Agency costs,property rights,and the evolution of labor unions

Collective bargaining requires that an agent represent workers. This paper examines the implications for the trade union movement of the resulting agency costs. Without transferable rights in the union, union members lack the means and incentive to bring forth the innovative agent controls common to the modern corporation. Considerations of the bargaining strengths of employers and employees, each represented by an agent, provide an explanation of the simultaneous decline of private sector union membership (corporate share holders have been more successful at lowering agency costs) and growth of public sector union representation (where the union official, a “double agent,” serves the interest of both employee and bureaucratic employer). The authors acknowledge the helpful remarks Donald L. Martin whose earlier research on property rights in unions inspired this effort. Don Bellante’s work was supported by a grant from the Research Committee of the College of Business Administration, University of South Florida.     

A note on inverse estimation in non-linear models

The problem of estimating the value of an x variable for a given proportion of an observed value of the y variable is consdered for a non-linear model. Asymptotic confidence limits are obtained and an application of the method is illustrated witreal data.     

Estimation of noncentrality parameters

We propose some estimators of noncentrality parameters which improve upon usual unbiased estimators under quadratic loss. The distributions we consider are the noncentral chi-square and the noncentral F. However, we give more general results for the family of elliptically contoured distributions and propose a robust dominating estimator.     

Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

Community involvement in land regularization for informal settlements in Tanzania: A strategy for enhancing security of tenure in residential neighborhoods

This paper documents and analyzes how landholders managed to uplift status of their neighborhood from hazard land as designated by the 1978 master plan to a regularized residential settlement through land regularization in Dar es Salaam city. Specifically, explores policy framework governing land regularization and how the local community explored the opportunities it offers. Documents the local community planning and land regularization processes undertaken focusing on land use planning, drainage construction, and cadastral survey and discuses how financial resources were raised, trust was built as well as factors which sustained community involvement towards meeting their interests of securing tenure. The paper also, draws challenges facing land regularization policy and recommends areas for further interventions commensurate with the human dimension challenges in securing tenure.Underlying community involvement, those aspects of community, which have been connected to the idea of social capital namely existence of committed leadership in land development matters, embracing mechanism for participatory decision making process and educational background to local leaders were particularly important in determining success for the case. Others include economic ability to contribute, high proportion of landholder settlers, land conflict task force formulation, local consensus to solve commonly felt problems, existence of strong community organization, and unwritten norms put in place to regulate individual behavior in building construction. Weak legal recognition of informal settlement, lengthy and bureaucratic procedures in planning and approval of regularization plans, weak knowledge on land management matters, short-term title deeds with low financial betterments, political popularity are identified critical challenges.Some of recommendations put forward include formalization of the grass-roots role in decision-making, decentralizing some of land development control functions to sub-ward leaders and training the same in basic land management matters. Others include definition of norms; by-laws and government facilitation of informal land parceling. Besides community support should be sought so as to create partnership in the promotion of security of tenure in informal areas. The study concludes that, unless land development activities ongoing in informal settlements are closely monitored and regulated as the settlement grow, it will be too costly socially and economically to retrofit once the settlement have identified.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

Gender,lineage, and fertility‐related outcomes in Ghana

A growing literature examines the empirical relationship between the joint reproductive preferences of marital partners and reproductive outcomes in Africa. Less explored is how spousal power in decision making may be influenced by lineage type. Using pooled data from Ghana, we investigate how lineage affects gendered reproductive decision outcomes and find some evidence that matrilineal women are more able than nonmatrilineal women to translate their reproductive preferences into action consistent with their goals.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.