IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v27y2022i2d10.1007_s13253-021-00476-w.html
   My bibliography  Save this article

Sample Design and Estimation When Using a Web-Scraped List Frame and Capture-Recapture Methods

Author

Listed:
  • Linda J. Young

    (USDA National Agricultural Statistics Service)

  • Michael Jacobsen

    (USDA National Agricultural Statistics Service)

Abstract

Surveys are often based on a sample drawn from a list frame. In recent years, the percentage of target population units on the list frames has been decreasing, making it important to adjust for this undercoverage in the estimation process. Multiple-frame methods generally assume that the union of the available list frames is equal to the target population; however, this assumption is often not satisfied, especially for hard-to-survey populations. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service has explored the use of web-scraped list frames to assess undercoverage of the NASS list frame, which is comprised of all known farms and potential farms in the USA. In 2020, NASS conducted the National Farmers Market Mangers Survey. Because NASS does not include farmers markets on its list frame, the USDA Agricultural Marketing Service (AMS) business register of farmers markets was the only list frame initially available. To assess its undercoverage, a web-scraped list frame was developed, and capture-recapture methods provided the foundation for estimation. This study made two advances in the use of capture-recapture methods when conducting a survey with two list frames. First, because record linkage was conducted prior to drawing the samples, the sample design incorporated information identifying records on only the AMS business register, on only the web-scraped list frame, or on both frames. Second, a composite estimator for this overlap design allowed full use of all sample information to produce survey estimates. Directions for future research are highlighted. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Linda J. Young & Michael Jacobsen, 2022. "Sample Design and Estimation When Using a Web-Scraped List Frame and Capture-Recapture Methods," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 261-279, June.
  • Handle: RePEc:spr:jagbes:v:27:y:2022:i:2:d:10.1007_s13253-021-00476-w
    DOI: 10.1007/s13253-021-00476-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-021-00476-w
    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/s13253-021-00476-w?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Alberto Cavallo, 2018. "Scraped Data and Sticky Prices," The Review of Economics and Statistics, MIT Press, vol. 100(1), pages 105-119, March.
    2. Shirley Pledger, 2000. "Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures," Biometrics, The International Biometric Society, vol. 56(2), pages 434-442, June.
    3. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    4. Lohr, Sharon & Rao, J.N.K., 2006. "Estimation in Multiple-Frame Surveys," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1019-1030, September.
    5. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
    6. Abreu, Denise A. & McCarthy, Jaki S. & Colburn, Leslie A., 2010. "Impact of the Screening Procedures of the June Area Survey on the Number of Farms Estimates," NASS Research Reports 234374, United States Department of Agriculture, National Agricultural Statistics Service.
    7. Linda J. Young & Andrea C. Lamas & Denise A. Abreu, 2017. "The 2012 Census of Agriculture: A Capture–Recapture Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 523-539, December.
    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. Yabu, Takuya, 2023. "On Discrete Probability Distributions to Grasp the Number of Samples in a Population," OSF Preprints yv24f, Center for Open Science.

    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. Fernando Alvarez & Francesco Lippi & Aleksei Oskolkov, 2022. "The Macroeconomics of Sticky Prices with Generalized Hazard Functions [“Optimal Inattention to the Stock Market With Information Costs and Transactions Costs,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 989-1038.
    2. David Staines, 2023. "Stochastic Equilibrium the Lucas Critique and Keynesian Economics," Papers 2312.16214, arXiv.org, revised Jun 2024.
    3. Kanika Mahajan & Shekhar Tomar, 2021. "COVID‐19 and Supply Chain Disruption: Evidence from Food Markets in India†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 35-52, January.
    4. Robert Sinclair & Jess Diamond, 2022. "Basic food and drink price distributions transcend time and culture," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-7, December.
    5. Fernando Alvarez & Francesco Lippi & Aleksei Oskolkov, 2023. "The Macroeconomics of Sticky Prices with Generalized Hazard Functions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(2), pages 989-1038.
    6. Alberto Cavallo & Francesco Lippi & Ken Miyahara, 2024. "Large Shocks Travel Fast," American Economic Review: Insights, American Economic Association, vol. 6(4), pages 558-574, December.
    7. Alexey S. Evseev & Rodion R. Latypov & Egor A. Postolit & Elena S. Sinelnikova-Muryleva, 2022. "Техданные О Ценах Онлайн-Ритейлеров Обладают Огромной Ценностью С Точки Зрения Экономической Науки: Их Использование Позволяет Уточнять Прогнозы Инфляции И Предвосхищать Будущие Тенденции В Моменте, К," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 11, pages 36-45, November.
    8. Doron Sayag & Avichai Snir & Daniel Levy, 2024. "Small Price Changes, Sales Volume, and Menu Cost," Papers 2403.07166, arXiv.org.
    9. Bogner Alexandra & Jerger Jürgen, 2023. "Big data in monetary policy analysis—a critical assessment," Economics and Business Review, Sciendo, vol. 9(2), pages 27-40, April.
    10. Kanika Mahajan & Shekhar Tomar, 2020. "Here Today, Gone Tomorrow: COVID-19 and Supply Chain Disruptions," Working Papers 28, Ashoka University, Department of Economics.
    11. Stéphane Dupraz, 2024. "A Kinked‐Demand Theory of Price Rigidity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(2-3), pages 325-363, March.
    12. Alberto Cavallo & Francesco Lippi & Ken Miyahara, 2023. "Large Shocks Travel Fast," NBER Working Papers 31659, National Bureau of Economic Research, Inc.
    13. Aparicio, Diego & Rigobon, Roberto, 2023. "Quantum prices," Journal of International Economics, Elsevier, vol. 143(C).
    14. Alexey S. Evseev & Rodion R. Latypov & Egor A. Postolit & Elena S. Sinelnikova-Muryleva, 2022. "Technical and Methodological Challenges of Collecting Price Data from Online Retailers [Технические И Методологические Проблемы Сбора Данных О Ценах Онлайн-Ритейлеров]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 11, pages 36-45, November.
    15. Hillen, Judith, 2018. "Web Scraping For Food Price Research," 58th Annual Conference, Kiel, Germany, September 12-14, 2018 275840, German Association of Agricultural Economists (GEWISOLA).
    16. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    17. Diego Aparicio & Alberto Cavallo, 2021. "Targeted Price Controls on Supermarket Products," The Review of Economics and Statistics, MIT Press, vol. 103(1), pages 60-71, March.
    18. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    19. Robert Laskowski, 2022. "Differences between Online Prices and the Consumer Prices Index During Covid-19 in Germany," ACTA VSFS, University of Finance and Administration, vol. 16(1), pages 76-87.
    20. Diego Aparicio & Roberto Rigobon, 2020. "Quantum Prices," NBER Working Papers 26646, National Bureau of Economic Research, Inc.

    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:jagbes:v:27:y:2022:i:2:d:10.1007_s13253-021-00476-w. 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.