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Fair trade and wellbeing improvements - evidence from Sri Lanka

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  • Hannah Holmes
  • Katsushi Imai

Abstract

This paper investigates whether and how Fair Trade certification improves the wellbeing of small-scale producers by drawing upon a field study carried out by the authors in 2009 in the Central Province of Sri Lanka. A point of departure from earlier studies is to use a mixed methods approach, combining qualitative and quantitative data to assess the impact of Fair Trade on a broader set of development indicators to capture both the monetary and non-monetary progress of farmers. Methodologically, to overcome the limitation of small sample sizes of non-experimental survey data, we propose the use of propensity-score weighted linear and non-linear regression models with and without instrumenting the farmers’ participation in Fair Trade. Here we have made treatment and control groups observationally comparable by applying propensity score matching (PSM) to match and weight the data, following Hirano and Imbens. We have found that Fair Trade certification increased farmers’ actual income from tea production significantly, with fewer hours of work per day and accelerated perceived improvement in overall household income, as well as empowering women in decision making. Our mixed methods approach led us to conclude that Fair Trade certification benefits Fair Trade tea farmers through increased tea income and risk reduction.

Suggested Citation

  • Hannah Holmes & Katsushi Imai, 2019. "Fair trade and wellbeing improvements - evidence from Sri Lanka," Global Development Institute Working Paper Series 402019, GDI, The University of Manchester.
  • Handle: RePEc:bwp:bwppap:402019
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    File URL: https://www.gdi.manchester.ac.uk/research/publications/gdi-working-papers/2019-040/
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    References listed on IDEAS

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    1. Raluca Dragusanu & Daniele Giovannucci & Nathan Nunn, 2014. "The Economics of Fair Trade," Journal of Economic Perspectives, American Economic Association, vol. 28(3), pages 217-236, Summer.
    2. Beuchelt, Tina D. & Zeller, Manfred, 2011. "Profits and poverty: Certification's troubled link for Nicaragua's organic and fairtrade coffee producers," Ecological Economics, Elsevier, vol. 70(7), pages 1316-1324, May.
    3. Alkire, Sabina & Foster, James, 2011. "Counting and multidimensional poverty measurement," Journal of Public Economics, Elsevier, vol. 95(7), pages 476-487.
    4. Matias Busso & John DiNardo & Justin McCrary, 2014. "New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 885-897, December.
    5. Eric W. Djimeu & Deo-Gracias Houndolo, 2016. "Power calculation for causal inference in social science: sample size and minimum detectable effect determination," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 8(4), pages 508-527, October.
    6. Raluca Dragusanu & Eduardo Montero & Nathan Nunn, 2022. "The Effects of Fair Trade Certification: Evidence from Coffee Producers in Costa Rica," Journal of the European Economic Association, European Economic Association, vol. 20(4), pages 1743-1790.
    7. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    8. Christopher Cramer & Deborah Johnston & Bernd Mueller & Carlos Oya & John Sender, 2017. "Fairtrade and Labour Markets in Ethiopia and Uganda," Journal of Development Studies, Taylor & Francis Journals, vol. 53(6), pages 841-856, June.
    9. Leonardo Becchetti & Marco Costantino, 2006. "The effects of Fair Trade on marginalised producers: an impact analysis on Kenyan farmers," Working Papers 41, ECINEQ, Society for the Study of Economic Inequality.
    10. Bacon, Christopher, 2005. "Confronting the Coffee Crisis: Can Fair Trade, Organic, and Specialty Coffees Reduce Small-Scale Farmer Vulnerability in Northern Nicaragua?," World Development, Elsevier, vol. 33(3), pages 497-511, March.
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    Cited by:

    1. Suchi Kapoor Malhotra & Swati Mantri & Neha Gupta & Ratika Bhandari & Ralph Nii Armah & Hamdiyah Alhassan & Sarah Young & Howard White & Ranjitha Puskur & Hugh Sharma Waddington & Edoardo Masset, 2024. "Value chain interventions for improving women's economic empowerment: A mixed‐methods systematic review and meta‐analysis," Campbell Systematic Reviews, John Wiley & Sons, vol. 20(3), September.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q17 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agriculture in International Trade

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