3.1 Introduction

The Mauritius touristry industry has been enduring badly from seasonality, accompanied by the jobs ensuing from it. In this chapter, a brief overview of the Mauritius touristry industry is provided and its general tendency and development are analysed. This would supply a general position of the touristry demand and the factors that affect it. Furthermore, by utilizing the Mauritius touristry informations, different methods and techniques would be applied in order to mensurate seasonality and to supply precise consequences of to what extent Mauritius is being affected by seasonality.

3.2 MAURITIAN TOURISM INDUSTRY

Tourism sector is the 1 of the really few surveies of Mauritanian economic system made by Professor G.E. Meade on 1960. Sing the natural facet of Mauritius Island, economic experts were optimistic for the development of Mauritius into a tourer Centre. Browneigg & A ; Greigg 1979 “ touristry has been viewed as a agency of presenting new growing into worsening economic systems ” and that absolutely matched the Mauritanian economic system at that clip which was sing heavy population growing force per unit area. Hence this has terrible deductions on unemployment, GDP and on other socio-economic jobs.

Sing the natural facet, harmonizing to Mark Twain “ Eden was copied from Mauritius as the island was made foremost and heaven second ” . In other words Mauritius has major natural advantages in footings of beaches, warm clear laguna, mountains, clime, exotica atmosphere and Mauritanian people who are welcoming and friendly. In the past 30 old ages, Mauritius has been traveling up the ladder from an developing 3rd universe state to that of a underdeveloped state as defined by the World-Bank. This encouragement was due to the variegation with sugar, fabric and touristry industries. However, so after the worsening of sugar monetary values and cost of textile production became uncompetitive touristry industry bit by bit become the back bone of the Mauritanian economic system.

3.3 GENERAL TREND AND EVOLUTION OF MAURITIAN TOURISM INDUSTRY

Tourism has become one of the major growing sectors in the Mauritanian economic system. It is amongst the top foreign exchange earner and an of import employer in Mauritius. Mauritanian touristry industry has experienced uninterrupted growing during the last 2 decennaries. Harmonizing to CSO, Mauritius had merely approximately 18,000 visitants in 1970, but between 1980 and 2000 the industry had a major encouragement in figure of tourer reachings by 340 % . Harmonizing to UNWTO, there were about 982 million international tourer reachings worldwide in 2011, i.e. a 4 % addition over 2010. Therefore international tourer is on the path to make the milepost one billion grade. However CSO Mauritius observed an addition of 4.8 % in tourer reachings between 2009 and 2010. Grosss from touristry on the other manus, harmonizing to the BoM rose by 9.54 % to make Rs 39456M.

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Again, in conformity to the CSO figures, as it can be identified from Table 1 there was a uninterrupted rise in employment and which bring a rise of 3.13 % to make 28039 between 2010 and 2011. The undermentioned chart 2 can exemplify the enlargement in the tourer industry in footings of tourer reachings during the last decennary 2000-2010.

From the graph, it can be easy being noticed that there has been a uninterrupted enlargement in tourer reachings between 2000 and 2010. In 2008, there was a little autumn in tourer reachings due to the fiscal dazes in the US and Europe where most of the Mauritanian touristry industry ‘s top 10 market are located. At the beginning of 2008, harmonizing to CSO study Mauritius, figure of direct employment climbed up to 28534 due to the uninterrupted rise in tourer reaching old ages after old ages, therefore figure of stakeholders in the industry besides follows this tendency. Table 1 shows how touristry industry has contributed to the Mauritanian Economy since twelvemonth 2000-2010.

Year

Tourist Arrival

Gross Earning Rs M

Direct Employment

2000

656453

14234

17433

2001

660318

18166

19522

2002

681648

18328

20729

2003

702018

19415

21860

2004

718861

23448

22613

2005

761063

25704

25377

2006

788276

31942

25798

2007

906971

40687

26322

2008

930456

41213

28534

2009

871356

35693

27002

2010

915000

39456

27161

On the economic system side tourer industry generate non merely direct employment but tonss of indirect occupations as good. These alone features of this industry has voluntary contributed to the economic system good being of Mauritius during the last two decennaries. As shown in table 1, touristry industry generates immense earning every bit good.

