COST AND PROFIT EFFICIENCIES IN THE SPANISH HOTEL INDUSTRY

Transcription

1 COST AND PROFIT EFFICIENCIES IN THE SPANISH HOTEL INDUSTRY ANTONIO ARBELO PILAR PÉREZ-GÓMEZ ENRIQUE GONZÁLEZ-DÁVILA FELIPE MANUEL ROSA-GONZÁLEZ University of La Laguna This article focuses on estimating and discussing cost and profit efficiencies related with the Spanish hotel sector. Managers have a special interest in controlling costs as a source of competitive advantage, which may enable companies to improve their results in a continuous manner. However, they usually do not attribute much importance to the improvement of profit efficiency, which is generally much lower than cost efficiency and as a result vital for achieving competitive advantage. In this article, cost and profit efficiencies are estimated in the Spanish hotel sector using a data panel for the years 2007 to 2011 and using distribution free approach methodology. The results show profit efficiency levels significantly lower than levels of cost efficiency, thereby confirming our working hypothesis. KEYWORDS: hotel sector; costs efficiency; profit efficiency; competitive advantage; data panel; distribution free INTRODUCTION Over the past decade, there have been numerous articles studying efficiency in the tourism industry, highlighting its importance in order to improve hotel performance (e.g., Anderson, Fok, & Scott, 2000; Assaf & Agbola, 2014; Assaf, Barros, & Josiassen, 2010; Assaf & Magnini, 2012; Barros & Alves, 2004; Barros, Dieke, & Santos, 2010; Barros & Mascarenhas, 2005; Barros & Santos, 2006; Brown & Ragsdale, 2002; T. H. Chen, 2009; Pulina, Detotto, & Paba, 2010; Reynolds & Biel, 2007; Untong, Kaosa-Ard, Ramos, & Rey-Maquieira, 2011). 1 All these articles used a standard methodology and a conceptual framework, where managerial efficiency is the core element that refers to the ability of a hotel to maximize its profits and minimize its costs under the given circumstances. However, most of the literature in this field focuses on investigating efficiency from the cost side (cost minimization), estimating economies of scale and scope to quantify the cost inefficiencies of operating at a nonoptimal level of production and/or with a combination of products (Lin & Liu, 2000; Weng & Wang, 2006); or estimating X-efficiencies to fix cost inefficiencies of a Journal of Hospitality & Tourism Research, Vol. XX, No. X, Month, 2015, 1 21 DOI: / International Council on Hotel, Restaurant and Institutional Education

2 nonoptimal management of resources (Anderson, Fish, Xia, & Michello, 1999; Assaf & Agbola, 2014; Assaf et al., 2010; Assaf & Magnini, 2012; Barros, 2004, 2005a, 2005b; Barros & Santos, 2006; Hwang & Chang, 2003; Pulina et al., 2010; Rodriguez & González, 2007). However, the goal of maximizing profits has had little to no research attention in the hotel industry. Not only does this goal require that goods and services be produced at minimum cost, but it also requires revenues to be maximized. The concept that best reflects these two important economic aims is profit efficiency (Berger & Mester, 1997), defined as the ratio between current profit and maximum profit. This is a more accurate concept than cost efficiency, as it takes into consideration both the effect of the choice of the portfolio of products and services on costs as well as revenues. Therefore, estimating profit efficiency is much more important for the management of hotels than the partial perspective given by cost efficiency. In fact, there is empirical evidence showing that other industries have much higher levels of profit inefficiencies than costs inefficiencies (Berger, Hancock, & Humphrey, 1993; Berger & Mester, 1997; Maudos, Pastor, Pérez, & Quesada, 2002). This highlights the importance of inefficiency on the side of revenue because of the failure to produce a higher output value. Given the superiority of the concept of profit efficiency in relation to cost efficiency in the study of economic efficiency, this article has an innovative goal since it estimates, in addition to cost efficiency, profit efficiency for a sample of hotels in Spain from 2007 to 2011, using a methodology also innovative in this sector, that is, the distribution free approach (DFA). This approach has not been previously used to estimate efficiency in the hotel industry and it differs from the two techniques that have dominated studies in the field to this day, that is, data envelopment analysis (DEA) and the stochastic frontier approach (SFA). The structure of the rest of this paper is as follows: In the second section, the concept of economic efficiency is analyzed; in the third section, a review of the literature in this area is undertaken; in the fourth section, the empirical model is introduced. The description of the data and empirical results are presented in the fifth section. Finally, the conclusions of the article are presented. ECONOMIC EFFICIENCY Economic literature considers cost and profit efficiencies the two most important concepts of economic efficiency. They are based on economic optimization as a reaction to price and market competition rather than the use of a particular technology (Berger & Mester, 1997). 2 In other words, these two concepts of efficiency respond in turn to two important economic goals: cost minimization and profit maximization. However, a review of the literature on efficiency in the hotel industry and other sectors shows a dominant interest in estimating cost efficiencies. In contrast, there are relatively few studies that have dealt with estimations of profit efficiency. These have primarily focused on the financial sector, mainly due to technical difficulties involving the use of traditional econometric methods. Nevertheless, profit efficiency is the most suitable concept of efficiency for the evaluation of the combined results because of the fact that it takes into consideration the effects of the activities of a company, both on the cost side and

