# Economic Methodology1 ECONOMIC METHODOLOGY ECO 2023 Principles of Economics Dr. McCaleb.

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Economic Methodology1 ECONOMIC METHODOLOGY ECO 2023 Principles of Economics Dr. McCaleb

Economic Methodology2 TOPIC OUTLINE I.Ceteris Paribus II.Correlation Does Not Imply Causation

Economic Methodology3 Ceteris Paribus

Economic Methodology4  Ceteris Paribus Conclusions about the effect of a variable require other things unchanged To determine the effect of a change in one variable on another variable requires that other things remain constant. Ceteris paribus means “other things remaining constant”. Laboratory experiments allow scientists to hold other things constant and focus on the effects of a single variable. Statistical techniques allow economists to do the same thing. CETERIS PARIBUS

Economic Methodology5  Ceteris Paribus Example The law of demand states that the quantity demanded any good decreases whenever the price of the good increases. But then you observe that consumption of steak has decreased in recent years even though the price of steak also decreased. Does this mean the law of demand is invalid? Explain. CETERIS PARIBUS

Economic Methodology6 Correlation Does Not Imply Causation

Economic Methodology7

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Economic Methodology9 CORRELATION DOES NOT IMPLY CAUSATION  Cause and Effect Relationships Methodological fallacy: Correlation does not imply causation Correlation is the tendency for the values of two variables to move in a predictable and related way. Because two variables appear to be correlated does not mean that movements of the first variable cause movements of the second variable.

Economic Methodology10  Cause and Effect Relationships There are four possible explanations for any observed correlation. Cause and effect Reverse causation Omitted variables Spurious correlation CORRELATION DOES NOT IMPLY CAUSATION

Economic Methodology11  Cause and Effect Relationships Example We observe that cities with more police have higher crime rates. Can we validly conclude that having more police causes a city to also have more crime? No. Consider the following possibilities. CORRELATION DOES NOT IMPLY CAUSATION

Economic Methodology12  Cause and Effect Relationships Cause and effect: More police cause higher crime rates The more police a city has, the more crimes get detected and reported. Therefore, a city’s reported crime rate is higher if it has more police because more of the crimes are detected and reported. Note that the true crime rate for a city with more police may not be any higher than for a city with fewer police, but more of the crimes are detected and reported. More police cause better detection and reporting of crimes. CORRELATION DOES NOT IMPLY CAUSATION

Economic Methodology13  Cause and Effect Relationships Reverse causation: More crimes cause cities to hire more police The correlation does indeed imply a cause and effect relationship, but it is the opposite of what it appears. It isn’t that more police cause more crimes. Instead, more crimes cause cities to hire more police. CORRELATION DOES NOT IMPLY CAUSATION

Economic Methodology14  Cause and Effect Relationships Omitted variables: Larger cities have both more police and higher crime rates There is no cause and effect relationship between police and crime rates. More police don’t cause more crimes and more crimes don’t result in more police. Instead, a third variable, the size of the city, is the cause and both the number of police and the number of crimes are the results. Police and crimes are correlated only because they are both results of a third omitted variable, city size. CORRELATION DOES NOT IMPLY CAUSATION

Economic Methodology15  Cause and Effect Relationships Spurious correlation: Police and crime are unrelated There is no cause and effect relationship between police and crime rates. More police don’t cause more crimes and more crimes don’t result in more police. Nor are they both the result of an omitted variable. The apparent correlation between police and crime is a coincidence. Perhaps the sample of cities is biased. It is too small or it isn’t truly representative. With a larger or more representative sample, the correlation would disappear. CORRELATION DOES NOT IMPLY CAUSATION

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