Manolis ; Kritikos

Dr Raphael N Markellos

Gregory Prastacos

Corporate Real Estate Analysis: Evaluating Telecom Branch Efficiency in Greece*

Keywords: C44 -Statistical Decision Theory|Operations Research &lt, C4 -Econometric and Statistical Methods: Special Topics &lt, C -Mathematical and Quantitative Methods, D24 -Production|Capital and Total Factor Productivity|Capacity &lt, D2 -Production and Organizations &lt, D -Microeconomics, L96 -Telecommunications Real Estate, Productivity Analysis, Data Envelopment Analysis (DEA) Real Estate, Productivity Analysis, Data Envelopment Analysis (DEA) Real Estate, Productivity Analysis, Data Envelopment Analysis (DEA) Real Estate, Productivity Analysis, Data Envelopment Analysis (DEA) Real Estate, Productivity Analysis, Data Envelopment Analysis (DEA)

This paper proposes productivity analysis for evaluating the relative efficiency in corporate real estate usage across decision-making units. Using data from the Greek Telecommunications Organization (GTO), we measure the productivity of 127 braches using the number of employees and the total area covered per building as inputs and the number of telephony access lines as outputs. We apply three non-parametric Data Envelopment Analysis (DEA) models assuming: constant returns to scale (CRS), variable returns to scale (VRS) and slacks-based measures (SBM), respectively. We discuss how the proposed approach can provide real estate managers and analysts a multi informational tool that allows the quantification of targets and may serve as a guide tool for the efficient employment of real estate assets.

approaches have been developed in order to measure inefficiency empirically, this paper adopts Data Envelopment Analysis (DEA), a popularly used family of nonparametric techniques (for a review see [START_REF] Ray | Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research[END_REF].

The remainder of the paper is organized as following. The next section makes a brief introduction to the methodology. Section 3 describes the data, the empirical application and the results. The final section summarizes our major findings and concludes the paper.

METHODOLOGY

In this paper we adopt the well-established methodological framework of technical efficiency analysis as defined by [START_REF] Farrell | The measurement of production efficiency[END_REF]: a DMU is said to be technically efficient if a reduction in any input requires an increase in the other input or decrease of output. In order to introduce the terminology and demonstrate how measures of DEA are applied we provide an illustrative example with five DMUs denoted by A, B, C, D and E. The empirically derived efficient frontier consists of the convex combination of A, B, C and D. Figure 1 shows the four efficient DMUs, A, B, C and D; the solid line segment connecting points ABCD constitutes an isoquant or level line that represents the different amounts of two inputs (x 1 , x 2 ) which can be used to produce the same amount. This line represents the efficient frontier of the production possibility set. By reason of Q, which exists on the line between A and B, E is inefficient. This is because Q uses less input to produce the same output as the efficient DMUs. The dashed line that intersects the isoquant at B is a minimum cost line since the pairs (x 1 , x 2 ) on this line yield the same total cost when unit costs are c 1 and c 2 , respectively. However, shifting this dashed line upwards in parallel fashion until it reaches a point of intersection with E would increase cost. In fact, point B is the minimum total cost needed to produce the specified output. This is because any parallel shift downwards below is said to provide a radial measure of technical efficiency (TE) with 0 1 ( ) 1 OQ OE yielding a measure of technical inefficiency. Technical inefficiency measures the proportion with which E could be replaced to maintain the same quantity of output. Now, consider the point P at the intersection of this cost line through B with the ray from the origin to E. The ratio 0 1 OP OQ is referred to as price efficiency or allocative efficiency (AE). The corresponding measure 1 OP OQ represents allocative inefficiency and denotes a possible reduction in cost by using appropriate input mixes. It provides a measure of the extent to which the technical efficiency point, Q, falls short of achieving minimal cost because of failure to make the substitutions, or reallocations, involved in moving from Q to B along the efficiency frontier.

We can also obtain a radial measure of overall efficiency (OEF) from the ratio 0 1 OP OE , we can express overall efficiency as the product of "allocative" and 'technical efficiency', ie., OEF TE AE = (see [START_REF] Sengupta | Economics of efficiency measurement by the DEA approach[END_REF]. The measure (1 ) OP OE represents overall inefficiency and denotes the possible reduction in cost due to changing from B (observed input quantities) to P (cost minimizing input quantities). Point D is efficient only when we allow for congestion.2 However, it will be inefficient under the standard assumption of no backward bending segment. The isoquant shown by the dashed line from point C represents the non-congested benchmark. The ratio OD OD represents the reduction in input x 1 required in order to reach the uncongested frontier from the congested frontier; that is we calculate the reduction that is achieved if congestion is eliminated. The present paper adopts the DEA approach in evaluating efficiency across DMUs. DEA refers to a mathematical programming family of techniques which are flexible and have the advantage of making very few assumptions (for a description see Cooper, Seiford and Tone, 2000). The method, developed by Charnes et al. (1978), has been widely used at numerous applications measuring the performance of decision-making units (DMU) in the public and private sector (e.g., see [START_REF] Boussofiane | The impact on technical efficiency of the UK privatization programme[END_REF]Giuffrida and Gravelle, 2001, inter alia). DEA is used so widely because of its simplicity and the useful interpretation of results it yields even with limited data sets. The determination of the efficiency score of the a DMU in a sample of n DMUs in the Constant Returns to Scale (CRS) model is equivalent to the optimization of the following linear programming problem (Charnes et al., 1978; hereafter CCR): To determine efficiency measures under the Variable Returns to Scale (VRS) model (see Banker et al., 1984; hereafter BCC), a further convexity constraint DMUs are flexible to adjust their size to the optimal firm size. On the other hand, the BCC method is less restrictive because it compares the productivity of companies only within similar sample sizes. The comparison between the two approaches also provides some information about the underling technology: if the results of the CCR and the BCC approaches are similar, then returns to scale do not play an important role in the process.

The CCR model score is called global technical efficiency because it is postulated that the radial expansion and reduction of all observed DMUs and their nonnegative combinations are possible. A DMU is said to display total technical efficiency if it produces on the best practice observed boundary of the production possibility set, i.e. maximizes output with given inputs and after having chosen technology. The BCC scores is called local pure technical efficiency because the model assumes the convex combinations of the observed DMUs as the production possibility set. Scale efficiency measure can be calculated by dividing the total technical efficiency by pure technical efficiency. Using these concepts, the total technical efficiency can be further decomposed into pure technical efficiency and scale efficiency (Cooper et al., 2000). The following relationship demonstrates the decomposition of Global Technical Efficiency (TE):

Technical efficiency = (Pure Technical efficiency) × (Scale Efficiency) Generally speaking, for each DMU the CCR model efficiency score will not exceed the BBC efficiency score. An efficiency score obtained using the CCR-model comprises both scale efficiency and pure technical efficiency. In a case where a DMU is found to be inefficient, one it assumes a value of 1, the DMU is efficient both under CCR and BCC. If SE is less than 1 then the DMU is not scale efficient.

The above-described approach does not allow identifying whether a DMU operates under increasing returns to scale (IRS) or decreasing returns to scale (DRS). This problem can be solved using the non-increasing returns to scale (NIRS) expression of DEA by setting the constraint which is non-price related and allocative inefficiency, which is price related (Färe and Grosskopf, 2000). Technical efficiency requires pure technical efficiency to be computed by relaxing the strong input disposability restriction (Byrnes et al., 1984) to allow for an input congestion component. [START_REF] Junoy | Partitioning input cost efficiency into its Allocative and Technical components. An empirical DEA application to Hospitals[END_REF]. Then, the Technical efficiency (TE) is defined as the product of the scale efficiency, input congestion, and pure technical efficiency components in the condition of weak disposal [START_REF] Färe | Production Frontiers[END_REF]:

TE(x,y)=SE(x,y) × C(x,y) × PTE(x,y) (6)
The theoretical discussion identifies some shortcoming of procedures for measuring congestion see, for example Cooper et al (2001), andCherchye et al. (2001). [START_REF] Färe | Production Frontiers[END_REF] Nonetheless, the [START_REF] Färe | Production Frontiers[END_REF] approach is still useful if one's aim to assess the impact of congestion on the overall technical efficiency. In the recent paper, [START_REF] Färe | Slacks and Congestion: a comment[END_REF] discuss the connection between slacks and congestion. We note, however, that precise measurement of congestion is not yet conclusive.

In (3), we measure the efficiency of a specific DMU 0 (Charnes et al.,1978 ) The model in The input congestion measure is then defined as the following ratio: returns to scale (VRS) approach. In addition to these two radial measures of efficiency, it is possible to adopt a non-radial slacks-based measure of efficiency (SBM) in the input orientation.

The decomposition of the non-radial technical efficiency can contribute in interpreting the sources of inefficiencies (see Cooper et al., 2000). In this paper we apply the CRS and VRS model for the calculation of technical efficiency using the CCR approach of Charnes et al. (1978) and the BCC approach of Banker et al. (1984), respectively. Moreover, we apply a non-radial slacks-based measure of efficiency (SBM) in the input orientation which allows us to interpret the sources of inefficiencies (see Cooper et al., 2000). 13 regions, which correspond to clients which make heavier usage of telecommunication services, will unfairly appear to be more efficient than branches operating in poorer regions. Since Greece has a wide variation of living standards and economic activity across rural and urban areas, large differences in traffic volume per customer between branches are to be expected. Regulatory constraints mean that the GTO is obliged to provide telecommunication services to all regions irrespective of the variations in profitability. This means that for the case of Greece and the purposes of this study telephony access lines constitute a more appropriate measure of output. As input variables we used the number of employees and the total area in m 2 covered by each branch.

