Moral and immoral in
economic quantification
1. What is the link between morality, immorality ... and statistics?
2. What is the role of statistical information in economic decision?
3. Opportunity or immediacy?
4. How to ignore or simplify the information through
measurement?
5. How to ignore or simplify the information through
quantification?
6. How to ignore or simplify the user information?
7. What role dose the education have in finding answers?
Doina Maria SIMION
In private, I want to be:
Healthy!!!
Activ !!!
Strong !!!
So I have to eat:
Identical in the professional life:
I want to be:
Cautious !
Consistent !
Fresh !
Healthy!!!
Activ !!!
Strong !!!
So I have to inform myself:
Therefore,
I need STATISTICS !!!
Cautious !
Consistent !
Fresh !
Which is the link between morality, immorality ... and statistics?
Today, we speak more and more about morality and immorality
 between individuals,
 in and between social grups,
 in the world of ideas and science.
We ask ourselves:
 What can be immoral in art?
 What can be immoral in medicin?
 What can be immoral in IT?
 But what can be immoral in Statistics (science and praxis)? etc. etc.
The answer was given by a famous painter:
“What is immoral in art is to make art without being talented”
Therefore I understand that imorality in science, art and praxis arises
and evolves where there is no:
 talent,
 vocation,
 calling,
 resonance,
 enough knowledge.
Immoral is to use the scientific knowledge unwise and incorect. It has
to be brought as close as possible to the real world, the social and
economic reality in which we live.
Role of statistical information in economic decision
Entropic law =>
work and information =>
anti-entropy factors
=>
>>>
<=
high entropy
LACK of work and information <=
<<<
proceeds
Decisional activities
conditions
Informational activity
succeeds
Opportunity or immediacy?

Information (in this case statistical) has to possess the following esential qualities:
 to be necesary,
 to be relevant (related to topic),
 to be exact and complete (without overflow ),
 to be recent and oportune (submitted in time),
 to be economically efficient, i.e. ocazional expenditures of collection,
transmission, processing and storage have to be compensated by the benefits
that derive from its use in economic decision making.
To be oportune !?
The scroll speed of life, of the economic and social phenomenon and processes, is
so fast at the beginning of this century it generates the immediacy of human
needs that we face today. This requires action priority, speed, sacrifice, kneading
and generates a pretty obvious feature of the modern mentality namely
oversimplification. Whose? Reality's.
Speed of action  immediacy  simplification
This state of affairs changes something in, what was called by the `80 literature
information opportunity, in this case statistical information.

Immediacy + ignorance of statistical theory =
= mistake and / or “lie”
STATISTICAL
INFORMATION PROVIDER
WITH COMPUTER
STATISTICAL
INFORMATION USER
WITHOUT COMPUTER
Where, when and how ... are we wrong?
Information
P
R
O
V
I
D
E
R
Information
Measurement,
quantification
Data systematization
Using the analysis
method
Graphic
representation
Result interpretation

Knowing the
statistical instrument,
the formula
Knowing the
statistical method
Ergodicity. The
Bayes principle
The need for
information

