The Relationship between the Inflation Rate and Inequality across US States

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Background and motivations

Increasing income and wealth inequality is a worldwide trend. We know that income inequality may lower the level of human capital by restricting education opportunities for lower income groups, may cause additional social cost by increasing rent-seeking, and may trigger social turmoil. The United States experienced a relatively low level of income inequality for about 30 years after the World War II. Since then U.S. income inequality has increased consistently. In 1963, John F. Kennedy could say that “a rising tide lifts all boats” as the average annual income for the bottom 90 percent kept pace with productivity growth between the 1950s and the 1970s. This trend, however, did not last forever and turned around in the 1980s. Income inequality began to increase and has continued to increase ever since. Income inequality has increased over the last three decades and a high tide lifts only a few boats. Despite average annual growth in U.S. output of around 3 percent in the 1980s and the 1990s, and around 2 percent since 2001, the average real income of the bottom 90 percent of the U.S. has stagnated. This prompts the question of whether income inequality may negatively affect the growth prospects of a country. The issue of income inequality has drawn great interest from researchers, politicians, and policy makers, since the well-being of many individuals often depends on the distribution of income. Consequently, the determinants of income inequality and the political and/or economic solutions to reduce inequality have become important discussions. Some policy makers assume that inequality is a natural and necessary result of growth and economic growth focused policy would resolve the income inequality problem. Since the financial crisis, the Federal Reserve has used monetary policy aggressively to promote economic growth and regain economic stability. When the Federal Reserve conducted such aggressive monetary policy, such as cutting the federal funds rate to zero and purchasing large amount of U.S. Treasury securities and mortgage-backed securities, the possible redistributive results of monetary policy can play an important role. In spite of their effort, inequality continues to worsen. A number of researchers point out skill biased technological change as a reason for increasing inequality during the period of technological innovation. When we look at only this determinant, however, it personalizes the income inequality issue. We need to consider both individual determinants and macroeconomic factors, which policy makers’ control. Thus, understanding the determinants and consequences of income inequality is central to macroeconomics.

Organization and summary of the study

The thesis consists of five independent papers corresponding to five chapters. As economic growth is a primary goal of every country and widely accepted tool for reducing economic inequality, our study starts with economic growth. The first paper examines the relationship between the U.S. per capita real GDP and income inequality over the period 1917 to 2012. The literature uncovers a complex set of interactions, which depends on the specific research method and sample, between inequality and economic growth and highlights the difficulty of capturing a definitive causal relationship. Inequality either promotes, retards, or does not affect growth. Most existing studies that examine the inequality-growth nexus exclusively utilize time-domain methods. We use wavelet analysis which allows the simultaneous examination of correlation and causality between the two series in both the time and frequency domains. Inflation targeting is a monetary policy where the central bank sets a specific inflation rate as its goal. The federal government spurs economic growth by adding liquidity, credit, and jobs to the economy and inflation stimulate the demand needed to drive economic growth. The second paper investigates the effects of the inflation rate on income inequality to see whether monetary policy and the resulting inflation rate can affect income inequality and improve the well-being of individuals. Our analysis relies on a cross-state panel for the United States over the 1976 to 2007 period to assess the relationship between income inequality and the inflation rate, employing a semiparametric instrument variable (IV) estimator. By using cross-state panel data, we minimize the problems associated with data comparability often encountered in cross-country studies related to income inequality. The researchers also examine the relationship between income inequality and growth in personal income, since personal income exerts a large effect on consumer consumption, and since consumer spending drives much of the economy. The third paper investigates the causal relationship between personal income and income inequality in a panel data of 48 states for the period of 1929-2012. Although inequality rose almost everywhere between 1980 to present, some states and regions experienced substantially greater increases in inequality than did others. The decentralisation allows different state level of policies, however, there is also a cross-state consistency in how those policies respond to the main economic shocks. Since U.S. states are subject to significant spatial effects given their high level of integration, ignoring cross-sectional dependency may lead to substantial bias and size distortions. We employ a causality methodology proposed by Emirmahmutoglu and Kose (2011), as it takes into account possible slope heterogeneity and cross-sectional dependency in a multivariate panel. The level of development of the United States is related to the sophistication of the financial structure which influences the ability to hedge against shocks and to loosen spending constraints. It leads us to investigate if the financial development affects income inequality in the U.S. In the fourth paper, we look into the role of financial development on U.S. state-level income inequality in a panel data of 50 states from 1976 to 2011. To our knowledge, this paper is the first regarding examining the role of financial development on U.S. state-level inequality. We analyze the data using Fixed Effect and Dynamic Fixed Effect regression. We also divide 50 states into two groups-states, with higher inequality measure and states with lower inequality measures than average of the cross-state average of the inequality, to examine the possible nonlinear impact of financial development on income inequality. Finally, literature mostly discovers that the volatility increases income inequality. However, researchers also find that income inequality may intensify the output volatility and inflation rate. This shows that possible bi-directional causality between economic volatility and inequality. In light of these considerations, the fifth paper explores the relationship between the U.S. economic growth volatility, and income and wealth inequality measures over the period 1917 to 2015 and 1962 to 2014. We consider the relationship between output volatility during positive and negative growth scenarios. In sum, this study looks at trends in the United States income and/or wealth inequality at the aggregate and state levels and examines its relationships with macroeconomic variables, such as output, inflation, level of financial development, and economic volatility. This study facilitates a better understanding of the dynamic relationship between inequality and key macroeconomic variables. This can serve as a prerequisite to the ability of policymakers to restrain the negative externalities associated with increasing inequality and implement measures to reduce the unexpected effects. Each contribution represents a different chapter.

