IDEAS home Printed from https://ideas.repec.org/a/jid/journl/y2017v25i1p1-30.html
   My bibliography  Save this article

Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia

Author

Listed:
  • Jose Cuesta

    (Georgetown University)

  • Gabriel Lara Ibarra

    (World Bank)

Abstract

Tunisia was showcased for a long time as an example of poverty reduction achievement and pro-poor growth. Yet, after halving its poverty rates a revolution took the world by surprise early in 2011 and since then nothing is known about its poverty levels. To fill that gap, this analysis develops and compares multiple cross-survey micro imputations (using household budgetary and labor force surveys) with macro poverty projections (based on sector GDP, unemployment and inflation). Results from both techniques are robust: poverty in post revolution Tunisia first increased in 2011 to then decrease in 2012. The magnitude of this swing oscillates between 1 and 2.3 percent points and accrues mostly from urban areas. Methods using readily available macro administrative data provide estimates of poverty levels and trends very close to those provided by analytically more sophisticated and data demanding micro imputation techniques. These findings for Tunisia provide relevant insights in data deprived contexts with serious deficiencies in the frequency and accessibility of welfare statistics.

Suggested Citation

  • Jose Cuesta & Gabriel Lara Ibarra, 2017. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, March.
    • Handle: RePEc:jid:journl:y:2017:v:25:i:1:p:1-30
    as

    Download full text from publisher

    File URL: http://www.glendon.yorku.ca/repec/uploads/repec/jid/articles/36210.pdf
    Download Restriction: Some fulltext downloads are only available to subscribers. See JID website for details.
    ---><---

    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. Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar & Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
    2. Newhouse, D. & Shivakumaran, S. & Takamatsu, S. & Yoshida, N., 2014. "How survey-to-survey imputation can fail," Policy Research Working Paper Series 6961, The World Bank.
    3. Francisco Ferreira & Jérémie Gignoux & Meltem Aran, 2011. "Measuring inequality of opportunity with imperfect data: the case of Turkey," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(4), pages 651-680, December.
    4. Astrid Mathiassen, 2013. "Testing Prediction Performance of Poverty Models: Empirical Evidence from U ganda," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(1), pages 91-112, March.
    5. Serajuddin,Umar & Uematsu,Hiroki & Wieser,Christina & Yoshida,Nobuo & Dabalen,Andrew L., 2015. "Data deprivation : another deprivation to end," Policy Research Working Paper Series 7252, The World Bank.
    6. Astrid Mathiassen, 2009. "A model based approach for predicting annual poverty rates without expenditure data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(2), pages 117-135, June.
    7. Sami Bibi, 2005. "When is Economic Growth Pro-Poor? Evidence from Tunisia," Cahiers de recherche 0522, CIRPEE.
    8. Jane Harrigan & Hamed El-Said, 2009. "Economic Liberalisation, Social Capital and Islamic Welfare Provision," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-00158-0.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Atamanov,Aziz & Tandon,Sharad Alan & Lopez-Acevedo,Gladys C. & Vergara Bahena,Mexico Alberto, 2020. "Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region : Data Gaps and Different Options to Address Them," Policy Research Working Paper Series 9259, The World Bank.
    2. Betti,Gianni & Molini,Vasco & Mori,Lorenzo, 2022. "New Algorithm to Estimate Inequality Measures in Cross-Survey Imputation : An Attemptto Correct the Underestimation of Extreme Values," Policy Research Working Paper Series 10013, The World Bank.
    3. Cuesta, Jose & Chagalj, Cristian, 2019. "Measuring poverty with administrative data in data deprived contexts: The case of Nicaragua," Economics Letters, Elsevier, vol. 183(C), pages 1-1.
    4. Dang,Hai-Anh H., 2018. "To impute or not to impute ? a review of alternative poverty estimation methods in the context of unavailable consumption data," Policy Research Working Paper Series 8403, The World Bank.
    5. Dang,Hai-Anh H. & Verme,Paolo, 2019. "Estimating Poverty for Refugee Populations : Can Cross-Survey Imputation Methods Substitute for Data Scarcity ?," Policy Research Working Paper Series 9076, The World Bank.
    6. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.

