Breusch-Pagan test of independence: chi2(45) = 100, Pr = 0.0000
Correlation matrix of residuals for Sweden and Norway:
\r\n\tThe book will present up to date knowledge on mentioned ADHD topics in order to be implemented in every day clinical practice.
",isbn:"978-1-83962-495-7",printIsbn:"978-1-83962-475-9",pdfIsbn:"978-1-83962-496-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"176f5275d9e1e06b24e0ae07b90c424f",bookSignature:"Prof. Hojka Gregoric Kumperscak",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9499.jpg",keywords:"Clinical Picture, Symptomatology, Symptoms, Clinical Presentation, Comorbidity, Pharmacotherapy, Nonpharmacological, Nutrition and Diet, Genetics, Neuroimaging, Neurotransmitters, Hormones",numberOfDownloads:493,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 10th 2020",dateEndSecondStepPublish:"August 6th 2020",dateEndThirdStepPublish:"October 5th 2020",dateEndFourthStepPublish:"December 24th 2020",dateEndFifthStepPublish:"February 22nd 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Prof. Kumperscak, MD, PhD graduated from the Faculty of Medicine in Ljubljana, Slovenia. She was trained in child and adolescent psychiatry in Slovenia and abroad. She has held the Chair of the Department of Psychiatry in the University of Maribor in Slovenia (2017) and has been Head of the Child and Adolescent Psychiatry Unit, University Clinical Center in Maribor (2008). She is a President of the Slovenian Association for Child and Adolescent Psychiatry and Adolescent Identity Treatment psychotherapist.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53417",title:"Prof.",name:"Hojka",middleName:null,surname:"Gregoric Kumperscak",slug:"hojka-gregoric-kumperscak",fullName:"Hojka Gregoric Kumperscak",profilePictureURL:"https://mts.intechopen.com/storage/users/53417/images/system/53417.jpg",biography:"Prof. Hojka Gregoric Kumperscak, MD, PhD was born in Maribor, Slovenia in 1970. She finished Faculty of Medicine in Ljubljana, Slovenia in 1996. She was trained in child and adolescent psychiatry in Slovenia and abroad (Italy, UK, Germany and Switzerland). \r\nShe has held the Chair of the Department of Psychiatry in the Faculty of Medicine, University of Maribor in Slovenia, since January 2017, and has been Head of the Child and Adolescent Psychiatry Unit, University Clinical Center in Maribor since 2008. She is a President of Slovenian Association for Child and Adolescent Psychiatry and Adolescent Identity Treatment psychotherapist. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"17704",title:"Using the SUR model of tourism demand for neighbouring regions in Sweden and Norway",doi:"10.5772/24403",slug:"using-the-sur-model-of-tourism-demand-for-neighbouring-regions-in-sweden-and-norway1",body:'\n\t\tThis chapter estimates the international demand for tourism in two neighbouring regions: the objective number 6 (SW:6) in Sweden and North Norway included – Tröndelag (NWT) in North Norway, from five different countries: Denmark, the United Kingdom, Switzerland, Japan, and the United States. For each visiting country, and for Sweden and Norway, we specify separate equations by including the relevant information. we then estimate these ten equations using Zellner’s Iterative Seemingly Unrelated Regressions (ISUR). The benefit of this model is that the ISUR estimators utilize the information within and the relation between the equations present in the error correlation of the cross regressions (or equations) and hence is more efficient than single equation estimation methods such as ordinary least squares. Monthly time series data from 1993:01 to 2006:12 are used. The results show that the consumer price index, some lagged dependent variables, and several monthly dummies (representing seasonal effects) have significant impacts on the number of visitors to the SW:6 region in Sweden and NWT region in Norway. We also find that, in at least some cases, relative prices and exchange rates have significant effects on international tourism demand.
\n\t\t\tTourism has important impacts on the economies of both developing and industrialized countries, resulting in job creation, additional income for the private and public sectors, foreign currency receipts, higher investment and growth. Indeed, tourism has acted as a catalyst to economic restructuring in many recipient countries, assisting a shift away from primary sector activities, towards greater reliance on services and manufacturing. Given the scale of tourism’s contribution to the macroeconomic dimension over time, knowledge concerning the nature of the demand upon which it is based is of both theoretical and practical relevance. It is well known that tourism demand is responsive to such variables as income, relative prices and exchange rates. What is not known is how the responsiveness of demand to changes in these variables alters during a country’s economic transition and integration into the wider world initial or subsequent years? Does the sensitivity of tourism demand to changes in its own prices, or those of its competitors, change between different periods? Further questions concern the degrees of complementarity or substitutability between tourism destinations and the extent to which these change during periods of economic transition. Complementarity occurs if holidays in different destinations are purchased as a package. Alternatively, there may be an intense degree of competition between destinations. Relationships of complementarity or substitutability may change over time as lower income destinations emerge from relative poverty to achieve a higher level of development. Little information is available about this issue. It is not known, for example, whether lower income destinations tend to become more or less competitive over time, either relative to other developing countries or relative to more industrialized nations.
\n\t\t\tDifferent models have been used to estimate tourism demand and some types of model are more appropriate for examining the above questions than others. The vast majority of studies of tourism demand have relied on single equation models of demand, estimated within a static context (for example, Uysal and Crompton, 1984; Gunadhi and Boey,1986). These models are not derived from consumer demand theory and fail to quantify the changes in demand behaviour that occur over time. Innovations in the methodology were subsequently introduced in the form of single equation models of demand estimated using an error correction methodology were subsequently introduced in single equation model of demand estimated using an error correction methodology (Syriopoulos, 1995). Kulendran (1996) used a general to specific, error correction model to estimate the Australia demand for tourism in the form of visits per capita to outbound destinations and demonstrated that the model has good forecasting ability. This modelling approach has the advantage of explicit treatment of the time dimension of tourism demand behaviour and allows for improved econometric estimation of the specified equations.
\n\t\t\tA More recently approach to tourism demand estimation involves a system -wide approach by using the ISUR Model (Salman et al. 2010). This system of study is particularly useful for testing the properties of homogeneity and symmetry which are basic to consumer demand theory. Hence, it provides a stronger theoretical basic for estimating the cross- price elasticities of demand than the single equation approach.
\n\t\t\tThis chapter was used system wise approach by the ISUR Model to examine tourism demand by the UK, Switzerland, Denmark, Japan and The USA in the neighboring destination number 6 (SW:6) and (NWT) in Norway. The UK, Switzerland, Denmark, Japan and The USA are major origin countries for tourism in the destinations under consideration. SW:6 in Sweden and NWT in Norway are key destinations, accounting for over one-third of all receipts from tourism in the European Union in 2005. The absolute value of their receipts from tourism is very high, at over $ 200 million in 2005. The choice of the countries as destinations for analysis is also appropriate owing to their position as geographic neighbours. Complementarity or substitutability in tourism demand, as indicated by the signs of the relative-price elasticities of demand, is of particular relevance in this context. This chapter pays attention to this issue, which has not previously been examined for the case of neighbouring countries using the ISUR approach. Sweden and Norway are interesting cases for consideration owing to their position as economies in transition during the period under consideration. By the early 1990s, the start years of the period under study, they had adopted new development policies, high dependence on industry, moving towards increased globalization, economic integration, and foreign competition. Sweden had joined the ranks of the more developed European economies in the period under study. Hence, an innovative feature of this chapter is its examination of the evolution of tourism demand during these countries’ transition to a new technological system and globalization status. It also permits examination of the extent to which the behaviour of demand become more or less similar over time with respect to changes in prices and exchange rates. Thus, this study provides useful information, at the cross-country level, about change in a major activity within each of the economies.
\n\t\t\tThe aim of this chapter is to estimate international tourism demand to the two neighbouring regions: objective number 6 (SW:6) in Sweden and North Norway included – Tröndelag
\n\t\t\tSwedish and Norwegian Maps; The Objective 6 region (SW: 6) in Sweden is the lightly shadowed area at the top and top-left of the map of Sweden. The North –Norway included and Tröndelag region in Norway (NWT) is the lightly shadowed part on the top right of the map of Norway.
(NWT) by five countries: namely, Denmark, the United Kingdom (UK), Switzerland, Japan, and the United States (US). Since these two regions are geographically very close to each others and highly competitive (although they are located in two different countries) we believe that they can be considered as one larger region with respect to the very similar nature and tourism facilities they supply. For this reason we study the demand for these two regions simultaneously together by specifying a separate equation for these regions from the respective visiting countries. We then merge the ten equations in one system of regression equations model that will be estimated by Zellner’s ISUR model.
\n\t\t\tPrevious Scandinavian studies did not study and compare the tourism demand for Sweden and Norway in one overall regression model. Further, previous studies of Norwegian tourism demand have not considered the relative price and substitution effect or complementarity, the real and nominal exchange rate, and personal income. With this method of estimation, I can come closer to studying the impact of the influences of cultural values, climate natural attraction on the tourism demand. This has earlier been difficult to quantify.
\n\t\t\tThe purpose of this chapter is to use Iterative Seemingly Unrelated Regressions (ISUR) to estimate the relationship between monthly tourist arrivals to Sweden and Norway from Denmark, the UK, Switzerland, Japan, and the US and the factors that influence arrivals. To this end, we use a demand function approach to tourism flow modelling. A large model consisting of ten equations for Sweden and Norway (five for each country) was estimated by the ISUR technique.