3 on the revenue side, as well as their interaction. This allows us to state that this type of efficiency better reflects the objective of maximizing profits than any other concept. Cost efficiency is defined as the ratio of minimum costs that can be achieved for a given production volume and the actual cost of production. These inefficiencies tell us how much higher the costs of one hotel are in relation to the costs of a more efficient hotel with the same combination of output and input prices, where the difference cannot be explained by random error. For Leibenstein (1966), most of them are caused by errors in management and/or organization as a result of improper motivation of workers by the management of the company. Consequently, we can say that cost inefficiency is due, first, to poor choices of production plans, defined as allocative inefficiency, and second, to poor implementation of these plans, which is called technical inefficiency. Cost efficiency defined in this manner simply refers to the capacity of a hotel to minimize its costs for a given quantity of output. However, this concept has, from our point of view, two main limitations: 1. Cost efficiency evaluates the efficiency of a given level of output, which normally does not have to correspond with optimal levels of production. Thus, while a hotel may be cost-efficient for its current level of output, it is very likely it is not efficient in terms of an optimal level of output. In this case, when a company deviates from its optimal scale of production, profit efficiency gives us a better estimation of possible inefficiencies (Berger & Mester, 1997). 2. Quality as a differentiating element has become a key mechanism in the search for greater competitiveness in the hospitality industry (Ingram & Daskalakis, 1999; López & Serrano, 2001; Poon, 1993). 3 What is more, as stated by Assaf and Magnini (2012), for a correct estimation of efficiency both the quantity and quality of output should be taken into account. However, cost efficiency does not reflect any differences in the service quality provided by hotels. If these differences in quality are not taken into consideration, and given that higher quality implies higher costs, one might make the mistake of considering that higher costs are inefficiencies, when in fact unmeasured differences in the quality of hotel service are. On the contrary, the concept of profit efficiency is more apt than cost efficiency because it does not have the above drawbacks, namely: Profit efficiency can be considered as overall efficiency, since it includes allocative efficiency, technical efficiency, and scale of production efficiency. This means that if a hotel is efficient from the point of view of profits, it would also be so from the point of view of these three efficiencies (Fitzpatrick & McQuinn, 2008). Profit efficiency integrates both the ability of a hotel to minimize its costs and to maximize its revenues, being therefore a concept that includes both cost and profit efficiencies. Profit efficiency is more consistent with the economical goal of profit maximization than other concepts of efficiency.

4 Literature in this field distinguishes between standard profit efficiency and alternative profit efficiency, depending on whether the hypothesis of perfect competition in output and input markets is assumed or not. Standard profit efficiency measures how close a hotel is operating to its maximum possible profit under the assumption of perfect competition in these markets, given a level of input and output prices. This means that a hotel takes as given the prices of inputs and outputs and maximizes its profits by adjusting the quantities of them. However, in practice the estimation of standard profit efficiency has some inconveniences (Berger & Mester, 1997): It is very sensitive to the measure of output prices, that is, an inaccurate measure of them also provides an erroneous measure of this efficiency. It does not adequately capture differences in the quality of hotel services. It is not recommended in markets without perfect competition conditions, as is the case in the hospitality sector (Bull, 1995; Davies, 1999); thus, some market power from price fixing by companies is not ruled out. In contrast, alternative profit efficiency does not present the problems of standard profit efficiency, since in this case the efficiency measures more or less how a hotel operates at maximum possible profit, given its level of output rather than its price. That is to say, in the alternative profit estimate the quantity of outputs is taken as a given and the price of inputs are allowed to vary freely and affect company profit, and therefore efficiency. However, possible errors in the measurement of the amounts of outputs will not affect alternative profit efficiency (Berger & Mester, 1997). In this way, inconveniences arising from inaccuracies in estimating the prices of outputs are avoided and, in addition, possible differences in the quality of services provided are taken into account. The additional revenue that generates a higher quality output is computed, which can far exceed the extra cost of this quality level. Finally, the concept of alternative profit efficiency is also very useful in situations where there are hotels that exercise some market power in the fixing of prices of services, since as mentioned previously, this concept does not take into account the prices of outputs. In short, alternative profit efficiency is closer to the reality of hotel service markets, characterized by a significant degree of imperfect competition and heterogeneity of hotels in the amount and quality of services provided. Profit efficiency understood in this way includes both cost efficiency and revenue efficiency and their interaction. Revenue efficiencies arise from market choice and/or erroneous competitive strategy, reflecting the failure to produce a higher value of output (given the level of output and input prices). Alternatively, a hotel may also have revenue inefficiencies if the response to the relative prices of outputs is poor and produces some high-margin services and lots of smallmargin ones. In this way, revenue inefficiencies are analogous to cost inefficiencies since in both cases a net loss of real value results, whether the loss is in terms of lower value produced or greater value of inputs consumed. 4 There is the possibility, contrary to what is expected, that cost and profit efficiencies are negatively correlated. This outcome may have two possible explanations (Berger & Mester, 1997):

