Productive Efficiency Measurement and Regulatory ...

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Keywords: Regulatory Reform, Productive Efficiency, Vehicle. Inspection Concessions. ∗ We would like to thank Mr. Joan Pau Clar of the Department of Industry ...
XXXI Congreso AECR Alcalá de Henares 17, 18 de noviembre de 2005

Productive Efficiency Measurement and Regulatory Reform: The Case of Vehicle Inspection Concessions∗

Francesc Trillas Universitat Autònoma de Barcelona and IEB Daniel Montolio and Néstor Ducha Universitat de Barcelona and IEB Preliminary version, April 2005

Abstract: The measurement of productive efficiency provides guidance about the likely effects of regulatory reform. We present a Data Envelopment Analysis (DEA) of vehicle inspection concessions for a sample of 38 inspection units between the years 2000 and 2004. The differences in the score efficiency figures show the potential in terms of technical efficiency of introducing some form of incentive regulation or of progressing towards liberalization. We also compute the scale efficiency scores, showing that only units in territories with very low density of population operate at a sub-optimal scale. Among those that operate with an optimal scale, however, there are significant differences in terms of size, the largest ones operating in territories with the highest population density. This reveals that the introduction of new units in the highest population density territories (a likely effect of some form of liberalization) would not be detrimental in terms of scale efficiency. We also show that in the years 2003 and 2004, characterized by high regulatory uncertainty for the units in the sample, there was no technical progress but the units operated with better managerial efficiency. Stations belonging to a large and diversified firm show more efficiency, reflecting economies of scale or scope at the firm level.

JEL: L23, L43, L62 Keywords: Regulatory Inspection Concessions



Reform,

Productive

Efficiency,

Vehicle

We would like to thank Mr. Joan Pau Clar of the Department of Industry of the Catalan Autonomous Government (Generalitat de Catalunya) for very helpful advice and for giving us access to the data. Please, do not quote without the authors’ permission. a Corresponding author: [email protected]

1. Introduction Vehicle Inspections in Spain are since 1985 under a regime of concession contracts,1 where tariffs and concession contract clauses are set by the regional governments, and the central government specifies the technical parameters that inspections have to fulfil. The legislation at that time prescribed a very rigid incompatibilities regime, by which firms in the automobile sector were not allowed into vehicle inspection concessions. This developed into a regime of regulated territorial monopolies. In 2000 and 2003 the central government passed a number of decisions by which it introduced a regime of authorizations to replace the concessions regime, but leaving regional governments discretion to introduce concrete steps in this direction. Regional governments have been very reluctant, albeit in different degrees, to implement liberalization steps. In this legal framework it is very important to note that vehicle inspections are part of an expanding sector worldwide, as it is clear from the fact that firms involved in vehicle inspections also issue other type of certifications such as quality, environmental or industrial. In the case of vehicle inspections, the demand is derived from the demand for vehicle use, which is increasing worldwide, and also depends on social preferences. Environmental and safety concerns have arguably increased worldwide in the recent past and are an increasing phenomenon. Legislation in Spain requires that technical inspections must be passed biannually by all vehicles aged from 4 to 10 years old, and annually for vehicles older than that. Moreover, it is a sector subject to regulatory change (opening up to private and/or foreign investment, competition, and regulatory reform in different forms and to varying degrees in most countries), and several multi-national firms (such as the German TÜV Rheinland, the Swiss SGS and the Spanish Applus+) are positioning themselves in the global market. In this context of regulatory change in an expanding sector, productive efficiency measurement is very important because it provides guidance about what is the minimum efficient scale of production, whether there is technical progress, whether there are

1

See Tribunal de Defensa de la Competencia (2004).

