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Understanding Forests Contribution to Poverty Reduction
Bringing Forest and Poverty into Focus in Argentina
Turkey Forest Villages: Socioeconomic study of forest villagers to better understand the causes of out-migration
Forest-SWIFT Methodology for High-Frequency Forest-Poverty data collection
Around 1.3 billion people – most of them living on less than $1.25 a day - rely on forests for some part of their livelihood. However, detailed, country-specific data is still lacking when it comes to forests’ socio-economic contributions and their role in poverty alleviation. Without this information, forests may be overlooked in national development strategies. This activity aims to contribute to knowledge on forest’s contribution to movements out of poverty by using a new and innovative approach to forest-poverty data collection.
A new tool, Forest-SWIFT (Survey of Wellbeing via Instant and Frequent Tracking) is being developed to collect information to document who the poor are, where they live and how they rely on forests. Forest-SWIFT combines the latest statistical methods with in-person interviews using smart phones or tablets, and also functions offline where mobile coverage or technology is limited. Survey data is collected on a central server, where it can be rapidly accessed, analyzed, and used to design more effective projects or policies.
The tool consists of 10-15 questions to strongly estimate forest reliance, a set of 10-15 questions to strongly estimate poverty, and a set of questions on governance. The tool methodology was developed by (i) modeling income (cash and non-cash) from forest to estimate forest reliance; (ii) modeling consumption/income models to estimate poverty; and (iii) identifying forest governance data. The tool is being field tested in Turkey, Argentina and Mozambique.
Forest-SWIFT can be used to complement the Living Standards Measurement Study (LSMS) Forestry Module. The LSMS Forestry Module is carried out every three years, and Forest-SWIFT can easily be carried out in the interim two years, to create a rich and comprehensive knowledge base on forests and poverty. Forest-SWIFT promises improved monitoring of forest projects, better targeting of beneficiaries of forest interventions, and more effective programs and policies that help to reduce poverty and enhance the economic, social and environmental benefits derived from forests.
This project has been completed. The main outcome of Forest-SWIFT was to equip policymakers with evidence emphasizing how forests are important for the poorest’ welfare and with a robust tool monitor the impacts of their forest interventions on beneficiaries’ welfare. The project emphasized the need for poverty reduction initiatives including more forest and its specificities.
Using Forest-SWIFT data collected in 2017, this activity found that poverty in Turkey was 23.2%, which reflects the poverty decline observed in the rest of the country. On average, forest households had more forest income in 2017 than they did in 2016. Forest dependence - as the ratio of forest income on consumption – was found to be 51% for poor households and 32.6% for non-poor households.
Thanks to the model and collected data, Forest-SWIFT work in Turkey highlights the importance of forest resources for households’ livelihoods. Measures of forest income show that households are more dependent on these resources but returns from these activities are low; analysis of this measure of income couldidegu the General Directorate of Forest (GDF) of Turkey on how to increase returns from forest activities. In addition, Turkey has now a reference point on poverty within forests, which could help monitor how projects and interventions affect poverty in these areas.
Using lessons from Forest-SWIFT in Turkey, the activity is informing additional forest projects on how to collect forest income data and work with the poverty team to strongly measure poverty in their projects.
In Armenia, Forest-SWIFT has been taken up by the Armenia Statistic Committee. This activity provided poverty rates not only for forest areas but also for the whole country. The data will inform the State Forest Committee on the importance of fuelwood to fulfil energy needs by poor and non-poor households in forest areas, rural and urban areas.
In Tunisia, although the existing data did not allow the use of the SWIFT methodology, the activity found that types of water source, assets, type of dwelling, occupation of household head are correlated to forest income. The data collected can be used to predict consumption and forest income in the future.
Forest-SWIFT continues to promote the need for more detailed data on forest livelihoods to understand poverty in forest areas and to have more robust measure of poverty in investment projects. The Brazil country team has expressed great interest to use Forest-SWIFT to analyse poverty in the Amazon ecoregion.
Thanks to Forest-SWIFT, teams can use a short set of questions to collect poverty and forest income determinants. These determinants are identified in a baseline model (one model for poverty and one model for forest dependence) using a linear regression but controlling for issues linked to over-fitting by using stepwise regressions. Once the determinants have been collected, the team can predict poverty and forest dependence using the betas from each baseline model and multiple imputation regression techniques. This activity consisted of testing a methodology to collect in a short and efficient manner data on poverty and forest dependence. This activity can be embedded in the monitoring and evaluation framework of IPF working on forests.
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Last Updated : 06-09-2020