LAUSANNE UNIVERSITY ECONOMETRICS AND POLITICAL … · growth determining that the trade of cultural goods were multiplied by five between 1980 and 1998.6 As the UNESCO published in

LAUSANNE UNIVERSITY

ECONOMETRICS AND POLITICAL ECONOMY DEPARTMENT

MASTER OF SCIENCE IN ECONOMICS

Modern culture and some factors that influence its demand

PAOLA A. RODRIGUEZ P.

LAUSANNE, JULY 2003 (revised final version)

Modern culture and some factors that influence its demand Paola Rodríguez

2

ABSTRACT This paper develops an empirical approximation for the demand for "modern" culture of two samples of nineteen and thirty-two countries, respectively, based on the construction of a cultural index for each of them, using the principal components method and seven / eight indicators of culture according to the sample. At the same time, a third sample of twenty-two countries is employed to test the impact of a relative price, available for just one of the indicators. After having obtained the cultural index, the model determines some of the factors that influence the demand for culture among the countries, being the most determinant: income, the educational level of the population and the language spoken in each of them; while the relative price does not seem to be quite significant.


3

MODERN CULTURE AND SOME FACTORS THAT INFLUENCE ITS DEMAND

1. Introduction Discussing about culture has never been an easy task due to the subjectivity that it

involves; first, because of the unlimited number of aspects of human life that are

possible to be considered as cultural, and then because of the characteristics that each

one has. What is certain is that it comprises some of the most basic forms of human

expression and that at the same time it is an important commodity in economic terms.

This paper examines some determinants of the demand of what will be defined as

modern culture. The first part gives an introduction to the culture definition and the

cultural market; the second part explains the indicators considered and the principal

components method applied for the construction of a “modern cultural index”, as well

as the variables and data used; the third and fourth parts develop the estimation of the

model as well as its results; the fifth part gives some additional remarks about the

model and finally some conclusions are drawn.

1.1 Definition of Culture

Culture can include many aspects of the social, political, intellectual, religious and

artistic life of people, but even more, their history and their heritage. There are two

world-wide definitions of culture given by the Concise Oxford Dictionary (1990):

« The customs, civilization and achievements of a particular time or people » and

« The arts and other manifestations of human intellectual achievement regarded

collectively». As these, it is possible to find thousands of other concepts, depending

on the point of view of the person that gives them.


4

Due to the difficulties that working with such an intangible subject involves, and

because -unlike most commodities which are readily definable in terms of a single

physical good or a specific identifiable service, cultural output and trade take many

forms-, the UNESCO established the concept of “cultural industries” for making more

“measurable” the idea of culture. These industries include the producction protected

by copyrights of publishing, printing, multimedia, music, video, cinema and crafts;

and for some countries it includes also architecture, visual arts, sports, musical

instruments, advertising and turism.

Even with this definition, it is hard to set up the proper parameters for measuring

these aspects. For example, in the case of the sports: how many people practice a

sport?, what is the average-time a person practices a sport?, how many sports are

practiced?, etc.

1.2 Why is culture important? Considering culture as the way that people live together, interact and co-operate,

together with how they justify such interactions through a system of beliefs, values

and norms, people’s culture is important not only because it enables them to live

together, but also because it enables them to live together well, as it comprises the

most basic forms of human expression, helping to conserve the world diversity and

the patrimony of the countries.

Besides, culture is quite important also in economic terms because: it comprises a

significant item in consumers’leisure, it provides a livelihood for countless workers

and employees and it is a key component in the globalised media and communications

industries.

In the first case, culture is usually identified with leisure activity and not with work.

The productive members of society have to generate enough of a surplus to support a

strata of artists, musicians, novelists, poets and other creative people whose works and

performances they can enjoy in their “free” time, where these activities are highly

demanded.


5

But culture is not usually thought of as a desirable characteristic of work itself, but it

should be, as it is the economic support for millions of people all over the world. For

example, in the United States only the subsector of cultural activities developed by

non-profit organizations employs almost the same number of people as the building

sector and more than the mining, legal services, police or forestry sectors. In the same

way, in the United Kingdom the contribution of the cultural industries to employment

is higher than the ones from the automobile and food industries, and is the same as

those of the chemical and synthetic textiles industries.1

Finally, in today’s rapidly changing world, driven relentlessly by the so-called

information revolution, people run the risk of becoming marginalized if they are not

literate and do not have access to modern means of communication. The concept of

communication enriched to include authentic cultural communication- real dialogue

among people of differing ethnic, religious and linguistic backgrounds, upholds and

strengthen cultural diversity, for not letting the standard media standardize cultural

values, beliefs and lifestyles. In this case, culture helps communication to enhance the

quality of human life and expand human capabilities.

1.3 The market of cultural goods

In the last fifty years the general trend of the world economy has been to open

markets. The world exports have grown from 8% to 27% as a component of the world

GDP between 1950 and 1998 and the total trade in 1997 was fourteen times bigger

that the one in 1950.2 This expansion of the international trade has developed

parallelly to multiple bilateral trade agreements established for eliminating the tariff

and no tariff barriers to the circulation of goods, services and investments.

In addition, on the other hand, the end of the Uruguay Round of the GATT in 1994,

started a new age of globalized economy, characterized by the apparition and

formation of business blocks - from the European Union to other less strong as the

1 STOLOVICH, Luis, La Cultura da Trabajo: Entre la creación y el negocio: economía y cultura en el Uruguay, Montevideo, 1997, pg.289. 2 UNESCO, Culture, Trade and Globalisation, Questions and Answers, 2000.


6

ASEAN3, NAFTA4 or MERCOSUR5. And, more recently, the evolution of

telecommunications and new technologies has reduced drastically the cost of

providing trade services, while INTERNET has transformed the nature of products

and services.

In this way, the market integration has allowed to the consumers to buy goods from

everywhere of the world in their local markets. While the local firms have to compete

with these foreign products, they also have new opportunities for developing their

markets of export, selling in all the other countries.

Inside this new pattern of production, consumption and trade, the cultural goods and

services have not been an exception. The cultural markets have been in constant

growth determining that the trade of cultural goods were multiplied by five between

1980 and 1998.6 As the UNESCO published in its Study of International Flows of

Cultural Goods between 1980-1998, in 1996 for example, cultural products as films,

recorded music, TV programs, books, journals and software were the greatest export

industry of the United States, exceeding for the first time other traditional industries

such as: automobiles, agriculture, aerospace or defense.

These facts are key elements for understanding the market of cultural goods and

services, especially now when it is evident that the world is living an economic and

cultural "boom".

