Search for fundamental articles in economics1
Tomáš Cahlík2
The aim of this article is to demonstrate on the scientific field “economics” the
search for fundamental articles.
Co-word analysis and citation analysis enable to visualize the structure of a
scientific field on the maps of science. Then we can find the fundamental themes on the
maps. After finding the articles belonging to these fundamental themes we can discuss
the fundamentality of the formers, too.
Keywords: co-word analysis, citation analysis, economics
Introduction
New ideas in economics – from the point of view of the world science - can be found
in about 140 economic journals documented in the SSCI database. It is clear that there are
some costs involved in finding fundamental ideas, the reason is the big number of journals.
That is why following questions are relevant: Which journals am I to read? Which are the
fundamental articles?
The answer to the first question is quite clear, important journals ought to be read. The
measure of importance could be for example the average number of citations of the journal’s
articles, the impact factor. This measure can be eventually more fully elaborated3. A deep
analysis of economic journals and their citation network was done by Stigler [11].
There are three possible answers to the second question. The first one is to take as
fundamental articles those that appear in important journals. The problem is that for example
more than 1000 articles appear yearly in the thirteen most important economic journals4. It is
just impossible to read them all. The second answer, the most usual one, takes as fundamental
articles the ones by known authors and those cited in the references in the read articles. The
third answer says that fundamental articles are those that belong to the fundamental themes of
the given scientific field. So the analysis of the given scientific field and identification of
fundamental themes must be done before we can judge the fundamental articles.
For the analysis of a scientific field we can imagine its structure in cognitive and
social layers. The cognitive layer can be captured as a set of networks at various levels of
aggregation – at the highest level is the net of scientific fields, scientific fields are nets of
scientific themes and scientific themes are nets of keywords or citations. The social layer is a
net of scientists, groups of scientists, control subjects and other actors mutually tied with
material, financial and informational flows. The cognitive layer can be analyzed by
scientometric methods, the social layer by various sociometric techniques. Both layers are
deeply tied. When a new theme appears in the cognitive layer, the scientists in the social layer
1
Results of grant 402/00/0999 „Research and Development in Economic Growth Models“, Grant Agency of the
Czech Republic are used in this article.
2
Charles University Prague, Faculty of Social Sciences, Institut of Economic Studies, Opletalova 26, Praha.
E-mail: [email protected]
3
4
For example Beckmann and Persson [1].
Beckmann a Persson [1]
move to another place. The change of flows in the social layer has an impact on the cognitive
layer. The corresponding aggregate in the social layer can be found to each aggregate in the
cognitive layer.
The life of an aggregate follows in principle the logistic curve. Its birth is the result of
activities of the best scientists. They create the main frame for farther elaboration of the
aggregate. Other scientists and flows enter this frame in the social layer. Scientists solve the
problems given in the cognitive layer by characteristic methods for the aggregate. There are
more and more questions that are unsolvable by means of those characteristic methods.
Scientists streaming to solve those questions begin to use other methods, aggregate begins to
stagnate and dies in the end. This model of cognitive and social layers has its origin in the
paradigms of T.S. Kuhn [9].
Scientometrics – „the science of science“ – concentrates on the analysis of the
cognitive layer on the base of publications. Its main scientific themes are at present in search
of the specific impact of publications on the structure of science. The two most important
methods are the co-word analysis [2,3,4,5,6,12] and citation analysis [7,10]. Both of these
methods enable to visualize the structure of a scientific field on the maps of science. Then we
can find the fundamental themes on the maps5. After finding the articles belonging to these
fundamental themes we can discuss the fundamentality of the formers, too.
Co-word analysis in search of fundamental themes and fundamental articles in economics
in 1997 - 1999
The co-word analysis has been elaborated mostly by French scientometricians (for
example Courtial, Callon, Turner). The keywords used for the description of the content of an
article are the basic building blocks of a research field structure. A cluster of keywords can be
understood as a short description of a research theme6. A research field is then described as a
structure of mutually connected research themes.
Each research theme obtained in this process has two parameters. The first one, called
"density", measures the strength of internal ties among all the keywords describing the
research theme. We can understand this parameter as a measure of the theme development.
The second one, called "centrality", measures the strength of external ties to other themes. We
can understand this parameter as a measure of importance of a theme in the development of
the entire analyzed field.
