal productivity which in turn is determined by the technology (Gollin, 2002). According to the textbook equilibrium approach, changes to the income distribution are exclusively determined by changes to the technology, and specifically by the introduction of directed technological change so that an increase in the capital share only reflects the introduction of capitalintensive technologies, and an increase in the share of labor only reflects the introduction of labor-intensive technological change (Stockhammer, 2017). According to the “out-of-equilibrium” approach the levels of and changes to the income distribution may not only and exclusively reflect the change to the state of the technology but also the changes in i) product markets and mark-up levels, ii) the inefficiency of factor markets (Hall, 1988; De Loecker and Warsynski, 2012), and iii) the effects of redistribution policies aimed at reducing income inequality. To identify the determinants of the income shares, we need first to test whether is exogenous or endogenous based on the changes to the slope of the isocost. According to our hypotheses, the slope of the isocost could affect both output elasticity and intensity of factors simultaneously. The SEM estimation seems an appropriate method for estimating both effects. Figure 3 shows that the first step in the analysis can be represented analytically by: (2) { where is a trend variable and and are the error terms. In particular, is the exogenous component of , and and measure the marginal effect of the slope of the isocost and the years on the labor output elasticities, respectively. Moreover, captures all other effects not attributable to the Euler theorem and the timing of the endogenous component of . Examples of these out-of-equilibrium variables include 19

changes to economic policy and institutional novelties, market rigidities, and worker creativity. Similar interpretations can be made for , , , and . [Figure 3] Table 2 and Figure 4 provide empirical estimates of the analyzed relationship described in (2). Columns 2 and 3 of Table 2 present the effects of factor costs on the output elasticity of labor and on the intensity of factors, respectively. We observe a positive relationship with highly significant coefficients. The effect of on is positive and significant also if its coefficient seems close to zero because is measured in millions. Indeed, from the conversion of the unit of measurement, the regression coefficient of over is positive ( ) and strongly significant (more than 99%). It is clear that affects and simultaneously and positively. Thus, both the neoclassical and localized technological change approaches are supported by data. Indeed, the variation in factor costs affects not only the factor-intensity but also the direction of technological change. However, the predictions about the induced technological change need two main revisions: 1) changes to factor costs affect the output elasticity but also the factor-intensity, and 2) the increase in wages induces an increase in the labor output elasticity. In other words, the regression confirms hypothesis 4: the higher the cost of labor and/or the lower the cost of capital, the higher will be the output elasticity of labor. [Table 2 and Figure 4] From (2), we can estimate the theoretical output elasticity of labor, which would emerge if the markets were in equilibrium: (3)

̂

̂

∑̂

(

),

where ̂ , ̂ , and ̂ are the estimated coefficients using the first regression in (2). Test of the relationship between ⁄ and the labor output elasticity exhibits relevant residuals which can be interpreted as the outcome of the out-of-equilibrium conditions in which the income allocation takes place, and can be considered a reliable measure of the 20

effects of income redistribution policies. The residual of the first equation in (2) and (3), , is measured as follows: (4)

.

If , the real share of labor is larger than the theoretical share and then it highlights that the out-of-equilibrium conditions triggered by economic policies aimed at reducing income inequality and poverty, increase the share of income paid to labor beyond the wage level. Alternatively, when , the out-of-equilibrium conditions -triggered primarily by product market imperfections, which increase profit marginsreduce . Table 3 presents the descriptive statistics of and . It shows that the theoretical always exhibits a lower variance than the real with a greater fitness of the estimators. [Table 3] We also measure the predicted labor output elasticity , i.e. the theoretical effect of on , and the un-explained residual , i.e. a set of the other variables that affect in addition to those measured in the first equation of (2), e.g. economic policies aimed at reducing income inequality. 3.2.2 Testing the Schumpeterian approach Building on the Schumpeterian framework, we assume that the new laborintensive direction of technological change is the endogenous and effective outcome of the creative response of firms to cope with the challenges raised by global product and factor markets relying on the stock of quasipublic tacit knowledge accumulated by localized learning processes. This allows us to test hypothesis 6 about whether the new observable trend in labor output elasticity -induced by the increase in wages- has a positive effect on TFP. The higher the relative wages and the larger the laborintensive direction of technological change, the larger will be TFP. We next move to the second step in the estimation procedure, which uses both the and the estimated in the previous subsection, as variables to estimate the following regression: 21

