Lies, Damn Lies, and Statistics

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”


• Currently, the poverty line is $9,359/yr for a single person

Defining Poverty





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





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





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


• 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



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






D. Choose to work, but can’t find a job: Unemployed

• Unemployment Rate = D/(C+D)

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 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?


• Selection bias: those that are unemployed are more likely to be home to answer the survey.


• 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



• 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%



Argentina

• i = 12.8%

• Annual inflation rate = 14.3%

United States

• i = 4.25%

• Annual inflation rate = 2.4%



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


• Nominal variables are measured in terms of some currency (e.g. your annual income is $70,000 per year)


• 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)


• 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




• 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


• Economic data series are generally believed to have four main components• Trend (many years)


• Economic data series are generally believed to have four main components• Trend (many years)• Business Cycle (1-2 yrs)


• Economic data series are generally believed to have four main components• Trend (many years)• Business Cycle (1-2 yrs)• Seasonal ( < 1 yr)


• 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)


• 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




• 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%



• A 5 year STRIP currently sells for around $90.25 per $100 of face value. This implies a return of around 10.8%



• 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?


• 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%)




• 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

Lies, Damn Lies, and Statistics

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