Female Labor Force Participation and Fertility: A Survey Based Study of Southern Punjab, Pakistan
##plugins.themes.academic_pro.article.main##
Abstract
Rapidly increasing population is challenging for almost all the developing countries which triggers the issues like poverty, ill-health, illiteracy, lower living standard and environmental degradation. Female participation in labor force may increase the opportunity cost for bearing and rearing children. This increased opportunity cost for having children may put pressure on families to have less number of children and offer more women time in labor market. This induces the women to allocate more time to work and develops their preferences to have less number of children. However, taking the 400 respondents as sample size, this study was conducted in Multan division, known as area of southern Punjab, Pakistan. Keeping in view the non-negative nature of dependent variable, this study used Negative Binomial Model to find out the relationship between fertility and female labor force participation. The estimates of the model indicates that monthly income of wife, education and use of birth control devices are major factors lessening the fertility rate of females. However, this mechanism for lowering demand for children by family may be helpful in reducing fertility rate (number of children per women) and increasing economic activity and wellbeing by involving more and more women in paid work. In this way, a society can achieve the targets of birth control in an invisible way to impede the pace of undesirable population growth. Finding of proposed research may help population welfare department, Punjab and Pakistan Population council, provincial and federal government in formulating an indirect and invisible population/birth control policies to overcome the burden of over-population.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- Al Kibria, G. M., Hossen, S., Barsha, R. A. A., Sharmeen, A., Uddin, S. I., & Paul, S. K. (2016). Factors affecting contraceptive use among married women of reproductive age in Bangladesh. Journal of Molecular Studies and Medicine Research, 2(01), 70-79. DOI: https://doi.org/10.18801/jmsmr.020116.09
- Bhandari, N., Shrestha, G. K., & Thakuri, P. C. (2013). Study of factors affecting contraceptive use among married women of reproductive Age. Journal of College of Medical Sciences-Nepal, 9(4), 24-29. DOI: https://doi.org/10.3126/jcmsn.v9i4.10233
- Bin, O., Landry, C. E., Ellis, C., & Vogelsong, H. (2005).Some consumer surplus estimates for North Carolina beaches. Marine Resource Economics, 20(2), 145–161. DOI: https://doi.org/10.1086/mre.20.2.42629466
- Bloom, D.E., D. Canning, G. Fink and J.E. Finlay, (2009).Fertility, female labor force participation and the demographic dividend. Journal of Economic Growth, 14: 79-101. DOI: https://doi.org/10.1007/s10887-009-9039-9
- Brand, J. E., & Davis, D. (2011). The impact of college education on fertility: Evidence for heterogeneous effects. Demography, 48(3), 863-887. DOI: https://doi.org/10.1007/s13524-011-0034-3
- Cameron, A.C., &Trived, P.K. (1998).Regression analysis of count data. Cambridge: Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511814365
- Choe, K. M., & Retherford, R. D. (2009). The Contribution Of Education To South Korea's Fertility Decline To ‘Lowest-Low’ Level. Asian Population Studies, 5(3), 267-288. DOI: https://doi.org/10.1080/17441730903351503
- Curtis, J.A. (2002). Estimating the demand for salmon angling in Ireland. The Economic and Social Review, 33(3), 319–332.
- Dunson, D. B., Baird, D. D., & Colombo, B. (2004). Increased infertility with age in men and women. Obstetrics and Gynecology, 103(1), 51-56. DOI: https://doi.org/10.1097/01.AOG.0000100153.24061.45
- Elborgh-Woytek, K, Newiak, M, Kochhar, K, Fabrizio, S, Kpodar, K, Wingender, P,Clements, B & Schwartz, G 2013, ‘Women, work, and the economy: macroeconomic gains from gender equity’, IMF Staff Discussion Note, DOI: https://doi.org/10.5089/9781475566567.006
- Englin ,J., & Shonkwiler,J.(1995).Estimating social welfare using count data models: An application under conditions of endogenous stratification and truncation. Review of Economics and Statistics, 77, 104–112. DOI: https://doi.org/10.2307/2109996
- Ghannam, A. R. E. (2005). An examination of factors affecting fertility rate differentials as compared among women in less and more developed countries. Journal of Human Ecology, 18(3), 181-192. DOI: https://doi.org/10.1080/09709274.2005.11905828
- Goldstein, S. (1972). The influence of labour force participation and education on fertility in Thailand. Population Studies, 26(3), 419-436. DOI: https://doi.org/10.1080/00324728.1972.10405911
- Green, W. (2008).Functional forms for the negative binomial model for count data. Economic Letters, 99, 5858–5890. DOI: https://doi.org/10.1016/j.econlet.2007.10.015
- Greenwood, J. Seshadri, A. and Yorukoglu, M. (2005). Engines of Liberation, The Review of Economic Studies, 72(1): 109-133. DOI: https://doi.org/10.1111/0034-6527.00326
- Grodstein, F., Goldman, M. B., & Cramer, D. W. (1994). Body mass index and ovulatory infertility. Epidemiology, 5(2): 247-250. DOI: https://doi.org/10.1097/00001648-199403000-00016
