How do prosperity and aspiration underlie leisure tourism expenditure patterns?

Abstract

This research advances the current knowledge of tourism expenditure by adapting a new analytical approach to understand expenditure differentials along their conditional distributions, based on multiple segmentation criteria. Using data from survey and secondary sources, we approximate tourists’ required utilities via prosperity at their countries of residence, a macro-level criterion, and individual-travel aspirations, a micro-level criterion. Subsequently, expenditure differentials between more and less prosperous/aspired tourists are decomposed into two components. First, group differences in expenditure covariates that represent tourists’ relative consumption behaviors and, second, differences in the estimated returns to those covariates, measuring potential third-degree price discrimination. Our results guide policy makers in the tourism industry to develop pricing strategies capable of generating mark-ups within all viable segmentations.

Keywords

prosperity aspiration expenditure decomposition conditional quantile regression

Introduction

Tourism expenditure is a complex and dynamic phenomenon. Socioeconomic attributes, trip-specific characteristics, and psychologic traits are fundamental covariates of tourism expenditures (Brida and Scuderi, 2013). Tourism expenditure boosts the economy as expenditure-based segmentation research recognizes higher-spending tourists as means for profit maximization (Mortazavi and Lundberg, 2020). Segmentation criteria to profile tourists are often confined to micro-level factors, such as nationality, tourists’ motivations, generational cohorts, transportation choice, and information search behavior, used as proxies for preference differentials (e.g., Laesser and Crouch, 2006; Park et al., 2020).

The paradigm of tourism expenditures shifts from advocacy to a sustainability platform which is indicative of its sophistication and complexity (Mehran and Olya, 2019). There is a need for methodological advances to decode the complexity of differentials in tourism expenditure patterns based on both macro- and micro-level criteria. This study fills this research gap in two ways. First, we approximate tourists’ required utilities via their countries’ prosperity levels and their individual travel aspirations. Second, we decompose tourists’ relative tourism expenditures into corresponding differences in their socioeconomic and behavioral expenditure covariates (aka their relative consumption behaviors), and differences in the estimated incremental returns at which such covariates transmute into monetary outlays via market exchange (aka potential third-degree price discrimination).

Methodology

A rational consumer minimizes expenditures subject to prices and a predetermined level of required utility (Mas-Colell et al., 1995). This yields consumers’ compensated demands, $f (x; p, u)$ , and their expenditure functions, $e (p; x, u)$ . For any two consumers with different levels of required utility, $(u^{h} > u^{l})$ , the differential in optimal expenditures could be due to higher quantities demanded at constant prices, $e^{h} (p; x^{h}, u^{h}) - e^{l} (p; x^{l}, u^{l}) > 0$ , higher prices paid for constant quantities, $e^{h} (p^{h}; x, u^{h}) - e^{l} (p^{l}; x, u^{l}) > 0$ , or both (Alfarhan et al., 2021a).

Since utility, $u$ , is a complex unobserved construct influenced by a dynamic set of elements within tourists’ macro- and micro-level environments, we approximate for differentials in their required utility via the Legatum Prosperity Index^TM (Legatum, 2020), and their revealed individual travel aspirations, simultaneously. Based on these two criteria, we disaggregate tourists’ relative optimal expenditures into correspondingly relative consumption behaviors, $e (p; \partial x, \partial u)$ , and an upper bound for third-degree price discrimination, $e (\partial p; x, \partial u)$ .

Data

For method demonstration, a rich set of data from international leisure tourists to Oman is used. To collect data using survey, we approached respondents who waited to depart from Muscat International Airport in 2019. In total, 1042 individuals completed the survey, reported staying within budget a priority and rated questions about their travel aspirations. Details on the sampling approach are provided in Alfarhan et al.’s (2021a, 2021b) research. Aspiration $(A)$ is the weighted average of three utility-generating components, namely, escape and relaxation (three items), self-deployment (five items), and stimulation (four items), following Pearce and Lee (2005). For each of the 12 items, respondents answered the question of “In considering your current trip, how important was it to you that you [item]?” on a five-point Likert scale. The associated component analysis rendered explained variations ranging between 50.3% and 77.8%, Kaiser–Meyer–Olkin statistics ranging between 0.812 and 0.915 and Cronbach’s alpha values of 0.829, 0.854, and 0.715 for the three components, respectively. The sample median for aspiration is 3.92.

