THE CAUSALITY BETWEEN INCOME INEQUALITY AND PRIVATE SECTOR INDEBTEDNESS

Abstract

In the three decades leading up to the financial crisis of 2008/2009, income inequality rose across most of the developed world. The causes of growing income gaps are complex and reflect both economic and social changes. Globalizations, and in particular the impact of technology on the workforce, is one of the many important factors. But in the last years, debates rise also as to whether widening inequality was to blame for the financial crises by driving private sector credit boom, or if credit boom provoked a higher inequality. The purpose of this paper, is that of providing an empirical evidence whether there exists a causality relationship, and its direction, between income concentration and private sector indebtedness. I will be using two different measures of income concentration (share of total income going to the top 10% earners, and GINI coefficient), based on a comparative analysis of the U.S and UK. The paper will have a double contribution: 1) testing whether it exists a causal relationship between Income concentration and Financial Boom and the direction of the causality 2) comparing this results between countries and data used.

Data: Our dependent variable is the level of domestic credit to the private sector, as a percentage of GDP, from the World Development Indicators database (World Bank 2016). It refers to financial resources provided to the private sector by financial corporations (i.e. banks, insurance corporations, pension funds, foreign exchange companies), such as loans, purchases of non-equity securities, trade credits and other accounts receivable, that establish a claim for repayment. It would have been preferable to use Household Debt (i.e. consumer debt, mortgage loans, credit cards, student loans) to GDP, but the data are not available for the time coverage of the study (data staring only from 2005). One important limitation of our dependent variable, is that it includes both household debt (which we are interested) but also debt of business and other private organizations. Since household debt data are available for a shorter period of time, we are using the private sector debt to GDP. To overcome this limitation, the necessary proxies for the part of credit demanded by non-household private sector are added:

a) Capital formation (% of GDP) as a proxy of credit demanded by firms for investment purposes, and b) Portfolio Investment (% of GDP) as a proxy of credit demand by firms driven by transactions in equity and debt securities.

Based on the existing literature, Real Interest Rates and Broad Money Supply (to GDP) ratio are used as proxies for the monetary policy. Many studies find that the overall level of economic development, is the strongest predictor of financial progress, hence GDP per Capita will be included among the regressors.

The last key variable is Credit Market Deregulation from Economic Freedom of the World, 2016 Annual Report. This component reflects conditions in the domestic credit market, the extent to which the banking industry is privately owned, the extent to which credit is supplied to the private sector and whether controls on interest rates interfere with the market for credit. Countries that use a private banking system to allocate credit to private parties and refrain controlling interest rates receive higher ratings.

Finally, as a proxy for the level of income concentration, the share of total income going to the top 10% of earners is used (1970-2012). Observation units are individual households, and includes income coming from labor, business and capital. The indicator also is market income (pre-tax and transfer), whereas it would be preferable to use disposable income, as a result disposable GINI coefficient will be used also.