This paper studies the causal relationship between safety, profitability and the load factor for US airlines taking airline deregulation into account. The results indicate that there is some evidence that profitability and the growth of load factor cause fatalities/accidents. Bivariate causality tests show that profitability does not cause fatalities/accidents.

Dipendra Sinha

English

Research Papers in Economics

Safety, profitability and the load factor for airlines in the
USA 
Dipendra Sinha
Ritsumeikan Asia Pacific University, Japan and Macquarie University, Australia
Abstract
This paper studies the causal relationship between safety, profitability and the load factor for
US airlines taking airline deregulation into account. The results indicate that there is some
evidence that profitability and the growth of load factor cause fatalities/accidents. Bivariate
causality tests show that profitability does not cause fatalities/accidents.
Citation: Sinha, Dipendra, (2007) "Safety, profitability and the load factor for airlines in the USA." Economics Bulletin, Vol.
12, No. 6 pp. 1-7
Submitted: June 6, 2006.  Accepted: March 19, 2007.
URL: http://economicsbulletin.vanderbilt.edu/2007/volume12/EB-06L90001A.pdf 
I. Introduction 
 
The Airline Deregulation Act of 1978 virtually deregulated the airline operations in the 
United States.  One of the main concerns with airline deregulation was that safety would 
be compromised.  It was feared that with deregulation, the replacement of jet services by 
commuter services would lead to less safety.  Almost all studies about airline safety, 
which have used data for jet services find that the safety record of jet services has 
improved after deregulation.  In fact, safety had been increasing for a long time before 
airline deregulation. However, small communities lost jet service and they were being 
serviced by turboprops.  More recently, these turboprops have often been replaced by 
small jets.    
It was also thought that the load factor (the percentage of seats in a plane that is filled 
by paying passengers) would go up after deregulation as more airlines start to offer “no 
frills” tickets to a greater extent.  Other things being equal, as the load factor goes up, it is 
sometimes feared that safety would be compromised.  The effect of airline deregulation 
on profitability is uncertain.  It was expected that airline deregulation would lead to more 
competition and thus drive down airfares.  It was also expected that removal of 
restrictions on airfares would lower airfares.  An increase in the load factor, other things 
being equal, was expected to increase profit.   
This paper studies the causal relationship between the load factor, safety and 
profitability in the United States when airline deregulation is taken into account.  No such 
study has been undertaken before.   
 
II. Literature Review 
 
Kanafani and Keeler (1990) is among the first studies to examine whether there was a 
change in safety level following deregulation.  They use monthly data from January 1966 
to December 1989. They run the following exponential (non-linear) regression: 
DPM = exp (a1 + a2 + a3  T . D )   +   u           ( 1 )  
where DPM stands for fatalities per revenue passenger-mile in regularly scheduled 
service, T is a time trend (taking the value of 1 in January 1966, a value of 2 in February 
1996 and so on) and D is the dummy variable having a value of 0 before January, 1979 
and a value of 1 thereafter.  a3 shows the shift in the trend in the change of fatalities after 
deregulation.  Because of the problem of heteroscedasticity, the equation is estimated by 
the method of nonlinear generalized weighted least squares.  The results show that a3 is 
not significant implying that there had been no change in the trend towards fatality.  In 
other words, deregulation did not have any effect (positive or negative) on the airline 
industry’s trend towards improved safety.  After deregulation, many small communities 
were being served by the commuter airlines.  Oster and Zorn (1989) find that commuter 
airlines had a higher fatality rate than that of trunk and local service airlines.  However, 
they also find that the largest commuter airlines were substantially safer than the jet 
carriers which they had replaced. A good review of the airline safety debate is found in 
Rose (1992).  Rose concludes by observing that airline safety had received much more 
attention relative to more significant risks.  She also observes that American national 
obsession with airline safety may explain the high safety standards of the US air carriers.  The first study to look at the profitability and safety issue using time series data is by 
Adrangi, Chow and Raffiee (1997).   They employ Granger causality tests between 
profitability and safety of airlines in the United States.  There are a number of differences 
between the present study and the study by Adrangi, Chow and Raffiee.  First, they do 
not take load factor into account in their causality tests.  Thus, while their tests are 
confined to bivariate causality tests, we perform both bivariate and multivariate causality 
tests. 
Second, to take airline deregulation into account, they split the data into two periods, 
pre-deregulation and post-deregulation periods and perform causality tests separately for 
these two periods.  Since their data end in 1994 (safety data are up to 1992 only), they are 
able to use only 14 years of post deregulation data to test for Granger causality.  This 
time period is too short to get any meaningful results.  In our view, a more satisfactory 
way of dealing with the event of airline deregulation is to treat airline deregulation as a 
form of structural break.  Our causality tests use data for the whole period. But, we treat 
airline deregulation as a structural break in our causality tests. 
Third, our measures of profitability are different from the measures that they use.  
We also normalize the measures of profitability as they do.  However, while we use 
operating profit per passenger and net operating profit per passenger, they use operating 
profit and net operating profit divided by total revenue.   
 
