Should You Donate to Disaster Relief?

As the effects of Hurricane Harvey continue decimating communities and displacing families throughout southern Texas, glimmers of hope can be found in everyday citizens’ responses to the disaster. People from neighboring states are bringing boats and supplies to assist with rescue efforts, and people are successfully challenging one another to donate $10 to disaster relief efforts over social media. These laudable actions deserve recognition and praise, and hopefully these examples will continue inspiring others to help those in need.

But if you want to make the biggest difference you can to those in need, is donating to disaster relief the best way you can spend your money?

According to William MacAskill, a University of Oxford philosopher and one of the founders of the Effective Altruism movement, the answer is probably not. MacAskill recognizes that disaster response efforts deserve funding, yet he argues that many people conflate doing good with doing the most good they can do. By making that mistake, people will donate to the most recent disaster relief efforts rather than addressing perennial problems – such as disease and starvation prevention in developing countries – that could benefit many more people for a fraction of the cost.

For instance, imagine you were prepared to give $1000 dollars to The Salvation Army for Hurricane Harvey relief efforts. That money would surely make a difference to those affected by hurricane; however, that same $1000 could double the annual income for a family in rural Kenya. $1000 isn’t nothing in America, but it is less likely to transform the lives of rural Texans than rural Kenyans.

Furthermore, as more people donate to The Salvation Army your $1000 will be making less of a difference. The law of diminishing returns affects charities as much as businesses, so as more people donate to a cause your individual donation is having less of an impact. When you have hundreds of millions or billions of dollars, an extra thousand dollars won’t change much. In fact, we sometimes give too much: eight months after Hurricane Sandy, the New York Attorney General’s Office found that $238 million, 42% of the money raised by charitable organizations, had not yet been spent on Sandy relief.  While most of those unused dollars were ultimately spent on relief efforts, much of that money could have been spent more effectively on issues that had been neglected by national or global attention.

Instead of thinking at the margins – trying to get the most prosocial bang for your buck – many people go with their guts when making decisions to donate to charity. MacAskill and the Effective Altruism community have researched the most effective ways to give, yet the sheer economics of charitable giving is often not enough to sway our hearts.

Why do we feel compelled to give in inefficient ways, even when we know we could be doing more net good in the world by donating to causes that are not getting enough attention?

Part of the reason, psychological scientist Paul Bloom suggests, is our reliance on empathy as motivator for moral and prosocial action. In his book Against Empathy: The Case for Rational Compassion, Bloom argues that empathy, which he defines as trying to feel what someone else is feeling, can affect our moral and prosocial decision-making in undesirable ways.

To illustrate how empathy can distort our morality, Bloom cites a classic experiment where participants were assigned to read the story of Sheri Summers, a child on a waiting list to receive expensive medication for a fatal disease. Before reading her tragic story, half of the participants were told to take “an objective perspective” on Sheri’s situation while the other half were told to “imagine how the child who is interviewed feels” about her situation. After reading Sheri’s story, all participants were told that they had the choice to move Sheri to the top of the waitlist, ahead of children who were higher up on the list “due to earlier application, greater need, or shorter life expectancy.”

While only 33% of participants in the low-empathy, objective evaluation condition recommended moving Sheri ahead of other children, 73% of those in the high-empathy condition recommend Sheri be moved ahead. These other, unnamed children surely had similar stories to Sheri as they were waiting for the same drug, but the act of empathizing with Sheri shifted participants’ judgments of what they should do.  What seemed fair from an objective perspective was unbearably cruel in an empathic state of mind, and what seemed unjust from an objective perspective seemed perfectly justifiable under the influence of empathy.

Such findings may help explain why we seem to value certain strangers over others. As Bloom notes, we are more likely to empathize with those who look like us or are similar to us in some way. Humans are social animals, and we tend to divide ourselves into in-groups and out-groups: us vs. them. No matter how we define our group — by race, religion, or what sports teams we root for — we usually empathize more with those in our group than with outsiders. Even random assignment to a meaningless social group (what psychologists call a minimal group paradigm) can induce an in-group empathy bias.

