Abstract:
This study examines determinants, trends and dynamics of rural poverty in Kenya using data collected in 2013 and 2015 from USAID’s Feed the Future (FTF) program zones of influence, Two methods are pursued in this study. The first uses the conventional poverty line based on expenditure approach, while the other uses the Multidimensional Poverty Index Approach. Results of the descriptive analysis show that socio-economic indicators play an important role in household poverty incidence. Households with lower level of education attainment, lower literacy level of the head, or are female-headed have a higher probability of falling into poverty than households with both male and female adults.
The regression results show that poverty is likely to be higher among households with higher dependency burden. While the share of own production in the food consumption expenditure is found to reduce the probability of a household falling into poverty in the pooled and the year 2015 model, it increases the chances of households falling into poverty in year 2013 survey. The study also shows that poverty incidences are negatively correlated with household physical asset wealth. Results further show that the probability of a household falling into poverty increased in the year 2015 compared to 2013, and that the probability of a household falling into poverty are lower in Semi-Arid (SA2) zone of Influence compared to the High Rainfall (HR1) zone of influence. With regard to poverty dynamics, results indicate that in 2015, the MPI poverty rate was about 35 percent of the population in the FTF zone of influence. This rate was higher in in HR1 (39%) compared to SA2 (26%). However, the average intensity of deprivation was, on average higher in
SA2 (46.5%) compared to HR1 (42.7%). The overall MPI index, which is a product of percent of poor people and average intensity of poverty was 0.15 implying that the poor in FTF-ZOI experiences 3/20th of the deprivation that would that would be experienced if all people in FTF-ZOI were deprived in all the indicators. The living standard dimension has the highest contribution to MPI poverty followed by health. These findings have key implications on addressing rural poverty from a program perspective
