EFFICIENCY ANALYSIS IN THE HOSPITALITY INDUSTRY OF GRANADA

Ibarrondo-Dávila, María Pilar1 Pérez-López, Gemma2 Abstract Granada occupies a relevant position in the touristic map of Andalusia. In 2016, the hotel industry in the province was integrated by 418 hotel establishments, which gives to Granada the second position in number of establishments, after Malaga, in the context of the Andalusian region. However, the average size of hotel establishments in Granada is 74.7 places per establishment, the third province of Andalusia with the smallest average size of hotel companies. Regarding the average occupation, Granada stood in 2016 below the average occupation of Andalusia, behind Malaga and Seville. The hotel sector of the province should explore the efficiency of the different companies in the market, in order to detect improving opportunities and their potential in the management of productive and tourism resources. In effect, the implementation of a strategy that enables the efficient use of productive resources will allow these companies to improve their profitability and competitiveness. In this sense, this work analyzes the efficiency of the sector, through the DEA (Data Envelopment Analysis) methodology, which constitutes a non-parametric evaluation technique that allows to evaluate the relative efficiency of a set of productive units, through the delimitation of the efficient frontier. The main objective of this work is to offer useful information for companies in the hotel sector in Granada, which will allow them to improve the efficiency and productivity with which they operate in the market. Keywords: DEA analysis, hospitality industry efficiency, touristic business management

1

Faculty of Business and Economics, Granada University.

2

Faculty of Social Science and Law, Campus of Melilla, Granada University, e-mail: [email protected]

1. INTRODUCTION Tourism is one of the main economic sectors in Spain, which promotes the generation of employment and the development of other sectors of activity, such as transport or communications. Likewise, the international development that this sector has faced and its implications for national economies as well as its environmental effects make this sector a key area in the political and economic context. Within the tourism industry, the study of the hospitality sector is particularly important due to the currently major challenges that the sector is facing: the rise of tourist apartments and the specialization of the supply of quality services. The development of innovative technologies and the growing supply of accommodation have intensified competition in the sector, leading to a reconversion of the services offered by hotel establishments. Therefore, the search for efficiency, in this context, becomes a key area in hotel management. In this sense, the main objective of this paper is to analyze the efficiency of the hotel establishments located in Granada, with the purpose of enabling benchmarking, which allows identifying the most efficient hotel companies, and carrying out a detailed study of the key points of the management of them. Moreover, the purpose of this procedure is supporting the management of the analyzed hotel establishments and promoting the improvement of their efficiency. In this framework, the preliminary results of the DEA efficiency analysis are presented in this working paper, which will serve as a basis to carry out a subsequent analysis. The work is organized as follows: in the second section the Granada hotel sector in the Andalusian set is contextualized; in the third one a review of the previous literature on the analysis of efficiency of the hotel sector is carried out; subsequently, the fourth and fifth sections explain the methodology used, the variables and the data used in the study; the main results are presented in the sixth section; and, finally, the last section highlights the main implications and future lines of research of this study. 2. THE HOSPITALITY SECTOR OF GRANADA IN THE CONTEXT OF ANDALUSIA Spain occupies a leading position in international tourism, having exceeded 75 million foreign tourists in 2016 and 54 billion euros in revenue, according to the data estimated by Exceltur (2017). In 2016, there was a growth in tourist GDP, standing at 4.9% and more than 80,000 new jobs created (Exceltur, 2017). As shown in Figure 1, during the period 2007 to 2009, the tourism sector was marked by the economic crisis, with a year-on-year variation rate of GDP lower than the average for the Spanish economy. However, from 2010 the trend is broken, although it is from 2012 when the tourist GDP grows continuously, to stand at 4.9% in 2016. As some studies show (González-Jiménez de la Espada, 2012, Cuadrado-Roura and López-Morales, 2015), the

change in the trend could be determined by the improvement of the disposable income of the issuing countries, as well as by the growth of travelers from new markets, and the political and social instability of Spain's tourist competitors. Within the tourism sector, the hospitality subsector has a special relevance, providing accommodation to more than 60% of foreign tourists (Arbelo, 2016). Figure 1. Interannual variation rate of tourist GDP and general GDP of the Spanish economy

Tourist GDP

General GDP

Source: Own elaboration from Exceltur data (2017)

If we observe the distribution of travelers by Autonomous Communities in Table 1, Catalonia, Andalusia, Madrid, the Balearic Islands, the Canary Islands and the Valencian Community concentrate, in order of importance, more than 75% of the tourists staying in hotel establishments. It is also possible to observe that the average stay in the national territory is 3.32 days, being 2.89 days in Andalusia.

