AN ECONOMIC ANALYSIS OF THE FACTORS DETERMINING THE ROLE OF RURAL WOMEN IN AGRICULTURAL PRODUCTION IN BAGHDAD GOVERNORATE.

Authors

  • M. N. Abed
  • A. F.

DOI:

https://doi.org/10.36103/fpn84410

Keywords:

Rural women, family size, logistic regression, tobit model, farm labour.

Abstract

The study aims to determine the impact of economic and social determinants on the size of women's contribution to agricultural work. The research used qualitative response models to determine the impact of the explanatory variables (age, marital status, educational level, training skills, experience in agricultural work, NGO support, health status, family size, experience in secondary agricultural work, use of agricultural machinery in plant production) and their impact on the variable affiliated with the participation of rural women for a random sample of (384) women in separate rural areas in Baghdad Governorate. The results of the analysis showed that the size of the family and the use of mechanization had a negative and weak effect on the contribution of women. As the increase in the size of the family leads to an increase in its household burdens at the expense of its work in farm work. And because the use of mechanization is limited to men, the increased use of technology, especially modern ones, works to reduce women's working hours on the farm. The rest of the variables had a positive and significant impact on their contribution. Accordingly, the research recommends the provision of health and educational services for women, as well as their involvement in training, extension, and economic and financial empowerment programs to raise their contribution to achieving sustainable agricultural development.            

References

1. Abbas, N. J. 2010. Comparison between methods of estimating economic functions with qualitative dependent variables. Tikrit for Administrative and Economic Sciences, Iss(18),vol 6,ppi 102-118.

2. Awf, A. A., E. H. Ali, and A. S. Shukr, 2024. Estimating off–farm labor supply and analysing relationship between risk and farm size. Iraqi Journal of Agricultural Sciences, 55(1), 526-541.‏ https://doi.org/10.36103/afwgrz97

3. Allison, P. D. 2014. Measures of fit for logistic regression. In Proceedings of the SAS global forum 2014 conference (pp. 1-13). SAS Institute Inc. Cary, NC, USA.

4. Ali, A. H. 2016. Using probability regression models to study the factors affecting efficiency of the production of tomato. Euphrates journal of agricultural science. 3rd Agricultural Conference. pp: 111-117.

5. Alex, J. P. 2013. Powering the women in agriculture: Lessons on women led farm mechanisation in South India. The Journal of Agricultural Education and Extension, 19(5), 487-503. https://doi.org/10.1080/1389224X.2013.817342

6. Al-Bakri ,R. A. R and M. S. M Al-Ezzi. 2017. Distinctive analysis and logistic regression in the presence of the problem of polylinearity (an applied study on anemia), Journal of Economic and Administrative Sciences, 23, 99,pp: 373-397. https://doi.org/10.33095/jeas.v23i99.261

7. Al-Thalabi, S. H. 2019. Analysis of binary data with special attention to the logit model. Economic, Administrative and Legal Sciences, 8(3), 131-157. https://doi.org/10.26389/AJSRP.T310119

8. Arab Organization For Agricultural Development. 1997. Project National Document For Active Rural Women In Rural Development. Sudan, Khartoum.pp:5

9. Teshale, A. B. 2022. Factors associated with unmet need for family planning in sub-Saharan Africa: A multilevel multinomial logistic regression analysis. PloS one, 17(2). https://doi.org/10.1371/journal.pone.0263885

10. Bierens, H. 2004. Introduction to the mathematical and statistical foundations of econometric. Cambridge university press. pp200. https://doi.org/10.1017/CBO9780511754012

11. Chambers, E. A., and D. R Cox,. 1967. Discrimination between alternative binary response models. Biometrika, (54), p573-578. https://doi.org/10.1093/biomet/54.3-4.573

12. Fahmy, M. U. 2005. Statistics Without Suffering, Concepts With Applications Using The Spss Program, Part Two. Kingdom Of Saudi Arabia,pp75.

13. Garson, D. 2006. Logistic Regression. Retrieved From Http:// www2.Class. Ncsu. Edu/ Garson/ Pa765 / Logistic, pp:50.

14. Ghanem, A., and F. X, El-Jaouni,.2011. Using the two-response logistic regression technique in studying the most important economic and social determinants of family income adequacy, an applied study on a random sample of families in Damascus governorate. Damascus University for Economic and Legal Sciences, (1)(27), PP:119-120.

15. Goldberger, A. 1964. Econometric Theory. New York. Wiley, PP:75. https://doi.org/10.1002/nav.3800110213

16. Gujarat. 1995. Basic Econometrics ,pp:.567-569.

17. Gujarati, N. D. 2004. Basic Econometrics (Mc-Graw Hill Co.Press.LTD Ed.) ,PP:150.

18. Hussein ,M..J and H.M ,Fayh, 2022. geographical analysis of diseases and epidemics in the world from 1918-2018. Journal Al-ustath of Human and Social sciences (4), Issue 4, pp:257. https://doi.org/10.36473/ujhss.v61i4.1933

19. Hussein, W. H., and, G. D AL-Zubidi. 2021. Employing agile leadership behaviors to enhance investment in human capital. journal of Economics and Administrative Sciences,27, pp:87. https://doi.org/10.33095/jeas.v27i126.2098

20.Jassam, Q. T., Ali E. H., and M. S ,Ghylan,. 2022. Estimating factors affecting wheat marketing efficincy using tobit model. Iraqi Journal of Agricultural Science, 53 (4), 941-949. https://doi.org/10.36103/ijas.v53i4.1606

21. Jawad, A. N. 2010. Comparison between methods of estimating economic functions with qualitative dependent variables. Tikrit for Administrative and Economic sciences, 18 (6), pp:106.

