Thank God you're here: the coming generation and their role in future‐proofing Australia from the challenges of population ageing

Like much of the industrialised world, Australia's population is ageing, the implications of which are twofold: increasing demand for publicly funded services and a decline in the supply of prime working‐age people. In grappling with the challenges of a diminishing workforce, the Australian Government is currently relying on its migration programs to provide both the much‐needed labour and skills for the resource boom and also to stimulate the economy through population growth. However, there may be another, not yet fully considered solution to the upcoming demographic problem. This paper investigates how the grandchildren of the baby boomers, termed here the Thank God You're Here generation (Gen TGYH), might impact on Australia's predicted workforce shortage. This generation of workers will enter the labour force as the last of the baby boomers reach retirement age, and will not only be bigger in size than preceding generations (Y, X and Baby Boomers), but also potentially be better educated than the retiring generation. This paper will also canvas the opportunities for both Gen TGYH and employers as well as the challenges for policymakers and governments in maximising the opportunity provided by this generation in the Australian economy.     

Record-breaking data: a parametric comparison of the inverse-sampling and the random-sampling schemes

In many industrial quality control experiments and destructive stress testing, the only available data are successive minima (or maxima)i.e., record-breaking data. There are two sampling schemes used to collect record-breaking data: random sampling and inverse sampling. For random sampling, the total sample size is predetermined and the number of records is a random variable while in inverse-sampling the number of records to be observed is predetermined; thus the sample size is a random variable. The purpose of this papper is to determinevia simulations, which of the two schemes, if any, is more efficient. Since the two schemes are equivalent asymptotically, the simulations were carried out for small to moderate sized record-breaking samples. Simulated biases and mean square errors of the maximum likelihood estimators of the parameters using the two sampling schemes were compared. In general, it was found that if the estimators were well behaved, then there was no significant difference between the mean square errors of the estimates for the two schemes. However, for certain distributions described by both a shape and a scale parameter, random sampling led to estimators that were inconsistent. On the other hand, the estimated obtained from inverse sampling were always consistent. Moreover, for moderated sized record-breaking samples, the total sample size that needs to be observed is smaller for inverse sampling than for random sampling.     

Robustness to non-normality and autocorrelation of individuals control charts

This paper studies the effects of non-normality and autocorrelation on the performances of various individuals control charts for monitoring the process mean and/or variance. The traditional Shewhart X chart and moving range (MR) chart are investigated as well as several types of exponentially weighted moving average (EWMA) charts and combinations of control charts involving these EWMA charts. It is shown that the combination of the X and MR charts will not detect small and moderate parameter shifts as fast as combinations involving the EWMA charts, and that the performana of the X and MR charts is very sensitive to the normality assumption. It is also shown that certain combinations of EWMA charts can be designed to be robust to non-normality and very effective at detecting small and moderate shifts in the process mean and/or variance. Although autocorrelation can have a significant effect on the in-control performances of these combinations of EWMA charts, their relative out-of-control performances under independence are generally maintained for low to moderate levels of autocorrelation.     

To be or not to be valid in testing the significance of the slope in simple quantitative linear models with autocorrelated errors

In this article, the validity of procedures for testing the significance of the slope in quantitative linear models with one explanatory variable and first-order autoregressive [AR(1)] errors is analyzed in a Monte Carlo study conducted in the time domain. Two cases are considered for the regressor: fixed and trended versus random and AR(1). In addition to the classical t -test using the Ordinary Least Squares (OLS) estimator of the slope and its standard error, we consider seven t -tests with n-2\,\hbox{df} built on the Generalized Least Squares (GLS) estimator or an estimated GLS estimator, three variants of the classical t -test with different variances of the OLS estimator, two asymptotic tests built on the Maximum Likelihood (ML) estimator, the F -test for fixed effects based on the Restricted Maximum Likelihood (REML) estimator in the mixed-model approach, two t -tests with n - 2 df based on first differences (FD) and first-difference ratios (FDR), and four modified t -tests using various corrections of the number of degrees of freedom. The FDR t -test, the REML F -test and the modified t -test using Dutilleul's effective sample size are the most valid among the testing procedures that do not assume the complete knowledge of the covariance matrix of the errors. However, modified t -tests are not applicable and the FDR t -test suffers from a lack of power when the regressor is fixed and trended ( i.e. , FDR is the same as FD in this case when observations are equally spaced), whereas the REML algorithm fails to converge at small sample sizes. The classical t -test is valid when the regressor is fixed and trended and autocorrelation among errors is predominantly negative, and when the regressor is random and AR(1), like the errors, and autocorrelation is moderately negative or positive. We discuss the results graphically, in terms of the circularity condition defined in repeated measures ANOVA and of the effective sample size used in correlation analysis with autocorrelated sample data. An example with environmental data is presented.     

