Journal of Transport Economics and Policy

Transcription

1 Journal of Transport Economics and Policy This article is the final accepted version to be published in a forthcoming issue volume to be determined later.

2 EVALUATING ALTERNATIVE POLICY RESPONSES TO FRANCHISE FAILURE: EVIDENCE FROM THE PASSENGER RAIL SECTOR IN BRITAIN Dr Andrew, S.J. Smith a and Phill Wheat b a Address for correspondence: Institute for Transport Studies, Room 213, University of Leeds, Leeds, LS2 9JT, UK. b Institute for Transport Studies, University of Leeds. Acknowledgements The authors are grateful to the UK Engineering and Physical Sciences Research Council (EPSRC) for funding this research, via Rail Research UK (RRUK), the universities centre for railway systems research. We would also like to acknowledge the support and advice offered by Professor Chris Nash, as well as the helpful suggestions and data provided by many people within the rail industry. Finally, we would like to thank the reviewers for their helpful comments. All remaining errors are the responsibility of the authors. Abstract One potential problem with franchising (competitive tendering) is how to deal with situations where the franchisee is unwilling to continue operating the franchise within the contract period. This paper studies the effects of the franchising authority s response to franchise failure in passenger rail in Britain, which saw the affected operators placed onto management or short-term re-negotiated contracts for an extended period. We find that operators on management contracts saw a sharp deterioration in efficiency. Further, the contract inefficiency persisted, though was eliminated by competitive re-franchising. In contrast, costs for re-negotiated franchises were no higher (statistically) than industry best practice. Date of receipt of final manuscript: February

3 1.0 Introduction Franchising (or competitive tendering) has become an important method for introducing competition for the market where competition in the market may be undesirable. Economic theory predicts that franchising should result in the tender being awarded to the most efficient operator. When introduced to a service previously operated by a state-owned monopoly, substantial cost reductions are therefore expected, driven by the fixed nature of the contract over a given period, and the profit maximising objective of the privatised firm. Of course, there are many problems in practice, most notably that the tender process may result in overly-optimistic bids, either due to winner s curse or strategic bidding, meaning that the most efficient operator may not be selected, and the expected cost reductions may not be achieved (Vickers and Yarrow, 1988 and Viscusi et. al., 2005). Ultimately, franchise failure may result, requiring a policy response from the franchising authority. In railways, starting with the 1991 European Commission Directive 91/440, Europe has embarked on a process of regulatory reform, progressively opening up rail markets to competition (both in and for the market). Via successive legislation, Europe s rail systems have been required to separate train operations and infrastructure (at least into separate divisions with their own accounts), which has been achieved via full institutional separation, organisation into separate subsidiaries within a holding company structure, or the separation of the key functions of train slot allocation and infrastructure charging into a separate body. Based on this separated model, competition in the market has been allowed to develop via third-party open- 2

4 access to the infrastructure (mainly for freight traffic), whilst competitive tendering, though not yet formally required by EU regulation, has been the chosen means of introducing competition to passenger services. Competitive tendering in rail has also been used outside Europe, for example in Melbourne, Latin America and for some North American commuter services. However, the expected productivity gains have not materialised in the British case. Despite some reported train operating company (TOC) cost savings during the early years after the completion of franchising in 1997, (see Affuso et. al., 2002; 2003), Smith et. al. (2009) find that TOC costs increased by around 45 per cent between 2000 and 2006 (or 35 per cent on a cost per train km basis; Figure 1). This cost rise is equivalent to around 1.5bn per year and, as a result, state subsidies to passenger train operators increased substantially over this period (see Smith et. al., 2009, Tables 2 and 7). [Figure 1 here] Importantly, during the period of our sample the franchising authority performed mid-term re-negotiations with a number of operators, resulting in half of the sector being placed on management or short-term re-negotiated contracts during the second half of our sample (see Smith et. al., 2009). The purpose of the paper is to test the impact of the British franchising authority s approach to dealing with failing franchises and the effects of the temporary contractual arrangements that were put in place. Of course, the franchising authority s response may have wider impacts on the bidding process through the signal that it gives regarding the likelihood that contracts 3

5 will be re-negotiated in future. Here we focus solely on the efficiency impact of the arrangements as they affected costs for the period of their duration and directly afterwards. There is an extensive literature analysing the efficiency and productivity performance of vertically integrated railways around the world (Oum et. al., 1999; Smith, 2006). More recently there has also been an interest in understanding the impact of vertical separation on total industry costs, mainly focussed on European evidence (Friebel, et. al., 2008; Asmild et. al., 2009; Growitsch and Wetzel, 2009; Cantos et. al., 2010); although one study considered evidence from North America (Bitzan, 2003). Overall, the results seem inconclusive, suggesting that much depends on the circumstances of the country concerned and the way in which the system is managed. There have also been a small number of studies focusing on the impact of competitive tendering on one part of the rail industry, namely passenger train operations. In Germany and Sweden the experience of competitive tendering has generally been positive, with the evidence suggesting that savings in the region of per cent can be achieved, alongside increased patronage (see Brenck and Peter, 2007; Lalive and Schmutzler, 2008; Alexandersson and Hulten, 2007; and Nash and Nilsson, 2009). Even here though, some franchises have failed, so our work is relevant for those countries. Kain (2009) describes the major problems that emerged in Melbourne, though the impact of the policy response is not described in any detail. Long-term passenger rail franchises have also been signed in Latin America, generally leading to radically improved performance, although in most cases re- 4