Prosperity $(P)$ is the five-year (2015–2019) average of the tourist’s country of residence’s Legatum index. This index is based on 12 pillars within the domains of Inclusive Societies, Open Economies, and Empowered People, with an in-sample median of 76.68. We stipulate that this index is more indicative of the complexity of tourism expenditure and more closely related to the distribution of tourists’ individual preferences than other macroeconomic measures (e.g., per capita GDP). As shown in Figure 1, sample $N_{A}^{P}$ where $P = {h, l}$ and $A = {h, l}$ is identified into four mutually exclusive and exhaustive subsamples, namely, tourists with above-median prosperity and aspiration $N_{h}^{h}$ (the north-east panel), below-median prosperity and above-median aspiration $N_{h}^{l}$ (the south-east panel), above-median prosperity and below-median aspiration $N_{l}^{h}$ (the north-west panel), and below-median prosperity and aspiration $N_{l}^{l}$ (the south-west panel).

Figure 1.

The distribution of tourists by prosperity and aspiration.

Figure 2 illustrates the descriptive statistics of our two main groups. $N_{h}^{h}$ tourists spend on average 37.4% more than $N_{l}^{l}$ tourists. They are more senior and wealthier. Furthermore, 7.3% less of $N_{h}^{h}$ tourists are married and 5.7% less travel via economy class. In terms of accommodation, 17.3% more of $N_{h}^{h}$ tourists stay at five-star hotels, whereas 13.7% less stay at four-star hotels. Also, $N_{h}^{h}$ visit more sites; 14.2% are more likely to be first-time visitors and stay one extra night at the destination. These socioeconomic and behavioral differences define their relative consumption behaviors in this paper, induced by the reported differences in their prosperity and aspiration.

Figure 2.

Means and standard deviations (between parentheses) of expenditures and expenditure covariates, based on relative prosperity and aspiration. *, **, and *** denote that the difference is statistically significant at 10%, 5%, or 1% probability levels.

Analytical approach

This research proposes a novel approach to model relative expenditures by integrating the conditional counterfactual quantile decomposition structure of Chernozhukov et al. (2013) with the mean-level structure proposed by Shamsuddin (1998). The former allows for the decomposition of an estimated differential on the basis of a single segmentation criterion, whereas the latter enables conducting the decomposition using multiple criteria.

Define $E$ as the natural logarithm of total personal tourism expenditures excluding airfare and $X$ as the set of expenditure covariates as reported in Figure 2. The distributional difference in expenditures between $N_{h}^{h}$ and $N_{l}^{l}$ tourists can be expressed as

Δ F_{E} = Δ F_{E}^{P} + Δ F_{E}^{A} = [\int^{​} F_{(E_{h}^{h} | X_{h}^{h})} d F_{X_{h}^{h}} - \int^{​} F_{(E_{h}^{l} | X_{h}^{l})} d F_{X_{h}^{l}}] + [\int^{​} F_{(E_{h}^{l} | X_{h}^{l})} d F_{X_{h}^{l}} - \int^{​} F_{(E_{l}^{l} | X_{l}^{l})} d F_{X_{l}^{l}}]

(1)

By means of the conditional quantile regression process

Q_{E} (τ | X) = X β (τ) + υ

(Koenker and Bassett, 1978)¹ and the counterfactual distributions

(C_{1}, C_{2})

of expenditures

Δ Q_{E}^{C_{1}} (τ | X) = X_{h}^{h} β_{h}^{l} (τ)

and

Δ Q_{E}^{C_{2}} (τ | X) = X_{l}^{l} β_{h}^{l} (τ)

, each of the two terms on the right-hand side of equation (1) may be disaggregated into the quantile

