IDEAS home Printed from
   My bibliography  Save this paper

Annualizing Labor Market, Inequality, and Poverty Indicators


  • Eduardo Lora
  • Miguel Benítez
  • Diego Gutiérrez


Widely, 12-month or 4-quarter average labor market, inequality and poverty indicators computed from repeated cross sections of household surveys are interpreted as annual. This is a valid interpretation only when several very specific criteria are met. Annual measures of indicators such as labor participation rates differ from their 12-month- or quarterly averages except when those who participate in a month or quarter also participate the other 11 months or three quarters. The same apply to unemployment rates and poverty rates. We propose several methods to accurately annualize sub-annual data. Some rely on ancillary questions often included in household surveys, others require econometric techniques such as predictive mean matching. Using data for Colombia we present annual measures of labor participation, occupation, unemployment, per capita labor income, average per capita household income, the Gini coefficients of labor income and per-capita household income, and moderate and extreme poverty rates.

Suggested Citation

  • Eduardo Lora & Miguel Benítez & Diego Gutiérrez, 2021. "Annualizing Labor Market, Inequality, and Poverty Indicators," Commitment to Equity (CEQ) Working Paper Series 113, Tulane University, Department of Economics.
  • Handle: RePEc:tul:ceqwps:113

    Download full text from publisher

    File URL:
    File Function: First version, 2021
    Download Restriction: no

    References listed on IDEAS

    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. 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.
    4. 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..
    5. 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.
    6. 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.
    7. repec:pri:rpdevs:deaton_grosh_consumption is not listed on IDEAS
    8. 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.
    9. Angus Deaton, 2003. "Household Surveys, Consumption, and the Measurement of Poverty," Economic Systems Research, Taylor & Francis Journals, vol. 15(2), pages 135-159.
    10. 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.
    11. 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.
    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, 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. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Mingyang Cai & Gerko Vink, 2022. "A note on imputing squares via polynomial combination approach," Computational Statistics, Springer, vol. 37(5), pages 2185-2201, November.
    18. Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2013. "Statistical Matching of Administrative and Survey Data," Sociological Methods & Research, , vol. 42(2), pages 192-224, May.
    19. Grabka, Markus & Westermeier, Christian, 2014. "Estimating the Impact of Alternative Multiple Imputation Methods on Longitudinal Wealth Data," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100353, Verein für Socialpolitik / German Economic Association.
    20. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.

    More about this item


    annualization; employment; income distribution; income poverty; Gini coefficient; labor income; labor participation; poverty; unemployment;
    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

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:tul:ceqwps:113. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: Nora Lustig (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.