∑

(5)

(

)

,

) is a dummy variable that drops the year 1995 to avoid where ( multicollinearity problems.8 Using the Schumpeterian approach, we test all the hypotheses formulated in sub-section 3.2, and in particular, we test whether , , , and are positive. Column 4 of Table 4 presents the econometric estimates of (5). The remaining columns in Table 4 present the results of the robustness checks. As suggested by hypothesis 6, Columns 1-4 show that the theoretical output elasticity of labor increases TFP. [Table 4] From , we can conclude that the stronger the labor-intensive direction of technological change, the larger will be TFP growth. The traditional capital-intensive direction of technological change can no longer support TFP growth. This result confirms that the labor-intensive direction of technological change is determined exclusively by the increase of and enhances the generation of localized technological change and consequently TFP. Second, the positive and significant results of can be interpreted as follows: the out-of-equilibrium conditions which push the share of income paid to labor beyond the theoretical output elasticity levels, support the increase in TFP because of the demand-pull effects triggered by the increased levels of aggregate demand stemming from income redistribution policies aimed at reducing income inequality (Gordon and Eisenbrey, 2012). Table 4 shows that hypotheses 1 and 2 hold. This evidence supports the hypothesis that in the knowledge economy, globalization triggers a search for knowledge-intensive technologies based on learning processes that parallel the substitution of the new knowledge-intensive industries for capital-intensive industries. Third, Columns 2 and 4 show that human capital positively affects technological change and that catch-up, a proxy for the level of access to knowledge spillovers and learning, has a positive effect on TFP. 8

Although all the results hold even without year dummy, we preferred to leave it in (5) because all the macroeconomics variables change not only in space but also over time.

22

In the context of hypotheses 4, 5, and 6, Column 4 of Table 4 summarizes all the previous hypotheses in a single regression. The results confirm that the Schumpeterian notion of creative response based on localized technological knowledge provides a consistent framework allowing articulation of a homogenous and coherent account of the endogenous determinants of the new labor-intensive direction of technological change and its positive effects on TFP. Columns 5 and 6 of Table 4 present the tests of (6) for OECD and nonOECD countries, respectively. We observe that hypothesis 4 and the other hypotheses hold for both the OECD and non-OECD subsets. In particular, we note that the effect of proximity to the technological frontier is particularly high for the OECD countries and that the effect of human capital is significant only for the OECD countries while the catching-up effect is more significant for the non-OECD countries. The results in Table 4 confirm the relevance of the out-of-equilibrium approach suggested by Schumpeter for analyzing and understanding the drivers of technological change. Indeed, both the theoretical and the explicit out-of-equilibrium conditions have a significant and positive effect on TFP. Labor shares larger than the levels of expected based on the traditional notion of induced technological change have a positive effect, while labor shares smaller than the theoretical levels of reduce the rate of TFP increase. Moreover, we observe that both effects are stronger for OECD countries. These regressions support the idea also that the positive effects of catch-up are especially relevant for the non-OECD countries, and that the positive effect of human capital is significant for the OECD countries. The effects of high wages on TFP are positive due to i) the enhanced generation of localized technological knowledge augmented by the more effective accumulation of tacit competence, and ii) the demand-pull effects triggered by the increased levels of aggregate demand as a result of income redistribution policies (Dosi et al. 2015; Franzini and Pianta, 2016). The results of the empirical analysis can be integrated into a broader interpretative framework that takes into account the role of organizational change at the firm level and structural change at the system level (Pianta, 2017). Globalization has induced a radical re-organization of the 23