Both median and mean values for density and centrality can be used in classifying
themes into four groups. Thereafter, a research field can be understood as a set of research
themes, mapped in a "strategic diagram" - graph made by plotting themes according to their
centrality and density rank values (if we use median for classifying clusters) or values (if we
use mean) along two axes, x-axe centrality, y-axe density. Strategic diagrams with rank values
are used more commonly than the ones with values, because of their legibility.
5
Deeper overview is in [5,10].
6
A research theme can be identified by using the information about common occurrences of keywords in some
articles. Let us calculate an "association index" as:
eij = fij2/(fi.fj) ,
where fij is the number of common presence of keywords i and j in an article, fi is the total number of
occurrences of the word i in all the articles. We can understand an association index as a measure of strength of
ties between keywords in a research field. This measure is then used for clustering the keywords into research
themes.
2
The themes in the first quadrant are both well developed and important for the
structuring of a research field. The themes in the fourth quadrant have well developed internal
ties but unimportant external ties and so are only of marginal importance for the field. The
themes in the third quadrant are both weakly developed and marginal. The themes in the
second quadrant are important for a research field but are not developed7.
The articles from the thirteen most cited economic journals8 have been excerpted from
the SSCI database for three successive periods 1997 - 1999. In 1997 it was 1235 articles, in
1998 -1202 articles and in 1999 - 1253 articles. Software system Lexidyn9 was then filled in
with these articles.
Strategic diagram for the first period is in Fig. 1. The number of the theme in the
strategic diagram is only a technical mark equal to the number of the most important
keywords10.
1
2
Fig. 1: Strategic diagram for the first period.
The structuring of the scientific field economics obtained by this method is only very
weak. The reason could be the insufficient description of articles by keywords. The solution
7
We use the clockwise convention of quadrant numbering and we hope it will not cause any misunderstandings
later in the text.
8
The same journals as in [1] are used: Journal of Economic Literature, Econometrica, Journal of Political
Economy, Quarterly Journal of Economics, American Economic Review, Review of Economic Studies, Rand
Journal of Economics, Journal of Economic Theory, Journal of International Economics, Economic Journal,
Journal of Public Economics, International Economic Review, Economica.
9
Software system Lexidyn filled in with this data has more than 25 MB. That is why this application is not
accessible through the Internet. A smaller application of Lexidyn filled in with different data is accessible on
http://tucnak.fsv.cuni.cz/~cahlik/, in the branch Economic Growth.
10
The numbering is created by system Lexidyn itself, the only reason is better legibility of the diagrams.
3
could be in this case the augmenting of the number of the processed articles. I haven’t done
this more profound analysis yet11.
Both themes on Fig. 1 are clusters of keywords. Theme 1-Model in the first quadrant
is formed by the following keywords: 1 model, 20 unemployment, 21 innovation, 26
inflation, 31 money, 45 monetary policy, 56 dynamics, 88 life cycle. Theme 2Equilibrium in the third quadrant is then cluster: 12 markets, 15 games, 152 tiebout, 16
oligopoly,
2 equilibrium, 24 existence, 4 information, 7 market.
Theme 2 demonstrates the main problem of this method, the quality of keywords. It is
clear that market and markets are in principle the same keyword and that only one of them
should be in the database. The solution to this problem lies in replacing synonyms by only one
keyword. Again, I haven’t done this more profound analysis yet.
The keywords in a theme are mutually connected12. In Fig. 2 we can see all the
internal ties of theme 2-Equilibrium.
16
15
152
2
4
24
7
12
Fig. 2: Internal ties of theme 2-Equilibrium in the first period.
Scientific fields develop in time. Themes change their positions and their contents, the
old ones can leave the field and the new ones can appear. That is why the strategic diagram in
the third period (year 1999, Fig. 3) differs from the one for the year 1997 (Fig.1).
11
Authors in different fields use different average numbers of keywords per article. The „optimal“ number of
articles can be found experimentally. Sensitivity of results on the number of processed articles is connected with
this problem, too. As far as I know this sensitivity analysis has not been done by anybody yet. Sensitivity
possibly differs in different scientific fields.
12
The strength of ties is given by the index of association (explained in footnote 6).
4
2
3
1
Fig. 3: Strategic diagram for the third period.
The key question is the identification of a specific research theme in different periods
of development of a scientific field when the theme can change its position on the map and
even the keywords that form a theme can change13. The solution to this problem lies in
defining as identical such themes on various maps that have the number of common keywords
over a (subjectively )given threshold14. In Fig. 4 a chain of themes describing the evolution of
theme 2 can be seen15.16
CHAINS OF THEMES
THRESHOLD : 2
2
-> 2
-> 2
Fig. 4: The evolution of themes in time.