3 Asociation of South East Asian Nations 4 North American Free Trade Agreement 5 Mercado Común del Cono Sur 6 UNESCO, Culture, Trade and Globalisation, Questions and Answers, 2000.


7

World Trade of Cultural Goods 1980-1998

(millions US Dollars)

SOURCE: Study on International Flows of Cultural Goods between 1980-1998, UNESCO.

This fast expansion of international cultural trade has responded to rising demand for

cultural goods and services. For example, in 1990, American consumers spent USD 5

billion on admissions to theater, opera, galeries and other non profit arts events (more

than on admissions to spectator sports), USD 4.1 billion on movie admissions, and

USD 17.6 billion on books. Because of difficulties in defining boundaries around the

arts industry, statistics on its contribution to GDP are problematical, but available data

suggest that the arts (theater, music, opera, dance, visual arts, crafts, literature,

community and folk arts) account for a little under 1% of the United States GDP and a

little over 1% of the civilian labor force. If we define “cultural industries” as

including the arts, motion pictures, radio and television, and printing and publishing,

an aggregate value of output can be measured for 1988 of about USD 130 billion or

2.5% of GDP (National Endowment for the Arts 1992). Moreover, in 1995 French

households spent on average 3.5 % of their budget on cultural products.

Changing consumption patterns in industrial and developing countries, more leisure

time and spare income, together with cheaper products, have helped generate this new

demand and, at the same time, they are the building blocks of the emerging

information society.


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While the consumption of cultural goods and services has expanded in the world, the

production has tended to concentrate in a few countries, generating an oligopolical

market with an asymmetric structure.

The effects of this type of markets are ambiguous: while everybody is conscious that a

high proportion of the cultural products circulating in the countries are produced out

of them, there is a little knowledge of the impact of this cultural global market on the

citizens, the business or the governments.

In this sense, it is important to consider the follow factors:

� Culture is on the vanguard. Since the late eighties, it can be appreciated a

growing interest in culture, due to the combination of different phenomena as

the globalization and the processes of regional integration, and the people

claiming their right to express; all this in a context were cultural industries are

quickly replacing the traditional forms of creation and distribution and

changing cultural practices.

� The combination of "culture and trade" has acquired a particular strategic

meaning. The cultural goods and services communicate and build cultural

values, produce and reproduce the cultural identity and contribute to the

society integration, at the same time that constitute a key production factor in

the new economy of knowledge, what makes the negotiations in the cultural

field quite controversial and hard. As it is affirmed by many experts, there is

no other industry that have generated greater debate en the political,

economical and institutional limits in the processes of regional and global

integration or in their legitimation, than the industries related with culture.

� Governments have understood that the international trade laws are constantly

exerting pressure in their capacity to influence the production and distribution

of the cultural goods and services. This has generated a growing polarization


9

of the positions in the trade business when talking directly or indirectly about

cultural affairs.

� As it is established by the UNPD7 in its Human Development Report of 1999,

two thirds of the humanity are not benefited with the new model of economic

growth based on the expansion of international trade and on the development

of new technologies, and are excluded from the global information. This

situation reveals the distances that exist in terms of capacity and of resources

among the countries for producing and consuming cultural goods and services.

In many small and developing countries, these capacities are being reduced

quickly. As a consequence, the trade flows of cultural goods are not balanced,

too much concentrated in a few countries, and the cultural industries show

great disparities in their structures.

2. Methodology

2.1 The Indicators The cultural indicators used in this work to compose the modern cultural index that

will be the dependent variable of the model, do not pretend to measure the world

culture -the culture of the world is far more complex and multi-faceted. What these

indicators pretend, is to present those aspects of the world that are readily measurable.

With this purpose and due to data limitation, eight indicators were taken into account:

three related to communication and five related to arts. That is why this is called

“modern” culture index, because it considers as cultural components some media

indicators and not just art indicators.

This inclusion is based on the idea that to be able to live together well, people need to

communicate and understand one another’s culture. In this way, communication

7 United Nations Program for Development


10

should be recognized as the basis of culture and so of cultural interchange, because it

facilitates the functioning of any multi-ethnic, multi-cultural society.

The indicators of communications that were considered are:

� INTERNET Users

� Number of Radios

� Number of Televisions In the case of the art indicators, traditionally considered as cultural expressions, there are:

� Cultural Paper Consumed

� Annual Cinema Attendances

� Music Unit Sales Retail Value

� Annual Museum Attendances

� Annual Attendance of Performing Arts Companies where the last one is given just for a sample of nineteen countries. These indicators were considered to build the cultural index, using the Principal

Components Method.

2.2 The Principal Components Analysis

The analysis of principal components is a technique used basically to summarize

several related series in one and consists in extracting from them one or some

common elements that explain a certain percentage of the variance matrix of the

group of series involved (a greater explanation is given in the appendix).

So long as all the constituent partial indicators are retained, a composite index may

serve the useful function of promoting discussion and debate about the relative

importance of the different dimensions involved in such an index. However, it is

sometimes objected that full aggregation does not make sense since very disparate

things, such as the consumption of music, films, etc. are being combined; or because


11

there may be a low correlation among the indicators constituing it, and indicators may

move in opposite directions as we try to compare countries. But these objections

would be valid if the primary purpose were to determine why the individual indicators

are moving in different directions; but this is not the case, as the principal aim of the

paper is to rank countries on the basis of their demand with respect to cultural aspects,

and so the risk of a lack of correlation among individual indicators is not particulary

problematic. Anyway, correlations in this case are not low as it can be seen in Table

1:

TABLE 1: CORRELATIONS AMONG THE COMPONENTS OF THE

CULTURAL INDEX

PAPER MUSIC CINEMA MUSEUMS INTERNET RADIO TV

PAPER 1 MUSIC 0.84524 1

CINEMA 0.53293 0.64129 1 MUSEUMS 0.53844 0.72036 0.38380 1 INTERNET 0.84481 0.85093 0.64764 0.70461 1

RADIO 0.79701 0.77694 0.67212 0.53291 0.77530 1 TV 0.68692 0.69300 0.51833 0.53611 0.68506 0.84190 1

Where it can be seen that all the cultural-index-components are very correlated.

2.3 The Explanatory Variables Again, due to the data limitation, just nine variables were used for explaining the

independent variable. Among these, five social and economic indicators were

considered:

� Real GDP per capita measured in PPP8

� Life expectancy at birth

� Adult literacy rate

� Combined first-second-third level gross enrolment ratio

� Urban population9

8 Purchasing Power Parity: the rate of currency conversion that corrects for the differences in price levels between countries. 9 In order to see the cultural index per country and its components for both samples see annex 2.