5 1. A company can compensate for low cost efficiency with high revenue efficiency if competitive pressure, for example, causes the company to have higher values for the production of output and achieve higher revenue results in terms of market power which derives from greater specialization. 2. As explained earlier, part of what are now considered cost inefficiencies are consequences of not incorporating differences in the quality of the output, that is, higher quality requires additional costs which cannot be considered as inefficiency. LITERATURE REVIEW Unlike studies up to and during the final decade of the 20th century, where the number of articles on efficiency in the hotel industry was sparse and generally used estimating methods based on simple ratios, literature on the topic over the past decade has had a very fast development and has included more complex methods of estimation, such as DEA or SFA, as a result of the changes and new challenges in the sector (Assaf & Magnini, 2012). Barros, Peypoch, and Solonandrasana (2009) and Assaf et al. (2010) make an extensive and comprehensive review of the literature on efficiency in the hotel industry up until 2009, so we will only analyze studies published after that date. Pulina et al. (2010) used DEA to compare, as a first step, hotel efficiency in 20 Italian regions. To further study the relationship between size and efficiency for a small sample of hotels in the Italian region of Sardinia from 2000 to 2002, they used a significantly superior number of observations compared with those made in other research articles using DEA so far. Assaf et al. (2010) introduced the meta-frontier concept as an appropriate methodological solution to solve the problem of heterogeneity presented by the hotels because of differences in size, market share, and access to technological advances in a study on the impact of environmental variables on hotel efficiency in a sample of 78 Taiwanese hotels from 2004 to Barros et al. (2010) employ a random frontier model to study technical efficiency in a sample of 12 hotels in Luanda from 1990 to These authors assume the heterogeneity of the hotels in the sample, and the results confirm the evidence that the use of heterogeneous frontiers is more advisable than the use of homogeneous frontiers. Hu, Chiu, Shieh, and Huang (2010) use SFA to estimate cost efficiency in a sample of 66 hotels in Taiwan from 1997 to Results show that the hotels in the sample were operating at a level of very high cost efficiency on average (around 91%) and that this efficiency is influenced very significantly by environment variables. Wu, Liang, and Song (2010) analyzed efficiency in a sample of 23 hotels in Taipei from 2002 to 2006 using a variation of DEA. Results show that efficiency decreased in the studied period, going from in 2002 to in Bernini and Guizzardi (2010) used SFA to study the relationship between competitiveness and efficiency in the Italian hospitality industry from 1998 to Assaf and Knezevic (2011) studied the effects of a set of variables on cost efficiency in tourist hotels in Slovenia, using Bayesian frontier methodology, with a sample of 23 hotels.

6 Assaf and Magnini (2012) studied the importance of incorporating customer satisfaction into the estimation of efficiency. These authors used SFA methodology to evaluate the efficiency of a sample of hotels in the United States of America from 1999 to 2009, concluding that the inclusion of customer satisfaction has a significant importance in the estimation of efficiency. Hadad, Hadad, Malul, and Rosenboim (2012) used DEA to compare the economic efficiency of the tourism industry in 105 countries, and finally, Assaf and Agbola (2014) use Bayesian techniques to estimate the efficiencies in the accommodation industry in Australian. Cost Efficiency EMPIRICAL MODEL Specification of a cost frontier enables an estimation of a cost function that relates the observed costs for a set of outputs, the price of inputs, random error, and inefficiency. This frontier can be expressed as follows: C C y, w, v, u, (1) c c where C measures the cost variable, y is a vector of quantities of outputs, w is a vector of prices of the variable inputs, v c is the random error, and u c represents the inefficiencies found. Inefficiency factor u c covers both allocative inefficiency, a consequence of a nonoptimal reaction to the relative prices of the inputs, w, and technical inefficiency, because of the use of many inputs to produce y. To facilitate the estimation of inefficiency, it is assumed that the random error and inefficiency, v c and u c, are separable. Taking logarithms on both sides of Equation 1, we get the following: ln C f y, w ln v ln u, (2) c where f is the chosen functional form and the terms ln vc ln u c are considered compound error terms. The cost efficiency of one company (CE) is estimated as the ratio between the minimum cost to produce the output vector min C and the cost incurred (C), that is, 5 f y w vc c c min C exp, exp ln CE exp ln uc. (3) C exp f y, w exp ln v exp ln u Cost efficiency defined like this can be interpreted as the proportion of resources that are used efficiently, so that if CE for a hotel is 0.80 it must be interpreted that this hotel is operating at an efficiency level of 80% or an inefficiency level of 20% of its costs. In other words, that hotel could produce the same number of products saving 20% of its costs. c