III. Methodology, Data and the Results 
 
We perform the block Granger (1969) block non-causality tests between profitability, 
safety and the load factor.  The Granger block non-causality test can be described as 
follows.  Consider the augmented vector autoregressive model: 
zt = a0 + a1t +  φi zt-i + Ψwt + ut            ( 2 )  
i
p
= ∑
1
 
where zt is an m x 1 vector of jointly determined (endogenous) variables, t is a linear time 
trend, wt is q x 1 vector of exogenous variables, and ut is an m x 1 vector of unobserved 
disturbances.  Let zt = (z’1t, z’2t)’, where z’1t and z’2t are m1 x 1 and m2 x 1 subsets of zt, 
and m = m1 + m2.  We can now have the block decomposition of (2) as follows: 
z1t = a10 + a11t +  φi, 11 z1,t-i +  φi, 12 z2,t-i + Ψ1wt + u1t      (3) 
i
p
= ∑
1 i
p
= ∑
1
 
z2t = a20 + a21t +  φi, 21 z1,t-i +  φi, 22 z2,t-i + Ψ2wt + u2t      (4) 
i
p
= ∑
1 i
p
= ∑
1
 
The hypothesis that the subset z2t do not ‘Granger cause’ z1t is given by 
HG: φ12 = 0 where  φ12 = (φ1,12, φ2,12 . . .,  φ1p,12).  
  However, before we perform such tests, we have to ensure that the variables 
involved are stationary.  If the variables are non-stationary in their levels, but stationary 
in their first differences, then cointegration tests can be performed.  If the variables are 
cointegrated, causality tests can still be performed but an error correction form needs to 
be used.  
  2We use Phillips-Perron (1988) test because the test is well suited for analyzing time 
series whose differences may follow mixed ARMA (p,q) processes of unknown order in 
that it the test statistic incorporates a nonparametric allowance for serial correlation and 
heteroscedasticity in testing the regression.  Consider the following equation:  
 
yt =   0 +  1 yt-1 + c 2 (t - T/2) + νt                        (5)   c
~
c
~ ~
 
where T is the number of observations and νt is the error term.  The null hypothesis of a 
unit root is: c 1 =1.  We can drop the trend term to test the stationarity of a variable 
without the trend. 
~
  All data are from the Air Transport Association website 
(http://www.airlines.org/home/default.aspx).  The annual data are for the period from 
1947 to 1998.  Data for the later years are not available. The variables are defined as 
follows.  Two measures of air safety are used.  These are fatal accidents per million 
aircraft miles (FAPMAM) and passenger fatalities per million aircraft miles (PFPMAM).   
Two measures of profitability are used.  These are real operating profit per passenger 
(ROPPP) and real net operating profit per passenger (RNOPP).  Finally, load factor (LF) 
is the system-wide load factor.   
The results of the Phillips-Perron unit root tests are given in table 1.  The results show 
that while FAPMAM, PFPMAM, ROPPP and RNOPPP are all stationary, LF is not.  We 
use critical values at the 5 percent level of significance.  Since LF is not stationary, we 
can use the growth rate of LF (which we call GLF) in our causality analysis provided that 
GLF is stationary.  The table shows that GLF is stationary.  Thus, in the causality tests, 
we use GLF along with other variables.  Our causality analysis takes airline deregulation 
into account as a structural break.  Following Kanafani and Keeler (1990), 1979 is taken 
to be the year of the structural break. In this study, the structural break is taken into 
account by treating it as an exogenous variable.    
    
Since we have two measures of profitability and two measures of safety, and the 
growth of load factor is also included in the causality tests, a large number of causality 
tests need to be conducted.  In all causality tests, we take into account the structural break 
for airline deregulation.  The results of multivariate and bivariate causality tests are given 
in table 2. 
 
