Using taxation statistics, we first derive consistently defined Gini coefficients for the period 1942-2013 for Australia as a whole as well as its eight states and territories. While income inequality exhibited a downward trend until 1979, it has since been on the rise not only over time, but also across states and territories. We then proceed to examine the effect of inequality on economic growth after controlling for changes arising from investment in physical and human capital using available panel data across all states and territories (1986-2013). We find that inequality adversely affects economic growth with a couple of years delay, an outcome consistent with similar studies undertaken in the United States and Europe. Our findings suggest that policymakers can address rising income inequality by implementing measures that support, and enhance, human capital accumulation given its long-run economic and social benefits.

Purpose - The purpose of this paper is to examine the usefulness of the yield spread for forecasting growth in the Australian economy since 1969. Design/methodology/approach - This paper applies time series analysis to evaluate the in-sample and out-of-sample forecasting power of the spread-growth nexus in Australia for the period spanning from 1969 to 2014. Findings - This paper concludes that the spread serves as a useful predictor of growth in output, private dwellings, private fixed capital formation, and inventories in Australia, both in-sample and out-of-sample. Its predictive content is not sensitive to the inclusion of monetary policy variables or the switch to the inflation-targeting regime by the Reserve Bank of Australia in the early 1990s. Originality/value - This paper provides significant evidence to policy makers and market participants on the usefulness of the spread in forecasting output growth for up to eight quarters ahead.

This paper examines the dynamic asymmetric relationship between changes in the Reserve Bank of Australia's (RBA) cash rate and the interest rate for small business loans using monthly data (1990-2011). The results provide support for the rockets-and-feathers hypothesis with respect to both the amount and adjustment asymmetries. While the RBA's rate rises exert a one-to-one and instantaneous impact on the loan rate, its rate cuts are only slowly and partially passed onto small businesses with a delay of 1-2 months. The results also suggest that the recent global financial crisis increased the cost of borrowing for small businesses in Australia by 2.21 %. These findings indicate that small businesses have limited time to respond to interest rate rises and not provided with the full benefit of interest rate decreases. Addressing this problem should ease the interest rate burden for small businesses and enhance their contribution to the economy.

This paper examines the dynamic and switching effects of volatility spillovers arising from US stock market returns and GDP growth on those of Australia, Canada and the UK. For this purpose, we use quarterly data (1961q1-2013q1) and a constant probability Markov regime switching model. We found that the US stock market volatility significantly affects the stock market volatility of all three countries at least in one of the two specified regimes over time. However, the stock market volatilities in none of the three countries are contemporaneously influenced by the US output volatility even after allowing for two distinct regimes. On the other hand, the US stock market volatility exerts significant influences on the output volatilities of both Australia and the UK. Compared with Australia and the UK, Canada and the US show substantial output volatility co-movements, thereby confirming the close association between the two neighbouring economies through the NAFTA (North American Free Trade Agreement). We conclude that shocks emanating from the US stock market have unequivocal flow-on effects on the output and return volatilities of the other economies.

Rising fuel prices can hamper economic activities in urban and regional areas. Despite following a mean reverting pattern over time, the spread between retail and wholesale prices of petrol exhibits significant differences across various geographical locations in Australia. Using a hierarchical cluster analysis, this paper classifies 109 retail locations into six heterogeneous groups with homogeneous contents. By identifying the whereabouts of those petrol stations that set relatively high gross profit margins within each comparable cluster, this study can provide important policy implications for both consumers and regulators. Contrary to popular belief, we found that excessively high margins are not necessarily observed only in remote and rural areas.

This thesis contains three empirical studies exploring income inequality and its impact on economic growth and productivity in Australia. The inequality-output nexus is examined using national, sub-national and gender-specific data calculated from Australian Taxation Office (ATO) statistics and data published by the Australian Bureau of Statistics (ABS).

Chapter 2 calculates Gini coefficients for Australia as a whole and in each of the states and territories from 1942 to 2013. These newly created series reveal that although national and sub-national income inequality exhibit similar trends over time, there are important short-term variations across regions. State-level Gini coefficients are then used in panel regressions to estimate Australia’s inequality-growth nexus. This study concludes that rising income inequality has negative implications for economic growth, while additional investment in education can boost output growth in the long-run. These results support the notion that policymakers should address rising income inequality by implementing measures that support and enhance human capital accumulation given the long-run economic and social benefits.

Following on from this finding, Chapter 3 examines how gender inequality affects productivity. To do this, gender wage gaps are calculated for all of Australia’s states and territories using Average Weekly Earnings data published by the ABS from 1982 to 2013. These data are then used as explanatory variables in four different models estimating the relationship between gender-based income inequality and productivity. Irrespective of the model chosen, it concludes that reducing gender income inequality has positive implications for economic growth that rival those associated with additional investment in human capital. This corroborates the conclusion of Chapter 2 and strengthens the case for policymakers to address rising inequality through additional investment in education.

Motivated by the findings of Chapters 2 and 3, Chapter 4 examines the long-run relationship between inequality and real per capita income using gender-specific Gini coefficients from 1942 to 2013. Using taxation statistics, this chapter derives Gini coefficients for men, women and all taxpayers separately, which are then used to estimate the inequality–growth nexus controlling for within-gender differences in inequality. It concludes that models which allow for differences in gender inequality offer more explanatory power than those models where such differences are overlooked.

This thesis argues that policymakers should not only concern themselves with income inequality for political and social reasons, but also because rising inequality has negative implications for economic growth. Rather than relying on redistributive income transfers which are potentially harmful to long-run productivity growth, this thesis proposes addressing inequality by implementing measures that promote human capital accumulation and economic mobility. Such policies may include greater funding for research and development or targeted investment in female education and training to help reduce gender-based occupational segregation.

Previous studies have reached mixed conclusions regarding the relationship between inequality and per capita income. These studies, however, fail to consider gender differences in income inequality and how these may impact on the relationship between income inequality and per capita income. Using Australian taxation statistics, we derive three sets of Gini coefficients (i.e. female, male and total) for the period 1950-2013. We then examine the relationship between inequality and real per capita income and find that a gender-specific threshold panel regression outperforms three other conventional models. Our findings suggest that 'one set of coefficients does not fit all' in that the use of aggregate and constant coefficients may mask variations within, and between, gender inequality over time.