ISSN 1881-6436

Discussion Paper SeriesNo. 18-01

Keynesian Elements
in Beveridge’s Free Society (1944):
A Text Mining Approach to the History of Economic Thought
Atsushi KOMINE, Hiroyuki SHIMODAIRA
September 2018

Faculty of Economics,
Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku,
Kyoto, Japan
612-8577

TitleKeynesian Elements in Beveridge’s Free Society (1944):
A Text Mining Approach to the History of Economic Thought
Authors
Atsushi KOMINE1, Hiroyuki SHIMODAIRA2
Abstract
This study has two conclusions. First, text mining can apply to the orthodox
approach regarding HET and also be a new method to encourage heuristicknowledge; however, such application is possible only to the extent that researchers
bear in mind the limitations of text mining. Second, we confirm the following
two-part hypothesis by utilizing frequency, co-occurrence network, cluster, andcompound word analyses, among others: (i) Beveridge’s Free Society focuses moreon insufficient effective demand, a surface cause of unemployment, than on the
intricate monetary economy, which is by far an essential for Keynes [thesimplification of problems], and (ii) Beveridge maintains his own views of society(which are comprehensive views from international and social perspectives) and theoriginal (supply-side) prescription for unemployment, even after absorbing
Keynesian elements [the maintenance of originality].
The two elements were in part already discussed in Komine (2007, p. 332) inapproximate form. However, this study demonstrates the two elements more precisely
by using numerical tables and figures, and supplementing the prior works with new
findings (Beveridge’s international perspective and Keynes’s view of the monetary
economy).
Keywords
text mining, methodology of the history of economic thought, compound analysis, full
employment policy, Keynes, Beveridge

JEL classification
B22, B25, B31, B41, C18, C80

Faculty of Economics, Ryukoku University (67 Fukakusa, Fushimi, Kyoto, 612-8577);
komine@econ.ryukoku.ac.jp
2 Faculty of Humanities and Social Sciences, Yamagata University (1-4-12 Kojirakawa-machi,
Yamagata-shi, 990-8560 Japan)1

6 September 2018

Keynesian Elements
in Beveridge’s Free Society (1944):
A Text Mining Approach to the History of Economic Thought

Atsushi KOMINE (Ryukoku University)
komine@econ.ryukoku.ac.jp

Hiroyuki SHIMODAIRA (Yamagata University)

Version 1.9.1(not to be quoted)

1. Introduction
2. Text mining
2.1 Definition and background
2.2 The application of text mining to the history of economic thought
2.3 Current studies of the digitalization technique
2.4 The degree of similarity between words
3. Beveridge’s Free Society
3.1 Background in the 1930s and 1940s
3.2 Frequency analysis
3.3 Cluster analysis
3.4 Co-occurrence network analysis
3.5 Compound analysis
4. Concluding remarks

11. Introduction1
Our study belongs to a larger project around the question, “how did
economic theories penetrate people, and then, contribute directly or
indirectly to the formation of economic policies?” The project is related toresearch branches, such as the professionalization of economics, history of
economic policy ideas, or the Keynesian Revolution in policy-making (Coatsed., 1981; Booth, 1989; and Furner & Supple eds., 1990). From 2010
onwards, our research group, based in Tohoku (northeast) Region, Japan, has
attempted to clarify, qualitatively and quantitatively, the dissemination
processes of British economic ideas (from 1750 to 1950), into other countries,groups, or among ordinary people.
This paper, as a case study of the project2, aims at a specific twofold
target. First, in the so-called big data area, it will argue whether it is possible
and suitable to apply a text mining approach to the traditional one in the HET
or not, by defining a seemingly new quantitative method, namely text mining,and by pointing out its merits and demerits. Second, the paper will
demonstrate how an influential theory affected contemporary leading
thinkers and persons in charge of policy-making. More specifically, it willargue to what extent the elements of Keynes’s General Theory of
Employment, Interest, and Money (GT, 1936) had an influence on
Beveridge’s Full Employment in a Free Society (FS, 1944) in the context ofaccepting full employment policy by the British government.
We will validate the following two-part hypothesis by using text
mining: (i) the FS focuses more on insufficient effective demand, a surface
cause of unemployment, than on the intricate monetary economy [thesimplification of problems], and (ii) Beveridge maintains his own views ofsociety and the original prescription for unemployment, even after absorbing
Keynesian elements [the maintenance of originality]. Komine (2007, p. 332)
has already examined, albeit approximately and partially, the hypothesis bya traditional method.
1

This paper is revised from the previous Japanese version, Komine & Shimodaira (2017).
Some of our results include Shimoraida, Komine, & Matsuyama (2012); Shimodaira & Fukuda(2014); Furuya (2014); and Matsuyama (2016).
22

