As generative artificial intelligence (AI) moves rapidly from promise to practice, its early labor-market effects pose a puzzle for economists. Standard frameworks of skill-biased and routine-biased technological change predict that new technologies either complement highly skilled workers or displace routine labor. Yet emerging evidence paints a more complex picture. Recent results from Brynjolfsson et al. (2025a) show that the initial impact of widespread AI adoption is characterized by a pronounced contraction in employment-rather than wages-concentrated among highly educated, entry-level workers in prediction-intensive occupations, with little discernible effect on aggregate employment. We argue that these patterns cannot be fully understood within existing paradigms and call for a new conceptual synthesis. By integrating task-based macroeconomic models of automation with a microeconomic view of AI as a technology that substitutes for implementation skills while complementing human judgment, we delineate the backbone of a unified framework to analyze how AI is likely to reshape job design, reallocate comparative advantage across tasks, and alter the distributional consequences of technological change. This perspective serves to structure a formal research agenda on the evolving interaction between AI and labor markets and its implications for productivity, employment, and inequality. 

This paper examines whether the structural architectures of production networks across Eurozone countries have converged over the period 1995–2022. While a large literature documents increasing – albeit irregular – convergence in macroeconomic outcomes following monetary integration, the underlying structural mechanisms behind this dynamic remain so far insufficiently explored. We construct a set of network-based indicators capturing key features of national input-output production systems and analyze the evolution of the first four moments of their cross-country distributions. Our findings provide new evidence on the role of production networks in shaping convergence patterns and on how aggregate shocks, including the legacy of the global financial and sovereign debt crisis, have influenced the trajectory of integration within the Eurozone.

We develop an agent-based model in which the architecture of credit intermediation — the split between regulated and shadow banking — emerges endogenously from regime-switching decisions by heterogeneous banks. Intermediaries are periodically allowed to adjust their business model by choosing between a regulated mode subject to capital requirements and Cournot oligopoly pricing, and a shadow mode funded via asset-backed securities with higher lending intensity. Three channels drive the dynamics: i) a Cournot mark-up channel that makes regulated banking more profitable as competitors migrate to shadow; ii) a balance-sheet contagion channel through which shadow stress spills over to regulated funding costs; iii) a procyclical lending channel enabling co-expansion of both credit volumes during booms. Simulations show that tighter capital requirements induce persistent migration toward shadow intermediation, with wavelet analysis revealing an endogenous financial cycle at lower frequencies. We document co-expansion of regulated and shadow credit during booms, and a markup effect whereby reduced competition raises the profitability of remaining regulated banks, partially offsetting the regulatory burden that triggered the migration. Monte Carlo experiments confirm robustness across regulatory parameterizations. These results suggest that microprudential capital requirements alone cannot control systemic leverage when the regulatory perimeter is permeable. 

We study how deposit runs on individual banks, and the ensuing reallocation of funds across institutions, interact with interbank market access to shape systemic fragility. We develop an agent-based liquidity framework in which banks hit by a run lose deposits that are endogenously reallocated toward alternative banks according to proximity and capacity, while all institutions can borrow or lend in an interbank funding market subject to liquidity hoarding and payment frictions. Monte Carlo simulations show that such reallocations can generate an endogenous leakage channel when recipient banks reach absorption limits, amplifying system-wide stress. Liquidity hoarding has non-monotonic effects, initially constraining interbank funding and increasing defaults, but potentially stabilizing outcomes at higher levels. Network structure further conditions these dynamics, with core–periphery configurations and endogenous concentration of inflows shaping the timing and synchronization of cascades. Overall, systemic risk reflects the joint evolution of deposit flows and interbank linkages, implying that policies affecting liquidity provision or deposit mobility have regime-dependent effects. 

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.