The results show that there is some evidence that profitability and the growth of load 
factor Granger caused fatalities/accidents.  The evidence is stronger when real net 
operating profit per passenger (RNOPPP) is used.  Bivariate causality tests between 
measures of profitability and the measures of safety show that there is no evidence that 
profitability Granger causes fatalities/accidents.  This leads us to conclude that the 
growth of load factor and not profitability contributes to the Granger causality.  We also 
conduct Granger causality tests between the growth rate of load factor and profitability.  
There is strong evidence that the causality flows from profitability to the growth of load 
factor.  This is true for both measures of profitability.  There is no evidence of reverse 
causality that the growth of load factor Granger causes profitability. 
 
  3References: 
 
Adrangi, B., Chow, G. and K. Rafiee (1997) “Airline Deregulation, Safety and 
Profitability in the U.S.” Transportation Journal 46, 44-52. 
 
Granger, C. W. J. (1969) “Investigating Causal Relations by Econometric Models and 
Cross Spectral Methods” Econometrica 37, 424-38. 
 
Kanafani, A. and T.E. Keeler (1990) “Air Deregulation and Safety: Some Econometric 
Evidence” Logistics and Transportation Review 26, 203-210.  
 
Oster, C. and C.K. Zorn (1989) “Airline Deregulation: Is It Still Safe to Fly?” in 
Transportation Safety in an Age of Deregulation by L. Moses and I. Savage, Eds., Oxford 
University Press: Oxford, 129-52. 
 
Phillips, P. C. B. and P. Perron (1988) “Time Series Regression with a Unit Root” 
Biometrika 75, 335-46. 
 
Rose, N. L. (1992) “Fear of Flying: Analyses of Airline Safety” Journal of Economic 
Perspectives 6, 75-94. 
  4 
Table 1.  Phillips-Perron (PP) Unit Root Tests (Truncation lag = 3
*)       
Variable  PP Test Statistic
** Critical  Value 
FAPMAN  Tµ = -3.5383  -2.9190 
FAPMAM  Tτ = -4.6078  -3.4987 
PFPMAM  Tµ = -5.0047  -2.9190 
PFPMAM  Tτ = -7.4327  -3.4987 
ROPPP  Tµ = -3.2766  -2.9190 
ROPPP  Tτ = -4.0352  -3.4987 
RNOPPP  Tµ = -3.6222  -2.9190 
RNOPPP  Tτ = -4.4117  -3.4987 
LF  Tµ = -1.4291  -2.9190 
LF  Tτ = -1.6990  -3.4987 
GLF  Tµ = -6.9432  -2.9202 
GLF  Tτ = -6.9729  -3.5005 
*The truncation lag of 3 was determined using the Schwert Criterion. The truncation lag = 
integer [ 4(T/100)1/4]  where T stands for the number of observations. 
 
** Tµ  and Tτ and are test statistics (1) with drift and no trend and (2) with drift and trend 
respectively.   
  5  6
Table 2.  Multivariate and Bivariate Granger Block Causality Tests 
Cause Effect  Test 
Stat.(*) 
Probability(**) 
ROPPP, GLOAD  FAPMM  12.25(3)  .057(6) 
ROPPP, GLOAD  PFMAM  11.77(3)  .067(6) 
RNOPPP, 
GLOAD 
FAPMM 13.70(3) .033(6) 
RNOPPP, 
GLOAD 
PFMAM 12.71(3) .048(6) 
ROPPP  FAPMM    3.97(3)  .265(3) 
ROPPP  PFMAM    5.67(3)  .129(3) 
RNOPPP  FAPMM    1.66(3)  .647(3) 
RNOPPP  PFMAM    2.01(3)  .570(3) 
ROPPP GLOAD  19.10(3)  .000(3) 
GLOAD  ROPPP    5.66(3)  .129(3) 
RNOPPP GLOAD  20.57(3)  .000(3) 
GLOAD  RNOPPP    3.86(3)  .277(3) 
Note: The test statistic indicates the chi-square value.  The probability refers to the 
probability of accepting the null hypothesis of no causality  
*indicates the number of lags which was determined by using the Akaike Information 
Criterion (AIC) 
**indicates the degrees of freedom of the chi-square distribution 
 
 

Safety, profitability and the load factor for airlines in the USA

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Safety, profitability and the load factor for airlines in the USA

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