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Reciprocity in bank regulatory reforms and income inequality: first evidence from a panel vector autoregression analysis

Abstract

Our study examines the dynamics between variations in bank regulatory policies and the income distribution, using an IMF database of quantified measures for financial reforms as well as the Standardized World Income Inequality Database. The unbalanced panel comprises 36 developed countries and covers a period over three decades from 1973 to 2005. We assess the potential endogeneity between bank regulations and inequality via a panel vector autoregression model. Among the regulatory reforms, we consider the deregulation of securities markets, entry barriers, credit and interest rate controls as well as the extent of privatization, international capital flows and banking supervision. We are able to provide support for the hypotheses that (i) overall, abolishing bank regulations enhances inequality in income and (ii) higher levels in inequality encourage laissez-faire policies in the banking sector. Moreover, our results highlight the importance of examining each regulatory reform individually. In particular, we endorse a relaxation of entry barriers for financial intermediaries while promoting interest rate controls, capital account restrictions and deliberate government intervention in securities markets.

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Notes

  1. Frequently dubbed “financialization” and comprising various phenomena such as increasing private household debt, securitization and financial deregulation (Koehler et al. 2016).

  2. As H. P. Minsky (1919–1996) has argued since 1954.

  3. Including the expansion of financial activities, improvements in financial markets and financial services, financial openness and direct access to capital.

  4. Such as the neoclassical allocative efficiency theory, a structural Keynesian narrative or neo-Kaleckian approach.

  5. We use the R package panelvar by Sigmund and Ferstl (2019) who basically extend the STATA code by Roodman (2009a) with the GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) to panel vector autoregression models.

  6. Unfortunately, Abiad et al. (2010) did not update their regulatory policy dataset beyond 2005.

  7. \({\bar{y}} = \int _{0}^{\infty } y f(y) \mathrm {d}y\).

  8. \(y_{i,t+1}^{\bot } = c_{i,t} (y_{i,t}-1/T_{i,t} \sum _{s>t} y_{i,s})\). Where \(c_{i,t} = \sqrt{T_{i,t}/(T_{i,t}+1)}\). This transformation is suggested by Arellano and Bover (1995) to minimize data losses due to data gaps.

  9. The first difference transformation exists for \(t \in \{p+2, \ldots , T\}\), and the forward orthogonal transformation exists for \(t \in \{p+1, \ldots , T-1\}\). We denote the set of indexes t, for which the transformation exists by \({\mathbb {T}}_{\varDelta }\).

  10. In the empirical literature, this problem is known as instrument proliferation (Roodman 2009b).

  11. We can also rely on an alternative proof of Koenker and Machado (1999) that the first difference GMM estimator remains consistent and asymptotically normally distributed in the case of \(T \rightarrow \infty \). Koenker and Machado (1999) state the following condition: \(q^3/N \rightarrow 0\).

  12. See also H. P. Minsky’s Financial Instability Hypothesis (FIH).

  13. Together with the exchange rate, par and the price level.

  14. The private interest view suggests that in the circumstance of a (natural) monopoly or oligopoly, regulations ought to be imposed in order to improve social welfare and to correct market failure. However, in this view, politicians and supervisors of the government do not aim at maximizing public welfare but their own. Therefore, independent monitoring of banks rather than official oversight should be strengthened in order to limit the abuse of power.

  15. Under “securitization” we understand the “process of turning illiquid assets, or certain aspect of that asset like their volatility, into financial assets that can be traded on markets. [...] This [is] supposed to slice risk into different parts and allocate it to those who [are] best equipped to hold it.” (Stockhammer 2016) On the role of banks as financial intermediaries, the “originate and distribute model of lending” (in contrast to the “originate to hold model,” banks originate credit and securitize or sell a fraction of it later or at the time of emergence) gives banks flexibility and reduces incentives so screen borrowers in the securitization process. That is, banks are building up riskier loans; the US sub-prime crisis 2007 has then infamously put securities markets back to the center stage of financial analysis.

References

  • Abiad A, Detragiache E, Tressel T (2010) A new database of financial reforms. IMF Staff Pap 57(2):281–302

    Article  Google Scholar 

  • Alvarez J, Arellano M (2003) The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71(4):1121–1159

    Article  Google Scholar 

  • Andrews D, Lu B (2001) Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. J Econom 101(1):123–164

    Article  Google Scholar 

  • Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297

    Article  Google Scholar 

  • Arellano M, Bover O (1995) Another look at the instrumental variable estimation of error-components models. J Econom 68(1):29–51

    Article  Google Scholar 

  • Baldacci E, Mello L, Inchauste G (2002) Financial crises, poverty and income distribution. IMF working paper: Fiscal Affairs Department, vol 02(4)

