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Previous inquiries into the relationship between education and housework productivity reveal that expectations differ along disciplinary (i.e., economics vs. non-economics) lines and empirical results from the economics literature are mixed. Expectations of a positive sign between education and housework productivity in the economics literature may be a function of misinterpretations of [J. Polit. Economy 81 (1973) 306] original theory pertaining to all non-market production, which is far more general than just housework. Mixed empirical results may be a function of incomplete or overly assumption-reliant econometric models derived previously. We streamline the procedures for estimating the parameters of a one-person, one-period housework production function such that the system of equations may be specified with a single, literature-based assumption. Our estimation of the production function parameter that measures the effect of education on housework productivity suggests that authors in the non-economics literature may have a point; the relationship between education and housework productivity may be negative due to ''morale'' effects.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jedc.2004.10.006 Byline: Stephen J. Turnovsky (a), William T. Smith (b) Abstract: The derivation of a closed-form solution for consumption based on the constant elasticity utility function in the presence of stochastic labor income has proved to be intractable. This paper derives a closed-form equilibrium relationship between consumption and wealth, one that holds along a balanced growth path in a stochastic Romer endogenous growth model. By employing more general recursive preferences, we can disentangle the coefficient of relative risk aversion from the intertemporal elasticity of substitution. The effects of key structural parameters on equilibrium consumption and its tradeoff with leisure are analyzed. A significant aspect of our analysis concerns the extent to which current risk in the economy is shared between labor and capital. This plays an important role in determining the impact of risk on the economy in general, and on consumption in particular. Formal analysis is supplemented with extensive numerical simulations. Author Affiliation: (a) Department of Economics, University of Washington, Seattle WA 98105, USA (b) University of Memphis, Memphis TN 38152, USA Article History: Received 2 June 2004; Accepted 20 October 2004 Article Note: (footnote) [star] This research was supported in part by the Castor endowment at the University of Washington. We are grateful to the constructive comments of two anonymous referees.

A study on the relationship between consumption and personal savings using a stochastic Romer endogenous growth model is presented.

Asymptotic waveform evaluation (AWE) is a recently developed technique for time-domain analysis of electrical interconnects. AWE is a computationally efficient method that asymptotically approximates the response of a large system with a lower-order transfer function. In this paper, asymptotic waveform evaluation is used to analyze lossy interconnects including the skin effect. The internal impedance of the interconnect conductors varies as a function of the square root of the frequency. First, an overview of AWE is presented. The AWE formulation for modeling frequency dependent loss in the conductors is derived using two different series expansions of the system response at both s = 0 and s [not equal to] 0 in the Laplace domain. The expansions for s [not equal to] 0 are determined using a transfer function formulated for inclusion of the frequency-dependent internal impedance. The network response is computed by extracting the dominant poles and residues using the Pade approximation. The proposed method is evaluated using time-domain examples of lossy multiconductor transmission lines.

The physical optics/aperture integration (PO/AI) formulation is often used to analyze the radiation patterns of reflector antennas. In this study, the PO/AI radiation integrals for distorted reflector antennas are addressed. The surface error of the antennas is approximated by a series of surface expansion functions. The radiation integral is decomposed into a series of radiation-type integrals, each of which corresponds to one of the surface expansion functions. Each of these radiation-type integrals is then weighted by amplitude coefficients. The advantage of performing the decomposition is that each of the radiation-type integrals can be computed and the pattern data stored. The computation of the pattern for a distorted reflector antenna with a changing error profile is performed by recalling the pattern data for each perturbation term and weighting it with the amplitude coefficient. This facilitates rapid evaluation of the radiation integral in cases where the error profile is changing (for example, time-varying errors). The superposition of integrals presented in this paper was shown to be valid for surface-error profiles up to 0.1 [Lambda] rms amplitude. Index Terms - Reflector antennas.

Uncertainty fundamentally alters the way in which taxes affect growth, because tax policies can change the riskiness of disposable income. An increase in the income tax rate, for example, reduces both the mean and variance of after-tax income. The reduction in the mean reduces savings, as predicted by models without uncertainty. However, the reduction in risk may decrease or increase savings depending upon whether consumers are averse to intertemporal substitution. If the elasticity of intertemporal substitution is small, then the fall in the variance reinforces the effects of the fall in the mean, so an increase in the tax rate reduces growth by much more than predicted by non-stochastic models. If the elasticity of intertemporal substitution is large, however, then the fall in the variance causes growth to decrease by less than predicted by non-stochastic models; theoretically, it is actually possible for a tax increase to increase growth. Keywords: Taxes; Uncertainty; Long-term growth

In this study, a neural network algorithm for electromagnetic compensation of reflector surface error effects is formulated. Sets of trained neural networks are used to compute the compensation excitations for array feeds. The networks were trained using data generated with the constrained least squares (CLS) compensation method. Once trained, the calculation of the excitations is accomplished in significantly less time than required by the original constrained least squares algorithm. The surface error profile for a distorted reflector antenna is expanded using bivariate surface basis functions. Each of the trained networks corresponds to one of the expansion functions. Excitations computed using the neural netwoks are superposed to produce composite compensation excitations for the distorted reflector. The compensation results for a distorted reflector are presented, and the neural network algorithm performance is compared to the original CLS technique.

An accurate and efficient method of including frequency-dependent conductor losses into the time-domain solution of the multiconductor transmission line equations is presented. It is shown that the usual A + B [square root of s] representation of these frequency-dependent losses is not valid for some practical geometries. The reason for this is the inaccurate representation of the internal inductance of the conductors at lower frequencies. A computationally efficient method for improving this representation in the finite-difference time-domain (FDTD) solution method is given and is verified using the conventional time-domain to frequency-domain (TDFD) solution technique.

Price stabilization schemes reduce investment when demand is uncertain, even when these price controls are not binding. When a firm anticipates that price will drop, it slows down its rate of capital accumulation. However, a corollary is that if there is a price ceiling, investment may actually decline with the instantaneous variance of price, contrary to current models of investment under uncertainty.