~ By Satvik Parashar
A recent study by Sandra Baqui´ from Columbia University and co-authors (including some NCCI members) assesses the effects of internal migration on poverty alleviation and reduced pressure on forests. Migration can, in theory, diversify income sources and increase asset ownership for a typical central Indian rural household. It can also contribute to forest restoration as a livelihood practice that is not dependent on forest extraction (compared to NTFP collection or cattle grazing). The study tests these very hypotheses with an extensive survey across rural central India.
The study was conducted in villages of central India, spanning the states of Maharashtra, Madhya Pradesh and Chhattisgarh, across a total geographical area of ~ 25 million hectares. Villages within 8km buffer of the forests were selected. They were split on the basis of distance from the town and then again on the basis of distance from the road, creating 4 groups. Total 5000 surveys were done in the 500 selected villages (10 households per village) equally distributed among the 4 groups.
Forest transition theory examines how forests evolve over time. This particular study focuses on a transition period of 5 years from 2013 to 2018. The household survey was designed to understand three major aspects in this period.
Firstly, the presence of internal migration in a household, the reasons and the extent of it. This was done by determining the number of household members that migrated from 2013 to 2018, the drivers of this migration, migration span and the year when migration first started in a household.
Secondly, the survey determined the economic benefits of migration with questions related to asset ownership, money sent back home by migrants, deposits, savings etc.
Thirdly, the effect on forest was analyzed by determining the extent of firewood and NTFP collection, LPG ownership and number of livestock owned. As these variables were measured over a five-year timeline, they relate directly to the forest transition for the same period.
The land cover classification was done using GIS and forest areas were defined as ones with greater than 10% tree cover. The study used Bare Ground Index (BGI), which is a normalized ratio of bare ground and tree cover, to measure forest degradation. Higher the BGI, more the forest degradation. Other geospatial data such as distance to town, road and protected areas, were also measured and used in analyses
Linear regression was used to assess the effect of migration on household assets as well as forest degradation. For, association with household assets, the outcome variable was chosen as ChangeOutcome, which is a measure of change in savings, assets and forest use, while factors such as demographics, income shocks etc. were the input variables. On the other hand, DegradationIndex was chosen as the outcome variable to measure forest degradation with input variables being; fraction of household involved in migration, income shocks, distances to road, towns, forests etc.
Proportion of households with at least one migrating member was found to be ~ 18%. Only three percent of these households had a member migrating permanently outside of the village in the last 5 years, an indication that migration was mostly seasonal. The main reason for migration was better pay (88 % of migrating households mentioned this), followed by better opportunities (29 % of migrating households).
Three different groups that were formed based on migration within households were: i) the households where migration started after 2013, ii) the households where there was no migration and, iii) the households where migration started prior to 2013. Associations with household assets and forest degradation were analysed for these groups.
In terms of household assets, households with migration before 2013 had less land than others in 2013, indicating that lack of permanent assets might be a driving factor for migration. The households with migration in 2013 were also more likely to own a mobile phone by that year, as it may be a useful asset for migrating households. Also, the education level for migrating households was higher than that for non-migrating ones. Households with migration received remittances from migrating members, but the rates of expenditure and savings were similar to that of non-migrating households.
The forest regeneration happened (as indicated by decreasing BGI) once the proportion of migrating households were more than 40% in a village. However, initially, the forest degradation increased until the proportion of migrating households was below this threshold.
The study implies that migration is linked to forest degradation only through indirect channels. Migration in the study region was primarily found to be seasonal and migrating households had less land ownership in the baseline year, suggesting a mechanism of push migration. Migrating households received significant remittances, however changes in liquid assets and expenditure were insignificant. Migration is associated with investment in improving housing and mobile phone adoption and majorly contributes to households’ subjective wellbeing. It has the potential to alleviate poverty, however the study found that the five-year time-period was too short to bridge the existing gap between migrating and non-migrating households. Forest regeneration and migration starts to have a constant or a positive relationship once the villages have approximately more than 40% of households with migrants. However, this relationship is unlikely to hold in the context of central India as only about 10% of households in the region are associated to migration.
Original Paper: Baquié, S., Urpelainen, J., Khanwilkar, S., Galletti, C. S., Velho, N., Mondal, P., ... & DeFries, R. (2021). Migration, assets, and forest degradation in a tropical deciduous forest of South Asia. Ecological Economics, 181, 106887.https://doi.org/10.1016/j.ecolecon.2020.106887
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