Table 1. National and foreign travelers by Autonomous Communities (2016) Number of travelers Provinces

%Total Travelers

TOTAL

Total

National

Foreign

Average

Travelers

Travelers

stay

100.0%

99,502,990

49,462,182

50,040,808

3.32

17.9%

17,829,730

9,651,183

8,178,547

2.89

Aragón

2.7%

2,687,870

2,137,321

550,549

1.93

Asturias, Principality de

1.7%

1,695,485

1,422,239

273,246

2.03

Balearic Islands

9.7%

9,659,723

1,158,162

8,501,561

6.04

Canary Islands

9.4%

9,350,669

1,807,299

7,543,370

7.44

Cantabria

1.2%

1,163,341

930,150

233,191

2.35

Castilla y Leon

4.8%

4,786,046

3,662,612

1,123,434

1.63

Castilla-La Mancha

2.1%

2,118,843

1,758,195

360,648

1.68

19.2%

19,126,022

7,468,977

11,657,045

2.89

Andalusia

Catalonia Valencian Community

8.3%

8,212,902

5,034,388

3,178,514

3.44

Extremadura

1.3%

1,321,948

1,116,628

205,320

1.73

Galicia

4.3%

4,324,820

3,105,378

1,219,442

1.91

11.5%

11,453,837

6,219,065

5,234,772

1.93

Murcia, Region of

1.2%

1,235,847

979,726

256,121

2.49

Navarra

1.0%

995,700

719,205

276,495

1.84

Basque Country

2.8%

2,831,596

1,740,611

1,090,985

1.90

Rioja, La

0.6%

577,765

462,593

115,172

1.76

Ceuta

0.1%

72,634

47,664

24,970

2.54

Melilla

0.1%

58,212

40,785

17,427

2.73

Madrid

Source: Own elaboration from EOH (INE, 2017)

In Andalusia, the provinces that constitute the main tourist destinations, by order of importance, are Malaga, Seville, Granada and Cadiz, which account for 77% of the total travelers staying in hotel establishments. Regarding Table 2, the province of Granada maintains the percentage of travelers it hosts at 15%, both national and foreign. On the other hand, Malaga and Seville receive a higher percentage of foreign tourists, while Cadiz receives a higher percentage of domestic travelers.

Table 2. National and foreign travelers by Andalusian provinces (2016) Number of Travelers

Total

Andalusia

Total

National

Percentage

Travelers

National Traveler percentage

Foreign Travelers

Foreign Traveler percentage

17,829,730

100.0%

9,651,183

100.0%

8,178,547

100.0%

Almeria

1,377,325

7.7%

1,060,852

11.0%

316,473

3.9%

Csdiz

2,454,247

13.8%

1,582,418

16.4%

871,829

10.7%

Cordoba

1,178,204

6.6%

691,997

7.2%

486,207

5.9%

Granada

2,831,192

15.9%

1,530,211

15.9%

1,300,981

15.9%

Huelva

1,006,381

5.6%

790,514

8.2%

215,867

2.6%

522,740

2.9%

441,851

4.6%

80,889

1.0%

Malaga

5,247,563

29.4%

2,005,175

20.8%

3,242,388

39.6%

Seville

3,212,072

18.0%

1,548,163

16.0%

1,663,909

20.3%

Jaen

Source: Own elaboration from EOH (INE, 2017)

Considering the characteristics of the hotel offer, the average number of open hotel establishments, as well as the average number of hotel rooms offered and the average size per establishment are shown in Table 3. Table 3. Oferta hotelera en Andalucía por provincias (2016)

Andalusia Almeria

Average number of

Average number of

Average size (No. rooms

establishments

rooms

/establishment)

2,470 (100%)

251,508 (100%)

101.84

194 (7.9%)

29,577 (11.8%)

152.33

404 (16.3%)

37,494 (14.9%)

92.90

Cordoba

195 (7.9%)

11,040 (4.4%)

56.50

Granada

418 (16.9%)

31,185 (12.7%)

74.69

144 (5.8%)

20,974 (8.3%)

145.74

Cadiz

Huelva Jaen

178 (7.2%)

8,405 (3.3%)

47.33

Malaga

566 (22.9%)

83,192 (33.1%)

146.92

Seville

371 (15.0%)

29,641 (11.8%)

79.84

Source: Own elaboration from EOH (INE, 2017)