\n\t\t\tThe idea of merging the equations from both countries into one overall model, in fact, is necessary to measure the tourism demand to neighbouring regions that are close in their geographical characters, see Figure 1. In other words, tourists can receive the same utility from either region. An important question is “Are there differences between the hedonic regressions for the two neighbourhoods, or not?” If there are no differences, then the data from the two neighbourhoods can be pooled into one sample without parameter restrictions and with no allowance made for differing slope or intercept. Moreover, the estimation has been done by using the ISUR regression.
\n\t\t\tThere is no previous application of this technique to tourism demand modelling for these two regions. With the ISUR technique, we estimate the entire system of equations by taking into account any possible correlations between the residuals from the different equations. Moreover, the ISUR technique provides parameter estimates that converge to unique maximum likelihood parameter estimates.
\n\t\t\tThe remainder of the chapter is organized as follows. Section 2 discusses tourism demand for Nordic countries and the data used. Section 3 presents the estimation and testing methodology. Section 4 provides the results. This chapter has a brief summary and conclusion in Section 5.
\n\t\tThe objective of this section is to analyze how the following macroeconomic and microeconomic variables and seasonal (monthly) conditions influence the demand for tourism for the (SW:6) and (NWT):
\n\t\t\tThe Swedish Consumer Price Index (CPI) represents the inflation rate and cost of living in Sweden and is in natural logarithms. The CPI has several advantages for this purpose: it is familiar to the public and is the most widely used measure of inflation in Sweden (Andersson and Berg, 1995). We adjust the CPI for any changes in indirect taxes and subsidies.
I use dummy variables for January to November to proxy for seasonal effects (December is the base category).
The exchange rate (EX) between the Swedish/Norwegian currencies and the visitors’ country of origin currency are included in natural logarithms.
The relative price (Pr) reflects opportunity cost. This represents the cost of living in relative terms for Norway and Sweden and a substitute price for an origin country tourist. These are also in natural logarithms.
The SW:6 is a major tourist destination worldwide, with the yearly demand for tourism in this part of Sweden and NWT consistently following an upward trend. However, interruption to these trends has taken place on a number of occasions due to economic conditions and/or international events. For example, September 11 and the first Gulf War had a detrimental effect on tourism demand in both Sweden and Norway.
\n\t\t\tA common model used in tourism demand studies is a single equation with demand explained by the tourist’s income in their country of origin, the cost of tourism in their chosen and alternative destinations, and a substitute price (Witt and Martin, 1987). To start with, the demand for tourism can be expressed in a variety of ways. The most appropriate variable to represent demand explained by economic factors is consumer expenditure or receipts (Grouch, 1992). Other measures of demand are the nights spent by the tourist or their length of stay. However, due to the lack of data on monthly GDP, personal income (GDP/Population) is not included in this analysis.
\n\t\t\tThe tourism price index (the price of the holiday) is also an important determinant of the decision a potential tourist makes. We can divide this into two components:
\n\t\t\tthe cost of living for the tourist at the destination, and
the cost of travel or transport to the destination.
I divide the cost of living into two components:
\n\t\t\tthe CPI in relative price form assuming that tourists have the option of spending their vacation in either SW:6 or NWT, and
tourist consumer expenditure, real consumer expenditure, real income, and per capita income (Salman, 2003).
In this chapter, CPI represents the cost of living. However, we measure transport costs by the weighted mean prices according to the transport mode used by tourists to reach the destination. Changes in travel costs, particularly airfares, can have a major impact on tourism demand. Unfortunately, data on economy class airfares between Stockholm and the capital cities of the countries of origin were not consistently available, so I could not use these in construction of the variables. Moreover, one should also take into account the small proportion of tourists who arrive in Sweden using charter flights destined for regional airports closer to the main tourist resorts, as the airfares for these may differ considerably from those to the capital city’s airport. Therefore, in the absence of a suitable proxy, I exclude travel costs from our demand system (Lathiras and Siriopoulos, 1998).
\n\t\t\tIn previous Scandinavian tourism demand studies, the cost of living component was defined in relative price form, assuming that the tourists have the option of spending their vacation in Sweden or at home. The probability of travel to the destination declines if the destination price level increased faster than that of the origin price due to a substitution effect, and also if the if the reserves occurred and hence the tourist’s real income decreased to the substitution effect.
\n\t\t\tIn this study, the cost of living (relative price) component is defined as the Norwegian price relative to the price of a holiday in Sweden. The underlying assumption is that for the tourists, SW: -6 in Sweden is a substitute long-haul holiday destination for NWT in Norway. In recent years, SW:6 in Sweden and NWT in Norway have been competing with each other to attractive more tourists. The tourists from these five countries have the option of spending vacations on SW:6 in Sweden or NWT in Norway, both having similar mountains and skiing facilities in winter and similar climate. Furthermore, for visitors from these five countries mentioned above, the travel distances to SW:- 6 in Sweden and NWT in Norway are almost the same. Therefore, for potential visitors, SW:6 is consider a substitute long-haul holiday destination for the NWT and the cost of living variable for the tourism demand model is defined as the cost of living in SW:6 relative to that for NWT in Norway.
\n\t\t\tFollowing previous research, we can specify the price of tourism at the destination in a variety of ways. For instance, we can represent prices in either absolute or relative terms. In this chapter, we employ the relative price as an opportunity cost. We define this as the ratio of the CPI of the host country (CPI\n\t\t\t\t\n\t\t\t\t\tSW\n\t\t\t\t) to the country of origin adjusted by the relative exchange rate (R\n\t\t\t\t\n\t\t\t\t\tit\n\t\t\t\t) to obtain a proxy for the real cost of living (Salman, 2004). Therefore, the real cost of tourism in Sweden and Norway are the relative CPIs given by:
\n\t\t\twhere i is the host country (Sweden or Norway), j is the visiting (or foreign) country, and t is time. Rp\n\t\t\t\t\n\t\t\t\t\tit\n\t\t\t\t is the relative CPI for country i in time t, CPI\n\t\t\t\t\n\t\t\t\t\tit\n\t\t\t\t is the CPI for Sweden or Norway, CPI\n\t\t\t\t\n\t\t\t\t\tjt\n\t\t\t\t is the CPI for the foreign country, and EX\n\t\t\t\t\n\t\t\t\t\tijt\n\t\t\t\t is the exchange rate between the Swedish krona/Norwegian krone and the foreign currency.
\n\t\t\tIn addition to the price variable, the exchange rate is a relevant factor in determining tourism demand. The rationale behind incorporation of the exchange rate as a separate explanatory variable is that tourists may be more aware of the relative exchange rate than the specific cost of tourism at the destination. A question that arises is whether the exchange rate should be included in our model system as an explanatory variable together with the price variable. In an attempt to find a variable to represent a tourist’s cost of living, Salman, Shukur, and Bergmann-Winberg (2007) concluded that the CPI (either alone or with the exchange rate) is a reasonable proxy of the cost of tourism. And also the exchange rate and price are used in this study as proxy variables for the structural transformation in these two small open economies and the expansion in international competitions faced by them. We define the exchange rate variable as the foreign exchange rate of the Swedish Krona or Norwegian Krona to the currency of the origin country. This variable represents the relationship between tourism demand and the international money market and international economic events (including recessions and financial crises) as well.
\n\t\t\tAs microeconomic theory suggests, the price of other goods influences the demand for a particular good. In the case of tourism, the identification and separation of substitute products is very difficult to achieve on an a priori basis. In our case, tourists consider NWT region (in the North of Norway) an alternative destination to the SW:6 region (in the north of Sweden). These destinations are among the most popular destinations in Scandinavia, at least in terms of arrivals, for tourists from the origin countries under:
\n\t\t\tRelative price of tourism for Denmark
\n\t\t\tRelative price of tourism for the UK
\n\t\t\tRelative price of tourism for Switzerland
\n\t\t\tRelative price of tourism for Japan
\n\t\t\tRelative price of tourism for the US
\n\t\t\tWhere:
\n\t\t\t\n\t\t\t\tCPI\n\t\t\t\t\n\t\t\t\t\tSwe\n\t\t\t\t: CPI in Sweden (1998 = 100).
\n\t\t\t\n\t\t\t\tCPINor: CPI in Norway (1998 = 100).
\n\t\t\t\n\t\t\t\tEX\n\t\t\t\t\n\t\t\t\t\tSEK/DKK\n\t\t\t\t: An index of the Swedish krona per unit of Danish krona (1998 = 100).
\n\t\t\t\n\t\t\t\tEX\n\t\t\t\t\n\t\t\t\t\tSEK/GBP\n\t\t\t\t: An index of the Swedish krona per unit of British pound (1998 = 100).
\n\t\t\t\n\t\t\t\tEX\n\t\t\t\t\n\t\t\t\t\tSEK/CHF\n\t\t\t\t: An index of the Swedish krona per unit of Swiss franc (1998 = 100).