12

Other variables that were introduced in the model were four linguistic dummies

explaining the origin of the language spoken by the majority of people in each

country, as follows:

� D1, Germanic origins

� D2, Latin origins

� D3, Slav origins

� D4, all others10

In selecting indicators to explain cultural demand, emphasis should be placed on

people’s abilities and on people’s historical cultural consumption, because culture is

considered as being a kind of “addictive” merchandise, in the sense that an increase in

an individual’s present consumption of it will increase her future consumption. Such a

view can in fact be traced back to Alfred Marshall, who recognized that the taste for

“good music” was an acquired taste that would increase over time with exposure

(among others, through education).11

In this respect, basic adult literacy and the combined first-second-third level gross

enrolment ratio were considered because they reflect an outcome, not an input; and

they reflect a human capability, not the means of capability.

Besides, life expectancy, urban population and the GDP per capita were considered as

proxis of demographic and geographical distribution, and of people’s income,

respectively. Finally the dummies where chosen to include the linguistic

especifications of each country.

10 In order to see the explanation of each dummy see annex 3. 11 In Chapter III of Book III of his Principles, Marshall wrote: “It is therefore no exception to the Law (of diminishing marginal utility) that the more good music a man hears, the stronger is his taste for it likely to become” (Marshall 1891, p.151)


13

2.4 Data Because of the lack of availability of the statistics, the data correspond to the period

1990 - 1996 and it was mainly obtained from the databases of the UNESCO, the

World Intellectual Properties Organization (WIPO) and the World Bank.

3. Estimating the model

For estimating the cultural index, the purpose was to collect data of eight indicators

for thirty-two countries, but because of data availability of the national statistics, it

was possible to find only seven indicators for all the countries and the eight indicators

just for nineteen countries.12

The cultural index was computed using the method of the principal components,

considering each of the indicators for the two samples, and it was important to find

that the first component explained in a 76% the variation of the original series, which

shows that this index is a very good condensation of its components.

Once calculated the cultural index, the model of depart was:

Cultural Index = � + �1 ln(real GDP pc) + �2 ln(life expectancy at birth) + �3

ln(adult literacy rate) + �4 ln(combined first-second-third gross enrolment ratio)

+ �5 ln(% of urban population) + �6 D1 + �7 D2 + �8 D3 + ��

The hypothesis were that the explanatory variables had a positive relation with the

cultural index.

12 In order to see the complete list of countries and indicators see annex 1.


14

4. Results

From the cultural index obtained for each country in both samples, it could be seen

that the group of countries with higher cultural index was also the one with higher

human development index that includes, among others, indicators of income,

education and health.13

RELATION BETWEEN THE CULTURAL AND THE HUMAN DEVELOPMENT INDEX

-2

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2

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3

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Cultural Index Human Development Index (1994)

SOURCE: UNPD and the Author.

As it is possible to see in the graph above, both series follow the same tendency, showing the great relation that exists among cultural and human development indexes. It is also interesting to notice that ten of the twelve countries with higher cultural index are countries whose languages come from Germanic roots, as can be seen on Table2:

13 In order to see the complete ranking of the sample see annex 4.


15

TABLE 2

CULTURAL

INDEX

HUMAN DEVELOPMENT

INDEX LINGUISTIC DUMMY 1

Norway 1.42945 0.943 1 Denmark 1.41033 0.927 1

Australia 1.38380 0.931 1

Canada 1.27531 0.960 1

Japan 1.24568 0.940 0

Sweden 1.22745 0.936 1

Switzerland 1.04903 0.930 1 Finland 1.01805 0.940 0

Germany 0.89540 0.924 1

Netherlands 0.81323 0.940 1

Austria 0.67733 0.932 1

New Zealand 0.61871 0.937 1

This fact is found again when we analyse the results of the run regressions.

When we have the sample of nineteen countries, GDP, life expectancy, adult literacy,

first-second-third level gross enrolment ratio, the percentage of urban population and

dummy 1, corresponding to the languages with Germanic origin, are the variables that

are significant in explaining the cultural index; as can be seen in equations 1 and 2

from Table 3.

Considering these equations, it results very interesting to see the negative relation of

the cultural index and the life expectancy and urban population indicators, given by

the negative signs of their betas.

In the first case this result is quite difficult to explain because it would mean

something like, for example, lower quality of health deals to higher demand for

culture; but in the other case, the result is comprehensible because it can be

interpreted as people that live in rural areas demand more of this modern culture

because of its level of media components, due to their need for communication given

by the less facilities that they usually have for living in non-urban areas. �


16

In the other hand, in equations 3 and 4 of Table 4, it can be seen that when the sample

is increased to 32 countries, GDP and dummies 1 and 2 are the only highly significant

variables.

This phenomenon could be explained because a larger number of countries adds more

heterogeneity to the sample and furthermore, the cultural index for this sample is

different from the one of the other simple as it excludes the art performing indicator,

that has more-educated consumers.

So, it is important to notice that no matter the sample, GDP and dummy 1, are always

significant to explain the cultural index, while dummy 3 is never important.

These facts appear again when working just with west-european countries where the

main explanatory variables are the GDP and the dummy of the latin origin. On the

other hand, when considering just the east-european countries, educational indicators

and life-expectancy at birth are also variables explaining the cultural index demand

(see annex 5 for these results).

So, these results show again that, for determining the cultural demand of a country,

what is mainly important is its income, and if the language spoken in the country has

Germanic or Latin roots; while education counts for the least developed countries.

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19

The above results are not surprising if we consider:

� The demand for cultural goods is influenced by the income because the highest

the income, the highest the possibility to access to other sorts of merchandises. In

this case, given the way the cultural index was constructed including

communication and arts indicators, and due to the fact that a number of these

means of communication are luxury items, their users must be primarily those

individuals with moderately high income and the countries that concentrate the

majority of these industries. The richer the country, the more cultural goods it

usually produces and consumes. Not only did rich countries have more data, but

they “score” higher in the area of producing and consuming cultural goods

through market mechanisms; so the higher the GNP per head of a country, the

more prevalent such items are likely to be, and not as in poor countries where the

cultural goods are luxurious goods and consumers have to choose between

consuming them or satisfying other more prioritary needs.

An important example is the case of performing arts, that can be seen as luxury

items, associated with social status and the desires of the wealthy for conspicuous

consumption, where the phenomenon of “first nights”, at which attendees are

more interested in looking at each other than at what is on the stage, is common in

many countries.

