Policy reform and productivity change in Chinese agriculture: A distance function approach

Agricultural policy reform has been an important source of change in the Chinese agricultural sector. The reforms led to productivity growth and helped China to pursue its self-sufficiency goal especially in the grain sector. In order to analyze whether observable productivity growth stems from technologically induced components or from the market induced parts, a multi-input–multi-output model is derived by using an econometric distance function framework. A decomposition allows to distinguish allocative effects, scale effects, technological change, and technical efficiency change. Data on farms in Zhejiang from 1986 to 2000 are used to analyze the impact of policy reform.     

Elicitation using multiple price list formats

We examine the properties of a popular method for eliciting choices and values from experimental subjects, the multiple price list format. The main advantage of this format is that it is relatively transparent to subjects and provides simple incentives for truthful revelation. The main disadvantages are that it only elicits interval responses, and could be susceptible to framing effects. We consider extensions to address and evaluate these concerns. We conclude that although there are framing effects, they can be controlled for with a design that allows for them. We also find that the elicitation of risk attitudes is sensitive to procedures, subject pools, and the format of the multiple price list table, but that the qualitative findings that participants are generally risk averse is robust. The elicitation of discount rates appear less sensitive to details of the experimental design. Electronic Supplementary Material Supplementary material is available in the online version of this article at . JEL Classification C9, D81, D91 An erratum to this article is available at .     

Uncovering the Risk–Return Relation in the Stock Market

There is ongoing debate about the apparent weak or negative relation between risk (conditional variance) and expected returns in the aggregate stock market. We develop and estimate an empirical model based on the intertemporal capital asset pricing model (ICAPM) that separately identifies the two components of expected returns, namely, the risk component and the component due to the desire to hedge changes in investment opportunities. The estimated coefficient of relative risk aversion is positive, statistically significant, and reasonable in magnitude. However, expected returns are driven primarily by the hedge component. The omission of this component is partly responsible for the existing contradictory results.     

From edgeworth to fisher to vickrey: A comment on Michael J. Boskin's Vickrey lecture

Irving Fisher has been overlooked as an influence on William Vickrey's work on taxation and as a link between Edgeworth and Vickrey. Vickrey was Fisher's last and greatest student.     

Symmetry and order in the portfolio allocation problem

Summary. This research studies the role of multivariate distribution structures on random asset returns in determining the optimal allocation vector for an expected utility maximizer. All our conclusions pertain for the set of risk averters. By carefully disturbing symmetry in the distribution of the, possibly covarying, returns, we ascertain the ordinal structure of the optimized allocation vector. Rank order of allocations is also established when a permutation symmetric random vector is mapped into the returns vector through location and scale shifts. It is shown that increased dispersion in the vectors of location and scale parameters benefit, ex-ante, investors as does a decrease in the rank correlation coefficient between the location and scale parameter vectors. Revealed preference comparative static results are identified for the location and scale vectors of asset returns. For most issues addressed, we arrive at much stronger inferences when a safe asset is available. Received: August 8, 2000; revised version: January 8, 2001     

Cognitive processing models in performance appraisal: evidence from the Malaysian education system

The effectiveness of the decision‐making process in appraisal has been of long‐standing interest. This paper reports the results of research exploring the extent to which those undertaking appraisals apply systematic processes to their decision‐making, represented in terms of the cognitive processing models applied by appraisers. Results of 22 cognitive mapping interviews exploring appraisal practice, undertaken with appraisers in the Malaysian education system, are described. The resultant cognitive maps have been explored for what they tell us about the cognitive processing models underlying appraisers' decision‐making. Results suggest that the practice of appraisal evidenced in interviews demonstrates the presence of cognitive processing that reflects theoretical cognitive processing models, although some differences are observed between less experienced and experienced appraisers' models. Implications from the interview findings for the practice of appraisal are identified.     

The Effects of Occupational Injuries After Returns to Work: Work Absences and Losses of On-the-Job Productivity

We extend the research on postinjury employment by estimating productivity losses for workers with permanent partial disabilities (PPDs) in the first three years after injury. Our method distinguishes between productivity losses attributed to spells of work absence versus reduced earnings during spells of employment. The method is applied to data for 800 Ontario workers with PPDs. The results document large productivity losses persisting at least three years after injury, with different loss patterns for workers returning to stable versus unstable employment. Human capital investments or job accommodations can reduce productivity losses, but the significant determinants of losses differ for the stable versus unstable employment groups.     

Combining information for prediction in linear regression

This article deals with the prediction problem in linear regression where the measurements are obtained using k different devices or collected from k different independent sources. For the case of k=2, a Graybill-Deal type combined estimtor for the regression parameters is shown to dominate the individual least squares estimators under the covariance criterion. Two predictors ŷ _c and ŷ _p are proposed. ŷ _c is based on a combined estimator of the regression coefficient vector, and ŷ _p is obtained by combining the individual predictors from different models. Prediction mean square errors of both predictors are derived. It is shown that the predictor ŷ _p is better than the individual predictors for k≥2 and the predictor ŷ _c is better than the individual predictors for k=2. Numerical comparison between ŷ _c and ŷ _p shows that the former is superior to the latter for the case k=2.