IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2024i4p144-156.html

Heterogeneity in the agricultural sector and its implications for modeling viability

Author

Listed:
  • Cecilia ALEXANDRI

    (Institute of Agricultural Economics, Romanian Academy.)

  • Corina SAMAN

    (Institute for Economic Forecasting, Romanian Academy. Institute of Agricultural Economics, Romanian Academy.)

  • Bianca PAUNA

    (Centre for Macroeconomic Modelling, National Institute of Economic Research, Romanian Academy.)

Abstract

The paper investigates the viability of crop farms in Romania, an important topic because in rural regions agriculture is the main activity of a large part of the labour force and plays an important role in food security. In the literature, only trends and changes in viability as a result of policy at the European level are analysed. This study advances the literature by identifying the determinants of viability with a new model (correlated random effects ordered probit model) that intrinsically captures unobserved heterogeneity of farms. The results show that the viability of crop farms dependents incrementally on the volume of resources (scale). The paper shows that there is scope to increase farms’ income if some changes are made in the structure of production factors (land, labor).

Suggested Citation

  • Cecilia ALEXANDRI & Corina SAMAN & Bianca PAUNA, 2024. "Heterogeneity in the agricultural sector and its implications for modeling viability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 144-156, December.
    • Handle: RePEc:rjr:romjef:v::y:2024:i:4:p:144-156
    as

