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Spanish air travel and the September 11 terrorist attacks: a note
ARTICLE IN PRESS
Journal of Air Transport Management 10 (2004) 441–443
Spanish air travel and the September 11 terrorist attacks: a note Vicente Ingladaa, Bele´n Reyb,* a b
University Carlos III and Ministry of Public Works, Paseo de la Castellana, 67. 28071 Madrid, Spain Faculty of Economics, University Complutense, Campus de Somosaguas, 28223 Pozuelo, Madrid, Spain
Abstract This paper studies the impact of the September 11 terrorist attacks and its after-effects on Spanish airline demand. Using monthly time series data from 1980–2003, we find that September 11th resulted in an ongoing negative demand shock although smaller than that experienced in the US but particularly in relation to international passengers. r 2004 Elsevier Ltd. All rights reserved. Keywords: Terrorism; Demand shocks; Spanish airlines
1. Introduction The financial position of the air transport sector was seriously damaged by the terrorist attacks of September 11, 2001. The US industry suffered particularly heavily where, in addition to the direct impact immediate on commercial aviation, many travellers subsequently avoided flying for fear of further attacks. Later, although the initial panic subsided, the new security conditions imposed at airports, that increased travel times and added to inconvenience, continued to suppress demand. As a result, a large number of airlines have been experiencing financial crisis generally leading to drastic cost reduction programs. Spain does not find itself exempt from this picture. Our objective here is model air transport demand in Spain so as to isolate the effects of the terrorist attacks of September 11. In particular, we attempt to ascertain whether there been a non-transitory downward shift in the demand function as some have suggested has happened in the US (Ito and Lee, 2003). National air transportation demand is influenced by a range of factors including, income levels, air fares, the relative speed of air travel compared to alternative modes, the economic and commercial structure of a country, population size, and social factors such as vacation patterns. But in many countries, such as Spain, *Corresponding author. E-mail addresses: [email protected] (V. Inglada), [email protected] (B. Rey). 0969-6997/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2004.06.002
the tourist trade is often a dominant consideration for international air travel and leads to cyclical effects (Inglada and Rey, 2002). Because of this, the study focuses specifically on separating seasonal and economic aspects of air travel demand from the effects of the September 11 factor. Air travel to and from Spain peaks in July and August and July and are at their nadir in January. Easter (be it in March or April) represents a mini-peak. Fig. 1 depicts the smoothed air traffic volumes over the past 20 years or and shows the longer term effects of economic cycles, air transport liberalisation (Rey, 2003), the impacts of specific ‘shocks’ (such as the 1992 Olympic Games in Barcelona), the Gulf War in 1991, and the September 11, 2001, terrorist attacks. It illustrates that the Spanish airline industry has faced a number of negative shocks since 1980, but that their impact has dissipated within a relatively short period.
2. The model and the data To explore the particular effects of the September 11th attacks in Box-Jenkins methodology is used (Box et al., 1994) incorporating developments introduced in ! Gomez and Maravall (1994) and Maravall (1995). The model, estimated using the maximum exact likelihood method, is: X LPAt ¼ Ikt þ Nt ; ð1Þ
ARTICLE IN PRESS 442
V. Inglada, B. Rey / Journal of Air Transport Management 10 (2004) 441–443
Fig. 1. Evolution of air line passenger traffic in Spain. Yearly rates.
Table 1 Parameter estimates Effectsa
Working days (WD) Leap year (LY) Easter (EA) Olympic games (OG) Liberalisation (LI) Gulf war (GW): o/(1d) o d Terrorist attacks (11S) Economic cycle (EC) THETA1 THETA2
M1. Domestic airline passengers
M3. Total airline passengers
Estimate
t-stat
Estimate
t
Estimate
t-stat
0.001 0.024 0.024 0.085 0.071
2.39 2.74 2.82 2.79 2.32
0.005 0.027 0.078 0.037 NS
14.10 3.20 8.33 1.15 —
0.003 0.027 0.060 0.027 NS
8.54 3.69 8.56 1.14 —
0.183 0.600 0.041 1.226 0.520 0.638
5.74 4.52 1.37 2.37 9.27 12.79
0.171 0.7256 0.086 0.836 0.521 0.537
5.24 6.41 2.75 1.48 9.32 10.10
0.178 0.682 0.077 1.110 0.569 0.571
7.38 7.88 3.46 2.88 10.56 10.70
R2 Residual standard deviation Ljung–Box statistical Q (24) t-value of mean (against zero) a
M2. International airline passengers
0.9890 0.038 12.4 0.0407
0.994 0.041 14.40 0.101
0.995 0.030 15.5 –0.052
The dummies reflecting daily variations are not shown.
