Deregulation and Firm Performance: Evidence from the Rail Industry. Lee Pinkowitz, and Rohan Williamson* June 2015

Transcription

1 Deregulation and Firm Performance: Evidence from the Rail Industry Lee Pinkowitz, and Rohan Williamson* June 2015 Preliminary Please do not cite without permission. Abstract This paper broadly examines the how deregulation is related to firm performance using the deregulation period from 1978 to 1982 as a pseudo natural experiment. The paper first examines the long-term performance of U.S. industries from 1963 to The paper then examines the performance of deregulated industries relative to other industries to explicitly examine whether there was a real impact on performance post deregulation. We use multiple definitions of performance that are both accounting and market-based. During the deregulation period, Congress passed the Staggers Act which served to deregulate the rail industry allowing firms greater freedom to set prices. Unlike many other deregulation actions, the Staggers Act explicitly intended to increase the profitability of the industry. More than three decades after its passage, a comprehensive analysis of the performance of the rail industry, its shippers and related industries is needed. Therefore, we focus on the rail industry and examine its performance and how it differs from other deregulated industries, its competitors and its shippers. The study provides for a broad based analysis on the impact of the deregulation of the rail industry and the potential spillover effects of Staggers. * Respectively, Associate Professor, Georgetown University and Stallkamp Research Fellow and Professor of Finance, Georgetown University. We would like to thank Murillo Campello, John Gray, Jeffrey Macher, John Mayo and Jason Schloetzer for their helpful comments and suggestions. This research was supported in part by the Georgetown Center for Business and Public Policy. 1

2 The Impact of Deregulation on Firm Performance: Evidence from the Rail Industry 1. Introduction For most of the 20 th century several large industries that were considered vital to the U.S. economy were regulated at the federal level. The general objectives for economic regulation were to improve quality of service, fairness in pricing and impact entry conditions in industries such as transportation and communications. Beginning in the 1970s there was a strong push for deregulation of these industries. When the government deregulated airlines, trucking, railroads, natural gas and banking in the 1970s, the intent was to give these industries more power to build the economy and reduce the cost of government subsidies. The ultimate intent was to give consumers more benefits through competitive pricing and better quality products and services. For the railroad industry specifically, in 1980 the Staggers Act (Staggers) served to deregulate the freight rail industry allowing firms greater freedom to set prices. Interestingly, unlike other deregulations, an explicit intent of Staggers was to increase the profitability of the industry. Prior examination of these deregulations have studied their impact on industry competitiveness, pricing and productivity along with consumer choice (Lowtan (2004), Martland (2006), and Martland, Lewis, and Kreim (2011)). In this study, we conduct a long-term investigation of deregulation and firm profitability. Specifically, we examine the performance of the railroad industry following the passage of Staggers which extends prior work on cost savings and productivity gains (see for instance, Wilson (1997) and Lim and Lovell (2009)). Given the implied intent of the deregulation, there are clear policy implications for taking a long-term view of Staggers and its impact on industry performance. The investigation focusses on the rail industry and relates it to other deregulated industries as well as industries which were not deregulated in the 50 year period. The impact relative to other deregulated industries allows us to examine whether the type, form, and intent of deregulation impacts firm performance. Importantly, this study takes a broad view of performance and examines accounting measures as well as those used by regulators and investors. 2

3 To investigate the performance of firms in the sample and the potential impact of deregulation, we examine the change in deregulated firm performance from before to after the deregulation and compare it to firms that were not deregulated over that time period. The main accounting measure is the operating margin of the firms which is a basic accounting measure of profitability. We next examine the standardized return on investment (SROI) which is the firm return on investment standardized using the industry cost of capital. The SROI is similar to that used in Macher, Mayo and Pinkowitz (2014) and based on a measure used by regulators like the Surface Transportation Board (STB) to determine revenue adequacy in the railroad industry. Finally, we use the primary market-based measure used in the finance literature which is the market to book (MB) measure. Market to book represents the market value of the assets relative to the book value of the assets and measures how investors value a firm. Overall the results show that there is considerable variation, both within and across industries, in how deregulation is related to firm performance. Given this, and the skewness that is observed in the results, regulators should be particularly careful about using simple averages to make assessments. To demonstrate this the paper uses quantile regressions to examine the impacts across the entire distribution of firms, and shows that the implications are quite sensitive to where in the distribution a firm lies. The results show that the change in operating margins is relatively similar for both deregulated firms and those which were not deregulated in the past fifty years. However, the accounting based performance measure that is used by the STB has improved in the post-deregulation period for industries that were deregulated relative to those that were not. Interestingly the market-based measure provides the opposite results, with deregulated industries performing worse in the post-deregulation period than industries which were not deregulated over that time. There are various motivations for the deregulation of industries. One of the intentions of the Staggers Act was to improve the economic performance of the rail industry. Using this as motivation, we examine whether the rail industry performed differently than others deregulated around the same time. We find evidence of improved accounting performance in the rail industry relative to other deregulated industries. However, the rail industry lags other industries when we focus on the market-based measure of 3

