Working paper 21 - Rural Poverty Dynamics, Agricultural Productivity and Access to Resources

Author(s):  Gamba, Paul; Mghenyi, Elliot


Introduction

The pursuance of Structural Adjustment Programmes (SAPS) in recent years has awakened most Governments, including Kenya’s, to the increasing prevalence of rural poverty in their countries. In 2000, more than 45 percent of sub-Saharan Africa’s population was estimated to be in poverty, and this situation has not improved in at least the last 15 years (World Bank, 2000).1 While efforts have been made to track poverty levels through standard welfare monitoring surveys and the computation of statistics on poverty prevalence, depth and severity, such information rarely provides insights for the design of specific anti-poverty programmes.

Rising poverty levels have prompted the international community to develop and seek consensus on internationally agreed development goals to be pursued by governments. This has led to the adoption of the International Development Goals and consequently the United Nations endorsed Millennium Development Goals (MDGs).

At the same time, multilateral lending agencies also developed their own version of development goals that focus on poverty alleviation strategies. As a result, loan recipient governments have been required to develop Interim Poverty Reduction Strategy Papers (IPRSP) as a prelude to more elaborate Poverty Reduction Strategy Papers (PRSP) that together with other requirements form the basis of continued lending programmes. The need to provide indications of progress towards achievement of these objectives has given new impetus to re-examination of the adequacy of welfare monitoring surveys as currently conducted.

In order to fill this void, the Ministry of Finance through the PRSP secretariat and the Central Bureau of Statistics (CBS) have devised innovative systems to capture information pertinent to monitoring poverty over time. This system involves the development of monitoring and evaluation protocols and poverty mapping tools to areas experiencing high and severe poverty and the associated socio-economic groups. The poverty mapping concept has been applied in the current welfare monitoring survey in Kenya but was limited to Nairobi and its environs.

However, our understanding of “poverty dynamics”, e.g., the extent to which poor households in one year remain poor in subsequent years as opposed to moving out of poverty, has not received commensurate attention from either the PRSP secretariat or the CBS. This can partly be attributed to the lack of appropriate panel data that tracks the poverty status of rural households over time in Kenya. This has also inhibited the ability to understand the reasons why some households that are below the poverty line in one period are able to climb out of poverty in subsequent periods, while others remain chronically mired in poverty. It should be noted that this problem is not peculiar to Kenya and is exhibited in a number of countries.

Even the World Bank, which is renowned for its eminent work in the area of poverty dynamics has little relevant information on Kenya. The PRSP monitoring and evaluation exercise and the CBS poverty mapping process can be complemented by rigorous analysis of panel data to provide gainful insights into the dynamics of poverty in Kenya through the analytical methods utilized in this study. In its endeavours to contribute to the policy process in Kenya by strengthening the quantitative information base, Tegemeo Institute of Egerton University, with technical support from Michigan State University, conducted surveys of roughly 1,400 households in 1997 to 2000. This panel database was designed to provide panel data that could be used to monitor the progress of rural households and the agricultural sector over the years.

In an effort to characterize rural poverty dynamics, panel data can provide insights unachievable through other means. The objectives of this paper are threefold:  First, we measure the prevalence of rural poverty in 1997 and 2000, based on the nationwide Tegemeo survey.  Second, we categorize households according to whether they were above the poverty line in both 1997 and 2000, entered into poverty or exited from poverty between 1997 and 2000, or were above the poverty line in both years. 

Third, the paper identifies the household-level and community-level factors associated with rural poverty through econometric analysis. Lastly, we consider the implications of these results for the design of appropriate poverty reduction strategies. Such analysis is intended to guide donor programs and interventions designed to attack the roots of chronic poverty. Characterization of poverty categories can also ensure that their relationship with access to physical and social capital, agricultural productivity growth and non-farm income are understood and utilized to ensure attainment of agreed poverty reduction objectives.

 

Rural Poverty Dynamics, Agricultural Productivity and Access to Resources