IDEAS home Printed from https://ideas.repec.org/a/bla/growch/v55y2024i1ne12691.html
   My bibliography  Save this article

Do female labor‐migrated households have lower productivity? Empirical evidence from rural rice farms in Bangladesh

Author

Listed:
  • Md Nazirul Islam Sarker
  • Md Abdus Salam
  • R. B. Radin Firdaus

Abstract

The labor movement from rural areas and the remittance flow from migrants is a common household livelihood strategy in rural Bangladesh. While migration can offer economic benefits through remittances, it can be a source of hardship for migrants and their families due to societal culture. This study examines the differences in farm productivity and technical efficiency between female and male labor migrants by focusing on female and male laborers who have lived away from their homes for 6 months or more within the country and its reflection on farm production. Using data on 2271 rice plots from Bangladesh Integrated Households Survey in 2018, we estimate plot‐level stochastic meta‐frontier approach for households with female‐labor migrants and male‐labor migrants separately emphasizing technological difference and heteroskedastic technical efficiency. The empirical result shows that the female‐labor migrants' farms have 10.3% lower production frontier (maximum frontier yield) and 6.1% higher technical efficiency than male migrants' farms, indicating that they have 4.2% lower productivity. Lower production frontier reflects lower management ability and less attention to farm practice. Moreover, the study reveals that female‐labor migrants' farms are closer to the meta‐frontier, suggesting smaller technology gaps. However, some farmers failed to achieve the highest possible output in relation to the meta‐frontier, indicating that farmers can boost their production by adopting and disseminating new rice production technology.

Suggested Citation

  • Md Nazirul Islam Sarker & Md Abdus Salam & R. B. Radin Firdaus, 2024. "Do female labor‐migrated households have lower productivity? Empirical evidence from rural rice farms in Bangladesh," Growth and Change, Wiley Blackwell, vol. 55(1), March.
  • Handle: RePEc:bla:growch:v:55:y:2024:i:1:n:e12691
    DOI: 10.1111/grow.12691
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/grow.12691
    Download Restriction: no

