International tourism demand,number of airline seats and trade triangle: Evidence from New Zealand partners

Abstract

This article examines the three-way relationship between international tourism demand, airline economy seats and international trade for New Zealand together with its key trading/tourism partners. We have found that airline economy seats are the important factor for determining tourism demand among New Zealand's tourism partners except for richer economies, like the United States and Japan. Trade volume does not have strong causality relationship to tourism demand in particular for trading partners, like Japan, Korea, Singapore and the United States. However, especially after the global financial crisis, it is observed that trade volumes help to boost the number of airline economy seats available (airline seat capacity) between New Zealand and its trading partners.

Keywords

airline economy seats international tourism demand international trade New Zealand

Introduction

In New Zealand, international tourism has been the key driver of growth in its economy (and the largest export item) as well as the aviation sector makes significant contribution to its economy (Balli and Tsui, 2016; Becken, 2002; Becken et al., 2009; Duval, 2013; Tsui et al., 2017). According to Statistics New Zealand, international tourism expenditure grew to approximately NZ$10.3 billion and contributed 15.3% to New Zealand’s total exports of goods and services in March 2014. There were also approximately 2.71 and 2.91 million of visitor arrivals by air to visit New Zealand during 2014 and 2015, respectively (Statistics New Zealand, 2016), which makes 99% of total tourism inflows for New Zealand. This dynamic link between tourism demand and air transport development has also been discussed in academia and New Zealand government to understand the rapid growth of New Zealand’s tourism and aviation sectors (e.g. Duval and Schiff, 2011; Kissling, 1998; Schiff and Small, 2013; Vowles and Tierney, 2007).

Likewise, the Ministry of Transport of New Zealand held similar viewpoint and indicated that “aviation connects New Zealand and New Zealanders to the world, provides access to global markets, and generates trade and tourism” (Ministry of Transport, 2013, 2017). Tourism New Zealand announced that it has formed strategic marketing partnership with Air New Zealand to promote New Zealand to new and existing international markets and collaboratively grow the value on inbound tourism (Tourism New Zealand, 2016). Therefore, a clear knowledge of the causal relationship between international inbound tourism (including business travellers and leisure travellers) and the growth of air transport activity in New Zealand may offer better estimation of its economic outlook as international tourist expenditure is one of New Zealand’s key inflows.

Empirically, in the air transport and tourism literature, a close relationship between air transport and tourism has been found as a two-way or interlinked relationship (Bieger and Wittmer, 2006; Lei and Papatheodorou, 2010; Page and Connell, 2014; Spasojevic et al., 2017). Bieger and Wittmer (2006) reviewed the relationship between tourism and air transport and claimed that tourism is a driving factor and a stimulator of changes in air transport, and air transport influences tourism by opening new destinations and tourism forms such as long-haul travel. Most notably, the close relationship between tourism and air transport (particularly the liberalization of air transport) is also well documented in other prior literature (e.g. Dobruszkes et al., 2016; Forsyth, 2006; Hall, 2004; Page, 2005; Papatheodorou and Zenelis, 2013; Prideaux, 2000; Warnock-Smith and Morrell, 2008; Warnock-Smith and O’Connell, 2011; Wheatcroft, 1998). In particular, Forsyth (2006) argued that aviation liberalization has greatly contributed to the boom in international tourism, as many tourists now travel by air. It is evident that the causal relationship between air transport and tourism is well examined in prior literature.

Recently, many studies have found a bilateral relationship between bilateral trade volumes and international inbound tourism (e.g. Eilat and Einav, 2004; Fischer and Gil-Alana, 2009; Kulendran and Wilson, 2000; Mishra et al., 2011; Tsui and Fung, 2016; Turner and Witt, 2001). For example, Kulendran and Wilson (2000) used the Granger causality approaches to analyse the causality between different travel and trade between Australia and its four key trading partners (Japan, New Zealand, the United Kingdom, and the United States) and found support for belief that there is a relationship between international travel and international trade. Tsui and Fung (2016) also found evidence of a long-run equilibrium relationship (cointegration) between business travel and trade volumes among Hong Kong and the United States.