� Considering the assumption that the more educated people are, the fuller and

richer their communication, the demand for cultural goods can be influenced by

the level of education of the people because the more people know, the more they

demand knowledge and try to enjoy the cultural environment around them.

� Finally, Germanic or Latin roots of the language spoken in the country can explain

its demand for culture because it is in English, Spanish, German or French in

which the majority of the cultural goods and services are offered and traded. In

addition, these languages are some of the more spoken in the world (not only as

mother tongue but as second-tongue) so it depends on the linguistic abilities of

their people, the diverse-origin cultural goods and services that they can afford.


20

The assumption is that as people learn one another’s languages, they would be

better able to communicate and more inclined to respect one another’s culture.

5. Some Remarks

One explanatory variable that should have been important to introduce in the model,

is the relative price for each cultural component. The problem was that these specific

data were not generated or collected by any international institution, so it is difficult

to find countries statistics about.14 This is particularly explained by the fact that it is

hard to establish just one price that averages or represents hundreds of different types

of books, disks in all their sizes or how to measure the consumption of one hour of

radio or TV.

So, it was possible just to have an approximation of the price of a cinema ticket in a

sample of twenty-two countries for which the data were available, and this variable

was introduced in the model to see if it was significant or not in explaining the

cultural index.

Considering the results of equations 5, 6 and 7 of Table 5, price for cinema seems to

be not highly significant.15 This conclusion is similar to the one found by David

Throsby in his article “The role of music in international trade and economic

development”, published in The World Culture Report 2000, where he makes an

analysis of the per capita demand for music in 1994, for a sample of 35 countries.

Among his results, he concludes that although price differentials between countries do

appear to exert some influence on demand, it is primarily per capita incomes that

explain relative purchases, and that it is consumer incomes that principaly determines

the amounts spent on music recordings.16

14 For many of the cultural dimensions, human development data are sparse or simply do not exist.

One reason is that these dimensions have not been considered important. 15 In order to see the results of the model with and without the price of the cinema tickets see annex 6. 16 For having additional information about this model, check Chapter 12 of the World Cultural Report, UNESCO 2000.


21

So, as when considering this sample the variable price seems to be not quite

explicative of the cultural index, it can be thought that this will not greatly change

when considering the samples of nineteen or thirty-two countries, and so their indexes

can be explained as before.

It is important to notice that in running the regression with this sample of twenty-two

countries, the origin of the languages spoken and the level of education are still key

factors for explaining the cultural index.

These results can be seen in Table 5, were equation 7 shows that a 1% change in the

adult literacy level, generates a positive 10% change in the demand for culture; while

a 1% change in the ticket price leads to an equal but negative change in the cultural

demand.

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ern

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eans

that

it is

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loga

rithm

of t

he v

aria

ble.


23

6. Conclusions In the process of global human development, culture plays a key role, as a language of

communication, as a mean of storage and transmission of cultural diversity, as a

vehicle for political and social comment and dissent, and as a source of economic

empowerment.

The challenge of this paper was how to consider the multicultural aspects of all

countries and try to determine the demand for them. For this purpose, the chosen

indicators had to reflect an important an measurable part of world culture. The aim

was to broaden the scope of the indicators so as to bring in cultural features that

existed in all countries, could be provided by all countries and could be presented in

indicator tables.

While trying to do this, it was evident the lack of basic cultural indicators because it

does not exist a rich, world-wide, comparable database for the majority of countries

and if it exist, it is not comparable or from recent years. This situation is particularly

worst in the poorest countries.

Due to this data limitation communication indicators where included and the concept

of cultural demand was changed for a “modern cultural demand”.

This demand function was expected to contain, among others, own price, price of

substitutes, consumer income, and other explanatory variables, but again it was

impossible due to data availability, and it was necessary to restrict these variables to

some other existing indictators.

When running the regressions and examining the most significant determinants of

demand, the results indicate that this demand depends mostly on income and

education levels as well as on the world importance of the most-spoken languge in the

country. Life expectancy, urban population and even the proxi for the relative price of

cinema (one of the cultural components) were not found to be statistically significant.


24

Empirical studies of demand for cultural aspects undertaken over a number of years

have been broadly consistent with the above observations. At the outset, several early

studies identified for example, performing arts audiences as being of significantly

higher educational, occupational and income status than the community at large (W.

Baumol and Bowen 1966; Ford Foundation 1974).

Although most (but not all) studies have identified a significant positive coeficient on

consumer income in estimated demand equations, the corresponding elasticity

estimates have varied above and below 1. Because live arts consumption is time-

intensive, gains in attendances over the long run due to increasing incomes are likely

to be offset to some extent by the increasing price of leisure. Glenn Withers (1980)

confirmed this proposition using data covering all U.S. performing arts for the period

1929-1973, and he found a “pure” income elasticity of around unity, composed of a

“full“ income effect (imputing leisure time as part of income) of 2.7, offset by a

leisure price effect of about –1.6.

Considering the relative price, even if it was not possible to introduce it in the model,

the approximation obtained with the cinema indicator in this paper and some

empirical experience by Throsby -already specified-, show that they do not seem to be

quite significant for determining the consumers demand for culture. Particularly,

Throsby (1983) in another research, identified several quality characteristics of some

cultural performances, including standard of script, acting, and production, and found

consumer demand to be strongly responsive to variations in the expected quality of

these characteristics and not responsive to the ticket price.

Finally, there might be possible to find other factors that explain better the behaviour

of the consumers' demand. As Leo Goldstone writes in his paper "Measuring culture:

prospects and limits", there are thousands of factors that can help to determine the

cultural demand among which we can find the taxes on cultural goods and services,

the leisure-time use, the cultural tolerance and freedom, etc. so we may hope that in

the future this data could be available and new research could be developed.


25

References

� CHATFIELD, C. and COLLINS, A.J., Introduction to Multivariate Analysis, Science Paperbacks, Cambridge Press, UK, 1980

� Culture, Trade and Globalization, UNESCO 2000

� Goldstone, Leo, Measuring culture: prospects and limits, World Cultural Report 1999, UNESCO 1999

� Human Development Report 1997, UNPD 1997

� Human Development Report 1999, UNPD 1999

� Johnson, J. and Lenartowicz, T. Culture, Freedom and Economic Growth: Do Cultural Values Explain Economic Growth? Journal of World Business 33(4)/1998

� McKinley, Terry, Measuring the contribution of culture to human well-being, UNESCO 1999.

