In most empirical work, the investigator's understanding of the economic process under study is only minimally reflected in the econometric methodology. This paper suggests that in many cases, the construction of a small-scale simulation can prepare the data for regression in a manner which takes cognizance of the theory of the process. Regression is then used to scale the output of the simulation up to observed magnitudes of the variable to be predicted. The simulation has the function of exploring for the nature of the nonlinearities and interactions and thus replaces the usual search for a form which maximizes R2. The simulation may also be helpful where colinear data are a problem. An example is presented in which the effects of wages, unemployment rates, and labor turnover on poverty are studied through a prepared regression. IN THE LAST three decades, regression analysis has become the Procrustean bed into which all economic data are fitted. In the usual empirical paper by an economist, the obligatory theoretical discussion which precedes the description of the regressions generally contributes little more to the empirical methodology than an indication of which variables ought to be included in which equation, what the signs of the coefficients might be expected to be, and whether the regressions should be run in linear or logarithmic form. One reaction to this state of affairs has been the commencement of construction of large systems of microsimulation, notably one at the Urban Institute emphasizing demography and the distribution of income [6], one at the National Bureau of Economic Research on urban problems [4], and one at the University of Maryland featuring money flows [2]. While these big microsimulations are designed to describe the processes of the economy in a more natural way than can be done exclusively by usual regression methods, they tend to take years to build and tend to be unavailable to economists not involved in their building. It is possible, however, to occupy a middle ground between the regression runners and the large-model microsimulators. In many cases, improvement over the usual regression procedures can be gained by a combination of a very simple do-it-yourself simulation model with regression. The simulation model has the function of preparing the data for regression, in the sense of exploring for the nonlinearities and variable interactions inherent in the phenomenon under study. The regression, which uses the output of the simulation as an explanatory variable, has the function of scaling the simulation results up to observed magnitudes of the variable under study.

To be a housewife is to be a member of a peculiar occupationone with characteristics quite different from all others. The nature of the duties to be performed, the form of the pay, the methods of supervision, the tenure system, the marketplace in which workers find jobs, and the physical hazards are all so different from conditions found in other occupations that one tends not to think of a housewife as belonging to occupation in the usual sense. Yet being a housewife certainly meets the American Heritage Dictionary's definition of occupation, as an activity that serves as one's regular source of In fact, to be a housewife is to be a member of the largest single occupation in the U. S. economy. Thus, it is certainly both legitimate and interesting to compare the advantages and disadvantages of the housewife's source of livelihood with those of other sources of livelihood. Few economists have studied the economic aspects of being a housewife. Gary Becker's economium to the advantages of the division of labor among spouses addresses some of the issues, but its perspective seems to be that of a male member of a traditional family. More recently, Marianne Ferber and Bonnie Birnbaum, as well as Clair (Vickrey) Brown have made contributions in which the interests of each family member are recognized as distinct.' In this paper, I focus on the economic risks of the housewife occupation, which are shown to be very high relative to those of other occupations.

Discrimination concentrates Negroes into certain occupations while virtually excluding them from others. In the occupations to which Negroes are relegated, marginal productivity may be lowered by the enforced abundance of supply. A model embodying this "crowding" hypothesis is used to estimate the effects on white incomes of a reduction in discrimination. Whites with only an elementary education might have a once-for-all loss on the order of 10 percent; on all other whites and on national income the effect is estimated to be trivial. Formulations by Becker and Thurow concerning Negro marginal productivity and wages are criticized.