A4 - Socioeconomic analysis of land use systems of rural households

Abstract
Project A4 investigates trade-offs and synergies between economic growth, poverty reduction, and environmental objectives. Its research findings show that 20.6% of the households in the research area live on less than 1 US$ PPP capita-1 day-1. Poorer households have less access to credit markets and live in locations with poorer market and road infrastructure, where lower prices and higher price variability for cocoa are observed. The income activities of the poorest households are highly dependent on natural resources, i.e. use of forest products and conversion of forest land for agriculture, whereas the better-off households are particularly involved in activities outside the agricultural sector. During the third phase, we complete our analysis of the critical triangle by a dynamic and spatially explicit component. Based on expenditure surveys from 2005 and 2007, we will also analyse food demand and vulnerability to various natural risk factors,
mainly ENSO drought.

Summary
The stability of tropical forest margins is being analyzed by project A4 at the household and village level, guided by the concept of the critical triangle of rural development that distinguishes trade-offs and synergies between economic growth, poverty reduction, and environmental objectives. In its second phase, project A4 generated important new findings for a better understanding of the situation with regard to these three major objectives of rural development.

Using the 1 US$ poverty line at purchasing power parity (PPP) rate, 20.6% of the households in the research area are classified as very poor. Poorer households have less access to credit markets and live in locations with poorer market and road infrastructure, where we observe lower prices and higher price variability for cocoa. Income from perennial crops and non-agricultural income are particularly important for the better-off households. Key factors influencing non-agricultural incomes are the possession of physical capital not related to agriculture, ethnic affiliation, and education. These results show the dilemma of the poorest households. While having the greatest needs for nonagricultural income, they are also the most constrained in terms of access to education and capital endowment not related to agriculture. Income from the selling of forest products (mainly rattan) is particularly important for the poorest households. The probability that households are selling forest products decreases with the area of irrigated land owned, the level of formal education, and distance to tarmac roads. Poor and indigenous households acquire land by clearing primary forest, whereas better-off and non-indigenous households acquire land by purchase. This is presumably caused by the high labour-to-capital ratio among the poor, and the relative lack of access of nonindigenous households to informal land use rights. Moreover, the share of poor and poorest households which cleared forest since 1999 is higher compared to better-off households. The likelihood that a household decides to clear forest decreases with wealth, the share of irrigated land owned, and non-agricultural income-earning activities.
Our empirical results suggests that investment into irrigation technology, better access to primary education, and the promotion of non-agricultural activities are win-win-win policy options to foster economic development, reduce poverty, and reduce forest encroachment.
During the third phase, we complete the analysis of trade-offs and synergies between economic growth, poverty reduction, and environmental objectives by estimating a spatially explicit and dynamic regression model. Based on expenditure surveys from 2005 and 2007, we will also analyse food demand and vulnerability to various natural risk factors, mainly ENSO drought. In collaboration with Project A5, we continue the micro-economic analysis of agroforestry systems and income from forest uses from the second phase. Project A4 contributes to all three foci and works closely with all subprojects of program area A, Projects B1 and Project B2, those projects of C that undertake experiments on management and technologies in cocoa production, and with D5 and D6.