2. Text mining
2.1 Definition and background
Text mining is defined as a set of processes and methods assisted bysoftware and hardware for a semantic and heuristic analysis of very large
amounts of natural, or unstructured, text. In this regard, text mining can
examine frequency, networks, patterns, trends, topics, and other charactersin the data (see Wiedemann 2016, pp. 2-3; Kida, 2018, p. 8).
Until the 1990s, it was expensive to obtain information because
computing power was at an insufficient level. Thus, researchers attempted todetermine ‘the real world’ through as little information as possible. This was
the age of the ‘classical statistical analysis’, the strong point of which was
ex-post validation of certain phenomena. Then, from approximately the
2000s onwards, it became inexpensive to acquire even large volumes of data
because computing power developed to an incredible level. Moreover,access to significant amounts of data became possible ‘thanks to … powerfulalgorithms and software’ (Ignatow & Mihalcea, 2017, p. 5). Researchers nolonger needed to make any special assumptions such as normal distribution
or particular variance.
Text mining, which is a mixture of natural language processing anddata mining in that order, has developed in the age of so-called big data. First,
analysts need to engage in natural language processing (morphological andsyntactic analyses). Morphological analysis means both resolution intomorphemes, which are meaningful and minimum units of ‘words’, andparsing, which resolves sentences into their component parts and syntactical
functions. Syntactic analysis indicates the relationship between modifiers
and modificands. Second, the analysts must undertake data mining, namely
frequency processing, numerous means of statistical processing (such asrecurrence, principal component, correspondence, decision tree, cluster,neural networks, and latent semantic analyses), and visualization. In normal
data mining, ‘data’, such as the names of commodities and numbers, mustbe ‘structured’ data, which consists of aggregated numeric data in tabular3

form (a matrix). However, in text mining, data is necessarily text, which isnatural, unstructured, and untagged, and has diverse meanings within
particular contexts.
As described, text mining has dual characteristics: it stands in the
middle of quantitative and qualitative analyses 3 . On the one hand,researchers can find out hidden patterns or networks which are automaticallyderived from processing big data [a data-oriented viewpoint]. On the other
hand, from the patterns or networks, researchers can select significant,meaningful, and useful principles which are relevant to their own disciplines
such as economics [a theory-oriented viewpoint]. At the former step,mathematical or quantitative processing helps with tools such as statisticalsoftware and algorithms can help. At the latter step, however, qualitative
inference is absolutely necessary when researchers have to judge which
categories are relevant, how many headlines are necessary, what words areimportant even if they are not frequently used, and so on. In other words,
text mining is an intermediate method between an analytical focus on data
(statistical processing) and one on human nature (fieldworks, case studies,and ethnography, among others). Text mining also involves an analyticalfocus on context by way of text.
Finally, from the very beginning of its use, text mining has had eclectic
characteristics. These characteristics provide the possibility of applying text
mining to the history of economic thought (HET).
2.2 The application of text mining to the history of economic thought
A new concept of ‘digital humanities’ now prevails in some fields;
however, it has been delayed in reaching academic areas such as history,
literature, the arts, and philosophy. A cause of the delay is the slow pace atwhich the text, which is the focal point of these areas, is digitalized. Besides,
digital humanities, which naturally includes social sciences, not only utilizes
digital tools and data; it also poses a challenge by eliminating the existing
3

Regarding the relationship between the two approaches, see Brady & Collier eds. (2010) and
Goertz & Mahoney (2012).

4

boundary lines of disciplines and thereby eventually remapping a system of
new knowledge (see Burdick et al., 2012, p. 1). Thus, is now the time toconsider the possible applications of digital humanities, or one of its tools,
text mining, for HET, a discipline which interprets large amounts of original
texts from dead economists?
The application of text mining to HET could present a twofold
opportunity or challenge, if we thoroughly understand its merits and demerits.
First, with regard to individual researchers, it may be possible toconfirm their own conclusions or find new interpretations by applying thismethod. By using quantitative tools, researchers can cover wider fields ofpossible interpretations (namely, wider or quicker descriptions).
Contemporary researchers are subject to the restrictions of their own
ideologies, visions, or generally accepted ideas at that time. Automatic
processing could bring their blind spots to their notice. This situation leads
to heuristic knowledge. By using qualitative devices, researchers answer
semantic questions based on immense and copious knowledge about cases,
contexts, and history (namely, deeper or thicker descriptions). These two
methods have a synergistic effect if employed well. Historians of economics
are best known for their sincere interpretations of original texts. They know
the importance of, for example, certain writings, documents, letters, andmemos before they start text mining. Thus, they are in a good position toreconstruct chopped morphemes, which have been cut from the original text
by machinery processing, into new meaningful texts.
Second, with regard to both the researchers’ community and the wholeworld, it may be possible to deliver past original texts to current (and future)people more relevantly and objectively. Generally, researchers’ processes ofinterpretations are apt to be in a black box if they firmly adhere to their own
methodologies and ideologies. In contrast, the text-mining approach includes
data set records, the clarified processes of interpretation, and numerical andvisualized results. This approach, even partly at least, has a common method
or viewpoint with that of theoretical or empirical economists, experimental
scientists, and researchers in other fields, and general intellectual readers.

