Fernando Cirelli


I am an assistant professor at Columbia University School of International and Public Affairs (SIPA)


My research interest include Macroeconomics, Monetary Economics, and Macro-Finance


Please click here for my CV


Email:  f.cirelli@columbia.edu


Twitter:  @fercirelli 

Research

Qatar Centre for Global Banking and Finance at King’s Business School Young Economist Prize 2023

I study the welfare cost of anticipated inflation with an emphasis on distributional considerations. Two facts motivate my approach. First, around 60% of U.S. households are Bank-Dependent: they save all their liquid assets in bank deposits. Second, there is imperfect passthrough of market interest rates to bank deposit rates, i.e. deposit rates move less than one-to-one with market rates. As a result, high expected inflation lowers the real return on liquid savings for Bank-Dependent households, which impairs their precautionary saving capacity. I study a model of non-competitive banks along with households that vary in financial sophistication. In the model, the joint distribution of households' portfolio choices and wealth shapes demand elasticities for deposits thereby influencing banks' optimal interest rates. I use the model to explore the consequences of permanent and temporary changes in inflation. The model predicts the welfare costs of high inflation to be disproportionately borne by low- and medium-wealth households that rely on deposits to smooth consumption.

As is well known, during the pandemic recession firms directly exposed to the virus, i.e. the “contact” sector, contracted sharply and recovered slowly relative to the rest of the economy. Less understood is how firms that “won” by offering safer substitutes for contact sector goods have affected this unequal downturn. Using both firm and industry data, we first construct disaggregated measures of revenue growth that distinguish between contact sector losers, contact sector winners, and the non-contact sector. We show that contact sector losers contracted roughly fifty percent more than the sector average, while winners grew. Further, forecast data suggests that the gap between winners and losers will persist at least through 2022. To explain this evidence, we then develop a simple three sector New Keynesian model with (i) a sector of firms that offers safe substitutes for risky contact sector goods and (ii) learning by doing. Overall, the model captures the unequal sectoral recession. It also accounts for inflation, including the sharp runup in 2021.

Journal of Development Economics, Volume 148, January 2021. (WP version) (BibTeX)

The high incidence of informality in the labor markets of middle-income economies challenges the provision of unemployment protection. We show that, despite informational frictions, the introduction of an unemployment insurance savings account (UISA) system may provide substantial benefits. This system improves welfare by providing insurance to the unemployed and creating incentives to work in the formal sector. The optimal scheme generates a reduction in unemployment (from 4 to 3 percent), an increase in formality (from 68 to 72 percent) and a rise in total output (by 4 percent). Overall, individuals obtain welfare gains equivalent to a 2.4 percent increase in consumption in every period.

Short paper examining the impact of a CBDC on bank lending in a stylized general equilibrium model

We examine the impact of a Central Bank Digital Currency (CBDC) on bank lending, emphasizing the role of different financial frictions. Within a stylized general equilibrium model, we integrate a banking sector characterized by market power on deposits and leverage constraints, together with liquidity in households' utility. Calibrating the model to US data and simulating a CBDC introduction as a shift in households' preferences for public money, our results indicate that a CBDC increases bank lending when market power is the primary operating friction in the banking sector. However, this outcome reverses when leverage constraints are binding for banks.