production process at the firm level with the vertical dis-integration. Manufacturing firms have changed the organization of the chain value specializing in knowledge-intensive activities and leaving the production activities to third parties located abroad. Research and development, prototype engineering, marketing, finance, logistic, and advertisement are retained “intramuros” and become the core of -former- manufacturing firms. The physical production is more and more provided by imports from third parties based in newly industrialized countries through a variety of organizational devices that range from equity participation in production plants located abroad, global outsourcing based on long term contracts and arm’s lengths commercial transaction. The range of activities traditionally carried out by and in headquarters become the core of the former manufacturing firms that become full-fledged knowledge-intensive service providers. They command but do not perform -any longer- the full production cycle (Siedschlag and Zhang, 2015). The shift away from the manufacturing industry into the knowledge economy has important implications: the decline of the traditional manufacturing industry characterized by high levels of capital-intensity and the new specialization in knowledge-intensive activities characterized by high levels of labor and human capital-intensity affects the aggregate evidence in terms of the direction of technological change. At the aggregate level, the shift in the specialization accounts for the laborintensive direction of technological change. Within the -resilient portionsof the manufacturing industry, the classical “induced” relationship between wages and the capital-intensive direction of technological change holds (Antonelli and Scellato, 2019). At the system level, the relationship is reversed by the dynamics of structural change and the new organization of the chain value. This broader interpretation seems to deserve additional research and at the same time, highlights the limits of this work. 4. Conclusions This paper identified a strong relationship between the labor-intensive direction of technological change in the global economy and increased TFP. To understand the determinants, we elaborate, apply, and test an interpretative framework integrating the induced technological change approach with the Schumpeterian creative framework and emphasizing the role of localized knowledge as a critical production factor. 24

In the advanced countries, the increase in wages together with the decline in performance due to the changing conditions in product and factor markets promoted by globalization triggers a creative response localized by the mechanisms underlying the recombinant generation of technological knowledge. We show that the direction of technological change is biased towards the introduction of labor-intensive technologies due to the central role of technological competence, which stems from the learning derived from the techniques practiced. The introduction of alternative more capital-intensive techniques and technologies would imply movements in the map of isoquants that would be inconsistent with the learning processes at work. The new direction of technological change is labor-intensive due to the central role of knowledge in the specialization of advanced countries. Industrializing countries also show evidence of the labor-intensive direction of technological change which provides them with opportunities to benefit from the international knowledge spillovers intrinsic to convergence and the new catch-up processes related to qualityimprovement efforts to allow participation in global value chains and increase the knowledge content of their production processes. The empirical analysis provides strong support for the proposed interpretative framework and confirms that world TFP and the laborintensive direction of technical change increased in parallel during the period 1995-2014. These changes were triggered by higher wages and supported by the availability of human capital which has favored the dissemination of knowledge spillovers from advanced to industrializing countries and increased the competition from industrializing countries in the domestic markets of advanced economies. The implications of both the interpretative framework implemented and the empirical evidence presented are important on several counts. First and most important, a new understanding of the effects of the localized processes of knowledge generation acts as a bridge between new growth theory and induced technological change. Second, our evidence suggests that the Euler theorem does not explain the increase in the share of income paid to capital which in contrast, can be regarded as the outcome of an outof-equilibrium process triggered by the augmented capability of capital to capitalize oligopolistic markups. Third, whenever and wherever the share 25

