Lies, Damn Lies, and Statistics
Using Economic Data
Empirical Questions
Empirical Questions
• What exactly are you trying to measure? Is your variable consistent with what you’re trying to measure?
Example:Poverty in the US
Defining Poverty
Poverty in the US
• Poverty was defined by Mollie Orshansky of the SSA in 1964 as 3 times the cost of the Dept. of Agriculture’s “economy food plan”
Defining Poverty
Poverty in the US
• Poverty was defined by Mollie Orshansky of the SSA in 1964 as 3 times the cost of the Dept. of Agriculture’s “economy food plan”
• Since 1964, that number has been updated annually for changes in inflation
Defining Poverty
Poverty in the US• Poverty was defined by
Mollie Orshansky of the SSA in 1964 as 3 times the cost of the Dept. of Agriculture’s “economy food plan”
• Since 1964, that number has been updated annually for changes in inflation
• Currently, the poverty line is $9,359/yr for a single person
Defining Poverty
Poverty in the US• Poverty was defined by
Mollie Orshansky of the SSA in 1964 as 3 times the cost of the Dept. of Agriculture’s “economy food plan”
• Since 1964, that number has been updated annually for changes in inflation
• Currently, the poverty line is $9,359/yr for a single person
International Poverty
• Of the 184 member countries of the world bank. 52 countries are considered “high income” – defined as a per capita income of more than $9,206/yr
Defining Poverty
Poverty in the US• Poverty was defined by
Mollie Orshansky of the SSA in 1964 as 3 times the cost of the Dept. of Agriculture’s “economy food plan”
• Since 1964, that number has been updated annually for changes in inflation
• Currently, the poverty line is $9,359/yr for a single person
International Poverty
• Of the 184 member countries of the world bank. 52 countries are considered “high income” – per capita income of more than $9,206/yr
• 66 countries are considered “low income” (less than $746/yr)
Defining Poverty
Poverty in the US• Poverty was defined by
Mollie Orshansky of the SSA in 1964 as 3 times the cost of the Dept. of Agriculture’s “economy food plan”
• Since 1964, that number has been updated annually for changes in inflation
• Currently, the poverty line is $9,359/yr for a single person
International Poverty• Of the 184 member countries
of the world bank. 52 countries are considered “high income” – per capita income of more than $9,206/yr
• 66 countries are considered “low income” (less than $746/yr)
• Currently the international poverty standard is $1/day
Empirical Questions
• What exactly are you trying to measure? Is your variable consistent with what you’re trying to measure?
• How is your variable measured?
Example: US Unemployment
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Measuring Unemployment
• Each month, the Department of Labor surveys 60,000 households. Each household is placed in one of four categories
Measuring Unemployment
• Each month, the Department of Labor surveys 60,000 households. Each household is placed in one of four categories
A. Under 16 or institutionalized
Measuring Unemployment
• Each month, the Department of Labor surveys 60,000 households. Each household is placed in one of four categories
A. Under 16 or institutionalized
B. Choose not to work: Not in Labor Force
Measuring Unemployment
• Each month, the Department of Labor surveys 60,000 households. Each household is placed in one of four categories
A. Under 16 or institutionalized
B. Choose not to work: Not in Labor Force
C. Choose to work and are working: Employed
Measuring Unemployment
• Each month, the Department of Labor surveys 60,000 households. Each household is placed in one of four categories
A. Under 16 or institutionalized
B. Choose not to work: Not in Labor Force
C. Choose to work and are working: Employed
D. Choose to work, but can’t find a job: Unemployed
Measuring Unemployment
• Each month, the Department of Labor surveys 60,000 households. Each household is placed in one of four categories
A. Under 16 or institutionalized
B. Choose not to work: Not in Labor Force
C. Choose to work and are working: Employed
D. Choose to work, but can’t find a job: Unemployed
• Unemployment Rate = D/(C+D)
Is the unemployment rate biased downward?
Is the unemployment rate biased downward?
• The unemployment rate doesn’t count underemployment (those that would like to work full time, but only work part time)
Is the unemployment rate biased downward?
• The unemployment rate doesn’t count underemployment (those that would like to work full time, but only work part time)
• The “discouraged worker effect”: Those that have given up trying to find a job are counted as not in the labor force rather than unemployed
Is the unemployment rate biased upward?
Is the unemployment rate biased upward?
• Selection bias: those that are unemployed are more likely to be home to answer the survey.
Is the unemployment rate biased upward?
• Selection bias: those that are unemployed are more likely to be home to answer the survey.
• Moral hazard: due to unemployment insurance, it is difficult to tell how hard individuals are trying to find work
Other Problems
• Should we interpret unemployment statistics differently when population demographics change? (e.g. individuals under the age of 25 are much more likely to be unemployed)
Other Problems
• Should we interpret unemployment statistics differently when population demographics change? (e.g. individuals under the age of 25 are much more likely to be unemployed)
• Should we count military personnel as employed or unemployed
Empirical Questions
• What exactly are you trying to measure? Is your variable consistent with what you’re trying to measure?
