This paper presents comparative qualitative and quantitative evidence from rural Kenya and Madagascar in an attempt to untangle the causality behind persistent poverty. We find striking differences in welfare dynamics depending on whether one uses total income, including stochastic terms and inevitable measurement error, or the predictable, structural component of income based on a household's asset holdings. Our results suggest the existence of multiple dynamic asset and structural income equilibria, consistent with the poverty traps hypothesis. Furthermore, we find supporting evidence of locally increasing returns to assets and of risk management behaviour consistent with poor households' defence of a critical asset threshold through asset smoothing.

Kenya is an agricultural nation, with over 12 million people residing in areas with degraded lands. Unfortunately, the food crop productivity growth in the country has failed to exceed the population growth. The growth of agricultural output in Kenya is constrained by many challenges including soil erosion, low productivity, agro-biodiversity loss, and soil nutrient depletion. Land exploitation devoid of proper compensating investments in soil and water conservation will lead to severe land degradation. This will translate to loss of rural livelihoods, diminished water supplies and threaten the wildlife habitat. This study explores the causes, extent and impacts of land degradation in Kenya, discusses the costs of action versus inaction in rehabilitating degraded lands, and proposes policy options for promoting sustainable land management (SLM). In order to appropriately support SLM, there is a need to account for the total economic value (TEV) of land degradation, i.e. including the value of both provisioning and indirect ecosystem services of land. Using such a TEV approach, findings show that the costs of land degradation due to land use and land cover changes (LUCC) in Kenya reach the equivalent of 1.3 billion USD annually between 2001 and 2009. Moreover, the costs of rangeland degradation calculated through losses in milk and meat production, as well as in livestock live weight decreases reach about 80 million USD annually. Furthermore, the costs of "soil nutrient mining" leading to lower yields for three crops, namely wheat, maize and rice in Kenya were estimated at about 270 million USD annually. The cost of taking action to rehabilitate lands degraded through LUCC is found to be lower than the cost of inaction by 4 times over a 30 year period, i.e. each dollar invested in land rehabilitation is likely to yield four dollars of returns. This may strongly justify the urgent need for taking action against land degradation. Addressing land degradation involves investments in SLM. Our econometric results show that improving access to information on SLM and to the markets (input, output, financial) may likely stimulate investments into SLM by agricultural households.

PurposeThe data used in this study is for the period 1980‐2000. Almost midway through this period (in 1992), the Kenyan government liberalized the sugar industry and the role of the market increased, while the government's role with respect to control of prices, imports and other aspects in the sector declined. This exposed the local sugar manufacturers to external competition from other sugar producers, especially from the COMESA region. This study aims to find whether there were any changes in efficiency of production between the two periods (pre and post‐liberalization).Design/methodology/approachThe study utilized two methodologies to efficiency estimation: data envelopment analysis (DEA) and the stochastic frontier. DEA uses mathematical programming techniques and does not impose any functional form on the data. However, it attributes all deviation from the mean function to inefficiencies. The stochastic frontier utilizes econometric techniques.FindingsThe test for structural differences in the two periods does not show any statistically significant differences between the two periods. However, both methodologies show a decline in efficiency levels from 1992, with the lowest period experienced in 1998. From then on, efficiency levels began to increase.Originality/valueTo the best of the authors' knowledge, this is the first paper to use both methodologies in the sugar industry in Kenya. It is shown that in industries where the noise (error) term is minimal (such as manufacturing), the DEA and stochastic frontier give similar results.

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