� Rodríguez, P, Análisis de la Industria Editorial Ecuatoriana y su Potencial Productivo, Ecuador, 2001.

� Statistical Yearbook 1999, UNESCO Publishing and Bernan Press

� Stolovich, Luis, La Cultura da Trabajo: Entre la creación y el negocio: economía y cultura en el Uruguay, Montevideo, 1997

� Study on International Flows of Cultural Goods between 1980-1998, UNESCO 2000

� Throsby, David, Economics and Culture, Cambridge Press, UK, 2001

� Throsby, David, The Production and Consumption of the Arts: A View of Cultural Economics, Journal of Economic Literature, Vol. XXXII, March 1994


26

� Throsby, David, The role of music in international trade and economic development, World Cultural Report 1999, UNESCO 1999

� World Cultural Report 1999, UNESCO 1999

� World Cultural Report 2000, UNESCO 2000

Databases from:

� United Nations Development Program

� UNESCO

� United Nations Organization

� World Bank

� World Intellectual Properties Organization


27

APPENDIX

The Principal Components Method

This is a technique used to examine the relationships among a set of p correlated variables, transformed to a new set of uncorrelated variables called principal components. These new variables are linear combinations of the original variables and are derived in decreasing order of importance so that, for example, the first principal component accounts for as much as possible of the variation in the original data.17 The transformation is in fact, an orthogonal rotation in p-space. As the usual objective of the analysis is to see if the first few components account for most of the variation in the original data, this means that if some of the original variables are highly correlated, they are effectively saying the same thing and there may be near-linear constraints on the variables. It this case, it is hoped that the first few components will be intuitively meaningful and will help to understand the data better, and will be useful in subsequent analysis where it is possible to operate with a smaller number of variables. The main stages for calculating the principal components are:

� To decide if it is worth or not to include all the variables recorded in the original data matrix, and if any of the variables need to be transformed.

� To calculate the correlation (or covariance) matrix, bearing in mind that a correlation coefficient should generally not be calculated for a pair of variables whose relationship is obviously non-linear.

� To look at the correlation matrix and observe any natural groupings of variables with "high" correlations. However, if nearly all the correlations are "small", then there is probably not much point in carrying out the principal components method.

� To calculate the eigenvalues and eigenvectors of the correlation (or covariance) matrix.

� To examine the eigenvalues and to try to decide how many are "large". This should indicate the effective dimensionality of the data.

� To look at the groupings of the variables suggested by the components and consider whether the components have some meaningful interpretation.

� To use the component scores in subsequent analyses as a way of reducing the dimensionality of the problem.

17 As in the case of this work, where the first principal component explains almost the 80% of the variation of the original data.


28

The derivation of the principal components: Suppose X T = [X1,...Xp] is a p-dimensional random variable with mean µ and

covariance matrix �. The problem is to find a new set of variables, say Y1, Y2,...Yp, which are uncorrelated and whose variances decrease from first to last. Each Yj is taken to be a linear combination of the X’s, so that:

Yj = a1jX1 + a2jX2 + ,... + apjXp (1)

= a Tj X

where a Tj = [a1j,...,apj] is a vector of constants. Equation (1) contains an arbitrary scale

factor. We therefore impose the condition that a Tj aj = �

�

p

k 1

a 2kj = 1. We shall see that

this particular normalization procedure ensures that the overall transformation is orthogonal, this means, that the distances in p-space are preserved.

The first principal component, Y1, is found by choosing a1 so that Y1 has the largest

possible variance. In other words, we choose a1 so as to maximize the variance of

a Tj X subject to the constraint that a T

1 a1 = 1. This approach originally suggested by

Harold Hotelling, gives equivalent results to that of Karl Pearson, which finds the line in p-space such that the total sum of squared perpendicular distances from the points to the line is minimized.

The second principal component is found by choosing a2 so that Y2 has the largest

possible variance for all combinations of the form of equation (1) which are uncorrelated with Y1. Similarly, we derive Y3,... Yp, so as to be uncorrelated and to have decreasing variance.

We begin by finding the first component. We want to choose a1 so as to maximize the

variance of Y1 subject to the normalization constraint that a T1 a1 = 1. Now,

Var (Y1) = Var (a T1 X) (2)

= a T1 � a1

that can be taken as the objective function. The standard procedure for maximizing a function of several variables subject to one or more constraints is the method of Lagrange multipliers. With just one constraint,


29

this method uses the fact that the stationary points of a differentiable function of p variables, say f(x1,...,xp), subject to a constraint g(x1,...,xp) = c, are such that there

exists a number , called the Lagrange multiplier, such that

dx

df - �

dx

dg = 0� � � i = 1,...,p (3)

� � � �� at the stationary points. These p equations, together with the constraint, are sufficient to determine the co-ordinates of the stationary points (and the corresponding values of

, which, however, are usually of little interest). Further investigation is needed to see

if a stationary point is a maximum, minimum or a saddle point. It is helpful to form a new function, L (x), such that

L (x) = f(x) - �[g(x) - c]

where the term in the square brackets is of course zero. Then the set of equations in (3) may be written simply as

xd

dL = 0

Applying this method to the problem, we write

L (a1) = a T1 � a1 - (a T

1 a1 - 1)

Then, considering that the function has a quadratic form xT � x, where ��is a (p x p) symmetric matrix, we have

1da

dL= 2 � a1 - 2 a1

Setting this equal to 0, we have

(�� a1 = 0 (4)

Note the insertion of the unit matrix I into equation (4) so that the term in brackets is of the correct order, namely (p x p). We now come to the crucial step in the argument.


30

If equation (4) is to have a solution for a1, other than the null vector, then (��

must be a singular matrix. Thus must be chosen so that

��

�

Thus a non-zero solution for equation (4) exists if and only if is an eigenvalue of �.

But ��will generally have p eigenvalues, which must all be nonnegative as � is positive semidefinite. Let us denote the eigenvalues by 1, 2,..., p, and assume for the moment that they are distinct, so that 1 > 2 > ... > p > 0. Now,

Var (a T1 X) = a T

1 � a1

= a T1 � a1 using eq. (4)

= �

�

As we want to maximize this variance, we choose to be the largest eigenvalue,

namely 1. Then, using equation (4), the principal component, a1, which we are

looking for must be the eigenvector of � corresponding to the largest eigenvalue.

The second principal component, namely Y2 = a T2 X, is obtained by an extension of

the above argument. In addition to the scaling constraint that a T2 a2 = 1, we now have

a second constraint that Y2 should be uncorrelated with Y1. Now,

Cov (Y2, Y1) = Cov (a T2 X, a T

1 X)

= E [a T2 (X - µ) (X - µ) T a1]

= a T2 ��a1 (5)

We require this to be zero. But since � a1 = 1 a1, an equivalent simpler condition is

that a T2 a1 = 0. In other words, a1 and a2 should be orthogonal.

