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Livelihood asset maps: a multidimensional approach to measuring risk-management capacity and adaptation policy targeting—a case study in Bhutan

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Abstract

The application of a livelihood asset-based approach to adaptation policy targeting is presented through the creation of maps highlighting the spatial contrasts of access to various types of livelihood assets utilizing primary household data. Thus, the livelihood maps provide policy-makers with a tool to quickly identify areas with limited access to certain types of assets, making the latter less able to react to a changing level of climate-related risks. In the case of Bhutan, distinct spatial patterns of asset endowments is identified using five different asset indicators drawing attention to the fact that some areas facing increased level of climate-related risks lack access to productive and human capital, while other areas facing a similar situation have relatively insufficient access to financial assets. This again shows that any non-targeted policy aiming at improving households’ risk-management capacities through asset-building would have quite diverse results even among closely located districts in Bhutan. Finally, relevant policy options concerning the various dimensions of asset holdings are discussed so as to identify options that may benefit poor and vulnerable no matter if the expected outcomes of a changing climate are realized or not.

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Notes

  1. The terms “no-regrets” and “pro-poor” should be seen as a way to identify policy options that would be beneficial to the poor and vulnerable, no matter what the uncertain future consequences of a changing climate.

  2. Bhutan is 47,000 km2 large and has around 700,000 citizens. Hence, 20 districts are quite disaggregated.

  3. Principal component analysis (PCA) has also been used to compile the necessary indicators regarding asset-based maps (Erenstein et al. 2010). While the assumptions behind factor analysis and PCA differ, the end result, that is, the inter-district ranking of asset holdings, is virtually the same. Factor analysis was finally chosen primarily due to its more relaxed assumptions regarding correlation structure.

  4. Small area estimations of, for example, poverty are suitable for identifying local pockets of poverty, but it is more difficult to highlight the possible differences between two poor areas by focusing solely on a single welfare outcome.

  5. Natural capital usually covers soil quality, rainfall and the like (and such factors can be difficult to allocate to specific households), while social capital is normally proxied by membership of community-level organisations or cooperatives and similar networks.

  6. The factor loadings were taken from the first factor. For all the indicators except human capital, the eigenvalue of the first factor confirmed that the first factor loading captured the most significant part of the variation. For the human capital indicator, the second set of factor loadings was also considered due to the high eigenvalue, but it was decided to use only the first factor, as this was the only intuitively appealing one.

  7. The categories are determined using natural jenks, meaning that within each group the average deviation from the mean is minimized, while the deviation from the other groups is maximized.

  8. Ruminants are included to capture easily sellable assets in terms of goats and the like.

  9. It is not surprising that ownership of physical and agricultural productive assets correlates well with the estimated community-level poverty rates, as these rely heavily on exactly the same variables.

  10. In this case, identifying the perceived climate-related risks is mostly linked to identifying the areas in Bhutan that are most likely to be affected by changing levels of climate-related risks.

  11. As a matter of fact, changing the timing of payments does not change primary enrolment rates, but it does a positive effect on the secondary and tertiary levels.

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  1. UNEP Risoe Centre on Energy, Climate and Sustainable Development, Roskilde, Denmark

    Kristian Jakobsen

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Jakobsen, K. Livelihood asset maps: a multidimensional approach to measuring risk-management capacity and adaptation policy targeting—a case study in Bhutan. Reg Environ Change 13, 219–233 (2013). https://doi.org/10.1007/s10113-012-0320-7

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