Too much finance?

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Notes

  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

References

  1. Aghion, P., Howitt, P., & Mayer-Foulkes, D. (2005). The effect of financial development on convergence: Theory and evidence. The Quarterly Journal of Economics, 120(1), 173–222.

    Google Scholar 

  2. Aizenman, J., Jinjarak, Y., & Park, D. (2015). Financial development and output growth in developing Asia and Latin America: A comparative sectoral analysis. NBER WP 20917. Cambridge, MA: National Bureau of Economic Research

  3. Arcand, J. L., Berkes, E., & Panizza U. (2011). Too much finance? IHEID WP.

  4. Arcand, J. L., Berkes, E., & Panizza U. (2012). Too much finance? IMF WP 12/161.

  5. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–97.

    Article  Google Scholar 

  6. Arellano, M., & Bover, O. (1995). Another look at the instrumental variables estimation of error-components models. Journal of Econometrics, 68(1), 29–51.

    Article  Google Scholar 

  7. Arestis, P., & Demetriades, P. (1997). Financial development and economic growth: Assessing the evidence. Economic Journal, 107, 83–99.

    Article  Google Scholar 

  8. Arestis, P., Demetriades, P. O., & Luintel, K. (2001). Financial development and economic growth: The role of stock markets. Journal of Money, Credit and Banking, 33(1), 16–41.

    Article  Google Scholar 

  9. Bagehot, W. (1873). Lombard street: A description of the money market. McMaster University archive for the history of economic thought: History of economic thought books.

  10. Barro, R., & Lee, J. W. (2010). A new data set of educational attainment in the world, 1950–2010. NBER WP 15902. Cambridge, MA: National Bureau of Economic Research.

  11. Barth, J., Caprio, G., & Levine, R. (2008). Bank regulations are changing: For better or worse. Comparative Economic Studies, 50(4), 537–563.

    Article  Google Scholar 

  12. Beck, T., & Levine, R. (2002). Industry growth and capital allocation: Does having a market- or bank-based system matter? Journal of Financial Economics, 64(2), 147–180.

    Article  Google Scholar 

  13. Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking and Finance, 28(3), 423–442.

    Article  Google Scholar 

  14. Beck, T., Levine, R., & Loayza, N. (2000). Finance and the sources of growth. Journal of Financial Economics, 58(1–2), 261–300.

    Article  Google Scholar 

  15. Beck, T., Demirgüç-Kunt, A., & Levine, R. (2010). Financial institutions and markets: Across countries and over time. World Bank Economic Review, 24(1), 77–92.

    Article  Google Scholar 

  16. Beck, T., Buyukkarabacak, B., Rioja, F., & Valev, N. (2012). Who gets the credit? And does it matter? Household vs. firm lending across countries. B.E. Journal of Macroeconomics: Contributions, 12, 1–44.

    Google Scholar 

  17. Beck, T., Degryse, H., & Kneer, C. (2014). Is more finance better? Disentangling intermediation and size effects of financial systems. Journal of Financial Stability, 10, 50–64.

    Article  Google Scholar 

  18. Bencivenga, V., & Smith, B. (1991). Financial intermediation and endogenous growth. The Review of Economic Studies, 58(2), 195–209.

    Article  Google Scholar 

  19. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

    Article  Google Scholar 

  20. Burbidge, J., Magee, L., & Robb, A. L. (1988). Alternative transformations to handle extreme values of the dependent variable. Journal of the American Statistical Association, 83(401), 123–127.

    Article  Google Scholar 

  21. Cecchetti, S. & Kharroubi, E. (2012). Reassessing the impact of finance on growth. BIS WP 381. Basel: Bank for International Settlements.

  22. Cecchetti, S., & Kharroubi, E. (2015). Why does financial sector growth crowd out real economic growth?. BIS WP 490. Basel: Bank for International Settlements.

  23. Cerra, V., & Saxena, S. C. (2008). Growth dynamics: The myth of economic recovery. American Economic Review, 98(1), 439–457.

    Article  Google Scholar 

  24. Coricelli, F., Driffield, N., Pal, S., & Roland, I. (2012). When does leverage hurt productivity growth? A firm-level analysis. Journal of Interantional Money and Finance, 31(6), 1674–1694.

    Article  Google Scholar 

  25. Coval, J., Jurek, J., & Stafford, E. (2009). The economics of structured finance. Journal of Economic Perspectives, 23, 3–25.

    Article  Google Scholar 

  26. Crotty, J. R. (2009) The bonus-driven “rainmaker” financial firm: How these firms enrich top employees. destroy shareholder value and create systemic financial instability. WP. Amherst: Department of Economics, University of Massachusetts Amherst.

  27. De Gregorio, J., & Guidotti, P. (1995). Financial development and economic growth. World Development, 23(3), 433–448.

    Article  Google Scholar 

  28. Deidda, L. (2006). Interaction between economic and financial development. Journal of Monetary Economics, 53, 233–248.