5Thus, it provides a good opportunity for academicians in adjacent fields who
have different methodologies to enter into the area of HET, and for historiansof economics to collaborate with them in turn. Indeed, this opportunity could
be one of the means to disseminate the usefulness of HET.
HET has four efficacious aspects which come from the use of text
mining. First, text mining extracts ‘specific words’ within a paper or between
papers as proper candidates for headlines or an index. The comparison of theextracted words drawn from automatic extraction and those from priorstudies’ interpretations leads to confirmation or heuristic knowledge. Second,
because stylometry is established, it is possible to identify anonymous
authors. Before freedom of speech was established (for instance, during theage of mercantilism), numerous books and pamphlets appears which had no
specific authors’ names attached. Third, text mining reveals differencesamong various editions by the same author and confirms changes in theauthor’s thoughts. Fourth, text mining traces certain transformations fromthe original text(s) into simplified and popularized discourse bypolicymakers and through public opinion. We have a special interest in thislast aspect.
2.3 Current studies of the digitalization technique
Although digital humanities has become the fashion even in the social
sciences, the examples relating to it are often from anthropology, education,sociology, policy studies, and business administration (Ignatow & Mihalcea,
2017, p. 3; Wiedmann, 2016, p. 1); Almost no studies consider in law andeconomics. This situation is a kind of mystery because, like other disciplines,
law and economics regard ‘text’ as their most important data and object ofstudy.
Despite such a trend, a few pioneering studies exist for HET. We
exemplify four such studies. Wright (2016) describes relations with diverse
individuals and groups beyond their own discipline in Vienna in the 1920s
by using ‘social network analysis’. Claveau & Gingras (2016) describes therise and decline of specific areas within economics from 1956 to 2014, in

6

combination with bibliometrics and dynamic network analysis. Binder &Jennings (2016) uses ‘topic model analysis’ to compare a currently dominantinterpretation of the Wealth of Nations, which is deeply influenced by
Cannan’s additions of headlines in his edition of the book, and the content
automatically extracted from the 1784 third edition. Finally, O’Neill (2016)
establishes a direct relationship between data which consists of a list of J. S.
Mill’s borrowed books from the London Library and of his donated books,
and his own corpus.
As far as we know, there is no study which directly applies text mining
to HET. Thus, our study is among the pioneers and heralds the beginning ofa process to determine with great care which aspects of the method are
relevant to HET and how shortcomings may be minimized.
2.4 The degree of similarity between words
Before we move on to a comparison of original texts by Keynes andBeveridge, we explain one example which indicates the transformation fromoriginal words into a quantitative index; namely, the degree of similarity
between words. This index originates from the distributional hypothesis. The
hypothesis states that words which are used and occur in the same contexts
tend to have similar meanings (Harris, 1954, p. 157).
have

new

drink

bottle

ride

speed

read

beer

36

14

72

57

3

0

1

wine

108

14

92

86

0

1

2

train

291

94

3

0

72

43

2

Table 1 Word-context matrix

Table 1 illustrates in matrix form the number of times of neighbouring
words (say, within an n words interval) that co-occur with a specific word
(Okazaki, 2016, p. 190). For instance, the word ‘train’ occurs 43 times
around the word ‘speed’ in a certain document. In this case, we have three‘word vectors’ as follows:7

beer (36, 14, 72, 57, 3, 0,1)
wine (108, 14, 92, 86, 0, 1, 2)
train (291, 94, 3, 0, 72, 43, 2)
Each word vector has several elements which indicate the frequency of cooccurrence near the word. This vector constitutes the ‘word-context matrix’.
Further, we can mathematically define the similarity between words byutilizing the cosine of vectors. If the direction of two vectors is near, theirangle becomes smaller (and the cosine of the angle becomes close to 1). Thus,
we define ‘cosine similarity’ as follows (Sarkar, 2016, p. 283):

Similarity (U, V) = cosθ=

!⋅#
! |#|

where the numerator on the right-hand side is the inner product of the two
vectors (U and V) and the denominator is the multiplication of the size ofeach vector (θ is the angle between U and V). According to Table 1, we
have two numerical values. For example:
Similarity (beer, wine)&'∗)*+,)-∗)-,./∗0/,1.∗+',&∗*,*∗),)∗/= &'2 ,)-2 ,./2 ,1.2 ,&2 ,*2 ,)2 )*+2 ,)-2 ,0/2 ,+'2 ,*2 ,)2 ,/2=)1')/
00&1 /../1= 0.9406

Similarity (beer, train) =)///'00&1 )**1'&=0.386795

In conclusion, the similarity between ‘beer’ and ‘wine’ is 0.9406,
whereas that between ‘beer’ and ‘train’ is 0.3868. It is clear that therelationship between words in certain writings can be expressed as numericalvalues by using a word-context matrix.

