EMERGENCE NEWS

Where the Butterfly Alights: the Global Location of eWork

As part of the EMERGENCE project, IES has been carrying out a review and analysis of all the existing statistical indicators of eWork both at the EU level and globally. Although these statistics fail to capture the full scope of eWork, they do provide some contextual information for the fuller picture which will be painted by the results of the EMERGENCE employer survey.

The results of the statistical analysis will be published by IES in the spring under the title: Where the Butterfly Alights: the Global location of eWork. One of the conclusions of the authors, Nick Jagger and Ursula Huws, is that particular types of eWork remain strongly clustered in particular regions. In a phenomenon which Huws has described as the ‘paradox of choice’ it seems that the opportunities offered by the new Information Society Technologies to relocate work are not resulting in a more even distribution of activities in all locations, but in the development of a more specialist global division of labour in which ‘like attracts like’, with a danger of increasing regional polarisation.

eWork in Europe

There is currently no satisfactory definition of eWork occupations in ISCO (the International Standard Classification of Occupations). In order to gain an impression of the regional distribution of IT-related work we combined three categories: (ISCO 213 - Computer professionals; ISCO 312 - Computer associate professionals, and ISCO 313 - Optical and electronic equipment operators) into a category which we refer to as ITCE employees.

Table 1 shows that in 1999, the European Regions in which these occupations formed the highest percentage of workers were strongly clustered around the capital cities of Stockholm, Paris, Brussels, London, Helsinki and Vienna, or in the densely-populated Netherlands.

Table 1: Top 12 regions in terms of ITCE occupational intensity, 1999
	NUTS code	Country Region	Numbers of ITCE employees	% ITCE
	SE01	Stockholm	40.6	4.9
	FR10	Île de France	207.1	4.2
	NL31	Utrecht	23.4	4.2
	FI16	Uusimaa	28.8	4.1
	NL33	Zuid-Holland	60.7	3.8
	UKJ1	Berkshire, Bucks, Oxfordshire	41.4	3.8
	NL32	Noord-Holland	45.6	3.7
	BE10	Rég. Bruxelles Cap.	12.0	3.6
	BE31	Brabant Wallon	4.8	3.5
	AT13	Wien	24.2	3.2
	UKH2	Bedfordshire, Hertfordshire	25.0	3.1
	UKI1	Inner London	35.4	3.1
Notes: Regions with data too low to be reliable excluded and UK data for 1998, no data available for Ireland Source: IES and a Eurostat special analysis of the Community Labour Force Survey

An alternative way of identifying eWorkers is to look at the sectors in which they are employed. Here too, the statistics are inadequate, making it impossible to identify many of the new eWork-intensive sectors which are developing, such as multimedia activities, or the kinds of eWork activity which takes place in user industries, such as finance or public administration. There are, however, two categories which can be regarded as covering ‘core’ eWork activities: NACE 30 - Manufacture of office machinery and computers, and NACE 72 - Computer and related activities. Combining the employees in these sectors and expressing them as a proportion of all employees in the regional workforce gives us Table 2.

Table 2: Top 12 regions in terms of IT sector employment intensity, 1999
	NUTS code	Country Region	Numbers employed in IT sectors	% of total employment in IT sector
	UKJ1	Berkshire, Bucks, Oxfordshire	60.7	5.6
	SE01	Stockholm	30.8	3.7
	UKH2	Bedfordshire, Hertfordshire	29.2	3.6
	FR10	Île de France	163.9	3.4
	UKJ2	Surrey, East-West Sussex	38.2	3.3
	FI16	Uusimaa	21.1	3.0
	UKK1	Avon, Gloucestershire,  Wiltshire & North Somerset	30.7	2.9
	UKJ3	Hampshire, Isle of Wight	23.8	2.8
	NL31	Utrecht	15.3	2.8
	ES30	Communidad de Madrid	51.0	2.7
	IT60	Lazio	47.5	2.6
	DE21	Oberbayem	50.7	2.6
Notes: Regions with data too low to be reliable excluded and UK data for 1998, no data available for Ireland Source: IES and a Eurostat special analysis of the Community Labour Force Survey

As can be seen, this too paints a picture of a clustering around major cities, although here, Rome, Madrid and Munich have made their way into the top twelve regions. The UK shows a slightly more dispersed pattern, reflecting the very dense population of Southern England, with a dispersal of many large computer companies outside the immediate Greater London area to adjoining regions to the South and West.

The results of the first phase of the EMERGENCE employer survey (currently being weighted and analysed ) will shed light on the extent to which less developed European regions are making up for this shortfall in ‘core’ IT employment by creating jobs in other forms of eWork, such as call centres.

