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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 non-linearities 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. [ABSTRACT FROM AUTHOR]

The article comments on anti-poverty policy. With respect to anti-poverty policy, two major positions have emerged-one emphasizing a structural approach, the other an aggregative approach. These positions are not mutually exclusive, their difference being a question of relative emphasis. Both hold that there is some link between the rate of economic growth and the rate at which poverty incidence is reduced. The essential point of departure between the two points of view lies with the strength of this linkage: the structural position holding that, although economic growth opens up possibilities to escape poverty, a substantial proportion of the poor are not able to exploit these opportunities and are thus left behind in a poverty backwash. In short, the crucial distinction between the two theses is disagreement over the effect of economic growth in reducing poverty incidence. The higher levels of poverty incidence among minority families indicates, of course, that family poverty is relatively more concentrated among these groups and is what one would expect to be likely.