چگونه نوآوری تکنولوژیکی و انتشار تحرک کارگران درون صنعت را تاثیر می گذارد؟

Does technological change amount to accumulation of general, and so transferable, human capital? To approach this question I rely on a theoretical framework in which the “technology distance” between industries reduces inter-industry transferability of workers’ skill. Empirically, I use US panel data on individual intra-industry and inter-industry mobility decisions between 1982 and 1990, a period of rapid technological change in all manufacturing sectors, to estimate a mixed logit econometric specification that does not rely on the IIA assumption. I find support to the main idea that technological innovation and diffusion have different effects on workers’ industrial mobility patterns. “Knowledge spillovers”, differently from “rent spillovers”, indeed boost the chances of workers’ inter-industry mobility. Surprisingly, this is more consistently so in low-tech industries than in high-tech industries. Consistently with the expectations developed in the theoretical framework, in low-tech industries skilled workers respond more sharply to technology diffusion than unskilled workers.

The Industrial Enlightenment was in part about the expansion of useful knowledge. According to McGee (2004) technological progress often depended on “analogical” thinking, in which inventors, consciously or subconsciously, transform an idea they have already seen into something novel. Since most of these ideas travel embodied in humans, workers’ mobility is paramount for technological progress. So, who are the fearful? In a “cognitive capitalism” it is the rivalrous nature of intellectual human capital that poses to employers’ the problem of skilled and technical workers’ mobility. Stated in other words, if R&D investment translates into human capital or knowledge that workers can transfer and utilize in other firms, workers’ mobility amounts to knowledge diffusion. While this is generally evaluated as an important condition for the spread of new firms and research units (e.g., in Zucker et al., 1998), mobility of workers and particularly of highly technical workers is potentially a threat to the firm whenever it’s the firm/industry that bears the cost of R&D activities (Kim and Marschke, 2005). In other words, intellectual human capital is rivalrous because it is characterized by natural excludability in the sense that its utilization in a firm usually excludes its simultaneous utilization in another firm.1 This argument is central to contextualize the interest in skilled workers’ mobility and in how the labor market for skilled technical workers works (Rosen, 1972 and Pakes and Nitzan, 1983; Song et al., 1996; Fallick et al., 2005). More fundamentally an analysis of workers’ mobility, what prevents and what engenders it, is paramount to understand the contemporary ways in which capitalism attends to its central imperative and challenge: namely, “to immobilize workers, to tie it to the labor relation and to prevent its flight, the breach of contract and the refusal to work” (Moulier-Boutang, 1998). This paper investigates the effects of industry R&D intensity on workers’ mobility. A so far overlooked aspect in the literature on how technology change impacts upon the labor markets is the effects of technological change that occurs by means of diffusion of existing knowledge. This oversight has persisted in spite of the intensification of phenomena of technology diffusion brought by the deepening of input dependence among firms and industries starting from the 1970s (Wolff, 1997) and by a rise in R&D performed by non-manufacturing industries (OECD, 1996). In the face of the rapid technology changes driven by both innovation and diffusion, this paper explores the hypothesis that the dual aspects of technological change are among the determinants of workers’ mobility. I structure the discussion as follows. Section 2 reviews the literature and introduces a simple model, which relies on the notion of “skill distance” developed in Silverberg et al. (1988) to discuss the impact of technological change on inter-industry mobility. Section 3 describes the data and discusses measurement of technology issues. Section 4 introduces the econometric strategies, namely multinomial models and mixed logit models of inter-industry mobility. Section 5 discusses the empirical findings, while Section 6 concludes.

Despite the importance for economic performance of a rapid adoption of new technologies and its successful utilization in workplaces, we do not know much about how technologies diffuse and the effect of technological diffusion on workers’ mobility. The opening citation suggests that R&D investment translates into human capital or knowledge that workers can transfer and utilize in other firms, a fact that opens the door for large private (firms’) losses and large social gains stemming from workers’ mobility. Primarily using a mixed logit econometric specification, this paper has tested a number of hypotheses stemming from a purported negative relationship between R&D-induced “technology distance” across industries and workers’ mobility opportunities. By this argument, technology diffusion across industries will enhance workers’ mobility chances by shortening the technology distance across industries. The main results can be summarized as follows: 1. In general, Hausman and Small-Hsiao tests for IIA rarely allow for a rejection of the IIA at the usual levels of statistical significance. The mixed logit econometric specification however, confirms the existence of a likely positive correlation between some or all of the mobility options depending on the sample utilized. 2. This study confirms a number of important results on the impact of workers’ socio-demographic characteristics on mobility often reported in the literature. Age, tenure and income usually reduce the relative odds of mobility but education and skill, as captured by dummy variables for employment in technical jobs, usually enhance the mobility options, particularly across 3-digit industries. 3. In measuring technology diffusion a clear distinction appears to be necessary between “knowledge spillovers” and “rent spillovers” as introduced by Griliches (1979). Consistently with a literature on the nature of R&D in low and high tech industries, mobility patterns in the face of technology innovation appear to be significantly different in high-tech and low-tech industries. The analytical framework employed in this study provides a key to interpret these differences. 4. In particular, in high-tech industries stock measures of innovation decreases mobility, particularly across 3-digit industries, a result that is consistent with the idea that innovation increases technological distance between the high-tech industry of current employment and other industries. Both stock and flow measures of innovation appear to positively impact upon mobility across 2-digit industries in a sample of workers in low-tech industries. Again this result suggests that innovation in low-tech industries decreases the technology distance between low-tech industries and other industries, thus increasing mobility out of the 2-digit industry of current employment. 5. Both flow and stock measures of technology diffusion impacts in the expected positive way upon mobility across 3-digit industries by workers in low-tech industries. However, only stock measures of technology diffusion, those capturing pure “knowledge spillovers” positively impact upon mobility across 3-digit industries in high-tech industries. 6. Both stock and flow measures of technology only mildly impact upon the predicted chances of workers’ mobility within 3-digit industries. 7. Consistently with our expectations, this study has found support to the hypothesis that the impact of technology diffusion on mobility is larger in a sample of skilled workers than in a sample of unskilled workers in low-tech industries. However, a comparison between skilled and unskilled workers’ results in high-tech industries does not lead to unambiguous results. Taken together these results provide support to the view that technological diffusion, particularly when accompanied by knowledge spillovers at the industry level, enhances skill transferability of all workers. In other words, human capital becomes less specific as the rate of technological diffusion increases. This result appears to be particularly strong for those workers employed in low-tech industries. A number of open questions remain. In particular, why and to what extent unskilled workers should be affected by technology innovation and diffusion. While this work has started to address this question, more work in this area is needed to ascertain the positive impact that technology diffusion has in enabling skilled and unskilled workers in low-tech industries to move towards possibly more technologically advanced industries. At the light of the recently observed increasing signs of stratification in the US labor market since the 1980s, with no sign of sustained upward mobility of low-wage workers (Gabriel, 2003) this study suggests that technology diffusion and the adoption of measures that speed this process may have significant effects on the labor market opportunities of workers employed in low-tech industries. Finding sustained evidence of the technology diffusion effect could have important policy implications.