In order to maximize the variance of Y2, namely a T2 ��a2, subject to the two

constraints, we need to introduce two Lagrange multipliers, which we will denote by

and , and consider the function

L (a2) = a T2 ��a2 - (a T

2 a2 - 1) - �a T2 a1


31

As the stationary point(s) we must have

2da

dL= 2 (�� a2 - a1 = 0 (6)

If we premultiply this equation by a T1 , we obtain

2 a T1 ��a2 - �= 0

since a T1 a2 = 0. But from equation (5), we also require a T

1 ��a2 to be zero, so that �is

zero at the stationary point(s). Thus equation (6) becomes,

(�� a2 = 0

With a little thought, we see that this time we choose to be the second largest

eigenvalue of ��and a2 to be the corresponding eigen vector.

Continuing this argument, the jth principal component turns out to be the eigenvector associated with the jth largest eigenvalue. There is no difficulty in extending the above argument to the case where some of the

eigenvalues of ��are equal. In this case there is no unique way of choosing the

corresponding eigenvectors, but as long as the eigenvectors associated with multiple roots are chosen to be orthogonal, then the argument carries through. Let us denote the (p x p) matrix of eigenvectors by A, where

A = [a1,...,ap]

and the (p x 1) vector of principal components by Y. Then Y = A T X (7)

The (p x p) covariance matrix of Y will be denoted by and is clearly given by


32

=

�

�

�

�

�

�

p ...

0 ... 2

0 ... 0 1

...

�

��

00

0

(8)�

Note that the matrix is diagonal as the components have been chosen to be uncorrelated.

Using the fact that E (A T X) = A T µ and Var (A T X) = A T �A we can also express

Var (Y) in the form AT�A, so that

= A T �A (9)

gives the important relation between the covariance matrix of X and the corresponding principal components. Note that equation (9) can be rewritten as

�� A T (10)

since A is an orthogonal matrix with � A T = I. We have already noted that the eigenvalues can be interpreted as the respective variances of the different components. Now the sum of these variances is given by

��

p

i 1

Var (Yi) = ��

p

i 1

i ��trace ( ��

But

trace ( ��= trace (A T �A)

� � � � ��= trace (��A T )

� � � � ��= trace (��

� � � � ��

p

i 1

Var (Xi)


33

Thus we have the important result that the sums of the variances of the original variables and of their principal components are the same. It is therefore convenient to make statements such as "the ith principal component accounts for a proportion

i / ��

p

j 1

j of the total variation in the original data", though it should be emphasized

that this is not an analysis of variance in the usual sense of the expression. We will

also say that the first m components account for a proportion ��

m

j 1

j / ��

p

j 1

j of the

total variation.

�


34

INDICATORSThe 8 indicators (7 + 1)

1 Cultural Paper Consumed kilos per person 1994

2 Annual Cinema Attendances per person 1990 - 1995

3 Music Unit Sales Retail Value USD / per capita 1996

4 Annual Museum Attendances per 100 people 1988 - 1994

5 INTERNET users per 10,000 inhabitants 2000

6 Radios per thousand people 1995

7 Televisions per thousand people 1995

8 Annual Attendance of Performing Arts Companies per thousand people 1980 - 1985

COUNTRIESWith 8 indicators, 19 countries

1 Egypt

2 Turkey

3 Chile

4 Mexico

5 Canada

6 Belgium

7 Denmark

8 Finland

9 Germany

10 Greece

11 Hungary

12 Italy

13 Netherlands

14 Norway

15 Poland

16 Portugal

17 Romania

18 Sweden

19 Switzerland

With 7 indicators, 32 countries (19 + 13)

20 Zimbabwe

21 Japan

22 Australia

23 Malaysia

24 New Zealand

25 Costa Rica

26 Austria

27 Czech Republic

28 France

29 Latvia

30 Russia

31 Slovakia

32 Spain

ANNEX 1

Mod

ern

cult

ure

and

som

e fa

ctor

s th

at in

flue

nce

its

dem

and

Pao

la R

odrí

guez

35

Cu

ltu

ral P

aper

A

nn

ual

Cin

ema

Mu

sic

Un

it S

ales

A

nn

ual

Mu

seu

m

INT

ER

NE

T U

sers

Rad

ios

Tele

visi

on

sA

rts

Co

mp

anie

sC

ult

ura

l In

dex

Cu

ltu

ral I

nd

ex(k

ilos

per

per

son

)(p

er p

erso

n)

(US

D /

per

cap

ita)

(per

100

p.)

(per

10.

000

p.)

(per

100

0 p

.)(p

er 1

000

p.)

(per

100

0 p

.)(7

ind

icat

ors

)(8

ind

icat

ors

)19

9419

90 -

199

519

9619

88 -

199

420

0019

9519

9519

80 -

198

5E

gypt

50.

30.

47

70.8

931

211

016

-1.2

5832

9-3

.520

637

Tur

key

60.

32.

611

304.

4116

418

944

-1.2

7715

3-3

.368

625

Chi

le16

0.6

6.3

516

57.6

534

821

579

-0.7

7501

3-2

.462

888

Mex

ico

110.

74.

411

274.

3126

321

925

-1.1

3155

5-2

.944

702

Can

ada

962.

837

.494

4130

.07

1053

714

385

1.27

5310

3.06

0784

Bel

gium

105

1.9

4424

2289

.41

790

454

144

0.61

3626

0.86

6647

5D

enm

ark

751.

758

.821

536

58.5

210

3457

454

71.

4103

283.

0279

24F

inla

nd16

61.

128

.872

3722

.95

1008

519

545

1.01

8050

2.04

0544

Ger

man

y10

31.

539

.211

529

17.6

944

554

503

0.89

5400

1.92

2643

Gre

ece

341

12.3

2393

9.41

430

220

205

-0.6

8822

1-1

.911

77H

unga

ry18

1.4

7.4

7214

51.4

164

343

366

4-0

.347

892

-0.4

0167

66Ita

ly63

1.6

1170

2303

.75

822

446

229

0.11

6459

0.17

3884

6N

ethe

rland

s93

142

.714

924

39.4

793

749

723

50.

8132

311.

3552

46N

orw

ay98

2.7

6119

149

05.2

480

843

328

11.

4294

523.