22. Khudair, M. S. 2007. Women between education, training and employment activities in the work sectors. Krbala Scientific University , 2, (5)pp:10.

23. Mahdi, H, S and A, Y Mustafa, 2021 .Social safety nets and sustainable development in fragile environments: a field social study of slums in the city of Baghdad /Al-karkh, 32, Issue 3, 75.

24. Mahmoud Al-Rahim, S. I. 2021. The Reality of iraqi rural women and ways to empower them. Developmental radiance, Issue 27: 475-499. https://www.doi.org/10.51424/Ishq.27.18

25. Nuri, A, Z. 2022 , Reducing the burden of women from the husband in the sunnah ,Journal Islamic Science College, Issue 72 .:539. https://doi.org/10.51930/jcois.21.72.0513.

26. Png, I. P. L, and C.W.J, Cheng,. 2011. China Economic Review, 16, (16)12.

27. Wahhab, N. K, and Y. H Aloisy ,. 2022, The ethical behavioral reasons for happiness in the holy qur’an, produced models, Journal of Islamic Sciences , (72), :411. https://doi.org/10.51930/jcois.21.72.0392.

28. Walsh, A. 1987. Teaching understanding and interpretation of logit regression. Teaching sociology, 178-183. https://doi.org/10.2307/1318033.

29. Wang, H., R. Zhu, and P. Ma, 2018. Optimal subsampling for large sample logistic regression. Journal of the American Statistical Association, 113(522), 829-844. https://doi.org/10.1080/01621459.2017.1292914.

30. Wang, P., and M. L. Puterman, 1998. Mixed logistic regression models. Journal of Agricultural, Biological, and Environmental Statistics, 175-200. https://doi.org/10.2307/1400650.

31. Wasserman, S., and P. Pattison, 1996. Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p. Psychometrika, 61(3), 401-425. http://dx.doi.org/10.1007/BF02294547.

32. Wells, B. L., and B. O. Tanner, 1994. The organizational potential of women in agriculture to sustain rural communities. Community Development, 25(2), 246-258. https://doi.org/10.1080/15575339409489884

33. Whitehead, A. 2000. Continuities and discontinuities in political constructions of the working man in rural Sub-Saharan Africa: The ‘lazy man’in African agriculture. The European Journal of Development Research, 12(2), 23-52. https://doi.org/10.1080/09578810008426764.

34. Wong, G. Y., and W. M. Mason, 1985. The hierarchical logistic regression model for multilevel analysis. Journal of the American Statistical Association, 80(391), 513-524. https://doi.org/10.1080/01621459.1985.10478148.

35. Wright, W., and A. Annes, 2016. Farm women and the empowerment potential in value‐ added agriculture. Rural Sociology, 81(4), 545-571. https://doi.org/10.1111/ruso.12105.

36. Yang, Y., and M. Loog, 2018. A benchmark and comparison of active learning for logistic regression. Pattern Recognition, 83, 401-415. https://doi.org/10.1016/j.patcog.2018.06.004.

37. Yuan, G. X., C. H. Ho, and C. J. Lin, 2012. An improved glmnet for l1-regularized logistic regression. The Journal of Machine Learning Research, 13(1), 1999-2030. http://dx.doi.org/10.1145/2020408.2020421.

38. Yuen, J., E. Twengström, and R. Sigvald, 1996. Calibration and verification of risk algorithms using logistic regression. European Journal of Plant Pathology, 102, 847-854. https://doi.org/10.1007/BF01877054.

39. Zaidi, A., and A. S. M. Al Luhayb, 2023. Two statistical approaches to justify the use of the logistic function in binary logistic regression. Mathematical Problems in Engineering, 2023(1), 5525675. https://doi.org/10.1155/2023/5525675.

40. Zhang, L., A. De Brauw, and S. Rozelle, 2004. China's rural labor market development and its gender implications. China Economic Review, 15(2), 230-247. https://doi.org/10.1016/j.chieco.2004.03.003.

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Published

2025-12-31

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How to Cite

Abed, M. N., & Ahmad, A. F. A. (2025). AN ECONOMIC ANALYSIS OF THE FACTORS DETERMINING THE ROLE OF RURAL WOMEN IN AGRICULTURAL PRODUCTION IN BAGHDAD GOVERNORATE. IRAQI JOURNAL OF AGRICULTURAL SCIENCES, 56(6), 2159-2169. https://doi.org/10.36103/fpn84410