Edward E. Leamer

International Trade Theory: The Evidence

Publisher Summary This chapter focuses on the international trade theory. It discusses the Ricardian and the Ricardo–Viner models. These models point to technological differences as the source of international comparative advantage. A simple Ricardian model has one input, labor, which is assumed to be mobile across the two sectors of the economy, but internationally immobile. The Ricardo–Viner model introduces into the model two additional factors that are sector-specific. This gives curvature to the production possibilities curve and also allows international commerce to affect the distribution of income. Although there is little or no direct empirical support for these simple models, there is nonetheless growing awareness that technological differences are a natural consequence of economic isolation and play a role in the integration process, following an economic liberalization. The chapter provides an overview of the Heckscher–Ohlin model and discusses the Rybcyzinski theorem and the Stolper–Samuelson theorem. It also reviews the studies of the Heckscher–Ohlin model based on cross country comparisons.

Paths of Development in the Three-Factor, n-Good General Equilibrium Model

The three-factor, n-good model is displayed graphically and tested empirically. The three-dimensional endowment vectors and expansion paths are represented by points in an endowment triangle. Features of this endowment triangle determine relative factor returns, Rybczy nski and Stolper-Samuelson derivatives, output mixes, and the directi on of trade. The effects of mobile factors and nontraded goods are al so considered. Capital accumulation is shown to induce changes in out puts, trade, and the returns to factors that depend on the abundance of land relative to labor. Weak evidence for this 3 A n model is foun d in a 1978 U.N. data set on the value of output. Copyright 1987 by University of Chicago Press.

Reporting the Fragility of Regression Estimates

E MPIRICAL results reported in economics journals are selected from a large set of estimated models. Journals, through their editorial policies, engage in some selection, which in turn stimulates extensive model searching and prescreening by prospective authors. Since this process is well known to professional readers, the reported results are widely regarded to overstate the precision of the estimates, and probably to distort them as well. As a consequence, statistical analyses are either greatly discounted or completely ignored. This unfortunate equilibrium in the market for information is a result of the current econometric technology, which generates inferences only if a precisely defined model were available, and which can be used to explore the sensitivity of inferences only to discrete changes in assumptions. The reporting of a complete sensitivity analysis is ruled out therefore first, because the econometric theory which takes models as given would be rendered explicitly inadequate if the sensitivity analysis were reported, and, second, because the econometric technology, if used to explore sensitivity issues, would generate vast numbers of estimated models that journals are rightfully reluctant to print. It is the purpose of this article to discuss an alternative econometric technology that could increase the value of our professions limited data resources. The basic assumption underlying this technology is that no econometric model can be taken as given. Because there are many models which could serve as a basis for a data analysis, there are many conflicting inferences which could be drawn from a given data set. If this fact of life is acknowledged, it deflects econometric theory from the traditional task of identifying the unique inferences implied by a specific model to the task of determining the range of inferences generated by a range of models. We propose that researchers be given the task of identifying interesting families of alternative models and be expected to summarize the range of inferences which are implied by each of the families. When a range of inferences is small enough to be useful and when the corresponding family of models is broad enough to be believable, we may conclude that these data yield useful information. When the range of inferences is too wide to be useful, and when the corresponding family of models is so narrow that it cannot credibly be reduced, then we must conclude that inferences from these data are too fragile to be useful. This contrasts greatly with the reporting schemes currently used by individuals. As a profession, however, we do suspend judgment on econometric results until they hold up to inspections by other researchers using other models. The advocacy process we use to accumulate professional opinion is therefore aimed in the same direction as our proposals, but the path we recommend is much more direct and the outcome is much more clearly stated. A simple introduction to this alternative econometric technology is given in section I of this paper. In writing this section we have attempted to communicate the main ideas as concisely as possible. As a consequence, there is no reference to any sophisticated statistical theory and especially no mention of the Reverend Thomas Bayes. For a more complete statement as well as theological fanfare, consult Leamer (1978). The proper test of our proposals is whether they are useful in practice. We believe that researchers will find them to be efficient tools for discovering the information in data sets and for communicating findings to the consuming public. In an effort to make clear the value of these techniques we present two examples in section II. These methods are not without their own problems, the most serious of which is their concentration on the point estimation problem and their neglect of hypothesis testing or interval estimation. The basic approach to studying and reporting the fragility of estimates which we describe in this paper can be readily extended to studying and reporting the fragility of t-values, though computational difficulties do arise. Received for publication June 15, 1981. Revision accepted for publication August 2, 1982. * University of California, Los Angeles, and Harvard University, respectively. Research supported by NSF Grant SOC78-09477. Comments of the referees have helped to improve both the content and the exposition. Thomas Wolff is thanked for able research assistance.

What’s the use of factor contents?