of income appropriated by capital increases, TFP levels increase less: the excess appropriation of income by capital has clear negative effects on dynamic efficiency. Fourth, our econometric evidence supports the existence of a wage-labor-productivity nexus based not only on demandpull effects but also and primarily due to the central role of localized knowledge generation. These results have important policy implications. The relevance of the direction of technological change is highlighted by analyses of its effects on income inequality. The positive effects of the labor-intensive direction of technological change have been acknowledged, and it has been suggested that they provide selective support for the introduction of new technologies, based on their direction. Innovation policy has usually targeted the rate of introduction of technological change with little attention to its direction. This approach has been supported by the standard assumption that technological change was intrinsically neutral. The new evidence about the changes in the direction of technological change and their effects calls attention to the possible role of public policies in shaping the direction of technological change. The provision of research incentives should be selective with respect to the direction of new technologies in terms of labor- and knowledge-intensity. Research incentives should be granted to innovation activities that exhibit a stronger labor-intensive direction and make more extensive use of human capital. The support to the introduction of laborintensive technologies is likely to exert positive effects not only in terms of augmented rates of increase of productivity but also in terms of demand for labor and reduction of income inequality (Atkinson, 2015).

26

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

Acemoglu, D.K. (1998), Why do new technologies complement skills? Directed technical change and wage inequality, Quarterly Journal of Economics 113:1055–1089. Acemoglu, D.K. (2002), Directed technical change, Review of Economic Studies 69:781–809. Acemoglu, D.K. (2007), Equilibrium bias of technology, Econometrica 75(5):1371–1409. Acemoglu, D.K. (2010), When does labor scarcity encourage innovation?, Journal of Political Economy 118(6):1037–1078. Acemoglu, D.K. (2015), Localised and biased technologies: Atkinson and Stiglitz’s new view: Induced innovations and directed technological change, Economic Journal 125(583):443–463. Aghion, P., Howitt, P. (2006), Appropriate growth theory: A unifying framework, Journal of the European Economic Association 4:269–314. Antonelli, C. (2017), Endogenous Innovation: The Economics of an Emergent System Property, Edward Elgar, Cheltenham. Antonelli, C. (2018), The Evolutionary Complexity of Endogenous Innovation. The Engines of the Creative Response, Edward Elgar, Cheltenham. Antonelli, C. (2019), The Knowledge Growth Regime: A Schumpeterian Approach, Palgrave MacMillan. Antonelli, C., Feder, C. (2019), A long-term comparative analysis of the direction and congruence of technological change, SocioEconomic Review, Forthcoming. Antonelli, C., Scellato, G. (2019), Wage inequality and directed technological change: Implications for income distribution, Technological Forecasting and Social Change 141, 59-65 Arrow, K.J. (1962), The economic implications of learning by doing, Review of Economic Studies 29:155–173. Atkinson, A.B. (2015), Inequality. What can be done?, Harvard University Press, Cambridge Mass. Atkinson, A.B., Stiglitz, J.E. (1969), A new view of technological change, Economic Journal 79:73–578.

27

[15] [16] [17] [18] [19] [20] [21]

[22] [23] [24] [25] [26] [27] [28]

Autor, D.H. (2015), Why are there still so many jobs? The history and future of workplace automation, Journal of Economic Perspectives 29(3):3–30. Autor, D.H., Levy, F., Murnane, R.J. (2003), The skill content of recent technological change: An empirical exploration, Quarterly Journal of Economics 118(4):1279–1334. Autor, D.H., Dorn, D., Katz, L.F., Patterson, C., Van Reenen, J. (2017), Concentrating on the fall of the labor share, American Economic Review 107(5):180–185. Baldwin, R. (2016), The Great Convergence Information Technology and the New Globalization, Harvard University Press, Cambridge. Barro, R.J., Sala-i-Martin, X. (1992), Convergence, Journal of Political Economy 100(2):223–251. Barro, R.J., Sala-i-Martin, X. (1995), Economic Growth, McGrawHill, New York. Bassanini, A., Manfredi, T. (2012), Capital’s grabbing hand? A cross-country/cross-industry analysis of the decline of labor share, OECD Social, Employment and Migration Working Papers 133, OECD Publishing. Berman, E., Bound, J., Machin, S. (1998), Implications of skillbiased technological change: International implications, Quarterly Journal of Economics 113:1245–1279. Berman, E., Machin, S. (2000), Skill-biased technology transfer around the world, Oxford Review of Economic Policy 16:12–22. Bernard, A.B., Jones, C.I. (1996a), Productivity across industries and countries: Time series theory and evidence, Review of Economics and Statistics 78(1):135–146. Bernard, A.B., Jones, C.I. (1996b), Technology and convergence, Economic Journal 106(437):1037–1044. Bivens, J., Mishel, L. (2015), Understanding the historic divergence between productivity and a typical worker’s pay: Why it matters and why it’s real, EPI Briefing Paper n. 406, Economic Policy Institute. Blanchard, O.J. (1997), The medium run, Brookings Papers on Economic Activity 28(2):89–158. Bloom, N., Draca, M., Van Reenen, J. (2016), Trade induced technological change? The impact of Chinese imports on innovation, IT and productivity, Review of Economic Studies 83(1):87–117. 28