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scope or diversification economies, and whether managers are behaving in a cost minimizing way. This informs about the desirability of introducing competition or bidding for monopoly concessions or the desirability of different forms of incentive regulation, such as price caps or yardstick competition. In this study, we use the Data Envelopment Analysis (DEA, hereafter) methodology to perform an exercise of production efficiency measurement for 38 vehicle inspection concessions for the period 2000-2004. Moreover, with the efficiency scores obtained with the DEA methodology we perform a secondstage Tobit analysis of the likely determinants of the observed productive efficiency among units. In the remainder of the paper, in Section 2 we briefly review the implications of regulatory reform on productive efficiency while in Section 3 we sketch the methodology employed. In Section 4 we present the data and the hypotheses to be tested. In Section 5 we show the efficiency scores that result from the DEA analysis. In Section 6 we present the results of a “second stage” panel Tobit analysis. Finally, Section 7 concludes. 2. Productive Efficiency and Regulatory Reform The measurement of relative productive efficiency for vehicle inspection stations in our sample provides guidance about the likely effects of regulatory reform. Efficiency scores measure the distance between observed inspection stations and the most efficient similar stations, giving a measure of the radial reduction in inputs that could be achieved for a given measure of output. The differences in efficiency score figures show the potential in terms of technical efficiency of introducing some form of incentive regulation or of progressing towards liberalization, if that kind of reform is to increase managerial efficiency. We also compute the scale efficiency scores, showing that only units in territories with very low density of population operate at a sub-optimal scale. Among those that operate with an optimal scale, however, there are significant differences in terms of size, the largest ones operating in territories with the highest population density. This reveals that the introduction of new units in the highest population density territories (a likely effect of some form of liberalization) would not be detrimental in terms of scale efficiency. We also show that in the years 2003 and 2004, characterized by high regulatory uncertainty for the units in the 3

sample, there was no technical progress although managers improved their efficiency relative to the frontier in each year. As Bogetoft (1997) argues, “DEA seems particularly well-suited for regulatory practice.” This is because it requires very little technological information a priori, allowing a flexible non-parametric modelling. Besides, DEA-based cost estimates are conservative or cautious, because they are based on an inner (minimal extrapolation) approximation of the production possibilities. Bogetoft (1997) also points out that this is attractive in regulatory practices, where firms may want to quit when budget deficits are foreseeable. Other authors have used a similar methodology to study productive efficiency in other sectors, such as Resende (2000) in telecommunications, or Affuso et al. (2002) and Kennedy and Smith (2003) in railways. The only study that we are aware of that uses a DEA methodology in the vehicle inspection sector is Ylvinger (1998) for the Swedish case, who examines efficiency by analyzing data on all decision-making units related to vehicle inspections. Data on inputs consisted of labour hours, the area of the inspection stations, and a composite of the costs of all other inputs (energy, maintenance and physical capital). Output was constructed as a weighted physical measure approximated by normalized annual compulsory automobile inspections. The author finds an average structural efficiency of 0,91 which indicates that if all units were to operate at the revealed best-practice frontier, the potential to reduce input use is about 9%. He argues that the potential for efficiency improvement is limited in comparison to other industry studies, which might be explained by the high quality of the data along with the extensive use of yardstick-competition in the industry, offering no empirical evidence to support a deregulation of the Swedish motor-vehicle inspection industry. In a second stage of the exercise, we make use of panel Tobit regressions to compute the determinants of efficiency scores. In general, if the results of the Tobit regression (about the determinants of variable returns to scale efficiency scores, i.e. efficiency scores that compare units of similar size) turn out not to be significant, this is evidence that the differences in efficiency (once scale

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efficiency is taken into account) are not due to exogenous environmental factors, but due to the differences in the cost minimization behaviour of Decision Making Units (DMU, hereafter). We show that the dummy variable for years 2003 and 2004 is a robustly significant determinant of computed efficiency scores relative to a yearly frontier. Stations belonging to a large and diversified firm show more efficiency, reflecting economies of scale or scope at the firm level. For the years that we studied, the units in the sample were de facto under a regime of rate of return regulation, that is, entry was not allowed and tariffs were set to sustain an “economic and financial balance”. There were no clearly established procedures to fix tariffs, and these were set at the initiative of the firms, the government having discretion whether to completely or partially accept, or reject, the operators’ demands. It is well known that under such type of regulation the power of incentives to reduce costs is minimal. Some authors have suggested, however, that such regulatory regimes may be the result of a political equilibrium and that they may be better at providing commitment to sustain adequate levels of investment (see for instance Armstrong and Sappington, 2003). 3. Methodology In this section we briefly comment on the methodology employed to study the efficiency of vehicle inspections units. The DEA methodology2 calculates an efficiency frontier for a set of units (vehicle inspection stations in our case), as well as the distance to the frontier for each unit. Here we concentrate on the input oriented DEA model that takes output as given, something consistent with efficiency measurement3 in regulated sectors since DMUs in these sectors have discretion to decide on parts of the inputs but not on the outputs. The DEA methodology builds a production frontier for the DMU’s in the sample and computes the distance of each unit to this frontier (efficiency scores); to be 2

For a more detailed exposition of these techniques and a review of some of its applications, see MurilloZamorano (2004). 3 However, it should be clear that these techniques abstract from the problems of allocative efficiency and are orientated to technical efficiency, ignoring the role inputs prices can have.

