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1 Inernaional Journal of Research and Review E-ISSN: ; P-ISSN: Original Research Aricle Forecasing Tourism Generaed Employmen in Sri Lanka: Mulivariae Time Series Konarasinghe Mudiyanselage Udaya Banda Konarasinghe Insiue of Mahemaics and Managemen, Ranala, Sri Lanka. ABSTRACT Tourism indusry in Sri Lanka is growing over he recen pas. As a resul, he employmen in he indusry also shows a rapid growh. Bu here were very few aemps in forecasing ourism generaed employmen in Sri Lanka. Hence, he objecive of he sudy was o forecas ourism generaed employmen in Sri Lanka. Annual employmen daa for he period of 1970 o 2015 were obained from he Sri Lanka Tourism Developmen Auhoriy (SLTDA). Karl Pearson s correlaion used o es he correlaion beween oal employmen and ouris arrivals. Simple Regression Model (SRM) and Auo Regressive Disribued Lag Model (ARDLM) esed for forecasing. The Anderson-Darling, Ljung-Box Q es, Auo-Correlaion Funcions (ACF) and Durbin-Wason (DW) es used for model validaion. Forecasing abiliy of he models was assessed by boh relaive and absolue measuremens of errors. The SRM was no successful, bu he ARDLM saisfied he validaion crierion. Boh relaive and absolue measuremens of ARDLM were very low. Hence, he ARDLM is suiable on forecasing ourism generaed employmen in Sri Lanka. The resuls of his sudy will be useful for planning and sraegy developmen o overcome he surplus, shorfall of employmen and workforce planning in boh public and he privae secor in he ourism indusry. Furher, he finding of he sudy can be used o assess he economic benefis o he hos communiy in various ourism areas in Sri Lanka. Keywords: Simple Regression Model, Auo Regressive Disribued Lag Model, Measuremens of Errors, Employmen INTRODUCTION The ourism indusry is one of he fas growing indusries in he global economy oday. I generaed 107,519,000 jobs in [1] Tourism indusry creaes jobs, accouning 1 in 11 worldwide. [1-2] Direc and indirec employmen opporuniies are major segmens of ourism. Direc employmen includes, employed by hoels, ravel agens, airlines and oher passenger ransporaion services, agencies providing recreaional faciliies, ouris guides, ouris shops and oher organizaions in he sae secor. Indirec employmen is businesses which sell goods and services o he ourism secor. Konarasinghe (2016) showed ha here is an increasing rend of ouris arrivals from all regions. [2] I could be an evidence of a growh of ourism indusry in Sri Lanka. The growh of indusry ensures he growh of ourism generaed employmen. [3] Figure 1 is he ime series plo of ourism generaed employmen in Sri Lanka. Inernaional Journal of Research & Review ( 61

2 Toal Employmen Year Figure 1: Time Series plo of oal employmen Figure 1 clearly shows a growh of ourism generaed employmen in Sri Lanka. Problem Saemen The growh of ourism indusry and he growh of employmen in he ourism indusry are in line. Therefore, i is imporan o selecing and raining individuals for specific job funcions and charging hem wih he associaed responsibiliies. The employmen forecasing is a process of esimaing he fuure numbers of employees required and he likely skills and compeencies needed. [4-6] Konarasinghe (2015) has shown he imporance of planning and forecasing employmen in he indusry, in order o overcome he surplus and shorfall of employmen. [7-1] However, fewer aemps were found in forecasing ourism generaed employmen in Sri Lanka. Hence, i is a imely requiremen of forecasing ourism generaed employmen of ourism indusry in Sri Lanka. Significance of he Sudy The indusry creaes millions of job opporuniies worldwide. Also, i provides soluions o unemploymen in Sri Lanka. The resuls of he sudy will be very helpful o develop sraegies relaed on saffing, and oher human resource managemen needs. In addiion, i is useful for decision making in boh macro and micro level in ourism indusry. Objecive of he Sudy To Forecasing ourism generaed employmen in Sri Lanka LITERATURE REVIEW The lieraure review of he sudy was focused on forecasing employmen on various indusries across he world. Chau (1970) used muliple regression models for forecasing civilian personal income and employmen of Hawaii. [8] The forecasing abiliy of he model is quie accurae in employmen forecasing. Auo Regressive Disribued Lag Models (ARDLM) approach used o forecas ourism generaed employmen in Denmark. [9] The measuremens of errors were saisfacorily small. ARDLM performed highly saisfacory in forecasing employmen growh in Missouri. [10] Log linear model used Paque, Sargen, and James (2006) o examine he pas and fuure behavior of he employmen rae of men and women in Canada. [11] Fied models are reliable for shor-erm forecasing, bu he same was no rue for in he long run. Sof compuing echniques namely; neural nework used o forecas regional employmen in boh he former Wes and Eas Germany. [12] The performance of forecasing is highly saisfacory. The neural nework performed well in employmen forecasing on hree disrics in Germany. [13] Logisic Regression did no performed well in forecas employmen demand in local governmen area in Norhern New Souh Wales, Ausralia. [14] Bayesian vecor auoregressive models used o forecas indusry employmen for a resource-based economy in a sae of Georgia. [15] The fied models perform well in long run. Vecor Auo- Regressive models used o forecas employmen growh in Sweden. [16] I was successful up o a cerain exen in shorerm forecasing. Linear and non linear rend models used in forecasing direc employmen rend in he ourism indusry in Sri Lanka. [7-2] The resuls of his sudy confirmed ha linear rend model is suiable for forecasing. Hybrid Trend- ARIMA is anoher univariae ime series approach was successful in forecasing ourism generaed employmen in Sri Lanka. [17] According o Inernaional Journal of Research & Review ( 62

