Visualització del contingut web Visualització del contingut web

      CV - Job Market Paper  
 

Manea, Cristina

Contact Information

Tel. +34 93 542 2693

[email protected]

Available for interviews at

European Job Market for Economists (EEA), December 18-19, Rotterdam, The Netherlands

Allied Social Science Associations (ASSA), January 3-5, San Diego, US

 

 

Research interests

Monetary Economics, Macroeconomics

Placement Officer

Filippo Ippolito
[email protected]
 

References

Jordi Galí (Advisor)
[email protected]

Alberto Martin (Advisor)
[email protected]

Boris Hofmann
[email protected]

Alain Durre
[email protected]

Research

"Monetary Policy with Financially-Constrained and Unconstrained Firms” (Job Market Paper)
As monetary policy affects financially-constrained and unconstrained firms to different degrees, its overall impact ought to vary with the share of constrained firms in the economy. But it is not clear how. The theoretical literature on the transmission channels and design of monetary policy is ill-equipped to address this question, because it relies on models that feature either constrained firms or unconstrained firms —but not both. In this paper, I enrich the basic New Keynesian model by allowing for both types of firms, and use it the revisit the literature. My model yields a number of novel insights. (i) The interactions of the two types of firms on input and output markets activate a new transmission channel (the “spillover channel”). (ii) Monetary policy affects constrained firms via input prices in the opposite direction of the standard balance-sheet channel (a new “input-price channel”). (iii) Aggregate output does not necessarily respond more strongly to monetary policy when the share of constrained firms is higher (contrary to the financial accelerator intuition) and (iv) “price puzzles” may emerge in equilibrium. (v) Because of the spillover channel, the optimal design of monetary policy does not necessarily change with the share of constrained firms. I use firm-level data to validate the predictions of the model, and to show how it can be used for monetary policy analysis.

“Inside-Money in the New Keynesian Model”
The textbook New Keynesian framework has become a common tool for monetary policy analysis in central banks. Policy makers are nonetheless often concerned that this framework abstracts away from endogenous money creation, and lacks realism. To address this concern, I introduce endogenous money creation by the private banking sector (like deposits), or “inside money”, into the textbook framework. I find that the new “inside money” model has the same equilibrium representation as the textbook “money-less” one, and hence transmission and optimal design of monetary policy in the two models are identical.

“Optimal Monetary-Fiscal Policy Mix with Zero Lower Bound and Fiscal Limit Constraints”(with A. Durré (Goldman Sachs and University of Paris Dauphine))
With their aging populations, many advanced economies are currently (i) approaching their fiscal limit, and (ii) facing a decline in their long-run real interest rates. In this paper, we study the optimal monetary-fiscal policy mix in the presence of fiscal limit and zero lower bond (ZLB) constraints. We conduct our analysis in an extension of the basic New Keynesian framework with an endogenous fiscal limit. We assume away both outright default on public debt and monetary financing. We find that, in a first instance, as the economy approaches its fiscal limit and the zero lower bound starts binding, the reduction in fiscal space limits the future boom that the policymaker can promise. In this case, the dynamics under the optimal policy mix are less inflationary. However, once the economy goes beyond its fiscal limit, the optimal monetary-fiscal policy mix necessarily implies an increase in the inflation target. As a result, the economy gravitates around an equilibrium with higher average inflation, and hence the optimal monetary-fiscal policy mix necessarily becomes more inflationary.

“Monetary Policy in a World of Intangible Capital and Firm Heterogeneity”(with R. Banerjee (BIS) and B. Hofmann (BIS))
The ratio of intangible to tangible capital ratio in advanced economies has been raising steadily over the past decades. We study whether/how this trend affects the transmission of monetary policy. Using firm-level data, we find that on average firms with a lower ratio of tangible capital (eg more R&D), respond more to monetary policy, and are less levered. We explain this result by the fact that intangible capital being less pledgeable than physical capital| these firms are relatively more financially constrained. We use a theoretical model to rationalize these results and to study the implications for monetary policy design.

 

Research in Progress

“Financial stability and monetary policy” (with F. Boissay (BIS) and F. Collard (TSE))
Since the Great Financial Crisis, accommodative monetary policy, with policy rates “too low for too long”, has been widely blamed for being conducive to financial imbalances and banking crises. The goal of our project is to study the relation between monetary policy and financial stability. In particular, we are interested in how transmission of monetary policy changes when the interbank market is subject to sudden freezes, and how its design should be adjusted.

“BigTech and the credit channel of monetary transmission“(with F. de Fiore (BIS and ECB) and L. Gambacorta (BIS))
Technology firms such as Alibaba, Amazon, Facebook have grown rapidly over the last two decades, and recently started to venture into financial services. As yet, financial services are only a small part of their business globally. But given their size and customer reach, BigTechs' entry into finance has the potential to spark rapid changes in the industry. The goal of our project is to study how financial intermediation by BigTechs could modify the transmission of monetary policy. To structure our analysis, we started by developing a theoretical model. Our plan, going forward, is to test the predictions of the model empirically using BigTechs' lending data.