6 negotiation has been required due to changed economic circumstances (in particular the severe economic recession in the late 1990s; see Kogan, 2006). Turning to studies of British TOCs, Affuso et. al. (2002; 2003) and (Cowie, 2002a, 2002b, 2005) study the early years after privatisation (prior to the major cost rises) and all find improving productivity during this period. Only two studies cover the post-2000 period, after which costs started to rise. Cowie (2009) finds declining productivity growth after 2000, with the absolute productivity level falling post In a paper presented at the Thredbo 11 conference, Smith and Wheat (2009) report productivity levels falling as early as 2000 and not recovering over the remainder of the sample (to 2006). Smith et. al. (2010) reviews this literature. The latter paper focused on the impact of franchising on total factor productivity (TFP) and efficiency since privatisation. It included a simplified treatment of management and re-negotiated contract effects, but importantly did not adequately address the inherent problem of endogeneity bias when testing the impact of contractual or institutional arrangements on productivity (the TOCs that ran into trouble may always have had characteristics which made them likely to end up as distressed franchises; see section 2). In the present paper we guard against the endogeneity problem, and thus present a robust and comprehensive treatment of the temporary contract arrangements put in place by the franchising authority following franchise failure. The findings, as compared to the earlier simplified treatment, are different as a result. 5

7 This paper is therefore positioned within a broader literature on rail efficiency, and a relatively small number of studies on the efficiency of passenger train operations. Specifically, we focus on the franchising authority s response to failing franchises on costs and efficiency, rather than the broader picture of sector productivity performance which has been covered by the aforementioned studies. We also use a new dataset which is more robust than that used in earlier work, having been derived from industry sources. Importantly, our data allows us to separately identify TOC costs from track access charges paid to the infrastructure manager, and thus focus attention closely on the costs under the direct control of TOCs. It also contains new data on important cost drivers, most notably vehicle-km 1 (we are not aware of any previous studies of rail costs that has utilised vehicle-km data). The analysis contained in this paper is important from a policy perspective both in the UK, but also more widely internationally, where franchising or competitive tendering arrangements have been put in place or are being considered. Given the importance of franchising (competitive tendering) as a policy device in rail and other sectors, it is important to understand the efficiency impact of alternative policy responses to franchise failure. The remainder of the paper is structured as follows. Section 2 formalises our research questions regarding the impact of management contracts. Sections 3 and 4 detail the methodology and the data used for the study. The results are shown and discussed in section 5. Section 6 offers some conclusions. 1 Total vehicle-kms is defined as the distance travelled by all of the vehicles operated by a TOC (where a vehicle is a sub-set of a train). For example, a 3 car diesel multiple unit, travelling one km, would be counted as 3 vehicle-km. 6

8 2.0 Research Questions Passenger rail franchises in Britain were generally awarded on the basis of minimum subsidy (or exceptionally highest premium for profitable franchises) and the winning subsidy profiles generally declined sharply over the course of the franchise as a result of assumed cost savings and/or revenue growth. However, despite strong revenue growth, and some cost reductions, those operators where farebox revenue was small relative to costs, and where therefore cost reduction was the key to success, ran into difficulties within a few years of the franchises being let. The evidence suggests that the problems were more to do with unrealistic assumptions, particularly on the cost side (winner s curse), rather than being a deliberate strategy of low-balling with a view to subsequent re-negotiation (see Smith et. al., 2009). In response, the then franchising authority (SRA) had to perform mid-term renegotiations with the affected operators, which made up around half of the 25 franchises let (Table 1). These franchises were mainly placed onto cost-plus type management contracts with higher subsidy (the level of subsidy payment was negotiated annually on the basis of projected costs, so the affected TOCs therefore retained some cost risks during the year). For a smaller number of operators the original franchise agreements were simply re-negotiated for a short period (typically 2-3 years), again with higher subsidy (see Smith et. al., 2009). Post re-negotiation, these franchises therefore faced the same incentives as other operators continuing on their original franchise agreements. 7

