“I used ai to help research the post; all arguments were reviewed and modified by me.”
Can Better Information Make Invisible Widows Visible to Opportunity?
Testing whether low-cost, community-generated information infrastructure can improve the identification, targeting, coordination and cost-effectiveness of support for rural widows in Kenya and beyond.
Executive proposition
Across Africa, millions of widows live at the intersection of poverty, caregiving responsibilities, insecure livelihoods, limited access to land and productive assets, financial exclusion, weak social protection and climate vulnerability.
Yet an important question is rarely asked:
How many widows are actually visible to the systems trying to support them?
The problem may not only be a shortage of resources. It may also be a shortage of actionable information. The Widow Opportunity Map proposes to test a simple but consequential hypothesis:
If development actors have better, verified and actionable information about who rural widows are, where they are, what they need, what assets and capabilities they possess, what risks they face and which opportunities exist around them, can scarce resources be targeted more effectively and at lower cost?
The first proposed test will be in Busia County, Kenya.
The goal is not to build another database.
It is to test whether a low-cost information layer can improve real-world decisions.
1. The Invisible Widow
Start with a human story rather than statistics. Imagine a widow living in a rural community.
She may be:
raising children or grandchildren
cultivating a small piece of land
running a tiny informal enterprise
participating in a savings group
struggling to access finance
facing land or inheritance insecurity
coping with climate-related agricultural shocks
caring for dependants or
possessing skills and productive assets that no programme knows about.
A development organization may know that poor households exist in her village.
A government programme may know the village.
A financial institution may know that women entrepreneurs exist in the county.
An agricultural programme may know that farmers need inputs.
But does anyone know her?
And more importantly:
Does anyone know enough about her to determine which intervention is most appropriate?
This is where the article introduces the central idea of information invisibility.
2. The Problem Is Not Only Poverty. It Is Information.
This becomes one of the essay’s central arguments.
There is substantial evidence that widowhood can be associated with economic and social disadvantage, while conventional household-level data can obscure differences between individuals within households and between different categories of widows.
Kenya’s census and gender analysis provide an important starting point for understanding the scale and gendered nature of widowhood. The World Bank has also highlighted how household-level poverty measurement can hide disadvantaged individuals, including widows.
But the deeper question is:
Even when widows are counted, are they sufficiently understood for effective programme targeting? Thus,
Counting is not the same as understanding. And understanding is not the same as connecting.
3. From Counting Widows to Understanding Widows
This introduces an important conceptual distinction as a conventional database might tell us, there are 10,000 widows in this area. Therefore, ‘The Opportunity Map’ should help us answer:
Who are they?
Where are they?
What are their household responsibilities?
What livelihoods do they have?
What assets do they control?
What skills do they possess?
What risks do they face?
What opportunities are available nearby?
Which services have they already accessed?
Which services are missing?
What interventions are appropriate for different groups?
This leads to the first major conceptual shift:
A widow is not an intervention category.
Widowhood may be the starting point for identification. It should not automatically determine the intervention.
4. The Opportunity Problem
Thus the word Opportunity deliberately introduced. Too often vulnerable populations are described entirely through deficits:
poor
vulnerable
excluded
food insecure
dependent
at risk
Those conditions matter. But a useful information system should also capture:
Is elderly, has limited earning capacity and significant dependency responsibilities.
Potential opportunity: social protection + care support + basic income/security interventions
The Map therefore asks: What is the best-fit opportunity for this particular woman?
5. What Is the Widow Opportunity Map?
The Widow Opportunity Map is a low-cost information infrastructure layer designed to make rural widows more visible to appropriate services, opportunities and investment.
It combines:
Community identification
Digital data collection
Verification
Structured profiling
Geographic mapping
Opportunity mapping
Service mapping
Referral/ Targeting
Follow-up and learning
The objective is not simply to collect data. The objective is to change decisions.
A key principle: A map that nobody uses is not an intervention.
6. What Information Would the Map Contain?
Introduce the proposed minimum useful dataset.
A. Identity and location
unique identifier
group affiliation
ward/village
sub-county/sub-location
county/district
appropriate geographic reference
B. Household
dependants
caregiving responsibilities
household composition
C. Economic profile
livelihood
land access
enterprise
livestock
skills
savings
financial access
market access
D. Vulnerability and risks
food security
asset insecurity
land/inheritance issues
climate exposure
livelihood shocks
social protection access
E. Opportunity profile
enterprise interests
agricultural opportunities
skills
productive assets
financing needs
training needs
market opportunities
F. Service environment
government programmes
NGO services
financial institutions
agricultural services
social protection
market actors
Then make a crucial point:
We should collect only information that can plausibly improve a decision.
