IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i12p2042-d1532310.html
   My bibliography  Save this article

Exploring the Effects of Farmland Transfer on Farm Household Well-Being: Evidence from Ore–Agriculture Compound Areas in Northwest China

Author

Listed:
  • Xueping Li

    (School of Economics and Management, Weinan Normal University, Weinan 714099, China
    Soft Science Research Base of Shaanxi Agricultural and Rural Modernization, Weinan 714099, China)

  • Xingmin Shi

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

  • Yuhan Qin

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China)

Abstract

Due to long-term interactions between intensive resource exploitation and rapid social development, there are multiple challenges to maintaining and improving the well-being of farm households in ore–agriculture compound areas in Northwest China. However, few studies have focused on the effects of farmland transfer on farm household comprehensive well-being. This study collected 485 valid questionnaires through a structured questionnaire technique and then compared the well-being level and its five components between farm households who participated and did not participate in farmland transfer based on an index system of well-being. Further, a propensity score matching (PSM) method was used to estimate the net effects of farmland transfer on farm household well-being and its heterogeneity. The results showed the following. (1) Overall, farm household well-being in ore–agriculture compound areas in Northwest China was at a moderate level (mean value was 0.433), but there were large differences among its five components. The orders of the five components of well-being in the three study sites were consistent, and the well-being index of farm households participating in farmland transfer was generally greater than that of those not participating in farmland transfer. (2) The results of the PSM revealed that farmland transfer only increased the levels of well-being, security, and freedom of choice and action by 4.9%, 8.8% and 6.1%, respectively. (3) The younger the household heads and the higher their education levels, the greater the effects of farmland transfer on farm household well-being. Local government sectors should continue to improve their farmland transfer system and strengthen institutional innovation. Meanwhile, venerable groups’ well-being should be paid more attention in the process of farmland transfer.

Suggested Citation

  • Xueping Li & Xingmin Shi & Yuhan Qin, 2024. "Exploring the Effects of Farmland Transfer on Farm Household Well-Being: Evidence from Ore–Agriculture Compound Areas in Northwest China," Land, MDPI, vol. 13(12), pages 1-23, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2042-:d:1532310
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/12/2042/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/12/2042/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tongwei Qiu & Qinying He & Biliang Luo, 2021. "Does land renting-out increase farmers’ subjective well-being? Evidence from rural China," Applied Economics, Taylor & Francis Journals, vol. 53(18), pages 2080-2092, April.
    2. Paul R. Rosenbaum, 2015. "Bahadur Efficiency of Sensitivity Analyses in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 205-217, March.
    3. Baomin Cui & Lingling Tang & Jianxu Liu & Songsak Sriboonchitta, 2023. "How Does Land Transfer Impact the Household Labor Productivity in China? Empirical Evidence from Survey Data in Shandong," Land, MDPI, vol. 12(4), pages 1-24, April.
    4. Kaili Peng & Chen Yang & Yao Chen, 2020. "Land transfer in rural China: incentives, influencing factors and income effects," Applied Economics, Taylor & Francis Journals, vol. 52(50), pages 5477-5490, October.
    5. Sollis, Kate & Yap, Mandy & Campbell, Paul & Biddle, Nicholas, 2022. "Conceptualisations of wellbeing and quality of life: A systematic review of participatory studies," World Development, Elsevier, vol. 160(C).
    6. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    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. Tan, Jing & Cai, Dongliang & Han, Kefei & Zhou, Kui, 2022. "Understanding peasant household’s land transfer decision-making: A perspective of financial literacy," Land Use Policy, Elsevier, vol. 119(C).
    2. Ruoqi Yu, 2021. "Evaluating and improving a matched comparison of antidepressants and bone density," Biometrics, The International Biometric Society, vol. 77(4), pages 1276-1288, December.
    3. Sven Resnjanskij & Jens Ruhose & Simon Wiederhold & Ludger Wößmann, 2021. "Mentoring verbessert die Arbeitsmarktchancen von stark benachteiligten Jugendlichen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 74(02), pages 31-38, February.
    4. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    6. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    7. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    8. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    9. Bonesrønning, Hans & Finseraas, Henning & Hardoy, Ines & Iversen, Jon Marius Vaag & Nyhus, Ole Henning & Opheim, Vibeke & Salvanes, Kari Vea & Sandsør, Astrid Marie Jorde & Schøne, Pål, 2022. "Small-group instruction to improve student performance in mathematics in early grades: Results from a randomized field experiment," Journal of Public Economics, Elsevier, vol. 216(C).
    10. Peydró, José-Luis & Jiménez, Gabriel & Kenan, Huremovic & Moral-Benito, Enrique & Vega-Redondo, Fernando, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," CEPR Discussion Papers 15277, C.E.P.R. Discussion Papers.
    11. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    12. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    13. Konrad Menzel, 2021. "Structural Sieves," Papers 2112.01377, arXiv.org, revised Apr 2022.
    14. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    15. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    16. Andrés Elberg & Pedro M. Gardete & Rosario Macera & Carlos Noton, 2019. "Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 1-58, March.
    17. Suresh de Mel & David McKenzie & Christopher Woodruff, 2019. "Labor Drops: Experimental Evidence on the Return to Additional Labor in Microenterprises," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 202-235, January.
    18. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    19. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    20. Jeon, Sung-Hee & Pohl, R. Vincent, 2019. "Medical innovation, education, and labor market outcomes of cancer patients," Journal of Health Economics, Elsevier, vol. 68(C).

    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:gam:jlands:v:13:y:2024:i:12:p:2042-:d:1532310. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.