\n\t\t\t\n\t\t\t\tEX\n\t\t\t\t\n\t\t\t\t\tSEK/JPY\n\t\t\t\t: An index of the Swedish krona per unit of Japanese yen (1998 = 100).
\n\t\t\t\n\t\t\t\tEX\n\t\t\t\t\n\t\t\t\t\tSEK/USD\n\t\t\t\t: An index of the Swedish krona per unit of US dollar (1998 = 100).
\n\t\t\tA lagged dependent variable may also be included to account for habit persistence and supply constraints. As for the signs of the explanatory variables, we expect a negative sign for the relative price variable and a positive sign for the exchange rate variable. In this study, monthly dummies represent seasonal effects on the number of arrivals from the origin countries. All variables are in natural logarithms, and the data are in index form (1998 = 100). All economic data employed in this study are from Statistics Sweden (Statistiska Centralbyrån) and Statistics Norway (Statistisk SENTRALBYRÅ). Estimation is with the STATA Ver. 10 and EViews Ver. 5.1 statistical program packages. We examine monthly time series data from 1993:01 to 2006:12.
\n\t\tIn the common stochastic specification of econometric models, the error terms are assumed to be normally distributed with mean zero, constant variance and serially uncorrelated. These assumptions must be tested and verified before one can have any confidence in the estimation results or conduct any specification tests, including standard t-tests of parameter significance or tests of theoretical restrictions. Because misspecification testing is a vast area of statistical/econometric methodology, there will only be a brief description of the methods used in this study (in the Appendix) with additional details in the cited references.
\n\t\t\t\tThe methodology used in this chapter for misspecification testing follows Godfrey (1988) and Shukur (2002). To test for autocorrelation, we apply the F-version of the Breusch (1978) and Godfrey (1978) test. We use White (1980) test (including cross products of the explanatory variables) to test for heteroscedasticity and Ramsey’s (1969) RESET test to test for functional misspecification (Ramsey, 1969). We also apply the Engle (1980) Lagrange Multiplier (LM) test for the possible presence of Autoregressive Conditional Heteroscedasticity (ARCH) in the residuals. Finally, we apply the Jarque–Bera (1987) LM test of non-normality to the residuals in model (4).\n\t\t\t\t
\n\t\t\t\tWhen building an econometric model, the assumption of parameter consistency is widely used because of the resulting simplicity in estimation and ease of interpretation. However, in situations where a structural change may have occurred in the generation of the observations, this assumption is obviously inappropriate. Particularly in the field of econometrics where data are not generated under controlled conditions, the problem of ascertaining whether the underlying parameter structure is constant is of paramount interest. However, to test for the stability of the parameters in the models, and in the absence of any prior information regarding possible structural changes, we conduct a cumulative sum (CUSUM) test following Brown et al. (1975). The CUSUM test is in the form of a graph and is based on the cumulative sum of the recursive residuals. Movement in these recursive residuals outside the critical lines is suggestive of coefficient instability.
\n\t\t\tIn this chapter, we aim to estimate the number of visitors to Sweden and Norway from five countries (Denmark, the UK, Switzerland, Japan, and the US). For each visiting country and for both Sweden and Norway, we specify a separate equation with the relevant information included in each equation. For this purpose, we follow a simple strategy on how to select an appropriate model by successively examining the adequacy of a properly chosen sequence of models for each country separately using diagnostic tests with known good properties. The methodology used for misspecification testing in this chapter follows Godfrey (1988) and Shukur (2002). We apply their line of reasoning to the problem of autocorrelation, and then extend it to other forms of misspecification. If we subject a model to several specification tests, one or more of the test statistics may be so large (or the p-values so small) that the model is clearly unsatisfactory. At that point, one has either to modify the model or search for an entirely new model.
\n\t\t\t\tOur aim is to find a well-behaved model that satisfies the underlying statistical assumptions, which at the same time agrees with aspects of economic theory. Given these equations, we estimate the whole system (consisting of ten equations) using Zellner’s ISUR. The ISUR technique provides parameter estimates that converge to unique maximum likelihood parameter estimates. Note that conventional seemingly unrelated regressions (SUR) does not have this property if the numbers of variables differ between the equations, even though it is one of the most successful and efficient methods for estimating SUR. The resulting model has stimulated countless theoretical and empirical results in econometrics and other areas (see Zellner, 1962; Srivastava and Giles, 1987; Chib and Greenberg, 1995). The benefit of this model for us is that the ISUR estimators utilize the information present in the cross regression (or equations) error correlation and hence it is more efficient than other estimation methods such as ordinary least squares (OLS).
\n\t\t\t\tConsider a general system of m stochastic equations given by:
\n\t\t\t\twhere Yi is a \n\t\t\t\t\t\t
and then combined into a larger model written as:
\n\t\t\t\tThis model can be rewritten compactly as:
\n\t\t\t\twhere Y and e are of dimension (TM\n\t\t\t\t\t\n\t\t\t\t\t\t
At this stage, I make the following assumptions:
\n\t\t\t\tXi is fixed with rank n\n\t\t\t\t\t\t\t\ti\n\t\t\t\t\t\t\t.
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t
addition, I assume that \n\t\t\t\t\t\t\t\t
The above expression can be written as:
\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t
definite symmetric matrix and \n\t\t\t\t\t\t
The OLS estimator of B in (9) is:
\n\t\t\t\twith the variance
\n\t\t\t\tThe SUR Generalized Least Squares (GLS) estimator of B is given by:
\n\t\t\t\tand the variance is given by:
\n\t\t\t\tHowever, the system of the five equations for Sweden and Norway are as follows:
\n\t\t\t\twhere Yit is a \n\t\t\t\t\t\t
The dependent variables Yi are the natural logarithms of the number of monthly visitors from Denmark, the UK, Switzerland, Japan, and the US to either Sweden or Norway. The matrix Xi is the natural logarithm of three vectors that contains monthly information about the CPI in Sweden (or Norway), the exchange rate (Ex) in Sweden (or Norway), and relative price (Rp) for Sweden (or Norway) with respect to each of the abovementioned countries.
\n\t\t\t\tAnother objective of this study is to test for the existence of any contemporaneous correlation between the equations. If such correlation exists and is statistically significant, then least squares applied separately to each equation are not efficient and there is need to employ another estimation method that is more efficient.
\n\t\t\t\tThe SUR estimators utilize the information present in the cross regression (or equations) error correlation. In this chapter, we estimated the model in Equation (10) by using the OLS method for each equation separately to achieve the best specification of each equation. We then estimate the whole system using ISUR, see Tables 2 and 3. The ISUR technique provides parameter estimates that converge to unique maximum likelihood parameter estimates and take into account any possible contemporaneous correlation between the equations.
\n\t\t\t\tTo test whether the estimated correlation between these equations is statistically significant, we apply Breusch and Pagan’s (1980) LM statistic. If we denote the covariances between the different equations as 12, 13…45, the null hypothesis is:
\n\t\t\t\tH0: 12 = 13... = 45 = 0, against the alternative hypothesis,
\n\t\t\t\tH1: at least one covariance is nonzero.
\n\t\t\t\tIn our three equations, the test statistic is:
\n\t\t\t\t= N(r2\n\t\t\t\t\t12 + r2\n\t\t\t\t\t13 +…r2\n\t\t\t\t\t45), where r2\n\t\t\t\t\t\n\t\t\t\t\t\tij\n\t\t\t\t\t is the squared correlation,
\n\t\t\t\tr2\n\t\t\t\t\t\n\t\t\t\t\t\tij\n\t\t\t\t\t = 2\n\t\t\t\t\t\n\t\t\t\t\t\tij\n\t\t\t\t\t / \n\t\t\t\t\t\tii\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tjj\n\t\t\t\t\t.
\n\t\t\t\tUnder H0, has an asymptotic 2 distribution with five degrees of freedom. I may reject H0 for a value of greater than the critical value from a 2\n\t\t\t\t\t(45) distribution (i.e. with 45 degrees of freedom) for a specified significance level. In this study, the calculated 2 value for Sweden and Norway together is equal to 100 (p-value = 0.000). This result, reported in Table 1 below for Sweden and Norway together, suggests a rejection of H0 at any conventional significance level. This implies that the residuals from each ISUR regressions are significantly positively
\n\t\t\t\tor negatively correlated with each other that might stand for the relation between these equations and the countries thereafter. Also this can show substitutability and complementarity among destinations by indicating positive and negative correlation of residuals. Clear conclusions about the complementarily or substitutability among destinations are not usually obtained in studies using the correlation matrix of residuals and SUR model.