This prevents the project becoming a technology-heavy data-collection exercise.
7. From Data Collection to Decision Infrastructure
This should contain a simple flow:
Community
↓
Identify
↓
Register
↓
Verify
↓
Profile
↓
Map
↓
Match
↓
Refer
↓
Follow up
↓
Measure
The information should potentially serve several decision-makers:
Widows/groups ⇒ “What opportunities exist for us?”
NGOs ⇒ “Who fits this programme?”
Government ⇒ “Where are the gaps?”
Financial institutions ⇒ “Where are potential underserved women entrepreneurs?”
Agricultural programmes ⇒ “Where are women farmers and what support do they need?”
Funders ⇒ “Where might investment produce additional impact?”
Researchers ⇒ “What interventions work, for whom and at what cost?”
8. The Busia Test
Now bring the concept down to earth.
Rather than claiming: “This can transform Africa.”
say: “We do not yet know whether it works. We propose to test it.”
Proposed first pilot
Location: Busia County, Western Kenya
Population: Rural widows and widow-led groups
Implementation partner: Samia Widows AiD & Protection Center (SWAPC)
Potential technical partners: Data/GIS platform, research/evaluation institution and relevant government actors
Potential data collection infrastructure: KoboToolbox and related low-cost digital tools
The pilot should deliberately be small enough to evaluate rigorously but large enough to generate meaningful operational evidence.
9. The Questions We Need to Answer
This becomes the intellectual heart of the EA version.
Question 1 - How many widows are currently invisible to participating programmes?
Question 2 - Can they be identified at reasonable cost?
Question 3 - Can the information be made sufficiently accurate and current?
Question 4 - Does the information actually change targeting decisions?
Question 5 - Does it improve referrals and access to opportunities?
Question 6 - Does it identify programme duplication and service gaps?
Question 7 - Does the value created justify the cost of maintaining the information system?
These questions prevent the article from becoming advocacy alone.
10. The Six Hypotheses
H1 — Identification
The Map identifies a meaningful number of widows who were previously unknown or poorly characterized by participating service providers.
H2 — Data Quality
Community-generated information can achieve adequate completeness and accuracy at an acceptable cost.
H3 — Targeting
Map-enabled targeting improves the proportion of selected participants who match the intended programme profile.
H4 — Referral
Map-enabled referrals increase successful access to appropriate services and opportunities.
H5 — Coordination
The Map identifies geographic or demographic service gaps and potential duplication.
H6 — Cost-effectiveness
The additional value generated by the Map is sufficiently large relative to the cost of building and maintaining it.
Then emphasize: ⇒ These hypotheses are meant to be tested, not assumed to be true.
11. The Theory of Change
The section directly linked to Document #4 – The Opportunity Problem
Problem
Invisible or poorly characterized widows
↓
Information gap
Incomplete / fragmented / outdated information
↓
Decision problem
Poor targeting + weak referrals + service gaps + duplication
↓
Intervention
Widow Opportunity Map
↓
Better information
Who + Where + Needs + Assets + Risks + Skills + Opportunities + Services
More appropriate support More successful referrals Fewer gaps Better opportunity matching
↓
Longer-term outcomes
Economic inclusion Food security Social protection Climate resilience Access to finance Improved livelihoods
↓
Ultimate test
Does this produce enough additional impact to justify its cost?
12. Measuring Cost-Effectiveness
This helps distinguish the project from ordinary NGO database development. Because;
We are not simply asking: - “How much does the database cost?”
We are asking: - “What additional value does the database create?”
Potential measures:
cost per widow identified
cost per verified widow
cost per complete profile
cost per appropriate referral
cost per successful referral
cost per additional appropriately targeted participant
cost per additional service/opportunity accessed
Eventually:
Incremental Cost-Effectiveness Ratio
ICER = Incremental Cost / Incremental Outcome
The outcome could initially be: successful referral
and eventually potentially: income, food security, productive assets, sustained enterprise or other meaningful outcomes.