These are the two main cost centers for each branch. Although capital expenses could also be included as an input variable, these cannot be easily allocated to each branch. Moreover, the nature and homogeneity of the technology within the GTO means that it is unlikely that variations in capital expenses can lead to significant relative differences in efficiency. In any case, such data were not available. The number of employees includes all technical, administrative and support personnel. The total area includes all types of real estate used: offices, technical support and commercial. It is reasonable to assume that the GTO can adjust the levels of the two inputs in use and hence aims to achieve the maximum potential decrease in inputs while remaining in the production possibility set. It is also reasonable to assume that output is fixed in a market with the obligation to serve all customers in a predefined region that demand a telephone line. The number of access lines for 2002 totaled to just under 3 million, while the number of employees and the corporate real estate area amounted to about 8,400 and 278,000 m 2 , respectively. Finally, it must be noted that all the data used in this study was provided by the GTO administration.

Figure 2: DEA analysis with CRS

The DEA models discussed in the previous section were applied in order to compute relative This implies that non-efficient units use, on average, 63.6 % more inputs per unit of output than efficiency units do. The distribution of all scores is summarized in Table 1.

Table 2. Distribution of technical efficiency scores of the branches grouped by area occupied

Table 2 gives the average technical efficiency score for branches grouped by the area they cover. The results according to both CCR and BCC suggest that braches occupying less (more) space are more (less) efficient. Branches occupying less than 2,000 m 2 appear to be the most efficient with an average score assuming constant (variable) returns to scale of about 41% (79%). As shown in Figure 3, there is a weak negative relationship between 15 the branch surface and its efficiency score. The Pearson correlation coefficient (correl) between these two variables is estimated at -15.4% which is marginally significant at the 10% level with the relevant two-tailed t-statistic equal to -1.74. Branches with scores between 15-25 % present a closing up at 1000 m2. The best performing braches occupy surface less than 2,000 m 2 . As can be seen in Figure 4, there is also a negative correlation between the number of personnel of branches and its efficiency score. The relevant correlation coefficient is estimated at -27.4% which is significant below the 1% level. Figures 7 and 8 depict the relationship between the efficiency score differences following the two approaches, and, the surface of branches and the number of employees, respectively. It can be inferred that branches with surface between 1,000 m 2 to 2,000 m 2 and with employees between 1 and 70 are of optimal size. There is a closing up when the number of employees reach 50 and the branch surface is 2000 m 2 . As can be seen in Figures 4 and8, the pure technical efficiency improvements should be primarily sought on branches with a number of employees between 1 and 70. However, after achieving pure technical efficiency, the smaller branches will still exhibit significant scale inefficiencies, i.e., difference in efficiency between CRS and VRS.

This indicates that after achieving pure technical efficiency, the resulting scale size of branches does not allow the maximization of productivity due to the inherent returns to scale properties of branches' activities. Similar conclusions can be drawn with respect to the second input concerning the area occupied by each branch. The pure technical efficiency improvements should be primary sought in branches occupying an area less than 2000 m 2 , although branches will still exhibit significant scale inefficiencies (see Figure 7). In the input oriented DEA approach, input slacks would be associated with the assumption of strong or weak disposability of inputs which permits zero marginal productivity of inputs and present among the branches with 40 units operating without congestion while 72 units showed a congestion score of around 90%. The average price was 95%, that is on average 5% of the inputs could be reduced to eliminate the congestion.

Table 5: Returns to scale analysis

Turning to the analysis of returns to scale (see Table 5), as identified by the input oriented CCR, BCC and NIRS model, three branches from 127 showed constant returns to scale, ninety branches increasing returns to scale (IRS) and 44 decreasing returns to scale, respectively. Interestingly, 90

branches have a possibility to improve their efficiency by scaling up their activities. This could be accomplished by, for example, merging low ranked branches into one branch. The returns to scale characteristics of the projected activity of branches can be identified on the basis of the reference set of branches. For example, the DMU No 127 reference set is composed by of 90, 31 and 23, all of them belonging to IRS. This means that the projected activity of the branch 127 belongs to IRS.

Finally, we also examined the robustness of the efficient branches. For an inefficient DMU O , the positive values of determines the set of dominating units (reference set) placed in the border of efficiency against the unit that is evaluated.

CONCLUSIONS

In this paper we propose productivity analysis to evaluate the efficiency in the exploitation of corporate real estate assets. This approach provides an objective and consistent way of assessment compared to ad hoc ratio measures which are largely subjective and atheoretic.

Productivity analysis produces a wealth of empirical results which can be used by managers as a multi-informative framework to quantify targets and serves as a guide tool for the efficient employment of real estate assets. In an empirical application, we applied four popular DEA models (CRS, VRS, SBM, and NIRS) to study radial and non-radial measures of efficiency for 127 branches of the Greek Telecommunications Organization. We used data for 2002 with the number of fixed telephony access lines as an output variable and the number of employees and the total area in m 2 covered by each branch as the two input variables. The results suggest that there is significant potential for efficiency improvements in the GTO. More specifically, we found significant elements of technical inefficiency with respect to the employment of real estate in the production process. The comparison of mean efficiency of CCR and VRS models showed that a significant difference is due to the scale inefficiency of branches. The analysis identified scale inefficiency as the main reason of overall inefficiency. In a nutshell, we found that branches operate in wrong scale. The correlation between branch surface to the difference in efficiency scores between the VRS and CRS approach suggests that the branches with size from 1,000 to 3,000 m 2 are in the region of the optimal scale. We found that there is a significant waste of branch surface (34.82%) indicating the need for strategic and technical allocation of the real Additional sources of inefficiency can be recognized in the framework of a cost efficiency analysis. The analysis of allocative efficiency, a basic component of cost efficiency, is based on actual market prices for inputs and outputs and may produce a different picture.

Although productivity analysis is very useful in analyzing production unit efficiency without the need to impose a pre-defined functional form for production, care must be taken to analyze the results in conjunction with the inputs and outputs used. Future research will focus on providing a more complete analysis by attributing not only the production performance but also the cost performance and additional production mix variables. Real estate is one of the major factors of production, cost and profit in most industries and may provide significant competitive advantages (Roulac, 2001). Notwithstanding, a number of studies suggest that firms do not generally exploit their real estate assets in the most efficient manner (eg., Zeckhauser and Silverman, 1983;Veale, 1989). Although some approaches have been developed for measuring corporate real estate effectiveness (see, for example, [START_REF] Pittman | A Survey of Corporate Real Estate Executives on Factors Influencing Corporate Real Estate Performance[END_REF]Apgar, 1995), most real estate managers rely on benchmarking via ad hoc comparative ratio analysis for evaluating real estate efficiency within the firm and against competition. 1 However, there is little consensus in the academic and professional literature as to what measures are correct and why (eg., see [START_REF] Duckworth | Realizing the Strategic Dimension of Corporate Real Property through Improved Planning and Control Systems[END_REF][START_REF] Noha | Benchmarking: The Search for Best Practices in Corporate Real Estate[END_REF][START_REF] Nourse | Measuring Business Real Property Performance[END_REF]. Roulac (2001) summarizes the concerns in the industry and literature by saying that: "a conceptual linear programming approach is necessary to implement multiple complementary and competing strategic objectives in corporate property economics functions". The present paper attempts to address these concerns.

This paper proposes the application of productivity analysis in assessing how efficiently real estate assets are used within the units of a firm or across competing firms. Productivity analysis is broad methodological framework which can be used to distinguish two types of efficiency: technical efficiency, which determines whether a production unit (DMU) achieves maximum output using the given factors of production, and, allocative efficiency, which determines whether the factors of production are used in proportion that ensure maximum output at given market prices (for review of this literature see Coelli et al., 2005). While a variety of [START_REF] Ray | Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research[END_REF].

The remainder of the paper is organized as following. The next section makes a brief introduction to the methodology. Section 3 describes the data, the empirical application and the results. The final section summarizes our major findings and concludes the paper.

METHODOLOGY

In this paper we adopt the well-established methodological framework of technical efficiency analysis as defined by [START_REF] Farrell | The measurement of production efficiency[END_REF]: a DMU is said to be technically efficient if a reduction in any input requires an increase in the other input or decrease of output. In order to introduce the terminology and demonstrate how measures of DEA are applied we provide an illustrative example with five DMUs denoted by A, B, C, D and E. The empirically derived efficient frontier consists of the convex combination of A, B, C and D. Figure 1 shows the four efficient DMUs, A, B, C and D; the solid line segment connecting points ABCD constitutes an isoquant or level line that represents the different amounts of two inputs (x 1 , x 2 ) which can be used to produce the same amount. This line represents the efficient frontier of the production possibility set. By reason of Q, which exists on the line between A and B, E is inefficient. This is because Q uses less input to produce the same output as the efficient DMUs. The dashed line that intersects the isoquant at B is a minimum cost line since the pairs (x 1 , x 2 ) on this line yield the same total cost when unit costs are c 1 and c 2 , respectively. However, shifting this dashed line upwards in parallel fashion until it reaches a point of intersection with E would increase cost. In fact, point B is the minimum total cost needed to produce the specified output. This is because any parallel shift downwards below x 1 required in order to reach the uncongested frontier from the congested frontier; that is we calculate the reduction that is achieved if congestion is eliminated.

