Urban-Rural Migration under the devolved governance system in Kenya: Subsequent implications for income and occupation
Keywords:
Urban-rural migration, Kenya devolution policy, income and occupational change, Probit modelAbstract
The study investigates the impact of urban-rural migration on income and occupation. The paper aims to estimate the probabilities and significance of income and occupational change across different socio-economic characteristics and demographic profiles upon return to the rural areas from Nairobi city, particularly after the enactment of 2013 Kenya devolution policy. The paper draws upon exploratory research using data comprising 69 interviews with the return migrants after they had established a stay in rural areas, two years subsequently after migrating from Nairobi city. By applying the empirical methods of probit regression model, the study finds that significant probability for income change varies across different socio-economic attributes and demographic profiles. Occupational change and associated probabilities are significantly determined by low education level, female gender, the old age, huge rural land size, and low migrant’s job-related expertise level. For both income and occupational change, rural land size more than 2.5 acres is a significant incentive for urban-rural migration; given the likelihood that return migrant shifts to agriculture and in a long run establishes a robust source of income. This, after assigning other dummy variables, and setting the baseline at two years.
References
Ajaero, C. K., & Onokala, P. C. (2013). The effects of rural-urban migration on rural communities of southeastern Nigeria. International Journal of Population Research, 2013.
Akinola, A. O. (2018). Women, culture and Africa’s land reform Agenda. Frontiers in psychology, 9, 2234.
Akobeng, A. K. (2016). Understanding type I and type II errors, statistical power and sample size. Acta Paediatrica, 105(6), 605-609.
Cali, M., & Menon, C. (2013). Does urbanization affect rural poverty? Evidence from Indian districts. The World Bank Economic Review, 27(2), 171-201.
Cortes, P., & Pan, J. (2018). Occupation and gender. The Oxford handbook of women and the economy, 425-452.
Dobryagina, N. (2019). Agricultural Entrepreneurship Motivation Policies: European Union Experience and Decision Theory Application. International Journal of Rural Management, 15(1), 97-115.
Ernst, K. C., Phillips, B. S., & Duncan, B. D. (2013). Slums are not places for children to live: vulnerabilities, health outcomes, and possible interventions. Advances in pediatrics, 60(1), 53– 87. https://doi.org/10.1016/j.yapd.2013.04.005
Eshetu, F., & Beshir, M. (2017). Dynamics and determinants of rural-urban migration in Southern Ethiopia. Journal of Development and Agricultural Economics, 9(12), 328-340.
Frane, A. V. (2015). Are per-family type I error rates relevant in social and behavioral science?. Journal of Modern Applied Statistical Methods, 14(1), 5.
Garip, F. (2012). An integrated analysis of migration and remittances: Modeling migration as a mechanism for selection. Population Research and Policy Review, 31(5), 637-663.
Government of Kenya (GoK). (2019). Kenya Population and Housing Census Results. Nairobi: KNBS.
Government of Kenya, (GoK). (2016). Policy on the Devolved System of Government. Nairobi: Government Printers.
Greene, W. H. (2012). Application: Binomial Probit Model. In Econometric Analysis 7th Edition (pp. 711-714). London: Prentice Hall, Pearson Education.
Greene, W. H. (2012). Endogeneity and Instrumental Variable Estimation. In Econometric Analysis 7th Edition (pp. 259-270). London: Prentice Hall, Pearson Education.
Hernández-Murillo, R., & Marifian, E. A. (2013). District Overview: Urban Areas Host the Largest Manufacturing and Service Employers. Regional Economist. Missouri.
Hill, H. D., & Ybarra, M. A. (2014). Less-Educated Workers' Unstable Employment: Can the Safety Net Help? Fast Focus. No. 19-2014. Institute for Research on Poverty.
Jung, Sin-Ho. (2014). Stratified Fisher's Exact Test and its Sample Size Calculation. Biometrical journal. 56(10), 129-40.
Kabir, M., Radović Marković, M., & Radulović, D. (2019). The determinants of income of rural women in Bangladesh. Sustainability, 11(20), 5842.
Kampelmann, S., Rycx, F., Saks, Y., & Tojerow, I. (2018). Does education raise productivity and wages equally? The moderating role of age and gender. IZA Journal of Labor Economics, 7(1), 1-37.
Kenya National Bureau of Statistics, Ministry of State for Planning, National Development and Vision 3030. (2012 b). Analytical Report on Migration. Nairobi: Government Printer.