In footings of direct part of tourer industry in economic system in footings of GDP, this is shown in table 2.

Table 2: Direct part of Tourist Industry in Economy 2000-2010 in Rs m

Sum

2005

2006

2007

2008

2009

Gross Domestic Product at Basic Prices Rs M

162171

182009

206971

234151

246979

Direct part of touristry in GDP ( % )

11.2

12

13

11.8

10.1

Beginning: Tourism orbiter Account CSO Feb ( 2010 )

A little lessening in per centum direct part of touristry in Mauritius GDP in 2008 was due to the planetary daze the fiscal crisis and Euro-Crisis.

3.4 MEASURING SEASONALITY IN MAURITIAN TOURISM INDUSTRY

Butler 2001, expressed seasonality in footings of figure of visitants, outgos, main roads traffic, employment or tenancy rates in hotels. So harmonizing to Butler and Mao ( 1997 ) , they classified three types of seasonality:

One-Peak

Two-Peak

Non-Peak

In Mauritius, touristry is a fast growth sector which brings of import part to the economic system good existences. Tourist normally considered by several factors before make up one’s minding where to pass their vacation or particular clip ; they normally choose alien sandy beaches, bluish lagunas, good clime, dramatic landscape and friendly and welcoming people, first category hotels which provide first-class services. Mauritius possessed about all the above mentioned features. However, despite these qualities in Mauritius touristry demand is chiefly driven by Sun and warm exotic conditions follow a one-peak form characterized by a marked extremum season in the summer months i.e. between Octobers to Februarys. This can be illustrated in the undermentioned monthly tourer reachings chart for 1997, 2001, 2006 and 2008 to 2010.

Chart 3: Monthly figure of tourer reachings for twelvemonth 1997, 2001, 2006 and 2008 to 2010

As illustrated in the above chart, one can easy detect that tourist demand form for Mauritius is on a one-peak form get downing in the month of October and stop in the month of March.

Non-peak seasonality means tourist activities occur throughout the twelvemonth significance that touristry is non seasonal indicating. Mentioning to Fernandez-Morales ( 2003 ) seasonal index is among the simple method to place extremum season. Assorted indexs have been used by research workers to mensurate seasonality. The most widely used are ( Nadal et al. , 2004 ; Koenig and Bischoff, 2005 ; Weidner, 2006 ; Chung, 2009 ; Kurtulus and Sevki, 2010 ) :

The Seasonality Ratio

The Seasonality Indicator

Gini Coefficient/ Lorenz Curve

Seasonality Index

In this survey, seasonality is measured in figure of tourer reaching in Mauritius and accent will be more focussed on specific old ages ; 1996, 2001, 2006, and 2008 to 2010. Seasonality is measured on a monthly footing and consequences depend on the Mauritius international tourer reachings

3.4.1 Seasonality Ratio

Seasonality ratio is implemented to detect and compare the development of the tourer reachings. It is calculated by taking the highest figure of visitants during the twelvemonth and spliting this by the mean figure of visitants ( Yacoumis 1980 ) . The greater the seasonality ratio, the greater would be the seasonal alteration and vice-versa. Yacoumis ( 1980 ) computed the seasonality ratio for the Sri Lanka ‘s touristry industry and put up a graph with these ratios. He farther analysed the seasonal fluctuations in demand in order to reason that whether the selected old ages had the similar seasonal demand construction or non.

Analyzing the off-peak, shoulder and peak seasonal months for a peculiar part or state can be really helpful to the hotels and other similar constitutions in indicating out which policies that can be used against the seasonal fluctuations for a given clip period. Table 1, provides informations which can be utile in measuring both for the similar parts and similar sort of adjustment constitutions.