Other strand of the literature indicates that air travel between two nations might promote bilateral trade volumes through business relationships or direct investment channels (e.g. Bilen et al., 2017; Brida, Cortes-Jimenez et al., 2016; Brida, Rodríguez-Brindis et al., 2016; Hakim and Merkert, 2016; Kulendran and Wilson, 2000; Tsui and Fung, 2016; Van De Vijver et al., 2014). In particular, Hakim and Merkert (2016) examined the casual relationship between air transport and economic growth in the South Asian context between 1973 and 2014 and confirmed a long-run unidirectional Granger causality which runs from GDP to air passenger traffic and also to air freight volumes.

Indeed, by merging two strands of the literature, we investigate the three-way relationship between tourism, air transportation and trade. On theoretical basis, Gray (1970) proposed that international travel is a component of international trade. Similarly, Kientz (1971) discovered that the total value of trade is a strong predictor of the demand for travel to the United States. He indicated that when there is much trade between two countries in physical goods or services, this would promote business travel between two countries, since businesses in both countries would like to sustain international trade. Having mentioned, the trade and tourism relationship, Borenstein (1992), both theoretically and empirically, investigated the air transportation and tourism nexus. He explored and identified the positive relationship between number of seats and tourism. In this study, we combine these theoretical assumptions into one empirical work and expose if trade, tourism and airline seats are interacting with each other.

To the best knowledge of authors, this article is the first empirical study in the tourism and aviation literature to investigate a three-way causal relationship between geographically isolated economy (like New Zealand) and its key tourist source markets and the key trading partners. Applying a popular Granger causality approach to study the linkages between these three time-series variables, we found that air transport (i.e. the number of airline economy seats) is significantly explaining international inbound tourism demand for New Zealand from its key tourist source markets, expect for the United States and Japan. For these two countries, their respective tourist arrivals to New Zealand is unresponsive to the number of airline economy seats available. In addition, international trade is found to be a significant factor that has an instrumental effect on tourist arrivals from Australia, Canada and China. However, for Japan, the United States, Korea and Singapore, a strong causal effect of bilateral trade volumes on New Zealand’s international inbound tourism growth cannot be found, which indicates the existence of the inelastic tourism demand for New Zealand for these countries. Concentrating airline capacity between New Zealand and its key tourist source markets, it is mostly triggered by tourism demand from most of New Zealand’s key tourist source markets (excluding China) and their bilateral trade volumes. Importantly, the key findings of this study pinpoint that the existing relationships between tourism and air transport help air carriers to plan and schedule more flights and seat capacity between New Zealand and its key tourist source markets to meet increasing international inbound tourism demand for New Zealand.

The format of this article is structured as follows: The second section provides an overview of three variables of this study – international tourism demand, airline economy seats and bilateral trade volumes for New Zealand. The third section describes the methodology used to investigate the interrelationship between international tourism demand, airline economy seats and bilateral trade of New Zealand and presents the empirical findings of the study. The final section summarizes the key findings and indicates a direction for future research.

Data and descriptive analysis

In a monthly basis, we collected namely ‘Tourist’, ‘EconSeat’ and ‘Int.Trade’ data between New Zealand and eight countries (Australia, Canada, China, Hong Kong, Japan, Korea, Singapore and the United States) for the period from April 1998 to July 2015. ‘Tourist’ is the number of inbound tourists with the holiday and vacation purpose only. ‘EconSeat’ is the number of direct scheduled airline economy-class seats from the origin country to New Zealand and it was sourced from the Official Airline Guide. ‘Int.Trade’ denotes the bilateral trade factors/indicators between New Zealand and its trading partners. These factors include import values, export values and the total trade volumes (import plus export values), respectively. ‘Tourist’, ‘Export’ and ‘Import’ data were sourced from Statistics New Zealand. The descriptive statistics for each time series that we collected are presented in Table 1. The table contains the basic statistics for all the variables above, indicating the reliability of the data set. The sample size reduces to 93 and 105 (from 208) for Canada and China due to unavailability of direct flight services to New Zealand from both countries.¹

Table 1.

Descriptive statistics of variables.

