Gravitational Lensing and the Variability of G

The four observables associated with gravitational lensing of distant quasars by intervening galaxies: image splittings, relative amplifications, time delays, and optical depths, provide separate measures of the strength of the gravitational constant $G$ at cosmological distances. These allow one, in principle, to factor out unknown lensing parameters to directly to probe the variation of $G$ over cosmological time. We estimate constraints on $\dot{G}$ which may be derivable by this method both now and in the future. The limits one may obtain can compete or exceed other direct limits on $\dot{G}$ today, but unfortunately extracting this information, is not independent of the effort to fix other cosmological parameters such as $H_0$ and $\Omega_0$ from lensing observations.Comment: 13 pages plus figures (not included

Inflation, Tax Rules, and the Long Term-Interest Rate

macroeconomics, inflation, tax rules, Long Term-Interest Rate

Problems in fluid dynamics

A scheme was developed for the parametric differentiation and integration of gas dynamics equations. A numerical integration of the gas dynamics equations is necessarily performed for a specific set of parameter values. The linear variational equations are obtained by differentiating the exact equations with respect to each of the relevant parameters. The resulting matrix of flow quantities is referred to as the Jacobi matrix. The subsequent procedure is then straightforward. The method was tested for two dimensional supersonic flow past an airfoil, with airfoil thickness, camber, and angle of attack varied. This approach has great potential value for rapidly assessing the effect of design changes. The other focus of the work was on problems in fluid stability, bifurcations, and turbulence

Is Theory Really Ahead of Measurement? Current Real Business Cycle Theories and Aggregate Labor Market Fluctuations

In the l93Os, Dunlop and Tarshis observed that the correlation between hours and wages is close to zero. This classic observation has become a litmus test by which macroeconomic models are judged. Existing real business cycle models fail this test dramatically. Based on this result, we argue that technology shocks cannot be the sole impulse driving post-war U.S. business cycles. We modify prototypical real business cycle models by allowing government spending shocks to influence labor market dynamics in a way suggested by Aschauer (1985), Barro (1981, 1987) and Kormendi (1983), This modification can, in principle, bring the models into closer conformity with the data. While the empirical performance of the models is significantly improved, they still fail to account for the Dunlop-Tarshis observation. Accounting for that observation will require further advances in model development. Consequently, we conclude that theory is behind, not ahead of, business cycle measurement.

Simulating Nonlinear Tax Rules and Nonstandard Behavior: An Application to the Tax Treatment of Charitable Contributions

This paper examines how the tax simulation method can be extended to incorporate nonlinear budget constraints and nonstandard economic behavior. We simulate the effect of extending the charitable deduction to nonitemizers and study the effect of alternative "floors". The specific simulations indicate that the econometric evidence on charitable giving implies that extending the charitable deduction to nonitemizers would raise individual giving by about 12 percent of the existing total amount or $4.5 billion at 1977 levels. The extension would reduce tax revenue by slightly less, about$4.1 billion. A floor of $300 or 3 percent of AGI would reduce the revenue loss by 30 to 40 percent, even if there is significant bunching. The effect of the floor on increased giving depends critically on whether taxpayers' behavior is guided by conventional demand principles or by the net altruism rule. A reasonable conclusion is that a floor would reduce giving by less than the increased revenue but that the difference between them would not be very large.