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Essays in Macroeconomic Models of Wealth Inequality.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Macroeconomic Models of Wealth Inequality./
作者:	Mohaghegh, Mohsen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	112 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-08, Section: A.
Contained By:	Dissertations Abstracts International81-08A.
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27692333
ISBN:	9781392477144

Essays in Macroeconomic Models of Wealth Inequality.
Mohaghegh, Mohsen.

Essays in Macroeconomic Models of Wealth Inequality. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 112 p.

Source: Dissertations Abstracts International, Volume: 81-08, Section: A.

Thesis (Ph.D.)--The Ohio State University, 2019.

This item must not be sold to any third party vendors.

In these essays, I explore two main quantitative theories of wealth inequality. These two theories are (1) uninsured earnings risk, and (2) entrepreneurship. For each theory, I build on the existing frameworks to study wealth concentration in the cross section -i.e. at a given point in time. I use micro-level data to quantitatively discipline, when needed, extend the existing theories. Also, I explore the ability of each model to explain the observed rise in the wealth inequality in the United states.In the first chapter, "Evolution of Inequality In the U.S.:Entrepreneurial Activity and Financial Intermediation", I focus on the importance of entrepreneurship on changes in the wealth inequality over time. The existing theories of wealth inequality and entrepreneurship - e.g. Quadrini (2000) and Cagetti and DeNardi (2006) - argue that occupational choice models explain cross sectional inequality. Building on these theories, I investigate whether changes in entrepreneurship over time also explain changes in inequality. There are two major trends in entrepreneurship in the U.S. data since 1975: the number of entrepreneurs has fallen, and the entrepreneurs' leverage has risen.In this chapter, I develop a general equilibrium, overlapping-generations model with occupational choice to examine if these trends have contributed to the observed rise in the wealth inequality. In the model, entrepreneurs borrow for investment in their projects. Production for entrepreneurs is subject an idiosyncratic random capital depreciation shock that is unknown at the time of investment. After observing their draw, entrepreneurs may default on their debt. A risk-neutral lender prices each individual debt contract consistent with the borrowers' risks of default. This is an extension of the cross-sectional theory of inequality proposed by Cagetti and DeNardi (2006), and allows me to use the available data about the entrepreneurial firms' default decisions to discipline the financial friction in the model.After calibration, I show that wealth concentration in the benchmark economy matches the data in 2007. Since risk of default depends on the idiosyncratic characteristics of entrepreneurs, their optimal borrowing decision -which determines the scale of operation in their firms- increase the concentration of wealth in the equilibrium.In order to study changes in wealth inequality over time, consistent with the evolution of the U.S. economy, I consider four channels: a greater ability on the part of lenders to make risky loans, an increase in costs of starting a business, a fall in unit cost of borrowing, and a change in exemptions in the bankruptcy code as happened in 1979. Through running quantitative experiments, I find that a change in the lenders' ability to issue risky loans as well as an increase in costs of startups reproduce trends in entrepreneurship. When trends in entrepreneurship are accounted for, the model explains almost all of the rise in the wealth share of the top 1 percent between 1975 and 2007.In the second chapter, "Are Earnings Risks High Enough to Explain Inequality?", I focus on models of uninsured earnings risk. Empirically speaking, wealth is more concentrated than earnings in the U.S. data. This implies that individuals' saving rates increase with wealth. However, models of uninsured earnings risk which are the most frequently used framework to study wealth inequality are unable to reproduce this feature of the data. As a result, wealth in these models is less concentrated than the data.In a seminal paper, Castaneda et al. (2003) propose a quantitative structure that accounts for both earnings and wealth inequality in the U.S. Their theory implies an extremely skewed distribution of earnings risk in the data. In particular, their modeling requires top earners to face disproportionately higher risks. In the second chapter, I examine this theory in an environment where the distribution of earnings risk is more consistent with the data.To do so, I use new empirical evidence from Guvenan et al. (2016) who report several moments about the earnings dynamics in the U.S. between 1975 and 2013. They use confidential data from the U.S. Social Security Administration (SSA) which include all of the tax payers in the economy. This important for my analysis as it significantly reduces sampling biases.I propose an innovative stochastic process for individual earnings that accurately reproduces several important data features: the cross sectional distribution of earnings, the distribution of earnings changes, and multiple skill-related aspects of the earnings data. I simulate a very large sample of individuals whose earnings follow this stochastic process, and use the Simulated Method of Moments (SMM) to determine the unknown parameters.In my model, instead of assuming a stochastic process for individuals' labor productivity -which is the common in the literature- I directly calibrate a stochastic process for individuals' earnings. This, on the one hand, improves the modeling of risk, and on the other, allows for calibrating time-varying earnings profiles. However, this approach restricts my ability to use standard solution method in the literature. In the second chapter, I show how we can identify labor productivity, hours worked, and the wage rate based on exogenous earnings draws.I develop an overlapping-generations production economy with skilled and unskilled workers whose earnings follow two independent calibrated stochastic processes. In the model, I also account for the progressivity of the U.S. tax system. My results show that even when all these factors are considered, when the distribution of risk is consistent with the data, wealth is noticeably less concenterated in the model than in the data. The main reason for this result is that as opposed to the predictions of Castaneda et al. (2003), when earnings risks are consistent with the data, top earners do not face extremely large risks.In this chapter, I also study changes in the wealth inequality when over time, the distribution of earnings, the skill-related aspects of earnings, and the tax system vary. Comparing the distribution of wealth with the data at various points between 1989 and 2013 confirms the model's ability to explain wealth concentration. In addition, I show that during the transitional period, the distribution of wealth evolves away from the data. This presents a challenge to the use of models of uninsured earnings risk for studying the dynamics of wealth inequality.

