What determines the allocative efficiency of markets? Why are double auctions, even with untrained human traders, allocationally efficient? We provide a simple explanation for these complex phenomena by showing how externally observable rules that define a market cause high allocative efficiency when individuals remain within the confines of these rules. We also show how the oft-ignored shape of extramarginal demand and supply affects efficiency by influencing the inverse relationship between the magnitude of efficiency loss and its probability.

Endogenous growth models have reignited interest in institutional and path dependencies in the economic growth process. One reason for the interest in endogenous growth models is that they may explain why countries consistently grow at different rates. In this vein, it has been recently proposed that greater economic inequality reduces future economic growth. An important paper in this literature is by Torsten Persson and Guido Tabellini (1994), who will be referred to as PT. PT's model shows why it is reasonable to expect a negative relationship between inequality and future economic growth. Moreover, their empirical evidence is consistent with their contention. If PT's findings are robust to other data sets, there would be important policy implications. For example, they imply that policy makers should not only be concerned with the distributional implications of government policies for political and social reasons, but also because income distribution has long-run effects on economic growth. This indicates that greater U.S. income inequality since the early 1970's may have resulted in lower subsequent economic growth. However, PT's results are somewhat fragile to various specifications, suggesting that they should be replicated with different data sets and over different time periods (PT p. 617). With these implications in mind, this study employs data from a panel of U.S. states to further explore the relationship between economic growth and income inequality. In what follows, Section I summarizes PT's study and discusses how this comment extends their findings. The empirical implementation and results in Section II directly examine the link between overall income inequality and growth. Section III expands the analysis to alternative measures of income distribution and government policy. One emphasis in Section III is the distinction between how the overall income distribution (especially at the tails) influences economic growth from how the relative well-being of the median voter affects economic growth. Section IV provides some concluding discussion.

Spatial separation of racial and ethnic groups may theoretically have positive or negative effects on the economic performance of those groups. We examine the effects of segregation on outcomes for blacks in schooling, employment, and single parenthood. We find that blacks in more segregated areas have significantly worse outcomes than blacks in less segregated areas. We control for the endogeneity of location choice using instruments based on political factors, topographical features, and residence before adulthood. A one standard deviation decrease in segregation would eliminate one-third of the black-white differences in most of our outcomes.

The recent decline in the unemployment insurance (UI) takeup rate has puzzled researchers. Using administrative data with accurate information on the potential level and duration of benefits, we examine whether a separating employee receives UI. We find a strong positive effect of the benefit level on takeup, and smaller effects of the potential duration and the tax treatment of benefits. Simulations indicate that the recent inclusion of UI in the income tax base can account for most of the previously unexplained decline in UI receipt.

