economics experiments examples

Famous Economics Experiment Reproduced Thousands of Times

In an open marketplace, such as a farmers' market where produce and other goods like candles and flowers are exchanged for money, the ideal prices for both consumers and sellers will quickly emerge. For example, if a seller tries to offer a bag of peaches for $10 but another vendor is willing to sell similar peaches for $5, the lower price will eventually win out and become the norm. This phenomenon, which is related to the law of supply and demand, was demonstrated experimentally starting in the 1960s by Caltech alumnus and Nobel Laureate Vernon Smith (BS '49), now at Chapman University, and later by Caltech's Charlie Plott , the William D. Hacker Professor of Economics and Political Science.

Now, nearly 60 years later, Caltech economists have analyzed data from 2,000 repetitions of these experiments, from researchers around the world, to demonstrate for the first time that the work of Smith, Plott, and others is reproducible on large scales. The research was published August 3 in the journal Nature Human Behaviour.

"Until now, there was nothing in the published experimental economics literature like this, in which a common design is done over and over, or in which somebody went back and meta-analyzed a body of data using a common design," says Colin Camerer, the Robert Kirby Professor of Behavioral Economics and director of the T&C Chen Center for Social and Decision Neuroscience in the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech . "It is like a chemistry experiment—if you combine the same exact chemicals many times over, do you get the same chemical reaction? We actually did not know. There is very little career incentive to replicate somebody else's experiment because it's not as creative as coming up with your own, but it's important to do."

The research was possible thanks to a collaboration with the economics education company MobLab, which provides online experiments for students across the globe. Camerer, who is a scientific advisor at the company, worked with his graduate student Po Hsuan Lin, who himself worked at MobLab prior to starting graduate school, to collect tens of thousands of records from the company. As was the case with Smith's 1960s experiments, student users of the online tool take part in a simple market game, where imaginary goods are exchanged for actual money or course credits. Within as little time as minutes, an equilibrium price that is ideal for both consumers and sellers is reached.

"We pulled the data of the simple market experiment, done by students in countries around the globe," says Lin, who is also affiliated with National Taiwan University. "Even with the variation inherent in having people from different countries participate in the same experiment, we get nearly the same results every time. The final equilibrium prices differ by only pennies."

Because professors using MobLab tools want to be consistent with other classes, the same experiments have been performed across classrooms. This meant that the Caltech economists could study the MobLab data to, in essence, repeat the same experiment over and over again. For the simple market game, they ended up analyzing 2,000 experiments and always got the same results.

"The idea that buyer-seller markets are highly efficient and converge to a single price is in every textbook. It's called perfect competition. But it is based largely on a theory," says Camerer. "Charlie Plott has done these market experiments all over the world, with younger children and other students, but the designs are not exactlly the same. With MobLab, we were able to use the exact same experimental designs and show how incredibly reproducible these experiments really are."

The study, titled " Evidence of General Economic Principles of Bargaining and Trade from 2000 Classroom Experiments ," was funded by Deutsche Forschungsgemeinschaft, CRETA (Center for Research in Economic Theory), National Taiwan University, the Ministry of Science and Technology in Taiwan, Caltech, and a MacArthur Foundation Fellowship. Other authors include Alexander L. Brown (MS '05, PhD '08) of Texas A&M University, Taisuke Imai (PhD '16) of LMU Munich, Joseph Tao-yi Wang, a former Caltech postdoc now at National Taiwan University, and Stephanie W. Wang, also a former Caltech postdoc now at the University of Pittsburgh and MobLab.

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Experimental Economics

By don coursey.

By Don Coursey,

W hen the Swedish Nobel Committee awarded the 2002 Nobel Memorial Prize in Economic Sciences to Vernon L. Smith , an economist at George Mason University, it simply affirmed what economists have long known: that experimental economics has arrived as a respected and powerful discipline within economics. The committee noted that the award was based on Smith’s “having established laboratory experiments as a tool in empirical economic analysis, especially in the study of alternative market mechanisms.” But what exactly are market experiments, and what can researchers learn from them? Of what importance, outside the academy, is the “study of alternative market mechanisms”?

Economic experiments are not simulations or role-playing exercises. They involve real people who make serious choices. Through their efforts, participants stand to make or lose substantial amounts of money.

The simplest form of economic transaction—and the simplest experiment to conduct—is a two-person exchange. This experiment addresses how a single buyer and seller of an item reach, or fail to reach, mutually agreeable terms of trade for that item. In this experimental setting, the researcher induces value on the item for the buyer and the seller. For example, the person assigned the role of seller might be handed a card that indicates that his cost of production for the item is $10. If he can sell the item to the buyer in the experiment for more than his cost of production, then he will be awarded the difference between his sales price and $10. Likewise, the person assigned the role of buyer might be handed a card indicating that his resale value of the item is $22. This means that if he is able to acquire the item for less than $22, he can then sell it back to the experimenter for $22 and keep the difference.

Although no actual physical object is traded, both the seller and the buyer have an incentive to behave exactly as if one were. The seller will desire a price well above $10 for his item; the buyer will wish to pay as little as possible for his. What will happen? Two outcomes are possible. Either the seller and buyer find a mutually agreeable price between $10 and $22, or they fail to reach agreement. Economics says that both sides have an incentive to make a deal, but it says nothing about how the benefits of that deal will be divided. Economics also has little to say about the frequency of occasions in which the seller and buyer part company without making a trade. Many versions of this simple experiment have been conducted to explore these empirical issues.

The simple experimental design outlined above provides a building block for all subsequent experimental market designs. After all, a market at its core is a place where bilateral trades are facilitated between multiple buyers and sellers. Suppose we want to construct a market with five sellers and five buyers. In this case, we would hand a card to each seller indicating the cost of production. For example, one seller would be given a card indicating a cost of $10. The other four sellers would have costs of $12, $14, $16, and $18. People assigned to be buyers would receive a card indicating their resale value. Continuing the example, suppose these values were $22, $20, $18, $16, and $14. Each seller and each buyer in this design would have the opportunity to make one transaction.

Given the range of values for buyers and the range of costs for sellers, what will occur when they are allowed to trade? Will sellers have the upper hand? Will all trades that might benefit both buyers and sellers occur, or will some beneficial trades fail to take place because of incomplete information or so-called market failure? When trades do take place, will they be across a wide range of prices or a narrow band?

Economic theory in its simplest incarnation of supply and demand makes a strong set of predictions. Consider a graph (see Figure 1 ) that has price on the vertical axis and quantity on the horizontal axis. The supply schedule answers the question: How many units would voluntarily be brought to the market at various prices? Thus, supply in this experimental structure is an ascending stair-step pattern that starts at $10 and rises $2 per step for each unit in the market. Above $18 the supply curve is vertical, for only five units can ever be purchased in this setting. Likewise, the demand schedule answers the question: How many units will be voluntarily purchased in the market at different prices? Using the same analysis as that for the sellers, we find that the demand schedule is a descending stair-step pattern that starts at $22 and falls $2 per step for each unit demanded in the market. Below a price of $14, the demand schedule also is vertical, for no more than the five units are desired in this setting. For this scenario, textbook economics predicts that equilibrium will be reached where supply equals demand. In this case, that means that four units would be traded at the identical price of $16.

Vernon Smith developed this basic structure for creating an experimental market in the mid-1950s. His first creative insight was motivated by the severity of the textbook prediction for all prices to be exactly $16, as noted above. He asked how everyone in the market could reach a single price. Neither the sellers, wanting high prices, nor the buyers, wanting low prices, would necessarily be happy with this outcome. In other words, how was Adam Smith ’s invisible hand to do its work? Vernon Smith found empirically that the market took care of both the buyers’ concerns and the sellers’ concerns simultaneously. This led to his first insight: a defined set of trading rules produces an efficient market price.

Smith’s second creative insight was that exploring these questions could not be done in an institutional vacuum. Half of the experimental structure was missing. The sellers and buyers in this structure cannot trade unless specific rules forming the structure of a trading institution are employed. In his early work, Smith opted to use the rules of a double-oral auction. These auction rules are similar to the rules used for trading at the New York Stock Exchange or the Chicago Board of Trade. It is “double” because both sellers and buyers participate (as opposed, for example, to a silent auction at a fund-raising event, where the seller is passive). It is “oral” because the participants call out their bids and offers publicly. They do this using an important rule called the “bid-asked-price-reduction rule.” What it means is that sellers call out asking prices, which are posted publicly, and all subsequent asking prices must descend from this starting asking price. On the other side of the market, once the first buyer makes his bid, all subsequent bids must ascend from this starting bid price. Trade can occur in two ways. Any buyer can accept a seller’s asking price, or a seller can accept any buyer’s bid.

When Smith ran these first experiments, the mechanics of the invisible hand became visible for the first time! Undergraduate student subjects produced single-price market equilibria, even though none of them desired this outcome. When they repeated the exercise, prices were even tighter around the equilibrium. The number of units being transacted was also “efficient,” exhausting the gains from trade without anyone being in charge of the market.

These results came as a big surprise. Textbooks say that for the market to equilibrate, there must be perfect information. But the subjects produced market equilibria having no knowledge about others and with little experience, if any, trading in the double-oral auction. Finally, when Smith manipulated the number of sellers and buyers, he found that astoundingly small numbers of sellers and buyers—for instance, four of each—could produce competitive equilibrium. Prior to this research, the textbooks said that infinite or “numerous” numbers of each were required. Smith’s early research challenged this convention and opened up the possibility that many apparently “thin” markets (having few sellers and buyers) in the real world produce competitive outcomes.

By the late 1970s Smith was examining all types of market institutions: English and Dutch single-object auctions , sealed-bid auctions, posted-offer markets (like a grocery market, where stores place take-it-or-leave-it prices), treasury bill markets, and others. Smith found a computer system at the University of Arizona that was progressive for its time, offering both real-time networking and touch-screen communication, and soon began computerizing all of his experimental markets.

I have conducted experiments of the simple-oral-double auction with groups ranging from eight-year-olds to Communists to professional traders. In every group, the auction has always produced the competitive result. It is the best economics education a student can absorb if a teacher has only one hour.

Experimental economics not only has allowed us to see how command and control regulations in the market affect behavior and produce unintended consequences , but also has helped address how public goods might be provided using market principles.

Another economist whose work on experimental economics has led to substantial insights about markets is Charles Holt of the University of Virginia. Holt, together with Anne Villamil and Loren Langan, conducted an experiment that showed that even when sellers had more than one unit of a good for sale and even when withholding output from the market could drive up the price substantially on the units they did sell, competitive pressures caused them to price at a level close to the competitive level. Work by Holt and others has also shown that when sellers can offer secret discounts from posted prices, collusive agreements tend to break down. These experiments, along with many others, buttress the late George Stigler ’s contention that competition is a hardy weed, not a delicate flower.

Experimental methods have been used to understand not only markets, but also politics. One leader in this area has been Charles Plott of the California Institute of Technology. Plott and Michael Levine showed that someone who rigs the agenda can push those who vote on the agenda to the outcome preferred by the rigger. The bottom line is that he who controls the agenda has a large say over the outcome.

Experimental economics is also used to solve some knotty problems in U.S. public policy. Consider two examples.

At certain times of the day in large American cities, more jets want to land and depart than can be handled by the airport. One obvious economic solution to this problem is to auction off the right to land and take off during the congested periods so that the fixed number of scarce “slots” is sold to the highest bidders. Although this logic is correct, Vernon Smith realized that it is incomplete. The problem is not just the fact that you want to land at O’Hare airport in Chicago on Friday in the 4:00 through 5:00 p.m. time slot. Typically you want other conditions to be met as well. You might also want a slot out of O’Hare between 6:00 and 7:00 p.m. Additionally, you may be flying from Chicago to another congested airport like Atlanta, and so you will want a landing slot there as well, and so on.

At its essence, slot allocation is a problem of balancing supply and demand. But the constraints associated with so many crowded, interconnected airports with so many airlines and aircraft competing for space make the simple problem seem impossibly complex. Smith did not think so. He was able to develop a system of combined auctions that solved this problem. These auctions were exhaustively tested in his laboratory and are now used as allocation tools in national airport management.

Experimental economics has also been applied to electricity regulation . Electricity has three properties that make it different from other economic commodities. First, it is the only product for which supply and demand have to be equal at all moments in time. Electricity suppliers promise to meet the use by demanders—when you switch on your lights, you expect them to come on. Second, electricity is hard to store. Third, electricity does not really move directly from its source to its ultimate user. Rather, when electricity is provided to a power grid, the grid is like a great pond whose water level has just increased. These three factors have inhibited the trading of electricity across regions in the United States. Smith, always on the lookout for market solutions that could improve efficiency , cracked the complex technical problems associated with how to trade something so seemingly amorphous as electricity. His work in this area provided the basis for a radical new system of electricity and energy trading that swept the country during the 1990s. Smith advocated an open trading system for electricity in both the wholesale and retail markets. States that have adopted his system fully—mainly western states other than California—have benefited greatly. Other states that still use old regulatory regimes, mainly eastern states, or, like California, that applied only a partial market framework, have struggled.

The tools of experimental economics allow us not only to understand known issues with new precision, but also to discover whole new classes of otherwise unknown phenomena.

About the Author

Don Coursey is the Ameritech Professor of Public Policy Studies at the University of Chicago’s Harris School of Public Policy.

Experimental Economics: What it Means, How it Works

What Is Experimental Economics?

Experimental economics is a branch of economics that studies human behavior in a controlled laboratory setting or out in the field, rather than just as mathematical models. It uses scientific experiments to test what choices people make in specific circumstances, to study alternative market mechanisms and test economic theories.

Key Takeaways

Experimental economics is concerned with studying the efficacy of economic principles and strategies in a laboratory setting with participants.
Experimental economics is used to help understand the reasoning and factors that influence the functioning of a market.
Vernon Smith pioneered the field and developed a methodology that allowed researchers to examine the effect of policy changes before they are implemented.

Understanding Experimental Economics

Experimental economics is used to help understand how and why markets function the way they do. These market experiments, involving real people making real choices, are a way of testing whether theoretical economic models actually describe market behavior, and provide insights into the power of markets and how participants respond to incentives—usually cash.

The field was pioneered by Vernon Smith, who won the Nobel Prize in Economics in 2002 for developing a methodology that allows researchers to examine the effects of policy changes before they are implemented to help policymakers make better decisions.

Experimental economics is mainly concerned with testing in a laboratory setting with appropriate controls to remove the effects of external influences. Participants in an experimental economics study are assigned the roles of buyers and sellers and rewarded with the trading profits they earn during the experiment.

The promise of a reward acts as a natural incentive for participants to make rational decisions in their self-interest. During the experiment, researchers constantly modify rules and incentives in order to record participant behavior in changed circumstances.

Smith’s early experiments focused on theoretical equilibrium prices and how they compared to real-world equilibrium prices. He found that even though humans suffer from cognitive biases , traditional economics can still make accurate predictions about the behavior of groups of people . Groups with biased behavior and limited information still reach the equilibrium price by becoming smarter through their spontaneous interaction.

Along with behavioral economics —which has established that people are a lot less rational than traditional economics had assumed—experimental economics is also being used to investigate how markets fail and to explore anticompetitive behavior.

Examples of Experimental Economics

The applications of experimental economics can be seen in various policy decisions. For example, the design of carbon trading emissions schemes has benefitted from experiments conducted by economists in different regions of the world in a laboratory setting. Different perspectives of political science have also come to the surface through experimentation and exposure to experimental economics.

The Nobel Prize. " Vernon L. Smith ." Accessed April 17, 2021.

Cambridge University Press. " What Can Laboratory Experiments Teach Us About Emissions Permit Market Design? " Accessed Sept. 11, 2021.

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August 4, 2020

Famous economics experiment reproduced thousands of times

by Whitney Clavin, California Institute of Technology

In an open marketplace, such as a farmers' market where produce and other goods like candles and flowers are exchanged for money, the ideal prices for both consumers and sellers will quickly emerge. For example, if a seller tries to offer a bag of peaches for $10 but another vendor is willing to sell similar peaches for $5, the lower price will eventually win out and become the norm. This phenomenon, which is related to the law of supply and demand, was demonstrated experimentally starting in the 1960s by Caltech alumnus and Nobel Laureate Vernon Smith (BS '49), now at Chapman University, and later by Caltech's Charlie Plott, the William D. Hacker Professor of Economics and Political Science.

"Until now, there was nothing in the published experimental economics literature like this, in which a common design is done over and over, or in which somebody went back and meta-analyzed a body of data using a common design," says Colin Camerer, the Robert Kirby Professor of Behavioral Economics and director of the T&C Chen Center for Social and Decision Neuroscience in the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech. "It is like a chemistry experiment—if you combine the same exact chemicals many times over, do you get the same chemical reaction? We actually did not know. There is very little career incentive to replicate somebody else's experiment because it's not as creative as coming up with your own, but it's important to do."

Journal information: Nature Human Behaviour

Provided by California Institute of Technology

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Experimental Economics

Do prices increase when demand increases?
How many traders are required for a market to be “competitive”?
Does a sense of fairness play a role in bargaining? If so, can people be paid to be less fair?
Is it more efficient to auction complementary products, such as pairs of vases, sequentially or simultaneously?

Questions such as these are difficult to answer completely using field data. Often it is difficult to find naturally-occurring environments simple enough to cleanly test economic predictions, and often information not easily obtained in the field is required to draw conclusions. However, these questions are ideally suited to economic experimentation.

Economic experiments ask human subjects respond to incentives in a controlled laboratory environment which represents the essential economic features of a theoretical or naturally-occurring economic problem of interest. They are distinguished from other social science laboratory techniques, such as those used in psychology, by monetary incentives which depend on decisions made by experiment participants; those who respond better to the incentives provided are paid more money, usually in cash, for their participation.

Experimental Incentives

In some experiments, it is easy to implement monetary incentives. In bargaining problems, for instance, the experimenter can simply provide a cash prize over which subjects must bargain, according to certain rules.

However, most experimental incentives are structured using a technique called induced value. Each subject is given redemption schedules, indicating how much real money the experimenter will give him or her for each unit of laboratory good he or she holds at the end of the experiment.

For instance, a subject with one unit of Y may be offered $1.50 for the first unit of X; $1.00 for the second unit of X; and $0.75 for the first unit of Y. This subject may trade her Y for an X with another subject, who values Y more highly than X, so that each will be paid more at the end of the experiment. These values are “induced” because they are given to subjects who value the goods only through the money the experimenter is willing to pay.

These performance-dependent incentives link results in the lab to the situations being modeled outside the lab. Experimentalists argue that laboratory tests provide information about naturally-occuring economic behavior. because economic behavior is governed by basic principles which apply both inside and outside the lab. These axioms of rationality are the basis of economic theory, which makes no distinction between, for instance, maximizing utility inside and outside the laboratory. The cash incentives ensure that better decisions inside the laboratory result in more utility outside the laboratory (money earned in the experiment can be spent outside the laboratory). The external validity of any particular result depends on how well the experimental design captures the incentives present in the naturally-occuring situation being modeled.

Advantages of the Experimental Approach

Laboratory analysis can complement traditional field data modeling by testing economic theories in several ways. First, laboratory experimentation offers a degree of control which allows economists to generate tests of alternative policies at low cost, or to give theories their “best chance” by testing them in environments which exactly satisfy their assumptions. Second, laboratory analysis facilitates replication; it is often difficult to find an independent field sample with which to accomplish this critical step in the scientific method. Finally, experimentation allows economists to have information they could not know in the field. For instance, many economic theories suggest that people respond to their beliefs in particular ways.

Beliefs are difficult or impossible to measure in the field, but they can be naturally integrated into an experimental design.

Types of Experiments

There are many ways to categorize experiments, but one of the more useful is by which models they are attempting to test. (The taxonomy also has the advantage of distinguishing experiments which test models from those which do not.) Most experiments can be characterized by one or more of these objectives:

Testing theoretical predictions . Economic theories make predictions which can be tested in the laboratory. For instance, general equilibrium theory predicts market prices at the intersection of supply and demand. Experiments have shown this prediction is accurate with a wide variety of trading institutions.
Testing robustness of theories . Economic theories often include very strong assumptions, and experiments can test the sensitivity of their predictions to weakening of those assumptions. For instance, general equilibrium theory predicts market prices at the intersection of supply and demand when there are a very large number of agents . Experiments have determined the “very large” number of agents at which competitive outcomes reliably obtain is three.
Testing assumptions . Rather than testing the predictions of a theory, models are sometimes examined by testing their assumptions. This technique is frequently used by behavioral economists who seek to replace standard rationality assumptions with more descriptively accurate, yet mathematically tractable, models.
Identifying stylized facts . Because replication is straightforward in the laboratory, experiments are often used to identify patterns in behavior which may or may not be consistent with theory. For instance, in the dictator game (in which two people are anonymously paired and one, the dictator, is asked to allocate $10 between the two) dictators often give money to their paired partner, although they could have kept the money for themselves. New models are being developed to integrate such stylized facts, and the real economic norms they imply, into economic theory.
Comparing institutional designs . Theory does not always provide guidance when deciding among institutions. The alternative institutions, or policies, can be implemented in laboratory and the outcomes compared on the basis of efficiency or other desiderata. This technique has been used to make design decisions in the auction the FCC uses to sell spectrum permits and the auctions used to sell transferable pollution permits by the EPA and by the South Coast Air Quality Management Board in Southern California.

Conspicuously absent from this list of applications is measurement and calibration . Experiments have not, in general, been used to successfully measure the level of efficiency attained in natural applications of institutions, or to determine how much people value goods or ideals. Obstacles to these kinds of research include focal and framing considerations which are difficult to translate from the naturally-occurring situation to the lab. Furthermore, the “ economic principles ” argument for external validity does not extend well to magnitudes. However, as economists continue to grapple with ways to measure values, especially non market values, laboratory techniques may be an important part of new methodologies.

Search Title

Michael R. Kremer

Nobel 2019 | Experimental economics examples: Can smaller scale experiments help save the world?

Michael Kremer is not an economist who speaks in theory. He answers questions directly and always with real world examples. But it wasn’t always this way. Having started out as a macroeconomist studying economic growth, Kremer spent a fair share of time writing models and studying theory. On a trip back to Kenya, a country he had lived and worked in before, fateful conversations led to the radical shift to take a more experimental approach in his work. While some people questioned the move, others saw its potential from the start. Two of those early supporters went on to be awarded, alongside Kremer, the prize for reducing poverty around the world. Suffice it to say, it was a move in the right direction.

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, 2019

At a glance

Born: 1964, New York, USA

Field: Development Economics

Awarded: The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, 2019 (shared)

Prize-winning work: Experimental approach to alleviating global poverty

Favorite countries to visit: Kenya and India

Most influenced by: His parents

A lesson in altruism: Kremer and his co-laureates donated their entire winnings to the Weiss Fund for Research in Development Economics

A More Experimental Approach to Economics

After graduating from college, Kremer taught secondary school in western Kenya. He returned to the US for graduate school and visited Kenya some years later to reconnect with old friends, one of which was working for a small NGO at that time. Focusing on different education programs, the goal was to better understand school performance and to learn which approaches may be most impactful.

“One thing about randomized evaluations is that they tend to involve very close collaboration between researchers and practitioners who bring very different backgrounds,” says Kremer. “These types of collaboration bring economists into very close contact with the people or institutions they’re learning about and I think that brings a much richer perspective.”

It was this type of collaboration and experimentation that led the group to isolate an unexpected single factor with huge impacts on education that also led to major policy influence.