7 Profit Efficiency In contrast to the cost function, the profit function includes as a dependent variable profit instead of costs and exogenous variables remain the same as the cost function. Thus, we define the profit function as follows: y, w, v, u, (4) where is the profit variable, y is a vector of quantities of outputs, w is a vector of prices of the variable inputs, v is the random error, and u represents the inefficiencies found that reduce profit. Again, in order to facilitate the estimation of efficiency, it is assumed that random error and inefficiency, v and u are separable. Taking logarithms on both sides of Equation 4, we get the following: ln f y, w ln v ln u, (5) where is a constant that is added to profit variable for all hotels to ensure a positive value thereof and so we can apply logarithms. 6 Profit efficiency (PE) is defined as the ratio between the actual profits of a company () and the maximum level that could be achieved by the most efficient company in the max sample, that is, PE max exp f y, w exp ln u exp(ln v) exp f y, w exp(ln v ). Profit efficiency defined like this is no more than the proportion of maximum potential profit a hotel can achieve; if PE for a hotel is 0.80 it would be indicate that because of excessive costs and/or poor revenues, the hotel would be losing 20% of its maximum potential profit. This efficiency is equal to one for the most efficient hotel included in the sample, but unlike cost efficiency, profit efficiency can be negative, since a hotel may waste more than 100% of its potential profit. Estimation Method The main problem when measuring inefficiencies is to separate the behavior of these from other random factors affecting costs and revenues. In the literature on efficiency analysis, we can find two estimation methodologies parametric and nonparametric each with its advantages and disadvantages. Among the nonparametric, a method that has dominated work on efficiency in the hotel industry is the DEA, which although allowing the use of multiple inputs and outputs does not impose any functional form on the data nor assume a particular distribution of the inefficiency term, yet has the drawback of considering all errors as inefficiency (Barros, 2004). However, parametric models have the advantage of allowing decomposition of error between random portion and inefficiency. Also, nonparametric methods usually ignore prices and can, thus, only detect technical inefficiencies that occur when using many inputs or producing few outputs. Therefore, these techniques are not suitable for estimating the concepts of cost and profit efficiencies defined in this article, (6)

8 since they focus more on technical optimization than economic optimization (Berger & Mester, 1997). Consequently, this article will use a parametric model to estimate the efficiencies in the hotel industry in Spain since it fits better with the concepts of cost and profit efficiencies explained above. 7 In parametric models a company is considered inefficient if its costs are higher or its profit lower than the most efficient company in the total sample, which is produced with the same combination of output and price of inputs, and where the difference cannot be explained by random error. However, there is no simple rule to determine which method, in parametric models, 8 is the one that best describes the true nature of the data. 9 The most commonly used parametric model in the study of efficiency in the hotel industry is SFA (Anderson et al., 1999; Barros, 2004; C. Chen, 2007; Hu et al., 2010). However, this model has the inconvenience of imposing a distribution for the inefficiency term. An alternative methodology that does not have this inconvenience is the DFA introduced by Berger (1993) based on data panel approaches developed by Schmidt and Sickles (1984). As DeYoung (1997) points out, this is a highly recommended technique because its statistical foundations are intuitive and easy to apply. DFA allows technologies to change from one year to another, without imposing any distribution for the inefficiency term. That is, the regression coefficients are allowed to vary in time to collect the possible effects of changes in technology and the environment. This method also assumes the hypothesis that there is a level of efficiency for each hotel that is constant for the whole period of estimation, while the random parts of the errors tend to offset each other over time. The cost and profit functions are estimated separately for each year of the data panel. The errors of each of the regressions separately consist of both inefficiency, u, and random part, v, but since it is assumed that the random parts of the error cancel each other out over time, the average errors for a hotel and all regressions, u ˆ, estimate the inefficiency term, u. However, if the number of periods in the sample is small, it is possible that the extreme values of û reveal that random parts of the errors are not fully compensated for over time. In this case, it is advisable to truncate these outliers to ensure the robustness of the results, it being necessary to test for different truncation points (Berger, 1993). On the other hand, if the number of periods is very large one might question the assumption that efficiency is constant for the entire period. DFA estimated cost and profit efficiency, respectively, as min min exp ˆ C f y, w exp ln uˆ uˆ CE C exp fˆ y, wexp ln uˆ uˆ PE max ˆ exp fˆ y, w exp(ln uˆ ) max exp f y, w exp(ln uˆ ) min., (7) (8)

