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on Corporate Finance |
| By: | Nicola Cortinovis |
| Abstract: | Entrepreneurs in rural areas face much greater difficulties than those located in cities, also with respect to the access to entrepreneurial finance. Recent developments in the provision of capital, however, have opened new opportunities for small firms and start-ups to obtain funding. In this empirical work, I hypothesize that crowdfunding provides crucial resources and support for rural-based entrepreneurs and that rural areas characterized by greater (bridging) social capital are better positioned to benefit from the opportunities of crowdfunding. Using a newly developed database linking crowdfunding campaigns to industry and counties in the U.S. (KIUS), county-level information on social capital and official U.S. census data, I test these hypotheses. My findings indicate that crowdfunding is indeed positively related to the number of ventures operating in the industry-location in the following period. In addition, this relationship is stronger for counties with higher levels of bridging social capital and of civic engagement. The results are robust to a number of checks, including a placebo test and matching exercises. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2535 |
| By: | Frederik Rech (School of Economics, Beijing Institute of Technology, Beijing, China); Fanchen Meng (Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, China); Hussam Musa (Faculty of Economics, Matej Bel University, Bansk\'a Bystrica, Slovakia); Martin \v{S}ebe\v{n}a (Faculty of Arts and Social Sciences, Hong Kong Baptist University, Hong Kong, China); Siele Jean Tuo (Business School, Liaoning University, Shenyang, China) |
| Abstract: | This study investigates whether firm-level artificial intelligence (AI) adoption improves the out-of-sample prediction of corporate financial distress models beyond traditional financial ratios. Using a sample of Chinese listed firms (2008-2023), we address sparse AI data with a novel pruned training window method, testing multiple machine learning models. We find that AI adoption consistently increases predictive accuracy, with the largest gains in recall rates for identifying distressed firms. Tree-based models and AI density metrics proved most effective. Crucially, models using longer histories outperformed those relying solely on recent "AI-rich" data. The analysis also identifies divergent adoption patterns, with healthy firms exhibiting earlier and higher AI uptake than distressed peers. These findings, while based on Chinese data, provide a framework for early-warning signals and demonstrate the broader potential of AI metrics as a stable, complementary risk indicator distinct from traditional accounting measures. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02510 |
| By: | Mattia Guerini (Università degli Studi di Brescia and Fondazione Eni Enrico Mattei); Giovanni Marin (Università degli Studi di Urbino Carlo Bo, Fondazione Eni Enrico Mattei and SEEDS, Sustainability Environmental Economics and Dynamics Studies); Francesco Vona (Università degli Studi di Milano, Fondazione Eni Enrico Mattei and OFCE Sciences-Po) |
| Abstract: | We study how monetary policy shapes firm level carbon emissions. Our identification strategy exploits the European Central Bank’s July 2012 move to the zero lower bound as a plausibly exogenous easing of credit supply, combined with rich administrative and survey data on French manufacturing firms from 2000–2019. Using a difference-in-differences design with debt-to-asset ratios as exposure, we find that financially constrained firms cut emissions by about 9.4% more than unconstrained ones. This effect primarily stems from improvements in energy efficiency, lower carbon intensity of energy, and general productivity improvements associated with capital deepening that outweighed modest scale effects. Small and medium firms drive these results, while large and EU ETS regulated firms show no significant response. On average, emissions fell by 3.3% per year, summing up to 5.3 million tonnes of CO2 saved. Despite the smaller marginal effects, total carbon savings due to the monetary easing are comparable to the savings from the EU ETS, highlighting the untargeted nature of the policy. |
| Keywords: | Financial constraints, credit supply, firm level carbon emissions, climate policies |
| JEL: | Q52 Q48 D22 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2025.31 |
| By: | Fiechter, Chad M.; Miller, Noah J.; Ifft, Jennifer; Nelson, Blaine |
| Keywords: | Agricultural Finance, Financial Economics, Risk and Uncertainty |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343924 |