Daniel SALLIER
THE 2ND KENZA LAW OF DEMAND: A QUALITATIVE AND QUANTITATIVE
APPROACH FOR ASSESSING LONG-TERM DEVELOPMENTS OF LEISURE
DESTINATIONS MARKETS
By Daniel SALLIER
Aéroports de Paris
ABSTRACT
In this contribution, I introduce a set of two non-econometric but very closely
related methods for demand modelling and forecasting. These methods are
called the Kenza laws of demand. The 1st law is dedicated to the modelling of
generic and hardly substitutable perishable goods or services, such as the
worldwide set of foreign destinations of the British leisure market, for instance,
while the 2nd law is to be used for more specific perishable goods or services,
such as the set of Tunisian destinations of the French or German leisure markets.
The 1st and 2nd Kenza law of demand have been initially used by Airbus S.A.S. in
the late 90s and the 1st law represents, today, the primary demand short, medium
and (very) long-term forecasting tool of Aéroports de Paris (CDG & Orly).
Tourism represents a major component of many countries economy in the world,
some European ones included: local airlines and hotels developments and
profitability, real estate, local employment, hard currency reserves, etc... It simply
means that airlines, airports, hotel groups, the State and local administrations, if
not the financial community, do have very strong methodological requirements
which can deliver fully supported qualitative and quantitative understanding of the
today’s up to long term developments of the tourist demand. One of the main
assets of the 2nd Kenza law of demand is to provide a better assessment and
understanding of the different "ages" of any leisure market: from the times of
being a fancy destination for very rich people to the times of being a very popular
destination; from the times of a highly lucrative niche market to the times of huge
volumes and low (unit) profits.
To start with the empirical concepts leading to the 1st and 2nd Kenza laws of
demand will be exposed and discussed. Then exclusive attention will be paid to
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Daniel SALLIER
the 2nd Kenza law of demand and more specifically to market structural evolution
over the time and the demand elasticity properties. To finish with we will have a
look at the French, German, Italian, UK and Spanish leisure markets of Tunisian
destinations as an illustration of the theoretical concepts.
As the second Kenza law of demand is a direct and simple derivative of the first
Kenza law it does not deserve specific bibliographic references with the only
exception of T. Veblen work.
1. INTRODUCTION
I started the research work on the 1st Kenza law of demand1 by the end of the
year 1994. Beginning 1995, I was given the opportunity to apply it on a real case
within the frame of an Airbus aircraft sales campaign for the renewal and
expansion of Tunisair fleet.
The issue was that a major component of Tunisair traffic is the leisure market of
Western Europeans to Tunisia. The 1st Kenza law of demand had to be adapted
to provide a better modelling of a leisure market and that is how we came up with
the second law of Kenza which is the very subject of this paper.
It was decided together with Tunisair fleet planning team that we will focus
exclusively on the global leisure market from a set of individual European
countries to Tunisia and that Tunisair market share evolution was no part of the
scope of analysis. Of course, Tunisiar team had the academic background and
the expertise to use classical econometric models for demand forecasting
purposes, but the Kenza approach provided a "physical" understanding of
markets behaviour, sometime paradoxical, which was highly praised and
appreciated at that time. I take the opportunity of this paper for thanking again
Tunisair and Mr. Moncef Ben-Dhahbi for their very friendly, unrestricted and even
enthusiastic support over the years which is the reason why the market examples
used in this article are all based on the Tunisian leisure market.
The Tunisian market is just an example of a leisure market which is an important
source of economical development in Tunisia (7% of 2009 GDP2). Beyond the
Tunisian case, the tourism economy worldwide represents billions of US dollars
or euros every year. Some orders of magnitude to start with. In 2007, the French
tourist sector accounted for3:
-
81.9 million foreign visitors having 1 overnight stay and more in France;
114 million same day visitors
18.5 million beds
199 million nights spent out of which 72.5 million by foreign visitors
€39.6 billion expenditure by foreign visitor in France
833,683 employments attached to the tourist activity.
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Daniel SALLIER
Tourist sector accounted for 6.2% of the French GDP in 20074, while it accounted
for 10.8% of the Spanish GDP5 and 18% of the Greek 2008 GDP6 but only 2.68%
of the US GDP in 2007 while being the 1st one in the world in terms of sector
turnover (US$ 594.1 billion)7.
Tourism shares with civil aviation the settlement of dedicated international
organisations in charge of these economical sectors: World Tourism Organisation
(UNWTO) for the 1st one and International Civil Aviation Organisation (ICAO) for
the second. This is another clear indication of how important the tourism sector is
considered worldwide.
The second Kenza law of demand is directly derived from the first law with which
it shares all the assumptions made among which:
1/ it is a modelling of annual passenger demand. It is not designed for
modelling quarterly, monthly, weekly, daily, ... traffic and for taking into