2 Congestion refers to the situation whereby increasing (decreasing) one or more inputs decreases (increases) some outputs without improving (worsening) other inputs or outputs. The present paper adopts the DEA approach in evaluating efficiency across DMUs. DEA refers to a mathematical programming family of techniques which are flexible and have the advantage of making very few assumptions (for a description see Cooper, Seiford and Tone, 2000). The method, developed by Charnes et al. (1978), has been widely used at numerous applications measuring the performance of decision-making units (DMU) in the public and private sector. DEA is used so widely because of its simplicity and the useful interpretation of results it yields even with limited data sets. The determination of the efficiency score of the a DMU in a sample of n DMUs in the Constant Returns to Scale (CRS) model is equivalent to the optimization of the following linear programming problem (Charnes et al., 1978; hereafter CCR):

0 0 1 max s r r u r z u y = = subject to 1 1 0, 1, 2,..., s m r rj i ij r i u y vx j n = = = 0 1 1 m i i i v x = = 0, 1, 2,... r u r s = 0, 1, 2,..., i v i m = (1)
For the above linear programming problem, the dual can be written for a given DMU as: To determine efficiency measures under the Variable Returns to Scale (VRS) model (see Banker et al., 1984; hereafter BCC), a further convexity constraint DMUs are flexible to adjust their size to the optimal firm size. On the other hand, the BCC method is less restrictive because it compares the productivity of companies only within similar sample sizes. The comparison between the two approaches also provides some information about the underling technology: if the results of the CCR and the BCC approaches are similar, then returns to scale do not play an important role in the process.

The CCR model score is called global technical efficiency because it is postulated that the radial expansion and reduction of all observed DMUs and their nonnegative combinations are possible. A DMU is said to display total technical efficiency if it produces on the best practice observed boundary of the production possibility set, i.e. maximizes output with given inputs and after having chosen technology. The BCC scores is called local pure technical efficiency because the model assumes the convex combinations of the observed DMUs as the production possibility set. Scale efficiency measure can be calculated by dividing the total technical efficiency by pure technical efficiency. Using these concepts, the total technical efficiency can be further decomposed into pure technical efficiency and scale efficiency (Cooper et al., 2000). The following relationship demonstrates the decomposition of Global Technical Efficiency (TE):

Technical efficiency = Pure Technical efficiency x Scale Efficiency

Generally speaking, for each DMU the CCR model efficiency score will not exceed the BBC efficiency score. An efficiency score obtained using the CCR-model comprises both scale efficiency and pure technical efficiency. In a case where a DMU is found to be inefficient, one can decompose this total inefficiency to see in what degree this due to scale inefficiency or technical inefficiency. The efficiency scale can be defined as: it assumes a value of 1, the DMU is efficient both under CCR and BCC. If SE is less than 1 then the DMU is not scale efficient.

The above-described approach does not allow identifying whether a DMU operates under increasing returns to scale (IRS) or decreasing returns to scale (DRS). This problem can be solved using the non-increasing returns to scale (NIRS) expression of DEA by setting the constraint This decomposition contributes to interpret sources of inefficiencies for each non-radial inefficient DMU. A more comprehensive decomposition of cost efficiency considers the input congestion as a piece of the decomposition of the cost inefficiency into technical inefficiencies, which is non-price related and allocative inefficiency, which is price related (Färe and Grosskopf, 2000). Technical efficiency requires pure technical efficiency to be computed by relaxing the strong input disposability restriction (Byrnes et al., 1984) to allow for an input congestion component. [START_REF] Junoy | Partitioning input cost efficiency into its Allocative and Technical components. An empirical DEA application to Hospitals[END_REF]. Then, the Technical efficiency (TE) is defined as the product of the scale efficiency, input congestion, and pure technical efficiency components in the condition of weak disposal [START_REF] Färe | Production Frontiers[END_REF]:

TE(x,y)=SE(x,y)*C(x,y)*PTE(x,y) (6)
The theoretical discussion identifies some shortcoming of procedures for measuring congestion see, for example Cooper et al (2001), andCherchye et al. (2001). The work of Brockett et al. (1998) improves upon the work of [START_REF] Färe | Production Frontiers[END_REF] Nonetheless, the Färe's et al.

(1994) approach is still useful if one's aim to assess the impact of congestion on the overall technical efficiency. In the recent paper, [START_REF] Färe | Slacks and Congestion: a comment[END_REF] discuss the connection In (3), we measure the efficiency of a specific DMU 0 (Charnes et al.,1978 ) The model in

The input congestion measure is then defined as the following ratio:

* * * * 0 ( , ) 1 C = (10) if and only if * * ( , ) 1 C
< the congestion is presence.

In the manner described above, DEA allows the partition technical efficiency in pure technical efficiency, congestion, and scale efficiency and investigate the different types of 12 inefficiency across DMUs. The analysis can be done assuming constant returns to scale (CRS) or variable returns to scale (VRS) approach. In addition to these two radial measures of efficiency, it is possible to adopt a non-radial slacks-based measure of efficiency (SBM) in the input orientation. The decomposition of the non-radial technical efficiency can contribute in interpreting the sources of inefficiencies (see Cooper et al., 2000). In this paper we apply the CRS and VRS model for the calculation of technical efficiency using the CCR approach of Charnes et al. (1978) and the BCC approach of Banker et al. (1984), respectively. Moreover, we apply a nonradial slacks-based measure of efficiency (SBM) in the input orientation which allows us to interpret the sources of inefficiencies (see Cooper et al., 2000).

Table 1. Distribution of DEA radial measure scores

EVALUATING TELECOM BRANCH RELATIVE PERFORMANCE

In 13 reasonable to assume that output is fixed in a market with the obligation to serve all customers in a predefined region that demand a telephone line. The number of access lines for 2002 totaled to just under 3 million, while the number of employees and the corporate real estate area amounted to about 8,400 and 278,000 m 2 , respectively.

Figure 2: DEA analysis with CRS

The DEA models discussed in the previous section were applied in order to compute relative measures of technical (TE), pure technical (PTE), scale (SE) and congestion (C) efficiencies, respectively. As shown in Table 1, the average efficiency for the CCR model is just under 40%

with three branches on the efficiency frontier and only five braches with efficiency above 90% (see also Figure 2). That means that the average branch, if producing its output on the efficient instead of at its current (virtual) location, would have needed only 38.36% of the inputs currently being used. In the terms of average inefficiency, it would have needed 160.6% more inputs to produce the same outputs as an efficient branch. Units would need to lower inputs by 61.64% if all were operating on the production efficiency frontier. The average efficiency score for nonfrontier units is 61.6% (VRS), implying that nonefficient units use, on average, 63.6% more inputs per units of output than efficient units do.

Table 2. Distribution of technical efficiency scores of the branches grouped by area occupied

Table 2 gives the average technical efficiency score for branches grouped by the area they cover.

The results according to both CCR and BCC suggest that braches occupying less (more) space are more (less) efficient. Branches occupying less than 2,000 m 2 appear to be the most efficient with an average score assuming constant (variable) returns to scale of about 41% (79%). As shown in 3, there is a weak negative relationship between the branch surface and its efficiency score. Branches with scores between 15-25 % present a closing up at 1000 m 2 . The best performing braches occupy surface less than 2,000 m 2 . As can be seen in Figure 4, there is also a a negative correlation between the number of personnel of branches and its efficiency score. If one uses the VRS model, the efficiency scores rise significantly (see Figure 5) with 13 out of the 127 branches on the frontier (100% efficiency) and an average efficiency of 65.13%. This can be explained by the fact that now branches of similar size are compared with each other and not with the best ones of the whole sample. Figure 6 depicts the differences in efficiency scores between the VRS and the CCR model. It appears that the optimal size of branches, i.e. the one where the VRS and CRS efficiency scores converge, is in the beginning which corresponds to the region of Athens. It is also suggests that many branches do not have an optimal size and may gain in efficiency by merging.

Figures 7 and8 depict the relationship between the efficiency score differences following the two approaches, and, the surface of branches and the number of employees, respectively. It can be inferred that branches with surface between 1,000 m 2 to 2,000 m 2 and with employees between 1 and 70 are of optimal size. There is a closing up when the number of employees reach 50 and the branch surface is 2000 m 2 . In the input oriented DEA approach, input slacks would be associated with the assumption of strong or weak disposability of inputs which permits zero marginal productivity of inputs and hence extensions of the relevant isoquants to form horizontal or vertical facets. In this case, units which are deemed to be radial efficient, that is no further proportional reductions in inputs is possible without sacrificing output, may nevertheless be able to implement further reductions in some inputs. Such additional potential input reductions are typically refereed to as non-radial input slacks, in contrast to the radial slacks associated with DEA inefficiency, i.e., radial deviations from the efficient frontier.

Table 3 summarizes the output of the slack variable analysis. The results suggest that if it were possible for the inefficient branches to perform like the best practicing one's savings of about 61% (34%) in the total surface and 89% (58%) in the number of personnel would be possible according to the CRS (VRS) approach. At the same time, potentially increased outputs can be observed with an average increment of 635 lines per branch. Moreover, all the branches show zero input slack in the total surface variable and in the personnel variable so the most of them are mix inefficient because the reduction to bring them to the frontier change the input proportions. However, 28 inefficient branches operate without altering the mix (proportions) they utilized. The average value of slacks is 20 units showing the further reduction in the number of personnel over the reduction determined by the efficiency score. present among the branches with 40 units operating without congestion while 72 units showed a congestion score of around 90%. The average price was 95%, that is on average 5% of the inputs could be reduced to eliminate the congestion.

