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American Recovery and Reinvestment Act : An Empirical Assessment. Bergen County Academies Team #119. Prompting a Stimulus. Rapid Rises in Unemployment. The American Recovery and Reinvestment Act 2009. Promised to “create or save” 3,000,000 jobs

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american recovery and reinvestment act an empirical assessment

American Recovery andReinvestment Act:An Empirical Assessment

Bergen County Academies Team #119

prompting a stimulus
Prompting a Stimulus

Rapid Rises in Unemployment

the american recovery and reinvestment act 2009
The American Recovery and Reinvestment Act 2009
  • Promised to “create or save” 3,000,000 jobs
  • $526 billion spent within the first two years
  • Peak spending year 2010
    • 64% spent in the first 19 months
  • 2009 and 2010:
    • Energy
    • Tax Provisions
    • Housing and Urban Development
    • Labor, Health, Education
assumptions
Assumptions
  • The impact of the stimulus bill on each sector will be completely reflected by a representative indicator
  • The stimulus bill will be 100% effective in stimulating the output of a given sector
  • Each sector functions as a closed economic unit
  • No unforeseeable economic crises will take place in the near term
  • GDP contraction will end by 2010, so only spending until then will be considered
statistical considerations
Statistical Considerations

Calculated coefficient of determination for data

Closer r is to 1.0, the stronger the correlation between indicator and employment

r2 is the coefficient of determination, describes linearity of model and provides basis for extrapolation

One r2 value per sector is not concrete

Employment growth can lag behind indicator by different periods of time

Necessary to test for, calculate this lag

Done by manually offsetting the graph of job growth by a variable amount of years and recalculating r2.

slide7

Testing for Lag

r2 = 0.3863340083815

r2 = 0.66159883791517

design of computer model
Design of Computer Model

Calculates an r and r2 value for every possible shift

Input: two text files with % change data

Calculates mean of two data sets, standard deviations, and r2

Shifts data by one year, recalculates

Iterates over all integer year shifts

Outputs text file with all r2values, and highlights the maximum values

sample program output
Sample Program Output

House Building Employment Vs. Housing Starts

0: R^2 Value = 0.38633401908193815

1: R^2 Value = 0.6615199883791517

2: R^2 Value = 0.2654074482789769

3: R^2 Value = 0.0030958809551214633

4: R^2 Value = 0.0060519004454571215

5: R^2 Value = 0.019212798363841033

6: R^2 Value = 0.008139124417791818

7: R^2 Value = 0.0003564005819154275

8: R^2 Value = 0.008700600239202307

9: R^2 Value = 0.2242270845152305

  • Outputs r2 values for every shift
  • An abridged set of raw output data shown to the left
  • Deviations of 0 & 1 were shown on the previously displayed graphs
sample program output cont
Sample Program Output, cont.
  • Then displays list of max r2values, shifts that produced them, and ranges they were a maximum on
  • Finally, outputs the most likely lag value and its associated r2 value

0: R^2 = 0.38633401908193815

is a maximum until 1

Length of range = 1

1: R^2 = 0.6615199883791517

is a maximum until 15

Length of range = 14

15: R^2 = 0.6943978383227343

is a maximum until 20

Length of range = 5

20: R^2 = 0.8058708588662884

is a maximum until 21

Length of range = 1

Lag = 1

Best R^2 value = 0.6615199883791517

a highly correlated sector housing
A Highly Correlated Sector: Housing
  • Provision in stimulus bill: $59.5 Billion (11.3%) by 2010
  • Representative indicator: Housing Starts
    • Number of new homes being built, reflective of fresh demand in the housing sector
    • Accounts for tendency to retain home ownership versus new home contracts and purchases
  • Yielded a coefficient of determination of .662 with sector employment after adjusting for lag
a misleadingly correlated sector health care
A Misleadingly Correlated Sector: Health Care
  • Provision in stimulus bill: $38.2 Billion (7.3%) by 2010
  • Representative indicator: Health Care Standard and Poor’s Depository Receipt (SPDR)
    • Tracks shares of health care-related industries in the S&P 500
    • Serves as an overall indicator of the health care sector
  • Yielded a coefficient of determination of .622, but with a negative correlation with sector employment
health care least squares regression line
Health Care: Least Squares Regression Line

Note: correlation is negative

national economic forecast predicted changes in employment by sector for highly correlated sectors
National Economic Forecast: Predicted Changes in Employment by Sector for Highly Correlated Sectors
a second stimulus
A Second Stimulus
  • Christina Romer, Chairperson of the Council of Economic Advisors:
    • Endogenous tax changes have a maximum fiscal multiplier of 3
  • In 2005, the IRS collected $253.3 billion (2000-chained dollars) in corporate income taxes
  • Eliminating the corporate income tax would increase GDP, even with a conservative estimate of the multiplier effect (1)
gdp and employment
GDP and Employment

r2 = 0.675 (after scaling data)

conclusion
Conclusion
  • Employment changes are difficult to predict and highly speculative
  • Construction, housing and energy show the most promise for improvement
  • Inconclusive data for health care and tax provisions
    • Lurking variables, lack of linearity
  • GDP will continue to show substantial declines
  • A corporate income tax rebate or cut should pump billions into the economy, prompting growth by mid-2010.
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