Table 1: Number of Mauritius touristry reachings by months ( in 000 ‘s individuals )

Months/years

1

2

3

4

5

6

7

8

9

10

11

12

T0tal

Average

1995

43

27

33

34

31

23

34

43

29

41

39

46

422

35

1996

43

31

42

39

36

28

36

50

37

46

46

54

487

41

1997

51

37

47

39

40

32

40

55

40

45

55

56

536

45

1998

55

42

48

45

44

30

41

55

39

49

53

57

558

47

1999

60

42

51

42

42

36

43

57

41

55

57

53

578

48

2000

62

50

55

57

50

40

52

60

48

62

57

65

656

55

2001

65

49

56

55

52

38

57

53

49

59

57

69

660

55

2002

64

51

68

48

54

39

57

53

50

65

61

72

682

57

2003

65

54

63

57

55

41

58

57

50

65

62

74

702

59

2004

67

54

64

56

54

39

62

55

53

71

67

78

719

60

2005

73

56

68

53

56

43

65

61

53

71

71

91

761

63

2006

80

65

58

57

51

43

66

64

56

75

70

96

788

66

2007

92

72

80

70

65

53

77

70

66

81

77

104

907

76

2008

95

78

89

73

68

54

81

72

66

84

75

97

930

78

2009

89

68

76

69

65

47

72

63

60

80

79

104

871

73

2010

92

72

86

65

71

53

77

65

65

87

86

115

934

78

Beginning: Central statistical office

Based on Yacoumis ( 1980 ) , the Mauritius touristry seasonality ratios for the twelvemonth 1995 to 2010 are calculated as shown in table 2 below. First of all monthly indices have been computed by spliting the entire figure of tourer reachings for a month by the mean figure of tourer reachings for a twelvemonth. Afterwards, in order to cipher the seasonality ratio, the greater seasonal indices for each have been divided by the mean seasonal which is assumed to be 100.

Table 2: Seasonality ratios ( 1995 to 2010 )

Months/years

1

2

3

4

5

6

7

8

9

10

11

12

Seasonality ratio

1995

121

76

94

98

87

66

96

122

82

115

112

131

1.31

1996

105

76

103

96

89

67

88

124

90

113

114

132

1.32

1997

113

83

105

86

89

72

89

123

899

101

124

125

1.25

1998

117

90

104

98

94

65

87

117

84

106

113

123

1.23

1999

124

87

105

86

88

75

89

119

86

114

117

110

1.24

2000

113

92

100

103

91

73

94

109

87

112

105

118

1.18

2001

118

90

102

100

94

70

104

96

89

108

104

125

1.25

2002

117

92

123

87

98

71

103

97

90

118

111

131

1.31

2003

111

92

91

98

94

72

100

97

86

111

106

127

1.27

2004

111

90

106

93

90

65

104

92

89

118

112

130

1.3

2005

123

89

107

84

88

68

103

96

84

112

112

143

1.43

2006

131

99

89

87

77

65

100

98

85

113

107

146

1.46

2007

121

96

106

93

86

70

102

93

87

107

102

137

1.37

2008

122

100

115

94

87

64

105

92

85

108

97

126

1.26

2009

122

93

105

95

89

65

99

87

83

110

108

143

1.43

2010

118

93

110

83

91

68

99

89

84

112

110

147

1.47

Since, seasonality ratio can be represented utilizing a graph, we have selected four specific old ages, viz. 1995, 2000, 2005 & A ; 2010 and graphed their seasonality ratio as shown in figure 1. As can be seen in figure 1, the graph shows a conventional connexion among the months. Peak season months have seasonality ratio greater than the mean index, for the shoulder season months they are close to the mean index and for the off-peak season months are below the mean index. As noticed, January and December are the peak season months, March, October and November are the shoulder season months and February, April, May, June, July, August and September are the off-peak season months. Practically, seasonality ratio can differ from 1 to 12. If every month receives the same figure of tourer reachings, the seasonality ratio would be 1. On the other manus, if one month receives all the figure of visitants, so the seasonality ratio would be 12. To reason we can state that the higher the seasonal fluctuation, the higher is the greater is the seasonality ratio.

Figure 1: Seasonality ratio by months ( 1995, 2000, 2005 & A ; 2010 )

3.4.2 Seasonality Indicator

The seasonality index is the contrary of seasonality ratio. It is the same as Maximal Annual use Factor Constrained by seasonality adopted by Baron ( 1975 ) where the seasonality index is normalized to 100. By utilizing this seasonality index, we will hold an thought about what per centums of the hotels have been accommodated. The seasonality index is computed by spliting the mean seasonality index by the highest seasonal index. In add-on, it besides indicates the capacity of adjustment which has been used since it implies as the mean tenancy rate. For illustration, if the seasonality index 0.8, this indicates that 80 per cent of the room capacity are used.