9 It should be noted of course that in the event of franchise failure, the franchising authority faces a number of alternatives. An operator of last resort could be set up to step-in when a franchise fails 2. Some use of a short-term management contract with the incumbent is an alternative while a new franchise competition is organised. However, the distinguishing feature of the British case is that TOCs were put onto management contracts for an extended period (several years); although it should be noted that this situation came about during a period of considerable uncertainty, where refranchising had been temporarily halted because of lack of funding resulting from cost increases on the infrastructure side, and due to a desire to redraw the franchise map (see Smith et. al., 2009). [Table 1 here] Whilst there may have been good reasons for the franchising authority s response to franchise failure, given the circumstances, our aim is to determine whether the chosen policy response weakened cost minimising incentives such that costs under the temporary contract arrangements increased over and above the changes in costs for unaffected TOCs, and whether costs of these operators was then higher than best industry practice for the duration of the contracts (as well as after competitive re-franchising of the affected franchises). 2 During the sample period covered by this paper, one TOC, South Eastern, was moved onto such an arrangement after a period on a re-negotiated contract. 8

10 We break this objective down into a number of specific research questions: IA Did the costs of TOCs on temporary contract arrangements increase in the first year of those arrangements over and above the change in costs for other operators over that period? IB Were costs for the affected operators higher than industry best practice during the first year of the temporary contract arrangements? IIA Did costs fall back to the best practice level over the duration of the temporary contract arrangements? IIB Did costs fall back to the best practice level once the franchise had undergone competitive re-franchising? Research questions IIA and IIB are important given that some TOCs spent four or five years on management contracts and so persistency of any contract effect is very important from a policy perspective. Finally, we specify two further research questions concerning the period prior to franchise failure. IIIA Were the costs of TOCs that subsequently failed higher, other things equal, to industry best practice at privatisation (the start of our sample)? 9

11 IIIB Did the TOCs that subsequently failed cut costs prior to franchise failure such that their costs were lower than the other TOCs? Research questions IIIA and IIIB are important since failure to explicitly account for any prior systematic cost differences between the problem TOCs and other TOCs could introduce endogeneity bias into our estimated parameters because the problem TOCs may always have had characteristics which made them likely to become a problem TOC. We guard against this bias by including a problem TOC specific dummy variable and problem TOC time trend for the period proceeding movement to a problem contract. Our approach is analogous to that adopted by Domberger et al (1987) whom studied the efficiency performance of hospital ancillary services. As noted earlier, Smith and Wheat (2009) contained a simplified treatment of the contract effects, and did not adequately address the endogeneity problem. Ultimately, our approach enables us to estimate and plot the time profile of costs of TOCs which spent some time on management or re-negotiated contracts, relative to other TOCs, over the whole period of our analysis. Thus we are able to highlight the specific impact of the franchise authority s response to franchise failure, and how that impact changed over the duration of the temporary contract arrangements, as well as after competitive re-franchising. 10

12 3.0 Methodology 3.1 General model We investigate our research questions through estimation of a cost frontier. Our model can be represented as: C it f (Y,P,Q,Z, ; ) v u (1) it it it it t it it where the first term ( f (Y,P,Q,Z, ; )) is the deterministic component, it it it and Y it is a vector of output measures, P it is a vector of prices of the variable inputs, Q it is a vector of output characteristic variables (for example, dummy variables denoting commuter versus intercity services), Z is a vector of exogenous policy related influences on firms costs and contain the modelling of contract effects (see section 3.2), t is a vector of time variables which represent technical change and is a vector of parameters to be estimated. A translog functional form is used (see section 5). it t it Cit represents total controllable TOC costs, and so excludes track access charges, which are outside the control of the operators. We are therefore estimating a total cost function in the sense that TOCs are assumed to minimise all costs under their control (see section 4). However, in the context of the wider literature on rail cost function estimation, our cost function could be thought of as a variable cost 11

13 function, in the sense that the size and associated costs of the rail infrastructure are assumed fixed in our analysis. The v it term is a random component representing unobservable factors that affect the firm s operating environment. This term is distributed symmetrically around zero (more specifically assumed to be normally distributed with zero mean and constant variance). A further one sided random component is then added to capture inefficiency ( u it ). The specification of the inefficiency term is discussed in section 3.3 below. 2 Technical change over time (frontier shift) is modelled as t t t, where t is a time index and is a dummy variable which takes the value unity for years after The motivation for the dummy variable comes from the analysis of the raw data (Figure 1; see section 1) which indicates a cost shock after The rail industry in Britain has seen a sharp rise in costs since Whilst the infrastructure cost rises resulted from a re-appraisal of maintenance and renewal activities after the Hatfield accident 3 in October 2000, the reasons for the cost rises in passenger train operations are less well understood. This paper considers one reason for the increase in train operating costs, namely the impact of the franchising authority s response to franchise failure (see also Smith et. al., 2009). 3 A train de-railment at a town called Hatfield, just north of London, caused by defective track, which led to the death of four people. 12

