This paper estimates the impact of economic insecurity on the mental health of Australian adults. Taking microdata from the 2001-2011 HILDA panel survey, we develop a conceptually diverse set of insecurity measures and explore their relationships with the SF-36 mental health index. By using fixed effects models that control for unobservable heterogeneity we produce estimates that correct for endogeneity more thoroughly than previous works. Our results show that exposure to economic risks has small but consistently detrimental mental health effects. The main contribution of the paper however comes from the breadth of risks that are found to be harmful. Job insecurity, financial dissatisfaction, reductions and volatility in income, an inability to meet standard expenditures and a lack of access to emergency funds all adversely affect health. This suggests that the common element of economic insecurity (rather than idiosyncratic phenomena associated with any specific risk) is likely to be hazardous. Our preferred estimates indicate that a standard deviation shock to economic insecurity lowers an individual's mental health score by about 1.4 percentage points. If applied uniformly across the Australian population such a shock would increase the morbidity rate of mental disorders by about 1.7%.

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There are two main types of data sources of income distributions in China: household survey data and grouped data. Household survey data are typically available for isolated years and individual provinces. In comparison, aggregate or grouped data are typically available more frequently and usually have national coverage. In principle, grouped data allow investigation of the change of inequality over longer, continuous periods of time, and the identification of patterns of inequality across broader regions. Nevertheless, a major limitation of grouped data is that only mean (average) income and income shares of quintile or decile groups of the population are reported. Directly using grouped data reported in this format is equivalent to assuming that all individuals in a quintile or decile group have the same income. This potentially distorts the estimate of inequality within each region. The aim of this paper is to apply an improved econometric method designed to use grouped data to study income inequality in China. A generalized beta distribution is employed to model income inequality in China at various levels and periods of time. The generalized beta distribution is more general and flexible than the lognormal distribution that has been used in past research, and also relaxes the assumption of a uniform distribution of income within quintile and decile groups of populations. The paper studies the nature and extent of inequality in rural and urban China over the period 1978 to 2002. Income inequality in the whole of China is then modeled using a mixture of province‐specific distributions. The estimated results are used to study the trends in national inequality, and to discuss the empirical findings in the light of economic reforms, regional policies, and globalization of the Chinese economy.

It is frequently hypothesized that feelings of social isolation are detrimental for an individual's mental health, however standard statistical models cannot estimate this effect due to reverse causality between the independent and dependent variables. In this paper we present endogeneity-corrected estimates of the mental health consequences of isolation (based on self-assessed loneliness scores) using Australian panel data. The central identification strategy comes from a natural source of variation where some people within our sample are required by work or study commitments to move home. This relocation may break individuals' social ties, resulting in significantly higher reported feelings of loneliness and consequently may lower mental health scores. The method gives results that are significant, robust and pass a battery of diagnostic tests. Estimates indicate that feelings of isolation have large negative consequences for psychological well-being, and that the effects are larger for women and older people. The results suggest that at current levels, a 10 % reduction applied to all individuals would reduce annual expenditure on mental illness in Australia by approximately $3B AUD, or around $150 AUD per person.

This paper studies income volatility using recent data from the Cross National Equivalence File (CNEF). Measures of downward instability are applied to household income streams and the results are interpreted as indicators of income insecurity. Using this method we examine (i) cross national differences in average insecurity levels, (ii) the effects of taxes and transfers, and (iii) relationships between the insecurity index and household income. Insecurity estimates based on pre-government incomes are highest in Britain and lowest in Germany, however results for post-government incomes are highest in the U.S. It is also shown that insecurity estimates based upon pre-government incomes are heavily concentrated at the lower end of the distribution; although governments are effective at smoothing the income streams of these households. We also search for determinants of our measure and find that gender, household size, health status, and industry affiliations of the household head are the most significant covariates. JEL Code: D31

This paper empirically examines whether and how similarity in country characteristics affects the changes in trade flows amongst member countries under a preferential trade agreement (PTA). It demonstrates that accounting for similarity in size, income and location of member countries is important in obtaining unbiased estimates of the trade creation effect of PTAs. Using both a nonparametric stratification and a parametric interaction term approach, we obtain consistent results that the more similar the member countries are in terms of size, income or location, the larger the level and the proportion increase in intra-bloc trade under a PTA. Extensive sensitivity analyses that account for potential biases due to self-selection of trade, the extensive margin of trade and omission of trade diversion variables confirm the robustness of our results. JEL Code: F15

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