ISBN: 9781392477144Subjects--Topical Terms:

517137
Economics.
Subjects--Index Terms:

Macroeconomics

Essays in Macroeconomic Models of Wealth Inequality.
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245 1 0 $a Essays in Macroeconomic Models of Wealth Inequality.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 112 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-08, Section: A.
500 $a Advisor: Khan, Aubhik.
502 $a Thesis (Ph.D.)--The Ohio State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a In these essays, I explore two main quantitative theories of wealth inequality. These two theories are (1) uninsured earnings risk, and (2) entrepreneurship. For each theory, I build on the existing frameworks to study wealth concentration in the cross section -i.e. at a given point in time. I use micro-level data to quantitatively discipline, when needed, extend the existing theories. Also, I explore the ability of each model to explain the observed rise in the wealth inequality in the United states.In the first chapter, "Evolution of Inequality In the U.S.:Entrepreneurial Activity and Financial Intermediation", I focus on the importance of entrepreneurship on changes in the wealth inequality over time. The existing theories of wealth inequality and entrepreneurship - e.g. Quadrini (2000) and Cagetti and DeNardi (2006) - argue that occupational choice models explain cross sectional inequality. Building on these theories, I investigate whether changes in entrepreneurship over time also explain changes in inequality. There are two major trends in entrepreneurship in the U.S. data since 1975: the number of entrepreneurs has fallen, and the entrepreneurs' leverage has risen.In this chapter, I develop a general equilibrium, overlapping-generations model with occupational choice to examine if these trends have contributed to the observed rise in the wealth inequality. In the model, entrepreneurs borrow for investment in their projects. Production for entrepreneurs is subject an idiosyncratic random capital depreciation shock that is unknown at the time of investment. After observing their draw, entrepreneurs may default on their debt. A risk-neutral lender prices each individual debt contract consistent with the borrowers' risks of default. This is an extension of the cross-sectional theory of inequality proposed by Cagetti and DeNardi (2006), and allows me to use the available data about the entrepreneurial firms' default decisions to discipline the financial friction in the model.After calibration, I show that wealth concentration in the benchmark economy matches the data in 2007. Since risk of default depends on the idiosyncratic characteristics of entrepreneurs, their optimal borrowing decision -which determines the scale of operation in their firms- increase the concentration of wealth in the equilibrium.In order to study changes in wealth inequality over time, consistent with the evolution of the U.S. economy, I consider four channels: a greater ability on the part of lenders to make risky loans, an increase in costs of starting a business, a fall in unit cost of borrowing, and a change in exemptions in the bankruptcy code as happened in 1979. Through running quantitative experiments, I find that a change in the lenders' ability to issue risky loans as well as an increase in costs of startups reproduce trends in entrepreneurship. When trends in entrepreneurship are accounted for, the model explains almost all of the rise in the wealth share of the top 1 percent between 1975 and 2007.In the second chapter, "Are Earnings Risks High Enough to Explain Inequality?", I focus on models of uninsured earnings risk. Empirically speaking, wealth is more concentrated than earnings in the U.S. data. This implies