Chapter 3 Content

Question 1

What is the difference between correlation and causation?

Answer 1

Correlation: When two variables move together but do not necessarily cause each other.
Causation: When a change in one variable directly results in a change in another.

The Identification Problem questions the direction of influence between two variables.

Question 2

What is the Russian Peasant Fallacy?

Answer 2

A mistake where peasants killed doctors during a cholera epidemic, believing they caused the disease.

This illustrates misinterpreting causation.

Question 3

What is the SAT Training Fallacy?

Answer 3

Students who took SAT prep courses performed worse on average, possibly due to weaker students self-selecting into prep courses.

This highlights issues in interpreting data related to causation.

Question 4

What defines a randomized trial?

Answer 4

An experiment where individuals are randomly divided into treatment and control groups.

This is considered the gold standard for establishing causality.

Question 5

Why do randomized trials work?

Answer 5

Random assignment removes bias and confounding variables, ensuring differences between groups are due to the intervention.

This allows for clearer causal inference.

Question 6

What was a significant finding from Estrogen Replacement Therapy (ERT) trials?

Answer 6

ERT trials found that hormone therapy increased heart disease risk, leading to reduced usage.

This is an example of how randomized trials can reveal important causal relationships.

Question 7

What is a limitation of randomized trials?

Answer 7

Results may not generalize beyond the study sample.

Attrition bias can also skew results if participants drop out.

Question 8

What is observational data?

Answer 8

Data collected from real-world behavior rather than controlled experiments.

This type of data can be biased, complicating causal inference.

Question 9

What is the challenge with observational data?

Answer 9

Observational data can contain bias, making it harder to determine causation.

This is due to uncontrolled external factors affecting outcomes.

Question 10

What methods can address bias in observational data?

Answer 10

• Time Series Analysis
• Cross-Sectional Regression
• Quasi-Experiments

These methods help clarify relationships and potential causal links.

Question 11

What does time series analysis track?

Answer 11

Changes in two variables over time.

It can help identify trends and correlations.

Question 12

What example illustrates time series analysis?

Answer 12

Cigarette prices and youth smoking rates showed a negative correlation (higher prices, lower smoking rates).

This example demonstrates how correlation can be misleading.

Question 13

What is cross-sectional regression analysis?

Answer 13

Examines relationships between variables at a single point in time.

It helps analyze data without considering changes over time.

Question 14

What finding was observed with TANF benefits and labor supply?

Answer 14

A $1 increase in TANF benefits reduced annual work hours by 0.2 hours.

This suggests a correlation that may not indicate causation.

Question 15

What is a quasi-experiment?

Answer 15

Natural changes in policies create treatment and control groups.

This method helps in understanding causal effects without randomization.

Question 16

What does the difference-in-difference estimator do?

Answer 16

Compares changes in two groups before and after a policy change.

This helps isolate the effect of the policy change.

Question 17

What was the net impact of benefit reduction on work hours in Arkansas compared to Louisiana?

Answer 17

Net impact of benefit reduction on work hours = 150-hour reduction.

Arkansas reduced TANF benefits while Louisiana did not, illustrating the policy impact.

Question 18

What is the biggest challenge in policy analysis?

Answer 18

Establishing causality.

This is crucial for effective policy-making.

Question 19

What alternative methods can be used when randomized trials are not possible?

Answer 19

• Time Series Analysis
• Cross-Sectional Regression
• Quasi-Experiments

These methods help analyze relationships and infer causality in different contexts.

Question 20

Fill in the blank: Randomized Trials are the ______ standard for causality.

Answer 20

[gold]

This highlights the importance of randomized trials in research.

Question 21

True or False: Correlation implies causation.

Answer 21

False

Correlation does not necessarily indicate that one variable causes the other.

Question 22

empirical public finance

Answer 22

The use of data and statistics methods to measure the impact of government policy on individuals and market

Question 23

correlation

Answer 23

to economic variables are correlated if they move together

Question 24

causal

Answer 24

two economic variables are causally related if the movement of one causes movement of the other

Question 25

empirical economics must distinguish between three explanations between A and B, what are the three explanations?

Answer 25

A causes B, B causes A, some third factor causes both, empirical economist must distinguish among three explanations, correlation alone does not imply causation

Question 26

first solution to solve the identification problem

Answer 26

Random trials solve the problem of identification

Question 27

why do randomized trials solve the problem to identification

Answer 27

randomized trials solve the problem to identification because they rule out reverse causation randomized assignment means the treatment and control group differ only by treatment, this rules out any third factors causing both treatment and effects

Question 28

gold standard for determining cauality

Answer 28

randomized trials because any different between treatment and control group must be due to treatment another problem with randomized trials is that the results might be only valid for the sample of individuals participating in the trial not for the whole population

Question 29

attrition

Answer 29

reduction in the size of a sample over time, which if not random, can lead to biased estimates

Question 30

time series analysis

Answer 30

co-movement of two series overtime, can be used to identify and measure correlation, potentially providing support for existing theories regarding causation time series analysis often produces striking patterns

Question 31

what is the problem with time series analysis

Answer 31

does not distinguish causation from correlation different sub periods can give different impressions, excluded variables may drive the results especially those like economic growth and wage subsidy programs

Question 32

cross sectional regression analysis

Answer 32

statistical analysis of the relationship between two or more variables exhibited by many individuals at one point in time with other factors held constant

Question 33

regression line

Answer 33

the line that shows the best linear approximation to the relationship between any two variables, regression analysis find the best fitting linear relationship between two variables

Question 34

control variables

Answer 34

variables included in the cross sectional regression models to account for differences between treatment and control groups that can lead to bias

Question 35

Quasi experiments

Answer 35

a Quasi experiments is a research design that resembles a true experiment but lacks random assignment of participants to treatment and control groups key features: no randomization, treatment and control groups but the assignment to these groups is not random, can help establish causal relationships