Journal article

How valid are synthetic panel estimates of poverty dynamics?

Nicolas Herault, Stephen P Jenkins

The Journal of Economic Inequality | Springer Verlag | Published : 2019

Abstract

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 n..

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Grants

Awarded by Australian Research Council Discovery Grant


Awarded by UK Economic and Social Research Council


Funding Acknowledgements

We dedicate this paper to the memory of Tony Atkinson. This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the Melbourne Institute. Our research is supported by an Australian Research Council Discovery Grant (award DP150102409). Jenkins's research is also partially supported by core funding of the Research Centre on Micro-Social Change at the Institute for Social and Economic Research by the University of Essex and the UK Economic and Social Research Council (award ES/L009153/1). For helpful discussions, we thank Hai-Anh Dang, Peter Lanjouw, David Garces Urzainqui, the handling editor (Markus Jantti), and an anonymous referee. We thank DLLM for making their Stata code freely downloadable. Our Stata code, which builds on theirs, is available on request. Helpful comments from audiences in Bristol, Dublin, Essex, Melbourne, and Oslo are also acknowledged.