Some additional information can be given to this chain to obtain a quite detailed look
at the dynamics of evolution of scientific themes17. Graphical presentation as in Fig. 5 can be
13
Theme 2-Equilibrium in the third period is following cluster: 10 competition, 125 exchange,
15 games, 16 oligopoly, 2 equilibrium, 24 existence, 31 money, 91 repeated games. We can see that
the keywords are quite different here from those that are in the first period.
14
Let’s imagine an analogous problem how we could distinguish the villages that have been moved by a giant to
a different place on the maps from various eras of Liliputs‘ Empire. We would have to look at the houses that
form the villages and if we could find more than for example 5o % of the same houses it would be the same
village.
15
So theme 2 lived as theme 2 farther both in the second and in the third period. The number of the theme is only
a technical mark created by program Lexidyn itself according to the most important keyword. That is why the
numbers of themes in the chain usually differ, according to the change of importance of keywords. A theme can
be interpreted only by an expert from the specific scientific field.
16
Theme 1 in the third period has only one common keyword with theme 1 in the first period (model). But a
theme cannot be specified by only one keyword.
17
Program Lexidyn, function Field Analysis - Parents and Children of Themes.
5
used. Here we can read, for example, that theme 2 was in the first period in the third quadrant
and had eight keywords, 2 keywords from this theme were not even in the dictionary in the
second period (+++), two were in theme 2 in the second period and three were in the second
period in the dictionary but they were not included in any theme (xxx). Other 4 keywords that
were not in the dictionary in the first period entered theme 2 in the second period. What
happened with theme 2 after the second period can be described similarly. What is interesting
here is only the movement from the second to the fourth quadrant.
3
3
28
2
xxx
+++
2
+++
4
2
3
28
2
+++
xxx
2
+++
3
3
xxx
4
28
Fig. 5: Detailed information about the evolution of theme 2-Equilibrium.
So far we described themes only as clusters of keywords. How can we go from those
clusters to a better description? The first possibility is to use the sensitivity of an expert for
combining the keywords. The expert can use the internal ties of the theme. In Fig. 2 we can
see that ties exist among keywords 2 equilibrium, 16 oligopoly, 12 markets, 7 market
(synonyms), 15 games. This theme can then be described for example as market games
resulting in equilibrium on oligopolistic markets. The second possibility is to find typical
articles for the theme and combine the abstracts of typical articles for the description18. We
can define as typical such articles that have the number of common keywords with the theme
over a certain threshold. In Fig. 6, two articles are described that have four common keywords
with theme 2-Equilibrium in the first period. We can see that the abstracts tell us more than
keywords themselves and so this possibility of themes’ description gives better results.19.
18
19
Software system Lexidyn, function Field Analysis- Documents´ Information..
Of course, two articles are insufficient, we could augment the number of articles by diminishing the threshold.
6
DOCUMENTS
PERIOD : 1
THEME : 2
THRESHOLD : 3
AU : Syropoulos C
TI Nontariff Trade Controls and Leader-Follower Relations in
International Competition
JN ECONOMICA 63(252):633-648
PY 1996
AB
A simple duopoly model is constructed in which leader-follower
relations arise as part of a subgame-perfect equilibrium in a game
of endogenous timing. I show that, in the absence of policy
intervention, cost asymmetries between firms can help sustain
collusive hierarchical organization of markets. On the basis of
this model, I then analyse the effects of VERs and import quotas in
the presence of foreign and international duopolies. My analysis
reveals that, in contrast to the existing literature, these
nontariff trade controls can break the stability of leader-follower
relations and thereby raise an importing country's welfare.
ID : OLIGOPOLY; POLICY; WELFARE; DUOPOLY; EQUILIBRIUM;
TARIFFS ; QUOTAS; MARKET; GAMES; PRICE
AU : Chen YM
TI Multidimensional Signalling and Diversification
JN RAND JOURNAL OF ECONOMICS 28(1):168-187
PY 1997
AB
This article offers a new explanation of why firms diversify.
I present a model in which a firm has private information about
both its own cost and the demand function of the market on which it
competes with another firm. I show that diversification can be used
by the informed firm to signal private information in order to
obtain competitive advantages. This provides an important motive
for a firm to diversify. The signalling explanation of
diversification is consistent with some empirical observations. A
phenomenon called natural signalling is also studied in the model
where both signals and private information are multidimensional.