The highest number of establishments is observed in Malaga (22.9%), followed by Granada (16.9%), Cadiz (16.3%) and Seville (15%). Considering the number of seats, it is also led by Málaga (33.1%), followed by Cadiz (14.9%), Granada (12.7%) and Seville (11.8%). Finally, the largest average size per establishment is presented in Almeria, followed by Huelva and Malaga. Granada is the third province with the smallest average

size of the establishments, calculated by quotient between the number of rooms and the number of establishments. Regarding the degree of occupation (Figure 2), Malaga is above the average occupation of Andalusia throughout the period considered (2008-2016), while Granada is around the average occupation, having suffered a decline in the last year. Figure 2. Degree of hotel occupancy for the period 2008-2016: Andalusia, Malaga, Granada, Seville and Cadiz

Source: Own elaboration from EOH (INE, 2017)

The most relevant economic indicators on hotel profitability, for which statistical information is available in the sector, are the average income per available room (RevPar) and the average daily rate per occupied room (ADR). Both indicators offer information on the financial status of a hotel, and together with the degree of occupation by rooms, represent a relevant source of information for hotel establishments, in the management of their pricing policy (Arbelo, 2016). Table 4 shows the data estimated for 2016, by the National Institute of Statistics (INE), in the Hotel Occupancy Survey (EOH). In this table are collected ADR, RevPar and the interannual variation rate of both, by Autonomous Communities.

Table 4. Estimated RevPar and ADR for 2016 by Autonomous Communities ADR (€)

Interannual variation rate

RevPAR (€)

Interannual variation rate

TOTAL

82.3

4.5

53.9

10.6

Andalusia

80.9

4.5

50.4

11.5

Aragón

55.3

1.8

22.9

13.2

Asturias. Principality of

59.6

5.3

26.5

11.3

Balearic Islands

92.6

6.8

75.5

11.9

Canary Islands

90.2

5.1

77.9

11.6

Cantabria

67.6

0.7

33.7

9.5

Castilla y Leon

54.7

0.9

22.1

6.1

Castilla - La Mancha

53.3

-1.3

18.0

6.6

Catalonia

91.3

3.3

61.0

9.1

Valencian Community

70.2

6.3

45.4

11.9

Extremadura

57.0

3.0

20.3

11.3

Galicia

55.8

1.2

22.3

8.0

Madrid

80.6

4.0

55.2

6.6

Murcia. Region f

59.7

0.2

32.5

10.8

Navarra

62.6

0.3

31.1

10.3

Basque Country

80.8

3.2

50.4

8.2

Rioja. La

61.6

1.5

32.3

4.3

Ceuta

68.7

-3.0

42.4

13.6

Melilla

66.0

2.0

43.2

7.6

Source: Hotel Occupancy Survey (INE. 2017)

As we can see in Table 4, the Autonomous Communities with the highest daily income per occupied room (ADR) are, in descending order, Balearic Islands, Catalonia, Canary Islands, Andalusia, Basque Country and Madrid. Between them, the largest interannual increases have occurred in the Balearic Islands, followed by the Canary Islands and Andalusia. In relation to RevPar, it is observed that the highest income per available room is reached in the Canary Islands, followed by the Balearic Islands, Catalonia, Madrid and the fifth place is occupied by Andalusia and the Basque Country. In this case, the highest interannual variation rates observed in these regions are reached in the Balearic Islands, Canary Islands and Andalusia. The analysis of interannual variations highlights that the increases in RevPar exceed those corresponding to ADR. As a result, there has been an increase in the average occupation that has favorably affected the generated RevPar.