\n\t\t\t\t\n\t\t\t\t\t\t\t | Dn/s | \n\t\t\t\t\t\t\tUK/s | \n\t\t\t\t\t\t\tSwit/s | \n\t\t\t\t\t\t\tJp/s | \n\t\t\t\t\t\t\tUS/s | \n\t\t\t\t\t\t\tDn/n | \n\t\t\t\t\t\t\tUK/n | \n\t\t\t\t\t\t\tSwt/n | \n\t\t\t\t\t\t\tJP/n | \n\t\t\t\t\t\t\tUS/n | \n\t\t\t\t\t\t
Dn/s | \n\t\t\t\t\t\t\t1.0000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
UK/s | \n\t\t\t\t\t\t\t0.0269 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
SWIT/s | \n\t\t\t\t\t\t\t0.2497 | \n\t\t\t\t\t\t\t-0.1475 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
JP/s | \n\t\t\t\t\t\t\t-0.0284 | \n\t\t\t\t\t\t\t0.1550 | \n\t\t\t\t\t\t\t0.0094 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
US/s | \n\t\t\t\t\t\t\t-0.1465 | \n\t\t\t\t\t\t\t-0.0940 | \n\t\t\t\t\t\t\t0.1320 | \n\t\t\t\t\t\t\t0.0389 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
DN/n | \n\t\t\t\t\t\t\t-0.0909 | \n\t\t\t\t\t\t\t0.0829 | \n\t\t\t\t\t\t\t-0.0716 | \n\t\t\t\t\t\t\t-0.0579 | \n\t\t\t\t\t\t\t0.0650 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
UK/n | \n\t\t\t\t\t\t\t0.0335 | \n\t\t\t\t\t\t\t-0.1719 | \n\t\t\t\t\t\t\t0.1384 | \n\t\t\t\t\t\t\t-0.0985 | \n\t\t\t\t\t\t\t-0.0632 | \n\t\t\t\t\t\t\t-0.0216 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
SWIT/n | \n\t\t\t\t\t\t\t0.0876 | \n\t\t\t\t\t\t\t0.0534 | \n\t\t\t\t\t\t\t0.0999 | \n\t\t\t\t\t\t\t-0.0606 | \n\t\t\t\t\t\t\t0.0350 | \n\t\t\t\t\t\t\t0.0231 | \n\t\t\t\t\t\t\t0.1036 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
JP/n | \n\t\t\t\t\t\t\t0.0195 | \n\t\t\t\t\t\t\t-0.0235 | \n\t\t\t\t\t\t\t0.0236 | \n\t\t\t\t\t\t\t-0.0828 | \n\t\t\t\t\t\t\t0.2230 | \n\t\t\t\t\t\t\t0.1444 | \n\t\t\t\t\t\t\t-0.0414 | \n\t\t\t\t\t\t\t0.1493 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t |
US/n | \n\t\t\t\t\t\t\t-0.1943 | \n\t\t\t\t\t\t\t-0.1549 | \n\t\t\t\t\t\t\t0.1279 | \n\t\t\t\t\t\t\t-0.1170 | \n\t\t\t\t\t\t\t-0.0330 | \n\t\t\t\t\t\t\t-0.0081 | \n\t\t\t\t\t\t\t0.3869 | \n\t\t\t\t\t\t\t-0.0895 | \n\t\t\t\t\t\t\t-0.0010 | \n\t\t\t\t\t\t\t1.000 | \n\t\t\t\t\t\t
Breusch-Pagan test of independence: chi2(45) = 100, Pr = 0.0000
Correlation matrix of residuals for Sweden and Norway:
In this section, we present the most important results from the ISUR method to model international tourism demand to SW:6 in Sweden and NWT in Norway.
\n\t\t\tWe first conduct single equations estimation on model (10) for the five equations for Sweden and the five equations for Norway, separately. We specify these equations according to a battery of diagnostic tests (see the Appendix). We then select the five most appropriate equations for Sweden and Norway and include them separately in ISUR estimation consisting of ten equations to achieve the best possible efficiency. We then discuss the results for each country separately and also compare them together. We first present the results for the three economic variables and then discuss the results for the seasonal dummy variables (with December as the base month), followed by the lagged dependent variables. Note that the macro variables are in logarithmic form and so we can interpret the estimated parameters as elasticities. The estimated coefficients are included even if they are not significant. For the dummy and lagged dependent variables, only coefficients significant at least at the 10% level in the single equation estimation are included in the ISUR estimation.
\n\t\t\tconsisting of ten equations to achieve the best possible efficiency. We then discuss the results for each country separately and also compare them together. We first present the results for the three economic variables and then discuss the results for the seasonal dummy variables (with December as the base month), followed by the lagged dependent variables. Note that the macro variables are in logarithmic form and so we can interpret the estimated parameters as elasticities. The estimated coefficients are included even if they are not significant. For the dummy and lagged dependent variables, only coefficients significant at least at the 10% level in the single equation estimation are included in the ISUR estimation.
\n\t\t\t\n\t\t\t\t\t\t\tSweden\n\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Equations | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t |
Parameters | \n\t\t\t\t\t\tDenmark | \n\t\t\t\t\t\tUK | \n\t\t\t\t\t\tSwitzerland | \n\t\t\t\t\t\tJapan | \n\t\t\t\t\t\tUS | \n\t\t\t\t\t
Constant | \n\t\t\t\t\t\t.0662(0.974) | \n\t\t\t\t\t\t–5.732 (0.103) | \n\t\t\t\t\t\t.879 (0.850) | \n\t\t\t\t\t\t16.210 (0.000) | \n\t\t\t\t\t\t1.288 (0.761) | \n\t\t\t\t\t
CPI | \n\t\t\t\t\t\t–. 197 (0.0.024) | \n\t\t\t\t\t\t2.474 (0.047) | \n\t\t\t\t\t\t2.474 (0.070) | \n\t\t\t\t\t\t–6.205 (0.000) | \n\t\t\t\t\t\t-.1348 (0.688) | \n\t\t\t\t\t
EX | \n\t\t\t\t\t\t1.821 (0.285) | \n\t\t\t\t\t\t0.782 (0.462) | \n\t\t\t\t\t\t–1.564 (0.013) | \n\t\t\t\t\t\t0.1923 (0.456) | \n\t\t\t\t\t\t0.140 (0.224) | \n\t\t\t\t\t
Rp | \n\t\t\t\t\t\t-.167( 0.000 ) | \n\t\t\t\t\t\t0.458 (0.000) | \n\t\t\t\t\t\t-2.250 (.000) | \n\t\t\t\t\t\t-6.205 ( 0.004) | \n\t\t\t\t\t\t-.135 ( 1.583) | \n\t\t\t\t\t
D1 | \n\t\t\t\t\t\t.605 (0.000) | \n\t\t\t\t\t\t-.367 (.000) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t |
D2 | \n\t\t\t\t\t\t.638 ( 0.000) | \n\t\t\t\t\t\t-.256 (.000) | \n\t\t\t\t\t\t.1651 (.024) | \n\t\t\t\t\t\t.1651 (.002 ) | \n\t\t\t\t\t\t\n\t\t\t\t\t |
D3 | \n\t\t\t\t\t\t.271 ( 0.069) | \n\t\t\t\t\t\t-.4119 (.000) | \n\t\t\t\t\t\t.0887 (.000) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | .132 (.007) | \n\t\t\t\t\t
D4 | \n\t\t\t\t\t\t-.397 ( 0.002) | \n\t\t\t\t\t\t-.432 (.000) | \n\t\t\t\t\t\t-.195 (.000) | \n\t\t\t\t\t\t.165 (.107) | \n\t\t\t\t\t\t-.235 (.000) | \n\t\t\t\t\t
D5 | \n\t\t\t\t\t\t-1.04 ( 0.000) | \n\t\t\t\t\t\t-.662 (.000) | \n\t\t\t\t\t\t-.133 (.000) | \n\t\t\t\t\t\t-.129 (.001) | \n\t\t\t\t\t\t-.109 (.000) | \n\t\t\t\t\t
D6 | \n\t\t\t\t\t\t-.230 (0 .000) | \n\t\t\t\t\t\t-.063 (.231) | \n\t\t\t\t\t\t.493 (.000) | \n\t\t\t\t\t\t.106 (.023) | \n\t\t\t\t\t\t.343 (.000) | \n\t\t\t\t\t
D7 | \n\t\t\t\t\t\t.323 (0 .000) | \n\t\t\t\t\t\t-.285 (.0000) | \n\t\t\t\t\t\t.813 (.000) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t |
D8 | \n\t\t\t\t\t\t-.373 ( 0.002) | \n\t\t\t\t\t\t-.385 (.000) | \n\t\t\t\t\t\t.479 (.000) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t |
D9 | \n\t\t\t\t\t\t-.788 (0 .000) | \n\t\t\t\t\t\t-.645 (.000) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | -.246 (.000) | \n\t\t\t\t\t
D10 | \n\t\t\t\t\t\t-.823 ( 0.000) | \n\t\t\t\t\t\t-.486 ( .000) | \n\t\t\t\t\t\t-.343 (.000) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | -.251 (.012) | \n\t\t\t\t\t
D11 | \n\t\t\t\t\t\t-.894 (0 .000) | \n\t\t\t\t\t\t-.294 (.000) | \n\t\t\t\t\t\t.314 (.000) | \n\t\t\t\t\t\t275 (.083) | \n\t\t\t\t\t\t-.137 (.000) | \n\t\t\t\t\t
Y(t–1) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | .6056 (.000) | \n\t\t\t\t\t\t0.095 (0.082) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t |
Y(t–2) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t |
Y(t–3) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t |
Y(t–4) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | -.169 (.001) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t |
Y(t–11) | \n\t\t\t\t\t\t.136 (.061) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | .116 (.032) | \n\t\t\t\t\t\t.208 (.000) | \n\t\t\t\t\t\t.551 (.001) | \n\t\t\t\t\t
Y(t–12) | \n\t\t\t\t\t\t.0411 (.552) | \n\t\t\t\t\t\t.275 (.000) | \n\t\t\t\t\t\t.142 (.010) | \n\t\t\t\t\t\t.594 (.000) | \n\t\t\t\t\t\t.215 (.063) | \n\t\t\t\t\t
R2 | \n\t\t\t\t\t\t0.941 | \n\t\t\t\t\t\t0.900 | \n\t\t\t\t\t\t0.824 | \n\t\t\t\t\t\t0.845 | \n\t\t\t\t\t\t0.742 | \n\t\t\t\t\t
The non significant results erased from the Table.
