manual for social impact assessment of land-based ... - Forest Trends
manual for social impact assessment of land-based ... - Forest Trends
manual for social impact assessment of land-based ... - Forest Trends
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T9.5 Disaggregating Indicators<br />
Multi-dimensional indicators <strong>of</strong>ten need to be disaggregated, or broken down, in order to reveal<br />
differences between their various components. The decision on the level <strong>of</strong> disaggregation <strong>of</strong><br />
indicators is as important as the choice <strong>of</strong> the indicator itself. Indicators can be disaggregated along<br />
various dimensions, including location, gender, income level, and <strong>social</strong> group (<strong>based</strong> on ethnicity,<br />
religion, tribe, caste). Aggregate, country-level indicators are useful, as they give an overall picture <strong>of</strong><br />
where a country stands in comparison with others. However, these can mask significant differences<br />
across areas, gender, or <strong>social</strong> groups which will affect how well an <strong>impact</strong> can be monitored and<br />
assessed (Prennushi et al., 2002). At the project level, disaggregation is <strong>of</strong>ten necessary.<br />
Although smaller projects may find it harder to disaggregate by geographical areas, other possible<br />
examples include disaggregating by gender, income, consumption, asset ownership and ethnicity. It<br />
is also important to recognize that disaggregating indicators by areas, groups, etc., can have political<br />
consequences, and must be done carefully.<br />
Gender considerations are perhaps the most frequent reason <strong>for</strong> disaggregating data. It is well<br />
established that men and women use <strong>land</strong>-<strong>based</strong> resources differently, have different access to<br />
programs, and are affected differently by activities/programs/projects. Project proponents must<br />
understand these differences in order to improve the efficiency and effectiveness <strong>of</strong> the project, and<br />
to ensure that women and men have equitable access to the project’s benefits, and that neither is<br />
negatively affected by the project. Table T28 demonstrates how indicator data can be disaggregated.<br />
Table T28: Data Disaggregation and Analysis, by Indicator<br />
Indicator<br />
Aggregate<br />
Analyze by:<br />
Activity<br />
Increase in income <strong>for</strong> community from carbon payments<br />
Value <strong>of</strong> carbon payments to community <br />
Number <strong>of</strong> community development projects<br />
completed<br />
<br />
Number <strong>of</strong> direct beneficiaries under Indicator <br />
T9.6 Stakeholder Participation in Indicator Selection<br />
<br />
<br />
Gender<br />
<br />
Youth/Adult<br />
<br />
Additional Analysis by:<br />
Cash, Material, Labour,<br />
Source<br />
Project type<br />
Intervention type<br />
Until recently, the most common approach <strong>for</strong> the selection <strong>of</strong> indicators was a priori external<br />
selection where indicators were selected at the beginning <strong>of</strong> an <strong>assessment</strong> by external assessors or<br />
by the project development staff. This resulted in subjective biases as the monitoring process was<br />
Social Impact Assessment <strong>of</strong> Land-Based Carbon Projects (1.0) – Part II | 114