This paper reports the findings of an empirical study on the low-cost airline market. A lot of literatures on low-cost carriers’ business model agree that low-cost airlines operate with high load factors. However, due to variations in the market development life cycle of low-cost carriers from one region to another, empirical evidences have shown mixed results of the effect of increasing airline capacity on load factor. The paper therefore extends this analysis by examining two airlines in Kenya over 72 months period; and explores such impact using panel data to capture both time-series and cross-sectional elements over the period. Findings indicate that fleet capacity is a significant positive predictor of load factor. The paper finally underlines that increasing capacity by 1 seat will result in an increase of 0.03% in load factor.
Literatures on the low-cost carrier’s business model reveal that low-cost carrier phenomenon has proved to be a robust service concept from the financial and operational view point. However, studies have reported mixed results on the effect of enhanced low-cost carrier’s fleet capacity on load factor that varies from one region and/or country to the other. Attempts have attributed these variations and mixed results to the conceptualization and configuration of low-cost carrier business model which have led to the uneven spread of this model around the world due to differing catalytic factors such as regulatory framework, degree of entrepreneurship, density of population and relative wealth; travelling culture, airport availability, and adherence to internet facilities, and thus, a variation in the market development life cycle of low-cost carriers [
Section 2 briefly outlines the concept of low-cost carrier’s business model and the associated constructs, i.e. fleet capacity and load factor. In addition, previous studies are compared, contrasted, critiqued and the gap established. Section 3 outlines the methodology. Statistical tests for the assumptions of linear regressions, panel unit root tests, panel cointegration tests are performed in Section 3. Results and discussions are outlined in Section 4. Section 5 summarizes, concludes and provides recommendations.
This section reviews the concepts of low-cost carrier’s business model with an extension to specific constructs such as fleet capacity and load factor. Previous empirical studies are highlighted. Comparisons, contrasting, critiquing and acknowledgement of the gap from the reviewed literature is also established in this section.
The chief difference between low-cost carriers and traditional airlines, or full service carriers (FSCs), fall into three groups: service savings, operational savings and overhead savings [
The planning of demand-responsive transport services requires addressing two fleet-related decision problems: what types of equipment to use and how many to use [
Load factor is the percentage of seats filled with passengers or the ratio of unit costs to unit yields [
Studies by [
Due to variations in the market development life cycle of low-cost carriers from region and/or country to the other, empirical evidences have shown mixed results of the effect of enhancing airlines’ fleet capacity on load factor in different countries. It is for this reason that the purpose of this study was to ascertain the effect of the rising low-cost airlines capacity on load factor in the Kenyan airline market.
This section addresses the research design, target population, type of data, statistical tests, and model specification.
The study adapted longitudinal design, which is a time series correlational research design that describes patterns of change and helps establish the direction and magnitude of causal relationships [
Before linear regression models are used for purposes of inference or prediction, there are four principal assumptions which must be tested to justify its use. If any of these assumptions is violated, then the forecasts, confidence intervals, and scientific insights yielded by a regression model may be (at best) inefficient or (at worst) seriously biased or misleading [
1) Test for Normality of the error distribution
Violations of normality create problems for determining whether model coefficients are significantly different from zero and for calculating confidence intervals for forecasts. Since parameter estimation is based on the minimization of squared error, a few extreme observations can exert a disproportionate influence on parameter estimates [
where S is the skewness, and K is the kurtosis.