How Health and Economic Growth are Interlinked

More than a billion people worldwide are at risk of worms, including certain areas of Kenya. It’s a common disease and one that is treated inexpensively, yet the diagnosis is logistically difficult and expensive. For this reason, the World Health Organization has recommended that schools routinely provide deworming medicine in high risk areas.

“It costs four to 10 times as much to do the testing as it does to provide the medicine,” says Kremer. “So, if 90 percent of children are infected, as they were in this particular region, just go ahead and make the medicine available to everyone.”

The NGO initially started working in seven schools but planned to introduce the program to 75 schools over time, phasing in 25 schools annually. By doing this, the results could easily be compared between those in the program and those not yet included. Absence fell by a quarter, disease transmission fell and the percentage of girls continuing and completing their education rose.

“The benefits of this school-based worming approach turns out to be 100 times the cost,” says Kremer. “I hate to be an economist about this but it's such a bargain that had the Kenyan government borrowed to finance this program, they would have made enough in tax revenue that they could have paid this off with interest and come out ahead from a purely financial point of view, even setting aside all of the benefits to the kids and future adults as they earn more.”

Kremer presented their findings to the World Bank offices and the Kenyan government and, after some time, the project was scaled up. Today, the Kenya government is reaching millions of children every year.

“What we’re doing in development economics might seem extremely different, but in each case, we’re engaging practical problems,” he says. “The insights from the practical problems lead to the development and advancement of the theory, which advances the ability to engage with practical problems.”

“If you're analyzing data, you can put in whatever statistical controls you want and there's more scope for the researcher to get the result that they want,” says Kremer, “There's much less scope to do that in a randomized evaluation or with the experimental method.”

Today, Kremer is seen as one of the pioneers of randomized controlled trials, and this approach has not only transformed economics but a range of other fields, including agriculture and healthcare.

These types of collaboration bring economists into very close contact with the people or institutions they’re learning about and I think that brings a much richer perspective.

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Committing to Innovation Collectively

The more that Kremer worked on randomized controlled trials, the more he saw how iterative the process could be, a feature more commonly linked to technology companies. While their pace at which some of these private firms operate is “incredible” according to Kremer, the issue is the direction of technological innovation.

“I think we as a society need to think about the institutions that we create, to try to ensure that there's the same level of attention, or at least some attention, being paid to problems that don't have as immediate opportunity for private profit,” he says.

An example of this are diseases that affect poor countries. Pharmaceutical firms successfully develop disease treatments for afflictions that are common in developed countries all the time. They are very profitable in doing so, while also generating huge health benefits. The incentives to research and develop treatments for diseases that primarily affect poor people in developing countries however aren’t as strong. Witnessing this inspired Kremer and Rachel Glennester, his partner and a fellow economist, to write the book Strong Medicine. In the book, they present an approach inspired by the energy of the private sector to tackle health problems affecting poor people around the world, an idea that is now referred to as advanced market commitments.

“The concept of an advanced market commitment is very simple,” says Kremer. “It's that donors commit in advance that if a product is developed that meets certain standards, then they all commit to help finance the purchase of this.”

Kremer and Glennester began with the more distant target of malaria and a closer target of vaccines for strains of pneumococcus, a lesser known disease but one that kills more than a million people annually. The Center for Global Development then got involved focusing on how to transform the theoretical idea into an actual proposal and a commission was set up bringing in people from various fields.

“In this case, the donors committed $1.5 billion to top up the payments that developing countries’ governments and organizations like UNICEF would have made to buy the vaccine in the first place,” he says.

This kind of financial commitment helps create the incentive for the development of the vaccine and ensures those working on the vaccine that it will reach the people who need it most.

“So that's an example of the benefits,” explains Kremer. “It’s to say let’s try to make sure that market incentives are working to serve the full range of human needs.”

There is a strong case for advanced market commitments for infectious diseases today perhaps more than ever. Ensuring a commitment to help pay for the development of vaccines for emerging diseases is vital when the spread and its speed is unknown.

“With coronavirus now and Ebola earlier, people are realizing outbreaks in one part of the world can be very important for the rest of the world,” says Kremer. “Figuring out how to deal with these is of public health importance for all of us and the advanced market commitment could indeed be applied to these. When it comes to communicable diseases, we all should be working together.”

Something that excites Kremer most about the design of advanced market commitments is the scope for iteration and improvement, not only for health needs but a whole range of needs.

“Part of what we need is new technologies,” he says. “This can be a useful tool to make sure we’re developing practical solutions that will actually be used, because with an advanced market commitment firms don't get paid unless they develop products that work to address the social need.”

When it comes to communicable diseases, we all should be working together.

Advanced Market Commitments as a Tool for Global Health

Climate change & economics: rethinking the rules of innovation.

As a development economist, Kremer says climate change affects almost every area he works in and that creating the appropriate institutions for technological change will be key going forward.

“I think there's a misperception that we can’t have economic growth and address climate change,” he says. “But we can have economic growth. That's something that people in every country want and people in poor countries need.”

The effects of climate change are already happening all over the world. While developed countries have some level of protection, those in developing countries are far more at risk. Kremer thinks that by coupling the right incentives to existing technology available today and lessons from Silicon Valley, that should be changeable now.

“Private sector firms are using A/B tests which are basically the same idea as the experimental approach that we use in development economics,” he says. “Try different approaches, measure, then go back. Design things that people actually want to use and that might lead to a broader range of firms to invest in developing these technologies.”

He gives the example of wood or charcoal cookstoves being used by people in some of the poorest countries. Not only are these environmentally harmful by emitting harmful types of emissions that cause much higher rates of global warming, they are harmful to human health as well.

“If we could develop alternative cookstoves, that could lead to reduced deforestation,” says Kremer. “It could lead to better human health and it could have a climate impact.”

Innovation to Address Climate Change in Developing Countries

Giving back to economics.

When Kremer was awarded the prize, alongside Esther Duflo and Abhijit Banerjee, they saw it as an opportunity for the field as a whole. It put their work, and development economics, under the spotlight.

Having an influx of policymakers, both in government and business, who are attracted to this approach—one that favors data rather than spending resources first—has been one of the most encouraging results of the award for Kremer.

“Every time I go to a conference, I see multiple papers where I think this is a great idea but more importantly, they have evidence that it's a great idea, and this is something that could potentially affect millions of people and improve lives,” he says.

Kremer has always encouraged his own students to focus on the research they believe in instead of a continuation of what others are already focusing on.

“Certainly, when I started doing this research, it was not what economists typically did. But in the long run it worked out,” he says. “I think there's sometimes a very glib message that people give, ‘Take more risks, and focus on the big questions.’ I encourage people to do the work they think is important, that they enjoy doing. The rest will usually take care of itself.”

Reflecting back on his own mentor, Larry Weaver, Kremer shares one of their most meaningful interactions. At the time, Kremer was taking physics classes taught by Weaver. Weaver had published a paper in which colleagues had found an error in. While Kremer assumed Weaver would be upset by this, perhaps even embarrassed, he was delighted and told Kremer he was grateful because it helped advance the journey.

He realized in that moment that having the right attitude about science and life should be focusing on the ultimate objective, to redo things if you get them wrong, and that science is a collective enterprise.

“I think that's something that Abhijit, Esther and I all feel very strongly about,” he says. “This is an award not just for what we've been able to accomplish, it's an award for the whole field and the whole movement of researchers, of practitioners, of survey enumerators, of the farmers, and teachers and students we've been talking to, and together that's produced some remarkable insights about better ways to improve education, agriculture, health that are making lives better for millions of people.”

I encourage people to do the work they think is important, that they enjoy doing. The rest will usually take care of itself.

Why do countries have to find better ways to grow?

Hear Michael Spence's view on how countries can grow sustainably while having a long-lasting positive impact.

How can we fight global poverty?

Ester duflo.

Nobel Laureate, 2019

Does wealth always improve your life?

Sir angus s. deaton.

Nobel Laureate, 2015

Can economics save lives?

Nobel Laureate, 2012

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Unit 4 Strategic interactions and social dilemmas

4.9 Using experiments to study economic behaviour

Understanding people’s motivations (altruism, reciprocity, inequality aversion, as well as self-interest) is essential to being able to predict how they will behave as employees, family members, custodians of the environment, and citizens.

Economists use experiments—like the public good experiment in the previous section—to observe behaviour under controlled conditions.

The nineteenth century scientist (and monk) Gregor Mendel used controlled experiments to discover the laws of genetic inheritance. He systematically cross-bred pea plants with different characteristics, and then observed and measured seven characteristics of each plant. We cannot hope to explain human behaviour with seven measurements; nevertheless, economists use a similar approach to identify the factors that matter for economic decision-making, and to infer the preferences underlying people’s decisions.

Experiments are designed to be as realistic as possible, while controlling the conditions in which decisions are made. Then the experimenter can observe how decisions change when just one of the conditions is changed and all others are held constant. Therefore, in the public good experiment:

Exactly the same experiment was conducted in all the cities.
All participants in the experiment were university undergraduates, with similar ages and sociodemographic backgrounds.
Every participant received the same instructions and information about the experiment.
Interactions were computer-mediated and took place anonymously.
Decisions had real consequences: each participant earned an amount of money equal to the total of their own pay-offs at the end of the experiment.

To isolate the effect of an option for players to punish others if they thought their contributions to the public good were too low, the participants were split randomly into a treatment group, who played the game with the option of punishment, and a control group, who did not have this option.

Many universities have computer laboratories where ‘lab experiments’ like this one are conducted, often with student participants. Juan Camilo Cárdenas, an economist at the Universidad de los Andes in Bogotá, Colombia, performs experiments about social dilemmas with people who are facing similar problems in their real life, such as overexploitation of a forest or a fish stock.

View transcript

In our ‘Economist in action’ video, Juan Camilo Cárdenas describes how his experiments help us understand why people cooperate even when there are apparent incentives not to.

For a summary of the kinds of experiments that have been run, the main results, and whether behaviour in the experimental lab predicts real-life behaviour, read the research done by some economists who specialize in experimental economics. For example, Colin Camerer and Ernst Fehr, 1 Armin Falk and James Heckman, 2 or the experiments done by Joseph Heinrich and a large team of collaborators around the world. 3

The way people behave in experiments can predict how they react in real-life situations. For example, fishermen in Brazil who acted more cooperatively in an experimental game also fished more sustainably than the fishermen who were less cooperative in the experiment.

Question 4.10 Choose the correct answer(s)

According to the ‘Economist in action’ video of Juan Camilo Cárdenas , which of the following have economists discovered using experiments simulating public goods scenarios?

The imposition of external regulation sometimes erodes the willingness of participants to cooperate.
Populations with greater inequality exhibit a greater tendency to cooperate.
Once real cash is used instead of tokens of hypothetical sums of money, people cease to act cooperatively.
People are often willing to cooperate rather than free-ride.
Professor Cárdenas mentions this finding in the video.
Professor Cárdenas finds that populations with greater inequality exhibit less trust and cooperation.
Cooperative behaviour occurs even when experimental participants are offered real cash, as in Professor Cárdenas’s experiments.

Exercise 4.10 How valid are laboratory experiments?

In 2007, Steven Levitt and John List published a paper called ‘What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?’ . Read the paper to answer these two questions.

According to their paper, 4 why and how might people’s behaviour in real life vary from what has been observed in laboratory experiments?
Using the example of the public goods experiment, explain why you might observe systematic differences between the observations recorded in Figures 4.14a and 4.14b , and what might happen in real life.

Field experiments

Economists also conduct experiments ‘in the field’: deliberately changing the economic conditions under which people make real decisions, and observing how their behaviour changes. An experiment conducted in Israeli day care centres in 1998 demonstrated that social preferences may be very sensitive to past experience.

It is common for parents to rush to pick up their children from day care. Sometimes parents are late, causing a negative effect on staff who have to work longer. What would you do to deter parents from being late?

Two economists ran a field experiment introducing fines in some day care centres (the treatment group) but not others (the control group). The ‘price of lateness’ went from zero to ten Israeli shekels (about $3 at the time) depending on how late a parent was. Figure 4.15 shows what happened. Surprisingly, after the fine was introduced, the frequency of late pickups doubled.

Figure 4.15 Average number of late-coming parents, per week.

Uri Gneezy and Aldo Rustichini. 2000. ‘A Fine Is a Price’ . The Journal of Legal Studies 29 (January): pp. 1–17.

Why did putting a price on lateness backfire?

One possible explanation is that before the introduction of fines, most parents were on time because they felt that it was the morally right or responsible thing to do, to avoid inconveniencing the day care staff. Perhaps they felt an altruistic concern for the staff, or regarded a timely pickup as a reciprocal responsibility in the joint care of the child.

But the imposition of the fine signalled that the situation was really more like shopping. Lateness had a price and so could be purchased, like vegetables or ice cream. 5 If you paid the price, you had the right to be late, without consequence.

In this article , Nobel Prize winner Esther Duflo explains how field experiments, also known as randomized control trials, can influence government policy.

The use of a market-like incentive—the price of lateness—had provided what psychologists call a new ‘frame’ for the decision, changing it so that self-interest rather than concern for others was acceptable. Even worse, Figure 4.15 shows that when the fine was removed, parents continued to pick up their children late. They seemed to have permanently adjusted their view of what was socially acceptable. They learned that it is acceptable to be late and updated their preferences accordingly. When fines and prices have these unintended effects, we say that incentives have crowded out social preferences.

Question 4.11 Choose the correct answer(s)

Figure 4.15 depicts the average number of late-coming parents per week in day care centres, where a fine was introduced in some centres and not in others. The fines were eventually abolished, as indicated on the graph.

Based on this information, read the following statements and choose the correct option(s).

The graph shows that the market-like incentive has been an effective way of reducing late-coming parents. The introduction of the fine successfully reduced the number of late-coming parents.
The fine can be considered as the ‘price’ for collecting a child.
The graph suggests that the experiment may have permanently increased the parents’ tendency to be late.
The crowding out of the social preference did not occur until the fines ended.
The graph shows that the number of late-coming parents more than doubled in the centre where the market-like incentive (the fine) was introduced.
The parents paid the fine if they were late and not otherwise. So it can be considered as a price for lateness.
The graph shows that the number of late-coming parents remained high after the fine was abolished, so it is possible that the experiment had a permanent effect.
The crowding out of social preferences occurs when the moral obligation of not being late is replaced by the market-like incentive of purchasing the right to be late without ethical qualms. This result is evident in the graph immediately after the introduction of the fines.

Colin Camerer and Ernst Fehr. 2004. ‘Measuring Social Norms and Preferences Using Experimental Games: A Guide for Social Scientists’ . In Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies , eds. Joseph Henrich, Robert Boyd, Samuel Bowles, Colin Camerer, and Herbert Gintis. Oxford: Oxford University Press. ↩

Armin Falk and James J. Heckman. 2009. ‘Lab Experiments Are a Major Source of Knowledge in the Social Sciences’. Science 326 (5952): pp. 535–538. ↩

Joseph Henrich, Richard McElreath, Abigail Barr, Jean Ensminger, Clark Barrett, Alexander Bolyanatz, Juan Camilo Cárdenas, Michael Gurven, Edwins Gwako, Natalie Henrich, Carolyn Lesorogol, Frank Marlowe, David Tracer, and John Ziker. 2006. ‘Costly Punishment Across Human Societies’ . Science 312 (5781): pp. 1767–1770. ↩

Steven D. Levitt, and John A. List. 2007. ‘What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?’ Journal of Economic Perspectives 21 (2): pp. 153–174. ↩

Samuel Bowles. 2016. The Moral Economy: Why Good Incentives Are No Substitute for Good Citizens . New Haven, CT: Yale University Press. ↩

Home — The Economy 2.0
Table of contents — Microeconomics
How to cite The Economy 2.0
A note to instructors
Producing The Economy 2.0
Great Economists
How economists learn from facts
When economists disagree
Building blocks
Notation and conventions
Who invented calculus?
1.1 Ibn Battuta’s fourteenth-century travels in a flat world
1.2 History’s hockey stick
1.3 Another hockey stick: Climate change
1.4 Inequality in global income
1.5 The continuous technological revolution
1.6 Explaining the flat part of the hockey stick: Production functions and the diminishing average product of labour
1.7 Explaining the flat part of the hockey stick: The Malthusian trap, population, and the average product of labour
1.8 Capitalist institutions
1.9 Structural transformation: From farm to firm
1.10 Capitalism, causation, and history’s hockey stick
1.11 Application: Did the British colonization of India reduce Indian living standards?
1.12 Varieties of capitalism: Institutions, government, and politics
1.13 Economics, the economy, and the biosphere
1.14 Summary
1.15 References
2.1 Kutesmart automates personalized tailoring
2.2 Economic decisions: Opportunity costs, economic rents, and incentives
2.3 Comparative advantage, specialization, and markets
2.4 Firms, technology, and production
2.5 Modelling a dynamic economy: Technology and costs
2.6 Modelling a dynamic economy: Innovation and profit
2.7 Cheap coal, expensive labour: The Industrial Revolution in Britain and incentives for new technologies
2.8 Economic models: How to see more by looking at less
2.9 Markets, cheap calories, and cotton: The colonies, slavery, and the Industrial Revolution in Britain
2.10 Growth: Escaping the Malthusian trap
2.11 Capitalism + carbon = hockey stick growth + climate change
2.12 How good is the model? Economists, historians, and the Industrial Revolution
2.13 Summary
2.14 References
3.1 Would you work fewer hours if your hourly wage doubled?
3.2 A problem of choice and scarcity
3.3 Goods and preferences
3.4 The feasible set
3.5 Decision-making and scarcity
3.6 Hours of work and technological progress
3.7 Income and substitution effects on hours of work and free time
3.8 Is this a good model?
3.9 Explaining our working hours: Changes over time
3.10 Application: Work hours, free time, and inequality
3.11 Explaining our working hours: Gender and working time
3.12 Explaining our working hours: Differences between countries
3.13 Summary
3.14 References
4.1 Climate negotiations: Conflicts and common interests
4.2 Social interactions: Game theory
4.3 Best responses in the rice–cassava game: Nash equilibrium
4.4 Dominant strategy equilibrium and the prisoners’ dilemma
4.5 Evaluating outcomes: The Pareto criterion
4.6 Public good games and cooperation
4.7 Social preferences: Altruism
4.8 Repeated interaction: Social norms, reciprocity, and peer punishment in public good games
4.10 Cooperation, negotiation, and conflicts of interest
4.11 The ultimatum game: Dividing a pie (or leaving it on the table)
4.12 Fair farmers, self-interested students? Experimental results of the ultimatum game
4.13 Coordination games and conflicts of interest
4.14 Modelling the global climate change problem
4.15 Summary
4.16 References
5.1 Pirate economics
5.2 Institutions and power
5.3 Evaluating institutions and outcomes: Fairness
5.4 Setting up a model: Technology and preferences
5.5 Institutions, and the case of the independent farmer
5.6 Case 1: Forced labour
5.7 Case 2: A take-it-or-leave-it contract
5.8 Case 3: Bargaining in a democracy
5.9 Case 3 continued: Negotiating to a Pareto-efficient sharing of the surplus
5.10 Lessons on the impact of institutions on efficiency and fairness
5.11 The distribution of income: Endowments, technology, and institutions
5.12 Measuring economic inequality
5.13 Application: A policy to redistribute the surplus and raise efficiency
5.14 Application: Conflicts of interest and bargaining over wages, pollution, and jobs
5.15 Summary
5.16 References
6.1 Exploding tyres: The mystery unravelled
6.2 The structure of the firm: Owners, managers, and workers
6.3 Other people’s money: The separation of ownership and control
6.4 Finding jobs and filling vacancies
6.5 Managing hiring and quitting: The reservation wage curve
6.6 Getting the work done: Contracts, principals, and agents
6.7 Employment rents: The cost of job loss
6.8 Counting the cost of job loss: Rents and reservation wages
6.9 Getting employees to work hard: The labour discipline model
6.10 Combining recruitment and labour discipline: The wage-setting model
6.11 Putting the wage-setting model to work: Wages, employment, and the rate of unemployment
6.12 How employers exercise power
6.13 Application: The minimum wage
6.14 Application: Another kind of business organization
6.15 Summary
6.16 References
7.1 Winning brands
7.2 Breakfast cereal: Choosing a price
7.3 Economies of scale and the cost advantages of large-scale production
7.4 Production and costs: The cost function for Beautiful Cars
7.5 Demand, elasticity, and revenue
7.6 Setting price and quantity to maximize profit
7.7 Gains from trade: The surplus and how it is divided
7.8 Price setting, competition, and the market
7.9 How firms differentiate their products
7.10 Markets with few firms: Strategic price setting
7.11 Firms and markets with decreasing long-run average costs
7.12 Influencing market power, and competition policy
7.13 Summary
7.14 References
8.1 Supply and demand: Markets with many buyers and sellers
8.2 Buying and selling: Demand, supply, and the market-clearing price
8.3 Competitive equilibrium and price-taking
8.4 Firms in competitive equilibrium
8.5 Gains from trade in competitive equilibrium: Allocation and distribution
8.6 Changes in supply and demand
8.7 Short-run and long-run equilibria
8.8 Application: Market dynamics in the oil market
8.9 How competition works: Transforming a cartel coordination game into a competitive prisoners’ dilemma
8.10 Supply, demand, and competitive equilibrium: Is this a good model?
8.11 Application: Why information about prices matters
8.12 The effect of a tax
8.13 Price controls
8.14 Summary
8.15 References
9.1 The importance of Chambar moneylenders
9.2 Income and wealth
9.3 Borrowing: Bringing consumption forward in time to the present
9.4 Reasons to borrow: The value of spending now
9.5 Application: Discounting, external effects, and the future of the planet
9.6 Lending and storing: Moving consumption to the future
9.7 Investing: Another way to move consumption to the future
9.8 Conflicts over the gains made possible by borrowing and lending
9.9 Borrowers and lenders: A principal–agent problem
9.10 Inequality: Lenders, borrowers, and those excluded from credit markets
9.11 How good is the model?
9.12 A poverty trap for those with limited wealth
9.13 Application: Policies to reduce risk exposure of less well off people
9.14 Summary
9.15 References
10.1 Bananas, fish, and cancer
10.2 The external effects of pollution: Private and social costs and benefits
10.3 Solving the problem: Private bargaining and property rights
10.4 Solving the problem: Regulation, taxation, and compensation
10.5 External effects: More examples and diagnoses
10.6 Public goods, non-rivalry, and excludability: A model of radio broadcasting
10.7 Public goods and bads, open access, and shared resources
10.8 Asymmetric information: Principal–agent relationships, hidden actions, and incomplete contracts
10.9 Hidden actions and risk: Market failure in insurance and credit markets
10.10 Asymmetric information: Hidden attributes and adverse selection
10.11 The limits of markets
10.12 Summary
10.13 References
Looking forward to economics after The Economy 2.0
Bibliography
Copyright acknowledgements

This ebook is developed by CORE Econ. More information and additional resources for learning and teaching can be found at www.core-econ.org .

Licensed under Creative Commons BY NC ND

A-Z Publications

Annual Review of Economics

Volume 14, 2022, review article, experimental economics: past and future.

Guillaume R. Fréchette 1 , Kim Sarnoff 2 , and Leeat Yariv 2,3,4
View Affiliations Hide Affiliations Affiliations: 1 Department of Economics, New York University, New York, NY, USA; email: [email protected] 2 Department of Economics, Princeton University, Princeton, New Jersey, USA; email: [email protected] [email protected] 3 Centre for Economic Policy Research, London, United Kingdom 4 National Bureau of Economic Research, Cambridge, Massachusetts, USA
Vol. 14:777-794 (Volume publication date August 2022) https://doi.org/10.1146/annurev-economics-081621-124424
Copyright © 2022 by Annual Reviews. All rights reserved

Over the past several decades, lab experiments have offered economists a rich source of evidence on incentivized behavior. In this article, we use detailed data on experimental papers to describe recent trends in the literature. We also discuss various experimentation platforms and new approaches to the design and analysis of the data they generate.