Table 3: Seasonal index ( 1995 to 2010 )

Months/years

1

2

3

4

5

6

7

8

9

10

11

12

Seasonality index

1995

121

76

94

98

87

66

96

122

82

115

112

131

0.76

1996

105

76

103

96

89

67

88

124

90

113

114

132

0.76

1997

113

83

105

86

89

72

89

123

899

101

124

125

0.8

1998

117

90

104

98

94

65

87

117

84

106

113

123

0.81

1999

124

87

105

86

88

75

89

119

86

114

117

110

0.81

2000

113

92

100

103

91

73

94

109

87

112

105

118

0.85

2001

118

90

102

100

94

70

104

96

89

108

104

125

0.8

2002

117

92

123

87

98

71

103

97

90

118

111

131

0.76

2003

111

92

91

98

94

72

100

97

86

111

106

127

0.79

2004

111

90

106

93

90

65

104

92

89

118

112

130

0.77

2005

123

89

107

84

88

68

103

96

84

112

112

143

0.7

2006

131

99

89

87

77

65

100

98

85

113

107

146

0.68

2007

121

96

106

93

86

70

102

93

87

107

102

137

0.73

2008

122

100

115

94

87

64

105

92

85

108

97

126

0.79

2009

122

93

105

95

89

65

99

87

83

110

108

143

0.7

2010

118

93

110

83

91

68

99

89

84

112

110

147

0.68

As it can be seen from table 3, the seasonal index values which have been calculated in table 2 have been used to cipher the seasonality index for the old ages. Furthermore, it can be noticed that in 2001 the adjustment capacity used has been the highest, that is 85 per cent and in 2006 and 2010 the adjustment capacity are 68 per cent, the lowest of other old ages. Here it is really important to presume that the figure of tourer reachings monthly have been used as the figure of room occupied in the hotels adjustment as informations for monthly room tenancy are non disposable. However, seasonality ratio and seasonality index have certain restrictions, one of which is that they can be condemn as being influenced by the highest monthly value. This is because in some months, the tenancy rates or tourer reachings are near the mean index whereas in other months of the twelvemonth, the tenancy rates or tourer reachings are above the mean index and this can ensue in high seasonality rates and seasonality indexs. As a consequence, the usage of the Gini coefficient is recommended to mensurate seasonality.

3.4.3 Gini Coefficient/ Lorenz Curve

The most common measuring of inequality is the Gini Coefficient. When the coefficient equal to zero that means complete equality and when it yield a value of one that indicate complete inequality in the information which is being observed. Therefore Gini Coefficient varies between nothing and one. As the Gini coefficient gets closer to 1 the greater will be the inequality. Lundtorp ( 2001 ) , Weidnen ( 2006 ) and Kurtulus and Sevki ( 2010 ) make usage of Lorenz Curve to stand for the grade of inequality in monthly tourer reaching. Likewise, in this survey the same method will be applied to demo the grade of inequality in figure of tourer reachings for certain old ages. See appendix for the monthly tourer reaching, cumulative % month and cumulative % of tourer reachings for the old ages 1995, 2000, 2005 and 2010.

First the monthly tourer reaching ratios have been calculated by spliting figure of monthly tourer reachings by entire figure of tourer reaching for each given twelvemonth. The ratio has been sorted in go uping order so their cumulative values have been calculated. The following are Lorenz curve for the twelvemonth 1995, 2000, 2005 and 2010.

Figure: Lorenz Curves- 1995 and 2000 tourer reaching ratios

Figure: Lorenz Curves- 2005 and 2010 tourer reaching ratios

From the above Lorenz Curve it can be easy noticed that there are unequal distribution of figure of tourer reachings from the distance between the 45 grade heterosexual and the curve. The Lorenz Curves in fig. and fig. Exemplify the distribution of tourer Numberss against the months of old ages. If the Numberss of tourer reachings distribution are equal, there would be a consecutive 45 degree line. In Mauritius, some months of the twelvemonth, figure of tourer reaching is low ; for cases usually during off-peak season is in May and September, and in some months is high for case during peak season like January, December and sometimes in August for some old ages. Therefore there is an unequal distribution of tourer reachings over a twelvemonth in Mauritius ; hence it yielded these Lorenz Curves. In this regard, it can besides state that the Winter-Summer seasons contributed to the unequal distribution in tourer reaching and seasonality consequence in Mauritius. Data has been computed from the CSO Mauritius web site and computations have been carried out to obtain the Lorenz Curve for each of the five old ages. See APPENDIX for figures on cumulative % tourer arrival ratios and cumulative % of months.