    Article  Google Scholar 

  29. Deidda, L., & Fattouh, B. (2002). Non-linearity between finance and growth. Economics Letters, 74, 339–345.

    Article  Google Scholar 

  30. de la Torre, A., Ize, A., & Schmukler, S. (2011). Financial development in Latin America and the Caribbean: The road ahead. Washington, DC: The World Bank.

    Google Scholar 

  31. Demetriades, P. O., & Hussein, K. A. (1996). Does financial development cause economic growth? Time-series evidence from 16 countries. Journal of Development Economics, 51(2), 387–411.

    Article  Google Scholar 

  32. Demetriades, P. O., & Law, S. H. (2006). Finance, institutions and economic development. International Journal of Finance and Economics, 11(3), 245–260.

    Article  Google Scholar 

  33. Demirgüç-Kunt, A., & Levine, R. (2001). Financial structures and economic growth: A cross-country comparison of banks, markets, and development. Cambridge, MA: MIT Press.

    Google Scholar 

  34. Demirgüç-Kunt, A., & Maksimovic, V. (2002). Funding growth in bank-based and market-based financial systems: Evidence from firm level data. Journal of Financial Economics, 65, 337–363.

    Article  Google Scholar 

  35. Demirgüç-Kunt, A., Feyen, E., & Levine, R. (2013). The evolving importance of banks and securities markets. World Bank Economic Review, 27(3), 476–490.

    Article  Google Scholar 

  36. Easterly, W., Islam, R., & Stiglitz, J. (2000). Shaken and stirred, explaining growth volatility. In Annual bank conference on development economics. Washington, DC: World Bank.

  37. Eichengreen, B., Gullapalli, R., & Panizza, U. (2011). Capital account liberalization, financial development and industry growth: A synthetic view. Journal of International Money and Finance, 30(6), 1090–1106.

    Article  Google Scholar 

  38. Eugster, J. (2013). Too much finance and the functional form: Non-linear marginal effects on growth. unpublished, IMF.

  39. Galor, O., & Moav, O. (2004). From physical to human capital accumulation: Inequality and the process of development. Review of Economic Studies, 71(4), 1001–1026.

    Article  Google Scholar 

  40. Galor, O., & Zeira, J. (1993). Income distribution and macroeconomics. Review of Economic Studies, 60(1), 35–52.

    Article  Google Scholar 

  41. Gaytan, A. & Rancière, R. (2004). Wealth, financial intermediation and growth. WP 851. Barcelona: Department of Economics and Business, Universitat Pompeu Fabra.

  42. Gennaioli, N., Shleifer, A., & Vishny, R. W. (2010). Neglected risks, financial innovation, and financial fragility. NBER WP 16068. Cambridge, MA: National Bureau of Economic Research.

  43. Goldsmith, R. W. (1969). Financial structure and development. New Haven: Yale University Press.

    Google Scholar 

  44. Grenwood, J., & Jovanovich, B. (1990). Financial development, growth and the distribution of income. Journal of Political Economy, 98(5), 1076–1108.

    Article  Google Scholar 

  45. Jordà, Ò., Schularick, M., & Taylor, A. (2014). Betting the House. NBER WP 20771. Journal of International Economics (forthcoming)

  46. Kaminsky, G., & Reinhart, C. (1999). The twin crises: The causes of banking and balance-of-payments problems. American Economic Review, 89(3), 473–500.

    Article  Google Scholar 

  47. Kindleberger, C. P. (1978). Manias, panics, and crashes: A history of financial crises. New York: Basic Books.

    Google Scholar 

  48. King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics, 108(3), 717–37.

    Article  Google Scholar 

  49. Kneer, C. (2013). Finance as a magnet for the best and brightest: Implications for the real economy. DNB WP 392.

  50. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. (1998). Law and finance. Journal of Political Economy, 106(6), 1113–1155.

    Article  Google Scholar 

  51. Laeven, L. & Valencia, F. (2010). Resolution of banking crises: The good, the bad, and the ugly. IMF WP 10/146. Washington, DC: International Monetary Fund

  52. Law, S. H., & Singh, N. (2014). Does too much finance harm economic growth? Journal of Banking and Finance, 41, 36–44.

    Article  Google Scholar 

  53. Levine, R. (2002). Bank-based or market-based financial systems: Which is better? Journal of Financial Intermediation, 11, 398–428.

    Article  Google Scholar 

  54. Levine, R. (2005). Finance and growth: Theory and evidence. in Aghion, P. & Durlauf, S. (Eds.), Handbook of economic growth, Chap. 12 (Vol. 1, pp. 865–34). Amsterdam: Elsevier.

  55. Levine, R., & Zervos, S. (1998). Stock markets, banks, and economic growth. The American Economic Review, 88(3), 537–58.

    Google Scholar 

  56. Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46(1), 31–77.