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precise, it computes the distance of each unit (it extrapolates this unit) to a composite possibly fictitious unit that is a convex combination of units operating at the frontier. It does so by using linear programming techniques as opposed to parametric econometric techniques.4 It has the advantage of being very flexile, as it does not require any functional assumptions on production technologies or any assumption on the distribution of statistical errors. One can compute the Constant Returns to Scale (CRS, hereafter) efficiency scores, where it is assumed that all units operate at their optimal scale, so that any unit can be compared in terms of efficiency to any other unit, and it is assumed that differences in efficiency have nothing to do with scale. However, this assumption may not be realistic in many settings. Then a Variable Returns to Scale (VRS, hereafter) DEA analysis is recommended. The latter adds a convexity restriction on the CRS formulation. The usefulness of convexity is to secure that any composite unit extrapolated is similar in size to the reference unit and not merely an extrapolation of another composite unit operating at a different sale size. Given that when calculating the CRS efficiency indexes we can be comparing units that operate in very different scales, the index mixes inefficiencies due to the managers’ deviation from a resource minimizing behaviour to reach a given level of output, with inefficiencies resulting from not operating in the optimal scale (the quantity of the product that minimizes the average cost of production). This mix would not cause any harm if the units operated with constant returns to scale for all the relevant levels of production, but there are few productive processes characterized by this technology. Nevertheless, it is possible to measure the degree by which firms operate at the efficient scale calculating the ratio between the efficiency scores obtained under the CRS assumption and those obtained with the VCR hypothesis. If the resulting indexes are identical, the DMU operates with 100% scale efficiency, getting a value of 1 in the index of scale efficiency.

4

Used, for instance, in another frontier method: the stochastic frontier analysis. For an excellent and extensive review of economic efficiency and frontier techniques see Murillo-Zamorano (2004).

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4. Data and Importance for Regulatory Reform We use data about two inputs (a unit of labour and inspection lines per station) and a measure of output (vehicles inspected) for 38 units between 2000 and 2004. The units are all the vehicle inspection stations that operate in Catalonia (Spain) under concession contracts granted by the Regional Government. This database allows us to explore changes in productive efficiency in the vehicle inspection sector. It is well known that changes in productivity (ability to produce outputs for a given measure of inputs) are the result of at least four interacting forces: •

Allocative efficiency: the ability of firms to exploit differences in input prices, i.e. choosing a point in the desired iso-quant that coincides with the lowest possible iso-cost line. All points in the iso-quant are technically efficient (minimum input requirements for a given level of output), but only the point were the iso-quant and the iso-cost line are tangent is allocatively efficient, the slope of the iso-cost line depending on input prices.



Technical change: this describes shifts in the production function due to technological progress.



X-efficiency: the ability of managers or workers to behave in a cost minimizing way.



Scale efficiency: the ability of the firm to operate close to the minimum efficient scale, i.e., the production level were average costs achieve a minimum.

In this paper we omit allocative efficiency issues and focus on technical change, X-efficiency and scale efficiency. In our exercise, the DMU’s are the different stations that perform vehicle inspections. All the stations belong to 3 firms (called A, B and C for confidentiality reasons). Two of these firms are subsidiaries of the same multinational group. We have named each station by its location in the four Catalan provinces (B = Barcelona, G = Girona, L = Lleida and T = Tarragona).

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We make use of 2 inputs and one output. The main source of data is the Department of Industry of the Catalan Autonomous Government (Generalitat de Catalunya). The measures of inputs and output used are: A) Inputs: The first input used is the number of operating lines available in each station. Operating lines are the corridors where vehicles are ordered to place themselves to undergo the inspection of breaks, suspension, emissions and engine. Each line replicates the others in size and number and quality of machinery, and determines (together with the labour input) the amount of inspections that a station can perform per unit of time. This variable reflects the fixed capital of each station directly involved in inspection activities. Other measures of physical input were, unfortunately, not available at station level. However, the number of operating lines is a good measure of that part of capital that, together with labour input, is involved in the production process of vehicle inspections. The second input is the labour input. This input is a weighted estimate of the labour engaged in inspection activities in each station. Hence, we assign weights to the 7 categories of workers a station can have. The categories are: manager of the station, team chief, mechanic, auxiliary mechanic, environment control mechanic and support staff (not directly related with inspection activities). We assign weights to each category of worker depending on his or her direct implication in inspection activities, obtaining an accurate measure of the labour input operating in each station. Moreover, given that some stations work during all day (in two shifts: morning and afternoon) and other stations just one, we have calculated our labour input per week accounting for the fact that a station that works all day is open 80 hours a week and the others only 40 hours a week. This procedure is the same used by the regulator (the Catalan government) to keep a record of the labour involved in the concessions. B) Output We use as an output measure the number of inspections per week performed by each station. We obtained data from the regulator of the total number of 8