Results in
2) Tests for Linearity or Addivity
Violations of linearity or additivity are extremely serious. If one fits a linear model to data which are nonlinearly or non-additively related, your predictions are likely to be seriously in error. In order to test for linearity, the researcher adopted Ramsey RESET (Regression Specification Error Test) to detect any incorrect functional form as proposed by [
Results in
Date: 04/08/16 Time: 21:31 | ||
---|---|---|
FLTC | LDFC | |
Jarque-Bera | 10.44967 | 2.315542 |
Probability | 0.005381 | 0.314186 |
Observations | 144 | 144 |
Ramsey RESET Test | ||||
---|---|---|---|---|
Equation: UNTITLED | ||||
Specification: LDFC FLTC | ||||
Omitted Variables: squares of fitted values | ||||
Value | df | Probability | ||
t-statistic | 14.60024 | 142 | 0.0000 | |
F-statistic | 213.1670 | (1, 142) | 0.0000 | |
Likelihood ratio | 213.1670 | 1 | 0.0000 | |
F-test summary: | ||||
Sum of Sq. | df | Mean Squares | ||
Test Deviance | 84516.03 | 1 | 84516.03 | |
Restricted Deviance | 140815.9 | 143 | 984.7267 | |
Unrestricted Deviance | 56299.88 | 142 | 396.4780 | |
Dispersion SSR | 56299.88 | 142 | 396.4780 | |
LR test summary: | ||||
Value | df | |||
Restricted Deviance | 140815.9 | 143 | ||
Unrestricted Deviance | 56299.88 | 142 | ||
Dispersion | 396.4780 | |||
Unrestricted Test Equation: | ||||
Dependent Variable: LDFC | ||||
3) Statistical independence of the errors
When data are ordered―for example, when sequential observations represent Monday, Tuesday, and Wed- nesday―then the neighboring error terms may turn out to be correlated. This phenomenon is called serial correlation [
4) Homoscedasticity (constant variance) of the errors
OLS makes the assumption that the variance of the error term is constant (Homoscedasticity). If the error terms do not have constant variance, they are said to be heteroscedastic. Heteroscedasticity does not cause ordinary least squares coefficient estimates to be biased, although it can cause ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true or population variance [
Heterokedasticity, serial correlations and presence of outliers were never perceived by the researcher to be problems at all due to the fact that Fully Modified Ordinary Least Squares (FMOLS) had been adopted in the panel cointegrating equations as outlined by [
5) Panel Unit Root Tests
While dealing with panel data, which is usually time series in nature, researcher may have to find out if the data is stationary [
PP Fisher Panel unit root testing was performed on the two variables. The results showed that LDFC was stationary at order 0, while the FLTC was stationary at order 1. The following 3 tables (Tables 3-5) show the results of the panel unit root analysis for the series:
The results in
Null Hypothesis: unit root (individual unit root process) | |||
---|---|---|---|
Series: FLTC | |||
Date: 03/28/16 Time: 09:35 | |||
Sample: 1 144 | |||
Exogenous variables: individual effects | |||
Newey-West automatic bandwidth selection and Bartlett kernel | |||
Total (balanced) observations: 142 | |||
Cross-sections included: 2 | |||
Method | Statistic | Prob.** | |
PP-Fisher Chi-square | 1.17204 | 0.8827 | |
PP-Choi Z-stat | 1.21574 | 0.8880 | |
**Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. | |||
Intermediate Phillips-Perron test results FLTC | |||
Cross | |||
Section | Prob. | Bandwidth | Obs |
FFV | 0.9290 | 1.0 | 71 |
JLX | 0.5991 | 4.0 | 71 |
Null Hypothesis: unit root (individual unit root process) | |||
---|---|---|---|
Series: D(FLTC) | |||
Date: 03/28/16 Time: 09:36 | |||
