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


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.


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



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

10888_2019_9408_MOESM1_ESM.pdf (7.1 mb)
(PDF 7.14 MB)


  1. Bane, M., Ellwood, D.: Slipping into and out of poverty: the dynamics of spells. J. Hum. Resour. 21, 1–23 (1986)CrossRefGoogle Scholar
  2. Bourguignon, F., Moreno, H.: On the construction of synthetic panels. Paper presented at the North East Universities Development Conference, Brown University, Providence RI (2015)Google Scholar
  3. Canberra Group: Handbook on Household Income Statistics, 2nd. United Nations Economic Commission for Europe, Geneva (2011)Google Scholar
  4. Cruces, G., Lanjouw, P., Lucchetti, L., Perova, E., Vakis, R., Viollaz, M.: Estimating poverty transitions using repeated cross-sections: a three-country validation exercise. J. Econ. Inequal. 13, 161–179 (2015)CrossRefGoogle Scholar
  5. Dang, H.-A., Dabalen, A.L.: Is poverty in Africa mostly chronic or transient? Evidence from synthetic panel data. Journal of Development Studies, online (2018)Google Scholar
  6. Dang, H.-A., Ianchovichina, E.: Welfare dynamics with synthetic panels: the case of the Arab world in transition. Rev. Income Wealth 64(S1), S114–S144 (2018)CrossRefGoogle Scholar
  7. Dang, H.-A., Lanjouw, P.: Measuring poverty dynamics with synthetic panels based on cross-sections. Policy Research Working Paper 6504, The World Bank (2013)Google Scholar
  8. Dang, H.-A., Lanjouw, P.: Poverty dynamics in India between 2004 and 2012: Insights from longitudinal analysis using synthetic panel data. Econ. Dev. Cult. Chang. 67(1), 131–170 (2018)CrossRefGoogle Scholar
  9. Dang, H.-A., Lanjouw, P., Luoto, L., McKenzie, D.: Using repeated cross-sections to explore movements into and out of poverty. J. Dev. Econ. 107, 112–128 (2014)CrossRefGoogle Scholar
  10. Dang, H.-A., Lanjouw, P., Swinkels, R: Who remained in poverty, who moved up, and who fell down? An investigation of poverty dynamics in Senegal in the 2000s. In: Nissanke, M., Ndulo, M. (eds.) Poverty Reduction in the Course of African Development. Oxford University Press, Oxford (2017)Google Scholar
  11. Deaton, A.: Panel data from time series of cross-sections. J. Econ. 30, 109–126 (1985)CrossRefGoogle Scholar
  12. Ferreira, F.H.G., Messina, J., Rigolini, J., López-Calva, L.-F., Lugo, M.A., Vakis, R.: Economic Mobility and the Rise of the Latin American Middle Class. The World Bank, Washington DC (2013)Google Scholar
  13. Fields, G., Viollaz, M.: Can the limitations of panel datasets be overcome by using pseudo-panels to estimate income mobility? Paper presented at the ECINEQ Conference, Bari, Italy (2013)Google Scholar
  14. Frick, J.R., Jenkins, S.P., Lillard, D.R., Lipps, O., Wooden, M.: The Cross-National Equivalent File (CNEF) and its member country household panel studies. Schmollers Jahrbuch J. Appl. Soc. Sci. Stud. 127(4), 627–654 (2007)Google Scholar
  15. Garcés Urzainqui, D.: Poverty transitions without panel data? An appraisal of synthetic panel methods. Paper presented at the ECINEQ Conference, New York City (2017)Google Scholar
  16. Jenkins, S.P.: Changing Fortunes. Income Mobility and Poverty Dynamics in Britain. Oxford University Press, Oxford (2011)CrossRefGoogle Scholar
  17. Jenkins, S.P., Van Kerm, P.: How does attrition affect estimates of persistent poverty rates? The case of EU-SILC. In: Atkinson, A.B., Guio, A., Marlier, E. (eds.) Monitoring Social Europe, 2017 Edition. Luxembourg: Eurostat, pp 401–417 (2017)Google Scholar
  18. Moffitt, R.: Identification and estimation of dynamic models with a time series of repeated cross sections. J. Econ. 59, 99–123 (1993)CrossRefGoogle Scholar
  19. OECD: Employment Outlook, 2015. OECD Publishing, Paris (2015)CrossRefGoogle Scholar
  20. OECD: A Broken Elevator? How to Promote Social Mobility. OECD Publishing, Paris (2018)CrossRefGoogle Scholar
  21. Perez, V.: Moving in and out of poverty in Mexico: what can we learn from pseudo-panel methods? ISER Working Paper 2015-16, University of Essex (2015)Google Scholar
  22. Rama, M., Béteille, T., Li, Y., Mitra, P.K., Newman, J.L.: Addressing Inequality in South Asia. The World Bank, Washington DC (2014)CrossRefGoogle Scholar
  23. Rigolini, J., Vakis, R., Lucchetti, L.: Left behind Chronic Poverty in Latin America and the Caribbean. The World Bank, Washington DC (2016)Google Scholar
  24. Rosenzweig, M.R.: Payoffs from panels in low-income countries: economic development and economic mobility. Am. Econ. Rev. Pap. Proc. 93(2), 112–117 (2003)CrossRefGoogle Scholar
  25. Summerfield, M., Freidin, S., Hahn, M., La, N., Li, N., Macalalad, N., O’Shea, M., Watson, N., Wilkins, R., Wooden, M.: HILDA user Manual – Release, 15. Melbourne Institute for Applied Social and Economic Research, Melbourne (2016)Google Scholar
  26. Verbeek, M.: Synthetic panels and repeated cross-sections. In: Matyas, L., Sevestre, P. (eds.) The Econometrics of Panel Data, pp 369–383. Springer-Verlag, Berlin (2008)Google Scholar
  27. Watson, N., Wooden, M.: Re-engaging with survey non-respondents: the BHPS, SOEP and HILDA survey experience, HILDA Project Discussion Paper 1/11 (2011)Google Scholar
  28. Wilkins, R.: The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 15. Melbourne Institute of Applied Social and Economic Research, Melbourne (2017)Google Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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

Personalised recommendations