Econometric analysis of labor mobility and economic growth in Kenya, (1990–2024)

Abstract

Kenya's labor sector has greatly shifted over the last thirty years while the labor market has been facing serious structural flaws. A large proportion of the working population is still stuck in precarious employment, especially in the unorganized- informal and agricultural sectors where labor conditions, pay, and job security are subpar. Over 65% of employed people work informally, and a sizable portion are underemployed, working fewer hours than they would like to or are capable of, or they are involved in low-productivity activities that are out of line with their qualifications and skill set, as per the Kenya National Bureau of Statistics reports. Thus, this study focused on Kenya's economic growth and labor mobility in the dynamic relationship between 1990 and 2024. In developing economies, labor mobility both sectoral and geographic is becoming more widely acknowledged as a key driver of productivity development and economic transformation. This study was anchored on Lewis Dual Sector Theory found in 1954. Using annual secondary data from reliable sources; the World Bank, and the International Labor Organization (ILO), the study estimated the effects of labor mobility on Kenya's Gross Domestic Product [GDP] growth using time-series and econometric techniques like quantitative design, unit root testing, pair-wise correlation analysis, and regression analysis. Using Augmented Dickey Fuller (ADF) and IM Peseran tests, the dataset for both independent and dependent variables was found to be stationary at levels. Pairwise correlation test showed a weak negative association, which is statistically insignificant between labour mobility and economic growth (r= -0.2694, p= .123). The OLS regression produced a negative coefficient for labour mobility (β = −0.58113), although the effect was not statistically significant at the 5% level, with R² = 0.639. A Jarque-Bera normality test, heteroscedasticity test, and CUSUM stability test indicate that the model is stable and can be estimated. The coefficient estimate indicates that for a one-unit change in the proxy for labour mobility, there is a reduction of 0.58113 units in economic growth, but this correlation is not statistically significant at the 5% level. Hence, policy interventions in order of priority should focus on: (i) making informal labor markets formal; (ii) ensuring that skills development programs match Kenya Vision 2030 and AfCFTA productive areas; and (iii) coordinating regional and national labor mobility and capacity building programs. The findings also have great significance in terms of designing national employment policies, integration at the regional level, and investments in human capital formation that is consistent with Kenya Vision 2030 and AfCFTA.