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Economic Classroom Experiments

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This page refers to information on economic classroom experiments and related material.

Numbers insist that an economic model of giving, can produce an economy of abundance in that, if all were to give consistently, a substantial amount, into, let's picture it as a river of abundance. What you do not need for right now goes to the river. And what you need when you need it comes from the river, for the most part. We see something similar to this in paying taxes to support the public welfare system. The problem is that these welfare systems that are in place, are terribly inefficient. And more money goes to paying employees of the system to manage the lesser amount of money actually going to public welfare.

I challenge anyone to run the numbers and prove the giving river wrong. Also, a challenge to anyone to say the public welfare system is not riddled with inefficiency...

Why use experiments in teaching economics?

They help students to understand an otherwise abstract model. In the double auction experiment they experience, for example, how demand and supply drive the market towards equilibrium. It's learning by doing.
To show that economic theory can work (for instance, in a Bertrand game ). This is important because students or the general public are often sceptical about the use of mathematical analysis in economics.
To show that economic theory might not work (for instance, in an ultimatum game ). This quickly leads to questions on the current frontier of our science.
An experiment can make it easier for students to grasp a threshold concept like Nash equilibrium .

List of Experiments

The Twenty pound auction
The Wallet Game
The Ultimatum and the Dictator Bargaining Games
The Public Good Game
Private Value Auctions
The Insurance Game
Currency Attack
Bertrand Competition
American Call Option
Diamond Dybvig Experiment
Hold-Up Problem
Kiyotaki Wright Hazlett Experiment
Monty Hall Paradox
Network Externalities
Price Discrimination
Warren Buffett
The Guessing Game
Complete List of Interactive Experiments

Useful Links and Related Literature

resource	description
and	Charlie Holt’s website has a variety computerized experiments. Students log in to an experiment via . Simply type "veconlab" into google. The first link shown will be for the instructor to start an experiment. The second is for participating students to log in. You will need a log-in name.
	has many hand-run versions of his computerized experiments on veconlab.
	has a beautifully written version of the double auction (Vernon Smith's basic demand- and supply curve experiment). Important information on how to alter the parameters of the experiment is at The site also offers a very useful on-line handbook for micro economics.
	provides access to a set of computerized experiments. You can quickly log in as a subject to try out various experimennts, both group-participation (playing against recorded data from a real-life session) and individual-progress (playing against the computer). If you want to set up and run your own experiments, you need to register your email address to obtain a username and password. The site is very similar in purpose to Charlie's site and intended to complement it.
	Loads of useful information on all aspects of teaching economics, including .
	provides information for 7 handrun macro experiments. FEELE computerized two of them.
	The site offers both links and a series of handrun classroom experiments some original and some standard.
	A web-based journal on classroom experiments published until 2003.
	One hundred and sixty classroom experiments described!
	Check for additional links.
	Plenty of questions on decision theory and game theory. Very easy to use. Select your own problem set and let students work on it via the web.
	Experiments-based elementary microeconomics course. See note on ClassEx, below.

	Popular economic games on all major browsers, iPhone, iPad, and Android smartphones.
	Highlights microeconomics notions such as marginal/average cost, variable/sunk costs, short run/long run costs, price elasticity of demand, demand shocks, impact of production capacity on price competition, oligopoly/monopoly and the logic behind competition, price discrimination, collusion, ... (also includes advanced features such as auctions for capacity or CO2 permits, mergers and takeovers, differentiation, ...)
	Several short games for teaching economics (market games, prisoner's dilemma, public goods, Cournot and Stackelberg....). Students play with their smartphones, tablets or laptops.
	18 interactive games to teach concepts in financial education and economics in a playful, stimulating, meaningful and practical environment. FinÉcoLab is targeted at high school and college students. It provides teachers with a pedagogical guide. Students play with their tablets or laptops.

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5 Examples of Behavioral Economics in Your Everyday Life

We see examples of behavioral economics more than we think in our day-to-day lives. How do these principles impact us?

Read the first post in this series, “ Q&A: Behavioral Economics 101 ”, to hear from Dr. Elizabeth Schwab on an overview of behavioral economics.

Read the third post in this series, “ Must-see media list for behavioral economics ” to discover a list of resources to help you learn about the field outside of the classroom.

Our days are a whirlwind of activities—rushing from work, to the gym, to the store, and filling our time with errands, meals, and whatever else we need to do before we start all over again the next day. We are so absorbed in our routines that it’s difficult to have an awareness of the factors that influence us. Behavioral economics is so applicable because it explains some of our behavior that we don’t think twice about.

In this post, we explore these questions: what is behavioral economics? How is it that we encounter this psychological phenomenon daily without recognizing it? What are some examples of behavioral economics?

What is Behavioral Economics?

Behavioral Economics is a study that intersects the teachings of psychology and economics. More specifically, as stated by Investopedia , behavioral economics “relates to the economic decision-making processes of individuals and institutions.”

Behavioral economics principles have major consequences for how we live our lives. By understanding the impact they have on our behavior, we can actively work to shape our realities.

Examples of behavioral economics

Example #1: playing sports.

Principle : Hot-Hand Fallacy —the belief that a person who experiences success with a random event has a greater probability of further success in additional attempts.

Example : When basketball players are making several shots in a row and feel like they have a “hot hand” and can’t miss.

Relation to BE : Human perception and judgment can be clouded by false signals. There is no “ hot hand ”—it’s just randomness and luck.

Example #2: Taking an exam

Principle : Self-handicapping —a cognitive strategy where people avoid effort to prevent damage to their self-esteem.

Example : In case she does poorly, a student tells her friends that she barely reviewed for an exam, even though she studied a lot.

Relation to BE : People put obstacles in their own paths (and make it harder for themselves) in order to manage future explanations for why they succeed or fail.

Example #3: Grabbing coffee

Principle : Anchoring —the process of planting a thought in a person’s mind that will later influence this person’s actions.

Example : Starbucks differentiated itself from Dunkin’ Donuts through their unique store ambiance and product names. This allowed the company to break the anchor of Dunkin’ prices and charge more.

Relation to BE : You can always expect a grande Starbucks hot coffee ( $2.10 ) to cost more than a medium one from Dunkin ( $1.89 ). Loyal Starbucks consumers are conditioned, and willing, to pay more even though the coffee is more or less the same.

Example #4: Playing slots

Principle : Gambler’s Conceit —an erroneous belief that someone can stop a risky action while still engaging in it.

Example : When a gambler says “I can stop the game when I win” or “I can quit when I want to ” at the roulette table or slot machine but doesn’t stop.

Relation to BE : Players are incentivized to keep playing while winning to continue their streak and to keep playing while losing so they can win back money. The gambler continues to perform risky behavior against what is in this person’s best interest.

Example #5: Taking work supplies

Principle : Rationalized Cheating —when individuals rationalize cheating so they do not think of themselves as cheaters or as bad people.

Example : A person is more likely to take pencils or a stapler home from work than the equivalent amount of money in cash.

Relation to BE : People rationalize their behavior by framing it as doing something (in this case, taking ) rather than stealing . The willingness to cheat increases as people gain psychological distance from their actions.

These behavioral economics principles have major consequences on how we live our lives. By understanding the impact they have on our behavior, we can actively work to shape our own realities.

As Dan Ariely, Ph.D ., says in his book, “ Predictably Irrational: The Hidden Forces That Shape Our Decisions , ,” “We usually think of ourselves as sitting in the driver’s seat, with ultimate control over the decisions we made and the direction our life takes; but, alas, this perception has more to do with our desires—with how we want to view ourselves—than with reality.”

Awareness of behavioral economics helps us comprehend our actions so we can make better choices and live our lives in the driver’s seat.

For more, see the first blog in this series, a Q&A with Elizabeth Schwab , Psy.D., Associate Department Chair for Business Psychology and Program Chair for Behavioral Economics.

Learn more about our online masters in behavioral economics here and must-see media options for behavioral economics here , or fill out the form below for more information.

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What Are Natural Experiments and How Do Economists Use Them?

Natural Experiments vs. Controlled Experiments

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A natural experiment is an empirical or observational study in which the control and experimental variables of interest are not artificially manipulated by researchers but instead are allowed to be influenced by nature or factors outside of the researchers' control. Unlike traditional randomized experiments, natural experiments are not controlled by researchers but rather observed and analyzed.

Natural Experiments Versus Observational Studies

So if natural experiments are not controlled but rather observed by researchers, what is there to distinguish them from purely observational studies? The answer is that natural experiments still follow the primary principles of experimental study. Natural experiments are most effective when they mimic as closely as possible the existence of test and control groups of controlled experiments, which is to say that there is a clearly defined exposure to some condition in a clearly defined population and the absence of that exposure in another similar population for comparison. When such groups are present, the processes behind natural experiments are said to resemble randomization even when researchers do not interfere.

Under these conditions, observed outcomes of natural experiments can feasibly be credited to the exposure meaning that there is some cause for belief in a causal relationship as opposed to simple correlation. It is this characteristic of natural experiments — the effective comparison that makes a case for the existence of a causal relationship — that distinguishes natural experiments from purely non-experimental observational studies. But that is not to say that natural experiments aren't without their critics and validation difficulties. In practice, the circumstances surrounding a natural experiment are often complex and their observations will never unequivocally prove causation. Instead, they provide an important inferential method through which researchers can gather information about a research question upon which data might otherwise not be available.

Natural Experiments in Economics

In the social sciences, particularly economics, the expensive nature and limitations of traditionally controlled experiments involving human subjects has long been recognized as a limitation for the development and progress of the field. As such, natural experiments provide a rare testing ground for economists and their colleagues. Natural experiments are used when such controlled experimentation would be too difficult, expensive, or unethical as is the case with many human experiments. Opportunities for natural experimentation are of the utmost importance to subjects like epidemiology or the study of health and disease conditions in defined populations in which experimental study would problematic, to say the least. But natural experiments are also used by researchers in the field of economics to study otherwise difficult to test subjects and are often possible when there is some change in law, policy, or practice in a defined space like a nation, jurisdiction, or even social group. Some examples of economics research questions that have been studied through natural experimentation include:

The "return on investment" of higher education in American adults
The effect of military service on lifetime earning
The effect of public smoking bans on hospital admissions

Journal Articles on Natural Experiment:

The Economic Consequences of Unwed Motherhood: Using Twin Births as a Natural Experiment
Natural and Quasi-Experiments in Economics
A Natural Experiment in "Jeopardy!"
Cost-Push Inflation vs. Demand-Pull Inflation
What Is International Economics?
Definition and Use of Instrumental Variables in Econometrics
So What Exactly Do Economists Do?
What Is Microeconomics?
What Is a Commodity in Economics?
What Are the Various Subfields of Economics?
Overview of Cost Curves in Economics
What Is Classical Liberalism? Definition and Examples
Online Microeconomics Textbook
White Noise Process Definition
Understand the Economic Concept of a Budget Line
What Is a Plant in the Study of Economics?
Quasiconcave Utility Functions
Learn the Definition What Is Okun's Law in Economics

If you don't find the experiment you need, consider designing your own !

Economists have written a large number of classroom experiments and published them in a wide variety of books, journals and websites .

Subject: Economics

Results 1 - 10 of 11 matches

Penalty Kicks—A Simultaneous Move Zero-Sum Game Christina Robinson, Central Connecticut State University Penalty Kicks—A Simultaneous Move Zero-Sum Game is an experiment that illustrates the importance of playing randomized strategies. This experiment is appropriate for undergraduate students who have completed a basic game theory module and can be completed in 15-20 minutes. Subject: Economics: Economics

An Interactive Introduction to Randomized Control Trials Utteeyo Dasgupta, Franklin and Marshall College This activity provides a classroom impact evaluation exercise that serves as an introduction to the primary investigative tool of current Development economics. Subject: Economics: Economics

The Value of Exchange: A Classroom Experiment Tisha Emerson, Baylor University This experiment illustrates the value of exchange to students through trading of candy. Subject: Economics: Economics

Airplane Production: A Law of Diminishing Marginal Product Exercise Tisha Emerson, Baylor University Classroom experiment illustrating the law of diminishing marginal productivity through the production of paper airplanes. Subject: Economics: Economics

Public Goods Experiment Todd Swarthout, Georgia State University Subject: Economics: Economics

Introduction of Backward Induction Technique Using a Classroom Experiment Utteeyo Dasgupta, Franklin and Marshall College This classroom activity serves as an intuitive introduction to backward induction solutions in an upper level undergraduate game theory course. Subject: Economics: Economics

Energy and the Environment Sheryl Ball, Virginia Polytechnic Institute and State Univ This experiment illustrates how seemingly harmless individual actions can, when taken collectively, develop into larger costs to society. Subject: Economics: Economics

Homegrown Demand Todd Swarthout, Georgia State University The professor sells an announced number of M&M packets (or other inexpensive good) through an auction to derive a classroom demand schedule. The resulting demand schedule is displayed as a "curve" and facilitates discussion of consumer demand. Subject: Economics: Economics

Foreign Exchange Rates: Solidifying a Student's Grasp of Supply and Demand Todd Easton, University of Portland In this assignment, students think about four events that would affect a country's exchange rate. Without actually drawing a supply and demand diagram, students say what direction, if at all, each curve would shift--and whether the currency would appreciate or depreciate as a result. Subject: Economics: Economics

Teaching Case: This American Life Episode 391: More is Less, 2009 Christina Robinson, Central Connecticut State University This case study is based on NPR's This American Life Episode 391: More is Less, which originally aired in 2009. The story highlights the role health care providers, patients, and health insurance companies play in driving up the cost of care. Subject: Economics: Economics

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Three economists win Nobel for their research on how real life events impact society

Scott Horsley

Displayed is a file photo of a Nobel Prize medal on Dec. 8, 2020. The Nobel Prize in economic sciences was awarded to three U.S-based professors for their pioneering work with "natural experiments." Jacquelyn Martin/AP hide caption

Displayed is a file photo of a Nobel Prize medal on Dec. 8, 2020. The Nobel Prize in economic sciences was awarded to three U.S-based professors for their pioneering work with "natural experiments."

Three U.S.-based economists will share this year's Nobel Memorial Prize in Economic Sciences for their innovative work with "natural experiments" – events or policy changes in real life that allow researchers to analyze their impact on society.

David Card of the University of California at Berkeley will receive half the prize, worth 10 million Swedish kronor, or about $1.1 million, the Royal Swedish Academy of Sciences said on Monday . Joshua Angrist of the Masschusetts Institute of Technology and Guido Imbens of Stanford University will share the other half.

The Nobel Prize in literature goes to a Black writer for the first time since 1993

Controlled experiments are common in science and medicine: they allow, for example, to test new drugs by carefully selecting participants and controlling vital aspects to ensure objectivity.

But they are harder in social sciences where it can often be impractical or unethical to conduct randomized trials – unless a real-life event or policy change happens that allow researchers to conduct what are called "natural experiments."

"Natural experiments are everywhere," said Eva Mork, a member of the prize committee. "Thanks to the contributions of the laureates, we researchers are today able to answer key questions for economic and social policy. And thereby the laureates work has greatly benefited society at large."

The Nobel Economics Prize committee members announce the winners of Nobel Memorial Prize in Economic Sciences on Monday. David Card, Joshua Angrist and Guido Imbens were given the award for their research of real-life events and policy changes. Claudio Bresciani/TT News Agency/AFP via Getty Images hide caption

The impact of the minimum wage

Card was recognized in part for his groundbreaking work in the early 1990s with the late Princeton economist Alan Krueger, which challenged conventional wisdom about minimum wages.

Economists had long assumed that there was a tradeoff between higher wages and jobs. If the minimum wage went up, it was thought, some workers would get higher pay but others would be laid off.

But when Card and Krueger looked at the actual effect of higher wages on fast food workers , they found no significant drop in employment.

They reached this conclusion by comparing fast food restaurants in New Jersey, which raised its minimum wage, with restaurants in neighboring Pennsylvania, which did not.

A McDonald's sign is shown on July 28 in Houston, Texas. One of the winners of the Nobel Prize in economics on Monday was cited for his work in studying the fast food industry to help determine how minimum wages impact employment. Brandon Bell/Getty Images hide caption

Studying cause-and-effect in real life

Meanwhile, Angrist and Imbens were recognized for methodological research that helps tease out cause and effect from these accidental case studies.

During the pandemic, natural experiments have allowed researchers to study the effects of mask mandates, social distancing policies, and supplemental unemployment benefits.

The Nobel Peace Prize goes to journalists in the Philippines and Russia

Imbens said he was "stunned" to get the congratulatory wake-up call at about 2 a.m. in California.

"I was absolutely thrilled to hear the news," Imbens told reporters. "In particular hearing that I got to share this with Josh Angrist and David Card, who are both very good friends of mine."

He noted that Angrist was best man at his wedding.

Imbens said he had no idea how he would spend his share of the prize money.

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Article contents

Behavioral experiments in health economics.

Matteo M. Galizzi Matteo M. Galizzi Department of Psychological and Behavioural Science, London School of Economics and Political Science
and Daniel Wiesen Daniel Wiesen Department of Business Administration and Health Care Management, University of Cologne
https://doi.org/10.1093/acrefore/9780190625979.013.244
Published online: 28 March 2018

The state-of-the-art literature at the interface between experimental and behavioral economics and health economics is reviewed by identifying and discussing 10 areas of potential debate about behavioral experiments in health. By doing so, the different streams and areas of application of the growing field of behavioral experiments in health are reviewed, by discussing which significant questions remain to be discussed, and by highlighting the rationale and the scope for the further development of behavioral experiments in health in the years to come.

behavioral experiments in health
behavioral health economics
experimental health economics
randomized controlled experiments
behavioral data linking
health economics

Introduction

In the past few decades, experiments have been successfully introduced in many fields of economics, such as industrial organization (e.g., Chamberlin, 1948 ; Sauermann & Selten, 1959 ; Plott, 1982 ), labor economics (e.g., Fehr, Kirchsteiger, & Riedl, 1993 ; Kagel, Battalio, Rachlin, & Green, 1981 ), and public economics (e.g., Andreoni, 1988 ; Bohm, 1984 ; Marwell & Ames, 1981 ). Despite the fact that the use of experiments was first advocated by leading health economists long ago (Frank, 2007 ; Fuchs, 2000 ; Newhouse et al., 1981 ), their introduction and employment has been relatively slow to be widely accepted in health economics, policy, and management.

Recently, however, two special issues in the Journal of Economic Behavior & Organization (Cox, Green, & Hennig-Schmidt, 2016 ) and in Health Economics (Galizzi & Wiesen, 2017 ) and a number of dedicated special sessions in major field conferences (e.g., the European Association of Health Economics, EuHEA, the International Health Economics Association, iHEA) indicate the increasing acceptance of experiments by the health economics, policy, and management communities.

The rise in interest in using experiments in health economics has coincided with the parallel growing interest in applying behavioral economics to health, as witnessed by an increasing number of books and articles on the topic (Bickel, Moody, & Higgins, 2016 ; Hanoch, Barnes, & Rice, 2017 ; Loewenstein et al., 2017 ; Roberto & Kawachi, 2015 ).

Among health policymakers and practitioners, the use of insights from behavioral economics, and, in particular, of “nudges” has recently led many governments around the world to set up behavioral or “nudge units” within their civil services, starting from the Behavioural Insights Team in the UK Cabinet Office, to the analogous initiatives within the UK Department of Health, the National Health Service (NHS), and Public Health England, in the governments of Australia, Canada, Denmark, Finland, France, Israel, Italy, the Netherlands, New Zealand, Norway, Singapore, and the United States, and in the European Commission (Dolan & Galizzi, 2014a ; Oliver, 2017 ; Sunstein, 2011 ).

We start with a simple operational definition of “behavioral experiments in health,” arguably the first such definition. We then identify ten key areas of potential debate about behavioral experiments in health that we think deserve explicit discussion. In what follows, we address one by one each of these ten areas of possible debate and controversy by answering ten corresponding questions. By doing so, we review the state of the art of the different streams and areas for applications of the growing field of behavioral experiments in health; we discuss which significant questions remain to be addressed; and we highlight the rationale and the scope for the further development of behavioral experiments in health in the years to come.

In a nutshell, “behavioral experiments in health” make use of a broad range of experimental methods typical of experimental and behavioral economics to investigate individual and organizational behaviors and decisions related to health and healthcare.

The behaviors and decisions considered in behavioral experiments in health therefore usually take place, or are framed, in a health, healthcare, or medical setting or context.

The term behavioral in “behavioral experiments in health” first requires a clarification. Common to experimental economics and behavioral science, the outcomes of behavioral experiments in health are “behavioral” in that they consist of directly observable and measurable behavioral responses or directly revealed preferences, rather than self-reported statements. For example, subjects in behavioral experiments in health are typically observed in real health or healthcare field situations, or, if not, they face real consequences for their choices or behaviors through aligned monetary and nonmonetary incentives. Behaviors and decisions of participants in a behavioral experiment in health are thus typically “natural”—that is they take place in naturalistic situations—or “incentive-compatible” in the usual experimental economics sense that participants bear some real behavioral consequences for their choices in the experiment (e.g., Cassar & Friedman, 2004 Friedman & Sunder, 1994 ; Smith, 1976 , 1982 ). This defining feature makes behavioral experiments in health distinct from “stated preference experiments,” such as contingent valuation studies, or “discrete choice experiments” (DCEs), which have long been used in health economics, and which do not typically consider real behavior or incentive-compatible choice situations (e.g., de Bekker-Grob, Ryan, & Gerard, 2012 ; Ryan & Farrar, 2000 ; Ryan, McIntosh, & Shackley, 1998 ).

Furthermore, methodologically, behavioral experiments in health purportedly cover the entire continuum spectrum of experimental methods spanning the lab to the field, passing through online and mobile experiments, and experiments pursuing “behavioral data linking” (for more, see Questions Three and Ten).

Finally, and following the usual methodological convention in experimental economics, behavioral experiments in health do not deceive subjects. Some behavioral experiments in health can, nonetheless, entail some degree of “obfuscation” when, in the attempt to minimize possible “experimenter demand effects” (Zizzo, 2010 ), subjects are not told about the exact purpose and research question of the experiment. This is in line with the spirit of those experiments that intend to minimize the alteration of, and interference with, naturally occurring behavior by not telling subjects that they are part of an experiment (i.e., in the spirit of “natural field experiments” according to the taxonomy by Harrison & List, 2004 , discussed in Question Three; or of “lab-field” experiments, as in Dolan & Galizzi, 2014a ).

To sum up, five characterizing features of behavioral experiments in health are therefore: (i) the fact that the decisions and behaviors are health-related; (ii) the fact that, whenever possible, the outcomes of the decisions in the experiment are “behavioral” in the sense of consisting of directly observable and measurable behavioral responses, or of bearing real consequences for the decision makers; (iii) the open-minded consideration of principles and insights from both behavioral economics and conventional economics, as well as their combination and integration; (iv) the use of a broad range of experiments spanning the lab to the field, passing through online and mobile experiments, as well as “behavioral data linking” experiments; and (v) the tendency to avoid deception, which, however, does not prevent the use of obfuscation, natural field experiments, and lab-field experiments.

