Habit persistence in tourist sub-industries

Fleissig, Adrian R. Article Habit persistence in tourist sub-industries Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Fleissig, Adrian R. (2021) : Habit persistence in tourist sub-industries, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 24, Iss. 1, pp. 103-113, https://doi.org/10.1080/15140326.2021.1896294 This Version is available at: https://hdl.handle.net/10419/314119 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Habit persistence in tourist sub-industries Adrian R. Fleissig To cite this article: Adrian R. Fleissig (2021) Habit persistence in tourist sub-industries, Journal of Applied Economics, 24:1, 103-113, DOI: 10.1080/15140326.2021.1896294 To link to this article: https://doi.org/10.1080/15140326.2021.1896294 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 23 Mar 2021. Submit your article to this journal Article views: 1668 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 ARTICLE Habit persistence in tourist sub-industries Adrian R. Fleissig Department of Economics, California State University, Fullerton, CA, USA ABSTRACT Habit persistence across six U.S. tourism sub-industries is estimated using a dynamic forward looking model. Estimates show that habits largely determine current expenditure for air transportation, shopping, accommodation, and other transportation. Estimated uncompensated price elasticities find that air transport and accommodation are price elastic in the short-run and long-run. Shopping is price inelastic in the short-run but price elastic in the long-run. An important result is that air transportation and other transportation are elastic substitutes for price changes in air transportation but inelastic substitutes for price changes in other related transportation. Estimates show that expenditure across most of the tourist subindustries is closely related because they are gross complements. Food and beverages are necessities, price inelastic, and relatively unresponsive to changes in expenditure across the sub-industries. The estimates show that policy makers and tourist marketing should account for habit persistence and differences between the short-run and long-run. ARTICLE HISTORY Received 18 March 2020 Accepted 13 February 2021 KEYWORDS Habit formation; short-run and long run estimates; tourist sub-industries 1. Introduction Consumer habits and the business cycle have an impact on tourism marketing strategies, public tourism policy, and revenue for the tourism industry. To evaluate the short-term and long-term impacts that consumer choices have on the tourism industry, many studies provide estimates of own-price elasticities, cross-price elasticities of substitution, and budget elasticities of demand. Recent studies like Croes, Ridderstaat, and Rivera (2018) found that the business cycle has a substantial impact on tourism demand and Mohammed (2019) finds that tourism imports are generally income and price elastic. The meta-analysis of Nunkoo, Seetanah, Jaffur, Moraghen, and Sannassee (2020) analyzed the relationship between economic growth and tourism and found support for the tourismled growth hypothesis. Peng, Song, Crouch, and Witt (2015) found that dynamic models that include a lagged dependent variable to model tourist loyalty and “word of mouth”, for example, Garín-Munoz (2006), Naude and Saayman (2005), Seetaram (2010), and Liu (2019), produce more elastic price and income elasticities. These studies typically focus on a single measure of tourism and fail to capture tourist habit formation across touristsub industries. Modelling habit formation is important for the impact on tourist revenue CONTACT Adrian R. Fleissig [email protected] Department of Economics, California State University, Fullerton, Fullerton, CA 92834, USA JOURNAL OF APPLIED ECONOMICS 2021, VOL. 24, NO. 1, 103–113 https://doi.org/10.1080/15140326.2021.1896294 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. by sector because habits are likely to differ over tourist sub-industries such as transportation, accommodation, food, recreation, and other tourist activities. Tourism in the United States is a significant part of the service sector. International and domestic tourism generated over one trillion dollars from the more than one billion of person-trips, US Travel Association (2019). Tourist-related employment generates millions of labor-intensive jobs and is often one of the largest employer industry in many states. Leisure