Michael W. Babcock

FORECASTING INLAND WATERWAY GRAIN TRAFFIC

Short-term forecasting of inland waterway traffic has been a neglected area in water transportation forecasting. This paper presents a time series model to forecast Mississippi River Lock 27 grain tonnage for the 1989:1 - 1999:4 period. The model was selected on the basis of several measures of goodness of fit and out-of sample forecasting performance. The out-of-sample forecasting performance of the model was good, as the percentage difference between the year 2000 actual and forecasted tonnage was less than 5.5% for three of the four quarters and only 2.3% for the year. Apparently, the forecast errors in the quarterly forecasts tend to offset each other so that the annual forecast is relatively close to the actual annual traffic. These forecasts could be used by inland waterway shippers to evaluate transportation equipment needs and establish marketing plans, government policymakers to measure the effects of past and prospective transportation policies, port authorities to monitor port utilization and inland waterway carriers to develop business plans and hire operating staff.

Time series forecasting of quarterly railroad grain carloadings

The participants in the grain logistics system need forecasts of railroad grain carloads. Although forecasting studies have been conducted for virtually every mode, no forecasting studies of quarterly railroad grain transportation have been published. The objectives of the paper are (1) specify a US quarterly railroad grain transportation forecasting model, and (2) empirically estimate the model. The selection of explanatory variables requires that they have a theoretical relationship to railroad grain transportation supply and/or demand, and that the data for the explanatory variables are published in quarterly frequency. However, there are relatively few potential explanatory variables that are published quarterly and those that are available appear to have weak correlation with quarterly railroad grain carloadings. The economic process generating quarterly railroad grain carloadings is quite complex and very difficult to model with regression techniques. Given this problem and the focus on short run forecasting, a time series model was employed to forecast quarterly railroad grain carloadings. An AR(4) model was estimated using the Maximum Likelihood estimation procedure for the 1987:4-1997:4 period. The actual railroad grain carloadings for this period were compared to the forecast carloadings generated by the time series model. For 92% of the 37 quarters the percentage difference between the actual and forecast values was 10% or less. Of the 9 annual observations, the per cent difference between the actual and forecast value was less than 2.6% for 8 of the 9 years. ©

SIMULATING THE EFFECTS OF RAILROAD MERGERS

The purpose of this paper is to add to the empirical literature regarding merger simulation analysis by examining the effect of railroad mergers on railroad market power. This is done by measuring railroad profits and revenue/variable cost ratios corresponding to different degrees of intrarailroad competition for movements of Kansas export wheat to Houston, Texas. Two models are developed to achieve the objectives of the study. A network model of the wheat logistics system is used to identify the least cost transportation routes from the Kansas study area to the market at Houston. A profit improvement algorithm, which identifies Nash equilibrium prices, is developed to measure the amount by which railroads can profitably raise their prices above variable cost. The results of the study have implications for U.S. railroad merger policy. The paper indicates that railroad mergers do not necessarily increase railroad market power or make railroad shippers worse off. Instead, the study demonstrates that the impact of railroad mergers on shippers and railroads depends on factors that vary geographically, such as the degree of intrarailroad and intermodal competition in the area.

The impact of railroad mergers on grain transportation markets: a Kansas case study

