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Cited by 196

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Inflation, Inflation Variability, and Corruption

Braun, Miguel, Di tella, Rafael·2004·Economics & Politics

Abstract

We present a model where agents can inflate the cost of goods needed to start an investment project and inflation variability increases monitoring costs. We show that inflation variability can lead to higher corruption and lower investment. We document a positive relationship between corruption and inflation variability in a sample of 75 countries. The effect is robust to the inclusion of country fixed effects, other controls, and 2SLS estimation. The results are economically significant: a one standard deviation increase in inflation variance from the median increases corruption by 12 percent of a standard deviation and reduces growth by 0.33 percentage points. Our paper highlights a new channel through which inflation reduces investment and growth, thus bridging the perception gap over the costs of inflation between economists and the public. We also find evidence that political competition reduces corruption and that corruption is pro‐cyclical.

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Crossref · 10.1111/j.1468-0343.2004.00132.x

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Metadata

Authors
Braun, M., & Di tella, R.
Venue
Economics & Politics
Publisher
Wiley
Published
2004-02-03
Volume
16
Issue
1
Pages
77-100
ISSN
  • 0954-1985 (ISSN-L)
  • 1468-0343
OpenAlex
W2021551161
Cited by
196
References
2
FWCI
7.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Inflation, Inflation Variability, and Corruption. https://app.cochat.ai/papers/10.1111/j.1468-0343.2004.00132.x
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Braun, M., & Di tella, R. (2004). Inflation, Inflation Variability, and Corruption. Economics & Politics. https://doi.org/10.1111/j.1468-0343.2004.00132.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.