(τ)

effects of tourists’ relative consumption behaviors and an upper bound of third-degree price discrimination such that

Δ Q_{E} (τ | X) = Δ Q_{E}^{P} (τ | X) + Δ Q_{E}^{A} (τ | X) = [(X_{h}^{h} - X_{h}^{l}) β_{h}^{l} (τ)] + [X_{h}^{h} (β_{h}^{h} (τ) - β_{h}^{l} (τ))] + [(X_{h}^{l} - X_{l}^{l}) β_{h}^{l} (τ)] + [X_{l}^{l} (β_{h}^{l} (τ) - β_{l}^{l} (τ))] + (υ_{h}^{h} - υ_{l}^{l})

(2)

The first and third terms on the right-hand side of equation (2), tourists’ relative consumption behaviors, are the distributional differences in their expenditure covariates, evaluated at estimated constant incremental returns. The first term corresponds to the differences induced by tourists’ relative prosperity and the third term corresponds to the differences induced by their relative aspiration.

The second and fourth terms, potential third-degree price discrimination, are the distributional differences in the estimated incremental returns at which constant expenditure covariates are transformed into monetary outlays via market exchange. The second term is based on relative prosperity and the fourth on relative aspiration.

Results

Results of the conditional counterfactual quantile decomposition of expenditures are reported in Table 1. Column (1) reveals that the expenditure differential between

N_{h}^{h}

and

N_{l}^{l}

tourists declines from 64.7% to 29.9% between the 10^th and the 90^th percentiles. The aggregate impact of tourists’ relative prosperity, the sum of columns (2) and (3), accounts for 84% at median-level expenditures. The impact of relative aspiration accounts for the remaining median-level differential by passing through tourists’ relative consumption behaviors, column (4), exclusively.²

Table 1.

The conditional counterfactual quantile decomposition of expenditures by prosperity and aspiration, equation (2).

Percentile (τ)	Expenditure differential	Prosperity		Aspiration
	Expenditure differential	Consumption behavior	Third-degree price discrimination	Consumption behavior	Third-degree price discrimination
	(1)	(2)	(3)	(4)	(5)
	$Δ Q_{E} (τ \| X)$	$[(X_{h}^{h} - X_{h}^{l}) β_{h}^{l} (τ)]$	$[X_{h}^{h} (β_{h}^{h} (τ) - β_{h}^{l} (τ))]$	$[(X_{h}^{l} - X_{l}^{l}) β_{h}^{l} (τ)]$	$[X_{l}^{l} (β_{h}^{l} (τ) - β_{l}^{l} (τ))]$
P10	0.647***†(0.089)	0.079*(0.053)	0.328***(0.104)	0.105**(0.061)	0.135**†(0.08)
P25	0.550***†(0.079)	0.097**(0.046)	0.28***(0.083)	0.093**(0.044)	0.08(0.078)
P50	0.343***(0.065)	0.076**(0.035)	0.212***(0.065)	0.078**(0.041)	−0.023(0.059)
P75	0.298***(0.079)	0.062**(0.036)	0.168***(0.065)	0.085**(0.038)	−0.017(0.058)
P90	0.299***(0.077)	0.067**(0.032)	0.139**(0.073)	0.084**(0.04)	0.009(0.069)
Bootstrap inference on the counterfactual quantile processes^a
No effect ∀ τ	0.000***	0.030**	0.000***	0.010**	0.480
Constant effect ∀ τ	0.010***	0.87	0.3	0.940	0.130

*, **, *** denote the statistical significance at 10%, 5%, or 1% probability.

† Statistically different from the median-level estimate.

Source: Authors’ calculations.

^a Cramer–von Mises–Smirnov probability values.

Columns (2) and (4) show that tourists are heterogeneous in terms of their consumption behaviors as induced by both prosperity and aspiration. The monetary impacts of these two behavioral components account for median-level shares of 22% and 23%, respectively. These shares are generalizable along the distributions of expenditures, as per the corresponding bootstrap inference.

