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on Information and Communication Technologies |
| By: | David Hirshleifer; Liang Ma |
| Abstract: | We test and compare the effects of introduction of two new financial information technologies, EDGAR and XBRL, on well-known asset pricing anomalies often attributed to mispricing. EDGAR facilitates easier access to public accounting information about public firms; XBRL reduces the cost of processing such information. Using stacked difference-in-differences regressions, we find that both EDGAR and XBRL reduce mispricing for accounting-based anomalies but not for non-accounting-based anomalies. The economic magnitudes of the effects on accounting-based anomalies are similar for EDGAR and XBRL. These results suggest that both easier access to and less costly processing of public information enhance market efficiency. |
| JEL: | G12 G14 G4 G40 M40 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32767 |
| By: | Natália Barbosa (School of Economics and Management, University of Minho) |
| Abstract: | The adoption of new digital technologies offers new opportunities and has the scope to engender positive effects on firms’ expansion and success in international markets. This paper examines the main factors driving the adoption of Artificial Intelligence (AI) and AI-related digital technologies that enable the Industry 4.0 transformation and whether these new generation of digital technologies affect exporting performance at firm level. Using a rich and representative sample of Portuguese firms over the period 2014-2020, the estimated results suggest that firm’s ex-ante performance, digital infrastructures and in-house ICT skills are the main drivers of digitalisation. However, conditional to ex-ante firm’s performance, there are heterogenous effects on exporting performance across digital technologies and across industries. Moreover, there is evidence of positive selection towards large firms, casting doubts on the inclusiveness of the adoption process and the performance effects of AI and AI-related technologies. |
| Keywords: | Artificial Intelligence, Industry 4.0 enabling digital technologies, firms’ exporting performance |
| JEL: | L20 H81 L25 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:183 |
| By: | Neifar, Malika |
| Abstract: | Purpose: The scope of this paper is to see if the aggregate information and communications technology index (ICT) drives firm performance (profitability and efficiency) for BRICS countries from a des-aggregate panel data of the firm-yearly level (by country) during 2014-2022, from an aggregate monthly time series data and a panel data of country-monthly level during 2014-01-2014-12, all covering the Covid outbreak event. Design/methodology/approach: Through static and dynamic long-run (LR) panel models, the Bayesian VAR-X short-run (SR) approach, and the time series and the panel (LR and SR) ARDL models, we investigate the stability of the linkage between firm performance and the aggregate ICT vis à vis the Covid outbreak. Findings: Using an international sample of 316 FinTech firms from BRICS countries, we find that ICT mechanisms on their own are in general negatively associated with firm performance (profitability and efficiency) with some exceptions. We also find that the ICT and the firm-performance relationship is more significant among countries with respect to the considered pre ou post Covid 19 outbreak period. Originality: The novelty of this research is based on the idea of studying the effect of the aggregate ICT on firm performance by using several dynamic approaches so that we can estimate the SR adjustments that arise from the impact of ICT to the LR relationship. |
| Keywords: | FinTech Firm performance and ICT; BRICS area; Dynamic Panel Regressions and GMM for firm level panel data; Bayesian VAR-X and ARDL models for TS data; PARDL for macro panel data; Covid 19 outbreak |
| JEL: | C11 C22 C23 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121772 |
| By: | Giacomo Damioli; Vincent Van Roy; Daniel Vertesy; Marco Vivarelli |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be “enabling technologies”, a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| Keywords: | Artificial Intelligence, Patents, Structural Change, Technological Paradigm |
| Date: | 2024–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:746877 |
| By: | Julia Schmidt; Graham Pilgrim; Annabelle Mourougane |
| Abstract: | By combining information from online job postings with firm-level financial data provided by Orbis, as well as firm-level merchandise trade data, this paper seeks to get a deeper understanding of the characteristics and performance of data-intensive firms in the United Kingdom since 2015. Data-intensive firms are defined here as firms which are hiring data-related skills. One key contribution of the analysis is to match in a more efficient way the two data sources, Lightcast and Orbis, which are now used extensively in the economic literature. Both the number and the share of data-intensive firms increased sharply in the United Kingdom from 2015 to 2021, with a peak in 2020. The number of highly data-intensive companies and data-intensive multinationals (MNEs) display the same pattern. A large share of data-intensive firms operate within the information and communication industry and are predominantly located in the Greater London area, especially in London itself. Those firms tend to employ more staff and are more capitalised than non data-intensive firms. They are on average more productive, generate more revenues and trade more in foreign markets. While data-intensive firms can be found in all firm size groups, the firms displaying on average the highest level of data intensity were medium sized in 2015 but are now small sized. In terms of international trade, UK dataintensive firms are, generally, more export intensive than non data-intensive firms, but estimates vary across industries. |
| Date: | 2024–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:oec:stdaaa:2024/07-en |