Five dimensions of impact | WHO
Introduction
The data categories under the “Who” dimension help enterprises and investors identify the people, communities, and aspects of the natural environment affected by their activities and assess how their experiences of social or environmental outcomes are shaped by their social and behavioral characteristics and geographic, social, and cultural contexts.
The individual people, places, and communities that enterprises and investors affect represent a diversity of experiences and interests. The further an enterprise or investor is from those people and communities, the more difficult it becomes to account for this heterogeneity. Enterprises and investors strive not to think about or describe those experiencing impact as an abstract or homogenous monolith.
To ground impact analysis and conclusions in concrete and specific experiences of affected people, communities, and aspects of the natural environment, enterprises and investors consider the following data categories:
Enterprises and investors can use these data to increase their impact by understanding the diverse needs and aspirations of affected people and communities and directing resources to meet those needs.
The premise behind the “Who” categories is simple: if you have limited resources to allocate towards creating impact, how should you direct those resources?
The impact data categories under the “Who” dimension help enterprises segment the people and communities experiencing impact based on their unique needs, allowing enterprises to develop responsive strategies. Addressing the barriers and gaps faced specifically by people experiencing outcomes below threshold can maximize impact by creating conditions for all people to thrive.
The remaining sections of this page provide guidance on the four impact data categories within the “Who” dimension.
What types of relationships do enterprises have with the people they affect?
The relationship data category allows enterprises and investors to identify the different relationships they have with the people they are affecting. Enterprises and investors have traditionally placed an emphasis on just one or two relationship types – such as customers and employees – although in reality they affect (and are affected by) more. If an enterprise or investor wants to understand its “total” impact, it needs to consider all of its relationships.
Enterprise and investor activities usually have an impact on the following groups / entities, defined here in relation to the enterprise (see diagram below for an illustrative example):
- Local communities who are directly or indirectly affected by an enterprise’s activities (e.g., unhealthy factory emissions that negatively affect surrounding local communities, or access to play space for local children through construction of a playground as part of an affordable housing complex)
- Suppliers and distributors who are affected by the enterprise’s volume of procurement, regulations, and quality control (e.g., a zero-tolerance policy on child labor that affects suppliers)
- The planet, which an enterprise affects by extracting, using, and creating environmental resources; and by the pollution that is emitted by these processes
- Customers who use the enterprise’s products and services
- Employees who work for the enterprise
- Leaders / owners of the enterprise (including members of governing bodies) accruing value (financial and otherwise) from products and services (e.g., profit)
- People affiliated with the enterprise’s capital providers (including investors, members of governing bodies, and their own capital providers) accruing value (financial and otherwise) from investment activities
It is not always possible to quantify impacts experienced by affected parties in the same way as value accruing to enterprises and investors. However, acknowledging the expected financial value accruing to an enterprise’s leaders and capital providers, alongside its intended impacts on other people, can provide a more complete picture of impact, clarify which perspectives are prioritized in decision-making, and identify opportunities to align activities with impact goals. For example, if an investor is considering investing in an enterprise that will increase monthly incomes for community members by $10 a month while also providing a $1,000 monthly benefit to investors, considering these impacts together could prompt investors to examine the impact targets they consider reasonable for each group and decide if they want to make any adjustments.
Categorizing these relationships is the first step towards understanding who is affected by an enterprise’s activities. This categorization can be further broken down for a more granular representation of relationships (suppliers could be categorized into upstream or downstream activities; the planet could be classified according to ecosystems).
Relevant Resources
- Capitals Coalition Framework for Integrated Decision-making: A framework to help organizations measure, value, and integrate considerations about natural, social, human, and produced capital into existing business processes.
- Social Value International Supplementary Guidance on Stakeholder Involvement: Guidance on using feedback and involvement by people experiencing impact to help identify the most important outcomes being delivered / needed.
What is the baseline?
The baseline refers to the level of outcome people experience prior to engaging with the enterprise.
Capturing outcomes before an initiative begins is essential for:
- Understanding who is experiencing positive and negative outcomes (defined in relation to thresholds), including differences among subgroups (e.g., gender, race / ethnicity)
- Setting impact targets
Estimating outcome changes once the product, service, or policy has been rolled out
Equipped with this knowledge, enterprises and investors can (re)allocate resources to address the barriers and gaps that people and communities experiencing below-threshold outcomes face.
The concept of baseline data is akin to market entry research: before entering a new market, enterprises take the “pulse” of the industry of focus by collecting data on a variety of factors including opportunities, challenges, growth dynamics, leading players, and consumer drivers.
An effective baseline indicator should mirror the outcome indicator as closely as possible. For example, if the outcome indicator is the % of people with an active savings account, then the baseline indicator should capture the same information. These data are crucial for assessing whether progress has been made since the product, service, or policy has been launched.