Third-degree price discrimination, on the other hand, is solely based on tourists’ prosperity levels. Column (3) indicates that estimated relative incremental returns to expenditure covariates within the prosperity criterion account for 62% of the median-level differential, whereas column (5) reveals no such effect within the criterion of aspiration. Intuitively, this signals that marketers at this destination are either unable to observe differentials in tourists’ travel aspirations or have no aspiration-based segmentation strategies in place.

Conclusion

This paper constitutes a methodological contribution by modeling the expenditure effects of tourists’ relative consumption behaviors and potential third-degree price discrimination within the double macro- and micro-level criteria of prosperity at the country of residence and individual travel aspiration. Prosperity and aspiration contribute to shaping tourists’ expenditure patterns.

We demonstrate that both criteria translate into observable heterogeneities in tourists’ consumption behaviors, both leading to corresponding differentials in their tourism expenditures (Supplementry Appendix Figures A3 and A4). Since third-degree price discrimination is a marketing strategy whose efficacy is contingent on, among other conditions, the ability to identify market segments who portray different consumption behaviors, the previously observed heterogeneities constitute ground for segmentation strategies based on both prosperity and aspiration together.

Evidence from the current destination, however, suggests that third-degree price discrimination is entirely based on observed relative prosperity levels. Relative aspirations, which may not be directly observable to destination marketers, induce no price mark-ups. These results imply room for revenue gains in the tourism industry, contingent on the ability of destination marketers to identify aspiration-induced differentials in behavioral covariates such as accommodation choice, sites visited, and visitation frequency (Supplementry Appendix Figure A4). Accordingly, marketing strategies may be introduced or redesigned, in order to generate price mark-ups within the aspiration criterion as well.

The results of this paper are based on post-travel data. Future research may capture data before and after travel to offer a comprehensive perspective on tourist’s behaviors including the travel decisions. Additionally, decomposing expenditure differentials via multiple segmentation criteria may turn data-demanding rather abruptly. That is because the sample must be subdivided into mutually exclusive and exhaustive subsamples, whilst each remaining sufficiently sizable for the quantile estimation processes. Furthermore, travel aspirations are a priori unobservable and may not be directly proposed as a segmentation criterion in practice. We recommend tourism marketers to find an observable instrument (e.g., information search behavior) to approximate for aspirations.

Supplemental Material

sj-pdf-1-teu-10.1177_13548166211064215 – Supplemental Material for How do prosperity and aspiration underlie leisure tourism expenditure patterns?

Supplemental Material, sj-pdf-1-teu-10.1177_13548166211064215 for How do prosperity and aspiration underlie leisure tourism expenditure patterns? by Usamah F. Alfarhan, Hossein Olya, and Khaldoon Nusair in Tourism Economics

Footnotes

Acknowledgments

We express our sincere gratitude to all public authorities and data collectors who contributed to the accessibility of respondents at Muscat International Airport.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by His Majesty’s Trust Fund for funding this research under agreement No. SR/EPS/MRKT/16/01.

Data availability statement

The data that support the findings of this study have been collected under the funding agreement No. SR/EPS/MRKT/16/01 and are not publicly accessible until conclusion of the agreement. Upon requests pertaining to the review process of this study, data are available from the corresponding author.

Ethics approval statement

This study is approved by His Majesty’s Trust Fund under agreement No. SR/EPS/MRKT/16/01.

ORCID iDs

Usamah F Alfarhan

Hossein Olya

Supplemental Material

Supplemental material for this article is available online.

Notes

Authors’ biographies

Usamah F. Alfarhan is an applied microeconomist at Sultan Qaboos University, Oman, and a scientific coordinator at the Gulf Labour Markets, Migration and Population Programme. For more information, see .

Hossein Olya is Director of Research Development for Marketing and Cultural Creative Industries at the Sheffield University Management School, Sheffield, UK. See more at .

Khaldoon Nusair is a full professor and chair of the Marketing Department at Sultan Qaboos University. His recent research interests are in digital marketing, smart retail, and brand management.

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Supplementary Material

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