But the article should be explicit: We should not claim cost-effectiveness before the evidence exists.
13. The Counterfactual (Particularly important for the EA audience.)
A simple before-and-after comparison may be insufficient.
We should ideally compare:
Existing practice
versus
Map-enabled practice.
Depending on feasibility, the pilot could explore:
comparison areas;
phased rollout;
matched groups;
randomized rollout;
quasi-experimental approaches.
The exact design should be developed with an independent research partner, key question being: What happens because the Map exists that would not otherwise have happened?
14. The Data Ethics Question
This is very essential because, the Map could create value but poorly governed data could also create risk.
Potential risks include:
privacy violations
misuse of personal information
unauthorized profiling
harmful disclosure of location
exclusion caused by incorrect data
outdated information
surveillance and
exploitation of vulnerable populations.
Therefore: Make communities visible to opportunity and not vulnerable to exploitation.
The pilot should thus incorporate:
informed consent
data minimization
purpose limitation
access controls
security
correction mechanisms
responsible geographic information
community participation
clear governance
appropriate retention and deletion policies.
15. Who Could Use the Map?
This expands the potential ecosystem.
Government ⇒ Better planning and service targeting.
NGOs ⇒ Better beneficiary identification and referrals.
Funders ⇒ Better understanding of underserved populations and investment gaps.
Financial institutions ⇒ Potential identification of underserved economic actors.
Agricultural programmes ⇒ Better targeting of farmers and climate-resilience interventions.
Researchers: Help us design a credible evaluation.
Technical partners: Help us build the lowest-cost appropriate information architecture.
Development partners: Help us test whether the information improves real programme decisions.
Funders: Help finance the pilot and independent evaluation.
Summary:
The question is not whether widows deserve to be visible. They do.
Whether better information can make that visibility economically and operationally useful and whether doing so creates enough additional impact to justify the cost.
We intend to find out;
The Widow Opportunity Map is not designed simply to count widows. It is designed to test whether better information can change decisions and whether those changed decisions can produce measurable additional impact at a cost worth paying.
By Michael Agundah, Gerry | Samia Widows AiD & Protection Center (SWAPC)
THE WIDOW OPPORTUNITY MAP
“I used ai to help research the post; all arguments were reviewed and modified by me.”
Can Better Information Make Invisible Widows Visible to Opportunity?
Testing whether low-cost, community-generated information infrastructure can improve the identification, targeting, coordination and cost-effectiveness of support for rural widows in Kenya and beyond.
Executive proposition
Across Africa, millions of widows live at the intersection of poverty, caregiving responsibilities, insecure livelihoods, limited access to land and productive assets, financial exclusion, weak social protection and climate vulnerability.
Yet an important question is rarely asked:
How many widows are actually visible to the systems trying to support them?
The problem may not only be a shortage of resources. It may also be a shortage of actionable information. The Widow Opportunity Map proposes to test a simple but consequential hypothesis:
If development actors have better, verified and actionable information about who rural widows are, where they are, what they need, what assets and capabilities they possess, what risks they face and which opportunities exist around them, can scarce resources be targeted more effectively and at lower cost?
The first proposed test will be in Busia County, Kenya.
The goal is not to build another database.
It is to test whether a low-cost information layer can improve real-world decisions.
1. The Invisible Widow
Start with a human story rather than statistics. Imagine a widow living in a rural community.
She may be:
raising children or grandchildren
cultivating a small piece of land
running a tiny informal enterprise
participating in a savings group
struggling to access finance
facing land or inheritance insecurity
coping with climate-related agricultural shocks
caring for dependants or
possessing skills and productive assets that no programme knows about.
A development organization may know that poor households exist in her village.
A government programme may know the village.
A financial institution may know that women entrepreneurs exist in the county.
An agricultural programme may know that farmers need inputs.
But does anyone know her?
And more importantly:
Does anyone know enough about her to determine which intervention is most appropriate?
This is where the article introduces the central idea of information invisibility.
2. The Problem Is Not Only Poverty. It Is Information.
This becomes one of the essay’s central arguments.
There is substantial evidence that widowhood can be associated with economic and social disadvantage, while conventional household-level data can obscure differences between individuals within households and between different categories of widows.
Kenya’s census and gender analysis provide an important starting point for understanding the scale and gendered nature of widowhood. The World Bank has also highlighted how household-level poverty measurement can hide disadvantaged individuals, including widows.
