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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?
9/1/2002
12/1/2001
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6/1/1997
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3/1/1992
6/1/1991
9/1/1990
Example: US Unemployment
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0
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.
B.
Under 16 or institutionalized
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.
B.
C.
Under 16 or institutionalized
Choose not to work: Not in Labor Force
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.
B.
C.
D.
Under 16 or institutionalized
Choose not to work: Not in Labor Force
Choose to work and are working: Employed
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.
B.
C.
D.
•
Under 16 or institutionalized
Choose not to work: Not in Labor Force
Choose to work and are working: Employed
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?
May-03
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Jan-03
Nov-02
Sep-02
Jul-02
May-02
Mar-02
Jan-02
Nov-01
Sep-01
Jul-01
May-01
Mar-01
Jan-01
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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NSA
SA
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 90Day return of around .2%
Example: Annualizing
• A 90-day T-Bill currently sells for $99.80
per $100 of face value. This implies a 90Day 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 90Day 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) = 1.108
x = 1.02 (2%)
• These two annualized rates can now be compared