[29] [30]

[31] [32] [33] [34]

[35] [36] [37] [38] [39] [40]

[41]

Bogliacino, F., Lucchese, M., Pianta, M. (2013), Job creation in business services: Innovation, demand, and polarisation, Structural Change and Economic Dynamics 25:95–109. Boyer, R. (1988), Formalizing growth regimes. In Technical change and economic theory, edited by G. Dosi, C. Freeman, R.R., Nelson, G., Silverberg and L., Soete, 608–630, Pinter Publishers, London and New York. Cameron, G., Proudman, J., Redding, S. (2005), Technological convergence, R&D, trade and productivity growth, European Economic Review 49:775–807. Cobb, C.W., Douglas, P.H. (1928), A theory of production, American Economic Review 18(1):139–165. De Loecker, J., Warsynski, F. (2012), Markups and firm-level export status, American Economic Review 102(6):2437–2471. Dosi, G., Nelson, R.R. (2010), Technical change and industrial dynamics as evolutionary processes. In Handbook of the Economics of Innovation, edited by B.H. Hall and N. Rosenberg, Volume 1, 51– 127, Elsevier, Amsterdam. Dosi, G., Sodini, M., Virgillito, M. (2015), Profit-driven and demand driven growth and fluctuations in different accumulation regime, Journal of Evolutionary Economics 25(4):707–728. Elsby, M.W.L., Hobijn, B., Sahin A. (2013), The decline of the U.S. labor share, Brookings Papers on Economic Activity (Fall):1–42. 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. Fellner, W. (1961), Two propositions in the theory of induced innovations, The Economic Journal 71(282):305–308. Fellner, W. (1966), Profit maximization, utility maximization, and the rate and direction of innovation, American Economic Review 56(1/2):24–32. Feenstra, R.C., Inklaar, R., Timmer, M.P. (2013), PWT 8.0 - A user guide, mimeo, available at: https://www.rug.nl/ggdc/productivity/pwt/ related-researchpapers/pwt_80_user_guide.pdf Feenstra, R.C., Inklaar, R., Timmer, M.P. (2015), The next generation of the Penn World Table, American Economic Review 105(10):3150–3182. 29

[42] [43] [44] [45] [46] [47] [48]

[49] [50] [51] [52] [53] [54] [55] [56] [57]

