Academic Research

Working Papers

The Importance of Working for Earnest: Climbing the Job Ladder through Firms’ Connectivity

^{🏆 Project selected for the VisitINPS 2019 program}

Abstract

Do workers consider a firm’s “springboard” value in terms of future job opportunities when choosing an employer? Using a search model of the labor market, I introduce the idea that firms differ in enhancing their employees’ chances of receiving external job offers. The model informs a firm-level proxy for outside job offers received by workers. This measure empirically aligns with key model predictions: 1) it negatively correlates with both firm-specific tenure and young workers’ entry salaries, revealing a compensating differential; and 2) it suggests that workers enjoy a salary premium upon leaving such firms, indicative of faster career progression. The model is estimated on administrative data from Italy and successfully captures key aspects of labor market dynamics. My channel explains 10% of the overall job-to-job transitions and shows how firm-induced variation in job search can be a significant driver of inequality, especially at the bottom of the wage distribution.

📄 Paper

The Heterogeneous Consequences of Reduced Labor Costs on Firm Productivity

joint with Paolo Zacchia

^{🏆 Project selected for the VisitINPS 2020 program}

Abstract

We document how a reduction in labor costs led to heterogeneous effects on manufacturing firms’ total factor productivity (TFP). Leveraging an Italian labor legislation reform and unique institutional features of the local collective bargaining system, we show that such effects vary along the TFP distribution. Relative to the counterfactual, TFP markedly declines on the left tail, which we explain via selection mechanisms; on the right, TFP mildly increases as firms are able to expand and reallocate their workforce. We develop a general equilibrium model featuring firm selection and frictions in input markets to guide the evaluation of welfare implications.

⏳ Submitted! 📄 Paper

Optimal Ramsey Taxation with Social Security

joint with Marco Francischello and Matteo Paradisi

^{🏆 Project awarded with a Netspar Comparative Research Grant 2021}

Abstract

We develop an OLG model with heterogeneous agents and aggregate uncertainty to study optimal Ramsey taxation when the government can use a credible set of social security instruments. Social security mitigates the income effect in optimal labor tax smoothing and, together with heterogeneity, adds new redistributive motives to both labor and capital taxes while crowding out others. We calibrate the model on three different economies: the US, Netherlands, and Italy. We argue that the three countries would experience heterogeneous gains, in redistributive and efficiency terms, by moving from the status-quo allocations to those prescribed by a utilitarian Ramsey planner. Our simulations show that retirement benefits in the current economies are higher than their Ramsey-optimal level while we argue that the use of funded social security schemes, neglected in current actual policies, could be welfare improving.

📄 Paper

Selected work-in-progress

Responsible Firms on the Labor Market

joint with Marc Fleurbaey

^{🏆 Project selected for the VisitINPS 2023 program}

Short abstract

Responsible firms prioritize maximizing stakeholder value, which entails balancing the surpluses of their customers and workers along with their own profits. In this paper, we center our attention on firms' behavior within the labor market and introduce a method for identifying responsible firms using administrative data. By integrating measures of labor market power and systematic utility provided to workers, we construct a multi-dimensional index of responsibility. This enables us to categorize companies and assess the degree of a firm's responsibility throughout its life cycle. Furthermore, we evaluate the influence of a firm's responsibility on income inequalities both within and between firms.

Human Capital Value Chains

joint with Paolo Zacchia

^{🏆 Project selected for the VisitINPS 2023 program}

Short abstract

In local labor markets, a pattern often emerges where workers transition during the early stages of their careers from lower-paying firms that provide comprehensive training to higher-paying specialized firms that predominantly employ already-trained workers. We refer to this mechanism as the "Human Capital Value Chain" (HCVC). We study and document its impact on the trajectory of workers' wages and local agglomeration externalities, thereby highlighting its role in the broader labor market dynamics.

Do AKM Effects Really Matter?

joint with Aslan Bakirov and Paolo Zacchia

Short abstract

We revisit the wage decomposition literature using machine learning. We show empirically that if both worker- and firm-level observable characteristics are treated non-parametrically via generalized random forests, the share of log-wages variance explained by typical “AKM” fixed effects falls precipitously.

Deep Structural Estimation

joint with Marco Francischello

Short abstract

Structural estimation using the Simulated Method of Moments (SMM) aims to iteratively minimize the difference between moments from simulated and real-world data. We propose an approach to speed up this process by fitting a neural network that approximates the map between parameters and moments—an object that can be built by exploiting parallel computation.

---

Policy papers

Una proposta per un APE volontario finalmente a regime

joint with Matteo Paradisi

Istituto Bruno Leoni, Briefing Papers 🗞️ Media coverage: Il Foglio

Abstract

We advocate for Ape (Anticipo Pensionistico, an early retirement option available to Italian employees) to become "structural". We claim that it responds to the need for greater flexibility in retirement while maintaining the sustainability of the pension system over the long-run. Moreover, we discuss some proposals that would make it easier for potential beneficiaries to claim Ape, reducing the low take-up problem observed in the first months after its implementation.

📄 Paper