会计数据信度与效度的特征性描述——基于会计变量及变量间关系的认识

无形会计数据信度即度量结果的重复性程度,是反映会计数据的客观性指标。效度是判断度量结果即研究对象结果的有效性程度。会计数据的信度与效度的探讨有助于会计信息质量的改善与质量标准的制定,对于进一步展开会计科学的研究也具有一定的意义。本文通过分析会计数据信度与效度的特征性,探讨与验证会计数据信度与效度路径研究方法。     

Efficiency outcomes of market concentration and managed care

The paper examines how hospital cost efficiency has reacted to extensive horizontal integrations of hospitals and rapid growth of managed care in the US health care industry. Cost efficiency is estimated by using panel data approaches to relax the assumptions for the hospital effects imposed in earlier studies. The paper shows that higher managed care penetration over time is associated with greater hospital efficiency, and higher market concentration is positively associated with efficiency when markets are highly competitive or highly concentrated.     

Why are Small Firms Different? Managers’ Views

Do incentives differ between large and small organizations? Results from a representative survey of compensation managers are used to shed light on the issues. I find that (i) small establishments rely less on pecuniary incentives, and have a significantly more hostile attitude towards incentive schemes based on competition and relative rewards; (ii) large units are more vulnerable to mechanisms of efficiency wages, effects that remain even after controlling for differences in monitoring ability; (iii) large units are more prone to indicate that negative reciprocity is important, and that their employees care about relative pay. I argue that these findings fit with behavioral stories of incentives and motivation, in particular those stressing group interaction effects, inequity aversion and gift exchange.     

高速移动数据传输新标准IEEE 802.20

介绍了未来高速移动通信新标准IEEE802．20的总体概况，具体分析了IEEE802．20的技术特性、系统参考结构以及系统的组成模型，并对IEEE802．20标准前景分析与展望进行了详细描述。     

LABOUR SUPPLY ESTIMATION AND PUBLIC POLICY

Abstract. Economists devote considerable energies towards refining their econometric techniques to overcome difficulties connected with conducting empirical research. Despite advances in technique. it is not clear whether further refinement in this direction is worthwhile for policy purposes. It may be that no further amount of statistical adjustment of inadequate data will increase understanding, and that better data is simply necessary to add to our knowledge. But rarely is sufficient credit paid to new forms of data. In short, econometric technique is emphasized to the neglect of data innovation, as if new data were merely lying about waiting for an ingenious suggestion for use. This paper surveys advances of the last twenty five years in estimating labour supply for policy purposes with a view towards appreciating the relative contribution of both improvements in econometric technique as well as developments of new data.
After briefly detailing the key parameters which economists have sought to estimate, we describe the early 'first generation' research (circa 1970), which is plagued by problems of unobservable variables, measurement errors, truncation and selectivity bias, and non linear budget constraints. 'Second generation' research constitute attempts to resolve one or more of these difficulties, and the respective contribution of econometric technique and new data is acknowledged and assessed, including the contribution of data generated by large scale social experiments in which participants are randomly assigned to different guaranteed income plans and their labour supply behaviour measured.     

Economics and underdetermination: a case study of urban land and housing economics

Underdetermination, associated with the Duhem–Quine thesis,is a familiar if under-researched theme in economics. In thelight of this, we examine the development of urban land andhousing economics. Through its Cartesian dualistic delineationof theory and data, the contemporary mainstream approach appearsunable to circumvent the problem of underdetermination. In effect,it employs the strong version of Duhem–Quine in its retentionof the assumption of a single, unitary competitive market (andassociated access–space trade-off). Conversely, we highlightthe affinity of Ely's (and the later Columbia School's) approachto pragmatists Dewey and Peirce, which provides a more fruitfulbasis for explanation.     

Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ

Bayesian multiple imputation (MI) has become a highly useful paradigm for handling missing values in many settings. In this paper, I compare Bayesian MI with other methods – maximum likelihood, in particular—and point out some of its unique features. One key aspect of MI, the separation of the imputation phase from the analysis phase, can be advantageous in settings where the models underlying the two phases do not agree.     

Measurement error and panel data1

The underidentification of linear models with measurement error does not necessarily extend to panel data models, as has been shown by GAiliches and Hausman (1986). We discuss and extend some of their results for a simple case and address particular issues concerning identification and asymptotic variances.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.