Partisan Dealignment in Germany: A rejoinder to Russell Dalton

The work of Russell Dalton has undoubtedly played a seminal role in the study of the relation between political sophistication and partisan dealignment. We furthermore acknowledge the presence of a consensus on the occurrence of lower levels of partisanship in Germany. Using panel data as well as pooled cross-sectional observations, however, it is clear that generational replacement is not the sole driving force of partisan dealignment, but that period effects should also be taken into account. While on an aggregate level rising levels of political sophistication have occurred simultaneously with decreasing partisanship, individual level analysis suggests clearly that the least sophisticated are most likely to feel alienated from the party system. We close with some very specific suggestion on how to address the democratic consequences of declining levels of partisanship.

Age, Period and Cohort effects in the Decline of Party Identification in Germany

Party identification traditionally is seen as an important linkage mechanism, connecting voters to the party system. Previous analyses have suggested that the level of party identity is in decline in Germany, and in this article, we first expand previous observations with more recent data. These suggest that the erosion of party identity continues up to the present time. An age-period-cohort analysis of the panel data of the SOEP panel suggests that period effects are significantly negative. Furthermore, it can be observed that throughout the 1992-2009 observation period, education level and political interest have become more important determinants of party identity. Contrary to some of the literature, therefore, it can be shown that the loss of party identity is concentrated among groups with lower levels of political sophistication, indicating that the socio-economic profile of the group with a sense of party identification has become more distinct compared to the population as a whole. In the discussion, we investigate the theoretical and democratic consequences of this trend.

Governança corporativa e custo de agência: uma visão do mercado brasileiro

Esse trabalho investiga empiricamente a relação entre custo de agência e as medidas de monitoramento interno disponíveis aos investidores brasileiros nas empresas nacionais, utilizando amostras de companhias abertas entre os anos de 2010 e 2014, totalizando 134 empresas analisadas e 536 observações. Para medir tal relação, foram utilizadas, como variáveis de monitoramento interno, informações sobre a remuneração variável dos executivos, entre elas o uso de outorgas de opções de compra de ações, a composição do conselho de administração, dando ênfase à representatividade dos conselheiros independentes e à dualidade entre Chairman e CEO, e o percentual do capital social das companhias que está sob propriedade dos executivos. Como proxy para custo de agência, foram utilizados os indicadores Asset Turnover Ratio e General & Administrative Expenses (G&A) como percentual da Receita Líquida. Neste contexto, foram estabelecidas duas hipóteses de pesquisa e estimados modelos de regressão em painel controlados por efeitos fixos de tempo e empresa, empregando como variável dependente as variáveis proxy do custo de agência e utilizando as variáveis endividamento e tamanho das empresas como variáveis de controle. Os resultados dos modelos demonstram que, na amostra selecionada, há uma relação positiva e significativa entre o percentual da remuneração variável e as proxies de custo de agência, comportamento este contrário ao esperado originalmente. Conclui-se assim que as empresas que apresentam uma maior composição variável no total remunerado ao executivo, incorrem em um maior custo de agência, o que leva à conclusão de que tais ferramentas não são boas estratégias de alinhamento de interesses entre executivos e acionistas. As demais variáveis de monitoramento interno não apresentaram significância.

How cost efficient are Australia's mining industries?

This paper uses a stochastic translog cost frontier model and a panel data of five key mining industries in Australia over 1968-1969 to 1994-1995 to investigate the sources of output growth and the effects of cost inefficiency on total factor productivity (TFP) growth. The results indicate that mining output growth was largely input-driven rather than productivity-driven. Although there were some gains from technological progress and economics of scale in production, cost inefficiency which barely exceeded 1.1% since the mid-1970s in the mining industries was the main factor causing low TFP growth. (C) 2002 Elsevier Science B.V. All rights reserved.