But the deeper question is:
Even when widows are counted, are they sufficiently understood for effective programme targeting? Thus,
Counting is not the same as understanding. And understanding is not the same as connecting.
3. From Counting Widows to Understanding Widows
This introduces an important conceptual distinction as a conventional database might tell us, there are 10,000 widows in this area. Therefore, ‘The Opportunity Map’ should help us answer:
Who are they?
Where are they?
What are their household responsibilities?
What livelihoods do they have?
What assets do they control?
What skills do they possess?
What risks do they face?
What opportunities are available nearby?
Which services have they already accessed?
Which services are missing?
What interventions are appropriate for different groups?
This leads to the first major conceptual shift:
A widow is not an intervention category.
Widowhood may be the starting point for identification. It should not automatically determine the intervention.
4. The Opportunity Problem
Thus the word Opportunity deliberately introduced. Too often vulnerable populations are described entirely through deficits:
poor
vulnerable
excluded
food insecure
dependent
at risk
Those conditions matter. But a useful information system should also capture:
assets + skills + capabilities + networks + opportunities + aspirations.
For example:
Widow A
Owns/accesses one acre, has farming experience and belongs to a savings group.
Potential opportunity: climate-smart agriculture + finance + market linkage
Widow B
Has tailoring skills, lives near a trading centre and has no secure productive asset.
Potential opportunity: enterprise equipment + working capital + market access
Widow C
Has farming experience but lives in an area experiencing increasing climate stress.
Potential opportunity: climate-resilient agriculture + diversification
Widow D
Is elderly, has limited earning capacity and significant dependency responsibilities.
Potential opportunity: social protection + care support + basic income/security interventions
The Map therefore asks: What is the best-fit opportunity for this particular woman?
5. What Is the Widow Opportunity Map?
The Widow Opportunity Map is a low-cost information infrastructure layer designed to make rural widows more visible to appropriate services, opportunities and investment.
It combines:
Community identification
Digital data collection
Verification
Structured profiling
Geographic mapping
Opportunity mapping
Service mapping
Referral/ Targeting
Follow-up and learning
The objective is not simply to collect data. The objective is to change decisions.
A key principle: A map that nobody uses is not an intervention.
6. What Information Would the Map Contain?
Introduce the proposed minimum useful dataset.
A. Identity and location
unique identifier
group affiliation
ward/village
sub-county/sub-location
county/district
appropriate geographic reference
B. Household
dependants
caregiving responsibilities
household composition
C. Economic profile
livelihood
land access
enterprise
livestock
skills
savings
financial access
market access
D. Vulnerability and risks
food security
asset insecurity
land/inheritance issues
climate exposure
livelihood shocks
social protection access
E. Opportunity profile
enterprise interests
agricultural opportunities
skills
productive assets
financing needs
training needs
market opportunities
F. Service environment
government programmes
NGO services
financial institutions
agricultural services
social protection
market actors
Then make a crucial point:
We should collect only information that can plausibly improve a decision.
This prevents the project becoming a technology-heavy data-collection exercise.
7. From Data Collection to Decision Infrastructure
This should contain a simple flow:
Community
↓
Identify
↓
Register
↓
Verify
↓
Profile
↓
Map
↓
Match
↓
Refer
↓
Follow up
↓
Measure
The information should potentially serve several decision-makers:
Widows/groups ⇒ “What opportunities exist for us?”
NGOs ⇒ “Who fits this programme?”
Government ⇒ “Where are the gaps?”
Financial institutions ⇒ “Where are potential underserved women entrepreneurs?”
Agricultural programmes ⇒ “Where are women farmers and what support do they need?”
Funders ⇒ “Where might investment produce additional impact?”
Researchers ⇒ “What interventions work, for whom and at what cost?”
8. The Busia Test
Now bring the concept down to earth.
Rather than claiming: “This can transform Africa.”
say: “We do not yet know whether it works. We propose to test it.”
Proposed first pilot
Location: Busia County, Western Kenya
Population: Rural widows and widow-led groups
Implementation partner: Samia Widows AiD & Protection Center (SWAPC)
Potential technical partners: Data/GIS platform, research/evaluation institution and relevant government actors
Potential data collection infrastructure: KoboToolbox and related low-cost digital tools
The pilot should deliberately be small enough to evaluate rigorously but large enough to generate meaningful operational evidence.
