Melbourne Institute of Applied Economic and Social Research - Theses
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ItemEarnings and Income Inequality in Germany and Australia: Evidence from Tax and Survey Data( 2021)Using tax data and combined tax-survey data, this thesis examines earnings inequality in Germany and income inequality in Australia. A particular focus is on combining tax record data and household survey data to obtain more accurate income data with the purpose of better understanding inequality in these two countries. Chapter 2, using tax data alone, creates historical series of top earnings shares. Using German wage tax statistics for 1926–2010, this chapter creates the first consistently measured long-term series of German top earnings shares. I find a U-shape pattern with top earnings shares falling from 1926 through the mid-1970s and almost continuously rising thereafter. I then compare my results to those from other studies examining top earnings shares in the US, UK, and France. Over the entire period it is reasonable to describe trends in the US, UK and Germany as U-shaped. All three decline from pre-WWII highs. The US shares begin to grow from the mid-1960s. The UK and German shares grow from the mid-1970s. In the mid-1990s, they begin to converge at a level substantially below the US. The French shares follow a different pattern that can be reasonably described as W-shaped. Over the entire period (1957–2010) that it is possible to disaggregate the data by gender, women are underrepresented among top earners. Despite an increase in their labour force participation from 1957 to 1986, their share in the top 10% and top 1% of all workers remained unchanged. Only after German reunification in 1990 did women’s presence among top earners begin to increase. Chapter 3, using a similar adjustment procedure as Burkhauser, Herault, et al. (2018) but applying it to earnings, combines data from tax records and from the German Socio-economic Panel (SOEP) to explore measures of annual earnings inequality that are broader than the top earnings shares of the previous chapter. I show that the unadjusted survey data, unlike the adjusted data, fail to replicate those shares. Using these enhanced data, I provide new evidence on German earnings inequality for the period 1992–2011. My results indicate higher levels of earnings inequality over this period than unadjusted survey data suggest. Inequality rose less quickly for measures that include all employees; it rose faster for measures of full-time, prime-aged employees. Chapter 4 turns to Australia, demonstrating the importance of capturing high incomes well and consistently when calculating and examining inequality measures of equivalised household income. Using a similar adjustment approach as in Chapter 3, we examine the two major sources of household income data in Australia—the Survey of Income and Housing (SIH) and the Household, Income and Labour Dynamics in Australia (HILDA) Survey. We show that under-capture of top incomes by these surveys leads to underestimation of the extent of income inequality in Australia. The rise in income inequality in the 2000s exhibited by the unadjusted SIH data is considerably reduced when top incomes are adjusted to match tax record data; under-capture of top incomes by the SIH is greater in the early-2000s.
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ItemEssays on financial stability( 2020)This thesis investigates several aspects of economic activity that are strongly linked to the stability of the financial system. First, I examine the possible build-up of systemic risk in the shadow banking system as an unintended consequence of the implementation of macroprudential policies. Second, I analyze the short-run and long-run interactions between house prices and household debt in Australia and study relevant policy implications. Finally, I propose the use of automatic model selection, namely Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation, to improve the forecasting performance of the conventional early warning system for systemic banking crises. The findings provide insights for policymakers about several aspects of potential vulnerabilities stemming from different segments of the financial sector, and improves the early warning system so as to help policymakers better identify and monitor systemic risk in the financial system. In Chapter 2, I investigate the impact of macroprudential policies on shadow banking activities using a panel data analysis with fixed effects containing 24 countries from 2002 to 2013. I find that the effectiveness of macroprudential policies targeting the demand for credit, namely loan-to-value and debt-to-income ratio caps, is partially undermined by regulatory arbitrage through the shadow banking system. Such a strategy is generally not found for supply-side policies with two exceptions. Sector-specific capital requirements are found to be associated with negative shadow banking growth, while loan-loss provisionings could lead to counter-productive outcomes. In Chapter 3, I empirically investigate the short-run and long-run interactions between house prices and their drivers, with a particular focus on household debt, in a Structural Error Correction Model (SVECM) framework for Australia over the sample period 1990-2016. The cointegration analysis suggests one equilibrium relationship exists between the real house prices and the long-run determinants: household debt, housing stock, household disposable income, and the real interest rate. Household debt and housing supply are the main drivers for the equilibrium house prices. Household disposable income and the real interest rate affect the equilibrium house prices through the credit channel. In the short run, house prices and household debt are found to be mutually reinforcing. The dynamic impacts of the structural shocks suggest that macroprudential policies, if they act in a manner similar to an exogenous tightening of credit conditions, may be preferable to monetary policy leaning against the wind, if and when policies are needed to reinforce financial stability. In Chapter 4, I propose using LASSO with cross-validation approach to automate the variable selection process of the conventional multivariate logit econometric framework, the purpose being to improve the prediction of systemic banking crises. Using a dataset covering 23 OECD countries with quarterly data from 1970Q1 to 2018Q3, the model performance is evaluated in a recursive out-of-sample forecasting exercise, taking policymakers’ preference of missed crises and false alarms into account. The results suggest that the automatic variable selection process can enhance the predictive performance of the early warning system. They also highlight the importance of extracting information from variable interactions and lags that may not be easily identified and accessed by typical subjective variable pre-selection. This simple approach is easy to interpret and is transparent, which are important aspects for effective policy communication. Five variables, namely credit growth, domestic and global credit gaps, real house price growth and the real effective exchange rate, are identified as the most important key indicators of systemic banking crises.
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ItemHousing prices, debt and beliefs( 2020)Large credit-fuelled swings in house prices can inflict substantial damage on the real economy. Indeed, the associated recessions historically have been particularly severe (Jorda, Schularick, and Taylor 2015). A literature points to house price belief formation as potentially being key to these swings. This thesis studies how these beliefs are best characterised as being formed using survey data, and examines the role house price belief formation plays in credit-fuelled house price swings and their transmission to the macroeconomy. It also evaluates how policies -- particularly macroprudential policies -- can best address volatility stemming from the housing market. I assess how house price belief formation is best characterised by testing the predictions of a wide range of expectations models on surveyed house price forecasts. I show that these forecasts are well characterised by a model in which house price growth is believed to follow an autoregressive-like process, similar to intuitive beliefs (described in Fuster, Laibson and Mendel 2010). Evidence is presented rejecting the hypotheses that house price beliefs are formed according to a wide range of other beliefs models, including fully rational, boundedly rational, diagnostic, adaptive, other autoregressive, and other vector-autoregressive expectations models. I show that if agents hold intuitive house price beliefs, a prominent macroeconomic model with housing, Iacoviello (2005), captures key stylised facts regarding the joint behaviour of house prices, household debt and GDP, including around large house price swings. In particular, the model captures that large house price swings, when fuelled by credit, are accompanied by especially severe recessions. The model does not capture these dynamics if agents form house price beliefs rationally or according to other commonly studied non-rational methods. Macroprudential policies – policies with the explicit aim of preserving the stability of the financial system as a whole – have been increasingly used since the financial crisis of 2007–08, particularly to stem risks emanating from the housing market (Akinci and Olmstead-Rumsey 2017). I evaluate which macroprudential policy instrument performs this role best, and how it should be set. These are assessed in a model where banks control the size of both their assets and liabilities, rather than passively intermediating the funds of savers, allowing them to effectively create credit. This approach captures key features of the dynamics of credit, deposits and bank capital. In this environment, instruments affecting credit supply, such as time-varying capital requirements, are found to not improve stability or welfare. Instruments affecting credit demand, such as loan-to-value ratio policies, reduce fluctuations in credit, house prices and defaults, and improve welfare.