8If data are quantitative or cardinal (namely, the absolute value of thedata is important), cosine similarity works well. However, if data are
qualitative or ordinal (namely, only the order within the data is important),
the Jaccard coefficient works well (Ishida & Jin eds., 2012, p. 12):
Jaccard coefficient =

|3∩5|
|3∪5|

(Jaccard distance = 1 – Jaccard coefficient)where X or Y indicates the number of elements in a set, the numerator on theright-hand side is the absolute value of co-occurrence of two documents, andthe denominator is the total number of the two sets.
3. Beveridge’s Free Society
3.1 Background in the 1930s and 1940s
Although Keynes believed in 1935 that he was ‘writing a book on
economic theory which will largely revolutionise --- not … at once but in thecourse of the next ten years --- the way the world thinks about economic
problems’ (Keynes, 1973, vol. 13., p. 492), ‘the Treasury’s conversion toKeynesian economics was still incomplete when war broke out’ (Peden,
1979, p. 61).
In 1940, Keynes regarded the war economy from wider viewpoints,
pointing out that the ‘importance of a war Budget is not because it will
“finance” the war. … Its importance is social: … to do this in a way which
satisfies the popular sense of social justice; whilst maintaining adequate
incentives to work and economy’ (Keynes, 1979, vol. 22., p. 218; theemphasis is in the original). Keynes also agrees with Ernest Bevin, theMinister of Labour, that ‘social security must be the first object of ourdomestic policy after the war’4. It was natural for Keynes in March 1942 toadmire Beveridge’s draft of a report on social security. Indeed, Keynes saysthat ‘I have read your Memoranda, which leave me in a state of wild
4

TNA, PREM 4/100/5, “Professor Keynes’ Memorandum on War Aims”, 13 January 1941.

9

enthusiasm for your general scheme. I think it a vast constructive reform of
real importance and am relieved to find that it is so financially possible’(Keynes, 1980, vol. 27., p. 204). Thus, Keynes, alongside James Meade andLionel Robbins in the British Government’s Economic Section, cooperated
with Beveridge’s creation of the report Social Insurance and Allied Services(published in December 1942)5.
Before finishing his report, Beveridge established his next target as the
avoidance of idleness, namely the maintenance of high employment. Afterbeing banned from the government and public contact, he organized a private
research group in Oxford, the members of which included Barbara Wootton,
Joan Robinson, Nicholas Kaldor, and E. F. Schumacher. In the period to
September 1943, Beveridge was influenced by the young scholars andabsorbed Keynesian ideas from broader perspectives, saying that the‘programme … is a programme of socialising demand rather than production.
It makes possible the retention of private enterprise to discover the mostefficient technical methods of production and to compete in meeting thesocial demand. … The programme allows of demand being directed bysocial policy, putting prior human needs first’6.
The British government also published landmark white papers onbudgets, social insurance, and employment in April 1941, May 1944, andSeptember 1944 respectively.
3.2 Frequency analysis
The main object of our research is the original 1944 version of
Beveridge’s Full Employment in a Free Society. We concentrate on the main
body of the book, excluding the notes and appendixes. The book is divided
into nine parts, from the Preface and Part I, to Part VII and the Postscript.
KH Coder (Ver. 2.00f), which is a free and multilingual software, recognized
the main body of the work as having 3,880 sentences, 661 paragraphs, and5

See Komine (2018) for a full account of Beveridge as an LSE economist.
Nuffield College Private Conferences, 1941-1955, Archive Collections, the Library of Nuffield
College, Oxford. “Full Productive Employment in a Free Society”, page 9, by W. H. Beveridge, 8September 1943. Dr Uemiya (Osaka University of Economics) thankfully gave us some information
about these documents.
610

nine parts.
The primary target of our research is the frequency of nouns. Suchfrequency often reflects significant ideas, concepts, and keywords. Although
the software automatically counted the top 20 most frequently used words,
it regarded many of them as proper nouns because of their capital letters.
Before the end of World War II, quite a few words were written in capitals.
Thus, we manually reclassified them into nouns if they were general words.
In this regard, automatic processing is not always relevant: human eyes must
always check the relevance of an entire text and its context. As Table 2 shows,the most frequent word is ‘employment’, the second ‘unemployment’, thethird ‘industry’, the fourth ‘demand’, and the fifth ‘war’.
Tables 2, 3, and 4 present the differences between Keynes’s General
Theory (GT) and Beveridge’s Free Society (FS). Only five nouns,‘employment’, ‘demand’, ‘labor’, ‘investment’, and ‘rate’ are among the 20
most frequently used in both books. There are 15 words which appear onlyin the GT, ‘interest’, ‘money’, (‘change’), ‘capital’, ‘income’, (‘cost’),(‘output’), (‘price’), (‘quality’), (‘term’), ‘level’, ‘consumption’, (‘increase’),(‘value’), and (‘efficiency’). Because the nine words in parentheses do notappear in book reviews of the GT7, the remaining six words are extremely
important, even though the FS omits them. In turn, 15 nouns appear in thelist of as the 20 most frequently used in both books only in the FS:‘unemployment’, ‘industry’, ‘war’, ‘policy’, ‘country’, ‘outlay’, ‘man’,‘trade’, ‘year’, ‘cent’, ‘problem’, ‘time’, ‘state’, ‘condition’, and‘fluctuation’. These 15 words may reflect Beveridge’s age and ideals.
Among them, ‘unemployment’, ‘industry’, ‘war’, and ‘policy’ have peculiar
characteristics. ‘Outlay’ (which is ranked ninth), is a substitute for ‘income’and ‘consumption’; consequently, it is also a unique term.

