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Search and active learning with correlated information: Empirical evidence from mid-Atlantic clam fishermen

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  • Marcoul, Philippe
  • Weninger, Quinn

Abstract

This paper examines search with active learning and correlated information. We first develop a simple model to show how correlation affects the decision to acquire information. A unique data set on fishing site choice by mid-Atlantic clam fishermen is used to test the model predictions. Results find that clam fishermen search new sites when the catch at familiar sites declines, i.e., when the opportunity cost of gathering information is low, and also when catch at familiar sites is on the rise. Search following a catch decline occurs at spatially distant sites whereas search following a catch increase occurs at nearby sites. Correlated learning is crucial for explaining the site choice patterns in our data. These results provide new insights that may extend to a variety of economic search problems where correlated learning is important.

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  • Marcoul, Philippe & Weninger, Quinn, 2008. "Search and active learning with correlated information: Empirical evidence from mid-Atlantic clam fishermen," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1921-1948, June.
  • Handle: RePEc:eee:dyncon:v:32:y:2008:i:6:p:1921-1948
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    Cited by:

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    2. Andrew T. Ching & Hyunwoo Lim, 2020. "A Structural Model of Correlated Learning and Late-Mover Advantages: The Case of Statins," Management Science, INFORMS, vol. 66(3), pages 1095-1123, March.
    3. Weninger, Quinn, 2012. "Search and Learning in the Alaskan Halibut Fishery: Overview of the Field Data Collection," Staff General Research Papers Archive 34759, Iowa State University, Department of Economics.
    4. In Chang Hwang & Richard S. J. Tol & Marjan W. Hofkes, 2019. "Active Learning and Optimal Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1237-1264, August.
    5. John Lynham, 2017. "Identifying Peer Effects Using Gold Rushers," Land Economics, University of Wisconsin Press, vol. 93(3), pages 527-548.
    6. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    7. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    8. Matthew N. Reimer & Joshua K. Abbott & James E. Wilen, 2014. "Unraveling the Multiple Margins of Rent Generation from Individual Transferable Quotas," Land Economics, University of Wisconsin Press, vol. 90(3), pages 538-559.
    9. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    10. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    11. Li, Shengyu, 2018. "A structural model of productivity, uncertain demand, and export dynamics," Journal of International Economics, Elsevier, vol. 115(C), pages 1-15.
    12. Hutniczak, Barbara & Münch, Angela, 2018. "Fishermen's location choice under spatio-temporal update of expectations," Journal of choice modelling, Elsevier, vol. 28(C), pages 124-136.

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