3. EFFICIENCY ANALISYS IN THE HOSPITALITY SECTOR In a globalized and highly competitive environment, as the tourism sector, it is essential that companies identify the relationship between the results obtained and the resources used, as well as to know the degree of efficiency of each company within the sector (Alberca and Parte, 2013). As Arbelo (2016) indicates, tourism is configured as a standardized product, difficult to differentiate, so cost control plays a fundamental role in the search of competitiveness. However, the reduction of costs must be achieved without reducing the quality required by customers, so that efficiency becomes a key factor in the competitiveness of the hotel company. Indeed, the efficient use of productive resources represents a strategy that allows the company to improve its profitability and competitiveness. Although productivity usually refers to the relationship between production and the productive factors used to obtain it, efficiency requires the use of the optimal combination of inputs and outputs in the transformation process. Several studies have been carried out in the hospitality sector focused on the analysis of efficiency. For this purpose, different techniques have been used, classified basically in two types: parametric and non-parametric methodologies. The first one presents as the main advantage the possibility of estimating the error of the model, but its main disadvantage is the fact that this procedure needs to specify the production function prior to the analysis. For its part, the non-parametric methodology presents as advantages a high degree of flexibility and not requiring the previous definition of the production function nor the homogeneity of the units of measurement, although its deterministic nature prevents estimating the standard error (Charnes et al., 1994). The DEA methology (Data Envelopment Analysis) is a non-parametric evaluation technique that allows to evaluate the relative efficiency of a set of productive units, by means of the delimitation of the efficient frontier. The efficient frontier will be determined by the productive units that have been more efficient in relation to the analyzed set of units. The rest of the units analyzed will be below the efficient frontier, obtaining an efficiency indicator (in percentage form) in relation to the efficient units. Alberca, Parte and Such (2011) and Arbelo (2016) highlight that this methodology has been the most used in the case of hotel companies. However, the analysis of efficiency in the hotel sector, despite its relevance in the global economy, is relatively scarce in the literature compared to other sectors such as banking (Arbelo, 2016). Efficiency studies in the hospitality sector, carried out in different countries and based on frontier analysis, can be consulted, among others, in Hwang and Chang (2003); Barros and Alves (2004); Pulina et al., (2010); Oliveira et al., (2013); Chen et al., (2017); and Amado et al., (2017). In the Spanish context, there are very few studies that analyze efficiency in the hotel sector (Alberca and Parte, 2013). In this regard, Table 5 shows different studies analyzing the efficiency of the Spanish hotel sector, through frontier analysis -the majority with DEA

methodology-. In addition, the inputs and outputs used in the analysis are identified, as well as their objectives. Table 5. Efficiency studies performed in the Spanish hospitality sector with frontier analysis AUTHORS Rubio and Román (2007)

INPUTS  Operational costs

OUTPUTS  Revenues

 Personnel costs  Equipment depreciation

 Other costs Alonso, Fernández  Assets  Sales and González  Consumptions  Operational (2009) revenues  Other operational costs  Personnel costs

OBJECTI VE Analysis of the efficiency (DEA) of the hotel sector of Andalusia and comparison with the national level. Period 2002-2004.

Efficiency study (DEA) in the hotels of the Meliá group, depending on the type of management contract, in a sample of 26 hotels in 2003.

 No. of rooms Alberca, Parte and Such (2011)

 Personnel costs

 Net sales

To analyze the influence of some factors on efficiency (DEA) and productivity, in a sample of 302 Spanish hotels. Period 2000-2005.

 Net sales

Regional analysis through efficiency evaluation (DEA) of hotel companies in the Community of Madrid. Period 2001-2008. Analysis of the influence of size on productivity and efficiency (DEA) in the Spanish hotel sector. Comparative analysis of independent hotels and hotel groups. Sample of 424 hotels. Period 2004-2006. Analysis of the efficiency (DEA) and the productivity of the hotel sector based on the location, 1,593 companies. Period 2001-2008. Efficiency analysis in the hostel sector in Andalusia in the period 2003-2012. DEA Methodology.

 Net fixed assets  Consumptions plus Other operational costs

Alberca, Parte, Muñoz and Such (2012)

 Number of employees  Fixed assets  Consumptions

Such and Mendieta  No. of rooms (2013)  No. of full-time

 Total revenues

employees  Personnel costs

Alberca and Parte (2013)

 No. of employees

 Net sales

 Fixed assets  Consumptions

Agabo-Mateos, Escobar-Pérez and Lobo-Gallardo (2013) Jackute (2014)

 Personnel costs

 Revenue per room

 No. of beds

 Revenue F&B

 Total operational costs

 Total revenues

 Purchases

 Operational

 Personnel costs

revenues

 Equipment depreciation Alberca (2014)

 Personnel costs  Net fixed assets

 Net sales plus Other operational income

Regional efficiency analysis (DEA) in the Spanish hotel sector, with a sample of 417 hotels. Period 2007-2012. Analysis of the incidence of the accounting result and the business

AUTHORS

INPUTS  Consumptions plus

OUTPUTS

Other operational costs De Jorge and Suárez (2014)

 No. of full-time employees

 Sales  Market share

 No. of rooms  Personnel costs

OBJECTI VE dimension in the efficiency (DEA) of the hotel companies, from a sample of 303 companies. Period 2000-2005. Study of the determinants of productivity and efficiency (DEA), from a sample of 303 hotels in Spain. Period 1999-2007.