7

Noun

Raw data

ProperNoun

Noun

Correction

employment

604

50

1 employment

654

unemployment565

2

2 unemployment

567

See Shimodaira, Komine & Matsuyama (2012, p. 17).

11

industry422

3

3 industry

425

demand404

2

4 demand

406

war

353

26

5 labor

374

labor

348

6 war

353

policy338

14

7 policy352

country295

4

8 country

299

outlay259

1

9 outlay

260

man

237

12

10 trade

247

trade

235

11 man

237

year

222

12 year222

investment

191

13 investment

207

rate

179

14 rate

179

cent

164

15 cent

164

problem

163

1

16 problem

164

time

152

2

17 time

154

state

148

18 state

148

condition143

19 condition148

fluctuation138

20 fluctuation

138

16

5

Table 2 The top 20 (Nouns in the FS)
Adj

Raw data

ProperNoun

Adj

Correction

full

389

30

1 full

419

other

307

2 other307

private222

3

3 private

225

first

183

6

4 first189

public158

55 public

163

more

145

6 international156

economic

141

3

7 social

151

total

140

2

8 national

150

general137

89 economic

148

international

133

23

10 more

145

such

131

11 general

145

12

new

120

possible

116

unemployed

112

same

111

social101

national

10

12 total142

13 such131

14 new

130

15 possible

116

50

16 unemployed

116

99

51

17 same111

industrial97

918 industrial

106

many

97

19 many

97

particular

95

20 particular

95

4

Table 3 The top 20 (Adjectives in the FS)

Table 3 presents Beveridge’s philosophy. ‘International’, ‘social’, and‘national’ are three adjectives which are highly important, while ‘economic’is of almost equal significance. This ranking did not take into account ourmanual correction of the raw data. Because the words of the headlines in theFS, all of which are in capitals, should be significant, it is important not todepend excessively on automatic processing.
Noun

Adjectiverate

709 marginal

342

interest

684 other251

employment

597 real219

investment

526 same

192

money

457 aggregate

160change394 full

150

capital392 current

148

income385 new

146

cost

304 such146

demand294 equal

135

output

274 more

128

price

151 effective

126

quantity

249 economic

113

term

231 different

109

13

level220 classical102

consumption

219 general102

increase

216 certain

94

labor

213 due

94value

210 net93

efficiency

202 prospective

82

Table 4 The top 20 (Nouns and adjectives in the GT)

Table 4 presents the characteristics in the GT. The shaded words
indicate common ones in both the GT and FS. Thus, the unshaded words,
such as ‘interest’, ‘money’, and ‘capital’ (noun) must be unique to Keynes.
It is interesting that particular adjectives, such as ‘marginal’, ‘aggregate’,‘effective’, and ‘classical’, imply a part of theoretical concepts.
Before this text mining analysis, as Komine (2007) says, Beveridge
and Keynes shared a common concept, ‘socialisation’ [of effective demandor effective investment]. Thus, we collect data related to ‘social’. Table 5
makes it clear that related words are frequently used in the FS. Such use
suggests that Beveridge considered economic problems from the viewpoints
of society, socialization, and socialism.
Extracted

frequency

Part of speechsocial101 Adjective

SOCIAL50 Proper Nounsociety

43 Nounsocialism

10 Nounsocialization

10 Nounsocialize

4 Verbsocially

3 Adjectival Verbsocialist

3 Adjective

anti-social

1 Adjective

socialized

1 Adjective

Table 5 Words related to ‘social’ in the FS

14

Keynes24

Kaldor

15

Pigou

5

Beveridge

3

Bevin

2

Clay

2

Table 6 Persons’ names in the FS

Table 6 displays the vulnerability of text mining without employing
professional knowledge about the original text. Nicholas Kaldor is famous
for assisting Beveridge: he served as a member of a private investigationcommittee organized by the latter and wrote with his signature Appendix C,‘The Quantitative Aspects of the Full Employment Problem in Britain’, inthe FS. Thus, it is natural that eminent scholars who were hostile to
Beveridge have highlighted such collaboration. For example, Hayek says:
He [Kaldor] wrote Beveridge’s book on employment. … Beveridge
would have been entirely unable to write such a book…. That essay
was just one that Beveridge didn’t want to commit to, because hecouldn’t understand it. (Hayek, 1994, p. 86)
Further, Robbins states that the FS is proof of Beveridge’s poor
understanding of economics ‘for it was notoriously written under advice’(Robbins, 1971, p. 136). Table 6 seems to prove their points.
However, the actual situation is more complicated. Using numerous
drafts and memos from Nuffield College Private Conferences, among others,
Harris (1997, pp. 432-443) explains the process of writing the FS in detail.
According to the unpublished materials, Beveridge completely understands
the essential points of the GT, apart from the technical aspects. Moreover, it
was E. F. Schumacher, not Kaldor, who persuaded Beveridge to accept theKeynesian ideas (Harris, 1997, p. 434). Further, Beveridge’s draft, ‘Full