  • Barro R, Lee J (2013) A new data set of educational attainment in the world 1950–2010. J Dev Econ 104:184–198

    Article  Google Scholar 

  • Barth J, Caprio G, Levine R (2002) Bank regulation and supervision: what works best? NBER working paper, 9323

  • Bazillier H, Hericourt J (2014) The circular relationship between inequality, leverage, and financial crises: intertwined mechanisms and competing evidence. CEPII working paper

  • Beck T, Demirgüç-Kunt A, Levine R (2009) Financial institutions and markets across countries and over time. World Bank Policy Research working paper, 4943

  • Beck T, Levine R, Demirgüç-Kunt A (2007) Finance, inequality and the poor. J Econ Growth 12(1):27–49

    Article  Google Scholar 

  • Beck T, Levine R, Levkov A (2010) Big bad banks? The winners and losers from bank deregulation in the United States. J Finance 65(5):1637–1667

    Article  Google Scholar 

  • Bertus M, Jahera J, Yost K (2007) The relation between bank regulation and economic performance: a cross-country analysis. Banks Bank Syst 2(3):32–45

    Google Scholar 

  • Birdsall N, Nellis J (2003) Winners and losers: assessing the distributional impact of privatization. World Dev 31(10):1617–1633

    Article  Google Scholar 

  • Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econom 87(1):115–143

    Article  Google Scholar 

  • Botero J, Djankov S, La Porta R, Lopez-de Silanes F, Shleifer A (2004) The regulation of labor. Q J Econ 119(4):1339–1382

    Article  Google Scholar 

  • Bryan D, Martin R, Rafferty M (2009) Financialization and marx: giving labor and capital a financial makeover. Rev Radic Polit Econ 41(4):458–472

    Article  Google Scholar 

  • Delis M, Hasan I, Kazakis P (2013) Bank regulations and income inequality: empirical evidence. Rev Finance 18(5):1–36

    Google Scholar 

  • Demirgüç-Kunt A, Laeven L, Levine R (2004) Regulations, market structure, institutions, and the cost of financial intermediation. J Money Credit Bank 36(3):623–626

    Google Scholar 

  • Demirgüç-Kunt A, Levine R (2009) Finance and inequality: theory and evidence. Ann Rev Financ Econ 1(1):287–318

    Article  Google Scholar 

  • Friedman M, Schwartz A (1969) The definition of money: net wealth and neutrality as criteria. J Money Credit Bank 1(1):1–14

    Article  Google Scholar 

  • Gastwirth J (1971) A general definition of the Lorenz curve. Econometrica 39(6):1037–1039

    Article  Google Scholar 

  • Greenwood J, Jovanovic B (1990) Financial development, growth, and the distribution of income. J Polit Econ 89(5):1076–1107

    Article  Google Scholar 

  • Hamilton J (1994) Time series analysis. Princeton University Press, Princeton

    Google Scholar 

  • Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50(4):1029–1054

    Article  Google Scholar 

  • Holtz-Eakin D, Newey W, Rosen H (1988) Estimating vector autoregressions with panel data. Econometrica 56(6):1371–1395

    Article  Google Scholar 

  • Jaumotte F, Strahan P (1997) The benefits of branching deregulation. Fed Reserve Bank N Y Econ Policy Rev 3:4

    Google Scholar 

  • Jayadev A (2007) Capital account openness and the labour share of income. Camb J Econ 31:423–443

    Article  Google Scholar 

  • Johansson A, Wang X (2014) Financial sector policies, poverty and income inequality. China Econ Rev 31:367–378

    Article  Google Scholar 

  • Kapetanios G (2008) A bootstrap procedure for panel data sets with many cross-sectional units. Econom J 11(2):377–395

    Article  Google Scholar 

  • Kearney M, Levine P (2016) Income inequality, social mobility, and the decision to drop out of high school. NBER working paper, vol 20195(1), pp 333–396

  • Klein M, Olivei G (2008) Capital account liberalization, financial depth, and economic growth. J Int Money Finance 27(6):861–875

    Article  Google Scholar 

  • Koehler K, Guschanski A, Stockhammer E (2016) How does financialization affect functional income distribution? A theoretical clarification and empirical assessment. Socio-Econ Rev 15:1–26

    Google Scholar 

  • Koenker R, Machado J (1999) GMM inference when the number of moment conditions is large. J Econom 93(2):327–344

    Article  Google Scholar 

  • Kumhof M, Ranciére R, Winant P (2015) Inequality, leverage, and crises. Am Econ Rev 105(3):1217–1245

    Article  Google Scholar 

  • La Porta R, Lopez-de Silanes F, Shleifer A (2002) Government ownership of banks. J Finance 57(1):265–301

