The rates for those who do buy insurance are going up because more people are driving without insurance.
Landsburg claimed that it was what happened in Philadelphia.
In Ithaca, the situation went the other way--many initially bought insurance, which meant that insurance costs for everyone were lower, which led to others buying insurance, which led to even lower rates.
Both equilibria were self-reinforcing, and it was very difficult to change without a government intervention.
In the supply/demand model, we see a model based on reasoning that people are rational and self interested.
Because it is not a supply/demand model with a single equilibrium, it leads to two possible equilibria.
It leads to a policy solution that requires all individuals to have insurance.
Landsburg's approach is very similar to Frank's.
He observes the events around him and tries to understand them using economic building blocks.
Frank is more willing to go beyond the traditional building blocks than Landsburg is.
This allows for a wide range of models and explanations, as well as a wide range of policy interven tions that follow from the model.
He presented two models in his book.
The first model that we will consider is designed to explain why people are more likely to return cash to a store when given too much change than to return merchandise for which they were not charged.
He reported the results of a survey in which 90 percent of the respondents said they would return $20 to the store if the cashier didn't charge for it, but only 10 percent said they would return a $20 lamp.
People should not return if they only took their own interests into account.
Most people don't want her to be punished.
It would be the store that would suffer the loss, not the individual, and people were less worried about hurting stores than they were about hurting people.
Frank's assumption is different from Landsburg's.
People were somewhat self-interested in Frank's model, but not completely.
The prediction would be that no one would return the money.
Frank's model allowed for the possibility that people cared about the impact of their actions on others.
Frank found a second model in his book that dealt with why people continue to wear shoes with shoelaces even though they are more practical.
Frank believes that the reason shoelaces are still used is because the young and old are associated with the same thing.
The ratio nality assumption was deviated from the traditional building blocks.
It doesn't make sense to use a technology that was less efficient than another.
Frank's model assumed that people care about what other people think about them and thus take social issues into account when making their decisions.
Social dimensions of problems are not taken into account by models.
Two models are recounted in the text.
Stores post signs saying Frank's book.
Men's fashions are put on the ground in most U.S. department stores.
Among the li 4, Frank followed can be found.
Behavioral economists have discovered other dimensions of my behavior.
If you study a model and use its assumptions as your own, you can influence your behavior by looking at the world in a different way.
It is possible that studying economics may change you.
The principles course is meant to do more than entertain; it is meant to teach, and only when they are writing for lay people.
The validity of the models is impossible to test because they are not sufficiently precise.
When we presented the models, we asked you how convinced you were.
Each was easy to modify and come up with a different conclusion.
The argument would be reversed.
It is a good question to ask if we really know anything more about the world after learning about the models that are embodied in the vignettes.
In a scientific sense, the answer is no, we don't.
Science is not based on models.
Scientists are hesitant to base their knowledge on anecdotes or models that are highly convincing.
Humans have a tendency to have a sense of understanding, but not necessarily a scientific understanding.
Scientists say that to extend a model to true understanding, you have to quantify and test your arguments.
The second important element of modern economics is highly empirical.
Modern economics is based on experiments that can be replicated.
The importance of empirical work has been in economics since the 1600s, but economics focused on reasoning until the 1940s.
That happened because of the lack of data and the lack of power to analyze it.
Because of limited data and computing power, empirical work in economics did not move to the forefront until the late 1980s.
Deduction was the economist's method for understanding the real world.
It is fair to say that the development of computing power has fundamentally changed the way economic research is done.
All modern economists rely on empirical work, even those who use traditional building blocks and those who use behavioral building blocks.
Today's empirical work in economics is not based on formal observations.
The economic scientist doesn't stop with the heuristic model, as did Frank's and Landsburg's presentations.
He or she builds an empirical model and supports the argument with empirical evidence.
Essentially, what he or she does is look at the relationships found in the heuristic model and see if they can be generalized.
You collect data and analyze it with statistical and econometric tools.
The researchers studied the relationship in their models.
Steve Levitt did a lot with his creativity and success.
A combination of points is shown here.
2.5 is the average grade.
He used informal models and hypotheses to structure his study.