4 performance. This could be driven by the fact that Staggers specifically mentioned the earnings of the industry and nothing about market-based performance. We then examine whether rail s improvement in accounting performance comes at the expense of industries who ship the most via rail. The results are not consistent with the improvement in the rail industry being driven by a deterioration in the performance of shippers. This supports other studies of improvement in the efficiency of the rail industry, see Caves, Christensen and Swanson (2011) and Eakin, Bozzo, Meitzen and Schoech (2011). An important contribution of this study for researchers and regulators is to be careful in putting too much weight on ordinary least squares (OLS) estimates as an indicator of the effectiveness of a particular action. OLS coefficients provide an estimate of the conditional mean of a distribution. This paper argues that going beyond the mean is very informative especially in cases with skewed distributions or variability within industry. This paper utilizes a quantile regression approach to address these concerns. Additionally, policymakers should try to make more explicit the intent of a policy to allow for better measurement of its success. This paper shows that even though there was an improvement in accounting performance for the rail industry, the implication from market-based measures is more mixed. Finally, long-term impact studies should be done on a relative basis, as with this study which employs a difference-in-differences approach. Relative performance provides additional insight in that it requires a benchmark which can be used to place performance in perspective. 2. What is the relative performance of industries over time? The overall goal of this study is to investigate the relative performance of industries over the fifty year period ending in 2013, and incorporate the impact of deregulation on that performance. Policymakers make many decisions that could have a lasting impact on firm performance and value. Such decisions include whether and how to regulate a particular industry, or when or if an industry will be deregulated. Over the past 50 years there have been a few deregulations of major U.S. industries. Interestingly, several 4

5 of these decisions occurred during a particular five year period in the late 1970s and early 1980s which we will exploit to get a clearer picture of the impact of deregulation on firm performance The Approach We first set out to get a general idea of the profitability of U.S. industries from 1963 to This fifty year period was one of the most eventful in U.S. business history complete with: post-war growth in the fifties and sixties; recession and high fuel cost of the seventies; a period of deregulations and high interest rates in the early eighties, followed by a stock market crash and contraction in the late eighties; the internet boom and bust in the 1990 s; ending with the recent financial crisis and the great recession. One of the contributions of this paper is to conduct a long-term examination of industry performance that captures many macroeconomic as well as policy changes. In this benchmark analysis we identify those industries that were deregulated relative to those that were not deregulated over that period. Firms that are not deregulated are either those firms that are still regulated or those that were never regulated in any material way. There was a rash of deregulation activity between 1978 and 1982 and we use this period to identify deregulated industries. During this period the Rail, Trucking, Airline and Telecom industries were deregulated, therefore we use firms in these industries as our deregulated sample. This identification strategy allows us to use a difference-in-differences approach to examine deregulation and industry performance while controlling for macroeconomic factors that may differentially affect firms. Importantly, even though we refer to industry performance, our analysis is done at the firm level and then the industry is constructed using the 2-digit SIC code or more coarsely, the Major Divisions of the SIC codes. This allows us to also control for firm level factors that may impact performance but are not influenced by deregulation, per se. 1 It is critical to note that data limitations make it impossible to assign causality to our results. Deregulations do not occur randomly to particular industries, nor do they occur randomly in time. Our empirical methodology attempts to control for other factors which could impact firm and industry performance over the long-run period we examine. However, we acknowledge that our results should be interpreted as correlations rather than causal. For brevity in the writing, we do not continually make this disclaimer and often use phrasing such as the impact of deregulation. 5

6 Having identified the deregulation period and sample, we then investigate the relative performance of industries that were deregulated compared to those that were not deregulated. We examine this relative performance in several steps. First, we do a univariate analysis of deregulated industries relative to other industries in both the pre- and post-deregulation period. Second, we examine the relative performance of deregulated firms controlling for macroeconomic, time-, industry- and firm-specific variables. Third, given one intent of Rail deregulation was to improve industry performance, we focus on the railroad industry and compare its performance to both other deregulated industries and those which were not deregulated. Finally we investigate whether the improved rail industry performance was at the expense of shippers. To analyze the performance of the industries we rely on measures of performance used in accounting, finance, and by regulators. This will allow a broad examination of relative performance across time and how it differs based on deregulation. We use three measures of firm performance to examine the long-term industry performance and the impact of deregulation; one accounting, one economic, and one market-based. The accounting measure is Operating Margin. Operating Margin is measured as Earnings before interest and taxes divided by net revenue and gives us an indication of the operating profitability of a firm. It can either be an indicator of pricing power or cost control. To avoid any excessive impact from outliers, we winsorize operating margin so that any observation greater (less) than 100% (-100%) is set to 100% (-100%). Our economic measure is Standardized Return on Investment (SROI) which is the ratio of return on investment (ROI) to the industry s cost of capital. This measure can be considered a hybrid of accounting and market measures. While return on investment is accounting based, industry cost of capital is market-based. We construct SROI to be similar to what the STB uses to measure revenue adequacy and is analogous to the measure ROI/COC in Macher, Mayo, and Pinkowitz (2014). Return on Investment is defined as after-tax earnings divided by average invested capital and gives us a metric to examine the return per dollar of capital invested in the business. 2 The denominator is the Weighted Average Cost of Capital 2 While unreported, the correlation between ROI and operating return on assets (measured as after-tax operating earnings divided by average assets) is The rank correlation is