We next review the existing literature by addressing ten areas of current debate about behavioral experiments in health. A few of these areas apply to behavioral experiments more generally and, when this is the case, we explicitly note it.

Question One: What Can Behavioral Experiments Tell Us That Non-Experimental Methods in Health Cannot Tell Us Already?

First of all, theory, experiments, and econometrics are complements to, not substitutes for, each other (Falk & Heckman, 2009 ; Galizzi, Harrison, & Miraldo, 2017b ); Harrison, Lau, & Rutström, 2015 . In particular, the way in which behavioral experiments are sometimes contrasted with econometric analysis is misleading. In fact, running any type of controlled behavioral experiments in health (see more in question three) is just the first step of the data collection process that should then feed into an appropriate econometric analysis of the experimental data. The broad range of behavioral experiments in health allows the researcher to gather rich data to delve empirically into the behavioral nuances and mechanisms of an observed change in health-related behavior. Indeed, as witnessed by the field of “behavioral econometrics,” experiments and econometric analysis are complementary, not substitute, methods (Andersen, Harrison, Lau, & Rutström, 2008a , 2010 , 2014 ; Harrison, Lau, & Rutström, 2015 ; Hey & Orme, 1994 ). A similar point holds for the theoretical underpinnings of a behavioral experiment in health.

Second, a key advantage of behavioral experiments is the ability to tightly control experimental conditions. In physics and social sciences, testing theory is a basic component of experiments and the scientific method relies upon explicit tests of theory (Charness & Fehr, 2015 ; Charness & Kuhn, 2011 ). While secondary data are often rich and abundant, at the same time they might be confounded by a variety of environmental factors. For example, a health economist who aims to test the effect of incentives inherent in performance pay on the physicians’ quality of medical care using secondary data might end up with confounded results because institutions such as public monitoring and reporting of physicians’ quality were introduced at the same time. Using secondary data, disentangling these factors seems prohibitively challenging, if not impossible. Taking a more general perspective, the key strength of behavioral experiments is the ability to test a specific theoretical model. One can then compare the behavioral predictions of the model to what happens. If a specific theory is rejected, it is then relatively neat to test competing explanations. For example, rational decision-making theory might not be suitable to explain the inconsistent choices of insurance plans in the United States (e.g., Abaluck & Gruber, 2011 ). Competing behavioral decision-making theories might then be called upon whose alternative explanations can then be tested in controlled experiments. This is consistent with the open-minded approach of behavioral experiments in health, which consider principles and insights from both behavioral economics and conventional economics.

Third, another reason to run experiments is the unique opportunity to study behavior and practices analyzed in the theoretical health economics models that are difficult to observe using field data. An example is the effect of referral payments from specialists to primary care physicians on primary care physicians’ referral behavior. While health economic theory (e.g., Pauly, 1979 ) suggests that referral fees enhance efficiency, payments for referrals are largely forbidden, in almost all Western healthcare markets. In a lab experiment, Waibel and Wiesen ( 2017 ) explicitly test model predictions on physicians’ diagnostic effort and referral decisions and find that the introduction of referral payments increase efficiency, although not to the levels predicted by theory. Another example is unethical behavior in healthcare, for example, diagnosis-related group (DRG) upcoding. Admittedly, at an aggregate level there is plenty of evidence that DRG upcoding exists (e.g., Jürges & Köberlein, 2015 ; Silverman & Skinner, 2004 ). However, what drives unethical behaviors is largely unknown. A study by Hennig-Schmidt, Jürges, and Wiesen ( 2017 ) complements field studies on DRG upcoding by analyzing dishonest behavior in a framed experiment in neonatology and by linking dishonest behavior to individuals’ characteristics to explore what drives dishonesty. They find that audits and fines significantly reduce dishonesty and that subjects’ personality traits and integrity relate to dishonest behavior. A further area in health, to which behavioral experiments have contributed, is a better understanding of the behavioral effects of professional norms. Exogenously changing professional norms in the field seems prohibitively challenging, and (if possible) drawing inferences seems difficult due to numerous confounding factors. In an online experiment with a large medical student sample, Kesternich, Schumacher, and Winter ( 2015 ) analyze the effect of making the Hippocratic oath salient on patient-regarding altruism and distributional preferences. In a series of experiments with physicians (from internal medicine and pediatrics), Ockenfels and Wiesen ( 2018 ) investigate the effect of a professional framing on physicians’ dishonest behavior (on behalf of themselves and others). Evidence from behavioral experiments in health that are “well-grounded” in theory is therefore not only useful to contrast behavior with model predictions but also to further stimulate the debate among healthcare policymakers on practices with little or no field evidence.

Fourth, one of the key strengths of experiments, in general, is that a researcher can empirically study the causal effects of different institutions, as defined by their rules, actors, and incentives. Thus, behavioral experiments seem ideal to serve as a test bed for analyzing the effect of institutional changes related to healthcare. Understanding behavioral mechanisms in health-related decisions is imperative before designing and implementing large-scale behavioral interventions in the field or ad hoc healthcare policy interventions, as there might be unknown or unintended effects for providers and patients alike. In this sense, excluding behavioral experiments from the research toolkit of a health economist would be somewhat similar to ignoring animal studies for medical or drug research:

While results from animal studies do not always apply to humans, the ability to test many hypotheses cheaply under carefully controlled conditions provides an indispensable tool for the development of models that work in the real world. (Charness & Kuhn, 2011 , p. 233)

Fifth, behavioral results from experiments might not only be insightful to better understand actual health-related decision-making and behavior, but also to inform the development of behavioral economics theories in health contexts (e.g., Hansen, Anell, Gerdtham, & Lyttkens, 2015 ; Kőszegi, 2003 , 2006 ; Frank, 2007 ). The observation of actual human behavior in experiments enables the researcher to identify behavioral deviations from theory and thus to extend health economic theories by taking into account issues like human motivation or behavioral phenomena like emotions or (patient-regarding) altruism. Two prominent research areas in which theory and experiments have already fruitfully complemented each other are: the matching markets for organ donations and for physicians and healthcare professionals (e.g., Herr & Normann, 2016 ; Kessler & Roth, 2012 , 2014a , 2014b ; Li, Hawley, & Schnier, 2013 ; Roth, 2002 ; Roth & Peranson, 1999 ); and the design of mixed systems of public and private healthcare finance (e.g., Buckley et al., 2012 , 2016 ).

In sum, running behavioral experiments in health allows the researcher to better understand the causal effects of health-related policy interventions on individual and organizational behavior and to contrast findings with predictions from theoretical models. Behavioral experiments therefore nicely complement and bridge non-experimental methods, in particular theory and empirical econometric analysis, and could therefore help bring closer together the different health research communities (Galizzi, 2017 ; Galizzi, Harrison, & Miraldo, 2017 ).

Question Two: Are Behavioral Experiments Really New to Health Economics, Policy, and Management?

No. First, health economists are particularly well aware of the importance of using randomized controlled experiments. Modern evidence-based medicine and pharmacology are all based on randomized controlled trials (RCTs), starting from the pioneering work on scurvy by James Lind in 1747 , to the first published RCT in medicine by Austin Bradford Hill and colleagues in 1948 . Thanks to the groundbreaking contributions of Charles Sanders Peirce, Jerzy Neyman, Ronald A. Fisher, and others, modern science has long considered randomized controlled experiments as fundamental and important scientific methods. Far from novel, the idea of using randomized controlled experiments has been advocated for decades even for policy applications (Burtless, 1995 ; Ferber & Hirsch, 1978 ; Rubin, 1974 ).

Second, arguably one of the most influential studies in health economics is indeed based on a large-scale randomized controlled experiment. The RAND Health Insurance Experiment conducted in the United States between 1974 and 1982 , in fact, analyzed the effects of randomly allocated co-payment rates and health insurance contracts on healthcare costs and utilization of healthcare (Manning et al., 1987 ; Newhouse et al., 1981 ). As a major finding, Joseph P. Newhouse and colleagues documented that cost sharing reduced the overutilization of medical care while it did not significantly affect the quality of care received by participating patients.

The spirit and the main features of the RAND Health Insurance Experiment later inspired the design of the Oregon Health Insurance Experiment. The latter was conducted in 2008 with uninsured low-income adults in Oregon. Adults allocated to the treatment group were given the chance to apply for Medicaid (via a lottery). This allowed researchers to analyze the effects of expanding access to public health insurance (Medicaid), for example, on the healthcare use and health of low-income adults. The researchers found that the treatment group had substantively and statistically significantly higher healthcare utilization and a better self-reported health than the control group (Finkelstein et al., 2012 ; Finkelstein & Taubman, 2015 ).

The launch of the Behavioural Experiments in Health Network (BEH-net) in 2015 can be seen as the response to a fast-increasing demand to systematically use behavioral experiments in health economics, policy, and management. The network aims precisely at integrating and bringing closer together the research communities at the interface between experimental and behavioral economics, and health economics.

Question Three: What Types of Experiments are Considered When Referring to Behavioral Experiments in Health?

There is an important initial conceptual distinction between behavioral experiments in health and RCTs. Many health practitioners and policymakers, in fact, tend to automatically associate behavioral experiments with RCTs.

In the health policy debate, the term RCT is sometimes used to denote large-scale field experiments conducted with entire organizations (e.g., hospitals, villages) without necessarily allowing the stakeholders in those organizations to explicitly express their views or their consent to the proposed manipulations. This is a major conceptual and practical difference with respect to proper RCTs in medicine or pharmacology, where subjects are always explicitly asked to give informed consent prior to taking part in RCT, and are allowed to drop out, with important ethical, political, and logistical implications. The term RCT is therefore conceptually inappropriate and practically misleading in a health economics, policy, and management context, since it conveys the false impression that subjects have been made aware of being part of an experiment and have been consulted and given their consent to it, when actually this may not be the case.

Moreover, even in the above narrow and inappropriate connotation, RCTs are only one specific type of experiment, namely field experiments. As mentioned, however, behavioral experiments in health purportedly cover the entire spectrum of experiments from the lab to the field. Harrison and List ( 2004 ) proposed an influential taxonomy of experiments along the lab-field spectrum that illustrates the diversity of experiments: (i) conventional lab experiments involve student subjects, abstract framing, a lab context, and a set of imposed rules; (ii) artefactual field experiments depart from conventional lab experiments in that they involve nonstudent samples; (iii) framed field experiments add to artefactual field experiments a field context in the commodity, stakes, task, or information; and, finally, (iv) natural field experiments depart from framed field experiments in that subjects undertake the tasks in their natural environment and subjects do not know that they are taking part in an experiment.

The main idea behind natural field experiments is equivalent to von Heisenberg’s “uncertainty principle” in physics: the mere act of observation and measurement necessarily alters, to some extent, what is being observed and measured. In key areas for health economics, for example, there may be potential experimenter demand effects, where participants change behavior due to cues about what represents “appropriate” behavior for the experimenter (Levitt & List, 2007 ), for example, when deciding on provision of medical services; Hawthorne effects, where simply knowing they are part of a study makes participants feel important and improves their effort and performance (Levitt & List, 2011 ); and John Henry effects, where participants who perceive that they are in the control group exert greater effort because they treat the experiment like a competitive contest and they want to overcome the disadvantage of being in the control group (Cook & Campbell, 1979 ).

More recently, other types of experiments have been conducted in experimental economics, beyond lab, artefactual field, frame field, and natural field experiments. For example, virtual experiments combine controlled experiments with virtual reality settings (Fiore, Harrison, Hughes, & Rutström, 2009 ). While not yet applied to health economics contexts, virtual experiments are a promising approach to make trade-offs more salient and vivid in health and healthcare decision-making.

Lab-field experiments consist of a first-stage intervention under controlled conditions (in the lab) linked to a naturalistic situation (in the field) where subjects are not aware that their behavior is observed. Lab-field experiments have been used to look at the unintended “behavioral spillover” effects of health incentives (Dolan & Galizzi, 2014b , 2015 ; Dolan, Galizzi, & Navarro-Martinez, 2015 ) or at the external validity of lab-based behavioral measures (Galizzi & Navarro-Martinez, 2019 ).

Virtual experiments and lab-field experiments are part of the growing efforts to bridge the gap between the lab and the field in health economics applications (Hennig-Schmidt, Selten, & Wiesen, 2011 ; Kesternich et al., 2015 ). They are also part of the more general “behavioral data linking” approach (Galizzi, Harrison, & Miraldo, 2017 ), that is, the linkage of behavioral economics experiments with longitudinal surveys, administrative registers, biomarkers banks, apps, mobile devices, scan data, and other big data sources (Andersen et al., 2015 ; Galizzi, Harrison, & Miniaci, 2017 ). Data linkage poses new ethical, practical, and logistical challenges when it seeks to link surveys and behavioral experiments with health records and healthcare registers (Galizzi, Harrison, & Miraldo, 2017 ). Nonetheless, there is currently an extraordinary, and still largely untapped, potential to apply the experimental approach to an unprecedented host of health and healthcare contexts, and by linking and augmenting behavioral experiments in health with the very rich data sources available in health (see more in Question Ten).

Taken together, there is not one type of experiment for potential health economics and policy purposes. Rather, the broad spectrum of different types of experiments spanning the lab to the field can prove useful and complementary for health applications, as well as the most recent online, mobile, and “behavioral data linking” experiments.

Question Four: Is There a Preferred Type of Behavioral Experiment in Health?

There is currently no consensus on which specific type of behavioral experiment is superior. The choice of the specific type of experiment depends on the specific research question. Lab, field, online, mobile, and “behavioral data linking” experiments all have strengths and weaknesses, and their relative merits have been systematically discussed elsewhere (Bardsley et al., 2009 ; Falk & Heckman, 2009 ; Guala, 2005 ; Harrison, 2013 ; Harrison & List, 2004 ; Kagel, 2015 ; Levitt & List, 2007 , 2009 ; Smith, 2002 ).

For example, it is generally reckoned that lab experiments allow for high internal validity because of their ability to tightly control the environment and frame, to minimize confounding factors, to closely simulate conditions of theoretical models, and to replicate past experiments. Furthermore, they provide insights into possible patterns prior to moving into the field, they uncover the mechanisms underlying decisions and behavior, and they require significantly fewer financial, time, and logistical resources than field experiments.

On the other hand, it is generally reckoned that field experiments can enhance the external validity of experimental results (see more about this in Question Five), because observations are made with subjects, environments, situations, tasks, rules, and stakes that are closer to the ones occurring in the real world (Brookshire, Coursey, & Schulze, 1987 ; Galizzi & Navarro-Martinez, 2019 ). Field experiments, however, come with less control and with several other limitations when used for policy purposes (Harrison, 2014 ). Moreover, they are inherently more difficult to replicate. This is a major limitation given the increasing attention to the replicability of experimental results in economics, psychology, and health sciences (Burman, Reed, & Alm, 2010 ; Camerer et al., 2016 ; Dolan & Galizzi, 2014a ; Galizzi, Harrison, & Miraldo, 2017 ; Open Science Collaboration, 2015 ).

More generally, it is important to reiterate the point that the different types of experiments are complementary, not substitutes (Falk & Heckman, 2009 ; Galizzi, Harrison, & Miraldo, 2017 ; Harrison, Lau, & Rutström, 2015 ). Recent behavioral experiments in health pick up on this important point and combine different types of experiments and thereby test for the validity of findings from lab experiments with conventional student samples. For example, Brosig-Koch, Hennig-Schmidt, Kairies-Schwarz, and Wiesen ( 2016a ) combine lab and artefactual field experiments to analyze the effect of fee-for-service and capitation regimes for medical service provision on the behaviors of medical students, nonmedical students, and physicians. Across the board, they found that all subject pools responded to incentives similarly—namely, patients were overtreated under fee-for-service and undertreated under capitation. Physicians, however, responded less to the incentives inherent in these two payment schemes. In experiments with medical and non-medical students, Hennig-Schmidt and Wiesen ( 2014 ) and Brosig-Koch, Hennig-Schmidt, Kairies-Schwarz, and Wiesen ( 2017a ) report similar findings. Wang, Iversen, Hennig-Schmidt, and Godager ( 2017 ) compare behavior of physicians and medical students in China and Germany. Comparing lab experimental data from medical students with artefactual field experimental data from a subsample of a representative sample of German resident physicians, Brosig-Koch, Hennig-Schmidt, Kairies-Schwarz, and Wiesen ( 2017b ) find that performance pay crowds out patient-regarding motivation for both subject pools.

Taken together, the experimental studies that systematically account for potential differences in the subject pools indicate that the direction of a treatment effect does not differ between (medical or nonmedical) students and medical professionals. Importantly, however, the intensity of a behavioral effect might vary across subject pools. Moreover, experimental designs in health typically abstract from the complexity of real-world settings in order to “isolate” treatment effects. A few experimental studies even employ neutral framings of the decision situation, presumably for reasons of control and salience of the incentives: see, for example, Green ( 2014 ) on provider incentives; Huck, Lünser, Spitzer, and Tyran ( 2016 ) on health insurance choice; and Mimra, Rasch, and Waibel ( 2016 ) on specialists’ second opinions. As experiments are “scalable,” adding more realism (health context) to these settings by employing medical frames seems desirable: Ahlert, Felder, and Vogt ( 2012 ) and Kesternich et al. ( 2015 ) find behavioral differences in the intensity of subjects’ responses when comparing neutral and medical frames.

In sum, researchers and healthcare policymakers alike might clearly need to be cautious in drawing conclusions about real-world settings when taking findings from behavioral experiments in health at face value. One might argue, however, that this key point applies to any type of behavioral experiments in health, not just to lab experiments or to field experiments. Moreover, the existing evidence indicates somewhat similar main directions of experimental treatment effects across subject pools and it is therefore useful to inform debates in healthcare policy and management. For a more general discussion of external validity and generalizability of experimental findings, see the next question.

Question Five: Can We Trust the External Validity of Behavioral Experiments in Health?

The point on the external validity of behavioral experiments in health is too important to be unduly misrepresented or oversimplified, as is often done in the research and policy debate. Most issues related to the external validity of experiments are actually not unique to behavioral experiments in health, but are common to all the economic experiments in general, and for this reason we answer this question more generally.

The main observation is that external validity means different things from different points of view. In a first connotation, external validity refers to the within-subjects question of whether the outcomes of a behavioral experiment in health are representative of the corresponding outcomes of interest that would occur outside of the behavioral experiment for the same pool of subjects. From this perspective, as mentioned, external validity is often contrasted with internal validity on the presumed ground that there is always an inherent trade-off between internal and external validity while one moves from the lab end to the field end of the spectrum in the Harrison and List ( 2004 ) taxonomy. This, however, is not always nor necessarily the case. In fact, to start with, if rigorously designed and conducted, all randomized controlled behavioral experiments in health are internally valid, whether they are lab, artefactual field, framed field, or natural field experiments. So, it is simply not true that internal validity is necessarily higher in lab than in natural field experiments. Moreover, it is also not true that the external validity (in the above explained connotation) is necessarily higher in natural field than in lab experiments. It obviously depends on how close is the correspondence between the outcomes measured in the behavioral experiment and the outcomes of interest that would occur outside of the behavioral experiments. In other words, it depends on what the final outcomes of interest are, and ultimately, what the research question is. For example, imagine that the main outcome of interest is about looking at how many calories subjects eat in a buffet, or how much time they wait until lighting up the next cigarette, which are both likely to be the outcomes of some automatic or “visceral” decision-making occurring without any conscious deliberation (Loewenstein, 1996 ). Then, a natural field experiment, where subjects do not know they are part of an experiment, would be a natural setting for observing such behaviors (Dolan & Galizzi, 2014b ). On the other hand, however, imagine that the main outcomes of interest are how subjects trade off and choose between different private health insurance schemes, or which groceries subjects purchase in an online supermarket, two highly deliberate decisions that are both likely to take place in online settings even when they occur outside of a behavioral experiment. Then, a conventional lab or an online experiment would be a natural setting for observing such behaviors.

In a second, slightly different, connotation, external validity refers to the, still within-subjects, question of whether the outcomes of a behavioral experiment in health are good predictors of the corresponding outcomes of interest that would occur outside of the behavioral experiment for the same pool of subjects. For example, are healthy food choices in a behavioral experiment good predictors of a subject’s healthy diet? Are experimental decisions about drugs, treatments, and health insurance good predictors of analogous decisions outside the experiment? It is true that, in principle, the experimental decisions can move closer to the decisions taken outside the experiment as one moves from lab experiments to natural field experiments in the Harrison and List ( 2004 ) taxonomy. But it is also true that this depends, again, on what the ultimate outcomes of interest and research questions are. And also, in principle, this type of external validity question can affect the entire spectrum of behavioral experiments in health, from lab to natural field experiments. In fact, the only rigorous strategy to empirically address this question is to design and implement a longitudinal augmentation of the original behavioral experiment that enables researchers to follow up over time in more naturalistic settings the same pool of subjects. When implemented in a systematic and transparent way, this strategy also allows the researcher to overcome a major limitation of the very few external validity analyses to date, namely the fact that they typically are ad hoc analyses. The typical analysis, in fact, reports the correlation between one specific experimental outcome and one specific variable outside the experiment and, when such a correlation is found to be statistically significant, it concludes that what is found in the experiment is externally valid. Such an approach, however, lacks systematization because it fails to provide full information on the whole set of pairwise correlations between all the experimental outcomes and all the variables outside the experiment, be they significant or not significant. Only a systematic and transparent testing and reporting of all such correlations would be the litmus test of the external validity of a behavioral experiment. Such exercises are rare in experimental economics and virtually not existent in health.

In a nonhealth context, for example, Galizzi and Navarro-Martinez ( 2019 ) systematically assess and report the associations between the whole set of eight social preferences experimental games and five different prosocial variables outside of the experiment, and they find that only one out of 40 pairwise correlations is statistically significant (and none is if there is proper correction for multiple hypothesis testing). They then relate their finding to a systematic review and meta-analysis of all the published and unpublished studies that have previously tested the external validity of those same experimental games, and conclude that the often proclaimed external validity of social preferences games is not supported by the empirical evidence: only the 39.7% of the reported pairwise correlations and 37.5% of the reported regressions find a statistically significant association between an experimental outcome and a variable outside the experiment.

The behavioral experiments in the health literature still lack a similar systematic and transparent approach to this dimension of the external validity question and, given the importance of this exercise for both research and policy purposes, we encourage more research in this direction (Galizzi, Machado, & Miniaci, 2016a ). For example, the lack of transparency and systematization is the main explanation behind the current sterile debate about whether lab-based behavioral economics measures for risk, time, and social preferences are externally valid in the health context. Very few studies in behavioral health economics have tied their hands by publishing a pre-analysis plan or a public protocol that clearly and explicitly states at the outset of the analysis which health behaviors in the field will be associated with the lab-based behavioral economics measures. Most studies only report ad hoc subsets of the correlations and regressions between those measures and the health behaviors, and do not report nor discuss how these results relate and compare to the whole set of statistically significant and not significant associations. Moreover, systematic replication is almost inexistent in behavioral experiments in health. It is even argued by strong proponents of the lab experiments that preferences can only be measured in the lab. This is tantamount to stating that the question of whether lab-based measures for those preferences are externally valid is a non-falsifiable question, which is the opposite of an evidence-based scientific approach to this key matter. If behavioral experiments in health are about to leave infancy for adulthood, they should better take seriously the lessons learned in the neighboring disciplines of medicine and health studies, where collective knowledge is systematically cumulated only through transparent replications, systematic reviews, and meta-analyses, as epitomized by major collective research infrastructures, such as the Cochrane Collaboration or the Campbell Collaboration.