inspections per year, calculating its weekly counterpart to have a comparable measure with the labour input. Before presenting the efficiency scores for our sample (section 4), we present the hypotheses we test: •

We test whether managerial inefficiencies prevent a fraction of the stations from operating at the frontier. Differences in efficiency are evidence that input quantities can be reduced for a given output level for those units showing efficiency scores below 1.



For the vehicle inspection stations in the sample, we compute the minimum size of a station that operates with scale efficiency. If a substantial number of stations operate at a scale larger than this, this is evidence that increasing the number of stations will not be detrimental for scale efficiency.



We compute a frontier for each of the years in the sample, and hence we are able to see whether the frontier shifts with time, i.e. whether there is technical change.

Coelli et al. (1999) suggest the use of environmental variables to assess the likely determinants of efficiency scores. Although several techniques are available, they recommend the Tobit procedure (especially when there are categorical variables) to regress the efficiency scores to a battery of “environmental variables” that may explain relative efficiencies. The justification for the Tobit regression technique is that efficiency scores are bounded between zero and one and a sub-set of the sample may be accumulated into the 1 value (the efficient units). In this case, Tobit regressions provide the right methodology. Coelli et al. (1999) suggest the following types of variables to include in such “second stage” regressions: •

Ownership differences, such as public/private or corporate/non corporate.



Location characteristics.

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Labour union power.



Government regulations.

It must be stressed that all units in our sample operated under the same regulatory regime, but this regime became tighter and more subject to regulatory uncertainty in the last years of the sample period. We capture location characteristics by measuring the GDP per capita, GDP and population density of the territories where the vehicle inspection units operate. We capture ownership differences in two ways: by differentiating those stations where the building is owned from those where the building is rented; and by classifying stations by the operating firm that manages them. This is important in this case because the stations belong to three different firms: two of them manage 85% of the stations and belong to a diversified multinational group, and the other is a focused small firm that only manages 15% of the stations in the sample and does not do anything else. The first two firms may enjoy economies of scope or diversification and scale economies at the firm level. It may also be that the first two being much larger than the other, union power is stronger in the larger ones. The explanatory variables directly related to the technical efficiency of vehicle inspections units used in the Tobit analysis are: •

DV-property is 0 if the station is not owned by the operating firms or the regional government but to a third party.



DV-Firm is 0 for firm C.



DV-0304 is 0 for 2002, (in which there was the last tariff change) and before.



DV-Metropolitan area is 0 is the station does not belong the metropolitan area of Barcelona.

Moreover, we make use of some other control variables that give us information on the activity levels where the stations are located. These control variables are

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Density, GDP and GDPpc, calculated for the territory5 at which the inspection station is located. We test the following hypotheses concerning the Tobit second stage regressions: •

Are there differences across firms in technical efficiency? If yes, this is evidence that there exist scope economies, economies of diversification or scale economies at the firm level, since two of the three operating firms belong to the same country and product diversified group. Also, differences may be related to union influence.



Are there differences in efficiency over the sample years? In particular, we focus on the two last years of the sample, where tariffs remained at the same level as in 2002 and it was a period characterized by high regulatory uncertainty as we develop below.



The ownership of the station buildings, the vintage (age) of stations, population density and income had any impact on productive efficiency?

5. Efficiency Scores and Scale Efficiency In Table 1 and Table 2 we report the summary statistics of the estimated efficiency scores assuming constant returns to scale and variable returns to scale respectively. Similarly, Table 3 shows the summary statistics for the scale efficiency scores of the units in the sample (CRS Technical Efficiency scores divided by the VRS Technical Efficiency scores, see Coelli et al., 1999). Table 1: Summary statistics for efficiency scores under CRTS CRTS N Average efficiency Standard deviation Efficient units

2000 2001 2002 2003 2004 38 38 38 38 38 61.7 61.9 62.6 67.9 67.2 25.39 26.42 25.91 25.28 24.82 3 4 2 4 3

Source: Own elaboration.