 

New global division of eWork
The quality of statistical information which is available from Eurostat in the EU at a regional level is not available in most countries, and it is not possible to carry out such a detailed analysis. Nevertheless, there is an urgent need for some reliable information about which countries are emerging as major suppliers and users of the new telemediated business services. From what little research already exists, we identified eight factors which seem to influence eWork location:
	relative service sector salaries
	graduate availability
	language
	time zone
	telecommunications infrastructure
	trust or previous contact
	internet literacy
	economic development and ‘openness’
We then looked for statistical indicators for these factors in order to study the characteristics of each country so that we could identify national strengths and weaknesses in any global competition to attract eWork. This resulted in the creation of an eIndicators database which covers 204 countries and includes 171 variables. We then carried out a cluster analysis of these data to see what sorts of groupings emerged and identify countries which seemed at particular risk of exclusion from the digital economy. Because of the lack of reliable indicators for some of these factors, and because of enormous differences in population size and other variables between countries, these clusters should not be regarded as definitive. In some large countries, for instance, the existence of highly dynamic pockets of new economy sector growth might be invisible because they are swamped, statistically speaking, by declining old economy industries. Conversely, a country, such as Botswana, with a small population and a great deal of mineral wealth, might present a similar profile to a highly developed economy although the majority of its people may still be living in poverty. Nevertheless, we feel this analysis does provide a starting point for future analysis, so we present it in summary here for discussion.

The six clusters which emerged are:
	e-Leaders: these countries define the shape of e-work and are likely to be the main source of relocated employment. The group consists of Australia, France, Germany, Japan, the United Kingdom and the United States.
	e-Capables: these countries although smaller operate at the same level as the e-leaders but are less likely to define the shape of e-work at a global level. They comprise Austria, Belgium, Cyprus, Denmark, Finland, Greece, Hongkong, Ireland, Israel, Italy, Malta, Macau, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovenia, Spain, Sweden, Switzerland, Taiwan and the Virgin Islands (US).
	e-Hares: these countries are relatively small with historically poor telecommunications infrastructure but rapid recent growth. They seem capable of capturing significant global eWork niches in the future. E-hares cover a diverse range including Cambodia, Chile, Ghana, Hungary, Indonesia, Mauritius and the Philippines.
	e-Tigers: these countries are large usually with relatively well-developed infrastructures and available human resources; often they are already significant players in global eWork, however they are perceived as raising problems of trust and in some cases are seen as relatively corrupt and therefore poor places to do business. They include China, Egypt, Guatemala, India, Jamaica, Korea, the Lebanon, , Mexico, Poland, Russia, Thailand and the Ukraine.
	e-Maybes: these countries are small in population with well-developed infrastructures and human resources as well as a reputation for trustworthiness - but often without the spare capacity to take on relocated employment. The cluster includes some centres of offshore banking, like Bermuda, Barbados and Jersey as well as developed economies like Canada, Iceland, Liechtenstein and Luxembourg.
	e-Losers: these countries tend to have neither the telecommunications infrastructure nor the human capital resources to benefit from eWork, whilst also being perceived as inefficient and corrupt. They include most of Africa, much of South America and clusters of countries in the Balkans and Central Europe. This large list of countries accounts for nearly three in ten of the world’s population and seems likely to be seriously at risk of outright exclusion from the emerging e-economy.

Table 3 shows that nearly half of the world’s population lives in e-tiger countries, whilst as much as 28 per cent lives in the e-loser countries, which make up over half of all countries. The e-leaders, although comprising only six countries, represent about a tenth of the world’s population. The e-capable countries, and especially the e-maybe countries, are relatively small in population terms, while the e-hare countries represent about a tenth of the world’s population.

Table 3: The clusters and their populations, 1998
Cluster Name	Number of countries		Total population (millions)	% of total population
e-Leaders	6		612.5	10.5
e-Capable	23		230.1	4.0
e-Hare	25		588.2	10.1
e-Tiger	17		2739.2	47.1
e-Maybe	19		44.9	0.8
e-Loser	114		1607.0	27.6
Total	204		5821.8	100.0
Source: IES cluster analysis of e-work indicators

Table 4 shows that the majority of African and South American countries fall into the e-loser category. At the same time, no e-leader, e-capable countries and only one each of e-maybe countries are found in these two continents. Europe has the highest concentration of e-leader and e-capable countries, while North America and Oceania have the highest concentration of e-maybes and Asia the highest concentration of e-tiger countries.

Table 4: Continental breakdown of the clusters
	Europe	North Americas	South Americas	Africa	Asia	Oceania
e-Leaders	3	1			1
e-Capable	16				5	2
e-Maybe	5	11	1	1		1
e-Hare	2	1	3	9	8	2
e-Tiger	3	4	1	4	5
e-Loser	14	11	11	39	29	10
Total	43	28	16	53	48	16
Source: IES analysis of e-work indicators database

Table 5 gives some examples of the differences between clusters. It shows the vast differences which still exist at a global level.

Table 5: Some differences between the clusters
Cluster names	Avg. number of internet hosts per country 1998	Avg. Cost of a business phone call to the US 1999	Avg. growth in mainlines per capita 1994 to 1998	Avg. Corruption Perception Indicator 1999
e-Leader	10,251,697	1.7	10.3	7.6
e-Capable	256,427	2.9	13.1	7.7
e-Hare	8,541	9.8	136.0	4.1
e-Tiger	66,153	8.2	67.5	3.3
e-Maybe	90,601	5.3	16.3	9.1
e-Loser	12,029	10.1	25.8	3.2
Total	353,827	8.5	38.5	4.7
Source: IES analysis of e-work indicators database derived from data from ITU and World Corruption Index

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