Richard M. H. Suen
Assistant Professor
Department of Economics
341 Mansfield Road, Unit 1063
University of Connecticut
Storrs, CT 06269-1063
Phone: (860) 486-4368
Fax: (860) 486-4463
Email: richard.suen@uconn.edu
Assistant Professor
Department of Economics
341 Mansfield Road, Unit 1063
University of Connecticut
Storrs, CT 06269-1063
Phone: (860) 486-4368
Fax: (860) 486-4463
Email: richard.suen@uconn.edu
Research
Concave Consumption Function and Precautionary Wealth Accumulation*
First Version: August 2010
This Version : November 2011
Abstract: This paper examines the theoretical foundations of precautionary wealth accumulation in
a multi-period model where consumers face uninsurable earnings risk and borrowing constraints.
We begin by characterizing the consumption function of individual consumers. We show that
consumption function is concave when the utility function has strictly positive third derivative and
the inverse of absolute prudence is a concave function. These conditions encompass all HARA utility
functions with strictly positive third derivative as special cases. We then show that when
consumption function is concave, a mean-preserving spread in earnings risk would encourage
wealth accumulation at both the individual and aggregate levels.
* This paper was previously circulated under the title "Concave Consumption Function under
Borrowing Constraints."
Time Preference and the Distributions of Wealth and Income
First Version: February 2010
This Version : January 2012
(Revised and resubmitted to Economic Inquiry)
Working paper version
Abstract: This paper analyzes the connection between time preference heterogeneity and economic
inequality. To achieve this, we extend the standard neoclassical growth model by introducing three
additional features, namely (i) heterogeneity in consumers' discount rates, (ii) direct preferences for
wealth, and (iii) human capital formation. The second feature prevents the wealth distribution from
collapsing into a degenerate distribution. The third feature generates a strong positive correlation
between earnings and capital income across consumers. A calibrated version of the model is able to
generate patterns of wealth and income inequality that are very similar to those observed in the
United States.
Technological Advance and the Growth in Health Care Spending
Abstract: The second half of the twentieth century recorded a rapid growth in health care spending
and a significant increase in life expectancy. This paper hypothesizes that technological progress in
medical treatment, combined with rising incomes, are the driving forces behind these two trends.
Using a stochastic, multi-period overlapping-generations model as the analytical vehicle, this paper
argues that the rapid growth in medical spending is not driven by factors associated with market
structures or insurance opportunities, but instead by factors underlying the production and
accumulation of health. According to this model, improvements in medical treatment and rising
incomes can explain all of the increase in medical spending and more than 60% of the increase in life
expectancy at age 25 during the second half of the twentieth century.
A Quantitative Analysis of Suburbanization and the Diffusion of the Automobile*
with Karen A. Kopecky
International Economic Review, vol. 51 (4), p. 1003-1037, 2010
Working paper version
Abstract: Suburbanization in the U.S. between 1910 and 1970 was concurrent with the rapid
diffusion of the automobile. A circular city model is developed in order to access quantitatively the
contribution of automobiles and rising incomes to suburbanization. The model incorporates a
number of driving forces of suburbanization and car adoption, including falling automobile prices,
rising real incomes, changing costs of traveling by car and with public transportation, and urban
population growth. According to the model, 60 percent of postwar (1940-1970) suburbanization can
be explained by these factors. Rising real incomes and falling automobile prices are shown to be the
key drivers of suburbanization.
*This paper was previously circulated with the title “Suburbanization and the Automobile.”
Finite State Markov-Chain Approximations to Highly Persistent Processes
with Karen A. Kopecky
Review of Economic Dynamics, vol. 13 (3), p. 701-714, 2010.
Longer version
Computer Codes
Abstract: The Rouwenhorst method of approximating stationary AR(1) processes has been
overlooked by much of the literature despite having many desirable properties unmatched by other
methods. In particular, we prove that it can match the conditional and unconditional mean and
variance, and the first-order autocorrelation of any stationary AR(1) process. These properties
make the Rouwenhorst method more reliable than others in approximating highly persistent
processes and generating accurate model solutions. To illustrate this, we compare the performances
of the Rouwenhorst method and four others in solving the stochastic growth model and an income
fluctuation problem. We find that (i) the choice of approximation method can have a large impact on
the computed model solutions, and (ii) the Rouwenhorst method is more robust than others with
respect to variation in the persistence of the process, the number of points used in the discrete
approximation and the procedure used to generate model statistics.