consideration traffic seasonality for instance;
2/ it is a modelling origin/destination leisure flows which means, for instance,
that modelling leisure traffic flows between the USA and Western Europe
would require, at least, two different models to be developed, one for the
passengers living in the USA and a second one for those living in Western
Europe.
To start with, we will quickly remind the empirical concept at the origin of the 1st
Kenza law of demand and we will detail those leading to the second Kenza law of
consumer demand. We will pay a specific attention to the structural market
evolution over the time as it results from the set of Kenza equations. Then we will
look at the demand elasticity as it results from the second Kenza law of demand.
To finish with we will use the Tunisian air markets to illustrate the ability of the 2nd
Kenza law of demand to provide both a good estimate of demand data and the
keys for a better understanding of market evolutions.
2. THE FIRST KENZA LAW OF DEMAND
Content of this chapter is a synthesis of the second chapter of the paper "LongTerm demand forecasting: the Kenza Approach" by Sallier, D. (2010), ATRS
2010, Porto.
The very idea at the origin of the empirical approach is that people are flying
because they "feel like" or have to, they can afford it and they are sensitive to the
relative ticket or inclusive tour price to their (annual) income. We assume that
they are not sensitive to the absolute price.
The following chart illustrates the successive steps of demand formation
according to the 1st Kenza law of consumer demand:
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Daniel SALLIER
In the former chart the red thick line F *  rn  represents the 1-complement of the
cumulative distribution of normalised individual income – Kenza distribution – it is
to say the percentage of the population of which the annual income is greater or
equal to rn which is equal to the individual annual revenue r divided by the
normalisation variable r which might be equal to the GDP per capita or the
average/median annual individual revenue, …
The equation of the 1st Kenza law of demand is:
D t   P t   K1  F *  K2  pn t 
Where:
p
pn 
r
is
K 2  pn
inclusive-tour sale price, which is actual
price divided by the normalisation quantity
at a given date t;
is the threshold of normalised income. K 2 is
constant;
is the proportion of elected population
relative to the total population which can
F * K 2  p n 
the
normalised
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average
ticket
or
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Daniel SALLIER
P
K1
D  P  K1  F *  K2  pn 
afford to buy the considered service or the
perishable good;
is the total population;
is a aggregated constant which aggregates
both the proportion of actual consumers out
of the elected population and the number of
the average number of goods/services
bought by each actual consumer.
is the total number of service/good units
actually bought/sold;.
3. THE SECOND KENZA LAW OF DEMAND
While the 1st Kenza law of demand can provide a good modelling tool of the
leisure demand of a given population for any destinations such as, for instance,
the German leisure market globally considered, the 1st Kenza law cannot be used
for modelling the demand for a given set of destinations such as the German
leisure market to Tunisia for instance.
It has to do with the fact that it is very likely that, generally speaking, the richer
among the German population will tend to be attracted by more fancy
destinations such as the Seychelles Islands for instance even if the service they
are ready to pay for is not that different from the one offered in Tunisia. It has to
do with self-consciousness of one's social status8. Of course this "conspicuous
consumption" behaviour as named by T. Veblen is not at all a specificity of the
German population and it can be generalised worldwide.
It means that a second threshold of income should be considered in the 1 st Kenza
law of consumer demand. While the first leftmost threshold of income keeps
determining what the elected population is, the second rightmost threshold of
income determines which part of the population will not go anymore to this set of
destinations for not being "classy" enough.
The following chart, illustrates the different steps of the leisure demand formation.
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Daniel SALLIER
This chart translates into 2 different versions of the equation of the 2 nd Kenza law
of consumer demand.
3.1 Equations of the 2nd Kenza law of consumer demand
1st version
D  pn   P   K1, L  F *  K 2, L  pn   K1, H  F *  K 2, H  pn 
In this equation P   K1, L  F *  K 2, L  pn  represents the demand which would
result from the leftmost lower threshold of normalised income  K2,L  pn  while
 P   K1, H  F *  K 2, H  pn   represents this part of the demand defined by the
rightmost upper threshold of normalised income
K
2,H
 pn  which is no more
attracted by the considered destination.
In this equation we should have:
-
K 2, H  K 2, L
being constant which means that the upper threshold is
proportional to the lower one;
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Daniel SALLIER
-
K1, H  K1, L being constant which means that only part of the richer customers
are considering a more fancy destination K1, H  K1, L or that all the richer
customers consider a more fancy one K1, H  K1, L
This version of the 2nd Kenza law of demand is
difference between the 2 normalised thresholds
characterised by the fact that the
 pn   K 2, H  K 2, L   is proportional