7 (WACC) for the industry in a particular year. The precise calculation of SROI as well as summary statistics for WACC are outlined in Appendix A. As a way to benchmark whether our WACC calculation is similar to the one that the STB uses, we examine how closely our estimates track those of rail industry, which are listed on page 20 of the Railroad Facts 2014 edition. Figure 1 shows both our estimates and the AAR estimates for the period. The figure indicates that our estimation of WACC for the railroad industry closely matches that used by the STB. The mean difference in estimates over that time period is a single basis point (0.01%), with a median difference of 3 basis points. We infer from this that our estimates of WACC for all industries are likely to be similar to what regulators would estimate if they followed the STB method. 3 Finally our market-based measure is Market to Book (MB) which examines the ratio of the market value of the firm to the book value of its assets and is analogous to Tobin s Q. It is constructed as (book value of liabilities plus market value of equity) / book value of assets. Market to book is widely used in the finance literature to measure the relative market value of a firm. To mitigate outliers, we winsorize MB at the 1% tails. Our data on publicly traded U.S. firms come from the CRSP/Compustat merged database. We include all publicly traded firms incorporated in the U.S. with assets and market capitalization (in year 2000 dollars) greater than $5 million. 4 Financial firms (SIC ) are excluded. 5 Table 1 shows the distribution of observations across 2-digit SIC codes for our full sample. We have 182,074 firm years in total with a mean of 3,570 firms per year. In addition to the standard 2-digit SIC codes, we construct four additional industries in order to isolate firms which were deregulated in the last 50 years. RAIL is the railroad industry and contains all firms with 4-digit SIC codes of TRUCK represents trucking and contains firms with the SIC codes 4210 and AIR is the passenger airline industry represented by SIC 3 The STB method for estimating WACC changed several times across that time period, which perhaps makes it more surprising that our technique obtains such close estimates. 4 CPI data are obtained from the St. Louis FRED database (data code CPIAUCNS) and adjusted to January 2000 dollars. 5 We also drop firm years where assets or sales are negative, zero, or missing. We retain only firms with basic ordinary shares (data item sharecode equal to 10 or 11). 7

8 codes 4511 and TELECOM represents long-distance telecommunications and is comprised of firms in the SIC codes 4812 and Our industry classifications are mutually exclusive. Thus, firms in SIC codes 4812 and 4813 appear in TELECOM, but not also in the Communications (SIC code 48) industry. We provide a detailed list of the firms in our RAIL industry in Appendix B. In addition to our 2-digit industry classifications, we examine firms using a coarser industry classification based on the Major Divisions of the SIC codes. The Major Divisions and their respective 2- digit SIC codes are: Agriculture, Forestry and Fishing (01-09); Mining (10-14); Construction (15-17); Manufacturing (20-39); Transportation, Communications and Utilities (40-49); Wholesale Trade (50-51); Retail Trade (52-59); and Services (70-89). We again separate out railroad, trucking, airline, and telecom firms based on the 4-digit SIC codes. 2.2 Industry Performance 1963 to 2013 To set a baseline for overall industry performance, we first examine the performance of all industries and identify those that we will use as deregulated industries. In Table 2, we show the performance based on our three measures over the fifty year period for firms split by Major Division. As described above there are four deregulated industries and eight that are not deregulated over the sample period. Examining Panel A, we see that mean (median) operating margin of firms which were deregulated over our sample was 4.2% (7.5%), while firms which were not deregulated had mean (median) operating margins of 1.3% (7.0%). The results show that industries which were deregulated have a higher operating margin than the not-deregulated industries at both the mean and the median. 7 6 Compustat sic codes are used, but some companies SIC codes change through time. Additionally, in certain years, the SIC codes also differ from those found in the CRSP database. As such, we make changes to the following firms: Railamerica is assigned SIC code 4011 while Lin Broadcasting, Millicom, Multiband Corp, PTV, Remote Dynamics, and Surewest Communications are assigned to We use the term not-deregulated to indicate an industry which did not experience a deregulation from It is not used to imply that those industries are regulated. In fact, most of the industries are deregulated both before and after the period. Utilities, on the other hand are regulated both before and after that period but still fall under our heading of not-deregulated firms. 8