14 In respect of input prices we include a labour price, together with variables that capture the characteristics of the rolling stock (rolling stock age and rolling stock type). Obtaining input prices for rolling stock and other costs proved problematic for two reasons. First, there are some classification issues between rolling stock and other costs which mean that our rolling stock price variable reported rolling stock lease costs divided by number of rolling stocks - is imperfectly measured. Second, it is problematic to select an appropriate denominator for other costs, since there is no associated physical input. Some previous studies have used train-km as the denominator (for example Sanchez and Villarroya, 2000), which is an output, not an input, and therefore runs the risk of capturing deterioration in efficiency as a rise in input prices. We therefore estimate a model that includes a labour price variable combined with a set of rolling stock price hedonic 4 variables, rolling stock age and rolling stock type variables as noted 5. In the final, preferred model, only the rolling stock variable was statistically significant, with the other variables being dropped (though the results of this paper are not sensitive to the decision to drop these variables). Further, we include TOC sector dummy variables that should capture systematic differences in rolling stock prices (and for that matter other costs) between the three TOC sectors. In this respect, the time invariant nature of rolling stock prices (fixed for the duration of franchises) matches with the time invariant nature of the sector dummies. As a final cross-check we note that a model comprising a labour price and our rolling stock input price variable gives almost identical results to those of the preferred model. 4 These variables play a similar role to rolling stock input prices, since the characteristic variables recognise that different types of rolling stock have different costs. 5 We also experimented with economy wide fuel price indices but these performed badly, mainly due to their invariance across TOCs. 13

15 3.2 Contract variable specification In order to capture the effects subsumed within the three research questions outlined in section 2, we model the contract effects as follows: Z jit j1 D jb j2 D jb T j3 D jo j4 D jo T * j5 D ja (2) where D ji are dummy variables corresponding to whether the TOC was eventually subject to a management contract (j=m) or re-negotiated contract (j=r). The i subscript denotes whether the time period is before or after the shift to the temporary contract arrangements (i=b refers to the time period prior to the temporary contract arrangements; i=o denotes the time onwards from the contract, extending also to the period after contract was terminated following competitive re-franchising; and i=a denotes the period after the contract was terminated). T is a time trend, whilst T* is a time trend starting at unity for the year that the TOC was placed onto the management or re-negotiated contract. These time trends are interacted with the relevant contract dummies to chart the progress of costs over time, both before, during and after the contract arrangements. jk are a set of parameters to be estimated (k=1,,5). As discussed above, this specification is adopted in order to avoid endogeneity bias, which means that we need to look at the cost characteristics of the problem TOCs both before and after the contract arrangements; and we also want to look at the path of costs whilst on the contract, and post re-franchising. 14

16 Using (2) we can test the following hypotheses which address research questions I to III 6. IA) If j 3 j4 T. * j1 j2 T 0, for T*=1 (first year of the temporary contract) and for T in the last year prior to the commencement of the temporary contract), then we can conclude that the problem TOCs costs rose as a result of the introduction of the temporary contracts relative to other operators. IB) If.T * 0, for T*=1 (first year of the temporary contract) costs j 3 j4 are found to be higher for the problem TOCs following them being placed on to the temporary contract relative to the cost for the other TOCs. IIA) If j 3 j4 T* 0 for T*=1 to 5 (corresponding to the duration of the contract arrangements), we can conclude that costs were still higher for the affected TOCs than best practice. This finding would suggest that some part of the contract effect is persistent. j 3 j4 * j5 IIB) If T 0 for T* after the completion of competitive refranchising, then we can conclude that costs did not fall back to the best practice level once the franchise had undergone competitive re-franchising. Note that there was insufficient data to include the R5 dummy and thus 6 Note j=m,r, that is there are two hypothesis; one for each group of problem TOCs (management contracts and re-negotiated contracts). 15

17 conduct this hypothesis for the re-negotiated TOCs (there are only four such operators, and only one saw re-franchising over the period of our analysis). IIIA) If j 1 j2 T. 0 for T=1 (first year of the sample) costs are found to be higher for the problem TOCs at the start of the sample relative to the other TOCs. IIIB) If j 1 j2 T 0 for T in the year directly preceding the move to the temporary contract, then we can conclude that problem TOCs costs were still above other TOCs costs prior to the introduction of problem contracts. If j 1 j2 T 0 then we can conclude that problem TOC costs were below those of other TOCs costs and so this is evidence that the problem TOCs cut costs to an unsustainable level and (at least part of) the cost increase following introduction of problem contracts was simply a reversion to a sustainable cost level. In all of the above cases we use t tests based on linear combinations of the relevant coefficients. To complete this section we note that the complete vector Z it comprises Zit Z Mit Z Rit Zit * (3) 16

