IDEAS home Printed from https://ideas.repec.org/a/bla/afrdev/v36y2024i2p306-319.html
   My bibliography  Save this article

Labor differentiation and cotton productivity in Burkina Faso

Author

Listed:
  • Aminata Zong‐naba
  • Aké G.‐M. N'gbo
  • Omer S. Combary

Abstract

Agriculture is a very important sector in Africa's economic development, particularly in Burkina Faso, as it employs a large proportion of the population. Given the importance of labor in this sector, a good allocation of the different types of labor could help increase agricultural productivity in Burkina Faso. This research contributes to the literature by determining the specific contributions of each type of labor in enhancing cotton productivity. The sample of this research is 477 cotton farms, and a semiparametric stochastic frontier model has been used in the analysis. The results show that the proportion of wage labor has a nonlinear effect and contributes to improving cotton productivity when the number of educated people in the household increases. But family labor decreases cotton productivity when the number of educated people in the household increase. The comparison between the findings of the semiparametric and parametric frontier shows that technical efficiency is 72.44% when education is used as the channel through which production factors affect cotton productivity. However, this technical efficiency is 54.96% when production factors directly affect cotton productivity in the parametric frontier model. Promoting education in rural areas will help to increase the number of people educated and consequently improve cotton productivity.

Suggested Citation

  • Aminata Zong‐naba & Aké G.‐M. N'gbo & Omer S. Combary, 2024. "Labor differentiation and cotton productivity in Burkina Faso," African Development Review, African Development Bank, vol. 36(2), pages 306-319, June.
    • Handle: RePEc:bla:afrdev:v:36:y:2024:i:2:p:306-319
    DOI: 10.1111/1467-8268.12751
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-8268.12751
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-8268.12751?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. Chaoran Chen & Diego Restuccia & Raul Santaeulalia-Llopis, 2022. "The Effects of Land Markets on Resource Allocation and Agricultural Productivity," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 45, pages 41-54, July.
    2. Dean Karlan & Robert Osei & Isaac Osei-Akoto & Christopher Udry, 2014. "Agricultural Decisions after Relaxing Credit and Risk Constraints," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(2), pages 597-652.
    3. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-422, July.
    4. Brown, Charles & Medoff, James, 1978. "Trade Unions in the Production Process," Journal of Political Economy, University of Chicago Press, vol. 86(3), pages 355-378, June.
    5. Li, Qi & Racine, Jeffrey S., 2010. "Smooth Varying-Coefficient Estimation And Inference For Qualitative And Quantitative Data," Econometric Theory, Cambridge University Press, vol. 26(6), pages 1607-1637, December.
    6. Alexander Bilson Darku & Stavroula Malla & Kien C. Tran, 2016. "Sources and Measurement of Agricultural Productivity and Efficiency in Canadian Provinces: Crops and Livestock," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(1), pages 49-70, March.
    7. Maha Kalai & Kamel Helali, 2016. "Technical Change and Total Factor Productivity Growth in the Tunisian Manufacturing Industry: A Malmquist Index Approach," African Development Review, African Development Bank, vol. 28(3), pages 344-356, September.
    8. Benjamin, Dwayne, 1992. "Household Composition, Labor Markets, and Labor Demand: Testing for Separation in Agricultural Household Models," Econometrica, Econometric Society, vol. 60(2), pages 287-322, March.
    9. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514, November.
    10. Nusrat Abedin Jimi & Plamen V. Nikolov & Mohammad Abdul Malek & Subal Kumbhakar, 2019. "The effects of access to credit on productivity: separating technological changes from changes in technical efficiency," Journal of Productivity Analysis, Springer, vol. 52(1), pages 37-55, December.
    11. Shengqin Wu & Degang Yang & Fuqiang Xia & Xinhuan Zhang & Jinwei Huo & Tianyi Cai & Jing Sun, 2022. "The Effect of Labor Reallocation and Economic Growth in China," Sustainability, MDPI, vol. 14(7), pages 1-22, April.
    12. Paul Terhemba Iorember & Gylych Jelilov, 2018. "Computable General Equilibrium Analysis of Increase in Government Agricultural Expenditure on Household Welfare in Nigeria," African Development Review, African Development Bank, vol. 30(4), pages 362-371, December.
    13. Alexandra E. Hill & Izaac Ornelas & J. Edward Taylor, 2021. "Agricultural Labor Supply," Annual Review of Resource Economics, Annual Reviews, vol. 13(1), pages 39-64, October.
    14. Jean-Jacques Laffont & Mohamed Salah Matoussi, 1995. "Moral Hazard, Financial Constraints and Sharecropping in El Oulja," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(3), pages 381-399.
    15. Sun, Kai & Kumbhakar, Subal C., 2013. "Semiparametric smooth-coefficient stochastic frontier model," Economics Letters, Elsevier, vol. 120(2), pages 305-309.
    16. Anna Gaviglio & Rosalia Filippini & Fabio Albino Madau & Maria Elena Marescotti & Eugenio Demartini, 2021. "Technical efficiency and productivity of farms: a periurban case study analysis," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 9(1), pages 1-18, December.
