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The demand curve isn’t simple when lives are on the line..
A 70-year-old woman goes to the pharmacy to pick up medication for her arthritis. How much should that cost her? Maybe $5 for the prescription? Or $20? Or should it be free?
There’s surely a lot going on. The woman might be grappling with poverty and discrimination and a plethora of challenges beyond arthritis. A pharmaceutical company may have priced the medication very aggressively. The woman might not have access to good medical advice. For now, though, we want to focus on one, narrow question: How much should she have to pay for her medical care?
Health economists have grappled with that question for as long as there have been health economists. The answer is not simple, and the debate continues to this day. The short answer: it depends. The long answer requires a tour of research on the issue, research that goes back nearly half a century.
High prices are awful. No one likes it when things are expensive. What kind of monster would like high prices?
An economist.
High prices do something important: they force people to agonize over whether or not they really want to make the purchase. It can be problematic for people to consume goods without having to grapple with their price.
That’s why, whenever the government wants to reduce the consumption of something, there’s a simple solution: just raise the price! If there’s too much traffic in a city center, raise the price on driving downtown through higher toll prices or “congestion charges.” If there’s too much pollution, raise the price on pollution through a carbon tax. If too many people smoke, raise the price of cigarettes through tobacco taxes.
Why turn to higher prices? Because higher prices lead people to consume less and force them to align their personal decisions with the true cost of production. High prices force people to “internalize” the full cost of production, whether those prices reflect the costs we typically think about or harder-to-measure costs like congestion and pollution. That is the benefit of high prices.
What does this have to do with healthcare? Health insurance fundamentally breaks the relationship between individual decisions and the costs of production. It breaks that relationship because, by definition, generous health insurance shields consumers from the high price of healthcare. A problem with generous health insurance is that it makes healthcare too cheap. And when healthcare is too cheap, people buy too much of it.
Health insurance, in other words, can eliminate the benefit of high prices, the way that they force people to grapple with the costs of production. And that can lead to waste. If a 70-year-old woman doesn’t have to pay anything for her arthritis medication, she might continue with the medication even if it’s not working. That medication still costs the healthcare system money, money that could be better spent on more effective forms of healthcare.
Health policy would be much simpler if people behaved like shrewd medical experts whenever they faced a deductible. Unfortunately, that’s not how it works.
There are plenty of contexts in medicine in which there’s a cheap option and an expensive option. Sometimes, the cheap option is just as good as the expensive one. For instance, an upper respiratory infection can be treated in an emergency room (expensive) or in an ordinary doctor’s office (cheap). Some conditions can be treated with generic drugs (cheap) or branded drugs (expensive). If the consumer pays the same price for either option, why not choose the expensive option? And if all consumers face the same incentives and behave in the same way, healthcare spending, overall, might rise in ways that don’t actually improve health.
There’s a technical term for this issue: it’s called a “moral hazard problem.” In general, moral hazard problems are situations in which there are two parties in a transaction and one party cannot control the actions of another. In the case of health insurance, there’s the insurer and the consumer, and the insurer bears the costs of the consumer’s healthcare decisions. The consumer can choose the cheap option or the expensive option, and the insurer pays either way.
Now, to be clear, that’s not to say that generous health insurance is a bad thing. Generous health insurance also protects consumers from risk. The issue here is that there’s a trade-off. On the one hand, we want health insurance to be generous so that people are protected from risk. On the other hand, we don’t want health insurance to be generous, because of moral hazard. The generosity of health insurance—how much the consumer has to pay for healthcare—has to balance those two forces.
But all of that is theory , words on a page that describe how some people think the world works. Theory needs to be tested. Next, let’s turn to real-world evidence on moral hazard in health insurance.
Health economists first examined moral hazard in health insurance with a field experiment. In the late 1970s, a team of health economists sat down at the RAND Corporation, a think tank in Santa Monica, California. Joseph P. Newhouse, now at Harvard, was a young economist just starting out at RAND. He asked his colleagues a simple question: How do people respond to the price of healthcare? If ordinary people have to pay more for healthcare, do they consume less of it? Newhouse found that lots of economists had opinions on that question, but no one had any good evidence.
