The influence of economic policy uncertainty and geopolitical risk on US citizens overseas air passenger travel by regional destination

Abstract

This research note extends the literature on the role of economic policy uncertainty and geopolitical risk on US citizens overseas air travel through the examination of the forecast error variance decomposition of total overseas air travel and by regional destination. Our empirical findings indicate that across regional destinations, US economic policy uncertainty explains more of the forecast error variance of US overseas air travel, followed by geopolitical risk with global economic policy uncertainty explaining a much smaller percentage of the forecast error variance.

Keywords

economic policy uncertainty geopolitical risk tourism demand US overseas air travel

Introduction

For many countries, tourism provides for the acquisition of foreign exchange, the generation of income from the consumption of goods and services by tourists, employment in tourism-related service sectors, and tax revenues from tourist expenditures and businesses. Thus, understanding the behavior of the determinants of tourism is essential for the planning necessary to accommodate both domestic and international tourists. In addition to the traditional determinants of tourism demand, such as exchange rates and income levels, uncertainty serves a prominent role in the decision-making process for tourists with respect to their consumption decisions and for firms in the tourism industry in terms of their investment decisions. As noted by Bernanke (1983) and Giavazzi and McMahon (2012), if the precautionary motive takes hold, an increase in uncertainty will likely reduce consumption and investment spending as individuals, firms, and governments seek to minimize their future financial risk. With the emergence of the respective news-based measures for economic policy uncertainty by Baker et al. (2016) and geopolitical risk by Cardara and Iacoviello (2018), researchers have begun to explore the influence of such measures on the tourism sector.¹

The vast majority of studies find that economic policy uncertainty adversely impacts tourism flow indicators² (see Akadiri et al., 2020; Balli et al., 2018; Chen et al., 2020; Demir and Gozgor, 2018; Dragouni et al., 2016; Ghosh, 2019; Gozgor and Demir, 2018; Gozgor and Ongan, 2017; Isik et al., 2020; Khan et al., 2020; Liu et al., 2020; Nguyen et al., 2020; Ongan and Gozgor, 2018; Payne et al., 2020; Sharma, 2019; Singh et al., 2019; Tsui et al., 2018; Wu and Wu, 2019, 2020). Moreover, studies by Alola et al. (2019), Akadiri et al. (2020), Balli et al. (2019), and Tiwari et al. (2019) show the negative impact of geopolitical risk on tourism flow indicators as well.³ With the exception of the study by Tiwari et al. (2019) who incorporate both the economic policy uncertainty index of India and geopolitical risk in the analysis of tourist arrivals, the remaining studies focus on measures of either economic policy uncertainty or geopolitical risk. However, none of the previous studies incorporate country-specific economic policy uncertainty, global economic policy uncertainty, and geopolitical risk to understand the relative impact of each on tourism flows.

With the US a primary source market for many tourist destinations across the globe, this research note attempts to address this omission in the literature by examining the dynamic interplay between US and global economic policy uncertainty alongside geopolitical risk with respect to US citizens overseas air travel in total and by regional destination. Specifically, we estimate a vector autoregressive (VAR) model that includes US citizens overseas air travel, the broad real effective exchange rate, per capita real personal disposable income, US economic policy uncertainty, global economic policy uncertainty, and geopolitical risk with the variables denoted in growth rates. Since the VAR model is expressed in a reduced form that explicitly considers the role of all endogenous variables within the model, the examination of the forecast error variance decomposition allows for the identification of the percentage of the forecast error variance explained by each variable in response to a shock to the respective measures of US citizens overseas air travel. The second section presents the data, methodology, and results. The third section provides concluding remarks.