18 where Z Mit, Z Rit are as described in (2), and Z it * comprises other policy variables that apply to all TOCs and not just the problem TOCs. We tested the inclusion of planned franchise length, and a dummy variable denoting the last year of a franchise contract; however only the last year of franchise dummy variable was found to be remotely statistically significant and so only this variable is retained in this vector. Given that the focus is on contract effects for this paper we do not consider these other policy variables further in the discussion. 3.3 Model Estimation We estimate the model as a stochastic frontier. This approach was first proposed independently by Aigner et. al. (1977) and Meeusen and van den Broeck (1977) for the cross sectional case. The advantage of the approach over conventional average response cost function techniques is that it permits the possibility that some firms may be inefficient. Since we have panel data there are a range of possible assumptions concerning the path of the inefficiency ( u it ) over time available from the literature. In this paper we adopt a model from a more general and flexible class of time varying efficiency models that allow for firm-specific time paths for inefficiency. These models therefore allow for the possibility that some firms may be getting more efficient, whilst others may be falling further behind. The general model can be written as: u u it u ( t ) i 2 i ~ N 0, u i (4) 17

19 where ( t ) is some function describing the variation in inefficiency over i time, and u i is a non-negative random variable. The model estimated in this paper is a special case of equation (4), and takes the following form (see Cuesta, 2000 and Alvarez et. al., 2006): t ) exp( t ) t 1,, T (5) i( i where the i are a set of firm specific parameters to be estimated. If i is positive for an individual firm, this indicates that efficiency is improving for that firm over time, and vice versa for a negative i. We do recognise however, that while this model is quite general, other nonnested specifications of inefficiency are possible. Particular mention should go to the class of models where inefficiency observations are assumed iid (independently and identically distributed) over time even though time or policy variables influence the mean and variances of the inefficiency distribution (models of this class include those proposed by Battese and Coelli (1995) and Alvarez et al (2006)). We have examined some variants of these classes of models and found the story regarding the impact of contracts to be similar to our preferred specification. As described in Section 3, a central aim of the paper is to understand the impact of franchise contract changes and Section 4.2 details how our formulation allows us to test a series of hypotheses about the effect of different contracts. We 18

20 include these terms in the Z it vector within the deterministic frontier function. This has the implication of implying that those TOCs were problem TOCs had different deterministic frontiers than those that were not. The inclusion of these terms in the frontier is a common approach in the literature for handling such variables (see for example, Coelli, Rao, O Donnell and Battese, 2005). 4.0 Data Table 2 shows the data used for the analysis. The dependent variable is TOC variable cost, defined as All TOC expenditure (excluding exceptional items), less any transfers to Network Rail (access charges and performance penalties / payments). Thus variable cost comprises staff costs (32 per cent), rolling stock leasing charges (27 per cent) and other TOC expenditure (41 per cent). Our sample covers a ten year period (1997 to 2006) covering 26 TOCs (since not all TOCs appear in all years, the total number of observations is 238). Note that the period 1997 to 2006 refers to the financial years 1996/97 to 2005/06. [Table 2 here] Given the highly regulated environment in which TOCs operate the companies are highly constrained in their ability to adjust prices to maximise passenger-km. We thus consider that TOCs produce train-km. This is consistent with other studies in this area (Oum et. al., 1999). In addition, TOCs also operate stations. In order to distinguish between the cost associated with running more trains and the cost of 19

21 lengthening trains, we define two separate outputs variables: train-km per route-km (train density) and average length of train. The third output variable is then the number of stations operated. The route-km variable is included alongside the other variables in order to distinguish scale and density effects, and passenger-km is also included in order to capture the separate cost impact of carrying more passengers on existing services. Thus we see our main output vector as comprising train-km, average length of train and number of stations; combined with the output characteristics variables route-km and passenger-km. Table 2 also contains data on output quality (public performance measure, which measures reliability and punctuality, and signals passed at danger which is a safety measure). Regarding input prices, we include a cost per worker measure derived from the TOC accounts. As noted in section 3, we seek to control for the factors likely to affect the price paid by TOCs for rolling stock by testing the impact on cost of a set of rolling stock characteristic variables, as shown in Table 2. We also include, as noted previously, TOC sector dummy variables that should capture systematic (time invariant) differences in rolling stock prices and other costs between the three TOC sectors 7. Two aspects of the data are worth noting. First, we have utilised industry-sourced data on track access charges in order to obtain a measure of those costs that are directly under the control of the TOCs (that is, track access charges, which are not controlled by the TOCs are excluded). Our paper therefore focuses attention closely on the costs 7 As noted in section 3, an alternative model that includes a rolling stock price variable in place of the rolling stock characteristics variables produces very similar results to those of our preferred model. 20