to the price and narrowing over the time as the normalised price is decreasing.
2nd version


D  pn   P   K1, L  F *  K 2  pn   K1, H  F * K 2   pn  pn ,min  


In this equation P   K1, L  F *  K2  pn  represents the demand which would result
from
the
leftmost
lower
threshold
of
income
 K2  pn  while


 P   K1, H  F * K 2   pn  pn ,min   represents this part of the demand defined by


the rightmost upper threshold of income  K 2   pn  pn,min  which is no more
attracted by the destination.
In this equation we should have:
-
K1, H  K1, L being constant which means that only part of the richer customers
are considering a more fancy destination K1, H  K1, L or that all the richer
customers consider a more fancy one K1, H  K1, L
This version is characterised by the fact that the difference between the 2
normalised thresholds of income  K2  pn,min  is constant. It is very likely that in
fact consumer behaviour moves from the 1st version to the 2nd one as the relative
price is decreasing.
3.2 Model calibration
1st version
Kenza calibration process requires that K1,L, K1,H, K2,L and K2,H to be determines
out of the sets of historical data. One way, a very classical one, is to minimise the
sum of quadratic errors between actual data and their estimate.
To start with, let us assume that we already know K2,L and K2,H values. The sum
of quadratic errors e² is equal to:
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Daniel SALLIER

e2   Di  Pi   K1, L  F *  K 2, L  pn,i   K1, H  F *  K 2, H  pn,i  
i

2
where
i
index, refers to the date i in the
set of data
Di
actual demand measured at the
date i
*
*
Pi   K1, L  F  K 2, L  pn,i   K1, H  F  K 2, H  pn,i  Kenza demand estimate at the
date i
For a given value of K2,L and K2,H, e² is minimised for
 D  P  F K
K1, L 
i
 pn ,i     Pi  F *  K 2, H  pn ,i     Di  Pi  F *  K 2, H  pn ,i     Pi 2  F *  K 2, L  pn ,i   F *  K 2, H  pn ,i  
2
*
i
2, L
i
i
i
 D  P  F K
*
K1, H 
i


i  Pi  F  K 2,L  pn,i   i  Pi  F  K 2,H  pn,i   i  Pi 2  F *  K 2,L  pn,i   F *  K 2,H  pn,i  
2
*
i
i
2, L
i
i
*
i
2
2
 pn ,i     Pi 2  F *  K 2, L  pn ,i   F *  K 2, H  pn ,i     Di  Pi  F *  K 2, H  pn ,i     Pi  F *  K 2, L  pn ,i  
  P  F  K
i
*
2, H
i
2
i
2
2


 pn ,i      Pi  F *  K 2, L  pn ,i      Pi 2  F *  K 2, L  pn ,i   F *  K 2, H  pn ,i  
i
 i

2
K2,L and K2,H can be determined by using iterative approach to minimise e² value.
2nd version
Kenza calibration process requires that K1,L, K1,H, K2 and pn ,min to be determines
out of the sets of historical data. As for the 1st version, one way, a very classical
one, is to minimise the sum of quadratic errors between actual data and their
estimate.
To start with, again, let us assume that we already know K2,L and K2,H values. The
sum of quadratic errors e² is equal to:



e2   Di  Pi   K1, L  F *  K 2  pn   K1, H  F * K 2   pn  pn ,min  


i

2
where
i
Di


Pi   K1, L  F *  K 2  pn   K1, H  F * K 2   pn  pn ,min  


index, refers to the date i in
the set of data
actual demand measured
at the date i
Kenza demand estimate at
the date i
For a given value of K2 and pn ,min , e² is minimised for
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Daniel SALLIER
 D  P  F K
*
K1, L 
i
i
2, L
i

