|
on Knowledge Management and Knowledge Economy |
|
Issue of 2024‒05‒06
four papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Cavalcanti, T.; Mohaddes, K.; Nian, H.; Yin, H. |
| Abstract: | This paper investigates the long-run effects of prolonged air pollution on firmlevel human capital, knowledge and innovation composition. Using a novel firm-level dataset covering almost all industrial firms engaged in science and technology activities in China, and employing a regression discontinuity design, we show that prolonged pollution significantly diminishes both the quantity and the quality of human capital at the firm level. More specifically, we show that air pollution affects firm-level human capital composition by reducing the share of employees with a PhD degree and master’s degree, but instead increasing the share of employees with bachelor’s degree. Moreover, the difference in the composition of human capital materially change the knowledge and innovation structure of the firms, with our estimates showing that pollution decreases innovations that demand a high level of creativity, such as publications and inventions, while increasing innovations with a relatively low level of creativity, such as design patents. Quantitatively, on the intensive margin, one μg/m 3 increase in the annual average PM 2.5 concentration leads to a 0.188 loss in the number of innovations per R&D employee. Overall, we show that air pollution has created a gap in human capital, knowledge, and innovation between firms in the north and south of China, highlighting the importance of environmental quality as a significant factor for productivity and welfare. |
| Keywords: | Pollution, human capital, knowledge, innovation, China |
| JEL: | O15 O30 O44 Q51 Q56 |
| Date: | 2023–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camjip:2301&r=knm |
| By: | Drydakis, Nick (Anglia Ruskin University) |
| Abstract: | There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term 'AI capital' as a vector of knowledge, skills and capabilities related to AI technologies, which could boost individuals' productivity, employment and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms. |
| Keywords: | artificial intelligence, artificial intelligence capital, employment, wages, higher education, education |
| JEL: | E24 I26 O14 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16866&r=knm |
| By: | Guillouët, Louise; Khandelwal, Amit K.; Macchiavello, Rocco; Malhotra, Madhav; Teachout, Matthieu |
| Abstract: | We study communication frictions within multinationals (MNCs), hypothesizing that language barriers reduce management knowledge transfers within the organization. A distinct feature of such MNCs is a three-tier hierarchy: foreign managers (FMs) supervise domestic managers (DMs) who supervise production workers. Tailored surveys from our setting – MNCs in Myanmar – reveal that language barriers impede interactions between FMs and DMs. A first experimental protocol offers DMs free English courses and confirms that lowering communications costs increases their interactions with FMs. A second experimental protocol that asks human-resource managers at domestic firms to rate hypothetical resumes reveals that multinational experience and, specifically, DM-FM interactions are valued in the domestic labor market. Together, these results suggest that reducing language barriers can improve transfers of management knowledge, an interpretation supported by improvements in soft skills among treatment DMs in the first experiment. A model in which communication within MNCs is non-contractible – a realistic feature of workplace life – reveals that the experimental results are consistent with underinvestment in language training and provide a rationale for policy intervention. |
| Keywords: | FDI; multinationals; knowledge transfers; language barriers; management |
| JEL: | F00 F23 L20 |
| Date: | 2024–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122568&r=knm |
| By: | Wouter DESSEIN; Desmond (Ho-Fu) LO; SHANGGUAN Ruo; OWAN Hideo |
| Abstract: | We explore the role of management in knowledge-intensive work. Our theory posits that the manager’s function in a project mainly consists of ex ante coordination, specifying and delegating tasks to the project team, and ex post coordination of the team’s execution of those tasks as the project unfolds. Consistent with the predictions generated from this view, our micro-level data from architectural design teams show a clear pattern of coordinated time use: (i) the involvement of both the manager and the project team is significantly higher ex ante than ex post; notably, this time pattern is more potent for more knowledge-intensive projects and projects subject to more information frictions, and (ii) the timing of the peak hours of the manager precedes those of the team. We also find that the team takes up the slack when the manager reduces ex-ante hours because of a heavier workload. Finally, projects in which managerial attention deviates from our predicted involvement correlate with higher team hours and lower overall profitability. Our study highlights the importance of managerial coordination and rational inattention in organizing knowledge workers. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:24044&r=knm |