    Article  Google Scholar 

  • Lapavitsas C, Powell J (2013) Financialisation varied: a comparative analysis of advanced economies. Camb J Reg Econ Soc 6(3):359–379

    Article  Google Scholar 

  • Lavoie M, Godley W (2002) Kaleckian models of growth in a coherent stock-flow monetary framework: a Kaldorian view. J Post Keynes Econ 24(2):277–311

    Article  Google Scholar 

  • Lim G, McNelis P (2014) Income inequality, trade and financial openness. In: RES-SPR Conference on “Macroeconomic Challenges Facing Low-Income Countries”, IMF

  • Lorenz M (1993) Methods of measuring the concentration of wealth. Publ Am Stat Assoc 9(70):209–219

    Google Scholar 

  • Lucas R (1972) Expectations and the neutrality of money. J Econ Theory 4:103–124

    Article  Google Scholar 

  • Luetkepohl H (2006) New introduction to multiple time series analysis. Springer, Berlin

    Google Scholar 

  • Mehrhoff J (2009) A solution to the problem of too many instruments in dynamic panel data GMM. Technical report. Research Centre, Deutsche Bundesbank, Frankfurt

    Google Scholar 

  • Minsky H (1993) On the non-neutrality of money. FRBNY Q Rev 18:77–82

    Google Scholar 

  • Newey W, Smith R (2004) Higher order properties of GMM and generalized empirical likelihood estimators. Econometrica 72(1):219–255

    Article  Google Scholar 

  • Nickell S (1981) Biases in dynamic models with fixed effects. Econometrica 49(6):1417–1426

    Article  Google Scholar 

  • Ortiz I, Cummins M (2011) Global inequality: beyond the bottom billion. Social and Economic Policies: Working Paper, UNICEF

  • Ortiz-Ospina E, Roser M (2016) Our world in data: income inequality. https://ourworldindata.org/income-inequality/. Accessed 22 Nov 2016 (online resource)

  • Palley T (2007) Financialization: what it is and why it matters. The Levy Economics Institute working paper

  • Perraton J (2016) Macroeconomic implications of inequality and household debt: European evidence. Working paper: University of Sheffield

  • Pesaran H, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Econ Lett 58(1):17–29

    Article  Google Scholar 

  • Piketty T, Saez E (2014) Inequality in the long run. Science 344(6186):838–843

    Article  Google Scholar 

  • Reuveny R, Li Q (2003) Economic openness, democracy, and income inequality: an empirical analysis. Comp Polit Stud 36(5):575–601

    Article  Google Scholar 

  • Roodman D (2009a) How to do xtabond2: an introduction to difference and system GMM in stata. Stata J 9(1):86–136

    Article  Google Scholar 

  • Roodman D (2009b) A note on the theme of too many instruments. Oxf Bull Econ Stat 71(1):135–158

    Article  Google Scholar 

  • Ryan-Collins J, Greenham T, Werner R, Jackson A (2012) Where does money come from? A guide to the UK monetary and banking system. New Economics Foundation, London

    Google Scholar 

  • Saraceno F (2014) High inequality and its impact on the economy. Revue de l’OFCE, Presses de Sciences Po, pp 177–187

  • Sigmund M, Ferstl R (2019) Panel vector autoregression in R with the package panelvar. Q Rev Econ Finance (forthcoming)

  • Solt F (2016) The standardized world income inequality database: Swiid version 51. Soc Sci Q 97(5):1267–1281

    Article  Google Scholar 

  • Stockhammer E (2016) Financialization: from financial deregulation to boom bust cycles. http://kcleconomics.com/financialization-and-growth/. Accessed 18 Jan 2017 (online resource)

  • Werner R (2005) New paradigm in macroeconomics. Palgrave Macmillan, Basingstoke

    Book  Google Scholar 

  • Wisman J (2013) Wage stagnation, rising inequality and the financial crisis of 2008. Camb J Econ 37(4):921–945

    Article  Google Scholar 

  • Wolfson M, Epstein G (2013) The handbook of political economy of financial crises. Oxford University Press, New York

    Google Scholar 

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Correspondence to Lea Steininger.

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Appendix: Data coverage and summary statistics

Appendix: Data coverage and summary statistics

See Tables 11, 12 and 13.

Table 11 Countries in the Sample (36)
Table 12 Variable definition and source
Table 13 Summary statistics of the variables included

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Steininger, L., Sigmund, M. Reciprocity in bank regulatory reforms and income inequality: first evidence from a panel vector autoregression analysis. Empir Econ 59, 1537–1572 (2020). https://doi.org/10.1007/s00181-019-01693-6

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Keywords

  • Income inequality
  • Bank regulation
  • Financial reform
  • Panel vector autoregression