Some argued that healthcare was different than other goods, that healthcare is always a matter of “your money or your life.” Therefore, they argued, people would pay whatever price they faced for healthcare—the price didn’t matter, because healthcare was so important. And, by extension, those people were not concerned with moral hazard: since healthcare is different from other goods, it doesn’t matter, they argued, that health insurance makes healthcare cheap.
Economists knew that prices matter for ordinary goods: coffee, wheat, motorcycles. If the price of coffee goes up, people buy less coffee. That is, as we say in Econ 101, a matter of the demand curve. Is the same true for healthcare? No one knew.
The RAND researchers, led by Newhouse, decided to run an experiment. They took 2,750 American families and randomized them into two groups. The study included both urban and rural households and spanned a broad range of income levels. One group of families was put on a free-care plan: for several years, all of the healthcare they needed would be free. Every doctor visit, every dentist visit, every medication: they would pay nothing.
Other families were put on a high-deductible healthcare plan. They would be responsible for all of their healthcare costs up to a thousand dollars. After $1,000, their health-insurance plan would kick in and cover everything. But until then, they had to foot the bill on their own.
Remember: randomization makes experiments valid—it means that both groups began the experiment with the same health, on average. They had the same average income, level of education, number of children, number of televisions, and, most importantly, the same average health. As a result, any differences in outcomes in the years following the RAND experiment can be interpreted as the impact of the health-insurance plans themselves.
The study became known as the RAND Health Insurance Experiment and it lasted from 1976 until 1982. For health economists, the experiment amounts to a combination of NASA launching a space shuttle, Bill Gates starting Microsoft, and Ayatollah Khomeini returning to Iran. It happened in the late 1970s, and it’s a big deal to us. The RAND experiment is one of the most expensive experiments ever performed by social scientists.
For years, the families participating in the experiment led their ordinary lives; the only thing out of the ordinary was that a team of researchers at RAND was handling their health insurance. Then, after years of being on either the free-care plan or the high-deductible plan, the participants were given a final physical exam, and the experiment was over.
Why is us healthcare so expensive.
Chicago Booth’s Matthew Notowidigdo discusses the high price tags many people face for medical care.
Regular, continuous care is good for patients, and could also benefit providers and insurers.
Newhouse and his colleagues spent years poring over the data, trying to understand how having to pay for healthcare affected families. The researchers studied the results from every possible angle, slicing and dicing the data every which way. The results of the experiment filled hundreds of academic papers and also a 516-page book. But, decades later, the most relevant discoveries from the experiment boil down to three main conclusions:
1. High prices really do cut how much care people choose to consume. First, the researchers compared the amount of healthcare consumed by families that were put on the free-care plan with that of families randomly assigned to the high-deductible plan. Those put on the free-care plan consumed an average of almost $2,000 (in 2021 dollars) as compared with about $1,600 in healthcare for those on the high-deductible plan. That’s a roughly 20 percent difference—a large difference.
In other words, incentives matter, even for healthcare. People who face a higher price for healthcare consume less healthcare. In the language of Econ 101, demand curves slope down, even for healthcare. Yes, healthcare is important, and people treat it as important, but, at the end of the day, the price still matters.
2. Deductibles lead families to cut back on all healthcare, regardless of whether it’s effective or ineffective. The second conclusion of the experiment arose as the researchers tried to figure out which healthcare the families on the deductible cut out. The researchers assembled a panel of physicians and gave them all of the medical charts associated with the experiment. They asked the physicians to categorize all of the healthcare as “highly effective” care or “rarely effective” care. Going to the ER for a runny nose: that’s rarely effective care. Going to the ER for a heart attack: that’s highly effective care.
The panel of physicians worked through the stack of charts, methodically categorizing all visits as highly effective or rarely effective. Then they studied how the high-deductible plan affected those two categories.