A third connotation of external validity has to do with whether the outcomes of a behavioral experiment in health are representative of the corresponding outcomes of the population of interest. This “out-of-sample” connotation of external validity clearly requires that the pool of subjects involved in the behavioral experiment in health has been drawn from a representative sample of the population of interest. So, for example, if the behavioral experiment aims at concluding something about decisions or behaviors of medical doctors, it should involve a representative sample of medical doctors, while if it aims at inferring anything at a population level, the behavioral experiment should involve a representative sample of the population.

Moreover, the debate about the external validity of behavioral experiments in health should be more generally conducted within the broader framework of the debate in terms of generalizability — that is, the question of which other populations, settings, contexts, or domains the findings from an experiment can be generalized to (Al-Ubaydli & List, 2015 ). Importantly, the generalizability question equally applies to the whole spectrum of behavioral experiments in health, from lab to natural field experiments.

There are three conceptually distinct threats to generalizability of behavioral experiments in health. The first threat comes essentially from participation bias. Unlike natural field experiments, lab, artefactual field, and framed field experiments recruit subjects through an explicit invitation to take part in an experiment. As a result, there is bias because subjects who choose to participate in experiments may be inherently different in their underlying characteristics from subjects who choose not to take part. Health researchers and policymakers should therefore be aware that, because of the participation bias, even if the initial sample of subjects is indeed representative of the target student population, the resulting subsample of actual respondents may not be.

The second threat comes from the fact that the environment, context, and frame of the experimental decisions and tasks in the lab may not be representative of real situations encountered by subjects in natural health and healthcare settings. This limitation can be easily overcome by redesigning tasks and contexts to more closely match naturalistic situations that subjects are more familiar with in real life—that is, to design framed field experiments in the sense of Harrison and List ( 2004 ). This strategy has been extensively employed in other application areas in experimental economics (e.g., Harrison & List, 2008 ; Harrison, List, & Towe, 2007 ) and has already been explored in the health economics area (e.g., Eilermann et al., 2017 ; Hennig-Schmidt et al., 2011 ; Hennig-Schmidt & Wiesen, 2014 ; Galizzi et al., 2016 ).

The last threat to generalizability is that experimental subjects may not be representative of the general population, especially when they are students or medical students (Levitt & List, 2007 ). To overcome this limitation, behavioral economists have started running artefactual field experiments with representative samples of the population (Andersen et al., 2008a , 2014 ; Galizzi, Machado, & Miniaci, 2016 ; Galizzi, Harrison, & Miniaci, 2017 ; Harrison et al., 2007 ; Harrison, Lau, & Williams, 2002 ). This is a promising avenue for behavioral experiments in health, given that the goals and priorities in designing health policies and health systems are typically set at a population level (Michie, 2008 ).

From the broader generalizability perspective, we can hardly see why the results of a natural field experiment with, say, female nurses in Tanzania, or health insurance customers in the rural Philippines, should be considered as more generalizable than a lab experiment with medical students in Germany, or an artefactual field experiment with a representative sample of the population in the United Kingdom. Too often, similar claims even forget to state what the population of interest is for the study.

More generally, Falk and Heckman ( 2009 ) state that causal knowledge requires a controlled variation. Whether a variation from, for example, a natural field experiment or a more controlled lab experiment is more informative depends on the research question and is still debated among researchers in social sciences. It is important to acknowledge, again, that empirical methods and different sources of data are complements. For example, both behavioral experiments, spanning the lab to the field, and econometric analyses of secondary data can all improve the state of knowledge in health economics research, with the issue of generalizability of results being applicable to all of them.

Taken together, behavioral and experimental health economists should take seriously the external validity and generalizability challenges by open-mindedly using all types of experiments in the lab-field spectrum, by embracing a transparent and systematic approach in gathering and reporting evidence, including reporting of all the statistically significant and not significant correlations and regressions (rather than cherry-picked subsets of the positive results). We see this as a fundamental requisite for behavioral experiments in health as a field moving, in the years to come, from infancy to adulthood.

Question Six: What About Experiments to Elicit Preferences in Health?

One of the above-discussed defining features of behavioral experiments in health is that they entail directly observable and measurable behavioral responses. For example, experimental decisions, tasks, and measures to elicit preferences and willingness-to-pay are incentive-compatible in the sense that subjects pay some real consequence in terms of monetary or nonmonetary outcomes for the choices they make. This raises the question of whether or not behavioral experiments in health also include the experimental studies that aim at eliciting health-related preferences.

Some distinctions should be made on this point. On the one hand, there is a vast literature in health economics that uses popular experimental methods, such as the Standard Gamble (SG) or the Time Trade Off (TTO), to elicit preferences for hypothetical health states (Attema & Brouwer, 2012 ; Bleichrodt, 2002 ; Bleichrodt & Johannesson, 2001 ). Given the hypothetical nature of the choices about different health states, these experiments are similar in nature to the already discussed “stated preference experiments,” such as contingent valuation studies or discrete choice experiments (DCEs), which do not typically consider real behavior or incentive-compatible choice situations. Using the same argument, therefore, the experiments in this literature should not be considered behavioral experiments in health.

On the other hand, there is also a small, but growing, literature looking at the relationships between incentive-compatible experimental measures of risk and time preferences and health-related behaviors. Harrison, Lau, and Rutström ( 2010 ), for example, elicit risk and time preferences of a representative sample of the Danish population and find no difference in the likelihood of smokers and nonsmokers exhibiting hyperbolic discounting, no significant association of smoking with risk aversion among men, and no significant association of smoking with discount rates among women. Galizzi and Miraldo ( 2017 ) measure the risk preferences of a convenience sample of students and find that, while there is no association between smoking or body mass index (BMI) with the estimated risk aversion, the latter is significantly associated with the Healthy Eating Index, an indicator of overall nutritional quality. Harrison, Hofmeyr, Ross, and Swarthout ( 2015 ) elicit risk and time preferences of a convenience sample of students at the University of Cape Town and find that smokers and nonsmokers differ in their baseline discount rates, but do not significantly differ in their present bias, risk aversion, or subjective perception of probabilities. In a longitudinal experiment with a representative sample of the UK population, Galizzi, Machado, and Miniaci ( 2016 ) systematically assess the external validity of different measures of risk preferences linked to the UK Longitudinal Household Survey (UKHLS), and find that the experimental measures are not significantly associated with subjects’ BMI and eating, smoking, or drinking habits. Several other ad hoc analyses have associated risk and time preferences with heavy drinking (Anderson & Mellor, 2008 ), BMI (Sutter, Kocher, Glätzle-Rützler, & Trautmann, 2013 ), and the uptake of vaccinations, preventive care, and medical tests (Bradford, 2010 ; Bradford et al., 2010 ; Chapman & Coups, 1999 ).

Given that all these latter experiments use incentive-compatible methods to elicit risk and time preferences, they should be considered behavioral experiments in health. A common aspect of the latter group of experiments, however, is that they measure individual risk and time preferences over (risky or intertemporal) monetary outcomes, and then link these to health-related behaviors. But what about the studies that elicit individual risk and time preferences for health outcomes, rather than for monetary outcomes?

We see the experiments eliciting risk and time preferences in health as an interesting middle ground between stated preferences experiments and behavioral experiments. When it comes to the measurement of risk and time preferences in the health domain, in fact, the current community of behavioral health experimentalists interprets behavioral experiments in health with a fair degree of tolerance, flexibility, and open-mindedness, and considers the elicitation of risk and time preferences in health a research field that is closely aligned with, and affine to, the core interests and methods of behavioral experiments in health.

This is not because the community disagrees with the traditional experimental economics view that answers to hypothetical questions can significantly differ from responses to incentive-compatible tests because “talk is cheap” if there are no real behavioral consequences (Battalio, Kagel, & Jiranyakul, 1990 ; Cummings, Elliott, Harrison, & Murphy, 1997 ; Cummings, Harrison, & Rutström, 1995 ; Harrison, 2006 ; Holt & Laury, 2002 ;). Moreover, behavioral health experimentalists are all well aware that, from a theoretical perspective, risk and time preferences are fundamental individual characteristics at the core of health behavior and decision-making (Gafni & Torrance, 1984 ). Risk and time preferences, in fact, directly inform the principles and practices of cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) in healthcare, and the assumptions beyond the quality adjusted life years (QALY), the measure of health benefits that is commonly employed in CEA and CUA and that relies on the above-mentioned SG and TTO methods (Bleichrodt, Wakker, & Johannesson, 1997 ).

Rather, it is because at the moment the literature on behavioral experiments in health lacks a systematic body of state-of-the-art consensus methods to measure health-related preferences with real nonmonetary consequences. Given the fundamental role of risk and time preferences in the health context, it is actually surprising that there is no consensus to date on a “gold standard” measurement methodology.

A multitude of different methods have been proposed to measure risk and time preferences in health contexts, which are heterogeneous in terms of underlying theoretical frameworks, methodological features, and links to formal econometric analysis (Galizzi, Harrison, & Miraldo, 2017 ). A major challenge in converging to a consensus methodology to measure risk and time preferences in health is related to the fact that, to date, the different proposed methods are substantially disconnected. On the one hand, the current methods to measure preferences for health outcomes only entail hypothetical scenarios. On the other hand, all the incentive-compatible methods to measure preferences with real consequences are based on monetary outcomes. From both a conceptual and an empirical point of view, however, it is unclear whether individual risk and time preferences are stable across the health and the monetary domains (Chapman, 1996 ).

There have been a number of exploratory analyses of whether these preferences are indeed stable across the finance and the health domains. Galizzi, Miraldo, and Stavropoulou ( 2016 ), for example, summarize the relatively limited number of studies that compare risk taking across the health and other domains, and find that, despite the broad heterogeneity of methods and frames used in the literature, there is general evidence that there are differences across domains, and that these differences also emerge when real consequences are at stake.

The elicitation of risk and time preferences with incentive-compatible methods in the health domain is a promising and challenging task and a collective priority in the research agenda of behavioral experimentalists in health. We expect this methodological and substantial gap to be filled soon by the increasingly collaborative community of behavioral experimentalists in health.

Question Seven: Which Topics Are Addressed by Behavioral Experiments in Health?

A first area of experimental research that has recently received considerable attention are “nudges,” that is, changes in the “choice architecture” made to induce changes in health behavior, mainly at an unconscious or automatic level (Thaler & Sunstein, 2008 ). In the spirit of “asymmetric paternalism” (Loewenstein, Asch, & Volpp, 2013 ; Loewenstein, Brennan, & Volpp, 2007 ), many behavioral experiments have in fact applied nudges to health and healthcare behavior spanning from risky behaviors in adolescents (Clark & Loheac, 2007 ) to exercise (Calzolari & Nardotto, 2016 ), from food choices (Milkman, Minson, & Volpp, 2014 ; Schwartz et al., 2014 ; VanEpps, Downs, & Loewenstein, 2016a ; Schwartz, Riis, Elbel, & Ariely, 2012 ; 2016b ) to drugs compliance (Vervloet et al., 2012 ), from medical decision-making (Ansher et al., 2014 ; Brewer, Chapman, Schwartz, & Bergus, 2007 ; Schwartz & Chapman, 1999 ) to dentists’ services (Altman & Traxler, 2014 ).

There is, however, much more in behavioral experiments in health than just nudging (Galizzi, Harrison, & Miraldo, 2017 ; Oliver, 2017 ). Behavioral experiments in health can, in fact, uncover the behavioral mechanisms beyond the change in health behavior, and thus inform the design and the implementation of a series of other types of health policies, including informational campaigns, salient labeling and packaging of healthy food items, the use of financial and nonfinancial incentives, and the design of effective tax, subsidy, health insurance plans, and regulatory schemes (Galizzi, 2014 , 2017 ).

In fact, a broad spectrum of lab to natural field experiments have already been applied to a variety of health economics, policy, and management areas, well beyond “nudges.” For example, behavioral experiments in health have investigated the effects of different co-payment rates and health insurance contracts on healthcare utilization and costs (Manning et al., 1987 ; Newhouse et al., 1981 ); the effects of public health insurance coverage on healthcare utilization and health outcomes (Baicker et al., 2013 ; Finkelstein et al., 2012 ; Finkelstein & Taubman, 2015 ; Finkelstein, Taubman, Allen, Wright, & Baicker, 2016 ); the effects of different providers’ incentives and the role of altruism (Ahlert et al., 2012 ; Brosig-Koch et al., 2016a , 2016b , 2017a , 2017b ; Fan, Chen, & Kann, 1998 ; Godager & Wiesen, 2013 ; Green, 2014 ; Hennig-Schmidt et al., 2011 ; Hennig-Schmidt & Wiesen, 2014 ; Kesternich et al., 2015 ; Kokot, Brosig-Koch, & Kairies-Schwarz, 2017 ); the role of audit, transparency, compliance, and gender bias in healthcare management (Godager, Hennig-Schmidt, & Iversen, 2016 ; Hennig-Schmidt et al., 2017 ; Jakobsson, Kotsadam, Syse, & Øien, 2016 ; Lindeboom, van der Klaauw, & Vriend, 2016 ); the role of different healthcare financing policies (Buckley, Cuff, Hurley, McLeod, Mestelman, & Cameron, 2012 ; Buckley, Cuff, Hurley, Mestelman, et al., 2015 , 2016 ); two-part tariffs for physician services (Greiner, Zhang, & Tang, 2017 ); provider competition (Bosig-Koch, Hehenkamp, & Kokot, 2017 ; Han, Kairies-Schwarz, & Vomhof, 2017 ); the matching markets for organ donations and for physicians and healthcare professionals (Herr & Normann, 2016 ; Kessler & Roth, 2012 , 2014a , 2014b ; Li, Hawley, & Schnier, 2013 ; Roth, 2002 ; Roth & Peranson, 1999 ); the role of subsidies for diagnostic tests and new health products (Cohen, Dupas, & Schaner, 2015 ; Duflo, Dupas, & Kremer, 2015 ; Dupas, 2014a , 2014b ; Dupas, Hoffman, Kremer, & Zwane, 2016 ); the choice of health insurance (Buckley, Cuff, Hurley, McLeod, Nuscheler, & Cameron, 2012 ; Huck et al., 2016 ; Kairies-Schwarz, Kokot, Vomhof, & Weßling, 2017 ; Kesternich, Heiss, McFadden, & Winter, 2013 ; Schram & Sonnemans, 2011 ); the economic and behavioral determinants of vaccination (Binder & Nuscheler, 2017 ; Böhm, Betsch, & Korn, 2016 ; Böhm, Meier, Korn, & Betsch, 2017 ; Bronchetti, Huffman, & Magenheim, 2015 ; Massin, Ventelou, Nebout, Verger, & Pulcini, 2015 ; Milkman, Beshears, Choi, Laibson, & Madrian, 2011 ; Tsutsui, Benzion, & Shahrabani, 2012 ); the effects of different types of HIV risk information and of SMS interventions on HIV treatment adherence (Dupas, 2011 ; Rana et al., 2015 ); the use of financial incentives for smoking cessation (Gine, Karlan, & Zinman, 2010 ; Halpern et al., 2015 ; 2016 ; Volpp et al., 2009 ), physical exercise (Charness & Gneezy, 2009 ; Royer, Stehr, & Sydnor, 2015 ), weight loss (John et al., 2011 ;John, Loewenstein, & Volpp, 2012 ; Kullgren et al., 2013 , 2016 ; Rao, Krall, & Loewenstein, 2011 ; Volpp et al., 2008 ), healthy eating (Loewenstein, Price, & Volpp, 2016 ), warfarin adherence (Kimmel et al., 2012 , 2016 ; Volpp et al., 2008 ), glucose control (Long, Jahnle, Richardson, Loewenstein, & Volpp, 2012 ), home-based health monitoring (Sen et al., 2014 ), mental exercises (Schofield, Loewenstein, Kopisc, & Volpp, 2015 ), immunization coverage (Banerjee, Duflo, Glennerster, & Kothari, 2010 ), nursing services (Banerjee, Duflo, & Glennerster, 2007 ), and medical drugs (Samper & Schwartz, 2013 ); the unintended carryover and spillover effects of financial incentives and nudges in health (Chiou, Yang, & Wan, 2011 ; Dolan et al., 2015 ; Dolan & Galizzi, 2014b , 2015 ; Müller et al., 2009 ; Wisdom, Downs, & Loewenstein, 2009 ); the behavioral effect of decision support systems and feedback (Cox et al., 2016 ; Eilermann et al., 2017 ); the elicitation of risk and time preferences in health and their links with health-related behaviors (Allison et al., 1998 ; Anderson & Mellor, 2008 ; Attema, 2012 ; Attema, Bleichrodt, & Wakker, 2012 ; Attema & Brouwer, 2010 , 2012 , 2014 ; Attema & Versteegh, 2013 ; Bleichrodt & Johannesson, 2001 ; Bradford, 2010 ; Bradford, Zoller, & Silvestri, 2010 ; Cairns, 1994 ; Cairns & van der Pol, 1997 ; Chapman, 1996 ; Chapman & Coups, 1999 ; Chapman & Elstein, 1995 ; Dolan & Gudex, 1995 ; Galizzi, Machado, & Miniaci, 2016 ; Galizzi & Miraldo, 2017 ; Galizzi, Miraldo, & Stavropoulou, 2016 ; Galizzi, Miraldo, Stavropoulou, & van der Pol, 2016 ; Garcia Prado, Arrieta, Gonzalez, & Pinto-Prades, 2017 ; Harrison et al., 2015 ; Harrison, Lau, & Rutström, 2010 ; Michel-Lepage, Ventelou, Nebout, Verger, & Pulcini, 2013 ; Sutter et al., 2013 ; Szrek, Chao, Ramlagan, & Peltzer, 2012 ; van der Pol & Cairns, 1999 , 2001 , 2002 , 2008 , 2011 ), the elicitation of preferences for retransplantation (Ubel & Loewenstein, 1995 ), and end-of-life decisions (Halpern et al., 2013 ).

Nudges are just one of the many areas of applications. They tend to be relatively effective in the health contexts where people suffer from “internalities,” costs that we incur because we fail to account for our future selves (Herrnstein, Loewenstein, Prelec, & Vaughan, 1993 ). Many other health situations are, however, also affected by externalities. Other policy tools, such as taxes, subsidies, and regulatory interventions, have been documented to deal effectively with externalities in health markets (Bhargava & Loewenstein, 2015 ; Galizzi, 2017 ). The application of behavioral experiments to these policy areas is at the moment almost nonexistent, and we foresee an increase of applications in this key area.

Another related aspect for both research and policy purposes is the rationale informing the legitimacy of nudging people. There is a major gap in the literature in measuring underlying preferences before the nudging interventions take place, and in monitoring their evolution (if any) before and after being nudged. This would help in understanding heterogeneity in behavioral change, as well as in identifying the behavioral channels, mechanisms, and mediating factors that are activated when individuals’ behavior is nudged. At the same time, it would inform the design of target-specific nudges, incentives, and behavioral regulatory tools (thus advancing the state-of-the-art evidence beyond knowing just “what works”). The issue of which set of preferences should be considered for drawing a welfare analysis of nudges and other behavioral interventions is one of the most relevant and pressing open questions from both a conceptual and an empirical perspective, as well as another area of promising development for the next waves of behavioral experiments in health.

Question Eight: How Do Framing and Subject Pool Matter in Behavioral Experiments in Health When Analyzing Healthcare Professionals’ Behavior?

While a neutral framing of the experimental decision is appropriate in an experiment on decision-making in games of strategic interactions, a medical framing appears natural for behavioral experiments on decision-making in medical contexts. Kesternich et al. ( 2015 ) show that framing in a health context affects subjects’ behavior in modified dictator and trilateral distribution games. In particular, in their health frame, subjects decide on the role of physicians in the provision of medical services with consequences for real patients outside the lab (similar to Hennig-Schmidt et al., 2011 ) in the modified dictator game. In a trilateral distribution game, consequences for the insured bearing the cost of medical service provision are also added.

More generally, recent practice in behavioral experiments in health is aligned with the belief that unless you frame a decision situation—for example in a medical or health frame—a researcher cannot be sure how subjects in an experiment have perceived the decision situation (Galizzi & Navarro-Martinez, 2019 ; Harrison & List, 2008 ). It may thus be crucial in chosen effort tasks to set subjects in the context the experimenter wants to study in order to avoid the possibility that subjects may impose a context on the abstract experimental task that is different from the experimenter’s intended context (e.g., Engel & Rand, 2014 ; Harrison & List, 2004 ). As Harrison and List ( 2004 ) notice, “it is not the case that abstract, context-free experiments provide more general findings if the context itself is relevant to the performance of the subjects” (p. 1022).

It remains unclear, however, whether a change in behavior due to framing more or less accurately reflects true behavior of healthcare professionals. One may argue that a healthcare professional in a health-framed study may be more willing to forgo earnings to avoid looking bad (“experimenter demand effect”; Zizzo, 2010 ). In practice, healthcare professionals in a neutrally framed decision situation could even become less responsive to how choices directly affect patients than when facing a series of choices in a framed task. Further, individuals may value their health and the health of others differently than any other good. Therefore, both unframed and framed experiments may misinterpret choices by healthcare professionals. For these reasons, it is important to study whether a medical framing in experiments more accurately reflects behavior in healthcare delivery (Cox, Green, & Hennig-Schmidt, 2016 ; Kesternich, Schumacher, & Winter, 2015 ).

Further, different subject pools used in health-related experiments (nonmedical students, medical students, physicians) may significantly change behavioral results, with nonmedical students exerting less patient-regarding altruism (Brosig-Koch et al., 2017a ; Hennig-Schmidt & Wiesen, 2014 ). Considering fraudulent behavior in a routine task in neonatal intensive care units (entry of weights in the birth reports), Hennig-Schmidt et al. ( 2017 ) found some evidence for more honest behavior of medical students compared to economics students. A few studies with healthcare professionals and medical students in developing countries correlate neutrally framed social preferences with actual health-related behaviors (e.g., Brock, Lange, & Leonard, 2016 ; Kolstad & Lindkvist, 2012 ).

Taken together, three promising avenues for behavioral experiments in health on this issue are (i) rigorously and systematically testing the behavioral effects of framing and subject pools; (ii) extending initial findings from the laboratory to field experiments, ideally with healthcare professionals (Cox, Sadiraj, Schnier, & Sweeney, 2016 ; Eilermann et al., 2017 ; Leeds et al., 2017 ); and (iii) linking findings from behavioral experiments to actual health-related behaviors. In this sense, it seems thus appropriate to call, again, for more systematic evidence—also from healthcare systems in developed countries—to be able to gather more conclusive predictions of providers’ behavior in the field.

Question Nine: Is Health Really Different from Other Policy Domains?

The specificity of health as a policy domain is self-evident. On the one hand, health is a very special area of policy application for obvious ethical and political reasons, and even more so for the application of randomized controlled behavioral experiments. Health, moreover, is a research and policy area that is uniquely rich in data: think about the millions of yearly entries in healthcare records and administrative registers (e.g., the Hospital Episode Statistics in the United Kingdom); the large epidemiological cohorts and clinical RCTs; and the complex databanks containing the genetic and epigenetic profiling at a population level. It is also unclear from both a conceptual and an empirical point of view whether behaviors and decisions in the health domain merely reflect behaviors and decisions in other domains in life, for example in the financial domain. As mentioned, the small experimental literature on cross-domain preferences seems to suggest that preferences are not stable across the health and monetary domains. Also the literature on the use of financial incentives in health finds that their effects are less straightforward and universally applicable than in other fields of applications (Gneezy, Meier, & Rey-Biel, 2011 ). A more specific example is healthcare provider incentives. Recent experimental findings suggest that healthcare providers’ behavior seems to be affected by pay-for-performance pay, but that this might also lead to adverse effects, such as motivation crowding-out (e.g., Brosig-Koch et al., 2016a , 2017b ; Oxholm, 2016 ). The latter pattern is, for example, not observed in other working domains (admittedly with different performance schemes), such as in field experiments with teachers (e.g., Muralidharan & Sundararaman, 2011 ). Therefore, a note of caution is in order when extrapolating lessons from experiments in other fields and generalizing them to the health domain.