F

Table 5: Returns to scale analysis

Turning to the analysis of returns to scale (see Table 5), as identified by the input oriented CCR, BCC and NIRS model, three branches from 127 showed constant returns to scale, ninety branches increasing returns to scale (IRS) and 44 decreasing returns to scale, respectively. Interestingly, 90

branches have a possibility to improve their efficiency by scaling up their activities. This could be accomplished by, for example, merging low ranked branches into one branch. The returns to scale characteristics of the projected activity of branches can be identified on the basis of the reference set of branches

CONCLUSIONS

In this paper we propose productivity analysis to evaluate the efficiency in the exploitation of corporate real estate assets. This approach provides an objective and consistent way of assessment compared to ad hoc ratio measures which are largely subjective and atheoretic.

Productivity analysis produces a wealth of empirical results which can be used by managers as a multi-informative framework to quantify targets and serves as a guide tool for the efficient employment of real estate assets. In an empirical application, we applied four popular DEA models (CRS, VRS, SBM, and NIRS) to study radial and non-radial measures of efficiency for 127 branches of the Greek Telecommunications Organization. We used data for 2002 with the number of fixed telephony access lines as an output variable and the number of employees and the total area in m 2 covered by each branch as the two input variables. The results suggest that there is significant potential for efficiency improvements in the GTO. More specifically, we found significant elements of technical inefficiency with respect to the employment of real estate in the production process. The comparison of mean efficiency of CCR and VRS models showed that a significant difference is due to the scale inefficiency of branches. The analysis identified scale inefficiency as the main reason of overall inefficiency. In a nutshell, we found that branches scores between the VRS and CRS approach suggests that the branches with size from 1,000 to 3,000 m 2 are in the region of the optimal scale. We found that there is a significant waste of branch surface (34.82%) indicating the need for strategic and technical allocation of the real estate portfolio. The analysis of returns to scale (80%, Increasing Returns to Scale) indicates the possibility of improving the overall efficiency by merging low efficient branches into one branch.

Additional sources of inefficiency can be recognized in the framework of a cost efficiency analysis. The analysis of allocative efficiency, a basic component of cost efficiency, is based on actual market prices for inputs and outputs and may produce a different picture.

Although productivity analysis is very useful in analyzing production unit efficiency without the need to impose a pre-defined functional form for production, care must be taken to analyze the results in conjunction with the inputs and outputs used. Future research will focus on providing a more complete analysis by attributing not only the production performance but also the cost performance and additional production mix variables.

Figure 1 .

 1 Figure 1. Technical, Allocative, Overall Efficiency and Congestion.

  a line that fails to intersect the production possibility set. The ratio 0 1 OQ OE

  linear programming problem, the dual can be written for a given DMU as: of constants. Assuming that the DMU uses m inputs and s outputs, X and Y represent m n × input and s n × output matrices, respectively. The x ij represents the input of the i-th type of the j-th DMU and y rj the observed amount of output of the r-th type for the j-th DMU. A DMU is said to be efficient if technical efficiency is equal to one. A technical efficiency score less than one indicates the extent by which a DMU should reduce inputs while maintaining the same output in order to produce the output of a technically efficient DMU.

  input-oriented BCC model for a DMU can be written formally as: model for each DMU, the BCC-efficiency scores are obtained. The model eliminates the scale part of the efficiency from the analysis. It is an interesting subject to investigate the sources of inefficiency that a DMU might have. The CCR approach suggests that

  total inefficiency to see in what degree this due to scale inefficiency or technical inefficiency. The efficiency scale can be defined as: CCR , and * BCC are the CCR and BCC scores, respectively. SE cannot exceed one, if

s.

  DMU operates under IRS and it is scaleinefficient since it has the potential to achieve bigger output. On the other hand, under DRS and inefficiency is due to excess output.Cooper et al. (2000) introduced a non -radial measure of efficiency called SBM (slacksbased measure of efficiency). The input orientation of the SMB model is equivalent to the optimization of the following equation considering five conditions: is the input excesses of inputs and s + is the output shortfalls.Based on the SBM efficiency score,Cooper et al (2000) defines the Mix efficiency This definition drives to the decomposition of the non-radial technical efficiency into MIX, pure technical efficiency and scale efficiency. to interpret sources of inefficiencies for each non-radial inefficient DMU. A more comprehensive decomposition of cost efficiency considers the input congestion as a piece of the decomposition of the cost inefficiency into technical inefficiencies,

( 3 )

 3 confronts to the condition of strong disposal. If we replace the first m inequalities in (3) by equations the model exhibits weak (input) disposal so there is no possibility of positive inputs slacks that may have to be disposed of:

Figure 3 :

 3 Figure 3: Correlation between the branch surface and its efficiency score

Figure 4 :Figure 5 :Figure 6 :

 456 Figure 4: Correlation between the number of personnel in branch and its efficiency score

Figure 7 :Figure 8 :

 78 Figure 7: Size evaluation of branches over surface

  The analysis of returns to scale (80%, Increasing Returns to Scale) indicates the possibility of improving the overall efficiency by merging low efficient branches into one branch.

Figure 1 :Figure 3 :

 13 Figure 1: Technical, Allocative, Overall Efficiency and Congestion.

Figure 4 :Figure 6 :Figure 8 :

 468 Figure 4: Correlation between the number of personnel in branch and its efficiency score

Figure 1 .

 1 Figure 1. Technical, Allocative, Overall Efficiency and Congestion.

  a line that fails to intersect the production possibility set. The ratio 0 1 OQ OE is said to provide a radial measure of technical efficiency (TE) with 0 technical inefficiency. Technical inefficiency measures the proportion with which E could be replaced to maintain the same quantity of output. Now, consider the point P at the intersection of this cost line through B with the ray from the origin to E. The ratio 0 1 OP OQ is referred to as price efficiency or allocative efficiency (AE). The corresponding measure 1 OP OQ represents allocative inefficiency and denotes a possible reduction in cost by using appropriate input mixes. It provides a measure of the extent to which the technical efficiency point, Q, falls short of achieving minimal cost because of failure to make the substitutions, or reallocations, involved in moving from Q to B along the efficiency frontier.We can also obtain a radial measure of overall efficiency (OEF) from the product of "allocative" and 'technical efficiency', ie., OEF TE AE = and denotes the possible reduction in cost due to changing from B (observed input quantities) to P (cost minimizing input quantities). Point D is efficient only when we allow for congestion. 2 However, it will be inefficient under the standard assumption of no backward bending segment. The isoquant shown by the dashed line from point C represents the non-congested benchmark. The ratio OD OD represents the reduction in input

  efficiency score and ( a n*1 vector of constants. Assuming that the DMU uses m inputs and s outputs, X and Y represent m*n input and s*n output matrices, respectively. The x ij represents the input of the i-th type of the j-th DMU and y rj the observed amount of output of the r-th type for the j-th DMU. A DMU is said to be efficient if technical efficiency is equal to one. A technical efficiency score less than one indicates the extent by which a DMU should reduce inputs while maintaining the same output in order to produce the output of a technically efficient DMU.

  input-oriented BCC model for a DMU can be written formally as: model for each DMU, the BCC-efficiency scores are obtained. The model eliminates the scale part of the efficiency from the analysis. It is an interesting subject to investigate the sources of inefficiency that a DMU might have. The CCR approach suggests that

  CCR , and * BCC are the CCR and BCC scores, respectively. SE cannot exceed one, if

s.

  DMU operates under IRS and it is scaleinefficient since it has the potential to achieve bigger output. On the other hand, under DRS and inefficiency is due to excess output.Cooper et al. (2000) introduced a non -radial measure of efficiency called SBM (slacksbased measure of efficiency). The input orientation of the SMB model is equivalent to the optimization of the following equation considering five conditions: is the input excesses of inputs and s + is the output shortfalls. This definition drives to the decomposition of the non-radial technical efficiency into MIX, pure technical efficiency and scale efficiency.

  congestion. We note, however, that precise measurement of congestion is not yet conclusive.

( 3 )

 3 confronts to the condition of strong disposal. If we replace the first m inequalities in (3) by equations the model exhibits weak (input) disposal so there is no possibility of positive inputs slacks that may have to be disposed of:

  figure of the installed client base irrespective of the frequency of usage. As input variables we used the number of employees and the total area in m 2 covered by each branch. The number of employees includes all technical, administrative and support personnel. The total area includes all types of real estate used: offices, technical support and commercial. It is reasonable to assume that the GTO can adjust the levels of the two inputs in use and hence aims to achieve the maximum potential decrease in inputs while remaining in the production possibility set. It is also

Figure 3 :

 3 Figure 3: Correlation between the branch surface and its efficiency score

Figure 4 :

 4 Figure 4: Correlation between the number of personnel in branch and its efficiency score

Figure 5 :Figure 6 :

 56 Figure 5: DEA analysis with VRS

Figure 7 :Figure 8 :

 78 Figure 7: Size evaluation of branches over surface

  scale. The correlation between branch surface to the difference in efficiency

Figure 1 :Figure 3 :

 13 Figure 1: Technical, Allocative, Overall Efficiency and Congestion.

Figure 4 :Figure 6 :Figure 8 :

 468 Figure 4: Correlation between the number of personnel in branch and its efficiency score

  The work ofBrockett et al.

	(1998) improves upon the work of
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Table 1 .