	EconSeat	Tourist	Int.Trade	Export	Import	EconSeat	Tourist	Int.Trade	Export	Import
	Australia (1998M04–2015M07)					Japan (1998M04–2015M07)
Mean	265,323.1	28,892.06	1.25E+09	6.44E+08	6.04E+08	12,238.57	7677.557	5.65E+08	2.77E+08	2.89E+08
Maximum	391,771.9	60,143.98	1.93E+09	1.05E+09	1.19E+09	20,694	17,430	7.56E+08	4.25E+08	4.79E+08
Minimum	165,192	7070.002	6.23E+08	2.79E+08	3.44E+08	4082.001	1008	3.44E+08	1.71E+08	1.67E+08
Std. Dev.	58,967.02	11,473.47	2.32E+08	1.66E+08	1.12E+08	3738.119	3787.866	78,154,536	46,434,351	51,197,419
Sample	208	208	208	208	208	208	208	208	208	208
	Canada (2007M11–2015M07)					Korea (1998M04–2015M07)
Mean	4043.721	2142.828	88,939,214	43,677,869	45,261,345	7406.548	4224.529	2.13E+08	1.12E+08	1.01E+08
Maximum	7506.001	5313.002	1.59E+08	74,251,807	1.13E+08	23,865.99	13,050	4.60E+08	1.87E+08	2.89E+08
Minimum	2224	420	41,899,456	16,319,936	18,177,061	2349	260	77,292,960	47,287,606	30,005,355
Std. Dev.	1114.924	1298.98	21,435,235	11,775,187	17,000,814	3462.043	2258.958	66,999,115	26,737,498	47,927,373
Sample	93	93	208	208	208	208	208	208	208	208
	China (2006M11–2015M07)					Singapore (1998M04–2015M07)
Mean	9930.438	10,186.2	7.14E+08	2.98E+08	4.16E+08	23,731.32	1701.654	1.72E+08	55,007,042	1.17E+08
Maximum	28,315.01	46,480.01	2.04E+09	1.35E+09	9.38E+08	34,128.99	8111.999	5.55E+08	2.69E+08	4.61E+08
Minimum	2869.999	648.0001	1.24E+08	42,425,945	74,542,358	15,779.99	364	54,365,948	18,655,212	28,350,617
Std. Dev.	5787.095	6550.854	4.65E+08	2.73E+08	2.19E+08	4144.443	1289.401	95,042,704	32,646,974	75,777,938
Sample	105	105	208	208	208	208	208	208	208	208
	Hong Kong (1998M04–2015M07)					The United States (1998M04–2015M07)
Mean	16,378.96	1248.529	71,507,000	57,789,400	13,717,600	36,509.2	9527.673	7.38E+08	3.54E+08	3.83E+08
Maximum	29,463.99	3424.001	1.19E+08	1.07E+08	42,167,498	44,654.99	20,112	1.15E+09	6.03E+08	8.38E+08
Minimum	8336.999	342	40,466,080	29,381,244	7,242,806	24,964.99	3312	4.74E+08	1.77E+08	2.31E+08
Std. Dev.	5459.751	664.6937	12,861,544	12,780,517	3,981,479	4134.002	3994.525	1.28E+08	78,272,251	1.00E+08
Sample	208	208	208	208	208	208	208	208	208	208

Note: All the time series variables above are stated in the monthly figures. Tourist is the number of inbound tourists with the holiday and vacation purpose only. EconSeat is the direct scheduled airline economy-class seats from the origin country to New Zealand and it was sourced from the Official Airline Guide. Int.Trade is the summation of Export from New Zealand to each country listed and Import from each country listed to New Zealand. Tourist, Export and Import data were sourced from Statistics New Zealand. Std. Dev. denotes the standard deviation.

Empirical model and findings

To be able to investigate the three-way causal relationships between trade, number of airline economy seats and tourism between New Zealand and its key trading partners/tourist source markets, we first run augmented Dickey–Fuller unit root tests for each time series separately and found that for most of the time series no evidence to reject the unit root null hypothesis at the level. However, we found strong statistical evidence for stationarity of all the first differenced time series, denoting that most of the time series are integrated of order one (I(1)).²

Vector autoregressive (VAR) model

The vector autoregressive (VAR) model can be applied to explore the causality between international tourism demand, number of airline economy seats and bilateral trade volumes between New Zealand and its trading partners (Australia, Canada, China, Hong Kong, Japan, Korea, Singapore and the United States) (Granger, 1988).