Sample: 1 144 | |||
Exogenous variables: individual effects | |||
Newey-West automatic bandwidth selection and Bartlett kernel | |||
Total (balanced) observations: 140 | |||
Cross-sections included: 2 | |||
Method | Statistic | Prob.** | |
PP-Fisher Chi-square | 57.3674 | 0.0000 | |
PP-Choi Z-stat | −6.86787 | 0.0000 | |
**Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. | |||
Intermediate Phillips-Perron test results D(FLTC) | |||
Cross | |||
Section | Prob. | Bandwidth | Obs |
FFV | 0.0000 | 1.0 | 70 |
JLX | 0.0000 | 6.0 | 70 |
Null Hypothesis: unit root (individual unit root process) | |||
---|---|---|---|
Series: LDFC | |||
Date: 04/12/16 Time: 22:37 | |||
Sample: 1 72 | |||
Exogenous variables: individual effects | |||
Newey-West automatic bandwidth selection and Bartlett kernel | |||
Total (balanced) observations: 142 | |||
Cross-sections included: 2 | |||
Method | Statistic | Prob.** | |
PP-Fisher Chi-square | 28.9699 | 0.0000 | |
PP-Choi Z-stat | −4.48546 | 0.0000 | |
**Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. | |||
Intermediate Phillips-Perron test results LDFC | |||
Cross | |||
Section | Prob. | Bandwidth | Obs |
FFV | 0.0024 | 2.0 | 71 |
JLX | 0.0002 | 3.0 | 71 |
The results in
The results in
6) Panel Cointegration Tests
The finding that many macro time series may contain a unit root has spurred the development of the theory of non-stationary time series analysis [
Pedroni Residual Cointegration Test | |||||||||
---|---|---|---|---|---|---|---|---|---|
Series: LDFC FLTC | |||||||||
Date: 04/12/16 Time: 22:45 | |||||||||
Sample: 1 72 | |||||||||
Included observations: 144 | |||||||||
Cross-sections included: 2 | |||||||||
Null Hypothesis: no cointegration | |||||||||
Trend assumption: no deterministic trend | |||||||||
User-specified lag length: 1 | |||||||||
Newey-West automatic bandwidth selection and Bartlett kernel | |||||||||
Alternative hypothesis: common AR coefs (within-dimension) | |||||||||
Weighted | |||||||||
Statistic | Prob. | Statistic | Prob. | ||||||
Panel v-Statistic | 0.665149 | 0.2530 | 0.005479 | 0.4978 | |||||
Panel rho-Statistic | −7.921038 | 0.0000 | −7.947623 | 0.0000 | |||||
Panel PP-Statistic | −5.302679 | 0.0000 | −5.239487 | 0.0000 | |||||
Panel ADF-Statistic | −4.865236 | 0.0000 | −5.038563 | 0.0000 | |||||
Alternative hypothesis: individual AR coefs. (between-dimension) | |||||||||
Statistic | Prob. | ||||||||
Group rho-Statistic | −6.643452 | 0.0000 | |||||||
Group PP-Statistic | −5.869738 | 0.0000 | |||||||
Group ADF-Statistic | −4.994443 | 0.0000 | |||||||
Cross section specific results | |||||||||
Phillips-Peron results (non-parametric) | |||||||||
Cross ID | AR (1) | Variance | HAC | Bandwidth | Obs | ||||
FFV | 0.439 | 26.91137 | 23.28485 | 3.00 | 71 | ||||
JLX | 0.421 | 64.96408 | 55.87042 | 4.00 | 71 | ||||
Augmented Dickey-Fuller results (parametric) | |||||||||
Cross ID | AR (1) | Variance | Lag | Max lag | Obs | ||||
FFV | 0.465 | 25.82550 | 1 | -- | 70 | ||||
JLX | 0.334 | 64.21142 | 1 | -- | 70 | ||||
Results in
By combining time series of cross-section observations, panel data give more informative, more variability, less collinearity among variables, more degrees of freedom and more efficiency [
To test if FLTC predicts LDFC →
where:
𝔦 = 1, 2 and is the individual airline dimension (cross-section identifier);
t = time period (1 to 72);
C is the overall effect of the independent variable X on Y;
β0 is the intercept (cross-section fixed effects) for the equation;
u is the error terms(both person-specific and idiosyncratic) in the equation.