Franzini, M., Pianta, M. (2016), Explaining Inequality. Routledge, London. Gollin, D. (2002), Getting income shares right, Journal of Political Economy 110:458–74. Gordon, C., Eisenbrey, R. (2012), As unions decline, inequality rises, Economic Snapshot, Economic Policy Institute. Griliches, Z. (1969), Capital-skill complementarity, The Review of Economics and Statistics 51(4):465–468 Hall, R.E. (1988), The relationship between price and marginal cost in the U.S. industry, Journal of Political Economy 96(5):921–947. Hicks, J.R. (1932), The Theory of Wages, Macmillan, London. Inklaar, R., Timmer, M.P. (2013), Capital, labor and TFP in PWT 8.0, mimeo, available at: https://www.rug.nl/ggdc/productivity/pwt/rela ted-researchpapers/capital_labor_and_tfp_in_pwt80.pdf Kaldor, N. (1961), Capital accumulation and economic growth. In The Theory of Capital, edited by F.A. Lutz and D.C. Hague, 177– 222, Stockton Press, New York. Karabarbounis, L., Neiman, B. (2014), The global decline of the labor share, Quarterly Journal of Economics 129(1): 61–103. Keller, W. (2004), International technology diffusion, Journal of Economic Literature 42(3):752–782. Kemeny, T., Rigby, D. (2012), Trading away what kind of jobs? Globalization trades and tasks in the US economy, Review of World Economics 148(1):1–16. Kennedy, C. (1964), Induced bias in innovation and the theory of distribution, The Economic Journal 74(295):541–547. Kennedy, C. (1966), Samuelson on induced innovation, The Review of Economics and Statistics 48(4):442–444. Kennedy, C. (1967), On the theory of induced invention-A reply, The Economic Journal 77(308):958–960. Kennedy, C. (1973), A generalisation of the theory of induced bias in technical progress, The Economic Journal 83(329):48–57. Kleinknecht, A., Van Schaik, F.N., Zhou, H. (2014), Is flexible labour good for innovation? Evidence from firm-level data, Cambridge Journal of Economics 38(5):1207–1219.

30

[58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72]

Leamer, E. (1996), Wage inequality from international competition and technological change: Theory and country experience, American Economic Review 86(2):309–314. Lee, K. (2013), Schumpeterian Analysis of Economic Catch-up. Knowledge Path Creation and the Middle Income Trap, Cambridge University Press, Cambridge. Leontief, W. (1953), Domestic production and foreign trade; the American capital position re-examined, Proceedings of the American Philosophical Society 97(4):332–349. McMillan, M., Rodrik, Verduzco-Gallo, I. (2014), Globalization, structural change and productivity growth, with an update on Africa, World Development 63(C):11–32. Montobbio, F., Sterzi, V. (2011), Inventing together: Exploring the nature of international knowledge spillovers in Latin America, Journal of Evolutionary Economics 21(1):53–89. Penrose, E.T. (1959), The Theory of the Growth of the Firm, Basil Blackwell, Oxford. Pianta, M. (2017) Innovation and economic change, Economics of Innovation and New Technology 26 (8): 683-688. Ranis, G., Stewart, F., Ramirez, A. (2000), Economic growth and human development, World Development 28(2):197–219. Rodrik, K. (2013), Unconditional convergence in manufacturing, Quarterly Journal of Economics 128(1):165–204. Ruttan, V.W. (2001), Technology Growth and Development. An Induced Innovation Perspective, Oxford University Press, Oxford. Samuelson, P.A. (1965), A theory of induced innovation along Kennedy-Weisäcker lines, The Review of Economics and Statistics 47(4):343–356. Samuelson, P.A. (1966), A summing up, The Quarterly Journal of Economics 80(4):568–583. Schumpeter, J.A. (1947), The creative response in economic history, Journal of Economic History 7:149–159. Schwellnus, C., Kappeler, A., Pionnier, P.A. (2017), Decoupling of wages from productivity. Technical report, OECD Economics Department Working Papers, No. 1373, OECD Publishing, Paris. Siedschlag, I., Zhang, X. (2015), Internationalisation of firms and their innovation and productivity, Economics of Innovation and New Technology, 24:3, 183-203. 31

[73] [74] [75] [76] [77] [78] [79]

Slaughter, M. (1999), Globalisation and wages: A tale of two perspectives, The World Economy 22(5):609–629. Solow, R.M. (1957), Technical change and the aggregate production function, The Review of Economics and Statistics 39(3):312–320. Solow, R.M. (1958), A skeptical note on the constancy of relative shares, The American Economic Review 48(4):618–631. Stiglitz, J.E. (1974), Alternative theories of wage determination and unemployment in LDC'S: The labor turnover model, Quarterly Journal of Economics 88(2):194–227. Stiglitz, J.E., Greenwald, B.C. (2014), Creating a Learning Society. A new Approach to Growth, Development, and Social Progress, Columbia University Press, New York. Stockhammer, E. (2017), Determinants of the wage share: A panel analysis of advanced and developing economies, British Journal of Industrial Relations 55:3–33. Syverson, C. (2017), Challenges to mismeasurement explanations for the US productivity slowdown, Journal of Economic Perspectives 31(2):165–86.

