Generalised Variance Function Estimation for Binary Variables in Large‐Scale Sample Surveys

Generalised variance function (GVF) models are data analysis techniques often used in large‐scale sample surveys to approximate the design variance of point estimators for population means and proportions. Some potential advantages of the GVF approach include operational simplicity, more stable sampling errors estimates and providing a convenient method of summarising results when a high number of survey variables is considered. In this paper, several parametric and nonparametric methods for GVF estimation with binary variables are proposed and compared. The behavior of these estimators is analysed under heteroscedasticity and in the presence of outliers and influential observations. An empirical study based on the annual survey of living conditions in Galicia (a region in the northwest of Spain) illustrates the behaviour of the proposed estimators.     

Cambodia: Reconstructing the Demographic Stab of the Past and Forecasting the Demographic Scar of the Future

Cambodia went through 5 years of violent internal conflicts (1970–1975), including the spread of the American–Vietnamese war into the country, followed by four more years of brutal and chaotic Khmer Rouge government (1975–1979). The result was almost 2 million deaths. This article attempts to contribute to the reconstruction of the demographic history of Cambodia. This is done in three ways. First, by analysing scars in the pyramids corresponding to the recent past, present and future; second, by estimating major demographic events during the 1970s, especially excess deaths; and third, by comparing the size and composition of a simulated normal population with the real population.     

Ranking opportunity sets: An approach based on the preference for flexibility

We describe a criterion to evaluate subsets of a finite set of alternatives which are considered as opportunity sets. The axioms for set comparison are motivated within the preference for flexibility framework. We assume the preference over the universal set of alternatives to be made of two disjoint binary relations. The result is the axiomatic characterization of a procedure which is formally similar to the leximax ordering, but in our case it incorporates the presence of some uncertainty about the decision-maker final tastes. Received: 20 January 1999/Accepted: 20 October 1999     

Measuring is Believing! Improving Conventional Indicators of Welfare State Development

Welfare effort (social spending as a percentage of GDP) has conventionally been the preferred measure for comparisons in space and time of the level of development of welfare states. However, a wide variety of other measuring systems are potentially available in this field, because there are different sources and different methods for calculating social spending, so a variety of indicators can be calculated, each of which best reflects a different aspect of social protection systems. This paper presents two innovations: first it proposes options other than those generally used to measure the relative development of welfare states. Secondly, it uses those new measurements to show that some widely accepted conclusions on the relative development of welfare states need to be reviewed.     

Integrative public leadership: Catalyzing collaboration to create public value

Integrative public leadership is a process of developing partnerships across organizational, sectoral and/or jurisdictional boundaries that create public value. This paper explores the concept in the context of the literature and illustrates some salient features of integrative public leadership through three cases involving extensive multi-sector collaboration in the western (Smoky Mountain) region of North Carolina. The cases are different in subject matter—sewer lines to a rural community, broadband infrastructure across a network of rural schools and colleges, and a major environmental preservation effort—but they all share some key elements. Leadership in each case is enacted through structure, process, and people. Boundary organizations provide a structural context for partnership development; boundary experiences and boundary objects serve to bridge differences and create a common purpose; and boundary spanners exhibit entrepreneurial qualities and leverage relationship capital in order to facilitate integration.     

Robust regression through robust covariances

This paper discusses the estimation of regression parameters after summarizing the data by a covariance matrix of the concatenated vector of explanatory variables and response variable. A robust estimate of the covariance matrix leads to a robust regression estimator. An M-estimator at the covariance estimation step is studied in the paper, and the resulting regression estimator is compared to a few previously proposed robust regression estimators.     

Adaptive and Generative Learning: Implications from Complexity Theories

One of the most important classical typologies within the organizational learning literature is the distinction between adaptive and generative learning. However, the processes of these types of learning, particularly the latter, have not been widely analyzed and incorporated into the organizational learning process. This paper puts forward a new understanding of adaptive and generative learning within organizations, grounded in some ideas from complexity theories: mainly self‐organization and implicate order. Adaptive learning involves any improvement or development of the explicate order through a process of self‐organization. Self‐organization is a self‐referential process characterized by logical deductive reasoning, concentration, discussion and improvement. Generative learning involves any approach to the implicate order through a process of self‐transcendence. Self‐transcendence is a holo‐organizational process characterized by intuition, attention, dialogue and inquiry. The main implications of the two types of learning for organizational learning are discussed.     

The Role of Automatic Stabilizers in the U.S. Business Cycle

Most countries have automatic rules in their tax‐and‐transfer systems that are partly intended to stabilize economic fluctuations. This paper measures their effect on the dynamics of the business cycle. We put forward a model that merges the standard incomplete‐markets model of consumption and inequality with the new Keynesian model of nominal rigidities and business cycles, and that includes most of the main potential stabilizers in the U.S. data and the theoretical channels by which they may work. We find that the conventional argument that stabilizing disposable income will stabilize aggregate demand plays a negligible role in the dynamics of the business cycle, whereas tax‐and‐transfer programs that affect inequality and social insurance can have a larger effect on aggregate volatility. However, as currently designed, the set of stabilizers in place in the United States has had little effect on the volatility of aggregate output fluctuations or on their welfare costs despite stabilizing aggregate consumption. The stabilizers have a more important role when monetary policy is constrained by the zero lower bound, and they affect welfare significantly through the provision of social insurance.