In 1995, the figure of tourer reaching in the first three quarters ( the first nine months of 1995 ) consisted of 68.8 % of the entire tourer reachings. However after five old ages the figure of tourer reachings in the first three quarters of 2000 ( first nine months of 2000 ) consisted of 71.26 % of the entire tourer reachings and a little bead in 2005 69.18 % of entire tourer reachings. Recently in 2010 the figure of tourer reachings in the first three quarters is 70.27 % of the entire tourer reachings. Depending on these consequences, it can be said that the inequality of tourer reachings among the months is higher in 1995. From the fig. and fig. it can be noticed that the highest inequality of tourer reachings twelvemonth is 1995 and the least unequal distribution is the twelvemonth 2010. These can be explained by the distance of the curve from the equality line in the figures.

In other words, ‘How much distribution of the existent figure of tourer reachings yield a curve, it means that the distribution of the figure of tourer reachings shows that such inequality ‘ ( Diner, 2003 ) . As can be seen from the two figures the 1995 Lorenz Curve has yielded more than the other curves. Same as the 2005 Lorenz Curve which yielded more than the 2010 Lorenz Curve.

Another manner to measure unequal distribution in tourer reachings is by ciphering GINI-COEFFICIENT which is the proportion of the country left between the equality line and the curve to the entire country above and below the curve. Harmonizing to Lundtorp ( 2001 ) , he used the undermentioned expression to cipher GINI-COEFFICIENT and the same is applied here. See APPENDIX for the expression and computation of tabular array for GINI-COEFFICIENT for the twelvemonth 1995, 2000, 2005 and 2010. The following tabular array showed the GINI-COEFFICIENT for the four old ages together.

Year

GINI-COEFFICIENT

1995

11.183169

2000

7.0093

2005

10.3066

2010

3.05475

Table: Gini-Coefficient for twelvemonth 1995,2000,2005,2010

As it can be seen from the tabular array above, the highest inequality of the monthly distribution of figure of tourer reaching is in 1995 compared to that of 2010, 3.05475 which is the lowest. In other words, harmonizing to Fernandez and Morales ( 2003 ) , degree of seasonal concentration lessenings when comparing 1995 with 2000 and 2010. However when comparing 2005 and 2000, degree of seasonal concentration is higher in 2005.

GINI-COEFFICIENT can besides be calculated for different nationalities by using the same ( Lundtorp, 2001 ) expression. State of abode is selected in such a manner that the two highest in each continent between 2006 and 2011 are chosen. Due to restrictions in informations handiness, monthly tourer reachings by nationalities are assumed to be the monthly tenancy rates of different nationalities in adjustment constitutions. In other words, GINI-COEFFICIENT is calculated for different nationality and it may “ assist the adjustment constitutions to use appropriate selling schemes on seasonality for assorted markets ” , Morales and Toledano ( 2008 ) . The tabular array below shows the Gini-coefficient of the highest tourer reaching from different states around different portion of the universe ; EUROPE, AFRICA, ASIA, OCEANIC and AMERICA.

Nationality

2006

2007

2008

2009

2010

2011

France

0.2405

0.1506

0.1386

0.1483

0.1626

0.1713

United kingdom

0.1138

0.1092

0.7953

0.8226

0.9214

0.8861

Reunion Island

0.3171

0.3027

0.304

0.291

0.2992

0.3001

South-Africa

0.1682

0.158

0.1492

0.1938

0.212

0.1874

India

0.166

0.1631

0.1664

0.1625

0.1882

0.1782

P.Rep of China

0.0922

0.1207

0.1

0.153

0.1538

0.1579

Australia

0.1484

0.1536

0.1154

0.1348

0.1764

0.1304

USA

0.0774

0.0857

0.2

0.2215

0.2376

0.1275

Canada

0.1589

0.1187

0.1526

0.1237

0.1891

0.1849

Table: The GINI-COEFFICIENT by state of abode ( 2006-2011 )

Beginning: CSO Website, the International Tourism Highlights Year 2006-2011

*Calculation is based on the figure of international tourer reachings by state of abode.