U
S
E
R
How to ignore or simplify the information by measurement?
What does it mean to measure?
1. We determine the value of an indicator, directly, as a ratio of the
indicator we measure to its measurement unit.
2. To measure also means to determine the value of an indicator
indirectly, by using a formula which uses values obtained by direct
measurement
Here we find the sensitive point:
the formula.
 When, where, and who launched (designed) it?
 Who uses it, when, where, and how?
There are theoretical areas of economic analysis which take the
formula out of its statistical context, and use it as one would use an
instrument taken out of a kit, without remembering to put it back in
its place every now and then, in the matrix where it was designed,
tested, used and eventually redesigned. In other words, forgetting to
re-place the formula within the category it belongs to, to recall again
the principles of this category in order to use it correctly.
We use indices, create derivatives based on them, and ignore or
maybe forget the „mother” category with all that is specific to it.
Ex 1:
The Dow Jones index is, at its origin,
an average index belonging to the
average category. When the stock
exchange activity diversified and
quotation divisions were introduced,
the index was taken out of its matrix
(statistical mathematics) and
adapted to the economic area
where it was used. The effect of
share divisions is corrected by the
calculation of a divider, which is
periodically updated. The indicator
is no longer an average value, an
instrument that can be generalized
for any domain, it was „ripped” out
of its matrix, particularized and
„stuck” in the subject matter of Wall
Street transactions.
C1  C 2  ...  Cn
DJ 
n
Average
Dividing the rate of change of
shares
No average
DJ 
C1  C 2  ...  Cn
D
The artifice of the correction factor reduces the strength and relevance of the
indicator, relativizes it and makes it more sensitive in time and space. Whether we
find ourselves in the position of the provider or of the user of statistical information,
knowing the artifice added to the basic construction helps our understanding and
reasoning.
How do we ignore or simplify the information by quantification?
What happens through quantification?
We establish the discontinue that values and indicator may have, we
impose conditions on an indicator such that its values vary in
waves. We go from quantics, from the atomicity of the economic
and social space to the macroscopic world of companies, of
institutions, of societies, of the structures which make up the
economic and social architecture of this world.
Here happens a great loss!
Reality! Truth!
Reality? Truth?
We synthesize the results, as they occur in every atom, simply by making them discrete
and then adding them up. Then we create their corresponding statistical design,
systemize them on the interval scale, obtain statistical distributions, distribution tables,
contingency tables, association tables, without asking ourselves how much of the
economic and social space we are studying stays in the „shapes” we have created
statistically.
Data on annual net profit achived and nominal value of shares issued
on the market for 27 companies of a range of activities have been
summarized in the following tables:
a. Using primary data:
Companies
achieving
annual net
profit (mil
lei)
Companies
nr
Aggregated values :
%
Annual net profit
achieved
Nominal value of issued
shares
mil.lei
%
mil.lei
%
1 - 50
16
60
232,140
11,4
133,790
69,4
50 - 100
2
7
160,658
7,9
26,000
13,5
100 - 150
4
15
441,614
21,8
28,000
14,6
150 - 200
1
4
175,000
8,6
1,000
0,5
200 - 250
2
7
476,210
23,4
2,000
1,0
250 - 300
2
7
546,210
26,9
2,000
1,0
Total
27
100
2 031,832
100
192,790
100
b. Using systemized data:
Companies
Companies
achieving
annual net
profit(mli lei)
Aggregated values :
Annual net profit achieved
nr
Nominal value of
issued shares
%
mil.lei
%
mil.lei
%
1 - 50
16
60
25*16=400
18,0
...
...
50 - 100
2
7
75*2=150
6,7
...
...
100 - 150
4
15
125*4=500
22,5
...
...
150 - 200
1
4
175*1=175
7,9
...
...
200 - 250
2
7
225*2=450
20,2
...
...
250 - 300
2
7
275*2=550
24,7
...
...
Total
27
100
2 225
100
...
...
What effect has this significant difference on further economic
analysis when the above indicator, a structural indicator, enters as
primary data?
Partial conclusion:
The social and economic reality is not some easily
modelled plasticine, it is a material and spiritual
mixture of existence, which theoretical systems we
must approach with caution
They must approach it:
 in good knowledge
 in wise patience,
 with deep understanding,
 with professional friendship
that is,
with talent and vocation.
How to ignore or simplify the user information?
The information does not consist only of the data and the indicators obtained from
the statistical analysis, but also of the specificity given by the user’s
professional, economic, social, and scientific context.
Ergodicity refers to the manner in which information users infer various things,
reach a conclusion on something based on information about something
else.
Low transinformation means low
understanding
Rich transinformation means high
understanding
We speak of transinformation, as a factor for reducing enthopy. From a theoretical
perspective, transinformation is defined as the information about the emitter
contained in the receiver field, or as a correspondence between the emitted and the
received message. How much of what information users know is statistical
information?
Ex: Analyzing the dynamics of a phenomenon using:
 - „annual change”
 - “change at end of year”
 - „changes in current year”
 - “changes after 12 month”
 - „annualized change”
Period
Orders (mil.lei)
Change after four
quarters (%)
Change related to 4th quarter 2005 (%)
real
annualized
2005
1st quarter
333
2nd quarter
338
3rd quarter
343
4th quarter
336
Annual average
337
2006
1st quarter
343
3,0
2,1
8,6
2nd quarter
360
6,5
7,1
14,8
3rd quarter
352
2,6
4,8
6,4
4th quarter
329
-2,1
-2,1
-2,1
Annual average
346
2,7
A subjective probability always depends on what the
receiving subject, the information user knows at a given
time about the phenomenon studied, or about the
method of analysis. In other words, the content of
information is equally determined by the provider
(emitter) and by the user (receiver).
If somebody knows the market evolution of a soft drink over
the past years, and the methods by which its dynamics
can be studied, then an evolution index, or a seasonality
index, or a future estimated level can be more or less
plausible, relevant or significant for the one who informs
himself.
More so for the situations when that someone is the
decision maker in a structure.
Final conclusion:
Any particular economic analysis (of value, of dynamics, of
correlation, etc) gives specificity to the scientific instrument
and methods used, fills them with meaning
To conduct such an analysis with professional responsibility is a
commendable act. To do is with professional virtue is
excellence. Professional responsibility involves effort,
professional virtue involves joy.
The philosophers of our times note with sadness that today we speak
more about professional responsibility than we do about
professional joy.
And this is another abyss in which the world of science turns into
darkness.
Some notes:
! Statistical procedures simply work. Not knowing them pushes
towards automatism. To be useful we must feed them real,
healthy data, with real social and economic content….. knowing
what we give and what we ask for.

A manager asks a statistician to determine in what extent two rows of data
representing changes over a period of time (9 years) of two variables
correlate. The statistician with or without a computer calculates the
correlation coefficient and finds that the variables correlate with a high
intensity of 93%. What were the variables? What has the statistician
calculated? No more and no less then the correlation between the level of
housing loans granted by the banking system in Romania with beans
production in these nine years.
! Do we give / receive complete information for a good decision?



A rate of sales growth of 177% is announced but the
factorial indexes for this analyzed case I(p) = 197% and I(q)
= 90 % are not known. What weighting systems was used?
Which indicator is made public by the analyst? But by the
decision-maker? How to use the information given and how
to request additional information for good decision?
A promise of a higher average salary is announced but no
one announces also the coefficient of variation to see how
credible this average is.
A statistical survey result is announced, but information
about the error with which they worked or the degree of
significance is not provided.
! Do we know how to discern the abundance of
information?
 Calculation by Hirschman, Gini-Corado, Stuck or
other methods applied on one and the same case
leads to different results. Dose the recipient of the
information have sufficient knowledge of
statistics to understand that the difference
between these levels mean the same thing?
 The calculation of correlation and regression on
the same volume of data through different
parametric or non-parametric methods lead to
different final results. Dose the statistics
information provider have the good professional
sense to give the most relevant version of his
study?
Thank you for your attention
