Can a science of philanthropy inform spending in a way that maximizes the benefit? This collection explores two concepts that proponents believe create the greatest possible impact: effective altruism and moneyball philanthropy. Rather than focusing on interventions that feel good, these approaches encourage the use of data-driven evidence to allocate resources.
Impact measurement appears as a Success Factor in these stories, which span several sectors, including philanthropy, education, and government spending. We meet “effective altruists,” who seek ways to mobilize their personal wealth for the greatest impact. Building on this, the two pieces by Tina Rosenberg’s describe efforts by organizations like ImpactMatters and Charity Navigator to increase the transparency of charities and social programs. David Bornstein’s article examines whether this kind of data-driven approach can help the government evaluate its own programs. We also see an example of the moneyball approach at work in higher education—using data to help boost student outcomes.
This collection also includes links to two external articles. One is about a former hedge fund owner turned moneyball philanthropist, while the asks us to consider the limitations of data in showing us system-changing ideas.
Click here to explore other social change Success Factors represented in the StoryTracker!
- Define the terms “effective altruism” and “moneyball philanthropy.” How do they relate to one another?
- Compare the two external articles attached to this collection. In the first we read about couple who is giving away $4 billion to programs that have data to support them. The second, by Caroline Fiennes and Eheren Reed, explains some of the challenges to quantifying philanthropic giving within complex systems. Evaluate the two perspectives given by the articles. Does one article make a stronger case? Why?
- Thinking beyond philanthropic giving, does the moneyball approach apply equally to governmental spending? What challenges does Bornstein describe to applying analysis to Federal agencies?
- Not all solutions are subject to quantification. Sometimes “feel-good” projects have powerful stories of how they changed people’s lives, but it’s hard to find statistics to back that up. If you were a funder considering funding such a project, what would you do? As an individual, would you give money to support a project you felt good about, even if there was no evidence (perhaps yet) of success? Why or why not? Can you use the articles in this collection to justify your decision?
- Are you persuaded by the argument in Save the refugees, become a banker to pursue a career that earns you the most money possible, and then donate your extra income following the five questions that guide effective altruism? Why or why not?
- Create a collection using stories in the StoryTracker about solutions that are measuring their impact. Drawing on what you have learned from reading the stories in this collection, discuss the effectiveness of these measures. Are they aimed at donors, or at improving initiatives? Can you create a collection based on another Success Factor? Share your collection to facilitate the discussion.
- Journalism is a collaborative practice: reporters are writing for their community, but they also depend on community members as sources for information. Indeed, the very purpose of journalism, according to the American Press Institute, is to provide citizens with the information they need to make the best possible decisions about their lives, their communities, their societies, and their governments. With that in mind, SJN wants to help connect news readers and journalists. Beside the name of the journalist on any of our story pages or the results page of the Solutions Story Tracker, you’ll find a Twitter icon that will link you directly to the journalists profile. Tweet at them with questions or compliments about their piece - you might be surprised by how much writers want to engage with their audiences! Don’t forget to tag us too (@soljourno) and use the hashtag #journalistintheclassroom if you are reading as part of an academic assignment.
- Effective altruism seeks to maximize the effectiveness of charitable giving by measuring and evaluating the impact of interventions. In the article by Sanne Blauw and Maite Vermeulen, effective altruism puts itself in contrast to “feel-good” altruism, which bases its decisions on emotional appeals. Related to this, moneyball philanthropy is used in several of the articles to describe the approach to quantifying and analyzing the success of an organization. The terms is borrowed from the title of the film Moneyball, which tells the story of a baseball team that employed computer data analysis to determine which players to acquire.
- In the WSJ article about John Arnold, we learn about the philanthropist’s desire to invest in large-scale, systems changing projects. Using data analysis, the goal is to avoid “feel-good” interventions. But Arnold is also interested in high-impact investments, some of which come with higher risk. Although the authors of the Forbes article note that data-driven giving can lead to investors to seek out low-risk interventions not necessarily aimed at systems change, Arnold appears to use his business acumen to weigh the risk and impact of his investments more than most donors. Still this approach does not address issues of disaster relief and immediate interventions. Although immediate interventions are not systems-changing, they also often rely on philanthropic donations. Have students delve further into the Forbes piece to explain how simple inputs and outputs are difficult to determine within complex systems. For instance, what is the timeframe of change? How proximate should a charity be to its beneficiaries?
- In his article, Bornstein explains the rise in using data analysis to determine how to improve government programs, especially during the Obama administration. However, the author also notes that many agencies lack the budget to conduct thorough data collection and analysis. Oftentimes implementing proposed improvements takes changing bureaucracies and institutional momentum. Stakeholders in existing programs have an incentive to keep the program going, even if the evidence questions whether its warranted.
- As the attached Forbes article explains, system-changing initiatives often lack an easily quantifiable input-output relationship. Relying only on the data can lead donors to look away from organizations with a high overhead, as well. Other articles, including the WSJ piece about John Arnold, acknowledge the ongoing need for crisis relief, which requires regular giving. All of these examples challenge the idea of using only the moneyball approach to philanthropic giving.
- Answers will vary! Encourage students to evaluate the five questions (how many people benefit; Is this the most effective thing I can do; What would have happened otherwise; What are the odds for success; Is this area neglected). What types of evidence can inform these questions? Are there any other questions your students would add to this list?
- Answers will vary! For more on Success Factors, click here. For more on creating collections, click here.