Harmonizing to figures in the tabular array of GINI-COEFFICIENT by state of Residence ( 2006-2011 ) , France ranked first in footings of tourer reachings and tourers remaining in the adjustments constitutions and the GINI-COEFFICIENT for French tourer reaching showed a diminishing tendency between 2006 and 2008. In this regard it can be said that Gallic tourer reachings have non concentrated on some months of the old ages but so after 2008 to 2011, the GINI-COEFFICIENT reached at a stable degree.

P. Rep. of China showed an increasing tendency, therefore can reason that as from 2008 China tourer has started to concentrate on some months when sing Mauritius. Likewise UK tourer reachings at that place has been a sudden addition in GINI-COEFFICIENT as from 2007, therefore once more started to concentrate on specific month of the twelvemonth to pass their vacations in Mauritius. India, Canada and Australia have been stable over the old ages 2006 to 2011. However Reunion Island showed a diminishing tendency bespeaking that tourer from Reunion has non concentrated on some months of the twelvemonth.

So this sort of analysis produced utile guidelines for adjustments constitution and authorities organic structure which carried out promotional activities for touristry in Mauritius like the Mauritius Tourism Promotion Authority ( MTPA ) in finding the right selling schemes, when make up one’s minding to take which tourer market to come in, in which months of a twelvemonth and to seek to decrease the negative consequence of seasonality in the touristry industry. However a tourer reaching consists of T ( Trend ) , S ( Seasonal ) , C ( Cyclical ) and I ( Irregular Fluctuation ) . So GINI-COEFFICIENT takes merely the component S in consideration, therefore qualified as insufficient in finding the monthly distribution of tourer reachings. Therefore Seasonality Index can farther be analyzed and calculated so as to break up tourist reachings from the impacting factors.

3.4.4 Seasonality Index

The seasonality index will affect the application of traveling mean method which will let the decomposition of tendency, seasonal, cyclical and irregular fluctuation. Table 4 below illustrates the method of traveling mean taking into history the figure of monthly tourer reachings between 2003 and 2007.

Table 4: Method of traveling norm ( 2006 to 2010 )