From
Std. Dev. (standard deviation) is a measure of dispersion or spread in the series. The standard deviation is given by:
where N is the number of observations in the current sample and
Observations | FLTC | LDFC |
---|---|---|
FLTC | 1.000000 | |
- | ||
- | ||
144 | ||
LDFC | 0.474066 | 1.000000 |
6.415926 | - | |
0.0000 | - | |
144 | 144 |
FLTC | LDFC | |
---|---|---|
Mean | 295.3542 | 65.37500 |
Median | 284.0000 | 65.00000 |
Maximum | 563.0000 | 89.00000 |
Minimum | 48.00000 | 39.00000 |
Std. Dev. | 179.7084 | 9.010386 |
Skewness | 0.156009 | 0.107684 |
Kurtosis | 1.717714 | 3.582701 |
Jarque-Bera | 10.44967 | 2.315542 |
Probability | 0.005381 | 0.314186 |
Sum | 42531.00 | 9414.000 |
Sum Sq. Dev. | 4618203. | 11609.75 |
Observations | 144 | 144 |
deviation of fleet capacity is 179.7 seats, and that of load factor is 9.01 percent. Skewness is a measure of asymmetry of the distribution of the series around its mean. Skewness is computed as:
where σ is an estimator for the standard deviation that is based on the biased estimator, for the Variance (σ = s
where σ is again based on the biased estimator for the variance. The kurtosis of the normal distribution is 3 [
This section sought to determine the nature of relationships that existed, as shown by the following scatter diagram, between fleet capacity and load factor.
It is well known that many economic time series are difference stationary which produce misleading results,
with conventional Wald tests for coefficient significance spuriously showing a significant relationship between unrelated series [
where: C represents the individual cross-section fixed effect, and is as follows:
Dependent Variable: LDFC | ||||
---|---|---|---|---|
Method: Panel Fully Modified Least Squares (FMOLS) | ||||
Date: 04/12/16 Time: 23:01 | ||||
Sample (adjusted): 2 72 | ||||
Periods included: 71 | ||||
Cross-sections included: 2 | ||||
Total panel (balanced) observations: 142 | ||||
Panel method: pooled estimation | ||||
Cointegrating equation deterministics: C | ||||
Coefficient covariance computed using default method | ||||
Long-run covariance estimates (Bartlett kernel, Newey-West fixed bandwidth) | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
FLTC | 0.026781 | 0.005374 | 4.983714 | 0.0000 |
R-squared | 0.227566 | Mean dependent var | 65.66901 | |
Adjusted R-squared | 0.216452 | S.D. dependent var | 8.697209 | |
S.E. of regression | 7.698617 | Sum squared resid | 8238.349 | |
Long-run variance | 100.1477 |
The results imply that, should the low-cost carriers add to its fleet 2 more fifty-seater airplanes, such as a Canadian Royal Jet (CRJ) which form a majority of their fleet, load factor will improve by 3%. This finding supports that of [
Correlational analyses indicate that fleet capacity is significantly and positively correlated with load factor; and the study has also established that fleet capacity is a significant positive predictor of load factor. Since airline fleet management and planning requires determining the size of service fleet that is most cost-effective, the study recommends that there is, therefore, a need to identify and adjust accordingly, from time to time, the optimal fleet capacity for their specific operating conditions and environments without under or over supplying the available seats. Airlines management also needs to work on the two key drivers, i.e. pricing and commercial success. This is because fare reductions will generally stimulate demand and commercial success in product design, promotions, marketing communications, distributions, and service delivery will influence load factors. Studies should be designed with a view to replicating the results of this research within the wider setting of the entire Kenyan aviation market to include even the full service carriers.
Michael O. Aomo,David O. Oima,Moses N. Oginda, (2016) An Empirical Investigation into the Effect of Enhancing Airline Capacity on Load Factor: A Case of Kenya’s Low-Cost Carriers. American Journal of Industrial and Business Management,06,717-731. doi: 10.4236/ajibm.2016.66066
FFV―Fly540 Aviation Limited
FMOLS―Fully Modified Ordinary Least Squares
LDFC―Load Factor
IATA―International Airlines Transport Association
ICAO―International Civil Aviation Organization
JLX―Jetlink Aviation Limited
FLTC―Fleet Capacity