The multivariate VAR regression models are shown in Equations (1) to (3):

Δ ln {(Tourist)}_{i, t} = α_{i, 0} + \sum_{j = 1}^{n} α_{i j, 1} Δ ln {(Tourist)}_{i, t - j} + \sum_{j = 1}^{n} α_{i j, 2} Δ ln {(EconSeat)}_{i, t - j} + \sum_{j = 1}^{n} α_{i j, 3} Δ ln {(Int . Trade)}_{i, t - j} + ε_{i, t}

Δ ln {(EconSeat)}_{i, t} = β_{i, 0} + \sum_{j = 1}^{n} β_{i j, 1} Δ ln {(Tourist)}_{i, t - j} + \sum_{j = 1}^{n} β_{i j, 2} Δ ln {(EconSeat)}_{i, t - j} + \sum_{j = 1}^{n} β_{i j, 3} Δ ln {(Int . Trade)}_{i, t - j} + ϑ_{i, t}

Δ ln {(Int.Trade)}_{i, t} = λ_{i, 0} + \sum_{j = 1}^{n} λ_{i j, 1} Δ ln {(Tourist)}_{i, t - j} + \sum_{j = 1}^{n} λ_{i j, 2} Δ ln {(EconSeat)}_{i, t - j} + \sum_{j = 1}^{n} λ_{i j, 3} Δ ln {(Int . Trade)}_{i, t - j} + ω_{i, 1 t}

where Δ and $ln$ are the notations for first difference and the natural logarithm, respectively; i and t are the notations for the sampled country and time, respectively; α, β, and λ are the coefficients to each equation; and $ε, ϑ$ and ω are the uncorrelated and white noise error terms. $ln {(Tourist)}_{i, t}$ is the natural logarithm of total international inbound tourists by air from country i to New Zealand at time t. $ln {(EconSeat)}_{i, t}$ is the natural logarithm of the total direct airline economy seats available from country i to New Zealand. $ln {(Int.Trade)}_{i, t}$ denotes the natural logarithm of the bilateral trade factors/indicators between country i and New Zealand. This variable includes import values, export values and the total trade volumes (import plus export values), respectively. Therefore, we have run three separate VAR systems in which one for each international trade indicator.³

To decide on the directions of Granger causality between the two-time series variables in this study, we established the hypotheses shown below and performed the conventional F-tests. For example, testing whether either Ln(EconSeat) or Ln(Int.Trade) granger causing to Ln(Tourist), thus we set up two following null hypotheses (H₀₁ and H₀₂) and tested them separately.

H₀₁ : Ln(EconSeat) does not Granger-cause Ln(Tourist) for each of the studied countries i or H₀₁ : $α_{i 1, 2} = 0, α_{i 2, 2} = 0, \dots ., α_{i n, 2} = 0$ .

H₀₂: Ln(Int.Trade) does not Granger-cause Ln(Tourist) for each of the studied countries i or H₀₂ : $α_{i 1, 3} = 0, α_{i 2, 3} = 0, \dots ., α_{i n, 3} = 0$ .

Rejecting these null hypotheses reflects the evidence for a causal relationship from either Ln(EconSeat) or Ln(Int.Trade) to Ln(Tourist) at a specified significance level.

Similarly, testing whether either Ln(Tourist) or ln(Int. Trade) granger causing to Ln(EconSeat), we set up two following null hypotheses (H₀₃ and H₀₄) and tested them separately. Rejecting these null hypotheses reflects the evidence for a statistically significant causal relationship from either Ln(Tourist) or Ln(Int.Trade) to Ln(EconSeat) at a specified significance level.

H₀₃ : Ln(Tourist) does not Granger-cause Ln(EconSeat) for each of the studied countries $i$ or H₀₃ : $β_{i 1, 1} = 0, β_{i 2, 1} = 0, \dots ., β_{i n, 1} = 0$ .

H₀₄ : Ln(Int. Trade) does not Granger-cause to Ln(EconSeat) for each of the studied countries i or H₀₄ : $β_{i 1, 3} = 0, β_{i 2, 3} = 0, \dots ., β_{i n, 3} = 0$ .

Rejecting these null hypotheses reflects the evidence for a causal relationship from either Ln(Tourist) or Ln(Int. Trade) to Ln(EconSeat) at a specified significance level.

Granger causality test indicates the causal relationship between the time series variables. From the theoretical assumptions, we have investigated the causal relationship between airline seats, tourism and trade volumes of New Zealand with its key tourist source markets/trading partners.