9 However, the aggregate numbers hide large variation across industries. At the mean, the industry with the highest operating margin is the Railroad industry at 14.9%, while mining and services both have negative average operating margins. Looking at medians, the highest operating margin industry is Transportation, while the lowest is Wholesale Trade. Table 2 also shows the 25 th and 75 th percentile for each industry. Combining those results with the means and medians indicates skewness in operating margin, where some firms severely over- or under-perform other firms in the industry. For instance, although Mining has the second lowest mean, it ranks fourth based on its median. In aggregate, the mean operating margin of all firms is below its first quartile indicating significant left skewness in the data. This is despite the fact that we winsorize our operating margin data to be between -100% and 100%. For the deregulated industries Railroad and Trucking exhibit little skewness while Airlines and especially Telecom exhibit negative skewness. Given these results, it will be important to examine more than just the mean in order to make robust inferences. While deregulated firms had higher operating margins than not-deregulated firms, we find the opposite when we measure performance as standardized return on investment. In Panel B we show that deregulated firms had lower SROI than firms which were not deregulated, with means of compared to (medians of and 0.710), respectively. SROI is designed to mimic the measure used by the STB to calculate revenue adequacy. To be deemed revenue adequate, a firm would be expected to have SROI greater than one. Our results suggest that according to that definition, more than half of the firmyears for U.S. publicly traded firms would have been deemed revenue inadequate over the past fifty years. It is clear from the first two panels though that inferences can change depending on the measure of performance used. Panel C shows that unlike our accounting measures, MB exhibits positive skewness with means roughly 50% larger than the medians. For the not-deregulated firms, Services has the highest mean and median MB while construction has the lowest. Within the deregulated industries, Telecom has the highest MB at both the mean and the median. Railroads have the lowest MB of any industry at both the mean and the median. 9

10 In fact it is below 1.0 which implies that, on average, the market perceives the value of Railroads to be below its book value. Overall the results in this section highlight the importance of using various measures in assessing general performance of an industry. We see that in the accounting based measures, the deregulated firms have better operating margins but exhibit lower SROI. The picture using market-based measures are quite different where deregulated firms have substantially lower MB. Highlighting the Railroad industry, we see that it exhibits the highest mean operating margin of all industries but the lowest MB. Generally, the results support the understanding that good accounting performance is not necessarily concomitant with good market-based performance. 3. Industry Performance Pre- and Post-Deregulation In the prior section we examined the performance of U.S. industries from 1963 to 2013 using several measures of performance. We also isolate those industries that were deregulated from those that were not deregulated. In this section we examine the industries and see how they performed before and after the period of deregulation. To do this analysis we need to identify when the industries were deregulated to get a clearer picture of the potential impact of deregulation on performance. The period in the late 1970s to the early 1980s saw a series of federal deregulatory actions across several industries. In 1978 passenger airlines were deregulated followed by railroad and trucking in Finally in 1982 the telecommunications industry was deregulated. In taking a long-term view of the data, we categorize the period of deregulation generally and do not attempt to precisely distinguish the level or form of the deregulation. Similarly, the use of these industries and the particular time period has been used previously in the literature by Barclay and Smith (1995) and Opler, Pinkowitz, Stulz and Williamson (1999). As stated in the prior section, we broadly classify 1978 to 1982 as our deregulation period and drop this period from our remaining analyses. We do so in order to examine the long-term performance of deregulated firms, rather than any immediate impact resulting from any specific deregulation act(s). As 10

11 such, our results should be interpreted more as an example of a regime shift, if any, rather than an event study of particular deregulations. Thus, our pre-deregulation period is from 1963 to 1977 and our postderegulation period is 1983 to We then do a univariate analysis for the pre- and post-deregulation period across industries to get a general idea of the effect of deregulation on performance. We first examine operating margins for industries in the pre- and post-deregulation periods. The results are shown in Panel A of Table 3. Since the post period covers 30 years while the pre period covers only 15 years, the sample size is much larger for the post period. Additionally, within given years, there is better coverage in the latter period as there are simply more publicly listed firms in the post-deregulation period. For all U.S. firms, operating margin is significantly higher in the pre-deregulation period with a mean of 10.2% while the mean post-deregulation is actually negative at -2.4%. The medians show a similar pattern, but are considerably closer at 8.8% and 5.9% respectively. As with the full sample period, there is considerable skewness in the operating margin data, but interestingly, it reverses across our two periods. Pre-deregulation, operating margins are positively skewed, while they are negatively skewed postderegulation. The skewness post-deregulation is particularly large as the mean is actually less than the 25 th percentile. Panel B shows similar patterns for SROI, with higher performance pre-deregulation, and a reversing of the skewness from pre- to post. It would initially appear that in the U.S., average firm performance decreased dramatically from pre- to post-deregulation. However, the market-based measure again contradicts our accounting measures as Panel C shows that MB increased substantially from pre- to post. Importantly, the aggregate results demonstrate that examining only deregulated firms would be insufficient to make inferences about the impact of deregulation because there appears to be a significant shift for all U.S. firms over that time. Using firms that were not deregulated as controls should give us a better estimate of the impact of deregulation. For instance, in Panel A, we see that the mean operating margin of deregulated firms went from 12.2% to 0.6% after the deregulation period. Thus, at the mean, deregulated firms dropped by 11.6 percentage points. Over that same time period, firms that were not deregulated dropped from 10.1% 11