travel in the U.S. accounts for about 80% of all domestic travel and an important part of the tourist industry. Some major attractions for both domestic and international travelers are the national parks, amusement and theme parks, entertainment, shopping and culinary choices. Food services and lodging generate significant revenue. Expenditure across different tourist sub-industries like accommodation, food, sightseeing, transport, shopping, entertainment, and miscellaneous expenditure have been analyzed by Divisekera and Deegan (2010) for Ireland, Divisekera (2010) for Australia, Wu, Li, and Song (2012) for Asian tourism, Ahn, Baek, Lee, and Lee (2018) for Korea, and Aratuo and Etienne (2019) for the United States. These studies do not focus on consumer habits but have varying degrees of substitution, complementarity, and budget elasticities across tourist commodities which have important consequences for tourist marketing, revenue, and policy. Habits often impact expenditure decisions of consumers. Evidence of habit formation is especially prevalent for commodities like tobacco and alcohol products as in Gallet (2007), Zhen, Wohlgenant, Karns, and Kaufman (2011), Fogarty (2010), Nelson (2014), Koksal and Wohlgenant (2016), Alexander and Neill (2017), and Goel and Saunoris (2018). These studies typically estimate a parameter that captures the degree of habit formation for tobacco and alcohol products. Relatively few studies on tourism focus directly on multiple parameter estimates for habit formation across sub-tourist industries, see Bakkal (1991), Divisekera (2003), and Lyssiotou (2000), and Cazanova, Ward, and Holland (2014). This study examines the impact of habit formation across each of six U.S. tourist subindustries using the rational dynamic approach of Spinnewyn (1981), Muellbauer and Pashardes (1992), Pashardes (1986), Lyssiotou (2000), Zhen et al. (2011), and Koksal and Wohlgenant (2016). In the rational dynamic habit formation approach, the impact of habits on current tourism expenditure is based on passed tourism expenditure and desired future service flows from tourism expenditure. Habit formation on current tourism expenditure can range from no impact on current expenditure to a significant impact on current expenditure due to much habit formation. A dynamic Almost Ideal Model demand system is used to estimate habit formation for each of the six sub-tourist industries. The impact that current tourism expenditure has on future utility allows for intertemporally rational consumer behavior where current preferences for tourism are based on past expenditures captured through preference endogeneity. The data are from Aratuo and Etienne (2019) who emphasize the importance of analyzing six sub-tourist industries because of the interaction between sub-sectors and the business cycle. They find that gross domestic product co-moves with accommodation and food and beverages but does not cointegrate with the remaining four sub-industries. They only find evidence of a long-run relationship between other transportation and air transportation and shortrun evidence of unidirectional causality from GDP to the six sub-industries. 104 A. R. FLEISSIG The estimates find that habits account for 33% of current tourist air transportation expenditure and around 24% for the three sub-tourist industries of shopping, accommodations, and other transportation. About 10% of expenditure on food and beverage and recreation and entertainment expenditure is determined by habits. Estimated uncompensated own-price elasticities are elastic for air transportation but inelastic for the remaining tourist sub-industries. In the long-run, shopping becomes elastic. While air transportation and other transportation are substitutes for each other, the majority of the remaining pairwise tourist sub-industries are gross complements. Based on the estimated budget elasticities, recreational expenditure is classified as a luxury in both the short-run and long-run. Air transportation becomes a luxury good in the long-run. The remaining tourist sub-industries are estimated as necessary goods. Tourist subindustries that are relatively habit forming, necessities in use, or price inelastic tend to generate a consistent stream of revenue over time and can be a main target for tourist marketing and policy. In contrast, revenue is likely to decline during economic downturns for tourist sub-industries that have less evidence of habit formation, are luxuries in use, or are price elastic. The remainder of the paper is as follows. Section 2 outlines the dynamic model of habit formation with the data being discussed in section 3, and the estimation and results examined in section 4. The last section concludes the paper and provides policy recommendations. 