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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01964 |
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| _version_ | 1866909564656418816 |
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| author | Ortiz, Alvaro Rodrigo, Tomasa Sarasa, David Vazquez, Sirenia |
| author_facet | Ortiz, Alvaro Rodrigo, Tomasa Sarasa, David Vazquez, Sirenia |
| contents | Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on firm size, industry, credit maturity, and value chain position of the firms. The empirical evidence confirms that public procurement awards significantly increase new lending, with NGEU-funded contracts generating stronger credit expansion than traditional procurement during the recent period. The results show that the impact of NGEU procurement programs aligns closely with historical procurement impacts, with differences driven mainly by lower utilization rates. Moreover, integrating high-frequency financial data with procurement records highlights the potential of Big Data in refining public policy design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What Can 240,000 New Credit Transactions Tell Us About the Impact of NGEU Funds? Ortiz, Alvaro Rodrigo, Tomasa Sarasa, David Vazquez, Sirenia Statistical Finance Machine Learning Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on firm size, industry, credit maturity, and value chain position of the firms. The empirical evidence confirms that public procurement awards significantly increase new lending, with NGEU-funded contracts generating stronger credit expansion than traditional procurement during the recent period. The results show that the impact of NGEU procurement programs aligns closely with historical procurement impacts, with differences driven mainly by lower utilization rates. Moreover, integrating high-frequency financial data with procurement records highlights the potential of Big Data in refining public policy design. |
| title | What Can 240,000 New Credit Transactions Tell Us About the Impact of NGEU Funds? |
| topic | Statistical Finance Machine Learning |
| url | https://arxiv.org/abs/2504.01964 |