    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. Jose Cuesta & Gabriel Lara Ibarra, 2018. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, March.
    2. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    3. Hai‐Anh Dang & Dean Jolliffe & Calogero Carletto, 2019. "Data Gaps, Data Incomparability, And Data Imputation: A Review Of Poverty Measurement Methods For Data‐Scarce Environments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 757-797, July.
    4. Cuesta, Jose & Chagalj, Cristian, 2019. "Measuring poverty with administrative data in data deprived contexts: The case of Nicaragua," Economics Letters, Elsevier, vol. 183(C), pages 1-1.
    5. Caroline Krafft & Ragui Assaad & Hanan Nazier & Racha Ramadan & Atiyeh Vahidmanesh & Sami Zouari, 2019. "Estimating poverty and inequality in the absence of consumption data: an application to the Middle East and North Africa," Middle East Development Journal, Taylor & Francis Journals, vol. 11(1), pages 1-29, January.
    6. Dang,Hai-Anh H., 2018. "To impute or not to impute ? a review of alternative poverty estimation methods in the context of unavailable consumption data," Policy Research Working Paper Series 8403, The World Bank.
    7. Hai-Anh H. Dang & Peter F. Lanjouw & Umar Serajuddin, 2017. "Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country," Oxford Economic Papers, Oxford University Press, vol. 69(4), pages 939-962.
    8. Dang, Hai-Anh H. & Serajuddin, Umar, 2020. "Tracking the sustainable development goals: Emerging measurement challenges and further reflections," World Development, Elsevier, vol. 127(C).
    9. Dang, Hai-Anh H. & Verme, Paolo, 2019. "Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity?," GLO Discussion Paper Series 429, Global Labor Organization (GLO).
    10. Theresa Beltramo & Hai-Anh H. Dang & Ibrahima Sarr & Paolo Verme, 2020. "Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad," Working Papers 536, ECINEQ, Society for the Study of Economic Inequality.
    11. Hai-Anh H. Dang & Peter F. Lanjouw, 2018. "Poverty Dynamics in India between 2004 and 2012: Insights from Longitudinal Analysis Using Synthetic Panel Data," Economic Development and Cultural Change, University of Chicago Press, vol. 67(1), pages 131-170.
    12. Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar & Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
    13. Atamanov, Aziz & Tandon, Sharad & Lopez-Acevedo, Gladys & Vergara Bahena, Mexico Alberto, 2020. "Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region: Data Gaps and Different Options to Address Them," IZA Discussion Papers 13363, Institute of Labor Economics (IZA).
    14. Astrid Mathiassen & Bjørn K. Wold, 2019. "Challenges in predicting poverty trends using survey to survey imputation. Experiences from Malawi," Discussion Papers 900, Statistics Norway, Research Department.
    15. Thomas Pave Sohnesen & Niels Stender, 2017. "Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment," Poverty & Public Policy, John Wiley & Sons, vol. 9(1), pages 118-133, March.
    16. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    17. Dang, Hai-Anh & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    18. Djavad Salehi-Isfahani & Nadia Belhaj Hassine, 2012. "Equality of Opportunity in Education in the Middle East and North Africa," Working Papers e07-33, Virginia Polytechnic Institute and State University, Department of Economics.
    19. Ferreira, Francisco H. G. & Peragine, Vito, 2015. "Equality of Opportunity: Theory and Evidence," IZA Discussion Papers 8994, Institute of Labor Economics (IZA).
    20. Assaad, Ragui & Hendy, Rana & Salehi-Isfahani, Djavad, 2019. "Inequality of opportunity in educational attainment in the Middle East and North Africa: Evidence from household surveys," International Journal of Educational Development, Elsevier, vol. 66(C), pages 24-43.

    More about this item

    Keywords

    Poverty; cross-survey imputation; macro projections; Tunisia; residuals–based imputation;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:jid:journl:y:2017:v:25:i:1:p:1-30. 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: Timm Boenke (email available below). General contact details of provider: https://edirc.repec.org/data/gyorkca.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.