- Haab,C.H.,& McConnell,K.(2002).Valuingenvironmentalandnaturalresources:Theeconometrics of non-market valuation. Cheltenham: Edward Elgar. DOI: https://doi.org/10.4337/9781840647044.00014
- Hagerty, D., & Moeltner, K. (2005). Specification of driving costs in models of recreation demand. Land Economics, 81(1), 127–143. DOI: https://doi.org/10.3368/le.81.1.127
- Hesseln, H., Loomis, J.B., Gonzalez Caban, A., & Alexander, S. (2003). Wildfire effects on hiking and biking demand in New Mexico: A travel cost study. Journal of Environmental Management, 69(4), 359–368. DOI: https://doi.org/10.1016/j.jenvman.2003.09.012
- Jain, A. K. (1981). The effect of female education on fertility: A simple explanation. Demography, 18(4), 577-595. DOI: https://doi.org/10.2307/2060948
- Joffe, M., & Barnes, I. (2000). Do parental factors affect male and female fertility?. Epidemiology, 11(6): 700-705. DOI: https://doi.org/10.1097/00001648-200011000-00015
- Kamal, S. M. (2009). Factors affecting utilization of skilled maternity care services among married adolescents in Bangladesh. Asian Population Studies, 5(2), 153-170. DOI: https://doi.org/10.1080/17441730902992075
- Kravdal, Ø. (2002). Education and fertility in sub-Saharan Africa: Individual and community effects. Demography, 39(2), 233-250. DOI: https://doi.org/10.1353/dem.2002.0017
- Kravdal, Ø., & Rindfuss, R. R. (2008). Changing relationships between education and fertility: A study of women and men born 1940 to 1964. American sociological review, 73(5), 854-873. DOI: https://doi.org/10.1177/000312240807300508
- Loomis, J. (2003).Travel cost demand model based river recreation benefit estimates withon-siteand household surveys: Comparative results and a correction procedure. Water Resources Research, 39(4), 1105. DOI: https://doi.org/10.1029/2002WR001832
- Ma, L. (2016). Female labour force participation and second birth rates in South Korea. Journal of Population Research, 33, 173-193. DOI: https://doi.org/10.1007/s12546-016-9166-z
- Martinez-Espineira, R., Amoako-Tuffour, J., &Hilbe, J. M. (2006). Travel cost demand model based on river recreation benefit estimates withon-site and household surveys: Comparative results and a correction procedure – Reevaluation. Water Resources Research, 42, W10418. DOI: https://doi.org/10.1029/2005WR004798
- Martinez-Espineira,R.,&Amoako-Tuffour,J.(2009).Multi-destination and multi-purpose trip effects in the analysis of the demand for trips to recreational site (Economics and Econometrics Research Institute [EERI] Research Paper Series). Brussels: EERI. DOI: https://doi.org/10.1007/s00267-008-9253-9
- Nag, A., & Singhal, P. (2013). Impact of education and age at marriage on fertility among Uttar Pradesh migrants of Ludhiana, Punjab, India. The Anthropologist, 15(2), 225-230. DOI: https://doi.org/10.1080/09720073.2013.11891310
- Nussbaum, M.C., (2001). Women and human development: The capabilities approach vol. 3: Cambridge University Press. September 2013, viewed 15 July 2016,https://www.imf.org/external/pubs/ft/sdn/2013/sdn1310.pdf.
- Pakistan Bureau of Statistics (2018). Pakistan Statistical Year Book 2018. PBS, Islamabad, Pakistan
- Pakistan Bureau of Statistics (2020). Pakistan Statistical Year Book 2020. PBS, Islamabad, Pakistan
- Pal, A., Mohan, U., Idris, M. Z., & Masood, J. (2014). Factors affecting unmet need for family planning in married women of reproductive age group in urban slums of Lucknow. Indian Journal of Community Health, 26(1), 44-49.
- Rouse, S., & Visweswaran, K. (2011). Women's Movement in Pakistan: State, Class, Gender’. Perspectives on modern South Asia: A reader in culture, history, and representation. Blackwell: John Wiley & Sons, 321-27.
- Sadaquat, M.B., (2011). Employment situation of women in Pakistan, International Journal of Social Economics, 38: 98-113. DOI: https://doi.org/10.1108/03068291111091981
- Saleem, S., & Bobak, M. (2005). Women's autonomy, education and contraception use in Pakistan: a national study. Reproductive Health, 2(1), 1-8. DOI: https://doi.org/10.1186/1742-4755-2-8
- Sathar, Z. A., & Mason, K. O. (1993). How female education affects reproductive behavior in urban Pakistan. Asian and Pacific Population Forum, 6(4): 93-103.
- Shrestha, R. K., Seidl, A. F., &Moraes, A. S. (2002). Value of recreational fishing in the Brazilian Pantanal: a travel cost analysis using count data models. Ecological economics, 42(1-2), 289-299. DOI: https://doi.org/10.1016/S0921-8009(02)00106-4
- Steinberg, C.,& Nakane, M.(2012). ‘Can women save Japan?’, Working Paper No.12/248, International Monetary Fund, Washington, DC. DOI: https://doi.org/10.5089/9781475512922.001
- Tawiah, E. O. (1997). Factors affecting contraceptive use in Ghana. Journal of Biosocial Science, 29(2), 141-149. DOI: https://doi.org/10.1017/S0021932097001417
- Wazir, M. A. (2013). Population Dynamics in Pakistan: Past, Present and Future, Policy Brief # 35. Sustainable Development Policy Institute (SDPI) Islamabad, Pakistan.
- Zawacki, W. T., Marsinko, A., &Bowker, J. M. (2000). A travel cost analysis of non-consumptive wildlife-associated recreation in the United States. Forest science, 46(4), 496-506. DOI: https://doi.org/10.1093/forestscience/46.4.496