This dissertation examines the relationships between productivity and short-run total costs, and short-run profits. The foci of this research are how productivity changes contribute to short-run cost variations over time and across firms, and how positive short-run profit change can be attributed to increased productivity. In the railroad industry, improvements in productivity do not necessarily imply either reductions in short-run total cost or increases in short-run profit. Though numerous studies have attempted to examine railroad productivity in recent decades, the existing economics literature has paid very little attention to these phenomena. Using an unbalanced panel of U.S. Class I railroads for the period of 1986 - 2000, a short-run total cost change decomposition model is used to attribute intertemporal and multilateral cost variation to its causal factors. The intertemporal and multilateral cost decompositions enable us to conduct a benchmarking exercise across firms or through time. In addition, a short-run profit change decomposition model is used to relate short-run profit change to its sources. These models are analyzed using linear programming techniques. The empirical findings are: (i) total cost change varied greatly across railroads; (ii) rail capital had a direct impact on total cost but not on profit in the short run; (iii) the railroad industry experienced significant technical progress over time; (iv) in the cost-efficient benchmarking exercise, the industry benchmarks were cost-efficient because they were large; (v) in both low-cost and cost-efficient benchmarking exercises, the benchmarking railroads were both technically and allocatively inefficient, they can learn from the industry benchmarks when it comes to cost savings; (vi) positive profit gains can be attributed to improvement in railroad productivity; and (vii) negative profit change can be attributed to falling rail rates and increased input prices.