IDEAS home Printed from https://ideas.repec.org/p/tul/ceqwps/113.html
   My bibliography  Save this paper

Annualizing Labor Market, Inequality, and Poverty Indicators

Author

Listed:
  • Eduardo Lora
  • Miguel Benítez
  • Diego Gutiérrez

Abstract

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
    as

    Download full text from publisher

    File URL: http://repec.tulane.edu/RePEc/ceq/ceq113.pdf
    File Function: First version, 2021
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. James E. Foster, 2007. "A Class of Chronic Poverty Measures," Vanderbilt University Department of Economics Working Papers 0701, Vanderbilt University Department of Economics.
    2. 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.
    3. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(2), pages 357-378.
    4. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    5. repec:pri:rpdevs:deaton_grosh_consumption is not listed on IDEAS
    6. Ward, Jason M. & Anne Edwards, Kathryn, 2021. "CPS Nonresponse During the COVID-19 Pandemic: Explanations, Extent, and Effects," Labour Economics, Elsevier, vol. 72(C).
    7. Francesco Figari & Maria Iacovou & Alexandra Skew & Holly Sutherland, 2012. "Approximations to the Truth: Comparing Survey and Microsimulation Approaches to Measuring Income for Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(3), pages 387-407, February.
    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. Stefan Dercon, 2002. "Income Risk, Coping Strategies, and Safety Nets," The World Bank Research Observer, World Bank, vol. 17(2), pages 141-166, September.
    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.
    12. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
    13. Sider, Hal, 1985. "Unemployment Duration and Incidence: 1968-82," American Economic Review, American Economic Association, vol. 75(3), pages 461-472, June.
    14. Ivar Krumpal, 2013. "Determinants of social desirability bias in sensitive surveys: a literature review," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2025-2047, June.
    15. 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.
    16. 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).
    17. 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.
    18. 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..
    19. 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.
    20. Pirmin Fessler & Maximilian Kasy & Peter Lindner, 2018. "Survey mode effects on measured income inequality," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(4), pages 487-505, December.
    21. 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.
    22. Manimay Sengupta, 2009. "Unemployment duration and the measurement of unemployment," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(3), pages 273-294, September.
    23. Angus Deaton, 2003. "Household Surveys, Consumption, and the Measurement of Poverty," Economic Systems Research, Taylor & Francis Journals, vol. 15(2), pages 135-159.
    24. 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.
    25. van Praag, Bernard M S & Hagenaars, Aldi J M & van Eck, Wim, 1983. "The Influence of Classification and Observation Errors on the Measurement of Income Inequality," Econometrica, Econometric Society, vol. 51(4), pages 1093-1108, 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. 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.
    2. 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.
    3. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," Working Papers hal-04093646, HAL.
    4. Anton Korinek & Johan Mistiaen & Martin Ravallion, 2006. "Survey nonresponse and the distribution of income," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 4(1), pages 33-55, April.
    5. 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.
    6. 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.
    7. Stephen Bazen & Xavier Joutard & Mouhamadou Niang, 2014. "The measurement of unemployment using completed durations: evidence on the gender gap in unemployment in France," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 517-534, December.
    8. 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.
    9. 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.
    10. Miguel Szekely & Nora Lustig & Martin Cumpa & Jose Antonio Mejia, 2004. "Do we know how much poverty there is?," Oxford Development Studies, Taylor & Francis Journals, vol. 32(4), pages 523-558.
    11. 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.
    12. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," Working Papers hal-04093646, HAL.
    13. Baltussen, Guido & Swinkels, Laurens & Van Vliet, Pim, 2021. "Global factor premiums," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1128-1154.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Coral Río & Olga Alonso-Villar, 2018. "Segregation and Social Welfare: A Methodological Proposal with an Application to the U.S," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 257-280, May.
    20. 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.

    More about this item

    Keywords

    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:

    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:tul:ceqwps:113. 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: Nora Lustig (email available below). General contact details of provider: https://edirc.repec.org/data/detulus.html .

    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.