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on Business Economics |
| By: | Leonardo Becchetti (CEIS & DEF, University of Rome "Tor Vergata"); Sara Mancini (University of Rome "Tor Vergata"); Nazaria Solferino (Università della Calabria) |
| Abstract: | Based on results of the different fields of the game theoretic literature on strategic interactions and social dilemmas, gift exchange and procedural utility, we argue that corporate social responsibility and relational skills i) with other firms; ii) between employers and workers iii) among workers and iv) with stakeholders are associated to positive effects on productivity. We test our research hypothesis on a large representative sample of Italian firms including the universe of medium and large companies and accounting for 91.3 percent of domestic employees. We find that companies with higher relational skills report significantly higher value added per worker after controlling for relevant concurring factors. More specifically, the identified significant skill related components are: i) corporate policies considering strategic workers’ wellbeing; ii) team working attitudes considered as priority soft skills when hiring workers; iii) initiatives in favour of the productive network operating in the same local area and iv) involvement of stakeholders in CSR projects. |
| Keywords: | relational skills, corporate productivity, gift exchange, team working |
| JEL: | L22 L25 L14 J53 |
| Date: | 2022–01–25 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:530&r= |
| By: | Tian Xiong (Europäisches Institut für Internationale Wirtschaftsbeziehungen (EIIW)) |
| Abstract: | This study aims to investigate the effects of mergers and acquisitions (M&As) by Chinese multinational firm in the EU28 on the subsequent innovation performance of acquiring firms with different technological intensities and types of corporate ownership The study does so by applying the Zero-Inflated Negative Binomial estimation to analyze novel longitudinal firm-level data covering the period from 2010 to 2018. The empirical evidence suggests that Chinese acquiring firms are generally able to enhance their innovation performance after merging or acquiring firms from the EU28 countries. Furthermore, this study reveals that medium low- and low-tech firms significantly improved their innovation performance after undertaking M&As, but the same effect cannot be identified for firms in the high- and medium high-tech groups. Finally, strong evidence confirms the significant increase in innovation output of private-owned enterprises in the post-acquisition era compared with state-owned or -controlled enterprises. |
| Keywords: | mergers and acquisitions, M&A, innovation performance, emerging market multinationals (EMNEs), learning, China, EU |
| JEL: | O1 O3 F23 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:bwu:eiiwdp:disbei310&r= |
| By: | Bischoff, Thore Sören; Hipp, Ann; Runst, Petrik |
| Abstract: | Generalized trust within regions represents an important firm resource. We provide empirical evidence on the impact of trust among people in regions on innovation using two distinct data sets. The first one contains firm-level data and is used to analyze how trust affects firm-level innovation in small and medium sized enterprises (SMEs). The second data set is used to analyze the trust-innovation relationship within regions. It allows us to capture innovation in the form of patents and explore spatial patterns. Our observation period ranges from 2004 to 2019. We apply a multilevel approach, panel data models as well as spatial techniques. The results show that generalized trust has a positive impact on a firm's innovativeness, which is particularly strong for small and medium-sized firms and in regions with relatively low levels of trust. |
| Keywords: | Trust,innovation,regional innovation systems,SMEs |
| JEL: | D02 D83 O12 O18 O31 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifhwps:322021&r= |
| By: | Thiago Revil T. Ferreira |
| Abstract: | I document business cycle properties of the full cross-sectional distributions of U.S. stock returns and credit spreads from financial and nonfinancial firms. The skewness of returns of financial firms (SRF) best predicts economic activity, while being a barometer for lending conditions. SRF also affects firm-level investment beyond firms' balance sheets, and adverse SRF shocks lead to macroeconomic downturns with tighter lending conditions in vector autoregressions (VARs). These results are consistent with a lending channel in which cross-sectional financial firms' balance sheets play a prominent role in business cycles. I rationalize this argument with a model that matches the VAR evidence. |
| Keywords: | Cross-Sectional; Skewness; Business Cycles; Lending Channel |
| JEL: | E32 E37 E44 |
| Date: | 2022–02–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1335&r= |
| By: | Martins, Pedro S. |
| Abstract: | This paper surveys the emerging economics literature on the relationship between employee training and firm performance. Most studies find very high returns to training, at least from the perspective of firms, indicating that the costs of training can be recouped in short periods of time. These results follow from different identification approaches, including randomised control trials. The training provided is typically of a general nature, which is consistent with employers' labour market power. Several areas for future research are also proposed, including the role of labour market institutions in promoting training and the extent to which the productivity effects of training are shared with employees. |
| Keywords: | Productivity,Skills,Competences,Human Capital,Lifelong Learning,Employment,Public Policy,Programme Evaluation |
| JEL: | M53 I26 J24 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1046&r= |
| By: | Michał Dzieliński (Stockholm Business School, Stockholm University); Florian Eugster (University of St. Gallen); Emma Sjöström (Stockholm School of Economics); Alexander F. Wagner (University of Zurich - Department of Banking and Finance; Centre for Economic Policy Research (CEPR); European Corporate Governance Institute (ECGI); Swiss Finance Institute) |
| Abstract: | Climate change is a major concern for many companies, but it has not historically featured much in earnings conference calls. We find a marked increase in climate talk on these calls in recent years. We also find that climate talk is negatively related to the change in CO2 emissions (especially Scope 2) in the year after the call, particularly in firms with high overall environmental and governance ratings. Conversely, investors react particularly negatively to climate talk when it comes from a firm with low levels of ESG performance or following poor earnings performance. Finally, a firm employs more climate talk when it is more material, when there is greater shareholder pressure or when it is better prepared for climate-related disclosure. Overall, these results suggest that investors and other stakeholders interested in corporate climate action should be paying attention to earnings conference calls as a source of useful information about companies' broader stance on climate-related issues. |
| Keywords: | climate talk, earnings calls, sustainability, CO2 emissions, greenwashing |
| JEL: | D83 G14 G34 G41 Q54 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2214&r= |
| By: | Lafond, François; Farmer, J. Doyne; Mungo, Luca; Astudillo-Estévez, Pablo |
| Abstract: | The vulnerability of supply chains and their role in the propagation of shocks has been high- lighted multiple times in recent years, including by the recent pandemic. However, while the importance of micro data is increasingly recognised, data at the firm-to-firm level remains scarcely available. In this study, we formulate supply chain networks' reconstruction as a link prediction problem and tackle it using machine learning, specifically Gradient Boosting. We test our approach on three di↵erent supply chain datasets and show that it works very well and outperforms three benchmarks. An analysis of features' importance suggests that the key data underlying our predictions are firms' industry, location, and size. To evaluate the feasibility of reconstructing a network when no production network data is available, we attempt to predict a dataset using a model trained on another dataset, showing that the model's performance, while still better than a random predictor, deteriorates substantially. |
| Keywords: | Supply chains, Network reconstruction, Link prediction, Machine learning |
| JEL: | C53 C67 C81 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:amz:wpaper:2022-02&r= |