Acculturation and adaptation of first‐ and second‐generation South Asians in Hong Kong

This study investigates the experiences of psychological and sociocultural adaptation among 404 first‐ and second‐generation South Asian immigrants in Hong Kong. Results indicate that for first‐generation immigrants, lack of host language fluency, fewer contacts and friendships with host members, the strategy of marginalisation, and perceived discrimination are all related to higher psychological distress, lower self‐esteem and less competence in sociocultural adaptation. For second‐generation individuals, although they reported higher knowledge of the host language and higher preferences the for assimilation strategy, the levels of psychological distress were higher compared with the first‐generation group. An interesting finding of this study is the preference for the marginalisation strategy as opposed to the assimilation and/or separation strategy. The findings of this study highlight the importance of considering the unique experiences of the second generation in order to further our understanding of immigration and acculturation processes.     

改革开放以来中共领导集体法律思想研究述要

学界对改革开放以来中共三代领导集体核心——邓小平、江泽民、胡锦涛法律思想的研究主要集中在对其历史地位、历史意义、基本内容、基本特征及形成过程的概括和总结上，对三者的相互关系、它们对中国法制建设的影响及三者在中国特色社会主义法制思想形成过程中所做出的历史贡献缺乏整体性研究，需要今后进一步深入探讨。     

The almost sure convergence rate of the estimator of optimized certainty equivalent risk measure under α-mixing sequences

Conditional value-at-risk (CVaR) model is a kind of financial risk measure that is extensively supported and accepted by international financial community. Its optimized form can be regarded as an optimized certainty equivalent (OCE) risk measurement. In this paper, we mainly discuss and analyze the strong laws of large numbers and the convergence rate of OCE's estimator under α-mixing sequences. The result shows that the almost sure convergence rate of CVaR estimator is given by the results of OCE estimator. Its convergence rate is inversely proportional to the square root of the sample size under certain conditions. Its effectiveness is verified by simulation experiments for two classical α-mixing sequences.     

The Comparison of Grandparents’, Parents’, and Young People's Attitudes Toward Lesbians and Gay Men in Turkey

ABSTRACT

The present study aimed to compare attitudes toward lesbians and gay men across three generations in Turkey. Participants were 112 young people, 169 parents, and 125 grandparents. A total of 406 men and women were involved. The short form of Herek's (1998) the Attitudes Toward Lesbians and Gay Men Scale was used. MANOVAs and ANOVAs were used to analyze the data. Results showed the attitudinal differences toward gay and lesbian people across generations. Grandparents have the most negative attitudes, whereas parents have more negative attitudes than their children. When comparing attitudes of each generation across sex, we found that young women have more liberal attitudes toward gay men compared to all other groups. Discussion focuses on understanding the results within the unique cultural context of Turkey.     

第一代独生子女择偶观初探

第一代独生子女群体与非独生子女群体在择偶观方面差异不太明显,＂道德品质＂成为择偶的首选标准,择偶的自主性上体现了中庸之道,＂男大女小＂和＂男女差不多＂是择偶的最佳年龄组和方式,＂趋同与求强＂是择偶的最优能力组合方式,＂自由恋爱＂的择偶方式备受青睐。第一代独生子女群体的择偶观总体体现了＂扬＂与＂弃＂的统一,其中内部各群体的择偶观体现了相似性和差异性的统一。     

Scanner Data and the Treatment of Quality Change in Nonrevisable Price Indexes

The recently developed rolling year GEKS procedure makes maximum use of all matches in the data to construct nonrevisable price indexes that are approximately free from chain drift. A potential weakness is that unmatched items are ignored. In this article we use imputation Törnqvist price indexes as inputs into the rolling year GEKS procedure. These indexes account for quality changes by imputing the “missing prices” associated with new and disappearing items. Three imputation methods are discussed. The first method makes explicit imputations using a hedonic regression model which is estimated for each time period. The other two methods make implicit imputations; they are based on time dummy hedonic and time-product dummy regression models and are estimated on bilateral pooled data. We present empirical evidence for New Zealand from scanner data on eight consumer electronics products and find that accounting for quality change can make a substantial difference.