i
P  F K
*
i
2, L
i
 D  P  F K
*
K1, H  
i
i

 pn,i    Pi  F * K 2   pn  pn,min 

2, L
2
i
 pn ,i    Pi F
*
i
*
i
2
i
2
i
*

K
2, L
 pn,i   F
*
K  p
2
n
i
P  F K  p
i
2
 pn,i    Pi  F * K 2   pn  pn,min 
2
i

   D  P  F  K   p
n
 pn ,min 

2
n



 pn,min    Di  Pi  F
2
*
i
i
2, L

i


   Pi 2 F *  K 2, L  pn,i   F * K 2   pn  pn,min  
 i

*
i
    P  F  K

 pn ,min    Pi 2 F *  K 2, L  pn ,i   F * K 2   pn  pn ,min 
K  p
2
n



 pn ,min    Pi  F
*
K
2, L
 pn , i 

2
i
2


 pn ,i    Pi 2 F *  K 2, L  pn,i   F * K 2   pn  pn,min  
 i


2


2
And once again an iterative approach can be used to determine K2 and pn ,min
values.
4 The 3 ages of ANY leisure market
Whatever the formula used for the 2nd Kenza law of demand, we will get the
same qualitative evolution of any leisure market.
1/ age of market development
The 2 thresholds of income are located right enough on the Kenza distribution of
income and are moving leftward not necessarily because of fares decreasing but
of the population standard of life improving. This translates into a rather dynamic
leisure destination which keeps developing, attracting visitors and making profit.
2/ age of market 1st stagnation
The 2 thresholds of income are now located on Kenza distribution of income so
that additional visitors/passengers induced by a higher standard of life, if not
decreasing fares are balanced by the number of former visitors which do not feel
like going to such a "popular" destination anymore. Most of the time the reaction
of the tourism sector (airlines, tour operators, hotel, etc…) is to enter a fare
competition which does not translate into a significant higher number of visitors
while significantly altering the overall profitability.
Last but not least the experts of the sector are very likely to misunderstand the
very structural reason at work in this stagnation and are more than likely ready to
consider it results from the competition of other sets of destinations.
3/ age of market decline & final stagnation
Further to fare competition and increase of the population standard of life, the 2
thresholds of income have moved left enough on Kenza distribution of income for
the additional visitors/passengers induced by a higher standard of life and
decreasing fares are out balanced by the number of former visitors which do not
feel like going to such a "popular" destination anymore.
The following chart illustrates those 3 ages of any leisure market:
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Daniel SALLIER
A quite interesting point here is that the "conspicuous consumption" as named by
T Veblen is at work all the time but does not necessarily translate into the leisure
destination behaving like a Veblen good.
In fact depending on the 2nd Kenza law parameters, mostly the
K
1, L
, K1, H 
couple, the 3 ages of any leisure market may be more or less "contrasted". This
is illustrated in the following chart.
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Daniel SALLIER
The issue is that whatever the 2nd Kenza law parameters are, the demand will
demonstrate a maturing process resulting in a structural decline (in absolute
value) of demand elasticity to GDP and fares which will characterise a market
segment which is less and less profitable.
5. DEMAND ELASTICITIES
1st Version
Assumed that the Kenza distribution is steady over the time, derivation of the 1st
version of the 2nd Kenza law leads to the following set of demand elasticities:
dD  pn  dP

D  pn 
P
*
*
dp  K1, L   K  pn  K 2, L   F  K 2, L  pn   K1, H   K  pn  K 2, H   F  K 2, H  pn  
 

p 
K1, L  F *  K 2, L  pn   K1, H  F *  K 2, H  pn 

*
*
drn  K1, L   K  pn  K 2, L   F  K 2, L  pn   K1, H   K  pn  K 2, H   F  K 2, H  pn  



rn 
K1, L  F *  K 2, L  pn   K1, H  F *  K 2, H  pn 

Where  K  xn  is the intrinsic Kenza elasticity.
Alike the 1st Kenza law of demand we have:
1/ demand elasticity to the population is equal to 1;
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Daniel SALLIER
2/ A "symmetrical" value of the demand elasticity to the price and the one to the
normalisation quantity: the average revenue per capita or the GDP per capita
most of the time.
Empirical study9 of the Kenza distribution shows that the intrinsic elasticity is an
almost linear function of the rate of elected population:  K Tn   A F * Tn   B
where A  0 and B   A (demand elasticity equal zero for 100% of the
population.
Assumed that the two normalised thresholds of income are located on the rather
linear section of the intrinsic elasticity of Kenza, the demand elasticity to price is
equal to:



 K  F* K  p  2  K  F* K  p 
1, L
2, L
n
1, H
2, H
n
 p  A 
*
*
 K1, L  F  K 2, L  pn   K1, H  F  K 2, H  pn 


2

 1


In the very extreme case of K1, H  K1, L which is a market which cannot secure the
loyalty of the richest visitors, the demand elasticity to price is equal to
 p  A   F *  K 2, L  pn   F *  K 2, H  pn   1 .
For
the
highest
normalised
price
we
have
F *  K2, L  pn   0.5
and
F *  K 2, H  pn   0.5 which results in  p  0 : a negative demand elasticity to price
which translates into a "normal" behaviour of the market.
On the other hand there is a normalised price threshold below which
 F *  K 2, L  pn   F *  K 2, H  pn    1 which results in a positive elasticity of the


demand to price and the leisure destination turns into a Veblen good.
2nd Version
The following equation gives the different components of the demand elasticity.
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Daniel SALLIER
dD  pn 
D  pn 


dP
P




 


 
*
*
dp  K1, L   K  pn  K 2   F  K 2  pn   K1, H   K K 2   pn  pn ,min   F K 2   pn  pn ,min 

p 
K1, L  F *  K 2  pn   K1, H  F * K 2   pn  pn ,min 

K1, H  K 2  pn ,min 

dF * K 2   pn  pn ,min 





d K 2   pn  pn ,min 
dp

*
p K1, L  F  K 2  pn   K1, H  F * K 2   pn  pn ,min 

*
*
drn  K1, L   K  pn  K 2   F  K 2  pn   K1, H   K K 2   pn  pn ,min   F K 2   pn  pn ,min 

rn 
K1, L  F *  K 2  pn   K1, H  F * K 2   pn  pn ,min 



K1, H  K 2  pn ,min 



dF * K 2   pn  pn ,min 





d K 2   pn  pn ,min 
drn

*
rn K1, L  F  K 2  pn   K1, H  F * K 2   pn  pn ,min 






Same comments as before can be done on the demand elasticity to population
and the symmetrical characteristics of the demand elasticity to price and the one
to the normalisation quantity.
Assumed the same almost linear shape of the intrinsic elasticity function of the
elected population, K1, H  K1, L and a small enough value of pn ,min for using 1st
order development, it can be demonstrated that:
 p  A   F *  K 2  pn   F *  K 2   pn  pn,min    1  1

which
is
positive
for

 F *  K 2  pn   F * K 2   pn  pn,min    1  1 . Once again a leisure market will


A
progressively evolve from a "normal" price stimulated behaviour towards the
Veblen good category.
6. EXAMPLES OF UTILISATION OF THE 2nd KENZA LAW OF DEMAND
We are using the Tunisian leisure markets of 5 different European countries as
an illustration of the 2nd Kenza law of demand: France, Germany, UK, Italy and
Spain. The choice of these markets results from the availability of the Kenza
distributions of income for those countries.
6.1 Dataset
Visitors, GDP, Population, average passenger income.
We have been given the average airline revenue per passenger by Tunisair
which are non-public figures which is the reason why the related data are shaded
in the table hereafter.
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Daniel SALLIER
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Daniel SALLIER
Kenza distributions
The Coste estimator10 of a Kenza distribution is