 Operational costs Parte-Esteban and Alberca-Oliver (2015)

 Sales

Efficiency analysis (DEA) and productivity of 1,385 hotels in Spain. Period 2001-2010.

 Operational costs

 Net sales

 EBIT

 Other operational

Analysis of stochastic frontier approximation (SFA) of the determinants of economic efficiency in the hotel industry in Spain, from a sample of 838 establishments. Period 2009-2013. To analyze the relationship between the competitiveness of tourist destinations and the competitiveness of international hotels, with a sample of 15 international hotel groups in 2010. First research DFA Costs and revenues in the hotel industry. Period 2007-2011.

 No. of full-time employees  Personnel costs  Book value of the property  Operational costs

Arbelo (2016)

 Prices of labor,

revenues

materials y capital Mendieta-Peñalver  No. of employees et al, (2016)  Salaries

 Total revenues  RevPar

 No. of rooms

Arbelo,  Operational costs Pérez-Gómez,  EBITDA González-Dávila and Rosa-González (2017)

 Operational revenues  Other revenues

The studies collected in Table 5 are focused on the evaluation of the efficiency of different samples of hotel companies using public economic data. Considering the global character of these works, they offer general conclusions and a generic vision of the efficiency of the sector and its evolution over time. Additionally, some studies also analyze the possible factors affecting the efficiency of these companies (Arbelo, 2016; Alonso et al., 2009) or their regional distribution (Pool and Part, 2013; Jackute, 2014). In the study by Rubio and Román (2006) in the hotel sector in Andalusia, the hotels analyzed presented in 2004 a technical efficiency higher than the national average, ranking the second place behind the Canary Islands. Moreover, this work, analyzes the efficiency of the sector in the different Andalusian provinces, where Granada is the fifth in technical efficiency, in the period 2002-2004, behind Cordoba, Seville, Cadiz and Malaga.

The study of the Spanish hotel sector by Such and Mendieta (2013) -that differentiate between hotels affiliated with a group hotel and independent hotels- shows that among the hotels belonging to a group, Andalusia ranks, according to their efficiency, the third place in 2006, after Murcia and the Valencian Community. In the case of the independent hotels, Andalusia goes to the fifth place, after Murcia, the Canary Islands, the Balearic Islands and the Valencian Community. Additionally, the regional efficiency analysis carried out by Alberca and Parte (2013) shows that in 2008, Andalusia is ranked the seventh in efficiency, behind Madrid, the Basque Country, Catalonia, Navarra, La Rioja, the Balearic Islands and Aragon, and below the national average. Finally, in the period 2007-2012, Jackute (2014) obtains in their analysis of the Spanish hotel sector, that Andalusia was in the third place, after Madrid and the Canary Islands. Although most of the studies analyzing the Spanish hospitality sector indicate the relevance of this type of analysis for the management of the hotels, they do not offer useful information for the CEO of the individual companies, since they cannot have information about their own situation in the sector, in terms of efficiency level. Indeed, the efficiency analysis can be used as a powerful benchmarking tool for companies, since it offers information on the position that each of them occupies in the sector, taking as reference the most efficient companies. In this line, in this paper we address an exploratory analysis of efficiency in the hospitality sector of Granada, with the aim of providing useful information for companies in that sector, as a source of competitive advantage, which allows them to guide their management towards the improvement of efficiency and productivity. This working paper is an initial study in which size is explored as a relevant feature in the performance of companies in the sector, in order to address a more detailed study in the future, in collaboration with the Provincial Federation of Hospitality and Tourism Companies of Granada, and extend it to other Spanish cities, with the aim of achieving a comparative analysis from which extrapolatable conclusions can be drawn.

