Institutional determinants of growth-aspiration entrepreneurship

The thesis is a country-level study on the institutional and human capital determinants of growth-aspiration entrepreneurial activity. By using country-level panel-data analysis, the study is to our knowledge the first to test to what extent country-level human capital accumulation is associated with the prevalence of growth-aspiration entrepreneurship. Overall findings of the study suggest that there are different effects of the institutional determinants on the prevalence of growth-aspiration entrepreneurship in developing countries and developed countries. The study also found that country-level human capital moderates the effects of the institutional environment.

Modal choice between air and rail : a social efficiency benchmarking analysis that considers CO2 emissions

The problem of modal choice between rail and air arises as public awareness of carbon dioxide (CO2) emissions by the transportation sector rises. In this paper, we answer this question quantitatively by performing an efficiency benchmarking analysis that takes into account life-cycle CO2 emission due to transport service provision. The paper employs nonparametric efficiency estimation methods, namely a slacks-based inefficiency measure, as well as a more conventional directional distance function approach. We apply them to a panel data set for three major railway companies and the aviation sector in Japan for the period from 1999 to 2007. Results shows that, contrary to the common argument, air transport can still be more socially efficient than rail transport, even when the environmental load due to CO2 emission is incorporated. This is due to the aviation sector's extremely low user cost, measured in terms of in-vehicle time. In other words, aviation is a necessary transportation mode for those with a very high willingness to pay for their time.

Productivity measures and effects from subsidies and trade : an empirical analysis for Japan's forestry

Several techniques have been proposed in the literature to measure productivity. While allowing for inefficiency of the production unit, we provide a methodological comparison of alternative approaches to measure total factor productivity. This article evaluates the effects of unintended policy outcomes such as government subsidies and foreign trade. Empirically, we analyse the forest productivity of timber in Japan by using panel data on 46 regions. The results suggest substantial variation in productivity between these two techniques although average trends are similar. We find that subsidies impede competition since the government is ready to rescue a loss-making firm with subsidies rather than allow it to close. In contrast, trade is shown to have positive effects on productivity.

Regulatory reforms and productivity : an empirical analysis of the Japanese electricity industry

The Japanese electricity industry has experienced regulatory reforms since the mid-1990s. This article measures productivity in Japan's steam power-generation sector and examines the effect of reforms on the productivity of this industry over the period 1978-2003. We estimate the Luenberger productivity indicator, which is a generalization of the commonly used Malmquist productivity index, using a data envelopment analysis approach. Factors associated with productivity change are investigated through dynamic generalized method of moments (GMM) estimation of panel data. Our empirical analysis shows that the regulatory reforms have contributed to productivity growth in the steam power-generation sector in Japan.

Understanding public response to a congestion charge : a random-effects ordered logit approach

Public acceptance is consistently listed as having an enormous impact on the implementation and success of a congestion charge scheme. This paper investigates public acceptance of such a scheme in Australia. Surveys were conducted in Brisbane and Melbourne, the two fastest growing Australian cities. Using an ordered logit modeling approach, the survey data including stated preferences were analyzed to pinpoint the important factors influencing people’s attitudes to a congestion charge and, in turn, to their transport mode choices. To accommodate the nature of, and to account for the resulting heterogeneity of the panel data, random effects were considered in the models. As expected, this study found that the amount of the congestion charge and the financial benefits of implementing it have a significant influence on respondents’ support for the charge and on the likelihood of their taking a bus to city areas. However, respondents’ current primary transport mode for travelling to the city areas has a more pronounced impact. Meanwhile, respondents’ perceptions of the congestion charge’s role in protecting the environment by reducing vehicle emissions, and of the extent to which the charge would mean that they travelled less frequently to the city for shopping or entertainment, also have a significant impact on their level of support for its implementation. We also found and explained notable differences across two cities. Finally, findings from this study have been fully discussed in relation to the literature.