0624

65P

olan

d12

0.4

2.9

4172

2.3

454

311

609

-0.8

3255

8-1

.896

265

Por

tuga

l33

0.8

1651

2494

.11

245

326

61-0

.462

449

-1.5

1678

5R

oman

ia4

0.7

0.6

2935

8.31

211

220

649

-1.2

2091

6-2

.485

686

Sw

eden

941.

745

.721

245

58.2

988

247

845

31.

2274

542.

6613

26S

witz

erla

nd12

22.

256

.712

629

62.2

285

141

951

81.

0490

292.

3375

69Z

imba

bwe

2.1

0.2

0.3

137

.08

8929

-1.5

2737

8Ja

pan

106

1.1

53.3

5937

09.4

591

668

41.

2456

79A

ustr

alia

873.

944

.710

234

45.2

213

0449

51.

3837

97M

alay

sia

272

513

1590

.03

432

164

-0.7

0105

2N

ew Z

eala

nd54

3.9

34.5

921

66.6

599

751

40.

6187

07C

osta

Ric

a11

0.5

2.5

2262

1.43

263

143

-1.1

3402

7A

ustr

ia72

1.5

51.3

228

2557

.54

620

497

0.67

7333

Cze

ch R

ep.

180.

99.

188

976.

1863

848

2-0

.388

866

Fra

nce

792.

239

.824

1443

.32

895

589

0.52

9112

Latv

ia17

0.4

5.1

5061

8.81

678

477

-0.5

0891

1R

ussi

a5

0.9

1.5

5421

0.98

340

377

-1.0

1109

4S

lova

kia

131.

12.

553

1202

.59

570

476

-0.5

3358

8S

pain

512.

414

.973

1327

.04

314

404

-0.5

0396

4

SO

UR

CE

: UN

ES

CO

, WIP

O

AN

NE

X 2

CU

LT

UR

AL

IND

EX

AN

D IT

S C

OM

PO

NE

NT

S F

OR

EA

CH

SA

MP

LE


36

GDP Real Gross Domestic Product per capita

LE Life expectancy at birth

AL Adult literacy rate

FST

UB Urban population

D1 GERMANIC DANISHDUTCHENGLISHGERMANNORWEGIANSWEDISH

D2 LATIN FRENCHITALIANPORTUGUESEROMANIANSPANISH

D3 SLAV CZECHHUNGARIANPOLISHRUSSIANSLOVAK

D4 ALL THE OTHERS

Source: INTERNET and the Author.

ANNEX 3

THE EXPLANATORY VARIABLES

Combined first-second-third level gross enrolment ratio

Mod

ern

cult

ure

and

som

e fa

ctor

s th

at in

flue

nce

its

dem

and

Pao

la R

odrí

guez

37

lingu

istic

dum

mie

s

Rea

l GD

P p

er c

apita

(P

PP

$) 1

994

Life

Exp

ecta

ncy

at

birt

h (y

ears

) 19

94A

dult

Lite

racy

Rat

e (%

)

199

4

Com

bine

d 1s

t, 2n

d an

d 3r

d Le

vel G

ross

E

nrol

men

t Rat

io (

%)

1994

Urb

an

Pop

ulat

ion

(%)

19

96

CU

LTU

RA

L IN

DE

X

HU

MA

N

DE

VE

LOP

ME

NT

IN

DE

X

19

941

23

4N

orw

ay21

346

77.5

9992

74.0

61.

4294

520.

943

10

00

Den

mar

k21

341

75.2

9989

85.1

41.

4103

280.

927

10

00

Aus

tral

ia19

285

78.1

9979

84.7

1.38

3797

0.93

11

00

0C

anad

a21

459

7999

100

76.7

81.

2753

10.

961

00

0Ja

pan

2158

179

.899

7878

.24

1.24

5679

0.94

00

01

Sw

eden

1854

078

.399

8283

.14

1.22

7454

0.93

61

00

0S

witz

erla

nd24

967

78.1

9976

67.7

1.04

9029

0.93

10

00

Fin

land

1741

776

.399

9764

.98

1.01

805

0.94

00

01

Ger

man

y19

675

76.3

9981

86.7

0.89

540.

924

10

00

Net

herla

nds

1923

877

.399

9189

.08

0.81

3230

60.

941

00

0A

ustr

ia20

667

76.6

9987

64.3

80.

6773

329

0.93

21

00

0N

ew Z

eala

nd16

851

76.4

9994

85.4

0.61

8706

50.

937

10

00

Bel

gium

2098

576

.899

8697

.06

0.61

3626

10.

932

01

00

Fra

nce

2051

078

.799

8974

.88

0.52

9111

70.

946

01

00

Italy

1936

377

.898

.173

66.6

80.

1164

590.

921

01

00

Hun

gary

6437

68.8

9967

63.2

-0.3

4789

170.

857

00

10

Cze

ch R

epub

lic92

0172

.299

7074

.54

-0.3

8886

570.

882

00

10

Por

tuga

l12

326

74.6

89.6

8158

-0.4

6244

90.

890

10

0S

pain

1432

477

.697

.190

76.7

2-0

.503

9641

0.93

40

10

0La

tvia

3332

67.9

9967

69-0

.508

9112

0.71

10

01

0S

lova

kia

6389

70.8

9972

57.0

8-0

.533

5884

0.87

30

01

0G

reec

e11

265

77.8

96.7

8259

.38

-0.6

8822

10.

923

00

01

Mal

aysi

a88

6571

.283

6254

.44

-0.7

0105

230.

832

00

01

Chi

le91

2975

.195

7284

.66

-0.7

7501

310.

891

01

00

Pol

and

5002

71.2

9979

64.0

8-0

.832

5578

0.83

40

01

0R

ussi

a48

2865

.798

.778

76.2

6-1

.011

094

0.79

20

01

0M

exic

o73

8472

89.2

6673

.6-1

.131

555

0.85

30

10

0C

osta

Ric

a59

1976

.694

.768

47-1

.134

027

0.88

90

10

0R

oman

ia40

3769

.596

.962

55.1

6-1

.220

916

0.74

80

10

0E

gypt

3846

64.3

50.5

6944

.56

-1.2

5832

90.

614

00

01

Tur

key

5193

68.2

81.6

6370

.42

-1.2

7715

30.