15

Productive Employment in a Free Society’, dated September 1943, goesbeyond Keynes in a sense because he rejects the careless rhetoric of the GT:… the employment secured should be productive and progressive. It
excludes a solution of the problem by occupation which is merely
time-wasting (digging holes and filling them) or destructive (war,armaments and Nazi drilling).
A footnote also states that in ‘the preparation of this draft for discussion
frequent use has been made of a typescript Memorandum of Mr. E. F.
Schumacher’8.
Because Schumacher’s name does not appear in Table 6 or even in theindex of the FS, and Kaldor is referred to 15 times (although he appears only
once in the index), Hayek and Robbins seem to be right. However, rigorousstudies have revealed that Schumacher was more important during thetransition of Beveridge into a Keynesian. This situation is further evidence
that machinery counting and ranking may sometimes be misleading.
3.3 Cluster analysis
We now consider another technique, cluster analysis. As discussed in
section 2.4, each item of data, or word, has a numerical value of similarityor ‘distance’. Cluster analysis is a method to divide an entire text into several
clusters by grouping similar words into one cluster (Ishida & Jin eds., 2012,
p. 11). Cluster analysis has two different approaches: hierarchic and nonhierarchic. With regard to the former, a small cluster is first made in order togroup the nearest words. Then, a larger cluster is made to contain the smaller
ones. Finally, the last cluster is established to cover the entire data. A
dendrogram visualizes the hierarchic situation of each cluster as a whole.
However, non-hierarchic analysis type divides the entire data into adesignated number of clusters because it is very difficult to instantly analyze
8

Nuffield College Private Conferences, 1941-1955, Archive Collections, the Library of Nuffield
College, Oxford. Box 6, 6/2/92-97. “Full Productive Employment in a Free Society”, by W. H.
Beveridge, 1-11, 8 September 1943.

16

large quantities of data comprehensively. Because a dendrogram varies
marketunemployment
industry
demandlabordepression
fluctuation
fact
same
more
year
other
way
generaltime
firstmillionrate
cent
numberunemployed
work
man
job
investment
privatepublictotaloutlaystate
homecountry
trade
international
policyemployment
full
war
peace
power
servicegovernment
new

13

price
problem
economic
condition
possiblepartchangesuch
600500400300200100 0

0.0

0.5

1.0

Figure 1 Hierarchic cluster analysis (10 clusters)

17extensively in accordance with different definitions of ‘distance’ between
data, it is safe not to take this method too seriously, and to regard it as one ofhints as a supplement to other methods (Ishida & Jin eds., 2012, p. 12).
In the context of a full understanding of the aforementioned strong
points and shortcomings, we apply hierarchic cluster analysis to the FS. The
unit of grouping is ‘paragraph’, the minimum number of appearances is 105,
the targets are nouns and adjectives only (excluding proper nouns), the Wardmethod and Jaccard coefficient are selected, and the number of clusters is‘Auto’. The result is seven clusters. However, because this places a great
many words in each cluster, 10 clusters were manually chosen instead (seeFigure 1).
The first cluster deals with unemployment, the demand for labour, andindustry; the second, depression, fluctuation, and time; the third, numericalunits; the fourth, the numbers of the unemployed and jobs; the fifth, private
and public investment and total outlay; the sixth, international trade; theseventh, full employment policy; the eighth, war and peace; the ninth, newservices of government; the tenth, economic problems, possible changes andconditions, and prices. In conclusion, these clusters correctly summarize themain topics in the FS.
3.4 Co-occurrence network analysis
Collocation, or co-occurrence, in natural language processing refers to
the phenomenon whereby a word occurs with a different word
simultaneously within a given range of text. Such text is an entire document,
certain paragraphs, several sentences, or n words before or after a word. With
regard to the frequency of co-occurrence, it is necessary to establish asummation unit, a minimum number of appearances, and parts of speech.
Because no general rule exists about how to establish these elements
appropriately, researchers must proceed by trial and error.
Co-occurrence network analysis is a representative method to visualize
the structure of the collocation of words (Scott, 2013). It is also an

18

application of mathematical graph theory. In this theory, a vertex corresponds
to each word, while there is an edge with the relationship to co-occurrence(or distance). Moreover, two ways can visualize the structure: the analysis ofcentrality (such as betweenness, degree, and eigenvector) and sub-graphdetection. The former refers to the degree to which each word centrality inthe network structure; the latter refers to the sort (and colour) of several
words, which firmly ties one to another, in several categories (see Figures 2
and 3).
unemployed

numberrate

centinternational

generalcountry
year

trade

first

other
policystate

employment
outlayinvestment

privatetime

industryunemployment

full

demandwar
publiclabormarket

peace

job

man

work

changeFigure 2 Co-occurrence network analysis (centrality): the FS

19

Let us analyze the FS compared with the GT. A unit of summation is aparagraph, the minimum number of appearances is 105, and the objects ofparts of speech are nouns and adjectives. In the FS, three words havecentrality (betweenness): ‘employment’, ‘unemployment’, and ‘private’.
The first and second terms are natural, whereas the third needs further
explanation. Taking sub-graph detection and centrality together into
consideration, the term ‘private’ might be related to not only ‘outlay’ or‘investment’, but ‘full’, ‘employment’, ‘policy’. Beveridge seems to say thatunemployed