2. A dynamic flexible demand system The forward looking dynamic model of Muellbauer and Pashardes (1992) and Lyssiotou (2000) is used to model habit formation where current expenditure on tourism (q it ) is determined by some desired level of tourism service flows (~ qit) and from an amount of past spending on tourism (q it-1 ): qit ¼~ qit þθiqit1(1) for i = 1, . . ., n, and 0 ≤θi≤1 captures habit formation. Habit formation has a larger impact on current tourism expenditure as θi→1 and no impact of habit formation when θi¼0. Preference endogeneity across sub-industry i is captured by the estimate of θi. The rational dynamic model has the user cost of a tourist sub-industry capturing the future costs of habit formation. Under static expectations and a real interest rate (r), Spinnewyn (1981) and Muellbauer and Pashardes (1992) show that the user cost is: ~ pit ¼1þr 1þrθi � �pit ¼λipit (2) with p it the price of tourist sub-industry i in period t and λi¼1þr 1þrθi � �. Maximizing utility uð~ q1t, . . ., ~ qntÞsubject to the budget constraint ~yt¼P i ~ pit~ qit, the rational dynamic forward looking model of Muellbauer and Pashardes (1992) gives: qit ¼gitð~ pt;utÞþθiqit1(3) which are converted into budget share equations w it using pit=P i pitqit: JOURNAL OF APPLIED ECONOMICS 105 wit ¼~witð~yt=λiytÞþθiqit1pit=yt  � (4) where wit;pitqit=yt and ~wit;~ pit~ qit=~yt. Using quarterly data, and the user cost the dynamic Almost Ideal Model (AIDS) model is: qit ¼ /iþXjγij ln ~ pjt þβiln ~ytln ~ Pt  � h i ~yt λipit � �þθiqit4(5) where ln ~ Pt¼ /0þP i/iln ~ pit þ1 2P iP j ~ pit~ pjt. Adding up requires P i αi¼1, P i βi¼0;P i γij ¼0forallj, homogeneity requires P j γij ¼0 for all i, and symmetry requires γij ¼γji for all i and j and these across equations restrictions are imposed when estimating the system of equations. The budget share equations are: wit ¼ /iþXjγij ln ~ pjt þβiln ~ytln ~ Pt  � h i ~yt λipit � �þθiqit4 � �pit=ytþμit (6) and are used in the estimation to reduce heteroscedasticity and μit is a random error term. Following Lyssiotou (2000), the uncompensated elasticity of demand for tourist subindustry i in period t is: eijt ¼1 wit � ��ij ~yt λiyt � �þdijθi qit1 yt � �� �dij (7) where �it;@~~wit @lnpjt with d ij = 1 for i = j and d ij = 0 for i ≠j. Since changes in log p jt during period t impact tourist expenditure for k periods, the elasticity of q it+k with respect to p jt is: eijk ¼θk ieijt qit=qitþk  � (8) giving the long run elasticity as k→∞: e� ij ¼eij=1θi ð Þ (9) with qit ¼qitþk¼qi for all k and r = 0. The budget elasticities evaluated with q it = q i for all t are: ei¼1θi ð Þ βi wiþ1 � � (10) giving long-run budget elasticities as in Lyssiotou (2000): e� i¼ei=1θi ð Þ (11) 3. Tourism data The quarterly real tourism data have been used by Tang and Jang (2009) and Aratuo and Etienne (2019) and are from the Bureau of Economic Analysis (BEA). The six tourism industries used by Aratuo and Etienne (2019) are air transportation, food and beverage, recreation and entertainment, shopping, travelers’ accommodations, and other transportation-related commodities. Food and beverages are transactions in restaurants and 106 A. R. FLEISSIG places that sell food and beverages. Recreation and entertainment cover leisure-time activities like gambling, amusement parks and arcades, museums, historical sites, skating rinks, ski lifts, day camps, sporting goods, and so on. Shopping are expenditure by tourists of nondurable commodities except gasoline. Travelers’ accommodations includes hotels, motels, and all other forms of lodging used by tourists. Rail, water transport, intercity bus, local bus, taxi, car rental, travel arrangement and reservation services, gasoline, and so on are part of other transportation. Tourist expenditures across all six industries declined from 2001–2003 and 2009–2011 with the largest decreases for accommodations and air transportation industries. The real tourism output are estimates of domestically produced goods and services sold to travelers and the seasonally adjusted quarterly real tourism data cover the period 1998.1 through 2017.3. Aratuo and Etienne (2019) provide a detailed explanation for each sub-industry. The estimates may be more representative of local travel since domestic tourism is about 80% of total U.S. tourism (OECD, 2018). 