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Targets

The aim of this project is to develop an integrated analysis of the anatomy and dynamics of innovation processes and the ensuing patterns of industrial change and employment dynamics, starting from the ways in which new knowledge is incorporated into business, down to the market processes of “Schumpeterian” competition.

Within this general research project, the research activities are organised around three main targets, which are in turn designed through the identification of tasks.

Target 1

A large stream of literature has focused on the patterns of firm learning, productivity growth, and market selection, focusing on the reallocation dynamics that take place between existing firms (and also between incumbents, new entrants, and exiting firms). Theoretically, there are good reasons to expect that inter-firm productivity differentials – and, dynamically, also firm-level productivity growth – would lead to growth in sales and market shares via market selection mechanisms. Theoretically, this pattern should reduce ‘misallocation’ (see Jovanovic, 1982; Ericson and Pakes, 1995; Nelson and Winter, 1982; Dosi et al., 2016b). The empirics, however, are not as clear-cut as that. The available evidence on different countries, including the US, France, Italy, UK, China (see Bottazzi et al., 2010, Dosi et al 2015b, Yu et al., 2017) broadly suggests that: (i) there is a relationship between relative productivity levels and relative growth rates across firms within industries, but it accounts only for a limited share in the variance of firms’ growth; (ii) a more pronounced relationship exists between productivity growth and sales growth, at firm level; however, (iii) what dominates in the interpretation of changes in the average productivity growth of a sector is the dynamics of firm-level learning (i.e. the “within” component), while, (iv) reallocation dynamics, according to which less efficient firms lose market shares in favour of the more efficient ones (the “between” component) appear to be relatively weaker even in countries such as the US whose markets are purportedly quite ‘efficient’.

In the first task, we will investigate the main drivers of productivity growth at the firm-level, by looking in detail at the role played by technological capabilities. The micro evidence suggests that in fact inter-firm productivity differentials are wide, persistent and indeed widening. The increasing productivity gap between the global frontier and laggard firms is one of the causes of the productivity slowdown that started before the Global Crisis (Andrews et al., 2016). What are the sources of the differential productivity growth (the “within” component) across firms? Despite the relevance of this research question, the debate is still open on the determinants of productivity performance at the firm-level (Syverson, 2011). In addressing these issues, we will employ standard proxies for innovative activities (e.g. variables contained in Community Innovation Surveys, data on R&D expenditures and patents), but we will also identify new proxies for innovative (and imitative) activities of the firms. As a practical example, for Italy we will use the so-called Frame-SBS dataset that, in its basic form, provides annual information on firms’ profit and loss accounts for every company operating in Italy, coupled with comprehensive microdata gathered from integrated administrative sources, and recently extended to improve analysis of productivity (Istat, 2017), firms’ internationalisation choices and labour market dynamics.

In the second task, we shall try to identify the effects of firm-specific patterns of innovation on the revealed performances of firms, both in terms of sales growth and export growth. Given established univariate properties of such data (e.g. fat-tails in growth rates, extensive and intensive margins in international activity), we will investigate how firm-specific technologies affect sales growth, as well as quantities and prices across different groups of exported products and destination countries. There is evidence that innovation, usually proxied by R&D expenditures or patents stocks, does have some role in explaining firm growth, although this effect seems to be relevant especially for high-growth firm (Coad and Rao 2008). In the same vein, technological capabilities seem to matter in determining export performance at the firm level (Dosi et. al, 2015). In this task, we will gain a deeper understanding of the innovation-growth relationship also by exploring new facets of the innovation process. At the firm-level, we will look into the different dimensions of firm learning and organisational architecture, and will assess their mediating role in the innovation-growth relationship. The export dimension will be investigated by focusing on the differential performance of exported products across destination countries, taking into account their technological content as measured by different indicators (for example, patent intensity at the product level).

In the third task, we will use decomposition analysis and other techniques to explore patterns of resource reallocation in the face of secular stagnation. In particular, we will investigate, first, the processes of firm growth, entry, exit and their consequences for job creation and destruction; second, the implication of these reallocation dynamics for industry-level productivity growth. Emphasis will be given to the changing patterns of reallocation across time: we shall try to shed new light on the cleansing hypothesis, according to which productivity-enhancing reallocation intensifies during economic downturns (Schumpeter 1934, 1942; Caballero and Hammour, 1994). In this respect, we also aim to contribute to the emerging empirical literature on the role of the Global Crisis in the productivity-growth relationship (Carreira and Teixeira 2016; Foster et al., 2016).

Target 2

Within this target, we will investigate the effects of trade and technology on job creation, job destruction, and wage dynamics at the industry and firm level. Scholars and policy-makers alike are paying growing attention to the role innovation may have in boosting or destroying employment and in wage dynamics (see Autor 2015); very similar concerns have been expressed about the role of trade in displacing workers in developed economies (see Autor et al. 2013).