Table 2: Summary statistics for efficiency scores under VRTS VRTS N Average efficiency Standard deviation Efficient units

2000 2001 2002 2003 2004 38 38 38 38 38 90.6 86.6 87.6 90.7 89.7 10.77 15.37 14.43 10.43 11.45 18 20 20 17 16

Source: Own elaboration.

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The territorial unit we use is the “Comarca”, which in Spain should correspond to the shire territorial division in UK.

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Table 3: Summary statistics for scale efficiency (TEcrts/TEvrs) Units N Average Scale Efficiency Standard deviation Scale efficient units

2000 2001 38 38 0.69 0.74 0.28 0.31 4 10

2002 38 0.74 0.30 4

2003 38 0.76 0.28 5

2004 38 0.76 0.28 5

Mean 38 0.74 0.29 2

Source: Own elaborations. Note: TE refers to technical efficiency.

In general terms, we can observe that average technical efficiency for the whole period increased (and its standard deviation decreased) for the CRS case, and was stable (if not decreasing) between 2000 and 2004 for the VRS estimates. However, the VRS efficiency scores show higher average values for the units under study, quantitatively being very similar to those obtained by Ylvinger (1998) –which does not mean than Catalan stations are as efficient as the Swedish ones, but as close to the their respective frontiers. On average productive units are around 10% far from the efficient frontier and, therefore, it seems there is some room to achieve efficiency gains in the vehicle inspection sector. For example, if we take into account the VRS scores, in 2004 there was 10,3% technical inefficiency on average relative to the sample frontier,6 meaning that 10,3% less of physical resources could be used to achieve the same output level. Focusing on Table 3, if we take the smallest station with the highest average scale efficiency in the period 2000-2004 in the province of Girona (0.95), Lleida (0.96) and Tarragona (0.95); they performed an average of 56,451, 53,815 and 49,580 inspections per year, respectively. Something similar happens for stations in the province of Barcelona outside its metropolitan area. They have a score close to 1 in the scale efficiency and they have a smaller scale, with lower number of annual inspections performed, than units in the Barcelona metropolitan area. On average, for the period 2000-2004, the vehicle inspection station that performed more inspections was located in Barcelona (in its metropolitan area) and performed 96,184 inspections per year.

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There are some vehicle inspection stations with low efficiency scores (for both the CRS and VRS). Hence, taken individually it seems that there are possibilities of some efficiency gains. Data on inputs and output is not shown but are available upon request.

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This means that for example this station can lose about half of its customers and still operate very close to the optimal scale, which provides an indication that new stations (a likely effect of liberalization) that capture customers from existing stations are compatible with scale efficiency to a certain extent. In Table 4 we show that the units in the territories with the lowest population density (those in the Lleida province) are the ones that clearly operated at a scale below optimal. We conclude that economies of scale are certainly present in vehicle inspections, but that these are exhausted for stations present in most of the territories in the sample:7 Table 4: Scale efficiency per province Barcelona Tarragona Lleida Girona

2000 0.86 0.67 0.34 0.67

2001 0.92 0.71 0.35 0.75

2002 0.91 0.72 0.36 0.75

2003 0.94 0.74 0.39 0.74

2004 Average 0.94 0.91 0.75 0.72 0.40 0.37 0.72 0.72

Source: Own elaborations.

6. The determinants of efficiency scores In a second stage of our analysis we provide the results of panel Tobit regressions, to quantify the impact of environmental variables on efficiency scores.8 Comparing Tables 5 and 6 we can see that if we use as endogenous variable the CRS efficiency scores, most of the explanatory variables are statistically significant. However, if we use as endogenous variable the VRS efficiency scores, fewer variables are significant (the dummy variable for years 2003 and 2004, the years that the station has been opened and the ownership of the station). The explanation for this is that the relative inefficiencies captured with a CRS analysis are largely due to the firms not operating at the optimal scale, and some of the explanatory variables may be correlated with scale. This is certainly the case of the Metropolitan area dummy variable. Stations located in the Barcelona Metropolitan area inspect more vehicles over the year than stations located elsewhere. However, once we use the VRS DEA efficiency scores, 7

The Barcelona province is divided in two very different regions: the urban Barcelona Metropolitan Area, with two thirds of the overall Catalan population, and the rest of the province. 8 For a detailed explanation on the Tobit methodology see Greene (2002).

