We analyzed the tax filings of more than 40,000 nonprofit organizations in Minnesota, and found that a select group saw remarkable growth — as high as 424% year-over-year between 2013 and 2015. We wondered what differentiated these high-velocity organizations from those with more modest increases, and this report is a look into some of the interesting patterns and correlations we uncovered.
While some strong trends emerged, we were surprised by the diversity of organizations in the top 500. Nearly every size, age, region of the state, and mission focus are represented, an encouraging sign that indicates rapid growth is at least possible for most organizations.
We’ve broken down our findings into several key takeaways, and provided some light background and analysis for each. A more comprehensive explanation of our methods can be found at the end of the report. We welcome questions at email@example.com.
Takeaway #1 – Size matters, but it’s not all that matters
Perhaps unsurprisingly, fast-growing organizations tended to be smaller — after all, it’s easier to make large proportional gains from a more modest starting point. However, we discovered that even very large organizations were capable of major growth.
Dataset: Comparison of Organizations by Revenue, 2013 & 2015 Tax Years
The table below compares organizations by growth class, which are segments we created based on an organization’s rank by growth rate. For the purposes of this study, we focused only on organizations with at least $100,000 revenue in 2013. The cohorts break down as follows:
Top 100, 200, etc.: Orgs ranked 1 – 100, 101 – 200, etc., respectively
Slow Growers: Orgs outside the top 500 who saw positive year-over-year growth in 2013 – 2015
Non-Growers: All other orgs which did not see positive year-over-year growth
The cells highlighted in green indicate values greater than a baseline using the Non-Growers cohort. Note: Not all organizations had a 2015 income code assigned by the IRS.
In 2013, the top 100 organizations were far more likely to be under $1M revenue than the baseline cohort. Interestingly, the Slow Growers cohort tended to be larger than the baseline, suggesting that perhaps larger organizations were more likely to see growth overall, even if not rapid growth.
By 2015, the most common size group for all of the Top 100 – 500 cohorts was $1M – $5M, which suggests a sort of “tree-line” effect at which it becomes increasingly difficult to sustain rapid growth.
Dataset: Top 10 by Revenue, 2015 Tax Year
The table below breaks out the top 10 organizations in each size category, based on their 2015 tax year revenue.
Takeaway #2 – Education organizations outperformed everyone else
While we saw organizations of nearly every mission focus in the Top 500, education organizations appeared to grow at a noticeably greater rate.
Dataset: Comparison of Organizations by Organization Type
The table below compares organizations in each growth class based on their top-level NTEE category, as a percentage of all organizations in that growth class. Cells highlighted in green indicate values greater than a baseline using the Non-Growers cohort. Note: Organizations without a known NTEE category were removed.
Education organizations were the only group to be more heavily represented in all cohorts than the baseline cohort, and were the only group to outperform the baseline with a high degree of statistical significance based on the sample sizes available (p = 0.02).
Dataset: Top Organizations by Organization Type
Below are the top organizations, up to 10, in each NTEE category, sorted by annual growth rate.
Takeaway #3 – Rapid growth is possible for organizations of nearly any age
It was no shock to see that newer nonprofits had an advantage when it came to growth rate. But our major finding was that organizations could see triple- or high-double-digit growth rates several decades after founding.
Visualized: Age of Organizations by Growth Class
The chart below shows the average and median age in years of organizations in each growth class cohort. While there is a distinct trend of the most rapid growth correlating to younger organizations, it is notable that the Slow Growers cohort is older than the Non-Grower cohort. This could suggest that established nonprofits are more likely to see consistent, if smaller, growth.
Dataset: Top Organizations by Age Category
The table below shows up to the top organizations by growth rate in age categories split by number of decades old.
Takeaway 4: Organizations relying on grants and contributions grew fastest
The two primary revenue sources cited throughout the organizations studied were charitable contributions/grants or program services revenue. The data indicate that the fastest-growing 100 organizations are more likely to receive the majority of their revenue from grants and donations by a wide margin.
Visualized: Revenue Source by Growth Class
The graph below plots the average portion of organizations’ revenue coming from either contributions or program service revenue. While the top 200 – 500 organizations and the non-growing Non-Grower cohorts saw a negligible difference between the two sources, the Top 100 and Slow Growers cohorts appeared to rely on much different funding streams. Perhaps most surprising was that the Slow Growers were also much more likely to be driven by program services revenue. This may indicate that program services revenue provided a more consistent growth path for organizations during the time period studied.
The data appeared to confirm some of our initial assumptions, but also revealed some nice surprises. From our view, there appears to be a distinct class of organizations — the Slow Growers — that differs in key ways from the high-growth and Non-Growing cohorts. They are older, larger, and tend to rely more on program services revenue than contributions.
Dataset: The Top 500 Fastest Growing Nonprofits
The final table below shows select data for all of the top 500 organizations by growth rate.
Some notes on methodology
Disclaimer: This study is not meant to pass academic muster – it is meant simply as a collection of different views of public data for the purposes of uncovering interesting patterns. Some of these may warrant further, more rigorous study, but we did not attempt to validate the results to the level needed for outside publication. That said, we are happy to share our methods and welcome input from the community on how to improve similar studies in the future.
The data we based this study on came from two main sources: data extracts from IRS 990 filings compiled in each of the last three years, and the IRS Business Master File for Minnesota. Having worked with these datasets before, we understand that they are subject to error by both the filing organization and the extraction software that compiled the information.
Instead of using all organizations in the dataset, we chose to focus only on those with more than $100,000 in revenue in the first year of the study, 2013. This was mainly because very small organizations would dominate the rankings by virtue of their quantity and the fact that even small objective growth would mean very large proportional growth. Our hope for the study was to uncover trends about established organizations that saw growth, which we find a much more interesting and useful goal.
For the growth ranking, we calculated the geometric mean of total revenue for each organization’s tax filings in the years 2013, 2014, and 2015 — the most recent available from the IRS for the vast majority of organizations. When comparing different cohorts, we looked for the central tendency — either arithmetic mean or median — that we judged to best fit the data. We tended to use arithmetic mean only when the standard deviation was sufficiently narrow, and where the data had relatively few outliers.
When determining the age of an organization, we used the date of the IRS tax exemption letter issuance.
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