Concave Consumption Function and Precautionary Wealth Accumulation*
First Version: August 2010
This Version : November 2011
Abstract: This paper examines the theoretical foundations of precautionary wealth accumulation in
a multi-period model where consumers face uninsurable earnings risk and borrowing constraints.
We begin by characterizing the consumption function of individual consumers. We show that
consumption function is concave when the utility function has strictly positive third derivative and
the inverse of absolute prudence is a concave function. These conditions encompass all HARA utility
functions with strictly positive third derivative as special cases. We then show that when
consumption function is concave, a mean-preserving spread in earnings risk would encourage
wealth accumulation at both the individual and aggregate levels.
* This paper was previously circulated under the title "Concave Consumption Function under
Borrowing Constraints."
Time Preference and the Distributions of Wealth and Income
First Version: February 2010
This Version : January 2012
(Revised and resubmitted to Economic Inquiry)
Working paper version
Abstract: This paper analyzes the connection between time preference heterogeneity and economic
inequality. To achieve this, we extend the standard neoclassical growth model by introducing three
additional features, namely (i) heterogeneity in consumers' discount rates, (ii) direct preferences for
wealth, and (iii) human capital formation. The second feature prevents the wealth distribution from
collapsing into a degenerate distribution. The third feature generates a strong positive correlation
between earnings and capital income across consumers. A calibrated version of the model is able to
generate patterns of wealth and income inequality that are very similar to those observed in the
United States.
Technological Advance and the Growth in Health Care Spending
Abstract: The second half of the twentieth century recorded a rapid growth in health care spending
and a significant increase in life expectancy. This paper hypothesizes that technological progress in
medical treatment, combined with rising incomes, are the driving forces behind these two trends.
Using a stochastic, multi-period overlapping-generations model as the analytical vehicle, this paper
argues that the rapid growth in medical spending is not driven by factors associated with market
structures or insurance opportunities, but instead by factors underlying the production and
accumulation of health. According to this model, improvements in medical treatment and rising
incomes can explain all of the increase in medical spending and more than 60% of the increase in life
expectancy at age 25 during the second half of the twentieth century.
A Quantitative Analysis of Suburbanization and the Diffusion of the Automobile*
with Karen A. Kopecky
International Economic Review, vol. 51 (4), p. 1003-1037, 2010
Working paper version
Abstract: Suburbanization in the U.S. between 1910 and 1970 was concurrent with the rapid
diffusion of the automobile. A circular city model is developed in order to access quantitatively the
contribution of automobiles and rising incomes to suburbanization. The model incorporates a
number of driving forces of suburbanization and car adoption, including falling automobile prices,
rising real incomes, changing costs of traveling by car and with public transportation, and urban
population growth. According to the model, 60 percent of postwar (1940-1970) suburbanization can
be explained by these factors. Rising real incomes and falling automobile prices are shown to be the
key drivers of suburbanization.
*This paper was previously circulated with the title “Suburbanization and the Automobile.”
Finite State Markov-Chain Approximations to Highly Persistent Processes
with Karen A. Kopecky
Review of Economic Dynamics, vol. 13 (3), p. 701-714, 2010.
Longer version
Computer Codes
Abstract: The Rouwenhorst method of approximating stationary AR(1) processes has been
overlooked by much of the literature despite having many desirable properties unmatched by other
methods. In particular, we prove that it can match the conditional and unconditional mean and
variance, and the first-order autocorrelation of any stationary AR(1) process. These properties
make the Rouwenhorst method more reliable than others in approximating highly persistent
processes and generating accurate model solutions. To illustrate this, we compare the performances
of the Rouwenhorst method and four others in solving the stochastic growth model and an income
fluctuation problem. We find that (i) the choice of approximation method can have a large impact on
the computed model solutions, and (ii) the Rouwenhorst method is more robust than others with
respect to variation in the persistence of the process, the number of points used in the discrete
approximation and the procedure used to generate model statistics.
