IDEAS home Printed from https://ideas.repec.org/p/ags/aaae16/246280.html
   My bibliography  Save this paper

Cash Transfer Programmes for Managing Climate Risk: Evidence from a Randomized Experiment in Zambia

Author

Listed:
  • Asfaw, Solomon
  • Carraro, Alessandro
  • Davis, Benjamin
  • Handa, Sudhanshu
  • Seidenfeld, David

Abstract

Cash transfer programmes are increasingly being utilized in order to combat poverty and hunger as well as to building the human capital of future generations. Even though most of these programmes are not explicitly designed to help households manage climate risk, there are good reasons to expect that cash transfers can be good instrument to build household resilience against climatic risk. The goal of this study is to provide an empirical analysis of the effect of weather risk on rural households’ welfare using impact evaluation data from the Zambia Child Grant Programme (CGP) together with set of novel weather variation indicators based on interpolated gridded and re-analysis weather data that capture the peculiar features of short term and long term variations in rainfall. In particular, we estimate the impact of weather shocks on a rich set of welfare and food security indicators (including total expenditure, food expenditure, non-food expenditure, calorie intake and dietary diversity) and investigate the role of cash transfer for managing climate risk. We find strong evidence that cash transfer programmes has a mitigating role against the negative effects of weather shocks. Our results in fact highlight how important the receipt of social cash transfer is for households lying in the bottom quantile of consumption and food security distributions in moderating the negative effect of weather shock. Hence, integrating climate change and social protection tools into a comprehensive poverty reduction and social protection strategy should be of primary interest for policy makers and government when setting their policy agenda.

Suggested Citation

  • Asfaw, Solomon & Carraro, Alessandro & Davis, Benjamin & Handa, Sudhanshu & Seidenfeld, David, 2016. "Cash Transfer Programmes for Managing Climate Risk: Evidence from a Randomized Experiment in Zambia," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246280, African Association of Agricultural Economists (AAAE).
    • Handle: RePEc:ags:aaae16:246280
    DOI: 10.22004/ag.econ.246280
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/246280/files/58.%20Cash%20transfer%20and%20weather%20risk_Zambia.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.246280?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. Angus Deaton & Christina Paxson, 1998. "Economies of Scale, Household Size, and the Demand for Food," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 897-930, October.
    2. Silvio Daidone & Luca Pellerano & Sudhanshu Handa & Benjamin Davis, 2015. "Is Graduation from Social Safety Nets Possible? Evidence from Sub‐Saharan Africa," IDS Bulletin, Blackwell Publishing, vol. 46(2), pages 93-102, March.
    3. Skoufias, Emmanuel, 2003. "Economic Crises and Natural Disasters: Coping Strategies and Policy Implications," World Development, Elsevier, vol. 31(7), pages 1087-1102, July.
    4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    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. Tancrède Voituriez, 2020. "The quest for green welfare state in developing countries," Working Papers hal-02567919, HAL.
    2. Fanzo, Jessica & McLaren, Rebecca & Davis, Claire & Choufani, Jowel, 2017. "Climate change and variability: What are the risks for nutrition, diets, and food systems?," IFPRI discussion papers 1645, International Food Policy Research Institute (IFPRI).
    3. Tancrède Voituriez, 2020. "The quest for green welfare state in developing countries," Working Papers hal-02876972, HAL.
    4. Chonabayashi, Shun & Jithitikulchai, Theepakorn & Qu, Yeqing, 2020. "Does agricultural diversification build economic resilience to drought and flood? Evidence from poor households in Zambia," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 15(1), March.
    5. Tancrède Voituriez, 2020. "The quest for green welfare state in developing countries," World Inequality Lab Working Papers hal-02876972, HAL.
    6. Ana Maria Loboguerrero & Bruce M. Campbell & Peter J. M. Cooper & James W. Hansen & Todd Rosenstock & Eva Wollenberg, 2019. "Food and Earth Systems: Priorities for Climate Change Adaptation and Mitigation for Agriculture and Food Systems," Sustainability, MDPI, vol. 11(5), pages 1-26, March.
    7. Hansen, James & Hellin, Jon & Rosenstock, Todd & Fisher, Eleanor & Cairns, Jill & Stirling, Clare & Lamanna, Christine & van Etten, Jacob & Rose, Alison & Campbell, Bruce, 2019. "Climate risk management and rural poverty reduction," Agricultural Systems, Elsevier, vol. 172(C), pages 28-46.

    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. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    2. Zhao, Zhibiao & Wu, Wei Biao, 2009. "Nonparametric inference of discretely sampled stable Lévy processes," Journal of Econometrics, Elsevier, vol. 153(1), pages 83-92, November.
    3. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    4. Asongu, Simplice A. & Odhiambo, Nicholas M., 2021. "Inequality, finance and renewable energy consumption in Sub-Saharan Africa," Renewable Energy, Elsevier, vol. 165(P1), pages 678-688.
    5. Masato Abe & Linghe Ye, 2012. "The impacts of natural disasters on global supply chains," Working Papers 11512, Asia-Pacific Research and Training Network on Trade (ARTNeT), an initiative of UNESCAP and IDRC, Canada..
    6. Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
    7. repec:hal:wpspec:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    8. Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2009. "Copula-based nonlinear quantile autoregression," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 50-67, January.
    9. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    10. Abeliansky, Ana & Krenz, Astrid, 2015. "Democracy and international trade: Differential effects from a panel quantile regression framework," University of Göttingen Working Papers in Economics 243, University of Goettingen, Department of Economics.
    11. Cheung, Chau-kiu & Ng, Sik Hung, 2012. "Impacts of financial crisis on social engagement in Hong Kong," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 41(5), pages 623-632.
    12. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    13. Mayya Zhilova, 2015. "Simultaneous likelihood-based bootstrap confidence sets for a large number of models," SFB 649 Discussion Papers SFB649DP2015-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    15. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    16. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    17. Houssa, Romain & Verpoorten, Marijke, 2015. "The Unintended Consequence of an Export Ban: Evidence from Benin’s Shrimp Sector," World Development, Elsevier, vol. 67(C), pages 138-150.
    18. Deborah A. Cobb-Clark & Sonja C. Kassenboehmer & Mathias G. Sinning, 2013. "Locus of Control and Savings," Ruhr Economic Papers 0455, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    19. Francis Teal, 2006. "Consumption and welfare in Ghana in the 1990s," Journal of Development Studies, Taylor & Francis Journals, vol. 42(7), pages 1252-1269.
    20. Qi Li & Juan Lin & Jeffrey S. Racine, 2013. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 57-65, January.
    21. Klaus Friesenbichler, 2013. "Firm Growth in Conflict Countries: Some Evidence from South Asia," Review of Economics & Finance, Better Advances Press, Canada, vol. 3, pages 33-44, May.

    More about this item

    Keywords

    Agricultural Finance; Financial Economics; Research Methods/ Statistical Methods;
    All these keywords.

    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:aaae16:246280. 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.