Assessing baseline outcomes in relation to thresholds
Thresholds define the levels of outcomes that are considered positive versus negative. Assessing baseline outcomes in relation to thresholds identifies whether people are experiencing positive and negative outcomes. This assessment helps enterprises tailor their strategies to address the barriers that those people and communities face and improve outcome performance.
Though thresholds provide useful reference points by which to interpret outcomes, strategies that create conditions for all people and communities to continue to thrive beyond the threshold level can increase impact for individuals and society as a whole through a positive feedback loop of human and social capital (see diagram below). For example, higher wages not only improve the well-being of individual employees but can also lead to improvements in educational and health outcomes of family members.
Note on “deficit-framing”
While many investors and/or enterprises may have an explicit commitment to reaching people experiencing negative outcomes, falling into “deficit-framing” while pursuing this mission can inadvertently define those people by their negative experiences and undermine rather than support their well-being.
“Deficit-framing,” a term introduced by social entrepreneur Trabian Shorters, is the practice of “defining people by their challenges, ignoring their aspirations or contributions, then remediating them to be less burdensome on society.” This can perpetuate stereotypical associations between people and problems (e.g., labeling low-income youth as “at risk” of violence and substance use versus discussing violence and substance use as risk factors of their surrounding environment).
Identifying the people and communities experiencing negative outcomes is an intermediate step to identifying the aspects of a current market or system that may impede or exclude them.
Reference: The Power of Perception: A Beginner’s Guide to Asset-Framing
Relevant Resources
- World Bank Reconstructing Baseline Data: Considers how baseline data can be reconstructed for impact evaluation and results measurement.
- IFRC Baseline Basics: Considers how enterprises can conduct reliable and useful baseline studies.
- FHI 360 Vulnerability Assessment Methodologies: An overview of the different methods used to measure vulnerability.
How can enterprises segment outcome data?
Disaggregating and segmenting outcome data can provide enterprises and investors with crucial insights on which subgroups experience disproportionately positive or negative outcomes so they can begin to examine the causes of differential outcomes and produce more focused interventions. Enterprises and investors can segment outcome data based on social identities (e.g., race, gender, or sexuality), behavioral characteristics, and demographics to identify outcome inequities that might otherwise go unaddressed.
Understanding how multiple social identities in combination (e.g., combinations of gender, race, and religion) can shape experiences of outcomes is particularly important when segmenting outcome data to uncover and address inequities. For example, if aiming to improve health outcomes for people with disabilities, an enterprise that does not segment data by both disability status and immigration status might overlook the disproportionately negative outcomes that recent immigrants with disabilities experience and fail to address the unique barriers they face when navigating unfamiliar health care systems.
The figure below visualizes the concept of multiple social identities creating unique lived experiences, known as intersectionality.
The following two case studies illustrate the value of assessing behavioral characteristics and social identities.
Using behavioral data to create solutions that fit the needs of consumers
Rwandan-based Nuru Energy faced a challenge: despite selling a significantly healthier and cheaper product (smoke-free rechargeable bulbs), many people still preferred using kerosene as a source of light. This was difficult to reconcile for the company, as kerosene fumes cause more deaths than malaria and equal the effects of smoking two packs of cigarettes a day.
To understand the low adoption of its product, Nuru Energy researched the habits of its potential consumer — those living on less than US$5 a day in rural areas. The process led the organization to identify two distinct consumer segments who saw kerosene as a more attractive solution. The first group, the inconvenience-averse, associated rechargeable bulbs with time away from productive activity, as visiting bulb recharging centers required traveling large distances. The second group, the blackout-averse, did not want to be caught without light, maybe because their children needed to study or their cattle to be fed after dark. For them, kerosene was a quick-fix solution.
These behavioral findings have prompted Nuru to test alternative solutions (including larger-capacity bulbs and door-to-door bulb recharge services) to improve adoption rates among these two segments.
Source: INSEAD (2017)
Segmenting market research by gender to meet the needs of women customers
The Kashf Foundation is a non-banking microfinance company that develops and distributes micro-insurance products in rural Pakistan with a mission of enhancing the financial capabilities of low-income families and reducing household poverty. To address gender-based economic inequity in Pakistan, they require that at least 55% of the loans across their portfolio be distributed to women farmers.
To meet this goal, they conducted segmented market research by gender and identified strategies to meet the specific needs of women. The Kashf Foundation was aware that repaying debts is a deep cultural value in Pakistan. However, their segmented research revealed that women in Pakistan are even more risk-averse to taking on debt than men. They also learned that women were uncomfortable with the process of tagging insured cattle by attaching a plastic tag to their ears.
