IDEAS home Printed from https://ideas.repec.org/a/spr/joecin/v22y2024i1d10.1007_s10888-023-09583-9.html
   My bibliography  Save this article

Annualizing labor market, inequality, and poverty indicators

Author

Listed:
  • Eduardo Lora

    (Harvard University, and Fedesarrollo)

  • Miguel Benítez

    (Inter-American Development Bank)

  • Diego Gutiérrez

    (Inter-American Development Bank)

Abstract

Widely, 12-month or 4-quarter average indicators, such as monetary poverty rates, are computed from repeated cross sections of household surveys and interpreted as annual. This is a valid interpretation only when individuals do not change their status within the year; for instance, those observed as poor in the month they are interviewed stay poor the other 11 months. First, we demonstrate that such misinterpretation affects the calculation of several labor market, inequality, and monetary poverty measures. Then, we propose several methods to accurately annualize sub-annual data. Some methods rely on ancillary questions often included in household surveys while others require econometric techniques such as predictive mean matching. Using data for Colombia, we apply the methods to compute annual measures of labor participation, occupation, per capita labor income, average per capita household income, the Gini coefficients of labor income and per-capita household income, and moderate and extreme monetary poverty indices (headcount, gap, and severity). We show that differences with respect to the usual calculations based on monthly averages can be substantial.

Suggested Citation

  • Eduardo Lora & Miguel Benítez & Diego Gutiérrez, 2024. "Annualizing labor market, inequality, and poverty indicators," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 22(1), pages 131-164, March.
  • Handle: RePEc:spr:joecin:v:22:y:2024:i:1:d:10.1007_s10888-023-09583-9
    DOI: 10.1007/s10888-023-09583-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10888-023-09583-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10888-023-09583-9?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Bierbaum, Mira & Gassmann, Franziska, 2012. "Chronic and transitory poverty in the Kyrgyz Republic: What can synthetic panels tell us?," MERIT Working Papers 2012-064, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    3. Jolliffe,Dean Mitchell & Serajuddin,Umar & Jolliffe,Dean Mitchell & Serajuddin,Umar, 2015. "Estimating poverty with panel data, comparably : an example from Jordan," Policy Research Working Paper Series 7373, The World Bank.
    4. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    5. Angus Deaton, 2003. "Household Surveys, Consumption, and the Measurement of Poverty," Economic Systems Research, Taylor & Francis Journals, vol. 15(2), pages 135-159.
    6. Angus Deaton & Margaret Grosh, 1998. "Designing Household Survey Questionnaires for Developing Countries Lessons from Ten Years of LSMS Experience, Chapter 17: Consumption," Working Papers 218, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
    7. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    8. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    9. Hai-Anh H. Dang & Andrew L. Dabalen, 2019. "Is Poverty in Africa Mostly Chronic or Transient? Evidence from Synthetic Panel Data," Journal of Development Studies, Taylor & Francis Journals, vol. 55(7), pages 1527-1547, July.
    10. Kashi Kafle & Kevin McGee & Alemayehu Ambel & Ilana Seff, 2017. "Once Poor always Poor? Exploring Consumption- and Asset-based Poverty Dynamics in Ethiopia," Ethiopian Journal of Economics, Ethiopian Economics Association, vol. 25(2), May.
    11. repec:pri:rpdevs:deaton_grosh_consumption is not listed on IDEAS
    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. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    2. Ines A. Ferreira & Vincenzo Salvucci & Finn Tarp, 2021. "Poverty and vulnerability transitions in Myanmar: An analysis using synthetic panels," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 1919-1944, November.
    3. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    4. Westermeier, Christian & Grabka, Markus M., 2016. "Longitudinal Wealth Data and Multiple Imputation: An Evaluation Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 237-252.
    5. Arif Mamun & David Wittenburg & Noelle Denny-Brown & Michael Levere & David Mann & Rebecca Coughlin & Sarah Croake & Heather Gordon & Denise Hoffman & Rachel Holzwart & Rosalind Keith & Brittany McGil, "undated". "Promoting Opportunity Demonstration: Interim Evaluation Report," Mathematica Policy Research Reports caa99d38a8b14f968ea3438e5, Mathematica Policy Research.
    6. Baltussen, Guido & Swinkels, Laurens & Van Vliet, Pim, 2021. "Global factor premiums," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1128-1154.
    7. Sean Mc Auliffe & Georg U. Thunecke & Georg Wamser, 2023. "The Tax-Elasticity of Tangible Fixed Assets: Evidence from Novel Corporate Tax Data," CESifo Working Paper Series 10628, CESifo.
    8. Leonie C. Steckermeier & Jan Delhey, 2019. "Better for Everyone? Egalitarian Culture and Social Wellbeing in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(3), pages 1075-1108, June.
    9. Saeideh Kamgar & Florian Meinfelder & Ralf Münnich & Hamidreza Navvabpour, 2020. "Estimation within the new integrated system of household surveys in Germany," Statistical Papers, Springer, vol. 61(5), pages 2091-2117, October.
    10. Filippo Battistoni & Marco Martinez, 2022. "Rome and the Polis: Tradition and Change in the Financial Accounts of Tauromenion, 1st Century B.C," Annals of the Fondazione Luigi Einaudi. An Interdisciplinary Journal of Economics, History and Political Science, Fondazione Luigi Einaudi, Torino (Italy), vol. 56(1), pages 149-176, June.
    11. Roderick J. A. Little & Donald B. Rubin, 1989. "The Analysis of Social Science Data with Missing Values," Sociological Methods & Research, , vol. 18(2-3), pages 292-326, November.
    12. Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
    13. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    14. Chenyang Gu & Roee Gutman, 2017. "Combining item response theory with multiple imputation to equate health assessment questionnaires," Biometrics, The International Biometric Society, vol. 73(3), pages 990-998, September.
    15. Chia-Ning Wang & Roderick Little & Bin Nan & Siobán D. Harlow, 2011. "A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories," Biometrics, The International Biometric Society, vol. 67(4), pages 1573-1582, December.
    16. Matthias von Davier & Youngmi Cho & Tianshu Pan, 2019. "Effects of Discontinue Rules on Psychometric Properties of Test Scores," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 147-163, March.
    17. Morris A. Davis & William D. Larson & Stephen D. Oliner & Benjamin Smith, 2019. "Mortgage Risk Since 1990," FHFA Staff Working Papers 19-02, Federal Housing Finance Agency.
    18. Fernandes, Mario & Hilber, Simon & Sturm, Jan-Egbert & Walter, Andreas, 2023. "Closing the gender gap in academia? Evidence from an affirmative action program," Research Policy, Elsevier, vol. 52(9).
    19. Patrick M. Joyce & Donald Malec & Roderick J. A. Little & Aaron Gilary & Alfredo Navarro & Mark E. Asiala, 2014. "Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 36-47, March.
    20. Mingyang Cai & Gerko Vink, 2022. "A note on imputing squares via polynomial combination approach," Computational Statistics, Springer, vol. 37(5), pages 2185-2201, November.

    More about this item

    Keywords

    Annualization; Labor participation; Occupation; Unemployment; Labor income; Income distribution; Gini coefficient; Monetary poverty; Poverty gap; Poverty severity;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

    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:spr:joecin:v:22:y:2024:i:1:d:10.1007_s10888-023-09583-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.