IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v53y2024i2p872-897.html
   My bibliography  Save this article

Comparing Single- and Multiple-Question Designs of Measuring Family Income in China Family Panel Studies

Author

Listed:
  • Qiong Wu
  • Liping Gu

Abstract

Family income questions in general purpose surveys are usually collected with either a single-question summary design or a multiple-question disaggregation design. It is unclear how estimates from the two approaches agree with each other. The current paper takes advantage of a large-scale survey that has collected family income with both methods. With data from 14,222 urban and rural families in the 2018 wave of the nationally representative China Family Panel Studies, we compare the two estimates, and further evaluate factors that might contribute to the discrepancy. We find that the two estimates are loosely matched in only a third of all families, and most of the matched families have a simple income structure. Although the mean of the multiple-question estimate is larger than that of the single-question estimate, the pattern is not monotonic. At lower percentiles up till the median, the single-question estimate is larger, whereas the multiple-question estimate is larger at higher percentiles. Larger family sizes and more income sources contribute to higher likelihood of inconsistent estimates from the two designs. Families with wage income as the main income source have the highest likelihood of giving consistent estimates compared with all other families. In contrast, families with agricultural income or property income as the main source tend to have very high probability of larger single-question estimates. Omission of certain income components and rounding can explain over half of the inconsistencies with higher multiple-question estimates and a quarter of the inconsistencies with higher single-question estimates.

Suggested Citation

  • Qiong Wu & Liping Gu, 2024. "Comparing Single- and Multiple-Question Designs of Measuring Family Income in China Family Panel Studies," Sociological Methods & Research, , vol. 53(2), pages 872-897, May.
  • Handle: RePEc:sae:somere:v:53:y:2024:i:2:p:872-897
    DOI: 10.1177/00491241221077238
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00491241221077238
    Download Restriction: no

    File URL: https://libkey.io/10.1177/00491241221077238?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John Micklewright & Sylke V. Schnepf, 2010. "How reliable are income data collected with a single question?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 409-429, April.
    2. Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
    3. Kirstine Hansen & Dylan Kneale, 2013. "Does How You Measure Income Make a Difference to Measuring Poverty? Evidence from the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(3), pages 1119-1140, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Cutillo & Michele Raitano & Isabella Siciliani, 2022. "Income-Based and Consumption-Based Measurement of Absolute Poverty: Insights from Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 689-710, June.
    2. Lourdes Diaz Olvera & Didier Plat & Pascal Pochet, 2015. "Assessment of mobility inequalities and income data collection. Methodological issues and a case study (Douala, Cameroon) [Evaluation des inégalités de mobilité et recueil des revenus. Questions mé," Post-Print halshs-01235185, HAL.
    3. Diaz Olvera, Lourdes & Plat, Didier & Pochet, Pascal, 2015. "Assessment of mobility inequalities and income data collection. Methodological issues and a case study (Douala, Cameroon)," Journal of Transport Geography, Elsevier, vol. 46(C), pages 180-188.
    4. Lourdes Diaz Olvera & Didier Plat & Pascal Pochet, 2015. "Assessment of mobility inequalities and income data collection. Methodological issues and a case study (Douala, Cameroon)," Post-Print halshs-01205776, HAL.
    5. Marco Francesconi & Holly Sutherland & Francesca Zantomio, 2011. "A comparison of earnings measures from longitudinal and cross‐sectional surveys: evidence from the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 297-326, April.
    6. Crossley, Thomas F. & Fisher, Paul & Low, Hamish, 2021. "The heterogeneous and regressive consequences of COVID-19: Evidence from high quality panel data," Journal of Public Economics, Elsevier, vol. 193(C).
    7. Angus Deaton, 2012. "The financial crisis and the well-being of Americans," Oxford Economic Papers, Oxford University Press, vol. 64(1), pages 1-26, January.
    8. Carlo Drago, 2021. "The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator Approach," Economies, MDPI, vol. 9(4), pages 1-17, October.
    9. Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
    10. Elvire Guillaud & Michaël Zemmour, 2017. "The redistributive preferences of the well-off," SciencePo Working papers Main halshs-01652706, HAL.
    11. Per Engzell, 2021. "What Do Books in the Home Proxy For? A Cautionary Tale," Sociological Methods & Research, , vol. 50(4), pages 1487-1514, November.
    12. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 23-50, National Bureau of Economic Research, Inc.
    13. Sabatini, Serena & Martyr, Anthony & Gamble, Laura D. & Jones, Ian R. & Collins, Rachel & Matthews, Fiona E. & Knapp, Martin & Thom, Jeanette M. & Henderson, Catherine & Victor, Christina & Pentecost,, 2023. "Are profiles of social, cultural, and economic capital related to living well with dementia? Longitudinal findings from the IDEAL programme," Social Science & Medicine, Elsevier, vol. 317(C).
    14. Andrew J. Healy & Mikael Persson & Erik Snowberg, 2016. "Digging into the Pocketbook: Evidence on Economic Voting from Income Registry Data Matched to a Voter Survey," CESifo Working Paper Series 6171, CESifo.
    15. Jake Anders, 2012. "Using the Longitudinal Study of Young People in England for research into Higher Education access," DoQSS Working Papers 12-13, Quantitative Social Science - UCL Social Research Institute, University College London.
    16. Elvire Guillaud & Michaël Zemmour, 2017. "The redistributive preferences of the well-off," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01652706, HAL.
    17. Cardoso, Ana Rute & Loviglio, Annalisa & Piemontese, Lavinia, 2015. "Information Frictions and Labor Market Outcomes," IZA Discussion Papers 9070, Institute of Labor Economics (IZA).
    18. Rita Abdel Sater, 2021. "Essays on the application of behavioural insights to environmental policy [Essais sur l’application des connaissances comportementales aux politiques environnementales]," SciencePo Working papers tel-03450909, HAL.
    19. Ahnert, Henning & Kavonius, Ilja Kristian & Honkkila, Juha & Sola, Pierre, 2020. "Understanding household wealth: linking macro and micro data to produce distributional financial accounts," Statistics Paper Series 37, European Central Bank.
    20. Florianne C. J. Verkroost, 2022. "A Bayesian multivariate hierarchical growth curve model to examine cumulative socio‐economic (dis)advantage among childless adults and parents," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2234-2276, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:somere:v:53:y:2024:i:2:p:872-897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.