numberrate

centinternational

generalcountry
year

trade

first

other
policystate

employment
outlayinvestment

privatetime

industryunemployment

full

demandwar
publiclabormarket

peace

job

man

work

changeFigure 3 Co-occurrence network analysis (sub-graph): the FS20policy may have something to do with private sections of the economy.
Indeed, he asserts that ‘the greater part of industry continued to be conductedby private enterprise at private risk’ (Beveridge, 1944, p. 37) and emphasizes
that private companies are compatible with full employment policy(Beveridge, 1944, pp. 205-207). In this sense, the interpretation from text
mining confirms traditional scholarly analysis.
otherchangenewterm

efficiency

interestmoney

capitalrate

quantity

marginal

investment
time

level

increase

propensity
same

full

equipment

cost

employment

effective

outputdemand

labor

value

entrepreneur

factoraggregate

real

price

amountequal

supplyincomeconsumption

Figure 4 Co-occurrence network analysis (centrality): the GT
However, only one word has centrality in the GT. As is obvious fromits title, the GT is concerned with the theoretical framework for determining

21

the level of employment. The interpretation is not inconsistent with theresults of text mining. Sub-graph detection indicates the fact that there are
six blocks both in the FS and the GT. However, the contents completely differ.
The FS has the following six blocks: private and public investment,international trade, unemployment and employment, the demand for labour,the number of the unemployed, and numerical units. The GT has investment,
marginal efficiency, and employment; the quantity of money; effective
demand and supply of labour; income; cost and equipment; and time. The
differences between the blocks confirm the books’ characteristics. The FS ispolicy-oriented, whereas the GT is theory-oriented.
otherchangenewterm

efficiency

interestmoney

capitalrate

quantity

marginal

investment
time

level

increase

propensity
same

full

equipment

cost

employment

effective

outputdemand

labor

value

entrepreneur

factoraggregate

real

price

amountequal

supplyincomeconsumption

Figure 5 Co-occurrence network analysis (sub-graph): the GT223.5 Compound analysis
The next analysis is ‘TermExtract’, a system within KH Coder for
extracting technical terms. A noun is apt to symbolically expresses a concept,
an idea, or an ideal. A thinker often uses a noun not only independently but
to create a new compound which includes the noun before or after the otherword (Nakagawa, Yumoto, & Mori, 2003, pp. 27-28). For example, ‘full
employment’ and ‘under-employment equilibrium’, which derive from
‘employment’, and ‘liquidity preference’ and ‘liquidity preference function’,
which derive from ‘liquidity’. There are three steps to extract important
technical terms. First, division of the text by morpheme analysis (the sameprocess as text mining); second, counting the number of types andfrequencies of words connected with a certain minimum noun; third, judging
the importance of a compound created in the text (Nakagawa, Yumoto, andMori, 2003, p. 29). Table 7 presents the 20 largest ‘scores’ (the weight ofimportance) of technical terms in the FS and GT.
Free Society

Score

General Theory

Score

1 full employment

38283.435

rate of interest

59714.952

marginal

efficiency

2 full employment policy

5163.317

of capital

6579.732

3 international trade3992.062

quantity of money

6536.692

4 labor market

3902.355

full employment

5807.993

5 first world war

2373.954

effective demand4334.557

6 unemployment rate2373.824

marginal efficiency

3995.97

7 united states

2118.38

efficiency of capital

3858.848

8 private investment

1838.44rate of investment

3666.585

9 public outlay

1637.109

terms of money

2209.979

10 multilateral trade1495.281

user cost

2105.101

11 unemployment insurance

1306.352

new investment

2068.328

12 mass unemployment

1276.042

real wage

1747.277

13 other countries

1233.579

real income

1619.27923

14 supply of labor

1230.923

classical theory

1612.696

15 total outlay

1059.734

level of employment

1523.044

volumeof

16 private outlay

1058.47

employment

1511.791

17 cyclical fluctuation

1029.943

net income

1390.755

18 unemployment rates

1021.576

capital equipment1290.096

19 social insurance

922.537

supply price1227.051

aggregate

20 total demand873.033

investment

1184.698

Table 7 Compound extraction (the top 20)
This analysis produces very interesting results, which we summarize
as follows. First, the top ranking compound for the FS, ‘full employment’, isnaturally the most significant. Its score is more than seven times greater than
the second-highest ranking compound. Moreover, the compounds ranked
sixth, eleventh, twelfth, and eighteenth are connected with (un)employment.
Second, the third highest-ranking compound, ‘international trade’, the tenth,‘multilateral trade’, and the thirteenth, ‘other countries’ all relate tointernational affairs. Beveridge considers full employment policy in the lightof international relations and trades. This approach contrasts with that ofKeynes’s General Theory, which is normally regarded as a closed system.
Third, as the fourth highest-ranking compound ‘labour market’ and thefourteenth, ‘supply of labour’ show, the FS considers supply-side problems,
which Beveridge had emphasized since his earlier days in the 1900s and1910s, and continued to do so even after absorbing Keynesian demand-side
solutions.
Fourth, as the fifteenth highest-ranking compound, ‘total outlay’, thesixteenth, ‘private outlay’, and the twentieth, ‘total demand’, indicate,special compounds relate to total demand. Whether or not it is significant
that these compounds are in lower positions than those relating to supply isunclear. Fifth, the fifth highest-ranking compound, ‘first world war’, theseventh, ‘united states’, the seventeenth, ‘cyclical fluctuation’, and24

