New cultural economies of marginality: revisiting the West Coast,South Island,New Zealand

Marginal regions have been the subject of political concern and remedial action in western states for several decades now. The West Coast of the South Island of New Zealand is an interesting case study in this regard, for recent economic growth has confounded earlier expectations of post-restructuring decline, while also contradicting several of the nostrums of new regionalism. In an effort to understand this trajectory, this paper draws on documents from public and private sector organisations, newspaper articles and field visits to examine developments in four key sectors of the West Coast's economy: mining, dairy farming, forestry and tourism. Economic growth is found to be closely linked to the cultivation of new markets for primary products, but efforts to rework the cultural dimensions of marginality have also been important. Value has been added to specific products through the insertion of references to the region's alpine and forested landscapes. Isolation and peripherality have been recast in more positive terms, echoing the broader reframing of New Zealand as a scenic, unspoiled destination. In adopting a cultural economic perspective on marginal regions, the paper illustrates the significance of symbolic forms of value, the potentially flexible nature of marginality as a discursive category, and the importance of the networks which connect regions to national and international flows of capital and tourists.     

Comparison of causal effect estimators under exposure misclassification

Over the past decades, various principles for causal effect estimation have been proposed, all differing in terms of how they adjust for measured confounders: either via traditional regression adjustment, by adjusting for the expected exposure given those confounders (e.g., the propensity score), or by inversely weighting each subject's data by the likelihood of the observed exposure, given those confounders. When the exposure is measured with error, this raises the question whether these different estimation strategies might be differently affected and whether one of them is to be preferred for that reason. In this article, we investigate this by comparing inverse probability of treatment weighted (IPTW) estimators and doubly robust estimators for the exposure effect in linear marginal structural mean models (MSM) with G-estimators, propensity score (PS) adjusted estimators and ordinary least squares (OLS) estimators for the exposure effect in linear regression models. We find analytically that these estimators are equally affected when exposure misclassification is independent of the confounders, but not otherwise. Simulation studies reveal similar results for time-varying exposures and when the model of interest includes a logistic link.     

Hypothesis Testing of Hazard Ratio Parameters in Marginal Models for Multivariate Failure Time Data

Marginal hazard models for multivariate failure time data have been studied extensively in recent literature. However, standard hypothesis test statistics based on the likelihood method are not exactly appropriate for this kind of model. In this paper, extensions of the three commonly used likelihood hypothesis test statistics are discussed. Generalized Wald, generalized score and generalized likelihood ratio tests for hazard ratio parameters in a marginal hazard model for multivariate failure time data are proposed and their asymptotic distributions examined. The finite sample properties of these statistics are studied through simulations. The proposed method is applied to data from Busselton Population Health Surveys.     

There's more to the picture than meets the eye: Measuring tangible and intangible capital in two marginal communities in rural Denmark

A missing link in economics has been what Veblen in 1908 termed intangible capital. This includes common norms, trust and high levels of cooperative performance. Intangibles are invisible to the eye and not easily measured in quantitative terms. They nevertheless involve visible, socioeconomic outcomes and should therefore rightly be seen as productive, like tangibles. Thus, uneven levels of intangible capital would explain Differential Economic Performance (DEP) between, say, two firms containing exactly the same stock of physical, economic and human capital. Despite this common sense observation, most economists have failed to see that ‘there's more to the picture than meets the eye’, as Neil Young once sang. We use statistical, historical and fieldwork data from two Danish, marginal rural communities both rich on intangible capital. This to show how intangible capital in the form of social, organisational and cultural capital is accumulated and utilised in situ, at the microlevel. We suggest that the difference between these two, very similar communities should be explained in their varying ability to utilise local stocks of tangible and intangible capital. Drawing on seminal ideas from Bourdieu [The forms of capital. In: Richardson, J.G. (Ed.) Handbook of Theory and Research for the Sociology of Education. Greenwood Press, New York, Westport, CT and London, 1986, pp. 241–58] and the DORA project [Bryden, Differential economic performance in rural areas. In: International Conference on Rural Communities and Identities in the Global Millennium. Malpasino University College, Nainamo, BC, Canada, 2000], we want to develop a ‘total capital’ assessment tool for mapping and measuring socioeconomic development in marginal rural communities. In this way, we hope to count in ‘all’ capital as Schultz [Investment in human capital. In: Kiker, B.F. (Ed.) Investment in Human Capital. Columbia, 1971, pp. 3–21] prophesised. This in order to explain what we term Differential Local Development (DLD), where ‘good’, sustainable development is associated with high economic performance and increase in population.     

Understanding the 2001 UK census migration and commuting data: the effect of small cell adjustment and problems of comparison with 1991

Summary.  Origin–destination statistics have been produced from the last three UK censuses. The paper describes what is new about the 2001 census interaction data on migration and commuting, considers the disclosure control methods that were applied to cells containing small values and demonstrates the problems that are associated with making comparisons with 1991 data. The effect of small cell adjustment procedures on the interaction data sets is investigated by means of selective analyses at different spatial scales. Some recommendations are made in light of the problems that were manifest in 2001.     

Bayesian Variable Selection Under Collinearity

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.     

Unit Root Tests in the Presence of Multi-Variance Break and Level Shifts That Have Power Against the Piecewise Stationary Alternative

Structural breaks in the level as well as in the volatility have often been exhibited in economic time series. In this paper, we propose new unit root tests when a time series has multiple shifts in its level and the corresponding volatility. The proposed tests are Lagrangian multiplier type tests based on the residual's marginal likelihood which is free from the nuisance mean parameters. The limiting null distributions of the proposed tests are the χ²distributions, and are affected not by the size and the location of breaks but only by the number of breaks.

We set the structural breaks under both the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests are locally more powerful than the OLSE-based tests, and that the powers of our tests, in a fixed time span, remain stable regardless the number of breaks. In our application, we employ the data which are analyzed by Perron (1990), and some results differ from those of Perron's (1990).     

我国“刘易斯转折点”的测定及其对策研究

“刘易斯转折点”是我国社会经济发展的重要时点。这里通过估算得出．我国将在2013年左右越过了“刘易斯第一转折点”,在2026年左右迎来“刘易斯第二转折点”。而要顺利地跨越“刘易斯转折点”．转变我国经济发展方式才是惟一出路。     

TWO-PASS CUSUM TO IDENTIFY AGE-CLUSTER OUTBREAKS

The paper introduces a two-pass adaptive cumulative sum (CUSUM) statistic to identify age clusters (age grouping) that significantly contribute to epidemics or unusually high counts. If epidemiologists know that an epidemic is confined to a narrow age group, then this information not only makes it clear where to target the epidemiological effort but also helps them decide whether to respond. It is much easier to control an epidemic that starts in a narrow age range of the population, such as pre-school children, than an epidemic that is not confined demographically or geographically.     

Bayesian Approaches to Nonparametric Estimation of Densities on the Unit Interval

This paper investigates nonparametric estimation of density on [0, 1]. The kernel estimator of density on [0, 1] has been found to be sensitive to both bandwidth and kernel. This paper proposes a unified Bayesian framework for choosing both the bandwidth and kernel function. In a simulation study, the Bayesian bandwidth estimator performed better than others, and kernel estimators were sensitive to the choice of the kernel and the shapes of the population densities on [0, 1]. The simulation and empirical results demonstrate that the methods proposed in this paper can improve the way the probability densities on [0, 1] are presently estimated.