ISUR estimation results for Sweden
\n\t\t\t\t\tTable 1 show that the CPI parameter for Denmark is negative and small in magnitude but not statistically significant, indicating Swedish CPI has no effect on the demand for tourism by Denmark. This could be due to low travel costs, whereas countries of origin that are more distant generally have higher price elasticity. The estimated CPISW elasticity is –6.205 and greater than that for Japan. This indicates that a 1% increase in CPISW results in a 6.2% decrease in tourist arrivals to SW6 from Japan. The low CPISW elasticity (-0.13) for the US could be a reflection of the depreciation of the Swedish Krona against the US dollar.
\n\t\t\t\tThe estimated elasticity of the relative (substitute) price ranges from 6.2% to 0.13% and is greater than one for Japan and Switzerland. This indicates that a 1% rise in the relative price level (price of tourism in Sweden relative to Norway) causes more than 1% fall in tourist arrivals from Japan and Switzerland. These estimates indicate that tourist arrivals in Sweden from these countries are elastic with respect to the relative price variable. This implies that Sweden must maintain its international price competitiveness to maintain high growth in tourist inflow. The estimated relative price level elasticity ranges from 0.2% to 0.8% and is less than one for Denmark and the US. These suggest that a 1% increase in the relative price results in a 0.2% and 0.1% decrease in tourist arrivals to SW6 from Denmark and the US, respectively. The low exchange rate elasticity for Japan, UK and the US may also be a reflection of the depreciation of the Swedish krona against the Japanese yen and US dollar. As expected, the estimated elasticities of CPI\n\t\t\t\t\t\tSW\n\t\t\t\t\t for the UK and Switzerland are positive.
\n\t\t\t\tParameters | \n\t\t\t\t\t\t\tDenmark | \n\t\t\t\t\t\t\tUK | \n\t\t\t\t\t\t\tSwitzerland | \n\t\t\t\t\t\t\tJapan | \n\t\t\t\t\t\t\tUS | \n\t\t\t\t\t\t
Constant | \n\t\t\t\t\t\t\t1.848 (0.034) | \n\t\t\t\t\t\t\t-2.235 (0.093) | \n\t\t\t\t\t\t\t643 (0.784 ) | \n\t\t\t\t\t\t\t-1.964 (0.260) | \n\t\t\t\t\t\t\t2.337 (0.110) | \n\t\t\t\t\t\t
CPI | \n\t\t\t\t\t\t\t-0.511 (0.256) | \n\t\t\t\t\t\t\t2.284 (0.001) | \n\t\t\t\t\t\t\t2.601 (0.000) | \n\t\t\t\t\t\t\t0.820 (0.272) | \n\t\t\t\t\t\t\t–0.5252 (0.373) | \n\t\t\t\t\t\t
Ex | \n\t\t\t\t\t\t\t0.241 (0.685 ) | \n\t\t\t\t\t\t\t–0.665 (0.048) | \n\t\t\t\t\t\t\t-1.395 (0.092) | \n\t\t\t\t\t\t\t– 0.463(0.121) | \n\t\t\t\t\t\t\t0.118 (0.579) | \n\t\t\t\t\t\t
Rp | \n\t\t\t\t\t\t\t0.204 ( 0.729) | \n\t\t\t\t\t\t\t–1.375 (0.019) | \n\t\t\t\t\t\t\t-1.615 (0.013) | \n\t\t\t\t\t\t\t–0.994 (0.176) | \n\t\t\t\t\t\t\t–0.5272 (0.365) | \n\t\t\t\t\t\t
D1 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | 0.098 (0.056) | \n\t\t\t\t\t\t
D2 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 0.1549 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | 0.122 (0.042) | \n\t\t\t\t\t\t
D3 | \n\t\t\t\t\t\t\t0.158 (0.002) | \n\t\t\t\t\t\t\t0.210 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | 0.161 (0.004) | \n\t\t\t\t\t\t
D4 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | –0.476 (0.000) | \n\t\t\t\t\t\t\t4 | \n\t\t\t\t\t\t
D5 | \n\t\t\t\t\t\t\t–0.135 (0.018) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 0.495 (0.000) | \n\t\t\t\t\t\t\t–0.161 (0.025) | \n\t\t\t\t\t\t\t0.357 (0.000) | \n\t\t\t\t\t\t
D6 | \n\t\t\t\t\t\t\t0.308 (0.000) | \n\t\t\t\t\t\t\t0.473 (0.000) | \n\t\t\t\t\t\t\t1.334 (0.000) | \n\t\t\t\t\t\t\t0.235 (0.000) | \n\t\t\t\t\t\t\t0.487 (0.000) | \n\t\t\t\t\t\t
D7 | \n\t\t\t\t\t\t\t0.265 (0.000) | \n\t\t\t\t\t\t\t0.443 (0.000) | \n\t\t\t\t\t\t\t1.334 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 0.441 (0.000) | \n\t\t\t\t\t\t
D8 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | 0.721 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 0.419 (0.000) | \n\t\t\t\t\t\t
D9 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | –0.227 (0.000) | \n\t\t\t\t\t\t\t0.220 (0.001) | \n\t\t\t\t\t\t
D10 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | –0.331 (0.000) | \n\t\t\t\t\t\t\t–0.284 (0.000) | \n\t\t\t\t\t\t\t0.172 (0.002) | \n\t\t\t\t\t\t
D11 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | –0.2136 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Y(t–1)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.212 (0.000) | \n\t\t\t\t\t\t\t0.0137 (0.789) | \n\t\t\t\t\t\t\t0.1768 (0.000) | \n\t\t\t\t\t\t\t0.430 (0.000) | \n\t\t\t\t\t\t\t0.334 (0.000) | \n\t\t\t\t\t\t
Y(t–2)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | –0.157 (0.016) | \n\t\t\t\t\t\t
Y(t–3)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t–0.135 (0.001) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Y(t–6)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t–0.116 (0.002) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | –0.183 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Y(t–7)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.144 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Y(t–9)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t–0.127 (0.005) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Y(t–10)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | –0.129 (0.002) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
Y(t–11)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.278 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | 0.187 (0.000) | \n\t\t\t\t\t\t\t4 | \n\t\t\t\t\t\t
Y(t–12)\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.363 (0.000) | \n\t\t\t\t\t\t\t0.309 (0.000) | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 0.263 (0.000) | \n\t\t\t\t\t\t\t0.269 (0.000) | \n\t\t\t\t\t\t
R2\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.924 | \n\t\t\t\t\t\t\t0.793 | \n\t\t\t\t\t\t\t0.950 | \n\t\t\t\t\t\t\t0.808 | \n\t\t\t\t\t\t\t0.850 | \n\t\t\t\t\t\t
The non significant results erased from the Table.
ISUR estimation results for Norway
In the case of the UK, we find that all dummies are significant, indicating clear seasonality in the demand for tourism. The demand in December is the highest for the year. we also find lags 4 and 12 are statistically significant. Note that the sign of lag 4 is negative while it is larger and positive for lags 11 and 12. For Switzerland, only the summer dummies are large, positive, and statistically significant, meaning that the Swiss are relatively more interested in summer tourism. The remaining dummies are either insignificant or small in magnitude. The estimated parameters of lags 1 and 12 are positive and significant.
\n\t\t\t\tIn general, the lags of the dependent variable for the months of January and December are also significant, supporting the hypothesis of a habit-forming or word-of-mouth effect. Some of the monthly dummies as proxies for seasonal effects are also significant, including January, March, May, June, July, September, October, and November. Estimates of the Denmark dummy show a clear seasonal variation in the pattern of Danish tourism demand in Sweden, such that demand in January, February, March, and July is higher than in December, with lower demand in other months.
\n\t\t\t\n\t\t\t\t\tTable 3 provides estimates of the monthly arrivals from Denmark, Japan, and the US to NWT in Norway. The estimated Norwegian CPI (CPI\n\t\t\t\t\t\tNor\n\t\t\t\t\t) long- run elasticity ranges from 0.5% to 0.8% and is lower than that for Denmark, Japan, and the US. The estimated CPI\n\t\t\t\t\t\tSW\n\t\t\t\t\t coefficients suggest that a 1% increase in CPI\n\t\t\t\t\t\tNor\n\t\t\t\t\t results in 0.5%, 0.52%, and 0.8% decreases in tourist arrivals to Norway from Denmark, Japan, and the US, respectively. The low CPI\n\t\t\t\t\t\tNor\n\t\t\t\t\t elasticity for Japan and the US may be a reflection of the depreciation of the Norwegian krone against the Japanese yen and the US dollar.