The productivity-inflation nexus: The case of the Australian mining sector

This paper examines the causal links between productivity growth and two price series given by domestic inflation and the price of mineral products in Australia's mining sector for the period 1968/1969 to 1997/1998. The study also uses a stochastic translog cost frontier to generate improved estimates of total factor productivity (TFP) growth. The results indicate negative unidirectional causality running from both price series to mining productivity growth. Regression analysis further shows that domestic inflation has a small but adverse effect on mining productivity growth, thus providing some empirical support for Australia's 'inflation first' monetary policy, at least with respect to the mining sector. Inflation in mineral price, on the other hand, has a greater negative effect on mining productivity growth via mineral export growth.

A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies

This paper presents a metafrontier production function model for firms in different groups having different technologies. The metafrontier model enables the calculation of comparable technical efficiencies for firms operating under different technologies. The model also enables the technology gaps to be estimated for firms under different technologies relative to the potential technology available to the industry as a whole. The metafrontier model is applied in the analysis of panel data on garment firms in five different regions of Indonesia, assuming that the regional stochastic frontier production function models have technical inefficiency effects with the time-varying structure proposed by Battese and Coelli ( 1992).

Industry-specific human capital, knowledge labour, and industry wage structure in Taiwan

This paper investigates the role of industry-specific human capital (ISHC) in determining industry wage structure. The model presented in this paper distinguishes between knowledge labour and physical labour. Knowledge labour is physical labour embodied with ISHC. It is postulated that more ISHC-intensive industries, such as high-tech industries, pay higher wages and the wage premiums increase with workers' experience. The hypothesis is tested using a merged sample of 1997 - 1999 manpower utilization survey data from a newly industrialized economy - Taiwan. The findings show support for the effect of ISHC.

Relationships of Efficiency to Reproductive Disorders in Danish Milk Production: A Stochastic Frontier Analysis

Relationships of various reproductive disorders and milk production performance of Danish dairy farms were investigated. A stochastic frontier production function was estimated using data collected in 1998 from 514 Danish dairy farms. Measures of farm-level milk production efficiency relative to this production frontier were obtained, and relationships between milk production efficiency and the incidence risk of reproductive disorders were examined. There were moderate positive relationships between milk production efficiency and retained placenta, induction of estrus, uterine infections, ovarian cysts, and induction of birth. Inclusion of reproductive management variables showed that these moderate relationships disappeared, but directions of coefficients for almost all those variables remained the same. Dystocia showed a weak negative correlation with milk production efficiency. Farms that were mainly managed by young farmers had the highest average efficiency scores. The estimated milk losses due to inefficiency averaged 1142, 488, and 256 kg of energy-corrected milk per cow, respectively, for low-, medium-, and high-efficiency herds. It is concluded that the availability of younger cows, which enabled farmers to replace cows with reproductive disorders, contributed to high cow productivity in efficient farms. Thus, a high replacement rate more than compensates for the possible negative effect of reproductive disorders. The use of frontier production and efficiency/ inefficiency functions to analyze herd data may enable dairy advisors to identify inefficient herds and to simulate the effect of alternative management procedures on the individual herd's efficiency.

A Bayesian approach to imposing curvature on distance functions

The estimated parameters of output distance functions frequently violate the monotonicity, quasi-convexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models-a fixed effects model and a random effects model-are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed. (c) 2004 Elsevier B.V. All rights reserved.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

Empirical investigation of investment behaviour in Australia's pastoral region

Optimal intertemporal investment behaviour of Australian pastoralists is modelled using panel data for the period 1979-1993. Results indicate that quasi-fixity of inputs of labour, capital, sheep numbers and cattle numbers is characteristic of production in the pastoral region. It takes about two years for labour, four years for capital and a little over two years for both sheep numbers and cattle numbers to adjust towards long-run optimal levels. Results also indicate that, after accounting for adjustment costs, own-price product supply and input demand responses are inelastic in both the short and long run.

Estimating state-contingent production frontiers

Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to conventional estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies, and other quantities of economic interest.

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.