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Volatility and growth: Credit constraints and the composition of investment

Open access
Aghion, Philippe, Angeletos, George-Marios, Banerjee, Abhijit, Manova, Kalina·2010·Journal of Monetary Economics

Abstract

How does uncertainty and credit constraints affect the cyclical composition of investment and thereby volatility and growth? This paper addresses this question within a model where firms engage in two types of investment: a short-term one; and a long-term one, which contributes more to productivity growth. Because it takes longer to complete, long-term investment has a relatively less cyclical return; but it also has a higher liquidity risk. The first effect ensures that the share of long-term investment to total investment is countercyclical when financial markets are perfect; the second implies that this share may turn procyclical when firms face tight credit constraints. A novel propagation mechanism thus emerges: through its effect on the cyclical composition of investment, tighter credit can lead to both higher volatility and lower mean growth. Evidence from a panel of countries provides support for the model's key predictions.

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Metadata

Authors
Aghion, P., Angeletos, G.-M., Banerjee, A., & Manova, K.
Venue
Journal of Monetary Economics
Publisher
Elsevier BV
Published
2010-04
Volume
57
Issue
3
Pages
246-265
ISSN
0304-3932 (ISSN-L)
OpenAlex
W2014339993
Cited by
642
References
3
FWCI
70.34
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Volatility and growth: Credit constraints and the composition of investment. https://app.cochat.ai/papers/10.1016/j.jmoneco.2010.02.005
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Aghion, P., Angeletos, G.-M., Banerjee, A., & Manova, K. (2010). Volatility and growth: Credit constraints and the composition of investment. Journal of Monetary Economics. https://doi.org/10.1016/j.jmoneco.2010.02.005

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.