IDEAS home Printed from https://ideas.repec.org/p/ags/aaae16/246977.html
   My bibliography  Save this paper

The role of maize storage in stabilizing annual household maize consumption: an application of generalized propensity score matching

Author

Listed:
  • Kariuki, Sarah W.
  • De Groote, Hugo
  • Ndegwa, Michael K.

Abstract

Rural households in developing countries face yield risks and seasonal production amidst the desire for stable household consumption. While storage has been cited as one of the ways of smoothing consumption during the lean periods, there is little empirical evidence on the subject. The current study used a generalized propensity score approach to examine the impact of storage on maize consumption smoothing. Maize was found to be the main crop, mostly grown for home consumption. The amount bought increased during the leaner periods when the prices were higher. In addition, the coefficient of variation for total maize consumption for decreased with increase in the length of storage, indicating that indeed storage helps to smoothen consumption across the year and consequently improve household food security.

Suggested Citation

  • Kariuki, Sarah W. & De Groote, Hugo & Ndegwa, Michael K., 2016. "The role of maize storage in stabilizing annual household maize consumption: an application of generalized propensity score matching," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246977, African Association of Agricultural Economists (AAAE).
    • Handle: RePEc:ags:aaae16:246977
    DOI: 10.22004/ag.econ.246977
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/246977/files/312.%20Maize%20storage%20in%20Kenya.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.246977?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
    ---><---

    References listed on IDEAS

    as
    1. Deaton, Angus, 1992. " Household Saving in LDCs: Credit Markets, Insurance and Welfare," Scandinavian Journal of Economics, Wiley Blackwell, vol. 94(2), pages 253-273.
    2. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," IZA Discussion Papers 3255, Institute of Labor Economics (IZA).
    3. Albert Park, 2006. "Risk and Household Grain Management in Developing Countries," Economic Journal, Royal Economic Society, vol. 116(514), pages 1088-1115, October.
    4. Ndegwa, Michael & De Groote, Hugo & Gitonga, Zachary & Bruce, Anani, 2015. "Effectiveness and Economics of Hermetic Bags for Maize Storage: Results of a Randomized Controlled Trial in Kenya," 2015 Conference, August 9-14, 2015, Milan, Italy 212524, International Association of Agricultural Economists.
    5. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    6. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    Full references (including those not matched with items on IDEAS)

    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. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Hilal Atasoy & Rajiv D. Banker & Paul A. Pavlou, 2016. "On the Longitudinal Effects of IT Use on Firm-Level Employment," Information Systems Research, INFORMS, vol. 27(1), pages 6-26, March.
    4. Rüdiger Wapler & Daniel Werner & Katja Wolf, 2018. "Active labour market policies in Germany: do regional labour markets benefit?," Applied Economics, Taylor & Francis Journals, vol. 50(51), pages 5561-5578, November.
    5. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Chung Choe & Alfonso Flores-Lagunes & Sang-Jun Lee, 2015. "Do dropouts with longer training exposure benefit from training programs? Korean evidence employing methods for continuous treatments," Empirical Economics, Springer, vol. 48(2), pages 849-881, March.
    7. Ida D'Attoma & Silvia Pacei, 2018. "Evaluating the Effects of Product Innovation on the Performance of European Firms by Using the Generalised Propensity Score," German Economic Review, Verein für Socialpolitik, vol. 19(1), pages 94-112, February.
    8. Sylvester Amoako Agyemang & Tomáš Ratinger & Miroslava Bavorová, 2022. "The Impact of Agricultural Input Subsidy on Productivity: The Case of Ghana," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(3), pages 1460-1485, June.
    9. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    10. Carina Steckenleiter & Michael Lechner & Tim Pawlowski & Ute Schüttoff, 2023. "Do local expenditures on sports facilities affect sports participation?," Economic Inquiry, Western Economic Association International, vol. 61(4), pages 1103-1128, October.
    11. Steckenleiter, Carina & Lechner, Michael & Pawlowski, Tim & Schüttoff, Ute, 2019. "Do local public expenditures on sports facilities affect sports participation in Germany?," Economics Working Paper Series 1905, University of St. Gallen, School of Economics and Political Science.
    12. A. Giffin & B. J. Reich & S. Yang & A. G. Rappold, 2023. "Generalized propensity score approach to causal inference with spatial interference," Biometrics, The International Biometric Society, vol. 79(3), pages 2220-2231, September.
    13. Emiliano Magrini & Pierluigi Montalbano & Silvia Nenci, 2013. "Are the EU trade preferences really effective? A Generalized Propensity Score evaluation of the Southern Mediterranean Countries' case in agriculture and fishery," Working Papers 2/13, Sapienza University of Rome, DISS.
    14. Jie Zhu & Blanca Gallego, 2021. "Continuous Treatment Recommendation with Deep Survival Dose Response Function," Papers 2108.10453, arXiv.org, revised Sep 2023.
    15. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.
    16. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    17. Guadalupe Serrano-Domingo & Francisco Requena-Silvente, 2013. "Examining the non-linear relationship between migration and trade," Working Papers 1310, Department of Applied Economics II, Universidad de Valencia.
    18. Wen Ci & Jose Galdo & Marcel Voia & Christopher Worswick, 2015. "Wage returns to mid-career investments in job training through employer supported course enrollment: evidence for Canada," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-25, December.
    19. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    20. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae (Simon) Lee, 2014. "The identification power of smoothness assumptions in models with counterfactual outcomes," CeMMAP working papers 17/14, Institute for Fiscal Studies.

    More about this item

    Keywords

    Crop Production/Industries; Farm Management;

    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:aaae16:246977. 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/aaaeaea.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.