4. METHODOLOGY As it has been shown in the previous section, in the present work we have opted for the application of the Data Envelopment Analysis (DEA) methodology to perform the efficiency analysis of the hotel companies of the Granada sector. In this type of frontier analysis, the unit that is evaluated (in our case, the hotels located in Granada) is commonly known as DMU (decision making unit). These production units obtain certain outputs from some inputs, so it is possible to estimate the efficiency of each DMU from the observations of the inputs and outputs through the resolution of an optimization model, which estimates the efficiency of the units by comparison with the best practices of the set of units analyzed, which make up the efficient frontier. In spite of the limitations that the DEA models present, since it is not possible to make statistical inference and the error in the variables is included in the estimated efficiency coefficients due to their deterministic nature; their flexibility makes them more appropriate when it is unknown previously the form of the production function (Barros and Dieke, 2008) because netiher the previous definition of this production function is not necessary nor there is homogeneity of the units of measurement (Charnes et al., 1994). In particular, the application of the DEA methodology can be carried out following constant returns of scale (model Charnes - Cooper - Rhodes or CCR) or with variable returns (model Banker - Charnes - Cooper or BBC). Initially, Charnes, Cooper and Rhodes (1978) developed a model in which the hypothesis of convexity is assumed, that is, that optimal units can be obtained from the combination of two units considered as efficient; and the hypothesis of constant returns of scale, which implies that the estimated efficiency through this model would be conditioned both by the management of the production unit and by the size (Avrikran, 1999). Thus, the efficiency estimated through the BBC model, proposed by Banker, Charnes and Cooper (1984), represents a measure of pure efficiency, when considering variable returns of scale. At the same time, DEA models can be estimated following an orientation to input or output. The decision between these two orientations lies essentially in the variables which the production unit has greater control (Coelli et al., 2005). Thus, output-oriented models are normally used when the production unit needs to study how much its capacity (output) could expand given a level of input. Whereas, input-oriented models are more appropriate to respond to operational objectives, such as reducing the costs of the production unit (Cullinane, Song and Wang, 2004; Pulina, Detotto and Paba, 2010). In the case of the hotel sector, as it is an activity intensive in structural costs (Pulina, Detotto and Paba, 2010), it could be justified the choice of the orientation to the output. Nevertheless, as underlined by Coelli et al. (2005), the choice of the orientation has a minor influence on the efficiency values that are estimated.

Thus, the present analysis is performed following a DEA model with variable returns of scale and output orientation, which in its primal version would be specified as follows:

Where: : parameter that measures the efficiency of the unit evaluated. : weights obtained as a solution to the program. : slack variables of outputs and inputs respectively. 5. DATA AND VARIABLES The application of the DEA models requires the selection of input and output variables. In the case of the hotel sector, the variables used in the efficiency analysis are usually related to economic-financial data that can be obtained from the information in the annual reports presented by the hotel companies (Alberca and Parte, 2013). In this sense, variables that reflect the different productive factors of the sector, such as labor, materials and capital, are often used as inputs (Hwang and Chang, 2003). Thus, the variables most commonly used are: the number of employees or their monetary equivalent, as a measure of the work factor; the cost of consumptions and other ordinary expenses, as a measure of the materials; and fixed assets or the amortization cost, as a measure of the capital factor. In addition, most of the previous studies include income from accommodation services, those derived from restoration services and those obtained from other complementary services such as outputs. However, when it is not possible to identify the different types of income, the net amount of turnover or net sales can be used as a substitute (Arbelo, 2016).

Additionally, there are other measures of output, such as the number of guests, the number of overnight stays or the average income per room (RevPar). Specifically, in this work, the number of employees, consumption costs and the value of fixed assets are considered as input variables. Net sales are included as a measure of output, which have previously been used in the efficiency analysis of the Spanish hotel sector (Alberca and Parte, 2013). The data have been obtained from the SABI database (Iberian Balance Sheet Analysis System), for hotel companies located in Granada whose main activity belongs to category 551. "Hotels and similar accommodation" of the National Classification of Economic Activities (CNAE-2009). So, we obtained a sample of 61 companies for 2016, which represents approximately 15% of the hotel establishments in the province of Granada. Table 6 contains the descriptive statistics of the input and output variables considered in the analysis and Figure 3 represents the number of companies analyzed according to the number of workers. From their joint analysis, it can be deduced that the hotel sector in Granada is composed mainly of small and medium-sized enterprises (SMEs) -the 69% of the hotels considered in the study have from 0 to 16 employees-, with an average turnover of 1,353,830€ and an average fixed investment of 3,252,329€. Table 6. Descriptive statistics Variable

Mean

Employees

15.13115

8

0

85

17.68943

Capital

3,252,329

821,661.3

2,003.92

3.68E+07

6,647,544

Consumption

270,927.3

68,321.09

-389.93

3,178,061

527,237.3

Net sales

1,353,830

593,591.2

33,441.74

9,540,463

1,842,901

Source: Own elaboration

Median

Min.

Max.

St. Dev.