Families facing a deductible cut back on highly effective care by about 30 percent relative to those on the free-care plan. And then the researchers found roughly the same effect for rarely effective care: a roughly 30 percent drop in utilization. The lack of contrast between those two findings is the second conclusion of the experiment.
And that finding alone is kind of disappointing. Health policy would be much simpler if people behaved like shrewd medical experts whenever they faced a deductible. Unfortunately, that’s not how it works. Patients are not physicians themselves—they don’t know what is effective and what is ineffective. When faced with a high price, they just cut back on all of it, both care that really matters and also care that is probably wasteful.
3. A high-deductible plan takes people from bad shape to worse shape. Lastly, the researchers studied what deductibles did to people’s health. Remember that families were randomized to the two health-insurance plans, so any differences in health outcomes during the RAND experiment years were probably a result of the impact of those plans.
After several years on the insurance plan that they were assigned to, everyone’s health was evaluated. Overall, there was no difference: people who spent 3–5 years on the high-deductible plan finished the experiment in roughly the same health as people who spent that time on the free-care plan, according to a paper by Newhouse and a team of researchers.
Things were different, however, for one group of participants. The researchers focused on what they called “elevated-risk participants.” That group consisted of people who were in poor health at the start of the experiment. Maybe they already had a chronic condition or maybe they were obese. For that group, the researchers found that the deductible plan harmed their health. A few years on a high-deductible plan took people from bad shape to worse shape.
That finding is, perhaps, intuitive. If you’re in good health, a deductible will induce you to consume less healthcare, and that’s going to have a very small impact on your health. After all, you’re in good health, so a bit more or a bit less healthcare is not going to have a big effect, at least on average. But if you’re already at elevated risk, a deductible leads you to consume less healthcare, and for you, that really matters.
The three conclusions of the RAND experiment paint a confusing picture of what deductibles do to people. On the one hand, deductibles lead people to cut back on healthcare a lot but they do not hurt people, on average. On the other hand, the participants who were assigned a high-deductible plan cut back on all healthcare, not just ineffective care. And the most vulnerable among them ended up worse off.
Matthew J. Notowidigdo is the David McDaniel Keller Professor of Economics and Business and Public Policy Fellow at Chicago Booth. Tal Gross is a professor in the Department of Markets, Public Policy & Law at Boston University. This is an edited excerpt from their book, Better Health Economics. Reprinted with permission from the University of Chicago Press. © 2024 by the University of Chicago. All rights reserved.
Works Cited
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Guest post by Dr. Lyric Jorgenson, NIH Associate Director for Science Policy, originally posted on the Under the Poliscope blog.
One of the hardest (and most fun) parts of my job is conjuring up my crystal ball to foresee the policy needs of tomorrow. Of course, forecasting the future isn’t really that easy. Technology moves at light speed, while policy… well, policy often moves slower (we can get into that in another blog). How can we ready the biomedical research enterprise for the future when the future itself is a moving target? Paradoxically, to quote Taylor Swift, “lookin’ backwards might be the only way to move forward.”
As policymakers, we are continuously striving to develop policies capable of evolving alongside science and technology, taking into consideration that we know there will be unexpected twists and turns along the way. This is why we built a certain degree of flexibility into the NIH Data Management and Sharing Policy – because new tools for creating, sharing, and accessing data are being developed every day.
This is especially the case for machine learning algorithms, natural language processing, and other forms of “artificial intelligence” that are creating new research opportunities and transforming a new wave of improved health outcomes. Artificial intelligence (AI) has been around in some shape or form for some time. However, the ease in which AI approaches can be developed and deployed (similar to what CRISPR did for gene editing) has leveled the playing field for researchers around the globe to find new patterns in rich and complex datasets.