He reasoned that if a wrestler was close to winning enough matches to raise his ranking, it wouldn't matter if he won a match or not.
He said that if wrestlers are self-interested and rational, they will have an incentive to cheat and throw a match in order to win a match.
He had a testable hypothesis.
He collected and analyzed the data.
Say you are wonder to another.
"Run a regression" means that you use a statistical package to find a line that "best fits" the data, where "best fit" means making the distances between that line and the points as small as possible.
If the "best fit" line is upward-sloping, then the regression model's answer to the question is a tentative yes, subject to all the things that were held constant.
Every point will be on the "best fit" line if it is a perfect fit.
This isn't a statistics class so I won't go into further explanation, but the short descrip tion should give you a sense of how empirical regression models work.
The workhorses of applied microeconomists are regression models, and modern economists are almost magicians at pulling information out of data.
Issues far from the standard domain of economics are explored by economists' empirical models.
He created a model to predict whether a particular year's wine would be a good vintage.
The quality of a wine depends on a number of factors, including weather and rainfall.
He ran a regression to get the data related to the price of wine.
The relationship tells us that the quality of a Bordeaux wine depends on the weather.
He argued that the regression model he used did a better job of determining a good year for wine than the tast ing method.
He argued that his model could determine the quality of the wine before it was even tasted.
When choosing a wine, forget about sniffing, swirling, and tasting; just get out your computer, collect the data, plug in the numbers, and solve the equation.
I don't like wine, but the people I talk to think he is.
Baseball teams use a regression model to determine how good a prospect is.
As hits, modeling is important.
They argued that a person's ability to draw a walk should be one of the variables considered in choosing a recruit.
Regression models can help a team win.
Oakland's success didn't go unrecognized when the Boston Red Sox were on the verge of a team's season record.
The modern micro economist's tool kit has become more important because of the increase in computing power and statistical software.
Economic researchers can find stable patterns in data much more easily now that computer power has increased.
Computers can find patterns and turn them into models with sophisticated econometric software.
The development of computer power and empirical models has led to an enormous change in how modern microeconomics is done.
As a principles student, you will not be developing regression models, but you will be building models based on data, which may change in the future since other social sciences are becoming more empirical as well, but for the next decade they will likely still lag behind economics.
The charts and graphs are useful even though they don't have the full scientific look of a regression model.
The modern economic way of thinking uses quantitative data to make an argument, often by presenting those data with a simple chart or graph.
Empirical models would replace all other types of modeling if economic modeling were only a matter of data mining.
Data has no meaning, they have to be interpreted and given meaning, and how one uses the data depends on the model and the building blocks one has in mind.
One's model can guide how one organizes the data.
That's why theory is important, and an important part of this principles course is meant to give you practice in understanding the theoretical structure of economic thinking.
You can see the importance of theory when you think about a magic eye picture.
The old woman shown here is a simpler example.
I think you might have seen the picture in a different light.
The implicit model or frame that you bring to the picture or the data affects which pattern your eye sees.
The difficulty of pulling information from an empirical model is highlighted by this issue.
Even with the same empirical model, two different economists may see different results.
Let's look at an example of a recent debate in economics.
The debate is about the deterrent effect of the death penalty.
One could do a controlled experiment to see if the death penalty has a deterrent effect by changing anonymous German postcards and isolating specific variables.
Experiments that were controlled in the late 19th century are not possible in economics.
An economist can't suggest that we try out the (c)Chronicle/Alamy Stock Photo death penalty to see what it would do.
The economists found a correlation between the number of murders and the death penalty.
Ehrlich found that an increase in the number of executions is associated with a decrease in the murder rate, while Shepherd found that one execution deterred seven to eight murders.
A number of economists disagree with the statistical relationships.
They pointed out that how the variables are interpreted is important.
Economists use the same data in multiple ways.
John and Justin came to different conclusions.
They said that the theoretical models would help them interpret the data.
I don't claim to know who is right, so I won't get into the debate.
I recount it to give you a sense that given the limited ability economists have to conduct controlled experiments, letting the data speak will not provide the definitive answer.
Modern economics will have implications from their work.