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on Technology and Industrial Dynamics |
| By: | D'Al, Francesco; Santarelli, Enrico; Vivarelli, Marco |
| Abstract: | In this paper we integrate the insights of the Knowledge Spillover Theory of Entrepreneurship and Innovation (KSTE+I) with Schumpeter's idea that innovative entrepreneurs creatively apply available local knowledge, possibly mediated by Marshallian, Jacobian and Porter spillovers. In more detail, in this study we assess the degree of pervasiveness and the level of opportunities brought about by AI technologies by testing the possible correlation between the regional AI knowledge stock and the number of new innovative ventures (that is startups patenting in any technological field in the year of their foundation). Empirically, by focusing on 287 Nuts-2 European regions, we test whether the local AI stock of knowledge exerts an enabling role in fostering innovative entry within AI-related local industries (AI technologies as focused enablers) and within non AI-related local industries, as well (AI technologies as generalised enablers). Results from Negative Binomial fixed-effect and Poisson fixed-effect regressions (controlled for a variety of concurrent drivers of entrepreneurship) reveal that the local AI knowledge stock does promote the spread of innovative startups, so supporting both the KSTE+I approach and the enabling role of AI technologies; however, this relationship is confirmed only with regard to the sole high-tech/AI-related industries. |
| Keywords: | KSTE+I, Artificial Intelligence, innovative entry, enabling technologies |
| JEL: | O33 L26 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1473 |
| By: | Falck, Elisabeth; Röhe, Oke; Strobel, Johannes |
| Abstract: | In recent years, there has been a controversial debate on how the rapid diffusion of digital technologies affects labour productivity in advanced economies. Using a multi-sector dynamic general equilibrium model, we show that cumulative labour productivity growth in the United States, Germany and France over the period from 1996 to 2020 would have been about half as high without the efficiency gains from the sectors producing digital goods - despite their relatively small size in terms of gross value added. This is not only because TFP growth in the digital sectors is exceptionally high, but also because other sectors benefit from these efficiency improvements via production linkages. |
| Keywords: | dynamic general equilibrium model, sectoral linkages, production network, digitalisation |
| JEL: | E17 E23 E24 O33 O41 O47 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:300703 |
| By: | Andres, Raphaela; Niebel, Thomas; Sack, Robin |
| Abstract: | Until today, the question of how digitalisation and, in particular, individual digital technologies affect productivity is still the subject of controversial debate. Using administrative firm-level data provided by the Dutch and the German statistical offices, we investigate the economic importance of data, in particular, the effect of the application of big data analytics (BDA) on labour productivity (LP) at the firm level. We find that a simple binary measure indicating the mere usage of BDA fails to capture the effect of BDA on LP. In contrast, measures of BDA intensity clearly show a positive and statistically significant relationship between BDA and LP, even after controlling for a firm's general digitalisation level. |
| Keywords: | big data analytics, productivity, administrative firm-level data |
| JEL: | L25 O14 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:300678 |
| By: | Friedrich, Jonathan (CIRCLE, Lund University); Stihl, Linda (CIRCLE, Lund University); Grillitsch, Markus (CIRCLE, Lund University) |
| Abstract: | The circular economy (CE) is argued as a possible model for dealing with value chain instabilities in global production networks. Since geographical proximity is central to unlocking circular potential, x-shoring (including concepts like reshoring, resourcing, or friendshoring) is arguably key to this process. Often, spatial restructurings of the CE are embraced without a critical examination of their multi-scalar effects. Nevertheless, spatial restructuring of the economy inevitably produces winners and losers. To navigate the tensions that arise in the context of uneven development and environmental (in)justice, we present a framework for anticipating plausible socio-economic and socio-environmental effects of x-shoring processes across place, scale, and time. We illustrate our framework with insights from the literature on old industrial regions and cases documented in the Environmental Justice Atlas. Our framework represents a holistic approach that integrates interdisciplinary literature from different disciplines. We discuss the ambivalent effects of x-shoring across space, scale, and time, principles for navigating the tensions that arise, and outline research avenues for a thorough exploration of the geography of x-shoring in the CE and beyond. Because of the ambivalence of these processes, we conclude that research must embrace the complexity of these developments by employing integrative, multi-scalar approaches that empower local agency. |
| Keywords: | global production networks; global value chains; trade-offs; circular economy; anticipation |