Year

Calendar months

Number of tourer reachings

12 months traveling norm

Cardinal traveling norm

Ratio of figure of tourer reachings to the cardinal moving norm

2006

January

86,218

February

64,894

March

58,136

April

57,361

May

50,773

June

42,755

65,690

July

65,540

66,132

65,911

0.994

August

64,307

66,756

66,444

0.968

September

56,138

68,572

67,664

0.830

October

75,451

69,650

69,111

1.092

November

70,394

70,861

70,256

1.002

December

96,309

71,680

70,771

1.361

2007

January

91,528

72,656

71,668

1.278

February

72,338

73,126

72,891

0.992

March

79,965

73,909

73,518

1.088

April

70,297

74,392

74,151

0.949

May

65,301

74,962

74,677

0.874

June

52,584

75,575

75,269

0.699

July

77,225

75,830

75,703

1.021

August

69,941

76,281

76,056

0.920

September

65,542

77,047

76,664

0.855

October

81,244

77,259

77,153

1.053

November

77,236

77,459

77,359

0.998

December

103,670

77,553

77,506

1.338

Year

Calendar months

Number of tourer reachings

12 months traveling norm

Cardinal traveling norm

Ratio of figure of tourer reachings to the cardinal moving norm

2008

January

94,579

77,882

77,718

1.217

February

77,763

78,021

77,951

0.998

March

89,152

78,020

78,021

1.143

April

72,837

78,210

78,115

0.932

May

67,705

78,056

78,133

0.867

June

53,722

77,533

77,795

0.691

July

81,169

77,034

77,284

1.050

August

71,605

76,211

76,623

0.935

September

65,532

75,150

75,681

0.866

October

83,524

74,828

74,989

1.114

November

75,380

74,583

74,706

1.001

December

97,388

74,011

74,297

1.311

2009

January

88,591

73,237

73,624

1.203

February

67,892

72,550

72,894

0.931

March

76,425

72,101

72,326

1.057

April

68,969

71,823

71,962

0.958

May

64,761

72,087

71,955

0.9

June

46,866

72,616

72,352

0.648

July

71,872

72,888

72,752

0.988

August

63,365

73,261

73,075

0.867

September

60,144

74,038

73,650

0.817

October

80,197

73,690

73,864

1.086

November

78,544

74,215

73,953

1.062

December

103,730

74,753

74,484

1.393

2010

January

91,857

75,181

74,967

1.225

February

72,366

75,325

75,253

0.962

March

85,748

75,764

75,545

1.135

April

64,797

76,359

76,062

0.852

May

71,055

76,979

76,669

0.927

June

53,327

77,905

77,442

0.689

July

77,009

August

65,093

September

65,404

October

87,340

November

85,982

December

114,849

Beginning: Central statistical office

Harmonizing to DeLurgio ( 1998 ) , the value of tourer reachings is equal to the undermentioned expression: T*S*C*I

Where,

T= Trend

S= Seasonal

C=cyclical

I-Irregular ( fluctuations )

The value of ( T*C ) is equal to the traveling norm and the ratio as shown in the tabular array 4, is a rating of the value ( S*I ) . Since the ratio of the figure of tourer reachings to the cardinal moving norm is made-up of irregular fluctuations, it should be dissolved from those irregular fluctuations. To make so, we have rearranged the monthly indices in table 5 and computed the median of each month norm and hence, by spliting the consequence from the entire average values, we obtained the monthly seasonal index.

Table 5: Calculation of the Seasonal index ( 2006-2010 )

Calendar months

Ratios ( *100 )

Median

Seasonality index

January

120.32

121.69

122.52

127.71 122.11

121.64

February

93.13

96.16

99.24

99.75 97.7

97.33

March

105.66

108.76

113.5

114.26 111.13

110.70

April

86.18

93.24

94.8

95.84 94.02

93.67

May

86.65

87.44

90

92.67 88.72

88.38

June

64.77

68.87

69.05

69.86 68.96

68.70

July

98.79

99.43

102.01

105.02 100.72

100.33

August

86.71

91.95

93.45

96.78 92.7

92.35

September

81.66

82.96

85.49

86.58 84.23

83.91

October

105.3

108.57

109.17

111.38 108.87

108.45

November

99.84

100.19

100.9

106.2 100.55

100.17

December

131.07

133.75

136.08

139.26 134.92

134.40

Entire

1204.16

1200.03

Harmonizing to the computation, it can be observed that while December has the highest seasonal index ( 134 ) for the twelvemonth 2006 and 2010, June has the lowest value ( 69 ) . Based on the values in table 5, we have graph the seasonality index in order to analyze the seasonal fluctuation among the months

Figure 2: Seasonal indices for the Mauritius tourer reachings from 2006 to 2010 by the method of a ratio to traveling norms

From the above figure, it can be analysed that February, April May, June, August and September are the off-peak seasonal months, March July, October and December are the shoulder seasonal months and January and December are the peak seasonal months. Based on these consequences, it can be measure that the Mauritius touristry include six months to be off seasons, four months to be mid-season and two months to be peak season for the clip period of 2006 to 2010.

Decision

This chapter has analysing the job of seasonality that Mauritius touristry industry is confronting by following different utile methods to mensurate seasonality from the touristry literature. The four different methods used reveal that Mauritius touristry is extremely seasonal with the high figure of tourer reachings in the summer months. In add-on, the Gini-coefficient which has measured seasonality by nationalities concluded that tourer reachings from _________ are less seasonal as comparing tourer reachings from________ . However, the survey reveals that none of the four different methods are superior to another. The usage of traveling mean method with the seasonal indices may supply more precise analysis that may assist the hotel advertizers to calculate out the form of seasonality in the touristry industry. All these methods have their failings and strengths. Hence it would be appropriate to unite all these methods together so that the seasonal fluctuation in demand are analysed in a best manner spread outing the apprehension of the form and the type of seasonality in Mauritius.

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