Economics - Theses
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ItemEssays on competition, product design and firm formation(University of Melbourne, 2008)
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ItemEssays in corporate governance, innovation and R&D alliances(University of Melbourne, 2008)
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ItemThe socially optimal level of education and human capital(University of Melbourne, 2008)
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ItemEssays on the economics of services outsourcing : the case of the Indian software services industry(University of Melbourne, 2007)
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ItemThe attainment and influence of skill in Australia(University of Melbourne, 2007)
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ItemNo Preview AvailableThe development of social services in Victoria(University of Melbourne, 1948)
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ItemCompetition in Quantity Constrained Markets with Nonprofit Provision: Early Childhood Education in Australia( 2023-10)Early Childhood Education and Care plays a critical role in a nation's development, supporting children's educational growth and enabling parents to engage in the workforce. Despite the importance of the sector, policy challenges remain in ensuring access to affordable, quality care. This thesis examines the Australian Early Childhood Education context, focusing on the interplay between mixed provision by both for-profit and nonprofit providers as well as capacity constraints. The observation that capacity constraints are frequently binding in this market, and associated with higher fees, is rationalised through a theoretical model of demand and competition between services which includes nonprofit strategic behaviour. I then estimate an empirical version of the theoretical model using data on Australian child care services. Simulations from the model shed light on how capacity constraints sustain higher fees, and highlight the challenge for policy-makers in improving access to affordable care. The first essay examines key characteristics of Australian early childhood education, and presents a novel dataset encompassing child care centres across Australia's most populous states. Overall, fees are relatively high at around $97 per day, with full-time care making up one-fifth to one-third of an average family's pre-tax income (pre-subsidy). Nonprofit service fees are typically lower than for-profits. The sector is highly-capacity constrained. On average, just two-fifths of all services reported having a vacancy, consistent across provider type. These findings highlight the important interplay between capacity constraints and profit status in the sector. The second essay develops a theoretical model with capacity constraints and competition between for-profit and nonprofit firms. In the presence of capacity constraints, not all consumers are allocated their most preferred option. This induces a reallocation to less-preferred alternatives. The model incorporates this reallocation mechanism into a competitive pricing equilibrium. It predicts persistent excess demand and high prices, consistent with empirical observation. The third essay estimates an empirical counterpart to the theoretical model using the dataset on Australian ECEC introduced in the first essay. I extend the standard approach to estimating differentiated products demand to incorporate reallocation according to the mechanism outlined in the second essay. The estimates reveal the relative influence of key characteristics on demand, including income, fees, and quality. I then estimate marginal costs, separately for for-profit and nonprofit services. Finally, I use the model estimates to compute a number of counterfactual scenarios. The results highlight the importance of capacity constraints in supporting high fees. Collectively, the findings point towards the importance of considering the unintended consequences of policy interventions, such as subsidies, and the powerful role low-fee public provision can play in reducing fees and increasing access to care. Further development of the model, supported by richer data, will ensure it can provide a robust foundation for evidence-based policy-making in the Australian Early Childhood Education sector.
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ItemDevelopment of Bayesian dynamic nonparametric models and inferences( 2023-06)Dynamic econometric models play an important role in the analysis of economics and finance. Motivated by this significance, this thesis presents novel Bayesian nonparametric models and inferences, offering fast and accurate approaches for analysing large data sets and gaining more insightful understanding in empirical studies. Chapter 2 proposes a new dynamic Bayesian nonparametric model designed to capture time varying distributions, with each distribution being a mixture of an infinite number of Normal distributions. Our model builds on the work of Gutierrez, Mena, and Ruggiero (2016) and Mena and Ruggiero (2016). We improve their algorithm by incorporating a break indicator and a hierarchical prior structure that govern the parameters of all components within the mixture. Using the Australian banking statistics from 1925 to 2019, we apply the model to estimate the time-varying bank size and growth distributions. Our results reveal that the skewness of the weighted bank growth distribution is procyclical to business and financial cycles. Furthermore, we find that different quantiles of the weighted bank growth distribution exhibit different correlations with financial cycles. Chapter 3 proposes two new inferences, a variational inference (VI) and a stochastic variational inference (SVI), which are employed to approximate the Bayesian dynamic nonparametric model with large data sets. We apply these new inferences to the dynamic Dirichlet process mixture (DDPM) model. The DDPM model has a dependence structure with a break indicator. The prior of this indicator is a slab-or-spike prior, which includes a degenerate distribution. The presence of this degenerate distribution and dependence structure causes