The net imports of labor embodied in international trade has been a fairly small and stable share of the US labor force. From this some conclude that trade has not been a major contributor to the income inequality trends. This is a non sequitur. The labor embodied in trade is jointly determined by tastes, technologies, factor supplies and the external goods market. Although it is impossible to use factor contents to disentangle trade from technology, the factor contents can be used to suggest the change of earnings shares if the country were to close down external trade entirely. However, this is a proper application of factor contents only if tastes and technologies are log-linear, if trade is balanced and if foreign input intensities are used to compute factor contents of non-competing imports. Factor contents are virtually useless if technologies and tastes are not log-linear, or if the external deficit is substantial and variable. Factor contents do not tell us anything about earnings levels as opposed to shares. They also do not inform us of the impact of partial trade barriers that change relative product prices but do not completely eliminate trade. In other words, the title question is rhetorical.

Model choice and specification analysis

Publisher Summary This chapter discusses statistical theories of model selection and traditional problems. One important source of model-selection problems is the existence of a priori opinion that constraints are “likely.” Statistical testing is then designed either to determine if a set of constraints is “true” or to determine if a set of constraints is “approximately true.” The solution to these two problems that might be supposed to be essentially the same, in fact diverge in two important respects: (1) the first problem leads clearly to a significance level—that is a decreasing function of sample size, whereas (2) the second problem selects a relatively constant significance level. The problem has a set of alternative models—that is determined entirely from a priori knowledge, whereas the second problem can have a data-dependent set of hypotheses. Quadratic loss functions are discussed in the chapter both with and without fixed costs. Quadratic loss does not imply a model-selection problem. The chapter also discusses problems that are not well known.

Labor Markets in Developing Countries: An Agenda for Research

The gap between rich and poor countries has grown over the past century. A hundred years ago, the wealthiest country was 11 times richer ( in per capita income) than the poorest country. By 1985, the ratio of wealth of the richest to the poorest country had grown from 11 to 50. This increase in inequality is a distressing outcome for a globe that seems smaller every day; it is particularly distressing for countries that form the denominator of that wealth ratio. Is this likely to get worse? What can be done to raise the income levels of the poorest countries? Policy prescriptions for the poorest nations are often contradictory. Although providing more employment should alleviate poverty, there is no clear consensus regarding the best policies for expanding employment opportunities in developing countries. Some argue that labor market regulations are necessary to protect the rights of workers and to improve working conditions. Others point out that most regulations discourage firms from hiring workers and thus have the unintended consequence of harming the very people they are designed to protect. Moreover, in developing countries, massive noncompliance is the norm, and regulations like a national minimum wage could simply encourage the expansion of an informal market, where wages are even lower and working conditions even worse. The papers in this issue focus on two issues. The studies by Bell,

False Models and Post-Data Model Construction

Abstract This article proposes a method of discounting evidence when linear regression models are constructed after the data have been partially analyzed. The solution parallels a formal decision theoretic analysis of a presimplification problem in which models or model spaces are simplified before observation in order to avoid observation or processing costs. Post-data model construction is interpreted as the data-dependent decision that this pre-simplification is undesirable. The discounting implications of this formal analysis may then be used to police data analyses with less formal post-data model construction.

Latin America as a target of trade barriers erected by the major developed countries in 1983

Abstract According to an UNCTAD data set, nontariff barriers were applied by 14 major industrialized importers against 19 percent of Latin American exports. This is not an unusually high coverage ratio – Australia and New Zealand, and Japan faced substantially more frequent barriers. The effects of these barriers on Latin American exports are difficult to estimate. The estimates vary substantially depending on the method of estimation. One method estimates that Latin American exports to this group of 14 importers have been reduced by trade barriers by a total of 34 percent, varying from 5 percent for Mexico to 75 percent for Argentina.

Errors in Variables in Linear Systems

This paper extends the simple errors-in-variable bound to the setting of systems of equations. Both diagonal and nondiagonal measurement error covariance matrices are considered. In the nondiagonal case, the analogue of the simple errors-in-variable interval of estimates is an ellipsoid with diagonal equal to the line segment connecting t he direct least squares with a two-stage least-squares estimate. For the diagonal case, the set of estimates under some conditions must li e within the convex hull of 2k points. Copyright 1987 by The Econometric Society.

Welfare computations and the optimal staging of tariff reductions in models with adjustment costs

Abstract This is an analysis of the second-best problem of choice of tariff level for period two given a positive tariff in period one. The models presented take the allocation of all factors of production except labor as fixed, and allow labor to transfer between industries at a positive cost. The period two tariff may then optimally be set to some positive level. Although these models involve voluntary unemployment, no adjustment to the goods market welfare triangle is necessary to compensate for the adjustment burden in computing the gains from tariff reduction. Lastly, it is shown that a staged reduction of tariffs may be preferred for income distribution reasons to a once-and-for-all reduction.