In the first task, we will try to disentangle the complex relationship between technology and employment dynamics. Although the theoretical question has been there since Ricardo (1821), almost three decades of modern empirical research indicate a nuanced and multifaceted relationship between technology adoption and its effects on employment (see Vivarelli, 2014; and Calvino and Virgillito, 2017). This interaction crucially depends upon: i) the level of aggregation of the analysis, be it at the firm, sectoral or macro-level, ii) the nature (embodied vis-à-vis disembodied) of the technological change in place iii) the balance between the direct effects and relevant compensation mechanisms, ultimately affected by the prevailing effects of process versus product innovation. According to extant literature, the labour creating effect of technological change tends to emerge at the firm-level, particularly among highly innovative firms, or firms in high-tech sectors, and when a proxy for disembodied technical change is used in the econometric analysis (R&D and/or patents). However, more controversial results emerge when looking at the sectoral and higher levels of aggregation, with a prevailing labour-shedding effect whereby productivity improvements are not accompanied by substantial product innovation.

How does the existing empirical evidence compare with the very latest wave of technological artefacts? And relatedly, how does the overall picture change when we consider more vertically integrated sectors, wherein the labour-creating effects in the upstream sectors propagate downstream? What is the relative role of expansion and replacement investment? Finally, what types of prediction can theoretical models and applied research provide in order to account for the patterns of employment creation and destruction due to new “Industry 4.0” products and processes? In addressing these questions, we will integrate the empirical analysis of detailed sector- and firm-level data, with the development of an agent-based model able to account for an endogenous process of technical change and for the emergence of both new technological paradigms and new industries. Moreover, in order to understand the impact of technological change not only on the quantity but also on the quality of employment, we shall document how the patterns of skills and tasks demanded in the current digitisation era are evolving. Linking information about occupations, skills and abilities, number of employees and wages, we will assess how the rise in the pervasiveness of digital technologies has affected occupations in terms of number of employees and wages since the 2000s.

In the second task, we aim to produce both new theoretical insights into and empirical evidence on the patterns and causes of wage inequality in Europe.

According to the skill-bias technical change (SBTC) hypothesis, technological change turns out to be complementary to skilled workers – for which demand increases – and a substitute for unskilled workers – for which demand decreases. Thus, the skill and wage divides tend to increase over time (see Katz and Murphy, 1992; Goldin and Katz, 2007; Piva et al., 2005). More recently a revamped SBTC hypothesis
has emerged in the form of a Routine-biased technological change (RBTC) hypothesis (see Autor et al., 2008, Goos et al., 2009 and Acemoglu and Autor, 2011). The falling cost of computing power has led to rapid introduction and diffusion of technologies which replace jobs involving routine tasks that are easily programmed, such as administrative and production jobs. These technologies might indeed become a complement for high-skilled non-routinised jobs. In addition, they might only marginally affect low-skilled non-routine workers who are not easily replaceable by this kind of automation. These dynamics would entail the gradual disappearance of medium-skilled jobs, with a relatively stable or increasing demand for both very low-skilled and high-skilled non-routinised jobs. If we accept this explanation, then the primary cause of polarisation, both in terms of remuneration and types of occupations, is the simultaneous growth of high-skill high-wage work and low-skill low-wage work. However, the validity of this explanation has not gone unchallenged by scholars who have stressed the importance of institutional factors such as de-unionisation and minimum wage policies (Fortin and Lemieux, 1997; Devroye and Freeman, 2001).

We aim to contribute to this debate by investigating the pattern of wage inequalities through the analysis of employer-employee matched datasets for selected countries. Departing from existing literature that mainly focuses on the technological determinants of inequality we will dig deeper into the institutional and organisational determinants of inequality. To explore the between-firm pattern of inequality we will focus on the wage-productivity nexus, uncovering the extent to which productivity gains are shared with workers or retained by firms, and how this nexus changes along the wage distribution. To investigate the pattern of within firm inequality, we will account for the organisational composition of the firm, and will study the role of occupational categories (managers, white collars, blue collars, etc….), analysing whether firm-level employment contraction/expansion gives rise to increasing/decreasing within-firm wage dispersion, controlling for contractual typologies (permanent, temporary, non-standards types of jobs).

In the third task, we aim to look at the joint role of trade and technology in determining employment dynamics. In particular, we will analyse the firm-level adjustments to trade shocks (i.e., the 2008-2009 trade collapse) in terms of worker flows, and the role played by the technological capabilities of firms. Our contribution to the literature is twofold. First, we will study firm adjustments to the trade shocks, focussing on gross worker flows. While many works have studied the impact of trade shocks on net employment growth, only few studies have identified gross worker flows at the firm level (i.e. jobs created and destroyed within a firm), and even fewer have related them to trade shocks (a notable example being Moser et al. 2010). In particular, no study has yet focused on the impact of the trade collapse on
firm-level worker flows. Our project will provide valuable evidence on the dynamics of gross worker flows over the 2008-2009 trade collapse. Second, we will pay special attention to the mediating role of exporter characteristics, using different proxy of technological capabilities. One of these proxies will be the complexity of exported products as in Hidalgo and Hausmann (2009).

