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on Technology and Industrial Dynamics |
| By: | Carioli, Paolo; Czarnitzki, Dirk |
| Abstract: | Skills shortage has become a key policy issue in highly developed and innovation-oriented economies, with non-negligible consequences on firms' innovation activities. We investigate the effect of skills shortage on firms' innovation openness, which is considered to be one of the key drivers of innovation performance. We hypothesize that scarcity of personnel causes firms to cooperate more broadly with external partners. Using cross-sectional data from the German contribution to the Community Innovation Survey (CIS), and exploiting detailed information on the extent to which firms could fill their job vacancies, we find that, on average, a one standard deviation increase in skills shortage more than doubles a firm's cooperation breadth. We contribute to the literature on human capital in relation to open innovation by characterizing the necessity of openness as a way to mitigate the scarcity of skills. |
| Keywords: | open innovation, R&D collaboration, skills shortage |
| JEL: | O36 J63 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:23031&r=tid |
| By: | Stefania Albanesi (University of Pittsburgh, NBER and CEPR); António Dias da Silva (European Central Bank); Juan F. Jimeno (Banco de España, Universidad de Alcalá, CEMFI, CEPR and IZA); Ana Lamo (European Central Bank); Alena Wabitsch (University of Oxford) |
| Abstract: | We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011-2019. Using data for occupations at the 3-digit level in Europe, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. This evidence is in line with the Skill-Biased Technological Change theory. While there is heterogeneity across countries, very few countries show a decline in the employment shares of occupations more exposed to AI-enabled automation. Country heterogeneity for this result appears to be linked to the pace of technology diffusion and education, but also to the level of product market regulation (competition) and employment protection laws. In contrast to the findings for employment, we find little evidence for any correlation between wages and potential exposures to new technologies. |
| Keywords: | artificial intelligence, employment, skills, occupations |
| JEL: | J23 O33 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2322&r=tid |
| By: | Camarero, Mariam (University Jaume I and INTECO, Department of Economics,); López-Villavicencio, Antonia (EconomiX-CNRS and University Paris Nanterre); Tamarit, Cecilio (University of València and INTECO, Department of Applied Economics II) |
| Abstract: | The participation of the European Union in Global Value Chains (GVCs) is significantly higher compared to North America and Asia and it has steadily increased with the creation of the Single Market and the launching of the euro. We provide empirical evidence on the consequences of GVC participation on aggregate unemployment. Using data for EU countries and impulse response functions derived from local projections, we show that a higher participa- tion reduces the unemployment rate in less advanced EU economies while it increases it in core countries. Our results also show that unemployment is particularly sensitive to GVCs when the labour cost is low. |
| Keywords: | Global Value Chains, EU, local projections, unemployment |
| JEL: | F14 F15 F62 C32 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:bda:wpsmep:wp2022/10&r=tid |
| By: | Alex Coad (Waseda Business School, Japan); Clemens Domnick (European Commission - JRC); Pietro Santoleri (European Commission - JRC); Stjepan Srhoj (University of Split, Croatia) |
| Abstract: | Policy-makers and scholars often assume that a higher incidence of high-growth firms (HGFs) is synonymous with vibrant regional economic dynamics. We test whether more developed regions, which presumably feature superior entrepreneurial ecosystems (EE), have a higher incidence of HGFs. Empirical evidence suggests that the highest shares of HGFs in Europe are found in peripheral regions, which goes against common assumptions and popular theories. The results call for i) a more nuanced interpretation of regional HGF shares, including a better understanding of their nature and drivers as well as ii) a refinement of the theoretical EE framework. |
| Keywords: | high-growth firms, regional policy, regional economic development |
| JEL: | R11 L26 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc134577&r=tid |
| By: | David Card; Jesse Rothstein; Moises Yi |
| Abstract: | We revisit the estimation of industry wage differentials using linked employer-employee data from the U.S. LEHD program. Building on recent advances in the measurement of employer wage premiums, we define the industry wage effect as the employment-weighted average workplace premium in that industry. We show that cross-sectional estimates of industry differentials overstate the pay premiums due to unmeasured worker heterogeneity. Conversely, estimates based on industry movers understate the true premiums, due to unmeasured heterogeneity in pay premiums within industries. Industry movers who switch to higher-premium industries tend to leave firms in the origin sector that pay above-average premiums and move to firms in the destination sector with below-average premiums (and vice versa), attenuating the measured industry effects. Our preferred estimates reveal substantial heterogeneity in narrowly-defined industry premiums, with a standard deviation of 12%. On average, workers in higher-paying industries have higher observed and unobserved skills, widening between-industry wage inequality. There are also small but systematic differences in industry premiums across cities, with a wider distribution of pay premiums and more worker sorting in cities with more highpremium firms and high-skilled workers. |
| Keywords: | Industry wage differentials; AKM models; mover design |
| JEL: | J31 J62 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:23-40&r=tid |
| By: | Qin Chen; Jinfeng Ge; Huaqing Xie; Xingcheng Xu; Yanqing Yang |
| Abstract: | This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following Eloundou et al. (2023)'s methodology. We then aggregate occupation exposure to the industry level to obtain industry exposure scores. The results indicate a positive correlation between occupation exposure and wage levels/experience premiums, suggesting higher-paying and experience-intensive jobs may face greater displacement risks from LLM-powered software. The industry exposure scores align with expert assessments and economic intuitions. We also develop an economic growth model incorporating industry exposure to quantify the productivity-employment trade-off from AI adoption. Overall, this study provides an analytical basis for understanding the labor market impacts of increasingly capable AI systems in China. Key innovations include the occupation-level exposure analysis, industry aggregation approach, and economic modeling incorporating AI adoption and labor market effects. The findings will inform policymakers and businesses on strategies for maximizing the benefits of AI while mitigating adverse disruption risks. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.08776&r=tid |