12 to -2.5%, a reduction of 12.6 percentage points at the means. Broadly speaking, we can use the difference between the two differences ( = 1.0) to say that deregulated firms outperformed other firms from before to after the deregulation period by about 1 percentage point. 8 The skewness in the data suggest that the mean is likely not sufficient to describe the distribution. A similar calculation using medians suggests that the typical deregulated firm performed equivalent to the typical not-deregulated firm. 9 We can perform similar calculations for our other measures which suggests that at the mean, deregulated firms underperformed with respect to SROI and MB. Combining Rail, Trucking, Air, and Telecom allows us to examine deregulation in aggregate, but masks the fact that there is considerable variation within those industries. From pre- to post-deregulation, Rail shows improvement in operating margin at both the mean and the median, while Trucking and Air show small decreases, and Telecom has a large decline. With our other measures, we see there is also variation among the four deregulated industries. As such, in addition to looking at the deregulated firms in aggregate, it will be important to examine the industries separately. 4. Does deregulation have an impact on industry performance? So far we have examined the performance of firms using accounting and market-based measures for the period from 1963 to 2013 and compared the performance of deregulated firms to that of the notderegulated firms. The above comparisons do not control for several factors that could impact our analysis of the effect of deregulation on industry performance. In particular, the previous comparison of performance in the pre- and post-deregulation periods does not control for confounding effects of secular, macroeconomic, or firm-related factors. In this section we investigate the performance of deregulated firms 8 Of course, the outperformance is relative. In this case, both sets of firms experienced a decline in operating margins, but the deregulated firms decrease was smaller. In the remainder of the paper, when we talk about over- or under-performance, we mean relative to the benchmark, rather than in absolute terms. 9 Deregulated firms had a median decrease of 2.9 percentage points ( ). This is the same decrease at the median for not-deregulated firms ( ). 12

13 relative to those which were not deregulated while controlling for other factors that could influence the results we observed previously. 4.1 Deregulation and Industry Performance In order to investigate the impact of deregulation on the performance we need to control for factors that could impact performance beyond the deregulation event. To control for other factors and isolate the relative performance of deregulated firms we estimate the following difference-in-differences regression: y = α + α + λgdp + λ Size + δderegulated + γpost Deregulation it, t FE 1 t 2 it, it, t + β( Deregulated * Post Deregulation ) + ε it, t it, (1) where the dependent variable is one of our performance measures: operating margin, SROI, or MB. The right-hand-side variables are: GDP, the U.S. per capita gross domestic product for the year; SIZE, the log of the firm s total assets in year 2000 dollars; Deregulated, an indicator variable that takes a value of one if the firm is in the Truck, Railroad, Telecom or Airline industry, and zero otherwise; and Post- Deregulation, an indicator variable set to one if the year is from 1983 to 2013, and zero otherwise. The regression is estimated with time fixed effects as well as industry fixed effects. For our tests we are primarily concerned with β which measures the interaction of deregulated industries in the post deregulation period. This coefficient is the difference-in-differences estimate of the relative performance of firms in deregulated industries from pre- to post-deregulation relative to firms in not-deregulated industries, from pre- to post-deregulation. As such, our estimate of β is similar to our calculation in Section 3, but allows us to control for other factors. We know from the finance literature that size is an important determinant of firm performance, as such we include it as a control variable. We include time fixed-effects to allow for any macroeconomic effects which would impact all firms in a given year. Additionally, we include GDP which is constant for all firms in a year, but its variation through time could play an important role in relative industry 13