that individuals' saving rates increase with wealth. However, models of uninsured earnings risk which are the most frequently used framework to study wealth inequality are unable to reproduce this feature of the data. As a result, wealth in these models is less concentrated than the data.In a seminal paper, Castaneda et al. (2003) propose a quantitative structure that accounts for both earnings and wealth inequality in the U.S. Their theory implies an extremely skewed distribution of earnings risk in the data. In particular, their modeling requires top earners to face disproportionately higher risks. In the second chapter, I examine this theory in an environment where the distribution of earnings risk is more consistent with the data.To do so, I use new empirical evidence from Guvenan et al. (2016) who report several moments about the earnings dynamics in the U.S. between 1975 and 2013. They use confidential data from the U.S. Social Security Administration (SSA) which include all of the tax payers in the economy. This important for my analysis as it significantly reduces sampling biases.I propose an innovative stochastic process for individual earnings that accurately reproduces several important data features: the cross sectional distribution of earnings, the distribution of earnings changes, and multiple skill-related aspects of the earnings data. I simulate a very large sample of individuals whose earnings follow this stochastic process, and use the Simulated Method of Moments (SMM) to determine the unknown parameters.In my model, instead of assuming a stochastic process for individuals' labor productivity -which is the common in the literature- I directly calibrate a stochastic process for individuals' earnings. This, on the one hand, improves the modeling of risk, and on the other, allows for calibrating time-varying earnings profiles. However, this approach restricts my ability to use standard solution method in the literature. In the second chapter, I show how we can identify labor productivity, hours worked, and the wage rate based on exogenous earnings draws.I develop an overlapping-generations production economy with skilled and unskilled workers whose earnings follow two independent calibrated stochastic processes. In the model, I also account for the progressivity of the U.S. tax system. My results show that even when all these factors are considered, when the distribution of risk is consistent with the data, wealth is noticeably less concenterated in the model than in the data. The main reason for this result is that as opposed to the predictions of Castaneda et al. (2003), when earnings risks are consistent with the data, top earners do not face extremely large risks.In this chapter, I also study changes in the wealth inequality when over time, the distribution of earnings, the skill-related aspects of earnings, and the tax system vary. Comparing the distribution of wealth with the data at various points between 1989 and 2013 confirms the model's ability to explain wealth concentration. In addition, I show that during the transitional period, the distribution of wealth evolves away from the data. This presents a challenge to the use of models of uninsured earnings risk for studying the dynamics of wealth inequality.
590 $a School code: 0168.
650 4 $a Economics. $3 517137
650 4 $a Economic theory. $3 1556984
653 $a Macroeconomics
653 $a Wealth Inequality
653 $a Entrepreneurship
653 $a Financial Intermediation
653 $a Default
653 $a Earnings Risk
653 $a Earnings Distribution
653 $a Tax Policy
690 $a 0501
690 $a 0511
710 2 $a The Ohio State University. $b Economics. $3 1673045
773 0 $t Dissertations Abstracts International $g 81-08A.
790 $a 0168
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27692333