Of course, with great power comes great responsibility. At NIH, we prioritize the safe and responsible development and use of algorithms and models for research. We also implement policies and practices to ensure deposition of AI-ready datasets that are reliable, representative, and robust. To achieve these aims, NIH relies on its forward-leaning policy infrastructure to safeguard our work. While these policies might not specifically state AI in the title, the anticipated use of AI and risks from those uses is what we intended to address. To help the research community understand how these policies guide AI-related research, OSP recently released a centralized NIH policy resource illustrating the applicability of existing policies to AI, including policies related to participant protections, intellectual property, peer review, and many other topics.
An important thing to keep in mind is that NIH’s current policy landscape is well positioned to ensure the responsible use of AI technologies. However, as a policy office, we know that sometimes new policies are indeed warranted to capture new risks as science and technology progresses. NIH is committed to monitoring the field of AI and other emerging technologies and we will continue to update this resource to make sure we are keeping pace from a policy perspective. I invite you to check out this new resource and let us know your thoughts. Research that leverages the most promising technologies in a responsible manner will lead to better health for all. That is a future we can all hope for.
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Bank payout policy is strongly affected by regulation and politics, especially for the largest banks. Banks, but not industrial firms, have consistently lower payouts in times of high regulation uncertainty and under democratic presidents. After the Global Financial Crisis, bank regulators’ influence on payout policies of the largest banks increases sharply and repurchases become more important than dividends for these banks. Repurchases respond more to regulatory climate changes than dividends. The stock-price reaction of the largest banks to the election of Donald Trump is larger than for small banks or industrial firms, and their repurchases increase sharply afterwards.
Fahlenbrach acknowledges financial support from the Swiss Finance Institute. We are grateful for conversations with Hamid Mehran. Stulz is at times retained by financial institutions or their attorneys as a consultant or as an expert. Address correspondence to René M. Stulz, Fisher College of Business, The Ohio State University, 806A Fisher Hall, Columbus, OH 43210, [email protected], Tel.: +1 614-292-1970. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
René Stulz has a consulting practice where he is at times retained by financial institutions or their attorneys. He also belongs to the board of trustees of the Global Association of Risk Professionals.
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CDC’s Global Health Strategic Framework is a bridge that connects all of CDC’s global health activities. Learn how we use it to measure progress and impact and guide informed decision-making for global health activities across CDC.
CDC’s Global Health Strategic Framework ensures CDC is tactically developing and maintaining a robust response to existing and emerging global public health needs. At the same time, it builds transparency and accountability, which helps us better support our partners and achieve U.S. Government goals.
The framework guides how we build, execute, and evaluate our global health work. It is used to measure progress and impact and guide informed decision-making for global health activities across CDC.
Together, the framework’s four global goals and six core capabilities represent the “why” and “how” of CDC’s global work. The framework is also the basis for creating indicators to track progress across the globe.
Stop health threats at their source before they spread to the United States and other countries
Contain disruptive global disease outbreaks
Use global data for disease prevention and mitigation programs in the United States and other countries
Save lives and improve health globally
Data and surveillance.
Ensuring interoperable data and surveillance systems that detect, identify and monitor disease threats and produce high quality, timely data to inform public health action.
Building public health laboratory systems that rapidly and accurately detect, track and inform public health action
Training and developing a multisectoral health workforce and coordinated essential public health services to prevent, detect, and respond to disease threats and integrate national public health functions
Developing systems, tools, and processes that enhance response to public health emergencies, including implementation of prevention and mitigation strategies and countermeasures to prevent transmission and treat diseases
Supporting research, implementation science, and public health evaluations to inform best practices for preventing diseases and countering health threats.
Fostering health diplomacy by building relationships that promote the use of evidence-based public health policy, communicate risk, and disseminate prevention messages in response to health threats
"No matter the disease or condition, this collaborative framework will increase CDC's impact as we continue to protect Americans and save lives across the globe."
– Dr. Kayla Laserson, Director, CDC Global Health Center
Implementing the framework allows CDC programs across the agency to:
CDC's Global Health Center works 24/7 to reduce illness and respond to health threats worldwide.
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