Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Economics

First Advisor's Name

Cem Karayalcin

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Hakan Yilmazkuday

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Sheng Guo

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Sneh Gulati

Fourth Advisor's Committee Title

Committee member

Keywords

macroeconomics, income inequality, household heterogeneity, consumption volatility, house prices, monetary policy

Date of Defense

6-22-2022

Abstract

Income inequality has been rising throughout the world. In the United States, for example, the income share of the population at the top 10% of the income distribution rose 34% from 1980 to 2019 according to data from the World Inequality Database. This dissertation studies how increasing levels of income inequality might affect the economy.

In the first chapter, I show that changes in the level of income inequality may affect consumption volatility through changes in household aggregate marginal propensity to consume (MPC). I propose a simple theoretical framework to explain this dynamic and evaluate it empirically, combining data from the Panel Study of Income Dynamics and from the Census Bureau's Annual Social and Economic supplement of the Current Population Survey. This is the first study to estimate US state-level measures of average and aggregate MPC out of labor income. I find that the average MPC increases as the level of average state income inequality increases and that among households of the same income level, household MPC is higher in states with more income inequality. I also find that if the covariance term of a state's aggregate MPC is positive, higher inequality increases its amplification effect; similarly, higher inequality increases the dampening effect of the covariance term in states with negative covariance. Given these results, I estimate a panel data model using US state-level aggregate data and find that, overall, when there is a shock in aggregate GDP, higher inequality is associated with bigger fluctuations in aggregate consumption.

The second chapter uses annual US county-level data from 2007 to 2015 to empirically investigate if an increase in income inequality affects house prices. I determine that higher inequality is positively correlated with prices measured by the Zillow index. A 1% increase in the county Gini coefficient is associated with a 0.04% increase in the average price of all houses. A rise in the inequality measure is also associated with increases of approximately 0.03% in the prices of bottom- and top-tier houses separately. When an interaction term between the Gini coefficient and mortgage interest rates is added to the empirical model, the overall positive relationship between inequality and housing prices is driven by an interest rate channel. Hence, I propose a simple theoretical framework to explain this phenomenon.

In the last chapter, I explore the common currency union formed by US states to examine how higher income inequality affects monetary policy effectiveness. I start the investigation with a cross-sectional analysis and find no statistically significant difference between the response of personal income and private employment in states with low and high historical levels of income inequality between 1990 and 2007. However, using quarterly US state-level Gini coefficients in a panel version of a local projection estimation, I contend that a one-percentage-point increase in the Gini coefficient is associated with a smaller change in both state personal income and private employment when a monetary policy shock hits the economy. The initial effect is small but significant and increases over time.

Identifier

FIDC010903

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