Ideally, a paper that attempts to evaluate a challenge to established theory should accomplish the following objectives: 1. Present the theoretical substance of the challenging theory, which is here called the dual and radical theories, and indicate how it differs from the existing theory, which I will refer to as neoclassical or orthodox theory; 2. Point out the empirical implications of the theories and explain their differences with the existing theory; 3. Assess the empirical basis for discriminating between the competing hypotheses or, if this is lacking, provide the theoretical or evidential counterarguments of the neoclassical response to the dual and radical challenge; and, finally, 4. Spell out the policy implications of the competing theories. Meeting these objectives is an impossible task for a short paper. The space constraint is compounded by the fact that in my judgment the dual and radical theories are too varied, incomplete, and amorphous to present concisely. The strategy I adopt to describe these theories in the longer paper consists of, first, discussing their linkages to historical criticisms of classical and neoclassical theory; second, developing their empirical challenges to orthodox theory. Only the second part is emphasized below. To establish the context of the challenge, let us begin with the observation of Leo Rogin (p. 13) that: new systems (of economic doctrines) first emerge in the guise of arguments in the context of social reform. The dual and radical (or D-R, for short) theories began to emerge in the 1960's when the movement for social reform mainly involved the war on poverty and the drive for full participation in the economy by minority groups, including women. Dissatisfaction with the pace and progress of reform in these areas and dissatisfaction with the conventional analysis of the problems and their remedies have led to arguments within the economics profession, especially on the part of the younger labor economists. Let me simply assert, without defending the proposition as I do in the longer paper, that the neoclassical school does dominate * Department of economics and Institute for Research on Poverty, University of Wisconsin. This is an abbreviated version of a longer paper, which is available from the Institute for Research on Poverty, University of Wisconsin, Madison, Wisconsin 53706. The longer version contains a bibliography, which is omitted here. I am indebted to a number of people at the U.S. Department of Labor, especially Fred Siskind, and at the Institute for Research on Poverty for their support. They are not responsible for errors, nor do they necessarily agree with the interpretations expressed in the report. I am grateful to Marc P. Freiman for his extensive assistance.

The elementary course in economic statistics must devote more time to statistics than to economics. Students who major in economics will take at least six courses in economics, but the course in economic statistics may be their only course in statistics. Nevertheless, the basic purposes of economics should be kept in mind in shaping the course. Although the statistical methods used in applied economics deal with measurable variables, it is my view that the ultimate purpose of economics as an applied social science is the improvement of human welfare, which is intrinsically an unmeasurable concept. Our recognition of the unattainable ideal does not stop us from obtaining less than ideal measurements that are nevertheless useful. The two basic problems in this task of economic measurement are (a) to define and measure the correct outcomes, and (b) to measure their determinants so that we can predict and, ideally, influence the outcomes. The contribution that the science of statistics can make to these tasks is fundamentally that of the art and craft of using observed sample data to make inferences about unknown population parameters in economics. Inferential statistics is fundamental. Descriptive statistics, which is the art and craft of organizing and summarizing sample data, is useful and necessary for learning inferential statistics, but it is not fundamental in its own terms. Turning to the structure of the course in economic statistics, let us apply a principle of economics to its teaching by specifying the constraints under which we seek to optimize our goals. Four major constraints face the teacher of elementary economic statistics: 1) the limited time in a one-semester course; 2) the typically large size and heterogeneity of the class; 3) the limited economic and, especially, mathematical background of most of the students; and 4) the limited skills of the teacher. A word about constraint 4, which implicitly qualifies much of what follows. Instructors of economic statistics have varying talents for and preferences about the course, and it is appropriate to play to one's strengths. If someone is a whiz at teaching probability, this topic by this instructor may captivate students and inspire them toward an understanding of inferential statistics. Another instructor may be skillful in using examples from such economic topics as income distribution or macroeconomics to illustrate how economists use sample evidence to estimate and test interesting relationships between outcome variables and their determinants. We each have our own styles of teaching, and my suggestions for content and methods should be viewed as subservient to any particular instructor's tastes and skills. With that qualification, I turn next to the content of the course.