difficulties when applying VI and SVI techniques because they assume exponential conjugacy and independence within the variational density family. To address these challenges, our paper proposes a novel VI and SVI algorithm that maintains the conditional conjugate prior and preserves the dependence structure. It reduces the computational cost of estimating the model with large data sets while maintaining reasonable precision. Using the data of US banking from 1954 to 2014, we apply the approach to approximate the model and find a significant reduction in estimation time. Chapter 4 proposes a new variational inference (VI) algorithm to estimate a large dimensional Markov switching model fast and accurately. Although the multivariate Markov switching model captures useful information, the Markov chain Monte Carlo algorithm's computational cost increases significantly with its dimension. My contributions are threefold. First, while taking substantially less time to compute, this method achieves comparable in-sample and out-of-sample results to its MCMC counterpart. Second, this inference allows for the inclusion of important restrictions to identify hidden states. Third, my novel VI forward filtering backward smoothing algorithm is comparable to the well-known algorithm in economic literature from Chib (1996). As a result, this new strategy is simple and accessible to implement. My paper presents several results derived from multiple simulations, illustrating the accuracy and timely benefit of the new technique. For example, identifying the bull and bear states, detecting regime switching, and providing forecasts for investment strategies. In addition, applications to three sets of stock returns that are listed in the S&P 500 and one set of industry portfolios provide similar insights.
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ItemOptimal risk sharing and dividend strategies under interacting external default risk( 2023-07)Most large insurance companies in the world are in the form of corporate groups with a collection of subsidiary corporations. Within the same group, the interconnectedness among the subsidiaries exerts substantial impacts on their reserve dynamics and actions. In this thesis, we explore studies related to the optimization problem of an insurance group with multiple subsidiaries subject to interacting external default risk. We first articulate the dynamics of the insurance group and present the mathematical formulation of our optimization problem. We formulate a recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs) that solves the group's optimization problem of reinsurance and dividend payments. Our formulation proposes a novel approach to examine the dividend optimization problem on an insurance group subject to interacting default risk. We discuss the analytical approach to the optimization problem under a particular assumption and the semi-analytical method that integrates different numerical techniques in a more general setting. We also utilize the Markov chain approximation method (MCAM), and the hybrid deep learning method to demonstrate the semi-analytical approach. In a general setting, the explicit solution to the recursive system has not yet been found, and the semi-analytical method also provides an innovative approach to tackling the variational inequalities of similar structures. We compare two distinct types of interacting external default risk, the contagious one and the competitive one. We first examine an insurance group subject to contagious external default risk, where the surviving subsidiaries are confronted with greater default risk if a default event occurs within the group. Following the ideas outlined before, we are able to solve the recursive system of HJBVIs analytically. Next, we turn to a different setting, where the interacting external default risk is subject to the competitive effect. We also utilize the semi-analytical approach but integrate MCAM and the hybrid deep learning method. The proposed methods enable us to compare the optimal strategies and value functions under two different types of interacting default risk and explore the economic interpretations behind them.
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ItemEssays in education economics: Children with immigration backgrounds in Australia from preschool to adolescence( 2022)This thesis is a collection of three empirical studies that examine issues related to children with immigrant backgrounds in Australia along their life course from preschool to adolescence. The first study investigates how outdoor time use at age 4 may affect children’s school readiness. I find that outdoor time may negatively affect children’s performance in fields requiring fine motor skills but does not have significant impacts on children’s vocabulary or language skills. The second chapter investigates the spillover effects of culturally and linguistically diverse (CALD) students at grade 4, aged 9-10, in Australia, Canada and France. I find that the effect of an increase in the concentration of CALD students on non-CALD student’s reading score is positive in Australia and Canada but negative in France. This provides evidence of dissimilar effects across countries with different immigration policies. The results from this study also suggest that teachers may need to change their teaching approaches to adapt to a change in classroom diversity. The last chapter investigates how migrant children’s self-assessment of English skills at the age of 15 may affect their choice to specialise in STEM fields. I find that children with lower self-assessment of English skills choose more STEM subjects at high school levels and are more likely to complete a degree in a STEM major. In addition, while the English skill self-assessment, which is the self-assessment of their performance in English subjects compared to their peers, significantly influences migrant children’s decision to specialise in STEM fields, their actual reading scores do not affect this choice. This provides evidence that children’s perception of their comparative advantage in language is a determinant of their STEM specialisation rather than the actual advantage.