Knowledge that Transforms

Bilateral trade flows are used to examine the Linder hypothesis and the effect of exchange-rate variability in a gra vity-type trade model derived from an underlying demand and supply mo del. A behavioral model is used to justify examining these issues joi ntly. The model performs well empirically using a sample of seventeen countries for the period 1974-82. The authors find overwhelming supp ort for the Linder hypothesis and this version of the gravity model. Moreover, they find strong support for the hypothesis that increased exchange-rate variability affects bilateral trade flows. Copyright 1987 by MIT Press.

Some previous discussions have dismissed screening theories of education partly on the ground that diploma years of education do not confer especially large earnings gains. Similarly, most empirical research on earnings functions has assumed an absence of effects. report evidence, however, of substantial and statistically significant sheepskin effects. Although this suggests that the previous dismissals of the screening hypothesis were premature, our evidence of sheepskin effects is amenable to nonscreening interpretations also. According to screening theories of education, individuals with more schooling tend to earn more not because (or, at least, not solely because) schooling makes them more productive, but rather because it credentiates them as more productive. A frequently cited article by Layard and Psacharopoulos (1974), however, dismissed the importance of the screening hypothesis on the grounds that several of its refutable predictions were not supported by available evidence. One of these was the prediction that wages will rise faster with extra years of education when the extra year also conveys a certificate. After surveying a number of studies, Layard and Psacharopoulous (henceforth LP) concluded that of return to dropouts are as high as to those who complete a course, which refutes the sheepskin version of the screening Since publication of the LP paper, an undergraduate labor economics textbook' has cited LP's analysis of sheepskin effects as telling criticism of the screening hypothesis. A prominent proponent of the screening hypothesis, Riley (1979), has accepted LP's summary of the empirical evidence, but responded that some versions of the screening hypothesis do not imply sheepskin effects. In the meantime, the ongoing flood of empirical research on earnings functions typically has continued to treat the natural logarithm of the wage rate as a linear (or occasionally quadratic) function of years of education, with no allowance for discontinuities in diploma years.2 The estimated rates of return used by LP were based on data that did not disaggregate dropouts' earnings by how many years of school they had LP acknowledged, We would have preferred to show the earnings gain associated with each year of the course, including the year when it was successfully completed. This note presents a reanalysis of sheepskin effects based on the type of data LP wished they had. The results contain very strong evidence of sheepskin effects after all. The next section describes our analysis, and the following section summarizes and discusses our

Closed-ended contingent valuation surveys can be very useful in the evaluation of nonmarket resources. Respondents merely state whether they would accept or reject a hypothetical threshold amount, either as payment for giving up access to the resource or as a fee for its use. The authors develop a maximum likelihood procedure which exploits the variation in the threshold values to allow direct and separate point estimates of regression-like slope coefficients and error standard deviations (without truncation bias). Their illustration uses data from a survey of recreational fisherman to examine factors which influence individuals' willingness-to-pay. Copyright 1987 by MIT Press.

The authors estimate labor dem and equations derived from a (restricted variable) cost function in which "experience" on a technology (proxied by the mean age of the capital stock) enters "non-neutrally." The specification of the underlying cost function isbased on the hypothesis that highly educated workers have a comparative advantage with re spect to the adjustment to, and implementation of, new technologies. The empiric al results are consistent with the implication of this hypothesis, that the rel ative demand for educated workers declines as the ages of plant and (particularl y) of equipment increase, especially in R&D-intensive industries. Copyright 1987 by MIT Press.

The hypothesis that the relative innovative advantage between large and small firms is determined by market concentration, the extent of entry barriers, the composition of firm size within the industry, and the overall importance of innovation activity is tested. The authors find that large firms tend to have the relative innovative advantage in industries that are capital intensive, concentrated, highly unionized, and produce a differentiated good. The small firms tend to have the relative advantage in industries that are highly innovative, utilize a large component of skilled labor, and tend to be composed of a relatively high proportion of large firms. Copyright 1987 by MIT Press.

Aggregate fluctuations in employment and unemployment are often explained within a market-clearing framework as intertemporal substitution in labor supply. Under this hypothesis, leisure in the current period is supposed to be highly substitutable with leisure (and goods) in other periods. Consequently, labor supply responds to perceived temporary changes in the real wage although it may be inelastic with respect to permanent changes in the real wage. A very important and influential empirical study of intertemporal substitution was presented by Robert Lucas and Leonard Rapping (1969, L-R). Lucas and Rapping estimated a simultaneous equations model of the aggregate labor market under an adaptive expectations forecasting scheme and found strong support for the intertemporal substitution hypothesis. Although the adaptive expectations assumption is now rarely used in such models, the intertemporal substitution hypothesis has become quite prominent in equilibrium business cycle theories where it is used to explain how fluctuations in aggregate demand can result in real changes in output, employment and unemployment.' Besides the L-R study, relatively few attempts have been made to verify the intertemporal substitution hypothesis, and most have failed to find the kind of intertemporal substitution elasticity estimated by Lucas and Rapping.2 In addition, the L-R results have never been tested, although Joseph Altonji made some minor data corrections, extended the time period, and reproduced them.3 This paper helps to reconcile the L-R results with later findings and also provides a convincing illustration of the importance and usefulness of specification testing. Here the L-R estimates are reproduced and the model is subjected to the kind of overidentification tests now widely used in econometric analysis. The restrictions on the model are easily rejected. This standard specification testing leads to very different conclusions about the empirical importance of the intertemporal substitution hypothesis than those suggested by Lucas and Rapping.

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.