    Download full text from publisher

    File URL: https://www.ipe.ro/ftp/RePEc/rjef4_2024/rjef4_2024p144-156.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cecilia ALEXANDRI & Corina SAMAN & Bianca PĂUNA & Lucian LUCA, 2022. "Will Romanian Crop Farms Be Affected Differently By Low-Input Farming?," Agricultural Economics and Rural Development, Institute of Agricultural Economics, vol. 19(2), pages 155-164.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    3. Jason Abrevaya & Yu-Chin Hsu, 2021. "Partial effects in non-linear panel data models with correlated random effects," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 519-535.
    4. Wooldridge, Jeffrey M., 2019. "Correlated random effects models with unbalanced panels," Journal of Econometrics, Elsevier, vol. 211(1), pages 137-150.
    5. Cecilia ALEXANDRI & Corina SAMAN & Bianca PAUNA, 2021. "Exploring the Relationship Between Farm Productivity and CAP Subsidies for the NMS," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 124-140, December.
    6. Jindrich Spicka & Tomas Hlavsa & Katerina Soukupova & Marie Stolbova, 2019. "Approaches to estimation the farm-level economic viability and sustainability in agriculture: A literature review," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(6), pages 289-297.
    7. Joseph G. Altonji & Rosa L. Matzkin, 2005. "Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 73(4), pages 1053-1102, July.
    8. Adele Coppola & Alfonso Scardera & Mario Amato & Fabio Verneau, 2020. "Income Levels and Farm Economic Viability in Italian Farms: An Analysis of FADN Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    9. O'Donoghue, Cathal & Devisme, Simon & Ryan, Mary & Conneely, Ricky & Gillespie, Patrick & Vrolijk, Hans, 2016. "Farm economic sustainability in the European Union: A pilot study," Studies in Agricultural Economics, Research Institute for Agricultural Economics, vol. 118(3), pages 1-9, December.
    10. Riju Joshi & Jeffrey M. Wooldridge, 2019. "Correlated Random Effects Models with Endogenous Explanatory Variables and Unbalanced Panels," Annals of Economics and Statistics, GENES, issue 134, pages 243-268.
    11. Nurmet, Maire & Omel, Raul, 2020. "Economic Viability by Farm Size of Estonian Family Farms," Problems of Agricultural Economics / Zagadnienia Ekonomiki Rolnej 311217, Institute of Agricultural and Food Economics - National Research Institute (IAFE-NRI).
    12. Poczta-Wajda, Agnieszka, 2020. "Economic Viability of Family Farms in Europe – A Literature Review," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2020(4).
    13. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    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. repec:rjr:romjef:v::y:2024:i:4:p:144-157 is not listed on IDEAS
    2. Godwin Kwabla Ekpe & Anna A. Klis, 2023. "Spillover Effects in Irrigated Agriculture from the Groundwater Commons," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 86(3), pages 469-507, November.
    3. David Aristei & Manuela Gallo & Pierluigi Murro, 2025. "Financial Knowledge and Financial Fragility: Longitudinal Evidence from Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 11(2), pages 667-702, July.
    4. Liu, Laura & Poirier, Alexandre & Shiu, Ji-Liang, 2025. "Identification and estimation of partial effects in nonlinear semiparametric panel models," Journal of Econometrics, Elsevier, vol. 252(PB).
    5. Mirela Tomaš Simin & Danica Glavaš-Trbić & Aleksandar Miljatović & Jelena Despotović & Tihomir Novaković, 2025. "Farm Sustainability Indicators—Exploring FADN Database," Land, MDPI, vol. 14(10), pages 1-15, September.
    6. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    7. Maria C. Lo Bue, 2024. "Drivers of changes in child nutritional conditions: A panel data‐based study on Indonesian households, 1997–2014," Review of Development Economics, Wiley Blackwell, vol. 28(2), pages 741-776, May.
    8. Arnd Kölling & Claus Schnabel, 2022. "Owners, external managers and industrial relations in German establishments," British Journal of Industrial Relations, London School of Economics, vol. 60(2), pages 424-443, June.
    9. Fattouh, Bassam & Pisicoli, Beniamino & Scaramozzino, Pasquale, 2024. "Debt and financial fragility: Italian non-financial companies after the pandemic," Economic Modelling, Elsevier, vol. 131(C).
    10. Ron Berman & Ayelet Israeli, 2022. "The Value of Descriptive Analytics: Evidence from Online Retailers," Marketing Science, INFORMS, vol. 41(6), pages 1074-1096, November.
    11. Shiu, Ji-Liang & Hu, Yingyao, 2013. "Identification and estimation of nonlinear dynamic panel data models with unobserved covariates," Journal of Econometrics, Elsevier, vol. 175(2), pages 116-131.
    12. Santiago Pereda-Fernández, 2021. "Copula-Based Random Effects Models for Clustered Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 575-588, March.
    13. Mark Musumba & Cheryl A. Palm & Adam M. Komarek & Patrick K. Mutuo & Bocary Kaya, 2022. "Household livelihood diversification in rural Africa," Agricultural Economics, International Association of Agricultural Economists, vol. 53(2), pages 246-256, March.
    14. Jie Wei & Yonghui Zhang, 2022. "Panel Probit Models with Time‐Varying Individual Effects: Reestimating the Effects of Fertility on Female Labour Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 799-829, August.
    15. Becker, Annette & Hottenrott, Hanna & Mukherjee, Anwesha, 2022. "Division of labor in R&D? Firm size and specialization in corporate research," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 1-23.
    16. Guelmamen, Mehdi & Garcia, Serge & Mayol, Alexandre, 2025. "Inter-municipal cooperation in drinking water supply: Trade-offs between transaction costs, efficiency and service quality," International Review of Law and Economics, Elsevier, vol. 84(C).
    17. Ogawa, Keishi & Garrod, Guy & Yagi, Hironori, 2023. "Sustainability strategies and stakeholder management for upland farming," Land Use Policy, Elsevier, vol. 131(C).
    18. Irina B. Grafova & Alan C. Monheit & Rizie Kumar, 2020. "How Do Economic Shocks Affect Family Health Care Spending Burdens?," Journal of Family and Economic Issues, Springer, vol. 41(3), pages 442-457, September.
    19. Thomas Slijper & Yann de Mey & P Marijn Poortvliet & Miranda P M Meuwissen, 2022. "Quantifying the resilience of European farms using FADN," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 121-150.
    20. Mehdi Guelmamen, 2025. "Does Intermunicipal Cooperation Affect Prices? An Economic Analysis of the French Drinking Water Sector," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 67(2), pages 209-226, August.
    21. Valizadeh, Pourya & Ng, Shu Wen, 2020. "The New school food standards and nutrition of school children: Direct and Indirect Effect Analysis," Economics & Human Biology, Elsevier, vol. 39(C).

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

    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:rjr:romjef:v::y:2024:i:4:p:144-156. 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: Corina Saman The email address of this maintainer does not seem to be valid anymore. Please ask Corina Saman to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.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.