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

  • S.I.: Recent Developments in Financial Modeling and Risk Management
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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.

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Notes

  1. Appendix A offers a detailed overview of the questions from the manufacturing survey used in this paper. For further details on the survey, see European Commission (2017).

  2. While our sample is limited to manufacturing firms, the BLS reports banking sector's view on the relationship between banks and domestic enterprises, thus including also those belonging to the building or service sectors.

  3. The monotonic transformation we have chosen yields virtually identical results to the logit function, \( \left[ {\frac{1}{{1 + \exp \left( { - cci} \right)}}} \right] \), while it looks preferable to alternatives like those based on the standardized normal distribution \( \varPhi \left( {cci_{t} /sd_{cci} } \right) \), where sdcci indicates the sample standard deviation of ccit, or the normalization \( \left( {cci_{t} - cci_{ \hbox{min} } } \right)/\left( {cci_{ \hbox{max} } - cci_{ \hbox{min} } } \right) \), where \( cci_{ \hbox{min} } \) and \( cci_{ \hbox{max} } \) denote the sample minimum and maximum value, respectively, because it turns out to be less dependent on possible outliers in the sample.

  4. Using a measure of shadow rate instead of the repo rate does not affect our conclusions. In an alternative specification, a measure of shadow rate for the euro area, based on Krippner (2016), is used in place of the repo rate. The resulting evidence is qualitatively similar to the results discussed in Sect. 5.3. All quarterly data used in Sects. 4 and 5.3 are taken via Datastream.

  5. See, among others, Bastos (2010) and Caporale and Girardi (2013) for a similar application of fraction regression models.

  6. Note that with the identity function the fraction regression model collapses to the standard OLS regression. The quasi-maximum likelihood estimator of θ in condition (6) is consistent regardless of the distribution of \( \widetilde{cci} \). conditional on the Q’s (Papke and Wooldridge 1996).

  7. Specifically, the bank lending channel operates through banks' liability side. It posits that a monetary contraction, by draining reserves from the banking system, tends to leave banks with fewer loanable funds, thereby reducing lending (Bernanke and Blinder 1988). At the same time, less accommodative monetary policy increases banks’ external finance premium pushing banks to respond by reducing the total amount of credit they are willing to supply (Stein 1998). When considering the balance sheet channel, tight monetary policy operates through banks' asset side by reducing the net worth of borrowers with weaker fundamentals (Bernanke et al. 1996; Bernanke and Gertler 1989). Furthermore, a less accommodative monetary stance tends to increase the real value that banks must pay to retain deposits, which causes banks to fund fewer long-term projects (Diamond and Rajan 2006).

  8. Since a large number of instruments can overfit the instrumented variables, leading to inaccurate estimations and wrong inference in the Sargan-J test (Roodman 2009) we have kept the number of over-identifying restrictions to its minimum, i.e. one. To assess the relevance of our instrument set, we have computed the correlation between each potentially endogenous variable xj,t with its own instrument, xj,t-1 (as well as xj,t−2 for the case of liq_1). The results (available upon request from the authors) show that each xj,t is strongly correlated with its lag xj,t−1; the same holds true for the degree of association between liq_1t and \( liq\_1_{t - 2} \), validating the relevance of the chosen instruments. As for the second step regression, the Sargan-J does not rejects the null of validity of the instruments at the usual confidence levels (p value of 0.28); moreover, both the tests for weak- and under-identification reject the corresponding null hypotheses, while the F-test rejects the null of irrelevance of the entire set of regressors.

  9. For the sake of brevity, we do not report the empirical evidence from the fractional-probit alternative. The estimated ape's as well as the conclusions from the scenario analysis exercises are virtually identical to those reported in the main text. The complete set of results is available from the authors upon request.

  10. Namely, Abruzzo, Campania, Apulia, Basilicata, Molise, Calabria, Sicily and Sardinia.

  11. For the sake of brevity, the complete set of estimation results is not reported but available from the authors upon request.

  12. All growth rates are defined in annualized terms, while the autoregressive order used in the estimation is set to one.

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Authors and Affiliations

  1. Parliamentary Budget Office, PBO, Rome, Italy

    Alessandro Girardi

  2. Department of Economics and Law, Sapienza University of Rome, Rome, Italy

    Marco Ventura

  3. Italian National Institute of Statistics, ISTAT, Rome, Italy

    Alessandro Girardi & Marco Ventura

Authors
  1. Alessandro Girardi
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Correspondence to Marco Ventura.

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Appendix

Appendix

Our analysis uses data collected within the Joint Harmonised EU Programme of Business and Consumer Surveys, which inquires every month about 120,000 enterprises, as well as 40,000 consumers, across Europe (see European Commission 2017). As for the business sectors, enterprises are asked to assess the development of concepts like production, order books, or employment. Data are typically qualitative in nature, in the sense that they convey firms’ opinions—rather than quantitative information—on production, demand, inventories and other variables relevant at the firm level. Questions usually ask the firm to choose among three possible answers arranged on a Linkert scale. As for the temporal horizon, survey questions refer to the present situation, developments over the past three, or expectations for the next 3 months. Table 8 reports the questions of the manufacturing survey and the associated firm-specific variables that have been used in the empirical analysis.

Table 8 Survey questions
Full size table

For the case of Italy, the National Institute of Statistics (ISTAT) collects information in the form of panels stratified by geographical location, sector and size. Respondents are extracted from the official register of active firms. As for the manufacturing sector, the samples sizes is about 4000 individuals. Interviews are conducted through computer-assisted methods in the first 2 weeks of each month. Survey results are typically published before the end of the reference month and not revised afterwards.

Since March 2008, a specific section focusing on the bank-firm relationship has been added to the manufacturing survey in order to collect some information about credit access conditions. Specifically, firms are asked to report their perceptions on credit conditions, with three possible answers arranged on a Likert scale (getting better, stable, getting worse). This question corresponds to the variable discussed in Sect. 2.1. Subsequently, firms have to indicate whether or not their appraisal is based on a formal contact with a credit institution.

If it is the case, respondents are asked to specify whether:

  1. a.

    their request for credit has been obtained at the same conditions as 3 months before;

  2. b.

    their request for credit has been obtained at worsening conditions.

    If it is the case, a question is additionally asked about its determinants by allowing for the following possible answers: (a) higher interest rates, (b) higher collateral (real or personal guarantees), (c) limits to the amount of loans, (d) higher costs;

  3. c.

    their request for credit has been denied.

    If it is the case, a question is additionally asked about whether credit is due to (a) an explicit denial by the financial institution or (b) withdraw by the firm due to excessively unfavorable conditions imposed by the financial institution;

  4. d.

    the contact with the bank was only motivated by a request of information.

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Girardi, A., Ventura, M. Measuring credit crunch in Italy: evidence from a survey-based indicator. Ann Oper Res 299, 567–592 (2021). https://doi.org/10.1007/s10479-019-03238-7

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