The paper examines seven methods of numerical integration, including
both special purpose algorithms designed for the multivariate normal density
and general algorithms such as Gauss—Legendre and Newton—Cotes methods.
With the aid of some five functions, the accuracy of these methods and their
computational cost are compared in matched experiments on an IBM 370/3081
Model K and a 2-pipe CYBER 205. The effect of vectorised computation is
also examined.

This paper reports nonparametric estimates of the effect of labor supply
behavior on the payments to families enrolled in the Seattle/Denver Income
Maintenance Experiment. The randomized assignment of families to the
treatment groups in this experiment was designed to permit the calculation
of these nonparametric estimates. However, the nonparametric estimates
have never been reported, even though they are easy to construct using a
simple weighting procedure. Unfortunately, responses to the data
collection instrument (which depended on costly surveys) were not random,
and this opens up some ambiguity in the results.