nineteenth, ‘social insurance’ may relate to full employment. For instance,‘social insurance’ reflects on Beveridge’s idea that full employment is an
assumption of social insurance and that the latter strengthens the former.
In addition, important noun + noun] compounds are common in
Japanese, whereas adjective + noun compounds are more popular in English.
The latter include ‘full employment’, ‘international trade’, ‘social insurance’,and ‘total demand’. Exceptions are ‘unemployment insurance’ for English
and ‘cyclical fluctuations’ for Japanese. Thus, it is necessary to understandthat the compounds of each language have their own features.
Even as a by-product, the automatic extraction of words from the GT
is very significant. Only one compound, ‘full employment’, appears in the
20 most significant compounds in both the FS and GT. However, there areseveral compounds (although not the same words) about employment,
investment, and total demand. The most striking feature is the order of theupper ranking: the highest-ranking compound is ‘rate of interest’, the second
is ‘marginal efficiency of capital’, and the third is ‘quantity of money’. Thisorder must reflect the causal relationship in the GT about determining thelevel of investment; namely, the rate of interest, the marginal efficiency of
capital, and the quantity of money are all determinants of investment.
Moreover, the tenth highest-ranking compound, ‘user cost’, the twentieth,‘real wage’, and the fourteenth, ‘classical theory’ are all familiar to generaland academic readers. What are lacking are ‘liquidity preference’ and‘multiplier’. It is unclear why these two important compounds are missing
from the 20 most significant compounds.
It is reasonable to say that the GT expresses theoretical concerns about
monetary factors which determine the level and direction of investment.
Traditionally, numerous scholars have argued about the essence of the GT:
is it liquidity preference or multiplier (Minoguchi, 1982)? Although thesetwo compounds have somehow not appeared from ‘TermExtract’, the 20
most significant compounds indicate the decisive importance of the ‘rate ofinterest’, the score of which is more than nine times that of the second-ranked‘marginal efficiency of capital’.

25‘Word Clouds’ are illustrations to visualize statistics based on
frequency of appearance. Figures 6 and 7 are generated from one of the free
software9 of Word Clouds. It is obvious which illustration shows the FS.
4. Concluding remarks
This study has two conclusions. These correspond to our objectives, as
discussed in our introduction. First, text mining can apply to the orthodox
approach regarding HET and also be a new method to encourage heuristicknowledge; however, such application is possible only to the extent that
researchers bear in mind the limitations of text mining. Second, we confirm
the following two-part hypothesis by utilizing frequency, co-occurrence
network, cluster, and compound word analyses, among others: (i)Beveridge’s Free Society focuses more on insufficient effective demand, asurface cause of unemployment, than on the intricate monetary economy,
which is by far an essential for Keynes [the simplification of problems], and(ii) Beveridge maintains his own views of society (which are comprehensive
views from international and social perspectives) and the original (supplyside) prescription for unemployment, even after absorbing Keynesianelements [the maintenance of originality]. The two elements were in part
already discussed in Komine (2007, p. 332) in approximate form. However,this study demonstrates the two elements more precisely by using numericaltables and figures, and supplementing the prior works with new findings(Beveridge’s international perspective and Keynes’s view of the monetary
economy).

9

Wordle: http://www.wordle.net/ (access: 28 August 2017).

26

27

28

Acknowledgements
This study is in part supported by the following two grants:‘War and Peace in the History of Economic Thought: Can the Disseminationof Economics decrease International Conflicts?’, Grant-Aid for Scientific
Research (B), 2016-2020; Principal Investigator: Atsushi Komine (Ryukoku
University).
‘From Popularization of Economic Theory to the Creation of Economic
Policy: An Empirical Study by use of Text Mining’, Grant-Aid for Scientific
Research (B), 2015-2020; Principal Investigator: Hiroyuki Shimodaira (Yamagata
University).
We should thank Dr Emman Quinlan, Nuffield College Library, Oxford, andDr Robin Darweall-Smith, University College Archives, Oxford, for assisting ourarchival works.

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(Software)
KH Coder: http://khc.sourceforge.net/
Wordle: http://www.wordle.net/
R: http://o-server.main.jp/r/about.html
R Commander, 1.8-x version: http://socserv.mcmaster.ca/jfox/Misc/Rc
TinyTextMiner βversion: http://mtmr.jp/ttm/
Macromill Inc.: http://www.macromill.com/method/index.html

33