The Returns to Scale Assumption in Incentive Rate Regulation Rajiv Banker Fox School of Business and Management, Temple University, 1801 N Broad St, Philadelphia, PA 19122, USA
[email protected] Daqun Zhang College of Business, Texas A&M University – Corpus Christi, 6300 Ocean Dr, Corpus Christi, TX 78412, USA
[email protected] Last revised: March 29, 2016 The Returns to Scale Assumption in Incentive Rate Regulation Abstract This paper challenges a common (though not universal) practice of maintaining the assumption of constant or non-decreasing returns to scale (CRS or NDRS) for benchmarking of local monopolies in incentive rate regulation. We consider the situation when firms get larger by acquiring customers that are costlier to serve, but detailed data on different types of customers are not available to regulators. If the output is specified only in terms of total number of customers or total sales (in monetary or quantity terms) without explicitly capturing the impact of different types of customers who may have different marginal costs, then we show that the estimated production function will appear to exhibit variable returns to scale (VRS) with a region of increasing returns followed by a region of decreasing returns to scale even when the true production technology has CRS or NDRS. This apparent region of decreasing returns is only an illusion caused by the mis-specification of the variables in the production function. We demonstrate the magnitude of the distortion in the efficiency of individual firms with extensive Monte Carlo simulation experiments. Our simulation results indicate that in most scenarios the VRS model performs much better than the CRS or NDRS models or a NDRS model that corrects for customer mix only in the second stage.