9. The Questions We Need to Answer
This becomes the intellectual heart of the EA version.
Question 1 - How many widows are currently invisible to participating programmes?
Question 2 - Can they be identified at reasonable cost?
Question 3 - Can the information be made sufficiently accurate and current?
Question 4 - Does the information actually change targeting decisions?
Question 5 - Does it improve referrals and access to opportunities?
Question 6 - Does it identify programme duplication and service gaps?
Question 7 - Does the value created justify the cost of maintaining the information system?
These questions prevent the article from becoming advocacy alone.
10. The Six Hypotheses
H1 — Identification
The Map identifies a meaningful number of widows who were previously unknown or poorly characterized by participating service providers.
H2 — Data Quality
Community-generated information can achieve adequate completeness and accuracy at an acceptable cost.
H3 — Targeting
Map-enabled targeting improves the proportion of selected participants who match the intended programme profile.
H4 — Referral
Map-enabled referrals increase successful access to appropriate services and opportunities.
H5 — Coordination
The Map identifies geographic or demographic service gaps and potential duplication.
H6 — Cost-effectiveness
The additional value generated by the Map is sufficiently large relative to the cost of building and maintaining it.
Then emphasize: ⇒ These hypotheses are meant to be tested, not assumed to be true.
11. The Theory of Change
The section directly linked to Document #4 – The Opportunity Problem
Problem
Invisible or poorly characterized widows
↓
Information gap
Incomplete / fragmented / outdated information
↓
Decision problem
Poor targeting + weak referrals + service gaps + duplication
↓
Intervention
Widow Opportunity Map
↓
Better information
Who + Where + Needs + Assets + Risks + Skills + Opportunities + Services
↓
Better decisions
Targeting + referrals + coordination + programme design
↓
Intermediate outcomes
More appropriate support
More successful referrals
Fewer gaps
Better opportunity matching
↓
Longer-term outcomes
Economic inclusion
Food security
Social protection
Climate resilience
Access to finance
Improved livelihoods
↓
Ultimate test
Does this produce enough additional impact to justify its cost?
12. Measuring Cost-Effectiveness
This helps distinguish the project from ordinary NGO database development. Because;
We are not simply asking: - “How much does the database cost?”
We are asking: - “What additional value does the database create?”
Potential measures:
cost per widow identified
cost per verified widow
cost per complete profile
cost per appropriate referral
cost per successful referral
cost per additional appropriately targeted participant
cost per additional service/opportunity accessed
Eventually:
Incremental Cost-Effectiveness Ratio
ICER = Incremental Cost / Incremental Outcome
The outcome could initially be: successful referral
and eventually potentially: income, food security, productive assets, sustained enterprise or other meaningful outcomes.
But the article should be explicit: We should not claim cost-effectiveness before the evidence exists.
13. The Counterfactual (Particularly important for the EA audience.)
A simple before-and-after comparison may be insufficient.
We should ideally compare:
Existing practice
versus
Map-enabled practice.
Depending on feasibility, the pilot could explore:
comparison areas;
phased rollout;
matched groups;
randomized rollout;
quasi-experimental approaches.
The exact design should be developed with an independent research partner, key question being: What happens because the Map exists that would not otherwise have happened?
14. The Data Ethics Question
This is very essential because, the Map could create value but poorly governed data could also create risk.
Potential risks include:
privacy violations
misuse of personal information
unauthorized profiling
harmful disclosure of location
exclusion caused by incorrect data
outdated information
surveillance and
exploitation of vulnerable populations.
Therefore: Make communities visible to opportunity and not vulnerable to exploitation.
The pilot should thus incorporate:
informed consent
data minimization
purpose limitation
access controls
security
correction mechanisms
responsible geographic information
community participation
clear governance
appropriate retention and deletion policies.
15. Who Could Use the Map?
This expands the potential ecosystem.
Government ⇒ Better planning and service targeting.
NGOs ⇒ Better beneficiary identification and referrals.
Funders ⇒ Better understanding of underserved populations and investment gaps.
Financial institutions ⇒ Potential identification of underserved economic actors.
Agricultural programmes ⇒ Better targeting of farmers and climate-resilience interventions.
Social-protection actors ⇒ Better identification of potentially underserved households and/ individuals.
Researchers ⇒ Better sampling frames and intervention targeting.