Consequently, it shouldn’t surprise Americans that strangers from Texas feel more deserving of aid to us than strangers from Kenya. We may know people from Texas, maybe even from the areas affected by the storm; even if we don’t know anyone who was directly affected, we know that they are Americans and share an identity with us that we don’t share with Kenyans. In certain moments we recognize that human lives are equally valuable, no matter their origin, yet at other times we will privilege members of our own tribe at the expense of others who need our help more.

Nevertheless, critics of Bloom’s “against empathy” argument contend that other emotions, and even cold reasoning, can bias us just as much as empathy. Some even propose that we need to expand our empathy to include out-group members rather than try to shut it off for certain types of decisions. Whether empathy is a less effective prosocial motivator than other emotions or cognitive dispositions remains an open question in the scientific literature, but if nothing else Bloom’s arguments force us to reassess the value we place in trying to “walk in someone else’s shoes” relative to seeking objective, rational evaluations of moral decisions.

Similarly, some critics of MacAskill and Effective Altruism suggest that we should have an affinity for those closest to and most like us. If everyone listened to the effective altruists, would there be anyone left to help the victims of Hurricane Harvey? The point is valid: certainly no one is expecting wealthy Kenyans to send relief to Houston. However, MacAskill responds by reminding critics that effective altruists aim to make the most difference they can, which usually comes through addressing neglected charitable causes. Effective altruists would recommend donating to disaster relief efforts if the victims were truly being underserved and such donations would be maximally utilized in giving to those efforts. While we may value those who seem like us more than distant strangers, the opportunities to help others tend to be far greater in poorer countries where the same amount of money provides more relief.


The AI Governance Challenge

Yet the most powerful retort to critics of effective altruism, as MacAskill points out in his book, Doing Good Better, is that most people in the West don’t have to choose between giving to those close by or far away. If you earn at least $28,000 annually, then you are part of the richest 5% of the world population; if you earn over $52,000 a year, you are a part of the global 1%. These statistics can be difficult to process, but the fact is that a modest living in the US puts one in more secure financial standing than the vast majority of people who have ever lived. Many people in developed countries can afford to give much more than they currently do.

So, while I encourage readers to reflect more on the good you can do by donating to the most effective charities and the most pressing global problems, you can also do good by helping those closer to home — especially those who are, at this moment, still greatly in need.

Click here to donate to Hurricane Harvey Relief efforts.

Click here to learn more about Effective Altruism, and click here to learn more about and donate to Effective Altruism Funds.

A Nudge for Coverage: Last-Mile Problems for Health Insurance

On March 23, 2010 President Obama signed into law the Affordable Care Act (ACA). One of the primary objectives of the ACA was to reduce the number of uninsured Americans. And depending on the source, it appears that the number of uninsured has decreased by approximately 25 percent within the first year.[1] However, as noted in a recent Wall Street Journal article, 30 million Americans are still uninsured, despite the fact that nearly 20 percent would be eligible for coverage at almost no cost beyond the time it takes to enroll.[2] And in an effort to reach many of those who are still uninsured, federal officials intend to spend $32 million to increase enrollment and close this gap.[3]

This poses an interesting problem. For many, choosing health care coverage would likely be financially more beneficial than forgoing it, especially for those eligible for near-free coverage. Yet so many do not take action despite the millions of dollars spent to increase enrollment.

An opportunity exists to increase participation through the combination of two disciplines, data science and behavioral science. Jim Guszcza, a chief data scientist at Deloitte, refers to scenarios like this as last-mile problems. These are situations that can benefit from analytical models for targeting and segmenting, but additionally, behavioral nudges are implemented to entice the desired action.

Last-mile problems: From presidential campaigns to fraud detection

Recently, combining data analytics and behavioral economics has yielded some encouraging outcomes.

Guszcza highlights President Barack Obama’s 2012 presidential campaign as an effective application of this last-mile approach. [4] At the time, the Obama campaign employed a team of data scientists to target undecided voters who were the most likely to be persuaded to vote in his favor. But absent the right intervention, the model could not inform the proper actions to take.