On the other hand, behavioral health economists should be careful in advocating a complete disconnection of health applications from other areas of application of behavioral and experimental economics. On the contrary, they should continue arguing that there is much that can be learned from health applications that is useful to other policy domains. This can also help to reduce the substantial disengagement between economic and medical journals. For example, generalist economic journals seem to regularly publish behavioral experiments in, for example, education, financial savings, and energy consumption more regularly than in health, which are sometimes dismissed as “too field-specific.” That health is of more, not less, general interest than other subfields of economics is directly confirmed by the stellar impact factor and international reputation and visibility of the medical journals.

Question Ten: What Can Behavioral Experiments in Health Tell Us About Long-Term Effects?

It is true that, at the moment, there is very little evidence on the long-term carryover effects and on the cross-behavioral spillover effects of nudges, incentives, and other health policy interventions (Dolan & Galizzi, 2014a , 2015 ; Dolan et al., 2015 ). This is also due to the fact that, in practice, it is difficult to design behavioral experiments that follow up subjects over time for periods of time longer than a couple of hours (in the lab) or a few weeks or months (in the field), or that track all the complex ramifications of an initial policy intervention on the whole set of targeted and nontargeted health behaviors.

There is, more generally, a sort of major gap and disconnection between two key sources of empirical evidence in health economics. On the one hand, the behavioral experiments in health are typically conducted with small samples of subjects and almost invariably are centered on a single observation window or a single data collection point. On the other hand, very comprehensive longitudinal data sets exist in health in forms of administrative records for healthcare access (e.g., the Hospital Episode Statistics in the United Kingdom), biomarkers banks (e.g., the UK Biobank in the United Kingdom), or medical records and biomarkers for epidemiological cohorts (e.g., Constances in France).

The time seems ripe to systematically link and integrate these two major data sources. The already mentioned recent spring of experiments on “behavioral data linking” has shown that it is indeed feasible to link and merge behavioral economics experiments with other data sources, such as longitudinal surveys, online panels, administrative records, biomarkers and epigenetics banks, apps and mobile devices, smart cards and scan data, clinical RCTs, and other big data sources (Andersen et al., 2015 ; Galizzi, Harrison, & Miniaci, 2017 ). Given the inherent data-richness of health as a research and policy domain, we expect behavioral data linking to become a key building block of the next generation of behavioral experiments in health. This will contribute to further integrating and cross-fertilizing insights, tools, and methods from behavioral, experimental, and health economics, and to shaping up a groundbreaking interdisciplinary area at the interface between behavioral, medical, and data sciences.

Conclusions

This article reviews the state of the art of behavioral experiments in health by critically discussing ten key areas of potential debate and misconception, by highlighting their theoretical and empirical rationale and scope, and by discussing the significant questions which remain.

As our discussions indicate, there are many areas within health economics where experimental methods can be applied fruitfully. To date, in fact, a broad spectrum of behavioral experiments from the lab to the field have already been applied to numerous different health-related areas, including, for example, the effects of different co-payment rates and health insurance contracts on healthcare utilization and costs; the effects of public health insurance coverage on healthcare utilization and health outcomes; the effects of different providers’ incentives and the role of altruism; the role of audit, transparency, compliance, and gender bias in healthcare management; the role of different healthcare financing policies; the matching markets for organ donations and for physicians and healthcare professionals; the role of subsidies for diagnostic tests and new health products; the choice of health insurance; the economic and behavioral determinants of vaccination; the effects of different types of HIV risk information and of SMS interventions on HIV treatment adherence; the use of financial incentives and nudges for smoking cessation, physical exercise, weight loss, healthy eating, warfarin adherence, glucose control, home-based health monitoring, mental exercises, immunization coverage, and medical drugs; the unintended carryover and spillover effects of financial incentives and nudges in health; the behavioral effect of decision support systems and feedback; the elicitation of risk and time preferences in health and their links with health-related behaviors; and the elicitation of preferences for retransplantation and end-of-life decisions. Many other health-related areas are expected to follow in the next years in both developing and developed countries.

Tailoring and fine-tuning the broad spectrum of lab, field, online, mobile, and “behavioral data linking” experiments in order to address pressing health policy challenges and key research questions is, both methodologically and substantially, one of the most promising and exciting areas of applications of behavioral experiments to health economics. Also, via the new international networks, the next cohort of behavioral experiments in health is likely to originate from a closer collaboration among behavioral and experimental economists, health economists, medical doctors, and decision makers in health policy and management. This forthcoming generation of behavioral experiments in health will likely scale up the current endeavors to systematically link behavioral economics measures to other data sources, in which health is naturally rich. In the years to come, the promise and the research and policy impact of behavioral experiments in health are only destined to grow.

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Classroom Experiments & Games

Last updated May 2023

Experiments and Games in Context

Individual games, computerised games and experiments.

Many students respond well to being involved in a game and the experience can fix a concept vividly in their minds. We have guides and, in some cases, printable materials to help you introduce games to your classes.

Economic Classroom Experiments is a chapter of the Handbook for Economics Lecturers with advice and examples.

Simulations, Games and Role-Play is an older Handbook chapter, discussing why, when and how to use games or simulations in teaching economics, with examples.

Classroom Experiments, Games and Role-Play a series of experiments and games from our Reflections on Teaching section.

IREE Volume 9 Issue 2 was a special issue on economic classroom experiments, including review papers as well as details of individual experiments.

Using Experiments and Activities in the Principles Class by John Eaton describes a number of games, used analogously to lab sessions for physical science students

John Sloman summarises seven games that can be used to increase student motivation (Powerpoint, with links to handouts and other materials)

Jon Guest describes using classroom experiments as a more active method of teaching microeconomics in a first-year context and an intermediate context .

The FEELE team have created an extensive guide to Economic Classroom Experiments , including The Twenty Pound Auction . It's part of Wikiversity, so you can log in to add your own experiences and variations.

Games Economists Play: Non-Computerized Classroom-Games for College Economics is an online guide to 180 games both for micro principles and macro principles (external link).

Health Economics education (HEe) lists several classroom experiments for teaching Health Economics .

The International Trade Game: Using just scissors, pencils, rulers and paper, large numbers of students experience a simulation of international trade.

The Tennis Balls Game: students form a "production line" to illustrate diminishing marginal returns. This is one of a number of games used by Mary Hedges and colleagues , including the oligopoly game (favourite TV show) , Money Supply Game and the Restaurant Game (an auction market).

A separate page discusses some games that can be used with school students, for example on open days. These include the public goods game and rent-seeking game (both using playing cards), auctioning a £1 coin (illustrating sunk cost and marginal cost) and a public goods game with altruistic punishment .

Some journal articles describing classroom experiments and games

A classroom experiment on the specific factors model by Yu-Hsuan Lin, 2021 "students act as decision-makers in an economy comprising two goods and three factors, and each is asked to maximize the value of marginal production by allocating his/her labor force between the two sectors."
The Microfinance Game: Experiencing the dynamics of financial inclusion in developing contexts by Javier Sierra and María-José Rodríguez-Conde, 2021 "an online role-play simulation in which the students represent several stakeholders from the microfinance sector in Uganda"
The potential of simulations for developing multiple learning outcomes: The student perspective by Javier Sierra, 2020 "a simulation in which students represent different countries and carry out international exchanges to implement a set of public policies"
A classroom experimental game to improve the understanding of asymmetric common-pool resource dilemmas in irrigation water management by Stefano Farolfi and Katrin Erdlenbruch, 2020 "A hand-run experimental game that combines a ludic learning approach with the rigorous testing of hypotheses. Multiple concepts about CPR management can be analysed simultaneously"
Using classroom games to teach core concepts in market design, matching theory , and platform theory by Melati Nungsari and Sam Flanders, 2020 "We explore some of the core concepts in understanding market design, matching theory, and platforms, and outline three classroom games with detailed instructions for instructors who may want to explore these topics in their own classes and curricula"
For want of a chair: Teaching price formation using a cap and trade game by Stefano Carattini, Eli P. Fenichel, Alexander Gordan and Patrick Gourley, 2020 "Students can learn several important tenets of economics by playing an in-class game based on musical chairs, which creates a market for pollution using a mobile app or paper-based interaction."
A demand and supply game exploring global supply chains by Bei Hong, 2020 Building on other market games, but introducing global supply chain management (GSCM) features "suitable for introductory economics courses and for class sizes of 30 to 50" 35-45 minutes
A classroom experiment on the causes and forms of bounded rationality in individual choice by Anna Rita, Adrian Gourlay and Chris M. Wilson, 2020 "the experiment shows how a range of factors can prompt bounded rationality and illustrates how it can manifest itself in the form of different behaviors." 40-50 minutes to conduct & debrief
An exchange rate risk experiment with multiple currencies by Paul Johnson and James Staveley-O’Carroll, 2020 "Student teams compete by managing virtual portfolios of six foreign currencies over a period of several weeks. Trading requires a few minutes in class. Students gain an understanding of currency movements, financial risk, and portfolio management."
Microeconomic education, strategic incentives, and gender: An oligopoly classroom experiment with social interaction by José Antonio García-Martínez, Carlos Gutiérrez-Hita, and Joaquín Sánchez-Soriano, 1999 "In our setting, students may interact in the classroom (indeed, everywhere) prior to submitting quantity bids to a virtual market. [i]nformation exchange among students was expected to take place (as it did)."
A two-round in-class trading game on the principle of comparative advantage and the theory of reciprocal demand by Bei Hong, 2019 "Each group is assigned a country with hypothetical productivity [...] Students simulate the trading of goods between countries with the objective of achieving the best possible terms of trade."
Can homo economicus be an altruist? A classroom experimental method by Tomasz Kopczewski and Iana Okhrimenko, 2019 " The classroom experiment utilizes the original Giving According to GARP experimental methodology in order to create storytelling about human nature. "
The psychology of sunk cost: A classroom experiment by Louis-Philippe Sirois, 2019 "an active learning activity that allows students to witness how their own judgment can be biased" 30-50 minutes, 50 students or more per class
Equilibrium with capacity-constrained firms: A classroom experiment by Gayane Barseghyan and Aram Grigoryan, 2019 "a two-stage classroom experiment to illustrate convergence to long-run equilibrium in a market where price-taking firms are capacity-constrained" "The game and discussion session require about 60 to 75 minutes."
A classroom experiment in monetary policy by John Duffy and Brian C. Jenkins, 2019 "a simple version of a New Keynesian model suitable for courses in intermediate macroeconomics and money and banking. Students play as either the central bank or members of the private sector."
A dynamic semester-long social dilemma game for economic and interdisciplinary courses by Silvia Secchi & Simanti Banerjee, 2019 "a semester-long game to teach the role of economics in natural resources management", requiring 5 to 10 minutes per session.
An endogenous equilibrium game on traffic congestion externalities by Leah H. Palm-Forster and Joshua M. Duke, 2019 75-minute exercise "to demonstrate how congestion externalities are generated, the effects on private and social welfare, and how appropriately priced tolls can address congestion externalities."
The great American health care debate: A classroom game to explore risk and insurance by Kelly Grogan, 2018 "provides students with a thorough understanding of some of the [health insurance] policy options under debate, in addition to demonstrating the classic problem of adverse selection."
Let's make a deal in the classroom: Institutional solutions to the Monty Hall Dilemma by Brandon Dupont and Yvonne Durham, 2018. "In addition to demonstrating the anomaly, the experiment can be used to introduce students to some institutional modifications that have been shown to ameliorate it."
A college athletics recruiting game to teach the economics of rent-seeking by Justin R. Roush and Bruce K. Johnson, 2018. "Students engage in an effort-based lottery, i.e., recruiting to sign a blue-chip prospect. The winner gets the prize—the player's marginal revenue product in excess of his grant in aid. The authors demonstrate the game's use in a principles course, but it is easily adaptable to other courses."
The automobile industry and new trade theory: A classroom experiment by Steven Yamarick, 2018. "[F]or undergraduate international economics and trade courses. There are five rounds in the experiment, starting with autarky in the 1960s and ending with the Great Recession of 2008–9."
An introductory microeconomics in-class experiment to reinforce the marginal utility/price maximization rule and the integration of modern theory by David G. Raboy, 2017 "The experiment that is the subject of this paper was conducted in a 200-level Principles of Microeconomics course, with 23 students."
A classroom game on a negative externality correcting tax: Revenue return, regressivity, and the double dividend by Joshua M. Duke & David M. Sassoon, 2017 "A classroom game that uses numerical examples to explain, specifically, how a social planner might optimally return emission tax revenue. [...] which conveys some of the political economy challenges of adopting a new tax-with-revenue-return policy. The problem requires approximately 75 minutes of class time"
A classroom experiment with bank equity, deposit insurance, and bailouts by Denise Hazlett, 2016 "Students see how low bank equity requirements can interact with deposit insurance to encourage excessive risk-taking. [...] It takes about 45 minutes to run and debrief, and requires no computerization."
A classroom market for extra credit: A semester-long experiment by James Staveley-O'Carroll, 2016. Using extra course credit as a kind of currency, students learn about market-clearing price.
When do first-movers have an advantage? A Stackelberg classroom experiment by Robert Rebelein and Evsen Turkay, 2016. A two-firm game to teach first-mover and second-mover advantage
Price discrimination: A classroom experiment by Paula Aguiló, Maria Sard & Maria Tugores, 2016. An exercise in distinguishing first-, second-, and third-degree price discrimination
Airing Your Dirty Laundry: A Quick Marketable Pollution Permits Game for the Classroom by Jill L. Caviglia-Harris & Richard T. Melstrom, 2015. A very quick-to-run game involving tradeable permits
Simulating Price-Taking by Lucas M. Engelhardt, 2015. Repeated rounds teach students about perfect competition
Examining Theories of Distributive Justice with an Asymmetric Public Goods Game by Stephen J. Schmidt, 2015. "Players choose whether to contribute tokens to a group investment or retain them for a private investment which produces a lower return. [...] The ability to distribute the group return asymmetrically creates an entangling link between efficiency on the one hand and ideas about fairness, rights, and equality on the other."
A model to assess students’ social responsibility behavior within a classroom experiment by Amalia Rodrigo-González, María Caballer-Tarazona, 2015 Ultimatum game/ dictator game and debate within a 90-minute class "encouraging students to reflect on and discuss a range of social values such as respect, solidarity, and social justice"
Adverse Selection in Health Insurance Markets: A Classroom Experiment by Ashley Hodgson, 2014. A short experiment to convey how asymmetric information manifests in health markets
Teaching economics with a bag of chocolate: A classroom experiment for elementary school students by Nicholas G. Rupp, 2014. Uses exchanges to illustrate the value of trade and the law of demand
Veconlab Classroom Clicker Games: The Wisdom of Crowds and the Winner's Curse by A. J. Allen Bostian & Charles A. Holt, 2013. Students are shown a jar of marshmallows and use clickers to place bids that represent their guesses as to the number of marshmallows, experiencing a winner's curse. Computerised version is available, which can be run in ten minutes.
Choosing Partners: A Classroom Experiment by Carl T. Bergstrom, Theodore C. Bergstrom & Rodney J. Garratt, 2013. Simple, introductory exercise that illustrates two-sided matching and the idea of a stable assignment
Using a Simple Contest to Illustrate Mechanism Design by Calvin Blackwell, 2011. Illustrates the effects of incentive structure on effort using a 50-minute session.
Exploring Strategic Behavior in an Oligopoly Market Using Classroom Clickers by Keith Brouhle, 2011 "Using classroom clickers to communicate pricing decisions, students explore first-hand the strategic nature of decision-making in an oligopoly market. Students see the diversity of equilibrium outcomes that can be supported in an oligopoly setting and better understand the conditions that lead to one equilibrium over another."
Illustrating Environmental Issues by Using the Production-Possibility Frontier: A Classroom Experiment by Nancy Carson & Panagiotis Tsigaris, 2011 "Resources are allocated toward the production of two goods—widgets and environmental cleanup. [...] Various production possibilities are observed as resources are reallocated between the two products."
Teaching Bank Runs with Classroom Experiments by Dieter Balkenborg, Todd Kaplan & Timothy Miller, 2011 Hand-run and computerised versions of a game, taking less than an hour including debrief, illustrating how bank runs can emerge from rational decisions.
A Bluff-Bidding Exercise by J. Patrick Meister, 2011 "An auction based on the idea of a firm making “bluff bids” (i.e., not bidding to win, but bidding simply to drive up the price that the eventual winner pays)."
Persuasive and Informative Advertising: A Classroom Experiment by Beth A. Freeborn & Jason P. Hulbert, 2011 "The student is placed in the role of a monopolist and must choose the optimal number of advertisements and a price to charge. Students analyze the effects of advertising on price, quantity, and consumer surplus."
The Pollution Game: A Classroom Game Demonstrating the Relative Effectiveness of Emissions Taxes and Tradable Permits by Jay R. Corrigan, 2011 "Divides students into three groups—a government regulatory agency and two polluting firms—and allows them to work through a system of uniform command-and-control regulation, a tradable emissions permit framework, and an emissions tax."
Understanding Credit Risk: A Classroom Experiment by Maroš Servátka & George Theocharides, 2011 75-minute classroom market trading two kinds of bond, to teach about credit risk, pricing of risk, risk aversion and related concepts
Status spending races, cooperative consumption and voluntary public income disclosure by Damian Diamonov and Shane Sanders, 2011 Students choose how to spend a budget on two goods, to learn about the effects of relative income and the effect of publicly disclosed income.
Tradable Discharge Permits: A Student-Friendly Game by Amy W. Ando & Donna Ramirez Harrington, 2010. See also The tradable pollution permit exercise: Three additional tools by Michael A. McPherson & Michael L. Nieswiadomy, 2014 Simple form of a Tradeable Discharge Permit game to illustrate "the relative cost-effectiveness of a permit system over a uniform standard, the nature of permit market equilibrium, and the comparative advantages of different regulatory regimes."
Discovering Economics in the Classroom with Experimental Economics and the Scottish Enlightenment by Taylor Jaworski, Vernon Smith and Bart Wilson, 2010 Three classroom experiments used to illustrate Adam Smith's ideas about markets, each of which are hand-run but use one computer to calculate outcomes. The three are 1) Specialisation and exchange, 2) Double oral auction, 3) One-shot extensive-form trust game.
Teaching Opportunity Cost in an Emissions Permit Experiment by Charles Holt, Erica Myers, Markus Wrake, Dallas Burtraw and Svante Mandell, 2010 "[A]n individual choice experiment that can be used to teach students how to correctly account for opportunity costs in production decisions. Students play the role of producers that require a fuel input and an emissions permit for production. Given fixed market prices, they make production quantity decisions based on their costs."
Patents and R&D: a Classroom Experiment by Amy Diduch, 2010 "This classroom experiment provides students with an introduction to two competing models of the impact of patents on R&D: the ‘winner-take-all’ model contains incentives for excessive research effort and the ‘knowledge spillover’ model contains incentives for free riding."
To Work or Not to Work … That is the Question: Labour Market Decisions in the Classroom by Arlene Garces-Ozanne and Phyll Esplin, 2010 "a simple classroom experiment for first-year university students of introductory economics, about participation in a competitive labour market. The students are designated as workers and [in] each period representing different conditions in the labour market, workers must decide whether or not to offer their services based on the information they have at hand."
A Classroom Inflation Uncertainty Experiment by Denise Hazlett, 2008. Uses a double oral auction credit market to illustrate effects of inflation uncertainty.
A Classroom Investment Coordination Experiment by Denise Hazlett, 2007. Gets students to privately choose firm's levels of investment, illustrating coordination failure
Portfolio Construction in Global Financial Markets by Dallas Brozik and Alina M. Zapalska, 2007. Describes a portfolio management game that can be played in a single session
Market Forces and Price Ceilings by Jamie Kruse et al. 2005 Describes a market to illustrate the effects of rent controls.
Strategic Voting and Coalitions by James Stodder, 2005. Uses a classroom game to illustrate the Concordet Voting Paradox.
Using Context in Classroom Experiments: A Public Goods Example by John Bernard and Daria Bernard, 2005. Describes a game played on paper to introduce the concept of a public good.
A Search-Theoretic Classroom Experiment with Money by Denise Hazlett 2003. Presents a market game in which one commodity emerges as a medium of exchange. Hazlett's site has details of six non-computerized classroom experiments for undergraduate macroeconomics courses .

Charles Holt's VEconLab is a set of 35 interactive games that can be configured by lecturers and played by students using only their web browsers. Each game has extensive instructions.

Econport is another site allowing you to run a variety of experiments using the web. They provide extensive documentation on how to integrate the experiments into courses.

Finance and Economics Experimental Laboratory at Exeter (FEELE) , at Exeter University, has created 13 online games. You can register for free to create, customise and run economic experiments online. Jon Guest's case study describes using one of these experiments in a class.

Economics-games.com is another site along similar lines. It has 14 simulations for students to play on their own, and 48 multiplayer games that lecturers can configure and share via a web link. The site is free and students can play from computers, phones or other internet devices.

LIONESS is a free web-based platform for interactive online experiments, developed at the University of Nottingham and University of Passau. The control panel lets experimenters design and conduct experiments. Participants take part via personalised links.

oTree is a framework for coding multi-player experiments and games, based on the Python language. Experiments can be created by writing code and/or assembling components in a point-and-click interface. They can be run on the Web or downloaded. There is a long list of experiments and games created by the community .

ClassEx , based at the University of Passau, is a widely-used platform for online surveys and classroom experiments. Students make their decisions via their computers or mobile devices, and the outcomes appear on the lecturer's computer for them to present to the group. The CORE Project have released a video of a lecture by Cloda Jenkins (UCL) in which Prof Jenkins incorporates three ClassEx games.

Ariel Rubinstein's site, Didactic Web-Based Experiments in Game Theory , has free online games and suggestions for using them in class. "After having registered, a teacher, will be able to allocate problems to the students and get basic statistics on their responses. [...] My goal as a teacher is to deliver a loud and clear message that game theoretic models are not meant to supply predictions of strategic behavior in real life."

MobLab is a classroom gaming and polling system that takes a paid subscription per student per class. Its dozens of games can be browsed by course and can be configured by the lecturer. Its grade book can sync to VLE software.

The Teach Better podcast has an episode from July 2017 in which Matt Olczak of Aston Business School discusses using economics-games.com and VEconLab, and Bob Gazzale of the University of Toronto discusses his use of MobLab. Matt Olczak also wrote a blog post for Inomics Teach introducing some online games .

zTree , the Zurich Toolbox for Readymade Economic Experiments, is a client/server software package that runs locally on Windows computers (including virtual machines), rather than over the Web.

Kiviq is a dedicated educational tool for running double auctions in a classroom setting.

For a stock market game , see HowTheMarketWorks.com .