Data, methodology, and results

Our analysis utilizes monthly data from 2000:1 to 2019:10. Data on US citizens overseas air passenger travel in total (TOTAL) and by eight regional destinations in order of air travel volume (Europe, EUR; Caribbean, CAR; Asia, ASIA; Central America, CAM; South America, SAM; Middle East, MIDE; Oceania, OCE; and Africa, AFR) were obtained from the US Department of Commerce, International Trade Administration, Office of Travel and Tourism Industries and seasonally adjusted using the X-11 procedure.⁴ Data for the broad real effective exchange rate (BREER) and per capita real personal disposable income (PYPC) were drawn from the St. Louis Federal Reserve Bank database, FRED II. In addition to the inclusion of the BREER and per capita real disposable income as traditional determinants underlying tourism demand (Song et al., 2012), we include three measures associated with uncertainty captured by the geopolitical risk (GPR) index, the US economic policy uncertainty (USEPU) index, and the global economic policy uncertainty (GEPU) index obtained from the website, www.policyuncertainty.com.⁵

The summary statistics for the respective variables are presented in Table 1. In evaluating US citizens overseas air travel by regional destination, we observe the average passenger travel is the greatest for EUR, followed by the CAR and ASIA and then CAM, SAM, the MIDE, OCE, and AFR. The relative variability across regional destinations, measured by the coefficient of variation, reveals the MIDE exhibits the greatest variation at 0.642, followed by AFR: 0.386, CAM: 0.321, CAR: 0.312, OCE: 0.229, SAM: 0.222, and ASIA: 0.207. In regard to the BREER and PYPC, the relative variation is 0.087 and 0.083, respectively. In terms of the three key variables of interest, GPR index shows the greatest variation at 0.681, followed by GEPU index 0.428 and the USEPU index 0.387.

Table 1.

Summary statistics.

Variable	Mean	Median	Min.	Max.	SD	CV
EUR	1,066,586.00	982,477.00	414,958.00	2,566,724.00	380,787.00	0.357
CAR	518,367.30	495,395.00	220,141.00	1,006,030.00	161,881.00	0.312
ASIA	399,548.40	392,314.00	176,244.00	611,415.00	82,784.82	0.207
CAM	200,369.60	95,348.50	73,723.00	364,499.00	64,281.57	0.321
SAM	167,880.80	162,754.00	99,264.00	290,232.00	37,327.28	0.222
MIDE	101,549.10	103,517.50	13,434.00	256,427.00	65,222.63	0.642
OCE	60,861.21	60,265.50	35,157.00	108,323.00	13,956.69	0.229
AFR	25,885.79	25,908.00	6,956.00	61,360.00	9,988.91	0.386
BREER	109.78	110.92	93.06	129.03	9.60	0.087
PYPC	38,711.17	38,211.50	33,112.00	45,809.00	3,213.93	0.083
USEPU	125.27	114.62	44.78	284.14	48.42	0.387
GEPU	120.52	109.63	48.37	306.83	51.55	0.428
GPR	103.79	82.62	27.21	545.09	70.67	0.681
TOTAL	2,541,048.00	2,424,824.00	1,422,363.00	5,131,219.00	648,864.80	0.255

Note: Min.: the minimum value; Max.: the maximum value; SD: standard deviation; CV: coefficient of variation; EUR: Europe; CAR: Caribbean; ASIA: Asia; CAM: Central America; SAM: South America; MIDE: Middle East; OCE: Oceania; AFR: Africa; BREER: broad real effective exchange rate; PYPC: per capita real personal disposable income; USEPU: US economic policy uncertainty; GEPU: global economic policy uncertainty; GPR: geopolitical risk.

In our analysis, we convert the variables to growth rates based on the first difference of the natural logarithms of the respective variables.⁶ We begin by estimating unrestricted VAR models in the spirit of Sims (1980) with the lag length determined by the Akaike information criterion in total and by regional destination.⁷

{\dot{Y}}_{t} = a_{0} + \sum_{i = 1}^{p} θ_{i} {\dot{Y}}_{t - i} + u

where ${\dot{Y}}_{t}$ is a m × 1 vector of jointly determined dependent variables defined in growth rates and u_t is a m × 1 vector of disturbances with $E (u_{t}) = 0, E (u_{t} u_{t}^{'}) = Σ$ for all t, $E (u_{t} u_{t}^{'}) = 0$ for all t ≠ t ′, and $Σ$ is a m × m positive definite covariance matrix. Within the VAR model each endogenous variable is explained by its lagged values and the lagged values of all the endogenous variables in the model. Equation (1) can be presented in an infinite moving average form as follows:

{\dot{Y}}_{t} = \sum_{j = 0}^{\infty} A_{j} u_{t - j}

where the matrices $A_{j} = θ_{1} A_{j - 1} + θ_{2} A_{j - 2} + \dots + θ_{p} A_{j - p}$ with $j = 1, 2, \dots$ , $A_{0} = I_{m}$ , $A_{j} = 0$ for $j < 0.$ Based on the Cholesky decomposition of the covariance matrix of the shocks, $u_{t},$ as $Σ = T T^{'}$ where T is a lower triangular matrix, the moving average representation of Equation (2) can be rewritten as:

{\dot{Y}}_{t} = \sum_{j = 0}^{\infty} (A_{j} T) (T^{- 1} u_{t - j}) = \sum_{j = 0}^{\infty} A_{j}^{*} ε_{t - j}

where $A_{j}^{*} = A_{j} T$ and $ε_{t} = T^{- 1} u_{t}$ . Thus, $E (ε_{t} ε_{t}^{′}) = T^{- 1} E (u_{t} u_{t}^{′}) T^{′ - 1} = T^{- 1} Σ T^{′ - 1} = I_{m}$ whereby the error terms $ε_{t}$ obtained from the transformation matrix, T, are contemporaneously uncorrelated, such that the shocks $ε_{t}$ are orthogonal to each other. With the orthogonalized moving average representation in Equation (3), the forecast error variance decomposition for the ith variable in the VAR model is given as follows:

ϕ_{i, j, N} = \frac{\sum_{l = 0}^{N} {(ε_{i}^{′} A_{l} T ε_{j})}^{2}}{\sum_{l = 0}^{N} ε_{i}^{′} A_{l} Σ A_{l}^{′} ε_{i}}, i, j = 1, 2, \dots, m

where T is defined by the Cholesky decomposition of $Σ$ , $ε_{i}$ is the m × 1 selection vector of the ith variable, and A_l ( $l = 1, 1, 2 \dots)$ represents the coefficient matrices in the moving average representation. Equation (4) captures the proportion of the N-step ahead forecast error variance of variable i, which is accounted for by the orthogonalized innovations in variable j.

The Cholesky decomposition isolates the structural errors by recursive orthogonalization, whereby the variables are ordered based on the speed by which the variables act in response to shocks. To this end, the variable ordering is the global economic policy uncertainty index, US economic policy uncertainty index, geopolitical risk index, broad real effective exchange rate, per capita real personal disposable income, and US overseas air travel.⁸ Finally, the forecast error variance decompositions are used to evaluate the relative impact of the traditional tourism demand determinants of the exchange rate and income along with the economic policy uncertainty measures and geopolitical risk on US overseas air travel.

The results from forecast error variance decompositions across all models at forecasting horizons of 1 month, 12 months, and 60 months are presented in Table 2. In the evaluation of forecast error variance for total US citizens overseas air travel at 60 months, we find the broad real effective exchange rate and per capita real personal disposable income explain 3.60% and 16.36% of the forecast error variance, respectively, whereas US economic policy uncertainty and the geopolitical risk explain 28.73% and 23.81%, respectively, with global economic policy uncertainty explaining only 6.04%. As we disaggregate total US citizens overseas air travel by regional destination, the results exhibit some variation across regional destinations, but in general are quite similar. The contribution of the broad real effective exchange rate in explaining the forecast error variance in US citizens overseas air travel by regional destination at 60 months ranges from 1.33% in OCE to 2.74% in EUR, while in the case of per capita real personal disposable income, the contribution ranges from 12.73% in AFR to 21.84% in the MIDE. As for US economic policy uncertainty, global economic policy uncertainty, and geopolitical risk measures, the contribution by each in explaining the forecast error variance of US citizens overseas air travel varies considerably across regional destinations. We show the percentage that US economic policy uncertainty explains the forecast error variance of the respective US overseas air travel by regional destination ranges from 16.32% for AFR to 38.02% in CAM. Geopolitical risk follows US economic policy uncertainty in terms of the percentage of forecast error variance explained, ranging from 12.30% in AFR to 23.90% in EUR. In only the cases of the MIDE and AFR does the percentage of the forecast error variance explained by per capita real personal disposable income slightly exceed the percentage of the forecast error variance explained by geopolitical risk. We find that global economic policy uncertainty explains a relatively minor portion of the forecast error variance, ranging from 1.23% for OCE to 5.24% in EUR.

