How valid are synthetic panel estimates of poverty dynamics?
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.
KeywordsSynthetic 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.
- 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
- Canberra Group: Handbook on Household Income Statistics, 2nd. United Nations Economic Commission for Europe, Geneva (2011)Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Rigolini, J., Vakis, R., Lucchetti, L.: Left behind Chronic Poverty in Latin America and the Caribbean. The World Bank, Washington DC (2016)Google Scholar
- 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
- 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
- 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
- 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
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.