IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v237y2024ics0165176524001095.html
   My bibliography  Save this article

Misreporting in household income and expenditure: Evidence from the Chinese Household Income Project

Author

Listed:
  • Li, Feng
  • Wang, Xintao

Abstract

This study provides new evidence on misreporting in household income and expenditure using recall and diary data from the 2013 Chinese Household Income Project. Using more accurate diary records as benchmarks, we observe substantial and systematic misreporting in income and expenditure from recall data. Two main patterns of misreporting are identified: mean reversion and correlation with subjective well-being (i.e., happier respondents tend to overreport).

Suggested Citation

  • Li, Feng & Wang, Xintao, 2024. "Misreporting in household income and expenditure: Evidence from the Chinese Household Income Project," Economics Letters, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:ecolet:v:237:y:2024:i:c:s0165176524001095
    DOI: 10.1016/j.econlet.2024.111626
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176524001095
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2024.111626?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Claus Thustrup Kreiner & David Dreyer Lassen & Søren Leth-Petersen, 2014. "Measuring the Accuracy of Survey Responses Using Administrative Register Data: Evidence from Denmark," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 289-307, National Bureau of Economic Research, Inc.
    2. Thomas Carver & Arthur Grimes, 2019. "Income or Consumption: Which Better Predicts Subjective Well‐Being?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(S1), pages 256-280, November.
    3. Terry Sicular & Shi Li & Ximing Yue & Hiroshi Sato, 2017. "Changing Trends in China’s Inequality: Key Issues and Main Findings," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 201712, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
    4. Xiaolu Wang & Wing Thye Woo, 2011. "The Size and Distribution of Hidden Household Income in China," Asian Economic Papers, MIT Press, vol. 10(1), pages 1-26, Winter/Sp.
    5. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    6. Peter Valet & Jule Adriaans & Stefan Liebig, 2019. "Comparing survey data and administrative records on gross earnings: nonreporting, misreporting, interviewer presence and earnings inequality," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 471-491, January.
    7. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "A comparison of recall and diary food expenditure data," Food Policy, Elsevier, vol. 72(C), pages 53-61.
    8. Stefan Angel & Franziska Disslbacher & Stefan Humer & Matthias Schnetzer, 2019. "What did you really earn last year?: explaining measurement error in survey income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1411-1437, October.
    9. Juwei Zhang & Wen Zhao, 2019. "The unreported income and its impact on Gini coefficient in China," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 17(3), pages 245-259, July.
    10. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    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. Stella Martin & Kevin Stabenow & Mark Trede, 2024. "Measurement Error in Earnings," CQE Working Papers 10824, Center for Quantitative Economics (CQE), University of Muenster.
    2. Jenkins, Stephen P. & Rios-Avila, Fernando, 2021. "Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data," IZA Discussion Papers 14405, Institute of Labor Economics (IZA).
    3. Burt S. Barnow & David Greenberg, 2015. "Do Estimated Impacts on Earnings Depend on the Source of the Data Used to Measure Them? Evidence From Previous Social Experiments," Evaluation Review, , vol. 39(2), pages 179-228, April.
    4. Ana Cinta G. Cabral & Norman Gemmell & Nazila Alinaghi, 2021. "Are survey-based self-employment income underreporting estimates biased? New evidence from matched register and survey data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(2), pages 284-322, April.
    5. García-Suaza, A & Lobo, J & Montoya, S & Ordóñez, J & Oviedo, J. D, 2022. "Impact of the collection mode on labor income data. A study in the times of COVID19," Documentos de Trabajo 20396, Universidad del Rosario.
    6. Marco Caliendo & Katrin Huber & Ingo E. Isphording & Jakob Wegmann, 2024. "On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages," Papers 2411.04751, arXiv.org.
    7. Li, Hao & Millimet, Daniel L. & Roychowdhury, Punarjit, 2023. "Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach," GLO Discussion Paper Series 1227, Global Labor Organization (GLO).
    8. Li, Hao & Millimet, Daniel L. & Roychowdhury, Punarjit, 2019. "Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach," IZA Discussion Papers 12505, Institute of Labor Economics (IZA).
    9. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    10. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    11. Sule Alan, 2012. "Do disaster expectations explain household portfolios?," Quantitative Economics, Econometric Society, vol. 3(1), pages 1-28, March.
    12. Meghir, Costas & Pistaferri, Luigi, 2011. "Earnings, Consumption and Life Cycle Choices," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 9, pages 773-854, Elsevier.
    13. Diogo G. C. Britto & Alexandre Fonseca & Paolo Pinotti & Breno Sampaio & Lucas Warwar, 2022. "Intergenerational Mobility in the Land of Inequality," CESifo Working Paper Series 10004, CESifo.
    14. Yingyao Hu & Geert Ridder, 2012. "Estimation of nonlinear models with mismeasured regressors using marginal information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 347-385, April.
    15. Daniel Wilhelm, 2018. "Testing for the presence of measurement error," CeMMAP working papers CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Francisca Antman & David McKenzie, 2007. "Poverty traps and nonlinear income dynamics with measurement error and individual heterogeneity," Journal of Development Studies, Taylor & Francis Journals, vol. 43(6), pages 1057-1083.
    17. Richard Blundell & Luigi Pistaferri & Itay Saporta-Eksten, 2016. "Consumption Inequality and Family Labor Supply," American Economic Review, American Economic Association, vol. 106(2), pages 387-435, February.
    18. Chay, Kenneth Y. & Lee, David S., 2000. "Changes in relative wages in the 1980s Returns to observed and unobserved skills and black-white wage differentials," Journal of Econometrics, Elsevier, vol. 99(1), pages 1-38, November.
    19. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    20. Moshe Buchinsky & Jennifer Hunt, 1999. "Wage Mobility In The United States," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 351-368, August.

    More about this item

    Keywords

    Misreporting; Income; Expenditure; Mean version; Subjective well-being;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

    Statistics

    Access and download statistics

    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:eee:ecolet:v:237:y:2024:i:c:s0165176524001095. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.