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Analysis of Projected Employment Allocation in the American Recovery and Reinvestment Act

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Analysis of Projected Employment Allocation in the American Recovery and Reinvestment Act

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  1. INTRODUCTION • ARRA was announced by the White House as “a nationwide effort to create jobs, jumpstart growth and transform [the American economy].” The 787 billion dollar package is estimated to generate about 3.5 million jobs nationally. However, from a distributive politics standpoint, federal outlays are seldom without partisan or institutional influences. In this project I will test several distributive hypotheses against the jobs/district variable to learn whether ARRA employment allocation is subject to any non-need or non-geographic factors. The null hypothesis of ‘need based’ distribution (and no partisan/institutional distribution) will be tested against several alternate distributive hypotheses: core voter model, swing voter model, presidential influence, sub-national influence and congressional power. • Dependent Variable: • The dependent variable is the projected amount of jobs that will be created in each of the 435 US Congressional districts, and the District of Columbia, through the American Recovery and Reinvestment Act (ARRA). The data is available publically since its release on February 13th 2009 and it is based on a study by Christina Romer and Jared Bernstein, “The Job Impact of the American Recovery and Reinvestment Act.” • Hypotheses/Independent Variables • A Core-Voter model would suggest positive relationships for: Democratic Governor, Democratic Senators, Democratic Congressional Representative, % of Vote for Obama/CD, Democratic Majority in State Legislature • A Swing-Voter model would suggest a positive relationship for: Standard Deviation of Democratic Vote for President 2008-00 (Sqrt), Republican CD won by Obama, Republican CD won by Obama • Congressional influence would suggest a positive relationship for: Congressional Power Score, Total Earmarks/CD, Total Earmarks/State, Interaction of Democratic CD’s and Democratic Senators/State • Incumbent vulnerability would suggest a positive relationship for: Vulnerable Republican Governor, Vulnerable Democratic Governor, Vulnerable Democratic Representative, Vulnerable Republican, Democratic CD in a Republican State • Controls: Total Household per District, Median HH Income/District, HH Gini Index/CD, Ratio of Rural/Urban Highway Mileage/State 2006, Highway Mileage 2006 (Urban), % Bridges Obsolete/Deficient, Renewable Energy Generation 2005 , %Minority • METHOD • To test the hypotheses I will run four regression models: • I: A multiple regression model of 22 variables, in a sample space of 434 observations clustered by state. The data is clustered to increase model efficiency and account for possible correlation of the standard errors. • II: A multiple regression model of 23 variables. The addition of the Congressional Power ranking reduces the sample space to 418 observations, clustered by state. • III: A fixed-effects multiple regression of 22 variables, clustered by state. Fixed effects should account for possible omitted variable bias. • IV: A random-effects multiple regression of 22 variables, clustered by state. RESULTS The data suggests that the Core-Voter model is less relevant, as Democratic governors, senators, and Congressional representatives and state legislatures received less jobs then their Republican counterparts. However, vulnerable Democratic politicians received more jobs then vulnerable Republicans. The % of Vote for Obama/CD was just outside the range of significance, but suggested a positive relationship. Combined with the lack of a Congressional effect, we may infer that the President had a strong impact in the distributive decision making. The Swing-Voter model is supported by the fact that the Standard Deviation of Democratic Vote for President 2008-00 (Sqrt) is significantly positive in every model, and suggests an increase of 1,300 jobs. The average deviation from the mean was not significant. The third model suggests that Congressional influence was not a significant explanation of job distribution, as the Congressional Power Score was insignificant. Interestingly, the data on earmarks in the 2009 Omnibus Spending Bill show that Democrats were favored in distributive decisions. The Interaction of Democratic CD’s and Democratic Senators/State is slight significant in the first two models, suggesting that a combination of a homogeneously Democratic Senate and Congressional representation leads to more jobs. A summary of the Control variables finds a predictable positive relationship between Total Household per District, Median HH Income/District, Foreclosure rate, Renewable Energy Generation and Urban Highway mileage. The Gini Index variable is significantly negative, perhaps proxying for a redistributive hypothesis. Also, the percentage change in the African American population in the district has a negative relationship with the dependent variable. It is possible that this variable may be capturing the effect of people moving away from poorer districts. DISCUSSION ‘Jobs/district’ is based on projections made by the Obama administration, and as such is not immune from political bias or estimation problems. Nonetheless, the estimates are in and of themselves valuable as indicators of the calculus employed by policymakers in the government; and, to the extent that the analysis is a reflection of bargains struck during the legislative process, it sheds a light on possible coalition building and electoral strategies. The results appear to support a swing-voter model which rewarded Republican districts, and favored vulnerable Democrats. Given the lack of Congressional influence, this analysis suggest the central role of a popular President expanding his electoral base, by providing credit-taking opportunities in the form of jobs. Future research may seek to analyze the actual distribution figures, to find whether the projections fit with reality. Mateusz Tomkowiak University of Chicago RESULTS Analysis of Projected Employment Allocation in the American Recovery and Reinvestment Act Test of Normality Test of Linearity Test of Heteroskedasticity Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05

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