    File URL: https://libkey.io/10.1111/grow.12691?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. Moon-Gi Suh, 2017. "Determinants of Female Labor Force Participation in South Korea: Tracing out the U-shaped Curve by Economic Growth," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 255-269, March.
    2. Almeida, Alexandre N. & Bravo-Ureta, Boris E., 2019. "Agricultural productivity, shadow wages and off-farm labor decisions in Nicaragua," Economic Systems, Elsevier, vol. 43(1), pages 99-110.
    3. Eric S. Owusu & Boris E. Bravo-Ureta, 2022. "Gender and Productivity Differentials in Smallholder Groundnut Farming in Malawi: Accounting for Technology Differences," Journal of Development Studies, Taylor & Francis Journals, vol. 58(5), pages 989-1013, May.
    4. Jin Yang & Hui Wang & Songqing Jin & Kevin Chen & Jeffrey Riedinger & Chao Peng, 2016. "Migration, local off-farm employment, and agricultural production efficiency: evidence from China," Journal of Productivity Analysis, Springer, vol. 45(3), pages 247-259, June.
    5. Nguyen, Duc Loc & Grote, Ulrike & Nguyen, Trung Thanh, 2019. "Migration, crop production and non-farm labor diversification in rural Vietnam," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 175-187.
    6. Habtamu Alem & Gudbrand Lien & J. Brian Hardaker & Atle Guttormsen, 2019. "Regional differences in technical efficiency and technological gap of Norwegian dairy farms: a stochastic meta-frontier model," Applied Economics, Taylor & Francis Journals, vol. 51(4), pages 409-421, January.
    7. Alan De Brauw, 2010. "Seasonal Migration and Agricultural Production in Vietnam," Journal of Development Studies, Taylor & Francis Journals, vol. 46(1), pages 114-139.
    8. Ligia Alba Melo-Becerra & Antonio José Orozco-Gallo, 2017. "Technical efficiency for Colombian small crop and livestock farmers: A stochastic metafrontier approach for different production systems," Journal of Productivity Analysis, Springer, vol. 47(1), pages 1-16, February.
    9. Raaj Kishore Biswas & Enamul Kabir & Hafiz T. A. Khan, 2019. "Causes of Urban Migration in Bangladesh: Evidence from the Urban Health Survey," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 38(4), pages 593-614, August.
    10. Haya Stier & Efrat Herzberg-Druker, 2017. "Running Ahead or Running in Place? Educational Expansion and Gender Inequality in the Labor Market," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(3), pages 1187-1206, February.
    11. Marc Jim Mariano & Renato Villano & Euan Fleming, 2011. "Technical Efficiency of Rice Farms in Different Agroclimatic Zones in the Philippines: An Application of a Stochastic Metafrontier Model," Asian Economic Journal, East Asian Economic Association, vol. 25(3), pages 245-269, September.
    12. Zhang, Jian & Mishra, Ashok K. & Zhu, Peixin & Li, Xiaoshun, 2020. "Land rental market and agricultural labor productivity in rural China: A mediation analysis," World Development, Elsevier, vol. 135(C).
    13. S. Chandrasekhar & Mousumi Das & Ajay Sharma, 2015. "Short-term Migration and Consumption Expenditure of Households in Rural India," Oxford Development Studies, Taylor & Francis Journals, vol. 43(1), pages 105-122, March.
    14. Takasaki, Yoshito, 2022. "Impacts of applying for international labor migration before migration occurs," World Development, Elsevier, vol. 157(C).
    15. M. Islam & Delwar Hossain, 2014. "Island Char Resources Mobilization (ICRM): Changes of Livelihoods of Vulnerable People in Bangladesh," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(3), pages 1033-1054, July.
    16. Murakami, Enerelt & Yamada, Eiji & Sioson, Erica Paula, 2021. "The impact of migration and remittances on labor supply in Tajikistan," Journal of Asian Economics, Elsevier, vol. 73(C).
    17. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    18. M. Rezaul Islam, 2018. "Climate Change, Natural Disasters and Socioeconomic Livelihood Vulnerabilities: Migration Decision Among the Char Land People in Bangladesh," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(2), pages 575-593, April.
    19. Gautam, Madhur & Ahmed, Mansur, 2019. "Too small to be beautiful? The farm size and productivity relationship in Bangladesh," Food Policy, Elsevier, vol. 84(C), pages 165-175.
    20. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    21. J. Edward Taylor & Scott Rozelle & Alan deBrauw, 1999. "Migration, Remittances, and Agricultural Productivity in China," American Economic Review, American Economic Association, vol. 89(2), pages 287-291, May.
    22. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    23. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    24. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    25. Ghazal Bayanpourtehrani & Kevin Sylwester, 2013. "Democracy and Female Labor Force Participation: An Empirical Examination," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(3), pages 749-762, July.
    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. Abebayehu Girma Geffersa & Frank Wogbe Agbola & Amir Mahmood, 2022. "Modelling technical efficiency and technology gap in smallholder maize sector in Ethiopia: accounting for farm heterogeneity," Applied Economics, Taylor & Francis Journals, vol. 54(5), pages 506-521, January.