Built environment impacts on walking for transport in Brisbane, Australia

This study examines the association between urban form and walking for transport in Brisbane, Australia based on both panel and cross-sectional data. Cross-sectional data are used to determine whether urban form was associated with walking for transport in 2011. Panel data are used to evaluate whether changes in the built environment altered walking behaviour between 2009 and 2011. Results from the cross-sectional data suggest that individuals are significantly more likely to be walkers if they live in an area with a well-connected street network and an accessible train station. The longitudinal analysis confirms these relationships; there also was however, a significant impact of travel attitudes and perceptions on walking behaviour. The findings suggest that the built environment continues to be an important factor to encourage walking; however, interventions are also required to change social norms in order to increase the receptiveness for and participation in walking.

Learning-based Relevance Feedback for Web-based Relation Completion

In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

Do dissonants in transit oriented development adjust commuting travel behaviour?

Residential dissonance signifies a mismatch between an individual’s preferred and actual proximal land use patterns in residential neighbourhoods, whereas residential consonance signifies agreement between actual and preferred proximal land uses. Residential dissonance is a relatively unexplored theme in the literature, yet it acts as a barrier to the development of sustainable transport and land use policy. This research identifies mode choice behaviour of four groups living in transit oriented development (TOD) and non-TOD areas in Brisbane, Australia using panel data from 2675 commuters: TOD consonants, TOD dissonants, non-TOD consonants, and non-TOD dissonants. The research investigates a hypothetical understanding that dissonants adjust their travel attitudes and perceptions according to their surrounding land uses over time. The adjustment process was examined by comparing the commuting mode choice behaviour of dissonants between 2009 and 2011. Six binary logistic regression models were estimated, one for each of the three modes considered (e.g. public transport, active transport, and car) and one for each of the 2009 and 2011 waves. Results indicate that TOD dissonants and non-TOD consonants were less likely to use the public transport and active transport; and more likely to use the car compared with TOD consonants. Non-TOD dissonants use public transport and active transport equally to TOD consonants. The results suggest that commuting mode choice behaviour is largely determined by travel attitudes than built environment factors; however, the latter influence public transport and car use propensity. This research also supports the view that dissonants adjust their attitudes to surrounding land uses, but very slowly. Both place (e.g. TOD development) and people-based (e.g. motivational) policies are needed for an effective travel behavioural shift.

Child health and the income gradient : evidence from Australia

The positive relationship between household income and child health is well documented in the child health literature but the precise mechanisms via which income generates better health and whether the income gradient is increasing in child age are not well understood. This paper presents new Australian evidence on the child health–income gradient. We use data from the Longitudinal Study of Australian Children (LSAC), which involved two waves of data collection for children born between March 2003 and February 2004 (B-Cohort: 0–3 years), and between March 1999 and February 2000 (K-Cohort: 4–7 years). This data set allows us to test the robustness of some of the findings of the influential studies of Case et al. [Case, A., Lubotsky, D., Paxson, C., 2002. Economic status and health in childhood: the origins of the gradient. The American Economic Review 92 (5) 1308–1344] and Currie and Stabile [Currie, J., Stabile, M., 2003. Socioeconomic status and child health: why is the relationship stronger for older children. The American Economic Review 93 (5) 1813–1823], and a recent study by Currie et al. [Currie, A., Shields, M.A., Price, S.W., 2007. The child health/family income gradient: evidence from England. Journal of Health Economics 26 (2) 213–232]. The richness of the LSAC data set also allows us to conduct further exploration of the determinants of child health. Our results reveal an increasing income gradient by child age using similar covariates to Case et al. [Case, A., Lubotsky, D., Paxson, C., 2002. Economic status and health in childhood: the origins of the gradient. The American Economic Review 92 (5) 1308–1344]. However, the income gradient disappears if we include a rich set of controls. Our results indicate that parental health and, in particular, the mother's health plays a significant role, reducing the income coefficient to zero; suggesting an underlying mechanism that can explain the observed relationship between child health and family income. Overall, our results for Australian children are similar to those produced by Propper et al. [Propper, C., Rigg, J., Burgess, S., 2007. Child health: evidence on the roles of family income and maternal mental health from a UK birth cohort. Health Economics 16 (11) 1245–1269] on their British child cohort.