Leigh, N. G. (2013). Strengthening urban industry: The importance of infrastructure and location. Infrastructure and Land Policies, 318-340.
Mahabir, R., Crooks, A., Croitoru, A., & Agouris, P. (2016). The study of slums as social and physical constructs: Challenges and emerging research opportunities. Regional Studies, Regional Science, 3(1), 399-419.
Mallach, A. (2018). The divided city: Poverty and prosperity in urban America.
Marra, G., Papageorgiou, G., & Radice, R. (2013). Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing. Australian & New Zealand Journal of Statistics, 55(3), 321-342.
Mberu, B., Béguy, D., & Ezeh, A. C. (2017). Internal migration, urbanization and slums in sub-Saharan Africa. In Africa's population: In search of a demographic dividend (pp. 315-332). Springer, Cham.
Moloi, T., & Marwala, T. (2020). The Dual-Sector Model. In Artificial Intelligence in Economics and Finance Theories (pp. 33-41). Springer, Cham.
Mudege, N. N., & Zulu, E. M. (2011). In their own words: assessment of satisfaction with residential location among migrants in Nairobi slums. Journal of Urban Health, 88(2), 219-234.
Mueller, V., & Thurlow, J. (2019). Youth and jobs in rural Africa: Beyond stylized facts (p. 336). Oxford University Press
Nelson, M. S., Wooditch, A., & Dario, L. M. (2015). Sample size, effect size, and statistical power: A replication study of Weisburd’s paradox. Journal of Experimental Criminology, 11(1), 141-163.
Oyefara J.L. (2018) Migration and Urbanization in Africa. In: Oloruntoba S., Falola T. (eds) The Palgrave Handbook of African Politics, Governance and Development. Palgrave Macmillan, New York. https://doi.org/10.1057/978-1-349-95232-8_27
Papaelias, T. (2013). A theory on the urban rural migration. International Journal of Economics & Business Administration. 1(4), 17-30.
Pieterse, D. E. (2013). City futures: Confronting the crisis of urban development. Zed Books Ltd.
Ross, SM. (2017). Testing Statistical Hypotheses. In S. M.Ross, Introductory statistics (pp. 381-432). London: Academic Press.
Sarah, A. (2012). Determinants of rural household income diversification in Senegal and Kenya. 6èmes Journées de recherches en sciences sociales SFER-INRA-CIRAD. SFER, INRA, CIRAD, Toulouse School of Economics. Paris: SFER, 18.
Stokes, E., Lauff, C., Eldridge, E., Ortbal, K., Nassar, A., & Mehta, K. (2015). Income generating activities of rural Kenyan women. Journal of Sustainable Development, 8(8), 42.
Tacoli, C., McGranahan, G., & Satterthwaite, D. (2015). Urbanisation, rural-urban migration and urban poverty. Human Settlements Group, International Institute for Environment and Development.
Uzunoz, M., & Akcay, Y. (2012). A case study of probit model analysis of factors affecting consumption of packed and unpacked milk in Turkey. Economics Research International, 2012. https://doi.org/10.1155/2012/732583
Verma, J. P., & Verma, P. (2020). Introduction to Sample Size Determination. In J. P. Verma, Determining Sample Size and Power in Research Studies: A manual for Researchers (pp. 1-7). Singapore: Springer.
Zulu, E. M., Beguy, D., Ezeh, A. C., Bocquier, P., Madise, N. J., Cleland, J., & Falkingham, J. (2011). Overview of migration, poverty and health dynamics in Nairobi City's slum settlements. Journal of Urban Health, 88(2), 185-199.
Downloads
Published
Data Availability Statement
Upon Request
Issue
Section
License
Copyright (c) 2025 T, Mbegera (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under this license:
-
Authors retain copyright of their work.
-
Users are free to:
-
Share — copy and redistribute the material in any medium or format
-
Adapt — remix, transform, and build upon the material for any purpose, even commercially
-
-
Conditions:
-
Appropriate attribution must be given to the original author(s) and source.
-
A link to the license must be provided.
-
Any changes made must be indicated clearly.
-
This license ensures the widest possible dissemination and use of published research, while giving authors credit for their work.
Why CC BY 4.0?
The CC BY 4.0 license supports:
-
Open Access principles, allowing anyone to read, share, and reuse articles without restriction.
-
Compliance with funder mandates (e.g., Plan S, Horizon Europe).
-
Academic citation and attribution integrity.
For full legal details of this license, please visit:
https://creativecommons.org/licenses/by/4.0/