1

F *  rn   min 1, a  1 
d
 1  ebcrn

We have been using the following set of parameters



Demand forecast beyond 2009
The forecast is based on an extrapolation of the recent GDP, population and
average fares growth over 2000-2007. The purpose is not so much to produce a
forecast as to try to identify if the related market has already, is about or will enter
is Veblen life cycle.
6.1 French originated leisure market to Tunisia
Kenza models are best fitted to historical data with the following set of
parameters:
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Daniel SALLIER
Actual traffic figures before 1990 are a bit paradoxical for showing a slightly
decreasing trend while at the very same time the French GDP is increasing,
average fares are decreasing and the market is still far from entering its Veblen
cycle.
Close analysis of the French market as modelled by the Kenza law tells us that:
1/
the 2 models are best fitted when considering 1 year shift of the GDP per
capita which means customers are sensitive to their former year wealth but
to today’s prices.
2/
the 2 models are best fitted considering an equal value of K 1, L and K 1, H . So
it is a market which still lacks the ability to secure the loyalty of its “upper
class segment”.
3/
the market moved from the 1st to the 2nd version of the 2nd Kenza law of
demand between the years 1995 and 2000. According to this model
p n ,m in  0.0009 represented a value of €18.03 in 1995 and €28.19 in 2009
(one way).
4/
It is more than likely that the French leisure market to Tunisia will reach is
stagnation phase in the incoming years before starting its decline.
6.2 German originated leisure market to Tunisia
The German market reflects the turmoil the country went through along its recent
history:
-
1989: fall of the Berlin Wall en German reunification. Before 1990, visitors,
GDP, population and average fares are those of the former West Germany;
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Daniel SALLIER
-
2000 & 2001: Tunisia enjoyed strong arrivals of German tourists further to
terrorist attacks in Egypt and Turkey;
-
2001 and aftermaths: 9/11 terrorist attack in the USA, war in Afghanistan and
Irak.
Kenza models are best fitted to the 1987-1994 historical data with the following
set of parameters:
Both versions of the Kenza model seem to deliver very similar accuracy.
Kenza models are best fitted to the 2000-2009 historical data with the following
set of parameters:
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Daniel SALLIER
While the lower threshold of income constant, K 2 , L keeps being the same for the
2 periods of time, it is the upper threshold of income constants K 2, H and p n ,m in
which change significantly and translate a move for a more “choosy” behaviour
with a set of lower and upper threshold constants very close to those of the
French market. Together with a more choosy behaviour market penetration of the
elected population is almost doubled between the 2 periods of time.
As for the French market, both models are fitted considering a 1 year shift of the
GDP per capita: 1999 GDP per capita for 2000 demand.
A conclusion left to be confirmed is that the German market has already entered
its Veblen life cycle.
6.3 Italian originated leisure market to Tunisia
Kenza models are best fitted to historical data with the following set of
parameters:
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Daniel SALLIER
Close analysis of the Italian market as modelled by the Kenza law tells us that:
1/
the 2 models are best fitted when considering 1 year shift of the GDP per
capita which means customers are sensitive to their former year wealth but
to today’s prices;
2/
the 2 models are best fitted considering an equal value of K 1, L and K 1, H . So
it is a market which still lacks the ability to secure the loyalty of its “upper
class customers”;
3/
the market is best modelled with the 1st version of the 2nd Kenza law of
demand;
4/
It is more than likely that the Italian leisure market to Tunisia has reached is
declining phase and already started behaving like a Veblen good;
5/
While the constant for the lower threshold of income K 2, H is very close to
the French and German ones, the upper one K 2, H is far greater 300 instead
of 195/200 which can but to mean that the Italian market is less “choosy”.
In fact it is the very nature of the market which is different, Tunisia being a
long weekend destination for the Italians while it is definitely a vacation
destination for French and German people.
6.4 UK and Spanish originated leisure market to Tunisia
Both markets prove to be rather badly modelled by a Kenza law, but for 2
different reasons which are:
-
UK market: It is traditionally a very low yield market mostly served by British
charter (low-cost?) airlines which means that Tunisair average passenger
© Association for European Transport and contributors 2010
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Daniel SALLIER
income data are not representative of the market and those are the only data
we have access to;
-
Spanish market: it is a rather narrow market which accounted 46,000 visitors