772

00

01

Zim

babw

e21

9649

84.7

6832

.5-1

.527

378

0.51

31

00

0

SO

UR

CE

: UN

ES

CO

, UN

DP

, WB

AN

NE

X 4

RA

NK

ING

OF

TH

E C

OU

NT

RIE

S B

Y T

HE

IR C

UL

TU

RA

L IN

DE

X

Mod

ern

cult

ure

and

som

e fa

ctor

s th

at in

flue

nce

its

dem

and

Pao

la R

odrí

guez

38

Dep

end

ent

Var

CU

LT

UR

AL

IND

EX

�

stan

dar

d

erro

rt

p-v

alu

e�

stan

dar

d

erro

rt

p-v

alu

e�

stan

dar

d

erro

rt

p-v

alu

e�

stan

dar

d

erro

rt

p-v

alu

e

Co

nst

ant

0.07

6264

26.2

3685

0.00

2907

0.99

78-1

5.38

596

4.80

7577

-0.3

2003

60.

0084

-28.

7270

02.

6421

34-9

.035

387

0.07

02-2

5.31

169

1.44

2552

-17.

5465

0.00

04

LG

DP

2.50

8279

0.95

5325

2.62

5576

0.03

931.

6600

310.

4843

473.

4273

580.

0056

0.13

3318

0.06

3184

2.11

0010

0.28

180.

1122

850.

0505

442.

2215

50.

1129

LL

E3.

3177

967.

3866

900.

4491

590.

6691

2.28

5244

0.71

8793

3.17

9280

0.19

402.

8507

810.

4123

876.

9128

80.

0062

LA

L-1

0.13

926

9.19

2303

-1.1

0301

70.

3123

5.72

2058

0.61

7931

9.26

0027

0.06

855.

3676

970.

2017

5826

.604

60.

0001

LF

ST

0.69

5514

1.15

8502

0.60

0356

0.57

02-3

.087

223

0.21

8352

-14.

1387

70.

0450

-3.0

4305

90.

1630

98-1

8.65

780.

0003

LU

P1.

3148

750.

9439

081.

3930

120.

2130

-0.1

4412

70.

1639

72-0

.878

976

0.54

09

D1

-0.5

8496

40.

4973

02-1

.176

274

0.28

40

D2

-1.2

9361

10.

5853

73-2

.209

891

0.06

92-0

.737

634

0.17

4049

-4.2

3807

30.

0014

-1.0

2184

90.

1010

40-1

0.11

329

0.06

27-0

.949

641

0.04

4766

-21.

2134

0.00

02

D3

-0.0

5705

30.

0863

60-0

.660

638

0.62

83

Adj

uste

d R

²0.

7906

160.

8110

740.

9899

730.

9927

16

S.E

. of r

egre

ssio

n0.

2863

920.

2720

420.

0349

600.

0297

97

Dur

bin-

Wat

son

stat

.2.

7413

281.

7981

310.

8000

001.

4297

27

Aka

ike

Info

Crit

erio

n0.

6326

510.

4216

91-4

.288

685

-3.9

5407

7

Sch

war

z C

riter

ion

0.99

7826

0.55

8631

-4.1

1337

4-3

.822

594

F-s

tatis

tic8.

0124

3028

.904

9211

3.83

7021

9.05

42

* T

he "

L" in

fron

t of t

he n

ames

of t

he v

aria

bles

mea

ns th

at it

is th

e lo

garit

hm o

f the

var

iabl

e.

SA

MP

LE

OF

TH

E W

ES

TE

RN

EU

RO

PE

AN

CO

UN

TR

IES

SA

MP

LE

OF

TH

E E

AS

TE

RN

EU

RO

PE

AN

CO

UN

TR

IES

AN

NE

X 5

Mod

ern

cult

ure

and

som

e fa

ctor

s th

at in

flue

nce

its

dem

and

Pao

la R

odrí

guez

39

CI

GD

PL

EA

LF

ST

UP

D1

D2

D3

CI

1

GD

P0.

9072

7798

6869

1

LE

0.72

4796

2659

870.

8447

6280

4223

1

AL

0.57

9342

5742

480.

5179

2568

683

0.71

9999

2126

841

FS

T0.

7719

8014

9607

0.72

3142

2457

990.

6914

0004

4889

0.41

3369

6557

611

UP

0.52

9893

6867

440.

5895

8563

9962

0.54

7649

4479

610.

5194

8746

1853

0.41

4794

8484

361

D1

0.80

7691

6062

970.

7331

3600

8236

0.53

2605

1791

870.

3365

9312

8242

0.54

9312

5911

050.

5012

3253

2205

1

D2

-0.4

0252

4580

948

-0.1

8760

3518

674

0.01

3056

1539

017

0.03

4339

7059

857

-0.3

7245

3416

660.

0428

1474

3076

4-0

.518

8745

2166

31

D3

-0.1

6739

3356

64-0

.409

4832

1192

6-0

.369

1389

5282

20.

1511

6017

4814

-0.1

9842

9726

21-0

.218

6918

4759

3-0

.261

9684

1599

8-0

.233

0206

9121

41

CI

GD

PL

EA

LF

ST

UP

D1

D2

D3

CI

1

GD

P0.

9228

8072

4864

1

LE

0.67

0964

0655

60.

7517

6092

0179

1

AL

0.49

6875

7965

690.

4432

9165

0067

0.50

7321

6269

761

FS

T0.

7219

8515

9738

0.72

4571

7965

30.

5539

0808

2361

0.41

2870

7650

911

UP

0.62

9726

7635

910.

6104

0514

6828

0.59

9145

6915

40.

5069

9415

1414

0.50

7946

2199

291

D1

0.62

9658

1551

020.

5566

6221

1432

0.12

0462

5787

440.

2153

2547

9155

0.47

1830

8859

130.

2807

7320

2642

1

D2

-0.2

8530

5522

315

-0.0

4848

5340

6201

0.21

0184

3137

990.

0320

9353

6544

3-0

.123

8928

1089

20.

0247

1696

9802

-0.4

5273

4960

103

1

D3

-0.3

0001

2426

066

-0.4

9632

3972

653

-0.3

2611

5191

613

0.20

8862

6788

18-0

.283

5455

5449

-0.0

9111

0426

3355

-0.3

4767

6747

683

-0.3

0050

1253

482

1

AN

NE

X 6

CO

RR

EL

AT

ION

S M

AT

RIX

OF

TH

E S

AM

PL

E O

F 1

9 C

OU

NT

RIE

S

CO

RR

EL

AT

ION

S M

AT

RIX

OF

TH

E S

AM

PL

E O

F 3

2 C

OU

NT

RIE

S

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LAUSANNE UNIVERSITY ECONOMETRICS AND POLITICAL … · growth determining that the trade of cultural goods were multiplied by five between 1980 and 1998.6 As the UNESCO published in