22 under the direct control of TOCs. The majority of the previous literature has focused on overall industry costs including infrastructure (see for example, Cowie, 2009 and Growitsch and Wetzel, 2009 in respect of British and European rail studies). Whilst the latter papers offer an industry-wide perspective, through capturing both infrastructure and operations, they do face the problem of measuring the capital input, which has to be proxied either by track length, or by track access charges, both of which are imperfect measures. Second, we have obtained data on vehicle-km as well as train-km, which allows the model to take account of both distance travelled and length of train. We are not aware of any previous studies of rail costs in other countries that has utilised vehicle-km data. 5.0 Results and Discussion This section outlines the econometric results and verifies that they are robust. We then we report on the results of our hypothesis tests relating to the imposition of problem contracts and discuss the implications. 5.1 Econometric results Table 3 shows the results of our preferred model. In general, this model performs well in terms of the signs and significance of the parameter estimates in respect of both the explanatory variables and the efficiency specification. We adopt a 21

23 (restricted) translog functional form, with squared and interaction terms on the main output vector, comprising train density, train length and number of stations operated. The translog is restricted in the sense that there are no second order terms for the output characteristic terms. Likewise second order terms for the input price variable are excluded. Our aim is to estimate a set of frontier parameters that are plausible and represent a good approximation to the underlying technology. We can then have confidence in the findings concerning the contract effects. A full translog model was estimated. However this model was unsatisfactory in several respects (translog estimation is often problematic, see for example, Morrison, 1999). The elasticity of cost with respect to stations at the sample mean is implausibly high and that on route is implausibly low (the stations elasticity also having a relatively tight confidence interval associated with it). We also note that the wage elasticity is negative for half of our observations, which violates economic theory. We therefore retain our restricted translog model as the preferred model. Even this restricted model includes squared and interaction terms for the key output vector (density, train length and stations). The restricted translog is also preferred to the Cobb-Douglas specification based on a likelihood ratio (LR) test (the Cobb-Douglas restriction is rejected at the 1 per cent level of significance) 8. [Table 3 here] 8 It should also be noted that a full TL produces a broadly similar profile of contract effects to that of the preferred model. 22

24 In general we find the coefficients to be of the expected sign and statistically significant. At the sample mean, the model exhibits broadly constant returns to scale and increasing returns to density which is in line with the general literature on rail costs. Since all data is transformed by the sample mean, the scale elasticity is computed as the sum of the elasticities on the first order route-km and stations variables; variables ROUTE and STAT1 in Table 3 (Scale Elasticity = 1.015). The density elasticity is derived from the first order coefficient on the train density variable (TDEN in table 3; Density Elasticity=0.776). Of the variables listed in Table 2 the planned franchise length, SPADs (safety) and PPM (punctuality and reliability) measures were excluded due to the very low t- statistics associated with the estimated coefficients. For the rolling stock characteristic variables, most of the parameter estimates were statistically insignificant and the signs on the variables were not intuitively plausible. The results in terms of our findings on the contract effects were little affected by the inclusion or exclusion of these variables and we thus decided to exclude them from the final model, with the exception of the age of rolling stock. The coefficient on this variable has the expected negative sign. As noted earlier, our preferred model produces a very similar result to an alternative which includes a rolling stock price in place of the rolling stock age variable (see sections 3 and 4). As described above, the main aim of the paper is to study the performance of three groups of TOCs: failing TOCs that were placed on management contracts; failing TOCs that were placed on re-negotiated contracts; and other TOCs that remained on their original franchise agreements. These effects are measured via 23

25 dummy variables included in the deterministic part of the model, which therefore imply three separate frontiers for these groups. As is standard in the literature we also include a one-sided inefficiency term ( u it ) to pick up variation between firms within each group. The null hypothesis that there are no inefficiency effects ( u it ) is rejected, giving us confidence that a stochastic frontier model is appropriate in this case. The nested time invariant efficiency model (Pitt and Lee, 1981) and the simpler time varying efficiency (Battese and Coelli, 1992) model can also both be rejected in favour of our preferred model (again based on LR tests; in all cases at the 1 per cent level of significance). The latter tests show that the time variation in efficiency (for most firms) is found to be significant, and that it is important to allow for different extents and directions of efficiency change between firms. In addition to the preferred model, we also estimated several alternative models which assume inefficiency to be iid across time periods (but does allow for such correlation through the means of the inefficiency distributions). Overall these models provide similar results to those of our preferred model with regard to the key issue of the contract effects. For the remainder of the paper we concentrate on the contract group effects, as identified by the dummy variables in the deterministic frontier for two reasons. Firstly we are interested in the effects of the contract types, and therefore the performance of the different groups, rather than the differential performance of firms within the 24

26 groups (and in any case the u it should be independent of the contract effects 9 ). Secondly, the one-sided inefficiency effects ( u it ), are not very large and do not change much over time (though as noted the effects are statistically significant, and the time variation is also statistically significant; hence why we retain a stochastic frontier model in preference to a standard cost function model). 5.2 The impact of temporary contract arrangements We now discuss the results of our hypothesis tests for the temporary contract effects (see Table 4). We also illustrate our findings graphically see Figure 2 - which shows the extent to which the costs of management and re-negotiated contract TOCs exceed those of industry best practice (as represented by other, unaffected TOCs). Two profiles are shown for the management TOCs, one for a typical management TOC that entered into a management contract in 2002 and saw competitive re-franchising in 2005; and the remaining management TOCs, which continued on these contract arrangements until the end of the sample (dotted line in Figure 2). Given the way the model is constructed (see section 3), the results for these two sub-groups are identical up to and including 2004, but diverge after that when some of the firms are re-franchised. [Table 4 here] [Figure 2 here] 9 The appropriateness of the random effects specification, where the inefficiency effects are uncorrelated with the regressors is confirmed by a Hausman test (which we fail to reject). 25