Starting Point activities: Classroom Experiments
Conference sessions in Experiments, games and role-play
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Published: 02 September 2024

The carbon emission reduction effect of green fiscal policy: a quasi-natural experiment

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Climate-change mitigation
Environmental impact

Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction ( ECER ) fiscal policies on enhancing carbon emission ( CE 1 ) reduction and carbon efficiency ( CE 2 ). It further analyzes the mediating role of Green Innovation ( GI ), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE 2 , a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE 1 reduction and CE 2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE 1 reduction and the improvement of CE 2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals.

Green taxation, regional green development and innovation: Mechanisms of influence and policy optimization

The impact of China’s energy saving and emission reduction demonstration city policy on urban green technology innovation

Can place-based policy reduce carbon emissions? Evidence from industrial transformation and upgrading exemplary zone in China

Introduction.

Efforts to mitigate global climate change through the reduction of CE 1 have emerged as a shared objective among nations globally 1 . From the initiation of the United Nations Framework Convention on Climate Change to the enactment of the Kyoto Protocol and the adoption of the Paris Agreement, these pacts reflect the unified resolve of nations to tackle global climate change 2 , 3 . With the acceleration of global industrialization and the continuous increase in energy demand, there has been a significant rise in the emissions of greenhouse gases, especially carbon dioxide, posing an unprecedented challenge to the Earth’s climate system 4 . These issues encompass the escalation of average global temperatures, a surge in severe weather occurrences, accelerated glacier melt, and a persistent increase in sea levels 5 , 6 , 7 , which threaten the balance of natural ecosystems and have profound impacts on the economic development and well-being of human societies. Therefore, adopting effective carbon reduction strategies to slow these climate change trends has become an urgent task faced globally.

In the current field of CE 1 reduction research, the focus is mainly on implementing policies such as carbon emission trading 8 , smart city pilot policies 9 , and low-carbon city pilot policies 10 . Among these policies, green fiscal policy, as a core strategy to mitigate the impact of climate change, is increasingly recognized by the academic community and policymakers for its importance in promoting CE 1 reduction 11 , 12 . This policy directly impacts CE 1 in economic activities through adjustments in the tax system, provision of fiscal subsidies, and increased investments in renewable energy and low-carbon technologies 13 . Green fiscal policies differ from traditional environmental protection measures by employing a mechanism that combines incentives and constraints, aiming to encourage enterprises to adopt emission reduction measures. In the implementation process of green fiscal policies, governments encourage enterprises to reduce CE 1 by adjusting tax policies 14 . Specifically, the ECER policy impacts the carbon emissions of demonstration cities through a combination of financial incentives and target constraints. The demonstration period lasts for three years, during which the central government provides reward funds for demonstration projects. The amount of these rewards is determined by the category of the city: 600 million RMB annually for municipalities and city clusters, 500 million RMB annually for sub-provincial cities and provincial capitals, and 400 million RMB annually for other cities. Local governments have the discretion to decide how to utilize these funds, while the central government is responsible solely for project record management. Additionally, the central government conducts annual and overall target assessments of the demonstration cities. The results of the annual assessment influence the reward funds for the following year: cities that perform excellently will receive an additional 20% of reward funds, while those that fail to meet the standards will have 20% of their funds withdrawn. The overall assessment results are linked to the demonstration qualification and reward funds; cities that fail to meet the overall targets or have serious issues will lose their demonstration status and have all reward funds withdrawn. This financial incentive mechanism ensures that local governments have sufficient financial support when implementing green technologies and projects, promoting increased energy efficiency and the widespread adoption of clean energy. Simultaneously, through the target constraint mechanism, the central government strictly supervises and incentivizes local governments’ efforts to reduce emissions, ensuring effective policy implementation. Under the dual pressure of financial incentives and performance assessments, local governments actively adopt various measures to promote energy conservation and emission reduction, including investing in green infrastructure, promoting energy-saving technologies, and optimizing energy structures, thereby achieving significant reductions in carbon emissions.

Furthermore, innovation and technological breakthroughs significantly enhance the effectiveness of green fiscal policies in reducing carbon emissions. Specifically, technological advancements improve energy efficiency, reducing the energy consumption per unit of output; they lower the production costs of clean energy, promoting its widespread adoption; and they advance carbon capture and storage technologies, directly reducing industrial carbon dioxide emissions. These technological improvements bolster the impact of green fiscal policies, making them more effective in achieving carbon reduction targets. However, the implementation of green fiscal policies also faces some challenges. Firstly, balancing the relationship between economic development and environmental protection to avoid potential negative impacts such as job losses and industrial relocation during policy execution is an issue that policymakers need to consider. Secondly, the effective implementation of green fiscal policies requires strong policy support and regulatory mechanisms to ensure that policy measures are effectively executed and can adapt to constantly changing economic and environmental conditions. Therefore, evaluating the carbon reduction effect of such policies is of significant importance for achieving long-term environmental sustainability and promoting the green economic transformation.

This paper analyzes the impact of green fiscal policies on carbon emissions and carbon efficiency. Relevant research mainly focuses on the following two areas: studies on the factors influencing carbon emissions, and research related to environmental regulations and energy conservation and emission reduction fiscal policies.

Firstly, a substantial body of literature focuses on the factors influencing carbon emissions, with some studies specifically examining the impact of government intervention and environmental regulation on CO2 emissions. These studies are closely related to the theme of this paper. From an economic perspective, numerous studies have demonstrated that economic growth significantly impacts carbon emissions 15 , 16 , 17 . Generally, increased economic activity is associated with higher energy consumption, leading to higher carbon emissions. However, as economies reach a certain level of development, the Environmental Kuznets Curve (EKC) phenomenon may occur, where carbon emissions begin to decrease after reaching a certain economic threshold 18 , 19 . Research has also confirmed that economic growth increases the ecological footprint, leading to environmental degradation 20 . For example, economic growth, income inequality, and energy poverty have increased environmental pressure in BRICS countries 21 . In Pakistan, institutional quality has led to higher CO 2 emissions, but economic development can help reduce these emissions 22 . From a social perspective, the acceleration of urbanization is typically accompanied by increased energy consumption, thereby raising carbon emissions. There is a long-term and short-term U-shaped relationship between urbanization and the environment 23 . Upgrading existing infrastructure can enable various sectors to produce minimal waste that impacts emissions 24 . Changes in consumption levels and population structure also significantly affect carbon emissions 25 . From a policy perspective, government-enacted environmental regulations and policies, such as carbon taxes, carbon trading markets, emission standards, and renewable energy subsidies, play a crucial role in reducing carbon emissions. Innovations and environmental policies contribute to emission reductions both in the long and short term. Additionally, carbon pricing can reduce emissions in specific regions, although its impact is often more targeted at specific countries 26 . Carbon taxes and mitigation technologies are helping to achieve sustainable development goals for carbon mitigation 27 . Green energy investments are significantly associated with greenhouse gas emissions and support environmental quality 28 . However, these studies often overlook the impact of energy conservation and emission reduction fiscal policies on carbon emissions.

Secondly, there is a body of literature focusing on environmental regulation, which can be divided into two main areas: the impact of environmental regulation on the environment and its impact on the economy. On the one hand, extensive research has explored the environmental impact of regulation. Studies generally agree that stringent environmental regulations help reduce pollutant emissions and improve environmental quality. Environmental regulations significantly enhance the synergy between carbon reduction and air pollution control 29 . Target-based pollutant reduction policies effectively constrain the sulfur dioxide emissions of regulated enterprises, lowering their sulfur dioxide emission intensity, thereby demonstrating that stringent environmental regulations facilitate green transitions for businesses 30 . However, in some developing countries or regions with weak enforcement, the effectiveness of environmental regulations may be compromised. Despite strict regulatory policies being in place, inadequate enforcement or a lack of regulatory capacity may result in actual pollutant reduction falling short of expectations. On the other hand, part of the literature examines the economic impact of environmental regulation. Some studies suggest that environmental regulation can drive technological innovation and industrial upgrading, thereby promoting economic growth 31 . Strict environmental standards force companies to improve production processes and develop new environmental technologies, which can create new economic opportunities and growth points 32 . Environmental regulations significantly enhance green technological innovation 33 , and they have notably promoted green innovation across European countries 34 . Conversely, environmental regulations may increase operational costs for businesses, particularly in the short term due to compliance costs, which could inhibit economic growth. This is especially true for regions or countries that rely heavily on high-pollution, high-energy-consumption industries, where environmental regulation might lead to a slowdown in economic growth. Given that energy conservation and emission reduction fiscal policies are a form of environmental regulation, it is necessary to evaluate their effectiveness.

Thirdly, some literature evaluates the governance effectiveness of energy conservation and emission reduction fiscal policies. From an environmental perspective, these policies can reduce pollutants and enhance efficiency. On average, such policies have reduced industrial SO2 (sulfur dioxide) emissions by 23.8% and industrial wastewater discharge by 17.5% 35 . Additionally, energy conservation and emission reduction fiscal policies can effectively improve green total factor carbon efficiency 36 . From an economic perspective, these policies can promote investment and economic growth 37 . They have significantly improved green credit for enterprises and can facilitate sustainable urban development 38 .

In summary, there are two significant gaps in the existing literature. Firstly, although numerous studies have extensively explored the factors influencing carbon emissions from economic, social, and policy perspectives, relatively few have examined the relationship between ECER policies and carbon emissions. Specifically, most of the existing literature focuses on the impact of macroeconomic policies, industrial structure adjustments, and technological innovation on carbon emissions. However, there is a lack of systematic empirical analysis on how specific fiscal incentives directly affect carbon emissions, limiting our comprehensive understanding of the actual effects of fiscal policies on emission reduction. Secondly, most of the existing studies investigate carbon dioxide emissions from a single perspective, such as focusing on total carbon emissions, carbon intensity, or carbon efficiency. These studies lack a multi-faceted exploration of the relationship between a single policy and carbon emissions. Typically, research adopts a specific metric to measure policy effects, but this approach overlooks how different metrics might reveal various aspects of policy impact. Consequently, these studies fail to capture the multi-dimensional effects of policies on reducing carbon emissions comprehensively. This single-perspective research methodology cannot adequately reflect the multiple impacts of policies on carbon emissions across different scenarios and time periods. This paper aims to evaluate the impact of the ECER policy, jointly introduced by the Ministry of Finance and the National Development and Reform Commission in 2011, on CE1 and CE2. Given that the ECER policy was implemented in three batches of pilot cities, this study employs a multi-period Difference-in-Differences (DID) model for analysis. The advantage of this model lies in its ability to compare the effects of the policy before and after its implementation across multiple time points, thereby capturing the dynamic impacts of the policy. Furthermore, this article explores the mediating role of green innovation in the impact process of the ECER policy, revealing the policy’s varying effects on CE 1 and CE2 across different regions through heterogeneity analysis.The marginal contributions of this article: Firstly, this paper evaluates the relationship between ECER policies and carbon emissions, addressing a significant gap in the existing research. Although numerous studies have explored various factors influencing carbon emissions from different perspectives, there is a lack of systematic research on the actual effects of specific fiscal policies on energy conservation and emission reduction, particularly their direct impact on carbon emissions. Through empirical analysis and data validation, this study thoroughly investigates the specific mechanisms and effects of ECER policies on carbon emissions in practice, thus filling this research gap. Secondly, this paper systematically assesses the relationship between ECER policies and carbon emissions from two key perspectives: total carbon emissions and carbon efficiency. By considering these two important indicators, this study not only examines the impact of ECER fiscal policies on overall carbon emissions but also analyzes their role in improving carbon efficiency. Through an in-depth analysis of these two metrics, this paper provides a more comprehensive and multi-dimensional view, systematically evaluating the effectiveness and mechanisms of ECER policies.

The remainder of the article is organized as follows: the second part discusses the policy background and theoretical analysis; the third part details the model settings and variable explanations; the fourth part presents the empirical analysis; the fifth part analyzes regional heterogeneity; and the last part concludes with conclusions and policy recommendations.

Policy background and theoretical analysis

Policy background.

In 2011, the Ministry of Finance and the National Development and Reform Commission issued the “Notice on Conducting Comprehensive Demonstration Work of Fiscal Policies for Energy Conservation and Emission Reduction,” deciding to carry out comprehensive demonstrations of fiscal policies for ECER in some cities during the “Twelfth Five-Year” period. Beijing, Shenzhen, Chongqing, Hangzhou, Changsha, Guiyang, Jilin, and Xinyu were selected as the first batch of demonstration cities. In the subsequent years of 2013 and 2014, 10 and 12 cities were respectively chosen as pilot cities for the fiscal policies on ECER . Specifically, this policy uses cities as platforms and integrates fiscal policies as a means to comprehensively carry out urban ECER demonstrations in various aspects, including industrial decarbonization, transportation clean-up, building greening, service intensification, major pollutant reduction, and large-scale utilization of renewable energy. Its main goal in terms of CE 1 reduction is to establish a concept of green, circular, and low-carbon development in the demonstration cities, achieve widespread promotion of low-carbon technologies in industries, construction, transportation, and other fields, lead the pilot cities in ECER efforts across society, and significantly enhance their capacity for sustainable development. Figure 1 presents the spatial distribution of ECER policy pilot cities in the years 2011, 2013, and 2014 (This figure was created using ArcMap software).

Distribution of ECER Policy Pilot Areas (Plan Approval Number GS(2019)1822).

Theoretical analysis

Carbon emission reduction effect of green fiscal policy.

Green fiscal policy, as a significant environmental governance tool, promotes the transformation of the economic and social system towards low-carbon, sustainable development through fiscal measures 39 . Its CE 1 reduction effects can be described from the following aspects. Firstly, green fiscal policy encourages the research and application of green technologies through economic incentives (such as tax reductions and fiscal subsidies) 40 . These technologies include energy efficiency improvement technologies, clean energy technologies, and carbon capture and storage technologies, which directly reduce energy consumption and CE 1 in economic activities. Secondly, green fiscal policy influences the behavior of consumers and producers by affecting the price mechanism. The imposition of a carbon tax raises the cost of CE 1 , reflecting the external cost of CE 1 on the environment, encouraging enterprises to take emission reduction measures, and prompting consumers to prefer low-carbon products and services 41 . The change in price signals promotes the transformation of the entire society’s energy consumption structure towards more efficient and low-carbon directions. Furthermore, green fiscal policy can support CE 1 reduction-related infrastructure construction and public service improvements through the guidance and redistribution of funds. This includes the construction and optimization of public transportation systems, urban greening, and forest conservation projects, which not only directly or indirectly reduce CE 1 but also enhance the carbon absorption capacity of cities and regions. Lastly, green fiscal policies, by raising public environmental awareness and participation, create a conducive atmosphere for all sectors of society to join in carbon reduction efforts 42 . Governments can increase public awareness of climate change and inspire a low-carbon lifestyle through the promotion and education of fiscal policies, providing broader social support for carbon reduction 43 .

Green fiscal policies not only drive a reduction in CE 1 but also stimulate sustainable economic growth. By taxing high-carbon activities, offering financial subsidies and incentives for green projects, these policies channel capital towards low-carbon and green industries. This not only mitigates negative environmental impacts but also fosters the development of emerging green technologies and sectors. As the green industry expands and low-carbon technologies become more widespread, economic growth increasingly relies on clean and efficient energy use 44 , thereby enhancing the CE 2 . Thus, the implementation of green fiscal policies demonstrates a commitment to transitioning towards a low-carbon economy, playing a crucial role in the global response to climate change, achieving a win–win for environmental protection and economic growth.

Based on this, the article proposes hypothesis 1: Green fiscal policies can promote CE 1 reduction effects and enhance CE 2 .

Mechanism analysis

Green innovation is a key factor in driving sustainable development, particularly playing a significant role in CE 1 reduction and efficiency enhancement. By introducing and adopting new environmentally friendly technologies and processes, green innovation not only significantly reduces greenhouse gas emissions but also enhances the efficiency of energy use and resource management, thus promoting a harmonious coexistence between economic activity and environmental protection. Green innovation, through the development and adoption of renewable energy technologies such as solar, wind, and biomass energy, directly reduces reliance on fossil fuels and the corresponding CE 1 . The application of these technologies not only reduces the carbon footprint but also promotes the diversification of energy supply and enhances energy security 45 . Green innovation also plays an essential role in improving energy efficiency. By adopting more efficient production processes and energy-using equipment, businesses and households can accomplish the same tasks or meet the same living needs with lower energy consumption, thus reducing CE 1 46 . Additionally, green innovation encompasses the concepts and practices of the circular economy, which encourages the reuse, recycling, and recovery of materials, reducing the extraction and processing of new materials and further lowering CE1s in the production process 47 . Green innovation includes the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, which can directly capture carbon dioxide from industrial emissions and either convert it into useful products or safely store it, thereby reducing the carbon content in the atmosphere 48 . On the policy and management level, green innovation also involves establishing and refining mechanisms such as carbon pricing, green taxes, and carbon trading, which promote the adoption of low-carbon and environmentally friendly technologies and behaviors among businesses and individuals through economic incentives 49 . Based on this, the article proposes hypothesis H2: Green fiscal policies can promote CE 1 reduction effects and CE 2 by fostering green innovation.

In conclusion, the theoretical framework, as shown in Fig. 2 .

Theoretical framework.

Model setting and variable description

To address the limitations faced by traditional regression models in evaluating policy implementation effects, this study utilizes DID model for analysis. Given the variation in the policy implementation years in this paper, the traditional DID model cannot be used 50 . Accordingly, this paper draws on the approach of Beck et al. 51 , employing a DID with multiple time periods to assess the policy effects, with the model set up as follows:

Y in the model is the explained variable, indicating CE 1 and CE 2 of the city i in the annual t . Treated i is the group variable, where it takes the value 1 if city i belongs to the treatment group, and 0 if it belongs to the control group; Post it is the post-treatment period dummy variable, where it takes the value 1 for city i in year t if ECER policy has been officially implemented, and 0 if it has not been officially implemented. This study investigates the impact of energy conservation and emission reduction fiscal policies on urban CE 1 and CE 2 by examining the effect of the interaction term Treated × Post it on the dependent variable. The coefficient β 1 measures the impact of the policy on the dependent variable. Controls in this study represent control variables, specifically urbanization rate ( lnur ), foreign direct investment level ( lnfdi ), industrial structure ( lnis ), level of scientific and technological expenditure ( lnsst ), and fiscal revenue and expenditure level ( lnfre ), among others. $\nu$ , $\tau$ and $\varepsilon$ represent city fixed effects, time fixed effects, and random error terms, respectively.

Considering the three-year implementation period of green fiscal policies, it is necessary to establish an exit mechanism for the treatment group. Drawing on existing literature 12 , this paper constructs the following treatment groups: the first batch of pilot cities from 2011 to 2014 is set to 1; the second batch of pilot cities from 2013 to 2016 is set to 1; the third batch of pilot cities from 2014 to 2017 is set to 1, with other years set to 0. The pilot cities are shown in Fig. 3 .

ECER policy implementation period.

Variables and data sources

Explained variables.

Carbon Emissions: Drawing from existing literature, this article utilizes current CE 1 data to calculate CE 1 52 , 53 . It follows the guidelines on greenhouse gas emission allocations by the IPCC , taking into account the emissions of carbon dioxide within the administrative boundaries of each city. Territorial emissions refer to emissions occurring within the managed territory and maritime areas under the jurisdiction of a region 54 , including emissions from socio-economic sectors and direct residential activities within regional boundaries 55 .

Carbon Efficiency: Following existing literature, this paper measures CE 2 using the ratio of CE 1 to GDP 56 .

In examining the correlation between CE 1 and economic efficiency, Fig. 4 a provides an overview of the evolution of CE 1 from 2003 to 2019, while Fig. 4 b offers a detailed portrayal of the progress in CE 2 over the same period. Figure 4 a reveals a steady increase in total CE 1 beginning in 2002, with a notable acceleration post-2009, peaking in 2017. Despite some fluctuations and a slight dip in 2018, the figures for 2019 remained just below the peak, overall indicating an upward trajectory. In contrast, Fig. 4 b demonstrates a year-on-year improvement in CE 2 , measured in tens of thousands of yuan output per ton of carbon emitted, starting in 2003. The pace of growth accelerated significantly after 2011, reaching its zenith in 2019. This signifies a substantial rise in the economic output efficiency per unit of carbon emitted, revealing a reduction in carbon dependency within economic activities. The combined analysis of both figures indicates that, alongside economic growth, there has been a notable advancement in optimizing CE 2 .

Trends in CE 1 ( a ) and CE 2 ( b ) (2003–2019).

Control variables

To eliminate the interference of omitted variables on the research results, this article selects the following control variables 57 , 58 : Urbanization rate ( lnur ), which refers to the ratio of urban population to total population; Level of foreign direct investment ( lnfdi ), the ratio of actual foreign investment to the GDP ; Industrial structure ( lnis ), the proportion of the secondary industry in GDP ; Level of science and technology expenditure ( lnsst ), the ratio of science and technology expenditure in ten thousand to GDP in hundred billion; Fiscal revenue and expenditure level ( lnfre ), the sum of local fiscal budget revenue and expenditure to GDP . To reduce heteroscedasticity in the data, this article takes the logarithm of all control variables. Table 1 reports the definitions of the main variables in this paper.

Sample selection and data source

We selects cities at the prefecture level in China from 2003 to 2019 as the research sample. Considering that missing data can affect the results, this paper excludes samples with missing data, ultimately obtaining 3134 samples. The CE 1 data in this paper comes from the China Emissions Accounts and Datasets (CEADs), which provides CE 1 data from 1997 to 2019, so the sample period for this paper ends in 2019. The control variable data are all sourced from the China City Statistical Yearbook covering the years 2004 to 2020. Table 2 provides descriptive statistics for the main variables in this paper.

Eliminating interference

In a quasi-natural experiment, various factors may influence the relationship between the implementation of green fiscal policies and the reduction of carbon emissions. To address this, we employed multiple methods to control for these potential confounding variables. Firstly, we introduced control variables to eliminate or reduce the interference of external factors on the main research relationship, ensuring the accurate estimation of the effects of green fiscal policies. Secondly, we adopted a two-way fixed effects model to control for time-invariant city characteristics and potential common time trends. Thirdly, we conducted parallel trend tests to verify whether the trends of the treatment and control groups were consistent before the policy implementation, ensuring the validity of the Difference-in-Differences (DID) estimates. Additionally, we performed multiple robustness checks, including propensity score matching and excluding the effects of other concurrent policies, to test the robustness of the results. Finally, we confirmed the reliability of the results through placebo tests. These methods collectively help to effectively reduce the interference of external variables, ensuring the accuracy and reliability of the research findings.

Empirical results

Benchmark regression analysis.

We employs a two-way fixed effects model for the empirical analysis of the CE 1 reduction effects of ECER policies, with the estimation results presented in Table 3 . Columns (1) to (3) of Table 3 report the estimation results of green fiscal policies on CE 1 . The results show that, when the model does not include control variables, the implementation of green fiscal policies has an estimated coefficient of − 0.070 for CE 1 , significant at the 1% level, indicating that the CE 1 of pilot cities are 7.0% lower than those of non-pilot cities. After adding control variables, the results do not change significantly. Columns (4) to (6) report the estimation results of green fiscal policies on CE 2 . The results indicate that, when the model does not include control variables, the implementation of green fiscal policies has an estimated coefficient of 0.099 for CE 2 , significant at the 1% level, suggesting that the CE 2 of pilot cities is 9.9% higher than that of non-pilot cities. After including control variables, the results remain largely unchanged. This provides evidence for Hypothesis 1: ECER policies have a significant CE 1 reduction effect and also significantly promote CE 2 .