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each station is compared to stations of similar size. That is reflected in efficiency scores that become higher for smaller stations. Once these scores are used in the regressions, the metropolitan area dummy variable ceases to be significant, implying that the higher productive efficiency of these stations is not due to higher managerial efficiency (X-efficiency) but to operating closer to the minimum efficient scale. Table 5: Random Effects Tobit model. Constant Returns to Scale Constant Years Opened DV-Property DV-Firm DV-0304 DV-Metropolitan area Density

Model 1 59.140 (23.25)*** 1.073 (6.105)*** 6.712 (3.458)*** -18.729 (-8.700)*** 2.747 (2.454)** 14.877 (2.170)** 0.0061 (1.296)

Model 2 64.732 (18.302)*** -0.259 (-0.785) 10.610 (3.479)*** -3.205 (-1.218) 6.072 (4.362)*** 9.230 (1.414)

Model 3 52.66 (24.800)*** 1.587 (10.231)*** 3.583 (2.697)*** -2.015 (-1.387) 1.423 (1.096) 23.186 (15.551)***

--.--

--.---.--

GDP

--.--

0.0018 (0.779)

GDPpc

--.--

--.--

38 5 8.42 (32.561)*** 17.183 (29.650)*** -736.30

38 5 8.355 (35.510)*** 14.818 (17.002)*** -743.39

N t Sigma (v) Sigma (u) Log-likelihood

-5.285 (-2.010)** 38 5 9.256 (27.434)*** 24.764 (28.161)*** -743.80

Notes: t-values in parenthesis. *, ** and *** indicate significance at 90, 95 and 99 percent level, respectively. Estimations performed with random individual and time effects (2-way REM model). Time span: 2000-2004. DV-property is 0 if the station is not owned by the operating firms or the regional government but to a third party. DV-Firm is 0 for firm C. DV-0304 is 0 for 2002 (tariff change) and before. DV-Metropolitan area is 0 if the station is located in the metropolitan area of Barcelona. Control variables (Density, GDP and GDPpc) are calculated for the “comarca” at which the inspection station is located.

The variables that remain significant when the VRS DEA scores are used are the number of year opened (weakly), the ownership of the stations (strongly) and the dummy variable for the years 2003 and 2004 (strongly).

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Since the firms operating the concessions are very different among themselves, it was expected that these differences do have an impact on the efficiency scores. The group operating most of the stations, and operating those in the territories with the highest population density, is an internationally and product diversified group. It operates inspection vehicles in 7 Spanish regions beyond Catalonia, and has investments in 24 different countries, including the US and China. Table 6: Random Effects Tobit model. Variable Returns to Scale Constant Years Opened DV-Property DV-Firm D-0304 DV-Metropolitan area Density

Model 1 84.045 (20.096)*** -0.565 (-1.901)* 3.278 (1.253) 12.073 (3.630)*** 3.370 (3.432)*** -5.272 (-0.493) -0.002 (-0.339)

Model 2 84.487 (35.573)*** -0.530 (-2.368)** 2.751 (1.316) 9.815 (4.866)*** 3.218 (1.805)* -3.114 (-0.641) --.--

GDP

--.--

0.001 (0.632)

GDPpc

--.--

--.--

38 5 7.726 (34.356)*** 12.616 (8.464)*** -701.23

38 5 11.140 (15.447)*** 2.837 (4.252)*** -726.94

N t Sigma (v) Sigma (u) Log-likelihood

Model 3 81.851 (6.081)*** -0.397 (-0.704) -0.834 (-0.211) 10.977 (1.768)* 1.928 (2.466)** -1.698 (-0.330)

-0.202 (-0.067) 38 5 8.009 (22.935)*** 9.396 (4.729)*** -705.11

Notes: t-values in parenthesis. *, ** and *** indicate significance at 90, 95 and 99 percent level, respectively. Estimations performed with random individual and time effects (2-way REM model). Time span: 2000-2004. DV-property is 0 if property of the station implies a cost for the firm. DV-Firm is 0 for firm C. DV0304 is 0 for 2002 (tariff change) and before. DV-Metropolitan area is 0 if the station is located in the metropolitan area of Barcelona. Control variables (Density, GDP and GDPpc) are calculated for the “comarca” at which the inspection station is located.

