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Measuring credit crunch in Italy: evidence from a survey-based indicator

Author

Listed:
  • Alessandro Girardi

    (Parliamentary Budget Office, PBO
    Italian National Institute of Statistics, ISTAT)

  • Marco Ventura

    (Sapienza University of Rome
    Italian National Institute of Statistics, ISTAT)

Abstract

This paper presents a micro–macro framework to derive a credit crunch indicator for the Italian manufacturing sector. Using qualitative firm-level data over the years 2008–2018, nonlinear discrete panel data techniques are first applied in order to identify the loan supply curve controlling for firm-specific observable characteristics. In the subsequent step, the variation of the estimated supply curve that cannot be explained by proxies for loan demand is interpreted as the degree of credit squeeze prevailing in the economy at a given point in time. The empirical evidence shows that credit crunch episodes are less likely to occur during periods of sustained economic growth, or when credit availability for the manufacturing sector is relatively abundant. In contrast, a tight monetary policy stance or a worsening of the quality of banking balance sheets tend to increase the likelihood of experiencing a credit squeeze.

Suggested Citation

  • Alessandro Girardi & Marco Ventura, 2021. "Measuring credit crunch in Italy: evidence from a survey-based indicator," Annals of Operations Research, Springer, vol. 299(1), pages 567-592, April.
  • Handle: RePEc:spr:annopr:v:299:y:2021:i:1:d:10.1007_s10479-019-03238-7
    DOI: 10.1007/s10479-019-03238-7
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    More about this item

    Keywords

    Business survey; Credit crunch; Access to credit;
    All these keywords.

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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