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Inequality and crises revisited


Recent debate has suggested that growing levels or high levels of inequality may be systematically associated with the occurrence of banking crises. Using the updated version of the Chartbook of Economic Inequality, this paper provides new empirical evidence on the ‘level’ hypothesis and reassesses the empirical validity of the ‘growth’ hypothesis. In line with previous work, the empirical analysis on the entire set of countries and years under investigation does not provide any conclusive and compelling statistical support to either of the hypotheses. However, the apparent statistical insignificance of the findings does not rule out the economic relevance of the question at hand, given that the hypotheses cannot be rejected for important crises and countries such as the US and the UK. Hence, the overall evidence is far from being conclusive and there are several reasons to shed further light on this important research topic.

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  1. A formalisation of this potential channel can be found in Iacoviello (2008) who links positive changes in earnings inequality (referred to as increased volatility of individual’s earnings pattern) to the surge in household indebtedness (in order to smooth out consumption). Similarly, Kumhof and Ranciere (2010) and Ravenna and Vincent (2014) construct, within a conventional DSGE framework, a theoretical set-up in which a surge in inequality increases endogenously the leverage of households by increasing both demand and supply of credit.

  2. Some of these aspects were already surveyed in recent work by Atkinson and Morelli (2010, 2011) and more recently in Van Treeck (2014). The main ideas underlying the suggested links between inequality and crisis are, however, not new to the academic debate. For instance Brown (2004) has explicitly asked in his work the question of how rising income inequality can create “the need for greater reliance on debt to sustain aggregate consumption expenditure”, formally analysing many aspects of the above mentioned conjectures about the inequality/crisis nexus well in advance of the 2007 crisis.

  3. The database and the working paper are downloadable from the web site

  4. The countries under investigation are: Argentina, Brazil, Australia, Canada, Finland, France, Germany, Iceland, India, Indonesia, Italy, Japan, Malaysia, Mauritius, Netherlands, New Zealand, Norway, Portugal, Singapore, South Africa, Spain, Sweden, Switzerland, the UK and the US.

  5. Note that the number where inequality is found increasing before a crisis rises to 23 out of 43 if one does not use any threshold of salience for the classification of changes in inequality.

  6. Atkinson (2003), argued, under specific assumptions, that the Gini coefficient could change by approximately 3 % points in response to a change in 5 % points in the income tax rate, a very difficult task for a policy maker (see also Atkinson and Marlier 2010, p. 112). In particular, this is true assuming a constant marginal tax rate on all incomes and uniform tax credit. Atkinson (2003, p. 484) also assumes that the Gini coefficient for disposable income amounts to 48 in case no distribution of income applies and that marginal tax rate is 20 %.

  7. When there are multiple inequality series, in order to calculate the standard deviation of the series, we first calculate the standard deviation for each series. Subsequently we take the average of those as long as the estimation rests on more than 10 observations.

  8. Using the wave VI of Luxembourg Income Study data––a dataset containing inequality data comparable across countries––one observes that Sweden––in the year 2004––has the third lowest Gini coefficient in the whole set of countries.

  9. The work by Bellettini and Delbono (2013) constitutes the first attempt to assess the validity of the ‘level’ hypothesis with cross-section information.

  10. For instance, by doing this we exclude entirely the data on Iceland for which we only have 19 observations.

  11. The LIS data are the most closely comparable across countries, but only provide observations at intervals. There are ‘waves’ every 5 years approximately, but not annual data (Waves we (around 1980), II (around 1985), III (around 1990), IV (around 1995), V (around 2000) and VI (around 2004)).

  12. As an example consider the case of the UK where the data on Gini estimated in the Chartbook in 2004 is very close to the correspondent value in the LIS database. The values are 34 and 34.4 respectively. The methodology above consists in adjusting the available value in the Chartbook (34) with a proportional factor (34.4/34). This proportional adjustment is kept constant for other years so to obtain a new adjusted Gini series.

  13. The work by Bellettini and Delbono (2013) makes use of a combination of data from the Chartbook of Economic Inequality, the OECD, the LIS and the WIID (UNU-WIDER 2008) databases and compare the average Gini in the 10 years preceding each crisis to the OECD Gini average relevant in the period (from both the OECD and the LIS database).

  14. In the conclusions to their paper, Bellettini and Delbono claim that “as long as high income inequality is associated to banking crises in ‘big’ countries, then such inequality should be of some concern for the whole system, given the interdependency of financial markets and the resulting contagion outside national boundaries”.

  15. The quotation is taken from the article “Good and bad inequality” appeared on Project Syndicate’s web site on the 11th of December 2014.


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We acknowledge funding from the INET (IN01100021). This paper has been prepared by SM on the basis of earlier joint research by ABA and SM (2010 and 2011), and of the paper by SM presented at the “Inequality and Economic Performance Conference”, at Columbia University, New York––December 2014. We thank all the participants of the conference for stimulating conversations. We also thank Andrew Berg, Mauro Caselli and Joseph Stiglitz for helpful comments and discussions.

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Correspondence to Salvatore Morelli.

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Morelli, S., Atkinson, A.B. Inequality and crises revisited. Econ Polit 32, 31–51 (2015).

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  • Inequality
  • Financial crisis
  • Top income shares
  • Poverty
  • Wealth

JEL Classification

  • D31
  • D39
  • G01