9 Functional Form The most usual functional form in the reviewed literature for estimation of cost and profit efficiency is the Translog (Christensen, Jorgenson, & Lau, 1973), which is expressed as follows: n m n n m m n m ln C 1 1 i 0 j ln y j l ln wl ln ln ln ln ln ln, 2 jk y j yk 2ò ls wl ws ij yi wj i j1 l1 j1 k1 l1 s1 i1 j1 where i ln vi ln u i, with v i as the random term and u i the hotel i inefficiency term; n and m are the numbers of outputs and prices, respectively; and 0, j, l, jk, ls, and ij are the regression coefficients. The conditions of linear homogeneity and symmetry are imposed on the price of inputs. In the case of profit function, it is almost the same specification except that the dependent variable in this case is profit rather than cost. Since this profit function does not contain the price of outputs, it is not necessary to impose the restriction that it be homogeneous in prices. Once cost and profit functions are estimated, the residues obtained will cover inefficiency, u, and random error, v. As it has been assumed that the average of these errors over time is zero, we will estimate the inefficiency, u, for each hotel as the average of the residues from each year in the sample. Data DATA AND RESULTS The hotel information contained herein was obtained from SABI database. 10 Of all the companies in the initial sample taken, 11 the companies that did not have complete information were eliminated. Also, in concordance with Rodriguez and González (2007), other companies were also eliminated because their activity not only involved the administration and operation of a hotel. Finally, the sample is composed of 1,725 observations, corresponding to 345 hotels in Spain over the years 2007 to The sample includes hotels in all regions of the country, competing to offer similar services to their customers and use similar inputs. The choice of variables was performed as a result of a literature survey and information provided by balance sheets and revenue statement given by SABI database. To choose the outputs has been taken into account that they reflect so far as a hotel achieves its objectives (Anderson et al., 2000; Barros, 2005a; Barros & Alves, 2004). We measured two outputs, the operating revenue, corresponding to the two main activities of a hotel, renting rooms and service of food and beverages, and other operating revenues, relating to revenues as lease of store spaces, laundry, beauty salons and hairdressing. We define the inputs as those items using a hotel to generate its total revenue (Anderson et al., 1999). Three inputs are defined, the price of labor, the price of materials, 12 and the price of physical capital. The price of inputs cannot be observed directly, therefore, must be approximated from the available information, which is common to all studies of efficiency because of the used data limitations. All these variables are detailed below. (9)

10 Outputs y 1 Operating revenue (rooms, food, and beverages) y 2 Other operating revenues Inputs w 1 Price of labor (measured by dividing the total salary expenditure, including social security costs, by the number of full time equivalent employees) w 2 Price of materials (measured by dividing the total food, beverage, and rooms expenditure by the total operating revenue) w 3 = Price of physical capital (measured by dividing the depreciation of the fixed assets plus other operating expenses by the total fixed assets) For the cost function model, the dependent variable is the total operating cost (C) comprising labor cost, materials cost, and other operating cost, and in the profit function, the dependent variable is EBITDA () defined as earnings before interest, taxes, depreciation, and amortization. Table 1 presents descriptive statistics for the study variables between 2007 and Results Table 2 shows the results for the efficiency of cost and profit estimated for the hotels in the sample. Also, to make possible comparisons between efficiencies, different truncation points at levels of 0%, 1%, 5%, and 10%, are shown. The level of cost efficiency changes significantly depending on the truncation point selected. Thus, the efficiency changes from 70.6% to 71.4% when only 1% of the extreme values observed are changed by the value of the corresponding point of truncation; when 5% truncation level is used, efficiency increase to 81.6%; beyond this point the gain is reduced. The result shows that, even after using panel data for 5 years, random error is not compensated for over the period, and this significantly affects the estimates of efficiency. However, when increasing from 5% to 10% in the level of truncation the efficiency does not change significantly, so we consider 5% a reasonable level of truncation to evaluate our results. According to this level of truncation, cost efficiency shows an average level of 81.6%, which means it would be possible to reduce costs by 18.4% simply by eliminating inefficiencies. For average operating costs of the hotels in the sample this would be 8,480 thousand euros, a saving in cost of approximately 1,560 thousand euros. This relatively high level for cost efficiency is very similar to that found by Anderson et al. (1999) and Hu et al. (2010), among others, although these authors used a different technique. The results for profit efficiency again show a significant variation in efficiency levels depending on the level of truncation chosen. As with cost efficiency, this change in efficiency, from 5% to 10% for the truncation point, is small, so the results should be evaluated at a truncation level of 5%. The hotel efficiency scores are displayed in the appendix for this level. Estimated average profit efficiency is 13.9%, and a degree of dispersion for the values is relatively