3 he lieraure; researchers have esed saisical and sof compuing echniques as forecasing ools. Mos of hem have used mulivariae echniques, while some of hem used univariae echniques. The ARDLM and Regression models were he commonly esed models. Hybrid approach is anoher echnique for forecasing. Excep logisic regression all oher echniques were successful in forecasing. Some fied models are suiable for shor erm forecasing. METHODOLOGY Annual daa on ouris arrivals and oal employmen in he ourism indusry in Sri Lanka for he period of 1970 o 2015 was obained from annual saisical repors from 2008 o 2015, published by Sri Lanka Tourism Developmen Auhoriy (SLTDA). Karl Pearson s correlaion used o es he correlaion beween oal employmen and ouris arrivals. Time series plos used for paern idenificaion. Simple Regression Model (SRM) and Auo Regressive Disribued Lag Model (ARDLM) used for forecasing oal employmen. Anderson- Darling es used o es he normaliy of daa and residuals. The LBQ es, Auo- Correlaion Funcions (ACF) and Durbin- Wason (DW) es used o es he independence of residuals. The Augmened Dickey-Fuller (ADF) es used o es he saionary of he series. Forecasing abiliy of he models assessed by Mean Absolue Percenage Error (MAPE), Mean Absolue Deviaion (MAD) and Mean Square Error (MSE). RESULTS Ouliers are he exremely large or small values of a daa se. They replaced by moving average of order hree. The sudy adoped he echnique used by Konarasinghe, Abeynayake, and Gunarane (2016) and Konarasinghe (2016) for oulier adjusmen. [18,19] Daa analysis is organized as follows; 1. Descripive Saisics 2. Correlaion Analysis 3. Forecasing ourism generaed employmen. Descripive Saisics Graphical summary of descripive saisics is shown in Figure 2; A nderson-darling Normaliy Tes A -Squared 0.69 P-V alue Mean SDev V ariance Skew ness Kurosis N Minimum s Q uarile Median rd Q uarile Maximum % C onfidence Inerval for Mean % C onfidence Inerv al for Median % C onfidence Inerv al for SDev 95% Confidence Inervals Mean Median Figure 2: Graphical Summary Inernaional Journal of Research & Review ( 63

4 Auocorrelaion Auocorrelaion Minimum ourism generaed employmen of Sri Lanka were whereas maximum were during he period. The firs quarile of generaed employmen is I means a mos 25% of employmen opporuniies generaed by ourism indusry in Sri Lanka is during he period. Median employmen opporuniies were and he hird quarile of employmen is Hisogram of he employmen looks symmerical. P value of he Anderson- Darling es is greaer han he significance level (α=0.05 <0.066). As such number of ourism generaed employmen follows he Normal disribuion. Correlaion Analysis The correlaion analysis was done in wo ways; correlaion beween variables and correlaion wihin he series (Auo Correlaion). Table 1 show ha here is a srong posiive significan correlaion beween ouris arrivals and oal employmen in he ourism indusry in Sri Lanka. I means he increasing of ouris arrivals affec on increasing of ourism generaed employmen in Sri Lanka. Table 1: Correlaion Marix Employmen Arrivals Employmen Correlaion ** Sig. (2-ailed) Arrivals Correlaion ** 1 Sig. (2-ailed) **. Correlaion is significan a he 0.01 level (2-ailed) Forecasing Employmen Then he SRM (1) was esed; Y + X (1) Where; Y = Toal employmen X = Toal arrivals = Consan = Random Error The normaliy was esed for oal employmen (Y). The P value is I confirms he normaliy of Y variable. The residuals of he model were no normally disribued. Therefore, log ransformed daa used for he SRM. The esed model is as follows; lny + lnx (2) The ANOVA es revealed ha he model (2) is significan. The adjused R-Sq of he model is high (92.2%). The residuals of he model were normally disribued bu no independen. Therefore, model (2) is no suiable for forecasing. Then he saionary of he series; lny and lnx esed wih he help of ACF and ADF es. Figure 3 and 4 are he ACF s of ourism generaed employmen and ouris arrivals. Boh figures confirmed wo lags from each series were significan Lag Lag Figure 3: Auocorrelaion Funcion for Employmen (lny ) Figure 4: Auocorrelaion Funcion for Arrivals by Year (lnx ) The series were saionary, herefore, ARDLM (3) is esed, and he resuls are given in Table 2 and Table 3: lny + 1 lnx 2lnY-1 3lnY-2 4lnX -1 5lnX -2 (3) Inernaional Journal of Research & Review ( 64

