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Chapter 2
Chapter 2
Economic Methods and Economic Questions
Questions
1.
What does it mean to say that economists use the
scientific method? How do economists distinguish between models that work and
those that don’t?
Answer: The
scientific method is the name for the ongoing process that economists and other
scientists use to develop models of the world, test those models with data and
evaluate how well they predict or describe behavior. While this process may not
reveal the ‘true’ model of the world, it does help in identifying models that
are useful in understanding the world.
In order to decide whether models make accurate
predictions or not, economists test them against real-world data. Data are
facts, measurements, or statistics that describe the world. This process of
testing models against data is called empiricism.
2. What
is meant by empiricism? How do empiricists use hypotheses?
Answer:
Empirical evidence is a set of facts established by observation and
measurement, which are used to evaluate a model. Empiricism refers to the
practice of using data to test economic models. When conducting empirical
analysis, economists refer to a model’s predictions as hypotheses. Hypotheses
are predictions (typically generated by a model) that can be tested with data.
3. What
are two important properties of economic models? Models are often simplified
descriptions of a real-world phenomenon.
Does this mean that they are unrealistic?
Answer: A
good economic model has two important properties. First, it is an
approximation. The model predicts what would happen on average. Second, it
makes predictions that can be tested with data.
A model is a simplified description, or representation,
of reality. Because models are simplified, they are not perfect replicas of
reality. However, this does not mean that they are unrealistic. Models are
usually simplified in order to be able to isolate the relationship between two
variables. Even if a model is based on simplified assumptions, it may still
help us make good predictions and plan for the future.
4. How
is the mean calculated from a series of observations? Suppose 5,000 people
bought popsicles on a hot summer day. If the mean of the average number of
popsicles that each person bought is 2, how many popsicles were sold that day?
Answer: The
mean is the average value of a set of observations. It is calculated as the sum
of all the different items divided by the number of items.
The average value is the sum of all popsicles sold
divided by the number of people who bought them. If each of the 5,000 people
bought an average of 2 popsicles, that means that 10,000 popsicles were sold
that day.
5. How
does the sample size affect the validity of an empirical argument? When is it
acceptable to use only one example to disprove a statement?
Answer: The
size of the sample used to test the argument can affect the results. A small
sample may bias the results of a study. A key strength of economic analysis is
the amount of data used. Using a large number of observations strengthens the
force of an empirical argument. For example, if you collect information on
consumption from 20,000 people as opposed to 20 people, you are likely to get a
more representative result. A single example can be used to contradict a
statement. For example, a single black swan can disprove the statement that all
swans are white.
6. Explain
why correlation does not always imply causation. Does causation always imply positive correlation? Explain your
answer.
Answer:
Correlation means that there is a relationship between two variables; as one
variable changes, another variable changes. Causation occurs when one variable
directly affects another through a cause-and-effect relationship. Correlation
suggests that there is some kind of connection, but not necessarily a cause and
an effect. For example, the number of storks in a region might be correlated
with the number of babies born in the region. But this doesn’t mean that storks
bring babies.
Positive correlation implies that two variables tend to
move in the same direction. However, causation need not only imply positive
correlation. For example, an increase in the price of bacon may cause people to
buy smaller amounts of bacon. In this example, the price of bacon and the
quantity of bacon purchased will show a negative correlation.
7.
Give an example of a pair of variables that have
a positive correlation, a pair of variables that have a negative correlation,
and a pair of variables that have zero correlation.
Answer: A
person’s IQ and his or her telephone number are likely to show zero
correlation. The number of winter coats sold and the temperature outside are
likely to show a negative correlation. The quantity of fertilizers used and
crop yield (e.g., the number of bushels of wheat grown per acre) are likely to
have a positive correlation.
8. What
is meant by randomization? How does randomization affect the results of an
experiment?
Answer:
Randomization is the assignment of subjects by chance, rather than by choice,
to a test group or control group. Assigning participants randomly will ensure
that the result of the experiment is not biased. For example, suppose students
with poor scores are assigned to one type of teaching method and students with
good scores are assigned to another type of teaching method. It will be
difficult to decide which teaching method is more effective as the students
with the higher scores are likely to do better than the students with the
poorer scores, irrespective of the teaching method used.
9.
This chapter discussed natural and randomized
experiments. How does a natural experiment differ from a randomized one? Which
one is likely to yield more accurate results?
Answer: A
natural experiment is an empirical study in which some process – out of the
control of the experimenter – has assigned subjects to control and test groups
in a random or nearly random way. The process of randomization involves the
assignment of subjects by chance, rather than by choice, to a test group or
control group. The test group and the control group are treated identically,
except along a single dimension that is intentionally varied across the two
groups. The impact of this variation is the focus of the experiment. Both types
of experiments can yield accurate results. Natural experiments are likely to be
used when there are budget or time constraints to conducting a randomized
experiment.
10.
Suppose you had to find the effect of seatbelt
rules on road accident fatalities. Would you choose to run a randomized
experiment or would it make sense to use natural experiments here? Explain.
Answer: It
would be difficult (and, in many people’s view, unethical) to conduct a
randomized experiment. Instead, the study should use a natural experiment. You
can study data on the causes of road accident fatalities in cities where
seatbelt rules were not enforced, or in cities that have recently adopted new,
more stringent seat belt laws. Controlling for other factors like an increase
in the number of cars, etc., you can then look at similar data when seatbelt
rules have been implemented.
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