
Description
By sharing knowledge and data, grant makers can make their giving more accessible, equitable, and — crucially — effective.
Today we release our data analyses and dashboards: a key feature to the analytics we hope to offer the philanthropy community.
Work with Lever for Change (LFC) and MIT Solve (Solve) has allowed us to collect applicant data from a wide variety of teams that span both organizations. Previous work has allowed us to collect a shared schema between the two organizations (link here) and describe that shared schema (link here). This will allow the Philanthrobotics community to grow and give other organizations who would like to join a template to share data.
As we thought about the data visualizations, we wanted to root it in a large issue that philanthropy is already tackling: mental health. We were able to collect many applications across LFC and Solve:
450 applications
6 competitions represented
2018-2022 data years represented
67 countries of origin represented
Data available from these applications includes the following fields from the shared schema (the list is non-exhaustive):
Organization
Year of Competition
Competition / Challenge Name HQ Location
Limiting Factors to Success
Financial Sustainability Plan
Future Work Locations Country
How will you measure your progress toward each outcome?
Human-centeredness of your solution
Key Partners
Months to develop a pilot
Organization headquarters
Organization Location
Organization Name
Within the application for each organization, we found other similar fields that were not necessarily public
Solution Team / Key Staff
Feasibility Scoring
We developed a range of visualizations, augmented in part by the Our World in Data Mental Health catalog. We used the DALY (disability-adjusted life years) values for mental health and prevalence numbers across various geographies to get an idea of how many dollars/projects/calls are following various mental health ailments relative to their severity.
The following visualizations do not represent all of the visualizations we have to-date, but the best overlap of data between organizations that we have. More visualizations will become available on request or as we obtain more data.
Mental Health Project Dollars per DALY
Schema (first 3 rows shown):
HQ Location | Number of Projects | Total Project Monies | Project Dollars per DALY |
Greece | 2 | 300000 | 326.9831035 |
United States | 174 | 15842100986 | 25081892.82 |
Canada | 13 | 1172000 | 2104.500064 |
Mexico | 5 | 200000 | 379.7634681 |
Using the proposed budgets of each project as the intended dollar amounts that follow each mental health ailment, we found the total DALYs for all mental health ailments researched by Our World in Data, then used that as a denominator on our aggregated budgets for each organization’s headquarter location. In the visualization, dark blue and purple countries have many dollars chasing few DALYs (relatively) whereas lighter countries have the inverse.
Percentage Breakdown: Mental Health Project Budgets by Organization Type
Schema used (first 3 rows shown):
Org Type | Number of Projects | Percent of Total Project Monies | Average Employee Count |
For-Profit | 43 | 0.05268801753 | 54 |
Non-Profit | 27 | 0.02422117678 | 37 |
Not Registered as Any Organization | 40 | 0.005069784419 | 34 |
We were curious to answer the questions: who is proposing the largest budgets in mental health? Do they have small (start-up) like teams? It turns out that over 60% of the budgets in the mental health calls we looked at were represented by NGO classified organizations (small red triangle in lower right hand corner, large blue triangle in upper right hand corner), and their team sizes (represented by the size of the triangle) spanned the limits of the dataset. NGOs are well represented in mental health projects, and tend to be both small and large, with larger teams proposing larger budgets. In contrast, for-profit organizations are not well represented, making up less than 10% of all applicants, while still representing small, nimble teams and budgets.
More Data Cleansing In Progress: Impacted Population Breakdown by Number of Projects, Monies (i.e., Budget) per Project, and Percent of Total (Budgets) per Project
Schema (first 3 rows shown):
Populations that will benefit from your solution | Number of Projects | Total Project Monies | Money per Project | Percent of Total Project Monies |
Infants / toddlers (0-2 yrs.), Children (3-9 yrs.), Caregivers | 3 | 1447976942 | 482658980.7 | 3.542290443 |
Infants / toddlers (0-2 yrs.), Children (3-9 yrs.), Parents | 3 | 2424600132 | 808200044 | 5.931474201 |
Children (3-9 yrs.), Families | 1 | 889048116 | 889048116 | 2.174942538 |
We wanted to get a good sense of where proposed monies (i.e., budgets) were going towards impacted populations in mental health. There is still some data cleansing to do here, as each organization has specific labels for impacted populations, but we have found that many projects propose impacted populations of infants, toddlers, and their parents overall. As we consolidate this data more, we can get a better percentage, but > 10% are focused on the family unit as the impacted population.
Research as a Main Stage of Mental Health Projects, Scaling Organizations with Highest Budgets
Schema used (first 3 rows shown):
Project/Solution Stage | Number of Projects | Total Project Monies | Money per Project | Percent of Total Project Monies |
Research | 282 | 14008288 | 49674.78014 | 0.03426948542 |
Early | 17598371 | 399962.9773 | 0.04305216443 | |
Growth | 13 | 6938235 | 533710.3846 | 0.01697350477 |
Highlighted in this visualization is the pieces of the pie that each project is “staged” in. As we continue to work with organizations, we will hone these into nicer categories, but we have found that, when asking the question: “what types of projects make up mental health philanthropy?” research projects take up a majority, but scaling projects tend to take up the most budgetary dollars in total. Meanwhile, new partnerships have the highest dollar amounts per project.
Schema used (first 3 rows shown):
Year | Number of Projects | Log(Projects) | Total Project Monies | Log(Sum Money) | Money per Project | Percent of Total Project Monies |
2018 | 339 | 2.530199698 | 38544894 | 7.585966856 | 113701.7522 | 0.09494637659 |
2019 | 10 | 1 | 4790803172 | 9.680408328 | 479080317.2 | 11.80102875 |
Over time, how has the number of projects per year changed relative to the project monies (i.e., budgets) proposed in that year? We found that both of these numbers stayed fairly stable over our dataset, but there are some particularities in that Solve’s data mainly makes up 2018 as a year. Moreover, average money per project surpassed $400,000 in 2022 and has remained fairly stable dating back to 2019. This graph is on the log scale. The log fields are calculated after dataset aggregation.
We will continue to take requests from organizations we work with (particularly from Solve and LFC) as well as ask for various fields as our shared schema grows. However, we have our sights on improving the aforementioned visualizations in a few ways:
Using total award amount rather than budget dollars as a more accurate way of tracking dollars in the ecosystem.
Mapping out the partnerships of each organization after de-duplications onto a acyclic graph to see geographical and organization relationship.
Reducing categorical groups in organization type, stage, and impact population to reduce text in various visualizations.
This publication is meant to serve as a reference for participants in Philanthrobotics and the Open Grants Commons who want to understand the underlying structure of our knowledge graph. There are also some slight updates to our other work maintained here.