32

List of figures

Figure 1: Share of labor compensation in GDP by countries: a sample

Note: To better observe the trend, each country has a different scale based on the Source: Penn World Table and our elaborations.

range. 33

Figure 2: Labor output elasticity and relative factors cost

Source: Penn World Table and our elaborations.

Figure 3: Structural modeling: the framework 𝑤 𝑟

Year

𝛽

𝐾 𝐿

𝜀

𝜀

Year

34

Figure 4: Structural model: the results 𝑤 𝑟

𝜀

𝐾 𝐿

-2.831*** (295)

Year

2,89e+09 (2,17e+08)

-0.004*** 𝛽 (0.000) 0.005 (0.000)

Year

𝜀

35

List of tables

Table 1: Descriptive statistics Variable Obs. Mean Std. Dev. 1100 0.979 0.452 Human Capital 1100 2.804 0.550 Catch-up 1100 0.073 0.164 1100 237,366 197,416 1100 173,277 123,003 1100 0.547 0.088

Min Max 0.115 3.963 1.350 3.734 0.001 1.019 5,945 1,140,485 6,498 552,395 0.265 0.748

Note: TFP, Human Capital, and Catch-up are indexes. The unit of measure of and is current PPPs in millions of 2011$ over millions of persons engaged. Finally, is in GDP at current national prices. Source: Penn World Table and our elaborations.

Table 2: Results of the structural equation modeling Dependent Variable Independent Variable 0.000*** 0.534*** (0.000) (0.015) -0.004*** 2,831*** Year (0.000) (295) 9.075*** -5,627,379*** Constant (0.772) (589,972) Observations 1100 Note: Structural Equation Modeling. Standard Errors are reported in parentheses. Significant at *10%, **5%, and ***1%. Source: Penn World Table and our elaborations.

36

Table 3: Descriptive statistics of the theoretical and its residual Variable Obs. Mean Std. Dev. Min Max 1100 0.547 0.056 0.443 0.787 1100 0.000 0.068 -0.213 0.185 Source: Penn World Table and our elaborations.

Table 4: Results of the empirical regressions

Out-ofequilibrium

[1]

[2]

[3]

[4]

[5]

[6]

9.137*** (0.302) 9.757*** (0.196)

9.423*** (0.300) 9.913*** (0.194) 0.837*** (0.135)

9.172*** (0.302) 9.792*** (0.196)

9.448*** (0.300) 9.939*** (0.195) 0.823*** (0.135) 0.145* (0.079) -4.749*** (0.275) Yes 0.764 1100 All

11.373*** (0.308) 12.484*** (0.258) 1.474** (0.641) 0.240** (0.112) -6.567*** (0.382) Yes 0.865 580 OECD

8.013*** (0.802) 8.276*** (0.269) 0.577*** (0.150) 0.101 (0.121) -3.501*** (0.432) Yes 0.714 520 non-OECD

Catch-up Human capital Constant Year dummy R-squared Observations Countries

-4.138*** (0.174) Yes 0.754 1100 All

-4.356*** (0.174) Yes 0.763 1100 All

0.172** (0.080) -4.609*** (0.279) Yes 0.755 1100 All

Note: Fixed effects panel model. Dependent variable: parentheses. Significant at *10%, **5%, and ***1%. Source: Penn World Table and our elaborations.

. Standard Errors are reported in

37