Empirical findings

To examine the causal relationship between international tourism demand, bilateral trade volumes and number of airline economy seats available, we performed the Granger causality tests and reported the results in Tables 2 to 6. In Table 2, the upper panel contains the Granger causality tests when the dependent variable is tourism demand. It is affected by the number of airline economy seats for almost all trading partners of New Zealand excluding Japan and the United States. Both the United States and Japan are richer economies compared to the rest of the sampled countries and that might be the reason we did not find a stronger relation with airline economy seats and international tourism demand for New Zealand. This finding is in line with Marsiglio (2015), which indicated the importance of richer tourists on tourism demand. He also discussed that tourist demand from richer economies would be relatively inelastic, supporting our argument above.

Table 2.

Granger causality between total international inbound tourists, total direct airline economy seats and trade volume.

Excluded variables/countries	Australia	Canada	China	Hong Kong	Japan	Korea	Singapore	United States
Dependent variable: Ln(Tourist)
Ln(EconSeat)	31.16***	47.18***	17.69**	28.46***	12.40	38.97***	45.76***	13.96
Ln(Int.Trade)	31.92***	34.21***	27.49***	19.45*	18.48	12.17	7.03	17.01
All excluded	66.86***	66.56***	59.55***	51.09***	31.36	56.14***	52.29***	30.15
Dependent variable: Ln(EconSeat)
Ln(Tourist)	49.34***	34.29***	13.82*	41.48***	36.70***	46.33***	61.34***	23.96**
Ln(Int.Trade)	30.38***	10.66	13.87*	14.29	25.94**	6.23	20.13*	11.99
All excluded	78.57***	56.08***	29.56**	59.59***	79.12***	54.87***	81.52***	36.4**
Up to lag	12	11	8	12	12	12	12	12

Note: The null hypothesis is that the dependent variable is not Granger caused by the other variables. Chi-square statistics are printed.

* Test statistics is significant at the 0.10 significance level.

** Test statistics is significant at the 0.05 significance level.

*** Test statistics is significant at the 0.01 significance level.

Table 3.

Granger causality between total international inbound tourists, total direct airline economy seats and export.

Excluded variables/countries	Australia	Canada	China	Hong Kong	Japan	Korea	Singapore	United States
Dependent variable: Ln(Tourist)
Ln(EconSeat)	29.59***	37.78***	30.13***	28.99***	9.32	39.10***	43.79***	11.55
Ln(Export)	38.08***	39.98***	38.92***	17.29	12.22	14.92	7.83	16.71
All excluded	73.97***	72.22***	72.72***	48.59***	24.72	59.46***	53.27***	29.82
Dependent variable: Ln(EconSeat)
Ln(Tourist)	50.11***	39.86***	13.50	43.18***	40.31***	49.92***	52.29***	17.23
Ln(Export)	27.62***	13.44	10.64	17.44	17.93	16.51	11.58	19.91*
All excluded	75.22***	68.62***	25.46	63.43***	69.19***	67.63***	70.54***	45.26***
Up to lag	12	11	12	12	12	12	12	12

Note: The null hypothesis is that the dependent variable is not Granger caused by the other variables. Chi-square statistics are printed.

* Test statistics is significant at the 0.10 significance level.

*** Test statistics is significant at the 0.01 significance level.

Table 4.

Granger causality between total international inbound tourists, total direct scheduled airline economy seats and import.

Excluded variables/countries	Australia	Canada	China	Hong Kong	Japan	Korea	Singapore	United States
Dependent variable: Ln(Tourist)
Ln(EconSeat)	34.09***	27.04***	31.03***	32.64***	13.20	38.07***	46.56***	15.17
Ln(Import)	26.74***	15.92	29.59***	45.46***	12.76	10.13	7.45	14.42
All excluded	60.88***	41.75***	63.33***	80.91***	25.30	53.66***	52.81***	27.39
Dependent variable: Ln(EconSeat)
Ln(Tourist)	36.54***	35.33***	15.83	30.86***	36.52***	42.09***	58.74***	20.67*
Ln(Import)	19.33*	12.32	17.76*	8.59	24.69**	14.29	15.04	14.87
All excluded	65.17***	66.85***	35.88**	52.65***	77.57***	64.87***	74.99***	39.62**
Up to lag	12	11	11	12	12	12	12	12

Note: The null hypothesis is that the dependent variable is not Granger caused by the other variables. Chi-square statistics are printed.

* Test statistics is significant at the 0.10 significance level.

** Test statistics is significant at the 0.05 significance level.

*** Test statistics is significant at the 0.01 significance level.

Table 5.