14 performance. Lastly, we include industry fixed effects in each regression so that our estimate is only examining the impact within industry. We estimate equation (1) and report the results in Table 4. Table 4 reports only the β coefficient, the interaction of deregulated industries and the post-deregulation period. 10 Columns (1) and (2) show OLS results where column (1) defines industry with the SIC Major Division level and column (2) defines industries at the 2-digit SIC level. The OLS results for operating margin show that the coefficient on the interaction term is (0.008) when we define industry at the major division (2-digit SIC) level. Neither estimate is significant at conventional levels. The interpretation is that the pre- to post-deregulation change in operating margin for firms in deregulated industries is 1.8% (0.8%) higher than the change for industries which were not deregulated. The OLS regressions give us an estimate of the conditional mean and are thus analogous to our estimate of 1.0% from Section 3. As pointed out in previous sections, there is significant skewness in the data which could impact our inferences. Therefore, we estimate quantile regressions at the median to examine the conditional median, which can be thought of as measuring the impact for the typical firm in the deregulated industries. Regressions (3) and (4) show the results of those estimates. We see that the estimates in both specifications are about -0.5%, and both are marginally significant. These estimates are analogous to our estimate of 0% using medians in Section 3. The fact that the OLS estimates straddle that calculated in Section 3, while the median estimates are smaller shows the importance of controlling for time-, firm-, and industry-specific factors. Overall, while skewness impacts the magnitudes, it appears that deregulated firms performed similarly with respect to operating margin from pre- to post-deregulation than firms which were not deregulated. The measure of economic performance which is preferred by the STB is SROI, which uses ROI normalized by the industry cost of capital. For this measure, the results show that firms in deregulated 10 We are not interested in the coefficients on GDP or size, nor the specific estimates of the fixed effects. Additionally, the estimate of γ is absorbed by the time fixed-effect while the estimate of δ is absorbed by the industry fixed-effect. Full results of all estimations are available from the authors. 14

15 industries outperform firms in deregulated industries. The significance is stronger when industries are defined by Major Division and is stronger using OLS instead of median regressions. The evidence of skewness in the SROI data is that the OLS coefficients are six to seven times as large as the corresponding estimates of the conditional medians. While OLS results would suggest that deregulated firms greatly outperformed not-deregulated firms, the evidence using the median regressions is more muted. Therefore, our inferences with respect to accounting performance and regulation depend on the measure we use as well as whether we examine conditional means or medians. We next examine market to book results which reflect the investors view of the relative performance. Using MB we see that consistent with the univariate results, deregulated firms have a lower value then not-deregulated firms in the post regulation period. Using the OLS approach we see that the under-performance in MB is significant only when we control for industry at the Major Division. While the estimate remains negative when we define industry at the 2-digit SIC level, it is no longer significant. However, the quantile regressions show that both are significantly negative when we estimate the conditional median. 4.2 Industry performance across the distribution of firms Our regression evidence in Table 4 provides us estimates of how regulation impacts the conditional mean and median of the distribution. However, it is also useful to understand what the effect of deregulation is across the entire distribution of firms. To do this analysis, we re-estimate equation (1) defining industries at the 2-digit SIC level using quantile regressions every 5 th percentile. The results are shown in Figure 2. Each panel of the figure illustrates 21 different estimations of equation (1). In each panel, the dotted line is the coefficient estimate for β for the quantile indicated on the x-axis. The shaded area indicates the 95% confidence interval around the estimate. The figure also highlights the OLS coefficient shown by the x, with the 95% confidence interval indicated by the error bars. To be specific, for the operating margin panel, the OLS estimate from specification (2) in Table 4 was and this is shown by the x, while the estimate of the median from specification (4) in Table 4 15

16 was and is indicated by the dashed line at the 0.5 quantile in Figure 2. As the confidence intervals straddle zero, indicated by the blue line, the figure allows us to see that both the conditional mean and median are insignificant at the 5% level, consistent with Table 4. The figure allows us to make additional inferences about the impact of deregulation on firm performance. The dotted line lies above zero through the 40 th percentile and below it for the remainder of the distribution. However, the coefficients are reliably positive only to the 10 th percentile, while they are reliably negative in the top-third of the distribution. This suggests that while the mean impact is zero, there is considerable variation across firms. Since regulators are concerned with the magnitude of policy changes, it is useful to be able to see the size of the effect across the entire distribution. For firms which had low operating margins, they subsequently outperformed by as much as 4%, while for firms which were in the top of the distribution, they underperformed by as much as 2%. Some might view this simply as evidence of regression to the mean in operating performance, except that the estimate represents the change of the deregulated firms relative to the change in the not-deregulated firms. Thus, it seems more appropriate to interpret the evidence that deregulation helps firms which were performing poorly more so than firms which were already profitable. The variation in operating margin around zero results in the insignificant positive OLS coefficient we observe in Table 4. The pattern of SROI results shows that the positive mean effect is driven by firms in the bottom of the distribution. The skewness in the results is clearly seen in the figure as the OLS coefficient lies above the dotted line at each of the estimated quantiles. However, the imprecision of the estimates can be seen from the wide error bands in both the OLS and quantile regressions. Market to book exhibits considerably less skewness. In fact, the results show that not only are the mean and median difference-in-differences negative, but deregulated firms appear to have smaller increases in MB than not-deregulated firms across the entire distribution. Additionally, the magnitude of the difference is relatively similar across the entire distribution. Thus, while the accounting results are mixed, the marketbased results seem to clearly indicate that deregulated firms had smaller increases in MB than notderegulated firms did. 16

