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Scoring and ‘tally’ methods to show attribution The main approach to attribution in PIA is to separate out the project and non-project factors as determinants of outcomes, rather than to use the matching methods (using controls) approach. This involves firstly developing a ‘causal diagram’ showing all the potential project and non-project factors contributing to a given impact. These are then ranked or scored. If all the factors are scored using proportional piling (100 counters is best for calculating percentages), the project (or nonproject) factors can be aggregated into a statement such as: “the project-related factors contributed X% to a project outcome/impact” For example, Table T13 shows the scoring for six project and non-project factors contributing to a positive change in food security status following an agricultural recovery project in a post-conflict setting. It was concluded that the project-related factors made a 29% relative contribution to improved food security. Table T13: Attribution by Ranking and Scoring for a Food Security Project Factor Project or Non-Project Factor Rank Score Improved rainfall Non-project 1 33 Improved security Non-project 2 26 Improved seeds Project 3 19 Government extension service Non-project 4 12 Provision of fertilizers Project 5 8 Provision of tools Project 6 2 Source: Catley, A., Burns, J., Abebe, D. and Suji, O. (2008). Participatory Impact Assessment: A guide for practitioners. Feinstein International Center, Medford. Another approach is to ask individuals to list all the factors they think have contributed to a particular outcome or impact. When everyone has been asked, the number of times each potential cause was mentioned is added up. This is known as the ‘tally method’. Table T14 presents an example of the tally method based on the responses (74) to an open-ended question: what has contributed to improved food security following the drought in Niger? (this followed a ‘before and after project’ scoring exercise on food sources). Table T14: Reasons for Improved Household Food Security in Niger Factors Project or non-project factor No. of responses (n = 74) Cereal Banks Project 68 Better farm inputs Project 59 More income to buy food Project 50 Livestock restocking Project 46 Vegetable production Project 38 Food Aid Non-project 10 Decrease in crop pests and diseases Non-project 8 Improved rainfall Non-project 5 Source: Reproduced with permission from Catley, A., Burns, J., Abebe, D. and Suji, O. (2008). Participatory Impact Assessment: A guide for practitioners. Feinstein International Center, Medford Social Impact Assessment of Land-Based Carbon Projects (1.0) – Part II | 55
A large sample is needed to be confident of the tally method. An advantage of the tally methods is that by not listing or pre-defining the potential factors, there is little risk of influencing people’s responses; there may however be a bias towards them mentioning project-related factors, especially if they know the study is being carried out to analyze project impacts, and important non-project factors could be omitted. As with all participatory methods, great care is needed to avoid bias. Given the danger that respondents are more likely to cite project-related factors if someone from the project undertakes the survey, it would be better to get an independent group to do it, e.g., a local NGO unconnected to the project. Advantages and Disadvantages of Participatory Impact Assessment Main Advantages or Benefits Main Disadvantages or Limitations • PIA methods can be designed with an explicit attribution focus • With 10-15 repetitions with different focus groups and systematic use of the methods, basic statistical analysis is possible • Any level of ‘differentiation’ is possible, e.g., analysis of wealth, gender and ethnic factors • Participatory and low cost approach using locally defined indicators • Flexible and adaptable – can be combined with other methods Main Source and Further Guidance • Methodology needs to be adapted to each locality and may require a relatively long planning process • Possible vulnerability to bias (e.g., strategic responses; project factors more likely to be mentioned in ‘tally tables’) • Quite demanding of community time • Variable levels of rigor and reliability • Some methods are time consuming, e.g., pairwise or matrix ranking Catley, A., Burns, J., Abebe, D. and Suji, O. 2008. Participatory Impact Assessment. A Guide for Practitioners. Feinstein International Center, Tufts University. Available at: http://wikis.uit.tufts.edu/confluence/display/FIC/Participatory+Impact+Assessment (also available in Spanish and French). Social Impact Assessment of Land-Based Carbon Projects (1.0) – Part II | 56