27 Taking the management contracts first, we find that management TOCs costs rose 21.6 per cent in the first year on the management contract, compared to the previous year, over and above the change in costs for other (unaffected) operators (Hypothesis IA reject the null; significant at the 0.01 per cent level). This is shown by the sharp upward shift in costs for these operators following the shift to management contracts in year 6 (2002). The relevant test statistic in Table 4 is Further, following the shift to management contracts (in 2002), these TOCs were found to be 23.2 per cent more expensive than industry best practice (Hypothesis IB reject the null; significant at the 0.01 per cent level). The relevant test statistic in Table 4 is which translates into an index number in Figure 2 of During the subsequent years costs fell for management TOCs relative to other TOCs, albeit from a very high base. For those management TOCs that saw refranchising during our sample, by the end of the management contract period (year 8; 2004), costs were still 14.8 per cent greater than best practice (Hypothesis IIA reject the null for this group of TOCs; significant at the 1 per cent level). The relevant test statistic in Table 4 is which translates into an index number in Figure 2 of Thus while TOCs were on management contracts, we find that they had a sustained negative effect on performance. However, once the management contract TOCs were re-franchised (year 9; 2005), we find a sharp fall in costs, such that this group of management TOCs did not have statistically different costs to the other 10 The relationship between the index calculation in Figure 2 and the test statistics in Table 4 is as follows: index = exp (test statistic). 26

28 TOCs (Hypothesis IIB fail to reject the null; index of in year 9 11 ). Competitive re-franchising thus resolves the problem of higher costs. Those management TOCs that did not see re-franchising during our sample follow a path shown by the dotted line in Figure 2. Thus costs continue to fall, but not as quickly as for TOCs that were re-franchised. The costs for this group of management TOCs also remain above the costs of the other TOC category throughout the sample (see Figure 2), this finding being statistically significant up to and including However, by the very last year of the sample, the excess cost over other TOCs, though positive, is no longer statistically significant (see Table 4). In respect of Hypothesis IIA, we can therefore reject the null for the period from 2002 to 2005, but cannot reject the null for this group of TOCs in the final year of the sample. That is, costs for this group did eventually return to normal levels whilst on the management contract arrangements. Taken together, from a policy perspective, these results suggest that management contracts were bad for efficiency, particularly given that they were allowed to persist for several years. However, following competitive re-franchising, costs returned to industry best practice levels, which is reassuring. Whilst it appears that after the initial cost rise, the franchising authority did have some success in bringing downward pressure on costs through the management contract arrangements, they did so from a very high base, and also costs came down much more slowly for those operators that continued on the arrangements as compared to those that saw re- 11 Test statistic of in Table 4. 27

29 franchising. Thus costs for management TOCs were persistently, substantially higher than other TOCs over a number of years. Further, it may be that the cost falls among this group of management TOCs was driven in part by the anticipation of future re-franchising as much by any pressure brought to bear under the management contracts. The threat of competition has been found to have impacts in a wide range of cases, quite apart from the impact of actual competition (see for example, Evenden and Williams, 2000). As noted earlier, to guard against endogeneity bias, we need also to consider the profile of costs prior to the contract arrangements. We find that those TOCs which subsequently ended up on a management contract had costs that were 19.0 per cent 12 higher than best practice at privatisation (Hypothesis III A reject the null; statistically significant at the 1 per cent level). We also find that these costs fell up to the period directly preceding the contract shift. However we do not find that costs were cut to levels below those for the other TOCs (Hypothesis IIIB fail to reject the null; index of in year 5 (2001) 13 ). Thus while the TOCs which subsequently were placed onto management contracts did make large cuts in their costs prior to getting into difficulties, we find no evidence that they cut costs below an efficient (and thus sustainable) level (see also Figure 2). As also noted earlier, we would expect a different story to emerge for the renegotiated contracts. Once signed, these contracts should have the same incentive 12 Test statistic of ; see Table Test statistic of ; see Table 4. 28