This research re‐examines whether there are differences in the forecast accuracy of financial analysts through a comparison of their annual earnings per share forecasts. The comparison of analyst forecast accuracy is made on both an ex post (within sample) and an ex ante (out of sample) basis. Early examinations of this issue by Richards (1976), Brown and Rozeff (1980), O'Brien (1987), Coggin and Hunter (1989), O'Brien (1990), and Butler and Lang (1991) were ex post and suggest the absence of analysts who can provide relatively more accurate forecasts over multiple years. Contrary to the results of prior research and consistent with the belief in the popular press, we document that differences do exist in financial analysts' ex post forecast accuracy. We show that the previous studies failed to find differences in forecast accuracy due to inadequate (or no) control for differences in the recency of forecasts issued by the analysts. It has been well documented in the literature that forecast recency is positively related to forecast accuracy (Crichfield, Dyckman, and Lakonishok 1978; O'Brien 1988; Brown 1991). Thus, failure to control for forecast recency may reduce the power of tests, making it difficult to reject the null hypothesis of no differences in forecast accuracy even if they do exist. In our analysis, we control for the differences in recency of analysts' forecasts using two different approaches. First, we use an estimated generalized least squares estimation procedure that captures the recency‐induced effects in the residuals of the model. Second, we use a matched‐pair design whereby we measure the relative forecast accuracy of an analyst by comparing his/her forecast error to the forecast error of another randomly selected analyst making forecasts for the same firm in the same year on or around the same date. Using both approaches, we find that differential forecast accuracy does exist amongst analysts, especially in samples with minimum forecast horizons of five and 60 trading days. We show that these differences are not attributable to differences in the forecast issuance frequency of the financial analysts. In sum, after controlling for firm, year, forecast recency, and forecast issuance frequency of individual analysts, the analyst effect persists. To validate our findings, we examine whether the differences in the forecast accuracy of financial analysts persist in holdout periods. Analysts were assigned a “superior” (“inferior”) status for a firm‐year in the estimation sample using percentile rankings on the distribution of absolute forecast errors for that firm‐year. We use estimation samples of one‐ to four‐year duration, and consider two different definitions of analyst forecast superiority. Analysts were classified as firm‐specific “superior” if they maintained a “superior” status in every year of the estimation sample. Furthermore, they were classified as industry‐specific “superior” if they were deemed firm‐specific “superior” with respect to at least two firms and firm‐specific “inferior” with respect to no firm in that industry. Using either definition, we find that analysts classified as “superior” in estimation samples generally remain superior in holdout periods. In contrast, we find that analysts identified as “inferior” in estimation samples do not remain inferior in holdout periods. Our results suggest that some analysts' earnings forecasts should be weighted higher than others when formulating composite earnings expectations. This suggestion is predicated on the assumption that capital markets distinguish between analysts who are ex ante superior, and that they utilize this information when formulating stock prices. Our study provides an ex ante framework for identifying those analysts who appear to be superior. When constructing weighted forecasts, a one‐year estimation period should be used because we obtain the strongest results of persistence in this case.

[This paper examines the factors influencing the relative weights placed on financial and non-financial performance measures in CEO bonus contracts. We find that the use of non-financial measures increases with the level of regulation, the extent to which the firm follows an innovation-oriented strategy, the adoption of strategic quality initiatives, and the noise in financial measures. We find no evidence that the choice of performance measures in bonus contracts is associated with the level of financial distress or the value of CEO equity holdings relative to salary and bonus. Our results also provide no support for the hypothesis that CEOs with greater influence over the board of directors are more likely to be compensated based on non-financial measures.]

We examine the fit between the ownership data provided by four surrogate databases and the data collected from proxy statements. We discover an unambiguous pecking order among the surrogates relative to the benchmark ownership statistics of corporate proxy statements. Corporate Text is first, followed in descending order by Compact Disclosure, Value Line, and Spectrum. Further tests show that reporting discrepancies in the Value Line and Spectrum databases could affect economic inferences drawn from regressions using their ownership data. A field guide describing each data source's reporting conventions, formats, and strate? gies for data aggregation may be downloaded from the Journal of Financial and Quantitative Analysis' web site (http.V/weber.u.washington.edu/~jfqa/hola7andeapdx.pdf).

In Imbens and Ingrist (1994), Angrist, Imbens and Rubin (1996) and Imbens and Rubin (1997), assumptions have been outlined under which instrumental variables estimands can be given a causal interpretation as a local average treatment effect without requiring functional form or constant treatment effect assumptions. We extend these results by showing that under these assumptions one can estimate more from the data than the average causal effect for the subpopulation of compliers; one can, in principle, estimate the entire marginal distribution of the outcome under different treatments for this subpopulation. These distributions might be useful for a policy maker who wishes to take into account not only differences in average of earnings when contemplating the merits of one job training programme vs. another. We also show that the standard instrumental variables estimator implicitly estimates these underlying outcome distributions without imposing the required nonnegativity on these implicit density estimates, and that imposing non-negativity can substantially alter the estimates of the local average treatment effect. We illustrate these points by presenting an analysis of the returns to a high school education using quarter of birth as an instrument. We show that the standard instrumental variables estimates implicitly estimate the outcome distributions to be negative over a substantial range, and that the estimates of the local average treatment effect change considerably when we impose nonnegativity in any of a variety of ways.