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A growing literature uses repeated cross-section surveys to derive 'synthetic panel' data estimates of poverty dynamics statistics. It builds on the pioneering study by Dang et al. ('DLLM', Journal of Development Economics, 2014) providing bounds estimates and the innovative refinement proposed by Dang and Lanjouw ('DL', World Bank Policy Research Working Paper 6504, 2013) providing point estimates of the statistics of interest. We provide new evidence about the accuracy of synthetic panel estimates relative to benchmarks based on estimates derived from genuine household panel data, employing high quality data from Australia and Britain, while also examining the sensitivity of results to a number of analytical choices. For these two high-income countries we show that DL-method point estimates are distinctly less accurate than estimates derived in earlier validity studies, all of which focus on low-and middle-income countries. We also demonstrate that estimate validity depends on choices such as the age of the household head (defining the sample), the poverty line level, and the years analyzed. DLLM parametric bounds estimates virtually always include the true panel estimates, though the bounds can be wide.

Survey under-coverage of top incomes leads to bias in survey-based estimates of overall income inequality. Using income tax record data in combination with survey data is a potential approach to address the problem; we consider here the UK's pioneering 'SPI adjustment' method that implements this idea. Since 1992, the principal income distribution series (reported annually in * Submitted December 2016. Households Below Average Income) has been based on household survey data in which the incomes of a small number of 'very rich' individuals are adjusted using information from 'very rich' individuals in personal income tax return data. We explain what the procedure involves, reveal the extent to which it addresses survey under-coverage of top incomes and show how it affects estimates of overall income inequality. More generally, we assess whether the SPI adjustment is fit for purpose and consider whether variants of it could be employed by other countries. Policy pointsr Household surveys are the main source of information about overall inequality levels and trends in most countries around the world but do not capture income at the extreme top of the distribution very well. This means that survey-based estimates of overall income inequality are biased downwards. Using income tax record data in combination with survey data is a potential approach to address this problem because tax data are likely to have much better coverage of top incomes.r A pioneering variant of this approach -the 'SPI adjustment' -has been employed in the UK's official income distribution statistics since 1992. However, there are potentially better ways to improve data quality at the top of the income distribution.r Our proposed refinements of the SPI adjustment approach better address issues of under-coverage of top incomes in UK survey data.r More generally, the scope for improving estimates of inequality levels and trends by taking better account of top incomes using tax data is contingent on the nature of the data available (and the nature of survey under-coverage).r The more important lesson is that improvements per se are possible and they could be implemented in many countries by the guardians of national statistical series on income distribution with appropriate coordination between the agency in charge of the survey and the national tax office.

In recent decades income inequality has increased in many developed countries but the role of tax and transfer reforms is often poorly understood. We propose a new method allowing for the decomposition of historical changes in income distribution and redistribution measures into: (i) the immediate effect of tax-transfer policy reforms in the absence of behavioral responses; (ii) the effect of labor supply responses induced by these reforms; and (iii) a third component allowing us to explore the effect of changes in the distribution of a wide range of determinants, including the effect of employment changes not induced by policy reforms. The application of the decomposition to Australia reveals that the direct effect of tax-transfer policy reforms accounts for half of the observed increase in income inequality between 1999 and 2008, while the increased dispersion of wages and capital incomes also played an important role.JEL Codes: D31, H23, J22

We examine trends in the redistributive impact of the tax‐benefit system in Australia between 1994 and 2009 using a framework that allows us to separate the contributions of taxes, benefits, and taxes and benefits combined. Furthermore, we identify the effect of tax‐benefit policy reforms on income redistribution over the period. We find that after reaching a peak value in the late 1990s, the redistributive effect of taxes and benefits declined sharply. Although reforms to the tax‐benefit system contributed to the decline in redistribution, their contribution was limited compared to the role played by the changes in market income distribution.

This paper contributes to the relatively limited literature on the correlation of labor market outcomes of parents and their children. This literature is relevant to the larger literature on intergenerational income mobility since correlation in intergenerational labor market outcomes is one of the potential factors contributing to the intergenerational correlation of permanent incomes. In this paper, we consider the time spent in unemployment by both sons and daughters, while accounting for the potential endogeneity of education. Using the Household, Income and Labour Dynamics in Australia (HILDA) data, we find evidence of a positive correlation of labor market outcomes between fathers and sons and, to a lesser extent, between mothers and daughters. In addition, the results reveal a significant relationship between parents' and children's education levels, indicating that there is an indirect association of parental education with their children's labor market outcomes through education.

This paper uses survival analysis to model exits over time from two alternative notions of homelessness. We are unique in being able to account for time-invariant, unobserved heterogeneity. We find that duration dependence has an inverted U-shape with exit rates initially increasing (indicating positive duration dependence) and then falling. Like previous researchers, we find results consistent with negative duration dependence in models which ignore unobserved heterogeneity. Exit rates out of homelessness fall with age and with the education level of mothers. Women are more likely than men to exit homelessness when it is broadly conceived, but appear to be less likely to exit when it is narrowly defined. Finally, higher paternal education and exemptions from welfare-related activity requirements due to either mental or physical health conditions are all associated with higher exit rates.JEL classification: I3, R2, C4