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The Journal of Economic Inequality

, Volume 17, Issue 1, pp 51–76 | Cite as

How valid are synthetic panel estimates of poverty dynamics?

  • Nicolas Hérault
  • Stephen P. JenkinsEmail author
Open Access
Article

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 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.

Keywords

Synthetic panel Pseudo panel Poverty dynamics Poverty entry Poverty exit BHPS HILDA 

Notes

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 Garcés Urzainqui, the handling editor (Markus Jäntti), 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.

Supplementary material

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Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Melbourne Institute of Applied Economic and Social ResearchUniversity of MelbourneMelbourneAustralia
  2. 2.ARC Centre of Excellence for Children and Families over the Life CourseMelbourneAustralia
  3. 3.Department of Social PolicyLondon School of EconomicsLondonUK
  4. 4.ISERUniversity of EssexColchesterUK
  5. 5.IZABonnGermany

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