Bounding the CRRA Utility Functions

The constant-relative-risk-aversion (CRRA) utility function is now predominantly used in quantitative macroeconomic studies. This function, however, is not bounded and thus creates problems when applying the standard tools of dynamic programming. This paper devises a method for "bounding" the CRRA utility functions. The proposed method is based on a set of conditions that can establish boundedness among a broad class of utility functions. These results are then used to construct a bounded utility function that is identical to a CRRA utility function except when consumption is very small or very large. It is shown that the constructed utility function also satisfies the Inada condition and is consistent with balanced growth.Utility Function; Elasticity of Marginal Utility; Boundedness

Technological Advance and the Growth in Health Care Spending

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 the combination of techno-logical progress in medical treatment and rising incomes is the driving force 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.Technological progress, life expectancy, medical spending, health

Concave consumption function and precautionary wealth accumulation

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.Consumption function, borrowing constraints, precautionary saving

Concave Consumption Function under Borrowing Constraints

This paper analyzes the optimal consumption behavior of a consumer who faces uninsurable labor income risk and borrowing constraints. In particular, it provides conditions under which the decision rule for consumption is a concave function of existing assets. The current study presents two main findings. First, it is shown that the consumption function is concave if the period utility function is drawn from the HARA class and has either strictly positive or zero third derivative. Second, it is shown that the same result can be obtained for certain period utility functions that are not in the HARA class.Consumption function, borrowing constraints, precautionary saving

Bounding the CRRA Utility Functions

The constant-relative-risk-aversion (CRRA) utility function is now predominantly used in quantitative macroeconomic studies. This function, however, is not bounded and thus creates problems when applying the standard tools of dynamic programming. This paper devises a method for "bounding" the CRRA utility functions. The proposed method is based on a set of conditions that can establish boundedness among a broad class of utility functions. These results are then used to construct a bounded utility function that is identical to a CRRA utility function except when consumption is very small or very large. It is shown that the constructed utility function also satisfies the Inada condition and is consistent with balanced growth.Utility Function; Elasticity of Marginal Utility; Boundedness

Finite State Markov-Chain Approximations to Highly Persistent Processes

This paper re-examines the Rouwenhorst method of approximating first-order autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the first-order autocorrelation of any AR(1) process. This paper provides the first formal proof of this and other results. When comparing to five other methods, the Rouwenhorst method has the best performance in approximating the business cycle moments generated by the stochastic growth model. In addition, when the Rouwenhorst method is used, moments computed directly off the stationary distribution are as accurate as those obtained using Monte Carlo simulations.Numerical Methods; Finite State Approximations; Optimal Growth Model

A Quantitative Analysis of Suburbanization and the Diffusion of the Automobile

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.automobile; suburbanization; population density gradients; technological progress

A Quantitative Analysis of Suburbanization and the Diffusion of the Automobile

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.automobile; suburbanization; population density gradients; technological progress

Finite State Markov-Chain Approximations to Highly Persistent Processes

This paper re-examines the Rouwenhorst method of approximating first-order autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the first-order autocorrelation of any AR(1) process. This paper provides the first formal proof of this and other results. When comparing to five other methods, the Rouwenhorst method has the best performance in approximating the business cycle moments generated by the stochastic growth model. It is shown that, equipped with the Rouwenhorst method, an alternative approach to generating these moments has a higher degree of accuracy than the simulation method.Numerical Methods; Finite State Approximations; Optimal Growth Model

Finite State Markov-Chain Approximations to Highly Persistent Processes

This paper re-examines the Rouwenhorst method of approximating first-order autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the first-order autocorrelation of any AR(1) process. This paper provides the first formal proof of this and other results. When comparing to five other methods, the Rouwenhorst method has the best performance in approximating the business cycle moments generated by the stochastic growth model. It is shown that, equipped with the Rouwenhorst method, an alternative approach to generating these moments has a higher degree of accuracy than the simulation method.Numerical Methods, Finite State Approximations, Optimal Growth Model