The Washington Post published an article on September 24, 2019 called “How the rise of urban nonprofits has exacerbated poverty” by Claire Dunning, a professor at the University of Maryland, which was both fascinating and baffling. Dunning discusses how the nonprofit sector was actively cultivated in urban centers in the ‘60s by policymakers to address poverty by 1) providing jobs for a shrinking manufacturing sector and 2) solving pressing societal problems within some of the country’s largest cities. Dunning suggests that instead of assisting residents in poverty, the increase in nonprofits over the years has left many nonprofit service workers without upward mobility and in worse economic shape. The argument laid out a perfect vicious circle: hire people to fight poverty and unwittingly create more poverty. The theory was compelling and I was generally not surprised with Dunning’s conclusion.
But then I clicked on the link Dunning referenced in her Washington Post article. I found myself reading Not Just An Urban Phenomenon—New Data on the Nonprofit Workforce which summarized a 2019 study conducted by Johns Hopkins University using U.S. Bureau of Labor Statistics Data. This lead me to another article, Nonprofit pay and benefits: estimates from the National Compensation Survey, describing an earlier study by Johns Hopkins and BLS from 2017 [1]. The results astounded me; there is evidence to show that nonprofit service workers are paid a premium over their for-profit counterparts. Huh?!
Conventional wisdom tells us that nonprofit employees accept lower salaries in place of the opportunity to serve a philanthropic mission. But there is also an argument that because nonprofits are not motivated to maximize returns, more profits can be paid to staff. I asked a close friend who spent decades working in management for large nonprofits for her opinion.
“I’d like to see [your] source,” she said. “I’m not buying it…When you choose between cutting programs for children in poverty and increasing staff salaries—even if that is the JUST thing to do, you are in an impossible position.”
So I sent her both studies.
“FIGURE 5” below comes from John Hopkins’ 2019 study, showing average wages in 2017 across different sectors where both nonprofits and for-profits exist. In many cases, nonprofit average wages exceeded similar for-profit wages.
Source: Johns Hopkins Center for Civil Society Studies based on U.S. Bureau of Labor Statistics data.
“TABLE 5” below comes from the 2016 study and shows mixed results when the data is controlled for occupation. In this slice of the data, the red boxes show that total compensation for nonprofit service and sales/office workers outpaced compensation for similar for-profit workers.
This leaves me with a number of questions:
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Why is there general belief that nonprofit workers don’t receive competitive compensation when the data has shown otherwise for a few years? (Watch Dan Pallotta’s compelling 2013 TED Talk on the subject.)
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When and why did some nonprofit workers’ compensation start to outpace for-profits?
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And does Dunning’s research on nonprofit service workers contradict the Johns Hopkins research, as it seems to suggest, or is it focused on something entirely different that is not obvious to me?
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And what is the moral?
Data matters. I cut my teeth in the investment industry where data is plentiful, reasonably accessible and recent. Real-time prices and analyses are a smart-phone click away. Such easy availability of data is necessary for strong risk management and is critical to make predictions about an organization’s potential financial success or failure. When data is sparse and released infrequently, it has very little predictive power and can allow old or false perceptions to persist.
As the philanthropy sector becomes more important to the US economy, organizations should be encouraged to crunch the nonprofit numbers for its continued success.
[1] John L. Bishow and Kristen Monaco, “Nonprofit pay and benefits: estimates from the National Compensation Survey,” Monthly Labor Review, U.S. Bureau of Labor Statistics, January 2016, https://doi.org/10.21916/mlr.2016.4.
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