arXiv subjects

Paolo Zacchia

Publications and source records attributed to Paolo Zacchia.

3 recordsLinked to original sources

arXiv2026

Generalized AKM: Flexible Controls and Interactions in Wage Decompositions

How much wage dispersion is attributed to workers, firms, and their sorting depends on how wages are adjusted for observed characteristics. Standard AKM decompositions impose a known linear adjustment. We develop Generalized AKM, a framework that permits an unknown smooth covariate function and group-specific nonlinear interactions while preserving the original variance components. We prove consistency and asymptotic normality with heteroskedastic errors and many fixed effects, and characterize the stronger smoothness required for quadratic forms. In Portuguese employer-employee data, adding worker and firm-input controls lowers the bias-corrected worker-effect variance from $0.551$ to $0.474$ of total wage variance, firm-effect variance from $0.144$ to $0.121$, and sorting from $0.080$ to $0.047$. Across three group-specific nonlinear bases, firm-effect variance remains between $0.114$ and $0.117$ and sorting between $0.041$ and $0.042$, while worker-effect variance ranges from $0.474$to $0.491$. Which controls enter matters more for firm variance and sorting than how flexibly they enter; worker variance remains more sensitive to the basis.

econ.EM↗

arXiv2026

Staged Entry

We develop a model of staged entry: to operate, monopolistically competitive firms must pay two sequential entry costs, each time acquiring a more informative signal of future performance. This model yields two implications for fiscal policy. First, the equilibrium outcome is constrained-efficient if preferences are CES and entry costs are exogenous. Second, when entry costs depend on how many firms pass any entry stage (due to positive knowledge spillovers or negative congestion effects) the resulting externalities can be offset via Pigouvian taxes or subsidies that are timed around the relevant entry decisions. A calibration exercise based on U.S. firm-entry data shows that these policies would raise welfare through a wider pool of entrants and sharper selection of productive firms.

econ.GN↗

arXiv2026

TWICE: Tree-based Wage Inference with Clustering and Estimation

How much do worker skills, firm pay policies, and their interaction contribute to wage inequality? Standard approaches rely on latent fixed effects identified through worker mobility, but sparse networks inflate variance estimates, additivity assumptions rule out complementarities, and the resulting decompositions lack interpretability. We propose TWICE (Tree-based Wage Inference with Clustering and Estimation), a framework that models the conditional wage function directly from observables using gradient-boosted trees, replacing latent effects with interpretable, observable-anchored partitions. This trades off the ability to capture idiosyncratic unobservables for robustness to sampling noise and out-of-sample portability. Applied to Portuguese administrative data, TWICE outperforms linear benchmarks out of sample and reveals that sorting and non-additive interactions explain substantially more wage dispersion than implied by standard AKM estimates.

econ.GN↗