This expansion has taken place recently and is still taking place (in 2004 the group acquired the privatized vehicle inspection concessions in Denmark, or the certification division of Soluziona, a division of energy firm Union Fenosa). Executives of the group to which this firm belongs (Agbar, S.A., owned by

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financial conglomerate “La Caixa” and by the French group Suez), have repeatedly stated that they want to focus their expansion using this firm as a vehicle. The technical efficiency of these stations appears to be significantly higher than stations belonging to another firm that was not diversified, but that was focused on managing 15% of the vehicle inspection stations in Catalonia.9 This data set is consistent, therefore, with of scope, or of scale economies, at the firm level, but we cannot distinguish between these two sources of efficiency, since we cannot tell whether gains in efficiency are the result of a diversified portfolio of activities or of producing a higher output in the vehicle inspection sector. Technical progress during the sample years does not exist according to the data, since there is no monothonic direction in the evolution of the year isoquants, as the following graph reveals. Figure 1: Iso-quants 0.24 0.22

Input 2/Output

0.2 0.18 0.16 0.14 0.12 0.1 0.0009 0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023 0.0025 0.0027 Input 1/Output 2000

2001

2002

2003

2004

Source: Own elaboration.

The iso-quants in the graph are the CRS iso-quants for each year (the relatively more efficient ways of combining inputs to produce a given amount of output), 9

The firm dummy variable weakly showed the opposite sign in the CRS Tobit regressions because all firms operating with a very small size belong to the more efficient group.

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but the graph of the VRS, being somewhat more complicated, does not provide different conclusions. It can be seen that there is no monotonic evolution towards higher productivity in the yearly frontiers, reflecting that there was no technical progress for the units in the sample for the analyzed periods. It is actually remarkable that in years 2003 and 2004 the most efficient units were less efficient than the most efficient units in the previous years. This is in contrast with the fact that relative to the most efficient units in the year, stations were on average closer to the frontier in the years 2003 and 2004 than in the previous years. We conclude that dynamic efficiency decreased or at least did not significantly change over the years of the sample, but static efficiency (relative to the yearly frontier) increased. Further research should clarify the source of this paradox, but we conjecture that it may have to do with the regulatory uncertainty in years 2003 and 2004. In September 2003, the Catalan government announced an extension of the concession contract period. This was going to expire in 2006 and it was extended until 2014. This was ahead of an election to the Catalan Parliament in November 2003, with all pre-election polls suggesting that the then opposition parties were favourite to have a new majority. These opposition parties announced that they would reverse this decision once in office. Although a narrower one than expected, the opposition left and center-left parties obtained a new majority in November and took office in a new coalition government in December 2003. In September 2004 the new government announced the suspension of the concession contracts extension, the extension going back to the previous one that finished in 2006. During all this period tariffs were not updated and stayed at the same level as in 2002, the last time that they were raised. Therefore, in 2003 and 2004 regulatory uncertainty co-existed with a tighter regulatory regime. Our conjecture is that regulatory uncertainty hurt dynamic incentives (new investments) but that the tighter regulatory regime improved static incentives, since the firms had to make more effort to make profits, given that the government was refusing to increase tariffs. 17

Table 7 confirms that the effect of the dummy variable for the years 2003 and 2004 is very different from a static efficient point of view than it is for a dynamic efficiency point of view. This table reports the results of a Tobit analysis of the determinants of efficiency scores when efficiency scores10 are computed relative to a unique intertemporal frontier. Then the efficiency scores integrate concerns of both dynamic and static efficiency. We can see that the effect of the years dummy variable is now non-significant, revealing that static and dynamic efficiency considerations are of opposite sign and cancel each other. Table 7: “Pooled” Tobit model. Variable Returns to Scale Constant Years Opened DV-Property DV-Firm DV-0304 DV-Metropolitan area Density

Model 1 94.48 (16.777)*** -0.971 (-2.319)** 2.344 (0.652) 16.107 (4.104)*** 2.031 (0.668) -13.717 (-2.947)*** 0.0058 (1.504)

Model 2 94.450 (16.672)*** -0.967 (-2.272)** 2.128 (0.579) 16.091 (4.085)*** 1.907 (0.620) -13.341 (-2.772)***

Model 3 92.408 (15.007)*** -0.773 (-1.913)* 0.212 (0.066) 15.704 (3.952)*** 0.608 (0.206) -9.723 (-2.577)**