    Article  Google Scholar 

  57. Lewbel, A. (2012). Using heteroskedasticity to identify and estimate mismeasured and endogenous regressor models. Journal of Business and Economic Statistics, 30, 67–80.

    Article  Google Scholar 

  58. Lind, J. T., & Mehlum, H. (2011). With or without U? The appropriate test for a U-shaped relationship. Oxford Bulletin of Economics and Statistics, 72(1), 109–118.

    Article  Google Scholar 

  59. Loayza, N., & Ranciere, R. (2006). Financial development, financial fragility, and growth. Journal of Money, Credit and Banking, 38(4), 1051–1076.

    Article  Google Scholar 

  60. Lucas, R. E., Jr. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.

  61. Minsky, H. P. (1974). The modeling of financial instability: An introduction. In Proceedings of the fifth annual pittsburgh conference on modelling and simulation (Vol. 5, pp. 267–272). Pittsburgh: Instruments Society of America.

  62. Pagano, M. (2012). Finance: Economic lifeblood or toxin? CSEF Working Papers 326. Napoli: University of Naples.

  63. Panizza, U. (2013). Financial development and economic growth: Known knowns, known unknowns, and unknown unknowns. IHEID WP 14-2013.

  64. Philippon, T., & Reshef, A. (2013). An international look at the growth of modern finance. Journal of Economic Perspectives, 27, 73–96.

    Article  Google Scholar 

  65. Rajan, R. G. (2005). Has financial development made the world riskier? In Proceedings of the 2005 Jackson hole conference Kansas City fed.

  66. Rajan, R. G. (2010). Fault lines: How hidden fractures still threaten the world economy. Princeton, NJ: Princeton University Press.

    Google Scholar 

  67. Rajan, R. G., & Zingales, L. (1998). Financial dependence and growth. American Economic Review, 88(3), 559–86.

    Google Scholar 

  68. Ramey, G., & Ramey, V. A. (1995). Cross-country evidence on the link between volatility and growth. American Economic Review, 85(5), 1138–1151.

    Google Scholar 

  69. Ranciére, R., Tornell, A., & Westermann, F. (2008). Systemic crises and growth. Quarterly Journal of Economics, 123(1), 359–406.

    Article  Google Scholar 

  70. Rigobon, R. (2003). Identification through heteroskedasticity. The Review of Economics and Statistics, 85(4), 777–792.

    Article  Google Scholar 

  71. Rioja, F., & Valev, N. (2004). Does one size fit all? A reexamination of the finance and growth relationship. Journal of Development Economics, 74(2), 429–447.

    Article  Google Scholar 

  72. Rodrik, D., & Subramanian, A. (2009). Why did financial globalization disappoint? IMF Staff Papers, 56(1), 112–138.

    Article  Google Scholar 

  73. Rousseau, P., & Wachtel, P. (2002). Inflation thresholds and the finance-growth nexus. Journal of International Money and Finance, 21(6), 777–793.

    Article  Google Scholar 

  74. Rousseau, P., & Wachtel, P. (2011). What is happening to the impact of financial deepening on economic growth? Economic Inquiry, 49, 276–288.

    Article  Google Scholar 

  75. Sasabuchi, S. (1980). A test of a multivariate normal mean with composite hypotheses determined by linear inequalities. Biometrika, 67, 429–39.

    Article  Google Scholar 

  76. Schularick, M., & Taylor, A. (2012). Credit booms gone bust: Monetary policy, leverage cycles, and financial crises, 1870–2008. American Economic Review, 102, 1029–1061.

    Article  Google Scholar 

  77. Schumpeter, J. A. (1911). A theory of economic development. Cambridge, MA: Harvard University Press.

    Google Scholar 

  78. Tobin, J. (1984). On the efficiency of the financial system. Lloyds Bank Review, 153, 1–15.

    Google Scholar 

  79. Wand, M. (2005). SemiPar: Semiparametic regression. R package version, 1.0–2. Cambridge: Cambridge University Press.

  80. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.

    Article  Google Scholar 

  81. World Bank. (2011). World Development Indicators 2011. Washington, DC: World Bank.

    Google Scholar 

  82. Yatchew, A. (2003). Semiparametric regression for the applied econometrician. Cambridge: Cambridge University Press.

    Google Scholar 

  83. Wright, P. (1928). The tariff on animal and vegetable oils. New York: MacMillan.

    Google Scholar 

Download references

Acknowledgments

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.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ugo Panizza.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (zip 798 KB)

Appendix

Appendix

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Arcand, J.L., Berkes, E. & Panizza, U. Too much finance?. J Econ Growth 20, 105–148 (2015). https://doi.org/10.1007/s10887-015-9115-2

Download citation

Keywords

  • Finance
  • Growth
  • Financial crises
  • Non-linearities

JEL Classification

  • O11
  • O16
  • E44
  • G1