5 Where; lny = Logarihm of oal employmen lnx = Logarihm of arrivals lny = Lag one of oal employmen lny -1-2 lnx -1 lnx -2 = Lag wo of oal employmen = Lag one of arrivals = Lag wo of arrivals = Consan = Random error Table 2: ANOVA able Source DF SS MS F P Regression Residual Error Toal Table 2, he P value of ANOVA = I clearly showed ha, a leas one regression coefficien is non zero. Table 3 is summary of regression coefficiens. According o he resuls; lnx and lny -1 are he significan lag variables. Table 3: Summary able for regression coefficiens Predicor Coef SE Coef T P Consan lnx lny lnx lny lnx Then he model was run only wih he significan lags and he resuls are given in Table 4 and Table 5: Table 4: ANOVA Source DF SS MS F P Regression Residual Error Toal The ANOVA confirmed he significance of he model. The hypoheses es for regression coefficiens confirmed ha lnx and lny -1 are significanly relaed o lny. lny + 1 lnx 2lnY-1 (4) Table 5: Summary able for regression coefficiens Predicor Coef SE Coef T P Consan lnx lny The summary of regression coefficiens confirmed, boh lnx and lny -1 is significan lag variables. Table 6 is summary of model fiing and verificaion: according o he Table 6, he R 2 (Adj) 98.3%. The Anderson- Darling es confirmed he residuals were normally disribued (P = 0.315).The Durbin-Wason saic D, ACF and LBQ es confirms ha residuals were no correlaed. Table 6: Model Summary Model Model Fiing Model Verificaion lny lnX R-Sq 98.4% R-Sq(adj) 98.3% 0.806lnY-1 MAPE MAPE MAD MAD MSE MSE Normaliy P =0.315 DW 1.53 MAPE of he model was 0.58% and 1.21% under he fiing and verificaion. MSE of he model was and MAD was and in he fiing and verificaion. Based on he significance, validaion, and verificaion crierion, model (5) is a suiable model for forecasing ourism generaed employmen in Sri Lanka: lny ln X 0.806lnY (5) 1 Figure 5 and Figure 6 is he ime series plo of acual vs. fis and acual vs. forecas of above model. Fis almos follow he paern of acual behavior. Boh are closer o each oher. The deviaion of acual and forecas is very less. Hence, he seleced model is suiable models for forecasing ourism generaed employmen in Sri Lanka. Inernaional Journal of Research & Review ( 65

6 lny lny 12.0 Variable Acual Fis 12.6 Variable Acual Forecas Year Year Figure 5: Acual Vs Fis Figure 6: Acual Vs Forecas DISCUSSION Konarasinghe [17] has concluded ha he Hybrid-rend ARIMA model was successful in forecasing ourism generaed employmen. The Hybrid rend ARIMA model is a Univariae ime series model. The presen sudy evidenced ha, he Mulivariae ime series models also serve for he purpose. As such; one can use eiher univariae approach or mulivariae approach, based on he purpose. For insance; if i is needed o forecas merely he head coun of employmen, hen he univariae echniques would do; bu if i necessary o see how he employmen depends on arrivals ec., hen he mulivariae echniques would do. The resuls of his sudy can be used for esimae of he number of employees required and, skill requiremens o mee objecives in ourism indusry. I will be useful o develop proacive sraegies more effecively and efficienly. To assess he needs of employees and oher adminisraive needs are anoher requiremens of forecasing. I will helps o avoid long-erm gaps in saffing needs by keeping on op of which of employees migh be reiring, leaving or asked o leave. I will be faciliae o creae or updae beer organizaion char. This model can be used o esimae employmen opporuniies for indusry level. And i will be a ligh house for expansion of he indusry and various produc developmens. The oucome of his model can help for proper saffing, including raining and recruimen of employees o address he defici or reducing saff when necessary for beer produciviy. Reduces HR coss is anoher benefi from forecasing employmen. I can be achieved by effecive recruimen plan (Inernal and exernal recruimen), apply he bes way of paymen sysems, work ou he employee needs wihou any wasage, work ou exra working hours ec. Increases differen ypes of organizaional flexibiliy are anoher significan achievemen from employmen forecasing. Mainly i ensures he funcional and numerical flexibiliy in employmen maers. Workou he requiremen of muli skilled workers, recruimen and downsize employees are he main aciviies under funcional and numerical flexibiliy. The resuls of forecasing ourism generaed employees will show he growh or decline of he ourism indusry. Therefore, i is useful for developing raining programs such as workshops, academic and professional courses relaed o hospialiy managemen. I will ensure he knowledge base indusry o maximize he benefis wihin he indusry and oher relaed business in ourism. CONCLUSION This sudy confirmed ha he increasing of ouris arrivals effec on increasing of ourism generaed employmen in Sri Lanka. The simple regression used for forecasing employmen from ouris arrivals. I was no successful due he nonrandom of residuals. Finally, he resuls of he sudy revealed ha ARDLM model is suiable for forecasing ourism generaed Inernaional Journal of Research & Review ( 66

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