17 This analysis shows that there are large differences in the relative performance of deregulated firms compared to not-deregulated firm. The variability exists within measures as well as across measures of performance. The results suggests that the relation between deregulation and firm performance should be examined more granularly. 5. What s special about Railroads? As the univariate results indicate, the railroad industry seems to have performed differently from other deregulated industries over the sample period. Without controlling for any other factors, the railroad industry exhibited the highest improvement in performance of all the deregulated industries. In the prederegulation period the railroad industry was in poor condition, and there was concern that it might not survive. During the late 1970s, Congress passed several laws designed to help the railroad industry. These culminated in 1980 when the U.S. Congress passed the Staggers Act which substantially deregulated the railroad industry. Staggers was unique in that it specifically mentioned the low profitability of the railroad industry, and that it was important for earnings in the industry to increase. One of the explicit goals of Staggers was to assist the rail system to remain viable in the private sector of the economy. 11 Following Staggers there was substantial consolidation in the industry with the number of Class 1 railroads falling from more than 30 to only five U.S. based railroads by the end of the 20 th century. In this section we focus on the railroad industry and its change in performance following deregulation. Relatedly, we examine the shippers that make use of railroads to investigate the possible influence of deregulation on their performance as well. Specifically, whether the change in performance of the railroad industry is related to changes in the performance of shippers. This analysis helps to shed light on the possible impact of deregulation on industry performance. Our approach allows for a pseudonatural experiment in that improvement in railroad performance was one of the explicit goals of Staggers. 11 Staggers Rail Act 3(3) 17

18 5.1 Railroad vs other deregulated Industry post-deregulation performance While univariate results indicated that the railroad industry outperformed other deregulated industries, we need to control for industry-, firm-, and time-specific factors as well as macroeconomic changes. To do this we augment our prior difference-in-differences methodology by estimating the following regression: y = α + α + λgdp + λ Size + γpost Deregulation + it, t FE 1 t 2 it, t δ RAIL + β ( RAIL * Post Deregulation ) + 1 it, 1 it, t δ TRUCK + β ( TRUCK * Post Deregulation ) + 2 it, 2 it, t δ AIR + β ( AIR * Post Deregulation ) + δ 3 it, 3 it, t 4 TELECOM it, + β4 ( TELECOM it, * Post Deregulationt) + εit, (2) As with our prior analysis, the dependent variables represent the accounting, economic, and market-based measures of performance. GDP, Size, and Post-deregulation are identical to their definitions in equation (1). As with equation (1), we also include annual fixed effects as well as industry fixed effects. The key difference between equation (1) and equation (2) is that we now allow for different effects among each of the four deregulated industries. Equation (1) is essentially a constrained version of (2) where we require β 1=β 2=β 3=β 4. Again, our coefficients of interest will be β for each deregulated industry. These are the only coefficients that we report in Table 5. Table 5 shows the results for each of our performance measures. The reported coefficients represent the difference-in-differences estimate for each deregulated industry relative to not-deregulated industries. However, we also perform Wald tests to examine the difference in the coefficients among the deregulated industries. This empirical design allows for two levels of comparison for the railroad industry. First, β 1 indicates the change in performance of the rail industry relative to the change in performance of not-deregulated industries from pre- to post-deregulation. Second, our Wald tests allow us to estimate whether the relative change of RAIL to not-deregulated industries was greater than the relative change of the AIR, TRUCK, and TELECOM industries to not-deregulated industries. This is essentially a diff-indiff-in-diff design and is shown at the bottom of each panel where we report the differences in coefficients 18