4. Estimation and results The share equations were estimated using TSP International 5.1 FIML with the across equations restrictions imposed to ensure adding up, homogeneity, and symmetry. The parameter estimates are in Table 1 and most of the parameters are statistically significant at the 1% or 5% level. The model fits the data well with relatively high R-squares, low root-mean-square errors, and the Berndt and Savin (1975) test for fourth order serial correlation with the across equation restrictions imposed fail to detect serial correlation. The parameters measuring habit persistence (θi) are all statistically significant at the 1% level. The largest degree of habit persistence is for air transportation. Habits account for 33% of current tourism expenditure on air transportation, and around 24% for the three sub-tourist industries of shopping, accommodations, and other transportation. For food and beverage, and recreation, habits account for only 11% and 10% of tourism expenditure, considerably less than the other tourism sectors. Lyssiotou (2000) also finds Table 1. Parameter estimates. α i β i γ i1 γ i2 γ i3 γ i4 γ i5 γ i6 θi Accommodations 0.1933 −0.0051 0.0384 −0.0008 −0.0076 −0.0243 −0.0041 −0.0016 0.2428 0.0555 0.0010 0.0198 0.0002 0.0023 0.0056 0.0030 0.0004 0.0589 Food and beverage 0.1231 −0.0812 −0.0008 0.0429 −0.0133 −0.0013 0.0414 −0.0688 0.1129 0.0847 0.0275 0.0003 0.0101 0.0038 0.0013 0.0098 0.0132 0.0343 Shopping 0.1938 −0.0081 0.0454 −0.0022 0.0013 −0.0236 0.2499 0.0598 0.0025 0.0110 0.0003 0.0012 0.0059 0.0391 Air transportation 0.2232 0.0234 0.0655 −0.0439 0.0062 0.3258 0.0426 0.0054 0.0203 0.0520 0.0009 0.0513 Recreation 0.1409 0.0796 0.0343 −0.0290 0.1025 0.1090 0.0375 0.0079 0.0292 0.0233 Other transportation 0.1256 −0.0085 0.1168 0.2398 0.0356 0.0120 0.0374 0.0618 a Estimation using TSP International 5.1 FIML. b Standard errors are boldface and most of the parameters are statistically significant at the 5% or 10% level. c R-squares Food and beverage (0.837), Shopping (0.824), Air transportation (0.868), Recreation (0.856), Other transportation (0.841). d Food and beverage (0.0243), Shopping (0.0122), Air transportation (0.0143), Recreation (0.0264), Other transportation (0.0354) e Test for serial correlation P-value = 0.868, Berndt and Savin (1975). JOURNAL OF APPLIED ECONOMICS 107 important evidence of habit persistence for international tourism of 36% for France, 24% for both USA–Canada and Spain–Portugal, and a smaller degree of 18% for Greece–Italy. The uncompensated price elasticities calculated at the mean of the data are statistically significant at the 5% level and are in Table 2. Tourism expenditure on air transportation is the only sub-industry that is price elastic (−1.255). Expenditure on air transportation is often a significant share of travel expenditure and tends to be more price elastic, especially when consumers have less expensive alternative options like cars, rail, and other forms of transportation. The meta-analysis of Peng et al. (2015) found that the average own-price elasticity estimate for international air transportation was inelastic at −0.920 while the estimate of Divisekera (2010) is inelastic at −0.52 and Ahn et al. (2018) elastic at −3.40. The price elastic estimate for air transportation may also be due to accounting for habit persistence. Other modes of transport are relatively price inelastic at −0.430 and similar to Divisekera (2010). Certain local attractions may not be accessible by air transportation so that other transportation may become price inelastic. Tourism expenditure on food and beverages is the most price inelastic (−0.217) which is expected as these commodities are typically considered necessary expenditures. However, Ahn et al. (2018) found evidence of elastic demand for food and beverages. Shopping expenditure in this study includes a wide range of nondurable goods that are typically less costly items. For shopping, the price elasticity is inelastic at −0.876 and slightly less inelastic than Divisekera (2010). Wu et al. (2012) found that shopping can be elastic or inelastic in demand for their analysis of tourist spending by Chinese, Japanese, or Taiwanese tourists and Ahn et al. (2018) have an elastic demand for Korean tourism. Accommodation is a necessary expenditure and often