\n\t\t\t\tThe estimated long run elasticities of the relative price variable for Denmark and the US are less than one (0.2% and 0.6%, respectively), indicating that a 1% rise in the relative price (price of tourism in Norway relative to Sweden) causes about a 1% fall in tourist arrivals from Denmark and the US. The estimated long run –run elasticity of the relative price for Japan is closed to unity (99%), which indicates that a 1% rise in the relative price (price of tourism in Norway relative to Sweden) causes around a 1% drop in tourist arrivals from Japan. The estimated long- run elasticity of the relative price variable for the UK and Switzerland are greater than one, indicating that the arrival of tourists in Norway from these countries is elastic with respect to the relative price variable. This implies that Norway must also maintain its international price competitiveness to maintain high growth in tourist inflow. Yet again, the low exchange rate long- run elasticity for Denmark, Japan, and the US can be a reflection of the depreciation of the Norwegian krona against the Danish krona, the Japanese yen, and the US dollar.
\n\t\t\tThis chapter has applied the ISUR model, a model not used in other studies that have estimated models for tourism to these two neighbouring regions. First, the model was applied to the neighbouring destinations for the period of transition from characteristics of a lower level of integration and of facing competition from other countries, to characteristics of a high level of integration, globalization, exposure to an international competitive market, and high levels of income and welfare.
\n\t\t\tSecond, the model allowed for comparison of the changes in the behaviour of tourism demand in each country over time, not only in terms of the number of visitors, price and exchange rate, but also of relative price elasticities. The estimated results show the model to be consistent with the data, as indicated by both the diagnostic statistic and the model’s good forecasting ability. Moreover, the results are consistent with the properties of homogeneity and symmetry. This accords with the microeconomic foundations of the model and increases the credibility of the elasticity values.
\n\t\t\tSubstitutability and complementarity among destinations are indicated by positive and negative relative price elasticities, respectively. Clear conclusions about the complementarity or substitutability among destinations are not usually obtained in studies using the ISUR model, which have produced few well defined relative price effects. However, the results in this study seem consistent and also coincide with a priori expectations. Hence they are taken as an indication of the relative magnitudes and directions of changes in demand.
\n\t\t\tThe main purpose of this chapter is to estimate the demand for tourism to two neighbouring regions in Sweden and Norway from five different countries: namely, Denmark, the UK, Switzerland, Japan, and the US. Monthly time series data from 1993:01 to 2006:12 is collected from Statistics Sweden for this purpose. For each visiting country, We specify a separate equation with the relevant information included in each equation. We conduct several diagnostic tests in order to specify the five equations for SW:6 in Sweden and NWT in Norway. We then estimate these equations using Zellner’s ISUR, which takes into consideration any possible correlation between the equations and hence is more efficient than other single equation estimation methods, such as OLS.
\n\t\t\tThe results also indicates that CPI, some lagged dependent variables, and several monthly dummy variables representing seasonal effects have a significant impact on the number of visitors to SW6 in Sweden and NWT in Norway. The results also show that the relative price and exchange rate have a significant effect on international tourism demand for some countries. However, although we could view this conclusion as supporting a theoretical framework that describes tourism demand model variable relationships, our demand system lacks a travel cost variable. Nonetheless, our results could also have important implications for the decision-making process of government tourism agencies in both countries when considering influential factors in their long run planning.
\n\t\tThis test is used for time series and checks for structural changes. In the Cusum test Recursive Residuals (RR) calculated by the Kalman Filter are used.
\n\t\t\t\t\tI now describe the construction of recursive residuals and the Kalman filter technique. The recursive residuals can be computed by forward or backward recursion. Only forward recursion is described, backward recursion being analogous.
\n\t\t\t\t\tGiven N observations, consider the linear model (2. 2. 1) but with the corresponding vector of coefficient β expressed as βt, implying that the coefficients may vary over time t. The hypothesis to be tested is β1 = β2 =,..., = βΝ = β. The OLS estimator based on N observations is:
\n\t\t\t\t\twhere X is a N by k matrix of observations on the regressors, and y is an N by 1 vector of observations for the dependent variable. Suppose that only r observations are used to estimate β. Then for r > k, where k is the number of independent variables,
\n\t\t\t\t\tUsing br, one may "forecast" yr at sample point r, corresponding to the vector Xr of the explanatory variables at that point.
\n\t\t\t\t\tRecursive residuals are now derived by estimating equation (2. 2. 1) recursively in the same manner, that is by using the first k observations to get an initial estimate of β, and then gradually enlarging the sample, adding one observation at a time and re-estimating β at each step. In this way, it is possible to get (N-k) estimates of the vector β, and correspondingly (N-k-1) forecast errors of the type:
\n\t\t\t\t\twhere br-1 is an estimate of β based on the first r - 1 observations. It can be shown that, under the null hypothesis, these forecast errors have mean zero and variance σ2 dr2, where dr is a scalar function of the explanatory variables, equal to [ 1 + Xr\'(X\'r-1Xr-1)-1 Xr ]1/2.
\n\t\t\t\t\tThen the quantity:
\n\t\t\t\t\tgives a set of standardized prediction errors, called "recursive residuals". The recursive residuals are independently and normally distributed with mean zero and constant variance σ2. As a result of a change in the structure over time, these recursive residuals will no longer have zero mean, and the CUSUM of these residuals can be used to test for structural change.
\n\t\t\t\t\tCUSUM involves the plot of the quantity:
\n\t\t\t\t\twhere σ* is the estimated standard deviation based on the full sample.
\n\t\t\t\t\tThe test finds parameter instability if the cumulative sum goes outside the area between the two error bounds. Thus, movements of Vt outside the error bounds are a sign of parameter instability.
\n\t\t\t\tThe Breusch-Godfrey test can be separated into several stages:
\n\t\t\t\t\tRun an OLS on:
This gives us \n\t\t\t\t\t\t\t
Run an OLS on:
This equation can be used for any AR(P) process. From this equation the unrestricted residual sum of squares (RSSu).
\n\t\t\t\t\tThe restricted residual sum of squares (RSSR) is given from the following equation:
\n\t\t\t\t\t\n\t\t\t\t\t\tThe null hypothesis is:
\n\t\t\t\t\tRun an F-test:
This has a distribution: F(P,T-k-P) under the null hypothesis.
\n\t\t\t\t\tThe Breusch-Godfrey test can be tested for AR(P) processes which gives this test a clear advantage over other available tests for autocorrelation.
\n\t\t\t\tRESET test stands for Regression Specification Error Test. The test is very general and can only tell you if you have a problem or not. It tests for omitted variables and incorrect functional forms or misspecified dynamics and also if there is a correlation between the error term and the independent variable. The null hypothesis is:
\n\t\t\t\t\t(and an omitted variable effect is present)
\n\t\t\t\t\tThus, by rejecting the null hypothesis indicates some type of misspecification. First a linear regression is specified:
\n\t\t\t\t\tThis gives the restricted residual sum of squares (RSSR). After the RSSR has been found the unrestricted model is presented by adding variables (three fitted values):
\n\t\t\t\t\tThis gives us the unrestricted residual sum of squares (RSSU). In the third step the RESET-test uses a F-test:
\n\t\t\t\t\tF= ((RSSR - RSSU)/number of restrictions under H0) / (RSSU / (N- number of parameters in unrestricted model))
\n\t\t\t\t\tThe F-test checks if θ1=θ2 =0, if θ1=θ2≠0 I have an omitted variable or a misspecification in the model.
\n\t\t\t\tThis test is a general test where I do not need to make any specific assumptions regarding the nature of the heteroscedasticity, whether it is increasing, decreasing etc. The test only tells us if I have an indication of heteroscedasticity.
\n\t\t\t\t\tThe alternative hypothesis is not H0, anything other than H0.
\n\t\t\t\t\tThe test can be divided into several steps:
\n\t\t\t\t\tRun an OLS on:
From this equation I get \n\t\t\t\t\t\t\t
Run an OLS on:
Where k is the number of parameters. The variance is considered to be a linear function of a number of independent variables, their quadratic and cross products. Thus, the X:s is used as a proxy for Z.
\n\t\t\t\t\tCalculate an F-test:
Restricted model:
\n\t\t\t\t\tFrom this test the restricted residual sum of squares (RSSR) is measured.
\n\t\t\t\t\tThe F-test is:
\n\t\t\t\t\tWhere
\n\t\t\t\t\tThis is a test for AutoRegressive Conditional Heteroscedasticity (ARCH). The ARCH process can be modeled as:
\n\t\t\t\t\twhere the Variance of \n\t\t\t\t\t\t\t
Use OLS on the original model and get:\n\t\t\t\t\t\t\t\t\t
Test whether \n\t\t\t\t\t\t\t
The test for non-normality is normally done before one test for heteroskedasticity and structural changes.
\n\t\t\t\t\tThe test used here for testing for normal distribution is the Jarque-Bera test. The Jarque-Bera test is structured as follows:
\n\t\t\t\t\tWhere T is the total number of observations, b1 is a measure for skewness and b2 is a measure for kurtosis. The μ are different moments. The test has a chi-square distribution with two degrees of freedom under the null hypothesis of normal distribution. The two degrees of freedom comes from having one for skewness and one for kurtosis.