r12 Nt ¼ ð1 Y1 BÞð1 Y12 B12 Þat ;
ð2Þ
where B the backward operator, such that Bj (zt )=ztj ; r and r12 represent the operator’s regular difference, (1–B), and seasonal difference, 1–B12. Eq. (1) specifies the regression variables, that is, the deterministic part of the series, while Eq. (2) specifies the ARIMA model—the stochastic part. Accordingly, the model estimated corresponds to the airline’ model ARIMA (0,1,1) (0,1,1)12 in logs and with no mean, where several adjustments are made to isolate ‘calendar effects’ such as Easter (Liu, 1980). Table 1 shows the values of the coefficients and t statistics for successive modelling of three data series— national (domestic), international and total scheduled and non-scheduled airline air passengers on Spanish
flights.1 The residuals in the table verify the appropriateness of the models, while a description of the variables is given in Table 2. The data for passenger departures and arrivals at Spanish airports were obtained from the series in the Quarterly Conjuncture Reports of the Ministry of Public Works and the Ministry of Economy, and the number of workers included in the Social Security Register were taken from the BTC Quarterly Bulletin (Ministry of Economy).2 1 The effects of other variables, such as the Iraq war of 2003, were not significant. 2 The data covers 1982 (January) to 2003 (November) to allow for full business cycle effects (Coto et al., 1997). The number of social security registrations is used instead of the monthly number of workers (Ito and Lee, 2003) because this information is not available in Spain.
ARTICLE IN PRESS V. Inglada, B. Rey / Journal of Air Transport Management 10 (2004) 441–443
443
Table 2 Variable definitions Variable name
Definitions
Airline passengers (LPAt) Working days (WD)t Leap year (LYt) Easter (EAt) Olympic games (OGt) Liberalisation (LIt) Gulf war (GWt) Terrorist attacks (11St) Economic cycle (ECt) Residuals (nt)
Natural log of number of airline passengers in month t (national, international and total) {Number of working daysNumber of weekend days} 5/2 Takes the value 1 if t is February during a leap year and 0 otherwise Takes the value 1 if t contains any Easter holidays and 0 otherwise Takes the value 1 if t is August 1992 and 0 otherwise Takes the value 1 if t is January 1993 and 0 otherwise Takes the value 1 if t is February 1991 and 0 otherwise. (Transitory outlier: o/(1d)) Takes the value 1 if t is from October 2001 to November 2003 and 0 otherwise Natural log of national employment (social security) in t White noise with significant zero mean
3. Results The October 2001 effect, associated with the September 11 terrorist attacks, is detected as a level shift (Box and Tiao, 1975) is represented by introducing a unitary root in the rational filter denominator vh : ð3Þ Vh ðBÞ ¼ 1B The impact of the attacks, as can be seen in Table 1, was felt in both the domestic and international markets, but more so in the latter. As an additional check on the effects of September 11th, the models were estimated using the number of planes as a variable. Again, the effects were significantly different from zero and with magnitudes similar to those estimated for passenger traffic. This would indicate that the airlines reacted to the situation on the supply side by limiting their capacity.
4. Conclusions The attacks of September 11th on the US are found, using an AREMA model, to have had a long-term effect on Spanish air travel, and particularly for the international passenger market. One possible factor influencing the durability of the effect may well be the numerous
security measures that have adversely impacted on the inconvenience of air travel. The results for Spain, albeit on a smaller scale coincide with those others have found for the US.
References Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis, Forecasting and Control 3rd Edition. Prentice Hall, Engle Woods, New Jersey. Box, G.E.P., Tiao, G.C., 1975. Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association 70, 177–193. ! Gomez, V., Maravall, A., 1994. Estimation, prediction and interpolation for non-stationary series with the Kalman Filter. Journal of the American Statistical Association 89, 611–624. Ito, H., Lee, D., 2003. Assessing the Impact of the September 11 Terrorist Attacks on U.S. Airline Demand. Working Paper: 200316, Brown University. Inglada, V., Rey, B., 2002. El transporte ae´reo espan˜ol ante la crisis internacional. Revista Economistas 96, 103–108. Liu, L.M., 1980. Analysis of time series with calendar effects. Management Science 26, 106–112. Maravall, A., 1995. Unobserved components in economic time series. In: Pesaran, H., Wickens, M. (Eds.), The Handbook of Applied Econometrics, Vol. 1. Basil Blackwell, Oxford. Rey, B., 2003. Structural changes in the Spanish scheduled flights markets as a result of air transport deregulation in Europe. Journal of Air Transport Management 9, 195–200.
