To what extent do personal acquaintances between bankers and firms distort lending decisions, operating like a financial friction? This paper investigates credit misallocation driven by the network structure of interlocking directorates in Portugal. Our empirical results show that personal ties between corporate directors and lenders contribute to preferential credit allocation, supporting the literature on favoritism as a financial distortion. However, when we simulate a counterfactual scenario where interlocks are removed and credit is artificially redistributed to equalize marginal returns, we do not observe any significant improvement in aggregate output. This suggests that the macroeconomic impact of such distortions is extremely limited. We argue that this unexpected finding can be explained by the fact that less efficient firms are typically more distant from banks in geodesic terms, which reduces the misallocation effects of favoritism in credit allocation. 

This paper develops a model in which strategic complementarities emerge in a banking system constrained by supervisory frictions. The supervisory authority operates under a binding budget constraint that limits its monitoring capacity, while compliance with regulatory oversight imposes costs on banks. Within this setting, macroprudential policy seeks to regulate aggregate credit dynamics by preventing the system from converging toward extreme equilibria - either excessive credit expansion or a credit freeze. We show that achieving this objective requires a two-pronged policy approach: countercyclical capital buffers effectively curb excessive credit growth during booms, while loan-support programs sustain credit provision in downturns. No single instrument is sufficient to ensure financial stability; instead, a combination of regulatory tools is required to address distinct phases of the financial cycle and counteract the amplification mechanisms driving systemic risk.

Accurate and timely statistics on business dynamics are essential for analyzing firm turnover, job creation, and sectoral contributions to aggregate activity. A key challenge in this context is distinguishing genuine new entrants from reactivated firms—entities that re-emerge after apparent exit but retain strong continuity with previously closed businesses. Official data sources often conflate these categories, leading to biased estimates of entry and exit rates. This paper proposes a NLP-based methodology to improve the classification of firm transitions by combining high-frequency administrative records with unstructured textual data. We first use supervised learning applied to firms’ self-declared business descriptions to impute missing sectoral classifications and introduce a filtering algorithm to isolate economically informative text. Secondly, we estimate a Random Forest model to predict firm reactivation and distinguish true entrants from regenerated firms. The resulting framework enhances the quality and timeliness of firm-level statistics and offers new tools for studying business demography and its micro- and macroeconomic implications.