lies, damn lies, and statistics using economic data

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 using economic data

Documents

mollie orshansky

member countries

high income

poverty line

low income

206yr66 countries

department of labor

yrdefining povertypoverty