Journal of Air Transport Management 10 (2004) 441–443
Spanish air travel and the September 11 terrorist attacks: a note Vicente Ingladaa, Bele´n Reyb,* a b
University Carlos III and Ministry of Public Works, Paseo de la Castellana, 67. 28071 Madrid, Spain Faculty of Economics, University Complutense, Campus de Somosaguas, 28223 Pozuelo, Madrid, Spain
Abstract This paper studies the impact of the September 11 terrorist attacks and its after-effects on Spanish airline demand. Using monthly time series data from 1980–2003, we find that September 11th resulted in an ongoing negative demand shock although smaller than that experienced in the US but particularly in relation to international passengers. r 2004 Elsevier Ltd. All rights reserved. Keywords: Terrorism; Demand shocks; Spanish airlines
1. Introduction The financial position of the air transport sector was seriously damaged by the terrorist attacks of September 11, 2001. The US industry suffered particularly heavily where, in addition to the direct impact immediate on commercial aviation, many travellers subsequently avoided flying for fear of further attacks. Later, although the initial panic subsided, the new security conditions imposed at airports, that increased travel times and added to inconvenience, continued to suppress demand. As a result, a large number of airlines have been experiencing financial crisis generally leading to drastic cost reduction programs. Spain does not find itself exempt from this picture. Our objective here is model air transport demand in Spain so as to isolate the effects of the terrorist attacks of September 11. In particular, we attempt to ascertain whether there been a non-transitory downward shift in the demand function as some have suggested has happened in the US (Ito and Lee, 2003). National air transportation demand is influenced by a range of factors including, income levels, air fares, the relative speed of air travel compared to alternative modes, the economic and commercial structure of a country, population size, and social factors such as vacation patterns. But in many countries, such as Spain, *Corresponding author. E-mail addresses: [email protected] (V. Inglada), [email protected] (B. Rey). 0969-6997/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2004.06.002
the tourist trade is often a dominant consideration for international air travel and leads to cyclical effects (Inglada and Rey, 2002). Because of this, the study focuses specifically on separating seasonal and economic aspects of air travel demand from the effects of the September 11 factor. Air travel to and from Spain peaks in July and August and July and are at their nadir in January. Easter (be it in March or April) represents a mini-peak. Fig. 1 depicts the smoothed air traffic volumes over the past 20 years or and shows the longer term effects of economic cycles, air transport liberalisation (Rey, 2003), the impacts of specific ‘shocks’ (such as the 1992 Olympic Games in Barcelona), the Gulf War in 1991, and the September 11, 2001, terrorist attacks. It illustrates that the Spanish airline industry has faced a number of negative shocks since 1980, but that their impact has dissipated within a relatively short period.
2. The model and the data To explore the particular effects of the September 11th attacks in Box-Jenkins methodology is used (Box et al., 1994) incorporating developments introduced in ! Gomez and Maravall (1994) and Maravall (1995). The model, estimated using the maximum exact likelihood method, is: X LPAt ¼ Ikt þ Nt ; ð1Þ
ARTICLE IN PRESS 442
V. Inglada, B. Rey / Journal of Air Transport Management 10 (2004) 441–443
Fig. 1. Evolution of air line passenger traffic in Spain. Yearly rates.
Table 1 Parameter estimates Effectsa
Working days (WD) Leap year (LY) Easter (EA) Olympic games (OG) Liberalisation (LI) Gulf war (GW): o/(1d) o d Terrorist attacks (11S) Economic cycle (EC) THETA1 THETA2
M1. Domestic airline passengers
M3. Total airline passengers
Estimate
t-stat
Estimate
t
Estimate
t-stat
0.001 0.024 0.024 0.085 0.071
2.39 2.74 2.82 2.79 2.32
0.005 0.027 0.078 0.037 NS
14.10 3.20 8.33 1.15 —
0.003 0.027 0.060 0.027 NS
8.54 3.69 8.56 1.14 —
0.183 0.600 0.041 1.226 0.520 0.638
5.74 4.52 1.37 2.37 9.27 12.79
0.171 0.7256 0.086 0.836 0.521 0.537
5.24 6.41 2.75 1.48 9.32 10.10
0.178 0.682 0.077 1.110 0.569 0.571
7.38 7.88 3.46 2.88 10.56 10.70
R2 Residual standard deviation Ljung–Box statistical Q (24) t-value of mean (against zero) a
M2. International airline passengers
0.9890 0.038 12.4 0.0407
0.994 0.041 14.40 0.101
0.995 0.030 15.5 –0.052
The dummies reflecting daily variations are not shown.