    2. Tai-Hsin Huang & Yi-Chun Lin & Kuo-Jui Huang & Yu-Wei Liao, 2022. "Comparing Cost Efficiency Between Financial and Non-financial Holding Banks and Insurers in Taiwan Under the Framework of Copula Methods and Metafrontier," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(4), pages 735-766, December.
    3. Toba Stephen Olasehinde & Fangbin Qiao & Shiping Mao, 2022. "Performance of Nigerian Rice Farms from 2010 to 2019: A Stochastic Metafrontier Approach," Agriculture, MDPI, vol. 12(7), pages 1-13, July.
    4. Richard Adjei Dwumfour & Eric Fosu Oteng-Abayie & Emmanuel Kwasi Mensah, 2022. "Bank efficiency and the bank lending channel: new evidence," Empirical Economics, Springer, vol. 63(3), pages 1489-1542, September.
    5. Bravo-Ureta, Boris E. & Higgins, Daniel & Arslan, Aslihan, 2020. "Irrigation infrastructure and farm productivity in the Philippines: A stochastic Meta-Frontier analysis," World Development, Elsevier, vol. 135(C).
    6. Phuc Trong Ho & Pham Xuan Hung & Nguyen Duc Tien, 2023. "Effects of varieties and seasons on cost efficiency in rice farming: A stochastic metafrontier approach," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society, vol. 13(2), pages 120-129.
    7. Ning Yin & Yapeng Wang, 2017. "Impacts of Rural Labor Resource Change on the Technical Efficiency of Crop Production in China," Agriculture, MDPI, vol. 7(3), pages 1-12, March.
    8. Nguyen, Hoa-Thi-Minh & Do, Huong & Kompas, Tom, 2021. "Economic efficiency versus social equity: The productivity challenge for rice production in a ‘greying’ rural Vietnam," World Development, Elsevier, vol. 148(C).
    9. Qian Liu & Yongmu Jiang & Carl‐Johan Lagerkvist & Wei Huang, 2023. "Extension services and the technical efficiency of crop‐specific farms in China," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(1), pages 436-459, March.
    10. Ito, Junichi & Li, Xinyi, 2023. "Interplay between China’s grain self-sufficiency policy shifts and interregional, intertemporal productivity differences," Food Policy, Elsevier, vol. 117(C).
    11. Bravo-Ureta, Boris E. & Jara-Rojas, Roberto & Lachaud, Michee A. & Moreira L., Victor H. & Scheierling, Susanne M., 2015. "Water and Farm Efficiency: Insights from the Frontier Literature," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206076, Agricultural and Applied Economics Association.
    12. Khanal, Uttam & Wilson, Clevo & Shankar, Sriram & Hoang, Viet-Ngu & Lee, Boon, 2018. "Farm performance analysis: Technical efficiencies and technology gaps of Nepalese farmers in different agro-ecological regions," Land Use Policy, Elsevier, vol. 76(C), pages 645-653.
    13. Malabayabas, Maria Luz L. & Mishra, Ashok K. & Mayorga, Joaquin, 2023. "Spouses' Access to Financial Services: Estimating Technological and Managerial Gaps in Production," IZA Discussion Papers 16578, Institute of Labor Economics (IZA).
    14. Roengchai Tansuchat, 2023. "A Copula-Based Meta-Stochastic Frontier Analysis for Comparing Traditional and HDPE Geomembranes Technology in Sea Salt Farming among Farmers in Phetchaburi, Thailand," Agriculture, MDPI, vol. 13(4), pages 1-23, March.
    15. Laure Latruffe & Andreas Niedermayr & Yann Desjeux & K Herve Dakpo & Kassoum Ayouba & Lena Schaller & Jochen Kantelhardt & Yan Jin & Kevin Kilcline & Mary Ryan & Cathal O’Donoghue, 2023. "Identifying and assessing intensive and extensive technologies in European dairy farming," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(4), pages 1482-1519.
    16. Chukwujekwu A. Obianefo & John N. Ng’ombe & Agness Mzyece & Blessing Masasi & Ngozi J. Obiekwe & Oluchi O. Anumudu, 2021. "Technical Efficiency and Technological Gaps of Rice Production in Anambra State, Nigeria," Agriculture, MDPI, vol. 11(12), pages 1-13, December.
    17. Carrer, Marcelo José & Filho, Hildo Meirelles de Souza & Vinholis, Marcela de Mello Brandão & Mozambani, Carlos Ivan, 2022. "Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    18. Zarkovic, Maja, 2020. "Cap-and-trade and produce at least cost? Investigating firm behaviour in the EU ETS," Working papers 2020/12, Faculty of Business and Economics - University of Basel.
    19. Owusu, Rebecca & Kwadzo, Moses & Ghartey, William, 2022. "Regional Productivity Differential and Technology Gap In African Agriculture: A Stochastic Metafrontier Approach," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 10(1), January.
    20. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Yan, Ming-Zhe & Wang, Jian-Lin & Xie, Bai-Chen, 2019. "Which provincial administrative regions in China should reduce their coal consumption? An environmental energy input requirement function based analysis," Energy Policy, Elsevier, vol. 127(C), pages 51-63.

    More about this item

    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:bla:growch:v:55:y:2024:i:1:n:e12691. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0017-4815 .

    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.