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787 Billion Dollars vs. Rising Unemployment

787 Billion Dollars vs. Rising Unemployment. A Presentation by The Wheeler School Team 5 May 2009 2009 Moody ’ s Mega Math Challenge. The Wheeler School Team. Matt Halpern Brett Musco Chris Shaw Karan Takhar Alex Wheelock Coach: Mr. George Lewis. Presentation Overview.

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787 Billion Dollars vs. Rising Unemployment

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  1. 787 Billion Dollars vs. Rising Unemployment A Presentation by The Wheeler School Team 5 May 2009 2009 Moody’s Mega Math Challenge

  2. The Wheeler School Team • Matt Halpern • Brett Musco • Chris Shaw • Karan Takhar • Alex Wheelock • Coach: Mr. George Lewis The Wheeler School Team

  3. Presentation Overview • 787B Stimulus Package • Key Assumptions • Relative Impact of the Key Components of the Stimulus Package • Measuring Success • Economic Stimulus - Round 2 • Conclusion The Wheeler School Team

  4. Economic Stimulus Cagle Cartoons The Wheeler School Team

  5. 787 Billion Dollar Stimulus • Structure of the Economic Stimulus • 4 Major Components evaluated • Tax Cuts + Credits ($231.6 Billion, 29.4% of bill) • Healthcare Funding ($152 Billion, 19.3%) • Green Energy ($42.5 Billion, 5.4%) • Education Funding ($48.4 Billion, 6.1%) • Other smaller components not evaluated The Wheeler School Team

  6. Key Assumptions • 1. Data we obtained is accurate • 2. We do not expect rapid inflation- for the purposes of our models, inflation remains constant • 3. We do not expect unforeseen political, social, or economic events, such as a war or natural disaster • 4. The correlation between the rate of unemployment and the rate of GDP can be expressed with a linear model. • 5. Keynesian curves are sufficient to model the short-term effects of elements of the stimulus package The Wheeler School Team

  7. General Calculations • Macroeconomic Equilibrium • E = C + I + G + NX • E = Total Planned Expenditures • C = Total Consumption • I = Investments • NX = Net Exports • Disposable Income • Y - T • Y = Total Income (GDP) • T = Taxes • GDP • Current value of GDP = $11.65T • Forecast for change in GDP without stimulus = -0.9%[*] [*] Federal Reserve Bank Forecast The Wheeler School Team

  8. General Calculations (Cont’d) • Unemployment level calculated indirectly from GDP • Linear relationship between each (Okun’s Law) • % Change in real GDP = -1.89* % change in unemployment + 3.33% Linear correlation between % real GDP change and change in the Unemployment rate The Wheeler School Team

  9. General Calculations (Cont’d) • Change in GDP measured using MPC • Measures percentage of tax relief or govt. spending that is passed forward • Marginal Propensity to Consume, MPC = 0.5[*] • Forms a geometric series used to calculate multiplier effect of stimulus package spending • nMPCn = MPC/ (1 – MPC) = 1 • nMPCn-1 = 1/ (1 – MPC) = 2 [*] Paul Krugman, New York Times The Wheeler School Team

  10. Tax Credits and Cuts • Total Value of Tax Relief = $231.6B • Use of Keynesian Cross to determine change in GDP due to Tax Relief • Determine the intersection of Actual Expenditures Curve with Planned Expenditures Curve • before Tax Relief • after Tax Relief • The difference in two values of GDP corresponding to two points of intersection is the net change in GDP due to Tax Relief • For evaluation of the effect of Tax Relief, hold I, G, NX constant in E = C + I + G + NX • For planned expenditures, C is linear function of disposable income (Y-T) • E is a linear function of (Y-T) • For actual expenditures, Y = E The Wheeler School Team

  11. Tax Credits and Cuts (Cont’d) The Wheeler School Team

  12. Tax Credits and Cuts (Cont’d) • Change in GDP •  MPCn * T = -Y = T * MPC/ (1 – MPC) = T • Net increase in GDP = $231.6B • % Change of Real GDP • Current value of GDP = $11.65T • Net change in GDP due to tax credits/cut = +1.99% • % Change in Unemployment • Using linear regression, % change in Unemployment = -1.04% • 1.04% of total number unemployed[*] = 1,609,990 [*] Bureau of Economic Analysis Estimated number of jobs created through tax cuts is 1,609,990 jobs The Wheeler School Team

  13. Healthcare • Total spending = $152B • Only $25.1 billion will generate new jobs (COBRA expansion) • Use Keynesian Cross (as in Tax Relief) • Hold C, G, NX constant in E = C + I + G + NX • For planned expenditures, E is a linear function of I • For actual expenditures, Y = E • Change in GDP •  MPCn-1 * I = Y = T / (1 – MPC) = 2*I • Net increase in GDP = $50.2B • % Change of Real GDP • Net change in GDP due to tax credits/cut = 0.43% • % Change in Unemployment • Using linear regression, % change in Unemployment = 0.22% • 0.22% of employed = 339,187 Estimated number of jobs created through healthcare is 339,187 jobs The Wheeler School Team