r12 Nt ¼ ð1 Y1 BÞð1 Y12 B12 Þat ;
ð2Þ
where B the backward operator, such that Bj (zt )=ztj ; r and r12 represent the operator’s regular difference, (1–B), and seasonal difference, 1–B12. Eq. (1) specifies the regression variables, that is, the deterministic part of the series, while Eq. (2) specifies the ARIMA model—the stochastic part. Accordingly, the model estimated corresponds to the airline’ model ARIMA (0,1,1) (0,1,1)12 in logs and with no mean, where several adjustments are made to isolate ‘calendar effects’ such as Easter (Liu, 1980). Table 1 shows the values of the coefficients and t statistics for successive modelling of three data series— national (domestic), international and total scheduled and non-scheduled airline air passengers on Spanish
flights.1 The residuals in the table verify the appropriateness of the models, while a description of the variables is given in Table 2. The data for passenger departures and arrivals at Spanish airports were obtained from the series in the Quarterly Conjuncture Reports of the Ministry of Public Works and the Ministry of Economy, and the number of workers included in the Social Security Register were taken from the BTC Quarterly Bulletin (Ministry of Economy).2 1 The effects of other variables, such as the Iraq war of 2003, were not significant. 2 The data covers 1982 (January) to 2003 (November) to allow for full business cycle effects (Coto et al., 1997). The number of social security registrations is used instead of the monthly number of workers (Ito and Lee, 2003) because this information is not available in Spain.
ARTICLE IN PRESS V. Inglada, B. Rey / Journal of Air Transport Management 10 (2004) 441–443
443
Table 2 Variable definitions Variable name
Definitions
Airline passengers (LPAt) Working days (WD)t Leap year (LYt) Easter (EAt) Olympic games (OGt) Liberalisation (LIt) Gulf war (GWt) Terrorist attacks (11St) Economic cycle (ECt) Residuals (nt)
Natural log of number of airline passengers in month t (national, international and total) {Number of working daysNumber of weekend days} 5/2 Takes the value 1 if t is February during a leap year and 0 otherwise Takes the value 1 if t contains any Easter holidays and 0 otherwise Takes the value 1 if t is August 1992 and 0 otherwise Takes the value 1 if t is January 1993 and 0 otherwise Takes the value 1 if t is February 1991 and 0 otherwise. (Transitory outlier: o/(1d)) Takes the value 1 if t is from October 2001 to November 2003 and 0 otherwise Natural log of national employment (social security) in t White noise with significant zero mean
3. Results The October 2001 effect, associated with the September 11 terrorist attacks, is detected as a level shift (Box and Tiao, 1975) is represented by introducing a unitary root in the rational filter denominator vh : ð3Þ Vh ðBÞ ¼ 1B The impact of the attacks, as can be seen in Table 1, was felt in both the domestic and international markets, but more so in the latter. As an additional check on the effects of September 11th, the models were estimated using the number of planes as a variable. Again, the effects were significantly different from zero and with magnitudes similar to those estimated for passenger traffic. This would indicate that the airlines reacted to the situation on the supply side by limiting their capacity.
4. Conclusions The attacks of September 11th on the US are found, using an AREMA model, to have had a long-term effect on Spanish air travel, and particularly for the international passenger market. One possible factor influencing the durability of the effect may well be the numerous
security measures that have adversely impacted on the inconvenience of air travel. The results for Spain, albeit on a smaller scale coincide with those others have found for the US.
References Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis, Forecasting and Control 3rd Edition. Prentice Hall, Engle Woods, New Jersey. Box, G.E.P., Tiao, G.C., 1975. Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association 70, 177–193. ! Gomez, V., Maravall, A., 1994. Estimation, prediction and interpolation for non-stationary series with the Kalman Filter. Journal of the American Statistical Association 89, 611–624. Ito, H., Lee, D., 2003. Assessing the Impact of the September 11 Terrorist Attacks on U.S. Airline Demand. Working Paper: 200316, Brown University. Inglada, V., Rey, B., 2002. El transporte ae´reo espan˜ol ante la crisis internacional. Revista Economistas 96, 103–108. Liu, L.M., 1980. Analysis of time series with calendar effects. Management Science 26, 106–112. Maravall, A., 1995. Unobserved components in economic time series. In: Pesaran, H., Wickens, M. (Eds.), The Handbook of Applied Econometrics, Vol. 1. Basil Blackwell, Oxford. Rey, B., 2003. Structural changes in the Spanish scheduled flights markets as a result of air transport deregulation in Europe. Journal of Air Transport Management 9, 195–200.