    17. Samuel Sekyi & Benjamin Musah Abu & Paul Kwame Nkegbe, 2020. "Effects of farm credit access on agricultural commercialization in Ghana: Empirical evidence from the northern Savannah ecological zone," African Development Review, African Development Bank, vol. 32(2), pages 150-162, June.
    18. Mikémina Pilo, 2019. "Dynamics of Agricultural Productivity and Technical Efficiency in Togo: The Role of Technological Change," African Development Review, African Development Bank, vol. 31(4), pages 462-475, December.
    19. Fan Zhang & Joshua Hall & Feng Yao, 2018. "Does Economic Freedom Affect The Production Frontier? A Semiparametric Approach With Panel Data," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1380-1395, April.
    20. 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.
    21. Kimseyinga Savadogo & Omer S. Combary & Denis B. Akouwerabou, 2016. "Impacts des services sociaux sur la productivité agricole au Burkina Faso : approche par la fonction distance output," Mondes en développement, De Boeck Université, vol. 0(2), pages 153-167.
    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. Taining Wang & Jinjing Tian & Feng Yao, 2021. "Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency," Empirical Economics, Springer, vol. 61(2), pages 587-636, August.
    2. Ahimbisibwe, Vianny & Zhunusova, Eliza & Kassa, Habtemariam & Günter, Sven, 2024. "Technical efficiency drivers of farmer-led restoration strategies, and how substantial is the unrealised potential for farm output?," Agricultural Systems, Elsevier, vol. 213(C).
    3. Sun, Kai & Kumbhakar, Subal C. & Tveterås, Ragnar, 2015. "Productivity and efficiency estimation: A semiparametric stochastic cost frontier approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 194-202.
    4. Kai Sun & Ruhul Salim, 2020. "A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry," Journal of Productivity Analysis, Springer, vol. 54(2), pages 139-156, December.
    5. Fan Zhang & Joshua Hall & Feng Yao, 2018. "Does Economic Freedom Affect The Production Frontier? A Semiparametric Approach With Panel Data," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1380-1395, April.
    6. Sun, Kai & Kumbhakar, Subal C., 2013. "Semiparametric smooth-coefficient stochastic frontier model," Economics Letters, Elsevier, vol. 120(2), pages 305-309.
    7. Nicola GALLUZZO, 2022. "Agritourism And Less Favored Areas Subsidies Impact On Technical Efficiency Of Italian Farms," Agricultural Economics and Rural Development, Institute of Agricultural Economics, vol. 19(1), pages 61-75.
    8. Markose Chekol Zewdie & Michele Moretti & Daregot Berihun Tenessa & Zemen Ayalew Ayele & Jan Nyssen & Enyew Adgo Tsegaye & Amare Sewnet Minale & Steven Van Passel, 2021. "Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach," Land, MDPI, vol. 10(3), pages 1-17, March.
    9. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2022. "Valuing the Time of the Self-Employed," Working Papers 2022-2, Princeton University. Economics Department..
    10. Aragón, Fernando M. & Restuccia, Diego & Rud, Juan Pablo, 2022. "Are small farms really more productive than large farms?," Food Policy, Elsevier, vol. 106(C).
    11. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    12. Bharadwaj, Prashant, 2015. "Fertility and rural labor market inefficiencies: Evidence from India," Journal of Development Economics, Elsevier, vol. 115(C), pages 217-232.
    13. Antony Andrews & Omphile Temoso & Sean Kimpton, 2021. "Persistent and Transient Inefficiency of Australian States and Territories in Providing Public Hospital Services: An Application of Bayesian Stochastic Finite Mixture Frontier Analysis," Economic Papers, The Economic Society of Australia, vol. 40(2), pages 104-115, June.
    14. Tran, Kien C. & Tsionas, Mike G. & Prokhorov, Artem B., 2023. "Semiparametric estimation of spatial autoregressive smooth-coefficient panel stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1189-1199.
    15. Im, Hyun Joong & Park, Young Joon & Shon, Janghoon, 2015. "Product market competition and the value of innovation: Evidence from US patent data," Economics Letters, Elsevier, vol. 137(C), pages 78-82.
    16. Béatrice D'HOMBRES & Jean-Louis ARCAND, 2006. "Testing for Separation in Agricultural Household Models and Unobservable Household-Specific Effects," Working Papers 200632, CERDI.
    17. Andersson, Fredrik N.G., 2023. "Income inequality and carbon emissions in the United States 1929–2019," Ecological Economics, Elsevier, vol. 204(PA).
    18. MAIMOUNA DIAKITE & Jean-François BRUN, 2016. "Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT’s Revenue," EcoMod2016 9537, EcoMod.
    19. Asaftei, Gabriel & Parmeter, Christopher F., 2010. "Market power, EU integration and privatization: The case of Romania," Journal of Comparative Economics, Elsevier, vol. 38(3), pages 340-356, September.
    20. Yuichi Watanabe & Haruko Noguchi & Yoshinori Nakata, 2020. "How efficient are surgical treatments in Japan? The case of a high-volume Japanese hospital," Health Care Management Science, Springer, vol. 23(3), pages 401-413, September.

    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:afrdev:v:36:y:2024:i:2:p:306-319. 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: https://edirc.repec.org/data/afdbgci.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.