\n\t\tOur relationship with our body has always been complicated. Although our body is an integral part of our life experience, many of us do not grow to like or appreciate our body as the way it is. Through the process of socialization, we develop ideas of what is preferred in our society and how we should look at our body. This belief system shapes our perception of and attitudes towards our body. While the size and shape of our body are an objective measure, our body image is not always as stable or realistic. It is not uncommon for one to hold a distorted view of his or her body, which could lead to body dissatisfaction and compromise one’s psychological well-being.
Psychologists have devoted an entire body of research on this particular topic and investigated how our perception of our body influences thoughts, beliefs, feelings, and behaviors. Some researchers looked into the origins of body image, others studied the influence of negative body image on our mental health. They raised many interesting questions that inspire us to reflect upon. For example, what role do our families and friends play in the shaping of our body image? What makes some individuals more vulnerable to social comparison than others? In this chapter, we will delve into some of these questions and reveal how our societal standards explicitly and implicitly influence our body image.
Before we get started, it is worth mentioning that body image is not a subject that only pertains to girls or women. As many of our male readers may attest, men can be equally troubled by their body images as well. Moreover, body image is not only about size. It would be unfair to assume that one is only concerned with his or her weight or size, given the rich diversity of human experiences. Literature has examined a great variety of body-related topics, such as physical diseases and injuries. For the purpose of this chapter, we will mainly look at the section of literature that relates to shapes and sizes. But it is not to say that other aspects of body image are not as equally important.
Body image is commonly understood as how one looks in the mirror. By staring at your body in the mirror, you will notice some physical characteristics including shape, size, height, skin tone, and so forth. However, do you think these characteristics truly reflect your body? Does your body seem bigger or smaller than you expect? I think you would agree with me that what we see is often not how we feel. There is a great amount of discrepancy between what our body actually looks like and what we perceive our body to be. Furthermore, how do you like the way you look? Do you have an opinion about your body every time you check yourself out in the mirror?
Psychologists coined the term body image to refer to one’s perceptions and attitudes towards his/her physical characteristics [1]. Body image is a multidimensional concept that subsumes cognitive, affective, behavioral, and perceptual facets [2, 3]. For instance, perceiving your body to be a certain way can give rise to various emotions, lead you to have positive or negative thoughts, and result in behaviors in an attempt to modify your body.
More specifically, psychologists have been interested in studying the development of body image and its influence on other aspects of people’s life. Plato once said, “The body, in which we are imprisoned like an oyster in its shell” [4]. One can easily imagine that our feelings towards and opinions of our bodies can fundamentally influence our day-to-day life experiences. Therefore, in order to understand one’s idea of beauty, we must inevitably take a close look at his/her body image.
The first question that researchers began to investigate was how accurate people’s body perception was. Researchers asked individuals to estimate their body sizes and compared the estimations against their actual measurements. The results show that some people have inaccurate estimations as they perceived their bodies to be either bigger or smaller than their actual sizes [5, 6]. Furthermore, people who tend to overestimate their sizes are more likely to develop eating disorders [7]. Among anorexic patients, researchers examined whether their body distortions stem from inaccurate visual inputs or distorted views of their bodies. They employed assessments such as digital photography techniques and figure drawing scales. It turns out that there is not much difference in the patients’ visual sensitivity (i.e. heightened ability in processing visual information) but disturbances in how they interpret the images of their bodies [8, 9]. Hence, their biased attitudes towards weight and size caused them to have a distorted body image.
This discovery naturally led to the next question about the attitudinal component of body image. In other words, what attitudes do people have in relation to their physical appearance? Researchers examined this question by first asking people if they were satisfied with their body image. The results show that around 61 to 93% of people were not satisfied with either their overall appearance or specific body areas [10, 11]. More specifically, around 50% of preadolescent girls and 30% of preadolescent boys reported dissatisfaction with their body [12, 13, 14]. In adults, approximately 60% of women and 40% of men see their body negatively, and these rates remain stable across the lifespan [15, 16]. In addition to being dissatisfied, people also reported experiencing emotional distress (including shame, anxiety, or discomfort) with regards to their body [1]. They could experience such distress at specific moments or as part of their general life experience.
Body image distortion and dissatisfaction can have serious consequences. People with negative body image are at risk of having low self-esteem, depression, social anxiety, impaired sexual functioning, and reduced quality of life [1, 17, 18]. They may engage in risky health behaviors including unhealthy eating, physical inactivity, unsafe sex, smoking, and so forth [19, 20, 21, 22]. Negative body image can also contribute to the development and maintenance of body dysmorphic disorder and eating disorders [23, 24].
Eating disorders are serious and sometimes life-threatening illnesses. They often involve serious medical complications that can cause permanent damage or death. People with eating disorders also have an increased risk of dying by suicide. According to the National Institute of Mental Health, the accumulated lifetime prevalence of eating disorders (including anorexia nervosa, bulimia nervosa, and binge eating disorder) was around 4% among adults and 2.7% among adolescents aged 13 to 18 years [25]. Overall, approximately 30 million Americans have struggled with an eating disorder over their lifetime. Moreover, probably twice the number of people or more are also struggling with eating disturbances even if their conditions do not yet meet the criterion of a clinical diagnosis [26]. This is why body image distortion and dissatisfaction are implicated in a range of public health concerns such as eating disorders [27].
For people with negative body image, their passage to beauty and self-appreciation is blocked, because having a healthy and positive body image sits at the core of beauty. Merriam-Webster defines beauty as “a quality or aggregate of qualities in a person or thing that gives pleasure to the senses or pleasurably exalts the mind or spirit” [28]. It is one thing to please the senses of others, but it is another to please oneself. To appreciate the beauty within him/herself, a person must be able to view his/her body positively in the first place. However, it would difficult if a person views him/herself unfavorably and struggles with unpleasant emotions and feelings towards his/her own body. Therefore, in order to reinstate the sense of beauty and self-appreciation within individuals, we must first understand the concept of body image.
At this point, you might wonder what causes people to have distorted and disapproving views of their body. Thomas F. Cash, a leading expert in the field of body image, proposed that there are two views of human appearance [29]. One is the “outside view” as how our physical appearance influences our interpersonal experiences. For example, physical attractiveness plays a role in an array of contexts such as friendship, romantic relationships, and job opportunities. The other is the “inside view” which is a person’s subjective experiences of his/her appearance. The inside view was later defined as “body image” [29]. When we talk about body image, we are referring to a person’s own perceptions, attitudes, emotions, and beliefs regarding his/her appearance. However, the inside view is built upon and largely influenced by the outside view.
Societal standards regarding body image have a prominent influence on an individual’s self-image. As a society, we hold standards for a large variety of qualities. Among these dimensions of self, one’s physical body is the most prominent [30]. The values and beliefs about physical attractiveness are referred to as societal standards of beauty. The concept of beauty is ever-changing, as it has constantly evolved over time and varied across cultures. The standards of different societies stem from two main sources of influence: biology and culture. [31] In developed countries, as the issue of survival becomes less of a concern, people’s preferences for ideal body shape have shifted from a sultry and voluptuous ideal to a thin and slender ideal. Nowadays, the modern ideal body shape has increasingly become both thin and very fit [32]. These ideals then circulate to other parts of the world due to globalization and have been infused into the standards of other countries and cultures. A cross-national comparison study found that the pressure to conform to Western ideals significantly predicts ideal body stereotype internalization for female participants from America, Poland, and the Czech Republic [32].
How are people influenced by these societal standards? Psychologists proposed a persuasive model - the Tripartite Influence Model [33]. It describes how social influence from media, family, and peers can predict body image and eating disturbances. The model also suggests that individual factors, such as internalization of the ideal body shape (regardless of how the ideal body shape is defined in a given society) and a chronic tendency towards social comparison, could mediate these social influences.
Societal standards are pervasively communicated through media messages. Traditional forms of media, such as TV commercials and magazines, have been advocating and promoting the desirability of an unrealistically thin ideal [34, 35]. For example, a study about print media found that adolescent girls would endorse their ideal as the models in fashion magazines specifically targeting teenage girls [36]. New social media, such as Instagram, Pinterest, or Tumblr, are image based. Seeing the images of thin and athletic peers provides a convenient target of upward social comparison for female viewers and motivates them to achieve a similar body shape. Many researchers in the field of eating disorders have criticized the media’s role in the formation of eating and body image disturbances (e.g., [34, 35, 36]).
The media equate an ideal body shape not only with attractiveness but also with success. American culture emphasizes that physical attractiveness helps to achieve success in every area of life [32]. It is believed that thinness is crucial for success and happiness and people with the ideal body shape are likely to have a high social status. On the contrary, overweight or obese people are under pervasive appearance-based social discrimination and are often associated with negative qualities such as unattractive, lazy, immoral and dishonest [37].