Granger causality between total international inbound tourists and total direct scheduled airline economy seats before the GFC period.

Dependent variable	Excluded variables	Australia	Hong Kong	Japan	Korea	Singapore	United States
Ln(Tourist)	Ln(EconSeat)	40.09***	22.03**^,Ⱡ	29.25***^,Ⱡ	45.51***	35.04***	18.82*
	Ln(Int.Trade)	22.48**	19.79**^,Ⱡ	54.60***^,Ⱡ	28.56***	15.08	24.25**
Ln(EconSeat)	Ln(Tourist)	79.14***	25.04***^,Ⱡ	66.89***^,Ⱡ	28.29***	37.06***	23.72**
	Ln(Int.Trade)	42.08***	10.33^Ⱡ	38.41***^,Ⱡ	13.96	49.85***	15.67
Ln(Tourist)	Ln(EconSeat)	30.97***	23.50**	19.20*	41.98***	27.57***	15.66
	Ln(Export)	24.89**	37.23***	14.73	18.90*	10.16	24.88**
Ln(EconSeat)	Ln(Tourist)	43.43***	25.20**	55.90***	48.67***	24.31**	19.29*
	Ln(Export)	22.87**	12.00	20.57*	42.82***	26.81***	21.70**
Ln(Tourist)	Ln(EconSeat)	41.32***	16.59	31.42***	32.39***	35.28***	32.16***
	Ln(Import)	19.83*	23.02**	34.06***	21.45**	13.84	33.96***
Ln(EconSeat)	Ln(Tourist)	71.48***	26.28***	40.17***	21.33**	33.89***	37.79***
	Ln(Import)	35.97***	13.29	11.31	26.42***	42.16***	28.91***

Note: GFC denotes global financial crisis 2008. The null hypothesis is that the dependent variable is not Granger caused by the other variables. Chi-square statistics are printed. For Canada and China, we don’t have enough samples to run the models for the pre-GFC period.

* Test statistics is significant at the 0.10 significance level.

** Test statistics is significant at the 0.05 significance level.

*** Test statistics is significant at the 0.01 significance level.

^ⱠModels with optimum lag chosen as 11, while the rest of the models’ optimum lag is selected as 12.

Table 6.

Granger causality between total international inbound tourists, total direct scheduled airline economy seats after the GFC period.

Dependent variable	Excluded variables	Australia	Canada	China	Hong Kong	Japan	Korea	Singapore	United States
Ln(Tourist)	Ln(EconSeat)	42.48***	47.57***	29.02***^,®	55.61***^,Ⱡ	17.77*^,Ⱡ	45.82***	41.17***	29.13***^,Ⱡ
	Ln(Trade volume)	56.53***	34.11***	31.74***^,®	16.03^Ⱡ	23.51**^,Ⱡ	16.66	14.84	23.34**^,Ⱡ
Ln(EconSeat)	Ln(Tourist)	32.61***	32.56***	11.65^®	49.74***^,Ⱡ	42.08***^,Ⱡ	39.85***	67.00***	137.05***^,Ⱡ
	Ln(Trade volume)	13.78	10.78	5.07^®	22.17**^,Ⱡ	31.42***^,Ⱡ	19.83*	19.34*	33.93***^,Ⱡ
Ln(Tourist)	Ln(EconSeat)	37.97***	38.71***^,Ⱡ	24.00***^,®	59.71***^,δ	12.04^Ⱡ	36.09***	57.41***	19.32*^,Ⱡ
	Ln(Export)	34.66***	39.81***^,Ⱡ	25.01***^,®	16.40*^,δ	16.60^Ⱡ	18.15	14.53	15.63^Ⱡ
Ln(EconSeat)	Ln(Tourist)	42.95***	34.62***^,Ⱡ	12.43^®	46.18***^,δ	43.36***^,Ⱡ	28.06***	53.14***	48.68***^,Ⱡ
	Ln(Export)	24.53*	13.15^Ⱡ	6.60^®	39.44***^,δ	27.37***^,Ⱡ	7.93	11.74	18.87*^,Ⱡ
Ln(Tourist)	Ln(EconSeat)	29.12***	27.44***^,Ⱡ	47.61***^,Ⱡ	57.47***	17.33	52.03***	40.29***	28.58***^,Ⱡ
	Ln(Import)	41.97***	14.46^Ⱡ	46.99***^,Ⱡ	28.40***	13.65	17.31	16.42	16.16^Ⱡ
Ln(EconSeat)	Ln(Tourist)	49.82***	33.19***^,Ⱡ	20.51**^,Ⱡ	25.83**	36.52***	44.15***	68.12***	143.97***^,Ⱡ
	Ln(Import)	29.46***	10.06^Ⱡ	11.86^Ⱡ	14.62	24.68**	32.97***	16.08	46.57***^,Ⱡ