TABLE OF CONTENTS :

• Chapter 1: Introduction
  • 1.1 Background and motivations
  • 1.2 Organization and summary of the study
• Chapter 2: Causality between Per Capita Real GDP and Income Inequality in the U.S.:
  • Evidence from a Wavelet Analysis
  • 2.1 Introduction and literature review
  • 2.2 Methodology: wavelet theory and methods
    • 2.2.1 Mother wavelet
    • 2.2.1 Continuous wavelet transform
    • 2.2.3 Wavelet coherency and phase difference
  • 2.3 Data
  • 2.4 Preliminary analysis
  • 2.5 Main analysis
  • 2.6 Conclusion
• Chapter 3: The Relationship between the Inflation Rate and Inequality across US States
  • A Semiparametric approach
  • 3.1 Introduction
  • 3.2 Literature review
  • 3.3 Methodology and data
    • 3.3.1 Methodology
    • 3.3.2 Data
  • 3.4 Empirical results
    • 3.4.1 Preliminary results
    • 3.4.2 Main results
  • 3.5 Conclusion
• Chapter 4: Causality between Personal Income and Income Inequality: A Heterogeneous Mixed Panel approach
  • 4.1 Introduction and literature review
  • 4.2 Data
  • 4.3 Methodology
    • 4.3.1 Testing for cross-sectional dependence
    • 4.3.2 Testing slope homogeneity
    • 4.3.3 Panel Granger causality analysis
  • 4.4 Empirical analysis
  • 4.5 Conclusion
• Chapter 5: Does Financial development affect Income Inequality in the U.S. states? A panel data analysis
  • 5.1 Introduction and literature review
  • 5.2 Data
  • 5.3 Methodology and model specification
  • 5.4 Empirical analysis
  • 5.5 Conclusion
• Chapter 6: Growth Volatility and Inequality in the U.S.: A Wavelet analysis
  • 6.1 Introduction and literature Review
  • 6.2 Methodology
    • 6.2.1 Continuous wavelet transform
    • 6.2.2 Wavelet coherency and phase difference
  • 6.3 Data
  • 6.4 Empirical analysis
  • 6.5 Conclusion
• Chapter 7: Conclusion
  • Bibliography

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