 1 Distribution of DEA radial measure scores

3. EVALUATING TELECOM BRANCH RELATIVE PERFORMANCE In the empirical application we evaluate the technical efficiency of the Greek

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	Telecommunications Organization (GTO) DMUs with respect to real estate usage. More specifically, we apply the input oriented DEA, as we are interested in examining the efficient R e v i e w
	usage of the real estate input. The data set used corresponds to year 2002 for each one of the 127
	branches of GTO spread over the 52 municipalities of Greece. The output variable was set as the
	number of fixed telephony access lines. Although traffic volumes or revenues could also be used
	as outputs, such data were not available for this study. The number of access lines has the
	advantage of providing an exact figure of the installed client base irrespective of the frequency of
	usage. Using traffic volumes or revenues as outputs could produce biased results in our
	application. This is because of the fact that for the same level of inputs, branches in more affluent

  In the terms of average inefficiency, it would have needed 160.6% more inputs to produce the same outputs as an efficient branch. Units would need to lower inputs by 61.64% if all were operating on the production efficiency frontier. Pure technical inefficiency scores show a lower level of inefficiency, the average being 34.87%. Average scale inefficiency is 40.58%

	respectively. As shown in Table 1, the average efficiency for the CCR model is just under 40%
	with three branches on the efficiency frontier and only five braches with efficiency above 90%
	(see also Figure 2). That means that the average branch, if producing its output on the efficient
	instead of at its current (virtual) location, would have needed only 38.36% of the inputs currently
	being used.
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	measures of technical (TE), pure technical (PTE), scale (SE) and congestion (C) efficiencies,
	14

while congestion efficiency is 4.5%. With respect to technical efficiency, 2.4% of the branches are operating on the frontier. The average efficiency score for non-frontier branches is 36.8%, implying that non-efficient branches use, on average, 171.7 % more inputs per unit of output than efficient branches do. According to the pure technical efficiency criterion, 13 of the branches (10.2%) operate efficiently, with an average efficiency score of 61.1 % for non-frontier branches.

  the relevant isoquants to form horizontal or vertical facets. In this case, units which are deemed to be radial efficient, that is no further proportional reductions in inputs is possible without sacrificing output, may nevertheless be able to implement further reductions in some inputs. Such additional potential input reductions are typically refereed to as non-radial input slacks, in contrast to the radial slacks associated with DEA inefficiency, i.e., radial deviations from the efficient frontier.

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	hence extensions of
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Table 3

 3 summarizes the output of the slack variable analysis. The results suggest that if it were possible for the inefficient branches to perform like the best practicing one's savings of

about 61% (34%) in the total surface and 89% (58%) in the number of personnel would be possible according to the CRS (VRS) approach. At the same time, potentially increased outputs can be observed with an average increment of 635 lines per branch. Moreover, all the branches show zero input slack in the total area variable while most of them show non-zero slack in the personnel variable. This means that most branches are mix inefficient since the reduction needed to bring them to the frontier changes the input proportions. However, 28 inefficient branches operate without altering the mix (proportions) they utilized. The average value of slacks is 20 units showing the further reduction in the number of personnel over the reduction determined by the efficiency score.

Table 3 :

 3 Slack variable analysis

Table 4 :

 4 Decomposition of non-radial SBM Technical Efficiency for the most efficientWe also decomposed the non-radial SBM scores of the most efficient branches and the results are given in Table4. It can be observed that, for example, for DMU No 21, the low SBM (76.3%) is caused by SE (79.1%). For DMU No 12, the SBM (61.8%) can be mainly attributed to the SE

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  This number indicates the robustness of the efficient branches. Indeed, we found that DMU 19 DMU 65 , DMU 72 , and DMU 90 appear more than 35 times in the reference sets of inefficient DMUs. On the other hand, five DMUs appear less than two times in any comparison group of inefficient branches.

	of inefficient branches.
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The magnitude of defines if DMU o has more similarity to one from the other efficient DMUs. To discriminate between relatively efficient branches, we count the number of efficient branches that appeared in the reference sets

Table 1 .

 1 Distribution of DEA radial measure scores

			TE			PTE		SE		C
	Mean		38.36%			65.13%		59.42%		95.42%
	Stdev		25.40%			23.07%		33.62%		9.13%
	Score Bin	#	%		#	%	#	%	#	%
	Less than 50%	84	66.1%	35	27.6%	49	38.6%	2	1.5%
	50% to 60% 60% to 70%	17 F o 13.4% 11 8.7%	21 15	6.5% 11.8%	8 6	6.3% 4.7%	0 0	0.0% 0.0%
	70% to 80%	6	r 4.7%	16	12.6%	14	11.0%	3	2.4%
	80% to 90%	4	P 3.1% 14	11.0%	16	23.6%	10	7.9%
	90% to 100% 100%	2 3	1.6% 2.4%	13 e r 10.2% e 13 10.2%	30 4	24.4% 2.4%	72 40	59.1% 29.1%
	Total	127	100%		127	100%	127	100%	127	100%
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Table 2 .

 2 Distribution of technical efficiency scores of the branches grouped by area occupied

	Group	# Branches	Area (m 2 )	CCR	BCC
	1	4	5,001-	19.11%	24.90%
	2	5	4,001 -5,000	24.15%	41.58%
	3	16	3,001 -4,000	39.47%	50.24%
	4	32	2,001 -3,000	37.72%	51.17%
	5	70	1,001 -2,000	40.53%	78.70%

Table 3 :

 3 Slack variable analysis

	CCR	BCC

Table 4 :

 4 Decomposition of non-radial SBM Technical Efficiency for the most efficient

				Submitted Manuscript	Page 30 of 57
		branches
	DMU # SBM	CRS	VRS	Mix Efficiency (MIX) Scale Efficiency (SE)
	19	100.0% 100.0% 100.0%	100.0%	100.0%
	28	100.0% 100.0% 100.0%	100.0%	100.0%
	2	97.1% 97.2% 100.0%	99.9%	97.2%
	9	84.6% 87.2% 87.5%	97.0%	99.6%
	21	76.3% 79.1% 100.0%	96.5%	79.1%
	37	66.7% 69.5% 100.0%	95.9%	69.5%
	14	65.3% 67.8% 68.6%	96.3%	98.7%
	12	61.6% 71.9% 93.9%	85.7%	76.5%
	23	56.8% 56.9% 100.0%	99.9%	56.9%
	4	53.2% 92.3% 100.0%	57.6%	92.3%
	Mean	24.3% 38.3% 65.1%	62.9%	59.4%

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 5 :

 5 Returns to scale analysis

	DMU #	Efficiency Score	Reference Set	RTS
	19	100.0%	-	CRS
	28	100.0%	-	CRS
	32	96.6%	(23, 90)	IRS
	45	46.4%	(19, 65)	IRS
	72	100.0%	-	IRS
	75	89.0%	(19, 21, 65)	DRS
	93	40.2%	(19, 65)	IRS
	96	38.8%	(65, 72)	DRS
	100	58.1%	(72, 90)	IRS
	127	87.4%	(90, 31, 23)	IRS

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Table 3 :

 3 Slack variable analysis

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Table 4 :

 4 Decomposition of non-radial SBM Technical Efficiency for the most efficient branchesWe also decomposed the non-radial SBM scores of the most efficient branches and the results are given in Table4. It can be observed that, for example, for DMU No 21, the low SBM (76.3%) is caused by SE (79.1%). For DMU No 12, the SBM (61.8%) can be mainly attributed to the SE (76.5%) and to MIX (85.7%). Although DMU No 12 is efficient with respect to VRS (93.9%), its low SE (76.5%) and SBM (61.6%), force it to be inefficient overall. The average result of nonradial slacks based model confirms the low efficiency of the 127 branches. Congestion was also

  . For example, the DMU No 127 reference set is composed by of 90, 31 and 23, all of them belonging to IRS. This means that the projected activity of the branch 127 belongs to IRS. also examined the robustness of the efficient branches. For an inefficient DMU O , the positive values of ( determines the set of dominating units (reference set) placed in the border of efficiency against the unit that is evaluated. The magnitude of ( defines if DMU o has more similarity to one from the other efficient DMUs. To discriminate between relatively efficient branches, we count the number of efficient branches that appeared in the reference sets of inefficient branches. This number indicates the robustness of the efficient branches. Indeed, we found that DMU 19 DMU 65 , DMU 72 , and DMU 90 appear more than 35 times in the reference sets of inefficient DMUs. On the other hand, five DMUs appear less than two times in any comparison group of inefficient branches.

	Finally, we
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Table 1 .

 1 Distribution of DEA radial measure scores

			TE		PTE		SE		C
	Mean		38.36%		65.13%		59.42%		95.42%
	Stdev		25.40%		23.07%		33.62%		9.13%
	Score Bin	#	%	#	%	#	%	#	%
	Less than 50%	84	66.1%	35	27.6%	49	38.6%	2	1.5%
	50% to 60%	17	13.4%	21	6.5%	8	6.3%	0	0.0%
	60% to 70%	11	8.7%	15	11.8%	6	4.7%	0	0.0%
	70% to 80%	6	4.7%	16	12.6%	14	11.0%	3	2.4%
	80% to 90%	4	3.1%	14	11.0%	16	23.6%	10	7.9%
	90% to 100%	2	1.6%	13	10.2%	30	24.4%	72	59.1%
	100%	3	2.4%	13	10.2%	4	2.4%	40	29.1%
	Total	127	100%	127	100%	127	100%	127	100%

Table 2 .