Figure 3. Histogram of the hotels included in the study, by number of employees

Source: Own elaboration

6. RESULTS Taking into account that hotel sector in Granada is mainly composed of SMEs, a classification of the companies analyzed in the study has been carried out, based on the criteria included in the Recommendation of European Commission of May 6, 2003, on the definition of micro, small and medium enterprises (DOUE-L-2003-8073). In this sense, small and medium enterprises are those that "occupy less than 250 people and whose annual turnover does not exceed 50 million of euros or whose annual balance sheet does not exceed 43 million euros" (Article 2, DOUE-L-2003-8073). In particular, they are micro-enterprises when they employ less than 10 workers and whose annual turnover or annual balance sheet does not exceed 2 million euros. The category of small companies comprises those that have less than 250 employees and whose annual balance sheet does not exceed 43 million euros or whose turnover is less than does not exceed 50 million euros. Thus, Table 7 shows the main results according to the hotel category. As can be seen, 54.1% of the sample is classified as micro-enterprises, 39.3% as small companies and approximately 1%, as medium-sized enterprises. In particular, the highest levels of average efficiency are concentrated in the medium-sized companies (90.6%), among which there is less variability. For its part, the average efficiency values of microenterprises and small businesses are very similar, 63.2 and 66.3%, respectively. If we compare these results with those presented in previous studies, we can see that the efficiency of the hotel sector in Granada is slightly higher than those obtained in previous studies (Alberca and Parte, 2013; Rubio and Román, 2006; Such and Mendieta, 2013; Jackute, 2014).

Table 7. Aggregated results by hotel category Hotel category

n

Mean

Micro-sized hotels

33

0.6318697

0.5794

0.2457

1

0.2708683

Small-sized hotels

24

0.6639958

0.60305

0.2533

1

0.2566649

0.90625

1

0.625

1

0.1875

0.6625016

0.6037

0.2457

1

0.2657422

Medium-sized hotels

4

Total

61

Median

Min.

Max.

St. Dev.

Source: Own elaboration

On the other hand, the composition of the efficient production frontier for the hotel sector in Granada is shown in Figure 4. Half of the hotel companies, whose efficiency reaches a value equal to 1, are micro-enterprises, however, it must be emphasized that, of the four medium-sized companies analyzed, three are part of the efficient production frontier (Table 8). Figure 4. Hotels in the frontier production Medium-sized 19%

Micro-sized 50% Small-sized 31%

Source: Own elaboration

Finally, Table 8 shows the benchmarking of the sample analyzed according to the estimated efficiency value3. In this sense, two different estimations have been made, the first one with the total sample of hotel companies in Granada (n = 61) and, the second one, excluding companies classified as medium-sized enterprises, in order to check whether their consideration affects the estimation of efficiency values. In this regard, it should be noted that the results obtained are very similar, so that the relative position of the hotels in Granada remains stable, except for six of them (H35, H36, H37, H41, H42 and H48) whose position in the ranking improves when the medium-sized companies are excluded. 3To

estimate the efficiency, the FEAR package developed by Wilson (2008) in R (2011) has been used.

Table 8. Benchmarking in the hotel sector of Granada, year 2016 Estimated Hotel

Estimated

Hotel

efficiency

Ranking

efficiency

Ranking

category

(n=61)

(n=61)

(n=57)

(n=57)

H1

Medium-sized

1

1

H2

Micro-sized

1

2

1

1

H3

Small-sized

1

3

1

2

H4

Micro-sized

1

4

1

3

H5

Micro-sized

1

5

1

4

H6

Small-sized

1

6

1

5

H7

Small-sized

1

7

1

6

H8

Micro-sized

1

8

1

7

H9

Medium-sized

1

9

H10

Micro-sized

1

10

1

8

H11

Medium-sized

1

11

H12

Small-sized

1

12

1

9

H13

Small-sized

1

13

1

10

H14

Micro-sized

1

14

1

11

H15

Micro-sized

1

15

1

12

H16

Micro-sized

1

16

1

13

H17

Small-sized

0.9981

17

0.9981

14

H18

Small-sized

0.9703

18

0.9822

15

H19

Micro-sized

0.9348

19

0.9348

16

H20

Micro-sized

0.9251

20

0.9251

17

H21

Micro-sized

0.8287

21

0.8287

19

H22

Small-sized

0.8102

22

0.9067

18

H23

Small-sized

0.807

23

0.807

20

H24

Micro-sized

0.7332

24

0.7332

21

H25

Small-sized

0.7115

25

0.7115

22

H26

Micro-sized

0.7036

26

0.7036

23

H27

Small-sized

0.6839

27

0.6839

24

H28

Small-sized

0.6439

28

0.6489

25

H29

Micro-sized

0.6338

29

0.6338

26

H30

Medium-sized

0.625

30

H31

Micro-sized

0.6037

31

0.6037

28

H32

Micro-sized

0.5881

32

0.5881

31

H33

Micro-sized

0.5794

33

0.5794

32

H34

Micro-sized

0.576

34

0.576

33

H35

Small-sized

0.5622

35

0.5926

29

Estimated Hotel

Estimated

Hotel

efficiency

Ranking

efficiency

Ranking

category

(n=61)