11 high. Again, these companies could improve profit by 86.1% on average, if they would enhance the management of resources, revenue, or both. For an average profit level of the hotels in the sample of 926 thousand euros, we would suppose an average increase of these profits by about 797 thousand Table 1 Descriptive Statistics for the Variables (Pooled): 2007 to 2011 Pearson Correlations Total Variables Average Maximum Minimum SD Cost EBITDA C, Total operating cost a 8, , ,186 1, EBITDA a ,047 26,165 2,759 1 y1, Operating revenue a 8, , , *.413* y2, Other operating 222 8, *.151* revenues a w1, Price of labor *.016 w2, Price of materials w3, Price of physical capital a. Thousands of Euros. *p <.001. Table 2 Cost and Profit Efficiencies Cost Efficiency Profit Efficiency Truncation Level 0% 1% 5% 10% 0% 1% 5% 10% Average Standard deviation Variation coefficient Table 3 2 Coefficients of Determination ( R ) and Adjusted ( R 2 ad ) for Periods Regressions Cost Efficiency Profit Efficiency Year 2 R 2 2 R 2 ad SE R R ad SE Note: SE = standard error. euros. Moreover, these levels of profit efficiency are much lower than those obtained for cost efficiency, which is very similar to those found in other industry results (Ariff & Can, 2008; Berger & Mester, 1997; Maudo et al., 2002). Coefficients of determination for cost model vary between 0.978, in 2008, and 0.985, in 2007, instead to (2011) and (2008) for profit model. Detailed values for each year are shown in Table 3. The Spearman correlation coefficient between cost and profit efficiencies evaluated for a level of truncation of 5% is.302 (p <.001), which is not too high

12 but is positive and statistically significant, as expected, which would indicate that hotels that are more profit efficient are also more cost efficient. Distribution of cost and profit efficiencies depending on the size of the hotels, measured by average total assets, is presented in Figure 1. It can be observed how cost efficiency is almost constant for both small and large hotels, Figure 1 Cost and Profit Efficiencies by Interval of Average Total Assets, 2007 to 2011 being only a little lower for the latter, probably because of some loss of control over costs that occurs in large organizations (Dhawan, 2001). However, the figure shows that the profit efficiency remains constant and at very low levels for the smaller hotels, improving substantially for medium hotels and reaching significantly higher levels (near 60% efficiency, but still lower that the average levels of cost efficiency) for hotels of higher average total assets. These levels of profit efficiency of the larger hotels, more than four time the sample average, would clearly reveal a more efficient management of these hotels for their revenues. CONCLUSIONS Normally, articles on efficiency in the hotel industry have focused more on the cost side, estimating scale, scope, or X-inefficiencies, and ignoring possible inefficiencies on the revenue side. However, studies in other areas have shown that profit inefficiencies are much greater than cost inefficiencies, which would reveal that a correct assessment of efficiency requires estimation and analysis of profit efficiency rather than of cost efficiency. Furthermore, profit efficiency is the concept more appropriate for evaluating the overall outcome efficiency because it takes into account the impact of the activities of a hotel both from the cost and the revenue side as well as their interaction. This article provides empirical evidence of the importance of profit efficiency in the estimation of overall efficiency in the hotel industry. From this perspective, the study analyses cost and profit efficiencies in the hotel industry in Spain for the years 2007 to 2011 using DFA methodology. The results reveal that the hotels in the sample operate with relatively high average levels of cost efficiency (81.6%) in relation to average levels for the estimated profit efficiencies (13.9%), thus confirming the importance of these inefficiencies in the sector and our working hypothesis. This difference could be explained because while the profit efficiency is based on comparing each of the companies with the company in the sample which best manages its profit maximization, cost efficiency evaluates the results for a given output level, which

13 usually does not correspond to the optimum level. Thus, though still being a cost efficient company according to its current level of output, it may not be right for its optimal level of output, which most probably involves a different level and composition of output. In this case, when there is a deviation of the optimum scale of production, profit efficiency better estimates potential inefficiencies (Berger & Mester, 1997). What is more, the low (though positive) correlation between the rankings of cost and profit efficiency would confirm the incomplete and partial analysis of cost efficiency (Maudos et al., 2002); in other words, hotels profits efficiency penalizes not only the use of more expensive inputs to produce the same amount of output but also the generation of a lower income because of the use of the same number of inputs. Consequently, this efficiency, which takes into account the efficiency of costs and profits, gives more useful information to managers and shareholders about the performance of the hotels. Furthermore, the analysis of the efficiency by the size of the hotels shows that cost efficiency remains at high levels and very similar for both large and small hotels. However, the result of profits efficiency reveals an interesting finding, that is, while profit efficiency levels of the smaller hotels are really low, large hotels experience a significant increase in the level of this efficiency. One possible explanation for this result is that the management of big hotels handles better its product portfolio, generating higher quality and greater value from its combination of outputs. An example of a Hotel Brand where its success is explained in part by improving their efficiency level is Sol Meliá Group, one of the most important hotel corporation in Spain. The group has achieved significant improvements in efficiency and cost savings by simplifying the management and a better control on their costs. This has resulted in an improvement in their economic results. Another corporation that saw improve their performance as a result of improved levels of efficiency is Barceló Group. They have implemented a flexible, decentralized organizational model that has enable each facility to better adapt to different markets and environments. Finally, these results are consistent with a not very competitive market, so we can conclude that the management of the hotels should focus their interest more on correcting strong profit inefficiencies by producing a set of higher value services, that is, improving the management of its own product portfolio and in consequence be able to gain sustained competitive advantages. The limitation of this study is basically derived from the database used. First, it would be desirable a higher level of disaggregation of outputs; second, we recognize that employing an approximation to estimate the prices of the inputs is not the most appropriate; and third, the heterogeneity of the sample used, because on one hand, there are companies that manage one establishment and others who manage more than one, and on the other, there are chains with a company for each establishment and others creating one or more companies to manage all their establishments. However, and assuming this limitation, we believe that this does not reduce the importance of the study. It should also be indicated that the empirical results of this study need further research that will confirm; because, to our knowledge, this is the first study to compare the efficiency of costs and profits in the hotel industry.