 2 Distribution of technical efficiency scores of the branches grouped by area occupied

	Group	# Branches	Area (m 2 )	CCR	BCC
	1	4	5,001-	19.11%	24.90%
	2	5	4,001 -5,000	24.15%	41.58%
	3	16	3,001 -4,000	39.47%	50.24%
	4	32	2,001 -3,000	37.72%	51.17%
	5	70	1,001 -2,000	40.53%	78.70%

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Table 3 :

 3 Slack variable analysis

	CCR	BCC

Table 4 :

 4 Decomposition of non-radial SBM Technical Efficiency for the most efficient

		branches
	DMU # SBM	CRS	VRS	Mix Efficiency (MIX) Scale Efficiency (SE)
	19	100.0% 100.0% 100.0%	100.0%	100.0%
	28	100.0% 100.0% 100.0%	100.0%	100.0%
	2	97.1% 97.2% 100.0%	99.9%	97.2%
	9	84.6% 87.2% 87.5%	97.0%	99.6%
	21	76.3% 79.1% 100.0%	96.5%	79.1%
	37	66.7% 69.5% 100.0%	95.9%	69.5%
	14	65.3% 67.8% 68.6%	96.3%	98.7%
	12	61.6% 71.9% 93.9%	85.7%	76.5%
	23	56.8% 56.9% 100.0%	99.9%	56.9%
	4	53.2% 92.3% 100.0%	57.6%	92.3%
	Mean	24.3% 38.3% 65.1%	62.9%	59.4%

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Table 5 :

 5 Returns to scale analysis

	DMU #	Efficiency Score	Reference Set	RTS
	19	100.0%	-	CRS
	28	100.0%	-	CRS
	32	96.6%	(23, 90)	IRS
	45	46.4%	(19, 65)	IRS
	72	100.0%	-	IRS
	75	89.0%	(19, 21, 65)	DRS
	93	40.2%	(19, 65)	IRS
	96	38.8%	(65, 72)	DRS
	100	58.1%	(72, 90)	IRS
	127	87.4%	(90, 31, 23)	IRS

Page 58 of 57 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Congestion refers to the situation whereby increasing (decreasing) one or more inputs decreases (increases) some outputs without improving (worsening) other inputs or outputs.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Färe, R., Grosskopt, S. (2000) Slacks and Congestion: a comment, Socio-Economic Planning Sciences, 34, 27-33 Farrell, M.J. (1957) The measurement of production efficiency, Journal of the Royal Statistical Society Series A, 120, 253-281. Giuffrida, A., Gravelle, H. (2001), Measuring performance in primary care: econometric analysis and DEA, Applied Economics, 33, 163-175. Junoy, J.P. (2000) Partitioning input cost efficiency into its Allocative and Technical components. An empirical DEA application to Hospitals, Socio-Economic Planning Sciences, 34, 199-218.

Noha, E. A. (1993) Benchmarking: The Search for Best Practices in Corporate Real Estate, Journal of Real Estate Research, 8, 511-523. Nourse, H. O. (1994) Measuring Business Real Property Performance, Journal of Real Estate Research, 9, 431-444. Pittman, R. H., Parker, J.R. (1989) A Survey of Corporate Real Estate Executives on Factors Influencing Corporate Real Estate Performance, Journal of Real Estate Research, 4, 107-19. Ray, S.C. (2004) Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research, Cambridge University Press. Page 23 of 57 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Ms S B Huda

Hamidreza Mani

Terence M Dovey

Jason Cg Halford

Emma Boyland

Christina Daousi

John P H Wilding

Jonathan H Pinkney

J C G Halford

M.S.B Huda

Ghrelin Inhibits Autonomic Function in Healthy Controls, but has No Effect on Obese and Vagotomized Subjects

Keywords: ghrelin, autonomic function, Insulin Ghrelin inhibits Autonomic function ghrelin, sympathetic nervous system, obese, gastrectomy

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Ghrelin was discovered as the natural ligand for the growth hormone secretagogue receptor GHS-R 1a, 1 and appears to have vasoactive properties; administration of ghrelin in humans causes a decrease in mean arterial pressure, but does not change heart rate or norepinephrine levels. 2 This has led to speculation as to whether ghrelin inhibits activation of the sympathetic nervous system (SNS) during hypotension.

In support of this, two rodent studies have shown that peripheral ghrelin can inhibit the expected increase in SNS activity seen in sepsis and post myocardial infarction. 3;4 Another study on conscious rabbits has shown that central administration of ghrelin can modulate the SNS by decreasing arterial pressure and suppressing renal sympathetic nervous activity. 5 Furthermore, the parasympathetic nervous system (PNS) may be involved in initiating ghrelin secretion. Cholinergic agonists and antagonists respectively have been found to increase and decrease circulating ghrelin levels in humans. 6 The vagus nerve has been shown to have an important role in signalling between peripheral ghrelin and central appetite centres. The detailed effects of ghrelin on SNS and PNS activity in humans have not previously been well described.

Ghrelin also regulates long-term bodyweight and promotes fat deposition and weight gain, 7 perhaps in part through suppression of energy expenditure and thermogenesis in brown adipose tissue via inhibition of the SNS, which has been shown recently by administering central ghrelin in rats. 8 Obese subjects have low circulating ghrelin and increased SNS activity, but whether there is a causal relationship between these observations is unknown.

We therefore studied the effects of peripheral ghrelin on heart rate variability (HRV) during a ghrelin/ placebo infusion in three groups of subjects; healthy controls, morbidly obese subjects and to examine the role of the vagus nerve, surgically vagotomized subjects. Subjects: We studied 7 healthy volunteers aged 31-62 years (mean ± standard error of the mean (SEM), 44.7±4.6), body mass index (BMI) 20.4-27.2 (23.6±0.9) kg/m 2 , 7 obese volunteers aged 31-45 years (mean 39.3±1.9), BMI 38.4-73.0 (50.9±4.4) kg/m 2 and 7 post gastrectomy volunteers aged 39-75 (mean 59.9±5.2) , BMI 17.0-26.0 (22.0±1.1) kg/m 2 . Subject numbers were based on recruitment and previous published work showing a minimum of seven subjects were needed to show clear differences in appetite and energy intake. 9 Obesity was defined as BMI>30 kg/m 2 according to the criteria of the World Health Organisation and the International Obesity Task Force. All gastrectomy subjects underwent total gastrectomy for gastric carcinoma at least 12 months previously and were in remission (none had any evidence of recurrence by gastroscopy within the last 12 months). All subjects had a truncal vagotomy as part of the procedure. The mean time between surgery and the study was 6.6 ± 1.6 yrs (mean ± SEM). None of the subjects had chemotherapy/ radiotherapy within the last three years. Subjects in all groups with ischaemic heart disease, type 2 diabetes and those aged over 75 years were excluded. None of the subjects had a diagnosis of obstructive sleep apnoea, cardiac failure or hypertension and no subjects were on any medications that could impact on the autonomic nervous system.

Protocol: Subjects were asked to fast and drink only water from 2100 on the night prior to the study, and to refrain from alcohol or strenuous exercise in the 24 hours preceding the study day. Each subject was studied on two occasions and received two infusions: 0.9% saline and ghrelin (5 pmol/kg/min), in a randomised, double-blind, crossover design. The dose of 5 pmol/kg/min was chosen, as previous studies have shown an intravenous infusion of ghrelin at this dose leads to a significant increase in food intake in lean and moderately obese subjects. 10;11 Human ghrelin was supplied in the acylated (octanoylated) form and produced by Clinalfa Products, Merck Biosciences AG, Weidenmattweg 4, CH-4448 Laufelfingen, Switzerland.

Subjects attended at 0730 on each study day. On arrival, external time cues were removed until the end of the study. After venous cannulation the subjects relaxed for 30 minutes. The infusion was started at 0830 (t=0) and lasted for 270 minutes. Previous studies have shown that plasma ghrelin levels during an infusion reach a steady state within 60 minutes, 11 and hence a fixed energy breakfast was given to all subjects at 60 minutes from the start of the infusion. This consisted of 40g cornflakes, 250ml whole milk, 3.5g sugar (1 sachet) and 100ml fresh orange juice (1550 kJ, 62% carbohydrate, 13% protein and 25% fat). This meal was chosen, as it represents a typical European style breakfast, which was palatable to most tastes and was a suitable meal for the timing of the study. The subjects remained relaxed on a couch tilted at 30 degrees for the remainder of the study.

They were subsequently offered a free buffet lunch at 1230 (240 minutes after starting the infusion). The items offered at the buffet lunch were consistent between subjects, and were designed to be acceptable to a wide range of palates. The infusion was stopped at 1300 after the buffet lunch was completed. The food was weighed pre-and post-prandially by a blinded investigator. The purpose of providing a fixed meal and a buffet meal was to be able to compare responses to a fixed energy meal, and then to subsequently assess food intake during the infusion. Blood samples were taken at baseline, 30, 60, 75, 90, 105, 120 minutes and then hourly until the end of the study. Samples for plasma ghrelin estimation were collected into plastic EDTA tubes containing 0.07 mg aprotonin (500 Kallikrein Inactivator Units (KIU)). Serum samples were collected in serum separator tubes (SST) and allowed to stand for 15 minutes at -4ºC

Assays

Plasma total (acylated and desacylated) ghrelin was determined in duplicate by a commercially available radioimmunoassay (Phoenix Pharmaceuticals, Belmont, CA). Intraassay and inter-assay coefficients were 7.3 % and 9.5 % respectively.

Insulin and growth hormone (GH) was measured using a solid phase, two site, chemiluminescent enzyme-labelled immunometric assay run on an Immulite 2000 automated analyser (Diagnostic Products Corporation-UK, Llanberis, Gwyynedd UK). Glucose was measured by the glucose hexokinase method using the ADVIA 1650 system (Bayer UK ltd).

