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American Recovery and Reinvestment Act : An Empirical Assessment

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

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  1. American Recovery andReinvestment Act:An Empirical Assessment Bergen County Academies Team #119

  2. Prompting a Stimulus Rapid Rises in Unemployment

  3. 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

  4. Plan of Attack

  5. 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

  6. 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.

  7. Testing for Lag r2 = 0.3863340083815 r2 = 0.66159883791517

  8. 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

  9. 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

  10. 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

  11. 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

  12. Housing: Raw Data (not adjusted for lag)

  13. Housing: Least Squares Regression Line, Adjusted for Lag

  14. 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

  15. Health Care: Raw Data

  16. Health Care: Least Squares Regression Line Note: correlation is negative

  17. Summary: Coefficient of Determination by Sector

  18. National Economic Forecast: Predicted Changes in Employment by Sector for Highly Correlated Sectors

  19. 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)

  20. National Economic Forecast

  21. GDP and Employment r2 = 0.675 (after scaling data)

  22. 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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