Through this socialization process, women are disproportionately influenced by the social standards of beauty. Studies have unanimously observed greater body dissatisfaction among women than men [8, 9, 15]. Psychologists propose that the gender differences in body image originate from a sexual dimorphism through the general developmental process and the subsequent divergent psychosocial experiences of both genders [8, 31]. From an evolutionary perspective, beauty and attractiveness are not merely a cultural concept, but rather an important factor in determining one’s odds of survival and reproductive success. Through the process of defining prominent features of attractiveness for both genders, different standards have emerged. Traditional gender roles associate femininity with beauty and the desire for an attractive appearance, while masculinity is associated with force and control [8]. This focus on esthetic qualities of the body creates a low level of body esteem and dissatisfaction among women [31]. Moreover, mass media portray beauty as a woman’s primary objective. They also normalize the pressure that women experience with body image as if it is normal and acceptable for a woman to be ashamed and anxious about her body and appearance [38]. However, this is by no means to say that men are not affected by societal standards of beauty. In fact, a growing trend of body dissatisfaction has been observed among men [39]. While some of them are affected by the thin ideal, others are actively pursuing a masculine body ideal with high muscle mass. This tendency could posit men at similar risk of developing body image disturbance and eating disorders.
Not all women are equally influenced by societal standards of beauty. Some individuals may not be affected by media messages at all, while others are greatly affected and tend to modify their behaviors in a dysfunctional way to model media-promoted images. Researchers speculated that some interpersonal and intrapersonal factors play a role in influencing an individual’s response to societal standards.
One specific individual difference variable, internalization of societal pressures regarding standards of attractiveness, appears to moderate or even mediate the media’s effects on women’s body dissatisfaction and eating dysfunction. Internalization is a process in which individuals assimilate the idea that possessing the ideal body shape is associated with being happy and successful and begin to hold themselves up against the societal standards [40]. Researchers identified two trends of internalization. One is thin-internalization, in which an individual wants to be thin or skinny; the other is athlete-internalization referring to the desire to possess a lean but muscular body [33]. While these two trends differ in the societal standards that one subscribes to, individuals who experience greater pressure to conform to either societal standard are more likely to internalize the ideal body stereotype [39]. Living in a culture that puts such a strong emphasis on the media and uses it to convey beauty standards increases the perceived pressure to conform for some individuals. This may increase their propensity to internalization and create a strong desire for them to achieve ideal beauty standards. Thus, they are at a greater risk of experiencing body image disturbances.
Empirical studies substantiate extensive evidence of internalization. For example, middle-school-aged girls who perceive higher peer influence and more television influence on the importance of attractiveness reported greater dissatisfaction with their body image and having pathological beliefs about eating [14]. College women who saw photographs of thin models from fashion magazines reported significantly higher levels of private body self-consciousness and anxiety than women in the control group [41]. Women with eating disorders demonstrated significant increases in overestimating their body size following exposure to photographs of models from popular fashion magazines. However, no such effect was found among women without eating disorders [41]. People with bulimic symptoms, regardless of the severity of their symptoms, experienced lower levels of self-esteem and weight satisfaction after seeing photographs of thinner models compared to after seeing photographs of larger models [42].
Why are some individuals more likely to internalize societal standards than others? Psychologists suggest that “People differ considerably in how they want to be seen, but they share in common an active pursuit of those desired self-images.” [30]. Femininity and attractiveness, among many other qualities, are important to women’s self-image. One study found that, in order to present themselves as feminine, female participants consumed less food when getting acquainted with a desirable male companion than with others [43]. Compared to the other qualities, societal standards of attractiveness is a highly accessible external source that can be used to define the self. Therefore, for women who lack a clear definition of their identity, internalizing the thin ideal and comparing their appearance to other women may serve as a means to gain self-knowledge [44].
Unfortunately, defining their self-concept in these ways can have negative implications for women’s body image. A sociocultural model stressed that the current societal standards for thinness, as well as other standards of beauty for women are impossible to achieve for the average woman [31, 34]. It is not difficult to notice discrepancies between the actual self and the ideal self through upward social comparison. For example, when flipping through fashion magazines, one may make comparisons between her body shape and the models’ and inevitably notice the differences in their sizes. Strauman and colleagues found that actual-ideal discrepancies among female undergraduates were correlated with dissatisfaction with weight and appearance-related beliefs about self [45].
Another source of self-knowledge comes from one’s interaction with others. Interpersonal contexts provide opportunities for one to gain insights into his/her self-knowledge [30]. In particular, they allow people to choose comparison targets and interaction partners in ways that maximize benefits to the self. People tend to choose interaction partners who see them as they see themselves due to the desire for self-verification [46]. They create environments that confirm their self-views, primarily by choosing appropriate interaction partners, and they interpret and remember their interactions as confirming their self-views. People choose and are highly committed to interaction partners who confirm their self-views, even if those self-views are negative [47]. Therefore, their negative self-views can be verified, maintained, or even reinforced through their interactions.
Messages about societal standards of attractiveness do not emanate just from media sources. Unfortunately, family, peers, coaches, teachers, and others help reinforce this socialization of women [48, 49, 50]. Their perception plays a critical role in influencing our self-image. Many interpersonal influences have been identified to contribute to the development and maintenance of shape- and weight-related disorders [49]. The factors include, but are not limited to, teasing or critical comments about one’s appearance from parents, peers, or other significant others. For example, one study found that body-related comments received in childhood predicted body esteem in adulthood [51].
The fear of interpersonal rejection leads to a high amount of stress for women. People’s sensitivity to rejection based on their appearance within interpersonal contexts are named appearance-rejection sensitivity (RS). Highly sensitive people are self-conscious about how they look. They anxiously anticipate that other people would reject them for their appearance. Moreover, when they are rejected, they attribute their appearance as the reason of rejection. Appearance-RS strongly predicted disruptive and excessive body image concerns [52]. The more sensitive participants were to being rejected based on their appearance, the more likely they were to report thoughts and behaviors characterizing body dysmorphic symptoms, to view cosmetic surgery as acceptable for both social and intrapersonal reasons, and to consider having cosmetic surgery in the future [52]. Appearance-RS also predicted social reasons for having cosmetic surgery [52]. This finding is consistent with research linking higher sensitivity with sociocultural influences, such as peer acceptance and feeling pressured to please others [48, 53, 54].
Women view their body image as an area that they can improve in order to gain social acceptance. In an experiment examining the implicit relations between rejection and appearance, female participants attempted to modify their body image in order to achieve self-enhancement [55]. Self-enhancement is one of two general sets of motives. When people are under threat (e.g., rejection, negative feedback, low self-esteem, depression, illness), their affective system and desires for self-enhancement are invoked [56]. Kunda proposed that the motivation to self-enhancement leads people to believe that they possess the desired traits, which in this case is smaller body size [57]. In another study, women who received self-esteem threats reported greater satisfaction with their appearance and less preoccupied with it than women who received positive feedback [58]. While the results indicate that participants held self-defensive perspectives immediately after receiving negative feedback, they might experience a paradoxical increase in investment in body image later on [59, 60]. As counterintuitive as this may seem, evidence shows that, after rejection and disapproval, people are motivated to protect their self-image by regulating their body image in order to maintain a balanced self-concept and diffuse the unbearable emotional distress. Compared to other aspects of the self, such as intelligence, one’s body seems to be salient yet more malleable.
As a result of the interplay between sociocultural influences and the internalization process, women experience dissatisfaction towards their appearance. On the perceptual level, they are likely to overestimate their weight and size and see themselves as bigger than their actual size. Such irrational and inaccurate perception could lead to greater distress and a stronger motive to change. Consequently, individuals may develop distorted beliefs and automatic thoughts about their appearance and its significance [61].
As a result of the negative self-schema, individuals may resort to two common approaches to help regulate their body image. One is the avoidance approach, as some individuals are prone to avoid situations that might generate body image distress. For example, they might wear baggy clothes, avoid tight-fitting or revealing clothes, avoid mirror, and voluntarily isolate from social situations [62]. Other individuals may be prone to actively pursue the ideal body image and try to minimize the actual-ideal discrepancies [63]. They might monitor the condition of their body through repeated weighing and mirror checking. They are also preoccupied with their appearance and spend time-consuming efforts to groom and manage specific body areas. The more dangerous forms of effort include extreme restraints of eating behaviors and cosmetic surgeries. Regardless of the differences in approach, these actions are inherently self-reinforcing. They might relieve the individuals of immediate distress, but perpetuate the problems in the long term.
Struggles with body image have accompanied men and women throughout history and across cultures. With the development of research, we are fortunate to uncover the underlying mechanisms and pathways that link sociocultural influences and individual risk factors. However, many aspects of this problem still remain unresolved. Despite the active effort of studying and intervention, many individuals are still dissatisfied with their appearance and resort to alter their body in dysfunctional manners.
As we are embracing increasing diversity within our culture, it is imperative to reevaluate the dichotomous nature of our societal standards. When we put a category of qualities on the pedestal, we are essentially labeling people who do not possess such qualities as inadequate. Subsequently, individuals are subject to social scrutiny and risk disapproval from their significant and/or desirable others. The stakes are raised obviously too high to the degree that it may erode individuals’ self-esteem and overall well-being. In order for individuals to regain control over their body image, the real effort that we should spend is to challenge media messages and commonly held beliefs about beauty and help individuals develop a secure and stable self-concept that captures their true essence.
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