price inelastic. For accommodations, the price elasticity is −0.675 and similar to the average price elasticity for accommodation at −0.727 of Peng et al. (2015), −0.52 of Divisekera (2010), −0.37 of Wu et al. (2012), and Ahn et al. (2018) −0.5. For recreation and entertainment, the price elasticity is −0.654. Since the data are more representative of local U.S. travel, some expenditure on recreation and entertainment may involve relatively short trips and consumer demand may be price inelastic. The inelastic estimate may reflect that the recreational and entertainment tourist sub-industry consists of a range of activities from typically more expensive Table 2. Uncompensated price elasticities. Accommodations Food and beverage Shopping Air transportation Recreation Other transport Accommodations −0.675 −0.329 −0.187 −0.593 −0.207 −0.328 0.193 0.156 0.193 0.139 0.067 0.063 Food & beverage −0.099 −0.217 −0.133 −0.103 0.077 −0.188 0.019 0.062 0.113 0.035 0.024 0.201 Shopping −0.033 −0.088 −0.876 −0.067 0.383 −0.023 0.011 0.099 0.250 0.016 0.116 0.008 Air transportation −0.729 −0.033 −0.218 −1.255 −0.302 1.347 0.208 0.043 0.066 0.180 0.099 0.630 Recreation −0.264 0.093 0.143 −0.109 −0.654 −0.017 0.087 0.074 0.129 0.040 0.249 0.008 Other transportation −0.427 −0.247 −0.259 0.736 −0.088 −0.430 0.080 0.295 0.264 0.137 0.044 0.112 a ε ij is the long run unconditional elasticity of substitution between goods i and j for a price change in good j. b Standard errors are boldface. 108 A. R. FLEISSIG options like amusement parks, museums, ski lifts, and gambling that tend to be more price elastic to typically less costly activities like arcades, historical sites, skating rinks, and sporting goods that are often less price elastic. The price elasticity estimate for recreation and entertainment is less inelastic than the entertainment elasticity estimates for the U.S., New Zealand, Japan, and UK. of Divisekera (2010) but Ahn et al. (2018) has an elastic demand for Korean tourism. The cross-price elasticities show that air transportation and other transportation are elastic substitutes for price changes in air transportation (1.347) but inelastic substitutes for price changes in other related transportation (0.736). In times of increasing prices for air transportation, tourism marketing may be better focused on other transportation instead of air transportation. There is generally little other evidence of substitution across tourist sub-industries. Shopping and recreation are inelastic substitutes for each other while food and recreation are highly inelastic substitutes for each other as in Divisekera (2010). The remaining pairwise tourist sub-industries are all complementary in use. The estimated cross-price elasticities find air transportation and accommodation to have the highest degree of complementarity in use. Food and beverage are complements in use for changes in the price of accommodations (−0.329) but less so for changes in the price of food and beverage (−0.099). Estimated cross-price elasticities for shopping and the other tourist subindustries are generally highly inelastic. In contrast, Wu et al. (2012) found that shopping, accommodation, and meals are substitutes using data from Hong Kong. Estimates show that other forms of transportation and accommodation are also complements in use. Aggregate estimates across four countries from Divisekera (2010) find accommodation, food, transportation, shopping, and entertainment as gross complements. Ahn et al. (2018) only found a statistically significant relationship of complementarity between transportation and food. Divisekera and Deegan (2010) find food is a gross complement to logging, transportation, shopping, and sightseeing. The short-run and long-run budget and price elasticities are statistically significant at the 1% level and displayed in Table 2 and Table 3. Habit persistence drives up the Table 3. Price and budget elasticities. LR Price Elasticity (e� ij ) SR Budget Elasticity (ei) LR Budget Elasticity (e� i) Accommodations −0.892 0.737 0.973 0.235 0.185 0.243 Food and beverage −0.244 0.374 0.422 0.064 0.127 0.112 Shopping −1.168 0.717 0.956 0.255 0.113 0.192 Air transportation −1.862 0.782 1.160 0.346 0.150 0.183 Recreation −0.729 1.500 1.671 0.172 0.500 0.515 Other Transportation −0.565 0.730 0.961 0.132 0.115 0.180 a Long-run price elasticities are from equation (9) a Long-run budget elasticities are from equation (11) JOURNAL OF APPLIED ECONOMICS 109