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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

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