The ABCE Model of Volunteer Motivation

To meet the growing social service needs of our societies, the social services and other Volunteer organizations need to understand the needs and motives of their volunteers to keep them retained. Although volunteer motivation scales are available and tested, different organizations have to amend and add volunteering motives to best fit their organization and environment. Furthermore, not much guidance is available to volunteer organizations to understand or measure motivation of their volunteers raising a need for a unified model that can be a guideline for managers. This study discusses different approaches to volunteering motivation and links them into four areas of affiliation using an ABCE model: affiliation (A), beliefs, (B) career development, (C) and (E) egoistic. Participants were 496 volunteers from a variety of NGOs including 239 (48.2%) from an international Faith Based Organization (FBO). Findings show that although differences exist in volunteering motivation the actual best fit was an ABCE model. Future research is needed on testing the scale with different cultures and different organizations. A deeper knowledge of volunteer motivations will enable organizations to prosper and utilize the continuous experience of the volunteers and their engagement, thereby ensuring enhanced quality social service delivery.     

Stein-rule estimation in models with a lagged-dependemt variable

There is an extensive literature on the Stein-rule estimation of the parameters in a regression model. An important result in this literature is that the Stein-rule does not dominate the least squares estimator in the lower mean squared error sense when there are two or less regressors in the model. However, we note that not much is known about the Stein-rule estimation in dynamic models. This paper is a modest attempt in this direction and it shows that Stein-rule estimation of the lagged coefficient does not dominate the least square indicating that the result of regresion model goes through in the dynamic case.     

Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling

We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also allowed for. We propose a nonparametric test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis as well as a sequence of local alternatives and global alternatives, and propose a bootstrap version for it. Simulations are conducted to evaluate both the estimator and test. Applications to the 1985 Australian Longitudinal Survey data indicate a clear rejection of the null hypothesis of the constant rate of return to education, and that the returns to education obtained in earlier studies tend to be overestimated for all the work experience.     

Bangladeshi Migrant Workers in Hong Kong: Adaptation Strategies in an Ethnically Distant Destination

The paper attempts to explore the adaptation‐related challenges that Bangladeshi migrant workers in Hong Kong face and the strategies they adopt to triumph over them. Data were collected through administering both closed and open‐ended questionnaires to 56 migrant workers in Hong Kong during 2005–2006. The study revealed that most of the respondents stayed in Hong Kong legally but worked illegally because of the fact that although they could extend their stay permits, they were not grated work permits. This exclusive circumstance places them in a number of difficult situations. In attempting to adapt to Hong Kong society, they embrace a number of interesting and unique strategies to surmount challenges.     

Estimation of the seemingly unrelated regression model when the error covariance matrix is singular

A two-step generalized least-squares (GLS) estimator proposed by Zellner for seemingly unrelated regression (SUR) models is implementable when the estimated covariance matrix of the errors in the SUR system is non-singular. Violating the premise of non-singularity is a common problem among many applications in economics, business and management. We present methods of resolving this problem and propose an efficient procedure. The simulation study shows that the estimator of Haff performs better for small-sized observations, whereas the estimator of Ullah and Racine performs better for larger sized observations. Furthermore, the Ullah-Racine estimate is simple to calculate and easy to use. The empirical analysis involves the study of the diffusion processes of videocassette recorders across different geographic regions in the US, which exhibits a singular covariance matrix. The empirical results show that the procedures efficiently deal with the problem and provide plausible estimation results.     

Regularised Manova for High‐Dimensional Data

The traditional and readily available multivariate analysis of variance (MANOVA) tests such as Wilks' Lambda and the Pillai–Bartlett trace start to suffer from low power as the number of variables approaches the sample size. Moreover, when the number of variables exceeds the number of available observations, these statistics are not available for use. Ridge regularisation of the covariance matrix has been proposed to allow the use of MANOVA in high‐dimensional situations and to increase its power when the sample size approaches the number of variables. In this paper two forms of ridge regression are compared to each other and to a novel approach based on lasso regularisation, as well as to more traditional approaches based on principal components and the Moore‐Penrose generalised inverse. The performance of the different methods is explored via an extensive simulation study. All the regularised methods perform well; the best method varies across the different scenarios, with margins of victory being relatively modest. We examine a data set of soil compaction profiles at various positions relative to a ridgetop, and illustrate how our results can be used to inform the selection of a regularisation method.     

Forecasting Equity Premium: Global Historical Average Versus Local Historical Average and Constraints

The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when multiple predictors are used. We construct the constrained LHA estimator via an indicator function which operates as “model-selection” between the unconstrained LHA and the bound of the constraint (zero for the positivity constraint). We smooth the indicator function by bagging, which operates as “model-averaging” and yields a combined forecast of unconstrained LHA forecasts and the bound of the constraint. The local combining weights are determined by the probability that the constraint is binding. Asymptotic properties of the constrained LHA estimators without and with bagging are established, which show how the positive constraint and bagging can help reduce the asymptotic variance and mean squared errors. Monte Carlo simulations are conducted to show the finite sample behavior of the asymptotic properties. In predicting U.S. equity premium, we show that substantial nonlinearity can be captured by LHA and that the local positivity constraint can improve out-of-sample prediction of the equity premium.     

三峡库区农村劳动力转移研究

文章以三峡库区万州为研究对象,在对整个区域及乡镇实地调查的基础上,结合土地利用总体规划的成果,分析了万州区农村剩余劳动力转移的现状、特征与制约因素.结果表明,区域内经济落后、自然条件差、产业空虚是造成农村劳动力大量异地流动的主要原因.大量青壮年劳力的外移,又引发了农村缺乏现代化建设的生力军,并不可避免地出现留守儿童等负面影响.随着库区农业产业化结构的调整,小城镇建设的加速,新农村建设和农业产业化的带动,区域内农村劳动力迁移不断发生新的变化,转移的渠道和途径呈现出多元化特征.