[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ags/iaae15/211850.html
   My bibliography  Save this paper

Same Question but Different Answer Experimental Evidence on Questionnaire Design's Impact on Pverty Measured by Proxies

Author

Listed:
  • Kilic, Talip
  • Sohnesen, Thomas
Abstract
Does the same question asked of the same population yield the same answer in face-to-face interviews when other parts of the questionnaire are altered? If not, what would be the implications for proxy-based poverty measurement? Relying on a randomized household survey experiment implemented in Malawi, this study finds that observationally equivalent as well as same households answer the same questions differently when interviewed with a short questionnaire versus the longer counterpart that, in a prior survey round, would have informed the prediction model for a proxy-based poverty measurement exercise. The analysis yields statistically significant differences in reporting between the short and long questionnaires across all topics and types of questions. The reporting differences result in significantly different predicted poverty rates and Gini coefficients. While the difference in predictions ranges from approximately 3 to 7 percentage points depending on the model specification, restricting the proxies to those collected prior the variation in questionnaire design, namely demographic variables from the household roster and location fixed effects, leads to same predictions in both samples. The findings emphasize the need for further methodological research, and suggest that short questionnaires designed for proxy-based poverty measurement should be piloted, prior to implementation, in parallel with the longer questionnaire from which they have evolved. The fact that at the median it took 25 minutes to complete the food and non-food consumption sections in the long questionnaire also implies that the implementation of these sections might not be as overly costly as usually assumed.

Suggested Citation

  • Kilic, Talip & Sohnesen, Thomas, 2015. "Same Question but Different Answer Experimental Evidence on Questionnaire Design's Impact on Pverty Measured by Proxies," 2015 Conference, August 9-14, 2015, Milan, Italy 211850, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae15:211850
    DOI: 10.22004/ag.econ.211850
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/211850/files/Kilic-Same%20Question%20but%20Different%20Answer-156.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.211850?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
    2. Houssou, Nazaire & Zeller, Manfred, 2011. "To target or not to target? The costs, benefits, and impacts of indicator-based targeting," Food Policy, Elsevier, vol. 36(5), pages 626-636, October.
    3. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    4. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    5. Frauke Kreuter & Susan McCulloch & Stanley Presser & Roger Tourangeau, 2011. "The Effects of Asking Filter Questions in Interleafed Versus Grouped Format," Sociological Methods & Research, , vol. 40(1), pages 88-104, February.
    6. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    7. Mohamed Douidich & Abdeljaouad Ezzrari & Roy Van der Weide & Paolo Verme, 2016. "Estimating Quarterly Poverty Rates Using Labor Force Surveys: A Primer," The World Bank Economic Review, World Bank, vol. 30(3), pages 475-500.
    8. 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.
    9. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    10. Linh Vu & Bob Baulch, 2011. "Assessing Alternative Poverty Proxy Methods in Rural Vietnam," Oxford Development Studies, Taylor & Francis Journals, vol. 39(3), pages 339-367, September.
    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. Fiala, Nathan & Rose, Julian & Aryemo, Filder & Peters, Jörg, 2022. "The (very) long-run impacts of cash grants during a crisis," Ruhr Economic Papers 961, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Adan Silverio‐Murillo & Jose Roberto Balmori de la Miyar, 2022. "Remittances and domestic violence," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 2274-2295, November.
    3. Abay, Kibrom A. & Berhane, Guush & Hoddinott, John F. & Tafere, Kibrom, 2021. "Assessing response fatigue in phone surveys: Experimental evidence on dietary diversity in Ethiopia," IFPRI discussion papers 2017, International Food Policy Research Institute (IFPRI).
    4. Brown, Caitlin & Ravallion, Martin & van de Walle, Dominique, 2018. "A poor means test? Econometric targeting in Africa," Journal of Development Economics, Elsevier, vol. 134(C), pages 109-124.
    5. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, The World Bank.
    6. Jeong, Dahyeon & Aggarwal, Shilpa & Robinson, Jonathan & Kumar, Naresh & Spearot, Alan & Park, David Sungho, 2023. "Exhaustive or exhausting? Evidence on respondent fatigue in long surveys," Journal of Development Economics, Elsevier, vol. 161(C).
    7. repec:lic:licosd:41819 is not listed on IDEAS
    8. Dillon, Andrew & Mensah, Edouard, 2024. "Respondent biases in agricultural household surveys," Journal of Development Economics, Elsevier, vol. 166(C).
    9. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    10. Deepti Sharma & Hema Swaminathan & Rahul Lahoti, 2024. "Does it matter who you ask for time-use data?," WIDER Working Paper Series wp-2024-1, World Institute for Development Economic Research (UNU-WIDER).
    11. Dang, Hai-Anh & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," GLO Discussion Paper Series 1392, Global Labor Organization (GLO).
    12. 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.
    13. Ligon, Ethan & Christiaensen, Luc & Sohnesen, Thomas P, 2020. "Should Consumption Sub-Aggregates be Used to Measure Poverty?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9b9929jh, Department of Agricultural & Resource Economics, UC Berkeley.
    14. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    15. Masselus, Lise & Fiala, Nathan, 2024. "Whom to ask? Testing respondent effects in household surveys," Journal of Development Economics, Elsevier, vol. 168(C).
    16. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.
    17. Fiala, Nathan & Masselus, Lise, 2022. "Whom to ask? Testing respondent effects in household surveys," Ruhr Economic Papers 935, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    18. 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.

    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. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    2. Dang, Hai-Anh H & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    3. 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.
    4. 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.
    5. Hai-Anh H. Dang & Peter F. Lanjouw, 2023. "Regression-based imputation for poverty measurement in data-scarce settings," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 13, pages 141-150, Edward Elgar Publishing.
    6. Dang, Hai-Anh H & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    7. 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.
    8. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    9. Lain,Jonathan William & Schoch,Marta & Vishwanath,Tara, 2022. "Estimating a Poverty Trend for Nigeria between 2009 and 2019," Policy Research Working Paper Series 9974, The World Bank.
    10. World Bank, 2015. "Tanzania Poverty Assessment," World Bank Publications - Reports 21871, The World Bank Group.
    11. F. Clementi & A. L. Dabalen & V. Molini & F. Schettino, 2017. "When the Centre Cannot Hold: Patterns of Polarization in Nigeria," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 608-632, December.
    12. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
    13. 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.
    14. Dang, Hai-Anh & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," GLO Discussion Paper Series 1445, Global Labor Organization (GLO).
    15. Ligon, Ethan & Christiaensen, Luc & Sohnesen, Thomas P, 2020. "Should Consumption Sub-Aggregates be Used to Measure Poverty?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9b9929jh, Department of Agricultural & Resource Economics, UC Berkeley.
    16. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    17. Theresa Beltramo & Hai-Anh Dang & Ibrahima Sarr & Paolo Verme, 2024. "Estimating poverty among refugee populations: a cross-survey imputation exercise for Chad," Oxford Development Studies, Taylor & Francis Journals, vol. 52(1), pages 94-113, January.
    18. Ahmed, Faizuddin & Dorji, Cheku & Takamatsu, Shinya & Yoshida, Nobuo, 2014. "Hybrid survey to improve the reliability of poverty statistics in a cost-effective manner," Policy Research Working Paper Series 6909, The World Bank.
    19. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    20. Betti, Gianni & Molini, Vasco & Mori, Lorenzo, 2024. "An attempt to correct the underestimation of inequality measures in cross-survey imputation through generalized additive models for location, scale and shape," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

    More about this item

    Keywords

    Agricultural and Food Policy;

    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:ags:iaae15:211850. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.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.