• How is your variable measured?
• Is your variable in real or nominal terms?
Example: Suppose that you have $100 to invest in either the US or Argentina. Given the current
interest rates, where should you invest?
Argentina
• i = 12.8%
United States
• i = 4.25%
Example: Suppose that you have $100 to invest in either the US or Argentina. Given the current
interest rates, where should you invest?
Argentina
• i = 12.8%
• Annual inflation rate = 14.3%
United States
• i = 4.25%
• Annual inflation rate = 2.4%
Example: Suppose that you have $100 to invest in either the US or Argentina. Given the current
interest rates, where should you invest?
Argentina
• i = 12.8%
• Annual inflation = 14.3%
• Real (inflation adjusted) return = -1.5%
United States
• i = 4.25%
• Annual inflation = 2.4%
• Real (inflation adjusted) return = 1.85%
Real vs. Nominal Variables
Real vs. Nominal Variables
• Nominal variables are measured in terms of some currency (e.g. your annual income is $70,000 per year)
Real vs. Nominal Variables
• Nominal variables are measured in terms of some currency (e.g. your nominal income is $70,000 per year)
• Real (inflation adjusted) variables are measured in terms of some commodity (e.g. your real income is 7,000 pizzas per year)
Real vs. Nominal Variables
• Nominal variables are measured in terms of some currency (e.g. your nominal income is $70,000 per year)
• Real (inflation adjusted) variables are measured in terms of some commodity (e.g. if pizzas cost $10/pizza your real income is 7,000 pizzas per year)
• Real = Nominal/Price ( 7000 = 70,000/10 )
Empirical Questions
• What exactly are you trying to measure? Is your variable consistent with what you’re trying to measure?
• How is your variable measured?
• Is your variable in real or nominal terms?
• Is your variable seasonally adjusted?
Example: Seasonality
Retail Sales
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Components of Economics Data
• Economic data series are generally believed to have four main components
Components of Economics Data
• Economic data series are generally believed to have four main components• Trend (many years)
Components of Economics Data
• Economic data series are generally believed to have four main components• Trend (many years)• Business Cycle (1-2 yrs)
Components of Economics Data
• Economic data series are generally believed to have four main components• Trend (many years)• Business Cycle (1-2 yrs)• Seasonal ( < 1 yr)
Components of Economics Data
• Economic data series are generally believed to have four main components• Trend (many years)• Business Cycle (1-2 yrs)• Seasonal ( < 1 yr)• Noise (very short term)
Components of Economics Data
• Economic data series are generally believed to have four main components• Trend (many years)• Business Cycle (1-2 yrs)• Seasonal ( < 1 yr)• Noise (very short term)• Typically, we are not interested in the seasonal
component, so we remove it.
Seasonally Adjusted Retail Sales
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NSASA
Empirical Questions
• What exactly are you trying to measure? Is your variable consistent with what you’re trying to measure?
• How is your variable measured?
• Is your variable in real or nominal terms?
• Is your variable seasonally adjusted?
• Is your variable annualized?
Example: Annualizing
• A 90-day T-Bill currently sells for $99.80 per $100 of face value. This implies a 90-Day return of around .2%
Example: Annualizing
• A 90-day T-Bill currently sells for $99.80 per $100 of face value. This implies a 90-Day return of around .2%
• A 5 year STRIP currently sells for around $90.25 per $100 of face value. This implies a return of around 10.8%
Example: Annualizing
• A 90-day T-Bill currently sells for $99.80 per $100 of face value. This implies a 90-Day return of around .2%
• A 5 year STRIP currently sells for around $90.25 per $100 of face value. This implies a return of around 10.8%
• How can we compare these two rates of return?
Example: Annualizing
• Annualizing converts any data series to a common time frame (1 year)
Example: Annualizing
• Annualizing converts any data series to a common time frame (1 year)
• Assuming that the 90 day interest rate stays constant at .2%, the annual return to 90 day T-bills would be (1.002)(1.002)(1.002)(1.002) = 1.008 = .8%
Example: Annualizing
• Annualizing converts any data series to a common time frame (1 year)
• Assuming that the 90 day interest rate stays constant at .2%, the annual return to 90 day T-bills would be (1.002)(1.002)(1.002)(1.002) = 1.008 = .8%
• What would your annual return need to be to receive a (compounded) 5 year return of 10.8%
(1+x)(1+x)(1+x)(1+x)(1+x) = 1.108
x = 1.02 (2%)
Example: Annualizing
• Annualizing converts any data series to a common time frame (1 year)
• Assuming that the 90 day interest rate stays constant at .2%, the annual return to 90 day T-bills would be (1.002)(1.002)(1.002)(1.002) = 1.008 = .8%
• What would your annual return need to be to receive a (compounded) 5 year return of 10.8%(1+x)(1+x)(1+x)(1+x)(1+x) = 1.108x = 1.02 (2%)
• These two annualized rates can now be compared