Too much finance?


This paper examines whether there is a threshold above which financial depth no longer has a positive effect on economic growth. We use different empirical approaches to show that financial depth starts having a negative effect on output growth when credit to the private sector reaches 100 % of GDP. Our results are consistent with the “vanishing effect” of financial depth and that they are not driven by endogeneity, output volatility, banking crises, low institutional quality, or by differences in bank regulation and supervision.

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  1. 1.

    Several economists thus remained of the view that a large financial system is simply a by-product of the overall process of economic development. Among the remaining skeptics, Levine (2005) cites Robert Lucas (1988). Rodrik and Subramanian (2009) also suggest that economists may overemphasize the role of finance in economic development. Demetriades and Hussein (1996) apply time series techniques to a sample of 16 countries and find no evidence of a causal relationship going from finance to growth. Arestis and Demetriades (1997) and Arestis et al. (2001) discuss how institutional factors may affect the relationship between finance and growth and warn against the one-size-fits-all nature of cross-sectional exercises. Demetriades and Law (2006) show that financial depth does not affect growth in countries with poor institutions and Rousseau and Wachtel (2002) find that finance has no effect on growth in countries with double digit inflation. De Gregorio and Guidotti (1995) show that in high-income countries financial depth is positively correlated with output growth over the 1960–1985 period but that the correlation between financial depth and growth becomes negative for the 1970–1985 period. They suggest that high-income countries may have reached the point at which financial depth no longer contributes to increasing the efficiency of investment. Rousseau and Wachtel (2011) also find a vanishing effect of financial depth and show that credit to the private sector has no statistically significant impact on GDP growth over the 1965–2004 period. For surveys with more details on causality see Levine (2005) and Panizza (2013).

  2. 2.

    Levine et al. (2000) instrumented their cross sectional regressions with legal origin (Porta et al. 1998) and Beck et al. (2000) argued for causality by using the dynamic panel data estimators developed by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998).

  3. 3.

    While the Rajan and Zingales (1998) approach can only be used to evaluate the relative effect of financial development, it does provide strong support for a channel through which finance could affect growth.

  4. 4.

    Gaytan and Rancière (2004) develop and test a model in which the contribution of finance to growth is increasing in national wealth.

  5. 5.

    Deidda and Fattouh (2002) used cross-country data and a threshold regression model to show that financial depth has a positive but statistically insignificant impact on output growth in countries with low level of economic development or financial depth and that financial depth has a positive and statistically significant impact on growth in countries with higher levels of economic development and financial depth. Rioja and Valev (2004) divided a panel of 72 countries into three groups and showed that there is no statistically significant relationship between finance and growth at low levels of financial depth, there is a strong and positive relationship at intermediate levels of financial depth, and a weaker but still positive and statistically significant effect of finance at higher levels of financial depth.

  6. 6.

    For a discussion of the concept and process of financial development per se, see also de la Torre et al. (2011).

  7. 7.

    We would like to thank an anononymous referee for helping us in thinking through this interpretation of our results.

  8. 8.

    There are two distortions that may create a wedge between private and social returns: bank bailouts and the remuneration structure of bank managers (Rajan 2010; Crotty 2009). The second distortion may also lead to a reduction of shareholder value. Deidda (2006) develops a model in which the financial sector can have a negative effect on growth because it subtracts resources from the productive sectors.

  9. 9.

    Most studies use the log of financial depth and therefore allow for a non-linear relationship between financial development and economic growth. However, they do not include higher polynomial terms and thus they do not allow for a non-monotonic relationship between these two variables.

  10. 10.

    Another issue that could affect our results in terms of the validity of our measure of financial depth is that of bond financing. Data on the size of the corporate bond market are available from the BIS. However, the sample starts in 1989 and only covers 33 countries. Coverage has increased over time. By 2005 the BIS sample included 42 countries. Capitalization is small. In 2005, average capitalization for the 42 countries for which data are available was 6 % of GDP. Only 12 countries have a capitalization greater than 10 % of GDP (Canada, Chile, Iceland, Italy, Japan, South Korea, Malaysia, Malta, Portugal, Taiwan Province of China, Thailand, and the United States) and 22 countries had a capitalization lower than 5 % of GDP. It is thus highly likely that this source of finance is at most marginal for the broad sample of countries that we consider.

  11. 11.

    The results are in Arcand et al. (2011). In the regressions that include turnover we find that there is a positive and monotonic relationship between the turnover ratio and economic growth, and that the non-monotone relationship between credit to the private sector and economic growth is robust to controlling for the turnover ratio.

  12. 12.

    Table 14 in the Appendix describes all the variables used in the empirical analysis and provides a list of sources. Table 15 in the Appendix reports the summary statistics.

  13. 13.

    In the last two columns of Table 1, we lose three observations for which we do not have recent GDP data. The results are unchanged if we estimate all regressions using the 64 observations sample of these columns.

  14. 14.

    A weak identification test signals that the instruments are weak (the Kleibergen–Paap weak instrument Wald F statistic is 0.583) and Hansen’s J tests rejects the overidentifying restrictions with a p value of 0.01. We face the same problems if we use the same sample and data as Levine et al. (2000).

  15. 15.

    We estimate the models by using all available observations. We obtain similar results if we use a constant sample of countries.

    Table 4 Cross-country IH regressions
  16. 16.