Table 2.

Forecast error variance decompositions of US overseas air travel (in months).

TOTAL:	TOTAL	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	43.09	3.39	20.12	16.65	2.54	14.21
60	21.46	6.04	28.73	23.81	3.60	16.36
EUR:	EUR	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	41.62	3.14	21.79	17.64	2.26	13.55
60	18.71	5.24	32.36	23.92	2.74	17.03
CAR:	CAR	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	50.16	2.71	18.54	14.69	2.08	11.82
60	23.74	2.89	33.61	20.75	2.46	16.55
ASIA:	ASIA	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	37.92	1.16	27.74	15.36	1.24	16.58
60	21.60	1.32	35.63	21.89	1.35	20.21
CAM:	CAM	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	46.22	3.64	19.15	16.44	2.30	12.25
60	14.27	5.85	38.02	19.66	3.51	18.69
SAM:	SAM	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	48.62	2.24	20.11	15.06	1.73	12.24
60	20.47	4.49	33.93	20.35	2.64	18.12
MIDE:	MIDE	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	41.58	1.29	23.18	16.39	1.40	16.16
60	28.36	1.45	26.71	20.05	1.59	21.84
OCE:	OCE	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	44.98	1.12	24.72	12.77	1.27	15.14
60	38.05	1.23	25.11	17.81	1.33	16.47
AFR:	AFR	GEPU	USEPU	GPR	BREER	PYPC
1	100.00	0.00	0.00	0.00	0.00	0.00
12	62.14	1.26	14.41	10.46	1.32	10.41
60	55.71	1.40	16.32	12.30	1.54	12.73

Note: EUR: Europe; CAR: Caribbean; ASIA: Asia; CAM: Central America; SAM: South America; MIDE: Middle East; OCE: Oceania; AFR: Africa; BREER: broad real effective exchange rate; PYPC: per capita real personal disposable income; USEPU: US economic policy uncertainty; GEPU: global economic policy uncertainty; GPR: geopolitical risk.

The results reveal that the broad real effective exchange rate and per capita real personal disposable income do not appear to be the primary determinants in explaining the forecast error variance of US overseas air travel relative to the influence of US economic policy uncertainty and geopolitical risk. Furthermore, our finding that US economic policy uncertainty serves a more dominant role than global economic policy uncertainty in explaining the forecast error variance parallels the findings of Singh et al. (2019) but runs counter to the results reported by Payne et al. (2020). However, neither of these studies incorporated geopolitical risk in their analysis. Likewise, our finding that US economic policy uncertainty explains a greater percentage of the forecast error variance than geopolitical risk is in contrast to the findings Tiwari et al. (2019) who find that the influence of geopolitical risk is stronger than the economic policy uncertainty index for India with respect to the country’s tourist arrivals.

Concluding remarks

This research note examines the forecast error variance decomposition associated with the estimation of VAR models that include the growth rates of US citizens overseas air travel (for total and by regional destination), the broad real effective exchange rate, per capita real personal disposable income, US economic policy uncertainty, global economic policy uncertainty, and geopolitical risk. We find that US economic policy uncertainty explains more of the forecast error variance associated with US citizens overseas air travel than either geopolitical risk or global economic policy uncertainty. We also find that traditional tourism demand determinants with respect to the broad real effective exchange rate and per capita real personal disposable income exhibit less of an influence on US citizens overseas air travel relative to US economic policy uncertainty and geopolitical risk. With domestic economic policy uncertainty and geopolitical risk serving as more prominent factors underlying US overseas air travel, policy makers should focus efforts toward maintaining policy stability and credibility to reduce the level of policy uncertainty and to the extent possible mitigate geopolitical risk factors through coordinated efforts across countries. As for the tourism industry, the consideration of the importance of including country-specific economic policy uncertainty and geopolitical risk in the modeling of tourism behavior is pertinent to planning and in the design of risk mitigation strategies for the tourism sector.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

James E Payne

Notes

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