While there have been many studies of the impact of railroad deregulation on agricultural transportation markets there have been very few that address the impact of railroad mergers on rail grain prices and the distribution of efficiency gains. The purpose of this paper is to add to the sparse literature regarding the effect of railroad mergers on agricultural transportation markets. Given the ever declining number of Class I railroads, this research is very timely. The specific objectives of the research are as follows: (1) Analyze the impact of the Burlington Northern (BN)-Santa Fe (SF) merger on the ability of the BNSF to increase prices on movements of Kansas wheat to Houston, Texas. (2) Analyze the impact of the Union Pacific (UP)-Southern Pacific (SP) merger on the ability of the UPSP to increase prices on movements of Kansas wheat to Houston, Texas. (3) Analyze changes in Kansas wheat logistics system costs as a result of the BN-SF and UP-SP mergers. Two models are developed to achieve the objectives of the study. A network model of the wheat logistics system is used to identify the least cost transportation routes from the Kansas study area to the market at Houston, Texas. A profit improvement algorithm is developed to measure the amount by which railroads can raise their prices above variable cost. The BNSF and UPSP achieve only minor increases in market power (measured by the ratio of revenue to variable cost) because the merged railroads have only slight advantages in cost relative to other railroads that serve the same areas as the merged railroads. Wheat shippers benefit from merger-induced reductions in transportation and handling costs. Shippers are likely to capture a significant share of these cost reductions since intrarailroad competition is present after the mergers. Transport cost reductions accompany mergers due to more direct routing of wheat shipments and the assumption that the merged railroad operates at the costs of the lower cost partner.

The impact of transportation rates on the location of the wheat flour milling industry

Since wheat and flour are shipped over long distances, railroad rates exert a key influence on the location of the wheat flour milling industry. Two locational models are employed to explain recent trends in the location of the industry. One of these is a transport locational model focusing on flour milling location in the eastern half of the U.S. The other is a linear programming model which analyzes flour milling location in the west. The empirical analysis indicates that high railroad flour rates relative to wheat rates should shift the industry out of the rural wheat producing areas and toward population centers. An analysis of actual industry trends over the 1960-1982 period confirms the empirical results of the locational models. Railroad deregulation does not appear to have affected locational trends in the flour milling industry.

DETERMINANTS OF PROFITABILITY OF GRAIN DEPENDENT SHORT LINE RAILROADS

This paper is the first empirical analysis of U.S. short line railroad profitability using primary cost and revenue data. Models of short line profitability are developed using Earnings Before Interest and Taxes (EBIT) as the profitability measure. The sample includes 34 short lines operating in 17 states in the midwest region of the U.S. for the fiscal years 1986-1995. The models are estimated by OLS regression and explain up to 75% of the variation in short line profitability. Nearly all the explanatory variables have the theoretically expected sign and are statistically significant. The variable DENS (number of carloads per mile of main-line track) is the most important influence on EBIT. However, several other variables are identified as important including management ability to control expenses, type of short line ownership, size and ownership of the short lines network, composition of traffic, and length of haul. The empirical results of the study indicate that a short line operating at the mean values of the explanatory variables is likely to only break even. About 25% of the sample short lines have a high probability of requiring government assistance to continue operating.

Analysis of structural changes in a grain logistics system: Trainload facilities and intermodal competition in northwest Kansas†

This article evaluates potential change in Central Plains grain logistics systems, focusing on a supply area in northwest Kansas and export markets at the Gulf of Mexico and Pacific Coast. Employing a network model, total efficiency gains as well as changes in storage and transport costs are measured. Specific logistical changes that are evaluated include construction of trainloading facilities (subterminals) and increases in rail rates. Construction of subterminals results in substantial system cost savings and nearly all export wheat moves through the subterminal-unit train marketing channel. Rail rate increases of 15% result in only minor changes in marketing patterns and intermodal substitution.

Energy Use and Pollutant Emissions Impacts of Shortline Railroad Abandonment

Railroad abandonment has potential negative effects especially in rural areas that rely on railroads for outbound and inbound shipments. The objectives of the study are (1) to develop a model that can measure wheat transport modal ton-mile shifts, (2) measure modal energy use changes, and (3) measure the modal emissions changes resulting from hypothetical shortline railroad abandonment. Total ton-miles are about the same in the simulated shortline abandonment and no shortline abandonment cases, with the abandonment scenario generating 2% fewer ton-miles. Total energy consumption is nearly identical in the two scenarios; 2.1% higher in the shortline abandonment case. Grand total emissions are 1.4% lower in the scenario that does not include shortlines in the logistics system.