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Constructing synthetic panels for the purpose of studying poverty dynamics: A primer

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  • David Garcés‐Urzainqui
  • Peter Lanjouw
  • Gerton Rongen

Abstract

We describe a recently developed approach for constructing synthetic panels from cross‐section data and consider how it can be employed to study poverty dynamics. Initially introduced as a means to estimating upper and lower bounds on poverty transitions in the absence of panel data, further refinements to the method aim to permit as well the calculation of point estimates. We describe the assumptions that underpin the basic approach and its extensions and discuss their plausibility. We chart applications of the method in various contexts, with a view to gauging its overall validity and robustness. While proper panel‐based analysis of welfare dynamics is clearly preferable, we suggest that the method can be useful in the all‐too‐common settings where panel data are unavailable or suffer from particularly pressing quality concerns.

Suggested Citation

  • David Garcés‐Urzainqui & Peter Lanjouw & Gerton Rongen, 2021. "Constructing synthetic panels for the purpose of studying poverty dynamics: A primer," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 1803-1815, November.
    • Handle: RePEc:bla:rdevec:v:25:y:2021:i:4:p:1803-1815
    DOI: 10.1111/rode.12832
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    References listed on IDEAS

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    1. François Bourguignon & A. Hector Moreno M., 2020. "On synthetic income panels," Working Papers halshs-01988068, HAL.
    2. Ethan Ligon & Laura Schechter, 2003. "Measuring Vulnerability," Economic Journal, Royal Economic Society, vol. 113(486), pages 95-102, March.
    3. Dang,Hai-Anh H. & Lanjouw,Peter F., 2013. "Measuring poverty dynamics with synthetic panels based on cross-sections," Policy Research Working Paper Series 6504, The World Bank.
    4. Nicolas Hérault & Stephen P. Jenkins, 2019. "How valid are synthetic panel estimates of poverty dynamics?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(1), pages 51-76, March.
    5. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    6. Shorrocks, A F, 1978. "The Measurement of Mobility," Econometrica, Econometric Society, vol. 46(5), pages 1013-1024, September.
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    2. Hai‐Anh Dang & Peter Lanjouw & Elise Vrijburg, 2021. "Poverty in India in the face of Covid‐19: Diagnosis and prospects," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 1816-1837, November.
    3. Tseday Jemaneh Mekasha & Finn Tarp, 2021. "Understanding poverty dynamics in Ethiopia: Implications for the likely impact of COVID‐19," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 1838-1868, November.
    4. Jehovaness Aikaeli & David Garcés‐Urzainqui & Kenneth Mdadila, 2021. "Understanding poverty dynamics and vulnerability in Tanzania: 2012–2018," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 1869-1894, November.
    5. Shiyu Yan & Jiao Wang & Zhineng Hu, 2023. "Assessment of the effects of targeted poverty alleviation policy on differently poverty‐stricken households: A multi‐propensity score weighting model," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1792-1833, August.
    6. Archana Dang & Mausumi Das & Indrani Gupta, 2023. "COVID-19 And The Unequal Distribution Of Poverty Risks: Evidence From Urban India," IEG Working Papers 458, Institute of Economic Growth.

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