The effect of incentive reforms upon productivity : evidence from the Vietnamese rice industry

In this study we use region-level panel data on rice production in Vietnam to investigate total factor productivity (TFP) growth in the period since reunification in 1975. Two significant reforms were introduced during this period, one in 1981 allowing farmers to keep part of their produce, and another in 1987 providing improved land tenure. We measure TFP growth using two modified forms of the standard Malmquist data envelopment analysis (DEA) method, which we have named the Three-year-window (TYW) and the Full Cumulative (FC) methods. We have developed these methods to deal with degrees of freedom limitations. Our empirical results indicate strong average TFP growth of between 3.3 and 3.5 per cent per annum, with the fastest growth observed in the period following the first reform. Our results support the assertion that incentive related issues have played a large role in the decline and subsequent resurgence of Vietnamese agriculture.

Sources of productivity growth in health services : a case study of Queensland public hospitals

Improving the performance of health sector is one of the most popular issues in Australia. This paper contributes to this important policy debate by examining the efficiency of health facilities in Queensland using the Malmquist Productivity Index (MPI). This method is selected because it is suitable for the multi-input, multi-output, and not-for-profit natures of public health services. In addition, with the availability of panel data we can decompose productivity growth into useful components, including technical efficiency changes, technological changes and scale changes. The results revealed an average of 1.6 per cent of growth in total factor productivity (TFP) among Queensland public hospitals in the study period. The main component contributing to the modest improvement of TFP during the period was catching-up at an average of 1.0 per cent. SFA estimates suggest that the number of nurses is the most influential determinant of output.

Copula-based spatial modelling of geometallurgical variables

The most important aspect of modelling a geological variable, such as metal grade, is the spatial correlation. Spatial correlation describes the relationship between realisations of a geological variable sampled at different locations. Any method for spatially modelling such a variable should be capable of accurately estimating the true spatial correlation. Conventional kriged models are the most commonly used in mining for estimating grade or other variables at unsampled locations, and these models use the variogram or covariance function to model the spatial correlations in the process of estimation. However, this usage assumes the relationships of the observations of the variable of interest at nearby locations are only influenced by the vector distance between the locations. This means that these models assume linear spatial correlation of grade. In reality, the relationship with an observation of grade at a nearby location may be influenced by both distance between the locations and the value of the observations (ie non-linear spatial correlation, such as may exist for variables of interest in geometallurgy). Hence this may lead to inaccurate estimation of the ore reserve if a kriged model is used for estimating grade of unsampled locations when nonlinear spatial correlation is present. Copula-based methods, which are widely used in financial and actuarial modelling to quantify the non-linear dependence structures, may offer a solution. This method was introduced by Bárdossy and Li (2008) to geostatistical modelling to quantify the non-linear spatial dependence structure in a groundwater quality measurement network. Their copula-based spatial modelling is applied in this research paper to estimate the grade of 3D blocks. Furthermore, real-world mining data is used to validate this model. These copula-based grade estimates are compared with the results of conventional ordinary and lognormal kriging to present the reliability of this method.

From ImageNet to mining: Adapting visual object detection with minimal supervision

This paper presents visual detection and classification of light vehicles and personnel on a mine site.We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.

Public and private mitigation for natural disasters in Japan

An increasing number of studies analyze the relationship between natural disaster damage and income levels, but they do not consider the distinction between public and private disaster mitigation. This paper empirically distinguishes these two types of mitigation using Japanese prefectural panel data from 1975 to 2007. Our results show that public mitigation rather than private mitigation has contributed to mitigating the total damage resulting from natural disasters. Our estimation of cost-benefit ratios for each prefecture confirms that the mitigation efforts of urban prefectures are less effective than those of rural prefectures in focusing on both large and frequent/small disasters. Hence, urban prefectures need to reassess their public mitigation measures. Furthermore, to lessen the damage resulting from extreme catastrophes, policy makers are required to invest in improved mitigation infrastructures when faced with a high probability of disasters.

Validation-Based Sparse Gaussian Process Classifier Design

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.