(n=61)

(n=57)

(n=57)

H36

Small-sized

0.543

36

0.6169

27

H37

Small-sized

0.5388

37

0.5912

30

H38

Micro-sized

0.5148

38

0.5148

37

H39

Small-sized

0.5094

39

0.5532

34

H40

Micro-sized

0.4928

40

0.4928

38

H41

Small-sized

0.4894

41

0.5176

36

H42

Small-sized

0.4882

42

0.5226

35

H43

Micro-sized

0.4705

43

0.4705

39

H44

Micro-sized

0.4451

44

0.4451

40

H45

Small-sized

0.4394

45

0.444

41

H46

Small-sized

0.4278

46

0.4278

43

H47

Micro-sized

0.4255

47

0.4255

44

H48

Small-sized

0.424

48

0.4353

42

H49

Micro-sized

0.4111

49

0.4111

45

H50

Micro-sized

0.3942

50

0.3942

46

H51

Micro-sized

0.3899

51

0.3899

47

H52

Micro-sized

0.3827

52

0.3827

48

H53

Micro-sized

0.3555

53

0.3555

49

H54

Small-sized

0.3502

54

0.3502

50

H55

Micro-sized

0.3457

55

0.3457

51

H56

Micro-sized

0.3355

56

0.3355

52

H57

Micro-sized

0.2898

57

0.2898

53

H58

Small-sized

0.2853

58

0.2853

54

H59

Small-sized

0.2533

59

0.2636

55

H60

Micro-sized

0.2465

60

0.2465

56

H61

Micro-sized

0.2457

61

0.2457

57

Source: Own elaboration

7. IMPLICATIONS The present work is a first approach to the efficiency of the Granada hospitality sector. For this purpose, an analysis of the main aggregated data of the province within the Spanish context and a literature review of the previous studies carried out at a national level have been exposed. In this sense, it should be noted that, even though Granada is the second Andalusian province in hotel supply, the average size of the establishments is very small. In addition, an efficiency analysis of the hotel companies for the hotel sector in Granada is performed, which yields slightly higher values than those presented in previous studies for the average of Andalusia. Specifically, the sample, obtained from the data contained in the database SABI, encompasses 61 hotel establishments located in Granada. The estimation of the efficiency has been performed based on three inputs (number of employees, fixed assets and consumption costs) and one output (net sales), through the DEA analysis, with variable returns of scale and output orientation. From the analysis of the results, it can be deduced that the average efficiency of the Granada hospitality sector reaches a value of 66.25%, which implies that hotel companies in Granada must improve their values of efficiency by almost 34%. In addition, the hotels analyzed have been classified according to their size. In this regard, the sample is made up of SMEs. The highest average efficiency value is reached by medium-sized companies, although they are the least numerous. For their part, small and micro enterprises reach inefficiency values similar to the average for the sector. This study is a preliminary analysis of the hotel efficiency in Granada that serves as a starting point for a more detailed analysis and whose main objective is to analyze the best practices. In this sense, it is intended, based on the benchmarking carried out, to contact the hotel companies that make up the efficient frontier and analyze their internal and qualitative information to identify the key aspects in hotel management, with the aim of helping the management of this type of companies. However, this study must also be completed with an analysis of the efficiency of the Granada hotel sector, in the Andalusian and national level, in order to identify the position of the hotel companies in Granada in both contexts. Likewise, future research is focused on the analysis of the hotel efficiency according to the number of rooms, the classification system by stars and according to the membership of a hotel group, as well as extending the study to the long-term analysis. BIBLIOGRAFÍA Agabo-Mateos, F.; Escobar-Pérez, B. and Lobo-Gallardo, A. (2014). Measuring efficiency of the youth hostel sector in Andalusia using an adapted DEA model. Cultura, desarrollo y nuevas tecnologías: VII Jornadas de Investigación en Turismo. Sevilla, June 12 -13th, 185-210.

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