The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the formula:

Insulin resistance= Fasting insulin x Fasting glucose/ 22.5. 12

Heart Rate Variability (HRV)

HRV (the measure of the beat-to-beat variation in heart rate) is a useful and safe tool to measure autonomic nervous system activity. 13 HRV was measured for 20 minute intervals at 0805, 0905, 1005, 1105, 1205, 1305 and 1405 ie. pre and post prandially at hourly intervals.

All tests were performed in a quiet temperature controlled (21±1°C) room. All HRV measurements were taken with subjects lying supine, with a 30° head up tilt. Fifteen minutes of continuous ECG data were collected using the BIOPAC (Biopac Systems Inc, SantaBarbara, CA) data acquisition system via 3 ECG limb leads. This system consists of an electrocardiogram amplifier module (ECG100B), signal conditioning module (UIM100) and an analogue to digital signal converter (MP100). The ECG signals were sampled at 200Hz and were transmitted to MP100 and stored on AcqKnowledge software (BIOPAC Systems Inc, Santa Barbara). Data stored in AcqKnowledge were analysed using a commercial HRV analysis software package (PowerMedic, OkiiMura, Taiwan). After manually excluding all ectopic beats, the data were resampled at 2Hz and the Hamming spectral window was employed to reduce spectral leakage. 14 The fast Fourier transformation (FFT) method was used to calculate the power spectral density. Total power (TP) is defined as the sum of absolute, very low frequency (VLF), low frequency (LF) and high frequency (HF), and is considered to be an indirect measure of cardiac autonomic tone. LF and HF were defined as the power in the spectral bands 0.04 and 0.15 and 0.15 and 0.4Hz, representing sympathetic and parasympathetic activity, and VLF as 0 to 0.04 Hz. LF and HF can also be expressed as normalised units (n.u.) ie nLF and nHF, which is an expression of the percentage of total power, with the VLF component subtracted. 14 The LF/HF ratio is considered an indirect measure of sympathovagal balance. Therefore, an increased LF/HF ratio indicates a sympathetic predominance. This interpretation of power spectral analysis has been validated by international consensus and by several independent investigators. [14][15][16] Matsumoto and colleagues also identified specific power spectral peaks associated with thermogenesis which they called the VLO frequency component (0.007 to 0.035 Hz). 17 An alternative method of representing sympathetic and parasympathetic nervous system activity is by calculating a SNS index (VLO + LF/HF) and a PNS index (HF/TP). 17-19

Statistical analysis

The Statistical Package for the Social Sciences version 15.0 (SPSS, Chicago, IL, USA) was used for data analysis. Data that was not normally distributed were log transformed before analysis. Mean values are expressed as arithmetic means +/-SEM; data that has been log transformed is presented as a geometric mean with a 95% confidence interval. Within-group and group interactions with time were analysed using ANOVA for repeated measures. If an overall interaction was found using ANOVA then post-hoc t-tests were done to identify significant differences between individual groups. Total area under the curve (AUC) was calculated using the trapezoid rule. Two tailed p values were considered significant when p<0.05.

RESULTS

Baseline characteristics

Baseline characteristics are shown in table 1. The lean and obese groups were well matched for age and gender, but the gastrectomy group tended to be older (lean 44.7±4.6 v gastrectomy 59.8±5.2 p=0.051). The mean BMI in the obese group (50.9±4.4 kg/m 2 ) was significantly higher than in the lean group (23.6±0.9 kg/m 2 ). Weight, waist circumference and body fat percentage were also higher in the obese group. There was no significant difference in BMI, weight, waist circumference and body fat percentage in the gastrectomy group compared to the lean group.

Plasma ghrelin

Fasting ghrelin levels were significantly lower in the obese group compared to the lean group (lean 792.4±40.6 pg/ml v obese 509.7±101.2 pg/ml p=0.02) and lower still in the gastrectomy group (lean 792.4±40.6 pg/ml v gastrectomy 266.3±70.4 pg/ml p<0.0001). Plasma ghrelin levels during the ghrelin infusion reached steady state after 60 minutes. There was no significant difference between the mean steady state plasma ghrelin or area under the curve (AUC) in any of the three groups (table 1).

Plasma insulin and glucose

Ghrelin increased serum insulin levels in the healthy controls, but not significantly in obese and gastrectomy subjects (table 2). All three groups had a significant increase in serum glucose levels during the ghrelin infusion compared to during the saline infusion (table 2). As expected obese subjects were more insulin resistant than lean controls (HOMA IR lean 0.77±0.12 v obese 8.38±2.38 p=0.02), but there was no significant difference in insulin resistance between lean controls and gastrectomy subjects (HOMA IR lean 0.77±0.12 v gastrectomy 0.69±0.13 p=0.63).

Plasma growth hormone

GH levels were low during the saline infusion but showed a marked GH response during the ghrelin infusion in all three groups. The obese group had a significantly impaired GH

Energy intake

Energy intake during the buffet lunch was increased in lean subjects by 41+/-14% on the ghrelin infusion day compared with the saline infusion visit (3707+/-414kJ v 2626+/-238kJ p=0.008). Obese subjects also increased their energy intake significantly by 35+/-14% during the ghrelin infusion (3873+/-575kJ v saline 2867+/-501kJ p=0.04). Gastrectomy subjects however did not increase energy intake during the ghrelin infusion (1923+/-226kJ v saline 1764+/-205kJ p=0.4). This data has been published and discussed previously. 20

HRV

Using ANOVA for repeated measures, ghrelin had an overall highly significant inhibitory effect on total power (p=0.001), HF (p=0.04), VLO (p=0.03) and a borderline effect on LF (p=0.07). Ghrelin also had an effect on nLF (p=0.03) but not on nHF (p=0.20). This ANOVA was used only as a preliminary analysis, looking for an overall effect as the three groups were different. We then followed this by detailed analysis for each group of subjects.

Hence, to ascertain the effect of ghrelin on HRV in individual subject groups the AUC was compared for each treatment. These are shown in detail in table 3. In lean subjects ghrelin had a significant effect on total power (geometric mean (95% confidence interval) (6.4x10 5 (4.2 to 9.7x10 5 ) ms 2 /hr v 9.2x10 5 (7.0 to 11.3x10 5 ) ms 2 /hr p=0.03) (figure 1a), a borderline effect on LF (1.3 x10 5 (0.9 to 2.3 x10 5 ) ms 2 /hr v 1.9 x10 5 (1.5 to 2.6 x10 5 ) ms 2 /hr p=0.06) but no effect on HF (1.7 x10 5 (0.7 to 3.6 x10 5 ) ms 2 /hr v 2.7 x10 5 (1.7 to 4.2 x10 5 ) ms 2 /hr p=0.1). By contrast ghrelin appeared to have no effect on obese subjects on total power (p=0.3) (figure 1b), LF (p=0.5) and HF (p=0.06), and had no significant effect in gastrectomy subjects (total power p=0.7 (figure 1c), LF p=0.7 and HF p=0.9). As shown in table 3, ghrelin had no significant effect on LF/HF ratio in any of the three groups. There was however a significant difference in LF/HF ratio between the groups in the saline condition; obese subjects had a higher LF/HF ratio than both lean subjects (p=0.007) and gastrectomy subjects ((p=0.003), but lean and gastrectomy LF/HF ratio were similar (p=0.06).

In addition, the SNS and PNS index were calculated for each of the groups during the saline condition and are shown in figures 2a and 2b. Obese subjects had a higher SNS index to both lean and gastrectomy subjects (p=0.006) and interestingly gastrectomy subjects had a higher PNS index than the other two groups (p=0.0002).

Heart rate and Blood pressure

Ghrelin had an overall significant inhibitory effect on systolic blood pressure (ghrelin 116.7±1.5mmHg v saline 120.0±1.7mmHg ANOVA p=0.02) in lean subjects but no effect on diastolic blood pressure (p=0.78) or heart rate (ghrelin 70.2±0.9 beats/minute vs 74.0±1.1 beats/minute p=0.07). Ghrelin had no significant effects on systolic/ diastolic blood pressure or heart rate in obese or post-gastrectomy subjects.

DISCUSSION

This is a novel study that attempts to ascertain the effects of ghrelin on the autonomic nervous system (ANS) in humans. Our study suggests that ghrelin does indeed have an inhibitory effect on the ANS in humans, and that this response is diminished in obese subjects. We also report for the first time that this response is absent in vagotomized subjects, suggesting an important role for the vagus nerve in ghrelin's effects on the ANS.

Our study shows for the first time that peripheral ghrelin acts directly on the ANS in healthy controls by altering various parameters of heart rate variability (HRV). Heart rate variability is considered a sensitive indirect measure of cardiac autonomic tone, and although has some limitations -autonomic tone can vary from organ to organ and the assumed generalisation of autonomic tone is not always appropriate -the method is non-invasive and has been extensively validated. 14 Ghrelin appeared to have the greatest effect on total power (TP) and low frequency (LF) power but did not reach significance with high frequency (HF) power. This suggests that ghrelin had a more pronounced effect on sympathetic activity rather than parasympathetic activity but this distinction is likely to be minimal as the LF/HF ratio was unchanged during the ghrelin infusion.

Previous data have suggested a post-prandial increase in cardiac autonomic sympathetic activity, but this was not seen in our cohort perhaps due to small numbers. Nevertheless, there was a significant reduction in total power during the ghrelin infusion in lean individuals, which was not apparent in the obese and gastrectomized subjects.

