Exploring County Truck Freight. By : Henry Myers

Transcription

1 Exploring County Truck Freight Transportation data By : Henry Myers

2 Part 1 is focused on explaining the spatial econometrics and statistics used Part 2 explains the economic production function Part 3 reviews the data and the data sources Part 4 results and estimation methods Part 5 making sense out of the current results and describes future work

3 Spatial Statistics focuses on finding clusters or dispersions that cannot otherwise be considered random Econometrics focuses on the model and the theory that generates the model The coefficients of the independent variables parameters are estimated using regression analysis

4 Local Moran s I Spatial lag model assumes spatial dependence in the parameters Spatial error model assumes spatial dependence in the errors Spatial regime model assumes that the location has some explanatory power of system being studied Anselin s spatial chow-test indicates the spatial regime model The spatial chow model test the evaluates the residuals of the constrained (spatial) model against the unconstrained model. The null hypothesis of the spatial chow test is that the there is no difference between the two models

5 A function the relates the output of an economy, firm, or industry back to factor inputs. Factor inputs are Capital, Labor, Natural Resources. They assume a technological relationship This relationship gives raise to the concept of substitution in industry behavior Industries act like an agent with budget constraint, the Marginal rate of (technical) Substitution The Constant elasticity of Substitution is centered around the elasticity of substitution CES Production Functions was first introduced in the work of Arrow, Chenery, Minhas, and Solow

6 If the substitution of elasticity equals 0 use a linear production function If the substitution of elasticity equals 1 use a Cobb Douglas If the substitution of elasticity approaches negative infinity use a Leontief(fixed proportions) No general accepted method for estimating CES with inputs above 2 All methods of estimation end up with equal partial substitutions <Uzawa, 1975>

7 Leontief assumes a technical knowledge of the system The ratio at which factors of production are used Linear production function Easy to read, but fails in demonstrating returns to scale Assume the Cobb-Douglas case, elasticity of substitution equals zero Trans-log Cobb-Douglas check for homogenous of degree one. Sum of the independent variable coefficients equal one and that cross elasticity equal zero If homogenous of degree one, it can be said that doubling of factor input leads to a doubling of output

8 Marginal and Total Production Marginal Production Total Production

9 U.S Department of Transportation Highway statistic series MV9: As reported to USDOT by the state, number of Tractor-Trucks registered to that state that year MV11: As reported to USDOT by the state, number of Semi-Trailers registered to that state that year BEA: estimated truck freight transportation value NCHRP: Provides the method for converting between receipts and tonnage transported per year BLS: Wage and Labor quantity for the state and MSA Implan: 2007 County level Total proprietor income, total employment in that sector Individual states for county level trailer and truck registrations

10

11 Local Moran s I Result for Trans-Log Moran s I of with a z-score of 2.03

12 From just the OLS residuals, Identified spatial dependence Moran s I confirms a Spatial Dependence primarily in the Center for Freight and Infrastructure Research and Education (CFIRE) region Test of both the Spatial Error and Spatial Lag model Without a clear theory on why spatial regime would exist in the CFIRE region, the regime area is unknown CFIRE encompasses Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Missouri, Ohio, and Wisconsin

13 Start with a single random location and give it the value of 1 Include a dummy variable in the regression for the random area Using Anselin s spatial chow test Test the residuals from the constrained spatial regime model against the standard model If we reject the spatial chow test null hypothesis and the Akaike information criterion (AIC) is lower, expand the random area by 1 neighbor Iterate the process until chow test statistic is maximized, the AIC is minimized, and the regime is greater than equal to 2

14 The Random Regime area starting with Illinois includes Wisconsin, Iowa, Missouri, Indiana, Ohio, Michigan, Kentucky, Tennessee, Arkansas, Mississippi In comparison to CFIRE Kansas and Minnesota are excluded What s going on Minnesota and Kansas? Tennessee, Arkansas, Mississippi are included Pros and Cons of the Random Regime We identify and area that is causing an omitted variable bias This does not tell us why that area is a regime.

15 Table 1 Model Coefficients Tests AIC Const Labor Truck Trailer Regime JB BP Standard N/A Regime Moran s I of with a z-score of 0.4

16 Visual inspection of the National OLS residuals, Identify a strong influence of CFIRE region on the System Moran s I confirms a Spatial Dependence primarily in the CFIRE region The Random Regime Area encompasses 80% of the CFIRE region The study should be conducted until the Random Regime Area omitted variable is found The area in the Random regime has increasing returns to scale

17 Table 2 Method Weight Coeficients R^2 LR AIC rho Const Labor Truck Trailer Lag Q

18 Marginal Rates of Technical Substitution Given the Marginal Productivity at the county level we can assume a substitution exist based on cost of the input and it s marginal production MSA wage data can be broke down to the county level IRS depreciation data for trucks and trailers Testing Marginal Production by Commodity purchases Implan provides business to business annual transactions Separate the counties by flow percentage Each type of flow could require a new type of Truck/ Trailer and have a different marginal production

19