Target 3

Within this target, we aim to investigate the main features and the evolution of innovation patterns as well as the industrial dynamics in sectors that are characterised by the emergence of radical innovations within 1) complex product-systems and 2) general purpose technologies. Much of existing evidence on Schumpeterian processes of sectoral creative destruction and creative accumulation is based on qualitative industry case studies. Our objective will be a detailed quantitative analysis of micro-dynamics in sectors that experience radical innovations and are at the same time – given their characteristics – engines of growth in broad value chains. In particular, we will use patent records and market data to map and explain the entry of new actors and the consequences of this for industrial structure. This shall involve an in-depth investigation of the patterns of innovation by incumbents and new entrants in order to address a series of research questions of high relevance for both business and policy: how frequently do we see changes in market leadership and why? Who challenges market leaders? How do leaders react and survive to new phases of creative destruction? With what consequences for the development and diffusion of new products?

In the first task, we aim to offer a comparative analysis of industry evolution in the domains of complex product systems and general purpose technologies. We shall consider four cases, two for each type of technologies. For complex product systems we will focus on 1) the automotive industry, with special focus on the green transformation of the sector and the emergence of electric vehicles and 2) renewable energy, a key sector in the development and implementation of sustainable growth. In order to explore the industrial dynamics of general purpose technologies we will focus on 1) the laser industry, a classic enabling technologies with multiple downstream applications and 2) robotics, which is a sector of major importance on both the demand and the supply side of technology markets, as well as one of the most important enabling technologies of the Fourth Industrial Revolution. In the study of these four cases will pay particular attention to the role of learning processes and the accumulation of specific competences in driving the entry of firms into different market segments through innovation.

In the second task, we will focus on the relationship between entrepreneurship and innovation in the four industries. Recent literature suggests that the pre-entry knowledge resources of new entrants, embodied in their founders, affects their entry strategy, their performance and their ability to survive (Agarwal et al. 2004, Bruderl et al. 1992, Chatterji 2009, Klepper and Simons 2000). Founders’ know-how and experience may be sourced both from the focal industry and from suppliers and users (Agarwal and Shah 2014, Adams et al., 2015). We will therefore investigate the pattern of entry and innovation by giving a prominent role to the founders’ pre-entry knowledge-experience and the emergence of global opportunities for technological leapfrogging which often characterise major techno-economic and socio-institutional paradigm shift (Perez 2013). This is clearly the case of large and fast-industrialising countries such as China and India (Fu and Zhang 2011). In this context, we will examine the knowledge flows and innovation patterns at the global level, in order to understand the changing modes of competition between existing leaders and new competitors facing new technological paradigms. New entrants may be less path-dependent on given technologies and may construct innovative developmental paths based on emergent technologies. This might give them competitive advantages vis-à-vis prevailing incumbents (Schmitz and Altenburg 2016). As for the methodology, this second task relies on the development of novel datasets with detailed information on start-ups and diversifying entrants in the industry, matched with information on the founders’ background, the type of product and market segment, innovation expenditures and activities, and firm survival and growth.

Data

In order to carry out our analysis, we will integrate data from different sources.

At the firm-level, we will employ a set of French datasets, which provide information on the balance sheets and profit and loss statement on the population of French firms (FICUS and FARE). We can merge this dataset to an employer-employee (DADS) and export dataset (Customs data) which cover the population respectively of employers and exporters in France. Similar datasets will be employed in order to analyse firm and industry dynamics in Italy, taking advantage of the collaboration between Scuola Superiore Sant’Anna and ISTAT. Firm-level evidence from other countries will be provided using ORBIS. We will also use STAN and ANBERD data (public available OECD source) for the sectoral analysis, as well as EU-KLEMS database. We will also have the opportunity to use firm-level EU R&D Scoreboard data from top-R&D investors worldwide that can be matched to accounting and financial information from ORBIS (Bureau Van Dijk) database we have access to. In addition, patent and patent quality information from the OECD PATSTAT dataset, using firm-patent concordance tables developed by the European Patent Office (EPO) and the Office for Harmonization in the Internal Market (OHIM), might also be used to focus on firm-level patent activity. Moreover, in the case of Spain, firm-level data from the Survey on Business Strategies (Encuesta Sobre Estrategias Empresariales, ESEE) are available and, in the case of Italy, different surveys from Community Innovation Survey data can be accessed.

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