Research

Abstract: While central banks have demonstrated policy coordination to address global economic crises, such as the Global Financial Crisis or the COVID-19 pandemic, the literature has difficulties finding a significant gain from cooperation. I revisit the question through the lens of household heterogeneity. I design a Two-Agent-New-Keynesian model of two interdependent economies and assess the role of household heterogeneity in the gains from monetary policy cooperation. I find that the gains from cooperating are higher when heterogeneity is introduced, compared to the usual representative agent framework. The additional gains generated by household heterogeneity decrease with the trade elasticity.

Abstract: I study the ACA’s Medicaid expansion as an automatic stabilizer in a Bewley-Huggett-Aiyagari model with joint health and employment risk. In partial equilibrium, Medicaid crowds out precautionary saving, producing a Simpson’s paradox: every household gains strictly pointwise, yet aggregate welfare declines as the asset distribution shifts leftward. In general equilibrium, an endogenous rise in the equilibrium interest rate partially restores welfare, which remains positive under all balanced-budget financing strategies. Building on these steady-state results, I extend the model to a HANK framework to quantify the stabilization power of public health insurance programs over the business cycle, providing a structural counterpart to McKay and Reis (2016) for health-care benefits.

Abstract: We examine how inflation shapes the wages of newly hired workers by distinguishing between Employment‑to‑Employment (EE) transitions and Unemployment‑to‑Employment (UE) transitions, following the framework of Gertler et al. (2020). Using SIPP data from 2018–2024, we capture worker flows during the recent high‑inflation period. The analysis incorporates interaction terms between worker‑type indicators and inflation measures to identify heterogeneous wage responses across EE and UE hires.

Abstract: The project studies how the frequency of macroeconomic data releases affects the quality of information available to economic agents. Using a state‑space framework, it compares forecast performance when agents observe monthly versus quarterly signals, and quantifies the trade‑off between timeliness and noise. Signal noise is estimated from revisions in the BLS Current Employment Survey, allowing an empirical evaluation of the costs and benefits of lower‑frequency data publication.