To overcome this hurdle, the campaign implemented behavioral-based outreach strategies to encourage the intended action: vote. One strategy involved commitment devices. The campaign understood from behavioral research that they could increase the likelihood of achieving their goal by requesting that people complete and sign a “commitment card” bearing a photograph of President Obama. Reinforcing this strategy, they capitalized on the power of social proof by explaining to these “persuadable” individuals how their neighbors intended to cast their votes.

Fraud detection has also benefited from these complementary sciences. In 2015, the New Mexico Department of Workforce Solutions (DWS) applied this thinking to reduce unemployment insurance fraud.[5] Specifically, they found that solely applying algorithms to detect dishonest behavior was not enough to adequately reduce unintended behavior. The reason: Algorithms are not 100 percent accurate and a false accusation of fraud can result in dire consequences for both the accuser and accused.

The DWS identified an opportunity to still use the algorithms to target those likely to commit undesirable behavior but they turned to more subtle nudge tactics to reduce the unintended behavior. First, they looked to lessons from priming research to encourage honest behavior; for example, they required people to certify accuracy since research suggested priming individuals with assertions of honesty increases the likelihood that honest behavior will follow. The work that the Behavioral Insights Team accomplished with the “190 million pound sentence” also proved helpful. By explaining to claimants that “9 out of 10 people” accurately report their earnings, they invoked social proof to nudge greater compliance. And through randomized control trials, the DWS was able to point to evidence that the low-cost nudges effectively kindled the honesty they desired.

Applying the last mile in health care

These last-mile lessons may be applicable to those tasked with reducing the number of uninsured. To start, data analytics and segmentation may identify individuals with the highest likelihood to opt-in to insurance coverage. It could be those eligible for free insurance or even more specifically, the algorithm could suggest a subset of zero-cost eligible individuals who would be even more likely to enroll due to special circumstances. In contrast, the data might suggest that young, relatively healthy people may be less inclined to seek or accept coverage. A tiered system could help policy makers determine how aggressive the enrollment tactics would need to be to alter behavior for each segment. Armed with this information, we can start hypothesizing the necessary tactics.

Low-effort segments: A well-targeted group may just require a gentle, low-cost nudge. These could include an easy to access form that employs smart default options when filling out paperwork. Slightly more aggressively, they could be presented with commitment cards for enrollment that include the “first three steps” to gaining coverage.

Medium-effort segments: These individuals may require more convincing or help. They may be prime candidates to offer a health insurance “coach” who would guide them through the decision making process, akin to a fitness coach. Another option could involve equipping the decision maker with a health cost insurance calculator that takes into account a variety of attributes such as age and family size. Doing so could help decision makers overcome the present bias that overly weighs the immediate cash outlay.


The AI Governance Challenge

High-effort segments: This would be a more resource-intensive group. Nudges may not be effective; instead, this group would require more of a “push” approach. Consequently, policy makers may need to consider the more traditional “carrots and sticks” of traditional economics theory to incentivize behavior. However, implementing a targeting mechanism such as an algorithm would hopefully reduce the overall universe of those who require more costly interventions.

Without testing, it’s difficult to predict the efficacy of these interventions. The good news is that we possess both a roadmap and a wide variety of success stories to help guide a well-informed strategy for reducing the number of uninsured citizens—often in a cost-effective, non-intrusive manner.

How Barcelona Took City Streets Back From Cars

This video originally appeared in [] and belongs to the creators.

If you imagine a typical American city street, and you take away the space that’s dedicated to cars, you aren’t left with very much. Cars dominate cities. Spend some time walking around most U.S cities and you’ll find yourself pushed to narrow sidewalks waiting for crosswalk lights. You’ll find cyclists navigating really narrow strips of space.

Americans are used to cars like fish are used to water. And that’s so ubiquitous in the U.S that I think for most people, it just never occurred to them that it could be otherwise. To give space back to pedestrians and bicyclists, and to make cities more friendly to life outside of the car?

How Barcelona is taking city street back from cars

A brilliant approach to making cities more walkable. We should all take a lesson from Barcelona:

Geplaatst door Ezra Klein op Zondag 9 april 2017