As a result of this segmented analysis, Kashf developed a livestock loan product specifically tailored to women farmers that incorporated these and other learnings. They designed their product to allow premiums to be paid in affordable installments rather than in one lump sum and developed a bracelet that attaches to the foot of the cow to identify insured cattle. As a result, they were able to serve 9,500 women farmers in their first 2 years of offering the product.
Source: InsuResilience Fund (2020)
Collecting social identity and behavioral data
The indicators and data values for social identity and behavioral categories can be qualitative or quantitative, sourced from company administrative data, government data, and surveys, among other sources. The table below shows illustrative indicators and data values.
Though social identity data can be vital to understand and address the unique needs of subgroups, data disaggregation and segmentation do not achieve this goal in and of themselves. Follow-on actions are required to act on the insights gained. Moreover, collecting data on social identities and demographics always carries some risk of harm to the people providing data, either through the process of providing the data itself (e.g., experiencing discomfort when disclosing neurodivergent status) or due to data security concerns (e.g., a data breach that results in releasing sensitive information, such as LGBTQ2S+ status in a country that criminalizes LGBTQ2S+ identity).
Effectively leveraging social identity data (e.g., race, gender, age) to equitably improve outcomes requires a clear and actionable purpose, an understanding of which social identities to take into account in a given setting, consideration of the benefits and risks of data collection and use from the perspective of affected people and communities, and transparency as to how data will be used.
The following questions can help investors and enterprises articulate the intended purpose and use of social identity data:
- Which insights do we hope to gain?
- How will these insights inform decisions?
- Which data are needed to derive these insights?
- How will collecting and using these data impact the people contributing them? Will the benefits to them outweigh the risks to them?
Managing tradeoffs and tensions in disaggregated data collection
As a partner of the Government of Canada’s Social Finance Fund, impact measurement organization Common Approach to Impact Measurement facilitated the development of a plan to collect data on the social identities of the management teams and board members of investment funds and investee enterprises. The goal of collecting these data is to understand how many managers and board members identify as belonging to “equity-deserving groups” including, but not limited to, Black, Indigenous, LGBTQ2S+, gender-diverse, and newcomers to Canada.
After speaking to actors in the social finance community who identify as belonging to equity-deserving groups, Common Approach identified tensions and trade-offs in data collection. For example, small sample sizes indicated a risk of re-identification, and some of the data identified was duplicative of existing data collection efforts.
Common Approach recommended mitigating strategies such as making use of existing social identity data when possible, collecting a limited set of social identity data at intake, and then collecting more (increasingly sensitive) information when the decision to invest became more likely. This illustrates the benefits of engaging the people that will be providing data when developing a data collection and analysis plan.
Source: Common Approach (2023)
The following resources provide detailed guidance on data ethics and equity standards:
Relevant Resources
- Informing Change Data Ethics Guidebook: Offers a framework to assess and respond to ethical issues throughout the data process.
- United Nations Human Rights-based Approach to Data: Provides a preliminary set of principles, recommendations, and best practices for a Human Rights-Based Approach to Data with a focus on data collection and disaggregation.
- University of Oxford Informed Consent Resources: An overview of the concept of informed consent (that is, an ethical research approach to ensure those providing data do so freely and with full understanding of the impacts to them) as well as process guides and templates.
- Urban Institute Principles for Advancing Equitable Data Practice: Applies Institutional Review Board (IRB) principles for protecting human subjects to frame a person-centered approach to data acquisition, processing and analysis, dissemination, and disposition.
- We All Count We Need to Fill in the Blanks on our Social Identity Data: Walks through transparent communication practices around social identity data including what identity categories were selected, what category choices were defined, and by whom.
- PolicyWise Demographic Datapedia: Offers standardized demographic questions with basic, detailed, and comprehensive options, and provides guidance to data collectors on which opton they should select given their context.
- Responsible Data for Children: Offers guidance and tools to support the responsible handling of data for and about children.
Note on gender-disaggregated data
Across issue areas and geographic and social contexts, women and gender-diverse people tend to experience worse outcomes than men do. Therefore, gender can be considered a cross-cutting issue relevant to any context and issue area. Disaggregating outcome data by gender is worth consideration by all investors and enterprises.
When disaggregating data, it is important to distinguish between sex (typically assigned at birth based on biological and physiological characteristics) and gender (behavior, expression, and identity that may or may not align with assigned sex at birth). Being clear about how these labels are being defined and used (e.g., if using one as a proxy for the other) and acknowledging any limitations can help avoid misrepresentation or oversimplification of results. Outside of specific contexts (e.g., medical research), using gender is recommended.
Data on gender is sensitive, and ethical guidelines remain relevant when weighing the benefits and risks of collecting and using such data. Investors and enterprises can consider offering the following options for self-identification:
- Woman
- Man
- Other / Write-In (if appropriate and safe to ask)
- Prefer Not to Disclose
RELEVANT RESOURCES
- Urban Institute | Do No Harm Guide: Collecting, Analyzing, and Reporting Gender and Sexual Orientation Data: Considerations and recommendations for collecting, analyzing, and communicating data on gender, gender identity, and sexual orientation.