  14. Green Energy • Total Spending: $42.5B consists of • Tax incentives (T) • Investments (I) and government (G) spending • The effect of Tax incentives is similar to the case for Tax Relief • Change in GDP = Tax incentives • The effect of Investments and Govt. spending is similar to the case of Healthcare spending • Change in GDP = 2* Change in Investment and Govt. Spending • Estimated decrease in Unemployment = 869,506 jobs • Amount of green energy = 42.5 billion / (20.5 cents/MJ of energy) = 207.2 billion MJ • US annual energy budget is 106.8 trillion MJ • Program will cover 0.2% of total energy production Estimated number of jobs created through green energy is 869,506 jobs The Wheeler School Team

  15. Education • Total Spending: $48.4B includes • $29.1B for K-12 education • $19.2B for high school education • $9.9B other • $15.9B for public colleges and universities • Direct correlation between test scores and GDP growth The Wheeler School Team

  16. Education (Cont’d) • Estimate of change in Unemployment based on • Average cost of high school education = $4525/year • Number of dropout years available = 2 years • Increase in high school student (due to stimulus) = $19.2B/(4525*2) = 2,121,546 • Those with no degree • 14% of the adult population • 9% of the above are unemployed • Those with at least a high school degree • 84% of the adult population • 5.7% of above are unemployed • Using the above percentages, and the increase in number of high school students • Decrease in Unemployment = 578,386 Estimated number of jobs created through education is 578,386 jobs The Wheeler School Team

  17. Measuring Results of the Stimulus • Assumptions • The United States’ Labor Force will continue to grow at the linear rate it has followed for the last 28 years • The jobs that result from the stimulus package will be created within the next few years, as all government models suggest • Approach • We decided that the best approach to this problem would be to estimate the effect of the stimulus package on percent change in GDP and the unemployment rate The Wheeler School Team

  18. Measuring Results of the Stimulus (Cont’d) • Predictions based on Economic Policy Institution’s estimates • Jobs created over next 2 years = 3,655,000 • Jobs created over next 3 years = 4,965,000 • Using a data about the size of the labor force over the last 28 years, we were able to model the size of the labor force as a linear function and predict the labor force size for 2009, 2010, and 2011 • Labor force in 2009 = 153,716,000 • Labor force in 2010 = 157,774,700 • Labor force in 2009 = 159,465,800 The Wheeler School Team

  19. Measuring Results of the Stimulus (Cont’d) • Predictions based on Economic Policy Institution’s estimates (continued) • Using these number in addition to the following two equations we were able to predict the expected effects of the stimulus package • Change in unemployment = (Total new jobs created)/(total labor force)*100 • Percent change in GDP = 3.33-1.89 (change in unemployment rate) • This equation was derived in question 1 • Effects in next 3 years The Wheeler School Team

  20. Measuring Results of the Stimulus (Cont’d) • Predictions based on our models • Seeing that the EPI’s estimates seemed very high we ran the same model with the values that we had obtained in question 1: • Jobs created in next 3 year = 3,397,069 • Change in unemployment: -2.16% • Percent change in GDP = 7.46% • Confidence • The major source of error in our work would be in the equation that relates GDP and unemployment. We calculated a 95% confidence interval on this graph using t-distribution for our set of 58 data points. • This showed that the slope of our regression line could vary between -1.81 and -1.97. However this is not significant enough of a range to restrict our confidence in our predictions The Wheeler School Team

  21. Stimulus – Round 2 • Stimulus – Round 1 • The 4 major components are estimated to create approximately 3.4 million new jobs • While other smaller components of the stimulus package are expected to create additional new jobs, our estimate is that the total number of new jobs created will be approximately 1 million less than than the approximately 5 million predicted by the Economic Policy Institute • Hence, the need for a second round of Stimulus The Wheeler School Team

  22. Stimulus – Round 2 (Cont’d) • Stimulus – Round 2 Structure • Round 2 should be structured similar to Round 1 • Should focus on components with highest payoff, e.g., Tax Relief • Should emphasize components with long lasting (after the economic recovery) impact, e.g, Healthcare, Green Energy, and Education • Other Components (alternatives) • With manufacturing jobs continuing to relocate abroad, job re-training should be part of the Round 2 • To maintain competitive edge, additional resources should be applied to “quality” education The Wheeler School Team

  23. Conclusion and Recommendations • We analyzed the 4 major components of the Stimulus • Most significant: Tax Relief - $1 of Tax Relief results in $1 increase in GDP (assuming MPC = 0.5) • However, the smaller COBRA sub-component of Healthcare adds $2 to GDP for every $1 of stimulus • Over the next 3 years • Estimated new jobs created exceeds 3 million- about 1 million less than the EPI projection • Estimated increase in GDP is about 1.5% less than that predicted by EPI • Stimulus Round 2 will be necessary • to bridge the gap between EPI estimates and expected results based on our analysis • Should focus on components with most impact on Unemployment and GDP, I.e., Tax Relief • Should emphasize/add components with long lasting impact such as Green Energy, Healthcare, Education, and Job Re-training The Wheeler School Team

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