--.--

--.---.--

GDP

--.--

0.0022 (1.252)

GDPpc

--.--

--.--

190 20.072 (12.502)*** -499.01

190 20.131 (12.494)*** -499.36

N Sigma Log-likelihood

0.0016 (1.212) 190 20.348 (12.540)*** -499.34

Notes: t-values in parenthesis. *, ** and *** indicate significance at 90, 95 and 99 percent level, respectively. Maximum Likelihood estimates. DV-property is 0 if property of the station implies a cost for the firm. DV-Firm is 0 for firm C. DV0304 is 0 for 2002 (tariff change) and before. DV-Metropolitan area is 0 if the station does not belong the metropolitan area of Barcelona. Control variables (Density, GDP and GDPpc) are calculated for the “comarca” at which the inspection station is located.

7. Conclusions and Policy Implications The results of this study show:

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We do not report these scores but they are available upon request.

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There are differences in technical efficiency among existing units, which implies that liberalization or incentive regulation have the potential to achieve improvements in productive efficiency.



Results in terms of scale efficiency allow us to conclude that the optimal scale is not achieved for low density territories, but stations in high density territories exhaust by far scale economies, so that more and smaller stations may still operate at an efficient scale.



Stations belonging to a large diversified group show better productive efficiency, reflecting economies of scale or scope at the firm level.



Regulatory uncertainty in 2003 and 2004 may be the reason for the absence of technical progress.

With the frontier methods employed in this paper it is easier to measure the relative inefficiencies of firms than to find the reasons for the differences in efficiency levels. In general, we believe that significant savings could be made in future years if liberalization or some form of incentive regulation (such as yardstick competition) is adopted. This is recommended when firms have superior technological information and make non verifiable cost reductions. If policy makers decide in favour of liberalization they must address issues related to the quality of service provision and of universal service (prices equal to or above average costs in some regions may be below the average cost of existing production levels in regions with a low density of population). We have uncovered evidence about the sources of productive efficiencies for the vehicle inspection units in our sample. The following table summarizes this evidence: Table 8: Sources of productive efficiency Yes Technical Change Managerial efficiency X Scale economies X Diversification economies X Source: Own elaboration.

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No X

However, results should be interpreted with care. Frontier methods such as DEA reveal relative efficiencies. It may well be that once compared with similar units in other jurisdictions other results emerge. Future research may address a number of issues that were just hinted at in this exercise, given the fact that (at least part of) firms operating concessions belong to multinational groups and operate in a number of jurisdictions: •

Connection with the literature on the regulation of multinationals.



Connection with the literature on multi-market contact.



Regulatory federalism and the political cycle.

References Affuso, L.; Angeriz, A.; Pollit, M.G., 2002. Measuring The Efficiency of Britain’s Privatised Train Operating Companies, Regulation Initiative Discussion Paper Series, 48, London Business School. Armstrong, M.; Sappington, 2003. Recent Developments in The Theory of Regulation, forthcoming in Handbook of Industrial Organization, vol. III. Bogetoft, P., 1997. DEA-based Yardstick Competition: The Optimality of Best Practice Regulation, Annals of Operations Research, 73: 277-298. Coelli, T.; Rao, D.S.P.; Battese, G.E., 1999. An Introduction to Efficiency and Productivity Analysis, Kluwer Academic Publishers. Greene, W. H. (2002): Econometric Analysis, 5th Edition. Upper Saddler River, NJ: Prentice Hall. Kennedy, J.; Smith, A.S.J., 2003. Assessing The Efficient Cost of Sustaining Britain’s Rail Network: Perspectives Based on Zonal Comparisons, mimeo. Morten Dalen, D.; Gómez-Lobo, A., 2003. Yardsticks on the road: regulatory contracts and cost efficiency in the Norwegian bus industry, Transportation, 30: 371-386. Murillo-Zamorano, L. R., 2004. Economic Efficiency and Frontier Techniques, Journal of Economic Surveys, 18(1): 33-77. Resende, M., 2000. Regulatory Regimes and Efficiency in US Local Telephony, Oxford Economic Papers, 52: 447-470. Tribunal de Defensa de la Competencia, 2004. Sobre la Prestación de Servicios de Inspección Técnica de Vehículos. Ylvinger, S., 1998. The operation of Swedish motor-vehicle inspections: efficiency and some problems concerning regulation. Transportation, 25: 23-36.

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