30 properties as the standard franchise contracts which other, unaffected operators continued operating under. On the other hand, the annually negotiated management contracts might be thought to have weaker incentive properties. Figure 2 shows a similar cost profile for the TOCs which entered into renegotiated contracts, as management contracts. However, importantly, the cost changes and differences are less dramatic and for most of the hypotheses we cannot reject the null 14. In particular we do not find evidence that following re-negotiation these TOCs had costs greater than the other TOCs (Hypothesis IB fail to reject the null), although we do find that the costs for re-negotiated problem TOCs did increase from the year preceding the renegotiation (Hypothesis 1A reject the null; significant at the 0.01 per cent level). As for the management TOCs, costs started higher than best practice at privatisation (Hypothesis III A reject the null; index of in ; significant at the 2 per cent level), and were not cut below those of other TOCs (Hypothesis III B fail to reject the null). Our findings are therefore in line with prior expectations. It appears that TOCs on re-negotiated contracts did see an increase in costs initially, which may have resulted from the improvements in quality demanded by the franchising authority at the time of re-negotiation. However, their costs were not statistically higher than best practice during the period of the arrangements. 14 We could not include a variable picking up the impact of competitive re-franchising on re-negotiated contract TOCs due to parameter significance and model convergence issues most likely generated by the small number of re-negotiated contract TOCs which were re-franchised into comparable TOCs. 15 Test statistic ; see Table 4. 29

31 Overall, our findings suggest that the decision to place TOCs onto management contracts for an extended period led to a substantial and statistically significant deterioration in efficiency for the affected TOCs relative to best practice. Through careful specification of the contract dummy variables, before and after their onset, we can reject the alternative possibility that the affected TOCs cut costs too far during the early period to try and ensure their survival, with the consequences felt in the later period. The TOCs which ended up on management contracts did not cut costs below those of other TOCs prior to the onset of the temporary contract arrangements (Hypothesis IIIB). Rather, the evidence instead supports the hypothesis that these TOCs started the period being less efficient than other TOCs, and then achieved partial catch-up savings relative to other operators this being the very result that franchising would be expected to deliver. The onset of the management contract arrangements then weakened incentives for cost control among the affected operators and thus caused efficiency and costs to diverge further from those of other TOCs (Figure 2). As expected a priori, whilst the pattern of cost change is directionally similar for the renegotiated contracts, the effects are not statistically significant, since these contracts retain the strong incentive properties of standard franchise agreements. At this point we note an alternative interpretation of the above which is as follows. The problem TOCs may have started with higher costs because of the nature of their operations, rather than due to relative inefficiency. In bringing their costs down to the levels of other operators, they thus reduced costs to unsustainable levels, thus causing costs to rise later. However, we do not think that the evidence supports 30

32 this interpretation. First of all, there is no reason to presume, a priori, that the problem TOCs should have had higher costs than other operators (they are a mix of London commuter, regional and intercity operators). Second our model contains a wide range of variables to deal with heterogeneity between operators. Finally, by the end of the sample, the cost gap between the problem TOCs and other TOCs has been closed, and there is no evidence to date to suggest that this position is unsustainable. Of course, from a policy perspective, when an operator runs into problems, the franchising authority always faces a choice between taking control of operations itself, an eventuality for which it may have call-off arrangements in place, or allowing the incumbent to run services on a management or short-term re-negotiated contract pending re-franchising. The option of allowing the incumbent to continue in the short term on some kind of temporary arrangement is, of course, likely to be more advantageous if a number of operators fail simultaneously as occurred here. The difference in this case, however, is that the temporary contract arrangements were allowed to persist for several years, not just for a few months whilst waiting for the outcome of a new competitive franchise competition. As noted earlier, there were good reasons for delaying competitive re-franchising (lack of funding and a desire to re-draw the franchise boundaries). However, our analysis shows that this was a costly decision. Further, given the different experience of the two types of temporary contract arrangements employed, our findings suggest that if the intention (a priori) is to delay competitive re-franchising, a re-negotiated contract is preferable to an extended period on a management contract. 31

33 As noted in the introduction, the results with regard to the effects of contracts differ from those reported in an earlier paper by the current authors as part of a wider study on the impact of rail franchising on productivity (Smith and Wheat, 2009). Importantly, that paper did not adequately address the inherent problem of endogeneity bias when testing the impact of contractual or institutional arrangements on productivity. As a result, Smith and Wheat (2009) were not able to discern any significant difference between the management and re-negotiated contracts which were both found to have a substantial negative impact on productivity. Further, due to the simplified treatment of the contract dummies, it was not possible to determine whether costs fell following re-franchising. Thus the comprehensive and robust approach to modelling the contracts in this paper has advanced our understanding of their effects, in particular in respect of the differential effects of the two alternative contract types, and the unwinding of the effects following competitive re-franchising. Finally, Smith and Wheat (2009) also report a general deterioration in productivity across the sector, even the frontier firms, which is in addition to the contract effects reported here. Cowie (2009) also reported a sector wide deterioration in TFP over this period. Whilst not central to this paper, our model likewise shows a deterioration in the frontier after 2000 in line with previous studies. Thus, whilst our paper has focussed on the contract effects, further research is needed to understand the wider trends as well. 32