14 APPENDIX Cost and Profit Efficiencies per Hotel for a Truncation Level of 5% Hotel Cost Efficiency Profit Efficiency Hotel Cost Efficiency Profit Efficiency Hotel Cost Efficiency Profit Efficiency Hotel Cost Efficiency Profit Efficiency

15

16

17 18 JOURNAL OF HOSPITALITY & TOURISM RESEARCH NOTES 1. For a detailed revision of recent literature about efficiency in the hotel industry, see Assaf et al. (2010). 2. As, for example, scale and scope economies. 3. A detailed discussion of the importance of service quality in the hotel industry can be found, for example, in Knutson, Stevens, Wullaert, Patton, and Yokoyama (1991); Saleh and Ryan (1991); Falces, Sierra, Becerra, and Briñol (1999); Oh (1999); and Olorunniwo, Hsu, and Udo (2006). 4. Normally revenue efficiency is not calculated directly because of the difficulties involved, so profit efficiency is estimated. 5. The range of cost efficiency is between [0, 1] and is equal to one for the most efficient company in the sample. In practice, efficiencies are generally defined in relation to the most efficient entity observed in the sector, rather than in reference to the true minimum cost, as underlying technology is unknown. Fortunately, for most economic hypotheses, it is more appropriate to use the concept of relative efficiency than absolute efficiency. 6. The constant must be considered when recovering the values of efficiency. 7. For a more detailed relation of the most commonly used models in the estimation of efficiency in the hotel industry over the past few years, see Bernini and Guizzardi (2010). 8. For a wider discussion of different parametric estimation methods, see Berger and Mester (1997). 9. This in itself would not be a problem if all methods reached the same conclusion, but that is not so. In fact, the estimated level of inefficiency is affected substantially by the chosen method (Berger, Hunter, & Timme, 1993). 10. SABI, Sistema de Análisis de Balances Ibéricos, System Analysis of Iberian Balances, is a database that includes information on balances of more than 1.2 million Spanish companies and more than 350,000 Portuguese companies. 11. The initial sample was selected according to Category 551 (hotels and similar accommodation) of the Spanish National Classification of Economic Activities, CNAE Materials include raw materials necessary to give the room and food and beverages services. REFERENCES Anderson, R. I., Fish, M., Xia, Y., & Michello, F. (1999). Measuring efficiency in the hotel industry: A stochastic frontier approach. International Journal of Hospitality Management, 18, Anderson, R. I., Fok, R., & Scott, J. (2000). Hotel industry efficiency: An advanced linear programming examination. American Business Review, 18, Ariff, M., & Can, L. (2008). Cost and profit efficiency of Chinese banks: A non-parametric analysis. China Economic Review, 19, Assaf, A., & Agbola, F. W. (2014). Efficiency analysis of the Australian accommodation industry: A Bayesian output distance function. Journal of Hospitality & Tourism Research, 38, Assaf, A., Barros, C. P., & Josiassen, A. (2010). Hotel efficiency: A bootstrapped metafrontier approach. International Journal of Hospitality Management, 29,