To further illustrate the step-by-step changes in the coefficients, this paper presents Fig. 5 . The horizontal axis of Fig. 5 represents the number of control variables, while the vertical axis indicates the coefficients, with the grey area denoting the error bars. As evident from Fig. 5 , the coefficients and error bars exhibit minimal variation with the increase in control variables, indicating a negligible impact of the number of control variables on the coefficients and highlighting their stability. This finding suggests that the primary regression coefficients remain consistent even when more control variables are included in the analysis, underscoring the model’s robustness.

Plot of coefficient variation based on the step by step method.

Parallel trend test

The prerequisite for using DID model to evaluate policies is the parallel trends assumption. This implies that, before the policy intervention, the treatment group and the control group should exhibit similar trends without systematic differences. After the policy intervention, the trends between these two groups should diverge significantly. Following existing literature 50 , 59 , 60 , this paper employs an event study approach to analyze the effects before and after the policy implementation.

In Eq. ( 2 ), the variable Treated still represents cities that have been approved to establish pilot ECER policies. To avoid perfect multicollinearity, this paper uses the year before policy implementation as the baseline group, meaning that k = − 1 is not included in the regression equation, and the other parts of the model are consistent with the baseline model. If the coefficient is not significant when k < 0 , it indicates that the estimated results satisfy the parallel trends assumption. Figure 6 shows that, before the implementation of the policy, all coefficients are not significant, and in the fifth year after policy implementation, the coefficients start to become significant. This indicates that the implementation of ECER policies has a significant promotional effect on CE 1 reduction and CE 2 in the pilot areas, but this effect has some lag.

Parallel trend test of CE 1 ( a ) and CE 2 ( b ).

Robustness test

Exclusion of contemporaneous policies.

The smart city construction policy began with the “Notice on Carrying out the National Smart City Pilot Work” issued by the Ministry of Housing and Urban–Rural Development in 2012, with smart city pilots being established in 2012, 2013, and 2014 61 . This paper excludes all smart pilot cities and re-runs the regression, with results shown in columns (1) and (2) of Table 4 . The results indicate that contemporaneous policies during the sample period caused some interference with the estimated coefficients, but the extent is very limited. The implementation of ECER policies still has statistically and economically significant effects on promoting CE 1 reduction and CE 2 in pilot cities.

We employs the Propensity Score Matching (PSM) method to process the data, aiming to reduce data bias and the impact of confounding factors 62 , 63 . Through PSM-DID analysis, the results show that after matching, the absolute bias (|bias|) of all variables decreases by more than 70%, and the p -values are not statistically significant. This comparative analysis reveals the effectiveness of PSM in reducing the initial bias between the treatment and control groups. Therefore, the matching process successfully achieves balance in characteristics between the two groups across key indicators, making the assessment of the treatment effect more accurate and reliable.

Table 4 reports the results of the PSM. The propensity score matching results show a substantial decrease in |bias| for variables, highlighting an enhanced balance between treated and control groups post-matching. For instance, the absolute bias for “lnur” dropped from 86.0% to just 3.3%, showcasing a 96.2% reduction in bias, which underscores the effectiveness of the matching process. Similarly, other variables like “lnfdi”, “lnis”, and “lnsst” experienced significant reductions in bias. The p >|t| values, mostly above 0.05 post-matching, indicate that the differences between groups are not statistically significant, affirming the success of the matching in minimizing discrepancies and improving comparability.

Figure 7 displays the matching results of PSM. The results indicate that after the matching process, the percentage bias (%bias) for the control variables all remain below 10%. This finding fully confirms the effectiveness of the PSM method in balancing key characteristics between the experimental and control groups, thereby ensuring the accuracy and reliability of subsequent analyses.

Balance test.

This paper conducts an empirical analysis using matched data, with the results shown in columns (3) and (4) of Table 5 . The results indicate that ECER policy still has a significant CE 1 reduction effect and also significantly promotes CE 2 . This suggests that there is no significant impact of self-selection bias on the regression results in this study.

To reduce the impact of outliers on regression analysis, this paper adopts a winsorization process 39 , 64 , which involves replacing observations below a certain threshold with the 1st percentile and those above the threshold with the 99th percentile before conducting the regression. Columns (5) and (6) of Table 5 display the analysis results after this treatment, showing that the impact of outliers on the regression results is not significant.

Replacement sample time

Considering the potential unique impact of the COVID-19 pandemic on CE 1 and CE 2 in 2019, this paper decided to exclude data from 2019 to ensure the robustness of the research results, thus avoiding the interference of pandemic-related outliers in the analysis. Subsequently, the paper conducted an empirical analysis based on the updated dataset, with the analysis results presented in columns (7) and (8) of Table 5 . The analysis results indicate that after excluding the special impact of the COVID-19 pandemic, the CE 1 reduction effect of the green fiscal policy remains significant, and there is still a significant promotional effect on CE 2 .

Placebo test

The DID model is based on the common trends assumption, which posits that, in the absence of an intervention, the trends of the treatment and control groups would have been similar 65 . By conducting a placebo test on data from before the intervention, this assumption can be tested for validity. If significant ‘intervention effects’ are also found during the placebo test conducted before the intervention or at irrelevant time points, this indicates that the effects estimated by DID are actually caused by other unobserved factors, rather than the intervention itself 66 . Referencing the placebo practices in existing literature 59 , this paper tests for the impact of unobservable factors on the estimation results. The study randomizes the impact of ECER policies across cities, selecting treatment groups randomly from 248 cities, with the remaining cities serving as control groups. This randomization process is repeated 500 times to generate a distribution graph of the regression coefficients, where the dashed line in the graph represents the actual regression coefficient, as specifically shown in Fig. 8 . Figure 8 a represents the placebo test for CE 1 , and Fig. 8 b for CE 2 . From Fig. 8 , it is evident that after randomizing the core explanatory variables, the mean of the coefficients is close to 0, and the mean of the coefficients after randomization significantly deviates from their true values. This indicates that, excluding the interference of other random factors on the empirical results, the green fiscal policy has a significant effect on CE 1 reduction and significantly promotes CE 2 .

Placebo test of CE 1 ( a ) and CE 2 ( b ).

Mechanism test

The analysis results presented earlier indicate that the ECER policy has significantly promoted CE 1 reduction and the improvement of CE 2 in pilot cities. Accordingly, this study will further explore the mechanism of action of ECER policy and has constructed the following model:

GI refers to green innovation. Following existing literature, this study uses the number of green invention patent grants ( lngi_invention ) and the total number of green patents per 10,000 people ( lnpgi_total ) as proxy variables for green innovation 67 , 68 . Due to the evident causal inference flaws in the three-stage mediation mechanism test 69 , this study refers to the mediation effect test model by Niu et al. 70 and employs the Sobel test to further evaluate the regression results, thereby enhancing the completeness and credibility of the mechanism test 71 . The regression results are shown in Table 6 . Columns (1) and (4) report the impact of the ECER policy on green innovation, with significant results. This confirms hypothesis H2: green fiscal policies can promote CE 1 reduction effects and CE 2 by fostering green innovation. Moreover, the Sobel Z coefficients are greater than 2.58, indicating that the mediating variable has a sufficiently strong explanatory power for the total effect.

Heterogeneity analysis

By city grade.

In the process of urbanization and industrialization, a city’s level often reflects its level of economic development, capacity for technological innovation, infrastructure completeness, and the comprehensiveness of its public services. This paper categorizes the sample cities based on their tier into higher-level cities (provincial capitals, sub-provincial cities, and municipalities directly under the Central Government) and general cities, and conducts regression analysis. The regression results shown in Table 7 , specifically in columns (1), (2), (6), and (7), indicate that in higher-tier cities, the coefficients of the ECER policy on CE 1 and CE 2 for pilot cities are -0.098 and 0.118, respectively, significant at the 1% level. However, in general cities, the absolute values of the coefficients are smaller and not significant. From this, we can conclude that the ECER policy’s effect on CE 1 reduction and the enhancement of CE 2 is more significant in higher-tier cities compared to general cities. Higher-level cities, with their advanced economic structures, abundant fiscal resources, high levels of technological innovation, and strong policy enforcement capabilities, make the green fiscal policy more effective in these areas in terms of CE 1 reduction and the promotion of CE 2 . Firstly, economically developed higher-tier cities have more sufficient fiscal funds and investment capacity, which can support large-scale green infrastructure construction and green technology R&D, thereby directly reducing urban CE 1 and improving energy use efficiency. Secondly, technological innovation is a key factor in improving CE 2 . As centers of technological innovation and information exchange, higher-level cities are more likely to attract and gather high-tech companies and research institutions, promoting the development and application of green technologies, and effectively reducing CE 1 . Additionally, higher-tier cities usually have more comprehensive laws, regulations, and policy enforcement mechanisms, ensuring the effective implementation and regulation of green fiscal policies. Also, residents in these cities often have higher environmental awareness and a preference for green consumption, which helps to create a favorable social atmosphere for the implementation of green fiscal policies. Finally, due to their strong regional influence and exemplary role, higher-tier cities can promote green transformation and low-carbon development in surrounding areas and even the entire country through policy guidance and market incentives, further amplifying the CE 1 reduction effect and enhancing the impact on CE 2 of green fiscal policies.

By geographic location

Given the significant differences in economic development levels, resource endowments, and institutional environments across regions in China, the implementation effects of the ECER policy may exhibit heterogeneity. Therefore, this paper divides the sample into eastern, central, and western regions for analysis and conducts regressions separately. The regression results are presented in Table 7 . Columns (3) to (5) and (8) to (9) of Table 7 show the regression results for CE1s and CE 2 , respectively, with columns (3) and (8) representing the results for the eastern region. The analysis indicates that, in the eastern region, the ECER policy significantly promotes carbon reduction and CE 2 . Although the policy’s effects in the central region are less than those in the eastern region, they still exhibit a positive impact. In contrast, in the western region, the ECER policy’s promotional effects on carbon reduction and CE 2 are not significant.

This analysis reveals that, within the regional development pattern of China, the eastern regions exhibit more significant outcomes in terms of the CE 1 reduction effect and the enhancement of CE 2 under green fiscal policies compared to the central and western regions. Firstly, as the most economically developed area in China, the eastern region, with its leading total economic output, industrialization, and urbanization levels, provides a solid fiscal support and technological foundation for the implementation of green fiscal policies. This economic advantage enables the eastern region to allocate more resources to the research, development, and application of green technologies, as well as related infrastructure construction, thereby effectively promoting CE 1 reduction and energy efficiency improvement. Secondly, environmental policies and regulations in the eastern region are generally stricter and more advanced. Coupled with a higher public awareness of environmental protection, this creates a favorable social environment and policy atmosphere for the implementation of green fiscal policies and carbon reduction. Additionally, the industrial structure in the eastern region is more optimized and high-end compared to the central and western regions, with a larger proportion of the service industry and high-tech industries, which typically have lower energy consumption intensity and CE 1 , facilitating the improvement of overall CE 2 . Furthermore, as an important gateway for international trade and investment, the eastern region is more open to adopting and introducing advanced green technologies and management practices from abroad, accelerating the pace of green transformation. Lastly, the dense urban network and well-developed transportation and logistics systems in the eastern region provide convenient conditions for the effective implementation of green fiscal policies. Therefore, due to comprehensive advantages in economic development level, industrial structure, policy environment, technological innovation capability, and infrastructure, the eastern region demonstrates more significant performance in the CE 1 reduction effect and the promotion of CE 2 under green fiscal policies.

Figure 9 reports the main regression coefficients and error bars from the heterogeneity analysis, clearly illustrating the distribution of coefficients.

Results of heterogeneity analysis.

Classification by resource-based city

Resource-based cities center on industries involved in the extraction and processing of local natural resources, including minerals and forests 72 , 73 , 74 . Due to their unique urban characteristics, these cities may have a specific impact on the efficacy of ECEP policy. Consequently, this paper follows the guidelines set forth by the State Council in the “National Plan for Sustainable Development of Resource-based Cities (2013–2020),” dividing the sample into resource-based and non-resource-based cities for separate regression analyses, the results of which are presented in Table 8 . Columns (1) and (2) detail the regression outcomes for CE 1 , while columns (3) and (4) address CE 2 . The findings reveal that, compared to resource-based cities, the effect of ECEP policies on carbon reduction is more pronounced in non-resource-based cities, with a similarly more substantial impact on the promotion of CE 2 .

Upon conducting a thorough analysis of the disparities in how non-resource-based cities and resource-based cities respond to ECER policies, a significant finding emerges: non-resource-based cities, due to their diversified industrial structures and lower reliance on highly polluting and energy-intensive heavy industries and mineral resource extraction, demonstrate a stronger capacity to adopt and promote new energy, clean energy, and energy-efficient technologies. This characteristic of their industrial structure not only facilitates effective carbon reduction efforts but also propels a shift in economic growth models towards services, high-tech industries, and innovation-driven sectors, which are associated with lower energy consumption and carbon intensities. Therefore, the potential for ECER policies to enhance CE 2 and reduce CE 1 is greater in these cities. In contrast, resource-based cities, due to their long-standing dependence on resource extraction, exhibit significant inertia in their economic structure, technological levels, and employment opportunities. This inertia not only complicates their transition and industrial restructuring but also increases the associated costs. Against this backdrop, non-resource-based cities are more likely to achieve notable successes in implementing ECER policies compared to their resource-based counterparts.

Conclusions and policy recommendations

Conclusions.

Based on the city-level dataset from 2003 to 2019, this paper employs a multi-time point difference-in-differences model to thoroughly explore the impact of the ECER policy on CE 1 reduction and CE 2 , reaching the following conclusions:

The ECER policy is confirmed to play a significant role in promoting the reduction of CE 1 and enhancing CE 2 . This conclusion remains robust even after controlling for factors that might affect the accuracy of the assessment, such as contemporaneous policy interferences, sample selection biases, extreme value treatments, and other random factors. This indicates that the ECER policy has important practical implications in mitigating climate change impacts, and its effects are not significantly influenced by the aforementioned potential interferences. The ECER policy effectively promotes CE 1 reduction and CE 2 improvements by incentivizing the research and application of green technologies. This finding underscores the mediating role of green innovation in environmental policies, highlighting that fiscal incentives such as tax breaks and subsidies are crucial for promoting technological innovation and application, and further achieving environmental benefits. The CE 1 reduction effect and CE 2 enhancement of the ECER policy are more pronounced in economically developed, higher-tier cities and in the eastern regions. This may be due to these areas having better infrastructure, higher technological innovation capabilities, more abundant fiscal resources, and stronger public environmental awareness, which all provide strong support for the effective implementation of the ECER policy. Moreover, this variation also suggests that policymakers need to consider regional characteristics when implementing relevant policies to maximize policy effectiveness.

Existing literature has explored the role of energy conservation and emission reduction fiscal policies in environmental protection, such as green credit 37 , ESG performance 75 , green total factor carbon efficiency 36 , and sustainable urban development 38 . These studies report the positive impact of such policies on the environment. However, they do not directly examine the impact of these policies on pollutants. Our study extends the existing literature by investigating the relationship between these policies and carbon emissions. Green fiscal policies significantly promote the reduction of carbon emissions (CE1) and the improvement of carbon efficiency (CE2) through economic incentives, price mechanisms, infrastructure support, and increasing public environmental awareness. Specifically, these policies encourage the research and application of green technologies, change consumer and producer behavior, optimize energy consumption structures, support related infrastructure construction, and increase public participation in low-carbon living. Additionally, green fiscal policies promote sustainable economic growth by directing funds towards low-carbon and green industries, fostering the development of green technologies and industries. Overall, green fiscal policies have not only achieved significant environmental protection results but also played a crucial role in realizing the dual goals of economic growth and environmental protection.

Despite the significant findings, our study has some limitations. Firstly, the data is limited to 248 cities from 2003 to 2019, which may not fully capture the long-term impact of ECER policies. Secondly, reliance on existing data may introduce biases, as not all relevant factors could be considered. Future research could address these limitations by expanding the dataset, including more diverse regions, and employing alternative methods to validate these findings.

Policy recommendations

Based on the above analysis, the policy recommendations of this paper are as follows:

Continue to increase fiscal support. The government should continue to enhance fiscal support for the ECER policy, including expanding the scope of tax reductions and increasing the level of fiscal subsidies, especially for those projects and technologies that can significantly improve energy efficiency and reduce CE 1 . This will further stimulate the innovation motivation of enterprises and research institutions, accelerating the research and development (R&D) and application of low-carbon technologies.

Optimize policy design and implementation mechanisms. Considering the robustness of the ECER policy effects, the government should further refine the policy design to ensure that measures precisely target sectors and aspects with high CE 1 . Concurrently, it is crucial to establish and enhance the supervision mechanism for policy execution, ensuring effective implementation of policy measures. This approach also necessitates timely adjustments and optimizations of the policy to tackle new challenges effectively.

Establish a dedicated Green Technology Innovation Fund. This fund aims to provide financial support specifically for R&D and promotion of green technologies with high CE 2 . By offering startup capital, R&D subsidies, and rewards for the successful commercialization of green technologies, the fund can not only stimulate the innovation drive of enterprises and research institutions but also accelerate the transformation of green technologies from theory to practice. Consequently, this will promote CE 1 reduction and CE 2 enhancement on a broader scale. This initiative directly responds to the importance of fiscal incentive measures for promoting technological innovation and application emphasized in the research, ensuring the ECER policy maximizes its benefits in promoting green development.

Differentiated policy design. Given the variations in the effects of the ECER policy across different regions, policymakers should design and implement differentiated energy-saving and emission reduction policies based on regional factors such as economic development level, industrial structure, and resource endowment. For economically more developed areas with a stronger technological foundation, CE 1 reduction can be promoted by introducing higher standards for environmental protection and mechanisms for rewarding technological innovation. For regions that are relatively less economically developed, the focus should be on providing technical support and financial assistance to enhance their capacity for CE 1 reduction.

Green fiscal policies play a crucial role in reducing carbon emissions and promoting sustainable economic growth, but their impact on social and income inequality needs careful consideration. Firstly, while policies like carbon taxes are effective in reducing emissions, they may place a significant burden on low-income households, as a larger proportion of their income goes towards energy and basic necessities. To mitigate this inequality, governments can implement redistributive measures, such as using carbon tax revenues for direct subsidies or tax reductions for low-income families, ensuring social equity while achieving emission reductions. Secondly, green fiscal policies encourage investment in green technologies and the implementation of green projects. However, these incentives often favor businesses and wealthy families capable of making such investments, potentially widening income disparities. Therefore, policy design should consider inclusive growth by providing green job training and encouraging small and medium-sized enterprises to participate in green projects, ensuring that various social strata benefit from the green economy. Furthermore, in terms of public investment, governments should prioritize low-income and marginalized communities, ensuring they also benefit from the construction of green infrastructure. This includes prioritizing the development of public transportation and renewable energy projects in these areas, thereby reducing living costs and improving the quality of life for these communities. By adopting these redistributive measures and inclusive policy designs, green fiscal policies can achieve the goals of environmental protection and economic growth while effectively mitigating their negative impacts on social and income inequality, promoting sustainable and inclusive development.

When evaluating various policy tools for achieving carbon reduction goals, it is evident that carbon taxes, renewable energy subsidies, ECER policies, emissions trading systems, and energy efficiency standards each have their unique advantages (see Table 9 ). Carbon taxes leverage price mechanisms to encourage emissions reduction and provide redistribution opportunities, while renewable energy subsidies promote technological advancement and market development. ECER policies offer direct incentives and support for infrastructure, resulting in long-term environmental benefits. Emissions trading systems combine cap-and-trade controls with market flexibility, and energy efficiency standards provide direct pathways to emissions reduction. In practical applications, the integrated use of multiple policy tools, fully utilizing their respective advantages, can more effectively achieve carbon reduction goals and drive the transition to a low-carbon economy. Policymakers must consider equity, economic impact, and public acceptance when designing these policies to balance environmental protection with economic growth. Through careful integration and balanced implementation, green fiscal policies can significantly reduce carbon emissions while promoting sustainable and inclusive economic development.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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This study is supported by the National Social Science Fund Major Project: “Research on the Policy System and Implementation Path to Accelerate the Formation of New Productive Forces,” Project Number: 23&ZD069.

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Over the past several decades, lab experiments have offered economists a rich source of evidence on incentivized behavior. In this article, we use detailed data on experimental papers to describe recent trends in the literature. We also discuss various experimentation platforms and new approaches to the design and analysis of the data they generate.
Experimental Economics
Economic Games for Social Scientists. Catherine Eckel, in Laboratory Experiments in the Social Sciences (Second Edition), 2014. I Introduction. Experimental economics is a relatively new field, with the first academic papers published in the 1960s. Only in the past few years has experimental economics become so widely accepted that virtually every economics department believes that it should ...
Economic Classroom Experiments
This page refers to information on economic classroom experiments and related material. Numbers insist that an economic model of giving, can produce an economy of abundance in that, if all were to give consistently, a substantial amount, into, let's picture it as a river of abundance. What you do not need for right now goes to the river.
5 Examples of Behavioral Economics in Your Everyday Life
Read the first post in this series, "Q&A: Behavioral Economics 101", to hear from Dr. Elizabeth Schwab on an overview of behavioral economics. Read the third post in this series, "Must-see media list for behavioral economics" to discover a list of resources to help you learn about the field outside of the classroom. Our days are a whirlwind of activities—rushing from work, to the gym ...
PDF Natural experiments help answer important questions
2(8) THE PRIZE IN ECONOMIC SCIENCES 2021 THE ROYAL SWEDISH ACADEM OF SCIENCES WWW.KVA.SE One example of a natural experiment Let us use a concrete example to illustrate how a natural experiment works. One question that is relevant to both society and to young people considering their future, is how much more you would
The Definition of Natural Experiment in Economics
A natural experiment is an empirical or observational study in which the control and experimental variables of interest are not artificially manipulated by researchers but instead are allowed to be influenced by nature or factors outside of the researchers' control. Unlike traditional randomized experiments, natural experiments are not ...
Examples
Subject: Economics: Economics. Penalty Kicks—A Simultaneous Move Zero-Sum Game is an experiment that illustrates the importance of playing randomized strategies. This experiment is appropriate for undergraduate students who have completed a basic game theory module and can be completed in 15-20 minutes.
Nobel-winning 'natural experiments' approach made economics more robust
The 'natural experiments' approach to economics that won three researchers the 2021 Sveriges Riksbank Prize in Economic Sciences has helped to make the field more robust, say economists.
Three economists win Nobel for their use of real-life experiments
Three economists win Nobel for their research on how real life events impact society. Displayed is a file photo of a Nobel Prize medal on Dec. 8, 2020. The Nobel Prize in economic sciences was ...
Behavioral Experiments in Health Economics
More recently, other types of experiments have been conducted in experimental economics, beyond lab, artefactual field, frame field, and natural field experiments. For example, virtual experiments combine controlled experiments with virtual reality settings (Fiore, Harrison, Hughes, & Rutström, 2009). While not yet applied to health economics ...
Classroom Experiments & Games
Simulations, Games and Role-Play is an older Handbook chapter, discussing why, when and how to use games or simulations in teaching economics, with examples. Classroom Experiments, Games and Role-Play a series of experiments and games from our Reflections on Teaching section. IREE Volume 9 Issue 2 was a special issue on economic classroom ...
The carbon emission reduction effect of green fiscal policy: a quasi
Theoretical analysis Carbon emission reduction effect of green fiscal policy. Green fiscal policy, as a significant environmental governance tool, promotes the transformation of the economic and ...

Famous Economics Experiment Reproduced Thousands of Times

Experimental Economics

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Famous economics experiment reproduced thousands of times

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