W E regard economic phenomena as resulting from an interaction of human actions within a field of nonhuman environmental conditions and restraints. Of the various phases of economic phenomena that may interest social scientists there is one which is of particular importance to policy-makers whether they are in business or in government. One of their special needs is for knowledge concerning the consequences or impacts of the various actions which they are able to take and which they consider taking. This paper deals with methods that more adequately attempt to meet this need. We assume that the policy-makers know what they are seeking to achieve. We also assume that they have a number of actions at their disposal and that they wish to know which of these will achieve the desired objectives in as satisfactory a manner as possible. We take it for granted that it will usually be necessary to observe discrepancies between the desired situation and the actual situation in order to guide whatever actions are taken, but we will not concern ourselves here with problems of this sort. Rather we will limit our attention to certain aspects of the problems involved in discovering and specifying the consequences of actions.

There is a growing interest in detecting manageable environmental changes which would improve overall health and life expectancy. The work we report explores differences in mortality rates in county groups (as defined by the Census) to discover whether environmental differences among them affect death probabilities. Casual inspection of Diagram I offers evidence that the probability of dying is significantly different for individuals in different parts of the country. However, these variations in mortality rates are not necessarily the result of environmental factors which differ across the country; they could also be the result of different personal characteristics of local populations. For example, an area with an unusually wholesome environment may have a high death rate because it possesses relatively more elderly citizens. The primary objective of tnis paper is to remove the influence of personal characteristics so that the effects of area specific factors can be detected. I. Research Strategy and Data Base Unfortunately, the absence of a micro data base providing extensive information about decedent's lifetime characteristics complicates the analysis of mortality rates. We were able to take advantage of three large data sets which, when combined, provide somewhat comprehensive information about individuals and their environment. Information from the two million recorded death certificates filed in 1970 were organized by county group into 28 race, sex, and age cells. The second body of data, also about two million observations, was the 1970 Census of Public Use Sample, which provides geographic identification down to the county group. The Public Use Sample provided information about the living population in race, sex, and age cells within each of the 405 county groups. Macro variables describing the entire population of the county groups were also computed from the Public Use Sample. Finally, the machine readable 1970 City and County Data Book provided additional information about the overall characteristics of county groups. The basic research strategy was to attribute as much as possible of the between-county group variation of death rates to person specific factors. Since spatial features of residual variation of death rates appears nonrandom between county groups, the existence of significant environmental influences on death probabilities are strongly suggested. The residual variation might reasonably be explained by area specific factors such as climate, industrialization, and economic vitality. Though we have not yet been able to *Professor of economics, Yale University; research associate, The Urban Institute; Ph.D. candidate, Yale University; visiting professor, Yale University, respectively. The research for this paper was conducted with financial support from the National Science Foundation to The Urban Institute, grant No. SOC73-05420-AOI for the Simulation of the Distribution of Income. Substantial support in the form of staff time, computing, housing and secretarial services was supplied by the Institution for Social and Policy Studies of Yale University. Valuable assistance in preparing this paper was received from Amihai Glazer and Jan Stolwijk. The views expressed are those of the authors and do not necessarily represent the views of the National Science Foundation, The Urban Institute or Yale University.