Communities ⇒ Better visibility of available opportunities and services.
This is where the Map starts looking like shared information infrastructure, rather than a SWAPC beneficiary database.
16. Why Start in Busia?
The explains the practical logic where Busia offers:
an established network of widow-led groups
an identifiable implementation structure through SWAPC
rural and agricultural livelihoods
climate and economic vulnerability
proximity to multiple development actors and
environment in which community-generated information can be tested.
Note: Busia is a test environment not proof that the model will work everywhere.
17. From Busia to Kenya and Potentially Africa
The scaling pathway is designed to be conditional:
Stage 1
Busia
Test feasibility.
↓
Stage 2
Western Kenya and surrounding regions
Test replication.
↓
Stage 3
Kenya
Test different contexts.
↓
Stage 4
East Africa and Sub Saharan Africa
Test cross-country adaptability.
↓
Stage 5
Other African contexts
Only if evidence supports replication.
This is a much more credible scaling argument than starting with:
“We will map millions of widows across Africa.”
18. What Could Make This Highly Valuable?
We introduce the potential leverage but suppose the Map costs relatively little to operate?
If it helps an organization:
identify previously missed participants
avoid targeting the wrong people
reduce duplication
find underserved geographic areas
improve referrals
connect widows to finance/ investments
connect farmers to agricultural services
then the value of the information could potentially exceed its direct operating cost.
But that is precisely what the pilot needs to establish, thus;
The central proposition becomes:
A relatively small investment in information could potentially improve the effectiveness of much larger downstream investments.
That is the leverage hypothesis.
19. What Could Make It Fail?
This make the essay considerably more credible. Where;
Failure 1 — Nobody uses the data
Lesson: information collection without decision use creates little value.
Failure 2 — Data are inaccurate
Lesson: community mapping requires verification and quality control.
Failure 3 — Data become outdated
Lesson: sustainability depends on refresh costs.
Failure 4 — Group-based mapping misses isolated widows
Lesson: we need to measure residual invisibility.
Failure 5 — Technology becomes too expensive
Lesson: build only what is necessary.
Failure 6 — Better information does not change outcomes
Lesson: information alone is not enough.
Failure 7 — Privacy risks outweigh benefits
Lesson: responsible data governance is part of the intervention, not an afterthought.
20. The Bigger Question
The question is no longer: “How do we help widows?”
It becomes:
“What information infrastructure would allow society to identify underserved people more accurately and connect them to appropriate opportunities?”
Widows provide a particularly useful test case because they are:
economically diverse
often underrepresented in conventional datasets
exposed to multiple forms of vulnerability
connected to household and community responsibilities
and potentially relevant to multiple sectors simultaneously.
If the model works, the underlying architecture might eventually have applications beyond widowhood.
But again: We should earn that conclusion through evidence.
=============================================================
21. The Connection to WApp and A Widow’s Basket
This is carefully included because it connects the Map to broader WApp architecture without turning the essay into a promotion.
The emerging architecture:
Widow Opportunity Map → SEE
WApp Solution → CONNECT
A Widow’s Basket → INVEST
Broadly meaning;
SEE her → UNDERSTAND her → CONNECT her → INVEST in her
and finally ⇒ MEASURE THE RESULT <=
where;
The Map identifies and profiles opportunities.
The WApp potentially provide the digital coordination layer.
A Widow’s Basket provide an economic and resilience investment framework.
This makes the three initiatives complementary rather than competing projects.
==============================================================
22. An Invitation to Test the Hypothesis
The conclusion is deliberately open.
Because: ⇒ “We have not solved the problem.”
But: ⇒ “We have a hypothesis worth testing.”
Thus we invite four categories of partners:
Researchers: Help us design a credible evaluation.
Technical partners: Help us build the lowest-cost appropriate information architecture.
Development partners: Help us test whether the information improves real programme decisions.
Funders: Help finance the pilot and independent evaluation.
Summary:
The question is not whether widows deserve to be visible. They do.
Whether better information can make that visibility economically and operationally useful and whether doing so creates enough additional impact to justify the cost.
We intend to find out;
The Widow Opportunity Map is not designed simply to count widows. It is designed to test whether better information can change decisions and whether those changed decisions can produce measurable additional impact at a cost worth paying.
By Michael Agundah, Gerry | Samia Widows AiD & Protection Center (SWAPC)