ID : PRODUCT QUALITY; INFORMATION; PERFORMANCE;
EQUILIBRIUM ; OLIGOPOLY; MARKETS; FIRMS; PRICE
Fig. 6: Two typical articles for theme 2-Equilibrium in the first period.
7
Let us return to the basic question which are the fundamental articles and remember
the hypothesis that fundamental articles belong to the fundamental themes. So which themes
are fundamental? Those from the first or the second quadrant and those that have a good
chance to live farther in the future. The first piece of information can be easily obtained from
the strategic diagram, but the second one is much more inaccurate. Following statements can
be made [2,3,5]:
1. Themes that live more periods often survive to further periods.
2. Themes that have had an interesting evolution survive more often than themes with simple
dynamics.
3. Themes from the first and second quadrants survive more often than themes from the third
or fourth quadrants.
4. One can see the tendency of the themes from the second quadrant to go to the first
quadrant. This development is not at all surprising, the themes that are central are
interesting for the field and thus have a tendency to be elaborated.
5. Themes from the fourth quadrant are mostly coming into the field as already elaborated in
another research field. If this spring-in succeeds i.e. if the connections of such a theme to
its new field are so interesting that they are elaborated, then such a theme becomes central
in further periods. But most of the themes from the fourth quadrant leave the field in the
next period. Those are the springs-ins that are not considered as interesting by researchers.
6. Themes from the first quadrant that will not survive can make another research field richer
or their development can continue (be hidden) in applications.
With the co-word analysis of economics we obtained only a few themes with weak
internal structure20. That is why we cannot say in this case which theme is fundamental.
Now let us suppose that fundamental themes and their typical articles have been found.
Are those typical articles also fundamental? A typical article for a fundamental theme is
fundamental in the sense that it has found the place of optimal imprint in the cognitive layer
of science. But we don’t know anything about the depth of the imprint. So what the article is
about is fundamental, but we don’t know anything about the quality of the article, about its
potential to push up the problems of the specific scientific field farther.
Citation analysis in search of fundamental themes and fundamental articles in economics
in 1997 - 1999
The citation analysis doesn’t use keywords but instead of them uses reference
citations, cited articles. The clusters of cited articles are interpreted here as scientific themes.
Algorithm for the clustering and diagrams for the presentation of results are analogous to coword analysis.21.
20
Fig. 2 would look as a star without ties between keywords 15 and 7 and between keywords 16 and 12. This is
the indicator of weak internal structuring.
21
Maps of science based on citation analysis are created by the Institute for Scientific Information (ISI) in
Philadelphia, USA. Detailed description of their method is for example in [7,10]. Comparing with the French,
that usually map with co-word analysis only one scientific field, ISI creates since 1983 on the base of
combination of databases SCI (Science Citation Index) and SSCI (Social Science Citation Index) a map of the
8
In Fig. 7, strategic diagram of scientific field economics in 1999 can be seen, with the
most important ties among themes. In this diagram reference citations are used for clustering
and not keywords as in Fig. 3.
1969
11 4 2
596
1828
2135
1857
1480
11 8 5
1815
451
507
425
2203
581
Fig. 7: Strategic diagram for the third period on the base of reference citations.
This strategic diagram is much richer than the diagrams created on the base of
keywords. The reason is the much bigger number of reference citations than keywords in
articles22.
For example theme 1828 is the cluster of following citations: hart o 1988 economet11,
chung ty 1991 rev ec5, macleod wb 1993 am e10, aghion p 1994 econom9, hart o 1999 rev
econ4,
hermalin be 1993 j l4, noldeke g 1995 rand 10, edlin as 1996 am eco6.
These citation codes are used by ISI, specific articles can be found in the SSCI
database through those codes.
The articles that form the themes cannot be found directly in the Lexidyn system, but
we must look for them in the SSCI database. This search is time consuming and I haven’t
done it yet.
whole science. It enables to see in the map the connections between sciences and social sciences. The second
difference is in the presentation of results, where ISI doesn’t use strategic diagrams for the space presentation of
the maps of science, but the so called technique of multidimensional scaling. We leave the practice of ISI here in
using the citation analysis only for scientific field - economics and in presenting the obtained results in strategic
diagrams.
22
The comparative advantage of keywords over citations is that they are common also in other databases than
SCI and SSCI and that they can be directly used for the verbal description of scientific themes.
9
As in the co-word analysis, we can say here that fundamental themes are the ones from
the first or the second quadrant.
As in the co-word analysis, themes that lived longer in the past can be found. Only
theme 1857 has lived more than one period with threshold higher than one23, from 1998 till
1999 (Fig. 8).