- Data 2X | The Gender Data Playbook for Women’s Financial Inclusion: A guide to boosting collection of high-quality, gender-disaggregated financial data.
What contextual factors influence outcomes?
Understanding the geographic, social, and cultural contexts that influence how people experience outcomes helps enterprises and investors develop tailored impact strategies. This can include defining the physical places where people experience outcomes and the values, customs, laws, policies, and institutional practices that shape their experiences.
By identifying these contextual factors, enterprises and investors can:
- Define an investment thesis with a geographical focus
- Scope their area of influence (also known as zone of control)
- Understand how political, economic, and social institutions support or impede subgroups on the basis of social identity
- Address the barriers and gaps that cause people to experience inequitable outcomes and / or outcomes below threshold
After assessing differences in outcomes across subgroups, understanding the conditions that cause these differences is the next step to improving outcomes. Often, people and communities do not experience negative or inequitable outcomes by chance, but rather as a result of historical or current marginalization that excludes or impedes them from full participation and benefit from cultural, social, political, and economic activities.
The specific social identities that experience marginalization vary based on geographic, social, and cultural context. The table below presents global examples of historical and / or current marginalization in relation to illustrative impact goals.
To achieve equitable outcomes across diverse groups, investors and enterprises identify the customs, beliefs, social norms, laws, policies, and institutional practices that cause outcomes to differ systematically on the basis of social identity. This can occur even if no one involved is intrinsically motivated by bias against any subgroup. That is, biases can exist in the laws, policies, and practices themselves rather than in the people implementing them.
The figure below differentiates structural racism (i.e., institution-level, community-level, and systemic-level) from intra- and interpersonal bias, with examples of racism constructs from the United States.
By examining the experiences of different subgroups across these levels, investors and enterprises can develop a nuanced understanding of the opportunities, obstacles, and risks for achieving impact goals and tailor their strategies to support diverse needs.
SPOTLIGHT ON SOCIAL EQUITY
Understanding the causes of below-threshold and inequitable outcomes and the specific barriers that historically and / or currently marginalized communities face is crucial to advancing social equity. For more detailed guidance and resources, see Advancing Social Equity Through Impact Management, Step 1: Set Strategy.
The table below presents illustrative examples of tailoring a health clinic strategy to meet the needs of historically and / or currently marginalized subgroups.
Tailored strategies can often end up benefitting communities beyond those facing the greatest obstacles. For example, a telework policy that is implemented for the benefit of employees with disabilities may also result in more flexibility for employees with children and commute time saved for all employees. While resources may be finite, impact is not zero-sum. Strategies that address exclusion for one group have the potential to expand opportunity for many others.
Reflecting Inward: Advantage, Influence, and Bias
In addition to examining how specific geographic, social, and cultural contexts support or impede well-being across subgroups, enterprises and investors can also examine how their own personal and institutional beliefs and practices influence outcomes.
Embedded discrimination in traditional financial management methodologies can cause investors to perpetuate bias and generate inequitable outcomes even if they have no intention of doing so. For instance, the widely accepted practice of using credit history and scores in mortgage underwriting in the United States disproportionately excludes Black households from homeownership due to the influence of historical and current discriminatory practices such as redlining, employment exclusion, and predatory lending. For tools and examples of addressing institutional bias in investment practices, see Due Diligence 2.0 Commitment; The Investor Blueprint for Racial and Economic Equity from PolicyLink, CapEQ, and FSG; the Racial Equity Asset Lab Racial Equity Assessment Framework; and the Value For Women Equity Lens Toolkit for Venture Capital Investors
Enterprises and investors may also perpetuate inequity due to personal, implicit bias (i.e., bias of which one is not consciously aware). The lack of diversity in the financial industry puts investors with impact goals in the difficult position of making decisions that affect communities and address challenges about which they may have little experience or knowledge. Approaches such as Asset Based Community Development (ABCD) demonstrate how incorporating the insights and expertise of affected people and communities can address information and power asymmetries in decision-making and mitigate the risk of investor bias. ABCD shifts community members from recipients of products and services to contributors designing solutions (e.g., a character-based lending model that leverages community leaders’ relationships with local applicants).
Relevant Resources
- Othering & Belonging Institute Targeted Universalism Primer: Guidance on developing tailored strategies to advance universal goal attainment.
- National Equity Project Lens of Systemic Oppression: A framework to understand how different forms of oppression may negatively impact people’s ability to achieve their goals.
- john a. powell Deepening Our Understanding of Structural Marginalization: An approach to shifting focus from people and individuals to structures and institutions.