    Our regressions use all available lags as instruments, but the results are robust to different lag lengths. One source of concerns when estimating fixed effect models is that the limited within-country variability of the data tends to amplify the attenuation bias brought about by the presence of measurement errors. However, our variables of interest display substantial cross-country and within-country variation. Credit to the private sector, for instance, has a between-country standard deviation of 0.30 and a within-country standard deviation of 0.22 (the overall standard deviation is 0.37).

  17. 17.

    As in Beck and Levine, we take logs of all these variables. We deal with zero values by applying the inverse hyperbolic sine transformation (\( \widehat{x}=\ln (x+\sqrt{x^{2}+1})\)) described by Burbidge et al. (1988).

  18. 18.

    The high p values of the OID test, however, suggest that we might be overfitting the model. By reducing the number of lags in the set of instruments, we find results which are similar to those of Table 5, but with slightly lower values of the OID test.

  19. 19.

    As in the regressions of Table 5, we find very high p values for the overidentification tests, an indication that we may be overfitting our first stage. The results are robust to reducing the number of lags in the set of instruments. For instance, if we re-estimate the models of columns 1–4 (Table 6), we always find that both the linear and quadratic terms are statistically significant, with turning points which are lower than those of Table 6 (97 vs 144 %, 110 vs 137%, 95 vs 103 %, and 76 vs 90 %). When we use a smaller number of lags, the p values of the OID tests drop to 0.29, 0.38, 0.72, and 0.92 (full regression results available upon request). Results are sensitive to the choice of lag length and, since this choice may introduce some arbitrariness, we decided to report results based on regressions that use the default of including all lags in the set of instruments.

  20. 20.

    In fact, a quadratic model without controls yields better out-of-sample forecasts (\(MSE=5.7\), when we use 1960–2005 to forecast 2005–2010) than the linear model with the full set of controls used in Table 5 (\( MSE=6.4 \), when we use 1960–2005 to forecast 2005–2010).

  21. 21.

    Full regression results are available upon request.

  22. 22.

    Note that our sample includes 24 LDCs, for a total of 136 observations, and that these are all countries with very low levels of financial depth. If we restrict our analysis to LDCs, the relationship between finance and growth (either linear or quadratic) is never statistically significant. If we drop the LDCs from the sample our results become even stronger.

  23. 23.

    In this regression, we have 818 observations with \(PC<90\,\%\) and 99 observation with \(PC>90\,\%\). Full regression results are available upon request.

  24. 24.

    Aghion et al. (2005) measure growth and GDP per capita as deviation with respect to the United States.

  25. 25.

    Aghion et al. (2005) instrument financial depth with legal origin. Since legal origin is not a good instrument for the square of financial depth, we use identification through heteroskedasticity.

  26. 26.

    While in (9) we describe our estimating equation by using the standard fixed effects approach, we are actually estimating it with a system GMM in which time-invariant heterogeneity is controlled for through first-differencing.

  27. 27.

    Eichengreen et al. (2011) find the same result when they look at the effect of capital account and financial liberalization.

  28. 28.

    We would like to thank an anonymous referee for suggesting this specification. Full regression results are available from the authors.

  29. 29.

    Details and regression results are available in Arcand et al. (2012).

  30. 30.

    We are grateful to the authors for sharing their data on household and firm credit.

  31. 31.

    We find an impact which is quantitatively smaller than that found by Rajan and Zingales (1998). In their estimations, the differential in growth between an industry at the 75th percentile level of external dependence with respect to an industry at the 25th percentile level when it is located in a country at the 75th percentile of credit to the private sector rather than in a country at the 25th percentile was about 1 % point. In our estimates, this differential in growth is approximately 0.4 % points.

    Table 13 Rajan and Zingales estimations


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This is a revised version of a paper that was first circulated in March 2011. We would like to thank without implication, three anonymous referees, the editor, Thorsten Beck, Jean-Claude Berthélémy, Gunther Capelle-Blancard, Stijn Claessens, Tito Codella, Fabrizio Coricelli, Augusto de La Torre, Giovanni Dell’Ariccia, Panicos Demetriades, Luigi Guiso, Luc Laeven, Ross Levine, Eduardo Levy Yeyati, Jörg Mayer, Nicolas Maystre, Halvor Mehlum, Juan Pablo Nicolini, Sandra Poncet, Andy Powell, Giuseppe Ragusa, Vincenzo Scoppa, Federico Sturzenegger, Filippo Taddei, Marie-Anne Valfort, and seminar participants at the IMF, Chicago FED, ECARES, Universidad Torcuato di Tella, University of Milan, LUISS, Collegio Carlo Alberto, LACEA, Università delle Marche, Universidad Autonoma del Estado de Mexico, Paris I Panthéon-Sorbonne, University of Bergen, and EUDN for helpful comments and suggestions.

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Correspondence to Ugo Panizza.

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See Tables 14, 15, 16 and 17.

Table 14 Data description and sources
Table 15 Summary statistics
Table 16 Credit to the private sector over GDP (selected years)
Table 17 Share of observations above and below thresholds

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Arcand, J.L., Berkes, E. & Panizza, U. Too much finance?. J Econ Growth 20, 105–148 (2015).

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  • Finance
  • Growth
  • Financial crises
  • Non-linearities

JEL Classification

  • O11
  • O16
  • E44
  • G1