19 between RAIL and the other deregulated industries. We again perform median regressions to control for the skewness of the distribution that may impact inferences using simple OLS. Table 5, Panel A shows the results for operating margin. Specifications (1) and (3) define industries at the Major Division level, while (2) and (4) define industries at the 2-digit SIC level. In specification (2), the estimate for RAIL is 0.15 which can be interpreted as the rail industry experienced a mean change in operating margin of 15 percentage points more than the change for not-deregulated firms over the same time period. This is significantly different from zero at the 1% level and is quite different from the zero difference for all deregulated firms shown in Table 4. The discrepancy results from the positive and significant coefficients on TRUCK and AIR and the large and negative and coefficient on TELECOM. The estimated coefficients on all deregulated firms in Table 4 is the weighted average of the coefficients on the individual deregulated industries in Table 5. However, there are fewer Railroad observations than other deregulated industries which masks the sizeable outperformance of RAIL in Table 4. The coefficients for the median regressions are considerably smaller than OLS for RAIL, TRUCK, and AIR, while TELECOM remains large, negative and significant. Therefore the combined operating margin leads to a lack of improvement in in Table 4. The bottom of the panel shows the differences between the RAIL coefficient and the other deregulated firms. We notice that the improvement in operating margin for RAIL was significantly larger than all of the other deregulated industries. Relative to the other deregulated industries, RAIL outperformed in operating margin anywhere from 8 to 30 percentage points. Not only are these statistically significant, but they are economically large as well. This shows that the deregulation of the railroad industry by Staggers resulted in better accounting performance for its firms than the deregulations of other industries around that time. The results are quite robust as RAIL shows statistically significant improvement in operating margin whether we use OLS or median regression and controlling for industries at the major division or the 2-digit SIC level. Panel B shows the results using SROI. The difference in SROI is significantly better for RAIL than for not-deregulated industries for all regressions estimated. Conversely, the change in SROI is not 19

20 significantly different from not-deregulated industries using OLS for TRUCK, AIR, or TELECOM. Using median regressions, there is no difference in SROI for AIR and TELECOM but TRUCK is significantly worse at the 1% level than not-deregulated industries. Not surprisingly, the change in SROI for RAIL is higher in the post-deregulated period relative to all other deregulated industries, and is significant at the 1% level in all instances. Overall, Panels A and B show there is clear relative improvement in accounting performance for the railroad industry compared to other deregulated industries in the post-deregulation period. As discussed earlier, the perspective of investors are not necessarily consistent with that of accounting measures and to get varying viewpoints on the impact of deregulation on industry performance one should investigate changes in shareholder value. To do this we examine MB in Panel C of Table 5. Generally the results using MB are mixed across the deregulated industries. In the post-deregulated period RAIL exhibits a worse MB when industry fixed effects are at the Major Division level but insignificant at the 2-digit SIC level. There is also no significant change using median regressions for RAIL in the postderegulation period. For TRUCK, there is no significant difference in MB except when we use median regressions with major division fixed effects. AIR is the only industry where the results are consistent, as their change in MB relative to that of not-deregulated firms is significantly worse across all regressions. TELECOM is the only deregulated industry with an improvement in MB relative to not-deregulated firms, but only when industry fixed effects are defined at the 2-digit SIC level. The MB difference for RAIL compared to other deregulated industries is sensitive to the regression technique and the definition of industry fixed effects. RAIL performs similarly to TRUCK, although it is marginally better using a median regression and Major Division industry definitions. RAIL outperforms AIR, but the difference is significant only when estimating median regressions. Finally, RAIL underperforms TELECOM, but the difference is only significant using OLS. 20

21 5.2 Relative performance across the distribution Given the skewness in the data as well as a desire to examine the entire distribution, we re-estimate equation (2) using quantile regressions at every fifth percentile. We show the results in Figure 3 where we plot the coefficients of each of the deregulated industries. As in Figure 2, the OLS coefficient is represented by the x, while the estimate at each quantile is shown with the dotted line. The 95% confidence interval is shown with the error bars and the gray region. The industry fixed effects are at the two-digit SIC code level and thus correspond to specifications (2) and (4) in Table 5. It is important to remember that the estimates in the graphs represent the difference between RAIL (or other deregulated industry) and the group of notderegulated firms for the period from pre- to post-deregulation. For operating margin we see that the significant outperformance of RAIL is evident not only at the conditional mean and median, but throughout the distribution. This suggests that for all railroad firms, their increase in operating margin was greater post-deregulation than not-deregulated firms. The magnitude across the entire distribution is also similar, about 7-9 percentage points. This is supportive of the idea that post-deregulation, the profitability of the industry increased for all remaining firms rather than firms profiting at the expense of each other. TRUCK also shows strong outperformance in operating margin with changes that are higher across nearly all of the conditional distribution. There is little difference across the distribution for AIR suggesting that the change from pre- to post-deregulation was no different than not-deregulated firms regardless of where in the operating margin distribution firms were. Conversely, the differential effect across the distribution is evident in TELECOM where the bottom quartile experienced substantial underperformance relative to even TELECOM firms at the median. The results using SROI still show that RAIL outperformed not-deregulated industries. However, the dashed line slopes slightly downward suggesting that firms which were in the bottom of the SROI distribution improved more than firms at the top of the distribution. In fact, for railroad firms which had the highest SROI, there is virtually no outperformance. The large underperformance for TELECOM with respect to operating margin is not evident with SROI. One interpretation is that while margins shrank in 21