18 Arbelo et al. / COST AND PROFIT EFFICIENCIES 19 Assaf, A., & Knezevic, L. (2011). Privatization, market competition, international attractiveness, management tenure and hotel performance: Evidence from Slovenia. International Journal of Hospitality Management, 30, Assaf, A., & Magnini, V. (2012). Accounting for customer satisfaction in measuring hotel efficiency: Evidence from the US hotel industry. International Journal of Hospitality Management, 31, Barros, C. P. (2004). A stochastic cost frontier in the Portuguese hotel industry. Tourism Economics, 10, Barros, C. P. (2005a). Evaluating the efficiency of small hotel chain with a Malmquist productivity index. International Journal of Tourism Research, 7, Barros, C. P. (2005b). Measuring efficiency in the hotels: An illustrative example. Annals of Tourism Research, 32, Barros, C. P., & Alves, P. (2004). Productivity in tourism industry. International Advances in Economic Research 10, Barros, C. P., Dieke, P., & Santos, C. A. (2010). Heterogeneous technical efficiency of hotels in Luanda, Angola. Tourism Economics, 16, Barros, C. P., & Mascarenhas, M. J. (2005). Technical and allocative efficiency in a chain of small hotels. International Journal of Hospitality Management, 24, Barros, C. P., Peypoch, N., & Solonandrasana, B. (2009). Efficiency and productivity growth in hotel industry. International Journal of Tourism Research, 11, Barros, C. P., & Santos, C. A. (2006). The measurement of efficiency in Portuguese hotels with DEA. Journal of Hospitality & Tourism Research, 30, Berger, A. N. (1993). Distribution-free estimate of efficiency in the US banking industry and tests of the standard distributional assumptions. Journal of Productivity Analysis, 4(3), Berger, A. N., Hancock, D., & Humphrey, D. B. (1993). Bank efficiency derived from the profit function. Journal of Banking and Finance, 17, Berger, A. N., Hunter, W., & Timme, S. (1993). The efficiency of financial institutions: A review and preview of research past, present, and future. Journal of Banking and Finance, 17, Berger, A. N., & Mester, L. J. (1997). Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking & Finance, 21, Bernini, C., & Guizzardi, A. (2010). Internal and locational factors affecting hotel industry efficiency: Evidence from Italian business corporations. Tourism Economics, 16, Brown, J. R., & Ragsdale, C. T. (2002). The competitive market efficiency of hotel brands: An application of data envelopment analysis. Journal of Hospitality and Tourism Research, 26, Bull, A. O. (1995). The economics of travel and tourism (2nd ed.). Melbourne, Victoria, Australia: Longman. Chen, C. (2007). Applying to stochastic frontier approach to measure hotel managerial efficiency in Taiwan. Tourism Management, 28, Chen, T. H. (2009). Performance measurement of an enterprise and business units with an application to a Taiwanese hotel chain. International Journal of Hospitality Management, 28, Christensen, L., Jorgenson, D., & Lau, L. (1973). Transcendental logarithmic production frontiers. Review of Economic and Statics, 55,

19 Arbelo et al. / COST AND PROFIT EFFICIENCIES 20 Davies, B. (1999). Industrial organization: The UK hotel sector. Annals of Tourism Research, 26, DeYoung, R. (1997). A diagnostic test for the distribution-free efficiency estimator: An example using U.S. commercial bank data. European Journal of Operational Research, 98, Dhawan, R. (2001). Firm size and productivity differential: Theory and evidence from a panel of US firms. Journal of Economic Behavior & Organization, 44, Falces, C., Sierra, B., Becerra, A., & Briñol, P. (1999). Hotelqual: A scale for measuring the perceived quality in hosting services. Estudios Turísticos, 139, Fitzpatrick, T., & McQuinn, K. (2008). Measuring bank profit efficiency. Applied Financial Econometrics, 18(1-3), 1-8. Hadad, S., Hadad, Y., Malul, M., & Rosenboim, M. (2012). The economic efficiency of the tourism industry: A global comparison. Tourism Economics, 18, Hu, J. L., Chiu, C. N., Shieh, H. S., & Huang, C. H. (2010). A stochastic cost efficiency analysis of international tourist hotels in Taiwan. International Journal of Hospitality Management, 29, Hwang, S. N., & Chang, T. Y. (2003). Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan. Tourism Management, 24, Ingram, H., & Daskalakis, G. (1999). Measuring quality gaps in hotels: The case of Crete. International Journal of Contemporary Hospitality Management, 11(1), Knutson, B., Stevens, P., Wullaert, C., Patton, M., & Yokoyama, F. (1991). Lodgserv: A service quality index for the lodging industry. Hospitality Research Journal, 14, Leibenstein, H. (1966). Allocative efficiency vs X-efficiency. American Economic Review, 56, Lin, B. H., & Liu, H. H. (2000). A study of economies of scale and economies of scope in Taiwan international tourist hotels. Asia Pacific Journal of Tourism Research, 5, López, C., & Serrano, A. (2001). Dimensions and measurement of service quality in hotel industry. Revista Colombiana de Marketing, 2(3), Maudos, J., Pastor, J. M., Pérez, F., & Quesada, J. (2002). Cost and profit efficiency in European banks. Journal of International Financial Markets, Institutions and Money, 12, Oh, H. (1999). Service quality, customer satisfaction, and customer value: A holistic perspective. International Journal of Hospitality Management, 18, Olorunniwo, F., Hsu, M. K., & Udo, G. J. (2006). Service Quality, customer satisfaction, and behavioral intentions in the service factory. Journal of Services Marketing, 20(1), Poon, A. (1993). Tourism, technology and competitive strategies. Oxford, England: CAB International. Pulina, M., Detotto, C., & Paba, A. (2010). An investigation into the relationship between size and efficiency of the Italian hospitality sector: A window DEA approach. European Journal of Operational Research, 204, Reynolds, D., & Biel, D. (2007). Incorporating satisfaction measures into a restaurant production index. International Journal of Hospitality Management, 26, Rodriguez, J., & González, E. (2007). Cost efficiency of the lodging industry in the tourist destination of Gran Canaria (Spain). Tourism Management, 28,