| JEL: | F63 F64 |
| Date: | 2024–08–09 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2024_009 |
| By: | Palmer, Thomas |
| Abstract: | This paper establishes that the rise in employer-provided training due to technological change has dampened the college wage premium. Using unique survey micro-data, I show that hightechnology firms provide more training overall, but the gap in training participation between high- and low-skill workers is smaller within these firms. To understand the aggregate implications of these patterns, I build a quantitative model of the labor market with endogenous technology and training investments. In a counterfactual exercise, I find that the increase in the college wage premium would be 63 percent greater if training costs remained constant between 1980 and the early 2000s. |
| Keywords: | Training, Technological Change, College Wage Premium, Education, Technology |
| JEL: | E24 I24 J24 J31 M53 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:clefwp:300865 |
| By: | Atin Aboutorabi; Ga\'etan de Rassenfosse |
| Abstract: | Macroeconomic data are crucial for monitoring countries' performance and driving policy. However, traditional data acquisition processes are slow, subject to delays, and performed at a low frequency. We address this 'ragged-edge' problem with a two-step framework. The first step is a supervised learning model predicting observed low-frequency figures. We propose a neural-network-based nowcasting model that exploits mixed-frequency, high-dimensional data. The second step uses the elasticities derived from the previous step to interpolate unobserved high-frequency figures. We apply our method to nowcast countries' yearly research and development (R&D) expenditure series. These series are collected through infrequent surveys, making them ideal candidates for this task. We exploit a range of predictors, chiefly Internet search volume data, and document the relevance of these data in improving out-of-sample predictions. Furthermore, we leverage the high frequency of our data to derive monthly estimates of R&D expenditures, which are currently unobserved. We compare our results with those obtained from the classical regression-based and the sparse temporal disaggregation methods. Finally, we validate our results by reporting a strong correlation with monthly R&D employment data. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.11765 |
| By: | Koenraadt, Jeroen; Martens, Tim; Sextroh, Christoph |
| Abstract: | We study the role of non-traditional investment research as a source of information for managerial learning and corporate investment decisions. Using a comprehensive sample of social media analyst reports from Seeking Alpha and exogenous variation in social media analysts' coverage overlaps, we show that firms are more likely to invest into technologies similar to firms covered by the same analyst. The effect is incremental to coverage by professional sell-side analysts and varies with social media analysts' characteristics and differences in their contributed content that capture their unique information set. Overall, our results are consistent with non-traditional investment research enhancing firms' information environment as an additional source of information that may guide corporate investment decisions. |
| Keywords: | social media analyst; seeking alpha; information intermediaries; managerial learning; information spillover; corporate innovation; patents |
| JEL: | J50 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:124417 |
| By: | Mungase, Sachin; Nikam, Supriya; Kothe, Satyanarayan |
| Abstract: | The BRICS countries (Brazil, Russia, India, China, and South Africa) have seen varying GDP growth rates, averaging around 5 percent in the 1990s, rising to 8 percent in the 2000s, and about 5.5 percent from 2011 to 2020. Structural change and labour productivity are key for sustained economic growth, achieved by reallocating resources to more productive activities. This study analyses employment changes and labour productivity from 1990 to 2018, focusing on labour shifts from less productive to more productive sectors, inter-sectoral changes in output and employment patterns, and the impact of structural changes on labour productivity. Using the Economic Transformation Database (ETD) and methodologies from various researchers, the study highlights significant structural changes in China and India, while Russia, Brazil, and South Africa show minimal change. It underscores the need for policies promoting education, vocational training, and reducing trade barriers to enhance productivity and economic growth. |
| Keywords: | BRICS Countries, GDP Growth Rates, Structural Change, Labour Productivity, Economic Growth, Resource Reallocation, Employment Patterns, Sectoral Shifts, Economic Transformation Database (ETD), Inter-Sectoral Changes, China Economic Growth, India Economic Growth, Russia Economic Growth, Brazil Economic Growth, South Africa Economic Growth, Education and Vocational Training, Trade Barriers, Policy Recommendations, Productivity Enhancement, 1990-2018 Economic Analysis |
| JEL: | E00 E24 F62 J01 O11 O14 O4 O47 O57 |
| Date: | 2024–07–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121607 |