But with the citation analysis there is a bottleneck of experiences that could allow us
to make predictions about the future of scientific themes on the base of their evolution in the
past. So we cannot judge whether the theme is fundamental on the basis of its chance to live
farther in the future24.
+++
7
1
3
1857 8
+++
5
xxx
2
2
1857 8
Fig. 8: Detailed information about the evolution of theme 1857 from the second to the
third period.
For the verbal description of themes we would have to use those articles that create
themes or those that are typical of themes. Typical articles are those that have the number of
common citations with a theme over a certain threshold.
We get a better description using articles that create themes. There is a disadvantage
here and it is the costly search for articles in the SSCI database, as has been mentioned above.
The use of typical articles for description gives usually a sufficient approximation.
Now let us answer our basic question, which are the fundamental articles? Let us
suppose now that fundamental themes have been found. Articles, that create fundamental
themes are fundamental in the sense that they are both concerned with fundamental
questions and have a high quality. These articles have often been cited, that is why they
have a high quality and they have had impact on structuring the fundamental themes, that is
why they are concerned with fundamental questions 25.
Conslusions
In the preceding text we were concerned with the search for fundamental articles.
Some farther directions for the elaboration and amelioration of the analysis were mentioned.
Let us summarize them now:
23
Threshold is explained similarly as in Fig. 4.
In the codes of citations forming the theme, the year can be distinguished. According to the age of citations we
can then judge its „modernity“. It opens space for discussions about the connection between modernity and
fundamentality.
25
Typical articles are again about fundamental questions but again we do not have any information about their
quality.
24
10
•
The first direction suggests to experiment with augmentation of the number of the
processed articles with the target to find the “optimal” number of articles26.
• The second is to replace synonyms.
It can be objected now that both directions lead to the amelioration of the co-word
analysis that according to the text above doesn’t lead to the finding of fundamental articles
anyway. That is true but the co-word analysis gives us overview about mutual relations of
themes in a certain period. It enables to answer another question, research in which themes
is to be subsidized? The natural answer here is that the research in fundamental themes.
• Another objection can be that fundamental themes can be found the citation analysis, too.
Here we come closer to another important question about the relation of themes found by
the co-word analysis and the themes found by the citation analysis. Preliminary results27
obtained during the analysis of another research field show that the relation is quite free
and that both methods are to be taken more as complementary than as substitutes. To
make this vague statement more specific on the base of the analysis of economics gives us
the third direction.
• Besides these three directions experiences can be summarized for farther research of the
dynamics of evolution of research themes obtained by the citation analysis. The target is
here to enable predictions of farther evolution of research themes on the base of their
evolution in the past. It would enable better specification of fundamental themes in
economics and could be specified as the fourth direction.
Elaboration and amelioration in these four directions enables more precious
determination of fundamental themes and fundamental articles. This would be of immense
importance for the effectiveness of research because it would allow us to concentrate the
research on fundamental research themes and spare the capacity of researchers in reading only
fundamental articles.
References
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Field. Neural Network World, 1997,No. 2.
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[4] Callon,M. Law,J. Rip,A.: Mapping the Dynamics of Science and Technology. MacMillan,
London, 1986.
[5] Courtial,J.P.: Introduction a la Scientométrie, Anthropos, Paris, 1991.
[6] Courtial, J.P.-Cahlik,T.-Callon,M.: A Model for Social Interaction Between Cognition and
Action through a Key-word Simulation of Knowledge Growth. Scientometrics, Vol. 31, No.2,
1994.
[7] Garfield,E.: Citation Indexing: its Theory and Application in Science, Technology and the
Humanities. John Wiley and Sons, New York 1979.
26
Look at footnote 11.
Look at http://tucnak.fsv.cuni.cz/~cahlik/, branch „Economic Growth“, article „Comparison of the Maps of
Science“.
27
11
[8] Jonáš, J a kol.: Oslava ekonomie. Academia, Praha, 1994.
[9] Kuhn,T.S. - The Structure of Scientific Revolutions. Chicago University Press, 1970
[10] Small,H.-Garfield,E.: The Geography of Science: Disciplinary and National Mappings.
Journal of Information Science 11 (1985), 147-159.
[11] Stigler, G.J.: The Journals of Economics. Journal of Political Economy, 103 (1995), 331359.
[12] Turner, W.A.-Rojouan,F.: Evaluating Input/Output Relationship in a Regional Research
Network Using Coword Analysis. Scientometrics 22 (1991).
12