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MANUAL FOR SOCIAL IMPACT ASSESSMENT
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Table of Contents T1 Introduction t
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List of Boxes Box T1. General Data
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Forest Trends’ mission is to main
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List of Acronyms (Core Guidance and
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Sections T5 and T6 present a range
Page 13 and 14: Figure T1: The Main Stages and Step
Page 15 and 16: Figure T2: Example of a Conceptual
Page 17 and 18: Figure T4: Generic Conceptual Model
Page 19 and 20: Stages 3-5 of Open Standards The re
Page 21 and 22: Figure T7: Diagram of the ROtI Anal
Page 23 and 24: table so they can be read, discusse
Page 25 and 26: Table T1: Theory of Change for Seyc
Page 27 and 28: T2.4 Participatory Impact Pathways
Page 29 and 30: Table T4: The PIPA Outcomes Logic M
Page 31 and 32: Main Sources and Further Guidance h
Page 33 and 34: T3 Sustainability Framework Approac
Page 35 and 36: Figure T12: SAPA Initiative Modifie
Page 37 and 38: 1. Identify a small group of key in
Page 39 and 40: Table T6: Example of LOAM Livelihoo
Page 41 and 42: T4 Matching Methods T4.1 Introducti
Page 43 and 44: Advantages and Disadvantages of ‘
Page 45 and 46: Box T1. General Data Collection and
Page 47 and 48: Box T3. Data Collection Methods Pro
Page 49 and 50: T6 Specific Data Collection Methods
Page 51 and 52: Table T8: Example of a Household BN
Page 53 and 54: The poverty index can range from 0%
Page 55 and 56: The PIA guide proposes the followin
Page 57 and 58: day. The ‘before project’ score
Page 59 and 60: Figure T15: “Before and After”
Page 61 and 62: Table T10: Pair-Wise Ranking Showin
Page 63: Impact calendars Impact calendars c
Page 67 and 68: Table T15: Scoring of Changes in Ag
Page 69 and 70: Table T17: QPA Scoring of Social Eq
Page 71 and 72: T6.4 Participatory Economic Valuati
Page 73 and 74: Advantages and Disadvantages of Par
Page 75 and 76: The stories are collected from thos
Page 77 and 78: Main Sources and Further Guidance D
Page 79 and 80: Table T19: Stakeholder Analysis Pro
Page 81 and 82: Figure T18. Venn Diagram with Stake
Page 83 and 84: T7.2 Problem Trees Assuming it is p
Page 85 and 86: T7.3 Scenario Analysis Scenarios ar
Page 87 and 88: Box T7. Driving Forces in a Communi
Page 89 and 90: Maack, J.N. 2001. Scenario analysis
Page 91 and 92: In terms of non-monetary benefits,
Page 93 and 94: and duration of employment, the sta
Page 95 and 96: Physical Capital Positive changes i
Page 97 and 98: these strategic resource off-limits
Page 99 and 100: einforce existing institutions or n
Page 101 and 102: PROJECT: PROFAFOR TYPE: Plantations
Page 103 and 104: their occurrence will depend in lar
Page 105 and 106: HUMAN CAPITAL Increase in perceptio
Page 107 and 108: Table T26: Potential Mid- to Long-T
Page 109 and 110: Figure T20: Example of Relationship
Page 111 and 112: Figure T22: Additional Potential So
Page 113 and 114: T9 Further Guidance on Indicator Se
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Box T9. Goals and Indicators Defini
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When selecting indicators, it is be
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Table T27: Indicator Quality Assess
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Box T10. Indicators Derived from th
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T9.5 Disaggregating Indicators Mult
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Mayoux, L. 2001. What Do We Want to
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Landscape Outcomes Assessment Metho
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• Access to essential services (w
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T10.3 Social Indicators Derived fro
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Empowerment of women indicators:
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CCBA, 2008. Climate, Community & Bi
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James A., Pangtey V., Singh P. & Vi
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SEEP Network. 2006. Social Performa
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