Note: GFC denotes global financial crisis 2008. The null hypothesis is that the dependent variable is not Granger caused by the other variables. Chi-square statistics are printed.

* Test statistics is significant at the 0.10 significance level.

** Test statistics is significant at the 0.05 significance level.

*** Test statistics is significant at the 0.01 significance level.

^{Ⱡ, δ, and ®}denotes the models with optimum lag chosen as 11, 9, and 8, respectively while the rest of the models' optimum lag is selected as 12.

In the second panel, the Granger causality tests for the trade variable has produced weaker results. It is observed that international tourism demand is granger caused by international trade between New Zealand and its trading partners like Australia, Canada, China and Hong Kong, whereas the trade and tourism nexus is not significant when we considered tourist flows from Japan, Korea, the United States and Singapore. This finding does not support Bilen et al. (2017) and Tsui and Fung (2016) who identified some relationship between trade and tourism flows (particularly business travel). The results do not change significantly when we decomposed the trade variable into exports and imports and explored the nexus once more (see Tables 3 and 4). Both Wu et al. (2015) and Reitsamer et al. (2016) argued that the attractiveness of the tourism destinations might be highly important in some aspects and overcome the impact of economic indicators. We might approach empirical finding on the weak effect of bilateral trade on tourism from this perspective. New Zealand’s tourism demand from Japan, Korea and the United States is immune to the fluctuations in bilateral trade volumes, might be a sign to indicate the attractiveness of New Zealand over the tourists of these countries.

The lower panel of Table 2 contains Granger causality tests for number of airline economy seats. It is quite clear and consistent with prior literature (Costa and Almeida, 2015; Ridderstaat and Croes, 2017) that international tourism demand is the main factor in determining airline capacity (i.e. the number of airline seats) to transport visitors to New Zealand. Performing for all of the studied tourist source markets, the Granger causality test is significant for the tourism demand variable. Since New Zealand is dominantly receiving international tourists by air transportation (over 95%), these findings are quite reasonable.

The impact of bilateral trade linkages on the number of airline economy seats is limited and statistically significant only for the availability of airline economy seats between New Zealand and its five major trading partners: Australia, China, Japan, Singapore and the United States (only for exports). This finding partially supports Endo (2007) and Ismaila et al. (2014) where they emphasized the strong relationship between trade and number of airline economy seats. Tables 5 and 6 show the estimation results when we decomposed the sampled period as before and after global financial crisis (GFC). The overall estimation results are highly similar with the previous ones where we analysed the entire sampled period, except that the trade volume impact on airline economy seats is stronger during the post-GFC period. This result indicates that fluctuations in bilateral trade volumes might hamper flight services and airline capacity between New Zealand and its trading partners, eventually.

Conclusion

In this article, we examined the three-way relationship between international tourism demand, airline economy seats and international trade for New Zealand together with its tourism/trading partners. From the theoretical basis, these three variables can be interlinked to each other. The number of airline seats available for visitors to travel to New Zealand drives its tourism demand for some countries, but this is not valid for richer economies like Japan and the United States. More importantly, unlike the previous literature, New Zealand tourism is – to some extent – immune to bilateral trading volumes with its trading partners. This study also found that tourism demand has a direct effect on the number of airline economy seats for all of New Zealand’s major tourism/trading partners as expected. However, for many trading partners of New Zealand – especially after the GFC period, it is observed that bilateral trade volumes help to boost the number of airline economy seats available between New Zealand and its trading partners. Overall, New Zealand is geopolitically an isolated country, and its international tourism demand and the number of airline seat capacity has exhibited a strong nexus, while bilateral trade relationships has some limited power in affecting both international tourism demand and the availability of airline seat capacity in New Zealand as its impact on other countries.

Footnotes

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 research was supported by the Massey University Research Fund 2016.

ORCID iD

Faruk Balli

Notes

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