Ghrelin decreases arterial pressure without altering heart rate, but the mechanism has not been examined previously. We report for the first time that human subjects who have had a previous vagotomy do not demonstrate any significant SNS response to the peripheral administration of ghrelin . This is demonstrated in our study by the clear lack of a change in HRV parameters between saline and ghrelin infusion arms. This is further supported by the observation that ghrelin did also not affect blood pressure or heart rate in these subjects. This suggests that peripheral ghrelin communicates with the NTS and DMV via vagal afferents.

Our group and others, have shown previously the lack of orexigenic effect of ghrelin in vagotomized subjects and this further supports the new findings. 20;21 It was interesting to note that despite vagotomy our gastrectomy subjects had significantly higher PNS index than the other two groups, but a similar SNS index, suggesting increased parasympathetic activity. Our gastrectomy group were older but previous studies have suggested that all HRV indices are thought to decrease with increasing age, so is unlikely to be a major factor. 22 We have also confirmed previous studies in showing that ghrelin decreases arterial pressure but does not alter heart rate in healthy controls. 2 This suggests that the normal compensatory response to lowering of blood pressure ie SNS mediated increase in heart rate, does not occur and further confirms that ghrelin is likely to inhibit SNS activity in healthy controls. It is known that the growth hormone secretagogue receptor (GHS-R) is abundant in human vasculature [START_REF] Katugampola | 125I-His(9)]ghrelin, a novel radioligand for localizing GHS orphan receptors in human and rat tissue: up-regulation of receptors with athersclerosis[END_REF] and recent data suggest that ghrelin inhibits vascular superoxide production in hypertensive rats and hence may act directly on vascular endothelium. [START_REF] Kawczynska-Drozdz | Ghrelin inhibits vascular superoxide production in spontaneously hypertensive rats[END_REF] Previous investigators have demonstrated that central ghrelin induces c-fos expression in the nucleus of the solitary tract (NTS) and the dorso-motor nucleus of the vagus (DMV) in the brainstem. [START_REF] Date | Ghrelin acts in the central nervous system to stimulate gastric acid secretion[END_REF] It is also known that the NTS is an important regulator of blood pressure and the SNS. It follows therefore that central ghrelin may act via the NTS and the DMV to modulate the SNS and blood pressure ; an elegant study by Lin and colleagues supports this hypothesis, showing that ghrelin acted directly on the NTS in rats to suppress the SNS and decrease arterial pressure. [START_REF] Lin | Ghrelin acts at the nucleus of the solitary tract to decrease arterial pressure in rats[END_REF] As mentioned above, previous animal studies have suggested that central and peripheral ghrelin administration suppresses SNS activity. 3-5;8 Interestingly, centrally administered leptin has the opposite effect by promoting increased SNS activity and increasing arterial pressure, [START_REF] Matsumura | Central effects of leptin on cardiovascular and neurohormonal responses in conscious rabbits[END_REF] and as there is good evidence that ghrelin and leptin antagonize each other in the regulation of appetite and feeding behaviour, [START_REF] Shintani | Ghrelin, an endogenous growth hormone secretagogue, is a novel orexigenic peptide that antagonizes leptin action through the activation of hypothalamic neuropeptide Y/Y1 receptor pathway[END_REF] it is possible that they have similar roles in SNS regulation. Ghrelin is known to increase neuropeptide Y (NPY) and agouti-related protein (AGRP) mRNA levels in the arcuate nucleus. [START_REF] Kamegai | Chronic central infusion of ghrelin increases hypothalamic neuropeptide Y and Agouti-related protein mRNA levels and body weight in rats[END_REF] Studies have shown that NPY decreases arterial pressure and renal sympathetic nerve activity in rabbits [START_REF] Matsumura | Central cardiovascular action of neuropeptide Y in conscious rabbits[END_REF] and AGRP antagonizes leptin activation of the melanocortin-3 and -4 receptors (MC3-R and MC4-R) [START_REF] Haynes | Interactions between the melanocortin system and leptin in control of sympathetic nerve traffic[END_REF] ; therefore the mechanism behind ghrelin's influence on the central control of SNS activity can be at least partially explained via increasing NPY and AGRP expression, 5 in addition to actions on the NTS and DMV as mentioned above.

From our data and the above published data we can therefore postulate that an intact vagus nerve is needed for peripheral ghrelin to signal to the NTS/DMV in the brainstem, and that central ghrelin may decrease SNS activity via NPY and possibly antagonises leptin's effects on the SNS. Although ghrelin is a potent stimulator of growth hormone (GH) release, intravenous GH injection in healthy volunteers failed to change arterial pressure or heart rate in a recent study. [START_REF] Bisi | Acute cardiovascular and hormonal effects of GH and hexarelin, a synthetic GH-releasing peptide, in humans[END_REF] However it is well known that growth hormone deficiency (GHD) is associated with abnormalities in cardiac autonomic tone, and these are partially corrected with treatment. [START_REF] Leong | Abnormal heart rate variability in adults with growth hormone deficiency[END_REF] It is possible that the acute cardiovascular and sympathetic effects are secondary to ghrelin alone but it is likely that enhanced GH release will play an important confounder here.

Another important finding was a diminished response on HRV parameters to ghrelin by the obese subjects. The obese group was typically a more heterogeneous group and this is reflected by the wide confidence intervals which are apparent in table 3. However the number of subjects was the same as the lean control group so should have been adequately powered to show a difference. Obese subjects are typically thought to have an impairment of vagal tone, but this was not demonstrated in our study. [34][35][36] The reason for a reduced effect of ghrelin on obese subjects may be related to the marked hyperinsulinaemia and insulin resistance demonstrated by these subjects. Obese subjects are well known to have hyperinsulinaemia and insulin resistance, and insulin is thought to have a physiologically relevant effect on increasing SNS activity. 35 More recent data has shown that insulin alters sympathovagal balance measured by HRV in insulin sensitive but not in insulin resistant individuals. 37 The precise mechanism of insulin's action on heart rate variability is complex, and furthermore the interactions between ghrelin and insulin both peripherally and centrally, are controversial. As shown by previous groups obese groups have a diminished GH response to ghrelin, but the GH response is preserved in the gastrectomy group. this effect is blunted in diet-induced obese mice. 40 It is possible that there is similar obesityinduced adipocyte resistance to the effects of peripheral ghrelin on the SNS; this is an area for future research.

The main limitation of this study was that the ghrelin infusion achieved supra-physiological plasma levels of total ghrelin. We, in common with other groups who have used a synthetic acylated ghrelin infusion, did not specifically measure acylated ghrelin however and this may have added valuable information. In addition, it is known that acylated ghrelin makes up only around 10% of circulating total ghrelin, and the administration of supra-physiological doses of acylated ghrelin may have overtaken the influence of non-acylated ghrelin. 41;42 Similarly we would accept that due to small numbers, conclusions from detailed sub-set analysis may have limitations but all the data and analysis has been presented and made available to the reader.

In conclusion we have shown that ghrelin inhibits autonomic nervous system activity in healthy controls, particularly the SNS. There appears to be a diminished effect of ghrelin on obese subjects and an absent effect in vagotomized gastrectomy subjects. The precise mechanism behind the lack of response in obese subjects may involve hyperinsulinaemia and insulin resistance or a diminished GH response. The absence of response in vagotomized subjects confirms the importance of the vagus nerve in communicating the peripheral gut signals to the brainstem.

METHODS

  Volunteers were recruited by advertisement, from a local weight management clinic and from a surgical database. The study was approved by the South Sefton Research Ethics Committee (project registration number EC.05.03) and was performed in accordance with the principles of the Declaration of Helsinki. Volunteers gave written informed consent and had a normal physical examination and electrocardiogram. Urea and electrolytes and fasting glucose were normal in all subjects.

  centrifugation. All samples were then immediately stored at -80ºC until assayed. Heart rate and blood pressure were measured in the supine position at hourly intervals.

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Figure 1

 1 Figure 1 Total power in a.)lean, b.)obese and c)gastrectomy subjects during the ghrelin and saline infusions.

Figure 2 a

 2 Figure 2 a) SNS index and b) PNS index in healthy controls, obese and gastrectomy subjects.

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Table 1 Baseline characteristics of lean, obese and gastrectomy subjects †

 1

		Lean	Obese	Gastrectomy
	n	7	7	7
	Sex (male)	3(43%)	2(29%)	2(29%)
	Age (yrs)	44.7±4.6	39.3±1.9	59.8±5.2*
	BMI (kg/m 2 )	23.6±0.9	50.9±4.4*	22.0±1.1
	Weight (kg)	64.4±4.8	141.0±19.2*	61.4±3.1
	Waist (cm)	80.6±5.8	14.3±13.2*	82.2±3.5
	% Body Fat	26.6±0.9	49.1±1.3*	28.3±3.0
	Fasting Ghrelin
	(pg/ml)	792.4±40.6	509.7±101.2*	266.3±70.4*
	Mean Steady state
	ghrelin (pg/ml)	5444.6±50.5	6112.6±447.5	5559.6±209.1
	Mean ghrelin AUC**
	(pg/ml.min)	1.4x10 6 ±0.2x10 6	1.7x10 6 ±0.2x10 6	1.5x10 6 ±0.2x10 6
	† Plus-minus values are means ± SEM
	* p<0.05 when compared to lean values
	** AUC area under the curve

ACKNOWLEDGEMENTS

The authors would like to thank Jackie Cleator, Janet Cashen, Barbara Martin and David Kerrigan for their contributions to the study, as well as all of the volunteers who took part.

This work was supported by an International Endocrine Research Prize from Pharmacia awarded to J Pinkney and PJ English.