in 1987 and about 100,000 visitors today; the sample size to work with is too
small for having a chance to fit any accurate Kenza model.
7. CONCLUSION
The second Kenza law of demand looks like delivering what it has been designed
for:
-
an ability to deliver a better understanding of the structural forces at work in
any leisure market which allows to differentiate between conjectural effects
and structural ones:
Is my record figure of 1 million German visitors in 2000 and 2001 something
likely to repeat? Is it worth spending millions of Euros of advertisement in
Germany to drain back German visitors? Have we to strongly suggest our
flag carrier – Tunisair – to have a more aggressive fare policy on the German
market? Is the German market still profitable? Isn’t it better to reallocate our
marketing expenses and efforts on blossoming East European markets such
as the Polish or the Russian ones?
Is my continuously declining Italian market since 2005 despite 5% fare
decrease over the period a consequence of tougher competition from new
destinations such as Croatia? It is a question which leads to exactly the same
set of additional questions as the ones of the German market.
It looks like that my French market, my bread and butter, is likely to start
being rather stagnant in the next future whatever the aggressiveness of my
fare policy. It looks like it has to do with the inability to keep the upper
segment of this market. Is there any way to revert this process?
-
A rather robust model which is not that sensitive to the value of its
parameters and of which parameters have a physical meaning which make
them comparable between different markets;
-
An unexpected output of the second Kenza law of demand is that it tell us
that any leisure market will potentially turn into a Veblen good at some time of
its product life cycle: the higher the price, the higher the demand. The tourism
sector and its actors may find in the luxury industry – a typical sector of
Veblen goods – the keys for keeping their business profitable;
-
200,000 visitors, 400,000 passengers a year seem to be a very minimum
demand/traffic volume below which it is pretty hard to fit any Kenza model;
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Daniel SALLIER
As for the 1st Kenza law of demand, the very "disturbing" issue with this approach
is the very demanding assumption made on K 1s and K2s of being constant over
the time. This is the reason why we have conducted additional research works,
yet to be published, which seem to prove the case; Ks property would result from
the aggregation to all a population of a probabilistic model of individual
consumption behaviour.
To finish with, we have developed and used the second Kenza law of demand for
modelling leisure air traffic and tourist visitors demand, but this approach far
exceed the only topic of leisure air transportation and can address all sort of
consumer demand issues.
8. REFERENCES
Sallier, D. (2010) Long-term demand forecast: the Kenza approach, ATRS 2010,
Porto
Veblen, T. (1898), The Theory of Leisure Class, Prometheus Books (New York)
French Ministry of Economy, Industry and Employment (2008) Les Comptes du
Tourisme – Compte 2007, Ministère de l'Economie, de l'Industrie et de l'Emploi,
Paris
Le Garrec, M.-A. (2008), Le Tourisme : Un Secteur Economique Porteur,
Ministère de l'Economie, de l'Industrie et de l'Emploi – Direction du Tourisme,
Paris
Instituto Nacional de Estadística (2010), Cuenta satélite del turismo de España.
Base 2000. Serie contable 2000-2008, Madrid
Griffith E., Zemanek S. (2009), U.S. Travel and Tourism Satellite Accounts for
2005–2008, US Bureau of Economic Analysis, Washington
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Daniel SALLIER
NOTES
1
2
3
4
5
6
7
8
9
10
Sallier D. (2010) Long-term demand forecast: the Kenza approach, ATRS 2010, Porto
Source Tunisair
French Ministry of Economy, Industry and Employment, Direction du Tourisme (2008) Les
Comptes du Tourisme – Compte 2007, Ministère de l'Economie, de l'Industrie et de l'Emploi,
Paris
Le Garrec, M.-A. (2008) Le Tourisme : Un Secteur Economique Porteur, Ministère de
l'Economie, de l'Industrie et de l'Emploi – Direction du Tourisme, Paris
Instituto Nacional de Estadística (2010), Cuenta satélite del turismo de España. Base 2000.
Serie contable 2000-2008, Madrid
Hellenic Statistical Authority (2010) Athens
S. Griffith E., Zemanek S. (2009) U.S. Travel and Tourism Satellite Accounts for 2005–2008,
US Bureau of Economic Analysis, Washington
This behaviour is at the origin of what it's called the Veblen good category which was initially
identified and described by the economist Thorstein Bunde Veblen (1857-1929): Veblen T.
(1898), "The Theory of Leisure Class", Prometheus Books (New York)
Unpublished work based on the study of the Kenza distributions of Brazil, Canada, Denmark,
France, Germany, India, Italy, Japan, Mexico, San Salvador, Spain, South Africa, UK, USA.
Unpublished work based on the study of the Kenza distributions. The Coste estimator is 1
order of magnitude more precise that the classical log-normal estimator which proves to be
significantly more precise than the Pareto law.
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